<html lang="en"> <head> <title>Patient-specific Boolean models of signalling networks guide personalised treatments </title> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <link href="https://unpkg.com/@stencila/thema@2/dist/themes/elife/styles.css" rel="stylesheet"> <script src="https://unpkg.com/@stencila/thema@2/dist/themes/elife/index.js" type="text/javascript"></script> <script src="https://unpkg.com/@stencila/components@<=1/dist/stencila-components/stencila-components.esm.js" type="module"></script> <script src="https://unpkg.com/@stencila/components@<=1/dist/stencila-components/stencila-components.js" type="text/javascript" nomodule=""></script> </head> <body> <main role="main"> <article itemscope="" itemtype="http://schema.org/Article" data-itemscope="root"> <h1 itemprop="headline">Patient-specific Boolean models of signalling networks guide personalised treatments</h1> <meta itemprop="image" content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=Patient-specific%20Boolean%20models%20of%20signalling%20networks%20guide%20personalised%20treatments"> <ol data-itemprop="authors"> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Arnau Montagud"><span data-itemprop="givenNames"><span itemprop="givenName">Arnau</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Montagud</span></span><span data-itemprop="emails"><a itemprop="email" href="mailto:arnau.montagud@bsc.es">arnau.montagud@bsc.es</a></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-1">1</a><a itemprop="affiliation" href="#author-organization-2">2</a><a itemprop="affiliation" href="#author-organization-3">3</a><a itemprop="affiliation" href="#author-organization-4">4</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Jonas Béal"><span data-itemprop="givenNames"><span itemprop="givenName">Jonas</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Béal</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-1">1</a><a itemprop="affiliation" href="#author-organization-2">2</a><a itemprop="affiliation" href="#author-organization-3">3</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Luis Tobalina"><span data-itemprop="givenNames"><span itemprop="givenName">Luis</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Tobalina</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-5">5</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Pauline Traynard"><span data-itemprop="givenNames"><span itemprop="givenName">Pauline</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Traynard</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-1">1</a><a itemprop="affiliation" href="#author-organization-2">2</a><a itemprop="affiliation" href="#author-organization-3">3</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Vigneshwari Subramanian"><span data-itemprop="givenNames"><span itemprop="givenName">Vigneshwari</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Subramanian</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-5">5</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Bence Szalai"><span data-itemprop="givenNames"><span itemprop="givenName">Bence</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Szalai</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-5">5</a><a itemprop="affiliation" href="#author-organization-6">6</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Róbert Alföldi"><span data-itemprop="givenNames"><span itemprop="givenName">Róbert</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Alföldi</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-7">7</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="László Puskás"><span data-itemprop="givenNames"><span itemprop="givenName">László</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Puskás</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-7">7</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Alfonso Valencia"><span data-itemprop="givenNames"><span itemprop="givenName">Alfonso</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Valencia</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-4">4</a><a itemprop="affiliation" href="#author-organization-8">8</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Emmanuel Barillot"><span data-itemprop="givenNames"><span itemprop="givenName">Emmanuel</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Barillot</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-1">1</a><a itemprop="affiliation" href="#author-organization-2">2</a><a itemprop="affiliation" href="#author-organization-3">3</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Julio Saez-Rodriguez"><span data-itemprop="givenNames"><span itemprop="givenName">Julio</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Saez-Rodriguez</span></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-5">5</a><a itemprop="affiliation" href="#author-organization-9">9</a></span> </li> <li itemscope="" itemtype="http://schema.org/Person" itemprop="author"> <meta itemprop="name" content="Laurence Calzone"><span data-itemprop="givenNames"><span itemprop="givenName">Laurence</span></span><span data-itemprop="familyNames"><span itemprop="familyName">Calzone</span></span><span data-itemprop="emails"><a itemprop="email" href="mailto:laurence.calzone@curie.fr">laurence.calzone@curie.fr</a></span><span data-itemprop="affiliations"><a itemprop="affiliation" href="#author-organization-1">1</a><a itemprop="affiliation" href="#author-organization-2">2</a><a itemprop="affiliation" href="#author-organization-3">3</a></span> </li> </ol> <ol data-itemprop="affiliations"> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-1" id="author-organization-1"><span itemprop="name">Institut Curie, PSL Research University</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Paris</span><span itemprop="addressCountry">France</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-2" id="author-organization-2"><span itemprop="name">INSERM, U900</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Paris</span><span itemprop="addressCountry">France</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-3" id="author-organization-3"><span itemprop="name">MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Paris</span><span itemprop="addressCountry">France</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-4" id="author-organization-4"><span itemprop="name">Barcelona Supercomputing Center (BSC)</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Barcelona</span><span itemprop="addressCountry">Spain</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-5" id="author-organization-5"><span itemprop="name">Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Aachen</span><span itemprop="addressCountry">Germany</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-6" id="author-organization-6"><span itemprop="name">Semmelweis University, Faculty of Medicine, Department of Physiology</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Budapest</span><span itemprop="addressCountry">Hungary</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-7" id="author-organization-7"><span itemprop="name">Astridbio Technologies Ltd</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Szeged</span><span itemprop="addressCountry">Hungary</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-8" id="author-organization-8"><span itemprop="name">ICREA</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Barcelona</span><span itemprop="addressCountry">Spain</span></address></li> <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-9" id="author-organization-9"><span itemprop="name">Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine</span><address itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span itemprop="addressLocality">Heidelberg</span><span itemprop="addressCountry">Germany</span></address></li> </ol><span itemscope="" itemtype="http://schema.org/Organization" itemprop="publisher"> <meta itemprop="name" content="Unknown"><span itemscope="" itemtype="http://schema.org/ImageObject" itemprop="logo"> <meta itemprop="url" content="https://via.placeholder.com/600x60/dbdbdb/4a4a4a.png?text=Unknown"> </span> </span><time itemprop="datePublished" datetime="2022-02-15">2022-02-15</time> <ul data-itemprop="genre"> <li itemprop="genre">Research Article</li> </ul> <ul data-itemprop="about"> <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span itemprop="name">Computational and Systems Biology</span></li> </ul> <ul data-itemprop="keywords"> <li itemprop="keywords">personalised medicine</li> <li itemprop="keywords">logical modelling</li> <li itemprop="keywords">prostate cancer</li> <li itemprop="keywords">personalised drug</li> <li itemprop="keywords">simulations</li> <li itemprop="keywords">drug combinations</li> <li itemprop="keywords">Human</li> </ul> <ul data-itemprop="identifiers"> <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier"> <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/publisher-id"><span itemprop="name">publisher-id</span><span itemprop="value" data-itemtype="http://schema.org/Number">72626</span> </li> <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier"> <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/doi"> <span itemprop="name">doi</span><span itemprop="value">10.7554/eLife.72626</span> </li> <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier"> <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/elocation-id"><span itemprop="name">elocation-id</span><span itemprop="value">e72626</span> </li> </ul> <section data-itemprop="description"> <h2 data-itemtype="http://schema.stenci.la/Heading">Abstract</h2> <meta itemprop="description" content="Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology."> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.</p> </section> <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="introduction">Introduction </h2> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Like most cancers, prostate cancer arises from mutations in single somatic cells that induce deregulations in processes such as proliferation, invasion of adjacent tissues and metastasis. Not all prostate patients respond to the treatments in the same way, depending on the stage and type of their tumour <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Chen and Zhou</span><span>2016</span></a></cite> and differences in their genetic and epigenetic profiles <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib108"><span>108</span><span>Toth et al.</span><span>2019</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib116"><span>116</span><span>Yang et al.</span><span>2018</span></a></cite></span>. The high heterogeneity of these profiles can be explained by a large number of interacting proteins and the complex cross-talks between the cell signalling pathways that can be altered in cancer cells. Because of this complexity, understanding the process of tumorigenesis and tumour growth would benefit from a systemic and dynamical description of the disease. At the molecular level, this can be tackled by a simplified mechanistic cell-wide model of protein interactions of the underlying pathways, dependent on external environmental signals.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Although continuous mathematical modelling has been widely used to study cellular biochemistry dynamics (e.g. ordinary differential equations) <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib44"><span>44</span><span>Goldbeter</span><span>2002</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib58"><span>58</span><span>Kholodenko et al.</span><span>1995</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib69"><span>69</span><span>Le Novère</span><span>2015</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib99"><span>99</span><span>Sible and Tyson</span><span>2007</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib112"><span>112</span><span>Tyson et al.</span><span>2019</span></a></cite></span>, this formalism does not scale up well to large signalling networks, due to the difficulty of estimating kinetic parameter values <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib6"><span>6</span><span>Babtie and Stumpf</span><span>2017</span></a></cite>. In contrast, the logical (or logic) modelling formalism represents a simpler means of abstraction where the causal relationships between proteins (or genes) are encoded with logic statements, and dynamical behaviours are represented by transitions between discrete states of the system <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib57"><span>57</span><span>Kauffman</span><span>1969</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib107"><span>107</span><span>Thomas</span><span>1973</span></a></cite></span>. In particular, Boolean models, the simplest implementation of logical models, describe each protein as a binary variable (ON/OFF). This framework is flexible, requires in principle no quantitative information, can be hence applied to large networks combining multiple pathways, and can also provide a qualitative understanding of molecular systems lacking detailed mechanistic information.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In the last years, logical and, in particular, Boolean modelling has been successfully used to describe the dynamics of human cellular signal transduction and gene regulations <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib13"><span>13</span><span>Calzone et al.</span><span>2010</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib21"><span>21</span><span>Cho et al.</span><span>2016</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib35"><span>35</span><span>Flobak et al.</span><span>2015</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib46"><span>46</span><span>Grieco et al.</span><span>2013</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib48"><span>48</span><span>Helikar et al.</span><span>2008</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib109"><span>109</span><span>Traynard et al.</span><span>2016</span></a></cite></span> and their deregulation in cancer <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib39"><span>39</span><span>Fumiã and Martins</span><span>2013</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib52"><span>52</span><span>Hu et al.</span><span>2015</span></a></cite></span>. Numerous applications of logical modelling have shown that this framework is able to delineate the main dynamical properties of complex biological regulatory networks <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib1"><span>1</span><span>Abou-Jaoudé et al.</span><span>2011</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib34"><span>34</span><span>Fauré et al.</span><span>2006</span></a></cite></span>.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">However, the Boolean approach is purely qualitative and does not consider the real time of cellular events (half time of proteins, triggering of apoptosis, etc.). To cope with this issue, we developed the MaBoSS software to compute continuous Markov Chain simulations on the model state transition graph (STG), in which a model state is defined as a vector of nodes that are either active or inactive. In practice, MaBoSS associates transition rates for activation and inhibition of each node of the network, enabling it to account for different time scales of the processes described by the model. Given some initial conditions, MaBoSS applies a Monte-Carlo kinetic algorithm (or Gillespie algorithm) to the STG to produce time trajectories <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib104"><span>104</span><span>Stoll et al.</span><span>2017</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib103"><span>103</span><span>Stoll et al.</span><span>2012</span></a></cite></span> such that the time evolution of the model state probabilities can be estimated. Stochastic simulations can easily explore the model dynamics with different initial conditions by varying the probability of having a node active at the beginning of the simulations and by modifying the model such that it accounts for genetic and environmental perturbations (e.g. presence or absence of growth factors or death receptors). For each case, the effect on the probabilities of selected read-outs can be measured <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib23"><span>23</span><span>Cohen et al.</span><span>2015</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib77"><span>77</span><span>Montagud et al.</span><span>2019</span></a></cite></span>.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">When summarising the biological knowledge into a network and translating it into logical terms, the obtained model is generic and cannot explain the differences and heterogeneity between patients’ responses to treatments. Models can be trained with dedicated perturbation experiments <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib30"><span>30</span><span>Dorier et al.</span><span>2016</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib93"><span>93</span><span>Saez-Rodriguez et al.</span><span>2009</span></a></cite></span>, but such data can only be obtained with non-standard procedures such as microfluidics from patients’ material <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib32"><span>32</span><span>Eduati et al.</span><span>2020</span></a></cite>. To address this limitation, we developed a methodology to use different omics data that are more commonly available to personalise generic models to individual cancer patients or cell lines and verified that the obtained models correlated with clinical results such as patient survival information <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Béal et al.</span><span>2019</span></a></cite>. In the present work, we apply this approach to prostate cancer to suggest targeted therapy to patients based on their omics profile (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1</a>). We first built 488 patient- and eight cell line prostate-specific models using data from The Cancer Genome Atlas (TCGA) and the Genomics of Drug Sensitivity in Cancer (GDSC) projects, respectively. Simulating these models with the MaBoSS framework, we identified points of intervention that diminish the probability of reaching pro-tumorigenic phenotypes. Lastly, we developed a new methodology to simulate drug effects on these data-tailored Boolean models and present a list of viable drugs and treatments that could be used on these patient- and cell line-specific models for optimal results. Experimental validations were performed on the LNCaP prostate cell line with two predicted targets, confirming the predictions of the model.</p> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1" title="Figure 1."> <label data-itemprop="label">Figure 1.</label><img src="index.html.media/fig1.jpg" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> <figcaption> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="workflow-to-build-patient-specific-boolean-models-and-to-uncover-personalised-drug-treatments-from-present-work"> Workflow to build patient-specific Boolean models and to uncover personalised drug treatments from present work.</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We gathered data from <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib39"><span>39</span><span>Fumiã and Martins</span><span>2013</span></a></cite> Boolean model, Omnipath <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib111"><span>111</span><span>Türei et al.</span><span>2021</span></a></cite> and pathways identified with ROMA <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib74"><span>74</span><span>Martignetti et al.</span><span>2016</span></a></cite> on the TCGA data to build a prostate-specific prior knowledge network. This network was manually converted into a prostate Boolean model that could be stochastically simulated using MaBoSS <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib104"><span>104</span><span>Stoll et al.</span><span>2017</span></a></cite> and tailored to different TCGA and GDSC datasets using our PROFILE tool to have personalised Boolean models. Then, we studied all the possible single and double mutants on these tailored models using our logical pipeline of tools <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib77"><span>77</span><span>Montagud et al.</span><span>2019</span></a></cite>. Using these personalised models and our PROFILE_v2 tool presented in this work, we obtained tailored drug simulations and drug treatments for 488 TCGA patients and eight prostate cell lines. Lastly, we performed drug-dose experiments on a shortlist of candidate drugs that were particularly interesting in the LNCaP prostate cell line. Created with <a href="https://biorender.com/" itemscope="" itemtype="http://schema.stenci.la/Link">BioRender.com</a>.</p> </figcaption> </figure> <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="results">Results</h2> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="prostate-boolean-model-construction">Prostate Boolean model construction</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A network of signalling pathways and genes relevant for prostate cancer progression was assembled to recapitulate the potential deregulations that lead to high-grade tumours. Dynamical properties were added onto this network to perform simulations, uncover therapeutic targets and explore drug combinations. The model was built upon a generic cancer Boolean model by <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib39"><span>39</span><span>Fumiã and Martins</span><span>2013</span></a></cite>, which integrates major signalling pathways and their substantial cross-talks. The pathways include the regulation of cell death and proliferation in many tumours.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This initial generic network was extended to include prostate cancer-specific genes (e.g. SPOP, AR, etc.), pathways identified using ROMA <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib74"><span>74</span><span>Martignetti et al.</span><span>2016</span></a></cite>, OmniPath <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib111"><span>111</span><span>Türei et al.</span><span>2021</span></a></cite>, and up-to-date literature. ROMA is applied on omics data, either transcriptomics or proteomics. In each pathway, the genes that contribute the most to the overdispersion are selected. ROMA was applied to the TCGA transcriptomics data using gene sets from cancer pathway databases (Appendix 1, Section 1.1.3, <a href="#app1fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 1</a>). These results were used as guidelines to extend the network to fully cover the alterations found in prostate cancer patients. OmniPath was used to complete our network finding connections between the proteins of interest known to play a role in the prostate and the ones identified with ROMA, and the list of genes already present in the model (Appendix 1, Sections 1.1.3 and 1.1.4, <a href="#app1fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 2</a> and <a href="#app1fig3" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">3</span></a>). The final network includes pathways such as androgen receptor, MAPK, Wnt, NFkB, PI3K/AKT, MAPK, mTOR, SHH, the cell cycle, the epithelial-mesenchymal transition (EMT), apoptosis and DNA damage pathways.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This network was then converted into a Boolean model where variables can take two values: 0 (inactivate or absent) or 1 (activate or present). Our model aims at predicting prostate phenotypic behaviours for healthy and cancer cells in different conditions. Nine inputs that represent some of these physiological conditions of interest were considered: <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Epithelial Growth Factor (EGF</em>), <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Fibroblast Growth Factor (FGF</em>), <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Transforming Growth Factor beta (TGFbeta)</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nutrients, Hypoxia, Acidosis, Androgen, Tumour Necrosis Factor alpha (TNF alpha</em>), and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Carcinogen</em>. These input nodes have no regulation. Their value is fixed according to the simulated experiment to represent the status of the microenvironmental characteristics (e.g. the presence or absence of growth factors, oxygen, etc.). A more complex multiscale approach would be required to consider the dynamical interaction with other cell types and the environment.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We defined six variables as output nodes that allow the integration of multiple phenotypic signals and simplify the analysis of the model. Two of these phenotypes represent the possible growth status of the cell: <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis. Apoptosis</em> is activated by Caspase 8 or Caspase 9, while <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> is activated by cyclins D and B (read-outs of the G1 and M phases, respectively). The <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> output is described in published models as specific stationary protein activation patterns, namely the following sequence of activation of cyclins: Cyclin D, then Cyclin E, then Cyclin A, and finally Cyclin B <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib109"><span>109</span><span>Traynard et al.</span><span>2016</span></a></cite>. Here, we considered a proper sequence when Cyclin D activates first, allowing the release of the transcriptional factor E2F1 from the inhibitory complex it was forming with the RB (retinoblastoma protein), and then triggering a series of events leading to the activation of Cyclin B, responsible for the cell’s entry into mitosis (Appendix 1, Section 2.2, <a href="#app1fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 5</a>). We also define several phenotypic outputs that are readouts of cancer hallmarks: <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Invasion, Migration,</em> (bone) <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Metastasis</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">DNA repair</em>. The final model accounts for 133 nodes and 449 edges (<a href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2</a>, <a href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>, and in GINsim format at the address: <a href="http://ginsim.org/model/signalling-prostate-cancer" itemscope="" itemtype="http://schema.stenci.la/Link">http://ginsim.org/model/signalling-prostate-cancer</a>). </p> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2" title="Figure 2."> <label data-itemprop="label">Figure 2.</label><img src="index.html.media/fig2.jpg" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> <figcaption> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="prostate-boolean-model-used-in-present-work">Prostate Boolean model used in present work.</h4> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Nodes (ellipses) represent biological entities, and arcs are positive (green) or negative (red) influences of one entity on another one. Orange rectangles correspond to inputs (from left to right: Epithelial Growth Factor (EGF), Fibroblast Growth Factor (FGF), Transforming Growth Factor beta (TGFbeta), Nutrients, Hypoxia, Acidosis, Androgen, fused_event, Tumour Necrosis Factor alpha (TNFalpha), SPOP, Carcinogen) and dark blue rectangles to outputs that represent biological phenotypes (from left to right: Proliferation, Migration, Invasion, Metastasis, Apoptosis, DNA_repair), the read-outs of the model. This network is available to be inspected as a Cytoscape file in the <a href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>.</p> </figcaption> </figure> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="prostate-boolean-model-simulation">Prostate Boolean model simulation</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The model can be considered as a model of healthy prostate cells when no mutants (or fused genes) are present. We refer to this model as the wild type model. These healthy cells mostly exhibit quiescence (neither proliferation nor apoptosis) in the absence of any input (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3A</a>). When <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nutrients</em> and growth factors (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EGF</em> or <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FGF</em>) are present, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> is activated (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3B</a>). <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Androgen</em> is necessary for AR activation and helps in the activation of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em>, even though it is not necessary when <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nutrients</em> or growth factors are present. Cell death factors (such as Caspase 8 or 9) trigger <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> in the absence of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">SPOP</em>, while <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Hypoxia</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Carcinogen</em> facilitate apoptosis but are not necessary if cell death factors are present (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3C</a>).</p> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3" title="Figure 3."> <label data-itemprop="label">Figure 3.</label><img src="index.html.media/fig3.jpg" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> <figcaption> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="prostate-boolean-model-maboss-simulations">Prostate Boolean model MaBoSS simulations.</h4> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) The model was simulated with all initial inputs set to 0 and all other variables random. All phenotypes are 0 at the end of the simulations, which should be understood as a quiescent state, where neither proliferation nor apoptosis is active. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) The model was simulated with growth factors (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EGF</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FGF</em>), <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nutrients</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Androgen</em> ON. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) The model was simulated with <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Carcinogen</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Androgen</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">TNFalpha</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Acidosis</em>, and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Hypoxia</em> ON.</p> </figcaption> </figure> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In our model, the progression towards metastasis is described as a stepwise process. <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Invasion</em> is first activated by known pro-invasive proteins: either β-catenin <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib38"><span>38</span><span>Francis et al.</span><span>2013</span></a></cite> or a combination of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">CDH2</em> <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib29"><span>29</span><span>De Wever et al.</span><span>2004</span></a></cite>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">SMAD</em> <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib27"><span>27</span><span>Daroqui et al.</span><span>2012</span></a></cite>, or <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EZH2</em> <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib88"><span>88</span><span>Ren et al.</span><span>2012</span></a></cite>. <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Migration</em> is then activated by <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Invasion</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EMT</em> and with either <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT</em> or <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AR</em> <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib17"><span>17</span><span>Castoria et al.</span><span>2011</span></a></cite>. Lastly, (bone) <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Metastasis</em> is activated by <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Migration</em> and one of three nodes: <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">RUNX2</em> <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib5"><span>5</span><span>Altieri et al.</span><span>2009</span></a></cite>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ERG</em> <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib3"><span>3</span><span>Adamo and Ladomery</span><span>2016</span></a></cite> or ERG fused with TMPRSS2 <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib102"><span>102</span><span>St John et al.</span><span>2012</span></a></cite>, FLI1, ETV1 or ETV4 <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Cancer Genome Atlas Research Network</span><span>2015</span></a></cite>.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This prostate Boolean model was simulated stochastically using MaBoSS <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib104"><span>104</span><span>Stoll et al.</span><span>2017</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib103"><span>103</span><span>Stoll et al.</span><span>2012</span></a></cite></span> and validated by recapitulating known phenotypes of prostate cells under physiological conditions (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3</a> and Appendix 1, Sections 2.2 and 2.3, <a href="#app1fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 5</a>–<a href="#app1fig7" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">7</span></a>). In particular, we tested that combinations of inputs lead to non-aberrant phenotypes such as growth factors leading to apoptosis in wild type conditions; we also verified that the cell cycle events occur in proper order: as CyclinD gets activated, RB1 is phosphorylated and turned OFF, allowing E2F1 to mediate the synthesis of CyclinB (see <a href="#supp2" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 2</a> for the jupyter notebook and the simulation of diverse cellular conditions).</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="personalisation-of-the-prostate-boolean-model">Personalisation of the prostate Boolean model</h3> <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="personalised-tcga-prostate-cancer-patient-boolean-models">Personalised TCGA prostate cancer patient Boolean models</h4> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We tailored the generic prostate Boolean model to a set of 488 TCGA prostate cancer patients (Appendix 1, Section 4, <a href="#app1fig9" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 9</a>) using our personalisation method (PROFILE) <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Béal et al.</span><span>2019</span></a></cite>, constructing 488 individual Boolean models, one for each patient. Personalised models were built using three types of data: discrete data such as mutations and copy number alterations (CNA) and continuous data such as RNAseq data. For discrete data, the nodes corresponding to the mutations or the CNA were forced to 0 or 1 according to the effect of alterations, based on a priori knowledge (i.e. if the mutation was reported to be activating or inhibiting the gene’s activity). For continuous data, the personalisation method modifies the value for the transition rates of model variables and their initial conditions to influence the probability of some transitions. This corresponds, in a biologically meaningful way, to translating genetic mutations as lasting modifications making the gene independent of regulation, and to translating RNA expression levels as modulation of a signal but not changing the regulation rules (see Materials and methods and in Appendix 1, Section 4.1, <a href="#app1fig10" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 10</a>–<a href="#app1fig11" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">14</span></a>).</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We assess the general behaviour of the individual patient-specific models by comparing the model outputs (i.e. probabilities to reach certain phenotypes) with clinical data. Here, the clinical data consist of a Gleason grade score associated with each patient, which in turn corresponds to the gravity of the tumour based on its appearance and the stage of invasion <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Chen and Zhou</span><span>2016</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib43"><span>43</span><span>Gleason</span><span>1992</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib42"><span>42</span><span>Gleason and Tannenbaum</span><span>1977</span></a></cite></span>. We gathered the output probabilities for all patient-specific models and confronted them to their Gleason scores. The phenotype <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">DNA_repair</em>, which can be interpreted as a sensor of DNA damage and genome integrity which could lead to DNA repair, seems to separate low and high Gleason scores (<a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4A</a> and Appendix 1, Section 4.1, <a href="#app1fig15" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 15</a>–<a href="#app1fig18" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">18</span></a>), confirming that DNA damage pathways are activated in patients <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib73"><span>73</span><span>Marshall et al.</span><span>2019</span></a></cite> but may not lead to the triggering of apoptosis in this model (Appendix 1, Section 4.1, <a href="#app1fig11" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 11</a>). Also, the centroids of Gleason grades tend to move following <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Migration</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Invasion</em> variables. We then looked at the profiles of the phenotype scores across patients and their Gleason grade and found that the density of high <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> score (close to 1, <a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4B</a>) tends to increase as the Gleason score increases (from low to intermediate to high) and these distributions are significantly different (Kruskal-Wallis rank sum test, p-value = 0.00207; Appendix 1, Section 4.1). The <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> phenotype probabilities, however, do not have a clear trend across grades (<a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4C</a>), even though the distributions are significantly different (Kruskal-Wallis rank sum test, p-value = 2.83E-6; Appendix 1, Section 4.1).</p> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="setup"><label data-itemprop="label">setup</label> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code># Load packages list.of.packages <- c("ggplot2", "tidyverse", "gghalves","patchwork", "factoextra", "ggpubr","ggsci") pacman::p_load(list.of.packages, character.only = TRUE) </code></pre> </stencila-code-chunk> </figure> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 4 data preparation"><label data-itemprop="label">Figure 4 data preparation</label> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>load(url("https://github.com/ArnauMontagud/PROFILE_v2/raw/main/Analysis%20of%20TCGA%20patients'%20simulations/data_plot_TCGA.RData.txt")) load(url("https://github.com/ArnauMontagud/PROFILE_v2/raw/main/Analysis%20of%20TCGA%20patients'%20simulations/res.pca.RData.txt")) coloursG3 <- c("Low" = "yellowgreen", "Interm" = "orange3", "High" = "black") r <- 7.16 </code></pre> </stencila-code-chunk> </figure> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 4."><label data-itemprop="label">Figure 4.</label> <figcaption> <h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="associations-between-simulations-and-gleason-grades-gg">Associations between simulations and Gleason grades (GG).</h5> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) Centroids of the Principal Component Analysis of the samples according to their Gleason grades (GG). The personalisation recipe used was mutations and copy number alterations (CNA) as discrete data and RNAseq as continuous data. Density plots of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) scores according to GG; each vignette corresponds to a specific sub-cohort with a given GG. Kruskal-Wallis rank sum test across GG is significant for Proliferation (p-value = 0.00207) and Apoptosis (p-value = 2.83E-6).</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">R code needed to obtain Figure 4.Processed datasets needed are Figure 4—source data 1 and Figure 4—source data 2 are located in the corresponding folder of the repository: here.Processed dataset needed to obtain the phenotype distributions of Figure 4B, C, with Figure 4—source code 1.Processed dataset needed to obtain the PCA of Figure 4A, with Figure 4—source code 1.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure 4</p> </figcaption> </figure> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code>p_prolif <- ggplot(data_plot_TCGA, aes(x=Proliferation, fill=GG3)) + geom_density(show.legend = FALSE) + facet_grid(GG3~.) + theme_bw()+ scale_fill_manual(values=coloursG3) p_apoptosis <- ggplot(data_plot_TCGA, aes(x=Apoptosis, fill=GG3)) + geom_density() + facet_grid(GG3~.) + scale_fill_manual(values=coloursG3, guide = guide_legend(direction = "vertical")) + theme_bw() + labs(fill="Gleason\ngroups") data_plot_3 <- filter(data_plot_TCGA, !is.na(GG3)) %>% group_by(GG3) %>% summarise(Dim.1=mean(Dim.1, na.rm=T),Dim.2=mean(Dim.2, na.rm=T)) p_bary3 <- fviz_pca_var(res.pca,scale. = r/5, repel = T, select.var = list(name=c("Proliferation", "Apoptosis", "DNA_Repair", "Migration", "Invasion"))) + geom_point(data = data_plot_3, aes(x=Dim.1, y=Dim.2, color=GG3), size=3, show.legend = FALSE ) + scale_color_manual(values=coloursG3, name="GG\nBarycent.") + theme_bw() + labs(title="") t <- (p_bary3 / (p_prolif | p_apoptosis) | guide_area()) + plot_layout(guides = 'collect', widths = c(6,1)) + plot_annotation(tag_level=c('A', 'B', 'C')) + theme_pubclean() + theme(plot.tag = element_text(size = 18)) t </code></pre> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {figalign="center"}</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#fig4}</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="personalised-drug-predictions-of-tcga-boolean-models">Personalised drug predictions of TCGA Boolean models</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Using the 488 TCGA patient-specific models, we looked in each patient for genes that, when inhibited, hamper <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> or promote <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> in the model. We focused on these inhibitions as most drugs interfere with the protein activity related to these genes, even though our methodology allows us to study increased protein activity related to over-expression of genes as well <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Béal et al.</span><span>2019</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib77"><span>77</span><span>Montagud et al.</span><span>2019</span></a></cite></span>. Interestingly, we found several genes that were found as suitable points of intervention in most of the patients (MYC_MAX complex and SPOP were identified in more than 80% of the cases) (Appendix 1, Section 4.2, <a href="#app1fig19" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 19</a> and <a href="#app1fig20" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">20</span></a>), but others were specific to only some of the patients (MXI1 was identified in only 4 patients, 1% of the total, GLI in only 7% and WNT in 8% of patients). All the TCGA-specific personalised models can be found in <a href="#supp3" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 3</a>, and the TCGA mutants and their phenotype scores can be found in <a href="#supp4" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 4</a>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Furthermore, we explored the possibility of finding combinations of treatments that could reduce the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> phenotype or increase the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> one. To lower the computational power need, we narrowed down the list of potential candidates to a set of selected genes that are targets of already-developed drugs relevant in cancer progression (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>) and analysed the simulations of the models with all the single and combined perturbations.</p><table id="table1" itemscope="" itemtype="http://schema.org/Table"><caption><label data-itemprop="label">Table 1.</label><div itemprop="caption"><h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="list-of-selected-nodes-their-corresponding-genes-and-drugs-that-were-included-in-the-drug-analysis-of-the-models-tailored-for-tcga-patients-and-lncap-cell-line">List of selected nodes, their corresponding genes and drugs that were included in the drug analysis of the models tailored for TCGA patients and LNCaP cell line.</h4></div></caption><thead><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><th itemscope="" itemtype="http://schema.stenci.la/TableCell">Node</th><th itemscope="" itemtype="http://schema.stenci.la/TableCell">Gene</th><th itemscope="" itemtype="http://schema.stenci.la/TableCell">Compound / Inhibitor name</th><th itemscope="" itemtype="http://schema.stenci.la/TableCell">Clinical stage</th><th itemscope="" itemtype="http://schema.stenci.la/TableCell">Source</th></tr></thead><tbody><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">AKT</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">AKT1, AKT2, AKT3</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">PI-103</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Enzastaurin</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 3</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Archexin, Pictilisib</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">AR</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">AR</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Abiraterone,Enzalutamide, Formestane, Testosterone propionate</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">5alpha-androstan-3beta-ol</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Caspase8</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">CASP8</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Bardoxolone</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">cFLAR</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">CFLAR</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">EGFR</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">EGFR</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Afatinib, Osimertinib, Neratinib, Erlotinib, Gefitinib</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Varlitinib</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 3</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Olmutinib, Pelitinib</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">ERK</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">MAPK1</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Isoprenaline</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Perifosine</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 3</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Turpentine, SB220025, Olomoucine, Phosphonothreonine</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">MAPK3, MAPK1</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Arsenic trioxide</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Ulixertinib, Seliciclib</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Purvalanol</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">MAPK3</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sulindac, Cholecystokinin</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">5-iodotubercidin</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">GLUT1</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">SLC2A1</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Resveratrol</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 4</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">HIF-1</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">HIF1A</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">CAY-10585</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">HSPs</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">HSP90AA1, HSP90AB1, HSP90B1, HSPA1A, HSPA1B, HSPB1</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cladribine</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">17-DMAG</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">NMS-E973</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">MEK1_2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">MAP2K1, MAP2K2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Trametinib, Selumetinib</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Perifosine</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 3</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">PD184352 (CI-1040)</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">MYC_MAX</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">complex of MYC and MAX</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">10058-F4 (for MAX)</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">p14ARF</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">CDKN2A</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">PI3K</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">PIK3CA, PIK3CB, PIK3CG, PIK3CD, PIK3R1, PIK3R2, PIK3R3, PIK3R4, PIK3R5, PIK3R6, PIK3C2A, PIK3C2B, PIK3C2G, PIK3C3</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">PI-103</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Pictilisib</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">ROS</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">NOX1, NOX3, NOX4</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Fostamatinib</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">NOX2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Dextromethorphan</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Approved</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Tetrahydroisoquinolines (CHEMBL3733336, CHEMBL3347550, CHEMBL3347551)</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">ChEMBL</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">SPOP</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">SPOP</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">-</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">TERT</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">TERT</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Grn163l</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phase 2</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Drug Bank</td></tr><tr itemscope="" itemtype="http://schema.stenci.la/TableRow"><td itemscope="" itemtype="http://schema.stenci.la/TableCell">BIBR 1532</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">Preclinical</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell">ChEMBL</td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td><td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td></tr></tbody></table><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We used the models to grade the effect that the combined treatments have in each one of the 488 TCGA patient-specific models’ phenotypes. This list of combinations of treatments can be used to compare the effects of drugs on each TCGA patient and allows us to propose some of them for individual patients and to suggest drugs suitable to groups of patients (<a href="#supp4" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 4</a>). Indeed, the inactivation of some of the targeted genes had a greater effect in some patients than in others, suggesting the possibility for the design of personalised drug treatments. For instance, for the TCGA-EJ-5527 patient, the use of MYC<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MAX complex inhibitor reduced _Proliferation</em> to 66%. For this patient, combining MYC<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MAX with other inhibitors, such as AR or AKT, did not further reduce the _Proliferation</em> score (67% in these cases). Other patients have MYC<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MAX as an interesting drug target, but the inhibition of this complex did not have such a dramatic effect on their _Proliferation</em> scores as in the case of TCGA-EJ-5527. Likewise, for the TCGA-H9-A6BX patient, the use of SPOP inhibitor increased <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> by 87%, while the use of a combination of cFLAR and SPOP inhibitors further increased <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> by 89%. For the rest of this section, we focus on the analysis of clinical groups rather than individuals.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Studying the decrease of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em>, we found that AKT is the top hit in Gleason Grades 1, 2, 3, and 4, seconded by EGFR and SPOP in Grade 1, by SPOP and PIP3 in Grade 2, by PIP3 and AR in Grade 3, and by CyclinD and MYC<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MAX in Grade 4. MYC_MAX is the top hit in Grade 5, seconded by AR (Appendix 1, Section 4.2, <a href="#app1fig19" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 19</a>). In regard to the increase of _Apoptosis</em>, SPOP is the top hit in all grades, seconded by SSH in Grades 1, 2, and 3 and by AKT in Grade 4 (Appendix 1, Section 4.2, <a href="#app1fig20" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 20</a>). It is interesting to note here that many of these genes are targeted by drugs (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>). Notably, AR is the target of the drug Enzalutamide, which is indicated for men with an advanced stage of the disease <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib97"><span>97</span><span>Scott</span><span>2018</span></a></cite>, or that MYC is the target of BET bromodomain inhibitors and are generally effective in castration-resistant prostate cancer cases <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib24"><span>24</span><span>Coleman et al.</span><span>2019</span></a></cite>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The work on patient data provided possible insights and suggested patient- and grade-specific potential targets. To validate our approach experimentally, we personalised the prostate model to different prostate cell lines, where we performed drug assays to confirm the predictions of the model.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="personalised-drug-predictions-of-lncap-boolean-model">Personalised drug predictions of LNCaP Boolean model</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We applied the methodology for personalisation of the prostate model to eight prostate cell lines available in GDSC <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib53"><span>53</span><span>Iorio et al.</span><span>2016</span></a></cite>: 22RV1, BPH-1, DU-145, NCI-H660, PC-3, PWR-1E, and VCaP (results in Appendix 1, Section 5 and are publicly available in <a href="#supp5" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 5</a>). We decided to focus the validation on one cell line, LNCaP.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">LNCaP, first isolated from a human metastatic prostate adenocarcinoma found in a lymph node <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib51"><span>51</span><span>Horoszewicz et al.</span><span>1983</span></a></cite>, is one of the most widely used cell lines for prostate cancer studies. Androgen-sensitive LNCaP cells are representative of patients sensitive to treatments as opposed to resistant cell lines such as DU-145. Additionally, LNCaP cells have been used to obtain numerous subsequent derivatives with different characteristics <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib26"><span>26</span><span>Cunningham and You</span><span>2015</span></a></cite>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The LNCaP personalisation was performed based on mutations as discrete data and RNA-Seq as continuous data. The resulting LNCaP-specific Boolean model was then used to identify all possible combinations of mutations (interpreted as effects of therapies) and to study the synergy of these perturbations. For that purpose, we automatically performed single and double mutant analyses on the LNCaP-specific model (knock-out and overexpression) <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib77"><span>77</span><span>Montagud et al.</span><span>2019</span></a></cite> and focused on the model phenotype probabilities as read-outs of the simulations. The analysis of the complete set of simulations for the 32,258 mutants can be found in the Appendix 1, Section 6.1 and in <a href="#supp6" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 6</a>, where the LNCaP cell line-specific mutants and their phenotype scores are reported for all mutants. Among all combinations, we identified the top 20 knock-out mutations that depleted <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> or increased <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> the most. As some of them overlapped, we ended up with 29 nodes: <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT, AR, ATR, AXIN1, Bak, BIRC5, CDH2, cFLAR, CyclinB, CyclinD, E2F1, eEF2K, eEF2, eEF2K, EGFR, ERK, HSPs, MED12, mTORC1, mTORC2, MYC, MYC_MAX, PHDs, PI3K, PIP3, SPOP, TAK1, TWIST1, and VHL</em>. We used the scores of these nodes to further trim down the list to have 10 final nodes (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT, AR, cFLAR, EGFR, ERK, HSPs, MYC_MAX, SPOP,</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">PI3K</em>) and added seven other nodes whose genes are considered relevant in cancer biology, such as <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AR_ERG</em> fusion, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Caspase8</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">HIF1</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">GLUT1, MEK1_2</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">p14ARF</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ROS,</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">TERT</em> (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>). We did not consider the overexpression mutants as they have a very difficult translation to drug uses and clinical practices.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To further analyse the mutant effects, we simulated the LNCaP model with increasing node inhibition values to mimic the effect of drugs’ dosages using a methodology we specifically developed for this purpose (PROFILE<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">v2 and available at <a href="https://github.com/ArnauMontagud/PROFILE_v2" itemscope="" itemtype="http://schema.stenci.la/Link">https://github.com/ArnauMontagud/PROFILE_v2</a>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib79"><span>79</span><span>Montagud</span><span>2022</span></a></cite>). Six simulations were done for each inhibited node, with 100% of node inhibition (proper knock-out), 80%, 60%, 40%, 20% and 0% (no inhibition) (see Materials and methods). A nutrient-rich media with EGF was used for these simulations that correspond to experimental conditions that are tested here. We show results on three additional sets of initial conditions in the Appendix 1, Section 6, <a href="#app1fig27" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 27</a>: a nutrient-rich media with androgen, with androgen and EGF, and with none, . We applied this gradual inhibition, using increasing drugs’ concentrations, to a reduced list of drug-targeted genes relevant for cancer progression (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>). We confirmed that the inhibition of different nodes affected differently the probabilities of the outputs (Appendix 1, Section 7.3.1, <a href="#app1fig34" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 34</a> and <a href="#app1fig35" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">35</span></a>). Notably, the _Apoptosis</em> score was slightly promoted when knocking out <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">SPOP</em> under all growth conditions (Appendix 1, Section 7.3.1, <a href="#app1fig35" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 35</a>). Likewise, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> depletion was accomplished when <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">HSPs</em> or <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MYC_MAX</em> were inhibited under all conditions and, less notably, when <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ERK, EGFR</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">SPOP,</em> or <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">PI3K</em> were inhibited (Appendix 1, Section 7.3.1, <a href="#app1fig35" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 35</a>).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Additionally, these gradual inhibition analyses can be combined to study the interaction of two simultaneously inhibiting nodes (Appendix 1, Section 7.3.2, <a href="#app1fig36" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 36</a> and <a href="#app1fig37" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">37</span></a>). For instance, the combined gradual inhibition of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ERK</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MYC_MAX</em> nodes affects the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> score in a balanced manner (<a href="#fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5A</a>) even though <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MYC_MAX</em> seems to affect this phenotype more, notably at low activity levels. By extracting subnetworks of interaction around <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ERK</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MYC_MAX</em> and comparing them, we found that the pathways they belong to have complementary downstream targets participating in cell proliferation through targets in MAPK and cell cycle pathways. This complementarity could explain the synergistic effects observed (<a href="#fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5A and C</a>).</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 5."><label data-itemprop="label">Figure 5.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="phenotype-score-variations-and-synergy-upon-combined-erk-and-mycmax-a-and-c-and-hsps-and-pi3k-b-and-d-inhibition-under-_egf-growth-condition">Phenotype score variations and synergy upon combined ERK and MYC<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MAX (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A and C</strong>) and HSPs and PI3K (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B and D</strong>) inhibition under _EGF</em> growth condition.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Proliferation score variation (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) and Bliss Independence synergy score (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) with increased node activation of nodes ERK and MYC_MAX. Proliferation score variation (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) and Bliss Independence synergy score (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">D</strong>) with increased node activation of nodes HSPs and PI3K. Bliss Independence synergy score <1 is characteristic of drug synergy, grey colour means one of the drugs is absent and thus no synergy score is available.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">R code needed to perform the drug dosage experiments and obtain Figure 5 from the main text and Appendix 1—figures 27, 34–39.Processed datasets needed is Figure 5—source data 1 and is located in the corresponding folder of the repository: here.Processed datasets needed to obtain the phenotype score variations and synergy values of Figure 5 with Figure 5—source code 1.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure 5</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code>load(url("https://github.com/ArnauMontagud/PROFILE_v2/raw/main/Gradient%20inhibition%20of%20nodes/drugs_figures_datasets.RData.txt")) interesants_prolif <- tot %>% filter(drug1 == "ERK" & drug2 == "MYC_MAX") interesants_bliss <- tot_Bliss_prolif %>% filter(drug1 == "ERK" & drug2 == "MYC_MAX") prolif <- ggplot(interesants_prolif %>% filter(Phenotype == "Proliferation"), aes(dose1, dose2)) + geom_tile(aes(fill = value), colour = "black") + facet_grid(drug2 ~ drug1, switch = "both") + scale_x_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_y_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_fill_gradient2(low = "blue", high = "white", limits = c(-1, 0.1)) + labs(x = "ERK node inhibition (%)", y = "MYC_MAX node\ninhibition (%)", fill = "Treated -\nuntreated\ncell line\nscore") + theme( legend.position = "none", axis.ticks = element_blank(), panel.background = element_blank(), strip.text = element_blank(), strip.background = element_rect(fill = NA), plot.title = element_text(hjust = 0.5) ) prolifBliss <- ggplot(interesants_bliss, aes(dose1, dose2)) + geom_tile(aes(fill = Bliss), colour = "black") + facet_grid(drug2 ~ drug1, switch = "both") + scale_x_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_y_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_fill_gradientn( colours = c("#8c5ba9", "white", "#7fbf7b"), breaks = (c(0, 0.5, 1, 1.5, 2)), labels = (c(0, 0.5, 1, 1.5, ">2")), limits = c(0, 2) ) + labs(x = "ERK node inhibition (%)", y = "MYC_MAX node\ninhibition (%)", fill = "Bliss\nscore") + theme( legend.position = "none", axis.ticks = element_blank(), panel.background = element_blank(), strip.text = element_blank(), strip.background = element_rect(fill = NA), plot.title = element_text(hjust = 0.5) ) interesants_prolif <- tot %>% filter(drug1 == "HSPs" & drug2 == "PI3K") interesants_bliss <- tot_Bliss_prolif %>% filter(drug1 == "HSPs" & drug2 == "PI3K") prolif2 <- ggplot(interesants_prolif %>% filter(Phenotype == "Proliferation"), aes(dose1, dose2)) + geom_tile(aes(fill = value), colour = "black") + facet_grid(drug2 ~ drug1, switch = "both") + scale_x_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_y_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_fill_gradient2(low = "blue", high = "white", limits = c(-1, 0.1)) + labs(x = "HSPs node inhibition (%)", y = "PI3K node inhibition (%)", fill = "Treated -\nuntreated\ncell line\nscore") + theme( axis.ticks = element_blank(), panel.background = element_blank(), strip.text = element_blank(), strip.background = element_rect(fill = NA), plot.title = element_text(hjust = 0.5) ) prolifBliss1 <- ggplot(interesants_bliss, aes(dose1, dose2)) + geom_tile(aes(fill = Bliss), colour = "black") + facet_grid(drug2 ~ drug1, switch = "both") + scale_x_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_y_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_fill_gradientn( colours = c("#8c5ba9", "white", "#7fbf7b"), breaks = (c(0, 0.5, 1, 1.5, 2)), labels = (c(0, 0.5, 1, 1.5, ">2")), limits = c(0, 2) ) + labs(x = "HSPs node inhibition (%)", y = "PI3K node inhibition (%)", fill = "Bliss\nscore") + theme( axis.ticks = element_blank(), panel.background = element_blank(), strip.text = element_blank(), strip.background = element_rect(fill = NA), plot.title = element_text(hjust = 0.5) ) prolif + prolif2 + prolifBliss + prolifBliss1 + plot_annotation(tag_levels = 'A') </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#fig5}</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure S36."><label data-itemprop="label">Figure S36.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="proliferation-phenotype-score-variations-of-the-lncap-model-upon-combined-nodes-inhibition-under-egf-growth-condition"><em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> phenotype score variations of the LNCaP model upon combined nodes inhibition under <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EGF</em> growth condition.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Figure 5A is a closer look at ERK and MYC_MAX combination and Figure 5B at HSPs and PI3K combination.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure S36</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code># Figure S36: Double drug Proliferation scores Double_EGF_Proliferation <- ggplot( tot %>% filter(Phenotype == "Proliferation"), aes(dose1, dose2)) + facet_grid(drug2 ~ drug1, switch = "both") + geom_tile(aes(fill = value), colour = "black") + scale_x_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_y_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_fill_gradient2( low = "blue", high = "firebrick", mid = "white", limits = c(-1, 1) ) + labs( title = paste0("Proliferation phenotype variations upon drugs inhibition"), x = "Node inhibition (%)", y = "Node inhibition (%)", fill = "Treated -\nuntreated\ncell line\nscore" ) + theme( axis.ticks = element_blank(), panel.background = element_blank(), strip.background = element_rect(fill = NA), plot.title = element_text(hjust = 0.5), strip.text.y.left = element_text(angle = 0), strip.text.x = element_text(angle = 90), axis.text.x = element_text(angle = 90) ) Double_EGF_Proliferation </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#app1fig36}</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure S37."><label data-itemprop="label">Figure S37.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="apoptosis-phenotype-score-variations-of-the-lncap-model-upon-combined-nodes-inhibition-under-egf-growth-condition">Apoptosis phenotype score variations of the LNCaP model upon combined nodes inhibition under EGF growth condition.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure S37</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code># Figure S37: Double drug Apoptosis scores Double_EGF_Apoptosis <- ggplot( tot %>% filter(Phenotype == "Apoptosis"), aes(dose1, dose2)) + facet_grid(drug2 ~ drug1, switch = "both") + geom_tile(aes(fill = value), colour = "black") + scale_x_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_y_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_fill_gradient2(low = "blue", high = "firebrick", mid = "white") + labs( title = paste0("Apoptosis phenotype variations upon drugs inhibition"), x = "Node inhibition (%)", y = "Node inhibition (%)", fill = "Treated -\nuntreated\ncell line\nscore" ) + theme( axis.ticks = element_blank(), panel.background = element_blank(), strip.background = element_rect(fill = NA), plot.title = element_text(hjust = 0.5), strip.text.y.left = element_text(angle = 0), strip.text.x = element_text(angle = 90), axis.text.x = element_text(angle = 90) ) Double_EGF_Apoptosis </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#app1fig37}</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure S38."><label data-itemprop="label">Figure S38.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="bliss-independence-synergies-scores-variations-in-proliferation-phenotype-of-the-lncap-model-upon-combined-nodes-inhibition-under-egf-growth-conditions">Bliss Independence synergies scores variations in <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> phenotype of the LNCaP model upon combined nodes inhibition under <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EGF</em> growth conditions.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Bliss Independence synergy score <1 is characteristic of drug synergy. Figure 5C is a closer look at ERK and MYC_MAX combination and Figure 5D at HSPs and PI3K combination, grey colour means one of the drugs is absent and thus no synergy score is available.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure S38</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code># Figure S38: Double drug Proliferation Bliss synergy values bliss_pro <- ggplot(tot_Bliss_prolif, aes(dose1, dose2)) + geom_tile(aes(fill = Bliss), colour = "black") + facet_grid(drug2 ~ drug1, switch = "both") + scale_x_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_y_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_fill_gradientn( colours = c("#8c5ba9", "white", "#7fbf7b"), breaks = (c(0.0, 0.5, 1, 1.5, 2)), labels = (c(0.0, 0.5, 1, 1.5, ">2")) ) + labs(x = "Node inhibition (%)", y = "Node inhibition (%)", fill = "Bliss score") + theme( axis.ticks = element_blank(), panel.background = element_blank(), strip.background = element_rect(fill = NA), plot.title = element_text(hjust = 0.5), strip.text.y.left = element_text(angle = 0), strip.text.x = element_text(angle = 90), axis.text.x = element_text(angle = 90) ) bliss_pro </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#app1fig38}</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure S39."><label data-itemprop="label">Figure S39.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="bliss-independence-synergies-scores-variations-in-apoptosis-phenotypes-of-the-lncap-model-upon-combined-nodes-inhibition-under-egf-growth-conditions">Bliss Independence synergies scores variations in <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> phenotypes of the LNCaP model upon combined nodes inhibition under <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EGF</em> growth conditions.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Bliss Independence synergy score <1 is characteristic of drug synergy, grey colour means one of the drugs is absent and thus no synergy score is available.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure S39</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code># Figure S39: Double drug Apoptosis Bliss synergy values bliss_apop <- ggplot(tot_Bliss_apop, aes(dose1, dose2)) + geom_tile(aes(fill = Bliss), colour = "black") + facet_grid(drug2 ~ drug1, switch = "both") + scale_x_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_y_continuous( breaks = c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0), labels = c(0, 20, 40, 60, 80, 100) ) + scale_fill_gradientn( colours = c("#8c5ba9", "white", "#7fbf7b"), breaks = (c(0, 0.5, 1, 1.5, 2)), labels = (c(0, 0.5, 1, 1.5, ">2")) ) + labs(x = "Node inhibition (%)", y = "Node inhibition (%)", fill = "Bliss score") + theme( axis.ticks = element_blank(), panel.background = element_blank(), strip.background = element_rect(fill = NA), plot.title = element_text(hjust = 0.5), strip.text.y.left = element_text(angle = 0), strip.text.x = element_text(angle = 90), axis.text.x = element_text(angle = 90) ) bliss_apop </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#app1fig39}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Lastly, drug synergies can be studied using Bliss Independence using the results from single and combined simulations with gradual inhibitions. This score compares the combined effect of two drugs with the effect of each one of them, with a synergy when the value of this score is lower than 1. We found that the combined inhibition of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ERK</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">MYC_MAX</em> nodes on the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> score was synergistic (<a href="#fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5C</a>). Another synergistic pair is the combined gradual inhibition of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">HSPs</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">PI3K</em> nodes that also affects the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> score in a joint manner (<a href="#fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5B</a>), with some Bliss Independence synergy found (<a href="#fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5D</a>). A complete study on the Bliss Independence synergy of all the drugs considered in the present work on <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> phenotypes can be found in Appendix 1, Section 7.3.2, <a href="#app1fig38" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 38</a> and <a href="#app1fig39" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">39</span></a>.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="experimental-validation-of-predicted-targets">Experimental validation of predicted targets</h3><h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="drugs-associated-with-the-proposed-targets">Drugs associated with the proposed targets</h4><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To identify drugs that could act as potential inhibitors of the genes identified with the Boolean model, we explored the drug-target associations in DrugBank <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib115"><span>115</span><span>Wishart et al.</span><span>2018</span></a></cite> and ChEMBL <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib40"><span>40</span><span>Gaulton et al.</span><span>2017</span></a></cite>. We found drugs that targeted almost all genes corresponding to the nodes of interest in <a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>, except for cFLAR, p14ARF, and SPOP. However, we could not identify experimental cases where drugs targeting both members of the proposed combinations were available (Appendix 1, Section 7.1 and in <a href="#supp6" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 6</a>). One possible explanation is that the combinations predicted by the model suggest, in some cases, to overexpress the potential target and most of the drugs available act as inhibitors of their targets.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Using the cell line-specific models, we tested if the LNCaP cell line was more sensitive than the rest of the prostate cell lines to the LNCaP-specific drugs identified in <a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>. We compared GDSC’s Z-score of these drugs in LNCaP with their Z-scores in all GDSC cell lines (<a href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 6</a> and Appendix 1, Section 7.2, <a href="#app1fig33" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 33</a>). We observed that LNCaP is more sensitive to drugs targeting AKT or TERT than the rest of the studied prostate cell lines. Furthermore, we saw that the drugs that targeted the genes included in the model allowed the identification of cell line specificities (Appendix 1, Section 7.1). For instance, target enrichment analysis showed that LNCaP cell lines are especially sensitive to drugs targeting PI3K/AKT/mTOR, hormone-related (AR targeting) and Chromatin (bromodomain inhibitors, regulating Myc) pathways (adjusted p-values from target enrichment: 0.001, 0.001, and 0.032, respectively, Appendix 1, Section 7.1, <a href="#app1table2" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—table 2</a>), which corresponds to the model predictions (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>). Also, the LNCaP cell line is more sensitive to drugs targeting model-identified nodes than to drugs targeting other proteins (Appendix 1, Section 7.1, <a href="#app1fig32" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 32</a>, Mann-Whitney U p-value 0.00041), and this effect is specific for LNCaP cell line (Mann-Whitney U p-values ranging from 0.0033 to 0.38 for other prostate cancer cell lines).</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 6 data preparation"><label data-itemprop="label">Figure 6 data preparation</label><stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-programminglanguage="r"><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>load(url("https://github.com/ArnauMontagud/PROFILE_v2/raw/main/Analysis%20of%20drug%20sensitivities%20across%20cell%20lines/data_plot_CL.Rdata.txt")) load(url("https://github.com/ArnauMontagud/PROFILE_v2/raw/main/Analysis%20of%20drug%20sensitivities%20across%20cell%20lines/correspondance.Rdata.txt")) listed_nodes <- c("HSPs", "AKT", "TERT", "ERK", "EGFR", "MEK1_2", "PI3K", "AR", "Caspase8", "cFLAR", "GLUT1", "HIF-1", "MYC_MAX", "p14ARF", "ROS", "SPOP") my_cell_lines <- c("BPH-1", "PC-3", "RWPE2-W99", "22RV1", "VCaP", "NCI-H660", "DU-145", "LNCaP-Clone-FGC", "PWR-1E", "RWPE-1") options(dplyr.summarise.inform = FALSE) plot_data <- filter(data_plot_CL, Patient_ID %in% my_cell_lines) %>% left_join(select(correspondance, Drug_Name, Drug_Target), by = "Drug_Name") %>% group_by(Patient_ID, Drug_Target, TCGA_DESC) %>% summarise(Z_SCORE=mean(Z_SCORE), N=n()) %>% mutate(DR=case_when( Drug_Target %in% listed_nodes ~ Drug_Target, TRUE ~ "Other targets" )) %>% ungroup %>% mutate(Patient_ID=if_else(Patient_ID=="LNCaP-Clone-FGC", "LNCap", Patient_ID), DR=factor(DR, levels=c("AKT", "AR", "EGFR", "ERK", "HSPs", "MEK1_2", "PI3K", "TERT", "Other targets")))</code></pre></stencila-code-chunk></figure><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 6."><label data-itemprop="label">Figure 6.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="model-targeting-drugs-sensitivities-across-prostate-cell-lines">Model-targeting drugs’ sensitivities across prostate cell lines.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">GDSC z-score was obtained for all the drugs targeting genes included in the model for all the prostate cell lines in GDSC. Negative values mean that the cell line is more sensitive to the drug. Drugs included in <a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a> were highlighted. ‘Other targets’ are drugs targeting model-related genes that are not part of <a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">R code needed to obtain Figure 6.Processed datasets needed are Figure 6—source data 1 and Figure 6—source data 2 are located in the corresponding folder of the repository: here.Processed dataset needed to obtain Figure 6 with Figure 6—source code 1.Processed dataset needed to obtain Figure 6 with Figure 6—source code 1.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure 6</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code>plot_data$Patient_ID2 = factor(plot_data$Patient_ID, levels=c("LNCap","22RV1","BPH-1","DU-145","PC-3", "PWR-1E","VCaP"), labels=c("LNCap","22RV1","BPH-1","DU-145","PC-3", "PWR-1E","VCaP")) plot_CL1 <- ggplot(plot_data, aes(y=Z_SCORE)) + geom_half_boxplot(outlier.shape = NA) + geom_jitter(aes(x=0.3, color=DR, size=DR, alpha=DR),height = 0, width = 0.2) + scale_color_manual(values=c("HSPs"="#800000FF", "AKT"="#767676FF", "ERK"="#FFA319FF","TERT"="#8A9045FF", "AR"="#155F83FF", "EGFR"="#C16622FF", "MEK1_2"="#8F3931FF", "PI3K"="#350E20FF", "Other targets"="grey70")) + scale_size_manual(values=c("HSPs"=3, "AKT"=3, "ERK"=3, "TERT"=3,"AR"=3, "EGFR"=3, "MEK1_2"=3, "PI3K"=3,"Other targets"=1)) + scale_alpha_manual(values=c("HSPs"=1, "AKT"=1, "ERK"=1, "TERT"=1, "AR"=1, "EGFR"=1, "MEK1_2"=1, "PI3K"=1,"Other targets"=0.5)) + labs(y="Z-score", color="Target nodes:", size="Target nodes:", alpha="Target nodes:") + theme_pubclean() + theme(legend.position = "top", legend.title = element_text(face="bold"), axis.text.x = element_blank(), axis.title.x = element_blank(), axis.ticks.x = element_blank(), legend.key=element_blank()) + facet_grid(~Patient_ID2) plot_CL1 </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#fig6}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Overall, the drugs proposed through this analysis suggest the possibility to repurpose drugs that are used in treating other forms of cancer for prostate cancer and open the avenue for further experimental validations based on these suggestions.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="experimental-validation-of-drugs-in-lncap">Experimental validation of drugs in LNCaP</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To validate the model predictions of the candidate drugs, we selected four drugs that target HSPs and PI3K and tested them in LNCaP cell line experiments by using endpoint cell viability measurement assays and real-time cell survival assays using the xCELLigence system (see Materials and methods). The drug selection was a compromise between the drugs identified by our analyses (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>) and their effect in diminishing LNCaP’s proliferation (see the previous section). In both assays, drugs that target HSP90AA1 and PI3K/AKT pathway genes retrieved from the model analyses were found to be effective against cell proliferation.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The Hsp90 chaperone is expressed abundantly and plays a crucial role in the correct folding of a wide variety of proteins such as protein kinases and steroid hormone receptors <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib96"><span>96</span><span>Schopf et al.</span><span>2017</span></a></cite>. Hsp90 can act as a protector of less stable proteins produced by DNA mutations in cancer cells <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib7"><span>7</span><span>Barrott and Haystead</span><span>2013</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib49"><span>49</span><span>Hessenkemper and Baniahmad</span><span>2013</span></a></cite></span>. Currently, Hsp90 inhibitors are in clinical trials for multiple indications in cancer <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib20"><span>20</span><span>Chen et al.</span><span>2020</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib54"><span>54</span><span>Iwai et al.</span><span>2012</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib68"><span>68</span><span>Le et al.</span><span>2017</span></a></cite></span>. The PI3K/AKT signalling pathway controls many different cellular processes such as cell growth, motility, proliferation, and apoptosis and is frequently altered in different cancer cells <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib16"><span>16</span><span>Carceles-Cordon et al.</span><span>2020</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib98"><span>98</span><span>Shorning et al.</span><span>2020</span></a></cite></span>. Many PI3K/AKT inhibitors are in different stages of clinical development, and some of them are approved for clinical use (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Notably, Hsp90 (NMS-E973,17-DMAG) and PI3K/AKT pathway (PI-103, Pictilisib) inhibitors showed a dose-dependent activity in the endpoint cell viability assay determined by the fluorescent resazurin after a 48 hr incubation (<a href="#fig7" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7</a>). This dose-dependent activity is more notable in Hsp90 drugs (NMS-E973,17-DMAG) than in PI3K/AKT pathway (Pictilisib) ones and very modest for PI-103.</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 7 data preparation"><label data-itemprop="label">Figure 7 data preparation</label><stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-programminglanguage="r"><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>end1pre <- read.table( "https://github.com/ArnauMontagud/PROFILE_v2/raw/main/Analysis%20of%20experimental%20validation/LNCAPendpoint.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE ) end_ids <- read.table( "https://github.com/ArnauMontagud/PROFILE_v2/raw/main/Analysis%20of%20experimental%20validation/LNCAPendpoint_id.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE ) %>% pivot_longer(-X) %>% mutate(., id = as.integer(gsub("X", "", .$name))) %>% rename(., drug = X, nM = value) %>% select(-name) end1 <- full_join(end1pre, end_ids, by = c("drug", "id")) end1$nM <- as.integer(end1$nM) end1$count_noise <- end1$count - (end1 %>% filter(drug == "NoCell") %>% .$count %>% mean()) end1$count_norm <- end1$count_noise / (end1 %>% filter(drug == "kontroll") %>% .$count_noise %>% mean()) end2 <- end1 %>% filter(!(drug=="NoCell")) %>% mutate(id=factor(id, levels=c(0,5,4,3,2,1))) %>% filter(!(is.na(count))) end2[is.na(end2$nM),]$id <- 0 end2[is.na(end2$nM),]$nM <- 0.0</code></pre></stencila-code-chunk></figure><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 7."><label data-itemprop="label">Figure 7.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="cell-viability-assay-determined-by-the-fluorescent-resazurin-after-a-48-hours-incubation-showed-a-dose-dependent-response-to-different-inhibitors">Cell viability assay determined by the fluorescent resazurin after a 48 hours incubation showed a dose-dependent response to different inhibitors.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) Cell viability assay of LNCaP cell line response to 17-DMAG HSP90 inhibitor. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) Cell viability assay of LNCaP cell line response to PI-103 PI3K/AKT pathway inhibitor. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) Cell viability assay of LNCaP cell line response to NMS-E973 HSP90 inhibitor. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">D</strong>) Cell viability assay of LNCaP cell line response to Pictilisib PI3K/AKT pathway inhibitor. Concentrations of drugs were selected to capture their drug-dose response curves. The concentrations for the NMS-E973 are different from the rest as this drug is more potent than the rest (see Materials and methods).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">R code needed to obtain Figure 7.Processed datasets needed are Figure 7—source data 1 and 2 and are located in the corresponding folder of the repository: here.Processed dataset needed to obtain Figure 7 with Figure 7—source code 1.Processed dataset needed to obtain with Figure 7—source code 1.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure 7</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code>cols <- c( "17-DMAG" = pal_uchicago("default")(6)[1], "NMS-E973" = pal_uchicago("default")(6)[4], "PI-103" = pal_uchicago("default")(6)[5], "Pictilisib" = pal_uchicago("default")(6)[6] ) `17-DMAGa` <- ggplot() + theme_bw() + scale_y_continuous(limits = c(0.25, 1.26)) + scale_x_discrete(labels=c("0" = "0", "5" = "1", "4" = "5", "3" = "25", "2" = "125", "1" = "625")) + geom_half_point(data=end2 %>% filter(drug=="17-DMAG" | drug=="kontroll"), aes(x = id, y = count_norm), colour = cols[1], transformation = position_jitter(width = 0.1,height = 0,seed = 4), size = 2) + geom_half_boxplot(data=end2 %>% filter(drug=="17-DMAG" | drug=="kontroll"), aes(x = id, y = count_norm), fill = cols[1], colour = cols[1], alpha = .3) + scale_fill_manual(values = cols) + scale_colour_manual(values = cols) + labs(title = "17-DMAG", x = "Dose (nM)", y = "Normalised cell viability") + theme(plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text =element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank()) `NMS-E973a` <- ggplot() + theme_bw() + scale_y_continuous(limits = c(0.25, 1.26)) + scale_x_discrete(labels=c("0" = "0", "5" = "2", "4" = "8", "3" = "32", "2" = "128", "1" = "512")) + geom_half_point(data=end2 %>% filter(drug=="NMS-E973" | drug=="kontroll"), aes(x = id, y = count_norm), colour = cols[2], transformation = position_jitter(width = 0.1,height = 0,seed = 4), size = 2) + geom_half_boxplot(data=end2 %>% filter(drug=="NMS-E973" | drug=="kontroll"), aes(x = id, y = count_norm), fill = cols[2], colour = cols[2], alpha = .3) + scale_fill_manual(values = cols) + scale_colour_manual(values = cols) + labs(title = "NMS-E973", x = "Dose (nM)", y = "Normalised cell viability") + theme(plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text =element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank()) `PI-103a` <- ggplot() + theme_bw() + scale_y_continuous(limits = c(0.25, 1.25)) + scale_x_discrete(labels=c("0" = "0", "5" = "1", "4" = "5", "3" = "25", "2" = "125", "1" = "625")) + geom_half_point(data=end2 %>% filter(drug=="PI-103" | drug=="kontroll"), aes(x = id, y = count_norm), colour = cols[3], transformation = position_jitter(width = 0.12,height = 0,seed = 4), size = 2) + geom_half_boxplot(data=end2 %>% filter(drug=="PI-103" | drug=="kontroll"), aes(x = id, y = count_norm), fill = cols[3], colour = cols[3], alpha = .3) + scale_fill_manual(values = cols) + scale_colour_manual(values = cols) + labs(title = "PI-103", x = "Dose (nM)", y = "Normalised cell viability") + theme(plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text =element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank()) Pictilisiba <- ggplot() + theme_bw() + scale_y_continuous(limits = c(0.25, 1.25)) + scale_x_discrete(labels=c("0" = "0", "5" = "1", "4" = "5", "3" = "25", "2" = "125", "1" = "625")) + geom_half_point(data=end2 %>% filter(drug=="Pictilisib" | drug=="kontroll"), aes(x = id, y = count_norm), colour = cols[4], transformation = position_jitter(width = 0.12,height = 0,seed = 4), size = 2) + geom_half_boxplot(data=end2 %>% filter(drug=="Pictilisib" | drug=="kontroll"), aes(x = id, y = count_norm), fill = cols[4], colour = cols[4], alpha = .3) + scale_fill_manual(values = cols) + scale_colour_manual(values = cols) + labs(title = "Pictilisib", x = "Dose (nM)", y = "Normalised cell viability") + theme(plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text =element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank()) patchwork = (`17-DMAGa` + `PI-103a`) / (`NMS-E973a` + Pictilisiba) # Remove titles from subplots patchwork[[1]][[1]] = patchwork[[1]][[1]] + theme(axis.title.x = element_blank()) patchwork[[1]][[2]] = patchwork[[1]][[2]] + theme( axis.title.x = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title.y = element_blank() ) patchwork[[2]][[2]] = patchwork[[2]][[2]] + theme( axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title.y = element_blank() ) patchwork + plot_annotation(tag_levels = "A") </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {height=5 width=6 figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#fig7}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We studied the real-time response of LNCaP cell viability upon drug addition and saw that the LNCaP cell line is sensitive to Hsp90 and PI3K/AKT pathway inhibitors (<a href="#fig8" itemscope="" itemtype="http://schema.stenci.la/Link">Figures 8</a> and <a href="#fig9" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">9</span></a>, respectively). Both Hsp90 inhibitors tested, 17-DMAG and NMS-E973, reduced the cell viability 12 hr after drug supplementation (<a href="#fig8" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 8A</a> for 17-DMAG and <a href="#fig8" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 8B</a> for NMS-E973), with 17-DMAG having a stronger effect and in a more clear concentration-dependent manner than NMS-E973 (Appendix 1, Section 8, <a href="#app1fig40" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 40</a>, panels B-D for 17-DMAG and panels F-H for NMS-E973).</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 8 data preparation"><label data-itemprop="label">Figure 8 data preparation</label><stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-programminglanguage="r"><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>b1pre <- read.table( "https://github.com/ArnauMontagud/PROFILE_v2/raw/main/Analysis%20of%20experimental%20validation//LNCAP_RT.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE ) b1_ids <- b1pre %>% .[1:2, ] %>% .[-c(1:2)] %>% t() %>% as.data.frame() %>% rename(., drug = 1, uM = 2) %>% mutate(cell = row.names(.)) b1pre$min <- NA b1pre$min[-(1:2)] <- c(as.matrix(read.table( text = b1pre$Time.Interval[-(1:2)], sep = ":" )) %*% c(1, 1 / 60, 1 / 3660)) b1 <- b1pre %>% select(., min, everything(), -contains("Time")) %>% t() %>% as.data.frame() colnames(b1) <- b1[1, ] b1 <- b1 %>% .[-1, ] colnames(b1)[1] <- "drug" colnames(b1)[2] <- "uM" b1 %<>% mutate(cell = row.names(.)) %>% select(cell, everything()) b1long <- b1 %>% pivot_longer(., !(c(drug, uM, cell))) %>% rename(., time = name, CI = value) %>% as.data.frame() b1long[which(b1long$drug == "Control"), ]$uM <- 0 b1long$uM <- factor(b1long$uM, levels = c("0", "3.3", "10", "30")) b1long$time <- as.numeric(b1long$time) b1long$CI <- as.numeric(b1long$CI) times <- c("25:41:08", "49:18:09", "74:16:37", "97:17:03") times_min <- c(as.matrix(read.table(text = times, sep = ":")) %*% c(1, 1 / 60, 1 / 3660)) times_hour <- c(times_min[1], 48.05246, 72.02869, 96.03388) times_hour <- c(times_min[1], times_min[1] + 24, times_min[1] + 48, times_min[1] + 72) times_hour2 <- times_hour - times_min[1] b1long <- b1long %>% mutate(time2 = time - times_min[1]) b1long_mean <- b1long %>% filter(grepl("Y.", cell)) b1long_sd <- b1long %>% filter(grepl("SD.", cell)) b2 <- left_join( b1long %>% filter(grepl("Y.", cell)) %>% rename(mean = CI) %>% .[, -1], b1long %>% filter(grepl("SD.", cell)) %>% rename(sd = CI) %>% .[, -1], by = c("drug", "uM", "time", "time2") ) summary_24 <- left_join( b1long %>% filter(time > 49 & time < 49.1) %>% filter(grepl("Y.", cell)) %>% rename(mean = CI) %>% .[, -1], b1long %>% filter(time > 49 & time < 49.1) %>% filter(grepl("SD.", cell)) %>% rename(sd = CI) %>% .[, -1], by = c("drug", "uM", "time", "time2") ) summary_48 <- left_join( b1long %>% filter(time > 73 & time < 73.1) %>% filter(grepl("Y.", cell)) %>% rename(mean = CI) %>% .[, -1], b1long %>% filter(time > 73 & time < 73.1) %>% filter(grepl("SD.", cell)) %>% rename(sd = CI) %>% .[, -1], by = c("drug", "uM", "time", "time2") ) summary_72 <- left_join( b1long %>% filter(time > 97 & time < 97.1) %>% filter(grepl("Y.", cell)) %>% rename(mean = CI) %>% .[, -1], b1long %>% filter(time > 97 & time < 97.1) %>% filter(grepl("SD.", cell)) %>% rename(sd = CI) %>% .[, -1], by = c("drug", "uM", "time", "time2") ) </code></pre></stencila-code-chunk></figure><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 8."><label data-itemprop="label">Figure 8.</label><figcaption><h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="hsp90-inhibitors-resulted-in-dose-dependent-changes-in-the-lncap-cell-line-growth">Hsp90 inhibitors resulted in dose-dependent changes in the LNCaP cell line growth.</h4><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) Real-time cell electronic sensing (RT-CES) cytotoxicity assay of Hsp90 inhibitor, 17-DMAG, that uses the Cell Index as a measurement of the cell growth rate (see the Materials and methods section). The yellow dotted line represents the 17-DMAG addition. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) RT-CES cytotoxicity assay of Hsp90 inhibitor, NMS-E973. The yellow dotted line represents the NMS-E973 addition.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Processed dataset to obtain Figures 8 and 9 with Figure 8—source code 1.R code needed to obtain Figures 8 and 9 with Figure 8—source data 1.Processed dataset needed is Figure 8—source data 1 and is located in the corresponding folder of the repository: here.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure 8</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code>pal <- pal_uchicago("default")(9) # 17-DMAG: cols <- c( "0" = pal[2], "3.3" = "#fa0000", "10" = "#8f0000", "30" = "#350000" ) subdata1 <- b2 %>% filter(drug == "17DMAG" | drug == "Control") `17-DMAGt_ribbon` <- ggplot() + theme_bw() + geom_ribbon( data = subdata1, aes( x = time2, y = mean, ymin = mean - sd, ymax = mean + sd, fill = uM ), alpha = 0.2 ) + geom_point(data = subdata1, aes(time2, mean, colour = uM)) + scale_x_continuous(breaks = seq(-24, 108, 12)) + scale_colour_manual(name = "Drug dose (uM)", values = cols) + scale_fill_manual(name = "Drug dose (uM)", values = cols) + geom_vline( xintercept = times_hour2, colour = c(pal[3], pal[7], pal[7], pal[7]), linetype = "dashed" ) + ylim(NA, 4) + labs(title = "", x = "Time (hours)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = c(0.15, 0.6), axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) # NMS-E973: cols <- c( "0" = pal[2], "3.3" = "#c1c960", "10" = "#6e7337", "30" = "#292b14" ) subdata2 <- b2 %>% filter(drug == "NMS-E973" | drug == "Control") `NMS-E973t_ribbon` <- ggplot() + theme_bw() + geom_ribbon( data = subdata2, aes( x = time2, y = mean, ymin = mean - sd, ymax = mean + sd, fill = uM ), alpha = 0.2 ) + geom_point(data = subdata2, aes(time2, mean, colour = uM)) + scale_x_continuous(breaks = seq(-24, 108, 12)) + scale_colour_manual(name = "Drug dose (uM)", values = cols) + scale_fill_manual(name = "Drug dose (uM)", values = cols) + geom_vline( xintercept = times_hour2, colour = c(pal[3], pal[7], pal[7], pal[7]), linetype = "dashed" ) + ylim(NA, 4) + labs(title = "", x = "Time (hours)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = c(0.15, 0.6), axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) patchwork_HSP3 = `17-DMAGt_ribbon` / `NMS-E973t_ribbon` patchwork_HSP3[[1]] = patchwork_HSP3[[1]] + theme(plot.title = element_blank()) patchwork_HSP3[[2]] = patchwork_HSP3[[2]] + theme(plot.title = element_blank()) patchwork_HSP3 + plot_annotation(tag_levels = "A") </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {height=6 width=7 figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#fig8}</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure S40."><label data-itemprop="label">Figure S40.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="hsp90-inhibitors-resulted-in-dose-dependent-changes-in-the-lncap-cell-line-growth-1">Hsp90 inhibitors resulted in dose-dependent changes in the LNCaP cell line growth.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) Real-time cell electronic sensing (RT-CES) cytotoxicity assay of Hsp90 inhibitor, 17-DMAG, that uses the Cell Index as a measurement of the cell growth rate (see the Material and Methods section). The yellow dotted line represents 17-DMAG addition. The brown dotted lines are indicative of the cytotoxicity assay results at 24 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>), 48 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) and 72 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">D</strong>) after 17-DMAG addition. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>) RT-CES cytotoxicity assay of Hsp90 inhibitor, NMS-E973. The yellow dotted line represents NMS-E973 addition. The brown dotted lines are indicative of the cytotoxicity assay results at 24 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>), 48 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">G</strong>) and 72 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>) after NMS-E973 addition.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure S40</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code># Figure S40: Hsp90 inhibitors # 17-DMAG: cols <- c( "0" = pal[2], "3.3" = "#fa0000", "10" = "#8f0000", "30" = "#350000" ) `17-DMAG24` <- ggplot(summary_24 %>% filter(drug == "17DMAG" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "24 hours", x = "Drug dose (uM)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `17-DMAG48` <- ggplot(summary_48 %>% filter(drug == "17DMAG" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "48 hours", x = "Drug dose (uM)", y = "Cell Index") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `17-DMAG72` <- ggplot(summary_72 %>% filter(drug == "17DMAG" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "72 hours", x = "Drug dose (uM)", y = "Cell Index") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) # NMS-E973: cols <- c( "0" = pal[2], "3.3" = "#c1c960", "10" = "#6e7337", "30" = "#292b14" ) `NMS-E97324` <- ggplot(summary_24 %>% filter(drug == "NMS-E973" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "24 hours", x = "Drug dose (uM)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `NMS-E97348` <- ggplot(summary_48 %>% filter(drug == "NMS-E973" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "48 hours", x = "Drug dose (uM)", y = "Cell Index") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `NMS-E97372` <- ggplot(summary_72 %>% filter(drug == "NMS-E973" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "72 hours", x = "Drug dose (uM)", y = "Cell Index") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) patchwork_HSP2 = `17-DMAGt_ribbon` / (`17-DMAG24` + `17-DMAG48` + `17-DMAG72`) / `NMS-E973t_ribbon` / (`NMS-E97324` + `NMS-E97348` + `NMS-E97372`) patchwork_HSP2[[1]] = patchwork_HSP2[[1]] + theme(plot.title = element_blank()) patchwork_HSP2[[2]][[1]] = patchwork_HSP2[[2]][[1]] + theme(plot.title = element_blank()) patchwork_HSP2[[2]][[2]] = patchwork_HSP2[[2]][[2]] + theme( plot.title = element_blank(), axis.text.y = element_blank(), axis.title.y = element_blank() ) patchwork_HSP2[[2]][[3]] = patchwork_HSP2[[2]][[3]] + theme( plot.title = element_blank(), axis.text.y = element_blank(), axis.title.y = element_blank() ) patchwork_HSP2[[3]] = patchwork_HSP2[[3]] + theme(plot.title = element_blank()) patchwork_HSP2[[4]][[1]] = patchwork_HSP2[[4]][[1]] + theme(plot.title = element_blank()) patchwork_HSP2[[4]][[2]] = patchwork_HSP2[[4]][[2]] + theme( plot.title = element_blank(), axis.text.y = element_blank(), axis.title.y = element_blank() ) patchwork_HSP2[[4]][[3]] = patchwork_HSP2[[4]][[3]] + theme( plot.title = element_blank(), axis.text.y = element_blank(), axis.title.y = element_blank() ) patchwork_HSP2 + plot_annotation(tag_levels = "A") + plot_layout(heights = c(.7, .3, .7, .3)) </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {height=10 width=7.5 figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#app1fig40}</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure 9."><label data-itemprop="label">Figure 9.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="pi3kakt-pathway-inhibition-with-different-pi3kakt-inhibitors-shows-the-dose-dependent-response-in-lncap-cell-line-growth">PI3K/AKT pathway inhibition with different PI3K/AKT inhibitors shows the dose-dependent response in LNCaP cell line growth.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) Real-time cell electronic sensing (RT-CES) cytotoxicity assay of PI3K/AKT pathway inhibitor, PI-103, that uses the Cell Index as a measurement of the cell growth rate (see the Materials and methods section). The yellow dotted line represents the PI-103 addition. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) RT-CES cytotoxicity assay of PI3K/AKT pathway inhibitor, Pictilisib. The yellow dotted line represents the Pictilisib addition.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure 9</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code># Figure 9: PI3K inhibitors # PI-103: cols <- c( "0" = pal[2], "3.3" = "#28baff", "10" = "#176a92", "30" = "#082736" ) subdata3 <- b2 %>% filter(drug == "PI-103" | drug == "Control") `PI-103t_ribbon` <- ggplot() + theme_bw() + geom_ribbon( data = subdata3, aes( x = time2, y = mean, ymin = mean - sd, ymax = mean + sd, fill = uM ), alpha = 0.2 ) + geom_point(data = subdata3, aes(time2, mean, colour = uM)) + scale_x_continuous(breaks = seq(-24, 108, 12)) + scale_colour_manual(name = "Drug dose (uM)", values = cols) + scale_fill_manual(name = "Drug dose (uM)", values = cols) + geom_vline( xintercept = times_hour2, colour = c(pal[3], pal[7], pal[7], pal[7]), linetype = "dashed" ) + ylim(NA, 4) + labs(title = "", x = "Time (hours)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = c(0.15, 0.6), axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) # Pictilisib: cols <- c( "0" = pal[2], "3.3" = "#ffc641", "10" = "#cc7125", "30" = "#4c2a0e" ) subdata4 <- b2 %>% filter(drug == "Pictilisib" | drug == "Control") `Pictilisibt_ribbon` <- ggplot() + theme_bw() + geom_ribbon( data = subdata4, aes( x = time2, y = mean, ymin = mean - sd, ymax = mean + sd, fill = uM ), alpha = 0.2 ) + geom_point(data = subdata4, aes(time2, mean, colour = uM)) + scale_x_continuous(breaks = seq(-24, 108, 12)) + scale_colour_manual(name = "Drug dose (uM)", values = cols) + scale_fill_manual(name = "Drug dose (uM)", values = cols) + geom_vline( xintercept = times_hour2, colour = c(pal[3], pal[7], pal[7], pal[7]), linetype = "dashed" ) + ylim(NA, 4) + labs(title = "", x = "Time (hours)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = c(0.15, 0.6), axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) patchwork_PI3K2 = `PI-103t_ribbon` / `Pictilisibt_ribbon` patchwork_PI3K2[[1]] = patchwork_PI3K2[[1]] + theme(plot.title = element_blank()) patchwork_PI3K2[[2]] = patchwork_PI3K2[[2]] + theme(plot.title = element_blank()) patchwork_PI3K2 + plot_annotation(tag_levels = "A") </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {height=6 width=7 figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#fig9}</p><figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="Figure S41."><label data-itemprop="label">Figure S41.</label><figcaption><h5 itemscope="" itemtype="http://schema.stenci.la/Heading" id="pi3kakt-pathway-inhibition-with-different-pi3kakt-inhibitors-shows-dose-dependent-response-in-lncap-cell-line-growth">PI3K/AKT pathway inhibition with different PI3K/AKT inhibitors shows dose-dependent response in LNCaP cell line growth.</h5><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) Real-time cell electronic sensing (RT-CES) cytotoxicity assay of PI3K/AKT pathway inhibitor, PI-103, that uses the Cell Index as a measurement of the cell growth rate (see the Material and Methods section). The yellow dotted line represents PI-103 addition. The brown dotted lines are indicative of the cytotoxicity assay results at 24 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>), 48 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) and 72 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">D</strong>) after PI-103 addition. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>) RT-CES cytotoxicity assay of PI3K/AKT pathway inhibitor, Pictilisib. The yellow dotted line represents Pictilisib addition. The brown dotted lines are indicative of the cytotoxicity assay results at 24 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>), 48 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">G</strong>) and 72 hours (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>) after Pictilisib addition.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">chunk: Figure S41</p></figcaption></figure><pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"><code># Figure S41: PI3K inhibitors # PI-103: cols <- c( "0" = pal[2], "3.3" = "#28baff", "10" = "#176a92", "30" = "#082736" ) `PI-103t` <- ggplot( data = b1long %>% filter(!grepl("SD.", cell)) %>% filter(drug == "PI-103" | drug == "Control"), aes(time2, CI, colour = uM) ) + theme_bw() + geom_point() + scale_x_continuous(breaks = seq(-24, 108, 12)) + scale_colour_manual(name = "Drug dose (uM)", values = cols) + geom_vline( xintercept = times_hour2, colour = c(pal[3], pal[7], pal[7], pal[7]), linetype = "dashed" ) + ylim(NA, 4) + labs(title = "PI-103", x = "Time (hours)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = c(0.15, 0.6), axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `PI-10324` <- ggplot(summary_24 %>% filter(drug == "PI-103" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "24 hours", x = "Drug dose (uM)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `PI-10348` <- ggplot(summary_48 %>% filter(drug == "PI-103" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "48 hours", x = "Drug dose (uM)", y = "Cell Index") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `PI-10372` <- ggplot(summary_72 %>% filter(drug == "PI-103" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "72 hours", x = "Drug dose (uM)", y = "Cell Index") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) # Pictilisib: cols <- c( "0" = pal[2], "3.3" = "#ffc641", "10" = "#cc7125", "30" = "#4c2a0e" ) `Pictilisibt` <- ggplot( data = b1long %>% filter(!grepl("SD.", cell)) %>% filter( drug == "Pictilisib" | drug == "Control"), aes(time2, CI, colour = uM) ) + theme_bw() + geom_point() + scale_x_continuous(breaks = seq(-24, 108, 12)) + scale_colour_manual(name = "Drug dose (uM)", values = cols) + geom_vline( xintercept = times_hour2, colour = c(pal[3], pal[7], pal[7], pal[7]), linetype = "dashed" ) + ylim(NA, 4) + labs(title = "Pictilisib", x = "Time (hours)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = c(0.15, 0.6), axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `Pictilisib24` <- ggplot(summary_24 %>% filter(drug == "Pictilisib" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "24 hours", x = "Drug dose (uM)", y = "Cell Index (a.u.)") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `Pictilisib48` <- ggplot(summary_48 %>% filter(drug == "Pictilisib" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "48 hours", x = "Drug dose (uM)", y = "Cell Index") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) `Pictilisib72` <- ggplot(summary_72 %>% filter(drug == "Pictilisib" | drug == "Control")) + theme_bw() + scale_colour_manual(values = cols) + geom_point(aes(uM, mean, colour = uM))+ geom_errorbar( aes( x = uM, y = mean, ymin = mean - sd, ymax = mean + sd, colour = uM ), width = .4 ) + scale_y_continuous(limits = c(-1, 4)) + labs(title = "72 hours", x = "Drug dose (uM)", y = "Cell Index") + theme( plot.title = element_text(hjust = 0.5), legend.position = "none", axis.text = element_text(colour = "black"), panel.background = element_blank(), panel.grid.minor = element_blank() ) patchwork_PI3K = `PI-103t_ribbon` / (`PI-10324` + `PI-10348` + `PI-10372`) / `Pictilisibt_ribbon` / (`Pictilisib24` + `Pictilisib48` + `Pictilisib72`) patchwork_PI3K[[1]] = patchwork_PI3K[[1]] + theme(plot.title = element_blank()) patchwork_PI3K[[2]][[1]] = patchwork_PI3K[[2]][[1]] + theme(plot.title = element_blank()) patchwork_PI3K[[2]][[2]] = patchwork_PI3K[[2]][[2]] + theme( plot.title = element_blank(), axis.text.y = element_blank(), axis.title.y = element_blank() ) patchwork_PI3K[[2]][[3]] = patchwork_PI3K[[2]][[3]] + theme( plot.title = element_blank(), axis.text.y = element_blank(), axis.title.y = element_blank() ) patchwork_PI3K[[3]] = patchwork_PI3K[[3]] + theme(plot.title = element_blank()) patchwork_PI3K[[4]][[1]] = patchwork_PI3K[[4]][[1]] + theme(plot.title = element_blank()) patchwork_PI3K[[4]][[2]] = patchwork_PI3K[[4]][[2]] + theme( plot.title = element_blank(), axis.text.y = element_blank(), axis.title.y = element_blank() ) patchwork_PI3K[[4]][[3]] = patchwork_PI3K[[4]][[3]] + theme( plot.title = element_blank(), axis.text.y = element_blank(), axis.title.y = element_blank() ) patchwork_PI3K + plot_annotation(tag_levels = "A") + plot_layout(heights = c(.7, .3, .7, .3)) </code></pre><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {height=10 width=7.5 figalign="center"}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">::: {#app1fig41}</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Likewise, both PI3K/AKT pathway inhibitors tested, Pictilisib and PI-103, reduced the cell viability immediately after drug supplementation (<a href="#fig9" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 9A</a> for Pictilisib and <a href="#fig9" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 9B</a> for PI-103), in a concentration-dependent manner (Appendix 1, Section 8, <a href="#app1fig41" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 41B-D</a>, for Pictilisib and panels F-H for PI-103). In addition, Hsp90 inhibitors had a more prolonged effect on the cells’ proliferation than PI3K/AKT pathway inhibitors.</p><h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="discussion">Discussion</h2><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Clinical assessment of cancers is moving toward more precise, personalised treatments, as the times of one-size-fits-all treatments are no longer appropriate, and patient-tailored models could boost the success rate of these treatments in clinical practice. In this study, we set out to develop a methodology to investigate drug treatments using personalised Boolean models. Our approach consists of building a model that represents the patient-specific disease status and retrieving a list of proposed interventions that affect this disease status, notably by reducing its pro-cancerous behaviours. In this work, we have showcased this methodology by applying it to TCGA prostate cancer patients and to GDSC prostate cancer cell lines, finding patient- and cell line-specific targets and validating selected cell line-specific predicted targets (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1</a>).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">First, a prostate cancer Boolean model that encompasses relevant signalling pathways in cancer was constructed based on already published models, experimental data analyses and pathway databases (<a href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2</a>). The influence network and the assignment of logical rules for each node of this network were obtained from known interactions described in the literature (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3</a>). This model describes the regulation of invasion, migration, cell cycle, apoptosis, androgen, and growth factors signalling in prostate cancer (Appendix 1, Section 1).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Second, from this generic Boolean model, we constructed personalised models using the different datasets, that is 488 patients from TCGA and eight cell lines from GDSC. We obtained Gleason score-specific behaviours for TCGA’s patients when studying their <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> scores, observing that high <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> scores are higher in high Gleason grades (<a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4</a>). Thus, the use of these personalised models can help rationalise the relationship of Gleason grading with some of these phenotypes.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Likewise, GDSC data was used with the prostate model to obtain cell line-specific prostate models (<a href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 6</a>). These models show differential behaviours, notably in terms of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Invasion</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> phenotypes (Appendix 1, Section 5, <a href="#app1fig21" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 21</a>). One of these cell line-specific models, LNCaP, was chosen, and the effects of all its genetic perturbations were thoroughly studied. We studied 32,258 mutants, including single and double mutants, knock-out and over-expressed, and their phenotypes (Appendix 1, Section 6.1, <a href="#app1fig28" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 28</a> and <a href="#app1fig29" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">29</span></a>). Thirty-two knock-out perturbations that depleted <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> and/or increased <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> were identified, and 16 of them were selected for further analyses (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>). The LNCaP-specific model was simulated using different initial conditions that capture different growth media’s specificities, such as RPMI media with and without androgen or epidermal growth factor (Appendix 1, Section 6, <a href="#app1fig27" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 27</a>).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Third, these personalised models were used to simulate the inhibition of druggable genes and proteins, uncovering new treatment’s combination and their synergies. We developed a methodology to simulate drug inhibitions in Boolean models, termed PROFILE_v2, as an extension of previous works <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Béal et al.</span><span>2019</span></a></cite>. The LNCaP-specific model was used to obtain simulations with nodes and pairs of nodes corresponding to the genes of interest inhibited with varying strengths. This study allowed us to compile a list of potential targets (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>) and to identify potential synergies among genes in the model (<a href="#fig5" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5</a>). Some of the drugs that targeted these genes, such as AKT and TERT, were identified in GDSC as having more sensitivity in LNCaP than in the rest of the prostate cancer cell lines (<a href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 6</a>). In addition, drugs that targeted genes included in the model allowed the identification of cell line specificities (Appendix 1, Section 5).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Fourth, we validated the effect of Hsp90 and PI3K/AKT pathway inhibitors on the LNCaP cell line experimentally, finding a concentration-dependent inhibition of the cell line viability as predicted, confirming the role of the drugs targeting these proteins in reducing LNCaP’s proliferation (<a href="#fig7" itemscope="" itemtype="http://schema.stenci.la/Link">Figures 7</a> and <a href="#fig8" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">8</span></a>). Notably, these targets have been studied in other works on prostate cancer <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib20"><span>20</span><span>Chen et al.</span><span>2020</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib68"><span>68</span><span>Le et al.</span><span>2017</span></a></cite></span>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The study presented here enables the study of drug combinations and their synergies. One reason for searching for combinations of drugs is that these have been described for allowing the use of lower doses of each of the two drugs reducing their toxicity <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib8"><span>8</span><span>Bayat Mokhtari et al.</span><span>2017</span></a></cite>, evading compensatory mechanisms and combating drug resistances <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib4"><span>4</span><span>Al-Lazikani et al.</span><span>2012</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib63"><span>63</span><span>Krzyszczyk et al.</span><span>2018</span></a></cite></span>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Even if this approach is attractive and promising, it has some limitations. The scope of present work is to test this methodology on a prostate model and infer patient-specific prostate cancer treatments. The method need to be adapted if it were to be expanded to study other cancers by using other models and target lists. The analyses performed with the mathematical model do not aim to predict drug dosages per se but to help in the identification of potential candidates. The patient-specific changes in <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> scores upon mutation are maximal theoretical yields that are used to rank the different potential treatments and should not be used as a direct target for experimental results or clinical trials. Our methodology suggests treatments for individual patients, but the obtained results vary greatly from patient to patient, which is not an uncommon issue of personalised medicine <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib22"><span>22</span><span>Ciccarese et al.</span><span>2017</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib76"><span>76</span><span>Molinari et al.</span><span>2018</span></a></cite></span>. This variability is an economic challenge for labs and companies to pursue true patient-specific treatments and also poses challenges in clinical trial designs aimed at validating the model based on the selection of treatments <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib25"><span>25</span><span>Cunanan et al.</span><span>2017</span></a></cite>. Nowadays, and because of these constraints, it might be more commercially interesting to target group-specific treatments, which can be more easily related to clinical stages of the disease.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Mathematical modelling of patient profiles helps to classify them in groups with differential characteristics, providing, in essence, a grade-specific treatment. We, therefore, based our analysis on clinical grouping defined by the Gleason grades, but some works have emphasised the difficulty to properly assess them <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Chen and Zhou</span><span>2016</span></a></cite> and, as a result, may not be the perfect predictor for the patient subgrouping in this analysis, even though it is the only available one for these datasets. The lack of subgrouping that stratifies patients adequately may undermine the analysis of our results and could explain the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> scores of high-grade and low-grade Gleason patients.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Moreover, the behaviours observed in the simulations of the cell line-specific models do not always correspond to what is reported in the literature. The differences between simulation results and biological characteristics could be addressed in further studies by including other pathways, for example, better describing the DNA repair mechanisms, or by tailoring the model with different sets of data, as the data used to personalise these models do not allow for clustering these cell lines according to their different characteristics (Appendix 1, Section 5, <a href="#app1fig24" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 24</a> and <a href="#app1fig25" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">25</span></a>). In this sense, another limitation is that we use static data or a snapshot of dynamic data to build dynamic models and to study its stochastic results. Thus, these personalised models would likely improve their performance if they were fitted to dynamic data <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib94"><span>94</span><span>Saez-Rodriguez and Blüthgen</span><span>2020</span></a></cite> or quantitative versions of the models were built, such as ODE-based, that may capture more fine differences among cell lines. As perspectives, we are working on integrating these models in multiscale models to study the effect of the tumour microenvironment <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib84"><span>84</span><span>Leon et al.</span><span>2021</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib85"><span>85</span><span>Leon et al.</span><span>2022</span></a></cite></span>, on including information to simulate multiple reagents targeting a single node of the model, on scaling these multiscale models to exascale high-performance computing clusters <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib78"><span>78</span><span>Montagud et al.</span><span>2021</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib95"><span>95</span><span>Saxena et al.</span><span>2021</span></a></cite></span>, and on streamlining these studies using workflows in computing clusters to fasten the processing of new, bigger cohorts, as in the PerMedCoE project (<a href="https://permedcoe.eu/" itemscope="" itemtype="http://schema.stenci.la/Link">https://permedcoe.eu/</a>).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The present work contributes to efforts aimed at using modelling <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib32"><span>32</span><span>Eduati et al.</span><span>2020</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib89"><span>89</span><span>Rivas-Barragan et al.</span><span>2020</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib45"><span>45</span><span>Gómez Tejeda Zañudo et al.</span><span>2017</span></a></cite></span> and other computational methods <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib71"><span>71</span><span>Madani Tonekaboni et al.</span><span>2018</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib75"><span>75</span><span>Menden et al.</span><span>2019</span></a></cite></span> for the discovery of novel drug targets and combinatorial strategies. Our study expands the prostate drug catalogue and improves predictions of the impact of these in clinical strategies for prostate cancer by proposing and grading the effectiveness of a set of drugs that could be used off-label or repurposed. The insights gained from this study present the potential of using personalised models to obtain precise, personalised drug treatments for cancer patients.</p><h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="materials-and-methods">Materials and methods</h2><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="data-acquisition">Data acquisition</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Publicly available data of 489 human prostate cancer patients from TCGA described in <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib50"><span>50</span><span>Hoadley et al.</span><span>2018</span></a></cite> were used in the present work. We gathered mutations, CNA, RNA and clinical data from cBioPortal (<a href="https://www.cbioportal.org/study/summary?id=prad_tcga_pan_can_atlas_2018" itemscope="" itemtype="http://schema.stenci.la/Link">https://www.cbioportal.org/study/summary?id=prad_tcga_pan_can_atlas_2018</a>) for all of these samples resulting in 488 with complete omics datasets.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Publicly available data of cell lines used in the present work were obtained from the Genomics of Drug Sensitivity in Cancer database (GDSC) <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib53"><span>53</span><span>Iorio et al.</span><span>2016</span></a></cite>. Mutations, CNA and RNA data, as well as cell lines descriptors, were downloaded from (<a href="https://www.cancerrxgene.org/downloads" itemscope="" itemtype="http://schema.stenci.la/Link">https://www.cancerrxgene.org/downloads</a>). In this work, we have used 3- and 5-stage Gleason grades. Their correspondence is the following: GG Low is GG 1, GG Intermediate is GG 2 and 3, and GG High is GG 4 and 5.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">All these data were used to personalise Boolean models using our PROFILE method <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Béal et al.</span><span>2019</span></a></cite>.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="prior-knowledge-network-construction">Prior knowledge network construction</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Several sources were used in building this prostate Boolean model and, in particular, the model published by <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib39"><span>39</span><span>Fumiã and Martins</span><span>2013</span></a></cite>. This model includes several signalling pathways such as the ones involving receptor tyrosine kinase (RTKs), phosphatidylinositol 3-kinase (PI3K)/AKT, WNT/β-Catenin, transforming growth factor-β (TGF-β)/Smads, cyclins, retinoblastoma protein (Rb), hypoxia-inducible transcription factor (HIF-1), p53 and ataxia-telangiectasia mutated (ATM)/ataxia-telangiectasia and Rad3-related (ATR) protein kinases. The model includes these pathways as well as the substantial cross-talks among them. For a complete description of the process of construction, see Appendix 1, Section 1.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The model also includes several pathways that have a relevant role in our datasets identified by ROMA <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib74"><span>74</span><span>Martignetti et al.</span><span>2016</span></a></cite>, a software that uses the first principal component of a PCA analysis to summarise the coexpression of a group of genes in the gene set, identifying significantly overdispersed pathways with a relevant role in a given set of samples. This software was applied to the TCGA transcriptomics data using the gene sets described in the Atlas of Cancer Signaling Networks, ACSN <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib65"><span>65</span><span>Kuperstein et al.</span><span>2015</span></a></cite> (<a href="http://www.acsn.curie.fr/" itemscope="" itemtype="http://schema.stenci.la/Link">http://www.acsn.curie.fr/</a>) and in the Hallmarks <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib70"><span>70</span><span>Liberzon et al.</span><span>2015</span></a></cite> (Appendix 1, Section 1.1.3, <a href="#app1fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 1</a>) and highlighted the signalling pathways that show high variance across all samples, suggesting candidate pathways and genes. Additionally, OmniPath <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib111"><span>111</span><span>Türei et al.</span><span>2021</span></a></cite> was used to extend the model and complete it, connecting the nodes from Fumiã and Martins and the ones from ROMA analysis. OmniPath is a comprehensive collection of literature-curated human signalling pathways, which includes several databases such as Signor <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib83"><span>83</span><span>Perfetto et al.</span><span>2016</span></a></cite> or Reactome <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib33"><span>33</span><span>Fabregat et al.</span><span>2016</span></a></cite> and that can be queried using pypath, a Python module for molecular networks and pathways analyses.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Fusion genes are frequently found in human prostate cancer and have been identified as a specific subtype marker <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Cancer Genome Atlas Research Network</span><span>2015</span></a></cite>. The most frequent is TMPRSS2:ERG, as it involves the transcription factor ERG, which leads to cell-cycle progression. ERG fuses with the AR-regulated TMPRSS2 gene promoter to form an oncogenic fusion gene that is especially common in hormone-refractory prostate cancer, conferring androgen responsiveness to ERG. A literature search reveals that ERG directly regulates EZH2, oncogene c-Myc and many other targets in prostate cancer <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib64"><span>64</span><span>Kunderfranco et al.</span><span>2010</span></a></cite>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We modelled the gene fusion with activation of ERG by the decoupling of ERG in a special node <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AR_ERG</em> that is only activated by the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AR</em> when the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">fused_event</em> input node is active. In the healthy case, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">fused_event</em> (that represents TMPRSS2:ERG fusion event) is fixed to 0 or inactive. The occurrence of the gene fusion is represented with the model perturbation where <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">fused_event</em> is fixed to 1. This <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AR_ERG</em> node is further controlled by tumour suppressor NKX3-1 that accelerates <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">DNA_repair</em> response, and avoids the gene fusion TMPRSS2:ERG. Thus, loss of NKX3-1 favours recruitment to the ERG gene breakpoint of proteins that promote error-prone non-homologous end-joining <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib12"><span>12</span><span>Bowen et al.</span><span>2015</span></a></cite>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The network was further documented using up-to-date literature and was constructed using GINsim <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib18"><span>18</span><span>Chaouiya et al.</span><span>2012</span></a></cite>, which allowed us to study its stable states and network properties.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="boolean-model-construction">Boolean model construction</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We converted the network to a Boolean model by defining a regulatory graph, where each node is associated with discrete levels of activity (0 or 1). Each edge represents a regulatory interaction between the source and target nodes and is labelled with a threshold and a sign (positive or negative). The model is completed by logical rules (or functions), which assign a target value to each node for each regulator level combination <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib2"><span>2</span><span>Abou-Jaoudé et al.</span><span>2016</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib18"><span>18</span><span>Chaouiya et al.</span><span>2012</span></a></cite></span>. The regulatory graph was constructed using GINsim software <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib18"><span>18</span><span>Chaouiya et al.</span><span>2012</span></a></cite> and then exported in a format readable by MaBoSS software (see below) in order to perform stochastic simulations on the Boolean model.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The final model has a total of 133 nodes and 449 edges (<a href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>) and includes pathways such as androgen receptor and growth factor signalling, several signalling pathways (Wnt, NFkB, PI3K/AKT, MAPK, mTOR, SHH), cell cycle, epithelial-mesenchymal transition (EMT), Apoptosis, DNA damage, etc. This model has nine inputs (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EGF, FGF, TGF beta, Nutrients, Hypoxia, Acidosis, Androgen, TNF alpha,</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Carcinogen</em> presence) and six outputs (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis, Invasion, Migration,</em> (bone) <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Metastasis,</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">DNA repair</em>). Note that a node in the network can represent complexes or families of proteins (e.g. AMPK represents the genes PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3). The correspondence can be found in “Montagud2022_interactions_sources.xlsx” and “Montagud2022_nodes_in_pathways.xlsx” in <a href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This model was deposited in the GINsim Database with identifier 252 (<a href="http://ginsim.org/model/signalling-prostate-cancer" itemscope="" itemtype="http://schema.stenci.la/Link">http://ginsim.org/model/signalling-prostate-cancer</a>) and in BioModels <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib72"><span>72</span><span>Malik-Sheriff et al.</span><span>2020</span></a></cite> with identifier MODEL2106070001 (<a href="https://www.ebi.ac.uk/biomodels/MODEL2106070001" itemscope="" itemtype="http://schema.stenci.la/Link">https://www.ebi.ac.uk/biomodels/MODEL2106070001</a>). <a href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 1</a> is provided as a zipped folder with the model in several formats: MaBoSS, GINsim, SBML, as well as images of the networks and their annotations. An extensive description of the model construction can be found in the Appendix 1, Section 1.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="stochastic-boolean-model-simulation">Stochastic Boolean model simulation</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">MaBoSS <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib104"><span>104</span><span>Stoll et al.</span><span>2017</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib103"><span>103</span><span>Stoll et al.</span><span>2012</span></a></cite></span> is a C++ software for stochastically simulating continuous/discrete-time Markov processes defined on the state transition graph (STG) describing the dynamics of a Boolean model (for more details, see <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib2"><span>2</span><span>Abou-Jaoudé et al.</span><span>2016</span></a></cite>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib18"><span>18</span><span>Chaouiya et al.</span><span>2012</span></a></cite>). MaBoSS associates transition rates to each node’s activation and inhibition, enabling it to account for different time scales of the processes described by the model. Probabilities to reach a phenotype (to have value ON) are thus computed by simulating random walks on the probabilistic STG. Since a state in the STG can combine the activation of several phenotypic variables, not all phenotype probabilities are mutually exclusive (like the ones in Appendix 1, Section 6.1, <a href="#app1fig28" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 28</a>). Using MaBoSS, we can study an increase or decrease of a phenotype probability when the model variables are altered (nodes status, initial conditions and transition rates), which may correspond to the effect of particular genetic or environmental perturbation. In the present work, the use of MaBoSS was focused on the readouts of the model, but this can be done for any node of the model.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">MaBoSS applies Monte-Carlo kinetic algorithm (i.e. Gillespie algorithm) to the STG to produce time trajectories <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib104"><span>104</span><span>Stoll et al.</span><span>2017</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib103"><span>103</span><span>Stoll et al.</span><span>2012</span></a></cite></span>, so time evolution of probabilities are estimated once a set of initial conditions are defined and a maximum time is set to ensure that the simulations reach asymptotic solutions. Results are analysed in two ways: (1) the trajectories for particular model states (states of nodes) can be interpreted as the evolution of a cell population as a function of time and (2) asymptotic solutions can be represented as pie charts to illustrate the proportions of cells in particular model states. Stochastic simulations with MaBoSS have already been successfully applied to study several Boolean models <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib13"><span>13</span><span>Calzone et al.</span><span>2010</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib23"><span>23</span><span>Cohen et al.</span><span>2015</span></a></cite><cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib87"><span>87</span><span>Remy et al.</span><span>2015</span></a></cite></span>. A description of the methods we have used for the simulation of the model can be found in the Appendix 1, Section 2.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="data-tailoring-the-boolean-model">Data tailoring the Boolean model</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Logical models were tailored to a dataset using PROFILE to obtain personalised models that capture the particularities of a set of patients <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Béal et al.</span><span>2019</span></a></cite> and cell lines <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib10"><span>10</span><span>Béal et al.</span><span>2021</span></a></cite>. Proteomics, transcriptomics, mutations and CNA data can be used to modify different variables of the MaBoSS framework, such as node activity status, transition rates and initial conditions. The resulting ensemble of models is a set of personalised variants of the original model that can show great phenotypic differences. Different recipes (use of a given data type to modify a given MaBoSS variable) can be tested to find the combination that better correlates to a given clinical or otherwise descriptive data. In the present case, TCGA patient-specific models were built using mutations, CNA and/or RNA expression data. After studying the effect of these recipes in the clustering of patients according to their Gleason grading (Appendix 1, Section 4.1, <a href="#app1fig10" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 10</a>–<a href="#app1fig11" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">14</span></a>), we chose to use mutations and CNA as discrete data and RNA expression as continuous data.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Likewise, we tried different personalisation recipes to personalise the GDSC prostate cell lines models, but as they had no associated clinical grouping features, we were left with the comparison of the different values for the model’s outputs among the recipes (Appendix 1, Section 5, <a href="#app1fig23" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 23</a>). We used mutation data as discrete data and RNA expression as continuous data as it included the most quantity of data and reproduced the desired results (Appendix 1, Section 5, <a href="#app1fig23" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 23</a>). We decided not to include CNA as discrete data as it forced LNCaP proliferation to be zero by forcing the E2F1 node to be 0 and the SMAD node to be 1 throughout the simulation (for more details, refer to Appendix 1, Section 5).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">More on PROFILE’s methodology can be found in its own work <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Béal et al.</span><span>2019</span></a></cite> and at its dedicated GitHub repository (<a href="https://github.com/sysbio-curie/PROFILE" itemscope="" itemtype="http://schema.stenci.la/Link">https://github.com/sysbio-curie/PROFILE</a>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib11"><span>11</span><span>Béal</span><span>2022</span></a></cite>). A description of the methods we have used for the personalisation of the models can be found in the Appendix 1, Section 3. The analysis of the TCGA personalisations and their patient-specific drug treatments can be found in Appendix 1, Section 4. The analysis of the prostate cell lines personalisations can be found in Appendix 1, Section 5, with a special focus on the LNCaP cell line model analysis in Section 6.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="high-throughput-mutant-analysis-of-boolean-models">High-throughput mutant analysis of Boolean models</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">MaBoSS allows the study of knock-out or loss-of-function (node forced to 0) and gain-of-function (node forced to 1) mutants as genetic perturbations and of initial conditions as environmental perturbations. Phenotypes’ stabilities against perturbations can be studied and allow to determine driver mutations that promote phenotypic transitions <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib77"><span>77</span><span>Montagud et al.</span><span>2019</span></a></cite>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Genetic interactions were thoroughly studied using our pipeline of computational methods for Boolean modelling of biological networks (available at <a href="https://github.com/sysbio-curie/Logical_modelling_pipeline" itemscope="" itemtype="http://schema.stenci.la/Link">https://github.com/sysbio-curie/Logical_modelling_pipeline</a>; <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib80"><span>80</span><span>Montagud</span><span>2022</span></a></cite>). The LNCaP-specific Boolean model was used to perform single and double knock-out (node forced to 0) and gain-of-function (node forced to 1) mutants for each one of the 133 nodes, resulting in a total of 32,258 models. These were simulated under the same initial conditions, their phenotypic results were collected, and a PCA was applied on the wild type-centred matrix (Appendix 1, Section 6.1, <a href="#app1fig28" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 28</a> and <a href="#app1fig29" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">29</span></a>). In addition, we found that the LNCaP model is very robust against perturbations of its logical rules by systematically changing an AND for an OR gate or vice versa in all of its logical rules (Appendix 1, Section 6.2, <a href="#app1fig30" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 30</a> and <a href="#app1fig31" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">31</span></a>).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The 488 TCGA patient-specific models were studied in a similar way, but only perturbing 16 nodes from <a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a> shortlisted for their therapeutic target potential (AKT, AR, Caspase8, cFLAR, EGFR, ERK, GLUT1, HIF-1, HSPs, MEK1<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">2, MYC_MAX, p14ARF, PI3K, ROS, SPOP, and TERT). Then, the nodes that mostly contributed to a decrease of _Proliferation</em> (Appendix 1, Section 4.2, <a href="#app1fig19" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 19</a>) or an increase in <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> (Appendix 1, Section 4.2, <a href="#app1fig20" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figure 20</a>) were gathered from the 488 models perturbed.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Additionally, the results of the LNCaP model’s double mutants were used to quantify the level of genetic interactions (epistasis or otherwise) <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Drees et al.</span><span>2005</span></a></cite> between two genetic perturbations (resulting from either the gain-of-function mutation of a gene or from its knock-out or loss-of-function mutation) with respect to wild type phenotypes’ probabilities <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib14"><span>14</span><span>Calzone et al.</span><span>2015</span></a></cite>. The method was applied to the LNCaP model studying <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Proliferation</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Apoptosis</em> scores (Appendix 1, Section 7.3.2, <a href="#app1fig34" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 34</a> and <a href="#app1fig35" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">35</span></a>).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This genetic interaction study uses the following equation for each gene pair, which is equation 2 in <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib14"><span>14</span><span>Calzone et al.</span><span>2015</span></a></cite>:</p><span itemscope="" itemtype="http://schema.stenci.la/MathBlock"><span class="mjx-chtml MJXc-display" style="text-align: center;"><span class="mjx-math" aria-label="{ϵ}_{\varphi }\left(A,B\right)={f}_{\varphi }^{AB}-\psi \left({f}_{\varphi }^{A},\text{}{f}_{\varphi }^{B}\right)"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">ϵ</span></span></span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.212em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span><span class="mjx-mrow MJXc-space1"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em;">A</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-mi MJXc-space1"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">B</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.077em; padding-bottom: 0.298em;">=</span></span><span class="mjx-msubsup MJXc-space3"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em;">A</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">B</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.446em;">−</span></span><span class="mjx-mi MJXc-space2"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.519em;">ψ</span></span><span class="mjx-mrow MJXc-space1"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-size2-R" style="padding-top: 0.961em; padding-bottom: 0.961em;">(</span></span><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em;">A</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-msubsup MJXc-space1"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">B</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-size2-R" style="padding-top: 0.961em; padding-bottom: 0.961em;">)</span></span></span></span></span></span></span><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">where <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{f}_{\varphi }^{A}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em;">A</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span></span></span></span></span> and <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{f}_{\varphi }^{B}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">B</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span></span></span></span></span> are phenotype <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="\varphi"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span> fitness values of single gene defects, <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{f}_{\varphi }^{AB}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em;">A</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">B</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span></span></span></span></span> is the phenotype <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="\varphi"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span> fitness of the double mutant, and <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="\psi \left(x,y\right)"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.519em;">ψ</span></span><span class="mjx-mrow MJXc-space1"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-mi MJXc-space1"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span></span></span></span></span></span> is one of the four functions:</p><span itemscope="" itemtype="http://schema.stenci.la/MathBlock"><span class="mjx-chtml MJXc-display" style="text-align: center;"><span class="mjx-math" aria-label="\begin{array}{l}{\psi }^{ADD}(x,y)=x+y\text{}(\mathrm{a}\mathrm{d}\mathrm{d}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e})\\ {\psi }^{LOG}(x,y)=lo{g}_{2}\left(\left({2}^{x}-1\right)\left({2}^{y}-1\right)+1\right)\text{}(\mathrm{log})\\ {\psi }^{MLT}(x,y)=x\ast y\text{}(\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{i}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e})\\ {\psi }^{MIN}(x,y)=min(x,y)\text{}(\mathrm{m}\mathrm{i}\mathrm{n})\end{array}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mtable" style="vertical-align: -2.817em; padding: 0px 0.167em;"><span class="mjx-table"><span class="mjx-mtr" style="height: 1.445em;"><span class="mjx-mtd" style="padding: 0px 0px 0px 0px; text-align: left; width: 19.42em;"><span class="mjx-mrow" style="margin-top: -0.03em;"><span class="mjx-msubsup"><span class="mjx-base"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.519em;">ψ</span></span></span></span></span><span class="mjx-sup" style="font-size: 70.7%; vertical-align: 0.631em; padding-left: 0px; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em;">A</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">D</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">D</span></span></span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-mi MJXc-space1"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.077em; padding-bottom: 0.298em;">=</span></span><span class="mjx-mi MJXc-space3"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.446em;">+</span></span><span class="mjx-mi MJXc-space2"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">a</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">d</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">d</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">i</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.372em;">t</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">i</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">v</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">e</span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-strut"></span></span></span></span><span class="mjx-mtr" style="height: 1.645em;"><span class="mjx-mtd" style="padding: 0.2em 0px 0px 0px; text-align: left;"><span class="mjx-mrow" style="margin-top: -0.038em;"><span class="mjx-msubsup"><span class="mjx-base"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.519em;">ψ</span></span></span></span></span><span class="mjx-sup" style="font-size: 70.7%; vertical-align: 0.631em; padding-left: 0px; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">L</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em;">O</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em;">G</span></span></span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-mi MJXc-space1"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.077em; padding-bottom: 0.298em;">=</span></span><span class="mjx-mi MJXc-space3"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">l</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">o</span></span><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.003em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.003em;">g</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.375em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mn"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">2</span></span></span></span></span></span><span class="mjx-mrow MJXc-space1"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mrow"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-msubsup"><span class="mjx-base"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mn"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">2</span></span></span></span></span><span class="mjx-sup" style="font-size: 70.7%; vertical-align: 0.591em; padding-left: 0px; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span></span></span></span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.446em;">−</span></span><span class="mjx-mn MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">1</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span></span><span class="mjx-mrow MJXc-space1"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-msubsup"><span class="mjx-base"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mn"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">2</span></span></span></span></span><span class="mjx-sup" style="font-size: 70.7%; vertical-align: 0.591em; padding-left: 0px; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span></span></span></span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.446em;">−</span></span><span class="mjx-mn MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">1</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.446em;">+</span></span><span class="mjx-mn MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">1</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span></span><span class="mjx-mo MJXc-space1"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">l</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">o</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.519em;">g</span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-strut"></span></span></span></span><span class="mjx-mtr" style="height: 1.622em;"><span class="mjx-mtd" style="padding: 0.2em 0px 0px 0px; text-align: left;"><span class="mjx-mrow" style="margin-top: -0.053em;"><span class="mjx-msubsup"><span class="mjx-base"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.519em;">ψ</span></span></span></span></span><span class="mjx-sup" style="font-size: 70.7%; vertical-align: 0.631em; padding-left: 0px; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.081em;">M</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">L</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.12em;">T</span></span></span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-mi MJXc-space1"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.077em; padding-bottom: 0.298em;">=</span></span><span class="mjx-mi MJXc-space3"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.298em;">∗</span></span><span class="mjx-mi MJXc-space2"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">m</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">u</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">l</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.372em;">t</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">i</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.519em;">p</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">l</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">i</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">c</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">a</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.372em;">t</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">i</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">v</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">e</span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-strut"></span></span></span></span><span class="mjx-mtr" style="height: 1.422em;"><span class="mjx-mtd" style="padding: 0.2em 0px 0px 0px; text-align: left;"><span class="mjx-mrow" style="margin-top: -0.053em;"><span class="mjx-msubsup"><span class="mjx-base"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.519em;">ψ</span></span></span></span></span><span class="mjx-sup" style="font-size: 70.7%; vertical-align: 0.631em; padding-left: 0px; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.081em;">M</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.064em;">I</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.085em;">N</span></span></span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-mi MJXc-space1"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.077em; padding-bottom: 0.298em;">=</span></span><span class="mjx-mi MJXc-space3"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">m</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">i</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">n</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-mi MJXc-space1"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">m</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">i</span></span></span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.372em;">n</span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span><span class="mjx-strut"></span></span></span></span></span></span></span></span></span></span><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To choose the best definition of <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="\psi \left(x,y\right)"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.519em;">ψ</span></span><span class="mjx-mrow MJXc-space1"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">x</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="margin-top: -0.144em; padding-bottom: 0.519em;">,</span></span><span class="mjx-mi MJXc-space1"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.006em;">y</span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span></span></span></span></span></span> , the Pearson correlation coefficient is computed between the fitness values observed in all double mutants and estimated by the null model (more information on <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Drees et al.</span><span>2005</span></a></cite>). Regarding the <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{f}_{\varphi }^{X}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.024em;">X</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span></span></span></span></span> fitness value, to a given phenotype <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="\varphi"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span>, <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{\displaystyle {f}_{\varphi }^{X}<1}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mstyle"><span class="mjx-mrow"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.024em;">X</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.225em; padding-bottom: 0.372em;"><</span></span><span class="mjx-mn MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">1</span></span></span></span></span></span></span></span></span></span> represents deleterious, <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{\displaystyle {f}_{\varphi }^{X}>1}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mstyle"><span class="mjx-mrow"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.024em;">X</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.225em; padding-bottom: 0.372em;">></span></span><span class="mjx-mn MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">1</span></span></span></span></span></span></span></span></span></span> beneficial and <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{f}_{\varphi }^{X}\approx 1"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.06em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.519em; padding-right: 0.06em;">f</span></span></span></span></span><span class="mjx-stack" style="vertical-align: -0.265em;"><span class="mjx-sup" style="font-size: 70.7%; padding-bottom: 0.53em; padding-left: 0.181em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.024em;">X</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em;">φ</span></span></span></span></span></span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.225em; padding-bottom: 0.298em;">≈</span></span><span class="mjx-mn MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.372em; padding-bottom: 0.372em;">1</span></span></span></span></span></span> neutral mutation.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="drug-simulations-in-boolean-models">Drug simulations in Boolean models</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Logical models can be used to simulate the effect of therapeutic interventions and predict the expected efficacy of candidate drugs on different genetic and environmental backgrounds by using our PROFILE<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">v2 methodology. MaBoSS can perform simulations changing the proportion of activated and inhibited status of a given node. This can be determined in the configuration file of each model (see, for instance, the ‘istate’ section of the CFG files in the <a href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary files 1; 3 and 5</a>). For instance, out of 5,000 trajectories of the Gillespie algorithm, MaBoSS can simulate 70% of them with an activated _AKT</em> and 30% with an inhibited <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT</em> node. The phenotypes’ probabilities for the 5000 trajectories are averaged, and these are considered to be representative of a model with a drug that inhibits 30% of the activity of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT</em>. The same applies for a combined drug inhibition: a simulation of 50% <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT</em> activity and 50% <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">PI3K</em> will have 50% of them with an activated <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT</em> and 50% with an activated <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">PI3K</em>. Combining them, this will lead to 25% of the trajectories with both <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">PI3K</em> active, 25% with both nodes inactive, 25% with <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">AKT</em> active and 25% with <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">PI3K</em> active.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In the present work, the LNCaP model has been simulated with different levels of node activity, with 100% of node inhibition (proper knock-out), 80%, 60%, 40%, 20%, and 0% (no inhibition), under four different initial conditions, a nutrient-rich media that simulates RPMI Gibco media with DHT (androgen), with EGF, with both and with none. In terms of the model, the initial conditions are <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Nutrients</em> is ON and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Acidosis</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Hypoxia</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">TGF beta</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Carcinogen,</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">TNF alpha</em> are set to OFF. <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">EGF</em> and <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Androgen</em> values vary upon simulations. We simulated the inhibition of 17 nodes of interest. These were the 16 nodes from <a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a> with the addition of the fused AR-ERG (Appendix 1, Section 7.3.1, <a href="#app1fig34" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 34</a> and <a href="#app1fig35" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">35</span></a>) and their 136 pairwise combinations (Appendix 1, Section 7.3.2, <a href="#app1fig36" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 36</a> and <a href="#app1fig37" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">37</span></a>). As we used six different levels of activity for each node, the resulting <a href="#app1fig36" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 36</a> and <a href="#app1fig37" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">37</span></a> comprise a total of 4,998 simulations for each phenotype (136 × 6 x 6 + 17 x 6).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Drug synergies have been studied using Bliss Independence. The Combination Index was calculated with the following equation <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib37"><span>37</span><span>Foucquier and Guedj</span><span>2015</span></a></cite>:</p><span itemscope="" itemtype="http://schema.stenci.la/MathBlock"><span class="mjx-chtml MJXc-display" style="text-align: center;"><span class="mjx-math" aria-label="CI=\left({E}_{a}+{E}_{b}-{E}_{a}*{E}_{b}\right)/{E}_{ab}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em; padding-right: 0.045em;">C</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.064em;">I</span></span><span class="mjx-mo MJXc-space3"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.077em; padding-bottom: 0.298em;">=</span></span><span class="mjx-mrow MJXc-space3"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">(</span></span><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.026em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.026em;">E</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.212em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">a</span></span></span></span></span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.446em;">+</span></span><span class="mjx-msubsup MJXc-space2"><span class="mjx-base" style="margin-right: -0.026em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.026em;">E</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.219em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">b</span></span></span></span></span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.298em; padding-bottom: 0.446em;">−</span></span><span class="mjx-msubsup MJXc-space2"><span class="mjx-base" style="margin-right: -0.026em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.026em;">E</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.212em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">a</span></span></span></span></span></span><span class="mjx-mo MJXc-space2"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.151em; padding-bottom: 0.298em;">∗</span></span><span class="mjx-msubsup MJXc-space2"><span class="mjx-base" style="margin-right: -0.026em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.026em;">E</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.219em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">b</span></span></span></span></span></span><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">)</span></span></span><span class="mjx-texatom MJXc-space1"><span class="mjx-mrow"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">/</span></span></span></span><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.026em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.026em;">E</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.219em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">a</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">b</span></span></span></span></span></span></span></span></span></span><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">where <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{E}_{a}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.026em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.026em;">E</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.212em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">a</span></span></span></span></span></span></span></span></span></span> and <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{E}_{b}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.026em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.026em;">E</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.219em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">b</span></span></span></span></span></span></span></span></span></span> is the efficiency of the single drug inhibitions and <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="{E}_{ab}"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-msubsup"><span class="mjx-base" style="margin-right: -0.026em;"><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em; padding-right: 0.026em;">E</span></span></span></span></span><span class="mjx-sub" style="font-size: 70.7%; vertical-align: -0.219em; padding-right: 0.071em;"><span class="mjx-texatom" style=""><span class="mjx-mrow"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">a</span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.446em; padding-bottom: 0.298em;">b</span></span></span></span></span></span></span></span></span></span> is the inhibition resulting from the double drug simulations. A Combination Index (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">CI</em>) below 1 represents synergy among drugs (Appendix 1, Section 7.3.2, <a href="#app1fig36" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 1—figures 36</a> and <a href="#app1fig37" itemscope="" itemtype="http://schema.stenci.la/Link"><span data-itemtype="http://schema.org/Number">37</span></a>).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This methodology can be found in its own repository: <a href="https://github.com/ArnauMontagud/PROFILE_v2" itemscope="" itemtype="http://schema.stenci.la/Link">https://github.com/ArnauMontagud/PROFILE_v2.</a></p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="identification-of-drugs-associated-with-proposed-targets">Identification of drugs associated with proposed targets</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To identify drugs that could act as potential inhibitors of the genes identified with our models (<a href="#table1" itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>), we explored the drug-target associations in DrugBank <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib115"><span>115</span><span>Wishart et al.</span><span>2018</span></a></cite>. For those genes with multiple drug-target links, only those drugs that are selective and known to have relevance in various forms of cancer are considered here.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In addition to DrugBank searches, we also conducted exhaustive searches in ChEMBL <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib40"><span>40</span><span>Gaulton et al.</span><span>2017</span></a></cite> (<a href="http://doi.org/10.6019/CHEMBL.database.23" itemscope="" itemtype="http://schema.stenci.la/Link">http://doi.org/10.6019/CHEMBL.database.23</a>) to suggest potential candidates for genes whose information is not well documented in Drug Bank. From the large number of bioactivities extracted from ChEMBL, we filtered human data and considered only those compounds whose bioactivities fall within a specific threshold (IC50/Kd/ Ki <100 nM).</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We performed a target set enrichment analysis using the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">fgsea</em> method <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib60"><span>60</span><span>Korotkevich et al.</span><span>2016</span></a></cite> from the <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">piano</em> R package <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib113"><span>113</span><span>Väremo et al.</span><span>2013</span></a></cite>. We targeted pathway information from the GDSC1 and GDSC2 studies <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib53"><span>53</span><span>Iorio et al.</span><span>2016</span></a></cite> as target sets and performed the enrichment analysis on the normalised drug sensitivity profile of the LNCaP cell line. We normalised drug sensitivity across cell lines in the following way: cells were ranked from most sensitive to least sensitive (using ln(IC50) as the drug sensitivity metrics), and the rank was divided by the number of cell lines tested with the given drug. Thus, the most sensitive cell line has 0, while the most resistant cell line has 1 normalised sensitivity. This rank-based metric made it possible to analyse all drug sensitivities for a given cell line without drug-specific confounding factors, like mean IC50 of a given drug, etc. (Appendix 1, Sections 7.1 and 7.2).</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="cell-culture-method">Cell culture method</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For the in vitro drug perturbation validations, we used the androgen-sensitive prostate adenocarcinoma cell line LNCaP purchased from American Type Culture Collection (ATCC, Manassas, WV, USA). ATCC found no <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">Mycoplasma</em> contamination and the cell line was identified using STR profiling. Cells were maintained in RPMI-1640 culture media (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) containing 4.5 g/L glucose, 10% foetal bovine serum (FBS, Gibco), 1 X GlutaMAX (Gibco), 1% PenStrep antibiotics (Penicillin G sodium salt, and Streptomycin sulfate salt, Sigma-Aldrich, St. Louis, MI, USA). Cells were maintained in a humidified incubator at 37 °C with 5% CO<sub itemscope="" itemtype="http://schema.stenci.la/Subscript"><span data-itemtype="http://schema.org/Number">2</span></sub> (Sanyo, Osaka, Japan).</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="drugs-used-in-the-cell-culture-experiments">Drugs used in the cell culture experiments</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We tested two drugs targeted at Hsp90 and two targeted at PI3K complex. 17-DMAG is an Hsp90 inhibitor with an IC50 of 62 nM in a cell-free assay <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib82"><span>82</span><span>Pacey et al.</span><span>2011</span></a></cite>. NMS-E973 is an Hsp90 inhibitor with DC50 of <10 nM for Hsp90 binding <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib36"><span>36</span><span>Fogliatto et al.</span><span>2013</span></a></cite>. Pictilisib is an inhibitor of PI3K <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="\alpha /\delta"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">α</span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">/</span></span></span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em; padding-right: 0.007em;">δ</span></span></span></span></span></span> with IC50 of 3.3 nM in cell-free assays <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib117"><span>117</span><span>Zhan et al.</span><span>2017</span></a></cite>. PI-103 is a multi-targeted PI3K inhibitor for p110 <span itemscope="" itemtype="http://schema.stenci.la/MathFragment"><span class="mjx-chtml"><span class="mjx-math" aria-label="\alpha /\beta /\delta /\gamma"><span class="mjx-mrow" aria-hidden="true"><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.298em;">α</span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">/</span></span></span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.446em; padding-right: 0.007em;">β</span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">/</span></span></span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.519em; padding-bottom: 0.298em; padding-right: 0.007em;">δ</span></span><span class="mjx-texatom"><span class="mjx-mrow"><span class="mjx-mo"><span class="mjx-char MJXc-TeX-main-R" style="padding-top: 0.446em; padding-bottom: 0.593em;">/</span></span></span></span><span class="mjx-mi"><span class="mjx-char MJXc-TeX-math-I" style="padding-top: 0.225em; padding-bottom: 0.519em; padding-right: 0.025em;">γ</span></span></span></span></span></span> with IC50 of 2–3 nM in cell-free assays and less potent inhibitor to mTOR/DNA-PK with IC50 of 30 nM <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib86"><span>86</span><span>Raynaud et al.</span><span>2009</span></a></cite>. All drugs were obtained from commercial vendors and added to the growth media to have concentrations of 2, 8, 32, 128, and 512 nM for NMS-E973 and 1, 5, 25, 125, and 625 nM for the rest of the drugs in the endpoint cell viability and of 3.3, 10, 30 µM for all the drugs in the RT-CES cytotoxicity assay.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="endpoint-cell-viability-measurements">Endpoint cell viability measurements</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In vitro toxicity of the selected inhibitors was determined using the viability of LNCaP cells, determined by the fluorescent resazurin (Sigma-Aldrich, Germany) assay as described previously <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib106"><span>106</span><span>Szebeni et al.</span><span>2017</span></a></cite>. Briefly, the ∼10,000 LNCaP cells were seeded into 96-well plates (Corning Life Sciences, Tewksbury, MA, USA) in 100 µL RPMI media and incubated overnight. Test compounds were dissolved in dimethyl sulfoxide (DMSO, Sigma-Aldrich, Germany), and cells were treated with an increasing concentration of test compounds: 2, 8, 32, 128, and 512 nM for NMS-E973 and 1, 5, 25, 125, and 625 nM for the rest of the drugs. The highest applied DMSO content of the treated cells was 0.4%. Cell viability was determined after 48 hours of incubation. Resazurin reagent (Sigma–Aldrich, Budapest, Hungary) was added at a final concentration of 25 µg/mL. After 2 hr at 37 °C 5%, CO<sub itemscope="" itemtype="http://schema.stenci.la/Subscript"><span data-itemtype="http://schema.org/Number">2</span></sub> (Sanyo) fluorescence (530 nm excitation/580 nm emission) was recorded on a multimode microplate reader (Cytofluor4000, PerSeptive Biosystems, Framingham, MA, USA). Viability was calculated with relation to blank wells containing media without cells and to wells with untreated cells. Each treatment was repeated in two wells per plate during the experiments, except for the PI-103 treatment with 1 nM in which only one well was used.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In these assays, a deviation of 10–15% for in vitro cellular assays is an acceptable variation as it is a fluorescent assay that detects the cellular metabolic activity of living cells. Thus, in our analyses, we consider changes above 1.00 to be the same value as the controls.</p><h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="real-time-cell-electronic-sensing-rt-ces-cytotoxicity-assay">Real-time cell electronic sensing (RT-CES) cytotoxicity assay</h3><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A real-time cytotoxicity assay was performed as previously described <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib81"><span>81</span><span>Ozsvári et al.</span><span>2010</span></a></cite>. Briefly, RT-CES 96-well E-plate (BioTech Hungary, Budapest, Hungary) was coated with gelatin solution (0.2% in PBS, phosphate buffer saline) for 20 min at 37 °C; then gelatin was washed twice with PBS solution. Growth media (50 µL) was then gently dispensed into each well of the 96-well E-plate for background readings by the RT-CES system prior to the addition of 50 µL of the cell suspension containing 2 × 10<sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span data-itemtype="http://schema.org/Number">4</span></sup> LNCaP cells. Plates were kept at room temperature in a tissue culture hood for 30 min prior to insertion into the RT-CES device in the incubator to allow cells to settle. Cell growth was monitored overnight by measurements of electrical impedance every 15 min. The next day cells were co-treated with different drugs with concentrations of 3.3, 10 and 30 µM. Treated and control wells were dynamically monitored over 72 hr by measurements of electrical impedance every 5 min. Each treatment was repeated in two wells per plate during the experiments, except for the 3.3 µM ones in which only one well was used. Continuous recording of impedance in cells was used as a measurement of the cell growth rate and reflected by the Cell Index value <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a href="#bib100"><span>100</span><span>Solly et al.</span><span>2004</span></a></cite>.</p><p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Note that around hour 15, our RT-CES reader had a technical problem caused by a short blackout in our laboratory and the reader detected a minor voltage fluctuation while the uninterruptible power supply (UPS) was switched on. This caused differences that are consistent across all samples and replicates: all wild type and drug reads decrease at that time point, except Pictilisib that slightly increases. 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