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1 2 Hybrid epithelial-mesenchymal phenotypes are controlled by

3 microenvironmental factors 4 5 Gianluca Selvaggio (1, 2) ±, Sara Canato (1, 3, 4) ±, Archana Pawar (1, 5), Pedro T. Monteiro 6 (6, 7), Patrícia S. Guerreiro (1,3, 4), M. Manuela Brás (3, 8, 9), Florence Janody* (1, 3, 4) 7 and Claudine Chaouiya* (1, 10) 8 9 (1) Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, P-2780-156 Oeiras, Portugal 10 (2) Fondazione The Microsoft Research - University of Trento Centre for Computational and 11 Systems Biology (COSBI), Rovereto (TN), Italy 12 (3) i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo 13 Allen, 208, 4200-135 Porto, Portugal. 14 (4) IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, 15 Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal. 16 (5) Haffkine Institute of Training Research and Testing, Acharya Donde Marg, Mumbai 17 400012, India 18 (6) Department of Computer Science and Engineering, Instituto Superior Técnico (IST), 19 Universidade de Lisboa, Lisbon, Portugal 20 (7) Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento 21 (INESC-ID), Lisbon, Portugal 22 (8) INEB – Instituto de Engenharia Biomédica, Universidade do Porto, 4200 – 135 Porto, 23 Portugal 24 (9) FEUP – Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 25 4200-465 Porto, Portugal 26 (10) Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France 27 28 Running Title: Microenvironment driving EMT plasticity 29 30 ± These authors contributed equally to this work. 31 *corresponding authors: 32 Claudine Chaouiya 33 Institute of Mathematics of Marseille (I2M UMR7373) 34 Avenue de Luminy - Case 907

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35 13288 MARSEILLE Cedex 9 FRANCE- 36 phone: +33 491269614 37 email: [email protected] 38 39 Florence Janody 40 i3S Rua Alfredo Allen, 208, 4200-135 Porto, Portugal 41 phone: +351 42 email: [email protected] 43 44 45 Conflict of interest statement 46 All the authors of this manuscript declare that they have no conflict of interest and no 47 competing financial interests in relation to the work described.

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48 Abstract 49 50 Epithelial-to-mesenchymal transition (EMT) has been associated with cancer cell 51 heterogeneity, plasticity, and metastasis. However, the extrinsic signals supervising these 52 phenotypic transitions remain elusive. To assess how selected microenvironmental signals 53 control cancer-associated phenotypes along the EMT continuum, we defined a logical model 54 of the EMT cellular network that yields qualitative degrees of cell adhesions by adherens 55 junctions and focal adhesions, two features affected during EMT. The model attractors 56 recovered epithelial, mesenchymal, and hybrid phenotypes. Simulations showed that hybrid 57 phenotypes may arise through independent molecular paths involving stringent extrinsic 58 signals. Of particular interest, model predictions and their experimental validations indicated 59 that: 1) stiffening of the ExtraCellular Matrix (ECM) was a prerequisite for cells 60 overactivating FAK_SRC to upregulate SNAIL and acquire a mesenchymal phenotype, and 61 2) FAK_SRC inhibition of cell-cell contacts through the Receptor-type tyrosine-protein 62 phosphatases kappa led to acquisition of a full mesenchymal, rather than a hybrid, phenotype. 63 Altogether, these computational and experimental approaches allow assessment of critical 64 microenvironmental signals controlling hybrid EMT phenotypes and indicate that EMT 65 involves multiple molecular programs. 66 67 Statement of significance: A multi-disciplinary study sheds light on microenvironmental 68 signals controlling cancer cell plasticity along epithelial-to-mesenchymal transition and 69 suggests that hybrid and mesenchymal phenotypes arise through independent molecular 70 paths. 71 72 Key words: Epithelial to Mesenchymal Transition / Microenvironmental signals / Cell 73 adhesions / Computational modeling / In vitro human cell models

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74 Introduction 75 76 Metastasis is a hallmark of cancer and the leading cause of mortality among cancer patients. 77 Despite intensive effort in basic and clinical research, metastatic cancers still present a major 78 barrier to favorable clinical outcomes (1). Therefore, the fight against cancer calls for a better 79 understanding of the involved cellular mechanisms. 80 81 The progression from carcinoma to metastatic cancer has been proposed to involve a shift 82 from an epithelial to a mesenchymal phenotype, in a highly plastic and dynamic process 83 referred to as Epithelial to Mesenchymal Transition (EMT). During this transition, epithelial 84 cells downregulate epithelial markers, lose their connections with neighboring cells through 85 the breakdown of E-Cadherin (ECad)-mediated adherens junction (AJs), upregulate 86 mesenchymal markers and acquire a marked migratory capacity mediated by the dynamic 87 remodeling of focal adhesions (FAs). Colonization at distant sites and metastatic outgrowth 88 may require that disseminated cancer cells lose their migratory capacities and re-establish AJs 89 through a Mesenchymal to Epithelial Transition (MET). However, cancer cells are rarely 90 purely mesenchymal nor purely epithelial. They often exhibit both epithelial and 91 mesenchymal features. Cells with such hybrid phenotypes appear to reside at intermediate 92 states along the epithelial to mesenchymal continuum. Unlike mesenchymal phenotypes, 93 these hybrid phenotypes may bear multiple advantages to cancer cells, including drug 94 resistance and tumor-initiating potential (2). 95 96 The cancer-associated EMT program is hardly activated in a cell autonomous manner. 97 Indeed, the tumor microenvironment, induces EMT in carcinoma cells by releasing paracrine 98 cell-cell signaling molecules, (WNT - the NOTCH ligand DELTA), growth factors 99 (Epidermal Growth Factor - EGF, Hepatocyte Growth Factor - HGF) and inflammatory 100 signals (Interleukin-6 - IL-6, Reactive Oxygen Species – ROS, Transforming Growth Factor 101  - TGFB) (2). In addition, recent evidence suggests that direct physical interactions of tumor 102 cells with their neighbors or with the ExtraCellular Matrix (ECM) greatly affect the 103 EMT/MET program. In particular, ECM stiffening can induce EMT and promote tumor 104 invasion and metastasis (3). In contrast, the cell adhesion molecule FAT4, which 105 heterophilically interacts with its ligand on adjacent cells, prevents EMT and metastasis of 106 gastric cancer cells (4). Other cell adhesion molecules, such as Receptor-type tyrosine-protein 107 phosphatases (RPTPs) of the R2B sub-family, that display homophilic cell-cell adhesion

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108 capabilities, could also have major impact on the EMT/MET program. Indeed, they are 109 regulators of AJs, are frequently mutated in solid cancers and appear to display tumor 110 suppressive capabilities (5). These microenvironmental signals cooperate to induce a group of 111 EMT-inducing transcription factors (EMT-TFs), including the zinc-finger proteins SNAIL 112 (SNAIL, SLUG) and the E-box-binding protein ZEB1. These EMT-TFs regulate each other 113 and, in different combinations, control the expression of genes associated with the epithelial 114 and mesenchymal phenotypes, respectively (2). However, it remains unclear which 115 combinations of external signals can stabilize carcinoma cells into hybrid phenotypes. 116 The complexity of the molecular networks involved in EMT has prompted numerous 117 modeling studies, recently reviewed in (6). Ordinary differential equation models focused on 118 core regulatory circuitries, and demonstrated the existence of stable, hybrid phenotypes (7,8). 119 In contrast to these continuous models, discrete logical models were developed that 120 encompass numerous players and signaling pathways (9–12). Steinway et al.’s model was 121 built around the TGFB pathway in the context of hepatocellular carcinoma, hybrid 122 phenotypes being obtained through model perturbations (10). Other models considered the 123 regulatory control of cell fate decisions between cell cycle arrest, apoptosis, EMT, invasion 124 and migration (11), or epithelial, mesenchymal and senescent states (12). Overall, these 125 studies demonstrated that, despite a coarse-grain representation, logical models are suitable to 126 account for large and intricate networks, recovering physiological cell phenotypes and 127 providing relevant predictions. 128 129 Here, we used computational modeling and experimental validations to investigate the role of 130 selected microenvironmental signals on two features largely affected during EMT: cell-cell 131 and cell-ECM adhesion properties (Workflow in Supplementary Fig. S1). In addition to 132 purely epithelial and mesenchymal phenotypes, our logical model recovers hybrid 133 phenotypes, which could represent terminal phenotypical steps and whose stability depends 134 on stringent inputs from the microenvironment. Among these, we have validated 135 experimentally the role of cell-cell contacts through RPTP-kappa and of ECM stiffening. 136 Altogether, our work identified key microenvironmental cues involved in maintaining hybrid 137 EMT phenotypes that could be used as targets for developing therapeutic strategies against 138 cancer cells with these phenotypes.

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139 Materials and Methods 140 141 Logical formalism 142 The logical formalism allows to handle the complexity of networks and the lack of 143 quantitative data for most regulatory mechanisms (13). In brief, it considers an influence 144 graph where the nodes embody regulatory components (e.g., genes, proteins or 145 phenomenological properties), and signed edges represent regulatory interactions activating 146 or inhibiting the target node. Each regulatory component is associated with a discrete 147 variable (Boolean or multi-valued). This variable represents the component qualitative 148 functional level (or state) that is, depending on the nature of the component, its level of 149 expression, of activity, the effective complex formation or phenotype acquisition, etc. Finally, 150 a logical regulatory rule defines the values of this variable depending on the states of the 151 components influencing it (i.e., its regulators). A model state is defined by a tuple of the 152 variable values. 153 The model evolution is defined by the logical rules that, for any model state, indicate 154 whether each component level should be updated or not. Here, we used an asynchronous 155 updating scheme, meaning that all concurrent transitions are generated, each transition 156 corresponding to the update of a single variable. Hence, any model state has as many 157 successors as the number of components that are called to update in that state. Note that such 158 an asynchronous updating scheme leads to non-deterministic dynamics (13). 159 The asymptotic behavior of the model is defined by its attractors. These are sets of mutually 160 reachable states in which the model dynamics is trapped (no outgoing transitions). These 161 attractors are defined by: a single stable state, with no successors, i.e. all the model 162 components are stable in that state, or by several states, corresponding to a maintained 163 oscillatory behavior. The model presented here has only stable states and no cyclical 164 attractors. 165 166 Computational tools and methods 167 The logical model of the EMT regulatory network (Fig. 1) was built using GINsim (version 168 3.0.0b, http://ginsim.org/), a software dedicated to the logical formalism (14). GINsim 169 implements the determination of the stable states, export facilities and other relevant features. 170 To validate the classification of the stable states into phenotypic categories based on the 171 model read-outs, we used the Multiple Correspondence Analysis (MCA), which extends the 172 principal component analysis to categorical (e.g. Boolean or discrete) variables and show

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173 how phenotypes cluster with respect to one another (15). . This analysis was performed with 174 R (version 3.5.0) using the two packages FactoMineR (16) and factoextra 175 (https://rpkgs.datanovia.com/factoextra). 176 To confirm the absence of cyclical attractors, the model was reduced and exported to 177 BoolSim (https://www.vital-it.ch/software/genYsis), a tool that efficiently constructs the 178 dynamics of Boolean models (17). 179 180 In non-deterministic, asynchronous dynamics, ia reachability probability can be calculated for 181 the model attractors, assuming that concurrent transitions are equiprobable. Such a 182 quantification was performed using the built-in GINsim functionality implementing Avatar, a 183 modified Monte Carlo simulation (18), with a number of runs set to 105, ensuring the 184 convergence of estimated probabilities. 185 The capacity of a system to adapt to environmental variation (plasticity) can be explored 186 through model-checking analyses (19). We used the model checker NuSMV-ARCTL (20) 187 with Action Restricted Computation Tree Logic (ARCTL) temporal operators to study 188 switches between phenotypes, while specifying input values (see Supplementary Methods 189 and Supplementary Fig. S7, S8). To ease this analysis, we used the reduction method 190 available in GINsim, which allowed to propagate the values of the inputs FAT4L=1, WNT=0 191 and DELTA=0 (21). 192 193 More quantitative view of the dynamics is provided by stochastic simulations as performed 194 by MaBoSS (22) (https://maboss.curie.fr/). The model was exported into MaBoSS format 195 (this export feature is available in GINsim). MaBoSS computes stochastic trajectories and 196 provides the time evolution of probabilities of the component values. We considered equal 197 transition rates, a time step of 0.1 and a maximum time of 50. This allowed to plot the 198 evolution of phenotype probabilities (Fig. 5B and Fig. 6B-C), which can be interpreted as the 199 dynamical constitution of a cell population (each run corresponding to a cell). 200 201 Cell lines, culture conditions and drug treatment 202 The MCF10A-ER-SRC cell line was kindly provided by K. Struhl (23) and was cultured as 203 described in (24). To treat cells with 4OH-TAM or EtOH, 50% confluent cells were plated 204 and allowed to adhere for at least 16 hours before being treated with 1 μM 4OH-TAM 205 (Sigma-Aldrich; H7904) or with identical volume of EtOH for the time period indicated. 206 MDCK-pTR cSRCY527F-GFP cell line was kindly provided by C. Hogan and were cultured

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207 as described in (25), except that cells were supplemented with 10% tet-free fetal bovine 208 serum (Biowest, Nauillé, France; S181T). All experiments were performed with passage 209 number between 8 and 25. 210 211 Collagen gel preparation 212 Collagen gels were prepared using Corning® Collagen I High Concentrated, Rat Tail 213 (Corning, Ny, USA; 354249), according to manufacturer’s instruction. 0.15 mL of 1 mg/mL 214 or of 5 mg/mL high-concentrated collagen were used to coat the most central 14 mm 215 diameter of 35 mm petri dishes (MatTek corporation, Ashland, MA, USA; P35G-1.5-14-C), 216 that were pre-coated at the periphery with 1.2 % Poly-HEMA (Sigma-Aldrich, Darmstadt, 217 Germany; 3932). 218 219 Generation of stable cell line overexpressing PTPRK 220 MCF10A-ER-SRC cells were plated at 50% confluence and allowed to adhere for 16 hours. 221 Cells were then infected with the PTPRK guide RNA or control lentiviral activation particles 222 (Santa Cruz Biotechnology, Dallas, TX, USA; sc-402287 LAC and sc-437282) in complete 223 growth medium containing 8 µg/mL Polybrene (Millipore, Burlington, MA, USA; TR-1003- 224 6). Infected cells were then selected using media containing 400 g/mL of Hygromycin B 225 (Gibco/ Thermo Fisher Scientific, Waltham, MA, USA; 10687-010), 5 g/mL of Blasticidin 226 S HCl (Santa Cruz Biotechnology, Dallas, TX, USA; sc-204655A; sc-) and 2 g/mL of 227 Puromycin dihydrochloride (Calbiochem, Darmstadt, Germany; 540411). 228 229 Phase-contrast microscope images 230 MCF10A-ER-SRC cells plated on collagen gels were imaged before or after treatments with 231 EtOH or TAM for 48 hours. Phase-contrast images were obtained on Leica DMi1 inverted 232 microscope equipped with a Leica MC170 HD camera (Leica Microsystems, Germany), 233 using a 10x 0.22 PH1 objective. Numbers of isolated cells per well were evaluated by 234 applying the following settings: Subtract Background; Gaussian Blur - sigma = 4. Single cells 235 (size = 500-20000 pixels and circularity = 0.80-1.00) and total cell (size = 500-inf pixels and 236 circularity = 0.00-1.00) were then measured using the Analyse Particles tool from Fiji. 237 238 Aggregation Assay

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239 20 000 cells, resuspended in 200 l of complete cell culture media, were plated on top of a 240 0.7% agarose solidified layer. Images were acquired 24 hours later on a commercial Leica 241 High Content Screening microscope Leica DMI6000 equipped with a Hamamatsu Flash Orca 242 4.0 sCMOS camera (Leica Microsystems, Germany), using a 10x 0.30 NA objective. 243 Numbers of isolated cells per well were evaluated on single microscopic fields using the 244 Analyse Particles tool from Fiji. Cell area between 500 and 2000 pixels or between 2000- 245 10000 pixels were labelled as isolated or aggregated cells, respectively. 246 247 Immunofluorescence analysis 248 Primary antibody used was rabbit anti-ECad monoclonal antibody 24E10 (1:50; Cell 249 signaling, 3195S). For extended immunofluorescence analysis methods, see Supplementary 250 Methods. 251 252 Immunoblotting Analysis 253 Primary antibodies used were rabbit anti-pSRC (pY419) (1:1000; Invitrogen; 44-660G), 254 rabbit anti-pFAK (pY397) (1:1000; Cell Signaling Technology; 3283S), mouse anti-total 255 FAK (1:500; BD Transduction, 610088), mouse anti-ECad (1:1000, BD Transduction, 256 610182), rabbit monoclonal anti-RPTPK (1:1000, clone H4) (26) or rabbit anti-GAPDH 257 (1:5000, Sigma-Aldrich; G9545). For extended immunoblot analysis methods, see 258 Supplementary Methods. 259 (26) 260 Atomic Force Microscopy (AFM) 261 A PicoPlus 5500 Atomic Force Microscope (AFM) (keysight Technologies, USA), coupled 262 to an Inverted Fluorescence Microscope (Observer.Z1, Zeiss, Germany), was used for force 263 mapping acquisition, in force spectroscopy mode. A MLCT-BIO-DC-D cantilever (Bruker, 264 USA) was used. Before measuring collagen stiffness, the tip calibration was performed using 265 the thermal K method obtaining a k = 0.048 N/m. Briefly, a laser beam was focused on the 266 back of the cantilever, which deflection of it was transduce in changing of the beam position 267 in a photodiode. The calculation of the applied force was determined according to the 268 Hooke’s law. Once the laser aligned, three samples of 1 mg/ mL or 5 mg/ mL collagen I gels 269 were measured in PBS 1x. Force maps were acquired using a grid of 6 x 6 points in an area of 270 5 x 5 µm2. The maximum force applied was 4.9 nN, with a loading rate cycle of 13 µm/s. The 271 force curve maps were analyzed using the AtomicJ software 1.8 version, using the following

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272 parameters: blunt pyramidal model, Poisson ratio 0.5 and log-normal distribution. The 273 collected approach force-displacement curves were fitted with the Hertz´s model to obtain the 274 Apparent Young’s moduli. The topography images of each collagen concentration 275 nanosurfaces, were acquired using the Tapping modeTM in PBS 1x, with a scan speed of 1 276 Hz, in an area of 5.1 x 5.1 µm2, displayed at 2D and 3D images using the WSxM5.0 software 277 (27). 278 279 Sample sizes and statistics 280 Quantifications of PTPRK and PTPRM mRNA levels in cells treated with EtOH or TAM for 281 36 hours were from six biological replicates in triplicates. Quantifications of PTPRU or ECad 282 mRNA were from four or three biological replicates, respectively, in triplicates. 283 Quantifications of PTPRK mRNA over time and of SNAIL, SLUG, ZEB1 and ZEB2 mRNA 284 levels were from three biological replicates in triplicates. Quantifications of PTPRK mRNA 285 levels in cells expressing the mock or PTPRK guide RNA (PTPRK+), treated for 24 or 36 286 hours were performed in three or five biological replicates in triplicate, respectively. 287 Quantifications of isolated cells by aggregation assays were from five biological replicates in 288 triplicates. Quantifications of the number of isolated MCF10A-ER-SRC cells grown on stiff 289 or soft collagen gels were from four biological replicates in triplicates. All analyses were 290 performed using Prism 7.0 software (GraphPad Inc.). For statistical comparison of two or 291 more than two independent groups unpaired t-tests or one-way ANOVA tests were used, 292 respectively. An unpaired t test with Welch’s correction was used to compare the stiffness 293 between soft and stiff collagen gels by AFM. A two-tailed significance level of 5% was 294 considered as statistically significant (P < 0.05). 295 296 Data and Software Availability 297 The computational model p is available in the GINsim repository at 298 http://ginsim.org/model/EMT_Selvaggio_etal. GINsim and other software tools used for the 299 model definition and analysis are freely available (see section Material and Methods). Scripts 300 used to facilitate the model-checking analysis (Supplementary Methods) are available upon 301 request. 302

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303 Results 304 305 Construction of a microenvironment-dependent EMT regulatory network revealing cell 306 adhesion properties 307 We built a minimal regulatory network encompassing EMT-TF, epithelial (ECad and 308 miR200, green in Fig. 1) and mesenchymal (SNAIL, SLUG, ZEB, TCF_LEF, BCat, dark 309 brown in Fig. 1) markers, as well as known EMT signaling pathways (RAS, NOTCH, WNT, 310 TGFB, JAK/STAT, Hippo and AKT), controlled by inputs from the tumor microenvironment 311 (Supplementary Table S1). We focused on molecular interactions validated in experimental 312 carcinoma models, prioritizing when possible human data. The regulatory rule of each 313 internal component was defined based on experimental evidence when available. If not 314 specified otherwise, a component activation requires the presence of at least one of its 315 activators combined with the absence of all its inhibitors (Supplementary Table S1). 316 317 Amongst the plethora of non-cell autonomous signals involved in EMT, we included HGF, 318 EGF, TGFB, IL6, ROS, WNT and DELTA. In addition, to evaluate the impact of cell-cell 319 and cell-ECM interactions, we incorporated the FAT4 ligand (FAT4L), the RPTP ligand of 320 the R2B sub-family (RPTPL) and the ECM. While the ECM levels indicate the elastic 321 property of the substrate, either stiff or soft (ECM = 1 or 0, respectively), all other input 322 levels convey the presence (1) or absence (0) of these signals in the microenvironment. 323 These 10 signals denoting environmental conditions constitute the input components of the 324 model (grey nodes in Fig. 1). 325 326 Internal components (intra-cellular components) were associated with binary levels 327 (present/active=1, absent/inactive=0), with the exception of FAK_SRC and ECad_AJ. The 328 former was assigned a three-valued level based on the observations that increasing levels of 329 SRC activity correlate with the acquisition of pre-malignant and malignant carcinoma 330 features, respectively, in vivo and in vitro (24,28). Because microarray profiling and RNA- 331 Sequencing indicate that oncogenic SRC downregulates PTPRK, the gene encoding for 332 RPTP-kappa (29,30), a predicted inhibitory interaction between FAK_SRC and RPTP was 333 included, such that this interaction takes place only at high FAK_SRC activity 334 (FAK_SRC=2) (blue interaction in Fig. 1).

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335 ECad_AJ, the other multivalued component, conveys the state of ECad at the membrane: 336 ECad_AJ=0 indicates that the protein is not located at the membrane; ECad_AJ=1 stands for 337 the correct localization and binding to BCat_AJ; finally, ECad_AJ=2 represents the complete 338 formation of the stable complex ECad, BCat and p120. 339 Two multivalued read-outs define the cell commitment in assembling AJs, an attribute of an 340 epithelial state, and in remodeling FAs, typical of migrating properties displayed by 341 mesenchymal cells (2). AJ values convey the capacity of ECad and BCat in assembling stable 342 AJs, and FA values denote the status of FA recycling, and consequently the cellular 343 migratory capacities (Supplementary Table S1). 344 345 Modeling cell adhesion properties accounts for phenotypical repertoire observed in 346 EMT 347 The model attractors were identified: there were 1452 stable states and no cyclic attractor. 348 For conciseness we focused on the 136 stable internal states, obtained by considering only the 349 internal components and ignoring the input components. Furthermore, we compressed these 350 states by using wildcards (*, ?, !), resulting in 31 patterns, each gathering multiple stable 351 internal states (Table 1, see also Supplementary Methods). These patterns were then grouped 352 together into eight phenotypes, each characterized by specific values of the read-out 353 components AJ and FA: 354  States in which ECad localized with BCat at AJs, while displaying basal levels of FA 355 recycling (AJ=2, FA=0), were defined as epithelial phenotypes (E1). 356  States in which epithelial and mesenchymal markers were present were classified as 357 hybrid phenotypes (H1, H2 and H3). Among these, H1 assembled AJs and showed 358 the presence of both TCF_LEF and BCat and a weak ability to recycle FAs (AJ=2, 359 FA=1). The H2 phenotype failed to assemble AJs but exhibited the presence of ECad, 360 miR200, TCF_LEF and BCat with an intermediate ability to recycle FAs (AJ=1, 361 FA=2). Finally, H3 assembled AJs, while activating TCF_LEF and BCat and showing 362 a high ability to recycle FAs (AJ=2, FA=3). 363  States having only mesenchymal markers (and no epithelial markers) were classified 364 as mesenchymal phenotypes (M1, M2 and M3). These phenotypes differed in their 365 increasing capacity of recycling FAs (AJ=0, FA=1, 2 or 3). 366  Four states referred to an undefined phenotype (UN) with the presence of 367 mesenchymal markers only, and basal levels of FA recycling (AJ=0, FA=0).

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368 This phenotypical classification based on cell adhesion properties (model read-outs) allowed 369 to draw a parallel with the continuum of phenotypes along the EMT axis including hybrid 370 phenotypes, such as H3, typical of a collective cell migration behavior (2). 371 Interestingly, the model pointed towards the epithelial phenotype as a reference phenotype in 372 the absence of specific external signal: simulations starting from any state with all inputs set 373 to 0 evolve towards an epithelial stable state. 374 375 To validate this classification, we performed a Multiple Correspondence Analysis (MCA), 376 displaying the 136 stable internal states in a low dimension space (Fig. 2). This analysis 377 showed that states adequately cluster together within each phenotype, with a clear separation 378 between those clusters. We also tested the robustness of the model by setting the functional 379 levels of some components to their maximum (i.e. X=1 or 2) or to their minimum (i.e. X=0), 380 mimicking gain or loss of function conditions, respectively. The phenotypes reached by the 381 perturbed model reproduced experimental observations reported in the literature 382 (Supplementary Fig. S2). Interestingly, for some perturbations, the phenotypic landscapes 383 were reshaped, leading to novel phenotypes and/or impairing the stability of others. We then 384 performed a systematic analysis of single and double mutants. As hybrid phenotypes may 385 provide pluripotent abilities to cancer cells (2), we searched for perturbations impeding these 386 phenotypes (Supplementary Table S2). Three single mutants (TGFBR E1, BCad KO and 387 TCF_LEF KO) promoted a shift towards mesenchymal phenotypes or towards the two 388 extreme phenotypes in the EMT continuum. In contrast, when excluding the effect of these 389 single mutants, 43 double mutants triggered a shift towards mesenchymal only, and 20 390 towards mesenchymal and epithelial phenotypes (Supplementary Table S2). 391 392 Taken together, the qualitative assessment of cell adhesion properties defined in our 393 computational model is capable of accounting for the phenotypic repertoire observed in the 394 EMT continuum and permit to formulate predictions discussed hereafter. 395 396 SRC downregulates PTPRK 397 FAK and SRC integrate signaling from numerous microenvironmental inputs and play a 398 central role in tumor-associated EMT (31). A predicted inhibitory interaction between 399 FAK_SRC and RPTP has been included in the model (Fig. 1). Because of the latency period 400 between SRC activation and the downregulation of PTPRK by microarray and RNA- 401 Sequencing (29,30), we assumed that this interaction takes place exclusively at high levels of

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402 FAK_SRC activity (FAK_SRC=2). Simulations of the overexpression of FAK_SRC 403 (FAK_SRC=2) indicated that when starting from an E1 phenotype with input configurations 404 set to [ECM, GF, IF, CC, WNT, DELTA]=[100100] and in the presence of an inhibitory 405 interaction of FAK_SRC on RPTP, the model reached a full M3 mesenchymal phenotype in 406 100% of simulation runs (Fig. 3A). In contrast, when forcing RPTP maintenance, we 407 observed a switch to the H3 phenotype in 50% of cases (Fig. 3B), suggesting that the 408 repression of RPTP by FAK_SRC promotes a full mesenchymal phenotype.

409 To validate experimentally the relevance of RPTP repression by FAK_SRC on EMT, we took 410 advantage of the MCF10A-ER-SRC cell line with conditional SRC activation. This normal 411 cell line contains a fusion of v-SRC and the ligand-binding domain of the estrogen receptor, 412 inducible with Tamoxifen (TAM) treatment. Upon induction, these cells acquire transformed 413 features within 24 to 36 hours and invade in three-dimensional matrigel cultures (23,24). 414 Consistent with an interdependency between SRC and FAK, TAM-treated MCF10A-ER- 415 SRC grown on plastic, displayed stepwise increases of phosphorylated ER-SRC (ER-pSRC) 416 and endogenous SRC (pSRC) (Fig. 3C), and of phosphorylated FAK (pFAK) (Fig. 3D). 417 Moreover, these cells acquire EMT features, including a significant downregulation of ECad 418 mRNA levels, which was reduced by 48% 24 hours after TAM treatment, and dropped by 419 72% at 36 hours (Fig. 3E). In addition, TAM-treated MCF10A-ER-SRC cells suffered a 420 morphological shift from epithelial to mesenchymal starting 24 hours after treatment (Fig. 421 3F). This was in contrast to control MCF10A-ER-SRC cells, which maintained an epithelial 422 morphology with ECad localized at cell-cell contacts during the 36 hours of EtOH treatment 423 (Fig. 3G). Finally, the set of genes deregulated in TAM-treated MCF10A-ER-SRC cells 424 showed a significant 4.47 enrichment (13%, 33/246 genes, p<0.0001, Hypergeometric test) 425 for core EMT signature identified in mammary cells (32).

426 We then analyzed if conditional SRC activation affected the expression of the four RPTP 427 members of the R2B subfamily, encoded by the PTPRM, PTPRK, PTPRU and PTPRT genes. 428 Consistent with microarray and RNA-Sequencing data (29,30), the ratio of PTPRK mRNA 429 levels between cells treated with TAM and EtOH indicated that PTPRK was significantly 430 downregulated by 33% 8 hours after SRC activation, and dropped by 70% at 36 hours (Fig. 431 3H and Supplementary Fig. S3A). Accordingly, the levels of the P subunit of RPTPK, 432 generated from one precursor protein, displayed a progressive reduction during the 36 hours 433 of transformation in TAM-treated cells (Fig. 3I). In contrast, SRC activation had no

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434 significant effect on PTPRU or PTPRM mRNA levels (Supplementary Fig. S3B, C), while 435 PTPRT mRNA could not be detected using two independent sets of primers. Although 436 PTPRK expression is known to increase with cell confluency (33), the reduction in PTPRK 437 levels by SRC is unlikely due to a density-dependent regulation of PTPRK expression, as it 438 precedes the loss of cell-cell adhesion (Fig. 3D). Thus, we conclude that PTPRK 439 downregulation results from SRC activation. 440 441 Forcing PTPRK expression in SRC overactivated cells restores cell-cell adhesion 442 To test the model prediction by which maintaining PTPRK expression in SRC overactivated 443 cells favors the acquisition of an H3 phenotype (Fig. 3B), we forced PTPRK expression in 444 MCF10A-ER-SRC cells using the Clustered Regularly Interspaced Short Palindromic 445 Repeats (CRISPR)-based activation system. At 24 hours after EtOH or TAM treatment, 446 MCF10A-ER-SRC cells stably expressing the PTPRK guide RNA (PTPRK+) displayed 447 significantly higher PTPRK levels, compared to those expressing the mock guide RNA 448 (mock) (Fig. 4A). However, forcing PTPRK expression did not prevent the repressive effect 449 of SRC on PTPRK expression 24 (Fig. 4A) and 36 hours (Fig. 4B) after TAM treatment. 450 Nevertheless, it was sufficient to restore PTPRK expression in TAM-treated cells to levels 451 similar to those of control EtOH-treated cells expressing the mock (Fig. 4A and B). 452 Accordingly, the P subunit of RPTPK accumulated at higher levels in MCF10A-ER-SRC 453 cells expressing PTPRK+ and treated with EtOH or TAM for 24 or 36 hours (Fig. 4C). Then, 454 using cell aggregation assays we tested if restoring PTPRK expression would impact on the 455 loss of cell-cell adhesion induced by SRC activation. MCF10A-ER-SRC cells expressing the 456 mock and treated with EtOH for 36 hours were mainly found aggregated, with only 13.6% of 457 isolated cells (Fig. 4D). As expected, treating these cells with TAM prevented the formation 458 of aggregate, and increased the number of isolated cells to 54%. Strikingly, restoring PTPRK 459 expression in TAM-treated MCF10A-ER-SRC cells significantly restored cell aggregation 460 with the number of isolated cells dropping to 36.6% (Fig. 4D). This effect did not result from 461 a negative feedback of RPTPK on SRC or FAK activity, as MCF10A-ER-SRC cells 462 overexpressing PTPRK still maintained high levels of ER-pSRC (Fig. 4E) and pFAK (Fig. 463 4F) levels, 24 or 36 hours after TAM treatment. Taken together, these observations support 464 the predicted requirement of the downregulation of PTPRK by SRC to promote the 465 emergence of a full mesenchymal phenotype. 466 467 ECM stiffening synergizes with SRC to induce a full mesenchymal phenotype

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468 Simulations indicate that stable states reached upon FAK_SRC overactivation would depend 469 on the ECM status. In a soft ECM, and with an input configuration set to [ECM, GF, IF, CC, 470 WNT, DELTA]=[000100], FAK_SRC overactivation (FAK_SRC=2) failed to generate an 471 M3 phenotype when starting from an E1 state. Instead, the M2 and H2 phenotypes were 472 reached in 22% and 78% of simulation runs, respectively (Fig. 5A). In addition, the model 473 indicated that SNAIL (Fig. 5B), SLUG and ZEB (Supplementary Fig. S4A, B) were 474 expressed in 100% simulation runs with a stiff ECM (ECM=1). In contrast, with a soft ECM 475 (ECM=0), SNAIL (Fig. 5B) was never expressed, while SLUG and ZEB were expressed in 476 20% of cases only (Supplementary Fig. S4A, B). Altogether, this would indicate a synergistic 477 effect between ECM stiffening and FAK_SRC to generate a full mesenchymal phenotype, 478 and to upregulate EMT-TF. 479 480 To test this prediction experimentally, we analyzed the effect of type-I collagen-coated rigid 481 plastic of distinct concentrations on the behavior of TAM-treated MCF10A-ER-SRC cells. 1 482 mg/mL collagen gels with an average Young´s moduli of 1.641 kPa ± 0.691 were used to 483 mirror soft ECM surrounding normal mammary epithelial cells (34). While 5 mg/mL 484 collagen gels of an average Young´s moduli of 3.084 kPa ± 1.458 (Supplementary Fig. S5A- 485 C) were used to resemble stiffer matrix reported for stroma adjacent to transformed cells (34). 486 Consistent with the model prediction, TAM-treated MCF10A-ER-SRC cells expressed 487 significantly higher levels of SNAIL when grown on stiff gels, compared to those plated on 488 soft gels (Fig. 5C). However, the presence of a stiff gel was not sufficient to significantly 489 induce SLUG, ZEB1 or ZEB2 expression in TAM-treated MCF10A-ER-SRC cells 490 (Supplementary Fig. S6A-C). The ECM stiffness-dependent effect on SNAIL expression in 491 this cell line was also associated with distinct cell morphologies. While on stiff gel, TAM- 492 treated cells appeared rounder and isolated, when grown on soft gels, these cells maintained 493 cell–cell contacts at ‘tip-like’ junctions (Fig. 5D). These differential effects were not due to 494 increased SRC activity following matrix stiffening, as the levels of ER-pSRC did not 495 significantly vary in TAM-treated MCF10A-ER-SRC between those grown on stiff and soft 496 gels (Fig. 5E). Moreover, on both, soft and stiff gels, SRC activation could downregulate 497 PTPRK (Fig. 5F). To confirm the synergistic effect between ECM stiffness and SRC, we 498 analyzed the effect of soft and stiff collagen gels on the Madin-Darby Canine Kidney 499 (MDCK) epithelial cell line expressing cSRCY527F in a tetracycline (tet)-inducible manner 500 (MDCK-pTR cSRCY527F-GFP). In contrast to tet-treated MDCK-pTR cSRCY527F-GFP cells 501 grown on soft gels, those plated on stiff gels poorly accumulated ECad at the cell membrane

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502 (Fig. 5G). Moreover, in this cell line, a stiff gel synergized with cSRCY527F to significantly 503 upregulate SNAIL (Fig. 5H) and SLUG (Supplementary Fig. S6D). ZEB1 and ZEB2 could 504 not be tested in this cell line using two independent sets of primers. 505 Taken together, our experimental assessments of the effect of SRC and ECM stiffness are 506 consistent with the model predictions by which ECM stiffening synergizes with SRC to 507 potentiate SNAIL and SLUG expression and to promote a mesenchymal phenotype.

508 Phenotypes are plastic upon changes in environmental conditions 509 To further analyze the impact of microenvironment signals on EMT dynamics, we performed 510 systematic reachability analyses of the model stable states upon switching input 511 combinations. For concision, EGF and HGF and ROS on the one hand, IL6 and TGFB on the 512 other hand were gathered to respectively denote the presence or absence of growth factors 513 (GF) and of inflammation (IF). The RPTPL value denoted the state of cell-cell contact (CC) 514 and the other inputs were assigned fixed values with DELTA and WNT, both considered 515 absent, and FAT4 considered present (FAT4=1). We were thus left with 4 input 516 configurations (ECM, GF, IF, CC). The model was then reduced by propagating the values of 517 the fixed inputs, and stable states reached by the model were classified into distinct 518 phenotypes, depending on the values of the output nodes AJ and FA, as shown previously 519 (Table 1). Note that setting WNT to value 0 discarded the UN phenotype, which requires the 520 presence of the WNT signal. 521 Considering each input configuration and for each phenotype, we checked the following 522 properties (Supplementary Methods): 523 (1) Does it exist a state in the set of states defining the phenotype such that there is a 524 trajectory leading directly to a target phenotype (i.e., without visiting any other 525 phenotype)? This resulted into a “weak” reprogramming graph (Supplementary Fig. 526 S7); 527 (2) "For every state of the phenotype, is there a trajectory leading directly to a target 528 phenotype (i.e., without visiting any other phenotype)? This resulted into a “strong” 529 reprogramming graph (Supplementary Fig. S8). 530 531 When property (1) does not hold for an input configuration, it means that all the states of the 532 phenotype are stable for that configuration. This “strong” stability property is reported as a 533 self-loop in the “strong” reprogramming graph (Supplementary Fig. S8). From this graph, we 534 discarded the phenotypes M1 and H1, as no input configurations ensure the stability of all the

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535 states of these phenotypes. This permitted to obtain a simplified version of the strong 536 reprogramming graph, which displays striking behaviors (Fig. 6A). First, while the H2, H3 537 and M2 phenotypes can evolve into all other phenotypes, this is not the case for the E1 and 538 M3 phenotypes. Moreover, none of the inputs considered (ECM, GF, IF, CC) permit to attain 539 H3 neither from E1 nor from M3, or to reach H2 from M3. However, some specific states in 540 the other phenotypes can lead to H2 and H3 (Supplementary Fig. S7). In contrast, while the 541 E1 and M3 phenotypes show strong stability, a large variety of environmental inputs allow to 542 leave the H2, H3 or M2 phenotypes. Another relevant observation is that switching off ECM 543 is necessary and sufficient to convert the M3 phenotype into M2 and/or E1 phenotypes (Fig. 544 6A). Again, the “weak” reprogramming graph shows that other phenotypes are possibly 545 reached from restricted sets of M3 states (Supplementary Fig. S7). 546 Relying on the “strong” reachability graph, we then explored possible scenarios by which 547 microenvironmental inputs could trigger EMT. We assumed that (1) alterations in 548 microenvironmental inputs are more likely to arise sequentially (only 1 input altered at a 549 time) and; (2) ECM stiffening is a late event, as it has been mainly associated with later 550 stages of cancer progression (35). Starting from an E1 phenotype with input configurations 551 set to [ECM, GF, IF, CC]=[0001 or 0000], four possible scenarios were achievable. 552 When the input IF was first switched on in the absence of CC (configuration [0010]), this led 553 to transient occurrences of the H1, H2 and M1 phenotypes, that all ultimately reached the M2 554 phenotype. Then, switching the ECM to 1 (configuration [1010]) led to the disappearance of 555 the M2 phenotype, which was replaced by M3 (Fig. 6B). In this scenario, the presence of CC 556 (configuration [0001]) had only subtle effects on the trajectory. The only divergence was the 557 transient emergence of the M3 rather than the M1 phenotype (Fig. 6B). Moreover, the 558 kinetics and probabilities of acquiring these phenotypes were not altered by switching GF to 559 1 after stabilization of the M2 or M3 phenotypes by IF or ECM, respectively. In contrast, 560 when GF was first switched on in the absence of CC (configuration [0100]), this led to a 561 transient appearance of an H1 phenotype, and ultimately a mixed stable population with 562 about 5% and 95% of cells with M2 and H2 phenotypes, respectively (Fig. 6C). Then, 563 switching IF to 1 (configuration [0110]) and then ECM to 1 (configuration [1110]) converts 564 H2 into a M2 and then a M3 phenotype in 100% of the cells (Fig. 6C). Strikingly, in this 565 scenario, the CC status drastically influences the response of epithelial cells, as in the 566 presence of CC all cells maintained an E1 phenotype upon GF stimulation (configuration 567 [0101], Fig. 6C). Several predictions arise from this analysis: (1) IF hampers the maintenance 568 of the Hybrid H2 and H3 phenotypes while favoring the mesenchymal M2 and M3

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569 phenotypes; (2) ECM stiffening promotes the emergence of the H3 and M3 phenotypes; (3) 570 CC (illustrated by RPTPL) prevents EMT in the presence of GF.

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571 Discussion 572 573 Our in silico model uses cell adhesions properties as read-outs for the acquisition of EMT 574 traits. It robustly recapitulates cell behaviors observed experimentally. First, the model 575 accounts for the phenotypic repertoire identified using experimental and mathematical 576 modeling assessments of EMT (7,9,36,37), including pure epithelial (E1) and mesenchymal 577 (M) phenotypes, as well as hybrid phenotypes (H). Second, the ease by which these hybrid 578 phenotypes evolve upon microenvironmental stimulations, could reflect the observed 579 pluripotent abilities of cancer cells in these hybrid states (2). Third, we experimentally show 580 that the downregulation of PTPRK by SRC is required to promote the emergence of a full 581 mesenchymal phenotype. Fourth, our experimental assessment of the effect of ECM 582 stiffening corroborates the model prediction by which a stiff ECM and FAK_SRC synergize 583 to potentiate SNAIL and SLUG expression, therein triggering a full mesenchymal phenotype. 584 Our observations that SLUG, ZEB1 and ZEB2 did not respond to increase gel stiffness in 585 TAM-treated MCF10A-ER-SRC could reflect cell type-specific effects of these EMT-TFs, as 586 reported in other context (38,39). In any case, as wisely underscored by Jolly et al., ‘no one- 587 size-fits-all’, i.e., no model, either biological or mathematical, can account for all biological 588 contexts (37). Fifth, consistent with our simulations indicating that ECM stiffening boosts 589 cell movement, irrespectively of the mode of migration (collective through H3 versus 590 mesenchymal through M3), a stiffer microenvironment promotes metastatic transition 591 (34,40), induces faster migration (41) and does not influence the mode of cell migration (42). 592 Sixth, as observed in malignant tissues and cultured cancer cell lines (43), our model suggests 593 the coexistence of cell populations with distinct phenotypes, revealing tumor heterogeneity. 594 595 Our model also allows to formulate diverse predictions. Hybrid phenotypes might not 596 constitute mere transient steps along an EMT continuum, but could represent independent 597 trajectories, as the acquisition and maintenance of the H2 and H3 phenotypes require more 598 stringent inputs than those required for gaining mesenchymal traits. In addition, simulations 599 of single or double mutants and of microenvironmental signals on EMT phenotypes predict 600 key players that would facilitate or hinder the maintenance of these hybrid phenotypes. As 601 these phenotypes have been associated with worse progression-free survival in cancer 602 patients (44), our work point to actionable molecular targets that could serve to design novel 603 therapeutic interventions. In particular, inhibitors against molecular targets that would drag 604 cancer cells into mesenchymal phenotypes only, could be beneficial for cancer patients, as

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605 they could also halt metastasis seeding. Among involved microenvironmental signals, 606 inflammation triggers the transient appearance of these phenotypes, but does not allow their 607 maintenance. Instead, inflammation favors the acquisition of mesenchymal phenotypes. 608 Accordingly, the autocrine production of inflammatory signals, such as TGFB, stabilizes a 609 mesenchymal phenotype in vitro (45). However, our model suggests that transient versus 610 sustained inflammatory signals would have different consequences on tumor cell behavior 611 and likely aggressiveness. 612 In contrast to inflammatory signals, the model predicts that growth factors support the 613 acquisition and maintenance of the hybrid H2 phenotype, if and only if cell-cell contacts 614 through RPTP are absent. Thus, RPTPs could fulfill tumor suppressor functions in the 615 presence of growth factors. Consistent with this hypothesis, the downregulation of PTPRK in 616 solid cancers correlates with a poor disease free-survival time (5). Moreover, knocking down 617 PTPRK increases invasiveness in breast cancer cells, while expressing PTPRK exogenously 618 in melanoma cells reduces cell migration (46,47). Furthermore, we provide evidence that 619 PTPRK is downregulated by SRC. The model also predicts that RPTP is absolutely required 620 to support an H3 phenotype. In agreement with this prediction, R2B RPTPs have been 621 proposed to promote the collective movement of tumor cells along nerve bundles (48). 622 Moreover, they have also been classified as oncogenes (5). Furthermore, we show that 623 forcing PTPRK expression in TAM-treated MCF10A-ER-SRC cells restores cell-cell 624 adhesion. Although PTPR downregulation is not an obligate requirement for a mesenchymal 625 phenotype, our computational and experimental approaches suggest that PTPRK 626 downregulation prevents collective cell migration to allow full mesenchymal transition. Thus, 627 RPTPK could have opposite effects during EMT: early, it could prevent the induction of an 628 EMT program, in particular in the presence of growth factors, whereas later, PTPRK could 629 determine the mode of cell migration (collective versus single cell migration). 630 631 Our in silico analysis suggests that, as observed experimentally (36), a soft ECM is not 632 required to maintain an epithelial phenotype. It further predicts that ECM softening is 633 sufficient to revert both mesenchymal M3 and hybrid H3 phenotypes into epithelial ones. 634 Thus, alterations in ECM rigidity could be a critical determinant for seeding metastasis. 635 Accordingly, diverse observations support a role of ECM remodeling during pre-metastatic 636 niche formation (49). 637

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638 Further model analyses would permit to identify non-cell autonomous role of WNT and 639 DELTA, as well as individual contributions of the growth factors EGF and HGF and of the 640 inflammatory inputs IL6, TGFB and ROS. Moreover, as EMT alters many cell behaviors in 641 addition to adhesion properties (2), the model could be extended by including relevant 642 pathways and associated read-outs, such as survival, proliferation and other cell fates . As 643 future work, we also aim at embedding the cellular model into a multi-cellular context, which 644 will allow to properly simulate collective dynamics, accounting for cell-cells interactions and 645 their synergistic shaping of the microenvironment.

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646 647 Acknowledgements 648 We specially thank K. Struhl and C. Hogan for providing the MCF10A-ER-SRC and MDCK- 649 Src cell lines, respectively, P. Hermanowicz for his assistance on the use of the AtomicJ 650 program and for AFM data analysis, H.J. Shape for sharing their anti-RPTPK antibody (26) 651 (26)(26)and R. Pais for defining a first version of the computational model. We acknowledge 652 the Imaging and Cytometry and Genomics facilities at IGC, and the Cell Culture and 653 Genotyping, the Genomics, the Bioimaging and the Advanced Light Microscopy platform, 654 both member of the national infrastructure PPBI-Portuguese Platform of BioImaging (POCI- 655 01-0145-FEDER-022122), as well as the Biointerfaces and Nanotechnology platforms at i3S. 656 Funds from Fundação para a Ciência e Tecnologia (FCT) (PTDC/BEX-BCB/0772/2014) to 657 C. Chaouiya supported this work and A. Pawar and S. Canato. Funds from FCT, co-financed 658 by Fundo Europeu de Desenvolvimento Regional (FEDER) through Programa Operacional 659 Competitividade e Internacionalização (POCI) (POCI-01-0145-FEDER-016390) to C. 660 Chaouiya and F. Janody supported this work and G. Selvaggio. G. Selvaggio and C. 661 Chaouiya were supported by the Gulbenkian Foundation. F. Janody was the recipient of 662 IF/01031/2012. 663 664

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813 814 815 Table 1: Stable states denoting diverse phenotypes. Stable states are grouped into patterns 816 (the table rows) classified into 4 phenotypes according to the model read-outs AJ and FA (E 817 for epithelial, H for hybrid, UN for undetermined, and M for mesenchymal). Cell colors 818 denote the levels of the components: 0 (red), 1 (green), 2 (dark green), 3 (olive). The wildcard 819 * (white) indicates any admissible level, the coupled wildcard ? (light grey) indicates any 820 same admissible level for the components marked with this symbol (the same applies for the 821 coupled wildcard ! in dark grey). The penultimate column indicates the different phenotypes 822 and their respective color codes. For each phenotype, the total number of stable states is 823 given in the last column; this is calculated as the sum of the number of states represented in 824 each pattern that depends on the wildcards: 2^(number of *) if there are no coupled wildcard, 825 2x2^(number of *) if there are only coupled wildcards ?, 2x2x2^(number of *) if there are both

826 coupled wildcards ? and ! (see Supplementary Methods).

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827 Figure Legends 828 829 Figure 1: Regulatory network of cell adhesion properties controlled by the 830 microenvironment including key components involved in EMT and their interactions. Inputs 831 from the microenvironment are denoted in grey, epithelial markers in green, mesenchymal 832 markers in dark brown. Inhibitions are denoted by red blunt arrows, activations by green 833 arrows. The black interaction indicates a putative regulatory interaction. Plain edges denote 834 documented regulatory interactions, whereas dashed edges correspond to indirect 835 interactions. Ellipsoidal components are associated with Boolean levels, whereas rectangles 836 indicate multivalued level components. The two read-outs AJ and FA define the cell

837 commitment in assembling AJs and in remodeling FAs, respectively.

838 839 Figure 2: Multiple Correspondence Analysis (MCA) of the model stable states. Each dot 840 is a stable state, colored according to the phenotype it belongs to. The numbers (#n) indicate

841 the number of stable states in the corresponding phenotype.

842 Figure 3: SRC downregulates PTPRK prior to acquiring mesenchymal features. 843 (A-B) Probabilities of the reachable phenotypes when FAK_SRC is overactivated when 844 starting from an E1 phenotype, (A) in the presence of the inhibitory interaction of FAK_SRC 845 on RPTP (FAK_SRC+) or (B) when maintaining RPTP expression (FAK_SRC+, RPTP+). 846 (C, D) Western blots on protein extracts from MCF10A-ER-SRC cells treated with TAM for 847 the time indicated, blotted with (C) anti-pSRC (upper panel), which reveals ER-pSRC and 848 endogenous pSRC or (D) anti-pFAK or anti-FAK. The corresponding blotting with anti- 849 GAPDH is shown below each blot. The same extracts have been used for comparison 850 between pFAK and FAK levels. 851 (E) ECad mRNA levels in extracts from MCF10A-ER-SRC cells treated with TAM for 24 or 852 36 hours, normalized to those of MCF10A-ER-SRC cells treated with EtOH for the same 853 period of time and to GAPDH. 854 (F-G) Standard confocal sections of MCF10A-ER-SRC cells treated with (F) TAM or (G) 855 EtOH for the time indicated, stained with ECad (green), Phalloidin (magenta) to mark actin 856 filaments and DAPI (blue) to mark nuclei. Scale bars represent 20 µm. 857

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858 (H) PTPRK mRNA levels on extracts from MCF10A-ER-SRC cells treated with TAM for 859 the time indicated, normalized to those of MCF10A-ER-SRC cells treated with EtOH for the 860 same period of time and to GAPDH. 861 (I) Western blots on protein extracts from MCF10A-ER-SRC cells treated with TAM for the 862 time indicated, blotted with anti-RPTPK or anti-GAPDH. Pre and P indicate the precursor 863 and P subunit of RPTPK, respectively. 864 Quantifications are presented as mean ± SD. n.s. indicate non-significant; * indicates P<0.05; 865 ** indicates P<0.005; *** indicates P<0.001 (one-way ANOVA). 866 867 Figure 4: Forced PTPRK expression restores the ability of TAM-treated MCF10A-ER- 868 SRC cells to aggregate. 869 (A-B) PTPRK mRNA levels normalized to GAPDH on extracts from MCF10A-ER-SRC 870 cells expressing mock guide RNA (mock) or PTPRK guide RNA (PTPRK+), treated with 871 EtOH or TAM for 24 (A) or 36 (B) hours. 872 (C) Western blots on protein extracts from MCF10A-ER-SRC cells expressing mock guide 873 RNA (mock) or PTPRK guide RNA (PTPRK+) and treated with EtOH or TAM for 24 or 36 874 hours, blotted with anti-RPTPK or anti-GAPDH. Pre and P indicate the precursor and P 875 subunit of RPTPK, respectively. 876 (D) (Left panels) Phase-contrast images of MCF10A-ER-SRC expressing mock guide RNA 877 (mock) or PTPRK guide RNA (PTPRK+) and treated with EtOH or TAM for 36 hours, plated 878 in agar. (Right panel) Percentage of isolated MCF10A-ER-SRC cells for each experimental 879 condition. 880 (E-F) Western blots on protein extracts from MCF10A-ER-SRC cells expressing mock guide 881 RNA (mock) or PTPRK guide RNA (PTPRK+) and treated with EtOH or TAM for 24 or 36 882 hours, blotted with (E) anti-pSRC (upper panels), which reveals ER-pSRC or anti-GAPDH 883 (lower panels) or (F) anti-pFAK (upper left panels) and anti-GAPDH (lower left panels) or 884 anti-FAK (upper right panels) and anti-GAPDH (lower right panels). The same extracts have 885 been used for comparison between pFAK and FAK levels. 886 Quantifications are presented as mean ± SD. n.s. indicate non-significant; * indicates P<0.05; 887 ** indicates P<0.005; *** indicates P<0.001; **** indicates P<0.0001 (one-way ANOVA). 888 889 Figure 5: Simulations and experimental validation identify a synergistic effect of ECM 890 stiffening and SRC on SNAIL expression and on the acquisition of EMT features.

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891 (A) Probabilities of the reachable phenotypes when FAK_SRC is overactivated (FAK-SRC+), 892 starting from an E1 phenotype, in condition of a soft ECM (ECM=0) or of a stiff ECM 893 (ECM=1). 894 (B) Simulation of SNAIL expression when FAK_SRC is overactivated (FAK_SRC+), starting 895 from an E1 phenotype, in condition of a soft ECM (ECM=0) or a stiff ECM (ECM=1). 896 (C) SNAIL mRNA levels on extracts from MCF10A-ER-SRC cells plated on soft (1 mg/mL) 897 or stiff (5 mg/mL) collagen gels and treated with EtOH or TAM for 24 hours. 898 (D) (Left panels) Phase-contrast images and their corresponding inverted images of 899 MCF10A-ER-SRC cells plated on soft (1 mg/mL) or stiff (5 mg/mL) collagen gels and 900 treated with EtOH or TAM for 48 hours. (Right panel) Percentage of isolated round 901 MCF10A-ER-SRC cells for each experimental condition. 902 (E) (Left panel) Western blots on protein extracts from MCF10A-ER-SRC cells plated on 903 soft (1 mg/mL) or stiff (5 mg/mL) collagen gels and treated with EtOH or TAM for 24 hours, 904 blotted with anti-pSRC, which reveals ER-pSrc and endogenous pSRC (lower panel) or anti- 905 GAPDH (lower panel). (Right panel) Quantification of ER-pSRC levels for each 906 experimental condition. 907 (F) PTPRK mRNA levels normalized to GAPDH on extracts from MCF10A-ER-SRC plated 908 on soft (1 mg/mL) or stiff (5 mg/mL) collagen gels and treated with EtOH or TAM for 24 909 hours. 910 (G) Standard confocal images of MDCK-pTR cSRCY527F-GFP plated on soft (1 mg/mL) or 911 stiff (5 mg/mL) collagen gels and treated with tet for 48 hours, stained with anti-ECad (green) 912 and DAPI (blue). Scale bars represent 100m. 913 (H) SNAIL mRNA levels normalized to GAPDH on extracts from MDCK-pTR cSRCY527F- 914 GFP cells plated on soft (1 mg/mL) or stiff (5 mg/mL) collagen gels and treated with EtOH 915 or tet for 48 hours. 916 Quantifications are presented as mean ± SD. n.s. indicate non-significant; * indicates P<0.05; 917 ** indicates P<0.005; *** indicates P<0.001; **** indicates P<0.0001 (one-way ANOVA). 918 919 Figure 6: Effects of altering microenvironmental conditions on the acquisition of 920 distinct EMT phenotypes (A) Simplified (“strong”) reprogramming graph showing 921 existence of plasticity trajectories between phenotypes. Nodes denote the phenotypes: 922 epithelial (E1), hybrid (H2, H3), and mesenchymal (M2, M3). Edge labels indicate the input 923 configurations under which the corresponding trajectory is feasible. Inputs are shown in the

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924 following order [ECM, GF, IF, CC], with ECM stiffening (ECM), inflammation (IF), growth 925 factors (GF), cell-cell contact (CC). 1 means activated/present, 0 means inactivated/absent, 926 and * means that both values 0 and 1 are possible (complete “strong” and “weak” 927 reprogramming graphs are shown in Supplementary Fig. S7 and S8). (A-B) Scenarios by 928 which microenvironmental inputs trigger EMT. Dynamics of the phenotype probabilities 929 starting from the epithelial phenotype E1 with input configurations set to [ECM, GF, IF, 930 CC]=[0000] (upper panels) or to [0001] (lower panel); cells change phenotypes upon 931 sequential stimulations by (B) inflammation (IF) and ECM stiffening (ECM) or (C) growth 932 factors (GF), inflammation (IF) and ECM stiffening (ECM). Phenotypes are epithelial (E1), 933 hybrid (H1, H2), and mesenchymal (M1, M2, M3).

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Hybrid epithelial-mesenchymal phenotypes are controlled by microenvironmental factors

Gianluca Selvaggio, Sara Canato, Archana Pawar, et al.

Cancer Res Published OnlineFirst March 26, 2020.

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