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Signalling in prostate cancer


Prostate cancer is the second most occurring cancer in men worldwide, and with the
advances made with screening for prostate-specific antigen, it has been prone to early
diagnosis and over-treatment. 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.
The model 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. The final model accounts for 133 nodes and 449 edges.
We applied a methodology to personalise this Boolean model to molecular data to reflect the
heterogeneity and specific response to perturbations of cancer patients, using TCGA and
GDSC datasets.

Aurelien Naldi

Response to BRAF treatment in melanoma and colorectal cancer


The study of response to cancer treatments has benefited greatly from the contribution of different
omics data but their interpretation is sometimes difficult. Some mathematical models based on
prior biological knowledge of signaling pathways, facilitate this interpretation but often require
fitting of their parameters using perturbation data. We propose a more qualitative mechanistic
approach, based on logical formalism and on the sole mapping and interpretation of omics data,
and able to recover differences in sensitivity to gene inhibition without model training. This
approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal
cancers who experience significant differences in sensitivity despite similar omics profiles.

We first gather information from literature and build a logical model summarizing the regulatory
network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors
involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by
automatically assessing that it qualitatively reproduces response or resistance behaviors identified
in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to
validate the model’s ability to explain differences in sensitivity. This generic model is transformed
into personalized cell line-specific logical models by integrating the omics information of the cell
lines as constraints of the model. The use of mutations alone allows personalized models to
correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and
CRISPR targeting, and even better with the joint use of mutations and RNA, supporting
multi-omics mechanistic models. A comparison of these untrained models with learning approaches
highlights similarities in interpretation and complementarity depending on the size of the datasets.


Immunogenic Cell Death


This Boolean model covers the major cell types that intervene in immunogenic cell death (ICD), namely cancer cells, DCs, CD8+ and CD4+ T cells. This model integrates intracellular mechanisms within each individual cell entity, and further incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell populations.

Model dynamics has been simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type.

With this model, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted. In addition, model analysis led to the identification of actionable targets for boosting ICD-induced antitumor response.

All computational analyses and results are compiled in interactive notebooks which cover the presentation of the network structure, model simulations, and calculations of parameter sensitivity analyses.

Aurelien Naldi

Microenvironment control of hybrid Epithelial-Mesenchymal phenotypes


Epithelial to Mesenchymal Transition (EMT) has been associated with cancer cell heterogeneity, plasticity and metastasis. However, the extrinsic signals supervising these phenotypic transitions remain elusive. To identify microenvironmental signals controlling cancer-associated phenotypes amid the EMT continuum, we defined a logical model of the EMT cellular network that access the qualitative degrees of cell adhesions by adherent junctions and focal adhesions, two features affected during EMT. Model attractors could recover epithelial, mesenchymal and hybrid phenotypes. In silico simulations provided evidences that hybrid phenotypes may arise through independent molecular paths, involving stringent extrinsic signals. Of particular interest, model predictions and their experimental validations indicated that: 1) ECM stiffening is a prerequisite for cells overactivating FAK-SRC to upregulate SNAIL1 and acquire a mesenchymal phenotype, and 2) FAK-SRC inhibition of cell-cell contacts through the Receptor Protein Tyrosine Phosphates kappa leads to the acquisition of a full mesenchymal rather than a hybrid phenotype. Altogether, our computational and experimental approaches permitted to identify critical microenvironmental signals controlling hybrid EMT phenotypes, and indicated that EMT involves multiple molecular programs.


p53-Mdm2 network involved in DNA repair


This model is a refined version of the logical model of the p53-mdm2 network described in Fig. 5a of Abou-Jaoudé et al. [1].

The regulatory graph describes the interactions between protein p53, the ubiquitin ligase Mdm2 in its nuclear and cytoplasmic forms, and DNA damage. It relies on biological data taken from literature.

In short, the nuclear component of Mdm2 down-regulates the level of active p53. This occurs both by accelerating p53 degradation through ubiquitination and by blocking the transcriptional activity of p53.

Protein p53 plays a dual role. It activates the expression of Mdm2 thereby up-regulating the level of cytoplasmic Mdm2, and down-regulates the level of nuclear Mdm2 by inhibiting Mdm2 nuclear translocation through inactivation of the kinase Akt.

DNA damage has a negative influence on the level of nuclear Mdm2, by accelerating its degradation through ATM-mediated phosphorylation and auto-ubiquitination.

Damage-induced Mdm2 destabilization enables p53 to accumulate and remain active.

Finally, high levels of p53 promote damage repair by inducing the synthesis of DNA repair proteins.

model network


Pedro T. Monteiro

Cell fate decision network in the AGS gastric cancer cell line (Flobak et al 2015)


This model accounts for cell fate decision network in the AGS gastric cancer cell line. A set of logical equations has been defined, wich recapitulates AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. These simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. All predicted non synergic pairs and four of the predicted synergic ones were confirmed in AGS cell growth real-time assays, including known synergic effects of MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions.

D. Thieffry / C. Chaouiya

Molecular Pathways Enabling Tumour Cell Invasion and Migration


Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.

L. Calzone / C. Chaouiya

Mutually exclusive and co-occurring genetic alterations in bladder tumorigenesis


Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modelling to explain patterns of genetic alterations seen in 178 patients with bladder tumours (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signalling, cell cycle and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publically. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signalling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3 activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favour invasiveness. Further, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness.

Claudine Chaouiya
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