Cancer

This logical network model integrate signalling, transcriptional and epigenetic regulatory mechanisms underlying the Acute Promyelocytic Leukaemia cell responses to RA treatment depending on their genetic background. The explicit inclusion of the histone methyltransferase EZH2 allowed us to assess its role in the maintenance of the resistant phenotype, distinguishing between its canonical and non-canonical activities. Ultimately, this model offers a solid basis to assess the roles of novel regulatory mechanisms, as well as to explore novel therapeutical approaches in silico.

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.

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.

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.

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.