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From the mechanistic modeling of signaling pathways in cancer to the interpretation of models and their contributions : clinical applications and statistical evaluation

Abstract : Beyond its genetic mechanisms, cancer can be understood as a network disease that often results from the interactions between different perturbations in a cellular regulatory network. The dynamics of these networks and associated signaling pathways are complex and require integrated approaches. One approach is to design mechanistic models that translate the biological knowledge of networks in mathematical terms to simulate computationally the molecular features of cancers. However, these models only reflect the general mechanisms at work in cancers.This thesis proposes to define personalized mechanistic models of cancer. A generic model is first defined in a logical (or Boolean) formalism, before using omics data (mutations, RNA, proteins) from patients or cell lines in order to make the model specific to each one profile. These personalized models can then be compared with the clinical data of patients in order to validate them. The response to treatment is investigated in particular in this thesis. The explicit representation of the molecular mechanisms by these models allows to simulate the effect of different treatments according to their targets and to verify if the sensitivity of a patient to a drug is well predicted by the corresponding personalized model. An example concerning the response to BRAF inhibitors in melanomas and colorectal cancers is thus presented.The comparison of mechanistic models of cancer, those presented in this thesis and others, with clinical data also encourages a rigorous evaluation of their possible benefits in the context of medical use. The quantification and interpretation of the prognostic value of outputs of some mechanistic models is briefly presented before focusing on the particular case of models able to recommend the best treatment for each patient according to his molecular profile. A theoretical framework is defined to extend causal inference methods to the evaluation of such precision medicine algorithms. An illustration is provided using simulated data and patient derived xenografts.All the methods and applications put forward a possible path from the design of mechanistic models of cancer to their evaluation using statistical models emulating clinical trials. As such, this thesis provides one framework for the implementation of precision medicine in oncology.
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Jonas Béal. From the mechanistic modeling of signaling pathways in cancer to the interpretation of models and their contributions : clinical applications and statistical evaluation. Human health and pathology. Université Paris sciences et lettres, 2020. English. ⟨NNT : 2020UPSLT005⟩. ⟨tel-03188676⟩

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