Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients.
作者： Jonas Béal , Arnau Montagud , Pauline Traynard , Emmanuel Barillot , Laurence Calzone
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摘要: Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. Most time they generic as apply large cohorts individuals. As a consequence, generally do not capture heterogeneity patient tumors and their therapeutic responses. We present here novel framework, referred to PROFILE, tailor logical particular biological sample such patient’s tumor. This methodology permits compare model simulations individual clinical data, i.e., survival time. Our approach focuses on integrating mutation copy number alterations (CNA), transcriptomics or proteomics models. These data need first be either binarized set between 0 1, can then incorporated in modifying activity node, initial conditions state transition rates. The use MaBoSS, tool based Monte-Carlo kinetic algorithm perform stochastic results probabilities, allows semi-quantitative study model’s phenotypes perturbations. proof concept, we published signaling molecular from METABRIC breast patients. test several combinations incorporation discuss that, with most comprehensive patient-specific obtained nodes mutations CNA altering rates RNA expression. conclude that these models’ show good correlation patients’ Nottingham prognostic index (NPI) subgrouping observe two highly derived personalized models, Proliferation Apoptosis, biologically consistent factors: patients both high proliferation low have worst rate, conversely. aims combine mechanistic insights modeling multi-omics integration provide patient-relevant work leads precision medicine will eventually facilitate choice drug treatments physicians
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