Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients.

ABSTRACT: Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic responses. We present here a novel framework, referred to as PROFILE, to tailor logical models to a particular biological sample such as a patient tumor. This methodology permits to compare the model simulations to individual clinical data, i.e., survival time. Our approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) to logical models. These data need first to be either binarized or set between 0 and 1, and can then be in

SUBMITTER: Beal J 

PROVIDER: S-EPMC6353844 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature