Thesis presented December 01, 2020

Abstract:
Cancer has been the leading cause of premature mortality (death before the age of 65) in France since 2004. For the same organ, each cancer is unique, and personalized prognosis is therefore an important aspect of patient management. The decrease in sequencing costs over the last decade has made it possible to measure the gene expression level in tumor samples. Thus, the TCGA database provides molecular profiling of tumors, clinical data, and associated patient follow-up times over several years. New discoveries are thus made possible in terms of prognostic biomarkers constructed from genomic data. In this context, the main goal of the thesis is to compare and adapt methodologies to construct prognostic risk scores for survival or recurrence of patients with cancer from sequencing and clinical data.

Keywords:
Cancer, Prediction, Cox model, Penalized regression, Survival data, RNA-seq

On-line thesis.