Francesco Stingo (Università di Firenze)

“Statistical methods for precision medicine: prognostic and predictive modeling”

Cancer is a heterogeneous disease at different molecular, genomic and clinical levels.  Identification of prognostic and predictive biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. I will first present a prognostic model for the identification of patient-specific biomarkers based on protogenomics data; this novel Bayesian hierarchical varying-sparsity regression (BEHAVIOR) model selects clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. In the second part of the talk I will present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the (previously identified) treatment predictive and disease prognostic characteristics of a particular patient’s disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection.