Supporting Personalized Treatment Decisions in Head and Neck Cancer through Big Data (SuPerTreat)

Head and Neck Carcinomas (HNC) are aggressive and heterogeneous tumors with a high fatality rate. Treatment may be extremely invasive and result in highly impairing late sequelae. Many prognostic profiles and models have been discovered, but neither molecular sub-classification nor prognostic models are routinely used in clinical practice, because both are currently inconsistent, platform- and population-dependent, highlighting the need for accurate patients’ classification at diagnosis for personalized treatment decision. This project will focus on: validation of multifactorial methods combining existing clinically annotated omics datasets; investigation of ethical and legal aspects of data-driven clinical decision making vs. current evidence-based approach. We start from one of the world largest pools of treated HNC patients (approximately 2500), where the efficacy of the treatment has been recorded in varying forms together with a rich pool of omics and clinical data. In a 3 years study, we will retrospectively analyze these multi-source data using various types of classification, regression and statistical learning methods. We will (a) assess the role of omics, in addition to a currently used staging system to assist outcome of HNC; (b) produce and validate actionable prognostic and predictive models and algorithms to orient personalized treatment decisions, and integrate these into decisions support tools. Clinical endpoints are to improve patients’ stratification for disease outcome and response to treatment, to inform tailored treatment decisions and design clinical confirmatory studies based on new models to personalized medicine. Translational endpoints are to (a) validate effective genomics signatures for HNC outcome and treatment response prediction, (b) propose treatment decision support tools, (c) test the acceptability of Big Data driven research through a small pilot study, drawing new ethical and regulatory frameworks.