Project Funding Details

Metastatic gastric cancer (mGC) is one of the most lethal tumors. Recent efforts towards improving the medical management of these patients have been hindered by a number of negative or inconclusive trials with molecular targeted agents and immune checkpoint inhibitors. Moreover, while large-scale genome sequencing studies are facilitating a clinically exploitable characterization of several tumors, the proposed molecular taxonomy of gastric adenocarcinoma is poorly informative in clinical practice. Tumor response to chemotherapy is widely heterogeneous. While some patients experience early progression (fast progressors), other patients have durable clinical benefit (exceptional responders). My previous studies provided hints that a multiplicity of molecular cues account for this inter-patient variability, and raised the hypothesis that a full appreciation of the molecular landscape of atypical responders (fast progressors and exceptional responders) may sharpen the therapeutic potential of established agents and foster the development of novel therapies. The first aim of this project is to develop a novel molecular classifier for predicting the pattern of disease evolution at the individual patient level in mGC (WP1). Once having framed the molecular backgrounds of exceptional responders, conventional progressors and fast progressors, we will study how anticancer treatments shape tumour evolution by searching specific molecular alterations in the blood (liquid biopsy, WP2). The third aim is to identify still unappreciated genotype-specific vulnerabilities to improve outcomes of patients with intrinsically chemoresistant tumors (fast progressors). To this purpose, patient-derived models will be combined with high-throughput drug screenings (WP3). A novel whole-exome sequencing (WES) approach in association with whole-transcriptome sequencing (WTS) will be used to analyze two independent and equally-sized cohorts of mGC patients (cohort A: retrospective, identification set; cohort B: prospective, validation set). A streamlined analytical workflow specifically ideated for this project, that utilizes clinical outcomes as the benchmark for comparative analyses, will be adopted to generate a molecular classifier applicable to all mGC patients and predicting atypical tumor responses (WP1). The molecular features differentially represented in exceptional responders and fast progressors will be monitored through liquid biopsy in patients included in cohort B, leveraging a highly sensitive technology that allows the detection of both genomic alterations and transcript-level expression (WP2). Finally, patient-derived cancer stem cells (CSCs) isolated from fast progressors will be challenged with more than 1,000 established or investigational anticancer agents to uncover unappreciated vulnerabilities (WP3). The first output of the project will be the generation of a tool to be used on a routine basis for individual risk prediction. Second, tracking the evolutionary trajectories of mGC in atypical responders will allow both the diagnosis of molecular progression and the identification of therapeutic targets to overcome intrinsic/acquired resistance. Finally, studies in CSCs will accelerate biomarker-driven trials with agents targeting specific molecular backgrounds. This project holds the potential to significantly broaden the therapeutic landscape of mGC by i) identifying patients who will obtain a prolonged benefit from current therapeutics, ii) clarifying the processes underlying therapeutic resistance, and iii) accelerating the development of novel compounds and companion diagnostics especially for patients with intrinsically chemoresistant tumors.