Title: Divided and conquered: analysis of patient inter- and intra-tumour genetic heterogeneity to identify dysfunctional processes in oesophageal cancer
Funder: Cancer Research UK

Abstract: Background: In recent years, the incidence of oesophageal adenocarcinoma (OAC) has increased substantially in the western world and particularly in the UK. Because of the high genetic heterogeneity, OAC molecular drivers are still mostly unknown with negative impact on patient stratification and precision therapy. Here we propose to develop a ‘divided and conquered’ approach to analyse OAC genomic data in a patient-specific way and overcome the analytical challenges posed by OAC genetic heterogeneity. Aims: The proposed programme has three objectives: (1) Discover cancer driver genes through novel computational methods based on the analysis of individual OAC patients. Predictions will be confirmed experimentally in vitro and in vivo and OAC driver genes will be further analysed to verify the disruption of similar biological processes across patients; (2) Quantify intra-tumour heterogeneity to rebuild OAC clone composition and monitor its variation in time and space. This will inform on the selection of precision therapies based on alteration clonality. A selection potential targets will be further validated experimentally; (3) Analyse individual inherited vulnerabilities, i.e. germline alterations leading to sub-optimal gene functions that may preferentially favour the selection of specific somatic driver events. Somatic drivers and germline vulnerabilities will be integrated to infer causal relationships and non-random patterns between patient inherited and acquired genotypes. Methods: Algorithms based on machine learning, rank-based statistics and Bayesian inference will be specifically designed to analyse OAC inter- and intra-tumour heterogeneity, identify driver dysfunctional processes and integrate somatic and germline data. The effects of induced gene perturbations in selected driver genes will be measured with a variety of assays in vitro and in vivo. Patient-derived cells will be used to evaluate the effect of predicted sensitivity and/or resistance to anti-cancer drugs. Genomic and transcriptomic data will derive from the International Cancer Genome Project, while samples and cell lines will be obtained through the OCCAMS Consortium. How the results of this research will be used: This research will be used to: (1) Identify OAC molecular drivers and dysfunctional processes; (2) Inform on therapeutic approaches based on intra-tumour heterogeneity and alteration clonality; (3) Stratify patients based on inherited genetic vulnerabilities and somatic driver events; (4) Develop methodological approaches of general applicability to other cancer types particularly in the setting of personalised genome initiatives.

Lay abstract: Background: In recent years, the incidence of oesophageal adenocarcinoma (OAC) has increased substantially in the western world and particularly in the UK. Because of the high genetic heterogeneity, OAC molecular drivers are still mostly unknown with negative impact on patient stratification and precision therapy. Here we propose to develop a ‘divided and conquered’ approach to analyse OAC genomic data in a patient-specific way and overcome the analytical challenges posed by OAC genetic heterogeneity. Aims: The proposed programme has three objectives: (1) Discover cancer driver genes through novel computational methods based on the analysis of individual OAC patients. Predictions will be confirmed experimentally in vitro and in vivo and OAC driver genes will be further analysed to verify the disruption of similar biological processes across patients; (2) Quantify intra-tumour heterogeneity to rebuild OAC clone composition and monitor its variation in time and space. This will inform on the selection of precision therapies based on alteration clonality. A selection potential targets will be further validated experimentally; (3) Analyse individual inherited vulnerabilities, i.e. germline alterations leading to sub-optimal gene functions that may preferentially favour the selection of specific somatic driver events. Somatic drivers and germline vulnerabilities will be integrated to infer causal relationships and non-random patterns between patient inherited and acquired genotypes. Methods: Algorithms based on machine learning, rank-based statistics and Bayesian inference will be specifically designed to analyse OAC inter- and intra-tumour heterogeneity, identify driver dysfunctional processes and integrate somatic and germline data. The effects of induced gene perturbations in selected driver genes will be measured with a variety of assays in vitro and in vivo. Patient-derived cells will be used to evaluate the effect of predicted sensitivity and/or resistance to anti-cancer drugs. Genomic and transcriptomic data will derive from the International Cancer Genome Project, while samples and cell lines will be obtained through the OCCAMS Consortium. How the results of this research will be used: This research will be used to: (1) Identify OAC molecular drivers and dysfunctional processes; (2) Inform on therapeutic approaches based on intra-tumour heterogeneity and alteration clonality; (3) Stratify patients based on inherited genetic vulnerabilities and somatic driver events; (4) Develop methodological approaches of general applicability to other cancer types particularly in the setting of personalised genome initiatives.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS