Identifying pan-cancer signatures of chromosomal instability
The broad genomic complexity caused by CIN is a hallmark of cancer, however, there is no systematic framework to measure different types of CIN and their impact on clinical phenotypes. We aim evaluate the extent, diversity and origin of chromosomal instability pancancer. We present a compendium of 17 copy number signatures characterising specific types of CIN, with putative aetiologies supported by multiple independent data sources. The signatures predict drug response and identify new drug targets. Our framework refines the understanding of impaired homologous recombination, one of the most therapeutically targetable types of CIN. Our results illuminate a fundamental structure underlying genomic complexity and provide a resource to guide future CIN research in human cancers.
Collaborators: Markowetz lab, Van Loo lab.

Inducing chromosomal instability in vitro
Here we induce CIN in cell lines by knocking-out key genes related to cell cycle and DNA repair processes using CRISPR/Cas 9 gene editing techniques. We aim to use these models to provide improved understanding of different CIN-related mechanisms and identify new treatments to target CIN. These models provide the scientific community with a valuable resource for future study of CIN.
Collaborators: Cortes lab, Quintela lab, Malumbres lab, Capetillo lab, Losada lab

Detecting ongoing chromosomal instability in premalignant lesions
A key goal of the computational oncology group is to develop techniques to identify and stop CIN at a premalignant stage, before the development of an aggressive tumour. Here we are developing new single cell genomic techniques that can detect ongoing CIN from small amounts of fixed, premalignant tissues. In collaboration with UCL, we are applying these techniques to premalignant lung lesions to identify those which will progress to difficult to treat, high-CIN cancers.
Collaborators: Janes Lab

Predicting treatment response in human cancers using copy number signatures
Here we are applying our copy number signature biomarkers to organoids treated with different therapies, and retrospective patient cohorts, to determine which signatures predict response to therapies. In our proof of concept study we have identified a signature which can predict resistance to doxorubicin, a therapy commonly used to treat ovarian cancer. This test has been validated across a small patient cohort and is currently being tested in a prospective observational study.
Collaborators: Brenton lab

Developing improved computational methods for copy number analysis
Here we aim to develop improved computational algorithms for making sense of genome-wide copy number profiles. This includes: better absolute copy number fitting, identification of single cell unique copy number events, comparison of copy number profiles across different samples (CNpare). 

Molecular characterisation of clear cell and endometrioid ovarian carcinomas
The aim of this project is to characterize the immunogenicity of Endometrioid and Clear Cell Ovarian Carcinomas and to gain insight into the carcinogenesis of both histotypes with a strong focus on the DNA Mismatch Repair pathway (MMR) and its clinical relevance. We seek to identify new therapeutic targets and to define novel prognostic and predictive factors for these ovarian cancer subtypes with bad prognosis once disseminated.
Project lead: Maria Garcia