A DNA test that reads the scars of past damage on cancer genomes could reveal which drugs will work against the most aggressive tumours. Chromosomal instability—where cells gain, lose, or scramble entire chromosomes—drives many of the deadliest cancers, including pancreatic, ovarian, and triple-negative breast cancer. Yet doctors have few ways to predict how these chaotic tumours will respond to treatment. Standard genetic tests look for single mutations, but they miss the broader genomic wreckage. This project tackles that gap by focusing on the *cause* of instability rather than its consequences. The team has developed a single-cell DNA sequencing method that distinguishes between mutational processes that are still actively driving cancer and those that have gone extinct. By testing this pharmacogenomic test across five highly unstable cancer types, they aim to identify robust biomarkers that predict drug response. If successful, this fundamental science could transform how clinicians treat cancers that have seen no survival improvement for decades. Instead of treating all unstable tumours the same, doctors could match drugs to the specific type of chromosomal chaos driving each patient’s disease.
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GOAL: Overcoming the genomic complexity of cancers with high chromosomal instability by using signatures of ongoing mutational processes as biomarkers for drug response. BACKGROUND: Chromosomal instability is a hallmark of most lethal cancers. Highly unstable tumours have few biomarkers to guide treatment decisions and patient survival has not improved for decades. My team pioneered approaches to dissect chromosomal instability using genome-wide DNA copy number patterns, so called CIN signatures, which are characteristic for different types of instability and underlying mutational processes. In pilot studies we found that CIN signatures predict drug response. Signature analysis focuses on the cause, rather than the consequence of chromosomal instability, and thus represents a radical departure from previous personalised medicine approaches. APPROACH: Here, I am building on genomic and computational technologies I have developed to design biomarkers for treating cancers with high chromosomal instability. I propose a novel single cell DNA sequencing approach to identify ongoing mutational processes from unique events in individual cells. This pharmacogenomic test is a major advance, because it allows to separate ongoing mutational processes, which are actively contributing to cancer development, from extinct mutational processes, which left marks in the genome but are no longer active. I will systematically identify robust and predictive biomarkers of drug response in a comprehensive experimental plan across five highly unstable cancers (esophageal, lung, pancreatic, ovarian and triple negative breast cancer) and measure the heterogeneity of CIN across tumour microenvironments in patients undergoing neo-adjuvant treatment. IMPACT: This pioneering project will result in fundamental insights into how cancers with different types of chromosomal instability react to drugs and how to exploit genomic complexity for patient benefit in cancers of unmet clinical need.
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