Leveraging copy-number informed genetic constraint for cancer driver discovery

Creating computational tools to uncover the drivers that cause cancer 

For many cancer patients, pinpointing the genetic mutation—or driver—that caused their cancer opens the door to targeted, more effective treatments. But for a significant number, no obvious driver is found, leaving clinicians with fewer options and patients without access to precision therapies. 

Nour Hanafi, a researcher at Princess Margaret Cancer Centre and recipient of a 2025 Health Informatics and Data Science Award from the Marathon of Hope Cancer Centres Network (MOHCCN), is working to change that. Her project aims to improve the discovery of rare or overlooked cancer drivers using a powerful, multi-layered approach that integrates genetics, protein biology, and copy number data. 

“I am deeply honoured to receive this award from the MOHCCN,” said Hanafi. “My driving research interest is in establishing new ways to identify disease-causing mutations, particularly in 'mystery' cases where conventional approaches have failed. Through this project, I am excited to develop a novel driver discovery approach. This will allow us to identify rarer and more complex events, and potentially open up precision therapeutics to patients who do not fit the most common genetic profiles.” 

Building on a tool her lab has already developed to prioritize rare disease mutations, Hanafi plans to adapt it for cancer by integrating data from thousands of tumour samples. This computational tool will sift through large amounts of data to better identify which parts of a gene are essential for normal function—and more likely to cause cancer when mutated. 

She will then apply the tool to lung cancer patients whose tumours have already been sequenced as part of the creation of the MOHCCN Gold Cohort who lack known driver mutations, aiming to uncover previously undetected targets for therapy. Promising results will be validated using RNA, protein, and mouse model data. 

“Improving driver discovery through this work will yield greater therapeutic options, as well as a better understanding of cancer biology in people who lack common drivers, opening up avenues of targeted treatment in individuals for whom the potential of precision medicine has remained unrealized,” says Hanafi. 

Funding envelope 

As part of the Health Informatics and Data Science Award, Hanafi will receive $40k over the next year from the Network, which is matched by her host institution for a total envelope of $80k.