Combined analysis of MOHCCN Gold Cohort and TCGA/ICGC data to improve detection of low-frequency cancer driver genes

Uncovering hidden cancer driver mutations through pan-Canadian data sharing

Cancer begins when changes in our DNA allow cells to grow out of control. Some of these changes, known as driver mutations, directly fuel cancer development. Because of this, finding these mutations is critical, helping scientists understand how cancers form, guide treatment decisions and develop new therapies.

Over the past decade, large international projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have identified many of the most common cancer driver mutations. But many rare yet important drivers remain undiscovered—particularly in cancers with complex genomes where it can be difficult to distinguish meaningful mutations from background noise.

A new project led by Dr. Ian Watson (McGill University) aims to change that by combining the power of the Network’s Gold Cohort with these global datasets to uncover low-frequency driver mutations that may hold the key to better cancer diagnosis and treatment.

“Large-scale data is essential to find these hidden drivers,” says Dr. Watson. “By pooling genomic and clinical information across Canada and linking it with international datasets, we can tackle questions that were impossible to answer before.”

Combining genomic, transcriptomic and functional data

The project will analyze whole-genome sequencing data from Canadian patients alongside data from TCGA and ICGC to identify mutations that appear more frequently than expected, including those in the non-coding regions of the genome that help regulate genes but are often overlooked.

To increase accuracy, the multi-institutions team, which includes Drs. Guillaume Bourque, Hamed Najafabadi and Yasser Riazalhosseini (Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University), Dr. Morag Park (Goodman Cancer Institute) and Dr. Anne-Marie Mes-Masson (Centre hospitalier de l'Université de Montréal), will integrate multiple layers of information:

• RNA data will help reveal whether a mutation actually changes gene activity
• Genomic structural data will show whether mutations co-occur with losses in key tumour-suppressing regions
• Functional datasets will pinpoint mutations in genes essential for cancer cell survival

Together, this combined approach will help researchers distinguish truly important driver mutations from background genetic noise.

Building capacity for collaborative cancer research in Canada

Funded through the Network’s Data Sharing and Use Pilot, this team will be amongst the first five groups to access combined Gold Cohort data after being selected to participate in the Data Sharing and Use Pilots program.

As part of the program, the team will not only seek to expand our understanding of cancer genetics but also demonstrate the power of data sharing across Canada, while validating the Network’s data sharing and access policies.

By doing this, the project will help build Canada’s capacity for large-scale collaborative cancer research while paving the way for more personalized cancer care in the future.

“The ultimate goal is to find new cancer driver genes that could lead to better diagnostics, new treatments and more precise care for patients,” says Dr. Watson. “But just as importantly, this project shows how collaboration and data sharing can accelerate discovery nationwide.”