Big Data: Tracking cancer risk genes

Cancer may be due to lifestyle or pure chance, but a major cause is genetic predisposition. Three researchers at the Barcelona Centre for Genomic Regulation (CRG) have developed a new statistical method to identify cancer risk genes from tumour sequencing data. The study was published in Nature Communications. 

The publication identifies ten new candidate cancer predisposition genes. We applied our method to the genome sequences of more than 10,000 cancer patients with 30 different tumour types and identified known and new possible cancer predisposition genes that have the potential to contribute substantially to cancer risk, says Research Professor Ben Lehner, principal investigator of the study.

Our computational method uses an old idea that cancer genes often require ‘two hits’ before they cause cancer. We developed a method that allows us to systematically identify these genes from existing cancer genome datasets explains Solip Park, first author of the study. The method allows researchers to find risk variants without a control sample. Now we have a powerful tool to detect new cancer predisposition genes and, consequently, to contribute to improving cancer diagnosis and prevention in the future, adds Park.

The researchers worked with genome data from several cancer studies from around the world, including The Cancer Genome Atlas project among others. We managed to develop and test a new method that hopefully will improve our understanding of cancer genomics and will contribute to cancer research, diagnostics and prevention just by using public data, states Solip Park.

Ben Lehner adds: Our work highlights how important it is to share genomic data. It is a success story for how being open is far more efficient and has a multiplier effect. We combined data from many different projects and by applying a new computational method were able to identify important cancer genes that were not identified by the original studies. Many patient groups lobby for better sharing of genomic data because it is only by comparing data across hospitals, countries and diseases that we can obtain a deep understanding of many rare and common diseases. Unfortunately, many researchers still do not share their data and this is something we need to actively change as a society.