Genome analysis plays a major role in cancer research. Identifying genetic variations in a person's genome can unearth predisposition to cancer. The cancer genome can provide information about the likely aggressiveness of the cancer or treatment effectiveness against it. Personalizing cancer treatment based on genetic analysis can inform therapy choices and minimize side effects without jeopardizing the success of the treatment.
Unsurprisingly, studies related to cancer research make up a large proportion of omics data generated - for example 55% of the 7PB of data hosted by EGA are related to cancer studies. Together with rare disease research, cancer research is expected to dominate the data generation in the immediate future. Especially since sequencing methods are advancing at incredible speed and at the same time the costs decrease, leading to increasing cases of routine sequencing for diagnostic purposes.
For these reasons, while engaging and encouraging data submission by researchers in all medical fields, GHGA is initially focussing on cancer and rare disease research to fully address their specific needs. To support these efforts, GHGA will not only accept raw datasets but also generate community reference data collections. Community-specific tailored access portals combined with the curation of reference data collections will ensure the utility of GHGA's datasets to researcher and clinician communities – who in turn will help shape the further development of GHGA.
Here we collected relevant materials for the cancer community, such as highlighting use cases and other news and events.
In early July, GHGA met with patients to understand their perspective on GHGA governance, particularly in terms of genomic data sharing. Based on these discussions a concrete strategy for patient participation will be developed.
In this episode we want to take a closer look at how our genes influence our risk to develope breast cancer and what impact the Angelina-Jolie-effect had on genetic testing.
The GHGA workflow workstream was involved in the release of bioinformatic workflows (sarek 3.0, nanoseq 3.0, and DROP 1.2), collaborating with the nf-core community and the Gagneur lab.