Research highlight

Cancer: Diving deep into the genomes of cancer

Nature

February 6, 2020

An international effort to analyse and sequence nearly 2,700 whole genomes of cancer samples across a range of types of tumour yields insights into the genetic complexity of cancer. This comprehensive analysis is detailed in six papers published in Nature this week, as part of a wider collection of 22 papers published in other Nature Research journals. This resource brings us closer to understanding the biological changes that drive the development of cancer, and will be used in future projects that could translate this knowledge into clinical treatments.

Multiple genetic anomalies contribute to the diverse set of cancer subtypes. Sequencing data from a single biopsy can provide a snapshot of the changes that occur in a specific location at a moment in time, but whole-genome sequencing of a large number of tumour samples could offer a more comprehensive view of the events that contribute to cancer development.

The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium presents an analysis of 2,658 whole-genomes of cancer samples and their matched normal tissues across 38 types of tumours. In an overview paper in Nature, Peter Campbell and colleagues highlight some of the key findings. They report that, on average, cancer genomes contain 4-5 driver mutations. In 91% of the cancer samples that they analysed, the researchers were able to identify at least one cancer driver gene, but 5% of the tumours had no apparent drivers — which indicates that more work is needed to identify new drivers.

Other studies in the collection identify new signatures of mutational processes that could offer insights into the processes that underlie cancer. Moreover, the analyses may help to determine the nature and timing of the mutational processes that define the cancer genome, and highlight opportunities for early cancer detection. The data collected also act as a resource for identifying other types of alteration by overlaying datasets for structural variation and gene regulation.

The next step is to use the PCAWG data and clinical data about patient outcomes and treatments to identify the genetic changes that can predict clinical outcomes, note Marcin Cieslik and Arul Chinnaiyanin in a related News and Views article. The long-term impact of these efforts will not be limited to the findings published today, but will also come from the collaborations that have formed and the knowledge exchanges that have taken place between members of this global consortium of researchers”, they conclude.

The collection of papers and associated content will be available via this link: (the URL will go live after the embargo ends)

doi: 10.1038/s41586-020-1969-6

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