Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Cancer-associated DNA mutations frequently occur many years prior to cancer diagnosis.

It is well known that mutations in certain genes can predispose to cancer development. However, the timing at which these mutations occur before cancer diagnosis is not fully understood. In order to study this further, 2,658 genomes of 38 different tumour types were analysed using models to infer the relative timing of mutational events in the genome. The results, published in Nature, indicate that mutations that prime for cancer development often occur many years before a cancer diagnosis, which creates a valuable window of opportunity for early cancer detection. This is particularly important for cancer types without detectable pre-malignant conditions, such as ovarian cancer.

David Wedge (Big Data Institute, Oxford) who co-led this study with Peter Van Loo (The Francis Crick Institute, London) and Paul Spellman (Oregon Health and Science University), said, “This study identified a small number of mutations that cause initial tumour growth and a much larger range of mutations, with different characteristics, that are associated with later tumour growth. Mutations that drive tumour growth had occurred in many cancers as much as 20 years or more before diagnosis, suggesting that there may be opportunities for earlier detection of many types of cancer.”

The work was part of a collection of reports published by the Pan-Cancer Analysis of Whole Genomes Consortium, a collaboration involving more than 1,300 scientists and clinicians from 37 countries.


For more information, see the Big Data Institute website.


Image credit: PCAWG consortium CC-BY 4.0