Inigo Martincorena

Group Leader

Inigo is a group leader at the Sanger Institute investigating somatic mutation in cancer and normal cells.

With a background in molecular biology, bioinformatics and evolutionary genomics, my research focuses on understanding cancer progression as a result of somatic mutation and selection.

Over the past few years, systematic sequencing of tumours has revolutionised our understanding of the genetics of cancer. This has revealed that most cancers carry thousands of mutations in their genomes, accumulated through the lifetime of their cells. However, owing to technical limitations, very little is known about the earliest steps of cancer and how normal cells in our tissues accumulate mutations during ageing and in their progression towards cancer. We investigate these early changes by studying somatic evolution in normal and precancerous tissues.

In 2015 we published the first comprehensive description of somatic mutation and selection in a healthy solid tissue, revealing that human skin is a patchwork of thousands of competing clones carrying cancer-driver mutations (Martincorena et al., 2015). At that time, it was unclear whether sun-exposed skin was an exceptional tissue, owing to a lifetime of sun damage. To our surprise, we later discovered that the same phenomenon takes place at even greater scale in normal oesophagus, with over half of all epithelial cells carrying canonical cancer-driving mutations by middle age as a result of hundreds of microscopic clonal expansions per square centimetre (Martincorena et al., 2018).

These findings revealed how little we know about how our cells mutate and compete during life and raised questions about their role in cancer and ageing. Our current research focuses on understanding the extent of somatic evolution in normal tissues, including studies on early cancer development, studies exploring the impact of somatic mutation in diseases unrelated to cancer and forays into somatic mutation in other species.

I also work on adapting evolutionary methods to cancer genomics and on the development of computational methods for discovering new cancer genes and non-coding driver mutations. This includes the development of dNdScv, an evolutionary method to study selection in cancer and identify driver genes from cancer genomics data.

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