A comprehensive catalogue of somatic mutations from a human cancer genome

Abstract

All cancers carry somatic mutations. A subset of these somatic alterations, termed driver mutations, confer selective growth advantage and are implicated in cancer development, whereas the remainder are passengers. Here we have sequenced the genomes of a malignant melanoma and a lymphoblastoid cell line from the same person, providing the first comprehensive catalogue of somatic mutations from an individual cancer. The catalogue provides remarkable insights into the forces that have shaped this cancer genome. The dominant mutational signature reflects DNA damage due to ultraviolet light exposure, a known risk factor for malignant melanoma, whereas the uneven distribution of mutations across the genome, with a lower prevalence in gene footprints, indicates that DNA repair has been preferentially deployed towards transcribed regions. The results illustrate the power of a cancer genome sequence to reveal traces of the DNA damage, repair, mutation and selection processes that were operative years before the cancer became symptomatic.

DNA replication timing and selection shape the landscape of nucleotide variation in cancer genomes

“…We wondered whether the association between SNV frequency in genic regions and replication timing was a spurious correlation driven by transcription level, as transcription-coupled repairs are known to influence somatic SNV frequencies in cancer cells 3,4 . Consistent with these previous observations, SNV frequency in functionally neutral intronic regions decreased with increasing transcription level.…”

“…Using next-generation DNA sequencing technologies, somatic mutations have been mapped for hundreds of cancer genomes [1][2][3][4][5][6][7][8][9][10] , with thousands more under way 11 . How such alterations influence the tumorigenesis is not completely understood.…”

“…On the other hand, for small-scale mutations, it is straightforward to delineate the two; the underlying mutation rate can be directly inferred from the observed mutation frequency in functionally neutral, or nearly neutral, regions. Of small-scale mutations, single-nucleotide variation is one of the most abundant, functionally important source of evolution [1][2][3][4][5][6][7][8][9][10]13,19,20 .…”

Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing

“…For instance, >500 somatic SNVs in a lung cancer tumor were validated using mass spectrometry 5 , whereas other studies resequenced hundreds of SNVs using Sanger sequencing [6][7][8] . An important drawback of such validation experiments is that they rapidly become as expensive and time-consuming as the wholegenome sequencing experiment itself.…”

“…Another commonly used approach is to apply quality filters that are aimed at selectively removing errors. Every whole-genome sequence reported so far has used filtering to some extent: the most commonly used filters being those that remove sequences with a too-low coverage depth, discard variants with a low-confidence score or eliminate variants located within a cluster of variants 3,7,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . Surprisingly, there is little consensus with respect to which filters should be used and at which threshold they should be applied.…”