A Comprehensive Assessment of Somatic Mutation Calling in Cancer Genomes
Tyler S. Alioto,
Timothy A Beck,
Paul C Boutros,
Matthew D Eldridge,
Nicholas J. Harding,
Lawrence E Heisler,
David T. W. Jones,
Andrew G Lynch,
Jared T Simpson,
Takafumi N. Yamaguchi,
Sergi Beltran Agullo,
Robert E. Denroche,
Simon C. Heath,
Charlotte L Anderson,
Francesc Castro Giner,
David A. Wheeler,
Daniela S. Gerhard,
Ivo Glynne Gut,
Thomas J. Hudson,
John D. McPherson,
Xose S Puente,
Ivo G. Gut
Posted 24 Dec 2014
bioRxiv DOI: 10.1101/012997 (published DOI: 10.1038/ncomms10001)
Posted 24 Dec 2014
The emergence of next generation DNA sequencing technology is enabling high-resolution cancer genome analysis. Large-scale projects like the International Cancer Genome Consortium (ICGC) are systematically scanning cancer genomes to identify recurrent somatic mutations. Second generation DNA sequencing, however, is still an evolving technology and procedures, both experimental and analytical, are constantly changing. Thus the research community is still defining a set of best practices for cancer genome data analysis, with no single protocol emerging to fulfil this role. Here we describe an extensive benchmark exercise to identify and resolve issues of somatic mutation calling. Whole genome sequence datasets comprising tumor-normal pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, were shared within the ICGC and submissions of somatic mutation calls were compared to verified mutations and to each other. Varying strategies to call mutations, incomplete awareness of sources of artefacts, and even lack of agreement on what constitutes an artefact or real mutation manifested in widely varying mutation call rates and somewhat low concordance among submissions. We conclude that somatic mutation calling remains an unsolved problem. However, we have identified many issues that are easy to remedy that are presented here. Our study highlights critical issues that need to be addressed before this valuable technology can be routinely used to inform clinical decision-making.
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