high-throughput gene mutation profiling
Wednesday 21 February 2007
Definition: Somatic mutation theory of cancer is a theory on the pathogenesis of cancer that essentially postulates that cancer in somatic cells arises from mutations. More recently, the theory has been adapted to specifically implicate mutations in oncogenes or tumour suppressor genes.
The genome of a cancer cell carries somatic mutations that are the cumulative consequences of the DNA damage and repair processes operative during the cellular lineage between the fertilized egg and the cancer cell. Remarkably, these mutational processes are poorly characterized.
Global sequencing initiatives are yielding catalogs of somatic mutations from thousands of cancers, thus providing the unique opportunity to decipher the signatures of mutational processes operative in human cancer.
Somatic mutation patterns in tumor cells can serve as a source of information on the processes that have gone awry in cancers and, in some cases, the environmental exposures to blame for these genetic glitches. Though, routinely finding such mutational signatures has been tricky.
However, until now there have been no theoretical models describing the signatures of mutational processes operative in cancer genomes and no systematic computational approaches are available to decipher these mutational signatures.
By modeling mutational processes as a blind source separation problem, it has been introduced a computational framework that effectively addresses these questions. (DOI:10.1016/j.celrep.2012.12.008 )
This approach provides a basis for characterizing mutational signatures from cancer-derived somatic mutational catalogs, paving the way to insights into the pathogenetic mechanism underlying all cancers.
In a study, the Sanger Institute’s Michael Stratton and colleagues presented their computational strategy for sifting through the sets of base substitutions, insertions and deletions, rearrangements, and copy-number shifts found within the growing collection of cancer genome sequences to identify potentially informative mutational signatures. (DOI:10.1016/j.celrep.2012.12.008 )
After applying this approach to simulated cancer sequence data, the researchers demonstrated its feasibility for finding mutational signatures in breast cancer using data from whole-genome and exome sequencing studies of the disease.
This approach provides a basis for characterizing mutational signatures from cancer-derived somatic mutation catalogs, paving the way to insights into the pathogenetic mechanisms underlying all cancers.
cancer driver mutations
cancer passenger mutations
cancer-associated somatic mutations
DNA mutations/genic mutations
high-throughput oncogene mutation profiling
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