Genome MedicineBioinformaticsPeer-reviewed journal article
Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer
Yoo-Ah Kim, Damian WójtowicziD, Rebecca Sarto Basso, Itay Sason, Welles RobinsoniD, Dorit S. HochbaumiD, Mark D.M. LeisersoniD, Roded SharaniD, Fabio Vadin, Teresa M. PrzytyckaiD
Abstract
Studies of cancer mutations typically focus on identifying cancer driving mutations. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of passenger somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Understanding the etiology of the mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. Considering mutational signatures as phenotypes, we asked two complementary questions (i) what are functional pathways whose gene expression profiles are associated with mutational signatures, and (ii) what are mutated pathways (if any) that might underlie specific mutational signatures? We have been able to identify pathways associated with mutational signatures on both expression and mutation levels. In particular, our analysis provides novel insights into mutagenic processes in breast cancer by capturing important differences in the etiology of different APOBEC related signatures and the two clock-like signatures. These results are important for understanding mutagenic processes in cancer and for developing personalized drug therapies.
- organism: human
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 26
- FWCI
- 1.97
- Field percentile
- top 13%
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