Department of Molecular, Cell and Cancer Biology
Cancer Biology | Computational Biology | Genetic Phenomena | Molecular Genetics | Neoplasms
BRCAness has important implications in the management and treatment of patients with breast and ovarian cancer. In this study, we propose a computational framework to measure the BRCAness of breast and ovarian tumor samples based on their gene expression profiles. We define a characteristic profile for BRCAness by comparing gene expression differences between BRCA1/2 mutant familial tumors and sporadic breast cancer tumors while adjusting for relevant clinical factors. With this BRCAness profile, our framework calculates sample-specific BRCA scores, which indicates homologous recombination (HR)-mediated DNA repair pathway activity of samples. We found that in sporadic breast cancer high BRCAness score is associated with aberrant copy number of HR genes rather than somatic mutation and other genomic features. Moreover, we observed significant correlations of BRCA score with genome instability and neoadjuvant chemotherapy. More importantly, BRCA score provides significant prognostic value in both breast and ovarian cancers after considering established clinical variables. In summary, the inferred BRCAness from our framework can be used as a robust biomarker for the prediction of prognosis and treatment response in breast and ovarian cancers.
Breast cancer, Cancer epigenetics, Computational models
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Copyright © The Author(s) 2017. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
DOI of Published Version
Sci Rep. 2017 Nov 16;7(1):15742. doi: 10.1038/s41598-017-16138-2. Link to article on publisher's site
Wang Y, Ung MH, Cantor SB, Cheng C. (2017). Computational Investigation of Homologous Recombination DNA Repair Deficiency in Sporadic Breast Cancer. Open Access Articles. https://doi.org/10.1038/s41598-017-16138-2. Retrieved from https://escholarship.umassmed.edu/oapubs/3307
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.