Mutations in BRCA1 and/or BRCA2 (BRCA1/2) are the most common indication of deficiency in the homologous recombination (HR) DNA repair pathway. However, recent genome-wide analyses have shown that the same pattern of mutations found in BRCA1/2-mutant tumors is also present in several other tumors. Here, we present a new computational tool called Signature Multivariate Analysis (SigMA), which can be used to accurately detect the mutational signature associated with HR deficiency from targeted gene panels. Whereas previous methods require whole-genome or whole-exome data, our method detects the HR-deficiency signature even from low mutation counts, by using a likelihood-based measure combined with machine-learning techniques. Cell lines that we identify as HR deficient show a significant response to poly (ADP-ribose) polymerase (PARP) inhibitors; patients with ovarian cancer whom we found to be HR deficient show a significantly longer overall survival with platinum regimens. By enabling panel-based identification of mutational signatures, our method substantially increases the number of patients that may be considered for treatments targeting HR deficiency.
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Detailed information on how to access the ICGC, TCGA, CCLE and GDSC data for the cell lines can be found in the Methods. Information about the ICGC and TCGA can be found at https://icgc.org/ and http://cancergenome.nih.gov, respectively All other remaining data are available within the article and in the Supplementary Data, or available from the authors upon request.
The code for SigMA is available on GitHub (https://github.com/parklab/SigMA) as an R package.
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This work was mainly supported by the Ludwig Center at Harvard. I.C.C. received funding from the European Union (Marie Curie Skłodowska-Curie grant no. 703543). We would like to thank S. Elledge, G. Wulf, J. Dry and Z. Lai for helpful discussions, A. Galor and J. Cook for careful reading of the manuscript and S. Ouellette for help with the website.
The authors declare no competing interests.
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