Detecting the mutational signature of homologous recombination deficiency in clinical samples

Abstract

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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Fig. 1: Overview of SigMA for Sig3 prediction.
Fig. 2: Performance of SigMA.
Fig. 3: Validation of SigMA on MSK-IMPACT data.
Fig. 4: Experimental validation using drug response data.
Fig. 5: Survival analysis for patients with Sig3+ ovarian cancer.

Data availability

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.

Code availability

The code for SigMA is available on GitHub (https://github.com/parklab/SigMA) as an R package.

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Acknowledgements

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.

Author information

D.C.G. and J.J.K.L. conceived the project. P.J.P. supervised the project. D.C.G. developed the algorithm, with suggestions and assistance from G.E.M.M., J.J.K.L. and I.C.C. In particular, G.E.M.M. helped with the simulation studies, J.J.K.L. suggested the application of signature analysis to PARP inhibitors and I.C.C. suggested the analysis of cell line/drug response data. D.C.G. and P.J.P. wrote the manuscript with input from all other authors.

Correspondence to Peter J. Park.

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Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Figures 1–27

Reporting Summary

Supplementary Table 1

Predicted labels for MSK-IMPACT panels

Supplementary Table 2

MVA classification.

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