Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Detecting the mutational signature of homologous recombination deficiency in clinical samples


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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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 and, 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 ( as an R package.


  1. Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).

    Article  CAS  Google Scholar 

  2. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  CAS  Google Scholar 

  3. Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).

    Article  CAS  Google Scholar 

  4. Alexandrov, L. et al. The repertoire of mutational signatures in human cancer. Preprint at (2018).

  5. Burns, M. B. et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature 494, 366–370 (2013).

    Article  CAS  Google Scholar 

  6. Drost, J. et al. Use of CRISPR-modified human stem cell organoids to study the origin of mutational signatures in cancer. Science 358, 234–238 (2017).

    Article  CAS  Google Scholar 

  7. Fedeles, B. I., Chawanthayatham, S., Croy, R. G., Wogan, G. N. & Essigmann, J. M. Early detection of the aflatoxin B1 mutational fingerprint: a diagnostic tool for liver cancer. Mol. Cell. Oncol. 4, e1329693 (2017).

    Article  Google Scholar 

  8. Haradhvala, N. J. et al. Distinct mutational signatures characterize concurrent loss of polymerase proofreading and mismatch repair. Nat. Commun. 9, 1746 (2018).

    Article  CAS  Google Scholar 

  9. Meier, B. et al. Mutational signatures of DNA mismatch repair deficiency in C. elegans and human cancers. Genome Res. 28, 666–675 (2018).

    Article  CAS  Google Scholar 

  10. Nik-Zainal, S. et al. The genome as a record of environmental exposure. Mutagenesis 30, 763–770 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Ohno, M. et al. 8-oxoguanine causes spontaneous de novo germline mutations in mice. Sci. Rep. 4, 4689 (2014).

    Article  Google Scholar 

  12. Zou, X. et al. Validating the concept of mutational signatures with isogenic cell models. Nat. Commun. 9, 1744 (2018).

    Article  Google Scholar 

  13. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    Article  CAS  Google Scholar 

  14. Davies, H. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 23, 517–525 (2017).

    Article  CAS  Google Scholar 

  15. Polak, P. et al. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat. Genet. 49, 1476–1486 (2017).

    Article  CAS  Google Scholar 

  16. Zámborszky, J. et al. Loss of BRCA1 or BRCA2 markedly increases the rate of base substitution mutagenesis and has distinct effects on genomic deletions. Oncogene 36, 746–755 (2017).

    Article  Google Scholar 

  17. Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).

    Article  CAS  Google Scholar 

  18. Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386.e10 (2018).

    Article  CAS  Google Scholar 

  19. Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).

    Article  CAS  Google Scholar 

  20. Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).

    Article  Google Scholar 

  21. Gehring, J. S., Fischer, B., Lawrence, M. & Huber, W. SomaticSignatures: inferring mutational signatures from single-nucleotide variants. Bioinformatics 31, 3673–3675 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S. & Swanton, C. deconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

    Article  Google Scholar 

  23. Kazanov, M. D. et al. APOBEC-induced cancer mutations are uniquely enriched in early-replicating, gene-dense, and active chromatin regions. Cell Rep. 13, 1103–1109 (2015).

    Article  CAS  Google Scholar 

  24. Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).

    Article  CAS  Google Scholar 

  25. Frampton, G. M. et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat. Biotechnol. 31, 1023–1031 (2013).

    Article  CAS  Google Scholar 

  26. Gorthi, A. et al. EWS-FLI1 increases transcription to cause R-loops and block BRCA1 repair in Ewing sarcoma. Nature 555, 387–391 (2018).

    Article  CAS  Google Scholar 

  27. Abkevich, V. et al. Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer. Br. J. Cancer 107, 1776–1782 (2012).

    Article  CAS  Google Scholar 

  28. Fraser, M. et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature 541, 359–364 (2017).

    Article  CAS  Google Scholar 

  29. Ledermann, J. et al. Olaparib maintenance therapy in platinum-sensitive relapsed ovarian cancer. N. Engl. J. Med. 366, 1382–1392 (2012).

    Article  CAS  Google Scholar 

  30. Waddell, N. et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 518, 495–501 (2015).

    Article  CAS  Google Scholar 

  31. Wu, Y.-M. et al. Inactivation of CDK12 delineates a distinct immunogenic class of advanced prostate cancer. Cell 173, 1770–1782.e14 (2018).

    Article  CAS  Google Scholar 

  32. Yang, W. et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41, D955–D961 (2013).

    Article  CAS  Google Scholar 

  33. Basu, A. et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151–1161 (2013).

    Article  CAS  Google Scholar 

  34. Alsop, K. et al. BRCA mutation frequency and patterns of treatment response in BRCA mutation-positive women with ovarian cancer: a report from the Australian Ovarian Cancer Study Group. J. Clin. Oncol. 30, 2654–2663 (2012).

    Article  CAS  Google Scholar 

  35. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2018. CA Cancer J. Clin. 68, 7–30 (2018).

    Article  Google Scholar 

  36. Roy, R., Chun, J. & Powell, S. N. BRCA1 and BRCA2: different roles in a common pathway of genome protection. Nat. Rev. Cancer 12, 68–78 (2012).

    Article  CAS  Google Scholar 

  37. Mirza, M. R. et al. Niraparib maintenance therapy in platinum-sensitive, recurrent ovarian cancer. N. Engl. J. Med. 375, 2154–2164 (2016).

    Article  CAS  Google Scholar 

  38. Telli, M. L. et al. Homologous-recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin. Cancer Res. 22, 3764–3773 (2016).

    Article  CAS  Google Scholar 

  39. Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016).

    Article  Google Scholar 

  40. Vilar Sanchez, E. et al. Preclinical testing of the PARP inhibitor ABT-888 in microsatellite instable colorectal cancer. J. Clin. Oncol. 27, 11028 (2009).

    Google Scholar 

Download references


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

Authors and Affiliations



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.

Corresponding author

Correspondence to Peter J. Park.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gulhan, D.C., Lee, J.JK., Melloni, G.E.M. et al. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nat Genet 51, 912–919 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer