Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Genetics and Genomics

Prognostic relevance of HRDness gene expression signature in ovarian high-grade serous carcinoma; JGOG3025-TR2 study

Subjects

Abstract

Background

This study aimed to evaluate the homologous recombination repair pathway deficiency (HRD) in ovarian high-grade serous carcinoma (HGSC).

Methods

In the ovarian cancer data from The Cancer Genome Atlas, we identified genes differentially expressed between tumours with and without HRD genomic scars and named these genes “HRDness signature”. We performed SNP array, RNA sequencing, and methylation array analyses on 274 HGSC tumours for which targeted sequencing of 51 genes and clinical data were available to generate JGOG3025-TR2 dataset. The HRDness signature was tested on external datasets, including the JGOG3025-TR2 cohort, by computational scoring and machine-learning prediction.

Results

High scores and positive predictions of the HRDness signature were significantly associated with BRCA alterations, genomic scar scores, and better survival. On the other hand, among cases with high scores and/or positive predictions, those with BRCA1 methylation showed poorer survival. In the JGOG3025-TR2 cohort, HRD status was significantly associated with the use of olaparib after relapse and progression-free survival after its initiation.

Conclusions

The HRDness gene expression signature is associated with a good prognosis, while BRCA1 methylation is associated with a poor prognosis. The newly generated JGOG3025-TR2 dataset will be useful in future HGSC studies.

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

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Analysis of TCGA-OV dataset for genomic scar, survival and HRDness gene expression signature.
Fig. 2: Generation of the JGOG-TR2 dataset and validation of the HRDness signature.
Fig. 3: Prognostic analysis of the JGOG3025-TR2 dataset.
Fig. 4: Survival analysis using the HRDness prediction and the optimised HRD score cut-off in the JGOG-TR2 cohort.
Fig. 5: Survival analysis of the GEO datasets.

Similar content being viewed by others

Data availability

The data from SNP array, RNA-seq, DNA methylation array in this study are available from the corresponding authors upon reasonable request. Data sources other than JGOG3025 were summarised in Supplementary Table S7. Controlled access data were obtained through dbGaP access permission (phs000178, phs000892). Data and codes to reproduce the results are available from the corresponding author upon reasonable request.

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72:7–33.

    Article  PubMed  Google Scholar 

  2. Seidman JD, Horkayne-Szakaly I, Haiba M, Boice CR, Kurman RJ, Ronnett BM. The histologic type and stage distribution of ovarian carcinomas of surface epithelial origin. Int J Gynecol Pathol. 2004;23:41–4.

    Article  PubMed  Google Scholar 

  3. Konstantinopoulos PA, Ceccaldi R, Shapiro GI, D’Andrea AD. Homologous Recombination deficiency: exploiting the fundamental vulnerability of ovarian cancer. Cancer Discov. 2015;5:1137–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Murai J, Huang SN, Das BB, Renaud A, Zhang Y, Doroshow JH, et al. Trapping of PARP1 and PARP2 by clinical PARP inhibitors. Cancer Res. 2012;72:5588–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Min A, Im SA. PARP inhibitors as therapeutics: beyond modulation of PARylation. Cancers. 2020;12:394.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Konstantinopoulos PA, Matulonis UA. Targeting DNA damage response and repair as a therapeutic strategy for ovarian cancer. Hematol Oncol Clin North Am. 2018;32:997–1010.

    Article  PubMed  Google Scholar 

  7. Kalachand RD, Stordal B, Madden S, Chandler B, Cunningham J, Goode EL, et al. BRCA1 promoter methylation and clinical outcomes in ovarian cancer: an individual patient data meta-analysis. J Natl Cancer Inst. 2020;112:1190–203.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Birkbak NJ, Wang ZC, Kim JY, Eklund AC, Li Q, Tian R, et al. Telomeric allelic imbalance indicates defective DNA repair and sensitivity to DNA-damaging agents. Cancer Discov. 2012;2:366–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Popova T, Manié E, Rieunier G, Caux-Moncoutier V, Tirapo C, Dubois T, et al. Ploidy and large-scale genomic instability consistently identify basal-like breast carcinomas with BRCA1/2 inactivation. Cancer Res. 2012;72:5454–62.

    Article  CAS  PubMed  Google Scholar 

  10. Abkevich V, Timms KM, Hennessy BT, Potter J, Carey MS, Meyer LA, et al. Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer. Br J Cancer. 2012;107:1776–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Telli ML, Timms KM, Reid J, Hennessy B, Mills GB, Jensen KC, et al. Homologous recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin Cancer Res. 2016;22:3764–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ray-Coquard I, Pautier P, Pignata S, Pérol D, González-Martín A, Berger R, et al. Olaparib plus bevacizumab as first-line maintenance in ovarian cancer. N Engl J Med. 2019;381:2416–28.

    Article  CAS  PubMed  Google Scholar 

  13. González-Martín A, Pothuri B, Vergote I, DePont Christensen R, Graybill W, Mirza MR, et al. Niraparib in patients with newly diagnosed advanced ovarian cancer. N Engl J Med. 2019;381:2391–402.

    Article  PubMed  Google Scholar 

  14. Polak P, Kim J, Braunstein LZ, Karlic R, Haradhavala NJ, Tiao G, et al. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat Genet. 2017;49:1476–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Konstantinopoulos PA, Spentzos D, Karlan BY, Taniguchi T, Fountzilas E, Francoeur N, et al. Gene expression profile of BRCAness that correlates with responsiveness to chemotherapy and with outcome in patients with epithelial ovarian cancer. J Clin Oncol. 2010;28:3555–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kosuke Y, Tsukasa B, Muneaki S, Koji N, Masayuki S, Shiro T, et al. Homologous recombination inquiry through ovarian malignancy investigations: The Japanese Gynecologic Oncology Group study JGOG3025. medRxiv 2022.07.06.22277241. [Preprint]. Available from: https://doi.org/10.1101/2022.07.06.22277241

  17. Van Loo P, Nordgard SH, Lingjærde OC, Russnes HG, Rye IH, Sun W, et al. Allele-specific copy number analysis of tumors. Proc Natl Acad Sci USA. 2010;107:16910–5.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Takamatsu S, Brown JB, Yamaguchi K, Hamanishi J, Yamanoi K, Takaya H, et al. Utility of homologous recombination deficiency biomarkers across cancer types. JCO Precis Oncol. 2022;6:e2200085.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011;12:R41.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sztupinszki Z, Diossy M, Krzystanek M, Reiniger L, Csabai I, Favero F, et al. Migrating the SNP array-based homologous recombination deficiency measures to next generation sequencing data of breast cancer. npj Breast Cancer. 2018;4:16.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Marquard AM, Eklund AC, Joshi T, Krzystanek M, Favero F, Wang ZC, et al. Pan-cancer analysis of genomic scar signatures associated with homologous recombination deficiency suggests novel indications for existing cancer drugs. Biomark Res. 2015;3:9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Babraham Bioinformatics. Taking appropriate QC measures for RRBS-type or other -Seq applications with Trim Galore! https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/.

  23. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    Article  CAS  PubMed  Google Scholar 

  24. Chen GM, Kannan L, Geistlinger L, Kofia V, Safikhani Z, Gendoo DMA, et al. Consensus on molecular subtypes of high-grade serous ovarian carcinoma. Clin Cancer Res. 2018;24:5037–47.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Ellrott K, Bailey MH, Saksena G, Covington KR, Kandoth C, Stewart C, et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 2018;6:271–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gulhan DC, Lee JJK, Melloni GEM, Cortés-Ciriano I, Park PJ. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nat Genet. 2019;51:912–9.

    Article  CAS  PubMed  Google Scholar 

  27. Huang KL, Mashl RJ, Wu Y, Ritter DI, Wang J, Oh C, et al. Pathogenic germline variants in 10,389 adult cancers. Cell 2018;173:355–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SF, et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 2007;17:1665–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:R29.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Larsson O, Wahlestedt C, Timmons JA. Considerations when using the significance analysis of microarrays (SAM) algorithm. BMC Bioinforma. 2005;6:129.

    Article  Google Scholar 

  32. Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462:108–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Patch AM, Christie EL, Etemadmoghadam D, Garsed DW, George J, Fereday S, et al. Whole-genome characterization of chemoresistant ovarian cancer. Nature. 2015;521:489–94.

    Article  CAS  PubMed  Google Scholar 

  35. McDermott JE, Arshad OA, Petyuk VA, Fu Y, Gritsenko MA, Clauss TR, et al. Proteogenomic Characterization of Ovarian HGSC Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability. Cell Rep Med. 2020;1:100004.

  36. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ducie J, Dao F, Considine M, Olvera N, Shaw PA, Kurman RJ, et al. Molecular analysis of high-grade serous ovarian carcinoma with and without associated serous tubal intra-epithelial carcinoma. Nat Commun. 2017;8:990.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Gautier L, Cope L, Bolstad BM, Irizarry RA. affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20:307–15.

    Article  CAS  PubMed  Google Scholar 

  39. Takaya H, Nakai H, Takamatsu S, Mandai M, Matsumura N. Homologous recombination deficiency status-based classification of high-grade serous ovarian carcinoma. Sci Rep. 2020;10:2757.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Cristescu R, Liu XQ, Arreaza G, Chen C, Albright A, Qiu P, et al. Genomic instability metric concordance between OncoScanTM, CytoSNP and an FDA-approved HRD test. Int J Gynecol Cancer. 2020;30:A130–1.

    Google Scholar 

  41. Verhaak RG, Tamayo P, Yang JY, Hubbard D, Zhang H, Creighton CJ, et al. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Invest. 2013;123:517–25.

    CAS  PubMed  Google Scholar 

  42. Coleman RL, Fleming GF, Brady MF, Swisher EM, Steffensen KD, Friedlander M, et al. Veliparib with first-line chemotherapy and as maintenance therapy in ovarian cancer. N Engl J Med. 2019;381:2403–15. Dec 19

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Maxwell KN, Wubbenhorst B, Wenz BM, De Sloover D, Pluta J, Emery L, et al. BRCA locus-specific loss of heterozygosity in germline BRCA1 and BRCA2 carriers. Nat Commun. 2017;8:319.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kommoss S, Winterhoff B, Oberg AL, Konecny GE, Wang C, Riska SM, et al. Bevacizumab may differentially improve ovarian cancer outcome in patients with proliferative and mesenchymal molecular subtypes. Clin Cancer Res. 2017;23:3794–801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Murakami R, Matsumura N, Mandai M, Yoshihara K, Tanabe H, Nakai H, et al. Establishment of a novel histopathological classification of high-grade serous ovarian carcinoma correlated with prognostically distinct gene expression subtypes. Am J Pathol. 2016;186:1103–13.

    Article  CAS  PubMed  Google Scholar 

  46. Kondrashova O, Topp M, Nesic K, Lieschke E, Ho GY, Harrell MI, et al. Methylation of all BRCA1 copies predicts response to the PARP inhibitor rucaparib in ovarian carcinoma. Nat Commun. 2018;9:3970.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Prieske K, Prieske S, Joosse SA, Trillsch F, Grimm D, Burandt E, et al. Loss of BRCA1 promotor hypermethylation in recurrent high-grade ovarian cancer. Oncotarget. 2017;8:83063–74.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Elazezy M, Prieske K, Kluwe L, Oliveira-Ferrer L, Peine S, Müller V, et al. BRCA1 promoter hypermethylation on circulating tumor DNA correlates with improved survival of patients with ovarian cancer. Mol Oncol. 2021;15:3615–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Tutt A, Tovey H, Cheang MCU, Kernaghan S, Kilburn L, Gazinska P, et al. Carboplatin in BRCA1/2-mutated and triple-negative breast cancer BRCAness subgroups: the TNT Trial. Nat Med. 2018;24:628–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Nakai H, Matsumura N. Individualization in the first-line treatment of advanced ovarian cancer based on the mechanism of action of molecularly targeted drugs. Int J Clin Oncol. 2022;27:1001–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ledermann J, Harter P, Gourley C, Friedlander M, Vergote I, Rustin G, et al. Olaparib maintenance therapy in platinum-sensitive relapsed ovarian cancer. N Engl J Med. 2012;366:1382–92.

    Article  CAS  PubMed  Google Scholar 

  52. Mirza MR, Monk BJ, Herrstedt J, Oza AM, Mahner S, Redondo A, et al. Niraparib maintenance therapy in platinum-sensitive, recurrent ovarian cancer. N Engl J Med. 2016;375:2154–64.

    Article  CAS  PubMed  Google Scholar 

  53. Coleman RL, Oza AM, Lorusso D, Aghajanian C, Oaknin A, Dean A, et al. Rucaparib maintenance treatment for recurrent ovarian carcinoma after response to platinum therapy (ARIEL3): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2017;390:1949–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank all the JGOG members who participated in the JGOG3025-TR2 study; Drs. Makio Shozu, Hiroyuki Shigeta, Kazuhiro Takehara, Akira Kikuchi, Toyomi Sato, Akinori Oki, Shinya Yoshioka, Shinya Sato, Ryuji Kawaguchi, Hisafumi Okura, Takeshi Iwasa, Shoji Kamiura, Masato Kamitomo, Yoichi Aoki, Nao Suzuki, Yoshio Yoshida, Tadashi Kimura, Daisuke Aoki, Kazuyoshi Kato, Hiroaki Kobayashi, Hidemichi Watari, Etsuko Miyagi, Tsuyoshi Saito, Yoshihito Yokoyama, Tsunekazu Kita, Takashi Matsumoto, Satoshi Nagase, Toshiya Yamamoto, Yukio Hirano, Tomoaki Ikeda, Shiro Suzuki, Keiya Fujimori, Nagamasa Maeda, Naohiko Umesaki, Masatoshi Sugita, and Akira Kouyama.

Funding

This work was supported by AstraZeneca K.K. and Merck Sharp & Dohme Corp as the programme of Externally Sponsored Research (ESR-19-14550).

Author information

Authors and Affiliations

Authors

Contributions

ST: data analysis and writing the manuscript; KY: sample collection and data analysis; TB: sample collection and review of the manuscript; MS: sample collection and review of the manuscript; HY: sample collection and review of the manuscript; AO: sample collection and review of the manuscript; HK: sample collection and review of the manuscript; KO: sample collection and data analysis; MM: data analysis and review of the manuscript; TE: design of this study; NM: data analysis and writing the manuscript.

Corresponding authors

Correspondence to Takayuki Enomoto or Noriomi Matsumura.

Ethics declarations

Competing interests

NM received a research grant from AstraZeneca. NM received lecture fees from AstraZeneca and Takeda Pharmaceutical. NM is also an outside director of Takara Bio. TB received lecture fees from AstraZeneca. KY received lecture fees and a research grant from AstraZeneca. There are no other competing interests related to this paper.

Ethics approval and consent to participate

For the JGOG3025 study, i.e., clinical data analysis, frozen tumour tissue collection, target sequencing, and future analyses of tumour tissue, written informed consent was obtained from all patients with approval from the Institutional Review Board at each JGOG participating site prior to the start of the study [16]. The JGOG3025-TR2 study was then conducted with the approval of the Ethics Committee of JGOG and the Institutional Ethics Committee of Kindai University (approval number; 29-167), with opt-out patient consent. This study was performed in accordance with the Declaration of Helsinki.

Consent to publish

Not applicable.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Takamatsu, S., Yoshihara, K., Baba, T. et al. Prognostic relevance of HRDness gene expression signature in ovarian high-grade serous carcinoma; JGOG3025-TR2 study. Br J Cancer 128, 1095–1104 (2023). https://doi.org/10.1038/s41416-022-02122-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41416-022-02122-9

Search

Quick links