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:

Clinical Research

A transcriptomic model for homologous recombination deficiency in prostate cancer

Abstract

Background

Tumors with mutations associated with homologous recombination deficiency (HRD) are uncommon in prostate cancer (PCa) and variably responsive to PARP inhibition. To better identify tumors with HRD, we developed a transcriptomic signature for HRD in PCa (HRD-P).

Methods

By using an established mutational signature, we created and validated HRD-P in six independent PCa cohorts (primary PCa, n = 8224; metastatic castration-resistant PCa [mCRPC], n = 328). Molecular and clinical features were compared between HRD-P+ tumors and those with single HR-gene mutations.

Results

HRD-P+ tumors were more common than tumors with single HR-gene mutations in primary (201/491, 41% vs 32/491 6.5%) and mCRPC (126/328, 38% vs 82/328, 25%) cases, and HRD-P+ was more predictive of genomic instability suggestive of HRD. HRD-P+ was associated with a shorter time to recurrence following surgery and shorter overall survival in men with mCRPC. In a prospective trial of mCRPC treated with olaparib (n = 10), all three men with HRD-P+ experienced prolonged (>330 days) PSA progression-free survival.

Conclusion

These results suggest transcriptomics can identify more patients that harbor phenotypic HRD than single HR-gene mutations and support further exploration of transcriptionally defined HRD tumors perhaps in conjunction with genomic markers for therapeutic application.

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: Genomic and clinical characteristics of HRD-P+ tumors in TCGA.
Fig. 2: Clinical outcomes and molecular pathways in primary HRD-P+ tumors.
Fig. 3: Clinical outcomes and molecular pathways in metastatic HRD-P+ tumors.

Similar content being viewed by others

References

  1. Castro E, Goh C, Leongamornlert D, Saunders E, Tymrakiewicz M, Dadaev T, et al. Effect of BRCA mutations on metastatic relapse and cause-specific survival after radical treatment for localised prostate cancer. Eur Urol. 2015;68:186–93.

    Article  CAS  Google Scholar 

  2. Castro E, Romero-Laorden N, Del Pozo A, Lozano R, Medina A, Puente J, et al. PROREPAIR-B: a prospective cohort study of the impact of germline DNA repair mutations on the outcomes of patients with metastatic castration-resistant prostate cancer. J Clin Oncol. 2019;37:490–503.

    Article  CAS  Google Scholar 

  3. Mateo J, Porta N, Bianchini D, McGovern U, Elliott T, Jones R, et al. Olaparib in patients with metastatic castration-resistant prostate cancer with DNA repair gene aberrations (TOPARP-B): a multicentre, open-label, randomised, phase 2 trial. Lancet Oncol. 2020;21:162–74.

    Article  CAS  Google Scholar 

  4. Abeshouse A, Ahn J, Akbani R, Ally A, Amin S, Andry CD, et al. The molecular taxonomy of primary prostate cancer. Cell. 2015;163:1011–25.

    Article  CAS  Google Scholar 

  5. de Bono J, Mateo J, Fizazi K, Saad F, Shore N, Sandhu S, et al. Olaparib for metastatic castration-resistant prostate cancer. N. Engl J Med. 2020;382:2091–102.

    Article  Google Scholar 

  6. Lord CJ, Ashworth A. BRCAness revisited. Nat Rev Cancer. 2016;16:110–20.

    Article  CAS  Google Scholar 

  7. Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, et al. Genomic and molecular landscape of dna damage repair deficiency across the cancer genome atlas. Cell Rep. 2018;23:239–254. e6

    Article  CAS  Google Scholar 

  8. Spratt DE, Alshalalfa M, Fishbane N, Weiner AB, Mehra R, Mahal BA et al. Transcriptomic heterogeneity of androgen receptor (AR) activity defines a de novo low AR-active subclass in treatment naïve primary prostate cancer. Clin Cancer Res. 2019. https://doi.org/10.1158/1078-0432.CCR-19-1587.

  9. Sztupinszki Z, Diossy M, Krzystanek M, Borcsok J, Pomerantz MM, Tisza V, et al. Detection of molecular signatures of homologous recombination deficiency in prostate cancer with or without BRCA1/2 mutations. Clin Cancer Res. 2020;26:2673–80.

    Article  CAS  Google Scholar 

  10. Roychowdhury S, Iyer MK, Robinson DR, Lonigro RJ, Wu Y-M, Cao X, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011;3:111ra121–111ra121.

    Article  Google Scholar 

  11. Davies H, Glodzik D, Morganella S, Yates LR, Staaf J, Zou X, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med. 2017;23:517–25.

    Article  CAS  Google Scholar 

  12. Peng G, Chun-Jen Lin C, Mo W, Dai H, Park Y-Y, Kim SM, et al. Genome-wide transcriptome profiling of homologous recombination DNA repair. Nat Commun. 2014;5:3361.

    Article  Google Scholar 

  13. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. 1st ed. New York: Springer; 2001.

    Book  Google Scholar 

  14. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13.

    Article  Google Scholar 

  15. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57.

    Article  CAS  Google Scholar 

  16. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015;1:417–25.

    Article  CAS  Google Scholar 

  17. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Disco. 2012;2:401–4.

    Article  Google Scholar 

  18. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1–pl1.

    Article  Google Scholar 

  19. 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  Google Scholar 

  20. Karnes RJ, Bergstralh EJ, Davicioni E, Ghadessi M, Buerki C, Mitra AP, et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J Urol. 2013;190:2047–53.

    Article  CAS  Google Scholar 

  21. Abida W, Cyrta J, Heller G, Prandi D, Armenia J, Coleman I, et al. Genomic correlates of clinical outcome in advanced prostate cancer. Proc Natl Acad Sci USA. 2019;116:11428–36.

    Article  CAS  Google Scholar 

  22. 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. JCO. 2010;28:3555–61.

    Article  CAS  Google Scholar 

  23. Karnes RJ, Sharma V, Choeurng V, Ashab HA-D, Erho N, Alshalalfa M, et al. Development and validation of a prostate cancer genomic signature that predicts early ADT treatment response following radical prostatectomy. Clin Cancer Res. 2018;24:3908–16.

    Article  CAS  Google Scholar 

  24. Larsen MJ, Kruse TA, Tan Q, Lænkholm A-V, Bak M, Lykkesfeldt AE et al. Classifications within Molecular Subtypes Enables Identification of BRCA1/BRCA2 Mutation Carriers by RNA Tumor Profiling. PLoS ONE 2013;8. https://doi.org/10.1371/journal.pone.0064268.

  25. Yuan J, Hu Z, Mahal BA, Zhao SD, Kensler KH, Pi J, et al. Integrated analysis of genetic ancestry and genomic alterations across cancers. Cancer Cell. 2018;34:549–560.e9.

    Article  CAS  Google Scholar 

  26. Ren B, Cam H, Takahashi Y, Volkert T, Terragni J, Young RA, et al. E2F integrates cell cycle progression with DNA repair, replication, and G2/M checkpoints. Genes Dev. 2002;16:245–56.

    Article  CAS  Google Scholar 

  27. Abida W, Patnaik A, Campbell D, Shapiro J, Bryce AH, McDermott R et al. Rucaparib in men with metastatic castration-resistant prostate cancer harboring a BRCA1 or BRCA2 gene alteration. JCO 2020; JCO.20.01035.

  28. Taylor RA, Fraser M, Livingstone J, Espiritu SMG, Thorne H, Huang V, et al. Germline BRCA2 mutations drive prostate cancers with distinct evolutionary trajectories. Nat Commun. 2017;8:13671.

    Article  CAS  Google Scholar 

  29. Xu X, Qiao W, Linke SP, Cao L, Li W-M, Furth PA, et al. Genetic interactions between tumor suppressors Brca1 and p53 in apoptosis, cell cycle and tumorigenesis. Nat Genet. 2001;28:266–71.

    Article  CAS  Google Scholar 

  30. Na R, Zheng SL, Han M, Yu H, Jiang D, Shah S, et al. Germline mutations in ATM and BRCA1/2 distinguish risk for lethal and indolent prostate cancer and are associated with early age at death. Eur Urol. 2017;71:740–7.

    Article  CAS  Google Scholar 

  31. Lalonde E, Ishkanian AS, Sykes J, Fraser M, Ross-Adams H, Erho N, et al. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncol. 2014;15:1521–32.

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the National Institutes of Health grant 5U01CA196390 (EMS), the Prostate Cancer Foundation (EMS), Department of Defense grant W81XWH-15-1-0661 (EMS and TLL), the 2019 Urology Care Foundation Residency Research Award Program and the Russell Scott, Jr., MD Urology Research Fund (ABW), as well as the University of Michigan Prostate Specialized Program of Research Excellence (SPORE) P50 CA186786-05 and the Early Detection Research Network grant UO1 CA111275 (AMC). The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author information

Authors and Affiliations

Authors

Contributions

Conception and design: ABW, ED, AMC, TLL, DES. Acquisition of data: ABW, YL, MMcF, PSB, XZ, ZL, TH, MH, RJK, ED, AMC, TLL. Analysis and interpretation of data: ABW, YL, MMcF, XZ, ZL. Drafting of manuscript: ABW, YL, MMcF, PSB, EVL, XZ, ZL, TH, MH, DES, EMS. Critical revision of the manuscript for important intellectual content: ABW, R. JK, ED, ZRR, AMC, TLL, DES, EMS. Statistical analysis: ABW, YL. Obtaining funding: ABW, AMC, TLL, EMS. Administrative, technical, or material support: TLL, AMC, TLL, DES, EMS. Supervision: TLL, AMC, TLL, DES, EMS. Other: none.

Corresponding author

Correspondence to Edward M. Schaeffer.

Ethics declarations

Ethics approval and consent to participate

The study was performed in accordance with the Declaration of Helsinki.

Competing interests

YL, XZ, ZL, ED are employees of Decipher Biosciences. AMC currently serves on the scientific advisory board of Tempus. The remaining authors declare no potential conflicts of interest.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weiner, A.B., Liu, Y., McFarlane, M. et al. A transcriptomic model for homologous recombination deficiency in prostate cancer. Prostate Cancer Prostatic Dis 25, 659–665 (2022). https://doi.org/10.1038/s41391-021-00416-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41391-021-00416-2

This article is cited by

Search

Quick links