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Pharmacogenomics in epithelial ovarian cancer first-line treatment outcome: validation of GWAS-associated NRG3 rs1649942 and BRE rs7572644 variants in an independent cohort

The Pharmacogenomics Journal volume 19, pages 25–32 (2019)Cite this article

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Abstract

The identification of predictive biomarkers for the first-line treatment of epithelial ovarian cancer (EOC) remains a challenge. Although genome-wide association studies (GWAS) have identified several genetic polymorphisms as predictors of EOC clinical outcome, the subsequent validation has not yet been performed. This study aims to validate the influence of Neuregulin 3 (NRG3) rs1649942 and Brain and reproductive organ-expressed (TNFRSF1A modulator) (BRE) rs7572644 GWAS-identified variants in an independent cohort of EOC patients from the North region of Portugal (n = 339) submitted to first-line treatment. Polymorphism genotypes were determined by real-time PCR using validated assays. Patients carrying the NRG3 rs1649942 A allele presented a significantly longer overall survival (OS) when compared to GG-genotype patients (log-rank test, P = 0.011) in the FIGO IV stage subgroup. No impact was observed for early-stage patients or considering disease-free survival (DFS) as an outcome. For FIGO I/II stage patients, BRE rs7572644 C allele carriers exhibit a decreased OS (P = 0.014) and DFS (P = 0.032) when compared to TT-homozygous patients. Furthermore, a Multivariate Cox regression analysis revealed a three-fold increase in the risk of death (HR, 3.09; P = 0.015) and recurrence (HR, 3.33; P = 0.009) for FIGO I/II C allele carriers. No significant impact was observed for late-stage patients. The BRE rs7572644 and NRG3 rs1649942 genetic variants were validated in an independent cohort of EOC Portuguese patients, particularly in specific subgroups considering FIGO staging. Further functional post-GWAS analyses are indispensable to understand the biological mechanisms underlying the observed results.

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References

  1. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer . 2015;136:E359–386.

    Article  CAS  Google Scholar 

  2. Pereira D, Assis J, Gomes M, Nogueira A, Medeiros R. Improvement of a predictive model in ovarian cancer patients submitted to platinum-based chemotherapy: implications of a GST activity profile. Eur J Clin Pharmacol. 2016;72:545–53.

    Article  CAS  Google Scholar 

  3. Diaz-Padilla I, Amir E, Marsh S, Liu G, Mackay H. Genetic polymorphisms as predictive and prognostic biomarkers in gynecological cancers: a systematic review. Gynecol Oncol. 2012;124:354–65.

    Article  CAS  Google Scholar 

  4. Caiola E, Broggini M, Marabese M. Genetic markers for prediction of treatment outcomes in ovarian cancer. Pharm J. 2014;14:401–10.

    CAS  Google Scholar 

  5. Assis J, Pereira D, Gomes M, Marques D, Marques I, Nogueira A, et al. Influence of CYP3A4 genotypes in the outcome of serous ovarian cancer patients treated with first-line chemotherapy: implication of a CYP3A4 activity profile. Int J Clin Exp Med. 2013;6:552–61.

    PubMed  PubMed Central  Google Scholar 

  6. Huang RS, Duan S, Shukla SJ, Kistner EO, Clark TA, Chen TX, et al. Identification of genetic variants contributing to cisplatin-induced cytotoxicity by use of a genomewide approach. Am J Hum Genet. 2007;81:427–37.

    Article  CAS  Google Scholar 

  7. Pinto R, Assis J, Nogueira A, Pereira C, Pereira D, Medeiros R. Rethinking ovarian cancer genomics: where genome-wide association studies stand? Pharmacogenomics. 2017;18:1611–25.

    Article  CAS  Google Scholar 

  8. Prat J, Oncology FCoG. FIGO’s staging classification for cancer of the ovary, fallopian tube, and peritoneum: abridged republication. J Gynecol Oncol. 2015;26:87–89.

    Article  CAS  Google Scholar 

  9. Rustin GJ, Vergote I, Eisenhauer E, Pujade-Lauraine E, Quinn M, Thigpen T, et al. Definitions for response and progression in ovarian cancer clinical trials incorporating RECIST 1.1 and CA 125 agreed by the Gynecological Cancer Intergroup (GCIG). Int J Gynecol Cancer. 2011;21:419–23.

    Article  Google Scholar 

  10. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45(D1):D896–901.

    Article  CAS  Google Scholar 

  11. Saccone SF, Bolze R, Thomas P, Quan J, Mehta G, Deelman E, et al. SPOT: a web-based tool for using biological databases to prioritize SNPs after a genome-wide association study. Nucleic Acids Res. 2010;38(Web Server issue):W201–9.

    Article  CAS  Google Scholar 

  12. Desmet FO, Hamroun D, Lalande M, Collod-Beroud G, Claustres M, Beroud C. Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res. 2009;37:e67.

    Article  Google Scholar 

  13. Coetzee SG, Coetzee GA, Hazelett DJ. motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites. Bioinformatics. 2015;31:3847–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45:228–47.

    Article  CAS  Google Scholar 

  15. Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010;363:166–76.

    Article  CAS  Google Scholar 

  16. Patel JN, McLeod HL, Innocenti F. Implications of genome-wide association studies in cancer therapeutics. Br J Clin Pharmacol. 2013;76:370–80.

    Article  CAS  Google Scholar 

  17. Manolio TA. Bringing genome-wide association findings into clinical use. Nat Rev Genet. 2013;14:549–58.

    Article  CAS  Google Scholar 

  18. Chen K, Ma H, Li L, Zang R, Wang C, Song F, et al. Genome-wide association study identifies new susceptibility loci for epithelial ovarian cancer in Han Chinese women. Nat Commun. 2014;5:4682.

    Article  CAS  Google Scholar 

  19. Huang RS, Johnatty SE, Gamazon ER, Im HK, Ziliak D, Duan S, et al. Platinum sensitivity-related germline polymorphism discovered via a cell-based approach and analysis of its association with outcome in ovarian cancer patients. Clin Cancer Res. 2011;17:5490–500.

    Article  CAS  Google Scholar 

  20. Lu Y, Chen X, Beesley J, Johnatty SE, Defazio A, Lambrechts S, et al. Genome-wide association study for ovarian cancer susceptibility using pooled DNA. Twin Res Human Genet. 2012;15:615–23.

    Article  Google Scholar 

  21. Earp MA, Kelemen LE, Magliocco AM, Swenerton KD, Chenevix-Trench G, Lu Y, et al. Genome-wide association study of subtype-specific epithelial ovarian cancer risk alleles using pooled DNA. Hum Genet. 2014;133:481–97.

    Article  CAS  Google Scholar 

  22. Mostowska A, Sajdak S, Pawlik P, Markowska J, Pawalowska M, Lianeri M, et al. Replication study for the association of seven genome- GWAS-identified loci with susceptibility to ovarian cancer in the Polish population. Pathol Oncol Res. 2015;21:307–13.

    Article  CAS  Google Scholar 

  23. Zhang D, Sliwkowski MX, Mark M, Frantz G, Akita R, Sun Y, et al. Neuregulin-3 (NRG3): a novel neural tissue-enriched protein that binds and activates ErbB4. Proc Natl Acad Sci USA. 1997;94:9562–7.

    Article  CAS  Google Scholar 

  24. Falls DL. Neuregulins: functions, forms, and signaling strategies. Exp Cell Res. 2003;284:14–30.

    Article  CAS  Google Scholar 

  25. Maihle NJ, Baron AT, Barrette BA, Boardman CH, Christensen TA, Cora EM, et al. EGF/ErbB receptor family in ovarian cancer. Cancer Treat Res. 2002;107:247–58.

    CAS  PubMed  Google Scholar 

  26. Sheng Q, Liu J. The therapeutic potential of targeting the EGFR family in epithelial ovarian cancer. Br J Cancer. 2011;104:1241–5.

    Article  CAS  Google Scholar 

  27. National Center for Biotechnology Information. Gene. Available from: https://www.ncbi.nlm.nih.gov/gene/?term= [Accessed Feb 2018].

  28. Fane M, Harris L, Smith AG, Piper M. Nuclear factor one transcription factors as epigenetic regulators in cancer. Int J Cancer. 2017;140:2634–41.

    Article  CAS  Google Scholar 

  29. Tak YG, Farnham PJ. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin. 2015;8:57.

    Article  Google Scholar 

  30. Yeung TL, Leung CS, Yip KP, Au Yeung CL, Wong ST, Mok SC. Cellular and molecular processes in ovarian cancer metastasis. A review in the theme: cell and molecular processes in cancer metastasis. Am J Physiol Cell Physiol. 2015;309:C444–56.

    Article  CAS  Google Scholar 

  31. De Smet F, Pochet NL, Engelen K, Van Gorp T, Van Hummelen P, Marchal K, et al. Predicting the clinical behavior of ovarian cancer from gene expression profiles. Int J Gynecol Cancer. 2006;16(Suppl 1):147–51.

    Article  Google Scholar 

  32. Breuleux M. Role of heregulin in human cancer. Cell Mol life Sci. 2007;64:2358–77.

    Article  CAS  Google Scholar 

  33. Pradeep S, Kim SW, Wu SY, Nishimura M, Chaluvally-Raghavan P, Miyake T, et al. Hematogenous metastasis of ovarian cancer: rethinking mode of spread. Cancer Cell. 2014;26:77–91.

    Article  CAS  Google Scholar 

  34. Fridley BL, Ghosh TM, Wang A, Raghavan R, Dai J, Goode EL, et al. Genome-wide study of response to platinum, taxane, and combination therapy in ovarian cancer: in vitro phenotypes, inherited variation, and disease recurrence. Front Genet. 2016;7:37.

    Article  Google Scholar 

  35. Rebbeck TR, Mitra N, Domchek SM, Wan F, Friebel TM, Tran TV, et al. Modification of BRCA1-associated breast and ovarian cancer risk by BRCA1-interacting genes. Cancer Res. 2011;71:5792–805.

    Article  CAS  Google Scholar 

  36. Ho DV, Chan JY. Induction of Herpud1 expression by ER stress is regulated by Nrf1. FEBS Lett. 2015;589:615–20.

    Article  CAS  Google Scholar 

  37. Huang H, Tindall DJ. Dynamic FoxO transcription factors. J Cell Sci. 2007;120(Pt 15):2479–87.

    Article  CAS  Google Scholar 

  38. Goto T, Takano M, Hirata J, Tsuda H. The involvement of FOXO1 in cytotoxic stress and drug-resistance induced by paclitaxel in ovarian cancers. Br J Cancer. 2008;98:1068–75.

    Article  CAS  Google Scholar 

  39. Shi W, Tang MK, Yao Y, Tang C, Chui YL, Lee KK. BRE plays an essential role in preventing replicative and DNA damage-induced premature senescence. Sci Rep. 2016;6:23506.

    Article  CAS  Google Scholar 

  40. Li Y, Qi K, Zu L, Wang M, Wang Y, Zhou Q. Anti-apoptotic brain and reproductive organ-expressed proteins enhance cisplatin resistance in lung cancer cells via the protein kinase B signaling pathway. Thorac Cancer. 2016;7:190–8.

    Article  CAS  Google Scholar 

  41. Chan BC, Li Q, Chow SK, Ching AK, Liew CT, Lim PL, et al. BRE enhances in vivo growth of tumor cells. Biochem Biophys Res Commun. 2005;326:268–73.

    Article  CAS  Google Scholar 

  42. Konig IR. Validation in genetic association studies. Brief Bioinform. 2011;12:253–8.

    Article  Google Scholar 

  43. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356–69.

    Article  CAS  Google Scholar 

  44. Freedman ML, Monteiro AN, Gayther SA, Coetzee GA, Risch A, Plass C, et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nat Genet. 2011;43:513–8.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank the Liga Portuguesa Contra o Cancro-Centro Regional do Norte, Ministério da Saúde de Portugal (CFICS-45/2007), IPO-Porto (CI-IPOP-22-2015), and Fundação para a Ciência e Tecnologia (FCT). Joana Assis (SFRH/BD/98536/2013), Augusto Nogueira (SFRH/BD/124155/2016), and Carina Pereira (SFRH/BPD/114803/2016) are grant holders from FCT.

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Author notes

  1. These authors contributed equally: Ricardo Pinto, Joana Assis

Authors and Affiliations

  1. Molecular Oncology and Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Porto, Portugal

    Ricardo Pinto, Joana Assis, Augusto Nogueira, Carina Pereira & Rui Medeiros

  2. ICBAS, Abel Salazar Institute for the Biomedical Sciences, Porto, Portugal

    Ricardo Pinto & Rui Medeiros

  3. FMUP, Faculty of Medicine, Porto University, Porto, Portugal

    Joana Assis & Augusto Nogueira

  4. CINTESIS, Center for Health Technology and Services Research, Faculty of Medicine, Porto University, Porto, Portugal

    Carina Pereira

  5. Oncology Department, Portuguese Institute of Oncology, Porto, Portugal

    Sara Coelho, Mariana Brandão, João Dias, Sara Alves & Deolinda Pereira

  6. Research Department, Portuguese League Against Cancer (NRNorte), Porto, Portugal

    Rui Medeiros

  7. CEBIMED, Faculty of Health Sciences, Fernando Pessoa University, Porto, Portugal

    Rui Medeiros

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Pinto, R., Assis, J., Nogueira, A. et al. Pharmacogenomics in epithelial ovarian cancer first-line treatment outcome: validation of GWAS-associated NRG3 rs1649942 and BRE rs7572644 variants in an independent cohort. Pharmacogenomics J 19, 25–32 (2019). https://doi.org/10.1038/s41397-018-0056-y

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