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Gene polymorphisms of TACR1 serve as the potential pharmacogenetic predictors of response to the neurokinin-1 receptor antagonist-based antiemetic regimens: a candidate-gene association study in breast cancer patients

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Abstract

Purpose

The current candidate gene association study aims to investigate tag SNPs from the TACR1 gene as pharmacogenetic predictors of response to the antiemetic guidelines-recommended, NK-1 receptor antagonist-based, triple antiemetic regimens.

Methods

A set of eighteen tag SNPs of TACR1 were genotyped in breast cancer patients receiving anthracycline and cyclophosphamide (with/without docetaxel) applying real-time PCR-HRMA.

Data analysis for 121 ultimately enrolled patients was initiated by defining haplotype blocks using PHASE v.2.1. The association of each tag SNP and haplotype alleles with failure to achieve the defined antiemetic regimen efficacy endpoints was tested using PLINK (v.1.9 and v.1.07, respectively) based on the logistic regression, adjusting for the previously known chemotherapy-induced nausea and vomiting (CINV) prognostic factors. All reported p-values were corrected using the permutation test (n = 100,000).

Results

Four variants of rs881, rs17010730, rs727156, and rs3755462, as well as haplotypes containing the mentioned variants, were significantly associated with failure to achieve at least one of the defined efficacy endpoints. Variant annotation via in-silico studies revealed that the non-seed sequence variant, rs881, is located in the miRNA (hsa-miR-613) binding site. The other three variants or a variant in complete linkage disequilibrium with them overlap a region of high H3K9ac-promoter-like signature or regions of high enhancer-like signature in the brain or gastrointestinal tissue.

Conclusion

Playing an essential role in regulating TACR1 expression, gene polymorphisms of TACR1 serve as the potential pharmacogenetic predictors of response to the NK-1 receptor antagonist-based, triple antiemetic regimens. If clinically approved, modifying the NK-1 receptor antagonist dose leads to better management of CINV in risk-allele carriers.

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Data availability

The data supporting the findings of this study are available upon reasonable request from the corresponding author.

References

  1. Aapro M (2018) CINV: still troubling patients after all these years. Support Care Cancer 26(Suppl 1):5–9. https://doi.org/10.1007/s00520-018-4131-3

    Article  PubMed  PubMed Central  Google Scholar 

  2. Yeo W, Mo FKF, Yip CCH et al (2021) Quality of life associated with nausea and vomiting from anthracycline-based chemotherapy: a pooled data analysis from three prospective trials. Oncologist 26(12):e2288–e2296. https://doi.org/10.1002/onco.13978

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kuchuk I, Bouganim N, Beusterien K et al (2013) Preference weights for chemotherapy side effects from the perspective of women with breast cancer. Breast Cancer Res Treat 142(1):101–107. https://doi.org/10.1007/s10549-013-2727-3

    Article  CAS  PubMed  Google Scholar 

  4. Schwartzberg L, Harrow B, Lal LS et al (2015) Resource utilization for chemotherapy-induced nausea and vomiting events in patients with solid tumors treated with antiemetic regimens. Am Health Drug Benefits 8(5):273–282

    PubMed  PubMed Central  Google Scholar 

  5. Roeland EJ, Ruddy KJ, LeBlanc TW et al (2020) What the HEC? Clinician adherence to evidence-based antiemetic prophylaxis for highly emetogenic chemotherapy. J Natl Compr Canc Netw 18(6):676–681. https://doi.org/10.6004/jnccn.2019.7526

    Article  CAS  PubMed  Google Scholar 

  6. Navari RM, Aapro M (2016) Antiemetic prophylaxis for chemotherapy-induced nausea and vomiting. N Engl J Med 374(14):1356–1367. https://doi.org/10.1056/NEJMra1515442

    Article  CAS  PubMed  Google Scholar 

  7. Hesketh PJ, Van Belle S, Aapro M et al (2003) Differential involvement of neurotransmitters through the time course of cisplatin-induced emesis as revealed by therapy with specific receptor antagonists. Eur J Cancer 39(8):1074–1080. https://doi.org/10.1016/S0959-8049(02)00674-3

    Article  CAS  PubMed  Google Scholar 

  8. Warr D (2014) Prognostic factors for chemotherapy induced nausea and vomiting. Eur J Pharmacol 722:192–196. https://doi.org/10.1016/j.ejphar.2013.10.015

    Article  CAS  PubMed  Google Scholar 

  9. Ettinger DS, Armstrong DK, Barbour S et al (2012) Antiemesis. JNCCN J Natl Compr Cancer Netw 10(4):456–485

    Article  CAS  Google Scholar 

  10. Navari RM (2020) Nausea and vomiting in advanced cancer. Curr Treat Options Oncol 21(2):14. https://doi.org/10.1007/s11864-020-0704-8

    Article  PubMed  Google Scholar 

  11. Ghorbani M, Dehghani M, Fahimfar N et al (2022) FLOT (a chemotherapy regimen for gastric/esophagogastric junction cancer): to be treated as a highly emetogenic regimen or a moderately emetogenic one? Comparison of the emetogenic potential of FLOT versus FOLFOX and TAC regimens. Support Care Cancer 30(5):3865–3873. https://doi.org/10.1007/s00520-022-06832-x

    Article  PubMed  Google Scholar 

  12. Trammel M, Roederer M, Patel J et al (2013) Does pharmacogenomics account for variability in control of acute chemotherapy-induced nausea and vomiting with 5-hydroxytryptamine type 3 receptor antagonists? Curr Oncol Rep 15(3):276–285. https://doi.org/10.1007/s11912-013-0312-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hashimoto H, Abe M, Tokuyama O et al (2020) Olanzapine 5 mg plus standard antiemetic therapy for the prevention of chemotherapy-induced nausea and vomiting (J-FORCE): a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol 21(2):242–249. https://doi.org/10.1016/s1470-2045(19)30678-3

    Article  CAS  PubMed  Google Scholar 

  14. Nishimura J, Satoh T, Fukunaga M et al (2015) Combination antiemetic therapy with aprepitant/fosaprepitant in patients with colorectal cancer receiving oxaliplatin-based chemotherapy (SENRI trial): a multicentre, randomised, controlled phase 3 trial. Eur J Cancer 51(10):1274–1282. https://doi.org/10.1016/j.ejca.2015.03.024

    Article  CAS  PubMed  Google Scholar 

  15. Bubalo JS, Herrington JD, Takemoto M et al (2018) Phase II open label pilot trial of aprepitant and palonosetron for the prevention of chemotherapy-induced nausea and vomiting (CINV) in patients receiving moderately emetogenic FOLFOX chemotherapy for the treatment of colorectal cancer. Support Care Cancer 26(4):1273–1279. https://doi.org/10.1007/s00520-017-3950-y

    Article  PubMed  Google Scholar 

  16. Zhang L, Lu S, Feng J et al (2018) A randomized phase III study evaluating the efficacy of single-dose NEPA, a fixed antiemetic combination of netupitant and palonosetron, versus an aprepitant regimen for prevention of chemotherapy-induced nausea and vomiting (CINV) in patients receiving highly emetogenic chemotherapy (HEC). Ann Oncol 29(2):452–458. https://doi.org/10.1093/annonc/mdx698

    Article  CAS  PubMed  Google Scholar 

  17. Moradian S, Shahidsales S, Ghavam Nasiri MR et al (2014) Translation and psychometric assessment of the Persian version of the Rhodes Index of Nausea, Vomiting and Retching (INVR) scale for the assessment of chemotherapy-induced nausea and vomiting. Eur J Cancer Care (Engl Lang Ed) 23(6):811–818. https://doi.org/10.1111/ecc.12147

    Article  CAS  Google Scholar 

  18. Ahmadi KR, Weale ME, Xue ZY et al (2005) A single-nucleotide polymorphism tagging set for human drug metabolism and transport. Nat Genet 37(1):84–89. https://doi.org/10.1038/ng1488

    Article  CAS  PubMed  Google Scholar 

  19. Ding K, Kullo IJ (2007) Methods for the selection of tagging SNPs: a comparison of tagging efficiency and performance. Eur J Hum Genet 15(2):228–236. https://doi.org/10.1038/sj.ejhg.5201755

    Article  CAS  PubMed  Google Scholar 

  20. The International HapMap Project (2003) Nature 426(6968):789–796. https://doi.org/10.1038/nature02168

  21. Xu Z, Taylor JA (2009) SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies. Nucl Acids Res 37(Web Server issue):W600-605. https://doi.org/10.1093/nar/gkp290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Conrad DF, Jakobsson M, Coop G et al (2006) A worldwide survey of haplotype variation and linkage disequilibrium in the human genome. Nat Genet 38(11):1251–1260. https://doi.org/10.1038/ng1911

    Article  CAS  PubMed  Google Scholar 

  23. de Bakker PIW, Burtt NP, Graham RR et al (2006) Transferability of tag SNPs in genetic association studies in multiple populations. Nat Genet 38(11):1298–1303. https://doi.org/10.1038/ng1899

    Article  CAS  PubMed  Google Scholar 

  24. Hayase T, Sugino S, Moriya H et al (2015) TACR1 gene polymorphism and sex differences in postoperative nausea and vomiting. Anaesthesia 70(10):1148–1159. https://doi.org/10.1111/anae.13082

    Article  CAS  PubMed  Google Scholar 

  25. Fattahi Z, Beheshtian M, Mohseni M et al (2019) Iranome: a catalog of genomic variations in the Iranian population. Hum Mutat 40(11):1968–1984

    Article  CAS  PubMed  Google Scholar 

  26. Reed GH, Kent JO, Wittwer CT (2007) High-resolution DNA melting analysis for simple and efficient molecular diagnostics. Pharmacogenomics 8(6):597–608. https://doi.org/10.2217/14622416.8.6.597

    Article  CAS  PubMed  Google Scholar 

  27. Vossen RH, Aten E, Roos A et al (2009) High-resolution melting analysis (HRMA): more than just sequence variant screening. Hum Mutat 30(6):860–866. https://doi.org/10.1002/humu.21019

    Article  CAS  PubMed  Google Scholar 

  28. Słomka M, Sobalska-Kwapis M, Wachulec M et al (2017) High resolution melting (HRM) for high-throughput genotyping-limitations and caveats in practical case studies. Int J Mol Sci 18(11):2316. https://doi.org/10.3390/ijms18112316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Er TK, Chang JG (2012) High-resolution melting: applications in genetic disorders. Clin Chim Acta 414:197–201. https://doi.org/10.1016/j.cca.2012.09.012

    Article  CAS  PubMed  Google Scholar 

  30. Reed GH, Wittwer CT (2004) Sensitivity and specificity of single-nucleotide polymorphism scanning by high-resolution melting analysis. Clin Chem 50(10):1748–1754. https://doi.org/10.1373/clinchem.2003.029751

    Article  CAS  PubMed  Google Scholar 

  31. Wojdacz TK, Dobrovic A, Hansen LL (2008) Methylation-sensitive high-resolution melting. Nat Protoc 3(12):1903–1908. https://doi.org/10.1038/nprot.2008.191

    Article  CAS  PubMed  Google Scholar 

  32. Erali M, Voelkerding KV, Wittwer CT (2008) High resolution melting applications for clinical laboratory medicine. Exp Mol Pathol 85(1):50–58. https://doi.org/10.1016/j.yexmp.2008.03.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Auton A, Abecasis GR, Altshuler DM et al (2015) A global reference for human genetic variation. Nature 526(7571):68–74. https://doi.org/10.1038/nature15393

    Article  CAS  PubMed  Google Scholar 

  34. Untergasser A, Cutcutache I, Koressaar T et al (2012) Primer3—new capabilities and interfaces. Nucl Acids Res 40(15):e115–e115. https://doi.org/10.1093/nar/gks596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ye J, Coulouris G, Zaretskaya I et al (2012) Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinform 13:134. https://doi.org/10.1186/1471-2105-13-134

    Article  CAS  Google Scholar 

  36. Owczarzy R, Tataurov AV, Wu Y et al (2008) IDT SciTools: a suite for analysis and design of nucleic acid oligomers. Nucl Acids Res 36(Web Server issue):W163–W169. https://doi.org/10.1093/nar/gkn198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ziegler A, Van Steen K, Wellek S (2011) Investigating Hardy–Weinberg equilibrium in case–control or cohort studies or meta-analysis. Breast Cancer Res Treat 128(1):197–201. https://doi.org/10.1007/s10549-010-1295-z

    Article  PubMed  Google Scholar 

  38. Barrett JC, Fry B, Maller J et al (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21(2):263–265. https://doi.org/10.1093/bioinformatics/bth457

    Article  CAS  PubMed  Google Scholar 

  39. Stephens M, Smith NJ, Donnelly P (2001) A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68(4):978–989. https://doi.org/10.1086/319501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Stephens M, Scheet P (2005) Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am J Hum Genet 76(3):449–462. https://doi.org/10.1086/428594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chang CC, Chow CC, Tellier LC et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. https://doi.org/10.1186/s13742-015-0047-8

    Article  PubMed  PubMed Central  Google Scholar 

  42. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575. https://doi.org/10.1086/519795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Purcell S, Cherny SS, Sham PC (2003) Genetic power calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19(1):149–150. https://doi.org/10.1093/bioinformatics/19.1.149

    Article  CAS  PubMed  Google Scholar 

  44. ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57–74. https://doi.org/10.1038/nature11247

    Article  CAS  Google Scholar 

  45. Moore JE, Purcaro MJ, Pratt HE et al (2020) Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583(7818):699–710. https://doi.org/10.1038/s41586-020-2493-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Tak YG, Farnham PJ (2015) 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 8:57. https://doi.org/10.1186/s13072-015-0050-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Saunders MA, Liang H, Li W-H (2007) Human polymorphism at microRNAs and microRNA target sites. Proc Natl Acad Sci USA 104(9):3300–3305. https://doi.org/10.1073/pnas.0611347104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. McGeary SE, Lin KS, Shi CY et al (2019) The biochemical basis of microRNA targeting efficacy. Science. https://doi.org/10.1126/science.aav1741

    Article  PubMed  PubMed Central  Google Scholar 

  49. Cummins JM, He Y, Leary RJ et al (2006) The colorectal microRNAome. Proc Natl Acad Sci USA 103(10):3687–3692. https://doi.org/10.1073/pnas.0511155103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kozomara A, Birgaoanu M, Griffiths-Jones S (2018) miRBase: from microRNA sequences to function. Nucl Acids Res 47(D1):D155–D162. https://doi.org/10.1093/nar/gky1141

    Article  CAS  PubMed Central  Google Scholar 

  51. Ludwig N, Leidinger P, Becker K et al (2016) Distribution of miRNA expression across human tissues. Nucl Acids Res 44(8):3865–3877. https://doi.org/10.1093/nar/gkw116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Duan Y, Veksler-Lublinsky I, Ambros V (2022) Critical contribution of 3′ non-seed base pairing to the in vivo function of the evolutionarily conserved let-7a microRNA. Cell Rep 39(4):110745. https://doi.org/10.1016/j.celrep.2022.110745

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ward LD, Kellis M (2011) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucl Acids Res 40(D1):D930–D934. https://doi.org/10.1093/nar/gkr917

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Boyle AP, Hong EL, Hariharan M et al (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22(9):1790–1797. https://doi.org/10.1101/gr.137323.112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Van Laere K, De Hoon J, Bormans G et al (2012) Equivalent dynamic human brain NK1-receptor occupancy following single-dose i.v. fosaprepitant vs. oral aprepitant as assessed by PET imaging. Clin Pharmacol Therap 92(2):243–250. https://doi.org/10.1038/clpt.2012.62

    Article  CAS  Google Scholar 

  56. Renner SP, Ekici AB, Maihöfner C et al (2009) Neurokinin 1 receptor gene polymorphism might be correlated with recurrence rates in endometriosis. Gynecol Endocrinol 25(11):726–733. https://doi.org/10.3109/09513590903159631

    Article  CAS  PubMed  Google Scholar 

  57. Seneviratne C, Ait-Daoud N, Ma JZ et al (2009) Susceptibility locus in neurokinin-1 receptor gene associated with alcohol dependence. Neuropsychopharmacol Offi Publ Am Coll Neuropsychopharmacol 34(11):2442–2449. https://doi.org/10.1038/npp.2009.65

    Article  CAS  Google Scholar 

  58. Singh KP, Dhruva AA, Flowers E et al (2018) A review of the literature on the relationships between genetic polymorphisms and chemotherapy-induced nausea and vomiting. Crit Rev Oncol Hematol 121:51–61. https://doi.org/10.1016/j.critrevonc.2017.11.012

    Article  PubMed  Google Scholar 

  59. Tsuji D, Matsumoto M, Kawasaki Y et al (2021) Analysis of pharmacogenomic factors for chemotherapy-induced nausea and vomiting in patients with breast cancer receiving doxorubicin and cyclophosphamide chemotherapy. Cancer Chemother Pharmacol 87(1):73–83. https://doi.org/10.1007/s00280-020-04177-y

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors would like to greatly appreciate the kind cooperation of the participating centers: Amir Oncology Hospital, Amir Oncology Clinic, and Medical Specialties and Subspecialties Clinic affiliated with Shiraz University of Medical Sciences (SUMS), Shiraz, Iran.

Funding

This study is the Pharm.D. thesis of Marziyeh Ghorbani. The financial support is received from the Vice-Chancellor for Research, Shiraz University of Medical Sciences (Grant Nos. 97-01-05-18056 and 98-01-05-19879).

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Authors and Affiliations

  1. Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran

    Marziyeh Ghorbani & Ali Dehshahri

  2. Research Center for Rational Use of Drugs, Tehran University of Medical Sciences (TUMS), Tehran, Iran

    Soha Namazi

  3. Department of Hematology and Medical Oncology, Hematology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

    Mehdi Dehghani

  4. Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

    Farideh Razi

  5. Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran

    Bahman Khalvati

  6. Biological Mass Spectrometry Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY, USA

    Bahman Khalvati

Authors
  1. Marziyeh Ghorbani
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  2. Soha Namazi
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  3. Mehdi Dehghani
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  4. Farideh Razi
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Contributions

M.G., A.D., S.N., M.D., F.R., and B.K. had substantial contributions to conception and design, M.G. and M.D. to the acquisition of data, M.G. to analysis and interpretation of data and, A.D., S.N., M.D., and F.R. to funding acquisition. The article is drafted by M.G. and critically revised by A.D., S.N., M.D., B.K., and F.R. for important intellectual content. All the authors approved the final manuscript to be published.

Corresponding author

Correspondence to Ali Dehshahri.

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The authors have no relevant financial or non-financial interests to disclose.

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The current study is conducted in compliance with the Declaration of Helsinki and the national norms and standards for conducting medical research in Iran (approval IDs: IR.SUMS.REC.1397.662 and IR.SUMS.REC.1398.623).

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All enrolled patients provided written informed consent to participate.

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Ghorbani, M., Namazi, S., Dehghani, M. et al. Gene polymorphisms of TACR1 serve as the potential pharmacogenetic predictors of response to the neurokinin-1 receptor antagonist-based antiemetic regimens: a candidate-gene association study in breast cancer patients. Cancer Chemother Pharmacol (2024). https://doi.org/10.1007/s00280-024-04661-9

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