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Genes and variants in hematopoiesis-related pathways are associated with gemcitabine/carboplatin-induced thrombocytopenia

The Pharmacogenomics Journal volume 20, pages 179–191 (2020)Cite this article

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Abstract

Chemotherapy-induced myelosuppression, including thrombocytopenia, is a recurrent problem during cancer treatments that may require dose alterations or cessations that could affect the antitumor effect of the treatment. To identify genetic markers associated with treatment-induced thrombocytopenia, we whole-exome sequenced 215 non-small cell lung cancer patients homogeneously treated with gemcitabine/carboplatin. The decrease in platelets (defined as nadir/baseline) was used to assess treatment-induced thrombocytopenia. Association between germline genetic variants and thrombocytopenia was analyzed at single-nucleotide variant (SNV) (based on the optimal false discovery rate, the severity of predicted consequence, and effect), gene, and pathway levels. These analyses identified 130 SNVs/INDELs and 25 genes associated with thrombocytopenia (P-value < 0.002). Twenty-three SNVs were validated in an independent genome-wide association study (GWAS). The top associations include rs34491125 in JMJD1C (P-value = 9.07 × 10−5), the validated variants rs10491684 in DOCK8 (P-value = 1.95 × 10−4), rs6118 in SERPINA5 (P-value = 5.83 × 10−4), and rs5877 in SERPINC1 (P-value = 1.07 × 10−3), and the genes CAPZA2 (P-value = 4.03 × 10−4) and SERPINC1 (P-value = 1.55 × 10−3). The SNVs in the top-scoring pathway “Factors involved in megakaryocyte development and platelet production” (P-value = 3.34 × 10−4) were used to construct weighted genetic risk score (wGRS) and logistic regression models that predict thrombocytopenia. The wGRS predict which patients are at high or low toxicity risk levels, for CTCAE (odds ratio (OR) = 22.35, P-value = 1.55 × 10−8), and decrease (OR = 66.82, P-value = 5.92 × 10−9). The logistic regression models predict CTCAE grades 3–4 (receiver operator characteristics (ROC) area under the curve (AUC) = 0.79), and large decrease (ROC AUC = 0.86). We identified and validated genetic variations within hematopoiesis-related pathways that provide a solid foundation for future studies using genetic markers for predicting chemotherapy-induced thrombocytopenia and personalizing treatments.

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Data availability

The raw sequencing datasets generated and analyzed in this study are not publicly available because this is not permitted according to the ethical approval of the study. However, the datasets are available from the corresponding author upon reasonable request with the appropriate ethical approval.

References

  1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108.

    Article  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65:5–29.

    Article  Google Scholar 

  3. Barton-Burke M. Gemcitabine: a pharmacologic and clinical overview. Cancer Nurs. 1999;22:176–83.

    Article  CAS  Google Scholar 

  4. Calvert AH, Harland SJ, Newell DR, Siddik ZH, Jones AC, McElwain TJ, et al. Early clinical studies with cis-diammine-1,1-cyclobutane dicarboxylate platinum II. Cancer Chemother Pharmacol. 1982;9:140–7.

    Article  CAS  Google Scholar 

  5. Gronberg BH, Bremnes RM, Flotten O, Amundsen T, Brunsvig PF, Hjelde HH, et al. Phase III study by the Norwegian lung cancer study group: pemetrexed plus carboplatin compared with gemcitabine plus carboplatin as first-line chemotherapy in advanced non-small-cell lung cancer. J Clin Oncol. 2009;27:3217–24.

    Article  Google Scholar 

  6. Imamura F, Nishio M, Noro R, Tsuboi M, Ikeda N, Inoue A, et al. Randomized phase II study of two schedules of carboplatin and gemcitabine for stage IIIB and IV advanced non-small cell lung cancer (JACCRO LC-01 study). Chemotherapy. 2011;57:357–62.

    Article  CAS  Google Scholar 

  7. Rudd RM, Gower NH, Spiro SG, Eisen TG, Harper PG, Littler JAH, et al. Gemcitabine plus carboplatin versus mitomycin, ifosfamide, and cisplatin in patients with stage IIIB or IV non-small-cell lung cancer: A phase III randomized study of the London Lung Cancer Group. J Clin Oncol. 2005;23:142–53.

    Article  CAS  Google Scholar 

  8. Sederholm C, Hillerdal G, Lamberg K, Kolbeck K, Dufmats M, Westberg R, et al. Phase III trial of gemcitabine plus carboplatin versus single-agent gemcitabine in the treatment of locally advanced or metastatic non-small-cell lung cancer: the Swedish Lung Cancer Study Group. J Clin Oncol. 2005;23:8380–8.

    Article  Google Scholar 

  9. Zatloukal P, Petruželka L, Zemanová M, Kolek V, Skřičková J, Pešek M, et al. Gemcitabine plus cisplatin vs. gemcitabine plus carboplatin in stage IIIb and IV non-small cell lung cancer: A phase III randomized trial. Lung Cancer. 2003;41:321–31.

    Article  Google Scholar 

  10. Green H, Hasmats J, Kupershmidt I, Edsgard D, de Petris L, Lewensohn R, et al. Using whole-exome sequencing to identify genetic markers for carboplatin and gemcitabine-induced toxicities. Clin Cancer Res. 2016;22:366–73.

  11. Han B, Gao G, Wu W, Gao Z, Zhao X, Li L, et al. Association of ABCC2 polymorphisms with platinum-based chemotherapy response and severe toxicity in non-small cell lung cancer patients. Lung Cancer. 2011;72:238–43.

    Article  Google Scholar 

  12. Kiyotani K, Uno S, Mushiroda T, Takahashi A, Kubo M, Mitsuhata N, et al. A genome-wide association study identifies four genetic markers for hematological toxicities in cancer patients receiving gemcitabine therapy. Pharmacogenet Genomics. 2012;22:229–35.

    Article  CAS  Google Scholar 

  13. Qian J, Qu HQ, Yang L, Yin M, Wang Q, Gu S, et al. Association between CASP8 and CASP10 polymorphisms and toxicity outcomes with platinum-based chemotherapy in Chinese patients with non-small cell lung cancer. Oncologist. 2012;17:1551–61.

    Article  CAS  Google Scholar 

  14. Low SK, Chung S, Takahashi A, Zembutsu H, Mushiroda T, Kubo M, et al. Genome-wide association study of chemotherapeutic agent-induced severe neutropenia/leucopenia for patients in Biobank Japan. Cancer Sci. 2013;104:1074–82.

    Article  CAS  Google Scholar 

  15. Lamba JK, Fridley BL, Ghosh TM, Yu Q, Mehta G, Gupta P. Genetic variation in platinating agent and taxane pathway genes as predictors of outcome and toxicity in advanced non-small-cell lung cancer. Pharmacogenomics. 2014;15:1565–74.

    Article  CAS  Google Scholar 

  16. Cao S, Wang S, Ma H, Tang S, Sun C, Dai J, et al. Genome-wide association study of myelosuppression in non-small-cell lung cancer patients with platinum-based chemotherapy. Pharmacogenomics J. 2016;16:41–46.

    Article  CAS  Google Scholar 

  17. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17:10–12.

    Google Scholar 

  18. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    Article  CAS  Google Scholar 

  19. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    Article  Google Scholar 

  20. 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;43:491–501.

    Article  CAS  Google Scholar 

  21. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.

    Article  CAS  Google Scholar 

  22. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  Google Scholar 

  23. RCoreTeam. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016.

    Google Scholar 

  24. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–5.

    Article  CAS  Google Scholar 

  25. Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X. Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet. 2013;92:841–53.

    Article  CAS  Google Scholar 

  26. Leandro-García LJ, Inglada-Pérez L, Pita G, Hjerpe E, Leskelä S, Jara C, et al. Genome-wide association study identifies ephrin type a receptors implicated in paclitaxel induced peripheral sensory neuropathy. J Med Genet. 2013;50:599–605.

    Article  Google Scholar 

  27. Strachan T, Read AP. Human molecular genetics. 4th ed. New York, USA: Garland Science Taylor & Francis Group; 2011. p. 482.

  28. Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, Herwig R. ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. 2011;39 SUPPL. 1:D712–7.

    Article  CAS  Google Scholar 

  29. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21:3940–1.

    Article  CAS  Google Scholar 

  30. Ding Q, Wang M, Xu G, Ye X, Xi X, Yu T, et al. Molecular basis and thrombotic manifestations of antithrombin deficiency in 15 unrelated Chinese patients. Thromb Res. 2013;132:367–73.

    Article  CAS  Google Scholar 

  31. van Boven HH, Vandenbroucke JP, Briet E, Rosendaal FR. Gene-gene and gene-environment interactions determine risk of thrombosis in families with inherited antithrombin deficiency. Blood. 1999;94:2590–4.

    Article  Google Scholar 

  32. Gieger C, Radhakrishnan A, Cvejic A, Tang W, Porcu E, Pistis G, et al. New gene functions in megakaryopoiesis and platelet formation. Nature. 2011;480:201–8.

    Article  CAS  Google Scholar 

  33. Eicher JD, Xue L, Ben-Shlomo Y, Beswick AD, Johnson AD. Replication and hematological characterization of human platelet reactivity genetic associations in men from the Caerphilly Prospective Study (CaPS). J Thromb Thrombolysis. 2016;41:343–50.

    Article  CAS  Google Scholar 

  34. Shameer K, Denny JC, Ding K, Jouni H, Crosslin DR, de Andrade M, et al. A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum Genet. 2014;133:95–109.

    Article  Google Scholar 

  35. Soranzo N, Spector TD, Mangino M, Kuhnel B, Rendon A, Teumer A, et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat Genet. 2009;41:1182–90.

    Article  CAS  Google Scholar 

  36. Kitajima K, Kojima M, Kondo S, Takeuchi T. A role of jumonji gene in proliferation but not differentiation of megakaryocyte lineage cells. Exp Hematol. 2001;29:507–14.

    Article  CAS  Google Scholar 

  37. Sham PC, Purcell SM. Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet. 2014;15:335–46.

    Article  CAS  Google Scholar 

  38. Lin CY, Xing G, Ku HC, Elston RC, Xing C. Enhancing the power to detect low-frequency variants in genome-wide screens. Genetics. 2014;196:1293–302.

    Article  Google Scholar 

  39. Slatkin M. Linkage disequilibrium-understanding the evolutionary past and mapping the medical future. Nat Rev Genet. 2008;9:477–85.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was financially supported by grants from the Swedish Cancer Society (HG), the Swedish Research Council (HG), Linköping University (HG), ALF grants Region Östergötland (HG), the Funds of Radiumhemmet (RL and LDP), Marcus Borgströms stiftelse (HG), and the Spanish Ministry of Economy and Competitiveness [SAF2015-64850-R] (CR-A). The funders had no role in study design, data collection, data analysis, decision to publish, or preparation of the manuscript. We gratefully acknowledge the Science for Life Laboratory (SciLifeLab, Stockholm, Sweden), National Genomics Infrastructure (Sweden), National Bioinformatics Infrastructure Sweden, and UPPMAX (Uppsala Multidisciplinary Center for Advanced Computational Science, Uppsala, Sweden) for providing massive parallel sequencing, computational infrastructure, and support.

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Author notes

  1. These authors contributed equally: Joakim Lundeberg, Henrik Gréen

Authors and Affiliations

  1. Clinical Pharmacology, Division of Drug Research, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden

    Niclas Björn, Anna Svedberg & Henrik Gréen

  2. Science for Life Laboratory, Division of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden

    Benjamín Sigurgeirsson, Sailendra Pradhananga, Joakim Lundeberg & Henrik Gréen

  3. School of Engineering and Natural Sciences, University of Iceland, Reykjavík, Iceland

    Benjamín Sigurgeirsson

  4. Department of Respiratory Medicine, Gävle Hospital, Gävle, Sweden

    Eva Brandén & Hirsh Koyi

  5. Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden

    Eva Brandén & Hirsh Koyi

  6. Thoracic Oncology Unit, Tema Cancer, Karolinska University Hospital, and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden

    Rolf Lewensohn & Luigi de Petris

  7. Hereditary Endocrine Cancer Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain

    Maria Apellániz-Ruiz & Cristina Rodríguez-Antona

  8. Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden

    Henrik Gréen

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Björn, N., Sigurgeirsson, B., Svedberg, A. et al. Genes and variants in hematopoiesis-related pathways are associated with gemcitabine/carboplatin-induced thrombocytopenia. Pharmacogenomics J 20, 179–191 (2020). https://doi.org/10.1038/s41397-019-0099-8

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