Integration of genetic and functional genomics data to uncover chemotherapeutic induced cytotoxicity

Abstract

Identifying genetic variants associated with chemotherapeutic induced toxicity is an important step towards personalized treatment of cancer patients. However, annotating and interpreting the associated genetic variants remains challenging because each associated variant is a surrogate for many other variants in the same region. The issue is further complicated when investigating patterns of associated variants with multiple drugs. In this study, we used biological knowledge to annotate and compare genetic variants associated with cellular sensitivity to mechanistically distinct chemotherapeutic drugs, including platinating agents (cisplatin, carboplatin), capecitabine, cytarabine, and paclitaxel. The most significantly associated SNPs from genome wide association studies of cellular sensitivity to each drug in lymphoblastoid cell lines derived from populations of European (CEU) and African (YRI) descent were analyzed for their enrichment in biological pathways and processes. We annotated genetic variants using higher-level biological annotations in efforts to group variants into more interpretable biological modules. Using the higher-level annotations, we observed distinct biological modules associated with cell line populations as well as classes of chemotherapeutic drugs. We also integrated genetic variants and gene expression variables to build predictive models for chemotherapeutic drug cytotoxicity and prioritized the network models based on the enrichment of DNA regulatory data. Several biological annotations, often encompassing different SNPs, were replicated in independent datasets. By using biological knowledge and DNA regulatory information, we propose a novel approach for jointly analyzing genetic variants associated with multiple chemotherapeutic drugs.

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References

  1. 1.

    FACT SHEET: investing in the National Cancer Moonshot | whitehouse.gov. 2016. https://www.whitehouse.gov/the-press-office/2016/02/01/fact-sheet-investing-national-cancer-moonshot.

  2. 2.

    Wheeler HE, Dolan ME. Lymphoblastoid cell lines in pharmacogenomic discovery and clinical translation. Pharmacogenomics. 2012;13:55–70.

    CAS  Article  Google Scholar 

  3. 3.

    Huang RS, Duan S, Kistner EO, Hartford CM, Dolan ME. Genetic variants associated with carboplatin-induced cytotoxicity in cell lines derived from Africans. Mol Cancer Ther. 2008;7:3038–46. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2743011&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  4. 4.

    Wheeler HE, Gamazon ER, Stark aL, O’Donnell PH, Gorsic LK, Huang RS, et al. Genome-wide meta-analysis identifies variants associated with platinating agent susceptibility across populations. Pharm J. 2011;13:35–43. http://www.nature.com/doifinder/10.1038/tpj.2011.38.

    Google Scholar 

  5. 5.

    Huang RS, Duan S, Bleibel WK, Kistner EO, Zhang W, Clark Ta, et al. A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity. Proc Natl Acad Sci USA. 2007;104:9758–63. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1887589&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  6. 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. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1950832&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  7. 7.

    Borghaei H, Langer CJ, Millenson M, Ruth KJ, Litwin S, Tuttle H, et al. Phase II study of paclitaxel, carboplatin, and cetuximab as first line treatment, for patients with advanced non-small cell lung cancer (NSCLC). J Thorac Oncol. 2008;3:1286–92. http://www.ncbi.nlm.nih.gov/pubmed/18978564.

    Article  Google Scholar 

  8. 8.

    McWhinney SR, Goldberg RM, McLeod HL. Platinum neurotoxicity pharmacogenetics. Mol Cancer Ther. 2009;8:10–6. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2651829&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  9. 9.

    Rabik CA, Dolan ME. Molecular mechanisms of resistance and toxicity associated with platinating agents. Cancer Treat Rev. 2007;33:9–23. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1855222&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  10. 10.

    Cassidy J, Saltz L, Twelves C, Van Cutsem E, Hoff P, Kang Y, et al. Efficacy of capecitabine versus 5-fluorouracil in colorectal and gastric cancers: a meta-analysis of individual data from 6171 patients. Ann Oncol. 2011;22:2604–9. http://www.ncbi.nlm.nih.gov/pubmed/21415237.

    CAS  Article  Google Scholar 

  11. 11.

    Kumar CC. Genetic abnormalities and challenges in the treatment of acute myeloid leukemia. Genes Cancer. 2011;2:95–107. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3111245&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  12. 12.

    Rowinsky EK, Wright M, Monsarrat B, Donehower RC. Clinical pharmacology and metabolism of taxol (paclitaxel): update 1993. Ann Oncol. 1994;5:S7–16. http://www.ncbi.nlm.nih.gov/pubmed/7865438.

    Article  Google Scholar 

  13. 13.

    Huang RS, Kistner EO, Bleibel WK, Shukla SJ, Dolan ME. Effect of population and gender on chemotherapeutic agent-induced cytotoxicity. Mol Cancer Ther. 2007;6:31–6. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2669540&tool=pmcentrez&rendertype=abstract.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Zhang W, Duan S, Kistner EO, Bleibel WK, Huang RS, Clark TA, et al. Evaluation of genetic variation contributing to differences in gene expression between populations. Am J Hum Genet. 2008;82:631–40. http://www.sciencedirect.com/science/article/pii/S0002929708001365.

    CAS  Article  Google Scholar 

  15. 15.

    Gamazon ER, Huang RS, Cox NJ, Dolan ME. Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci. Proc Natl Acad Sci USA. 2010;107:9287–92. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2889115&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  16. 16.

    Schork AJ, Thompson WK, Pham P, Torkamani A, Roddey JC, Sullivan PF, et al. All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet. 2013;9:e1003449. http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003449.

    CAS  Article  Google Scholar 

  17. 17.

    Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009;106:9362–7. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2687147&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  18. 18.

    O’Donnell PH, Stark AL, Gamazon ER, Wheeler HE, McIlwee BE, Gorsic L, et al. Identification of novel germline polymorphisms governing capecitabine sensitivity. Cancer. 2012;118:4063–73. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3413892&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  19. 19.

    Abecasis GR, Auton A, Brooks LD, DePristo Ma, Durbin RM, Handsaker RE, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3498066&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  20. 20.

    Lappalainen T, Sammeth M, Friedländer MR, ’t Hoen PaC, Monlong J, Rivas Ma, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506–11. http://www.ncbi.nlm.nih.gov/pubmed/24037378.

    CAS  Article  Google Scholar 

  21. 21.

    Hartford CM, Duan S, Delaney SM, Mi S, Kistner EO, Lamba JK, et al. Population-specific genetic variants important in susceptibility to cytarabine arabinoside cytotoxicity. Blood. 2009;113:2145–53. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2652364&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  22. 22.

    Wen Y, Gorsic LK, Wheeler HE, Ziliak DM, Huang RS, Dolan ME. Chemotherapeutic-induced apoptosis: a phenotype for pharmacogenomics studies. Pharm Genom. 2011;21:476–88. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3134538&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  23. 23.

    McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2928508&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  24. 24.

    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. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1950838&tool=pmcentrez&rendertype=abstract.

  25. 25.

    Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9. Epub 2006 Jul 23.

    CAS  Article  Google Scholar 

  26. 26.

    Bush WS, Dudek SM, Ritchie MD. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac Symp Biocomput. 2009. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2859610&tool=pmcentrez&rendertype=abstract.

  27. 27.

    Aken BL, Ayling S, Barrell D, Clarke L, Curwen V, Fairley S, et al. The Ensembl gene annotation system. Database. 2016. http://www.ncbi.nlm.nih.gov/pubmed/27337980.

  28. 28.

    Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–30. https://academic.oup.com/nar/article-lookup/doi.org/10.1093/nar/gkt1223.

    CAS  Article  Google Scholar 

  29. 29.

    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25:25–9. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3037419&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  30. 30.

    Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=102409&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  31. 31.

    Vastrik I, D’Eustachio P, Schmidt E, Joshi-Tope G, Gopinath G, Croft D, et al. Reactome: a knowledge base of biologic pathways and processes. Genome Biol. 2007;8:R39. http://www.ncbi.nlm.nih.gov/pubmed/17367534.

    Article  Google Scholar 

  32. 32.

    Kim D, Li R, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD. Knowledge-driven genomic interactions: an application in ovarian cancer. BioData Min. 2014;7:20. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4161273&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  33. 33.

    Holzinger ER, Dudek SM, Frase AT, Krauss RM, Medina MW, Ritchie MD. ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels. Pac Symp Biocomput. 2013. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3587764&tool=pmcentrez&rendertype=abstract.

  34. 34.

    Holzinger ER, Dudek SM, Frase AT, Pendergrass Sa, Ritchie MD. ATHENA: the analysis tool for heritable and environmental network associations. Bioinformatics. 2014;30:698–705. http://www.ncbi.nlm.nih.gov/pubmed/24149050.

    CAS  Article  Google Scholar 

  35. 35.

    Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. https://doi.org/10.1038/nature11247.

    CAS  Article  Google Scholar 

  36. 36.

    Wheeler HE, Gamazon ER, Wing C, Njiaju UO, Njoku C, Baldwin RM, et al. Integration of cell line and clinical trial genome-wide analyses supports a polygenic architecture of paclitaxel-induced sensory peripheral neuropathy. Clin Cancer Res. 2013;19:491–9. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3549006&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  37. 37.

    Ricci MS. Chemotherapeutic approaches for targeting cell death pathways. Oncology. 2006;11:342–57. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3132471&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  38. 38.

    Brown JM, Attardi LD. The role of apoptosis in cancer development and treatment response. Nat Rev Cancer. 2005;5:231–7. http://www.ncbi.nlm.nih.gov/pubmed/15738985.

    CAS  Article  Google Scholar 

  39. 39.

    Moen EL, Zhang X, Mu W, Delaney SM, Wing C, McQuade J, et al. Genome-wide variation of cytosine modifications between European and african populations and the implications for complex traits. Genetics. 2013;194:987–96. http://www.ncbi.nlm.nih.gov/pubmed/23792949.

    CAS  Article  Google Scholar 

  40. 40.

    Quintela-Fandino M, Arpaia E, Brenner D, Goh T, Yeung FA, Blaser H, et al. HUNK suppresses metastasis of basal type breast cancers by disrupting the interaction between PP2A and cofilin-1. Proc Natl Acad Sci USA. 2010. 107(6):2622–7. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2823890&tool=pmcentrez&rendertype=abstract.

  41. 41.

    Sinilnikova OM, McKay JD, Tavtigian SV, Canzian F, DeSilva D, Biessy C, et al. Haplotype-based analysis of common variation in the acetyl-coA carboxylase alpha gene and breast cancer risk: a case-control study nested within the European prospective investigation into cancer and nutrition. Cancer Epidemiol Biomark Prev. 2007;16:409–15. http://www.ncbi.nlm.nih.gov/pubmed/17372234.

    CAS  Article  Google Scholar 

  42. 42.

    Amend K, Hicks D, Ambrosone CB. Breast cancer in African-American women: differences in tumor biology from European-American women. Cancer Res. 2006;66:8327–30. http://www.ncbi.nlm.nih.gov/pubmed/16951137.

    CAS  Article  Google Scholar 

  43. 43.

    Liu H, Yang Y, Xiao J, Yang S, Liu Y, Kang W, et al. Semaphorin 4D expression is associated with a poor clinical outcome in cervical cancer patients. Microvasc Res. 2014;93:1–8. http://www.ncbi.nlm.nih.gov/pubmed/24603190.

    CAS  Article  Google Scholar 

  44. 44.

    Shen Y-M, He X, Deng H-X, Xie Y-P, Wang C-T, Wei Y-Q, et al. Overexpression of the hBiot2 gene is associated with development of human cervical cancer. Oncol Rep. 2011;25:75–80. http://www.ncbi.nlm.nih.gov/pubmed/21109960.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Rositch AF, Nowak RG, Gravitt PE. Increased age and race-specific incidence of cervical cancer after correction for hysterectomy prevalence in the United States from 2000 to 2009. Cancer. 2014;120:2032–8. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4073302&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  46. 46.

    Houston KA, Henley SJ, Li J, White MC, Richards TB. Patterns in lung cancer incidence rates and trends by histologic type in the United States, 2004–2009. Lung Cancer. 2014;86:22–8. http://www.ncbi.nlm.nih.gov/pubmed/25172266.

    Article  Google Scholar 

  47. 47.

    Israël A. The IKK complex, a central regulator of NF-kappaB activation. Cold Spring Harb Perspect Biol. 2010;2:a000158. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2829958&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  48. 48.

    Karin M. NF- B as a Critical Link Between Inflammation and Cancer. Cold Spring Harb Perspect Biol. 2009;1:a000141. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2773649&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  49. 49.

    Oh CS, Toke DA, Mandala S, Martin CE. ELO2 and ELO3, homologues of the Saccharomyces cerevisiae ELO1 gene, function in fatty acid elongation and are required for sphingolipid formation. J Biol Chem. 1997;272:17376–84. http://www.ncbi.nlm.nih.gov/pubmed/9211877.

    CAS  Article  Google Scholar 

  50. 50.

    Pizer ES, Wood FD, Pasternack GR, Kuhajda FP. Fatty acid synthase (FAS): a target for cytotoxic antimetabolites in HL60 promyelocytic leukemia cells. Cancer Res. 1996;56:745–51. http://www.ncbi.nlm.nih.gov/pubmed/8631008.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Lupu R, Menendez JA. Pharmacological inhibitors of fatty acid synthase (FASN)—catalyzed endogenous fatty acid biogenesis: a new family of anti-cancer agents? Curr Pharm Biotechnol. 2006;7:483–93. http://www.ncbi.nlm.nih.gov/pubmed/17168665.

    CAS  Article  Google Scholar 

  52. 52.

    Kuhajda FP. Fatty-acid synthase and human cancer: new perspectives on its role in tumor biology. Nutrition. 2000;16:202–8. http://www.ncbi.nlm.nih.gov/pubmed/10705076.

    CAS  Article  Google Scholar 

  53. 53.

    Kuhajda FP, Pizer ES, Li JN, Mani NS, Frehywot GL, Townsend CA. Synthesis and antitumor activity of an inhibitor of fatty acid synthase. Proc Natl Acad Sci USA. 2000;97:3450–4. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=16260&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

  54. 54.

    Angeles TS, Hudkins RL. Recent advances in targeting the fatty acid biosynthetic pathway using fatty acid synthase inhibitors. Expert Opin Drug Discov. 2016;11:1187–99. https://www.tandfonline.com/doi/full/10.1080/17460441.2016.1245286.

    CAS  Article  Google Scholar 

  55. 55.

    Zhang J-S, Lei J-P, Wei G-Q, Chen H, Ma C-Y, Jiang H-Z. Natural fatty acid synthase inhibitors as potent therapeutic agents for cancers: a review. Pharm Biol. 2016;209:1–7.

    Google Scholar 

  56. 56.

    Reymond N, d’Água BB, Ridley AJ. Crossing the endothelial barrier during metastasis. Nat Rev Cancer. 2013;13:858–70. https://doi.org/10.1038/nrc3628.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 2010;6:e1000888. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2848547&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  58. 58.

    Wheeler HE, Aquino-Michaels K, Gamazon ER, Trubetskoy VV, Dolan ME, Huang RS, et al. Poly-omic prediction of complex traits: OmicKriging. Genet Epidemiol. 2014;38:402–15. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4072756&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  59. 59.

    Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype–phenotype interactions. Nat Rev Genet. 2015;16:85–97. https://doi.org/10.1038/nrg3868.

    CAS  Article  PubMed  Google Scholar 

  60. 60.

    Stark AL, Zhang W, Zhou T, O’Donnell PH, Beiswanger CM, Huang RS, et al. Population differences in the rate of proliferation of international HapMap cell lines. Am J Hum Genet. 2010;87:829–33. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2997375&tool=pmcentrez&rendertype=abstract.

    CAS  Article  Google Scholar 

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Acknowledgements

This work is supported, in part, by NSF graduate fellowship DGE1255832 (RL), NIH HG006389 (MDR), NIH P50GM115318 (MDR), and NIH/NIGMS Pharmacogenomics of Anticancer Agents Research Grant U01 GM61393 (MED).

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Correspondence to Marylyn D. Ritchie.

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Li, R., Kim, D., Wheeler, H.E. et al. Integration of genetic and functional genomics data to uncover chemotherapeutic induced cytotoxicity. Pharmacogenomics J 19, 178–190 (2019). https://doi.org/10.1038/s41397-018-0024-6

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