Germline Genetics in Immuno-oncology: From Genome-Wide to Targeted Biomarker Strategies

  • Tomas KirchhoffEmail author
  • Robert Ferguson
Part of the Methods in Molecular Biology book series (MIMB, volume 2055)


In immuno-oncology (IO), the baseline host factors attract significant clinical interest as promising predictive biomarker candidates primarily due to the feasibility of noninvasive testing and the personalized potential of IO outcome prediction catered to individual patients. Growing evidence from experimental or population-based studies suggests that the host genetic factors contribute to the immunological status of a patient as it plays out at the multiple rate-limiting steps of the cancer immunity cycle. Recent observations suggest that germline genetics may be associated with tumor microenvironment phenotypes, autoimmune toxicities, and/or efficacy of immunotherapy regimens and overall cancer survival. Despite these highly intriguing indications, the potential of germline genetic factors as personalized biomarkers of immune-checkpoint inhibition (ICI) remains vastly unexplored. In this chapter, we review the rationale for exploring the germline genetic factors as novel biomarkers predictive of IO outcomes, including ICI efficacy, toxicity, or survival, and discuss the approaches for the identification of such germline genetic surrogates. Specifically, we focus on strategies for mapping the germline genetic biomarkers of ICI using genome-wide scans (genome-wide association analyses, next-generation sequencing technologies), followed by targeted assays, to be applied in clinical use. As we discuss the limitations, we highlight a need for large collaborative consortia in these efforts and sketch possible avenues for incorporating germline genetic factors into emerging multifactorial approaches for more personalized prediction of ICI outcomes.

Key words

Immunotherapy Germline variants Immune responsiveness Hereditability GWAS Next-generation sequencing Targeted genotyping 


  1. 1.
    Schadendorf D et al (2015) Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma. J Clin Oncol 33:1889–1894CrossRefGoogle Scholar
  2. 2.
    Ribas A et al (2013) Phase III randomized clinical trial comparing tremelimumab with standard-of-care chemotherapy in patients with advanced melanoma. J Clin Oncol 31:616–622CrossRefGoogle Scholar
  3. 3.
    Hodi FS et al (2010) Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med 2010:711–723CrossRefGoogle Scholar
  4. 4.
    Ribas A et al (2015) Pembrolizumab versus investigator-choice chemotherapy for ipilimumab-refractory melanoma (KEYNOTE-002): a randomised, controlled, phase 2 trial. Lancet Oncol 16:908–918. Scholar
  5. 5.
    Weber JS et al (2015) Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol 16:375–384. Scholar
  6. 6.
    Wolchok JD et al (2013) Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med 369:122–133CrossRefGoogle Scholar
  7. 7.
    Larkin J et al (2015) Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N Engl J Med 373:23–34CrossRefGoogle Scholar
  8. 8.
    Bertrand A, Kostine M, Truchetet M-E, Schaeverbeke T, Barnetche T (2015) Immune related adverse events associated with anti-CTLA-4 antibodies: systematic review and meta-analysis. BMC Med 13:211CrossRefGoogle Scholar
  9. 9.
    Snyder A et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371:2189–2199CrossRefGoogle Scholar
  10. 10.
    Carbognin L et al (2015) Differential activity of nivolumab, pembrolizumab and MPDL3280A according to the tumor expression of programmed death-ligand-1 (PD-L1): sensitivity analysis of trials in melanoma, lung and genitourinary cancers. PLoS One 10:e0130142CrossRefGoogle Scholar
  11. 11.
    Orru V et al (2013) Genetic variants regulating immune cell levels in health and disease. Cell 155:242–256. Scholar
  12. 12.
    Patin E et al (2018) Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors. Nat Immunol 19:302. Scholar
  13. 13.
    Roederer M et al (2015) The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161:387–403. Scholar
  14. 14.
    Duffy D et al (2014) Functional analysis via standardized whole-blood stimulation systems defines the boundaries of a healthy immune response to complex stimuli. Immunity 40:436–450. Scholar
  15. 15.
    Li Y et al (2016) A functional genomics approach to understand variation in cytokine production in humans. Cell 167:1099. Scholar
  16. 16.
    Urrutia A et al (2016) Standardized whole-blood transcriptional profiling enables the Deconvolution of complex induced immune responses. Cell Rep 16:2777–2791. Scholar
  17. 17.
    Ben-Ali M et al (2011) Functional characterization of naturally occurring genetic variants in the human TLR1-2-6 gene family. Hum Mutat 32:643–652. Scholar
  18. 18.
    Pickrell JK et al (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464:768–772. Scholar
  19. 19.
    Cheung VG et al (2003) Natural variation in human gene expression assessed in lymphoblastoid cells. Nat Genet 33:422–425. Scholar
  20. 20.
    Stranger BE et al (2005) Genome-wide associations of gene expression variation in humans. PLoS Genet 1:695–704. Scholar
  21. 21.
    Parkes M, Cortes A, van Heel DA, Brown MA (2013) Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat Rev Genet 14:661–673. Scholar
  22. 22.
    Lim YW et al (2018) Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration. Proc Natl Acad Sci U S A 115:E11701-E11710. Scholar
  23. 23.
    Breunis WB et al (2008) Influence of cytotoxic T lymphocyte-associated antigen 4 (CTLA4) common polymorphisms on outcome in treatment of melanoma patients with CTLA-4 blockade. J Immunother 1997(31):586CrossRefGoogle Scholar
  24. 24.
    Queirolo P et al (2013) Association of CTLA-4 polymorphisms with improved overall survival in melanoma patients treated with CTLA-4 blockade: a pilot study. Cancer Invest 31:336–345CrossRefGoogle Scholar
  25. 25.
    Karasaki T et al (2017) An Immunogram for the cancer-immunity cycle: towards personalized immunotherapy of lung cancer. J Thorac Oncol 12:791–803. Scholar
  26. 26.
    Mehrotra M et al (2018) Detection of somatic mutations in cell-free DNA in plasma and correlation with overall survival in patients with solid tumors. Oncotarget 9:10259–10271. Scholar
  27. 27.
    van Dijk N et al (2019) The cancer Immunogram as a framework for personalized immunotherapy in Urothelial cancer. Eur Urol 75(3):435–444. Scholar
  28. 28.
    Carr EJ et al (2016) The cellular composition of the human immune system is shaped by age and cohabitation. Nat Immunol 17:461. Scholar
  29. 29.
    Marson A, Housley WJ, Hafler DA (2015) Genetic basis of autoimmunity. J Clin Invest 125:2234–2241. Scholar
  30. 30.
    Brodin P et al (2015) Variation in the human immune system is largely driven by non-heritable influences. Cell 160:37–47. Scholar
  31. 31.
    Ahmadi KR et al (2001) Genetic determinism in the relationship between human CD4(+) and CD8(+) T lymphocyte populations? Genes Immun 2:381–387. Scholar
  32. 32.
    Mangino M, Roederer M, Beddall MH, Nestle FO, Spector TD (2017) Innate and adaptive immune traits are differentially affected by genetic and environmental factors. Nat Commun 8:13850. Scholar
  33. 33.
    Borsellino G et al (2007) Expression of ectonucleotidase CD39 by Foxp3(+) Treg cells: hydrolysis of extracellular ATP and immune suppression. Blood 110:1225–1232. Scholar
  34. 34.
    Antonioli L, Blandizzi C, Pacher P, Hasko G (2013) Immunity, inflammation and cancer: a leading role for adenosine. Nat Rev Cancer 13:842–857. Scholar
  35. 35.
    Tumeh PC et al (2014) PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515:568. Scholar
  36. 36.
    Aran D, Sirota M, Butte AJ (2015) Systematic pan-cancer analysis of tumour purity. Nat Commun 6:8971. Scholar
  37. 37.
    Nicolae DL et al (2010) Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet 6:e1000888. Scholar
  38. 38.
    Gamazon ER, Huang RS, Cox NJ, Dolan ME (2010) Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci. Proc Natl Acad Sci U S A 107:9287–9292. Scholar
  39. 39.
    Aran D, Hu Z, Butte AJ (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18:220. Scholar
  40. 40.
    Gajewski TF et al (2013) Cancer immunotherapy strategies based on overcoming barriers within the tumor microenvironment. Curr Opin Immunol 25:268–276. Scholar
  41. 41.
    Hegde PS, Karanikas V, Evers S (2016) The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin Cancer Res 22:1865–1874. Scholar
  42. 42.
    Thorsson V et al (2018) The immune landscape of. Cancer Immun 48:812–830. e814. Scholar
  43. 43.
    Vacchelli E et al (2015) Chemotherapy-induced antitumor immunity requires formyl peptide receptor 1. Science 350:972–978. Scholar
  44. 44.
    Zitvogel L, Kepp O, Kroemer G (2011) Immune parameters affecting the efficacy of chemotherapeutic regimens. Nat Rev Clin Oncol 8:151–160. Scholar
  45. 45.
    Breunis WB et al (2008) Influence of cytotoxic T lymphocyte-associated antigen 4 (CTLA4) common polymorphisms on outcome in treatment of melanoma patients with CTLA-4 blockade. J Immunother 31:586–590. Scholar
  46. 46.
    Hamid O et al (2011) A prospective phase II trial exploring the association between tumor microenvironment biomarkers and clinical activity of ipilimumab in advanced melanoma. J Transl Med 9:204. Scholar
  47. 47.
    Chowell D et al (2018) Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359:582–587. Scholar
  48. 48.
    Aldous AR, Dong JZ (2018) Personalized neoantigen vaccines: a new approach to cancer immunotherapy. Bioorg Med Chem 26:2842–2849. Scholar
  49. 49.
    Cotsapas C et al (2011) Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet 7:e1002254. Scholar
  50. 50.
    Gutierrez-Arcelus M, Rich SS, Raychaudhuri S (2016) Autoimmune diseases - connecting risk alleles with molecular traits of the immune system. Nat Rev Genet 17:160–174. Scholar
  51. 51.
    Kawasaki A et al (2008) Role of STAT4 polymorphisms in systemic lupus erythematosus in a Japanese population: a case-control association study of the STAT1-STAT4 region. Arthritis Res Ther 10:R113. Scholar
  52. 52.
    Kobayashi S et al (2008) Association of STAT4 with susceptibility to rheumatoid arthritis and systemic lupus erythematosus in the Japanese population. Arthritis Rheum 58:1940–1946. Scholar
  53. 53.
    Gupta V et al (2018) Association of ITGAM, TNFSF4, TNFAIP3 and STAT4 gene polymorphisms with risk of systemic lupus erythematosus in a North Indian population. Lupus 27:1973–1979. Scholar
  54. 54.
    Gao X, Wang J, Yu Y (2018) The association between STAT4 rs7574865 polymorphism and the susceptibility of autoimmune thyroid disease: a meta-analysis. Front Genet 9:708. Scholar
  55. 55.
    Ciofani M et al (2012) A validated regulatory network for Th17 cell specification. Cell 151:289–303. Scholar
  56. 56.
    Gustafsson M et al (2015) A validated gene regulatory network and GWAS identifies early regulators of T cell-associated diseases. Sci Transl Med 7:313ra178. Scholar
  57. 57.
    Hu G, Chen J (2013) A genome-wide regulatory network identifies key transcription factors for memory CD8(+) T-cell development. Nat Commun 4:2830. Scholar
  58. 58.
    Qu K et al (2015) Individuality and variation of personal regulomes in primary human T cells. Cell Syst 1:51–61. Scholar
  59. 59.
    Chat V et al (2019) Autoimmune genetic risk variants as germline biomarkers of response to melanoma immune-checkpoint inhibition. Cancer Immunol Immunother 68(6):897–905. Scholar
  60. 60.
    Vogelsang M et al (2016) The expression quantitative trait loci in immune pathways and their effect on cutaneous melanoma prognosis. Clin Cancer Res 22:3268–3280. Scholar
  61. 61.
    Lees CW, Barrett JC, Parkes M, Satsangi J (2011) New IBD genetics: common pathways with other diseases. Gut 60:1739–1753. Scholar
  62. 62.
    Wu MC et al (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89:82–93. Scholar
  63. 63.
    Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X (2013) Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet 92:841–853. Scholar
  64. 64.
    Bernardini G, Antonangeli F, Bonanni V, Santoni A (2016) Dysregulation of chemokine/chemokine receptor axes and NK cell tissue localization during diseases. Front Immunol 7:402. Scholar
  65. 65.
    Le DT et al (2017) Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357:409–413. Scholar
  66. 66.
    Le DT et al (2015) PD-1 blockade in Tumors with mismatch-repair deficiency. N Engl J Med 372:2509–2520. Scholar
  67. 67.
    Rendleman J et al (2015) Genetic associations of the interleukin locus at 1q32.1 with clinical outcomes of cutaneous melanoma. J Med Genet 52:231–239. Scholar
  68. 68.
    Sutton BC et al (2017) Assessment of common somatic mutations of EGFR, KRAS, BRAF, NRAS in pulmonary non-small cell carcinoma using iPLEX (R) HS, a new highly sensitive assay for the MassARRAY (R) system. PLoS One 12:e0183715. Scholar
  69. 69.
    Rogers TM et al (2017) Multiplexed transcriptome analysis to detect ALK, ROS1 and RET rearrangements in lung cancer. Sci Rep 7:42259. Scholar
  70. 70.
    Mock A et al (2016) LOC283731 promoter hypermethylation prognosticates survival after radiochemotherapy in IDH1 wild-type glioblastoma patients. Int J Cancer 139:424–432. Scholar
  71. 71.
    Saffroy R et al (2017) MET exon 14 mutations as targets in routine molecular analysis of primary sarcomatoid carcinoma of the lung. Oncotarget 8:42428–42437. Scholar
  72. 72.
    Pesenti C et al (2018) MassARRAY-based simultaneous detection of hotspot somatic mutations and recurrent fusion genes in papillary thyroid carcinoma: the PTC-MA assay. Endocrine 61:36–41. Scholar
  73. 73.
    Millstein J, Zhang B, Zhu J, Schadt EE (2009) Disentangling molecular relationships with a causal inference test. BMC Genet 10:23. Scholar
  74. 74.
    Hemani G, Tilling K, Smith GD (2017) Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet 13:e1007149. Scholar
  75. 75.
    Sade-Feldman M et al (2018) Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175:998–1013 e1020. Scholar
  76. 76.
    Miraldi ER et al (2019) Leveraging chromatin accessibility for transcriptional regulatory network inference in T helper 17 cells. Genome Res 29(3):449–463. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Perlmutter Cancer CenterNew York University School of MedicineNew YorkUSA

Personalised recommendations