Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Germline variants associated with toxicity to immune checkpoint blockade

Nature Medicine volume 28, pages 2584–2591 (2022)Cite this article

Subjects

Abstract

Immune checkpoint inhibitors (ICIs) have yielded remarkable responses but often lead to immune-related adverse events (irAEs). Although germline causes for irAEs have been hypothesized, no individual variant associated with developing irAEs has been identified. We carried out a genome-wide association study of 1,751 patients on ICIs across 12 cancer types. We investigated two irAE phenotypes: (1) high-grade (3–5) and (2) all-grade events. We identified 3 genome-wide significant associations (P < 5 × 10−8) in the discovery cohort associated with all-grade irAEs: rs16906115 near IL7 (combined P = 3.6 × 10−11; hazard ratio (HR) = 2.1); rs75824728 near IL22RA1 (combined P = 3.5 × 10−8; HR = 1.8); and rs113861051 on 4p15 (combined P = 1.2 × 10−8, HR = 2.0); rs16906115 was replicated in 3 independent studies. The association near IL7 colocalized with the gain of a new cryptic exon for IL7, a critical regulator of lymphocyte homeostasis. Patients carrying the IL7 germline variant exhibited significantly increased lymphocyte stability after ICI initiation, which was itself predictive of downstream irAEs and improved survival.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Manhattan plot of irAE GWAS associations.
Fig. 2: Discovery associations and replication in MGH and CT cohorts.
Fig. 3: IL7 SNP effect cryptic exon activity in GTEx data.
Fig. 4: IL7 SNP effect on lymphocyte homeostasis in patients on ICIs.

Similar content being viewed by others

Data availability

Full summary association statistics for the discovery cohort are available at Zenodo https://zenodo.org/record/6800429. The deidentified clinical outcomes and three main associations are available in Supplementary Table 18 for all-grade and Supplementary Table 19 for high-grade irAEs. The UK Biobank association statistics for autoimmune disease were previously computed by BOLT-LMM v.2.3 and used to estimate the autoimmune disease PRS (https://data.broadinstitute.org/alkesgroup/UKBB/UKBB_409K/). The RNA-seq data from the GTEx and TCGA was accessed through the ReCount2 interface and API (https://jhubiostatistics.shinyapps.io/recount/). Cell-sorted data across six immune cell subsets from individuals with autoimmune diseases and healthy controls were accessed from Chowell et al.11 and the GEO (SRP045500).

Code availability

We used the R programming language v.3.5.1 and the survival v.2.44-1.1, mstate v.0.2.11 and coxmeg for GWAS v.1.0.11 packages. SuSIE v.0.9.57 was used for fine-mapping. Analysis scripts can be found at https://github.com/stefangroha/GWAS_IL7.

References

  1. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).

    Article  CAS  Google Scholar 

  2. June, C. H., Warshauer, J. T. & Bluestone, J. A. Corrigendum: Is autoimmunity the Achilles’ heel of cancer immunotherapy? Nat. Med. 23, 1004 (2017).

    Article  CAS  Google Scholar 

  3. Esfahani, K. et al. Moving towards personalized treatments of immune-related adverse events. Nat. Rev. Clin. Oncol. 17, 504–515 (2020).

    Article  CAS  Google Scholar 

  4. Boutros, C. et al. Safety profiles of anti-CTLA-4 and anti-PD-1 antibodies alone and in combination. Nat. Rev. Clin. Oncol. 13, 473–486 (2016).

    Article  CAS  Google Scholar 

  5. Koon, H. & Atkins, M. Autoimmunity and immunotherapy for cancer. N. Engl. J. Med. 354, 758–760 (2006).

    Article  CAS  Google Scholar 

  6. Postow, M. A., Sidlow, R. & Hellmann, M. D. Immune-related adverse events associated with immune checkpoint blockade. N. Engl. J. Med. 378, 158–168 (2018).

    Article  CAS  Google Scholar 

  7. Wang, D. Y. et al. Fatal toxic effects associated with immune checkpoint inhibitors: a systematic review and meta-analysis. JAMA Oncol. 4, 1721–1728 (2018).

    Article  Google Scholar 

  8. Eggermont, A. M. M. et al. Association between immune-related adverse events and recurrence-free survival among patients with stage III melanoma randomized to receive pembrolizumab or placebo: a secondary analysis of a randomized clinical trial. JAMA Oncol. 6, 519–527 (2020).

    Article  Google Scholar 

  9. Khan, Z. et al. Genetic variation associated with thyroid autoimmunity shapes the systemic immune response to PD-1 checkpoint blockade. Nat. Commun. 12, 3355 (2021).

    Article  CAS  Google Scholar 

  10. Khan, Z. et al. Polygenic risk for skin autoimmunity impacts immune checkpoint blockade in bladder cancer. Proc. Natl Acad. Sci. USA 117, 12288–12294 (2020).

    Article  CAS  Google Scholar 

  11. Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).

    Article  CAS  Google Scholar 

  12. Chowell, D. et al. Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nat. Med. 25, 1715–1720 (2019).

    Article  CAS  Google Scholar 

  13. Cubas, R. et al. Autoimmunity linked protein phosphatase PTPN22 as a target for cancer immunotherapy. J. Immunother. Cancer 8, e001439 (2020).

    Article  Google Scholar 

  14. Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).

    Article  CAS  Google Scholar 

  15. Martins, F. et al. Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nat. Rev. Clin. Oncol. 16, 563–580 (2019).

    Article  CAS  Google Scholar 

  16. Barrett, J. H. Genome-wide association studies of therapeutic response: addressing the complexities. Pharmacogenomics 20, 213–216 (2019).

    Article  CAS  Google Scholar 

  17. Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).

    Article  CAS  Google Scholar 

  18. Taylor, C. et al. Genetic variation at IL7 provides mechanistic insights into toxicity to immune checkpoint blockade. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-1531341/v1 (2022).

  19. Aguet, F. et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  CAS  Google Scholar 

  20. Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548.e24 (2019).

    Article  CAS  Google Scholar 

  21. Zeng, T. & Li, Y. I. Predicting RNA splicing from DNA sequence using Pangolin. Genome Biol. 23, 103 (2022).

    Article  CAS  Google Scholar 

  22. Li, T. et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 48, W509–W514 (2020).

    Article  CAS  Google Scholar 

  23. Rosenberg, S. A. et al. IL-7 administration to humans leads to expansion of CD8+ and CD4+ cells but a relative decrease of CD4+ T-regulatory cells. J. Immunother. 29, 313–319 (2006).

    Article  CAS  Google Scholar 

  24. Barata, J. T., Durum, S. K. & Seddon, B. Flip the coin: IL-7 and IL-7R in health and disease. Nat. Immunol. 20, 1584–1593 (2019).

    Article  CAS  Google Scholar 

  25. Penaranda, C. et al. IL-7 receptor blockade reverses autoimmune diabetes by promoting inhibition of effector/memory T cells. Proc. Natl Acad. Sci. USA 109, 12668–12673 (2012).

    Article  CAS  Google Scholar 

  26. Totsuka, T. et al. IL-7 is essential for the development and the persistence of chronic colitis. J. Immunol. 178, 4737–4748 (2007).

    Article  CAS  Google Scholar 

  27. Dooms, H. Interleukin-7: fuel for the autoimmune attack. J. Autoimmun. 45, 40–48 (2013).

    Article  CAS  Google Scholar 

  28. Belarif, L. et al. IL-7 receptor blockade blunts antigen-specific memory T cell responses and chronic inflammation in primates. Nat. Commun. 9, 4483 (2018).

    Article  Google Scholar 

  29. Johnson, D. B., Nebhan, C. A., Moslehi, J. J. & Balko, J. M. Immune-checkpoint inhibitors: long-term implications of toxicity. Nat. Rev. Clin. Oncol. 19, 254–267 (2022).

    Article  Google Scholar 

  30. Booth, C. M., Karim, S. & Mackillop, W. J. Real-world data: towards achieving the achievable in cancer care. Nat. Rev. Clin. Oncol. 16, 312–325 (2019).

    Article  Google Scholar 

  31. Price, A. L., Zaitlen, N. A., Reich, D. & Patterson, N. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11, 459–463 (2010).

    Article  CAS  Google Scholar 

  32. André, F. et al. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).

    Article  Google Scholar 

  33. van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019).

    Article  CAS  Google Scholar 

  34. Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).

    Article  CAS  Google Scholar 

  35. Emdin, C. A., Khera, A. V. & Kathiresan, S. Mendelian randomization. JAMA 318, 1925–1926 (2017).

    Article  Google Scholar 

  36. Garcia, E. P. et al. Validation of OncoPanel: a targeted next-generation sequencing assay for the detection of somatic variants in cancer. Arch. Pathol. Lab. Med. 141, 751–758 (2017).

    Article  CAS  Google Scholar 

  37. Gusev, A., Groha, S., Taraszka, K., Semenov, Y. R. & Zaitlen, N. Constructing germline research cohorts from the discarded reads of clinical tumor sequences. Genome Med. 13, 179 (2021).

    Article  CAS  Google Scholar 

  38. Liu, J. et al. An integrated TCGA Pan-Cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400–416.e11 (2018).

    Article  CAS  Google Scholar 

  39. Fast algorithms for conducting large-scale GWAS of age-at-onset traits using Cox mixed-effects models. Genetics 215, 1191 (2020).

  40. Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Series B Stat. Methodol. 82, 1273–1300 (2020).

    Article  Google Scholar 

  41. Aalen, O. O. & Johansen, S. An empirical transition matrix for non-homogeneous Markov chains based on censored observations. Scand. Stat. Theory Appl. 5, 141–150 (1978).

    Google Scholar 

  42. Garrido-Martín, D., Palumbo, E., Guigó, R. & Breschi, A. ggsashimi: sashimi plot revised for browser- and annotation-independent splicing visualization. PLoS Comput. Biol. 14, e1006360 (2018).

    Article  Google Scholar 

  43. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  Google Scholar 

  44. Linsley, P. S., Speake, C., Whalen, E. & Chaussabel, D. Copy number loss of the interferon gene cluster in melanomas is linked to reduced T cell infiltrate and poor patient prognosis. PLoS ONE 9, e109760 (2014).

    Article  Google Scholar 

  45. Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

    Article  CAS  Google Scholar 

  46. Collado-Torres, L. et al. Reproducible RNA-seq analysis using recount2. Nat. Biotechnol. 35, 319–321 (2017).

    Article  CAS  Google Scholar 

  47. Orechia, J. et al. OncDRS: an integrative clinical and genomic data platform for enabling translational research and precision medicine. Appl. Transl. Genom. 6, 18–25 (2015).

    Google Scholar 

  48. Nalichowski, R., Keogh, D., Chueh, H. C. & Murphy, S. N. Calculating the benefits of a Research Patient Data Repository. AMIA Annu. Symp. Proc. 2006, 1044 (2006).

    Google Scholar 

  49. Davies, R. W., Flint, J., Myers, S. & Mott, R. Rapid genotype imputation from sequence without reference panels. Nat. Genet. 48, 965–969 (2016).

    Article  CAS  Google Scholar 

  50. Chen, C.-Y. et al. Improved ancestry inference using weights from external reference panels. Bioinformatics 29, 1399–1406 (2013).

    Article  CAS  Google Scholar 

  51. Anderson, J. R., Cain, K. C. & Gelber, R. D. Analysis of survival by tumor response and other comparisons of time-to-event by outcome variables. J. Clin. Oncol. 26, 3913–3915 (2008).

    Article  Google Scholar 

  52. Austin, P. C. & Fine, J. P. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat. Med. 36, 4391–4400 (2017).

    Article  Google Scholar 

Download references

Acknowledgements

We thank all the patients who consented to participate in this study and the institutional data collection efforts that made this study possible. We thank M. Hassett, N. Lindeman, D. Liu, P. Lukasse, L. MacConnaill, P. Polak, S. Rodig, N. Zaitlen and E. Ziv for helpful discussions; the DFCI Oncology Data Retrieval System for the aggregation, management and delivery of the clinical and operational research data used in this project; and the DFCI/Brigham and Women’s Hospital Data Sharing Group for the aggregation, management and delivery of the clinical and genomics data used in this project. A.G. is supported by National Institutes of Health (NIH) grant nos. R01CA227237, R01CA244569 and R21HG010748, and awards from the Claudia Adams Barr Foundation, Louis B. Mayer Foundation, Doris Duke Charitable Foundation, Emerson Collective and Phi Beta Psi Sorority. S.A.S. acknowledges support from the National Cancer Institute (no. R50RCA211482). S.G. was supported by NIH grant no. R01CA227237 and a DFCI Trustee Fellowship. T.K.C. is supported in part by the Dana-Farber/Harvard Cancer Center Kidney SPORE (no. 2P50CA101942-16) and Program no. 5P30CA006516-56, the Kohlberg Chair at Harvard Medical School and the Trust Family, Michael Brigham, Pan-Mass Challenge and Loker Pinard Funds for Kidney Cancer Research at the DFCI. T.E.K. acknowledges grant support from the NIH (no. T32CA009172).

Author information

Author notes

  1. These authors contributed equally: Matthew L. Freedman, Toni K. Choueiri, Alexander Gusev.

Authors and Affiliations

  1. Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Stefan Groha, Deborah Schrag, Kenneth L. Kehl & Alexander Gusev

  2. Broad Institute of Harvard & MIT, Cambridge, MA, USA

    Stefan Groha, Sachet A. Shukla, Tanya E. Keenan, Eliezer Van Allen, Alexandra-Chloe Villani, Matthew L. Freedman & Alexander Gusev

  3. Harvard Medical School, Boston, MA, USA

    Stefan Groha, Guihong Wan, Alexandra-Chloe Villani, Kerry Reynolds, Yevgeniy Semenov, Toni K. Choueiri & Alexander Gusev

  4. Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Sarah Abou Alaiwi, Amin H. Nassar, Talal El Zarif, Elio Adib, Pier V. Nuzzo, David A. Braun & Toni K. Choueiri

  5. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Wenxin Xu, Vivek Naranbhai, Ziad Bakouny, Talal El Zarif, Renee Maria Saliby, Andrew L. Schmidt, Chris Labaki, Sachet A. Shukla, Tanya E. Keenan, Eliezer Van Allen, Michael Manos, Osama Rahma, Matthew L. Freedman & Toni K. Choueiri

  6. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Sarah Abou Alaiwi, Amin H. Nassar, Ziad Bakouny, Elio Adib, Osama Rahma & Toni K. Choueiri

  7. Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA

    Guihong Wan, Ahmad Rajeh & Yevgeniy Semenov

  8. Department of Internal Medicine and Medical Specialties, School of Medicine, University of Genoa, Genoa, Italy

    Pier V. Nuzzo, Joao Victor Alessi & Mark M. Awad

  9. Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Biagio Ricciuti

  10. Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA

    David A. Braun

  11. Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA

    Sachet A. Shukla

  12. Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA, USA

    Tanya E. Keenan

  13. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA, USA

    Eliezer Van Allen

  14. Massachusetts General Hospital, Boston, MA, USA

    Leyre Zubiri

  15. Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Alexandra-Chloe Villani

  16. Department of Oncology, University of Oxford, Oxford, UK

    Benjamin Fairfax

  17. Genentech, South San Francisco, CA, USA

    Christian Hammer & Zia Khan

  18. Division of Medical Oncology, Bartlett, Massachusetts General Hospital, Boston, MA, USA

    Kerry Reynolds

  19. Division of Genetics, Brigham and Women’s Hospital, Boston, MA, USA

    Alexander Gusev

Authors
  1. Stefan Groha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarah Abou Alaiwi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenxin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vivek Naranbhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amin H. Nassar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ziad Bakouny
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Talal El Zarif
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Renee Maria Saliby
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Guihong Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ahmad Rajeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Elio Adib
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Pier V. Nuzzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Andrew L. Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Chris Labaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Biagio Ricciuti
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Joao Victor Alessi
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. David A. Braun
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Sachet A. Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Tanya E. Keenan
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Eliezer Van Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Mark M. Awad
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Michael Manos
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Osama Rahma
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Leyre Zubiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Alexandra-Chloe Villani
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Benjamin Fairfax
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Christian Hammer
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Zia Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Kerry Reynolds
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Yevgeniy Semenov
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Deborah Schrag
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Kenneth L. Kehl
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Matthew L. Freedman
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Toni K. Choueiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Alexander Gusev
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.G., D.S., K.L.K., M.L.F., T.K.C. and A.G. conceived the study. S.G., M.L.F., T.K.C. and A.G. designed the study. S.G., S.A.A., W.X., M.L.F., T.K.C. and A.G. interpreted the study. S.G. and A.G. analyzed the discovery cohort data. C.H., Z.K., K.R., Y.S. and B.F. replicated the main findings. S.A.A., W.X., V.N., A.H.N., Z.B., T.E.Z., R.M.S., G.W., A.R., E.A., P.V.N., A.L.S., C.L., B.R., J.V.A., D.A.B., S.A.S., T.E.K., E.V.A., M.M.A., M.M., O.R., L.Z., A.-C.V., K.R., Y.S., D.S. and K.L.K. contributed to data collection.

Corresponding author

Correspondence to Alexander Gusev.

Ethics declarations

Competing interests

D.A.B. reports nonfinancial support from Bristol Myers Squibb, honoraria from LM Education/Exchange Services and personal fees from MDedge, Exelixis, Octane Global, Defined Health, Dedham Group, Adept Field Solutions, Slingshot Insights, Blueprint Partnerships, Charles River Associates, Trinity Group and Insight Strategy, outside of the submitted work. K.K. reports receiving honoraria from IBM and Roche. M.M.A. reports grants and personal fees from Genentech, grants and personal fees from Bristol Myers Squibb, personal fees from Merck, grants and personal fees from AstraZeneca, grants from Lilly and personal fees from Maverick, Blueprint Medicine, Syndax, Ariad, Nektar, Gritstone, ArcherDX, Mirati, NextCure, Novartis, EMD Serono and Panvaxal/NovaRx, outside the submitted work. O.R. reports research support from Merck. He is speaker for activities supported by educational grants from Bristol Myers Squibb and Merck; consultant for Merck, Celgene, Five Prime, GSK, Bayer, Roche/Genentech, Puretech, Imvax, Sobi and Boehringer Ingelheim; and has a patent pending for ‘Methods of using pembrolizumab and trebananib’. S.A.S. reports nonfinancial support from Bristol Myers Squibb and equity in Agenus, Agios Pharmaceuticals, Breakbio Corp., Bristol Myers Squibb and Lumos Pharma. T.K.C. reports research/advisory board/consultancy/honoraria (institutional and personal, paid and unpaid) from AstraZeneca, Aveo, Bayer, Bristol Myers Squibb, Eisai, EMD Serono, Exelixis, GSK, IQVA, Ipsen, Kanaph, Lilly, Merck, Nikang, Novartis, Pfizer, Roche, Sanofi/Aventis and Takeda, Tempest; travel, accommodation, expenses and medical writing in relation to consulting, advisory roles or honoraria; stock options in Pionyr, Tempest, Osel and Recede Bio; UpToDate royalties for CME-related events (for example, OncLive, PVI, MJH Life Sciences) honoraria; National Cancer Institute Genitourinary Steering Committee, American Society of Clinical Oncology and European Society of Medical Oncology; patents filed, royalties or other intellectual property (no income as of current date) related to biomarkers of immune checkpoint blockers and circulating tumor DNA. Z.B. reports research support from the imCORE Network on behalf of Genentech and Bristol Myers Squibb and honoraria from UpToDate. A.G., T.K.C. and M.L.F. are inventors on a patent related to germline predictors of irAEs. The other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Zlatko Trajanoski, Leng Han and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Anna Maria Ranzoni in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Probability of irAE by treatment class.

Probability of patients experiencing high-grade irAEs (a) or all-grade irAEs (b) stratified by therapy class. The difference between CTLA4 and combination therapy was not statistically significant in a log-rank test of the equivalent Kaplan-Meier estimator (p = 0.39 all-grade, p = 0.68 high-grade). The shaded areas correspond to 95% confidence intervals.

Extended Data Fig. 2 Discovery associations of locus near IL22RA1 and 4p15.

Discovery associations with rs75824728 (a) and rs113861051 (b) stratified by cancer type. The error bars correspond to the 95% confidence interval around the mean effect size. Significance was obtained from a two-sided Wald test.

Extended Data Fig. 3 Agreement between discovery cohort and MGH cohort at IL7 locus.

(a) Logarithmic hazard rates (effect sizes) and (b) p-values for association in the discovery DFCI cohort and the MGH cohort for the 8q21 locus, restricted to suggestive significant associations in the discovery cohort (p < 1.0 × 10−5). The shaded area of the linear fit corresponds to the 95% confidence interval. Significance was tested using a two-sided t-test on the Pearson correlations. (c) Comparison of the association strengths of variants around the top association locus in DFCI and MGH. The 95% credible set in the DFCI cohort is colored in blue. The upper red line signifies genome wide significance, the lower red line bonferroni corrected significance for SNPs tested in the MGH cohort.

Extended Data Fig. 4 De novo isoform reconstruction using Cufflinks.

De novo isoform reconstruction using Cufflinks. There is a novel transcript spanning chr8:78732772-78746671, which initiates at rs7816685 and is highly specific to carriers.

Extended Data Fig. 5 Response and overall survival in TCGA Melanoma for carriers and non-carriers of rs16906115.

Response and overall survival in TCGA Melanoma for carriers and non-carriers of rs16906115. Significance was obtained using a log-rank test.

Extended Data Fig. 6 Hypothesized mechanistic schematic.

Hypothesized schematic of how lymphocyte stability is a marker of an active host immunity with down-stream effects on both overall survival through better anti-tumor response, as well as higher rate of irAE due to increased auto-immunity.

Supplementary information

Supplementary Information

Supplementary Figs. 1–21 and Tables 1–20.

Reporting Summary

Supplementary Table 1

Supplementary Tables.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Groha, S., Alaiwi, S.A., Xu, W. et al. Germline variants associated with toxicity to immune checkpoint blockade. Nat Med 28, 2584–2591 (2022). https://doi.org/10.1038/s41591-022-02094-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-022-02094-6

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer