Kumar, V. et al. Current diagnosis and management of immune related adverse events (irAEs) induced by immune checkpoint inhibitor therapy. Front. Pharmacol. 8, 49 (2017).
Google Scholar
Larkin, J. et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N. Engl. J. Med. 373, 23–34 (2015).
Google Scholar
Hodi, F. S. et al. Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol. 19, 1480–1492 (2018).
Google Scholar
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).
Google Scholar
Wolchok, J. D. et al. Overall survival with combined nivolumab and ipilimumab in advanced melanoma. N. Engl. J. Med. 377, 1345–1356 (2017).
Google Scholar
Hamid, O. et al. Five-year survival outcomes for patients with advanced melanoma treated with pembrolizumab in KEYNOTE-001. Ann. Oncol. 30, 582–588 (2019).
Google Scholar
Robert, C. et al. Pembrolizumab versus ipilimumab in advanced melanoma (KEYNOTE-006): post-hoc 5-year results from an open-label, multicentre, randomised, controlled, phase 3 study. Lancet Oncol. 20, 1239–1251 (2019).
Google Scholar
Robert, C. et al. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl. J. Med. 372, 2521–2532 (2015).
Google Scholar
Schachter, J. et al. Pembrolizumab versus ipilimumab for advanced melanoma: final overall survival results of a multicentre, randomised, open-label phase 3 study (KEYNOTE-006). Lancet 390, 1853–1862 (2017).
Google Scholar
Johnson, D. B. et al. Fulminant myocarditis with combination immune checkpoint blockade. N. Engl. J. Med. 375, 1749–1755 (2016).
Google Scholar
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).
Google Scholar
Haanen, J. B. A. G. et al. Management of toxicities from immunotherapy: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 28, iv119–iv142 (2017).
Google Scholar
Das, S. & Johnson, D. B. Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors. J. Immunother. Cancer 7, 306 (2019).
Google Scholar
Johnson, D. B. et al. A case report of clonal EBV-like memory CD4+ T cell activation in fatal checkpoint inhibitor-induced encephalitis. Nat. Med. 25, 1243–1250 (2019).
Google Scholar
Tahir, S. A. et al. Autoimmune antibodies correlate with immune checkpoint therapy-induced toxicities. Proc. Natl Acad. Sci. USA 116, 22246–22251 (2019).
Google Scholar
Shahabi, V. et al. Gene expression profiling of whole blood in ipilimumab-treated patients for identification of potential biomarkers of immune-related gastrointestinal adverse events. J. Transl. Med. 11, 75 (2013).
Google Scholar
Tarhini, A. A. et al. Baseline circulating IL-17 predicts toxicity while TGF-β1 and IL-10 are prognostic of relapse in ipilimumab neoadjuvant therapy of melanoma. J. Immunother. Cancer 3, 39 (2015).
Google Scholar
Fujimura, T. et al. Serum levels of soluble CD163 and CXCL5 may be predictive markers for immune-related adverse events in patients with advanced melanoma treated with nivolumab: a pilot study. Oncotarget 9, 15542–15551 (2018).
Google Scholar
Das, R. et al. Early B cell changes predict autoimmunity following combination immune checkpoint blockade. J. Clin. Invest. 128, 715–720 (2018).
Google Scholar
Subudhi, S. K. et al. Clonal expansion of CD8 T cells in the systemic circulation precedes development of ipilimumab-induced toxicities. Proc. Natl Acad. Sci. USA 113, 11919–11924 (2016).
Google Scholar
Chaput, N. et al. Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab. Ann. Oncol. 30, 2012 (2019).
Google Scholar
Dubin, K. et al. Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis. Nat. Commun. 7, 10391 (2016).
Google Scholar
Jing, Y. et al. Multi-omics prediction of immune-related adverse events during checkpoint immunotherapy. Nat. Commun. 11, 4946 (2020).
Google Scholar
Lim, S. Y. et al. Circulating cytokines predict immune-related toxicity in melanoma patients receiving anti-PD-1-based immunotherapy. Clin. Cancer Res. 25, 1557–1563 (2019).
Google Scholar
Pavan, A. et al. Peripheral blood markers identify risk of immune-related toxicity in advanced non-small cell lung cancer treated with immune-checkpoint inhibitors. Oncologist 24, 1128–1136 (2019).
Google Scholar
Andrews, M. C. et al. Gut microbiota signatures are associated with toxicity to combined CTLA-4 and PD-1 blockade. Nat. Med. 27, 1432–1441 (2021).
Google Scholar
Hutchinson, J. A. et al. Virus-specific memory T cell responses unmasked by immune checkpoint blockade cause hepatitis. Nat. Commun. 12, 1439 (2021).
Google Scholar
Yasuda, Y. et al. CD4+ T cells are essential for the development of destructive thyroiditis induced by anti-PD-1 antibody in thyroglobulin-immunized mice. Sci. Transl. Med. 13, eabb7495 (2021).
Google Scholar
Morad, G., Helmink, B. A., Sharma, P. & Wargo, J. A. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell 184, 5309–5337 (2021).
Google Scholar
Marschner, D. et al. MicroRNA-146a regulates immune-related adverse events caused by immune checkpoint inhibitors. JCI Insight 5, e132334 (2020).
Google Scholar
Auslander, N. et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat. Med. 24, 1545–1549 (2018).
Google Scholar
Krieg, C. et al. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 24, 144–153 (2018).
Google Scholar
Liu, D. et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat. Med. 25, 1916–1927 (2019).
Google Scholar
Jacquelot, N. et al. Predictors of responses to immune checkpoint blockade in advanced melanoma. Nat. Commun. 8, 592 (2017).
Google Scholar
Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e16 (2017).
Google Scholar
Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).
Google Scholar
Fairfax, B. P. et al. Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat. Med. 26, 193–199 (2020).
Google Scholar
Bieber, A. K., Yin, L. & Lo Sicco, K. Pruritus and tense bullae after discontinuation of pembrolizumab in a patient with renal cell carcinoma. JAMA 324, 1453–1454 (2020).
Google Scholar
Lopez, A. T. & Geskin, L. A case of nivolumab-induced bullous pemphigoid: review of dermatologic toxicity associated with programmed cell death protein-1/programmed death ligand-1 inhibitors and recommendations for diagnosis and management. Oncologist 23, 1119–1126 (2018).
Google Scholar
Singer, S., Nelson, C. A., Lian, C. G., Dewan, A. K. & LeBoeuf, N. R. Nonbullous pemphigoid secondary to PD-1 inhibition. JAAD Case Rep. 5, 898–903 (2019).
Google Scholar
Croft, M., So, T., Duan, W. & Soroosh, P. The significance of OX40 and OX40L to T-cell biology and immune disease. Immunol. Rev. 229, 173–191 (2009).
Google Scholar
Campbell, J. J. et al. CCR7 expression and memory T cell diversity in humans. J. Immunol. 166, 877–884 (2001).
Google Scholar
Shifrut, E. et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 175, 1958–1971.e15 (2018).
Google Scholar
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Google Scholar
Chiffelle, J. et al. T-cell repertoire analysis and metrics of diversity and clonality. Curr. Opin. Biotechnol. 65, 284–295 (2020).
Google Scholar
Rényi, A. On the foundations of information theory. Rev. Int. Stat. Inst. 33, 1–14 (1965).
Robert, L. et al. CTLA4 blockade broadens the peripheral T-cell receptor repertoire. Clin. Cancer Res. 20, 2424–2432 (2014).
Google Scholar
Sims, J. S. et al. Diversity and divergence of the glioma-infiltrating T-cell receptor repertoire. Proc. Natl Acad. Sci. USA 113, E3529–E3537 (2016).
Google Scholar
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Google Scholar
Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).
Google Scholar
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Google Scholar
Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).
Google Scholar
Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).
Google Scholar
Eggermont, A. M. M., Crittenden, M. & Wargo, J. Combination immunotherapy development in melanoma. Am. Soc. Clin. Oncol. Educ. Book 38, 197–207 (2018).
Google Scholar
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).
Google Scholar
Burczynski, M. E. et al. Molecular classification of Crohn’s disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. J. Mol. Diagn. 8, 51–61 (2006).
Google Scholar
Carpintero, M. F. et al. Diagnosis and risk stratification in patients with anti-RNP autoimmunity. Lupus 24, 1057–1066 (2015).
Google Scholar
Hung, T. et al. The Ro60 autoantigen binds endogenous retroelements and regulates inflammatory gene expression. Science 350, 455–459 (2015).
Google Scholar
Kennedy, W. P. et al. Association of the interferon signature metric with serological disease manifestations but not global activity scores in multiple cohorts of patients with SLE. Lupus Sci. Med. 2, e000080 (2015).
Google Scholar
Palmer, N. P. et al. Concordance between gene expression in peripheral whole blood and colonic tissue in children with inflammatory bowel disease. PLoS ONE 14, e0222952 (2019).
Google Scholar
Peters, L. A. et al. A functional genomics predictive network model identifies regulators of inflammatory bowel disease. Nat. Genet. 49, 1437–1449 (2017).
Google Scholar
Menzies, A. M. et al. Anti-PD-1 therapy in patients with advanced melanoma and preexisting autoimmune disorders or major toxicity with ipilimumab. Ann. Oncol. 28, 368–376 (2017).
Google Scholar
Brown, L. J. et al. Combination anti-PD1 and ipilimumab therapy in patients with advanced melanoma and pre-existing autoimmune disorders. J. Immunother. Cancer 9, e002121 (2021).
Google Scholar
Johnson, D. B. et al. Ipilimumab therapy in patients with advanced melanoma and preexisting autoimmune disorders. JAMA Oncol. 2, 234–240 (2016).
Google Scholar
Tang, S.-Q. et al. The pattern of time to onset and resolution of immune-related adverse events caused by immune checkpoint inhibitors in cancer: a pooled analysis of 23 clinical trials and 8,436 patients. Cancer Res. Treat. 53, 339–354 (2021).
Google Scholar
Nabet, B. Y. et al. Noninvasive early identification of therapeutic benefit from immune checkpoint inhibition. Cell 183, 363–376.e13 (2020).
Google Scholar
Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
Google Scholar
Steele, N. G. et al. Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer. Nat. Cancer 1, 1097–1112 (2020).
Google Scholar
Amir, E. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).
Google Scholar
Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).
Google Scholar
Amir, E. D. et al. Development of a comprehensive antibody staining database using a standardized analytics pipeline. Front. Immunol. 10, 1315 (2019).
Google Scholar
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Google Scholar
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).
Google Scholar
Chen, G. M. et al. Integrative bulk and single-cell profiling of pre-manufacture T-cell populations reveals factors mediating long-term persistence of CAR T-cell therapy. Cancer Discov. 11, 2186–2199 (2021).
Google Scholar
Kaech, S. M., Wherry, E. J. & Ahmed, R. Effector and memory T-cell differentiation: implications for vaccine development. Nat. Rev. Immunol. 2, 251–262 (2002).
Google Scholar
Sprent, J. & Surh, C. D. T cell memory. Annu. Rev. Immunol. 20, 551–579 (2002).
Google Scholar
van den Broek, T., Borghans, J. A. M. & van Wijk, F. The full spectrum of human naive T cells. Nat. Rev. Immunol. 18, 363–373 (2018).
Google Scholar
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).
Jiang, H., Lei, R., Ding, S.-W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).
Google Scholar
Borcherding, N. et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun. Biol. 4, 122 (2021).
Google Scholar
Motulsky, H. J. & Brown, R. E. Detecting outliers when fitting data with nonlinear regression—a new method based on robust nonlinear regression and the false discovery rate. BMC Bioinformatics 7, 123 (2006).
Google Scholar
Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).
Google Scholar
Lipták, T. On the combination of independent tests. Magyar Tud. Akad. Mat. Kutató Int. Közl. 3, 171–197 (1958).
Stouffer, S. A., Suchman, E. A., Devinney, L. C., Star, S. A., & Williams, R. M., Jr. in Studies in Social Psychology in World War II (Princeton Univ. Press, 82–154 1949).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Google Scholar
Cohen, J. Statistical Power Analysis for the Behavioral Sciences (L. Erlbaum Associates, 1988).

