Abstract
Treatment with combined immune checkpoint blockade (CICB) targeting CTLA-4 and PD-1 is associated with clinical benefit across tumor types, but also a high rate of immune-related adverse events. Insights into biomarkers and mechanisms of response and toxicity to CICB are needed. To address this, we profiled the blood, tumor and gut microbiome of 77 patients with advanced melanoma treated with CICB, with a high rate of any ≥grade 3 immune-related adverse events (49%) with parallel studies in pre-clinical models. Tumor-associated immune and genomic biomarkers of response to CICB were similar to those identified for ICB monotherapy, and toxicity from CICB was associated with a more diverse peripheral T-cell repertoire. Profiling of gut microbiota demonstrated a significantly higher abundance of Bacteroides intestinalis in patients with toxicity, with upregulation of mucosal IL-1β in patient samples of colitis and in pre-clinical models. Together, these data offer potential new therapeutic angles for targeting toxicity to CICB.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
WES data from this study are available from the European Genome-Phenome Archive (EGA) under study accession no. EGAS00001003857. Human fecal 16S rRNA gene sequencing and WMS data are available from the EGA under study accession no. EGAS00001004885. Murine fecal 16S rRNA gene sequence reads from this study have been submitted to the NCBI under the Bioproject ID PRJNA484225.
Code availability
No unique software or computational code was created for this study. All tumor growth curves were analyzed using software developed in G. Kroemer’s laboratory and information about statistical analyses is available at https://kroemerlab.shinyapps.io/TumGrowth/. Code detailing implementation of established tools/pipelines as described in detail in the Methods is available upon request from the corresponding author.
References
Larkin, J. et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N. Engl. J. Med. 373, 23–34 (2015).
Hammers, H. J. et al. Safety and efficacy of nivolumab in combination with ipilimumab in metastatic renal cell carcinoma: the CheckMate 016 Study. J. Clin. Oncol. 35, 3851–3858 (2017).
Sznol, M. et al. Pooled analysis safety profile of nivolumab and ipilimumab combination therapy in patients with advanced melanoma. J. Clin. Oncol. 35, 3815–3822 (2017).
Attia, P. et al. Autoimmunity correlates with tumor regression in patients with metastatic melanoma treated with anti-cytotoxic T-lymphocyte antigen-4. J. Clin. Oncol. 23, 6043–6053 (2005).
Das, S. & Johnson, D. B. Immune-related adverse events and anti-tumor efficacy of immune checkpoint inhibitors. J. Immunother. Cancer 7, 306 (2019).
Robert, C. et al. Nivolumab in previously untreated melanoma without BRAF mutation. N. Engl. J. Med. 372, 320–330 (2015).
Robert, C. et al. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl. J. Med. 372, 2521–2532 (2015).
Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013).
Postow, M. A. et al. Nivolumab and ipilimumab versus ipilimumab in untreated melanoma. N. Engl. J. Med. 372, 2006–2017 (2015).
D’Angelo, S. P. et al. Efficacy and safety of nivolumab alone or in combination with ipilimumab in patients with mucosal melanoma: a pooled analysis. J. Clin. Oncol. 35, 226–235 (2017).
Hellmann, M. D. et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N. Engl. J. Med. 378, 2093–2104 (2018).
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
Roh, W. et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci. Transl. Med. 9, eaah3560 (2017).
Ekmekcioglu, S. et al. Inflammatory marker testing identifies CD74 expression in melanoma tumor cells, and its expression associates with favorable survival for stage III melanoma. Clin. Cancer Res. 22, 3016–3024 (2016).
Peng, W. et al. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. 6, 202–216 (2016).
Tanese, K. et al. Cell surface CD74-MIF interactions drive melanoma survival in response to interferon-γ. J. Invest. Dermatol. 135, 2775–2784 (2015).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).
Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).
Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018).
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
Vetizou, M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350, 1079–1084 (2015).
Chaput, N. et al. Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab. Ann. Oncol. 28, 1368–1379 (2017).
Frankel, A. E. et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 19, 848–855 (2017).
Peters, B. A. et al. Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients. Genome Med. 11, 61 (2019).
Seo, S. U. et al. Distinct commensals induce interleukin-1β via NLRP3 inflammasome in inflammatory monocytes to promote intestinal inflammation in response to injury. Immunity 42, 744–755 (2015).
Moro-Garcia, M. A., Alonso-Arias, R. & Lopez-Larrea, C. Molecular mechanisms involved in the aging of the T-cell immune response. Curr. Genomics 13, 589–602 (2012).
Chen, Y., Gorelik, G. J., Strickland, F. M. & Richardson, B. C. Decreased ERK and JNK signaling contribute to gene overexpression in ‘senescent’ CD4+CD28− T cells through epigenetic mechanisms. J. Leukoc. Biol. 87, 137–145 (2010).
Oh, D. Y. et al. Immune toxicities elicted by CTLA-4 blockade in cancer patients are associated with early diversification of the T-cell repertoire. Cancer Res. 77, 1322–1330 (2017).
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).
Routy, B. et al. The gut microbiota influences anticancer immunosurveillance and general health. Nat. Rev. Clin. Oncol. 15, 382–396 (2018).
Perez-Ruiz, E. et al. Prophylactic TNF blockade uncouples efficacy and toxicity in dual CTLA-4 and PD-1 immunotherapy. Nature 569, 428–432 (2019).
Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).
Bai, Y., Ni, M., Cooper, B., Wei, Y. & Fury, W. Inference of high resolution HLA types using genome-wide RNA or DNA sequencing reads. BMC Genomics 15, 325 (2014).
Nielsen, M. et al. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE 2, e796 (2007).
Hoof, I. et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61, 1–13 (2009).
Chen, P. L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).
The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
The Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012).
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10, e65088 (2021).
Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Dridi, B., Henry, M., El Khechine, A., Raoult, D. & Drancourt, M. High prevalence of Methanobrevibacter smithii and Methanosphaera stadtmanae detected in the human gut using an improved DNA detection protocol. PLoS ONE 4, e7063 (2009).
Angelakis, E. et al. Glycans affect DNA extraction and induce substantial differences in gut metagenomic studies. Sci. Rep. 6, 26276 (2016).
Million, M. et al. Increased gut redox and depletion of anaerobic and methanogenic prokaryotes in severe acute malnutrition. Sci. Rep. 6, 26051 (2016).
Morgan, X. C. & Huttenhower, C. Chapter 12: human microbiome analysis. PLoS Comput. Biol. 8, e1002808 (2012).
Furet, J. P. et al. Comparative assessment of human and farm animal faecal microbiota using real-time quantitative PCR. FEMS Microbiol. Ecol. 68, 351–362 (2009).
Suzuki, M. T., Taylor, L. T. & DeLong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5′-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614 (2000).
Odamaki, T. et al. Distribution of different species of the Bacteroides fragilis group in individuals with Japanese cedar pollinosis. Appl. Environ. Microbiol. 74, 6814–6817 (2008).
Tong, J., Liu, C., Summanen, P., Xu, H. & Finegold, S. M. Application of quantitative real-time PCR for rapid identification of Bacteroides fragilis group and related organisms in human wound samples. Anaerobe 17, 64–68 (2011).
Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, 2000).
Enot, D. P., Vacchelli, E., Jacquelot, N., Zitvogel, L. & Kroemer, G. TumGrowth: an open-access web tool for the statistical analysis of tumor growth curves. Oncoimmunology 7, e1462431 (2018).
Acknowledgements
This research was supported by generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Moon Shots Program from the Lyda Hill Foundation and utilized platform assistance from the Cancer Genomics Laboratory and Immunotherapy Platform, from the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation and the AIM at Melanoma Foundation. Additional support was provided to P.A.F. from the Cancer Prevention Research Institute of Texas and the Welch Foundation. M.A.D. is supported by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, the AIM at Melanoma Foundation, the NIH/NCI (1 P50 CA221703-02 and 1U54CA224070-03), the American Cancer Society and the Melanoma Research Alliance, Cancer Fighters of Houston, the Anne and John Mendelsohn Chair for Cancer Research, and philanthropic contributions to the Melanoma Moon Shots Program of MD Anderson. M.C.A. is supported by a National Health and Medical Research Council of Australia CJ Martin Early Career Fellowship (#1148680). W.-S.C. was supported by a National Institutes of Health T32 Training Grant (T32CA163185). A.P.C. is supported by the CPRIT Research Training Program (RP170067), the Fulbright France Commission Franco-Americainé and the John J. Kopchick Foundation. A.R. is supported by the Kimberley Clark Foundation Award for Scientific Achievement provided by MD Anderson’s Odyssey Fellowship Program. P.-O.G. was supported by the Fonds de Recherche Québec–Santé’s (FRQS) Resident Physician Health Research Career Training Program (32667). M.G.W. was supported by National Institutes of Health (T32CA009599) and an MD Anderson Cancer Center support grant (P30 CA016672). M.A.P. received support from the MSK Cancer Center Support Grant/Core Grant (P30 CA008748). We acknowledge the assistance of the animal facility team at Gustave Roussy. L. Zitvogel is funded by grants from EU H2020 ONCOBIOME, Ligue contre le Cancer (équipe labelisée); Agence Nationale de la Recherche (ANR)—Projets blancs; ANR under the frame of E-Rare-2, the ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; Chancellerie des universités de Paris (Legs Poix), Fondation de France; Fondation pour la Recherche Médicale (FRM); a donation by Elior; Fondation Carrefour; Institut National du Cancer (INCa); Inserm (HTE); ANR germanofrench; LabEx Immuno-Oncology; the French Ministry of Health PIA2, RHU Torino Lumière (ANR-16-RHUS-0008); the Swiss Bridge Foundation; the Seerave and Carrefour Foundation; the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE). This work was supported by the French Government under the ‘Investissements d’avenir’ (Investments for the Future) program managed by the Agence Nationale de la Recherche (ANR, fr: National Agency for Research, Méditerranée Infection 10-IAHU-03; L. Zitvogel). M.K.C. is supported by an institutional grant (NIH P30 CA008748). L.D. is supported by ‘Parcours d’excellence en cancérologie—Fondation Philanthropia’. This work was supported by Région Provence Alpes Côte d’Azur and European funding FEDER PRIMI. R.C.P. was supported by a Strategic Innovation Grant from the Division of Medical Oncology, University of Toronto. R.C.P. was supported by a Strategic Innovation Grant from the Division of Medical Oncology, University of Toronto. J.A.W. is supported by the National Institutes of Health (1R01CA219896-01A1), the Melanoma Research Alliance (4022024), American Association for Cancer Research Stand Up To Cancer (SU2C-AACR-IRG-19-17) and the MD Anderson Melanoma Moonshot Program. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Author information
Authors and Affiliations
Contributions
Conceptualization was provided by L. Zitvogel, J.A.W. and P.A.F. Investigations were carried out by M.C.A., C.P.M.D., V.G., V.I., W.-S.C., L.D., M.A.W.K., A.P.C., M.C.W., G.F., A.F., M.P.R., P.O., M.T.A., S.Y., W.R., C.N.S., I.F.C., L.V., A.R., S.J., L.L., C.G., Z.A.C., P.A.P., K.W., A.J.L., M.T.T., C.W.H., M.A., P.-O.G., L.N., L. Zhao and B.R. Provision and acquisition of data and materials were provided by A.P.C., M.C.W., C.N.S., Z.A.C., M.K.C., M.A.P., C.E.A., L.E.H., J.M.S., H.A.T., J.M., P.H., W.-J.H., R.N.A., E.M.B., S.E.W., S.W., A.D., S.P.P., I.C.G., M.K.W., J.Z., N.J.A., J.P., R.R.J., M.A.D., J.E.G., D.R., C.M., A.E., R.C.P., P.A.F., P.S., J.P.A., B.R., L. Zitvogel and J.A.W. Formal analysis was performed by M.C.A., C.P.M.D., V.G., V.I., W.-S.C., M.A.W.K., M.C.W., M.A., N.J.A. and L. Zitvogel. Data curation was carried out by M.C.A., V.G., V.I., W.-S.C., M.A.W.K., M.G.W., M.C.W., R.A., G.M., M.L. and N.J.A. The original draft was written by M.C.A., W.-S.C. and L. Zitvogel. Review and editing were carried out by M.C.A., C.D., V.G., A.P.C., M.G.W., N.J.A., L. Zitvogel and J.A.W. Review and approval of the final manuscript was provided by all authors. Visualization was provided by M.C.A., V.G., C.D., N.J.A., M.G.W. and M.A.W.K. Supervision was provided by P.A.F., L. Zitvogel and J.A.W. Funding acquisition was carried out by P.A.F., L. Zitvogel and J.A.W.
Corresponding authors
Ethics declarations
Competing interests
M.C.A. reports advisory board participation and honoraria from Merck Sharp and Dohme, outside the submitted work. V.G. and Z.A.C. are currently employees of AstraZeneca. R.N.A. reports research funding from Bristol-Myers Squibb (BMS), Merck and Genentech, all outside the submitted work. R.C.P. reports honoraria EMD Serono, Merck, Norvartis and Pfizer as well as consulting for Merck, BMS, Novartis, Pfizer and AstraZeneca, and research funding from Merck and Novartis outside the submitted work. H.A.T. reports personal fees from Novartis, grants from Merck and Celgene and grants and personal fees from BMS and Genentech, all outside the submitted work. M.A.D. has been a consultant to Roche/Genentech, Array, Novartis, BMS, GlaxoSmithKline (GSK), Sanofi-Aventis, Vaccinex and Apexigen, and he has been the PI of research grants to UT MD Anderson by Roche/Genentech, GSK, Sanofi-Aventis, Merck, Myriad and Oncothyreon. W.-J.H. reports research grants from Merck, BMS, MedImmune and GSK and has served on an advisory board for Merck, all outside the submitted work. J.E.G. reports advisory board participation with Merck, Regeneron, BMS, Novartis and Syndax. A.J.L. reports personal fees from BMS, Novartis, Genentech/Roche and Merck; personal fees and non-financial support from ArcherDX and Beta-Cat; grants and non-financial support from Medimmune/AstraZeneca and Sanofi; grants, personal fees and non-financial support from Janssen, all outside the submitted work. M.T.T. reports personal fees from Myriad Genetics, Seattle Genetics and Novartis, all outside the submitted work. A.P.C. reports advisory roles and/or stock ownership for Immunai and Vastbiome. M.A.P. reports honoraria from BMS and Merck, consulting fees from BMS, Merck, Array BioPharma, Novartis, Incyte, NewLink Genetics, Aduro, Eisai and Pfizer and institutional support from RGenix, Infinity, BMS, Merck, Array BioPharma, Novartis and AstraZeneca. S.P.P. reports institutional support for a clinical trial from InxMed. J.L.M. reports honoraria from Roche, BMS and Merck. R.R.J. has consulted for Karius, Merck, Microbiome DX and Prolacta, and is on the scientific advisory boards of Kaleido, LISCure, Maat Pharma and Seres, and has received patent royalties licensed to Seres. P.S. reports consulting, advisory roles and/or stocks/ownership for Achelois, Adaptive Biotechnologies, Apricity Health, BioAlta, BioNTech, Codiak Biosciences, Constellation, Dragonfly Therapeutics, Forty-Seven Inc., Hummingbird, ImaginAb, Infinity Pharma, Jounce Therapeutics, Lave Therapeutics, Lytix Biopharma, Marker Therapeutics, Oncolytics, Phenomics and Polaris, and owns a patent licensed to Jounce Therapeutics. J.P.A. reports consulting, advisory roles and/or stocks/ownership for Achelois, Adaptive Biotechnologies, Apricity Health, BioAlta, BioNTech, Codiak Biosciences, Constellation, Dragonfly Therapeutics, Forty-Seven Inc., Hummingbird, ImaginAb, Jounce Therapeutics, Lave Therapeutics, Lytix Biopharma, Marker Therapeutics, Phenomics and Polaris, and owns a patent licensed to Jounce Therapeutics. B.R. reports advisory board membership for Vedanta and research funding from Vedanta, Davoltera and Kaleido. V.G., C.N.S., A.R. and J.A.W. are co-inventors on US patent PCT/US17/53,717, relating to the microbiome. J.A.W., V.G., M.C.A., L. Zitvogel and V.I. are co-inventors on a provisional US patent (WO2020106983A1) relating to the microbiome, relevant to the current work. L. Zitvogel is the main founder of EverImmune, a biotech company devoted to the use of commensal bacteria for the treatment of cancers, is on the board of administrators of Transgene and in the scientific advisory board of EpiVax, Lytix Biopharma, and has received research contracts from Kaleido, BMS, Incyte, Transgene, MERUS and GSK. J.A.W. reports speaker fees from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, MedImmune and BMS; consultant/advisor roles or advisory board membership for Roche-Genentech, Novartis, AstraZeneca, GSK, BMS, Merck/MSD, Biothera Pharma and Microbiome DX; and receives clinical trial support from GSK, Roche-Genentech, BMS and Novartis, all outside the current work. The remaining authors declare no competing interests.
Additional information
Peer review information Nature Medicine thanks Adil Daud, Thomas Tüting and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Cohort description and tumor intrinsic genomic parameters.
a, Kaplan-Meier curve of progression-free survival in the patient cohort stratified by melanoma subtype (n = 77, n = 63 cutaneous/unknown primary, n = 8 mucosal, n = 6 uveal). b, Landscape of non-synonymous variants (NSV) identified by whole-exome sequencing (n = 26 tumors) affecting selected genes recurrently mutated in melanoma, IFN-signaling genes and antigen processing/presentation genes. c, Differences in counts of total predicted neoantigens, and all binding neoantigens in patients grouped by best overall response (R=responder (blue), n = 20; NR=non-responder (red), n = 6, two-sided Mann-Whitney test left panel p = 0.123, right panel p = 0.121). d, Genome-wide SGOL scores and (e) barplot of the number of genes affected by copy number losses aggregated by chromosome, demonstrating dominant copy number loss burden within chromosomes 5, 10 and 15. f, Copy number loss-affected genes located on chromosome 10 include a broad variety of functional classes. g, Entropy of pre-treatment intratumoral T cell receptor (TCR) repertoires comparing R (n = 19) versus NR (n = 6) repertoires (p = 0.058, two-sided Mann-Whitney test). Box plots present the median bar with the box bounding interquartile range (IQR) and whiskers to the most extreme point within 1.5 x IQR.
Extended Data Fig. 2 Fecal microbiome composition and diversity at baseline in CICB-treated patients.
a, Stacked bar plot depicting 16S microbial composition of each analyzed fecal sample from the cutaneous and unknown primary cohort at the order level (n = 39). b,c, Comparison of group-wise abundances of Firmicutes (b) (p = 0.39) and Clostridiales (c) (p = 0.38) by response outcome in the cutaneous/unknown primary cohort (n = 39). d, Inverse Simpson alpha diversity of the fecal microbiome grouped by response in CICB-treated patients with cutaneous or unknown primary melanomas (n = 39) taken at baseline (p = 0.68, Mann-Whitney test; R=responder, NR=non-responder). Box plots present the median bar with the box bounding interquartile range (IQR) and whiskers to the most extreme point within 1.5 x IQR. All tests are two-sided unless otherwise specified.
Extended Data Fig. 3 Microbial associations with CICB response are confirmed by whole metagenomic sequencing.
a, Volcano plot of pairwise comparisons of bacterial taxa (at all levels) identified from 16S sequencing (n = 40) dichotomized by response to CICB using Mann-Whitney tests applied to 1000 permutations of differential bacterial abundance. b, Procrustes analysis demonstrating high concordance between taxonomic identification using either 16S or WMS methods within the response cohort (Mantel: r=0.650, p = 0.001). c, A strong positive correlation was observed between abundance of Bacteroides stercoris quantified using 16S versus WMS (Spearman’s rho=0.934 p = 2.2e-16). d, Confirmation of bacterial candidate associations with response using WMS. Box plots present the median bar with the box bounding interquartile range (IQR) and whiskers to the most extreme point within 1.5 x IQR. All tests are two-sided unless otherwise specified.
Extended Data Fig. 4 Validation of microbial composition and response taxa across additional datasets.
a, Ordination of microbial beta diversity contrasting compositional differences between responders (R, n = 59) and non-responders (NR, n = 24) across the pooled CICB and previously published anti-PD-1 monotherapy (Science 2018) cohorts (Weighted UniFrac, PERMANOVA two-sided p = 0.002). b, Abundance of key response-associated taxa identified in the CICB cohort were evaluated in re-processed microbiome data from several published cohorts, indicating taxa enriched in responders (blue), taxa enriched in non-responders (red), or not detected (white) in each cohort by two-sided Mann-Whitney test (Ruminococcus; Gopalakrishnan, Science 2018 one-sided p = 0.0240, Frankel, Neoplasia 2017 one-sided p = 0.0487).
Extended Data Fig. 5 Associations between prevalent bacterial taxa and tumor response in murine models.
a, Experimental setting for murine studies shown in Figs. 2 and 3. Treatment of established transplantable tumors (MCA205 sarcoma or RET melanoma) by intraperitoneal (i.p.) administrations of CICB and feces collection at three time points for 16S rRNA gene sequencing. Feces collection time points: T0=before treatment initiation (Day 0), T2=48 hours after 2 treatments (Day 5), T5=48 hours after 5 treatments (Day 14). In studies utilizing antibiotic (ATB) treatment, ATB was commenced 14 days prior to tumor inoculation and continued throughout. b, Pearson correlation between the relative abundance of Parabacteroides distasonis (at T0, T2, and T5) and standardized tumor size at T5 in MCA205 and RET tumor-bearing mice (two-sided p = 0.010, r = −0.614). c, Heatmap of Spearman correlations between the most prevalent (>20%) bacterial species identified in mouse feces at different time points (T0, T2, T5) from RET tumor-bearing mice and colon inflammatory infiltrates. Data are derived from combined discovery and validation cohort animals. Red represents a positive correlation, while blue represents a negative correlation with colonic infiltrate score. Following FDR adjustment, no significant correlations were observed.
Extended Data Fig. 6 Microbial associations with immune-related toxicity are confirmed by whole metagenomic sequencing.
a, Inverse Simpson alpha diversity from 16S sequencing of baseline fecal microbiota in CICB-treated patients (n = 54) was not associated with subsequent development of high-grade immune-related adverse events (irAE). Mann-Whitney test (p = 0.71). b, Volcano plot of pairwise comparisons of bacterial taxa (at all levels) dichotomized by experience of high-grade (≥Grade 3) immune-related adverse events (n = 54 patients) using Mann-Whitney tests applied to 1000 permutations of differential bacterial abundance. Unadjusted p-values shown, adjusted values in supplemental tables 5 and 8. c, Procrustes analysis demonstrating high concordance between taxonomic identification using either 16S or WMS methods (Mantel: r=0.665, p = 0.001). d, Confirmation of bacterial candidate associations with toxicity using WMS ( ≥ Gr3 irAE: n = 25 Yes, n = 21 No). Significant associations existed for Bacteroides intestinalis (p = 0.032) and Dorea formicigenerans (p = 0.020) all other associations were non-significant. e, A strong positive correlation was observed between abundance of Bacteroides intestinalis quantified using 16S versus WMS (Spearman’s rho=0.62, p = 4.2e-6). f, Box-whisker plot of relative abundance of Bacteroides intestinalis in the combined McGill/University of Toronto cohort of melanoma patients treated with immune checkpoint blockade demonstrating identification of this species exclusively in patients developing irAE (≥Gr1 n = 37 Yes, n = 8 No; One-tailed Mann Whitney test p = 0.2269). Box plots present the median bar with the box bounding interquartile range (IQR) and whiskers to the most extreme point within 1.5 x IQR. All tests are two sided unless otherwise specified.
Extended Data Fig. 7 Immune markers of CICB toxicity.
a, b, Comparison of Ki67+ cells within CD8+ T effectors (Teff; a) and T central memory (TCM; b) cells in early on-treatment blood samples between patients with available blood samples (n = 14) grouped according to high-grade irAE (Mann-Whitney test left panel p = 0.0044, right panel p = 0.013). c, Gating strategy for key CD4/8+ T cell populations. d, e, Percentage of CD28+ cells within CD4+ Teff (c) and CD27+ cells within CD8+ Teff (d) measured at baseline in this patient cohort (MDACC; left panels) and a separate cohort of patients treated with CICB at Memorial Sloan-Kettering Cancer Center (MSKCC; right panels). Data are grouped by experience of high-grade irAE (Mann-Whitney test (d) left panel p = 0.014, right panel p = 0.050 (e) left panel p = 0.072, right panel p = 0.32)). f, Boxplot depicting a higher diversity of the peripheral T cell repertoire as measured by TCR Vβ sequencing in patients experiencing high-grade irAE (n = 24, Mann-Whitney test; p = 0.028). g, Boxplot showing the number of significantly expanded T cell clones (pre- to on-treatment) detected by TCR sequencing of the peripheral blood immune repertoire, grouped by presence or absence of high-grade irAE (n = 16, Mann-Whitney test: p = 0.22). Box plots present the median bar with the box bounding interquartile range (IQR) and whiskers to the most extreme point within 1.5 x IQR. All tests are two sided unless otherwise specified.
Supplementary information
Supplementary Tables 1–10
Supplementary Table 1 Patient characteristics. Supplementary Table 2 Clinical outcomes. Supplementary Table 3 Biospecimen use overview. Supplementary Table 4 Human fecal microbial OTU table. Supplementary Table 5 Pairwise comparison of human fecal bacterial abundances by response. Supplementary Table 6 Multivariable adjustment of human fecal microbial candidates from whole metagenomic sequencing. Supplementary Table 7 Characteristics of patients in colonic cytokine analysis sample cohort. Supplementary Table 8 Pairwise comparison of human fecal bacterial abundances by toxicity. Supplementary Table 9 Characteristics of patients in the McGill/University of Toronto sample cohort. Supplementary Table 10 Characteristics of patients in the MSKCC sample cohort.
Rights and permissions
About this article
Cite this article
Andrews, M.C., Duong, C.P.M., Gopalakrishnan, V. et al. Gut microbiota signatures are associated with toxicity to combined CTLA-4 and PD-1 blockade. Nat Med 27, 1432–1441 (2021). https://doi.org/10.1038/s41591-021-01406-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41591-021-01406-6
This article is cited by
-
Gut microbiome for predicting immune checkpoint blockade-associated adverse events
Genome Medicine (2024)
-
Unveiling the gastric microbiota: implications for gastric carcinogenesis, immune responses, and clinical prospects
Journal of Experimental & Clinical Cancer Research (2024)
-
A gut microbial signature for combination immune checkpoint blockade across cancer types
Nature Medicine (2024)
-
Clinical and translational attributes of immune-related adverse events
Nature Cancer (2024)
-
When smoke meets gut: deciphering the interactions between tobacco smoking and gut microbiota in disease development
Science China Life Sciences (2024)