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Gut microbiota signatures are associated with toxicity to combined CTLA-4 and PD-1 blockade

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.

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Fig. 1: Molecular and immune predictors of response.
Fig. 2: Gut microbial associations with CICB response.
Fig. 3: Role of gut microbiota and ileal IL-1β in CICB-induced intestinal inflammation in tumor-bearing mice.
Fig. 4: Bacteroides intestinalis is associated with intestinal IL-1β and colitis in the melanoma cohort.

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.

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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.

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Authors

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

Correspondence to Laurence Zitvogel or Jennifer A. Wargo.

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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.

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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

Reporting Summary

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.

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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 (2021). https://doi.org/10.1038/s41591-021-01406-6

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