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Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1

Abstract

Ample evidence indicates that the gut microbiome is a tumor-extrinsic factor associated with antitumor response to anti-programmed cell death protein-1 (PD-1) therapy, but inconsistencies exist between published microbial signatures associated with clinical outcomes. To resolve this, we evaluated a new melanoma cohort, along with four published datasets. Time-to-event analysis showed that baseline microbiota composition was optimally associated with clinical outcome at approximately 1 year after initiation of treatment. Meta-analysis and other bioinformatic analyses of the combined data show that bacteria associated with favorable response are confined within the Actinobacteria phylum and the Lachnospiraceae/Ruminococcaceae families of Firmicutes. Conversely, Gram-negative bacteria were associated with an inflammatory host intestinal gene signature, increased blood neutrophil-to-lymphocyte ratio, and unfavorable outcome. Two microbial signatures, enriched for Lachnospiraceae spp. and Streptococcaceae spp., were associated with favorable and unfavorable clinical response, respectively, and with distinct immune-related adverse effects. Despite between-cohort heterogeneity, optimized all-minus-one supervised learning algorithms trained on batch-corrected microbiome data consistently predicted outcomes to programmed cell death protein-1 therapy in all cohorts. Gut microbial communities (microbiotypes) with nonuniform geographical distribution were associated with favorable and unfavorable outcomes, contributing to discrepancies between cohorts. Our findings shed new light on the complex interaction between the gut microbiome and response to cancer immunotherapy, providing a roadmap for future studies.

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Fig. 1: Compositional differences in the fecal microbiome of anti-PD-1-treated patients with melanoma are associated with differential progression-free survival.
Fig. 2: Relationship between microbiota composition and associated host variables in relation to clinical response.
Fig. 3: Fecal microbial signatures are differentially associated with immune-related adverse events and progression-free survival in PD-1-treated patients with melanoma.
Fig. 4: Gut microbiome meta-analysis of five independent cohorts of patients with melanoma treated with anti-PD-1 identifies organisms and microbial genes differentially enriched in responders and nonresponders.
Fig. 5: Machine learning shows significant prediction of cohort response using models trained on other cohorts combined.
Fig. 6: Mapping of combined 16S rRNA gene amplicon sequencing data from PD-1-treated patients with melanoma to the American Gut Project dataset identifies favorable and unfavorable enteric microbiotypes.

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Data availability

The corresponding authors will comply with all requests for raw and analyzed data and materials after verification whether the request is subject to any patients’ confidentiality obligation. Patient-related data not included in the paper were generated as part of clinical trials and might be subject to patient confidentiality. All sequencing (human and microbiome) data and de-identified metadata that support the findings have been deposited in NCBI databases and are all accessible via BioProject accession no. PRJNA762360. AGP data are available at the ENA database (https://www.ebi.ac.uk/) under accession no. PRJEB11419. Access to publicly available sequencing data of the other cohorts analyzed in this study was obtained through BioProject accession nos. PRJNA399742 (Chicago), PRJNA541981 (New York), PRJNA397906 (Dallas) and PRJEB22893 (Houston). Source data are provided with this paper. The Sequence Read Archive accession numbers for each sample from each of these cohorts is the stated name of the sample on the spreadsheet in the source data. All other data are provided within the paper and its Supplementary Information files.

Code availability

All codes used for shotgun sequencing analysis can be found within the in-house JAMS_BW package, version 1.5.7, publicly available on GitHub (https://github.com/johnmcculloch/JAMS_BW/). GSEA analysis was done in R using fgsea package 1.19.4. Codes for transkingdom network analysis are available at https://github.com/richrr/TransNetDemo/. Additional codes used are part of the packages mentioned in the text or can be found on GitHub at https://github.com/trinchierilab/microbiotapd1melanoma2021/.

References

  1. Sivan, A. et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350, 1084–1089 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Vetizou, M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350, 1079–1084 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Dzutsev, A., Goldszmid, R. S., Viaud, S., Zitvogel, L. & Trinchieri, G. The role of the microbiota in inflammation, carcinogenesis, and cancer therapy. Eur. J. Immunol. 45, 17–31 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).

    Article  CAS  PubMed  Google Scholar 

  7. 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).

    Article  CAS  PubMed  Google Scholar 

  8. Dubin, K. et al. Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis. Nat. Commun. 7, 10391 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 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).

  10. Baruch, E. N. et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science 371, 602–609 (2021).

    Article  CAS  PubMed  Google Scholar 

  11. Davar, D. et al. Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science 371, 595–602 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Peters, B. A. et al. Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients. Genome Med. 11, 61 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gharaibeh, R. Z. & Jobin, C. Microbiota and cancer immunotherapy: in search of microbial signals. Gut 68, 385–388 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Limeta, A., Ji, B., Levin, M., Gatto, F. & Nielsen, J. Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma. JCI Insight 5, e140940 (2020).

    Article  PubMed Central  Google Scholar 

  16. Shaikh, F. Y. et al. A uniform computational approach improved on existing pipelines to reveal microbiome biomarkers of non-response to immune checkpoint inhibitors. Clin. Cancer Res. 27, 2571–2583 (2021).

  17. Byrd, A. L. et al. Gut microbiome stability and dynamics in healthy donors and patients with non-gastrointestinal cancers. J. Exp. Med. 218, e20200606 (2021).

  18. Derosa, L. et al. Gut bacteria composition drives primary resistance to cancer immunotherapy in renal cell carcinoma patients. Eur. Urol. 78, 195–206 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Viaud, S. et al. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342, 971–976 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ogluszka, M., Orzechowska, M., Jedroszka, D., Witas, P. & Bednarek, A. K. Evaluate Cutpoints: adaptable continuous data distribution system for determining survival in Kaplan–Meier estimator. Comput. Methods Prog. Biomed. 177, 133–139 (2019).

    Article  Google Scholar 

  21. Capone, M. et al. Baseline neutrophil-to-lymphocyte ratio (NLR) and derived NLR could predict overall survival in patients with advanced melanoma treated with nivolumab. J. Immunother. Cancer 6, 74 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Valero, C. et al. Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors. Nat. Commun. 12, 729 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ascierto, P. A. et al. Proteomic test for anti-PD-1 checkpoint blockade treatment of metastatic melanoma with and without BRAF mutations. J. Immunother. Cancer 7, 91 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yang, A. P., Liu, J. P., Tao, W. Q. & Li, H. M. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int. Immunopharmacol. 84, 106504 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Knight, J. M. et al. Noninvasive analysis of intestinal development in preterm and term infants using RNA-sequencing. Sci. Rep. 4, 5453 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Whitfield-Cargile, C. M. et al. The noninvasive exfoliated transcriptome (exfoliome) reflects the tissue-level transcriptome in a mouse model of NSAID enteropathy. Sci. Rep. 7, 14687 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rodrigues, R. R., Shulzhenko, N. & Morgun, A. Transkingdom networks: a systems biology approach to identify causal members of host–microbiota interactions. Methods Mol. Biol. 1849, 227–242 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yambartsev, A. et al. Unexpected links reflect the noise in networks. Biol. Direct 11, 52 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Das, S. et al. Immune-related adverse events and immune checkpoint inhibitor efficacy in patients with gastrointestinal cancer with food and drug administration-approved indications for immunotherapy. Oncologist 25, 669–679 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Matsuoka, H. et al. Correlation between immune-related adverse events and prognosis in patients with various cancers treated with anti PD-1 antibody. BMC Cancer 20, 656 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Suo, A. et al. Anti-PD-1-induced immune-related adverse events and survival outcomes in advanced melanoma. Oncologist 25, 438–446 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Horvath, A. et al. Biomarkers for oralization during long-term proton pump inhibitor therapy predict survival in cirrhosis. Sci. Rep. 9, 12000 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Cortellini, A. et al. Integrated analysis of concomitant medications and oncological outcomes from PD-1/PD-L1 checkpoint inhibitors in clinical practice. J. Immunother. Cancer 8, e001361 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Stacy, A. et al. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell 184, 615–627 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kelsey, C. M. et al. Gut microbiota composition is associated with newborn functional brain connectivity and behavioral temperament. Brain Behav. Immun. 91, 472–486 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Benito, M. et al. Adjustment of systematic microarray data biases. Bioinformatics 20, 105–114 (2004).

    Article  CAS  PubMed  Google Scholar 

  37. Lazar, C. et al. Batch effect removal methods for microarray gene expression data integration: a survey. Brief. Bioinform. 14, 469–490 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Balomenou, S. et al. Distinct functions of polysaccharide deacetylases in cell shape, neutral polysaccharide synthesis and virulence of Bacillus anthracis. Mol. Microbiol. 87, 867–883 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Bessman, N. J. et al. Dendritic cell-derived hepcidin sequesters iron from the microbiota to promote mucosal healing. Science 368, 186–189 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kjer-Nielsen, L. et al. MR1 presents microbial vitamin B metabolites to MAIT cells. Nature 491, 717–723 (2012).

    Article  CAS  PubMed  Google Scholar 

  45. Fan, S. et al. Cloning, characterization and production of three alpha-l-fucosidases from Clostridium perfringens ATCC 13124. J. Basic Microbiol. 56, 347–357 (2016).

    Article  CAS  PubMed  Google Scholar 

  46. Wright, D. P., Rosendale, D. I. & Robertson, A. M. Prevotella enzymes involved in mucin oligosaccharide degradation and evidence for a small operon of genes expressed during growth on mucin. FEMS Microbiol. Lett. 190, 73–79 (2000).

    Article  CAS  PubMed  Google Scholar 

  47. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Brooks, A. W., Priya, S., Blekhman, R. & Bordenstein, S. R. Gut microbiota diversity across ethnicities in the United States. PLoS Biol. 16, e2006842 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dwiyanto, J. et al. Ethnicity influences the gut microbiota of individuals sharing a geographical location: a cross-sectional study from a middle-income country. Sci. Rep. 11, 2618 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gorvitovskaia, A., Holmes, S. P. & Huse, S. M. Interpreting Prevotella and Bacteroides as biomarkers of diet and lifestyle. Microbiome 4, 15 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Knights, D. et al. Rethinking ‘enterotypes’. Cell Host Microbe 16, 433–437 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhang, R., Walker, A. R. & Datta, S. Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge. Biol. Direct 16, 1 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. McDonald, D. et al. American Gut: an open platform for citizen science microbiome research. mSystems 3, e00031–18 (2018).

  54. Levine, J. H. et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Costea, P. I. et al. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3, 8–16 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. He, Y. et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat. Med. 24, 1532–1535 (2018).

    Article  CAS  PubMed  Google Scholar 

  57. Luke, N. R. et al. Identification and characterization of a glycosyltransferase involved in Acinetobacter baumannii lipopolysaccharide core biosynthesis. Infect. Immun. 78, 2017–2023 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Freeman-Keller, M. et al. Nivolumab in resected and unresectable metastatic melanoma: characteristics of immune-related adverse events and association with outcomes. Clin. Cancer Res. 22, 886–894 (2016).

    Article  CAS  PubMed  Google Scholar 

  59. Weber, J. S. et al. Safety profile of nivolumab monotherapy: a pooled analysis of patients with advanced melanoma. J. Clin. Oncol. 35, 785–792 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Weber, J. S., Kahler, K. C. & Hauschild, A. Management of immune-related adverse events and kinetics of response with ipilimumab. J. Clin. Oncol. 30, 2691–2697 (2012).

    Article  CAS  PubMed  Google Scholar 

  61. Asnicar, F. et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat. Med. 27, 321–332 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gevers, D. et al. The Human Microbiome Project: a community resource for the healthy human microbiome. PLoS Biol. 10, e1001377 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012).

  64. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

  65. Amir, A. et al. Correcting for microbial blooms in fecal samples during room-temperature shipping. mSystems 2, e00199–16 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Davar, D. et al. Phase Ib/II study of pembrolizumab and pegylated-interferon alfa-2b in advanced melanoma. J. Clin. Oncol. 36, JCO1800632 (2018).

  67. 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).

    Article  CAS  PubMed  Google Scholar 

  68. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Li, D. et al. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).

    Article  CAS  PubMed  Google Scholar 

  72. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Article  CAS  PubMed  Google Scholar 

  74. Gu, Z., Eils, R. & Schlesner, M. Complex heat maps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

  75. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Haratani, K. et al. Association of immune-related adverse events with nivolumab efficacy in non-small-cell lung cancer. JAMA Oncol. 4, 374–378 (2018).

    Article  PubMed  Google Scholar 

  77. Robert, C. et al. Long-term safety of pembrolizumab monotherapy and relationship with clinical outcome: a landmark analysis in patients with advanced melanoma. Eur. J. Cancer 144, 182–191 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  79. Ho, T. K. Random decision forests. in Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, 278–282 (IEEE Computer Society, 1995).

  80. Boser, B. E., Guyon, I. M. & Vapnik, V. N. A training algorithm for optimal margin classifiers. in Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 144–152 (Association for Computing Machinery, 1992).

  81. McCullagh P. & Nelder, J. A. & Generalized Linear Models (Chapman and Hall, 1989).

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Acknowledgements

We acknowledge all patients and families affected by metastatic melanoma. We acknowledge J. Wargo for providing unpublished metadata of the Houston melanoma patient cohort. This work was supported in part by the Intramural Research Program of the NIH, NCI, Center for Cancer Research. D.D. is supported by the Melanoma Breakthrough Foundation Breakthrough Consortium. H.M.Z. is supported by the NIH/NCI (R01 CA228181 AND R01 CA222203) and the James W. and Frances G. McGlothlin Chair in Melanoma Immunotherapy Research. M.V. was supported by an Irvington postdoctoral fellowship from the Cancer Research Institute. Work of the University of Pittsburgh Medical Center HCC Microbiome Shared Facility and Cytometry Facility is supported by the NIH NCI Comprehensive Cancer Center Support Core grant (P30 CA047904). This research was supported in part by the University of Pittsburgh Center for Research Computing and Unified Flow Cytometry Core of the University of Pittsburgh’s Department of Immunology through the resources provided.

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Authors and Affiliations

Authors

Contributions

J.A.M., D.D., R.R.R., H.M.Z., G.T. and A.K.D. conceived the study; D.D., A.M.C., M.V., S.P., M.R.F., R.G.F.C., W.Y., R.S., S.R., R.N.D., J.-M.C., Q.D., B.Z., A.L., S.C., W.G., O.P., S.J.E., A.R., N.K.N. and A.K.D. were involved in sample collection, processing, preparation and sequencing; J.A.M., R.R.R., J.H.B., J.R.F., E.B., R.M.M., N.K.N., A.M. and A.K.D. performed computational analysis; H.M.Z., G.T. and A.K.D. supervised the entirety of the project. All authors approved the manuscript.

Corresponding authors

Correspondence to Hassane M. Zarour, Giorgio Trinchieri or Amiran K. Dzutsev.

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Competing interests

D.D. reports the following disclosures: Arcus, Bristol-Myers Squibb, Checkmate Pharmaceuticals, CellSight Technologies, Merck, GlaxoSmithKline/Tesaro (research support); Array Biopharma, Checkmate Pharmaceuticals, Finch, Incyte, Immunocore, Merck; Shionogi (consulting); and Vedanta Biosciences (scientific advisory board). H.M.Z. reports the following disclosures: Bristol-Myers Squibb, Checkmate Pharmaceuticals, GlaxoSmithKline (research support); Bristol-Myers Squibb, Checkmate Pharmaceuticals, GlaxoSmithKline, Vedanta (consulting). D.D., H.M.Z., J.A.M., R.R.R., G.T. and A.K.D. are inventors on a patent application (US patent no. 63/208,719) submitted by the University of Pittsburgh that covers methods to enhance checkpoint blockade therapy by the microbiome. The other authors declare no competing interests.

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Nature Medicine thanks R. Dummer, A. Bhatt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Editor Recognition Statement: Javier Carmona and Saheli Sadanand are the primary editors 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 Kaplan-Meier plots of progression-free survival and overall survival in the Pittsburgh early sample cohort and progression-free survival after dichotomization for abundance of select bacterial species.

a and b. Kaplan-Meier plots of probability of progression-free survival (PFS) (a) and overall survival (OS) (b) of PD-1-treated Pittsburgh early cohort melanoma patients. Vertical ticks show censored data. Central line is median OS or PFS probability, shaded area shows 95% confidence interval. c. Optimal cutpoints of bacterial abundance determined using Evaluate Cutpoints. Different plots show the effect on PFS of abundance (high vs. low) of the top four most significantly increased (left) and decreased (right) individual bacterial species in non-progressors at 10 months, determined using Mann-Whitney U test (Fig. 1c). Number of people at risk in in either group (high vs. low abundance) is shown below each panel. Vertical ticks show censored data. Hazard Ratio (HR) and score (logrank) test two-tailed p-value from Cox proportional hazards regression analysis.

Extended Data Fig. 2 Microbiota composition of non-progressing patients in the Pittsburgh cohort whose stool samples were collected 4–41 months after initiation of therapy is not predictive of late therapy failure but is enriched for similar bacterial taxa as observed in the initial microbiome of patients who did not progress at 10 months.

a. Plot of time of stool sample acquisition from 31 patients whose samples were collected after >4 months from therapy initiation. b. Progressor (P) and non-progressor (NP) groups identified at serial timepoints after late stool collection (top panel) were used to calculate the significance (two-tailed p-value) of compositional differences of the late-collected fecal microbiome using PERMANOVA (bottom panel). Fecal microbiota composition was determined using metagenomic sequencing. Progression during continued therapy was evaluated using RECIST v1.1 every 3 months or by clinical observation during follow-up visits. Number of patients on follow-up at each timepoint in relation to response status is shown in top panel. c. t-distributed uniform manifold approximation and projection (t-UMAP) plot depicts fecal microbiota compositional differences between early-collected patients who progressed (red) or did not progress (blue) in the first 10 months after initiation of therapy and late-collected long-term responders (green). Distance between centroids calculated as described in Fig. 1a, and significance (two-tailed p-value) of the differences by PERMANOVA are shown in lower table. d. Heatmap shows differentially abundant taxa (p < 0.05 and FC > 2) between the late Pittsburgh cohort compared with Ps (top) and NPs (bottom) at 10 months from the early Pittsburgh cohort. Columns denote patients grouped by each cohort before clustering; rows denote bacterial taxa enriched (black) or depleted (red) in early-sampled P versus late-sampled long-term NP clustered based on microbiota composition. Two-tailed p-values were calculated using two-tailed Mann-Whitney U test. e. ROC curve for manual model trained on the organisms associated with increased and decreased PFS in the Pittsburgh cohort from Supplementary Tables 4 and 5. Note that the model predicts late Pittsburgh samples well even though they were not included in the data used in training.

Extended Data Fig. 3 Entire time-to-event progression data analysis by Cox regression method of baseline fecal microbiome composition identifies additional favorable and unfavorable taxa linked with response to anti-PD-1 immunotherapy.

a. Volcano plot depicting bacteria identified by effect on progression-free survival (PFS) in the Pittsburgh early sample cohort using Cox regression analysis in Evaluate Cutpoints software. Taxa with q < 0.05 are shown as red dots. b. Cladogram visualization (favorable taxa – blue; unfavorable taxa – red) of bacterial taxa at different phylogenetic levels identified using approach described in (a).

Extended Data Fig. 4 Differential abundance analysis reveals relationship of baseline gut microbial taxa with high vs. low neutrophil-lymphocyte ratio in Pittsburgh early sample cohort.

a. t-distributed uniform manifold approximation and projection (t-UMAP) plots depicting fecal microbiota compositional differences between patient groups with high (≥3.82; orange) and low (<3.82; green) pre-treatment neutrophil-lymphocyte ratio (NLR). Optimal cutoff for NLR (3.82) was determined by time serial PERMANOVA as shown in Fig. 1a. Two-tailed p-value was calculated using PERMANOVA. b. Heatmap of differentially abundant taxa (p < 0.05 and FC > 2) in high-pre-treatment NLR (orange) and low-NLR (green) patients, using optimal cutoff (3.82). Columns denote patients grouped by NLR status and clustered within each group; rows denote bacterial taxa enriched (red) in patients with high NLR clustered based on microbiota composition; no bacterial taxa significantly enriched in the low-NLR patients were identified. Statistical significance was calculated using two tailed Mann‐Whitney U test. Bar plot to left of heatmap indicates extent of association between corresponding taxa and PFS probability [scaled hazard ratio (HR)] with Storey’s q-values <0.1 displayed within cells. Proportion of Gram-negative bacteria among those associated with high NLR was 58%, significantly higher than the average proportion of Gram-negative in patients’ fecal microbiome (28%, Chi-squared p = 0.0004).

Extended Data Fig. 5 Gut microbial gene differences discriminate between non-progressors and progressors during anti-PD-1 therapy in the Pittsburgh early sample cohort.

a. t-distributed uniform manifold approximation and projection (t-UMAP) plot depicting genetic differences of gut microbiomes between non-progressors (NPs; blue) and progressors (Ps; red) at time of maximal difference from start of therapy (10 months). Filled circles represent centroids, with connecting lines corresponding to samples from each group. Two-tailed p-value was calculated using PERMANOVA. b. Metagenomic shotgun sequencing of fecal microbiota samples identifies differentially abundant genes in Ps vs. NPs at 10 months from start of therapy. Heatmap shows differentially abundant genes identified by metagenomic shotgun sequencing (FDR < 0.2 and FC > 1.5). Columns denote patients grouped by progression status and clustered within P/NP groups; rows denote bacterial genes significantly upregulated (red) or downregulated (blue) in Ps versus NPs. c and d. Select genes involved in representative microbial processes of lipopolysaccharide (LPS) processing (c) and iron metabolism (d).

Extended Data Fig. 6 Metagenomic sequencing identifies distinct taxa associated with various immune-related adverse events in PD-1-treated melanoma patients in Pittsburgh early cohort.

Heatmap depicts metagenomic compositional differences between patients with a given immune-related adverse event (irAE) as compared to patients with other irAEs using scaled fold differences (high – red; low – blue) in abundances of specific bacteria. Values in individual cells represent unadjusted p-values calculated using two-tailed Mann-Whitney U test, with p-values <0.1 displayed within cells. Bar plot to left of heatmap indicates extent of association between corresponding taxa and progression-free survival probability [scaled hazard ratio (HR)], with Storey q-values <0.1 displayed within cells (from Supplementary Tables 4 and 5).

Extended Data Fig. 7 Reanalysis of four previously published individual cohorts using the same bioinformatic pipeline.

a. Analysis of α-diversity from five PD-1-treated melanoma patient cohorts (n = 185), including the Pittsburgh early sample cohort (n = 63), using either shotgun metagenomic (5 cohorts, red) or 16S rRNA gene amplicon (4 cohorts, black) sequencing. Details of each individual cohort are summarized in Supplementary Table 3. Forest plots depict α-diversity-based association tests including inverse Simpson, Shannon, and observed operational taxonomic units. Within each fixed-effect plot, names of each cohort are shown on a separate line, while log odds ratio of α-diversity (squares, size proportional to sample size used in meta-analysis) and associated 95% confidence intervals (bars) are shown, along with the dotted vertical line of no effect. The p-values reported for each cohort are two-tailed p values computed from the z statistic. To control for unobserved heterogeneity, we separately evaluated α-diversity using a random effects model on both pooled shotgun and 16S sequencing data from the 5 cohorts and performed I2 test for heterogeneity as shown. The p-value reported for heterogeneity is a one-tailed Cochran’s Q-test. b. t-distributed uniform manifold approximation and projection (t-UMAP) plot before (left) and after (right) correction for study-related batch effect using ComBat R package for all cohorts together including Pittsburgh cohort. P-values were calculated using PERMANOVA. c. t-UMAP plot of batch-corrected pooled metagenomic sequencing data from five separate cohorts of melanoma patients treated with anti-PD-1 therapy depicting fecal microbiota compositional differences with two-tailed p-value calculated using PERMANOVA between responders (Rs) and non-responders (NRs). d. Heatmap of differentially (p-values were calculated using non-parametric two-tailed Mann-Whitney U test) abundant gut microbiome taxa (p < 0.05, FC > 2) evaluated with shotgun sequencing in five melanoma patient cohorts, including Pittsburgh early sample cohort. Study-related batch effect was removed using ComBat R package. Response to therapy in published cohorts was determined as described in each study (Supplementary Table 3). Response to therapy in the Pittsburgh early sample cohort was defined as non-progression at 10 months after initiation of treatment. Columns represent patients grouped by clinical response and clustered within R/NR groups; rows depict bacterial taxa enriched (black) or depleted (red) in Rs versus NRs clustered based on gut microbiota composition.

Extended Data Fig. 8 Meta-analysis of all cohorts using random effects model identifies organisms differentially enriched in melanoma patients treated with anti-PD-1 therapy in separate cohorts by response status.

a. Random effect model meta-analysis of differentially abundant bacteria between responders (Rs) and non-responders (NRs) from five cohorts (n = 185) including Pittsburgh early sample cohort (n = 63) using shotgun metagenomic sequencing. All significant bacterial taxa enriched in Rs and NRs are shown. b. Forest plots depict association of representative bacterial species with response to anti-PD-1 therapy. Within each plot, names of various cohorts are shown on separate lines, while Hedge’s g (squares, standardized mean differences, size proportional to sample size) and associated 95% confidence intervals (bars) are shown, along with the dotted vertical line of no effect. To control for unobserved heterogeneity, we separately evaluated Hedge’s g using random effect model on metagenomic data and performed I2 test for heterogeneity as shown. P-values were calculated using random effect model.

Extended Data Fig. 9 Expression levels of selected taxa in different American Gut Project enteric microbiotypes.

t-distributed stochastic neighbor embedding (t-SNE) plots depicting American Gut Project (AGP) dataset with visualization of abundances of select taxa (blue – low; red – high).

Extended Data Fig. 10 Geographic differences determine sampling variability between cohorts.

a. t-distributed stochastic neighbor embedding (t-SNE) plots depicting mapping of individual melanoma patient cohorts to American Gut Project (AGP) dataset revealed distinct compositional differences between them. Each cohort is represented by a different color that is maintained in the overlay. Colors in the overlay are semi-translucent and were stratified starting from the Pittsburgh to New York cohort. Gut microbiota compositions of the different cohorts were significantly different (PERMANOVA, two-tailed p < 0.001). b. and c. Heatmaps represent scaled abundances of each enteric microbiotypes across 28 states from which AGP data were available on the left, with the four states from which anti-PD-1-treated melanoma cohorts originated separately on the right using three individuals per county per cluster as a cutoff. Data were scaled by number of individuals per state and per cluster and are depicted in relation to the 28 states that met the cutoff and in relation to the four states from which the four studied cohorts originated. b. Heatmaps are (b) scaled only by number of samples from state (to reflect local abundance of microbiotypes) or (c) scaled by both number of samples per state and by number of samples per microbiotype (to reflect distribution of different microbiotypes across the US). d. Geographic representation in the US of four representative enteric microbiotypes, with most uneven distribution between four states (right panel in c).

Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and Supplementary Tables 1–10

Reporting Summary

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Source data for microbiome analysis

Clinical metadata and microbiome analysis in all the five cohorts analyzed.

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McCulloch, J.A., Davar, D., Rodrigues, R.R. et al. Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1. Nat Med 28, 545–556 (2022). https://doi.org/10.1038/s41591-022-01698-2

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