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Diet-driven microbial ecology underpins associations between cancer immunotherapy outcomes and the gut microbiome

Nature Medicine volume 28, pages 2344–2352 (2022)Cite this article

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Abstract

The gut microbiota shapes the response to immune checkpoint inhibitors (ICIs) in cancer, however dietary and geographic influences have not been well-studied in prospective trials. To address this, we prospectively profiled baseline gut (fecal) microbiota signatures and dietary patterns of 103 trial patients from Australia and the Netherlands treated with neoadjuvant ICIs for high risk resectable metastatic melanoma and performed an integrated analysis with data from 115 patients with melanoma treated with ICIs in the United States. We observed geographically distinct microbial signatures of response and immune-related adverse events (irAEs). Overall, response rates were higher in Ruminococcaceae-dominated microbiomes than in Bacteroidaceae-dominated microbiomes. Poor response was associated with lower fiber and omega 3 fatty acid consumption and elevated levels of C-reactive protein in the peripheral circulation at baseline. Together, these data provide insight into the relevance of native gut microbiota signatures, dietary intake and systemic inflammation in shaping the response to and toxicity from ICIs, prompting the need for further studies in this area.

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Fig. 1: Associations between gut microbiota and treatment response and irAEs in Australian patients with melanoma.
Fig. 2: Microbial functions and dietary nutrient intake that promote gut integrity are associated with protection from irAEs and lack of response.
Fig. 3: Geographical differences in microbial communities in cohorts from Australia and the Netherlands.
Fig. 4: Ruminococcaceae-associated microbial signatures support the response to checkpoint inhibitor immunotherapy.

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Data availability

Sequencing data are available from the European Nucleotide Archive under accession number PRJEB54666. Supporting de-identified metadata have been provided. Publicly available datasets were attained from The NCBI Sequence Read Archive (SRA) under accession number SRP116709 and the European Nucleotide Archive under accession numbers PRJEB22894 and PRJNA770295. Further details, data and code are available upon request from the authors.

Code availability

No unique software or computational code was created for this study. Implementation of established tools and pipelines are described in the methods.

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Acknowledgements

We thank G. Giles of the Cancer Epidemiology Division, Cancer Council Victoria, for permission to use the Cancer Council Victoria Dietary Questionnaire for Epidemiological Studies (DQES v3.2), Melbourne, Australia, 1996. We acknowledge the technical assistance provided by the Sydney Informatics Hub, a core research facility of the University of Sydney. G.V.L and R.A.S are supported by NHMRC Program Grant R.A.S is supported by an NHMRC Practitioner Fellowship and G.V.L by an NHMRC Investigator Grant. G.V.L is also supported by the University of Sydney Medical Foundation. Support from the Cameron Family and Ainsworth Foundation, as well as from colleagues at Melanoma Institute Australia, Royal Prince Alfred Hospital and NSW Health Pathology is gratefully acknowledged. E.R.S. acknowledges financial support from the The William Arthur Martin à Beckett Cancer Research Trust (University of Sydney Fellowship). Funding support was provided by a Tour de Cure Australia project grant (RSP-00054-19/20) (R.A.S, M.B., E.R.S).

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Author notes

  1. These authors contributed equally: Rebecca C. Simpson, Erin R. Shanahan, Marcel Batten.

Authors and Affiliations

  1. Melanoma Institute Australia, The University of Sydney, Sydney, Australia

    Rebecca C. Simpson, Marcel Batten, Ines P. Silva, Chandra Adhikari, Alexander M. Menzies, Robyn P. M. Saw, Maria Gonzalez, Kerwin F. Shannon, Andrew J. Spillane, Rebecca Velickovic, James S. Wilmott, Richard A. Scolyer & Georgina V. Long

  2. Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

    Rebecca C. Simpson, Marcel Batten, Alexander M. Menzies, Robyn P. M. Saw, Rebecca Velickovic, James S. Wilmott, Richard A. Scolyer & Georgina V. Long

  3. Charles Perkins Centre, The University of Sydney, Sydney, Australia

    Rebecca C. Simpson, Erin R. Shanahan, Mark Read, Rosilene Ribeiro, Alexandra S. Angelatos, Jian Tan, Laurence Macia, Andrew J. Holmes, James S. Wilmott, Richard A. Scolyer & Georgina V. Long

  4. School of Life and Environmental Science, Faculty of Science, The University of Sydney, Sydney, Australia

    Erin R. Shanahan, Rosilene Ribeiro, Andrew J. Spillane & Andrew J. Holmes

  5. Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

    Marcel Batten

  6. Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands

    Irene L. M. Reijers, Judith M. Versluis & Christian U. Blank

  7. School of Computer Science, The University of Sydney, Sydney, Australia

    Mark Read

  8. The Westmead Initiative, The University of Sydney, Sydney, Australia

    Mark Read

  9. Westmead and Blacktown Hospitals, Sydney, Australia

    Ines P. Silva

  10. School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia

    Alexandra S. Angelatos, Jian Tan & Laurence Macia

  11. Department of Medical Oncology, Royal North Shore and Mater Hospitals, Sydney, Australia

    Alexander M. Menzies & Georgina V. Long

  12. Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Sydney, Australia

    Robyn P. M. Saw & Kerwin F. Shannon

  13. Breast and Melanoma Surgery Department, Royal North Shore Hospital, St Leonards, Australia

    Andrew J. Spillane

  14. Department of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Alexander J. Lazar

  15. Platform for Innovative Microbiome and Translational Research, Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Alexander J. Lazar, Ashish V. Damania, Aditya K. Mishra, Nadim J. Ajami & Jennifer A. Wargo

  16. Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Manoj Chelvanambi & Jennifer A. Wargo

  17. The University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA

    Anik Banerjee

  18. Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, Australia

    Richard A. Scolyer

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  1. Rebecca C. Simpson
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  2. Erin R. Shanahan
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  30. Georgina V. Long
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Contributions

G.V.L., R.A.S, R.C.S., E.R.S. and M.B. conceived the study. R.C.S., E.R.S., M.B., M.R., A.S.A., J.T., I.L.M.R. and J.V. conducted the experiments. R.C.S., E.R.S., M.B. and M.R. analyzed the data. I.P.S., R.R., C.A., A.M.M., R.P.S., M.G., K.R.S., A.J.S., R.V., A.J.L., A.V.D., A.K.M., M.C., A.B., N.J.A., L.M., and A.J.H. contributed to patient recruitment, biospecimen processing, data analysis and/or critical review of data. J.A.W., C.U.B., R.A.S. and G.V.L. contributed resources to the study. R.C.S., E.R.S. and M.B. wrote the original manuscript draft. All authors wrote, reviewed and edited the manuscript. G.V.L., R.A.S. and J.S.W. supervised the study. G.V.L., R.A.S., M.B. and E.R.S. obtained funding for the study.

Corresponding author

Correspondence to Georgina V. Long.

Ethics declarations

Competing interests

A.M.M has received fees for advisory board membership from BMS, MSD, Novartis, Roche, Pierre Fabre and Qbiotics. R.P.M.S. has received honoraria for advisory board participation from MSD, Novartis and Qbiotics, and speaking honoraria from BMS and Novartis. A.J.S has received fees for professional services from Eli Lily Australia. G.V.L. is a consultant advisor for Agenus, Amgen, Array Biopharma, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb, Evaxion Biotec, Hexal AG (Sandoz Company), Highlight Therapeutics S.L., Innovent Biologics USA, Merck Sharpe and Dohme, Novartis, OncoSec, PHMR Limited, Pierre Fabre, Provectus, Qbiotics and Regeneron. R.A.S has fees for professional services from Roche, Evaxion, Provectus Biopharmaceuticals Australia, Qbiotics, Novartis, Merck Sharp and Dohme, NeraCare, AMGEN, Bristol-Myers Squibb, Myriad Genetics and GlaxoSmithKline. All compensation was provided for work completed outside of the current work. The remaining authors declare no competing interests.

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Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Javier Carmona and Joao Monteiro, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1

Cohort and project schematic outlining clinical trial timeline and analyses conducted at baseline.

Extended Data Fig. 2

(a) Rarefaction analysis shows sequencing depth was sufficient to comprehensively catalogue the unique microbial strains present. (b) Composition of each Australian patient sample classified at the ‘family’ taxonomic level (n = 71). (c-e) Inverse Simpson’s index of alpha diversity for individual patients grouped by (c) maximum irAE grade experienced by each patient split by severe irAEs, (d) response/low irAE (R/G0-G2) (pink) and non-response/severe irAE (NR) (NR/G3- G5)) (orange) or (e) maximum irAE grade experienced by each patient split by severe gastrointestinal irAEs and non-gastrointestinal irAEs (NR with severe irAE ‘Adverse’ indicated in orange). (AUS, n = 7) (f & g) Tumour mutational burden (TMB) and tumour IFN-gamma signature split by response. Subset of patients Rozeman etal (2021)17 (n = 25). (h) Inverse Simpson’s index of alpha diversity for individual patients with high TMB grouped by response and non-response. (i-j) Absolute bacterial/archaeal faecal loads assessed using qPCR, with patients grouped by irAEs and ‘Adverse’ outcome groups (n = 71). (k) Correlation of diversity with 16 S rRNA gene number/mg faeces for each patient (n = 71). Each symbol represents an individual patient, bars indicate the median. Mann-Whitney U rank sum test (c-j). For linear regressions, p value was calculated on Spearman’s rank correlation (k). All statistical tests are two-sided where appropriate.

Extended Data Fig. 3

(a-f) Linear discriminant analysis (LDA) scores for differentially abundant taxa in (a) response, (c) irAE or (e) ‘Benefical/benign’ and ‘Adverse’ outcome groupings, as determined by LEfSe analysis (Australian cohort n = 71). LDA score indicates the confidence of the association, p < 0.05 for the Kruskal-Wallis H statistic, LDA score >3. (b, d & f) Dot plots show the relative abundance of sequence reads corresponding to each taxa for individual patients, where each dot represents a patient, colours correspond to the legend as indicated. (g-h) Quantitative PCR using taxa specific primers was used to determine bacterial copy number per mg faecal matter grouped according to response, irAEs and ‘Adverse’ outcome groups (n = 71). (g) Taxa associated with response or mild irAE based off LefSe analysis (h) taxa associated with non-response or severe irAE. Each symbol represents an individual patient, bars indicate the median. Mann-Whitney U rank sum test (g-h). All statistical tests are two-sided where appropriate.

Extended Data Fig. 4

(a-c) Archaeal ASV in pre-treatment faecal samples were classified using 16 S rRNA gene sequencing. Relative abundance of Archaeal ASVs (Methanobacteriaceae) from 16 S rRNA amplicon sequencing was compared by response, maximum irAE grade and ‘Beneficial/benign’ vs ‘Adverse’ outcomes (n = 71). (d) Quantitative PCR on faecal DNA using methanogen specific primers, grouped by response (left) or maximum irAE grade (right) (‘Adverse’ patients are indicated in orange). Each symbol represents an individual patient, bars indicate the median. Mann-Whitney U rank sum test (a-d). All statistical tests are two-sided where appropriate.

Extended Data Fig. 5

(a-e) Consumption of key dietary nutrients were estimated from food intake surveys. Patients were categorised as low or high according to the Australian dietary recommendations and grouped according to response. (f-i) Estimated total (f) protein (g/day), (g) fat (g/day), (h) carbohydrates (g/day) and (i) fibre (g/day) consumption from dietary surveys of food intake grouped by response (responder = R (yellow), non-responder = NR (green)) (AUS, n = 63). Each symbol represents an individual patient, bars indicate the median. Mann-Whitney U rank test (f-i). All statistical tests are two-sided where appropriate.

Extended Data Fig. 6

(a) MetaCyc pathways were predicted in the metagenomes of faecal samples of a subset of 38 Australian patients. Linear discriminant analysis (LDA) scores for differentially abundant pathway in the ‘Beneficial/benign’ (all R or NR irAE < G3) verses ‘Adverse’ (NR, irAE≥3) outcome group was determined by LEfSe analysis. LDA (log10) score on the left-hand side of the panel indicates the confidence of the association. The heat map indicates relative abundance (%) of each outcome-associated pathway in individual patients. Clinical groupings are indicated by coloured bars at the top. (b) Dot plots show the relative abundance of metabolic pathways identified by LefSe analysis as indicative of ‘Beneficial/benign’ or ‘Adverse’ outcome groups, where each dot represents a patient (subset of Australian cohort n = 38).

Extended Data Fig. 7

(a) Correlation of the relative abundance of outcome-associated butyrate pathways (metagenomic data) versus faecal butyrate concentrations, as assessed by NMR. For linear regressions, p values were calculated on Spearman’s rank correlation. (b-c) Serum butyrate levels (uM) detecting using NMR grouped according to (b) Beneficial/benign’ or ‘Adverse’ outcome or (c) response groupings (subset AUS, n = 38). Mann-Whitney U rank test. (d-g) Linear discriminant analysis (LDA) scores for differentially abundant taxa in (d) response and (f) irAE groupings (Dutch cohort, n = 32), as determined by LEfSe analysis. LDA score indicates the confidence of the association, p < 0.05 for the Kruskal-Wallis H statistic, LDA score >3. (e & g) Dot plots show the relative abundance of sequence reads corresponding to each taxa for individual patients, where each dot represents a patient. All statistical tests are two-sided where appropriate.

Extended Data Fig. 8

(a-b) Assessing clustering of samples (a) by country or (b) by DMM community type (combined AUS & NL, n = 103), by contrasting distances between samples within the same groups and between groups; distribution of distances shown. Significance assessed through the Kolmogorov-Smirnov statistic (D = effect magnitude value); p-values were corrected for multiple comparisons using the Bonferroni method. (c) Average relative abundance of Bacteroidaceae and Ruminococcaceae per community type. (d) Inverse Simpson’s index of alpha diversity for individual patients grouped by DMM community type (combined AUS & NL, n = 103). Each symbol represents an individual patient, bars indicate the median. (e-l) Relative abundance of genus level taxa by DMM community type (combined AUS & NL, n = 103). Each symbol represents an individual patient, bars indicate the median. Kruskal-Wallis with post hoc Dunn test. FDR adjusted p-values presented (d-l). All statistical tests are two-sided where appropriate.

Extended Data Fig. 9

(a) Correlation between Bacteroidaceae with Diversity in the Australian cohort (n = 71). (b-c) Correlation between (b) Bacteroidaceae or (c) Ruminococcaceae with Diversity in the Dutch cohort (n = 32). Each symbol represents an individual patient. For linear regressions, p value was calculated on Spearman’s rank correlation. (d-e) Estimated fibre consumption (g/day) from food intake surveys for (d) Australian (n = 63) and (e) Dutch patients (n = 32), grouped by DMM community type. Each symbol represents an individual patient, bars indicate the median. Mann-Whitney U rank sum test. (f-g) Baseline microbial signatures that distinguish patient outcomes (‘classes’) were determined based on microbial variables through sparse partial least squares discriminant analysis (sPLS-DA). Models were constructed from 16 S rRNA gene profiles to discriminate (f) responders (R) and non-responders (NR) to immunotherapy and (g) absent/mild (G0-G2) and severe (G3-G5) irAE development. Models were developed for the combined cohort (n = 103) and per community type: community type 1 (n = 37); community type 2 (n = 36) and community type 3 (n = 30). Classes are not equally populous; thus, plots depict the classification accuracy per class, with the percentage correctly assigned in black and the mean across classes. Colours indicate p-values derived under permutation testing. All statistical tests are two-sided where appropriate.

Extended Data Fig. 10

(a-d) Estimated fibre (g/day) from food intake surveys and Inverse Simpson diversity grouped by dominant taxa grouping (NL cohort (n = 32) & US neo-adjuvant cohort (Spencer et al n = 31). (e) Relative abundance of Faecalibacterium prausnitzii (top) or Ruminococcaceae (bottom), for patients by response (R vs NR) with in dominant taxa groups (Ba & Ru) (presented by country and combined n = 218). Symbols indicate mean, bars indicate standard error (f) Relative abundance of Ruminococcaceae, for individual patients within dominant taxa groups (Ba & Ru) by response (combined AUS/NL/US, n = 218). (g-h) Relative abundance of Ruminococcaceae (left) or Faecalibacterium prausnitzii (right) for patients by (g) maximum irAE grade (G0-G2 vs G3-G5) or (h) ‘Beneficial/benign’ (all R or NR irAE < G3) and ‘Adverse’ (NR, irAE≥ G3) outcomes (AUS/N, n = 103). (i) Inverse Simpson’s index of alpha diversity for individual patients grouped by ‘Beneficial/benign’ verses ‘Adverse’ outcome groups (n = 103, AUS circle, NL square). Each symbol represents an individual patient, bars indicate the median. Mann-Whitney U rank sum test (a-i). All statistical tests are two-sided where appropriate.

Supplementary information

Supplementary Information

Supplementary InformationSupplementary Tables 1-12. - Supplementary Table 1: Baseline patient characteristics.- Supplementary Table 2: Clinical outcomes by treatment arm combined Australia and Netherland.- Supplementary Table 3: Clinical outcomes and safety by country (Australia & Netherlands).- Supplementary Table 4- Univariate analysis for associations with pathological response.- Supplementary Table 5- Univariate analysis for associations with country.- Supplementary Table 6- Genus CLR abundance comparison of key taxa across studies.- Supplementary Table 7- Model components community type 1 response.- Supplementary Table 8- Model components community type 1 irAE.- Supplementary Table 9- Model components community type 2 response.- Supplementary Table 10- Model components community type 2irAE.- Supplementary Table 11- Model components community type 3 response.- Supplementary Table 12- Model components community type 3 irAE

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Supplementary Table 1

Supplementary Table 13- 16 S sequencing metadata

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Simpson, R.C., Shanahan, E.R., Batten, M. et al. Diet-driven microbial ecology underpins associations between cancer immunotherapy outcomes and the gut microbiome. Nat Med 28, 2344–2352 (2022). https://doi.org/10.1038/s41591-022-01965-2

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