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  • Review Article
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Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy

Nature Reviews Clinical Oncology volume 20, pages 583–603 (2023)Cite this article

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Abstract

Oncogenesis is associated with intestinal dysbiosis, and stool shotgun metagenomic sequencing in individuals with this condition might constitute a non-invasive approach for the early diagnosis of several cancer types. The prognostic relevance of antibiotic intake and gut microbiota composition urged investigators to develop tools for the detection of intestinal dysbiosis to enable patient stratification and microbiota-centred clinical interventions. Moreover, since the advent of immune-checkpoint inhibitors (ICIs) in oncology, the identification of biomarkers for predicting their efficacy before starting treatment has been an unmet medical need. Many previous studies addressing this question, including a meta-analysis described herein, have led to the description of Gut OncoMicrobiome Signatures (GOMS). In this Review, we discuss how patients with cancer across various subtypes share several GOMS with individuals with seemingly unrelated chronic inflammatory disorders who, in turn, tend to have GOMS different from those of healthy individuals. We discuss findings from the aforementioned meta-analysis of GOMS patterns associated with clinical benefit from or resistance to ICIs across different cancer types (in 808 patients), with a focus on metabolic and immunological surrogate markers of intestinal dysbiosis, and propose practical guidelines to incorporate GOMS in decision-making for prospective clinical trials in immuno-oncology.

Key points

  • Oncogenesis can cause a stress ileopathy (characterized by an ectopic accumulation of enteroendocrine cells and an imbalance between sympathetic and cholinergic signalling) associated with an intestinal dysbiosis.

  • Studies of the links between intestinal dysbiosis and microbial tissue colonization or infection could provide novel insights relevant to the aetiology, prevention and treatment of various cancer types, such as pancreatic adenocarcinomas and urothelial carcinomas.

  • Patients with cancer share gut microbiome signatures with individuals with seemingly unrelated disorders characterized by an imbalance between health-related and chronic inflammatory disease-related commensals.

  • In the past decade, investigators have identified Gut OncoMicrobiome signatures (GOMS) that share profile commonalities across cancer histotypes.

  • GOMS might constitute a promising, non-invasive and cost-effective approach for early diagnosis of various different cancer types.

  • GOMS are candidate predictors of resistance to immune-checkpoint inhibitors.

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Fig. 1: Cancer-associated confounding factors (ageing, comorbidities and comedications) associated with or causatively link to intestinal dysbiosis.
Fig. 2: Pan-cancer GOMS compared with healthy metagenomic profiles.
Fig. 3: GOMS related to response or lack of response to immune checkpoint inhibitors identified by mega-analysis.
Fig. 4: GOMS related to response or lack of response to immune checkpoint inhibitors identified by meta-analysis.
Fig. 5: Challenges in using microbiota-related biomarkers in oncology.
Fig. 6: Proposed pathway to define GOMS-based clinical guidelines.

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Acknowledgements

M.F. has received funding from the Seerave Foundation. B.R. has received support from the Canadian Institute for Health Research (CIHR), Fonds de la Recherche Québec-Santé (FRQS), the Seerave Foundation, the Terry Fox Marathon of Hope Program and the Weston Foundation. J.A.W. receives support from the American Association for Cancer Research Stand Up To Cancer (SU2C-AACR-IRG-19–17), MD Anderson Cancer Center’s Melanoma Moon Shots Program, Melanoma Research Alliance (4022024) and the National Institutes of Health (NIH) (1 R01 CA219896–01A1). N.S. has received support from the European Union Horizon 2020 programme (ONCOBIOME-825410 project, MASTER-818368 project and IHMCSA-964590), European Research Council (ERC-STG project MetaPG-716575 and ERC-CoG microTOUCH-101045015), the National Cancer Institute of the NIH (1U01CA230551) and Premio Internazionale Lombardia e Ricerca 2019. L.Z. has received support from ANR grant–French-German Ileobiome 19-CE15-0029-01, European Union Horizon 2020 research and innovation programme under grant agreement number 825410 (project acronym ONCOBIOME, project title Gut OncoMicrobiome Signatures (GOMS) associated with cancer incidence, prognosis, and prediction of treatment response), European Union Horizon Europe research and innovation programme under grant agreement number 101095604–GAP-101095604 (project acronym PREVALUNG-EU, project title Personalized lung cancer risk assessment leading to stratified Interception), RHU5 “ANR-21-RHUS-0017” IMMUNOLIFE, SIGN’IT ARC foundation and SIRIC Stratified Oncology Cell DNA Repair and Tumour Immune Elimination (SOCRATE). L.Z. and G.K. have received donations from Elior and the Seerave Foundation; and.support from ANR projets blancs, Badinter Philantropia, Cancéopole Ile-de-France; Dassault, FHU CARE, Fondation pour la Recherche Médicale (FRM), Inserm (HTE), Institut National du Cancer (INCa), Institut Universitaire de France, LabEx Immuno-Oncology and Ligue contre le Cancer (Equipe labelisée).

Author information

Author notes

  1. These authors contributed equally: Andrew Maltez Thomas, Marine Fidelle, Bertrand Routy.

Authors and Affiliations

  1. Department CIBIO, University of Trento, Trento, Italy

    Andrew Maltez Thomas & Nicola Segata

  2. Gustave Roussy Cancer Campus, Villejuif, France

    Marine Fidelle & Laurence Zitvogel

  3. Institut National de la Santé Et de la Recherche Médicale (INSERM) UMR 1015, ClinicObiome, Equipe Labellisée–Ligue Nationale contre le Cancer, Villejuif, France

    Marine Fidelle & Laurence Zitvogel

  4. Pharmacology Department, Gustave Roussy, Villejuif, France

    Marine Fidelle

  5. Center of Clinical Investigations in Biotherapies of Cancer (BIOTHERIS) 1428, Villejuif, France

    Marine Fidelle & Laurence Zitvogel

  6. Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, Quebec, Canada

    Bertrand Routy

  7. Hematology-Oncology Division, Department of Medicine, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, Quebec, Canada

    Bertrand Routy

  8. Centre de Recherche des Cordeliers, INSERM U1138, Equipe labellisée — Ligue Nationale contre le cancer, Université de Paris, Institut Universitaire de France, Paris, France

    Guido Kroemer

  9. Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France

    Guido Kroemer

  10. Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France

    Guido Kroemer

  11. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Jennifer A. Wargo

  12. Platform for Innovative Microbiome and Translational Research (PRIME-TR), MD Anderson Cancer Center, Houston, TX, USA

    Jennifer A. Wargo

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

    Jennifer A. Wargo

  14. IEO, European Institute of Oncology IRCCS, Milan, Italy

    Nicola Segata

  15. Université Paris-Saclay, Gif-sur-Yvette, France

    Laurence Zitvogel

Authors
  1. Andrew Maltez Thomas
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  2. Marine Fidelle
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  3. Bertrand Routy
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  4. Guido Kroemer
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  5. Jennifer A. Wargo
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  6. Nicola Segata
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  7. Laurence Zitvogel
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Contributions

L.Z. wrote the main text except the description of the CRC and melanoma GOMS performed by A.M.T. and N.S. A.M.T. and N.S. performed the meta-analysis and mega-analysis. G.K. and J.A.W. language-edited the manuscript. M.F. and B.R. prepared the display items for the submitted manuscript.

Corresponding author

Correspondence to Laurence Zitvogel.

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Competing interests

A.M.T. is a new employee of Microbiotica. B.R. has received grants from AstraZeneca, Bristol Myers Squibb, Davoltera, Kaleido, Merck and Vedanta outside the submitted work; and has a patent pending (G17004–00006-AD, use of castalagin or analogues thereof for anticancer efficacy and to increase the response to immune-checkpoint inhibitors). G.K. has held research contracts with Daiichi Sankyo, Elior, Kaleido, Lytix Pharma, PharmaMar, Samsara, Sanofi, Tollys, Vascage and Vasculox/Tioma; is on the Board of Directors of the Bristol Myers Squibb Foundation in France; is a scientific co-founder of EverImmune, Osasona Therapeutics, Samsara Therapeutics and Therafast Bio; and is an inventor in patents covering therapeutic targeting of ageing, cancer, cystic fibrosis and metabolic disorders. J.A.W. is an inventor on a US patent application (PCT/US17/53.717) submitted by the University of Texas MD Anderson Cancer Center, which covers methods to enhance immune-checkpoint inhibitor responses by modulating the microbiome; reports compensation for speaker’s bureau and honoraria from Bristol Myers Squibb, Dava Oncology, Exelixis, Gilead, Illumina, Imedex, MedImmune, Omniprex, PeerView and Physician Education Resource; has served as a consultant or advisory board member for AstraZeneca, Bristol Myers Squibb, EverImmune, GlaxoSmithKline, Merck Novartis, Roche/Genentech, Micronoma and OSE therapeutics; and receives stock options from Micronoma and OSE therapeutics. L.Z. is the scientific cofounder and President of the scientific advisory board of EverImmune, a company devoted to the use of commensal microorganisms (Oncobax) for the treatment of cancers; has received research grants from 9 meters, Daichi Sankyo, EverImmune and Pileje; is a former member of the board of directors of Transgene; and is a former consulting expert for Bristol Myers Squibb, GlaxoSmithKline, Lytix biotherapeutics and Tusk. M.F. and N.S. declare no competing interests.

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Supplementary information

Glossary

16S ribosomal RNA sequencing

Targeted, high-throughput method that consists of amplifying and sequencing the small-subunit ribosomal 16S RNA (rRNA) gene present in a sample (for example, a microbial community in stools). Sequences are generally the result of targeted amplification of one or more variable regions within the 16S rRNA gene, and can be used to profile the taxonomic composition of both archaeal and eubacterial members of the microbial community. This method provides lower taxonomic resolution than shotgun metagenomics but is generally less computationally intensive. Nevertheless, a variety of processing steps can introduce bias and limit the ability to combine data from different studies.

Alpha and beta diversities

Alpha diversity or richness measures the variability of species within a faecal sample while beta diversity accounts for the differences in composition between individuals.

Dysbiosis

Imbalance of microbiota that involves changes in composition and/or functions.

Exfoliome

Shed intestinal luminal cells contained within faecal samples.

Exposome

Encompasses the large number of individual exposures from various origins, such as chemical, physical, biological or psychological stimuli, and also takes into account the time dimension of the exposure (short or long, early or late, punctual or repeated). The exposome has an impact on individual physiology and, to a larger extent, overall health.

Leave-one-dataset-out

Analytical approach in which data from one cohort are set aside as an external validation set whereas data from the remaining cohorts are pooled together as a single training set, iterating along all the cohorts.

Linear discriminant analysis effect size

Determines the features (organisms, clades, operational taxonomic units, genes or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.

Mega-analysis

Analysis that gathers raw data across multiple studies.

Random forest model

Supervised machine learning algorithm used to solve classification and regression problems that averages multiple decision trees.

Short-chain fatty acids

Metabolic by-products derived from the fermentation of carbohydrates by anaerobic bacteria in the gut.

Shotgun metagenomic sequencing

Non-targeted, high-throughput method that consists of sequencing all DNA present in a sample (for example, a microbial community). These DNA sequences are computationally analysed and can be used to profile the taxonomic composition of members of the microbial community, including bacteria, fungi and viruses.

Species-level genome bins

Large-scale metagenomic analyses have uncovered a myriad of bacteria never before described. The species-level genome bin (SGB) nomenclature enables the annotation of bacterial sequences to the species level.

Stress ileopathy

Ileal mucosa atrophy associated with dominance of sympathetic over cholinergic signalling provoked by intra-intestinal and extra-intestinal malignancies or chronic inflammatory disorders (inflammatory bowel disease, stroke).

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Thomas, A.M., Fidelle, M., Routy, B. et al. Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy. Nat Rev Clin Oncol 20, 583–603 (2023). https://doi.org/10.1038/s41571-023-00785-8

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