Gut OncoMicrobiome Signatures (GOMS) as Next-Generation Biomarkers for Cancer Immunotherapy

Authors and Affiliations

• Andrew Maltez Thomas, Marine Fidelle, Bertrand Routy, Guido Kroemer, Jennifer A. Wargo, Nicola Segata, Laurence Zitvogel
• Affiliated with institutions across Italy, France, Canada, USA, and others.

Abstract & Introduction

• Oncogenesis & Dysbiosis: Oncogenesis linked to intestinal dysbiosis; stool shotgun metagenomic sequencing as a diagnostic tool.
• Immune-Checkpoint Inhibitors (ICIs): GOMS developed to predict ICI efficacy.
• Shared Signatures: Cancer patients share GOMS with individuals with different chronic inflammatory disorders.

Gut Microbiota and Aging

• Increased Cancer Incidence with Age: Aging affects gut microbiota; unhealthy aging linked to specific taxa.
• Gut Microbiome and Diseases: Dysbiosis linked to various diseases including cancer; comedications impact microbiota diversity.

Gut OncoMicrobiome Signatures (GOMS)

• Definition & Relevance: GOMS observed in cancer patients can serve as biomarkers for predicting treatment responses, especially ICIs.
• Meta-Analysis Findings: Specific bacteria and pathways associated with cancer identified through meta-analysis.

Specific Cancer Types

• Breast Cancer: GOMS associated with disease stage and treatment resistance.
• Pancreatic Ductal Carcinoma (PDAC): PDAC GOMS predicts mortality and relates to SCFA producers.
• Colorectal Cancer (CRC): CRC GOMS used for diagnosis; differences noted between early and late onset CRC.

GOMS and Cancer Treatment

• Prediction of ICI Response: GOMS can predict response or resistance to ICIs in cancers like NSCLC and melanoma.
• Beneficial and Harmful GOMS: Identified bacterial families correlated with positive or negative treatment outcomes.

Mechanisms Behind GOMS

• Immunity: T cell responses against gut microbiota, effects on immunity, and potential in cancer treatment.
• Metabolism: Gut microbiota impact on host metabolism; metabolites as biomarkers for treatment efficacy.

Challenges and Future Directions

• Microbiota-Centric Interventions: Need for integrating GOMS in clinical practice for treatment planning.
• Biomarker Validation: Ongoing research to validate GOMS as consistent biomarkers across various cancer types and therapies.

Conclusion

• Predictive Power of GOMS: GOMS as potential non-invasive markers for cancer diagnosis and treatment response prediction.
• Integration with Other Biomarkers: Combining GOMS with other biomarkers to enhance predictive accuracy in immunotherapy.

Acknowledgements

• Funding from multiple foundations and research initiatives supporting the study.

Supplementary Material

• Additional data available online.

Key Takeaways

• Oncogenesis and Dysbiosis: Connection between cancer and gut microbiome.
• GOMS as Predictors: Role of GOMS in predicting treatment outcomes.
• Microbiota Role in Therapy: Influence of gut microbiota on efficacy of cancer immunotherapy.
• Research and Development: Continued research is crucial for validating and integrating GOMS into clinical oncology.