Research Papers:

Predicting cancer immunotherapy response from gut microbiomes using machine learning models

Hai Liang, Jay-Hyun Jo, Zhiwei Zhang, Margaret A. MacGibeny, Jungmin Han, Diana M. Proctor, Monica E. Taylor, You Che, Paul Juneau, Andrea B. Apolo, John A. McCulloch, Diwakar Davar, Hassane M. Zarour, Amiran K. Dzutsev, Isaac Brownell, Giorgio Trinchieri, James L. Gulley and Heidi H. Kong _

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Oncotarget. 2022; 13:876-889. https://doi.org/10.18632/oncotarget.28252

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Hai Liang1, Jay-Hyun Jo1, Zhiwei Zhang2, Margaret A. MacGibeny1,3, Jungmin Han1, Diana M. Proctor4, Monica E. Taylor1, You Che1, Paul Juneau5,6, Andrea B. Apolo7, John A. McCulloch8, Diwakar Davar9, Hassane M. Zarour9, Amiran K. Dzutsev10, Isaac Brownell1,11, Giorgio Trinchieri10, James L. Gulley11 and Heidi H. Kong1,10

1 Dermatology Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD 20892, USA

2 Biostatistics Branch, Division of Cancer Treatment and Diagnostics, National Cancer Institute, NIH, Bethesda, MD 20892, USA

3 Department of Medical Education, West Virginia University, Morgantown, WV 26506, USA

4 Translational and Functional Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA

5 NIH Library, Division of Library Services, Office of Research Services, NIH, Bethesda, MD 20892, USA

6 Zimmerman Associates Inc., Fairfax, VA 22030, USA

7 Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA

8 Genetics and Microbiome Core, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA

9 Department of Medicine and UPMC Hillman Cancer Center University of Pittsburgh, Pittsburgh, PA 15213, USA

10 Laboratory of Integrative Cancer Immunology, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA

11 Center for Immuno-Oncology, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA

Correspondence to:

Heidi H. Kong, email: [email protected]

Keywords: gut microbiome; immunotherapy; 16S rRNA; machine learning; metagenomics

Received: June 01, 2022     Accepted: June 20, 2022     Published: July 19, 2022

Copyright: © 2022 Liang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Cancer immunotherapy has significantly improved patient survival. Yet, half of patients do not respond to immunotherapy. Gut microbiomes have been linked to clinical responsiveness of melanoma patients on immunotherapies; however, different taxa have been associated with response status with implicated taxa inconsistent between studies. We used a tumor-agnostic approach to find common gut microbiome features of response among immunotherapy patients with different advanced stage cancers. A combined meta-analysis of 16S rRNA gene sequencing data from our mixed tumor cohort and three published immunotherapy gut microbiome datasets from different melanoma patient cohorts found certain gut bacterial taxa correlated with immunotherapy response status regardless of tumor type. Using multivariate selbal analysis, we identified two separate groups of bacterial genera associated with responders versus non-responders. Statistical models of gut microbiome community features showed robust prediction accuracy of immunotherapy response in amplicon sequencing datasets and in cross-sequencing platform validation with shotgun metagenomic datasets. Results suggest baseline gut microbiome features may be predictive of clinical outcomes in oncology patients on immunotherapies, and some of these features may be generalizable across different tumor types, patient cohorts, and sequencing platforms. Findings demonstrate how machine learning models can reveal microbiome-immunotherapy interactions that may ultimately improve cancer patient outcomes.

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