Listen to the press release about a new research paper published in Volume 13 of Oncotarget, “Predicting cancer immunotherapy response from gut microbiomes using machine learning models.”
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A new research paper was published in Oncotarget on July 19, 2022, entitled, “Predicting cancer immunotherapy response from gut microbiomes using machine learning models.”
“In the last decade, the use of cancer immunotherapy targeting immune checkpoint inhibitors (ICIs) to boost T cell mediated cancer cell clearance has significantly improved cancer patient survival [1].”
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.
In this new study, by 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 from the National Institutes of Health Library, National Cancer Institute, National Human Genome Research Institute, West Virginia University, Zimmerman Associates Inc., and the University of Pittsburgh, researchers used a tumor-agnostic approach to find common gut microbiome features of response among immunotherapy patients with different advanced stage cancers.
Full press release - https://www.oncotarget.com/news/pr/oncotarget-predicting-cancer-immunotherapy-response-from-gut-microbiomes-using-machine-learning-models/
DOI - https://doi.org/10.18632/oncotarget.28252
Correspondence to - Heidi H. Kong - konghe@mail.nih.gov
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Keywords - gut microbiome, immunotherapy, 16S rRNA, machine learning, metagenomics
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