Machine studying fashions assist forecast immunotherapy response from intestine microbiomes
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A brand new analysis paper was revealed 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.”

Cancer immunotherapy has considerably improved affected person survival. Yet, half of sufferers don’t reply to immunotherapy. Gut microbiomes have been linked to medical responsiveness of melanoma sufferers on immunotherapies; nonetheless, totally different taxa have been related to response standing with implicated taxa inconsistent between research.

In this new examine, 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 Okay. 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 strategy to seek out frequent intestine microbiome options of response amongst immunotherapy sufferers with totally different superior stage cancers.

“Using the combined dataset, we trained and validated models with machine learning algorithms to predict patients’ clinical responses, followed by cross-sequencing-platform validation using shotgun metagenomic sequencing data.”

A mixed meta-analysis of 16S rRNA gene sequencing knowledge from a combined tumor cohort and three revealed immunotherapy intestine microbiome datasets from totally different melanoma affected person cohorts discovered sure intestine bacterial taxa correlated with immunotherapy response standing no matter tumor sort.

Using multivariate selbal evaluation, the researchers recognized two separate teams of bacterial genera related to responders versus non-responders. Statistical fashions of intestine microbiome neighborhood options confirmed sturdy prediction accuracy of immunotherapy response in amplicon sequencing datasets and in cross-sequencing platform validation with shotgun metagenomic datasets.

Results counsel baseline intestine microbiome options could also be predictive of medical outcomes in oncology sufferers on immunotherapies, and a few of these options could also be generalizable throughout totally different tumor varieties, affected person cohorts, and sequencing platforms. Findings show how machine studying fashions can reveal microbiome-immunotherapy interactions which will finally enhance most cancers affected person outcomes.

In conclusion, analyses of our cohort and the combined microbiome dataset have provided a robust assessment of immunotherapy patients’ gut microbiomes. The development of reliable models provides additional opportunity to distinguish and predict immunotherapy responders from non-responders. However, the interactions between key microbial taxa and host immunity still need to be elucidated. Ultimately, this research will assist in identifying microbial biomarkers or novel therapeutic targets to improve immunotherapy outcomes and the overall survival of cancer patients.”


Journal reference:

Liang, H., et al. (2022) Predicting most cancers immunotherapy response from intestine microbiomes utilizing machine studying fashions. Oncotarget.

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