Machine learning reveals genetic modifiers of the immune microenvironment of cancer.

Autor:	Riley-Gillis B; Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA., Tsaih SW; Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA., King E; Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA., Wollenhaupt S; Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany., Reeb J; Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany., Peck AR; Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA., Wackman K; Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA.; Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.; Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA., Lemke A; Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA.; Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.; Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA., Rui H; Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.; Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA., Dezso Z; Genomics Research Center, AbbVie Bay Area, 1000 Gateway Boulevard, South San Francisco, CA 94080, USA., Flister MJ; Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA.; Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA.; Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.; Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Jazyk:	angličtina
Zdroj:	IScience [iScience] 2023 Aug 09; Vol. 26 (9), pp. 107576. Date of Electronic Publication: 2023 Aug 09 (Print Publication: 2023).
DOI:	10.1016/j.isci.2023.107576
Abstrakt:	Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1 , were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets. Competing Interests: B.R.G., E.K., S.W., J.R., Z.D., and M.J.F. are employees of AbbVie. All animal studies and histological analysis of human breast cancer specimens were conducted at the Medical College of Wisconsin (MCW), at which time M.J.F. was a full-time faculty member of MCW. S.W.T., A.R.P., K.W., A.L., and H.R. are employees of MCW and have no financial relationship with AbbVie to disclose. The design, study conduct, and financial support for all other research were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication. (© 2023 The Author(s).)
Databáze:	MEDLINE
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