Machine learning reveals genetic modifiers of the immune microenvironment of cancer
Bridget Riley-Gillis,
Shirng-Wern Tsaih,
Emily King,
Sabrina Wollenhaupt,
Jonas Reeb,
Amy R. Peck,
Kelsey Wackman,
Angela Lemke,
Hallgeir Rui,
Zoltan Dezso,
Michael J. Flister
Affiliations
Bridget Riley-Gillis
Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA; Corresponding author
Shirng-Wern Tsaih
Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
Emily King
Genomics Research Center, AbbVie Inc, 1 North Waukegan Road, North Chicago, IL 60064, USA
Sabrina Wollenhaupt
Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
Jonas Reeb
Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
Amy R. Peck
Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI, USA
Kelsey Wackman
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
Angela Lemke
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
Hallgeir Rui
Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA; Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA
Zoltan Dezso
Genomics Research Center, AbbVie Bay Area, 1000 Gateway Boulevard, South San Francisco, CA 94080, USA
Michael J. Flister
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
Summary: 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.
