Riley-Gillis Bridget, Tsaih Shirng-Wern, King Emily, Wollenhaupt Sabrina, Reeb Jonas, Peck Amy R, Wackman Kelsey, Lemke Angela, Rui Hallgeir, Dezso Zoltan, Flister Michael J
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.
iScience. 2023 Aug 9;26(9):107576. doi: 10.1016/j.isci.2023.107576. eCollection 2023 Sep 15.
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, and , 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.
免疫肿瘤微环境（iTME）中的遗传力已被广泛观察到，但在很大程度上仍未得到充分表征。在这里，我们开发了一种机器学习方法，用于在全基因组关联研究（GWAS）的乳腺癌（BrCa）发病位点内绘制iTME修饰因子。在一组阳性免疫肿瘤学（I-O）靶点上训练随机森林模型，然后用于为与155个BrCa GWAS位点处于连锁不平衡状态的1362个候选基因分配I-O靶点概率分数。对最有可能的候选基因进行聚类分析，发现了与效应功能和适应性免疫反应相关的两个基因亚家族，这表明iTME修饰因子会影响抗癌免疫的多个方面。排名靠前的两个BrCa候选基因，分别使用BrCa患者活检和比较图谱研究，被正交验证为iTME修饰因子。总体而言，这些数据证明了一个强大且灵活的框架，用于对GWAS风险位点进行功能精细定位，以识别可转化的治疗靶点。
