Deep learning analysis of breast cancer histology predicts ATM pathogenic variant carrier status

We performed deep learning analysis of histopathological whole-slide (full-face) images (WSI) to predict ATM pathogenic or likely pathogenic variant (PV/LPV) status of women with breast cancer and identify specific histological patterns of their tumor.In the discovery set composed of tumors from PV/LPV carriers (58 WSI) and noncarriers (129 WSI), our deep learning model predicted ATM status of patients with an area under the curve of 0.90 [95%CI: 0.85-0.95] and a balanced accuracy of 0.80 [95%CI: 0.72-0.88]. In the replication set (29 WSI from carriers and 22 WSI from noncarriers), corresponding results were 0.85 [95%CI: 0.70-1.00] and 0.67 [95%CI: 0.51-0.83]. We found that tumors developed by ATM PV/LPV carriers often displayed discohesive neoplastic cells as observed in invasive lobular carcinomas, and dense lymphocytic infiltrate reflecting an immune-enriched microenvironment.Recognizing these tumors at the time of diagnosis is a critical first step toward precision medicine in affected women and precision prevention in family members.