Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular biology assays. 1 These tests can be a bottleneck in oncology workflows because of high turnaround time, tissue usage and costs. 2 Here, we show that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures 3,4 directly from routine histological images of tumor tissue. We developed and systematically optimized a one-stop-shop workflow and applied it to more than 4000 patients with breast 5 , colon and rectal 6 , head and neck 7 , lung 8,9 , pancreatic 10 , prostate 11 cancer, melanoma 12 and gastric 13 cancer. Together, our findings show that a single deep learning algorithm can predict clinically actionable alterations from routine histology data. Our method can be implemented on mobile hardware 14 , potentially enabling point-of-care diagnostics for personalized cancer treatment in individual patients.

Pan-cancer image-based detection of clinically actionable genetic alterations