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

Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular biology assays.[1][1] These tests can be a bottleneck in oncology workflows because of high turnaround time, tissue usage and costs.[2][2] Here, we show that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures[3][3],[4][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][5], colon and rectal[6][6], head and neck[7][7], lung[8][8],[9][9], pancreatic[10][10], prostate[11][11] cancer, melanoma[12][12] and gastric[13][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][14], potentially enabling point-of-care diagnostics for personalized cancer treatment in individual patients. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14