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

Nat Cancer. 2020 Aug;1(8):789-799. doi: 10.1038/s43018-020-0087-6. Epub 2020 Jul 27.


Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype-phenotype links in cancer.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Deep Learning*
  • Eosine Yellowish-(YS)
  • Hematoxylin
  • Humans
  • Mutation
  • Neoplasms* / diagnosis


  • Eosine Yellowish-(YS)
  • Hematoxylin