Pan-cancer image-based detection of clinically actionable genetic alterations
Jakob Nikolas Kather, Lara Rosaline Heij, Heike Irmgard Grabsch, Chiara Maria Lavinia Loeffler, Amelie Echle, Hannah Sophie Muti, Jeremias Krause, Jan Niehues, Kai A. J. Sommer, Peter Bankhead, Loes F. S. Kooreman, Jefree J. Schulte, Nicole A. Cipriani, Roman David Buelow, Peter Boor, Nadina Ortiz‐Brüchle, Andrew M. Hanby, Valerie Speirs, Sara E. Kochanny, Akash Patnaik · Nature Cancer · 2020
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.