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

Kather, Jakob Nikolas; Heij, Lara R.; Grabsch, Heike I.; Loeffler, Chiara Maria Lavinia; Echle, Amelie; Muti, Hannah Sophie; Krause, Jeremias; Niehues, Jan; Sommer, Kai; Bankhead, Peter; Kooreman, Loes; Schulte, Jefree J.; Cipriani, Nicole A.; Buelow, Roman D.; Boor, Peter; Ortiz‐Brüchle, Nadina; Hanby, Andrew M.; Speirs, Valerie; Kochanny, Sara; Patnaik, Akash; Srisuwananukorn, Andrew; Brenner, Hermann; Hoffmeister, Michael; Brandt, Piet A. van den; Jäger, Dirk; Trautwein, Christian; Pearson, Alexander T.; Luedde, Tom

doi:10.1038/s43018-020-0087-6

articleNature CancerJul 27, 2020HYBRID OA

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

JNJakob Nikolas Kather LRLara R. Heij HIHeike I. Grabsch CMChiara Maria Lavinia Loeffler AEAmelie Echle

German Cancer Research Center · National Center for Tumor Diseases · +15 more institutions

PubMed

Indexed incrossrefpubmed

Abstract

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…

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Topics

Keywords

Histology
H&E stain
Cancer
Phenotype
Pathology
Digital pathology
Histopathology
Biology

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