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

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

Research output: Contribution to journalArticlepeer-review


Molecular alterations in malignant tumors can cause phenotypic changes in tumor cells and their microenvironment. Routine histopathology tissue slides – which are ubiquitously available for patients with solid tumors – 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 images of cancer. We developed, systematically optimized, validated and publicly released a one stop-shop workflow and applied it to routine tissue slides of more than 5000 patients across a broad spectrum of common solid tumors including lung, colorectal, breast and gastric cancer. 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 yield spatially resolved predictions. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach can be used to elucidate and quantify genotype-phenotype links in cancer.
Original languageEnglish
Pages (from-to)789–799
Journalnature cancer
Issue number8
Early online date27 Jul 2020
Publication statusPublished - 1 Aug 2020


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