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

Jakob Nikolas Kather*, Lara R. Heij, Heike I. Grabsch, Chiara Loeffler, Amelie Echle, Hannah Sophie Muti, Jeremias Krause, Jan M. Niehues, Kai A. J. Sommer, Peter Bankhead, Loes F. S. Kooreman, Jefree J. Schulte, Nicole A. Cipriani, Roman D. Buelow, Peter Boor, Nadina Ortiz-Brüchle, 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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

159 Citations (Scopus)
8 Downloads (Pure)


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
Number of pages11
JournalNature Cancer
Issue number8
Early online date27 Jul 2020
Publication statusPublished - Aug 2020


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