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

246 Citations (Scopus)
11 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

Bibliographical note

The results are in part based upon data generated by the TCGA Research Network: Our funding sources are as follows. J.N.K.: RWTH University Aachen (START 2018-691906). V.S.: Breast Cancer Now, P.Bo: DFG: (SFB/TRR57, SFB/TRR219, BO3755/3-1, and BO3755/6-1), the German Ministry of Education and Research (BMBF: STOP-FSGS-01GM1901A) and the German Ministry of Economic Affairs and Energy (BMWi: EMPAIA project). A.T.P.: NIH/NIDCR (#K08-DE026500), Institutional Research Grant (#IRG-16-222-56) from the American Cancer Society, Cancer Research Foundation Research Grant, and the University of Chicago Med470 icine Comprehensive Cancer Center Support Grant (#P30-CA14599). T.L.: Horizon 2020 through the European Research Council (ERC) Consolidator Grant PhaseControl (771083), a Mildred Scheel-Endowed Professorship from the German Cancer Aid (Deutsche Krebshilfe), the German Research Foundation (DFG) (SFB CRC1382/P01, SFB-TRR57/P06, LU 1360/3-1), the Ernst-Jung Foundation Hamburg and the IZKF (interdisciplinary center of clinical research) at RWTH Aachen.

Correction to: Nature Cancer, published online 27 July 2020.


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