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-Bruechle, Andrew M. Hanby, Valerie Speirs, Sara Kochanny, Akash PatnaikAndrew Srisuwananukorn, Hermann Brenner, Michael Hoffmeister, Piet A. van den Brandt, Dirk Jaeger, Christian Trautwein, Alexander T. Pearson*, Tom Luedde*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

114 Citations (Web of Science)

Abstract

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their microenvironment. 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 5,000 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. Two papers by Kather and colleagues and Gerstung and colleagues develop workflows to predict a wide range of molecular alterations from pan-cancer digital pathology slides.

Original languageEnglish
Pages (from-to)789-799
Number of pages28
JournalNature Cancer
Volume1
Issue number8
DOIs
Publication statusPublished - Aug 2020

Keywords

  • COMPREHENSIVE MOLECULAR CHARACTERIZATION
  • GENOMIC CHARACTERIZATION
  • COLORECTAL-CANCER
  • MUTATIONS
  • SUBTYPES
  • HEAD

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