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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

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Fu, Yu, Jung, Alexander W., Torne, Ramon Viñas, Gonzalez, Santiago, Vöhringer, Harald, Shmatko, Artem, Yates, Lucy R., Jimenez-Linan, Mercedes, Moore, Luiza, Gerstung, Moritz·2020·Nature Cancer
DOI 10.1038/s43018-020-0085-8

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Abstract

We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal tissue distinction. Automatically learned computational histopathological features correlate with a large range of recurrent genetic aberrations across cancer types. This includes whole-genome duplications, which display universal features across cancer types, individual chromosomal aneuploidies, focal amplifications and deletions, as well as driver gene mutations. There are widespread associations between bulk gene expression levels and histopathology, which reflect tumor composition and enable the localization of transcriptomically defined tumor-infiltrating lymphocytes. Computational histopathology augments prognosis based on histopathological subtyping and grading, and highlights prognostically relevant areas such as necrosis or lymphocytic aggregates. These findings show the remarkable potential of computer vision in characterizing the molecular basis of tumor histopathology. 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.

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Cited by

Metadata

Authors
Fu, Y., Jung, A. W., Torne, R. V., Gonzalez, S., Vöhringer, H., Shmatko, A., Yates, L. R., Jimenez-Linan, M., Moore, L., & Gerstung, M.
Venue
Nature Cancer
Publisher
Springer Science and Business Media LLC
Published
2020-07-27
Volume
1
Issue
8
Pages
800-810
ISSN
2662-1347 (ISSN-L)
DOI
10.1038/s43018-020-0085-8
OpenAlex
W3043835773
Cited by
653
References
22
FWCI
34.76
Type
journal article
Language
en
Open access
green
Licence
Funders
  • Novo Nordisk Fonden (NNF17OC0027594)
  • Cancer Research UK (C20/A20917)
  • Wellcome Trust (214584/Z/18/Z)

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