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

View ORCID ProfileYu Fu, View ORCID ProfileAlexander W Jung, Ramon Viñas Torne, View ORCID ProfileSantiago Gonzalez, View ORCID ProfileHarald Vöhringer, Mercedes Jimenez-Linan, View ORCID ProfileLuiza Moore, View ORCID ProfileMoritz Gerstung
doi: https://doi.org/10.1101/813543
Yu Fu
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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Alexander W Jung
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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Ramon Viñas Torne
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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Santiago Gonzalez
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
2Institute for Research in Biomedicine (IRB Barcelona), Parc Científic de Barcelona, Barcelona, Spain
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Harald Vöhringer
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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Mercedes Jimenez-Linan
3Department of Pathology, Addenbrooke’s Hospital, Cambridge, UK
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Luiza Moore
3Department of Pathology, Addenbrooke’s Hospital, Cambridge, UK
4Wellcome Sanger Institute, Hinxton, UK
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Moritz Gerstung
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
5European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
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  • For correspondence: moritz.gerstung@ebi.ac.uk
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Abstract

Here we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

Key points

  • Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types

  • Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations

  • Wide-spread correlations with gene expression indicative of immune infiltration and proliferation

  • Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted October 25, 2019.
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Yu Fu, Alexander W Jung, Ramon Viñas Torne, Santiago Gonzalez, Harald Vöhringer, Mercedes Jimenez-Linan, Luiza Moore, Moritz Gerstung
bioRxiv 813543; doi: https://doi.org/10.1101/813543
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Yu Fu, Alexander W Jung, Ramon Viñas Torne, Santiago Gonzalez, Harald Vöhringer, Mercedes Jimenez-Linan, Luiza Moore, Moritz Gerstung
bioRxiv 813543; doi: https://doi.org/10.1101/813543

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