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Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer

Levy-Jurgenson, Alona ; Tekpli, Xavier ; Kristensen, Vessela N ; Yakhini, Zohar

Scientific reports, 2020-11, Vol.10 (1), p.18802, Article 18802 [Periódico revisado por pares]

England: Nature Publishing Group

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Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer
Autor: Levy-Jurgenson, Alona ; Tekpli, Xavier ; Kristensen, Vessela N ; Yakhini, Zohar
Assuntos: Automation ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Breast Neoplasms - genetics ; Breast Neoplasms - mortality ; Deep Learning ; Female ; Gene Expression ; Gene mapping ; Genetic Heterogeneity ; Humans ; Image Processing, Computer-Assisted - methods ; Lung cancer ; Lung Neoplasms - diagnostic imaging ; Lung Neoplasms - genetics ; Lung Neoplasms - mortality ; Male ; Medical prognosis ; MicroRNAs - genetics ; MicroRNAs - metabolism ; miRNA ; Pathology ; RNA, Messenger - genetics ; RNA, Messenger - metabolism ; Statistical analysis ; Survival ; Survival Rate
É parte de: Scientific reports, 2020-11, Vol.10 (1), p.18802, Article 18802
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
EC/H2020/847912
Descrição: Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular traits that were not previously recognized in pathology H&E whole-slide images. Simultaneous to these developments, it is becoming increasingly evident that tumor heterogeneity is an important determinant of cancer prognosis and susceptibility to treatment, and should therefore play a role in the evolving practices of matching treatment protocols to patients. State of the art diagnostic procedures, however, do not provide automated methods for characterizing and/or quantifying tumor heterogeneity, certainly not in a spatial context. Further, existing methods for analyzing pathology whole-slide images from bulk measurements require many training samples and complex pipelines. Our work addresses these two challenges. First, we train deep learning models to spatially resolve bulk mRNA and miRNA expression levels on pathology whole-slide images (WSIs). Our models reach up to 0.95 AUC on held-out test sets from two cancer cohorts using a simple training pipeline and a small number of training samples. Using the inferred gene expression levels, we further develop a method to spatially characterize tumor heterogeneity. Specifically, we produce tumor molecular cartographies and heterogeneity maps of WSIs and formulate a heterogeneity index (HTI) that quantifies the level of heterogeneity within these maps. Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival. Our methods potentially open a new and accessible approach to investigating tumor heterogeneity and other spatial molecular properties and their link to clinical characteristics, including treatment susceptibility and survival.
Editor: England: Nature Publishing Group
Idioma: Inglês;Norueguês

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Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer

Levy-Jurgenson, Alona ; Tekpli, Xavier ; Kristensen, Vessela N ; Yakhini, Zohar

England: Nature Publishing Group

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