A deep learning model to predict RNA-Seq expression of tumours from whole slide images

Benoît Schmauch (), Alberto Romagnoni, Elodie Pronier, Charlie Saillard, Pascale Maillé, Julien Calderaro, Aurélie Kamoun, Meriem Sefta, Sylvain Toldo, Mikhail Zaslavskiy, Thomas Clozel, Matahi Moarii, Pierre Courtiol and Gilles Wainrib ()
Additional contact information
Benoît Schmauch: Owkin Lab, Owkin, Inc.
Alberto Romagnoni: Owkin Lab, Owkin, Inc.
Elodie Pronier: Owkin Lab, Owkin, Inc.
Charlie Saillard: Owkin Lab, Owkin, Inc.
Pascale Maillé: INSERM U955, Team “Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers”
Julien Calderaro: INSERM U955, Team “Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers”
Aurélie Kamoun: Owkin Lab, Owkin, Inc.
Meriem Sefta: Owkin Lab, Owkin, Inc.
Sylvain Toldo: Owkin Lab, Owkin, Inc.
Mikhail Zaslavskiy: Owkin Lab, Owkin, Inc.
Thomas Clozel: Owkin Lab, Owkin, Inc.
Matahi Moarii: Owkin Lab, Owkin, Inc.
Pierre Courtiol: Owkin Lab, Owkin, Inc.
Gilles Wainrib: Owkin Lab, Owkin, Inc.

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability.

Date: 2020
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DOI: 10.1038/s41467-020-17678-4

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