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

Author

Listed:
  • 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”
    Hôpital Henri Mondor, Université Paris-Est)

  • Julien Calderaro

    (INSERM U955, Team “Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers”
    Hôpital Henri Mondor, Université Paris-Est)

  • 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.)

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.

Suggested Citation

  • Benoît Schmauch & Alberto Romagnoni & Elodie Pronier & Charlie Saillard & Pascale Maillé & Julien Calderaro & Aurélie Kamoun & Meriem Sefta & Sylvain Toldo & Mikhail Zaslavskiy & Thomas Clozel & Matah, 2020. "A deep learning model to predict RNA-Seq expression of tumours from whole slide images," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17678-4
    DOI: 10.1038/s41467-020-17678-4
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    Cited by:

    1. Duy Pham & Xiao Tan & Brad Balderson & Jun Xu & Laura F. Grice & Sohye Yoon & Emily F. Willis & Minh Tran & Pui Yeng Lam & Arti Raghubar & Priyakshi Kalita-de Croft & Sunil Lakhani & Jana Vukovic & Ma, 2023. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
    2. Marija Pizurica & Yuanning Zheng & Francisco Carrillo-Perez & Humaira Noor & Wei Yao & Christian Wohlfart & Antoaneta Vladimirova & Kathleen Marchal & Olivier Gevaert, 2024. "Digital profiling of gene expression from histology images with linearized attention," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Weiwei Wang & Yuanshen Zhao & Lianghong Teng & Jing Yan & Yang Guo & Yuning Qiu & Yuchen Ji & Bin Yu & Dongling Pei & Wenchao Duan & Minkai Wang & Li Wang & Jingxian Duan & Qiuchang Sun & Shengnan Wan, 2023. "Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Bao Feng & Jiangfeng Shi & Liebin Huang & Zhiqi Yang & Shi-Ting Feng & Jianpeng Li & Qinxian Chen & Huimin Xue & Xiangguang Chen & Cuixia Wan & Qinghui Hu & Enming Cui & Yehang Chen & Wansheng Long, 2024. "Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    5. Omar S. M. El Nahhas & Chiara M. L. Loeffler & Zunamys I. Carrero & Marko Treeck & Fiona R. Kolbinger & Katherine J. Hewitt & Hannah S. Muti & Mara Graziani & Qinghe Zeng & Julien Calderaro & Nadina O, 2024. "Regression-based Deep-Learning predicts molecular biomarkers from pathology slides," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Petr Holub & Heimo Müller & Tomáš Bíl & Luca Pireddu & Markus Plass & Fabian Prasser & Irene Schlünder & Kurt Zatloukal & Rudolf Nenutil & Tomáš Brázdil, 2023. "Privacy risks of whole-slide image sharing in digital pathology," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    7. Yuanning Zheng & Francisco Carrillo-Perez & Marija Pizurica & Dieter Henrik Heiland & Olivier Gevaert, 2023. "Spatial cellular architecture predicts prognosis in glioblastoma," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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