SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.

Journal: Scientific reports
Published Date: Oct 15, 2020

Abstract

Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&E images from the same tissue to be spatially registered. We show that deep learning-extracted morphological feature representations of histological images can guide representative sample selection, which improved SHIFT generalizability in a small but heterogenous set of human pancreatic cancer samples. With validation in larger cohorts, SHIFT could serve as an efficient preliminary, auxiliary, or substitute for panCK IF by delivering virtual panCK IF images for a fraction of the cost and in a fraction of the time required by traditional IF.

Authors

  • Erik A Burlingame
    Computational Biology Program, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
  • Mary McDonnell
    OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
  • Geoffrey F Schau
    Computational Biology Program, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
  • Guillaume Thibault
    School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551 Singapore; Mechanobiology Institute, National University of Singapore, Singapore 117411 Singapore. Electronic address: thibault@ntu.edu.sg.
  • Christian Lanciault
    Department of Pathology, Oregon Health and Science University, Portland, OR, USA.
  • Terry Morgan
    Department of Pathology, Oregon Health and Science University, Portland, OR, USA.
  • Brett E Johnson
    OHSU Center for Spatial Systems Biomedicine, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
  • Christopher Corless
    Knight Diagnostic Laboratories, Oregon Health and Science University, Portland, OR, USA.
  • Joe W Gray
    Department of Biomedical Engineering and the Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA.
  • Young Hwan Chang
    Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.

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