Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.

Journal: Cell reports. Medicine
Published Date: May 1, 2024

Abstract

Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TME information based on histological images for patients without ST data, thereby empowering precise cancer prognosis. The system provides two connections to bridge existing gaps. The first is the integrated graph and image deep learning (IGI-DL) model, which predicts ST expression based on histological images with a 0.171 increase in mean correlation across three cancer types compared with five existing methods. The second connection is the cancer prognosis prediction model, based on TME depicted by spatial gene expression. Our survival model, using graphs with predicted ST features, achieves superior accuracy with a concordance index of 0.747 and 0.725 for The Cancer Genome Atlas breast cancer and colorectal cancer cohorts, outperforming other survival models. For the external Molecular and Cellular Oncology colorectal cancer cohort, our survival model maintains a stable advantage.

Authors

  • Ruitian Gao
    Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • Xin Yuan
  • Yanran Ma
    Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Ting Wei
    Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • Luke Johnston
    SJTU-Yale Joint Centre for Biostatistics and Data Sciences, Shanghai Jiao Tong University, Shanghai, China.
  • Yanfei Shao
    Department of General Surgery, Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Wenwen Lv
    Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Tengteng Zhu
    Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Yue Zhang
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Junke Zheng
    Key Laboratory of Cell Differentiation and Apoptosis of the Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Guoqiang Chen
    Foshan Hospital, Foshan, China.
  • Jing Sun
    Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yu Guang Wang
    School of Mathematics and Statistics, The University of New South Wales, Sydney, Australia. Electronic address: yuguang.wang@unsw.edu.au.
  • Zhangsheng Yu
    Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

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