Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer.

Journal: bioRxiv : the preprint server for biology
Published Date: May 22, 2025

Abstract

Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers. Based on a comprehensive benchmarking comparison, EmitGCL outperformed other computational tools across six cancer types from seven cohorts of patients with superior sensitivity and specificity. It captured occult metastatic cells in a patient with a lymph node-negative breast cancer, who was declared to have no evidence of disease by conventional imaging methods but was later confirmed to have a metastatic disease. Notably, EmitGCL identified and as predictable biomarkers for future breast cancer metastasis, which was validated across five independent cohorts of patients (n=420). Furthermore, we demonstrated YY1 transcription factor as a key driver of breast cancer metastasis which was validated through and CRISPR-based migration assays, suggesting that YY1 is a potential therapeutic target for deterring metastasis.

Authors

  • XiaoYing Wang
  • Maoteng Duan
    Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore City, 637371, Singapore.
  • Po-Lan Su
    Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, 494 Biomedical Research Tower, 460 W 10th Ave., Columbus, OH 43210, USA.
  • Jianying Li
    CT Research Center, GE Healthcare China, Beijing 100176, China.
  • Jordan Krull
    Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
  • Jiacheng Jin
    Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA.
  • Hu Chen
  • Yuhan Sun
    Clinical Data Center, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 310052 Hangzhou, Zhejiang, China.
  • Weidong Wu
    School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, China.
  • Kai He
    Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
  • Richard L Carpenter
    Medical Sciences, Indiana University, Bloomington, IN 47405.
  • Chi Zhang
    Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Sha Cao
    Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, GA 30602, USA.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Guangyu Wang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Lang Li
    Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
  • Gang Xin
    Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA.
  • David P Carbone
    Division of Medical Oncology, The Ohio State University Medical Center and Pelotonia Institute for Immuno-Oncology, Columbus, Ohio.
  • Zihai Li
    Pelotonia Institute for Immuno‑Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.
  • Qin Ma
    Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, GA 30602, USA BioEnergy Science Center, TN 37831, USA.

Keywords

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