Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors

Biaobin Jiang, Quanhua Mu, Fufang Qiu, Xuefeng Li, Weiqi Xu, Jun Yu, Weilun Fu, Yong Cao and Jiguang Wang ()
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Biaobin Jiang: The Hong Kong University of Science and Technology
Quanhua Mu: The Hong Kong University of Science and Technology
Fufang Qiu: The Hong Kong University of Science and Technology
Xuefeng Li: The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital
Weiqi Xu: Fudan University Shanghai Cancer Center
Jun Yu: The Chinese University of Hong Kong
Weilun Fu: Capital Medical University
Yong Cao: Capital Medical University
Jiguang Wang: The Hong Kong University of Science and Technology

Nature Communications, 2021, vol. 12, issue 1, 1-15

Abstract: Abstract Metastatic cancer is associated with poor patient prognosis but its spatiotemporal behavior remains unpredictable at early stage. Here we develop MetaNet, a computational framework that integrates clinical and sequencing data from 32,176 primary and metastatic cancer cases, to assess metastatic risks of primary tumors. MetaNet achieves high accuracy in distinguishing the metastasis from the primary in breast and prostate cancers. From the prediction, we identify Metastasis-Featuring Primary (MFP) tumors, a subset of primary tumors with genomic features enriched in metastasis and demonstrate their higher metastatic risk and shorter disease-free survival. In addition, we identify genomic alterations associated with organ-specific metastases and employ them to stratify patients into various risk groups with propensities toward different metastatic organs. This organotropic stratification method achieves better prognostic value than the standard histological grading system in prostate cancer, especially in the identification of Bone-MFP and Liver-MFP subtypes, with potential in informing organ-specific examinations in follow-ups.

Date: 2021
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DOI: 10.1038/s41467-021-27017-w

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