A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

Yingying Zhang, Alden K. Leung, Jin Joo Kang, Yu Sun, Guanxi Wu, Le Li, Jiayang Sun, Lily Cheng, Tian Qiu, Junke Zhang, Shayne D. Wierbowski, Shagun Gupta, James G. Booth and Haiyuan Yu ()
Additional contact information
Yingying Zhang: Cornell University
Alden K. Leung: Cornell University
Jin Joo Kang: Cornell University
Yu Sun: Cornell University
Guanxi Wu: Cornell University
Le Li: Cornell University
Jiayang Sun: Cornell University
Lily Cheng: Cornell University
Tian Qiu: Cornell University
Junke Zhang: Cornell University
Shayne D. Wierbowski: Cornell University
Shagun Gupta: Cornell University
James G. Booth: Cornell University
Haiyuan Yu: Cornell University

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies—one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology—offer complementary strengths. We present NetFlow3D, a unified, end-to-end 3D structurally-informed protein interaction network propagation framework that maps the multiscale mechanistic effects of mutations. Built upon the Human Protein Structurome, which incorporates the 3D structures of every protein and the binding interfaces of all known protein interactions, NetFlow3D integrates atomic, residue, protein and network-level information: It clusters mutations on 3D protein structures to identify driver mutations and propagates their impacts anisotropically across the protein interaction network, guided by the involved interaction interfaces, to reveal systems-level impacts. Applied to 33 cancer types, NetFlow3D identifies 2 times more 3D clusters and incorporates 8 times more proteins in significantly interconnected network modules compared to traditional methods.

Date: 2025
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DOI: 10.1038/s41467-024-54176-3

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