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Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity

Author

Listed:
  • Lida Qiu

    (Fujian Normal University
    Minjiang University)

  • Deyong Kang

    (Fujian Medical University Union Hospital)

  • Chuan Wang

    (Fujian Medical University Union Hospital)

  • Wenhui Guo

    (Fujian Medical University Union Hospital)

  • Fangmeng Fu

    (Fujian Medical University Union Hospital)

  • Qingxiang Wu

    (Fujian Normal University)

  • Gangqin Xi

    (Fujian Normal University)

  • Jiajia He

    (Fujian Normal University)

  • Liqin Zheng

    (Fujian Normal University)

  • Qingyuan Zhang

    (Harbin Medical University Cancer Hospital)

  • Xiaoxia Liao

    (University of Illinois at Urbana-Champaign)

  • Lianhuang Li

    (Fujian Normal University)

  • Jianxin Chen

    (Fujian Normal University)

  • Haohua Tu

    (University of Illinois at Urbana-Champaign
    University of Illinois at Urbana-Champaign)

Abstract

Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.

Suggested Citation

  • Lida Qiu & Deyong Kang & Chuan Wang & Wenhui Guo & Fangmeng Fu & Qingxiang Wu & Gangqin Xi & Jiajia He & Liqin Zheng & Qingyuan Zhang & Xiaoxia Liao & Lianhuang Li & Jianxin Chen & Haohua Tu, 2022. "Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31771-w
    DOI: 10.1038/s41467-022-31771-w
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