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

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

doi:10.1038/s41467-022-31771-w

Nature Communications (Jul 2022)

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

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

Affiliations

Lida Qiu: Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University
Deyong Kang: Department of Pathology, Fujian Medical University Union Hospital
Chuan Wang: Department of Breast Surgery, Fujian Medical University Union Hospital
Wenhui Guo: Department of Breast Surgery, Fujian Medical University Union Hospital
Fangmeng Fu: Department of Breast Surgery, Fujian Medical University Union Hospital
Qingxiang Wu: Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University
Gangqin Xi: Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University
Jiajia He: Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University
Liqin Zheng: Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University
Qingyuan Zhang: Department of Medical Oncology, Harbin Medical University Cancer Hospital
Xiaoxia Liao: National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
Lianhuang Li: Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University
Jianxin Chen: Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University
Haohua Tu: Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign

DOI: https://doi.org/10.1038/s41467-022-31771-w
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 12

Abstract

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Cancer prognosis using multiregion sampling is costly and not completely reliable due to the required biomarker homogenisation step. Here, the authors develop an intratumor graph neural network for prognosis in multiregion cancer samples based on in situ biomarkers and gene expression that does not need homogenisation.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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