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

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 biomarke...

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Published inNature communications Vol. 13; no. 1; p. 4250
Main Authors Qiu, Lida, Kang, Deyong, Wang, Chuan, Guo, Wenhui, Fu, Fangmeng, Wu, Qingxiang, Xi, Gangqin, He, Jiajia, Zheng, Liqin, Zhang, Qingyuan, Liao, Xiaoxia, Li, Lianhuang, Chen, Jianxin, Tu, Haohua
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 22.07.2022
Nature Publishing Group
Nature Portfolio
Subjects
13
639/705/794
692/4028/67/1347
Biomarkers
Breast cancer
Cancer
Computer applications
Extracellular matrix
Gene expression
Graph neural networks
Heterogeneity
Homogenization
Human tissues
Humanities and Social Sciences
Medical prognosis
multidisciplinary
Neural networks
Precision medicine
Prognosis
Regional development
Sampling
Science
Science (multidisciplinary)
Spatial heterogeneity
Tumors
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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. 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.
AbstractList 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.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.
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.
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. 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.
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.
ArticleNumber 4250
Author Fu, Fangmeng
Chen, Jianxin
Kang, Deyong
Guo, Wenhui
He, Jiajia
Zheng, Liqin
Tu, Haohua
Wu, Qingxiang
Zhang, Qingyuan
Liao, Xiaoxia
Xi, Gangqin
Qiu, Lida
Wang, Chuan
Li, Lianhuang
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  organization: Department of Pathology, Fujian Medical University Union Hospital
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  fullname: Wang, Chuan
  organization: Department of Breast Surgery, Fujian Medical University Union Hospital
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  organization: National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
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  surname: Li
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  givenname: Haohua
  surname: Tu
  fullname: Tu, Haohua
  email: htu@illinois.edu
  organization: Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
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Snippet Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial...
Abstract Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample...
Cancer prognosis using multiregion sampling is costly and not completely reliable due to the required biomarker homogenisation step. Here, the authors develop...
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SubjectTerms 13
639/705/794
692/4028/67/1347
Biomarkers
Breast cancer
Cancer
Computer applications
Extracellular matrix
Gene expression
Graph neural networks
Heterogeneity
Homogenization
Human tissues
Humanities and Social Sciences
Medical prognosis
multidisciplinary
Neural networks
Precision medicine
Prognosis
Regional development
Sampling
Science
Science (multidisciplinary)
Spatial heterogeneity
Tumors
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Title Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity
URI https://link.springer.com/article/10.1038/s41467-022-31771-w
https://www.proquest.com/docview/2692888168
https://search.proquest.com/docview/2693774166
https://pubmed.ncbi.nlm.nih.gov/PMC9307796
https://doaj.org/article/d3c7863bb93d4192b8eb0f7c2bec318c
Volume 13
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