Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an inte...

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Vydáno v:Cancer research (Chicago, Ill.) Ročník 77; číslo 21; s. e91
Hlavní autoři: Cheng, Jun, Zhang, Jie, Han, Yatong, Wang, Xusheng, Ye, Xiufen, Meng, Yuebo, Parwani, Anil, Han, Zhi, Feng, Qianjin, Huang, Kun
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.11.2017
Témata:
Adult
Aged
Aged, 80 and over
Biomarkers, Tumor - genetics
Carcinoma, Renal Cell - diagnosis
Carcinoma, Renal Cell - genetics
Female
Gene Expression Profiling - methods
Gene Expression Profiling - statistics & numerical data
Gene Expression Regulation, Neoplastic
Genomics - methods
Humans
Kaplan-Meier Estimate
Kidney - metabolism
Kidney - pathology
Kidney Neoplasms - diagnosis
Kidney Neoplasms - genetics
Male
Middle Aged
Neoplasm Staging
Prognosis
Proportional Hazards Models
ISSN:1538-7445, 1538-7445
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Abstract In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas ( = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. .
AbstractList In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. ©2017 AACR.In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. ©2017 AACR.
In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas ( = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. .
Author Han, Yatong
Han, Zhi
Wang, Xusheng
Ye, Xiufen
Feng, Qianjin
Zhang, Jie
Parwani, Anil
Cheng, Jun
Meng, Yuebo
Huang, Kun
Author_xml – sequence: 1
  givenname: Jun
  surname: Cheng
  fullname: Cheng, Jun
  organization: Guangdong Province Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
– sequence: 2
  givenname: Jie
  surname: Zhang
  fullname: Zhang, Jie
  organization: Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
– sequence: 3
  givenname: Yatong
  surname: Han
  fullname: Han, Yatong
  organization: College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China
– sequence: 4
  givenname: Xusheng
  surname: Wang
  fullname: Wang, Xusheng
  organization: Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
– sequence: 5
  givenname: Xiufen
  surname: Ye
  fullname: Ye, Xiufen
  organization: College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China
– sequence: 6
  givenname: Yuebo
  surname: Meng
  fullname: Meng, Yuebo
  organization: College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, China
– sequence: 7
  givenname: Anil
  surname: Parwani
  fullname: Parwani, Anil
  organization: Department of Pathology, The Ohio State University, Columbus, Ohio
– sequence: 8
  givenname: Zhi
  surname: Han
  fullname: Han, Zhi
  organization: Department of Pathology, The Ohio State University, Columbus, Ohio
– sequence: 9
  givenname: Qianjin
  surname: Feng
  fullname: Feng, Qianjin
  email: kunhuang@iu.edu, 1271992826@qq.com
  organization: Guangdong Province Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. kunhuang@iu.edu 1271992826@qq.com
– sequence: 10
  givenname: Kun
  surname: Huang
  fullname: Huang, Kun
  email: kunhuang@iu.edu, 1271992826@qq.com
  organization: Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
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Snippet In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with...
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SubjectTerms Adult
Aged
Aged, 80 and over
Biomarkers, Tumor - genetics
Carcinoma, Renal Cell - diagnosis
Carcinoma, Renal Cell - genetics
Female
Gene Expression Profiling - methods
Gene Expression Profiling - statistics & numerical data
Gene Expression Regulation, Neoplastic
Genomics - methods
Humans
Kaplan-Meier Estimate
Kidney - metabolism
Kidney - pathology
Kidney Neoplasms - diagnosis
Kidney Neoplasms - genetics
Male
Middle Aged
Neoplasm Staging
Prognosis
Proportional Hazards Models
Title Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis
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