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...

Full description

Saved in:
Bibliographic Details
Published in:Cancer research (Chicago, Ill.) Vol. 77; no. 21; p. e91
Main Authors: Cheng, Jun, Zhang, Jie, Han, Yatong, Wang, Xusheng, Ye, Xiufen, Meng, Yuebo, Parwani, Anil, Han, Zhi, Feng, Qianjin, Huang, Kun
Format: Journal Article
Language:English
Published: United States 01.11.2017
Subjects:
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
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. .
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1538-7445
1538-7445
DOI:10.1158/0008-5472.CAN-17-0313