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

Cancer Research
Jun ChengKun Huang

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 (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...Continue Reading

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Citations

Jul 10, 2019·Molecular & Cellular Proteomics : MCP·Xiaohui ZhanKun Huang
Jan 8, 2020·Cancers·Reza AlaghehbandanOndrej Hes
Jun 24, 2020·Nature Biomedical Engineering·Bryan HeJames Zou
Jul 10, 2019·Computational and Mathematical Methods in Medicine·Ziang PeiWufan Chen
Aug 9, 2019·Molecular & Cellular Proteomics : MCP·Xiaohui ZhanKun Huang
Mar 12, 2021·Genomics, Proteomics & Bioinformatics·Zhi HuangJie Zhang
Mar 9, 2021·Bioinformatics·Isabelle BichindaritzChristopher Bartlett
Aug 2, 2021·Computer Methods and Programs in Biomedicine·Jeremias GomesGeorge Teodoro

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