Cheng Jun, Zhang Jie, Han Yatong, Wang Xusheng, Ye Xiufen, Meng Yuebo, Parwani Anil, Han Zhi, Feng Qianjin, Huang Kun
Guangdong Province Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio.
Cancer Res. 2017 Nov 1;77(21):e91-e100. doi: 10.1158/0008-5472.CAN-17-0313.
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. .
在癌症中，组织病理学图像和基因组特征都用于诊断、预后评估和亚型分类。然而，将组织病理学图像与基因组数据相结合以预测预后以及它们之间的关系，这方面的研究很少。在本研究中，我们提出了一个整合基因组学框架，用于构建透明细胞肾细胞癌的预后模型。我们使用了来自癌症基因组图谱的患者数据（n = 410），从数字化全切片图像中提取了数百个细胞形态学特征，并从功能基因组学数据中提取了特征基因来预测患者的预后。我们的模型生成的风险指数与生存率密切相关，优于单独考虑形态学特征或特征基因的预测。预测的风险指数也有效地对早期（I期和II期）肿瘤患者进行了分层，而仅使用分期未观察到显著的生存差异。我们模型的预后价值独立于透明细胞肾细胞癌患者的其他已知临床和分子预后因素。总体而言，这个工作流程和共享的软件代码为在其他癌症中应用类似方法提供了基础。
