Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis and therapy of COAD. Methods: We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct integrative prognostic model and calculate histopathological-genomic prognosis factor (HGPF). Results: There were 5 prognostic image features and one co-expression gene module involved in model construction. The time-dependent receiver operating curve showed that prognostic model had significant prognostic value. Patients were divided into high-risk group and low-risk group based on HGPF. Kaplan-Meier analysis indicated that the overall survival of low-risk group was significantly better than high-risk group. Conclusions: These results suggested that histopathological image features had a certain ability to predict survival of COAD patients. The integrative prognostic model based on histopathological images and genomic features could further improve prognosis prediction in COAD, which may assist the clinical decision in the future.