Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Abstract

Background: Colon adenocarcinoma (COAD) is one of the highest morbidity cancers all over the world. Its 5-year survival is no more than 60% even in European countries with the highest survival rates. The histopathological information is crucial for the prognosis and therapy of COAD. Application of the digital whole slide imaging system enables us to read histopathological sections digitally. Apart from that, cancer genomics is also an important prognostic factor.Methods: To identify prognosis biomarkers of COAD, we downloaded whole-slide histopathological images from TCIA database. After processing these images, histopathological features were extracted by CellProfiler. Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination were followed applied, screening out 5 prognosis-related features. Weighted gene co-expression network analysis (WGCNA) was operated to find co-expression gene module correlated with prognosis-related features. The samples were divided into a training set and a testing set on a scale of 70% and 30%. Random forest was applied to construct histopathologic-genomic prognosis factor (HGPF) using prognosis-related features and genomic data. After that, we combined HGPF and clinical characteristics with nomogram and verify its predictive efficacy.Results: The time-dependent ROC was drawn to evaluate the efficacy of prognostic model. In the training set, 1-year, 3-year and 5-year AUCs are respectively 0.948, 0.916, 0.933. In the testing set, 1-year, 3-year and 5-year AUCs are respectively 0.913, 0.894, 0.924. In addition, patients were separated into high-risk survival group and low-risk survival group by HGPF. Survival analysis indicates that the low-risk patients’ survival was significantly better than high-risk patients’ in both training set and testing set. It is suggested that histopathological image features have certain ability to predict COAD survival, which can be further improved by means of multi-omics combination.Conclusions: In conclusion, this study constructs an integrative prognosis model based on histopathological and genomic features, which may have some guidance effect on prognosis and clinical decision of COAD patients. Furthermore, the underlying biological mechanisms of this multi-omics model require further study.