Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma

Abstract

Objectives: To assess the feasibility of predicting molecular characteristics by computed tomography (CT) radiomics features, and predicting overall survival (OS) using combination of omics data in clear cell renal cell carcinoma (ccRCC). Methods: Genetic data of 207 ccRCC patients was retrieved from The Cancer Genome Atlas (TCGA) and matched contrast-enhanced CT images were obtained from The Cancer Imaging Archive (TCIA). Another cohort of 175 ccRCC patients from West China Hospital was used as external validation. We first applied radiomics features and machine learning algorithms to predict genetic mutations and mRNA-based molecular subtypes. Next, we established predictive models for OS based on single omics, combined omics (radiomics+genomics, radiomics+transcriptomics, radiomics+proteomics) and all features (multi-omics). Results: Using radiomics features, random forest algorithm showed good capacity to identify the mutations VHL (AUC=0.971), BAP1 (AUC=0.955), PBRM1 (AUC=0.972), SETD2 (AUC=0.949), and molecular subtypes m1 (AUC=0.973), m2 (AUC=0.968), m3 (AUC=0.961), m4 (AUC=0.953). The TCGA cohort was divided into training (n=104) and validation (n=103) sets. The radiomics model had promising prognostic value for OS in validation set (5-year AUC=0.775) and external validation set (5-year AUC=0.755). In the validation set, the radiomics+omics models enhanced predictive accuracy than single-omics models, and the multi-omics model made further improvement (5-year AUC=0.846). High-risk group of validation set predicted by multi-omics model showed significantly poorer OS (HR=6.20, 95%CI: 3.19-8.44, p<0.0001). Conclusions: CT radiomics might be a feasible approach to predict genetic mutations, molecular subtypes and OS in ccRCC patients. Integrative analysis of radiogenomics may improve the survival prediction of ccRCC patients.

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

“…All of the ccRCC studies showed significant results regarding the radiomic and radiogenomics models’ ability to predict outcomes in the form of PFI [ 44 , 45 ], MFS [ 42 ], and OS [ 42 , 43 , 46 , 47 ]. Zeng et al integrated radiomics with genomics, transcriptomics, and proteomics into a multi-omics model which was significantly better in terms of predicting 1-year, 3-year, and 5-year OS compared to models formed by single omics and the radiogenomics model, which shows potential, but, as the authors outline, the correlation between different omics features is complex and needs to be investigated further [ 47 ].…”

“…The genetic validation in the ccRCC studies ranged from gene expressions found correlated to the radiomic features [ 42 , 44 , 45 , 46 , 47 ] and hypoxia-related genes associated with survival [ 43 ]. Several of the studies showed associations between radiomics features and molecular functions in the form of mRNA expression [ 46 , 47 ] and biological pathways such as T cell activation [ 44 ] and proteasome, cell cycle, and p53 signaling pathway genes [ 45 ]. Regulatory T cells play an essential role in the progression of ccRCC in internal and peripheral tissues [ 57 , 58 ].…”

Commentary: Radiomics in oncology: A 10-year bibliometric analysis

“…Multimodality images may enable radiomic model to achieve more accurate predictions (7), but the feasibility and the additive value remain unknown for certain circumstance. While radiomics can predict the molecular features for carcinoma, they can also create multi-omics prediction model by integrating genomics, proteomics data etc (8). Anyway, the implication of modelling with multi-source data including radiomics needs massive investigations.…”

The Next Paradigm Shift in the Management of Clear Cell Renal Cancer: Radiogenomics—Definition, Current Advances, and Future Directions

“…Additional integration of existing clinical predictors and other -omic analysis into these models will also help improve prediction of clinically relevant outcomes. For instance, Zeng et al [27] demonstrated that a combined radiomic, genomic, transcriptomic, and proteomic model had higher AUC than any single model alone in predicting overall survival of patients with ccRCC. Additionally, Yin et al [22] showed that a model combining radiomic and clinical features (tumor size; stage; and grade) outperformed a radiomics only model in predicting ccRCC molecular subtype (91.3% vs. 86.96% accuracy).…”