Precision Medicine Approaches in Renal Cell Carcinoma: Integrating MultiOmics Data for Personalized Therapeutic Strategies

The inherent heterogeneity of Renal Cell Carcinoma (RCC) is a major obstacle to its treatment which necessitates the implementation of precision medicine approaches. The presented work introduces a full-scale computational model for quantitative modeling and evaluation of therapy plans by means of multi-omics data integration. A scaled synthetic dataset of 1,200 RCC patients was generated, including clinical information, gene expression data, and patient somatic mutation data, which carry intrinsic molecular signals of simulated treatment efficacy and patient survival. In order to predict treatment response, trained three machine learning models: Logistic Regression (accuracy, the Random Forest (accuracy and the Gradient Boosting (area under the curve (AUC)). All models achieved an ideal value of 1.00. This validated their good ability to determine molecular drivers and they ran a good analysis using Random Forest feature importance analysis to determine the important genes affecting their prediction. Treatment efficacy was therapeutically relevant, showing a highly significant difference in prognosis between Non-Responders and Responders (p<0.005) as shown by the survival comparison analysis based on Kaplan-Meier curves and using a Log-rank test. Multi-omics characteristics were also tested to be prognostically independent of survival in a Cox Proportional Hazards model. Unsupervised K-Means clustering has revealed that various groups of patients existed and that UMAP visualization showed an excellent level of agreement with such molecular groupings and response to treatment. This article demonstrates a successful proof of principle of an integrative computational approach that is able to accurately predict the outcome of a treatment protocol, discover important biomarkers and characterize a population into physiologically meaningful subpopulations. These findings demonstrate the tremendous potential of the multi-omics model to improve patient care for individuals with renal cell carcinoma.