Modeling of prognostication and differential genomic expression in the tumor microenvironment of clear cell renal cell carcinoma.

Abstract

557 Background: Stroma in the tumor microenvironment (TME) influences prognosis and response to therapy. Few mathematical models exist to prognosticate patients (pts), based on mRNA expressivity in the TME. Methods: Clinical outcomes data and mRNA-seq of 533 pts with clear cell renal cancer were obtained from TCGA. Expressivity of 191 genes enriched in cellular and structural elements of TME and clinical data were analyzed via machine learning, multivariate nonlinear regression with confined optimization, and Kaplan-Meier (KM) analysis. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis in each stage (Table). P/G is the ratio of genes associated with poor (61 genes) to good (14) prognosis (refer to presentation). Based on RS, pts in each stage were clustered into 2 groups (high and low RS), showing 2 KM curves with p < 0.001 in each stage. Analysis of immune profiles in these 2 groups shows that in stage 1, expression of genes related to immune activation (IA) is not statistically different in high and low RS groups, but expression of genes related to immune inhibition (II) is higher in high RS group. In high RS groups of stage 2-4, IA genes are highly co-expressed with II genes. In high RS groups of all stages, expression of both IA and II genes increases as stage increases. In low RS groups, IA genes increase as stage increases, but II genes do not. Conclusions: Machine learning and mathematical modeling of RS and gene analysis show that IA genes are suppressed by high degree of II in high RS groups of advanced stages, contributing to worse prognosis. RS enables prognostication of pts encountered in the clinic, given genomic profiles. [Table: see text]