Risk score and prognostication modeling based on mRNA expressivity in the tumor microenvironment of hepatocellular carcinoma.

Abstract

e16658 Background: Stromal elements in the tumor microenvironment (TME) impact prognosis and response to therapy. Advances in mRNA-sequencing improved understanding of gene expressivity, but few models exist to model prognosis in association with mRNA expression. Methods: Clinical data and mRNA-seq of 256 patients (pts) with hepatocellular carcinoma (HCC) were obtained from TCGA. The expressivity of 191 genes enriched in cellular and structural components of the TME and clinical data were analyzed using machine learning, multivariable COX model, and Kaplan-Meier (KM) analysis to model risk score (RS) for prediction of prognosis. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis. Gene expression associated with poor (P) and good (G) in stage 1 and 2 HCC was identified (refer to presentation). RS (stage 1) = 5.997 - 0.589 × (Age at diagnosis−7.979E-06) - 4.818 × (P/G−0.009); RS (stage 2) = -5.704 - 0.780 × (Age at diagnosis−9.383E-06) + 7.228 × (P/G−0.004). Based on RS, pts were clustered into 2 groups in each stage - high and low RS groups, showing two KM curves with P < 0.05, HR = 3.213 (95% CI 2.212 – 4.347) in stage 1; HR = 2.733 (95% CI 2.131 – 3.426) in stage 2, confirming the validity of RS modeling. Analysis of immune profiles in high and low RS groups shows that expression of genes associated with immunosuppressive factors, desmoplastic reaction, neutrophils, and co-inhibitory factors of T-cells are higher in high RS group in both stages (p < 0.05). Conclusions: Machine learning-assisted mathematical modeling of RS and gene analysis identified TME-related genes and gene groups that are strongly associated with worse prognosis in stage 1 and 2 of HCC. RS could potentially prognosticate pts in the clinic with available genomic profiles.