Gene-expression-based T-Cell-to-Stroma Enrichment (TSE) score predicts response to immune checkpoint inhibitors in urothelial cancer

Abstract

Immune checkpoint inhibitors (ICI) improve overall survival in patients with metastatic urothelial cancer (mUC), but therapeutic success at the individual patient level varies significantly. Here we identify predictive markers of response, based on whole-genome DNA (n = 70) and RNA-sequencing (n = 41) of fresh metastatic biopsy samples, collected prior to treatment with pembrolizumab. We find that PD-L1 combined positivity score does not, whereas tumor mutational burden and APOBEC mutagenesis modestly predict response. In contrast, T cell-to-stroma enrichment (TSE) score, computed from gene expression signature data to capture the relative abundance of T cells and stromal cells, predicts response to immunotherapy with high accuracy. Patients with a positive and negative TSE score show progression free survival rates at 6 months of 67 and 0%, respectively. The abundance of T cells and stromal cells, as reflected by the TSE score is confirmed by immunofluorescence in tumor tissue, and its good performance in two independent ICI-treated cohorts of patients with mUC (IMvigor210) and muscle-invasive UC (ABACUS) validate the predictive power of the TSE score. In conclusion, the TSE score represents a clinically applicable metric that potentially supports the prospective selection of patients with mUC for ICI treatment.

Immune checkpoint inhibitor treatment improves overall survival in metastatic urothelial cancer, but response of individual patients varies significantly. Authors here perform whole-genome DNA and bulk RNA sequencing on samples from metastatic tumours and based on these data, they set up a single metric, T cell-to-stroma enrichment (TSE) score, that reflects the relative abundance of T cells versus stromal cells and their products, accurately predicting therapeutic outcome.