Avelumab maintenance in advanced urothelial carcinoma: biomarker analysis of the phase 3 JAVELIN Bladder 100 trial

In a recent phase 3 randomized trial of 700 patients with advanced urothelial cancer (JAVELIN Bladder 100; NCT02603432), avelumab/best supportive care (BSC) significantly prolonged overall survival relative to BSC alone as maintenance therapy after first-line chemotherapy. Exploratory biomarker analyses were performed to identify biological pathways that might affect survival benefit. Tumor molecular profiling by immunohistochemistry, whole-exome sequencing and whole-transcriptome sequencing revealed that avelumab survival benefit was positively associated with PD-L1 expression by tumor cells, tumor mutational burden, APOBEC mutation signatures, expression of genes underlying innate and adaptive immune activity and the number of alleles encoding high-affinity variants of activating Fc gamma receptors. Pathways connected to tissue growth and angiogenesis might have been associated with reduced survival benefit. Individual biomarkers did not comprehensively identify patients who could benefit from therapy; however, multi-parameter models incorporating genomic alteration, immune responses and tumor growth showed promising predictive utility. These results characterize the complex biologic pathways underlying survival benefit from immune checkpoint inhibition in advanced urothelial cancer and suggest that multiple biomarkers might be needed to identify patients who would benefit from treatment.

Automated summary

In a recent study of advanced urothelial cancer patients, avelumab in combination with best supportive care significantly prolonged overall survival compared to care alone, with factors such as PD-L1 expression, tumor mutational burden, immune activity genes, and Fcγ receptor variants being positively associated with survival benefit. Individual biomarkers alone were not sufficient to predict therapy response, but multi-parameter models integrating genomic alterations, immune responses, and tumor growth showed potential for predictive utility. These findings highlight the complexity of biological pathways in immune checkpoint inhibition therapy and suggest the need for multiple biomarkers to identify patients who would benefit from treatment.