Predictive biomarkers of response to immune checkpoint inhibitors in mismatch repair-deficient endometrial cancer

The introduction of immune checkpoint inhibitors (ICIs) has represented a major therapeutic breakthrough for patients with mismatch repair-deficient (MMRd) endometrial cancer (EC). However, despite initial clinical success, a considerable subset of patients does not experience meaningful clinical benefit from these therapies. The lack of accurate predictive biomarkers to differentiate responders from non-responders remains a key clinical challenge. There is a pressing need for robust predictors of response that can more reliably identify patients with MMRd EC who are unlikely to benefit from ICIs, thereby guiding treatment decisions in routine practice and refining patient stratification in future clinical trials. A range of potential biomarkers has been explored in this context, including genomic, epigenomic, transcriptomic, and proteomic features of both the tumor and its microenvironment. In this review, we evaluate the predictive utility of conventional biomarkers, namely, programmed death-ligand 1 expression and tumor mutation burden, and survey emerging candidates, including proteomic immune signatures, for predicting response or resistance to ICIs in the MMRd EC population. We also examine machine-learning approaches that integrate multi-omics and clinicopathological data to improve stratification, and consider how mechanistic insights into ICI resistance may inform novel therapeutic strategies.