Computational Algorithm Driven Evaluation of Monocytic Myeloid Derived Suppressor Cell Frequency For Prediction of Clinical Outcomes

Abstract

Evaluation of myeloid derived suppressor cells (MDSC), a cell type implicated in T cell suppression, may inform immune status. However, uniform methodology is necessary for prospective testing as a biomarker. We report use of a computational algorithm driven analysis of whole blood and cryopreserved samples for monocytic MDSC (m-MDSC) quantity that removes variables related to blood processing and user definitions. Applying these methods to melanoma patients identifies differing frequency distribution of m-MDSC relative to healthy donors (HD). Patients with a pre-treatment m-MDSC frequency outside a preliminary definition of HD range (<14.9%) were significantly more likely to achieve prolonged overall survival following treatment with ipilimumab, an antibody that promotes T cell activation and proliferation. m-MDSC frequencies inversely correlated with peripheral CD8+ T cell expansion following ipilimumab. Algorithm driven analysis may enable not only development of a novel pre-treatment biomarker for ipilimumab therapy, but also prospective validation of peripheral blood m-MDSC as a biomarker in multiple disease settings.