Computational algorithm-driven evaluation of monocytic myeloid-derived suppressor cell frequency for prediction of clinical outcomes

S Kitano, MA Postow, CGK Ziegler, D Kuk… - Cancer immunology …, 2014 - AACR
S Kitano, MA Postow, CGK Ziegler, D Kuk, KS Panageas, C Cortez, T Rasalan, M Adamow…
Cancer immunology research, 2014AACR
Abstract Evaluation of myeloid-derived suppressor cells (MDSC), a cell type implicated in T-
cell suppression, may inform immune status. However, a uniform methodology is necessary
for prospective testing as a biomarker. We report the 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 samples from patients with melanoma identifies differing frequency …
Abstract
Evaluation of myeloid-derived suppressor cells (MDSC), a cell type implicated in T-cell suppression, may inform immune status. However, a uniform methodology is necessary for prospective testing as a biomarker. We report the 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 samples from patients with melanoma identifies differing frequency distribution of m-MDSC relative to that in healthy donors. Patients with a pretreatment m-MDSC frequency outside a preliminary definition of healthy donor 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 were inversely correlated with peripheral CD8+ T-cell expansion following ipilimumab. Algorithm-driven analysis may enable not only development of a novel pretreatment biomarker for ipilimumab therapy, but also prospective validation of peripheral blood m-MDSCs as a biomarker in multiple disease settings. Cancer Immunol Res; 2(8); 812–21. ©2014 AACR.
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