Background Certain cell subsets have been identified to have a negative impact on cancer immunotherapies by promoting angiogenesis and immunosuppression in the tumor microenvironment. One of these cell subsets is a heterogeneous population of immature myeloid cells that have been named Myeloid Derived Suppressor Cells (MDSC). MDSC are increased in states of cancer and their numbers have been shown to inversely correlate with a positive clinical outcome. These findings have prompted the measurement of MDSC in order to predict clinical outcome during treatment with immunotherapies. In this study, flow cytometry was used to measure M-MDSC in frozen PBMC samples and hence predict medical outcome in melanoma patients treated with an anti-CTLA-4 drug (ipilimumab).
Methods M-MDSC were measured in frozen PBMC from 20 healthy donors and 68 patients with melanoma treated with ipilimumab. M-MDSC were enumerated using a Lin-CD14+CD11b+HLA-DRlow/- phenotype. In order to prevent subjectivity during gating, caused by the lack of bi-modality with HLA-DR staining, a computational algorithm was used. As distinct HLA-DR spread can be observed in the different subjects, measuring the CV (a ratio between GMFI and SD) of this spread allows to calculate a standardized ad hoc quantitative measure of MDSC frequency in cancer patients. This measurement enables identification of M-MDSC in an objective manner and was used to determine whether the percentage of M-MDSC in patients could be linked with overall survival.
Results The relative frequency of M-MDSC was determined in 68 melanoma patients treated with two different doses of ipilimumab. By comparing the percentage of M-MDSC at baseline (pre-treatment) and after two doses of ipilimumab with the overall survival data and applying log-rank statistics, a cut-off was defined allowing the separation of ‘high’ and ‘low’ M-MDSC expressers. Patients with ‘low’ M-MDSC were associated with improved overall survival with a hazard ratio of 0.35.
Conclusions The reliable measurement of immune suppressive cells such as MDSC gives the ability to predict the clinical outcome of cancer treatments. In turn, these measurements will permit the design of patient-specific treatments as inhibitors to these cell subsets become available, making personalized medicine a reality in contemporaneous cancer treatment. The identification of specific phenotypes and activation markers for MDSC may improve the prediction ability of the test described in this study. These results highlight the importance of linking the frequency of immune suppressive cells with clinical outcome.
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