There is an increasing interest for monitoring circulating myeloid-derived suppressor cells (MDSCs) in cancer patients, but there are also divergences in their phenotypic definition. To overcome this obstacle, the Cancer Immunoguiding Program under the umbrella of the Association of Cancer Immunotherapy is coordinating a proficiency panel program that aims at harmonizing MDSC phenotyping. After a consultation period, a two-stage approach was designed to harmonize MDSC phenotype. In the first step, an international consortium of 23 laboratories immunophenotyped 10 putative MDSC subsets on pretested, peripheral blood mononuclear cells of healthy donors to assess the level of concordance and define robust marker combinations for the identification of circulating MDSCs. At this stage, no mandatory requirements to standardize reagents or protocols were introduced. Data analysis revealed a small intra-laboratory, but very high inter-laboratory variance for all MDSC subsets, especially for the granulocytic subsets. In particular, the use of a dead-cell marker altered significantly the reported percentage of granulocytic MDSCs, confirming that these cells are especially sensitive to cryopreservation and/or thawing. Importantly, the gating strategy was heterogeneous and associated with high inter-center variance. Overall, our results document the high variability in MDSC phenotyping in the multicenter setting if no harmonization/standardization measures are applied. Although the observed variability depended on a number of identified parameters, the main parameter associated with variation was the gating strategy. Based on these findings, we propose further efforts to harmonize marker combinations and gating parameters to identify strategies for a robust enumeration of MDSC subsets.

Study of myeloid cells endowed with suppressive activity is an active field of research which has particular importance in cancer, in view of the negative regulatory capacity of these cells to the host's immune response. The expansion of these cells, called myeloid-derived suppressor cells (MDSCs), has been documented in many models of tumor-bearing mice and in patients with tumors of various origin, and their presence is associated with disease progression and reduced survival. For this reason, monitoring this type of cell expansion is of clinical importance, and flow cytometry is the technique of choice for their identification. Over the years, it has been demonstrated that MDSCs comprise a group of immature myeloid cells belonging both to monocytic and granulocytic lineages, with several stages of differentiation; their occurrence depends on tumor-derived soluble factors, which guide their expansion and determine their block of differentiation. Because of their heterogeneous composition, accurate phenotyping of these cells requires a multicolor approach, so that the expansion of all MDSC subsets can be appreciated. This review article focuses on identifying MDSCs and discusses problems associated with phenotyping circulating and tumor-associated MDSCs in humans and in mouse models.

Activated T cells sustain myeloid-derived suppressor cell-mediated immune suppression

Ipilimumab, the first immune-checkpoint inhibitor extending overall survival (OS) in metastatic melanoma patients, has a survival benefit only in a proportion of patients and the development of reliable predictive biomarkers is still an unmet need. To meet this request, we used a multivariate statistical approach to test whether myeloid-derived suppressor cells (MDSC) or other tumor-associated and immunological parameters may serve as predictive or prognostic biomarkers in melanoma patients receiving ipilimumab. By using a standardized approach to determine the circulating levels of four MDSC subsets, we observed a significant expansion of three MDSC subsets at baseline, as compared to controls and, upon treatment, that high levels of CD14⁺/IL4Rα⁺ MDSCs were an independent prognostic factor of reduced OS. On the contrary, longer OS was associated to low levels of the proinflammatory proteins IL-6 and CRP and tumor-associated factors S100B and LDH both at baseline and after treatment. Increasing number of total T cells and especially of PD-1⁺/CD4⁺ T cells were associated with better prognosis, and upregulation of PD-1⁺ expression on CD4⁺ T cells upon treatment was associated with lower toxicity. As several parameters were associated to OS, we included these factors in a multivariate survival model, and we identified IL-6 and ECOG PS as independent biomarkers associated with improved OS, whereas high levels of LDH and CD14⁺/IL4Rα⁺ MDSCs were negative independent markers of reduced OS.

Some anti-cancer treatments (e. g., immunotherapies) determine, on the long term, a durable survival in a small percentage of treated patients; in graphical terms, long-term survivors typically give rise to a plateau in the right tail of the survival curve. In analysing these datasets, medians are unable to recognize the presence of this plateau. To account for long-term survivors, both value-frameworks of ASCO and ESMO have incorporated post-hoc corrections that upgrade the framework scores when a survival plateau is present. However, the empiric nature of these post-hoc corrections is self-evident. To capture the presence of a survival plateau by quantitative methods, two approaches have thus far been proposed: the milestone method and the area-under-the-curve (AUC) method. The first approach identifies a long-term time-point in the follow-up (“milestone”) at which survival percentages are extracted. The second approach, which is based on the measurement of AUC of survival curves, essentially is the rearrangement of previous methods determining mean lifetime survival; similarly to the milestone method, the application of AUC can be “restricted” to a pre-specified time-point of the follow-up. This Mini-Review examines the literature published on this topic. The main characteristics of these two methods are highlighted along with their advantages and disadvantages. The conclusion is that both the milestone method and the AUC method are able to capture the presence of a survival plateau.