Myeloid-derived suppressor cells a potential biomarker for metastatic melanoma;claims study
Defining the level and the type of spontaneous tumor immunity can help in implementing patient prognostication and tailoring treatment intervention. Preclinical models clearly proved that the onset of protective tumor immunity requires the coordination of multiple cell types and tissues at organism-wide level.However, the assessment of tumor immunological features has been largely focused on tumor biopsies through the quantification and the spatial distribution of the immune infiltrate.
This holds particularly true for Myeloid-derived suppressor cells (MDSC), which are generated in the bone marrow by myelopoiesis, enter peripheral blood and then colonize tumor and immune-relevant sites to exert their regulatory functions.MDSC are notoriously a marker of cancer-associated immunosuppression and the hallmark of a poorly controllable disease in most human cancers. Despite the massive evidence of the role of MDSC as a cornerstone in cancer progression, these cells remain a yet-to-be-exploited biomarker in real-life clinical practice.
According to a recent research report, a myeloid index score greater than zero identifies melanoma patients with a more aggressive disease, thus acting as a simple blood biomarker that can help tailoring therapeutic choices in real-life oncology. The findings have been put forth in Journal of Immunotherapy Cancer.
In the current study, researchers used a multistep downsizing process to verify whether a core of few markers could be sufficient to capture the prognostic potential of myeloid cells in peripheral blood mononuclear cells (PBMC) of metastatic melanoma patients.
Regarding the study design, in baseline frozen PBMC from a total of 143 stage IIIc to IV melanoma patients, the team first assessed the relevant or redundant expression of myeloid and MDSC-related markers by flow cytometry (screening set, n=23 patients). Subsequently, they applied the identified panel to the development set samples (n=59 patients undergoing first/second-line therapy) to obtain prognostic variables associated with overall survival (OS) and progression-free survival (PFS) by machine learning adaptive index modeling. Finally, the identified score was confirmed in a validation set (n=61) and compared with standard clinical prognostic factors to assess its additive value in patient prognostication.
Data analysis revealed some key facts.
- This selection process led to the identification of what we defined myeloid index score (MIS), which is composed by four cell subsets (CD14+, CD14+HLA-DRneg, CD14+PD-L1+ and CD15+ cells), whose frequencies above cut-offs stratified melanoma patients according to progressively worse prognosis.
- Patients with a MIS=0, showing no over-threshold value of MIS subsets, had the best clinical outcome, with a median survival of >33.6 months, while in patients with MIS 1→3, OS deteriorated from 10.9 to 6.8 and 6.0 months as the MIS increased (p<0.0001, c-index=0.745).
- MIS clustered patients into risk groups also according to PFS (p<0.0001).
- The inverse correlation between MIS and survival was confirmed in the validation set, was independent of the type of therapy and was not interfered by clinical prognostic factors.
- MIS HR was remarkably superior to that of lactate dehydrogenase, tumor burden and neutrophil-to-lymphocyte ratio.
For full article follow the link: 10.1136/jitc-2020-001167
Primary source: Journal of Immunotherapy Cancer
