Adriana Cassaro

Differential and transferable modulatory effects of mesenchymal stromal cell-derived extracellular vesicles on T, B and NK cell functions.

Mesenchymal stromal cells (MSCs) are multipotent cells, immunomodulatory stem cells that are currently used for regenerative medicine and treatment of a number of inflammatory diseases, thanks to their ability to significantly influence tissue microenvironments through the secretion of large variety of soluble factors. Recently, several groups have reported the presence of extracellular vesicles (EVs) within MSC secretoma, showing their beneficial effect in different animal models of disease. Here, we used a standardized methodological approach to dissect the immunomodulatory effects exerted by MSC-derived EVs on unfractionated peripheral blood mononuclear cells and purified T, B and NK cells. We describe here for the first time: i. direct correlation between the degree of EV-mediated immunosuppression and EV uptake by immune effector cells, a phenomenon further amplified following MSC priming with inflammatory cytokines; ii. induction in resting MSCs of immunosuppressive properties towards T cell proliferation through EVs obtained from primed MSCs, without any direct inhibitory effect towards T cell division. Our conclusion is that the use of reproducible and validated assays is not only useful to characterize the mechanisms of action of MSC-derived EVs, but is also capable of justifying EV potential use as alternative cell-free therapy for the treatment of human inflammatory diseases.

Identification of granulocytic myeloid-derived suppressor cells (G-MDSCs) in the peripheral blood of Hodgkin and non-Hodgkin lymphoma patients

Human granulocytic myeloid-derived suppressor cells (G-MDSCs) have been described as low-density immunosuppressive CD66b+CD33dimHLA-DR-granulocytes that co-purify with mononuclear cells after density gradient centrifugation of blood from cancer patients. The role of G-MDSCs in Hodgkin (HL) and non-Hodgkin lymphoma (NHL) remains unclear. The percentage and immunophenotype of CD66b+CD33dimHLA-DR-cells were analyzed in PBMCs from HL and B-cell NHL patients (n = 124) and healthy donors (n = 48). The immunosuppressive functions of these cells were tested in vitro. Correlations between CD66b+CD33dimHLA-DR-cells and patient clinicopathological features and outcome, were evaluated. CD66b+CD33dimHLA-DR-cells were increased in PBMCs from HL and B-cell NHL patients as compared to healthy donors: 2.18 (0.02–70.92) vs 0.42 (0.04–2.97), p < 0.0001. Their percentage remained significantly higher even considering HL (n = 31), indolent (n = 31) and aggressive (n = 62) B-cell NHL patients separately: 1.54 (0.28–26.34), 2.15 (0.02–20.08), and 2.96 (0.25–70.92), respectively, p < 0.0001. CD66b+CD33dimHLA-DR-cells in patient PBMCs were mostly composed of mature CD11b+CD16+ low-density neutrophils in an activated status, as revealed by their higher CD11b and CD66b expression as compared to conventionally isolated (normal-density) autologous or healthy donor neutrophils. The in vitro depletion of CD66b+ cells from patient PBMCs restored the proliferation of autologous T cells. Higher frequencies of CD66b+CD33dimHLA-DR− G-MDSCs correlated significantly with unfavorable prognostic index scores and a shorter freedom from disease progression. PBMCs from HL and B-cell NHL patients contain a population of CD66b+CD33dimHLA-DR− G-MDSCs, mostly composed of activated low-density neutrophils with immunosuppressive properties. These findings disclose a previously unknown G-MDSC-mediated mechanism of immune-escape in lymphomas, therefore anticipating possible targets for therapeutic interventions.

Notch signalling drives bone marrow stromal cell-mediated chemoresistance in acute myeloid leukemia

Both preclinical and clinical investigations suggest that Notch signalling is critical for the development of many cancers and for their response to chemotherapy. We previously showed that Notch inhibition abrogates stromal-induced chemoresistance in lymphoid neoplasms. However, the role of Notch in acute myeloid leukemia (AML) and its contribution to the crosstalk between leukemia cells and bone marrow stromal cells remain controversial. Thus, we evaluated the role of the Notch pathway in the proliferation, survival and chemoresistance of AML cells in co-culture with bone marrow mesenchymal stromal cells expanded from both healthy donors (hBM-MSCs) and AML patients (hBM-MSCs*). As compared to hBM-MSCs, hBM-MSCs* showed higher level of Notch1, Jagged1 as well as the main Notch target gene HES1. Notably, hBM-MSCs* induced expression and activation of Notch signalling in AML cells, supporting AML proliferation and being more efficientin inducing AML chemoresistance than hBM-MSCs*. Pharmacological inhibition of Notch using combinations of Notch receptor-blocking antibodies or gamma-secretase inhibitors (GSIs), in presence of chemotherapeutic agents, significant lowered the supportive effect of hBM-MSCs and hBM-MSCs* towards AML cells, by activating apoptotic cascade and reducing protein level of STAT3, AKT and NF-κB. These results suggest that Notch signalling inhibition, by overcoming the stromal-mediated promotion of chemoresistance,may represent a potential therapeutic targetnot only for lymphoid neoplasms, but also for AML.

Endothelin-1 receptor blockade as new possible therapeutic approach in multiple myeloma

New effective treatments are needed to improve outcomes for multiple myeloma (MM) patients. Receptors with restricted expression on plasma cells (PCs) represent attractive new therapeutic targets. The endothelin‐1 (EDN1) axis, consisting of EDN1 acting through EDN‐receptor A (EDNRA) and B (EDNRB), was previously shown to be overexpressed in several tumours, including MM. However, there is incomplete understanding of how EDN1 axis regulates MM growth and response to therapy. Besides EDNRA, the majority of MM cell lines and primary malignant PCs express high levels of EDNRB and release EDN1. Similarly, bone‐marrow microenvironment cells also secrete EDN1. Investigating the extent of epigenetic dysregulation of EDNRB gene in MM, we found that hypermethylation of EDNRB promoter and subsequent down‐regulation of EDNRB gene was observed in PCs or B lymphocytes from healthy donors compared to EDNRB‐expressing malignant PCs. Pharmacological blockade with the dual EDN1 receptor antagonist bosentan decreased cell viability and MAPK activation of U266 and RPMI‐8226 cells. Interestingly, the combination of bosentan and the proteasome inhibitor bortezomib, currently approved for MM treatment, resulted in synergistic cytotoxic effects. Overall, our data has uncovered EDN1‐mediated autocrine and paracrine mechanisms that regulate malignant PCs growth and drug response, and support EDN1 receptors as new therapeutic targets in MM.

Role of Wnt/β-Catenin Signalling in Acute Myeloid Leukemia (AML) Cell Response to Chemotherapy

Although drug-induced thrombotic microangiopathy (TMA) is commonly reported, evidence for a causal association is uncommonly documented. In this issue of Blood, Kavanagh et al use multiple methods to clearly establish a causal association of type I interferon with TMA.1

In reply to Schäfer et al: new evidence on the role of endothelin-1 axis as a potential therapeutic target in multiple myeloma

frequently found among those with positive correlation whereas upregulated genes were more frequent among those with negative expression correlation. Table SI. Biological features of the 85 CLL cases from CRO (Italy). Table SII. Baseline characteristics of the 296 CLL cases (ICGC series). Table SIII. Summary of probes for transcripts overlapping UCR loci found significantly differentially expressed by CpGODN in the discovery CLL series. Table SIV. Univariate Cox regression analyses of TTT in the 67 CLL series. Table SV. Multivariate Cox regression analyses of TTT in the 67 CLL series. Table SVI. Statistical analyses of uc.70-related transcripts impact on TTT in the 296 CLL ICGC series. Table SVII. Pearson correlation of ICGC CLL gene expression in reference to the transcript AC092652.2-202 (5th percentile). Data S1. Supplementary methods.