Human Isogenic Cell Line Models for Neutrophils and Myeloid-Derived Suppressor Cells.

Neutrophils with immunosuppressive activity are polymorphonuclear myeloid-derived suppressor cells (MDSCs) and may contribute to the resistance to cancer immunotherapy. A major gap for understanding and targeting these cells is the paucity of cell line models with cardinal features of human immunosuppressive neutrophils and their normal counterparts, especially in an isogenic manner. To address this issue, we employ the human promyelocytic cell line HL60 and use DMSO and cytokines (granulocyte macrophage-colony stimulating factor (GM-CSF) and interleukin 6 (IL6)) to induce the formation of either neutrophils or MDSCs. The induced MDSCs are CD11b+ CD33+ HLA-DR-/low and are heterogeneous for CD15 and CD14 expression. The induced MDSCs abrogate IL2 production and activation-induced cell death of the human T cell line Jurkat stimulated by CD3/CD28 antibodies, whereas the induced neutrophils enhance IL2 production from Jurkat cells. The induced MDSCs upregulate the expression of C/EBPß, STAT3, VEGFR1, FATP2 and S100A8. Lastly, the immunosuppressive activity of the induced MDSCs is inhibited by all-trans retinoic acid and STAT3 inhibitor BP-1-102 through cellular differentiation and dedifferentiation mechanisms, respectively. Together, our study establishes a human isogenic cell line system for neutrophils and MDSCs and this system is expected to facilitate future studies on the biology and therapeutics of human immunosuppressive neutrophils.

Radiomics-Based Image Phenotyping of Kidney Apparent Diffusion Coefficient Maps: Preliminary Feasibility & Efficacy.

Given the central role of interstitial fibrosis in disease progression in chronic kidney disease (CKD), a role for diffusion-weighted MRI has been pursued. We evaluated the feasibility and preliminary efficacy of using radiomic features to phenotype apparent diffusion coefficient (ADC) maps and hence to the clinical classification(s) of the participants. The study involved 40 individuals (10 healthy and 30 with CKD (eGFR < 60 mL/min/1.73 m2)). Machine learning methods, such as hierarchical clustering and logistic regression, were used. Clustering resulted in the identification of two clusters, one including all individuals with CKD (n = 17), while the second one included all the healthy volunteers (n = 10) and the remaining individuals with CKD (n = 13), resulting in 100% specificity. Logistic regression identified five radiomic features to classify participants as with CKD vs. healthy volunteers, with a sensitivity and specificity of 93% and 70%, respectively, and an AUC of 0.95. Similarly, four radiomic features were able to classify participants as rapid vs. non-rapid CKD progressors among the 30 individuals with CKD, with a sensitivity and specificity of 71% and 43%, respectively, and an AUC of 0.75. These promising preliminary data should support future studies with larger numbers of participants with varied disease severity and etiologies to improve performance.