Stromal STAT5-Mediated Trophic Activity Regulates Hematopoietic Niche Factors.

Signal transducer and activator of transcription 5 (STAT5a and STAT5b) are intrinsically critical for normal hematopoiesis but are also expressed in stromal cells. Here, STAT5ab knockout (KO) was generated with a variety of bone marrow hematopoietic and stromal Cre transgenic mouse strains. Vav1-Cre/+STAT5abfl/fl, the positive control for loss of multipotent hematopoietic function, surprisingly dysregulated niche factor mRNA expression, and deleted STAT5ab in CD45neg cells. Single-cell transcriptome analysis of bone marrow from Vav1-Cre/+ wild-type or Vav1-Cre/+STAT5abfl/fl mice showed hematopoietic stem cell (HSC) myeloid commitment priming. Nes+ cells were detected in both CD45neg and CD45+ clusters and deletion of STAT5ab with Nes-Cre caused hematopoietic repopulating defects. To follow up on these promiscuous Cre promoter deletions in CD45neg and CD45+ bone marrow cell populations, more stroma-specific Cre strains were generated and demonstrated a reduction in multipotent hematopoietic progenitors. Functional support for niche-supporting activity was assessed using STAT5-deficient mesenchymal stem cells (MSCs). With Lepr-Cre/+STAT5abfl/fl, niche factor mRNAs were downregulated with validation of reduced IGF-1 and CXCL12 proteins. Furthermore, advanced computational analyses revealed a key role for STAT5ab/Cish balance with Cish strongly co-expressed in MSCs and HSCs primed for differentiation. Therefore, STAT5ab-associated gene regulation supports the bone marrow microenvironment.

Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.

The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well-established for evaluating exposure-response (E-R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evaluating E-R relationships has not been widely explored. We developed a workflow to train regularized LR/CoxPH and tree-based XGboost (XGB) models, and derive the odds ratios for best overall response and hazard ratios for overall survival, across exposure quantiles to evaluate the E-R relationship using clinical trial datasets. The E-R conclusions between LR/CoxPH and XGB models are overall consistent, and largely aligned with historical pharmacometric analyses findings. Overall, applying this interpretable ML workflow provides a promising alternative method to assess E-R relationships for impacting key dosing decisions in drug development.

Analyzing immune response to engineered hydrogels by hierarchical clustering of inflammatory cell subsets.

Understanding the immune response to hydrogel implantation is critical for the design of immunomodulatory biomaterials. To study the progression of inflammation around poly(ethylene glycol) hydrogels presenting Arg-Gly-Asp (RGD) peptides and vascular endothelial growth factor, we used temporal analysis of high-dimensional flow cytometry data paired with intravital imaging, immunohistochemistry, and multiplexed proteomic profiling. RGD-presenting hydrogels created a reparative microenvironment promoting CD206+ cellular infiltration and revascularization in wounded dorsal skin tissue. Unbiased clustering algorithms (SPADE) revealed significant phenotypic transition shifts as a function of the cell-adhesion hydrogel properties. SPADE identified an intermediate macrophage subset functionally regulating in vivo cytokine secretion that was preferentially recruited for RGD-presenting hydrogels, whereas dendritic cell subsets were preferentially recruited to RDG-presenting hydrogels. Last, RGD-presenting hydrogels controlled macrophage functional cytokine secretion to direct polarization and vascularization. Our studies show that unbiased clustering of single-cell data provides unbiased insights into the underlying immune response to engineered materials.

Risk-associated alterations in marrow T cells in pediatric leukemia.

Current management of childhood leukemia is tailored based on disease risk determined by clinical features at presentation. Whether properties of the host immune response impact disease risk and outcome is not known. Here, we combine mass cytometry, single cell genomics, and functional studies to characterize the BM immune environment in children with B cell acute lymphoblastic leukemia and acute myelogenous leukemia at presentation. T cells in leukemia marrow demonstrate evidence of chronic immune activation and exhaustion/dysfunction, with attrition of naive T cells and TCF1+ stem-like memory T cells and accumulation of terminally differentiated effector T cells. Marrow-infiltrating NK cells also exhibit evidence of dysfunction, particularly in myeloid leukemia. Properties of immune cells identified distinct immune phenotype-based clusters correlating with disease risk in acute lymphoblastic leukemia. High-risk immune signatures were associated with expression of stem-like genes on tumor cells. These data provide a comprehensive assessment of the immune landscape of childhood leukemias and identify targets potentially amenable to therapeutic intervention. These studies also suggest that properties of the host response with depletion of naive T cells and accumulation of terminal-effector T cells may contribute to the biologic basis of disease risk. Properties of immune microenvironment identified here may also impact optimal application of immune therapies, including T cell-redirection approaches in childhood leukemia.

Correlation Patterns Between DNA Methylation and Gene Expression in The Cancer Genome Atlas.

BACKGROUND: DNA methylation is a form of epigenetic modification that has been shown to play a significant role in gene regulation. In cancer, DNA methylation plays an important role by regulating the expression of oncogenes. The role of DNA methylation in the onset and progression of various cancer types is now being elucidated as more large-scale data become available. The Cancer Genome Atlas (TCGA) provides a wealth of information for the analysis of various molecular aspects of cancer genetics. Gene expression data and DNA methylation data from TCGA have been used for a variety of studies. A traditional understanding of the effects of DNA methylation on gene expression has linked methylation of CpG sites in the gene promoter region with the decrease in gene expression. Recent studies have begun to expand this traditional role of DNA methylation. RESULTS: Here we present a pan-cancer analysis of correlation patterns between CpG methylation and gene expression. Using matching patient data from TCGA, 33 cancer-specific correlations were calculated for each CpG site and the expression level of its corresponding gene. These correlations were used to identify patterns on a per-site basis as well as patterns of methylation across the gene body. Using these identified patterns, we found genes that contain conflicting methylation signals beyond the commonly accepted association between the promoter region methylation and silencing of gene expression. Beyond gene body methylation in whole, we examined individual CpG sites and show that, even in the same gene body, some sites can have a contradictory effect on gene expression in cancers. CONCLUSIONS: We observed that within promoter regions there was a substantial amount of positive correlation between methylation and gene expression, which contradicts the commonly accepted association. We observed that the correlation between CpG methylation and gene expression does not exhibit in a tissue-specific manner, suggesting that the effects of methylation on gene expression are largely tissue independent. The analysis of correlation associated with the location of the CpG site in the gene body has led to the identification of several different methylation patterns that affect gene expression, and several examples of methylation activating gene expression were observed. Distinctly opposing or conflicting effects were seen in close proximity on the gene body, where negative and positive correlations were seen at the neighboring CpG sites.

Quantification and tracking of genetically engineered dendritic cells for studying immunotherapy.

PURPOSE: Genetically encoded reporters can assist in visualizing biological processes in live organisms and have been proposed for longitudinal and noninvasive tracking of therapeutic cells in deep tissue. Cells can be labeled in situ or ex vivo and followed in live subjects over time. Nevertheless, a major challenge for reporter systems is to identify the cell population that actually expresses an active reporter. METHODS: We have used a nucleoside analog, pyrrolo-2'-deoxycytidine, as an imaging probe for the putative reporter gene, Drosophila melanogaster 2'-deoxynucleoside kinase. Bioengineered cells were imaged in vivo in animal models of brain tumor and immunotherapy using chemical exchange saturation transfer MRI. The number of transduced cells was quantified by flow cytometry based on the optical properties of the probe. RESULTS: We performed a comparative analysis of six different cell lines and demonstrate utility in a mouse model of immunotherapy. The proposed technology can be used to quantify the number of labeled cells in a given region, and moreover is sensitive enough to detect less than 10,000 cells. CONCLUSION: This unique technology that enables efficient selection of labeled cells followed by in vivo monitoring with both optical and MRI. Magn Reson Med 79:1010-1019, 2018. © 2017 International Society for Magnetic Resonance in Medicine.