Effect of ²²⁵Ac-E4G10 therapy on tumor histology, vascularity and apoptosis.

<p>A, Light microscopy depicting numerous RBC-filled vascular spaces (arrows) in dual control tumor and fewer, but relatively normal-looking vessels (arrowheads) in the ²²⁵Ac-E4G10 treated tumor. B, Top: Immunohistochemical staining of tumor-sections for vWF, an endothelial cell marker (top). TUNEL staining of tumor sections to detect apoptosis (bottom). Quantification of vWF staining (C) and apoptosis (D) in 4 randomly selected fields. Data are mean ± S.E.M.</p

²²⁵Ac-E4G10 treatment results in a relatively normal remaining tumor vasculature.

<p>A, Greater coverage of tumor blood vessels (CD31 positive) by pericytes (α-SMA-positive cells) in ²²⁵Ac-E4G10 treated tumor relative to dual control. B, Transmission electron micrographs of blood vessels in dual control and ²²⁵Ac-E4G10 treated tumor. The dual control tumor contains extravasated RBC-filled vascular spaces that are not lined with endothelial cells, whereas blood vessels in ²²⁵Ac-E4G10 treated tumor display a continuous endothelial lining (arrow) resting on a basement membrane (BM) that is shared with the surrounding pericyte. Scale bar, 50 µm</p

²²⁵Ac-E4G10 therapy inhibits the growth of LnCap prostate tumors.

<p>A, Flow cytometric analysis depicting the lack of E4G10 binding to LnCap cells; J591, mouse-anti prostate specific membrane antigen is the positive control. Mouse and rat isotype controls were also evaluated. B, Photographs of in situ (left) and excised tumor (right) in a representative dual control and ²²⁵Ac-E4G10 treated animal. C, Tumor volume in various treatment groups at described time-points. D, Serum prostate specific antigen (PSA) levels in the three treatment groups at 22 days post-implantation with 5 million LnCap cells. E, Kaplan Meier curve showing enhancement of survival with ²²⁵Ac-E4G10 treatment. Data in C, D are mean ± S.E.M. <i>Scale bar</i>, 1 cm.</p

A combination of ²²⁵Ac-E4G10 with paclitaxel enhances the anti-tumor response.

<p>A, Tumor volume in the four treatment groups over time. Data are mean ± S.E.M. B, Kaplan Meier survival curve of treated animals showing significant enhancement of animal survival when ²²⁵Ac-E4G10 therapy is followed by a course of paclitaxel. C, Absence of histopathologic damage in normal organs, assessed 10 days after cessation of ²²⁵Ac-E4G10 treatment.</p

Heritabilities of measured CVD biomarkers.

<p>Presented is the distribution of heritability and its corresponding p-value for all 18 biomarkers, with the heritability estimate on the X-axis and the –log base 10 of the associated p-value on the Y-axis. The 95% confidence interval is represented by a horizontal error bar. The threshold for significance is represented by a dashed line.</p

Genome-Wide Linkage Analysis of Cardiovascular Disease Biomarkers in a Large, Multigenerational Family

<div><p>Given the importance of cardiovascular disease (CVD) to public health and the demonstrated heritability of both disease status and its related risk factors, identifying the genetic variation underlying these susceptibilities is a critical step in understanding the pathogenesis of CVD and informing prevention and treatment strategies. Although one can look for genetic variation underlying susceptibility to CVD <i>per se</i>, it can be difficult to define the disease phenotype for such a qualitative analysis and CVD itself represents a convergence of diverse etiologic pathways. Alternatively, one can study the genetics of intermediate traits that are known risk factors for CVD, which can be measured quantitatively. Using the latter strategy, we have measured 21 cardiovascular-related biomarkers in an extended multigenerational pedigree, the CARRIAGE family (Carolinas Region Interaction of Aging, Genes, and Environment). These biomarkers belong to inflammatory and immune, connective tissue, lipid, and hemostasis pathways. Of these, 18 met our quality control standards. Using the pedigree and biomarker data, we have estimated the broad sense heritability (H2) of each biomarker (ranging from 0.09–0.56). A genome-wide panel of 6,015 SNPs was used subsequently to map these biomarkers as quantitative traits. Four showed noteworthy evidence for linkage in multipoint analysis (LOD score ≥ 2.6): paraoxonase (chromosome 8p11, 21), the chemokine RANTES (22q13.33), matrix metalloproteinase 3 (MMP3, 17p13.3), and granulocyte colony stimulating factor (GCSF, 8q22.1). Identifying the causal variation underlying each linkage score will help to unravel the genetic architecture of these quantitative traits and, by extension, the genetic architecture of cardiovascular risk.</p> </div

Multipoint genome-wide linkage scans for GCSF, MMP3, paraoxonase, and RANTES.

<p>Vertical dashed lines represent boundaries between chromosomal regions and the cumulative cM position is indicated on the X axis.</p

Identification of Reprogrammed Myeloid Cell Transcriptomes in NSCLC

<div><p>Lung cancer is the leading cause of cancer related mortality worldwide, with non-small cell lung cancer (NSCLC) as the most prevalent form. Despite advances in treatment options including minimally invasive surgery, CT-guided radiation, novel chemotherapeutic regimens, and targeted therapeutics, prognosis remains dismal. Therefore, further molecular analysis of NSCLC is necessary to identify novel molecular targets that impact prognosis and the design of new-targeted therapies. In recent years, tumor “activated/reprogrammed” stromal cells that promote carcinogenesis have emerged as potential therapeutic targets. However, the contribution of stromal cells to NSCLC is poorly understood. Here, we show increased numbers of bone marrow (BM)-derived hematopoietic cells in the tumor parenchyma of NSCLC patients compared with matched adjacent non-neoplastic lung tissue. By sorting specific cellular fractions from lung cancer patients, we compared the transcriptomes of intratumoral myeloid compartments within the tumor bed with their counterparts within adjacent non-neoplastic tissue from NSCLC patients. The RNA sequencing of specific myeloid compartments (immature monocytic myeloid cells and polymorphonuclear neutrophils) identified differentially regulated genes and mRNA isoforms, which were inconspicuous in whole tumor analysis. Genes encoding secreted factors, including osteopontin (OPN), chemokine (C-C motif) ligand 7 (CCL7) and thrombospondin 1 (TSP1) were identified, which enhanced tumorigenic properties of lung cancer cells indicative of their potential as targets for therapy. This study demonstrates that analysis of homogeneous stromal populations isolated directly from fresh clinical specimens can detect important stromal genes of therapeutic value.</p></div

RNA-seq analysis of BM immature monocytic myeloid cells and epithelial cells from NSCLC patients and controls.

<p>(A) Summary of RNA-seq reads from adenocarcinoma (tumor)- and adjacent lung-derived IMMCs (immature monocytic myeloid cells), neutrophils, and epithelial cells isolated from 6 adenocarcinoma patients and mapped to known human mRNA, genome, and novel mRNA of Aceview gene model. P, unique patient identifier; A, adjacent non-neoplastic lung tissue; T, neoplastic tumor tissue. (B) Spearman correlation analysis showing clustering of stromal cells derived from IMMCs, neutrophils, and epithelial cells based on global RNA-seq gene expression profiles into distinct tumor and adjacent lung groups. P, unique patient identifier; A, adjacent non-neoplastic lung tissue; T, neoplastic tumor tissue. (C) Venn diagrams showing total number of differentially expressed genes in immature IMMCs, neutrophils, and epithelial cells from adenocarcinoma compared to non-neoplastic adjacent lung. Cutoff of at least 50 unique mapped reads and FDR <5%. The genes in the list show differential expression with p<0.05, and fold change >2. (D) Differentially regulated stromal genes from (C), enriched for potential paracrine functions as determined by Gene Ontology annotation as secreted, extracellular space, or membrane (except membranes of organelles including golgi and endoplasmic reticulum). Of these, genes with the functions in key tumorigenic pathways including angiogenesis, ECM breakdown, cell migration, proliferation, invasion, cytokine function, chemokine function, and chemotactic function were selected. Genes selected for analysis are denoted in blue, transmembrane; red, secreted.</p

RNA-deep sequencing analysis unravels differentially regulated mRNA isoforms in intratumoral BM-derived myeloid cells.

<p>(A) List of stroma-specific genes differentially regulated at the mRNA isoform level. (B) Wiggle plots showing read coverage across the Flt-1 gene in IMMCs from adenocarcinoma of lung (n = 3 patients) and IMMCs form adjacent lungs (n = 3 patients). The status of sFLT1 and mFLT1 is shown. (C) RNA-seq analysis showing <i>FLT-1</i> isoform expression levels of total <i>FLT1</i>, soluble FLT1 (<i>sFLT1</i>), and membrane binding FLT1 (<i>mFLT1</i>) in myeloid cells sorted from adjacent lung and tumor. (D) RT-PCR validation of <i>FLT1</i> and <i>mFLT-1</i> isoform expression in myeloid cells sorted from adjacent lung and tumor.</p