12 research outputs found

    Prediagnosis aspirin use and outcomes in a prospective cohort of esophageal cancer patients

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    Background: Esophageal cancer remains associated with poor outcomes, yet little is known regarding factors that influence survival. Aspirin use prior to cancer diagnosis may influence outcomes. We aimed to assess the effects of prediagnosis aspirin use in patients with esophageal cancer. Methods: We conducted a prospective cohort study of newly-diagnosed esophageal cancer patients at two tertiary care centers. We assessed history of prediagnosis aspirin use, and prospectively followed patients and assessed mortality, cause of death, and development of metastases. Results: We enrolled 130 patients, the majority of whom were male (81.5%) and had adenocarcinoma (80.8%). Overall, 57 patients (43.9%) were regular aspirin users. In unadjusted analyses, we found no difference in all-cause mortality between aspirin users and nonusers. In multivariate analyses, prediagnosis aspirin use was not associated with all-cause mortality [hazard ratio (HR) 0.86, 95% confidence interval (CI) 0.48–1.57] or esophageal cancer-specific mortality (HR 1.07, 95% CI 0.52–2.21). Prediagnosis aspirin use was associated with a significantly increased risk of interval metastasis (HR 3.59, 95% CI 1.08–11.96). Conclusions: In our cohort of esophageal cancer patients, prediagnosis aspirin use was not associated with all-cause or cancer-specific mortality. However, risk of interval metastatic disease was increased among those who took aspirin regularly prediagnosis. Future studies are warranted to assess whether aspirin influences the molecular characteristics of esophageal tumors, with potential prognostic and therapeutic implications

    Identification of Reprogrammed Myeloid Cell Transcriptomes in NSCLC

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

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    <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

    Depletion of Ly6G<sup>+</sup> neutrophils impair growth of lung adenocarcinoma in mice.

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    <p>(A) Quantitation of orthotopic lung tumors in mice treated with either anti-Ly6G (αLy6G) or control (IgG) antibodies as assessed by bioluminescence (BLI) measurements as a function of time (in days, n = 7 and 6 per group respectively). Arrows indicate days at which antibody was administered. Data represent mean ± SEM. (B) Representative BLI images of animals. Color scale bar depicts the photon flux (photons per second) emitted from these mice at day 22. (C) Representative micro-CT slice images of lungs showing tumors in mice treated with anti-Ly6G or control IgG antibody at day 25. Axial, coronal, and sagittal views shown. Bright objects are high density (bone) and black represents air voids within the animal. Axial images were taken from same relative position in each animal; cross-hair points to the same location in all views. D, dorsal; V, ventral; L, left; R, right. (D) Quantification of lung tumor burden by micro-CT analysis (n = 3 per group) HU, Hounsfield Unit (where -1000 is air, -700 is lung, and +100 to +300 is soft tissue). Data represent mean ± SEM. (E) Flow cytometry scatter plots of peripheral blood showing depletion of Ly6G<sup>+</sup> cells in anti-Ly6G treated mice as compared with control IgG treated mice at 3 days post-treatment. (F) Flow cytometry analysis of peripheral blood showing numbers (cells per ul of blood) of Ly6G<sup>+</sup> and CD11b<sup>+</sup> myeloid cells, CD4<sup>+</sup> and CD8<sup>+</sup> T cells, B220<sup>+</sup> B cells, and Ly6C<sup>+</sup>Ly6G<sup>-</sup> monocytes. n = 4 per group. Data represent mean ± SEM.</p

    Increased number of bone marrow hematopoietic cells infiltrate lung compared to matched adjacent non-neoplastic lung.

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    <p>(A) H&E staining of lung tissue from an adenocarcinoma patient. The dotted line separates the tumor from the adjacent non-neoplastic lung. Image is at 20X magnification. (B) Representative immunofluorescence image of tumor and matched adjacent non-neoplastic lung of adenocarcinoma patient stained for epithelial cells (EpCAM<sup>+</sup>, red) and BM-derived hematopoietic cells (CD45<sup>+</sup>, green). DAPI (blue) was used to label cell nuclei. (C) Flow cytometry scatter plots showing CD45<sup>+</sup>EpCAM<sup>-</sup> BM hematopoietic cells and CD45<sup>-</sup>EpCAM<sup>+</sup> epithelial cells in tumor and matched adjacent lung. (D) Quantitation of CD45<sup>+</sup>EpCAM<sup>-</sup> BM-derived hematopoietic cells in NSCLC patients (n = 5). Data represents mean ± SEM. (E) Flow cytometry scatter plots showing EpCAM<sup>-</sup>CD11b<sup>+</sup>CD33<sup>-</sup> BM-derived neutrophils and EpCAM<sup>-</sup>CD11b<sup>+</sup>CD33<sup>+</sup> BM-derived immature myeloid cells in tumor and matched adjacent lung. (F) Flow cytometry scatter plots showing CD11b<sup>+</sup>CD33<sup>-</sup> neutrophils are CXCR2<sup>+</sup> while CD11b<sup>+</sup>CD33<sup>+</sup> immature myeloid cells are CXCR2<sup>-</sup> (left panel). Microscopy of flow cytometry sorted cells stained with H&E showing nuclear morphology CD11b<sup>+</sup>CD33<sup>-</sup> and CD11b<sup>+</sup>CD33<sup>+</sup> cells (right panel).</p

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

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    <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

    Human erythroleukemia genetics and transcriptomes identify master transcription factors as functional disease drivers.

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    Acute erythroleukemia (AEL or acute myeloid leukemia [AML]-M6) is a rare but aggressive hematologic malignancy. Previous studies showed that AEL leukemic cells often carry complex karyotypes and mutations in known AML-associated oncogenes. To better define the underlying molecular mechanisms driving the erythroid phenotype, we studied a series of 33 AEL samples representing 3 genetic AEL subgroups including TP53-mutated, epigenetic regulator-mutated (eg, DNMT3A, TET2, or IDH2), and undefined cases with low mutational burden. We established an erythroid vs myeloid transcriptome-based space in which, independently of the molecular subgroup, the majority of the AEL samples exhibited a unique mapping different from both non-M6 AML and myelodysplastic syndrome samples. Notably, >25% of AEL patients, including in the genetically undefined subgroup, showed aberrant expression of key transcriptional regulators, including SKI, ERG, and ETO2. Ectopic expression of these factors in murine erythroid progenitors blocked in vitro erythroid differentiation and led to immortalization associated with decreased chromatin accessibility at GATA1-binding sites and functional interference with GATA1 activity. In vivo models showed development of lethal erythroid, mixed erythroid/myeloid, or other malignancies depending on the cell population in which AEL-associated alterations were expressed. Collectively, our data indicate that AEL is a molecularly heterogeneous disease with an erythroid identity that results in part from the aberrant activity of key erythroid transcription factors in hematopoietic stem or progenitor cells
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