Nicola Johnson

Hypoxia-induced neutrophil survival is mediated by HIF-1α-dependent NF-κB activity

Neutrophils are key effector cells of the innate immune response and are required to migrate and function within adverse microenvironmental conditions. These inflammatory sites are characterized by low levels of oxygen and glucose and high levels of reductive metabolites. A major regulator of neutrophil functional longevity is the ability of these cells to undergo apoptosis. We examined the mechanism by which hypoxia causes an inhibition of neutrophil apoptosis in human and murine neutrophils. We show that neutrophils possess the hypoxia-inducible factor (HIF)-1α and factor inhibiting HIF (FIH) hydroxylase oxygen-sensing pathway and using HIF-1α–deficient myeloid cells demonstrate that HIF-1α is directly involved in regulating neutrophil survival in hypoxia. Gene array, TaqMan PCR, Western blotting, and oligonucleotide binding assays identify NF-κB as a novel hypoxia-regulated and HIF-dependent target, with inhibition of NF-κB by gliotoxin or parthenolide resulting in the abrogation of hypoxic survival. In addition, we identify macrophage inflammatory protein-1β as a novel hypoxia-induced neutrophil survival factor.

Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling

Image analysis of breast cancer tissue improves and complements genomic data to predict patient survival. Digitizing Pathology for Genomics The tumor microenvironment is a complex milieu that includes not only the cancer cells but also the stromal cells, immune cells, and even normal, healthy cells. Molecular analysis of tumor tissue is therefore a challenging task because all this “extra” genomic information can muddle the results. Conversely, biopsy tissue staining can provide a spatial and cellular readout (architecture and content), but it is mostly qualitative information. In response, Yuan and colleagues have developed a quantitative, computational approach to pathology. When combined with molecular analyses, the authors were able to uncover new knowledge about breast tumor biology and, in turn, predict patient survival. Yuan et al. first collected histopathology images, gene expression data, and DNA copy number variation data for 564 breast cancer patients. Using a portion of the images (the “discovery set”), they developed an image processing approach that automatically classified cells as cancer, lymphocyte, or stroma on the basis of their size and shape. This approach was validated on the remaining samples, and any errors in this analysis were digitally corrected before obtaining a plot of tumor cellular heterogeneity. With exact knowledge of the tumor’s cellular composition, the authors were able to correct copy number data to more accurately reflect HER2 status compared with uncorrected data. Yuan and colleagues combined their digital pathology with genomic information to devise an integrated predictor of survival for estrogen receptor (ER)–negative patients. Higher number of infiltrating lymphocytes (immune cells) as quantified by their image analysis platform were found in a subset of patients with better clinical outcome than the rest of ER-negative patients, and this outcome difference was significantly enhanced with the addition of gene expression. The quantitative and objective nature of this integrated predictor could benefit diagnosis and prognosis in many areas of cancer by using the rich combination of tumor cellular content and genomic data. Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin–stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor–negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.

Application of Array CGH on Archival Formalin-Fixed Paraffin-Embedded Tissues including small numbers of microdissected cells

Array-based comparative genomic hybridisation (aCGH) has diverse applications in cancer gene discovery and translational research. Currently, aCGH is performed primarily using high molecular weight DNA samples and its application to formalin-fixed and paraffin-embedded (FFPE) tissues remains to be established. To explore how aCGH can be reliably applied to archival FFPE tissues and whether it is possible to apply aCGH to small numbers of cells microdissected from FFPE tissue sections, we have systematically performed aCGH on 15 pairs of matched frozen and FFPE astrocytic tumour tissues using a well-established in-house human 1 Mb BAC/PAC genomic array. By spiking tumour DNA with normal DNA, we demonstrated that at least 70% of tumour DNA was required for reliable aCGH analysis. Using aCGH data from frozen tissue as a reference, it was found that only FFPE astrocytic tumour tissues that supported PCR amplification of >300 bp DNA fragment provided high quality, reproducible aCGH data. The presence of necrosis in a tissue specimen had an adverse effect on the quality of aCGH, while fixation in formalin for up to 96 h of fresh tissue did not appear to affect the quality of the result. As little as 10–20 ng DNA from frozen or FFPE tissues could be readily used for aCGH analysis following whole genome amplification (WGA). Furthermore, as few as 2000 microdissected cells from haematoxylin-stained slides of archival FFPE tissues could be successfully used for aCGH investigations when WGA was used. By careful assessment of DNA integrity and review of histology, to exclude necrosis and select specimens with a high proportion of tumour cells, it is feasible to preselect archival FFPE tissues adequate for aCGH analysis. With the help of microdissection and WGA, it is also possible to apply aCGH to histologically defined lesions, such as carcinoma in situ.

New insights into the function and regulation of endothelial cell apoptosis.

The sculpting of blood vessels to meet the changing requirements of the tissues they supply is essential for life. Many researchers believe that endothelial cell apoptosis plays an important role in this process. This belief is bolstered by the detection of endothelial apoptosis within remodeling vessels in vivo, the dramatic vascular phenotypes of mice in which regulators of endothelial apoptosis have been inactivated and the apparent dependence of angiogenesis on endothelial apoptosis in vitro. However, when examined carefully, the evidence for or against endothelial cell apoptosis playing an important role in vascular biology is largely indirect and is far from clear-cut. In this review, we will discuss the idiosyncratic process of endothelial cell apoptosis. We will then examine its complex regulation and weigh the in vitro and in vivo evidence that it plays a significant role in mammalian vascular biology.

Endothelial cells preparing to die by apoptosis initiate a program of transcriptome and glycome regulation

The protein‐based changes that underlie the cell biology of apoptosis have been extensively studied. In contrast, mRNA‐ and polysaccharide‐based changes have received relatively little attention. We have combined transcriptome and glycome analyses to show that apoptotic endothelial cell cultures undergo programmed changes to RNA transcript abundance and cell surface polysaccharide profiles. Although a few of the transcriptome changes were protective, most appeared to prepare cells for apoptosis by decreasing the reception and transduction of pro‐ survival signals, increasing pro‐death signals, increasing abundance of apoptotic machinery, inhibiting cellular proliferation, recruiting phagocytes to regions of cell death, and promoting phagocytosis. Additional transcriptomal changes appeared to alter the synthesis and modification of cell surface glycosaminoglycans. The resultant reduced abundance of sulphated cell surface glycosaminoglycans may further promote cell death by inhibiting the presentation of extracellular matrix‐tethered survival factors to their receptors on dying cells. We propose that the transcriptome and glycome regulation presented here synergize with previously described protein‐based changes to guide the apoptotic program.

Splenic marginal zone lymphoma: characterization of 7q deletion and its value in diagnosis

The diagnosis of splenic marginal zone lymphoma (SMZL) is frequently a challenge, due to its lack of specific histological features and immunophenotypic markers, and the existence of other poorly characterized splenic lymphomas defying classification. Moreover, the clinical outcome of SMZL is variable, with 30% of cases pursuing an aggressive clinical course, the prediction of which remains problematic. Thus, there is a real need for biomarkers in the diagnosis and prognostication of SMZL. To search for genetic markers, we comprehensively investigated the genomic profile, TP53 abnormalities, and immunoglobulin heavy gene (IGH) mutation in a large cohort of SMZLs. 1 Mb resolution array comparative genomic hybridization (aCGH) on 25 SMZLs identified 7q32 deletion (44%) as the most frequent copy number change, followed by gains of 3q (32%), 8q (20%), 9q34 (20%), 12q23–24 (8%), and chromosome 18 (12%), and losses of 6q (16%), 8p (12%), and 17p (8%). High‐resolution chromosome 7 tile‐path aCGH on 17 SMZLs with 7q32 deletion identified by 1 Mb aCGH or interphase FISH screening mapped the minimal common deletion to a 3 Mb region at 7q32.1–32.2. Although it is not yet possible to identify the genes targeted by the deletion, interphase FISH screening showed that the deletion was seen in SMZL (19/56 = 34%) and splenic B‐cell lymphoma/leukaemia unclassifiable (3/9 = 33%), but not in 39 cases of other splenic lymphomas including chronic lymphocytic leukaemia (n = 14), hairy cell leukaemia (4), mantle cell lymphoma (12), follicular lymphoma (6), and others. In SMZL, 7q32 deletion was inversely correlated with trisomy 18, but not associated with other copy number changes, TP53 abnormalities, or IGH mutation status. None of the genetic parameters examined showed significant and independent association with overall or event‐free survival. In conclusion, 7q32 deletion is a characteristic feature of SMZL, albeit seen in isolated cases of splenic B‐cell lymphoma/leukaemia unclassifiable, and its detection may help the differential diagnosis of splenic B‐cell lymphomas. Copyright

Primary effusion lymphoma: genomic profiling revealed amplification of SELPLG and CORO1C encoding for proteins important for cell migration

Primary effusion lymphoma (PEL) is associated with Kaposi sarcoma herpesvirus (KSHV) but its pathogenesis is poorly understood. Many KSHV‐associated products can deregulate cellular pathways commonly targeted in cancer. However, KSHV infection alone is insufficient for malignant transformation. PEL also lacks the chromosomal translocations seen in other lymphoma subtypes. We investigated 28 PELs and ten PEL cell lines by 1 Mb resolution array comparative genomic hybridization (CGH) and found frequent gains of 1q21–41 (47%), 4q28.3‐35 (29%), 7q (58%), 8q (63%), 11 (32%), 12 (61%), 17q (29%), 19p (34%), and 20q (34%), and losses of 4q (32%), 11q25 (29%), and 14q32 (63%). Recurrent focal amplification was seen at several regions on chromosomes 7, 8, and 12. High‐resolution chromosome‐specific tile‐path array CGH confirmed these findings, and identified selectin‐P ligand (SELPLG) and coronin‐1C (CORO1C) as the targets of a cryptic amplification at 12q24.11. Interphase FISH and quantitative PCR showed SELPLG/CORO1C amplification (>4 extra copies) and low levels of copy number gain (1–4 extra copies) in 23% of PELs, respectively. Immunohistochemistry revealed strong expression of both SELPLG and coronin‐1C in the majority of PELs, irrespective of their gene dosage. SELPLG is critical for cell migration and chemotaxis, while CORO1C regulates actin‐dependent processes, thus important for cell motility. Their overexpression in PEL is expected to play an important role in its pathogenesis. Copyright

Bioinformatic analysis of primary endothelial cell gene array data illustrated by the analysis of transcriptome changes in endothelial cells exposed to VEGF-A and PlGF.

We recently published a review in this journal describing the design, hybridisation and basic data processing required to use gene arrays to investigate vascular biology (Evans etal. Angiogenesis 2003; 6: 93--104). Here, we build on this review by describing a set of powerful and robust methods for the analysis and interpretation of gene array data derived from primary vascular cell cultures. First, we describe the evaluation of transcriptome heterogeneity between primary cultures derived from different individuals, and estimation of the false discovery rate introduced by this heterogeneity and by experimental noise. Then, we discuss the appropriate use of Bayesian t-tests, clustering and independent component analysis to mine the data. We illustrate these principles by analysis of a previously unpublished set of gene array data in which human umbilical vein endothelial cells (HUVEC) cultured in either rich or low-serum media were exposed to vascular endothelial growth factor (VEGF)-A165 or placental growth factor (PlGF)-1131. We have used Affymetrix U95A gene arrays to map the effects of these factors on the HUVEC transcriptome. These experiments followed a paired design and were biologically replicated three times. In addition, one experiment was repeated using serial analysis of gene expression (SAGE). In contrast to some previous studies, we found that VEGF-A and PlGF consistently regulated only small, non-overlapping and culture media-dependant sets of HUVEC transcripts, despite causing significant cell biological changes.

MMP1 bimodal expression and differential response to inflammatory mediators is linked to promoter polymorphisms

BackgroundIdentifying the functional importance of the millions of single nucleotide polymorphisms (SNPs) in the human genome is a difficult challenge. Therefore, a reverse strategy, which identifies functionally important SNPs by virtue of the bimodal abundance across the human population of the SNP-related mRNAs will be useful. Those mRNA transcripts that are expressed at two distinct abundances in proportion to SNP allele frequency may warrant further study. Matrix metalloproteinase 1 (MMP1) is important in both normal development and in numerous pathologies. Although much research has been conducted to investigate the expression of MMP1 in many different cell types and conditions, the regulation of its expression is still not fully understood.ResultsIn this study, we used a novel but straightforward method based on agglomerative hierarchical clustering to identify bimodally expressed transcripts in human umbilical vein endothelial cell (HUVEC) microarray data from 15 individuals. We found that MMP1 mRNA abundance was bimodally distributed in un-treated HUVECs and showed a bimodal response to inflammatory mediator treatment. RT-PCR and MMP1 activity assays confirmed the bimodal regulation and DNA sequencing of 69 individuals identified an MMP1 gene promoter polymorphism that segregated precisely with the MMP1 bimodal expression. Chromatin immunoprecipation (ChIP) experiments indicated that the transcription factors (TFs) ETS1, ETS2 and GATA3, bind to the MMP1 promoter in the region of this polymorphism and may contribute to the bimodal expression.ConclusionsWe describe a simple method to identify putative bimodally expressed RNAs from transcriptome data that is effective yet easy for non-statisticans to understand and use. This method identified bimodal endothelial cell expression of MMP1, which appears to be biologically significant with implications for inflammatory disease. (271 Words)

Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium

Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large‐scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65–70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose‐response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96–98%), but yielded many false positives (positive predictive value = 30–32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large‐scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker‐specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.