Left ventricular area on non-contrast cardiac computed tomography as a predictor of incident heart failure – The Multi-Ethnic Study of Atherosclerosis

BackgroundThe use of non-contrast cardiac computed tomography measurements to predict heart failure (HF) has not been studied. In the present study we evaluated the prognostic value of left ventricular area adjusted for the body surface area (LVA-BSA) measured by non-contrast cardiac CT to predict incident HF and cardiovascular disease (CVD) events.MethodsWe studied left ventricular dimensions and calculated LVA-BSA in 6781 participants of the MESA study (mean age: was 62 ± 10 years, 53% females; 62% non-white) free from prior HF who underwent non-contrast cardiac CT to evaluate the coronary artery calcium score (CAC) at baseline and were followed up for a median of 10.2 years.ResultsDuring follow up, 237 (3.5%) incident HF and 475 (7.0%) CVD events occurred. After adjustment for clinical variables and CAC, LVA-BSA was significantly associated with incident HF (hazard ratio [HR]: 1.10 per 100 mm2/m2, p < 0.001) and CVD events (HR: 1.07 per 100 mm2/m2, p < 0.001). The area under the ROC curve for the prediction of incident HF improved from 0.787 on a model including only risk factors to 0.798 when CAC was added (p = 0.02), and to 0.816 with the additional inclusion of LVA-BSA (p = 0.007). Similar improvements for the prediction of CVD events were noted.ConclusionIn an ethnically diverse population of asymptomatic individuals free from baseline CVD or HF, the left ventricular area measured by non-contrast cardiac CT is a strong predictor of incident HF events beyond traditional risk factors and CAC score

Gene Profiling of Mta1 Identifies Novel Gene Targets and Functions

BACKGROUND: Metastasis-associated protein 1 (MTA1), a master dual co-regulatory protein is found to be an integral part of NuRD (Nucleosome Remodeling and Histone Deacetylation) complex, which has indispensable transcriptional regulatory functions via histone deacetylation and chromatin remodeling. Emerging literature establishes MTA1 to be a valid DNA-damage responsive protein with a significant role in maintaining the optimum DNA-repair activity in mammalian cells exposed to genotoxic stress. This DNA-damage responsive function of MTA1 was reported to be a P53-dependent and independent function. Here, we investigate the influence of P53 on gene regulation function of Mta1 to identify novel gene targets and functions of Mta1. METHODS: Gene expression analysis was performed on five different mouse embryonic fibroblasts (MEFs) samples (i) the Mta1 wild type, (ii) Mta1 knock out (iii) Mta1 knock out in which Mta1 was reintroduced (iv) P53 knock out (v) P53 knock out in which Mta1 was over expressed using Affymetrix Mouse Exon 1.0 ST arrays. Further Hierarchical Clustering, Gene Ontology analysis with GO terms satisfying corrected p-value<0.1, and the Ingenuity Pathway Analysis were performed. Finally, RT-qPCR was carried out on selective candidate genes. SIGNIFICANCE/CONCLUSION: This study represents a complete genome wide screen for possible target genes of a coregulator, Mta1. The comparative gene profiling of Mta1 wild type, Mta1 knockout and Mta1 re-expression in the Mta1 knockout conditions define "bona fide" Mta1 target genes. Further extensive analyses of the data highlights the influence of P53 on Mta1 gene regulation. In the presence of P53 majority of the genes regulated by Mta1 are related to inflammatory and anti-microbial responses whereas in the absence of P53 the predominant target genes are involved in cancer signaling. Thus, the presented data emphasizes the known functions of Mta1 and serves as a rich resource which could help us identify novel Mta1 functions

Evaluation of biological pathways involved in chemotherapy response in breast cancer

INTRODUCTION: Our goal was to examine the association between biological pathways and response to chemotherapy in estrogen receptor-positive (ER+) and ER-negative (ER-) breast tumors separately. METHODS: Gene set enrichment analysis including 852 predefined gene sets was applied to gene expression data from 51 ER- and 82 ER+ breast tumors that were all treated with a preoperative paclitaxel, 5-fluoruracil, doxorubicin, and cyclophosphamide chemotherapy. RESULTS: Twenty-seven (53%) ER- and 7 (9%) ER+ patients had pathologic complete response (pCR) to therapy. Among the ER- tumors, a proliferation gene signature (false discovery rate [FDR] q = 0.1), the genomic grade index (FDR q = 0.044), and the E2F3 pathway signature (FDR q = 0.22, P = 0.07) were enriched in the pCR group. Among the ER+ tumors, the proliferation signature (FDR q = 0.001) and the genomic grade index (FDR q = 0.015) were also significantly enriched in cases with pCR. Ki67 expression, as single gene marker of proliferation, did not provide the same information as the entire proliferation signature. An ER-associated gene set (FDR q = 0.03) and a mutant p53 gene signature (FDR q = 0.0019) were enriched in ER+ tumors with residual cancer. CONCLUSION: Proliferation- and genomic grade-related gene signatures are associated with chemotherapy sensitivity in both ER- and ER+ breast tumors. Genes involved in the E2F3 pathway are associated with chemotherapy sensitivity among ER- tumors. The mutant p53 signature and expression of ER-related genes were associated with lower sensitivity to chemotherapy in ER+ breast tumors only.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Air pollution and the microvasculature: A cross-sectional assessment of in vivo retinal images in the population-based multi-ethnic study of atherosclerosis (MESA)

10.1371/journal.pmed.1000372PLoS Medicine711

Applying unmixing to gene expression data for tumor phylogeny inference

<p>Abstract</p> <p>Background</p> <p>While in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of "sub-types," each characterized a roughly equivalent sequence of mutations by which it progresses in different patients. There is currently great interest in identifying the common sub-types and applying them to the development of diagnostics or therapeutics. Phylogenetic methods have shown great promise for inferring common patterns of tumor progression, but suffer from limits of the technologies available for assaying differences between and within tumors. One approach to tumor phylogenetics uses differences between single cells within tumors, gaining valuable information about intra-tumor heterogeneity but allowing only a few markers per cell. An alternative approach uses tissue-wide measures of whole tumors to provide a detailed picture of averaged tumor state but at the cost of losing information about intra-tumor heterogeneity.</p> <p>Results</p> <p>The present work applies "unmixing" methods, which separate complex data sets into combinations of simpler components, to attempt to gain advantages of both tissue-wide and single-cell approaches to cancer phylogenetics. We develop an unmixing method to infer recurring cell states from microarray measurements of tumor populations and use the inferred mixtures of states in individual tumors to identify possible evolutionary relationships among tumor cells. Validation on simulated data shows the method can accurately separate small numbers of cell states and infer phylogenetic relationships among them. Application to a lung cancer dataset shows that the method can identify cell states corresponding to common lung tumor types and suggest possible evolutionary relationships among them that show good correspondence with our current understanding of lung tumor development.</p> <p>Conclusions</p> <p>Unmixing methods provide a way to make use of both intra-tumor heterogeneity and large probe sets for tumor phylogeny inference, establishing a new avenue towards the construction of detailed, accurate portraits of common tumor sub-types and the mechanisms by which they develop. These reconstructions are likely to have future value in discovering and diagnosing novel cancer sub-types and in identifying targets for therapeutic development.</p

The ErbB signalling pathway: protein expression and prognostic value in epithelial ovarian cancer

Ovarian cancer is the most frequent cause of death from gynaecological cancer in the Western world. Current prognostic factors do not allow reliable prediction of response to chemotherapy and survival for individual ovarian cancer patients. Epidermal growth factor receptor (EGFR) and HER-2/neu are frequently expressed in ovarian cancer but their prognostic value remains unclear. In this study, we investigated the expression and prognostic value of EGFR, EGFR variant III (EGFRvIII), HER-2/neu and important downstream signalling components in a large series of epithelial ovarian cancer patients. Immunohistochemical staining of EGFR, pEGFR, EGFRvIII, Her-2/neu, PTEN (phosphatase and tensin homologue deleted on chromosome 10), total and phosphorylated AKT (pAKT) and phosphorylated ERK (pERK) was performed in 232 primary tumours using the tissue microarray platform and related to clinicopathological characteristics and survival. In addition, EGFRvIII expression was determined in 45 tumours by RT–PCR. Our results show that negative PTEN immunostaining was associated with stage I/II disease (P=0.006), non-serous tumour type (P=0.042) and in multivariate analysis with a longer progression-free survival (P=0.015). Negative PTEN staining also predicted improved progression-free survival in patients with grade III or undifferentiated serous carcinomas (P=0.011). Positive pAKT staining was associated with advanced-stage disease (P=0.006). Other proteins were expressed only at low levels, and were not associated with any clinicopathological parameter or survival. None of the tumours were positive for EGFRvIII. In conclusion, our results indicate that tumours showing negative PTEN staining could represent a subgroup of ovarian carcinomas with a relatively favourable prognosis

Algorithms for effective querying of compound graph-based pathway databases

<p>Abstract</p> <p>Background</p> <p>Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.</p> <p>Results</p> <p>Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool P<smcaps>ATIKA</smcaps><it>web </it>(Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases.</p> <p>Conclusion</p> <p>The P<smcaps>ATIKA</smcaps> Project Web site is <url>http://www.patika.org</url>. P<smcaps>ATIKA</smcaps><it>web </it>version 2.1 is available at <url>http://web.patika.org</url>.</p