The health of an individual is determined by the interaction of genetic and individual factors with wider social and environmental elements. Public health approaches to improving the health of disadvantaged populations will be most effective if they optimise influences at each of these levels, particularly in the early part of the life course. In order to better ascertain the relative contribution of these multi-level determinants there is a need for robust studies, longitudinal and prospective in nature, that examine individual, familial, social and environmental exposures. This paper describes the study background and methods, as it has been implemented in an Australian birth cohort study, Environments for Healthy Living (EFHL): The Griffith Study of Population Health. EFHL is a prospective, multi-level, multi-year longitudinal birth cohort study, designed to collect information from before birth through to adulthood across a spectrum of eco-epidemiological factors, including genetic material from cord-blood samples at birth, individual and familial factors, to spatial data on the living environment. EFHL commenced the pilot phase of recruitment in 2006 and open recruitment in 2007, with a target sample size of 4000 mother/infant dyads. Detailed information on each participant is obtained at birth, 12-months, 3-years, 5-years and subsequent three to five yearly intervals. The findings of this research will provide detailed evidence on the relative contribution of multi-level determinants of health, which can be used to inform social policy and intervention strategies that will facilitate healthy behaviours and choices across sub-populations.

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

BackgroundAldehyde dehydrogenases belong to a superfamily of detoxifying enzymes that protect cells from carcinogenic aldehydes. Of the superfamily, ALDH1A1 has gained most attention because current studies have shown that its expression is associated with human cancer stem cells. However, ALDH1A1 is only one of the 19 human ALDH subfamilies currently known. The purpose of the present study was to determine if the expression and activities of other major ALDH isozymes are associated with human ovarian cancer and ovarian cancer sphere cultures.MethodsImmunohistochemistry was used to delineate ALDH isozyme localization in clinical ovarian tissues. Western Blot analyses were performed on lysates prepared from cancer cell lines and ovarian cancer spheres to confirm the immunohistochemistry findings. Quantitative reverse transcription-polymerase chain reactions were used to measure the mRNA expression levels. The Aldefluor® assay was used to measure ALDH activity in cancer cells from the four tumor subtypes.ResultsImmunohistochemical staining showed significant overexpression of ALDH1A3, ALDH3A2, and ALDH7A1 isozymes in ovarian tumors relative to normal ovarian tissues. The expression and activity of ALDH1A1 is tumor type-dependent, as seen from immunohistochemisty, Western blot analysis, and the Aldefluor® assay. The expression was elevated in the mucinous and endometrioid ovarian epithelial tumors than in serous and clear cell tumors. In some serous and most clear cell tumors, ALDH1A1 expression was found in the stromal fibroblasts. RNA expression of all studied ALDH isozymes also showed higher expression in endometrioid and mucinous tumors than in the serous and clear cell subtypes. The expression of ALDH enzymes showed tumor type-dependent induction in ovarian cancer cells growing as sphere suspensions in serum-free medium.ConclusionsThe results of our study indicate that ALDH enzyme expression and activity may be associated with specific cell types in ovarian tumor tissues and vary according to cell states. Elucidating the function of the ALDH isozymes in lineage differentiation and pathogenesis may have significant implications for ovarian cancer pathophysiology.

Increased inclusion cyst formation in the ovary is associated with ovarian cancer development. We employed in vitro three-dimensional (3D) organotypic models formed by normal human ovarian surface epithelial (OSE) cells and ovarian cancer cells to study the morphologies of normal and cancerous ovarian cortical inclusion cysts and the molecular changes during their transitions into stromal microenvironment. When compared with normal cysts that expressed tenascin, the cancerous cysts expressed high levels of laminin V and demonstrated polarized structures in Matrigel; and the cancer cells migrated collectively when the cyst structures were positioned in a stromal-like collagen I matrix. The molecular markers identified in the in vitro 3D models were verified in clinical samples. Network analysis of gene expression of the 3D structures indicates concurrent downregulation of transforming growth factor beta pathway genes and high levels of E-cadherin and microRNA200 (miR200) expression in the cancerous cysts and the migrating cancer cells. Transient silencing of E-cadherin expression in ovarian cancer cells disrupted cyst structures and inhibited collective cell migration. Taken together, our studies employing 3D models have shown that E-cadherin is crucial for ovarian inclusion cyst formation and collective cancer cell migration.

Ovarian cancer survival rates have stagnated in the last 20 years despite the development of novel chemotherapeutic agents. Modulators of gene expression, such as histone deacetylase (HDAC) inhibitors, are among the new agents being used in clinical trials. Predictors of sensitivity to chemotherapy have remained elusive. In this study, we show that the expression of the transcriptional corepressor C-terminal binding protein-2 (CtBP2) is elevated in human ovarian tumors. Downregulation of CtBP2 expression in ovarian cancer cell lines using short-hairpin RNA strategy suppressed the growth rate and migration of the resultant cancer cells. The knockdown cell lines also showed upregulation of HDAC activity and increased sensitivity to selected HDAC inhibitors. Conversely, forced expression of wild-type CtBP2 in the knockdown cell lines reversed HDAC activity and partially rescued cellular sensitivity to the HDAC inhibitors. We propose that CtBP2 is an ovarian cancer oncogene that regulates gene expression program by modulating HDAC activity. CtBP2 expression may be a surrogate indicator of cellular sensitivity to HDAC inhibitors.