Cross-Study Projections of Genomic Biomarkers: An Evaluation in Cancer Genomics

Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a “common currency” that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies – in cancer and other diseases – have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.     

Attenuation of CXCR4 responses by CCL18 in acute lymphocytic leukemia B cells

CCL18 and CXCL12 are homeostatic chemokines with high constitutive concentrations in serum. Elevated levels of CCL18 have been described in various diseases including childhood acute lymphocytic leukemia (ALL) but its functions remain poorly characterized. Its receptor has not been identified, but functional cellular responses like lymphocyte chemotaxis have been described. CXCL12 is a pivotal chemokine for hematopoiesis and B cell homing processes. We demonstrate that CCL18 interferes with CXCL12‐mediated pre‐B ALL cell activation. CXCL12‐induced calcium mobilization, chemotaxis, pseudo‐emperipolesis and cellular proliferation could be significantly reduced by CCL18 in pre‐B ALL cell lines. The results could be observed in primary cells from patients suffering from pre‐B ALL, but not in cells from patients suffering from common ALL. Direct effects of CCL18 on the receptor for CXCL12, CXCR4, could be excluded. Moreover, we found that CCL18 modulations of CXCL12‐induced responses are mediated through the chemokine‐like receptor GPR30. CCL18 bound to GPR30 expressing cells, and antibodies against GPR30 abolished this binding as well as CCL18‐mediated functional effects. We also observed that, CCL18 interferes with the activation of GPR30 by previously identified ligands (17β‐estradiol and chemical agonists). We therefore suggest that CCL18 is an important modulator of CXCR4‐dependent responses in pre‐B ALL cells via interactions with GPR30. J. Cell. Physiol. 225: 792–800, 2010. © 2010 Wiley‐Liss, Inc.     

Monitoring equal employment opportunity at the workplace: The crucial role of the 1991 Census

The fact that the 1991 Census of Population included a race and ethnic question followed evidence from the 1989 test Census that this was not likely to arouse strong public opposition ‐ while the case for race and ethnic data in the Census was overwhelming. This article focuses on the necessity of the data for the proper implementation of the Race Relations Act 1976, especially in relation to employment. It demonstrates how the linking of race and ethnic manpower statistics in the Census to the analysis of local labour‐market areas could provide employers with the means by which they can measure whether or not they are achieving genuine equality of employment opportunity under the 1976 Act. The article illustrates the recommended approach and shows the substantial effect upon the ‘benchmark’ data that results from analysing each major occupation group separately. Finally, the article notes some possible weaknesses in the 1991 Census data that will be available for monitoring equal employment opportunity in Britain.     

What if fertility decline is not permanent? The need for an evolutionarily informed approach to understanding low fertility

‘Demographic transition theory’ assumes that fertility decline is irreversible. This commonly held assumption is based on observations of recent and historical reductions in fertility that accompany modernization and declining mortality. The irreversibility assumption, however, is highly suspect from an evolutionary point of view, because demographic traits are at least partially influenced by genetics and are responsive to social and ecological conditions. Nonetheless, an inevitable shift from high mortality and fertility to low mortality and fertility is used as a guiding framework for projecting human population sizes into the future. This paper reviews some theoretical and empirical evidence suggesting that the assumption of irreversibility is ill-founded, at least without considerable development in theory that incorporates evolutionary and ecological processes. We offer general propositions for how fertility could increase in the future, including natural selection on high fertility variants, the difficulty of maintaining universal norms and preferences in a large, diverse and economically differentiated population, and the escalating resource demands of modernization.     

Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales,Australia

Aim We consider three questions. (1) How different are the predicted distribution maps when climate‐only and climate‐plus‐terrain models are developed from high‐resolution data? (2) What are the implications of differences between the models when predicting future distributions under climate change scenarios, particularly for climate‐only models at coarse resolution? (3) Does the use of high‐resolution data and climate‐plus‐terrain models predict an increase in the number of local refugia? Location South‐eastern New South Wales, Australia. Methods We developed two species distribution models for Eucalyptus fastigata under current climate conditions using generalized additive modelling. One used only climate variables as predictors (mean annual temperature, mean annual rainfall, mean summer rainfall); the other used both climate and landscape (June daily radiation, topographic position, lithology, nutrients) variables as predictors. Predictions of the distribution under current climate and climate change were then made for both models at a pixel resolution of 100 m. Results The model using climate and landscape variables as predictors explained a significantly greater proportion of the deviance than the climate‐only model. Inclusion of landscape variables resulted in the prediction of much larger areas of existing optimal habitat. An overlay of predicted future climate on the current climate space indicated that extrapolation of the statistical models was not occurring and models were therefore more robust. Under climate change, landscape‐defined refugia persisted in areas where the climate‐only model predicted major declines. In areas where expansion was predicted, the increase in optimal habitat was always greater with landscape predictors. Recognition of extensive optimal habitat conditions and potential refugia was dependent on the use of high‐resolution landscape data. Main conclusions Using only climate variables as predictors for assessing species responses to climate change ignores the accepted conceptual model of plant species distribution. Explicit statements justifying the selection of predictors based on ecological principles are needed. Models using only climate variables overestimate range reduction under climate change and fail to predict potential refugia. Fine‐scale‐resolution data are required to capture important climate/landscape interactions. Extrapolation of statistical models to regions in climate space outside the region where they were fitted is risky.