Generation and Characterisation of a Canine EGFP-HMGA2 Prostate Cancer In Vitro Model

The architectural transcription factor HMGA2 is abundantly expressed during embryonic development. In several malignant neoplasias including prostate cancer, high re-expression of HMGA2 is correlated with malignancy and poor prognosis. The let-7 miRNA family is described to regulate HMGA2 negatively. The balance of let-7 and HMGA2 is discussed to play a major role in tumour aetiology. To further analyse the role of HMGA2 in prostate cancer a stable and highly reproducible in vitro model system is precondition. Herein we established a canine CT1258-EGFP-HMGA2 prostate cancer cell line stably overexpressing HMGA2 linked to EGFP and in addition the reference cell line CT1258-EGFP expressing solely EGFP to exclude EGFP-induced effects. Both recombinant cell lines were characterised by fluorescence microscopy, flow cytometry and immunocytochemistry. The proliferative effect of ectopically overexpressed HMGA2 was determined via BrdU assays. Comparative karyotyping of the derived and the initial CT1258 cell lines was performed to analyse chromosome consistency. The impact of the ectopic HMGA2 expression on its regulator let-7a was analysed by quantitative real-time PCR. Fluorescence microscopy and immunocytochemistry detected successful expression of the EGFP-HMGA2 fusion protein exclusively accumulating in the nucleus. Gene expression analyses confirmed HMGA2 overexpression in CT1258-EGFP-HMGA2 in comparison to CT1258-EGFP and native cells. Significantly higher let-7a expression levels were found in CT1258-EGFP-HMGA2 and CT1258-EGFP. The BrdU assays detected an increased proliferation of CT1258-HMGA2-EGFP cells compared to CT1258-EGFP and native CT1258. The cytogenetic analyses of CT1258-EGFP and CT1258-EGFP-HMGA2 resulted in a comparable hyperdiploid karyotype as described for native CT1258 cells. To further investigate the impact of recombinant overexpressed HMGA2 on CT1258 cells, other selected targets described to underlie HMGA2 regulation were screened in addition. The new fluorescent CT1258-EGFP-HMGA2 cell line is a stable tool enabling in vitro and in vivo analyses of the HMGA2-mediated effects on cells and the development and pathogenesis of prostate cancer.

Nonparametric Evaluation of Quantitative Traits in Population-Based Association Studies when the Genetic Model is Unknown

Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible multiplicity-adjusted p-values associated with the proposed maximum test.