A test for comparing two groups of samples when analyzing multiple omics profiles

Background: A number of statistical models has been proposed for studying the association between gene expression and copy number data in integrated analysis. The next step is to compare association patterns between different groups of samples.Results: We propose a method, named dSIM, to find differences in association between copy number and gene expression, when comparing two groups of samples. Firstly, we use ridge regression to correct for the baseline associations between copy number and gene expression. Secondly, the global test is applied to the corrected data in order to find differences in association patterns between two groups of samples. We show that dSIM detects differences even in small genomic regions in a simulation study. We also apply dSIM to two publicly available breast cancer datasets and identify chromosome arms where copy number led gene expression regulation differs between positive and negative estrogen receptor samples. In spite of differing genomic coverage, some selected arms are identified in both datasets.Conclusion: We developed a flexible and robust method for studying association differences between two groups of samples while integrating genomic data from different platforms. dSIM can be used with most types of microarray/sequencing data, including methylation and microRNA expression. The method is implemented in R and will be made part of the BioConductor package SIM

Genomic profiling by DNA amplification of laser capture microdissected tissues and array CGH.

Comparative genomic hybridization by means of BAC microarrays (array CGH) allows high-resolution profiling of copy-number aberrations in tumor DNA. However, specific genetic lesions associated with small but clinically relevant tumor areas may pass undetected due to intra-tumor heterogeneity and/or the presence of contaminating normal cells. Here, we show that the combination of laser capture microdissection, phi29 DNA polymerase-mediated isothermal genomic DNA amplification, and array CGH allows genomic profiling of very limited numbers of cells. Moreover, by means of simple statistical models, we were able to bypass the exclusion of amplification distortions and variability prone areas, and to detect tumor-specific chromosomal gains and losses. We applied this new combined experimental and analytical approach to the genomic profiling of colorectal adenomatous polyps and demonstrated our ability to accurately detect single copy gains and losses affecting either whole chromosomes or small genomic regions from as little as 2 ng of DNA or 1000 microdissected cells

Glucocorticoid-induced glucocorticoid-receptor expression and promoter usage is not linked to glucocorticoid resistance in childhood ALL

Glucocorticoid (GC) resistance is an adverse prognostic factor in childhood acute lymphoblastic leukemia (ALL), but little is known about causes of GC resistance. Up-regulation of the glucocorticoid receptor (GR) has been suggested as an essential step to the induction of apoptosis in leukemic cells. In this study we investigated whether baseline mRNA expression levels of the 5 different GR promoter transcripts (1A1, 1A2, 1A3, 1B, and 1C) or differences in the degree of regulation of the GR or GR promoter transcripts upon GC exposure are related to GC resistance. Therefore, mRNA levels of the 5 GR promoter transcripts and of the GR were measured by quantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR; Taqman) technology in primary ALL cells prior to and after 3, 8, and 24 hours of prednisolone exposure. GR expression is induced upon GC exposure in primary ALL patient samples, which is opposite to what is found in tissues in which GCs do not induce apoptosis. GC resistance in childhood ALL cannot be attributed to an inability of resistant cells to up-regulate the expression of the GR upon GC exposure, nor to differences in GR promoter usage (at baseline and upon GC exposure)