Analysing multiple types of molecular profiles simultaneously: Connecting the needles in the haystack

Background: It has been shown that a random-effects framework can be used to test the association between a gene's expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information. Methods and results: Here, we extend the model introduced by Menezes et al. 2009 to consider the effect of not just copy number, but also of other molecular profiles such as methylation changes and loss-of-heterozigosity (LOH), on gene expression levels. We will consider again sets of measurements, to improve robustness of results and increase the power to find associations. Our approach can be used genome-wide to find associations and yields a test to help separate true associations from noise. We apply our method to colon and to breast cancer samples, for which genome-wide copy number, methylation and gene expression profiles are available. Our findings include interesting gene expression-regulating mechanisms, which may involve only one of copy number or methylation, or both for the same samples. We even are able to find effects due to different molecular mechanisms in different samples. Conclusions: Our method can equally well be applied to cases where other types of molecular (high-dimensional) data are collected, such as LOH, SNP genotype and microRNA expression data. Computationally efficient, it represents a flexible and powerful tool to study associations between high-dimensional datasets. The method is freely available via the SIM BioConductor package

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

Chemopreventive targeted treatment of head and neck precancer by Wee1 inhibition

HPV-negative head and neck squamous cell carcinomas (HNSCCs) develop in precancerous changes in the mucosal lining of the upper-aerodigestive tract. These precancerous cells contain cancer-associated genomic changes and cause primary tumors and local relapses. Therapeutic strategies to eradicate these precancerous cells are very limited. Using functional genomic screens, we identified the therapeutic vulnerabilities of premalignant mucosal cells, which are shared with fully malignant HNSCC cells. We screened 319 previously identified tumor-lethal siRNAs on a panel of cancer and precancerous cell lines as well as primary fibroblasts. In total we identified 147 tumor-essential genes including 34 druggable candidates. Of these 34, 13 were also essential in premalignant cells. We investigated the variable molecular basis of the vulnerabilities in tumor and premalignant cell lines and found indications of collateral lethality. Wee1-like kinase (WEE1) was amongst the most promising targets for both tumor and precancerous cells. All four precancerous cell lines were highly sensitive to Wee1 inhibition by Adavosertib (AZD1775), while primary keratinocytes tolerated this inhibitor. Wee1 inhibition caused induction of DNA damage during S-phase followed by mitotic failure in (pre)cancer cells. In conclusion, we uncovered Wee1 inhibition as a promising chemopreventive strategy for precancerous cells, with comparable responses as fully transformed HNSCC cells

Relative power and sample size analysis on gene expression profiling data

Background: With the increasing number of expression profiling technologies, researchers today are confronted with choosing the technology that has sufficient power with minimal sample size, in order to reduce cost and time. These depend on data variability, partly determined by sample type, preparation and processing. Objective measures that help experimental design, given own pilot data, are thus fundamental. Results: Relative power and sample size analysis were performed on two distinct data sets. The first set consisted of Affymetrix array data derived from a nutrigenomics experiment in which weak, intermediate and strong PPARα agonists were administered to wild-type and PPARα-null mice. Our analysis confirms the hierarchy of PPARα-activating compounds previously reported and the general idea that larger effect sizes positively contribute to the average power of the experiment. A simulation experiment was performed that mimicked the effect sizes seen in the first data set. The relative power was predicted but the estimates were slightly conservative. The second, more challenging, data set describes a microarray platform comparison study using hippocampal δC-doublecortin-like kinase transgenic mice that were compared to wild-type mice, which was combined with results from Solexa/Illumina deep sequencing runs. As expected, the choice of technology greatly influences the performance of the experiment. Solexa/Illumina deep sequencing has the highest overall power followed by the microarray platforms Agilent and Affymetrix. Interestingly, Solexa/Illumina deep sequencing displays comparable power across all intensity ranges, in contrast with microarray platforms that have decreased power in the low intensity range due to background noise. This means that deep sequencing technology is especially more powerful in dete

More useful standard errors for group and factor effects in generalized linear models

SIGLEAvailable from British Library Document Supply Centre-DSC:D206609 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A global x global test for testing associations between two large sets of variables

Development and application of statistical models for medical scientific researc

mRNA degradation controls differentiation state-dependent differences in transcript and splice variant abundance

Genomics, epigenetics, population genetics and bioinformatic

A cell-based high-throughput screening assay for radiation susceptibility using automated cell counting

Contains fulltext : 154857.pdf (publisher's version ) (Open Access)BACKGROUND: Radiotherapy is one of the mainstays in the treatment for cancer, but its success can be limited due to inherent or acquired resistance. Mechanisms underlying radioresistance in various cancers are poorly understood and available radiosensitizers have shown only modest clinical benefit. There is thus a need to identify new targets and drugs for more effective sensitization of cancer cells to irradiation. Compound and RNA interference high-throughput screening technologies allow comprehensive enterprises to identify new agents and targets for radiosensitization. However, the gold standard assay to investigate radiosensitivity of cancer cells in vitro, the colony formation assay (CFA), is unsuitable for high-throughput screening. METHODS: We developed a new high-throughput screening method for determining radiation susceptibility. Fast and uniform irradiation of batches up to 30 microplates was achieved using a Perspex container and a clinically employed linear accelerator. The readout was done by automated counting of fluorescently stained nuclei using the Acumen eX3 laser scanning cytometer. Assay performance was compared to that of the CFA and the CellTiter-Blue homogeneous uniform-well cell viability assay. The assay was validated in a whole-genome siRNA library screening setting using PC-3 prostate cancer cells. RESULTS: On 4 different cancer cell lines, the automated cell counting assay produced radiation dose response curves that followed a linear-quadratic equation and that exhibited a better correlation to the results of the CFA than did the cell viability assay. Moreover, the cell counting assay could be used to detect radiosensitization by silencing DNA-PKcs or by adding caffeine. In a high-throughput screening setting, using 4 Gy irradiated and control PC-3 cells, the effects of DNA-PKcs siRNA and non-targeting control siRNA could be clearly discriminated. CONCLUSIONS: We developed a simple assay for radiation susceptibility that can be used for high-throughput screening. This will aid the identification of molecular targets for radiosensitization, thereby contributing to improving the efficacy of radiotherapy

Evidence for Immune Response, Axonal Dysfunction and Reduced Endocytosis in the Substantia Nigra in Early Stage Parkinson's Disease.

Subjects with incidental Lewy body disease (iLBD) may represent the premotor stage of Parkinson's disease (PD). To elucidate molecular mechanisms underlying neuronal dysfunction and alpha-synuclein pathology in the premotor phase of PD, we investigated the transcriptome of the substantia nigra (SN) of well-characterized iLBD, PD donors and agematched controls with Braak alpha-synuclein stage ranging from 0-6. In Braak alpha-synuclein stages 1 and 2, we observed deregulation of pathways linked to axonal degeneration, immune response and endocytosis, including axonal guidance signaling, mTOR signaling, EIF2 signaling and clathrin-mediated endocytosis in the SN. In Braak stages 3 and 4, we observed deregulation of pathways involved in protein translation and cell survival, including mTOR and EIF2 signaling. In Braak stages 5 and 6, we observed deregulation of dopaminergic signaling, axonal guidance signaling and thrombin signaling. Throughout the progression of PD pathology, we observed a deregulation of mTOR, EIF2 and regulation of eIF4 and p70S6K signaling in the SN. Our results indicate that molecular mechanisms related to axonal dysfunction, endocytosis and immune response are an early event in PD pathology, whereas mTOR and EIF2 signaling are impaired throughout disease progression. These pathways may hold the key to altering the disease progression in PD. Copyright