Ryan van Laar

Bio: Ryan van Laar is an academic researcher from Peter MacCallum Cancer Centre. The author has contributed to research in topics: Melanoma & Cancer. The author has an hindex of 17, co-authored 35 publications receiving 2366 citations. Previous affiliations of Ryan van Laar include Australian Animal Health Laboratory.

Identification and functional significance of genes regulated by structurally different histone deacetylase inhibitors

Abstract: Histone deacetylase inhibitors (HDACis) inhibit tumor cell growth and survival, possibly through their ability to regulate the expression of specific proliferative and/or apoptotic genes. However, the HDACi-regulated genes necessary and/or sufficient for their biological effects remain undefined. We demonstrate that the HDACis suberoylanilide hydroxamic acid (SAHA) and depsipeptide regulate a highly overlapping gene set with at least 22% of genes showing altered expression over a 16-h culture period. SAHA and depsipeptide coordinately regulated the expression of several genes within distinct apoptosis and cell cycle pathways. Multiple genes within the Myc, type β TGF, cyclin/cyclin-dependent kinase, TNF, Bcl-2, and caspase pathways were regulated in a manner that favored induction of apoptosis and decreased cellular proliferation. APAF-1, a gene central to the intrinsic apoptotic pathway, was induced by SAHA and depsipeptide and shown to be important, but not essential, for HDACi-induced cell death. Overexpression of p16INK4A and arrest of cells in G1 can suppress HDACi-mediated apoptosis. Although p16INK4A did not affect the genome-wide transcription changes mediated by SAHA, a small number of apoptotic genes, including BCLXL and B-MYB, were differentially regulated in a manner consistent with attenuated HDACi-mediated apoptosis in arrested cells. We demonstrate that different HDACi alter transcription of a large and common set of genes that control diverse molecular pathways important for cell survival and proliferation. The ability of HDACi to target multiple apoptotic and cell proliferation pathways may provide a competitive advantage over other chemotherapeutic agents because suppression/loss of a single pathway may not confer resistance to these agents.

Empirical array quality weights in the analysis of microarray data

Abstract: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment. This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.

Options available--from start to finish--for obtaining data from DNA microarrays II.

Abstract: Microarray technology has undergone a rapid evolution. With widespread interest in large-scale genomic research, an abundance of equipment and reagents have now become available and affordable to a large cross section of the scientific community. As protocols become more refined, careful investigators are able to obtain good quality microarray data quickly. In most recent times, however, perhaps one of the biggest obstacles researchers face is not the manufacture and use of microarrays at the bench, but storage and analysis of the array data. This review discusses the most recent equipment, reagents and protocols available to the researcher, as well as describing data analysis and storage options available from the evolving field of microarray informatics.

Gene-expression and immunohistochemical study of specific T-cell subsets and accessory cell types in the transformation and prognosis of follicular lymphoma.

Abstract: Purpose Despite the generally favorable clinical course in follicular lymphoma (FL), a minority of patients have a poor prognosis - with death within 3 years of diagnosis - most often due to transformation to aggressive disease. Patients and Methods In this study, we analyzed the potential of predicting early transformation on the basis of gene expression and immunologic parameters in FL biopsy samples taken at diagnosis. Results At the gene-expression level, FL is a highly uniform disease at the time of diagnosis, precluding the detection of sufficiently validated prognostic gene-expression profiles suitable for a clinical setting. Combinations of differentially expressed genes indicate that immunologic mechanisms play a differential role in the risk of early transformation. Using immunohistochemistry for specific cell populations, the spatial distribution to neoplastic follicles and the activation of CD4-positive T-helper cells (P = .002) and specifically T-helper 1 (P = .004) were shown to be highly discriminatory to predict early transformation. A role for functional modulation of follicular dendritic cells could also be supported (P = .04). Other cell populations, including CD68-positive macrophages and regulatory T cells, were not differentially present. Conclusion These results support the identification of FL as an immunologically functional disease in which an interaction of the tumor cells and the functional composition of the microenvironment determines the clinical behavior.

An Expression-Based Site of Origin Diagnostic Method Designed for Clinical Application to Cancer of Unknown Origin

Abstract: Gene expression profiling offers a promising new technique for the diagnosis and prognosis of cancer. We have applied this technology to build a clinically robust site of origin classifier with the ultimate aim of applying it to determine the origin of cancer of unknown primary (CUP). A single cDNA microarray platform was used to profile 229 primary and metastatic tumors representing 14 tumor types and multiple histologic subtypes. This data set was subsequently used for training and validation of a support vector machine (SVM) classifier, demonstrating 89% accuracy using a 13-class model. Further, we show the translation of a five-class classifier to a quantitative PCR-based platform. Selecting 79 optimal gene markers, we generated a quantitative-PCR low-density array, allowing the assay of both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue. Data generated using both quantitative PCR and microarray were subsequently used to train and validate a cross-platform SVM model with high prediction accuracy. Finally, we applied our SVM classifiers to 13 cases of CUP. We show that the microarray SVM classifier was capable of making high confidence predictions in 11 of 13 cases. These predictions were supported by comprehensive review of the patients' clinical histories.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

limma: Linear Models for Microarray Data

Abstract: A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

Abstract: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

Abstract: A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.