Joshua M. Stuart

PRINCIPAL COMPONENTS ANALYSIS TO SUMMARIZE MICROARRAY EXPERIMENTS: APPLICATION TO SPORULATION TIME SERIES

A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. It is often not clear whether a set of experiments are measuring fundamentally different gene expression states or are measuring similar states created through different mechanisms. It is useful, therefore, to define a core set of independent features for the expression states that allow them to be compared directly. Principal components analysis (PCA) is a statistical technique for determining the key variables in a multidimensional data set that explain the differences in the observations, and can be used to simplify the analysis and visualization of multidimensional data sets. We show that application of PCA to expression data (where the experimental conditions are the variables, and the gene expression measurements are the observations) allows us to summarize the ways in which gene responses vary under different conditions. Examination of the components also provides insight into the underlying factors that are measured in the experiments. We applied PCA to the publicly released yeast sporulation data set (Chu et al. 1998). In that work, 7 different measurements of gene expression were made over time. PCA on the time-points suggests that much of the observed variability in the experiment can be summarized in just 2 components--i.e. 2 variables capture most of the information. These components appear to represent (1) overall induction level and (2) change in induction level over time. We also examined the clusters proposed in the original paper, and show how they are manifested in principal component space. Our results are available on the internet at http:¿www.smi.stanford.edu/project/helix/PCArray .

Whole Genome Analysis Informs Breast Cancer Response to Aromatase Inhibition

To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathways activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients. Availability:Source code available at http://sbenz.github.com/Paradigm Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Genome 10K: A Proposal to Obtain Whole-Genome Sequence for 10 000 Vertebrate Species

The human genome project has been recently complemented by whole-genome assessment sequence of 32 mammals and 24 nonmammalian vertebrate species suitable for comparative genomic analyses. Here we anticipate a precipitous drop in costs and increase in sequencing efficiency, with concomitant development of improved annotation technology and, therefore, propose to create a collection of tissue and DNA specimens for 10,000 vertebrate species specifically designated for whole-genome sequencing in the very near future. For this purpose, we, the Genome 10K Community of Scientists (G10KCOS), will assemble and allocate a biospecimen collection of some 16,203 representative vertebrate species spanning evolutionary diversity across living mammals, birds, nonavian reptiles, amphibians, and fishes (ca. 60,000 living species). In this proposal, we present precise counts for these 16,203 individual species with specimens presently tagged and stipulated for DNA sequencing by the G10KCOS. DNA sequencing has ushered in a new era of investigation in the biological sciences, allowing us to embark for the first time on a truly comprehensive study of vertebrate evolution, the results of which will touch nearly every aspect of vertebrate biological enquiry.

Chromosomal clustering of muscle-expressed genes in Caenorhabditis elegans

Chromosomes are divided into domains of open chromatin, where genes have the potential to be expressed, and domains of closed chromatin, where genes are not expressed. Classic examples of open chromatin domains include ‘puffs’ on polytene chromosomes in Drosophila and extended loops from lampbrush chromosomes. If multiple genes were typically expressed together from a single open chromatin domain, the position of co-expressed genes along the chromosomes would appear clustered. To investigate whether co-expressed genes are clustered, we examined the chromosomal positions of the genes expressed in muscle of Caenorhabditis elegans at the first larval stage. Here we show that co-expressed genes in C. elegans are clustered in groups of 2–5 along the chromosomes, suggesting that expression from a chromatin domain can extend over several genes. These observations reveal a higher-order organization of the structure of the genome, in which the order of genes along the chromosome is correlated with their expression in specific tissues.

Integrating genotype and phenotype information : an overview of the pharmGKB project

Pharmacogenetics seeks to explain how people respond in different ways to the same drug treatment. A classic example of the importance of pharmacogenomics is the variation in individual responses to the anti-leukemia drug, 6-mercaptopurine. Most people metabolize the drug quickly. Some individuals, with a genetic variation for the enzyme thiopurine methyltransferase (TPMT),1 do not. Consequently, they need lower doses of 6-mercaptopurine for effective treatment as normal doses can be lethal. One of the many promises of the human genome project is an ability to pharmacologically treat individuals in a more personalized rather than statistical manner.

Subtype and pathway specific responses to anticancer compounds in breast cancer

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

GenMAPP 2: new features and resources for pathway analysis

BackgroundMicroarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses.ResultsTo address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP), a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP), and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages.ConclusionGenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms.

The UCSC cancer genomics browser: update 2011

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) comprises a suite of web-based tools to integrate, visualize and analyze cancer genomics and clinical data. The browser displays whole-genome views of genome-wide experimental measurements for multiple samples alongside their associated clinical information. Multiple data sets can be viewed simultaneously as coordinated ‘heatmap tracks’ to compare across studies or different data modalities. Users can order, filter, aggregate, classify and display data interactively based on any given feature set including clinical features, annotated biological pathways and user-contributed collections of genes. Integrated standard statistical tools provide dynamic quantitative analysis within all available data sets. The browser hosts a growing body of publicly available cancer genomics data from a variety of cancer types, including data generated from the Cancer Genome Atlas project. Multiple consortiums use the browser on confidential prepublication data enabled by private installations. Many new features have been added, including the hgMicroscope tumor image viewer, hgSignature for real-time genomic signature evaluation on any browser track, and ‘PARADIGM’ pathway tracks to display integrative pathway activities. The browser is integrated with the UCSC Genome Browser; thus inheriting and integrating the Genome Browser’s rich set of human biology and genetics data that enhances the interpretability of the cancer genomics data.

Voltage-Gated Na + Channel SCN5A Is a Key Regulator of a Gene Transcriptional Network That Controls Colon Cancer Invasion

Voltage-gated Na(+) channels (VGSC) have been implicated in the metastatic potential of human breast, prostate, and lung cancer cells. Specifically, the SCN5A gene encoding the VGSC isotype Na(v)1.5 has been defined as a key driver of human cancer cell invasion. In this study, we examined the expression and function of VGSCs in a panel of colon cancer cell lines by electrophysiologic recordings. Na(+) channel activity and invasive potential were inhibited pharmacologically by tetrodotoxin or genetically by small interfering RNAs (siRNA) specifically targeting SCN5A. Clinical relevance was established by immunohistochemistry of patient biopsies, with strong Na(v)1.5 protein staining found in colon cancer specimens but little to no staining in matched-paired normal colon tissues. We explored the mechanism of VGSC-mediated invasive potential on the basis of reported links between VGSC activity and gene expression in excitable cells. Probabilistic modeling of loss-of-function screens and microarray data established an unequivocal role of VGSC SCN5A as a high level regulator of a colon cancer invasion network, involving genes that encompass Wnt signaling, cell migration, ectoderm development, response to biotic stimulus, steroid metabolic process, and cell cycle control. siRNA-mediated knockdown of predicted downstream network components caused a loss of invasive behavior, demonstrating network connectivity and its function in driving colon cancer invasion.