Todd R. Golub

Bio: Todd R. Golub is an academic researcher from Harvard University. The author has contributed to research in topics: Cancer & Gene expression profiling. The author has an hindex of 164, co-authored 422 publications receiving 201457 citations. Previous affiliations of Todd R. Golub include Rush University Medical Center & Boston Children's Hospital.

Solution-based methods for rna expression profiling

Abstract: The present invention is directed to novel high-throughput, low-cost, and flexible solution-based methods for RNA expression profiling, including expression of microRNAs and mRNAs.

CRISPR-Cas9 screen reveals a MYCN -amplified neuroblastoma dependency on EZH2

Abstract: Pharmacologically difficult targets, such as MYC transcription factors, represent a major challenge in cancer therapy. For the childhood cancer neuroblastoma, amplification of the oncogene MYCN is associated with high-risk disease and poor prognosis. Here, we deployed genome-scale CRISPR-Cas9 screening of MYCN-amplified neuroblastoma and found a preferential dependency on genes encoding the polycomb repressive complex 2 (PRC2) components EZH2, EED, and SUZ12. Genetic and pharmacological suppression of EZH2 inhibited neuroblastoma growth in vitro and in vivo. Moreover, compared with neuroblastomas without MYCN amplification, MYCN-amplified neuroblastomas expressed higher levels of EZH2. ChIP analysis showed that MYCN binds at the EZH2 promoter, thereby directly driving expression. Transcriptomic and epigenetic analysis, as well as genetic rescue experiments, revealed that EZH2 represses neuronal differentiation in neuroblastoma in a PRC2-dependent manner. Moreover, MYCN-amplified and high-risk primary tumors from patients with neuroblastoma exhibited strong repression of EZH2-regulated genes. Additionally, overexpression of IGFBP3, a direct EZH2 target, suppressed neuroblastoma growth in vitro and in vivo. We further observed strong synergy between histone deacetylase inhibitors and EZH2 inhibitors. Together, these observations demonstrate that MYCN upregulates EZH2, leading to inactivation of a tumor suppressor program in neuroblastoma, and support testing EZH2 inhibitors in patients with MYCN-amplified neuroblastoma.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Abstract: DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

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.