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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.


Papers
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Proceedings ArticleDOI
TL;DR: Mounting evidence supports a role for epidermal growth factor during chronic liver disease and hepatocellular transformation and the hypothesis that blocking the EGF-EGF receptor (EGFR) pathway may be an effective strategy for inhibiting fibrogenesis and hepatocarcinogenesis is addressed.
Abstract: Background: Hepatocellular carcinoma (HCC) is the sixth most common solid tumor worldwide and due to its poor prognosis it is the third leading cause of cancer-related death. Given the lack of successful treatment options, chemoprevention in high-risk patients has been proposed as an alternative strategy. Mounting evidence supports a role for epidermal growth factor (EGF) during chronic liver disease and hepatocellular transformation. Based on these findings, we address the hypothesis that blocking the EGF-EGF receptor (EGFR) pathway may be an effective strategy for inhibiting fibrogenesis and hepatocarcinogenesis. Methods: A rat model of diethylnitrosamine (DEN)-induced cirrhosis was used to examine the effects of erlotinib on underlying chronic liver disease and HCC formation. DEN (50 mg/kg) was administered weekly throughout the study while erlotinib (either 0.5 or 2 mg/kg) was administered 5 days per week for 6 weeks beginning at the onset of cirrhosis. At the end of the study, rats were sacrificed and tumor nodules were counted. Disease progression was assessed by HE 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2925.

2 citations

Journal ArticleDOI
18 Nov 2011-Blood
TL;DR: The data suggest that driver mutations in DLBCL may not be randomly distributed across the genome, but may occur in a directed fashion, and suggest that many histone methyltransferases are enriched as a geneset in addition to MLL2.

2 citations

Journal ArticleDOI
TL;DR: A direct-to-patient nationwide research initiative where patients can contribute their medical records and biospecimens to accelerate research into genomic understanding of metastatic prostate cancer is piloted.
Abstract: 279Background: While there has been substantial advancement in the genomic understanding of metastatic prostate cancer (MPC), there is still much to be discovered. Additional progress is dependent upon obtaining a large amount of clinically-annotated genomic data. Therefore, we piloted a direct-to-patient nationwide research initiative where patients can contribute their medical records and biospecimens to accelerate research (mpcproject.org). Methods: In collaboration with patients and advocacy groups, we have developed a website (mpcproject.org). Participants are asked to complete a 17-question survey about their experiences with prostate cancer and an electronic informed consent. All participants receive a saliva kit for germline DNA and blood kit for circulating tumor DNA (ctDNA). Additionally, medical records are collected and archived tissue samples are requested if available. Ultra low pass whole genome sequencing (ULP-WGS) and whole exome sequencing (WES) are performed on the whole blood samples. ...

2 citations

Proceedings ArticleDOI
TL;DR: In 3 cohorts of patients with ER+ breast cancer, a signature of FGF2 signaling was significantly associated with poor prognosis and predictive of anti-estrogen resistance, including in a multivariate analysis including age, tumor grade, tumor stage, and FGFR amplification status.
Abstract: Despite the clinical success of anti-estrogen therapies, phosphatidylinositol 3-kinase inhibitors (PI3Ki), and mechanistic target of rapamycin complex I inhibitors (mTORC1i) for the treatment of patients with ER+ breast cancer, disease recurrence and progression are common. We found that a tumor transcriptional profile reflecting high stromal fibroblast content was associated with poor outcome in 3 cohorts of patients with ER+ breast cancer. We hypothesized that individual factors in the tumor microenvironment (TME) significantly contribute to drug resistance. To test this hypothesis, we screened 297 recombinant secreted proteins for ability to confer resistance to the anti-estrogen fulvestrant in MCF-7 and T47D ER+ breast cancer cells. Screen results were validated, and expansion screening included the anti-estrogen tamoxifen, the PI3Ki pictilisib, and the mTORC1i everolimus in 4 cell lines. To identify hits are most likely to be relevant to ER+ breast cancer, a bioinformatics filter was developed utilizing gene and protein expression in human tissues relevant to the TMEs of ER+ breast cancer. After filtering, the top screening hit was fibroblast growth factor 2 (FGF2), which confers resistance to anti-estrogens, PI3Ki, and mTORC1i, and is highly expressed in tissues and cell types associated with ER+ breast cancer. FGF2 did not rescue cells from the CDK4/6i palbociclib or the DNA-damaging agent doxorubicin, demonstrating pathway selectivity in the rescue phenotype. FGF2 rescued cells from anti-estrogen-, PI3Ki-, and mTORC1i-induced apoptosis and cell cycle arrest via activation of FGFR signaling through FRS2a, MEK1/2, ERK1/2, and downstream upregulation of cyclin D1 and degradation of Bim. FGF2-mediated anti-cancer effects were abrogated by co-treatment with the FGF2-neutralizing antibody GAL-F2, the pan-FGFR inhibitor PD-173074, the MEK inhibitor trametinib, or palbociclib. Cell cycle- and apoptosis-specific effects of FGF2 were abrogated by RNAi targeting cyclin D1 and Bim, respectively. We generated a transcriptional signature of FGF2 response by RNA-seq of fulvestrant-treated MCF-7 and T47D cells treated +/- FGF2. In 3 cohorts of patients with ER+ breast cancer, a signature of FGF2 signaling was significantly associated with poor prognosis and predictive of anti-estrogen resistance, including in a multivariate analysis including age, tumor grade, tumor stage, and FGFR amplification status. Finally, the therapeutic potential of targeting FGF2 was confirmed in 3 mouse models of ER+ breast cancer: 1) FGF2 rescue MCF-7 xenografts from fulvestrant; 2) GAL-F2 synergized with fulvestrant to suppress growth of 59-2-HI murine mammary adenocarcinomas that recruit FGF2-secreting stroma; 3) GAL-F2 synergized with fulvestrant to induce regression of HCI-003 patient-derived xenografts. Therapeutic effects coincided with increased tumor cell apoptosis and decreased proliferation, but not changes in tumor vasculature. These findings warrant consideration of FGF2 as a novel therapeutic target in ER+ breast cancer. Citation Format: Shee K, Hinds JW, Yang W, Hampsch RA, Patel K, Varn FS, Cheng C, Jenkins NP, Kettenbach AN, Demidenko E, Owens P, Lanari C, Faber AC, Golub TR, Straussman R, Miller TW. A microenvironment secretome screen reveals FGF2 as a mediator of resistance to anti-estrogens and PI3K/mTOR pathway inhibitors in ER+ breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD4-08.

2 citations


Cited by
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Journal ArticleDOI
04 Mar 2011-Cell
TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.

51,099 citations

Journal ArticleDOI
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
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.

34,830 citations

Journal ArticleDOI
TL;DR: 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.
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.

32,980 citations

Journal ArticleDOI
TL;DR: 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.
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.

31,015 citations

Journal ArticleDOI
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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.

22,147 citations