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.

The Connectivity Map Links Iron Regulatory Protein-1-Mediated Inhibition of Hypoxia-Inducible Factor-2a Translation to the Anti-inflammatory

Abstract: Hypoxia-inducible factors 1 and 2 (HIF1 and HIF2) are heterodimeric transcription factors consisting of α regulatory subunits and a constitutively expressed β subunit. The expression of α regulatory subunits is promoted by hypoxia, cancer-associated mutations, and inflammatory cytokines. Thus, HIF1 and HIF2 provide a molecular link between cancer and inflammation. We have recently identified novel small molecules that selectively inhibit translation of the HIF2a message and thereby powerfully inhibit the expression of HIF2a target genes. We report here that Connectivity Map analysis links three of these compounds to the anti-inflammatory cytokine 15-deoxy-Δ 12,14 -prostaglandin J2 (PGJ2). As with our identified compounds, PGJ2 inhibits translation of the HIF2a message in a mammalian target of rapamycin–independent manner by promoting the binding of iron regulatory protein-1 (IRP1) to a noncanonical iron responsive element (IRE) embedded within the 5′-untranslated region of the HIF2a message. The IRE is necessary and sufficient for mediating the effect. Mutation of the IRE sequence, or downregulation of IRP1 expression, blocks the effect of PGJ2 on HIF2a translation. This is the first report of an endogenous natural molecule regulating HIF2a translation, and it suggests that part of the anti-inflammatory and putative antineoplastic effects of PGJ2 may be mediated through inhibition of HIF2a within tumor epithelial cells themselves and/or mesenchymal cells of the tumor microenvironment. Cancer Res; 70(8); OF1–9. ©2010 AACR.

Corrigendum: The genomic landscape of juvenile myelomonocytic leukemia.

Abstract: Nat. Genet. 47, 1326–1333 (2015); published online 12 October 2015; corrected after print 7 December 2015 In the version of this article initially published, two patients were stated on page 5 to have been excluded owing to insufficient follow-up data. These patients were included in the final analysis, but two additional patients were excluded owing to the presence of Noonan syndrome.

Abstract 5371: The Metastatic Breast Cancer Project: Partnering with patients to accelerate progress in cancer research

Abstract: The Metastatic Breast Cancer Project (MBCproject) is a research study that directly engages patients (pts) through social media and advocacy groups, and empowers them to share samples, clinical data, and experiences. The goal is to create a publicly available database of genomic, molecular, clinical, and patient-reported data to enable research. Working with pts and advocates, a website (MBCproject.org) was developed that allows pts with metastatic breast cancer (MBC) to register. Registered pts are sent an online consent form that asks for permission to obtain and analyze their medical records and samples. Once enrolled, pts are sent a saliva kit and asked to mail back a saliva sample, which is used to extract germline DNA. We contact participants9 medical providers and obtain medical records and a portion of their stored tumor biopsies. Pts may be asked to mail in a blood sample, which is used to extract cell free DNA (cfDNA). Whole-exome sequencing (WES) is performed on tumor DNA, germline DNA, and cfNDA; transcriptome sequencing is performed on tumor RNA. Clinically annotated genomic data are used to study specific pt cohorts (including outliers) and to identify mechanisms of response and resistance to therapies. All de-identified data are shared via public databases. Study updates are shared with participants regularly. From 10/2015-11/2017, 4237 MBC pts registered, representing over 1,000 institutions. 95% answered the 16-question survey about their cancer, treatments, and demographic information. 2471 (58%) completed the consent form. 2,136 saliva kits were mailed to pts and 1,523 saliva samples were sent in (71%). 408 blood kits were mailed to pts and 175 blood samples have been received for cfDNA analysis. To date, we have obtained medical records from 311 pts and 190 tumors from 127 pts. In 10/2017, all data generated so far were publicly released on cbioportal.org, including WES for 103 tumors from 78 pts linked to clinical data including pathology (22 elements), medical record abstraction including all treatments and timelines/durations (67 elements), and patient-reported data (11 elements). 81% of biopsies included in this release were from the breast and 19% from metastatic sites. 75% were obtained prior to any therapy, 24% following therapy. New data will be released 4/2018 and every six months thereafter, as they are generated. Additional patient-reported data, including treatments, side effects, quality of life, family history, pregnancies, and sites of metastasis, will also be collected and shared. In summary, a patient-driven approach enabled rapid identification of thousands of MBC pts willing to share samples and clinical data. Remote acquisition of medical records, saliva, blood, and tumor tissue for pts across the U.S. is feasible. This shared clinico-genomic database should enable research in MBC and may serve as a model for patient-driven research in other cancers. Citation Format: Nikhil Wagle, Corrie Painter, Elana Anastasio, Michael Dunphy, Mary McGillicuddy, Rachel Stoddard, Esha Jain, Dewey Kim, Simona Di Lascio, Brett N. Tompson, Sara Balch, Beena Thomas, Priti Kumari, Shawn Johnson, Jamie Holloway, Ofir Cohen, Erik H. Knelson, Katie Larkin, Sam Pollock, Alicia Wong, Samira Bahl, Simone Maiwald, Andrew Zimmer, Esme O. Baker, Jen Hendry Lapan, Scott Sutherland, Scott Sassone, Viktor Adalsteinsson, Eric S. Lander, Todd R. Golub. The Metastatic Breast Cancer Project: Partnering with patients to accelerate progress in cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5371.

Abstract B085: High mutation burden and response to immune checkpoint inhibitors in angiosarcomas of the scalp and face

Abstract: Objective: Angiosarcoma (AS) is a rare soft tissue sarcoma, with an incidence of 300 cases/yr and a 5-year DSS of 30%. The low incidence has impeded large-scale research efforts. To address this, we launched a patient-partnered genomics study which seeks to empower patients to accelerate research by remotely sharing their samples and clinical information. Methods: We developed a website (ASCproject.org) to allow remote acquisition of medical records (MR), saliva, blood, and archival tissue from patients in the US and Canada. Whole-exome sequencing (WES) of ~20,000 genes is performed on tumor and matched germline DNA. Transcriptome analysis is performed on tumor RNA. Ultra-low pass whole-genome sequencing (ULP-WGS) and in some cases WES is performed on cell free DNA (cfDNA) obtained from blood samples. Clinical data including information about demographics, diagnosis, treatments, and responses are obtained via patient-reported data (PRD) and through MR abstraction. The resulting clinically annotated genomic database is shared widely to identify genomic drivers and mechanisms of response and resistance to therapies. Results: Since launch on March 13 2017, 321 patients with AS have registered. The average age of patients is 56 yrs (range 22-89). Primary locations of AS were primary breast (24%), breast with prior radiation (20%), head/face/neck/scalp (HFNS) (21%), bone/limb (9%), abdominal (3%), heart (3%), lung (1%), liver (1%), lymph (0.5%), multiple locations (11%), and other locations (5%). 142 (48%) reported being disease free at the time of enrollment. To date, 153 saliva kits, 167 MRs, 43 blood samples, and 97 tissue samples have been obtained. WES analysis is complete for 14 samples.ULP-WGS is complete for 10 cfDNA samples, and WES on 4 cfDNA samples. Transcriptome sequencing is complete for 9 tumor samples. We identified several previously described genes known to be altered in AS, including recurrent alterations in KDR and TP53. Tumor mutational burden (TMB) and mutational signature activities were quantified for each tumor sample. All three of the AS from the HFNS in the initial cohort exhibited a high TMB (>150 mutations) and dominant UV light signature (COSMIC Signature 7). Based on this, we hypothesized that HFNS AS might respond well to immune checkpoint inhibitors. We identified through PRD 56 patients with HFNS AS who reported what medications they received. Of these, 2 reported receiving immune checkpoint inhibitors for the treatment of metastatic disease. Both patients had refractory metastatic HFNS AS and reported receiving off-label anti-PD1 therapy. Both had complete or near-complete responses following immunotherapy, and currently report having no evidence of disease. Clinical responses were confirmed through review of MRs. Sequencing is currently being performed on tumor samples from both patients. Conclusion: A patient-partnered approach enabled rapid identification and enrollment of over 300 patients with AS, an exceedingly rare cancer, in 15 months. We were able to obtain tumor, blood, saliva samples to perform genomic analyses, which were then merged with detailed clinical information. PRD, clinical, and genomic data generated from the first 12 patients and 14 samples have been released on cbioportal.org. Additional data will be released in six-month intervals. Initial results show high TMB and a UV signature in 3 out of 3 patients with HFNS AS. In addition, we identified 2 patients with HFNS AS who had extraordinary responses to immunotherapy. These findings suggest a common genomic basis for HFNS AS and could provide rationale for clinical interventions using checkpoint inhibitors for these AS. Analyses of additional samples are under way to further characterize mutational signatures in HFNS AS and implications for patient care. This study serves as proof of principle that patient-partnered genomics efforts can democratize cancer research for exceedingly rare cancers. Citation Format: Corrie Painter, Esha Jain, Michael Dunphy, Elana Anastasio, Mary McGillicuddy, Rachel Stoddard, Beena Thomas, Sara Balch, Kristin Anderka, Katie Larkin, Niall Lennon, Yen-Lin Chen, Andrew Zimmer, Esme O. Baker, Simone Maiwald, Jen Lapan, Jason L. Hornick, Chandrajit Raut, George Demetri, Eric S. Lander, Todd Golub. High mutation burden and response to immune checkpoint inhibitors in angiosarcomas of the scalp and face [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B085.

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.