Institution
Broad Institute
Nonprofit•Cambridge, Massachusetts, United States•
About: Broad Institute is a nonprofit organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 6584 authors who have published 11618 publications receiving 1522743 citations. The organization is also known as: Eli and Edythe L. Broad Institute of MIT and Harvard.
Papers published on a yearly basis
Papers
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TL;DR: A population-based cohort of men with localized prostate cancers followed by expectant (watchful waiting) therapy with 15% (17/111) TMPRSS2:ERG fusion is reported, finding a statistically significant association between TMPR SS2-ERG fusion and prostate cancer specific death.
Abstract: The identification of the TMPRSS2:ERG fusion in prostate cancer suggests that distinct molecular subtypes may define risk for disease progression. In surgical series, TMPRSS2:ERG fusion was identified in 50% of the tumors. Here, we report on a population-based cohort of men with localized prostate cancers followed by expectant (watchful waiting) therapy with 15% (17/111) TMPRSS2:ERG fusion. We identified a statistically significant association between TMPRSS2:ERG fusion and prostate cancer specific death (cumulative incidence ratio=2.7, P<0.01, 95% confidence interval=1.3–5.8). Quantitative reverse-transcription–polymerase chain reaction demonstrated high estrogen-regulated gene (ERG) expression to be associated with TMPRSS2:ERG fusion (P<0.005). These data suggest that TMPRSS2:ERG fusion prostate cancers may have a more aggressive phenotype, possibly mediated through increased ERG expression.
638 citations
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TL;DR: Functional study of genes marked by super-enhancers identifies DLBCLs dependent on OCA-B and suggests a strategy for discovering unrecognized cancer dependencies.
638 citations
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TL;DR: A combination of yeast two-hybrid analysis and genome-wide expression profiling implicated hundreds of human factors in mediating viral-host interactions and pointed to potential roles for some unanticipated host and viral proteins in viral infection and the host response.
638 citations
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TL;DR: In this article, the authors review conceptual parallels between the respective biological phenomena, highlighting important interrelationships among transcription factors, chromatin regulators, and preexisting epigenetic states, and provide insights into oncogenic transformation, tumor heterogeneity, and cancer stem cell models.
Abstract: The demonstration of induced pluripotency and direct lineage conversion has led to remarkable insights regarding the roles of transcription factors and chromatin regulators in mediating cell state transitions. Beyond its considerable implications for regenerative medicine, this body of work is highly relevant to multiple stages of oncogenesis, from the initial cellular transformation to the hierarchical organization of established malignancies. Here, we review conceptual parallels between the respective biological phenomena, highlighting important interrelationships among transcription factors, chromatin regulators, and preexisting epigenetic states. The shared mechanisms provide insights into oncogenic transformation, tumor heterogeneity, and cancer stem cell models.
637 citations
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TL;DR: A new, low-cost, high throughput reduced representation expression profiling method, L1000, is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
Abstract: We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.
636 citations
Authors
Showing all 7146 results
Name | H-index | Papers | Citations |
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Eric S. Lander | 301 | 826 | 525976 |
Albert Hofman | 267 | 2530 | 321405 |
Frank B. Hu | 250 | 1675 | 253464 |
David J. Hunter | 213 | 1836 | 207050 |
Kari Stefansson | 206 | 794 | 174819 |
Mark J. Daly | 204 | 763 | 304452 |
Lewis C. Cantley | 196 | 748 | 169037 |
Matthew Meyerson | 194 | 553 | 243726 |
Gad Getz | 189 | 520 | 247560 |
Stacey Gabriel | 187 | 383 | 294284 |
Stuart H. Orkin | 186 | 715 | 112182 |
Ralph Weissleder | 184 | 1160 | 142508 |
Chris Sander | 178 | 713 | 233287 |
Michael I. Jordan | 176 | 1016 | 216204 |
Richard A. Young | 173 | 520 | 126642 |