A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

Subramanian, Aravind; Narayan, Rajiv; Corsello, Steven M.; Peck, Donald J.; Natoli, Ted; Lü, Xiaodong; Gould, Joshua; Davis, John F.; Tubelli, Andrew A.; Asiedu, Jacob K.; Lahr, David L.; Hirschman, Jodi E.; Liu, Zihan; Donahue, Melanie; Julian, Bina; Khan, Mariya; Wadden, David; Smith, Ian C. P.; Lam, Daniel D.; Liberzon, Arthur; Toder, Courtney; Bagul, Mukta; Orzechowski, Marek; Enache, Oana; Piccioni, Federica; Berger, Alice H.; Shamji, Alykhan F.; Brooks, Angela N.; Vrcic, Anita; Flynn, Corey; Rosains, Jacqueline; Takeda, David Y.; Davison, Desiree; Lamb, Justin; Ardlie, Kristin; Hogstrom, Larson; Gray, Nathanael S.; Clemons, Paul A.; Silver, Serena J.; Wu, Xiaoyun; Zhao, Wen-Ning; Read-Button, Willis; Wu, Xiaohua; Haggarty, Stephen J.; Ronco, Lucienne; Boehm, Jesse S.; Schreiber, Stuart L.; Doench, John G.; Bittker, Joshua A.; Root, David E.; Wong, Bang; Golub, Todd R.

doi:10.1101/136168

Cited by 552 publications

(1,056 citation statements)

References 69 publications

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“…Additionally, Connectivity Map analysis predicted that translation inhibitors may overcome the drug-resistant state; 6,7 we confirmed this prediction by showing that omacetaxine mepesuccinate (homoharringtonine) — the first translation inhibitor to be approved by the United States Food and Drug Administration — displayed potent cytotoxicity on the proteasome inhibitor-resistant multiple myeloma cells. 4,8 …”

supporting

confidence: 64%

Correlating Chemical Sensitivity with Low Level Activation of Mechanotransduction Pathways in Hematologic Malignancies

Hawley

2017

Exploratory Research and Hypothesis in Medicine

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Large-scale screening has revealed that human hematopoietic cancer cell lines are generally more sensitive to various classes of drugs than cell lines established from solid tumors. A detailed examination of data in the Cancer Therapeutics Response Portal (http://portals.broadinstitute.org/ctrp/) suggests that this enhanced sensitivity is due to lower basal levels of activation of TAZ-TEAD mechanotransduction pathways in hematopoietic versus non-hematopoietic cells. Translation inhibitors such as omacetaxine mepesuccinate (homoharringtonine) fall into this category of hematopoietic-selective compounds. Moreover, additional molecular determinants of sensitivity suggest that homoharringtonine might show therapeutic efficacy in certain patients with advanced hematologic malignancies despite activation of these pathways.

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supporting

confidence: 64%

Correlating Chemical Sensitivity with Low Level Activation of Mechanotransduction Pathways in Hematologic Malignancies

Hawley

2017

Exploratory Research and Hypothesis in Medicine

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“…Additionally, allele-specific molecular assays, massively parallel assays, and CRISPR screens are increasingly yielding high-resolution experimental information about the effects of genetic variation on gene expression 29,45,[160][161][162][163] as well as cellular processes such as growth [164][165][166] and inflammation 167 . Finally, perturbational differential expression experiments can yield signed predictions for the relationships of genes to a variety of biological processes such as drug response 168 , immune stimuli 169 , and many others 170 . Though converting such data to signed functional annotations will require care, doing so could allow us to leverage them to make detailed statements about disease mechanism.…”

Section: Discussionmentioning

confidence: 99%

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Reshef

Finucane

Kelley

et al. 2017

Preprint

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Biological interpretation of GWAS data frequently involves analyzing unsigned genomic annotations comprising SNPs involved in a biological process and assessing enrichment for disease signal. However, it is often possible to generate signed annotations quantifying whether each SNP allele promotes or hinders a biological process, e.g., binding of a transcription factor (TF). Directional effects of such annotations on disease risk enable stronger statements about causal mechanisms of disease than enrichments of corresponding unsigned annotations. Here we introduce a new method, signed LD profile regression, for detecting such directional effects using GWAS summary statistics, and we apply the method using 382 signed annotations reflecting predicted TF binding. We show via theory and simulations that our method is well-powered and is well-calibrated even when TF binding sites co-localize with other enriched regulatory elements, which can confound unsigned enrichment methods. We apply our method to 12 molecular traits and recover many known relationships including positive associations between gene expression and genome-wide binding of RNA polymerase II, NF-κB, and several ETS family members, as well as between known chromatin modifiers and their respective chromatin marks. Finally, we apply our method to 46 diseases and complex traits (average N = 289, 617) and identify 77 significant associations at per-trait FDR < 5%, representing 12 independent signals. Our results include a positive association between educational attainment and genome-wide binding of BCL11A, consistent with recent work linking BCL11A hemizygosity to intellectual disability; a negative association between lupus risk and genome-wide binding of CTCF, which has been shown to suppress myeloid differentiation; and a positive association between Crohn's disease (CD) risk and genome-wide binding of IRF1, an immune regulator that lies inside a CD GWAS locus and has eQTLs that increase CD risk. Our method provides a new way to leverage functional data to draw inferences about causal mechanisms of disease.

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“…The computation of RGES and the summarization RGES were detailed elsewhere and recently implemented as a standalone R package 31 . Briefly, we quantified the reversal of disease gene expression as RGES (Reversal Gene Expression Score), a measure modified from the connectivity score developed in other studies 20 . To compute RGES scores, we first rank genes based on their expression values in each drug signature.…”

Section: Reversal Correlationmentioning

confidence: 99%

Analysis of Infected Host Gene Expression Reveals Repurposed Drug Candidates and Time-Dependent Host Response Dynamics for COVID-19

Xing

Shankar

Drelich

et al. 2020

Preprint

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Repurposing existing drugs is a timely option to cope with COVID-19. We predicted therapeutic candidates that could reverse the gene expression of coronavirus-infected host cells. Thirteen expression signatures computed from various experimental conditions and preclinical models could be reversed by those compounds known to be effective against SARS-or MERS-CoV, as well as the drug candidates recently shown to be effective against SARS-CoV-2. We selected ten novel candidates to further evaluate their in vitro efficacy against SARS-CoV-2 infection. Four compounds bortezomib, dactolisib, alvocidib and methotrexate inhibited the formation of virus infection-induced cytopathic effect in Vero E6 cells at 1 µM, yet such a concentration seems toxic to the cells as well. While the evaluation in other permissive cells and

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A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

Cited by 552 publications

References 69 publications

Correlating Chemical Sensitivity with Low Level Activation of Mechanotransduction Pathways in Hematologic Malignancies

Correlating Chemical Sensitivity with Low Level Activation of Mechanotransduction Pathways in Hematologic Malignancies

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Analysis of Infected Host Gene Expression Reveals Repurposed Drug Candidates and Time-Dependent Host Response Dynamics for COVID-19

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