Author
Rajiv Narayan
Other affiliations: Boston University, Harvard University, Massachusetts Institute of Technology
Bio: Rajiv Narayan is an academic researcher from Broad Institute. The author has contributed to research in topics: Gene expression profiling & Auditory cortex. The author has an hindex of 22, co-authored 37 publications receiving 3885 citations. Previous affiliations of Rajiv Narayan include Boston University & Harvard University.
Papers
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TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that 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.
1,943 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
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TL;DR: This work hand-curated a collection of 4,707 compounds, experimentally confirmed their identities, and annotated them with literature-reported targets, to assemble a comprehensive library of drugs that have reached the clinic and established a blueprint for others to easily assemble such a repurposing library.
Abstract: To the Editor: Drug repurposing, the application of an existing therapeutic to a new disease indication, holds promise of rapid clinical impact at a lower cost than de novo drug development. So far, there has not been a systematic effort to identify such opportunities, limited in part by the lack of a comprehensive library of clinical compounds suitable for testing. To address this challenge, we hand-curated a collection of 4,707 compounds, experimentally confirmed their identities, and annotated them with literature-reported targets. The collection includes 3,422 drugs that are marketed around the world or that have been tested in human clinical trials. Compounds were obtained from more than 50 chemical vendors, and the purity of each sample was established. We have thus established a blueprint for others to easily assemble such a repurposing library, and we have created an online Drug Repurposing Hub (http:// www.broadinstitute.org/repurposing) that contains detailed annotation for each of the compounds. Repurposing is attractive and pragmatic, given the substantial cost and time requirements—on average, a decade or more—for drug development1. In addition, a large number of potential drugs never reach clinical testing. Moreover, fewer than 15% of compounds that enter clinical development ultimately receive approval, despite the majority of them being deemed safe2. For either approved or failed drugs for which safety has already been established, finding new indications can rapidly bring benefits to patients. Prior drug-repurposing successes span disease areas; examples include the cyclooxygenase inhibitor aspirin to treat coronary-artery disease, the phosphodiesterase inhibitor sildenafil to treat erectile dysfunction, and the antibiotic erythromycin for impaired gastric motility (Supplementary Table 1)3. Even drugs associated with troubling side effects merit reconsideration, as evidenced by the successful repurposing of the antiemetic thalidomide to treat multiple myeloma4. Risk-mediating measures for avoiding the potential teratogenicity of thalidomide and its derivatives are reasonable in patients with life-threatening cancer, whereas the use of these drugs to treat nausea remains unacceptable. Although the benefits of repurposing are clear, successes thus far have been mostly serendipitous. Systematic, large-scale repurposing efforts have not been possible owing to the lack of a definitive physical drug collection, the low quality of drug annotations, and insufficient readouts of drug activity from which new indications can be predicted. Recent technological advances have enabled a step change in our ability to assess drug activities comprehensively. For example, perturbational gene expression profiles can now be obtained at high throughput across multiple cell types5. Gene expression profiling has enabled recent repurposing discoveries, including sirolimus for glucocorticoid-resistant acute lymphocytic leukemia, topiramate for inflammatory-bowel disease, and imipramine for small-cell lung cancer. For cancer therapeutics, a recently developed assay known as PRISM, which uses barcoded cell lines, enables rapid testing of many drugs against a large number of cancer cell lines in pools6. Molecular features of the cell lines (for example, gene expression, mutation, or copy-number variation) can then be used to identify predictive biomarkers of drug sensitivity (Supplementary Table 2). Finally, morphologic changes in cells can be assessed using high-throughput microscopy and machine-learning approaches. Such imaging-based screening unexpectedly identified the cholesterol drug lovastatin as a potent inhibitor of leukemia stem cells. To take advantage of these advances in experimental methods, we sought to assemble a comprehensive library of drugs that have reached the clinic. Surprisingly, we found that no such chemical library of approved and clinical trial drugs is available for purchase. In particular, drugs that have been tested in clinical trials but did not reach approval are not readily accessible. Even obtaining a complete list of such drugs and their annotations is challenging. A prior effort led by the US National Institutes of Health (NIH) focused on drugs approved by the US Food and Drug Administration (FDA), but the library has few compounds that have yet to achieve FDA approval7. Some chemical vendors offer a subset of approved drugs, but most of these commercial libraries overlap in their content and include only a small fraction of the approximately 10,000 drugs that have reached the clinic in the United States and Europe. Given that no complete collection exists, we launched a three-step effort to create the Repurposing Library by (i) identifying and purchasing compounds; (ii) comprehensively annotating their known activities and clinical indications; and (iii) experimentally confirming drug identity and purity. We employed two approaches to identify clinical-drug structures for the Repurposing Library. First, we searched existing databases, both publicly accessible and proprietary, for clinically tested drugs and then manually integrated them to ensure sufficient drug coverage and chemical-structure reliability (Supplementary Table 3). Sources included DrugBank, the NCATS NCGC Pharmaceutical Collection (NPC), Thomson Reuters Integrity, Thomson Reuters Cortellis, and Citeline Pharmaprojects7–9. Second, we located marketed or approved ingredient lists from regulatory agencies worldwide, including the FDA. After structure standardization and the removal of duplicates, approximately 10,000 small-molecule drugs with disclosed structures were found to have reached clinical development. Most of these drugs are not widely available in commercial screening libraries. Through structure-matching (as opposed to relying on compound names), chemical suppliers were identified for 5,691 compounds (Fig. 1). Controlled substances, nonpharmaceutical substances, and redundant elemental formulations were not pursued further. To assemble the collection, we ultimately purchased 8,584 samples (representing 5,691 unique compounds) from 75 chemical vendors, at an average cost of $29 per sample. We performed chemical-structure analysis on all clinical-drug structures (whether commercially available or not) to assess the extent of The Drug Repurposing Hub: a next-generation drug library and information resource
619 citations
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TL;DR: Systematic gain-of-function resistance studies suggest that oncogenic dysregulation of a melanocyte lineage dependency can cause resistance to RAF–MEK–ERK inhibition, which may be overcome by combining signalling- and chromatin-directed therapeutics.
Abstract: Malignant melanomas harbouring point mutations (Val600Glu) in the serine/threonine-protein kinase BRAF (BRAF(V600E)) depend on RAF-MEK-ERK signalling for tumour cell growth. RAF and MEK inhibitors show remarkable clinical efficacy in BRAF(V600E) melanoma; however, resistance to these agents remains a formidable challenge. Global characterization of resistance mechanisms may inform the development of more effective therapeutic combinations. Here we carried out systematic gain-of-function resistance studies by expressing more than 15,500 genes individually in a BRAF(V600E) melanoma cell line treated with RAF, MEK, ERK or combined RAF-MEK inhibitors. These studies revealed a cyclic-AMP-dependent melanocytic signalling network not previously associated with drug resistance, including G-protein-coupled receptors, adenyl cyclase, protein kinase A and cAMP response element binding protein (CREB). Preliminary analysis of biopsies from BRAF(V600E) melanoma patients revealed that phosphorylated (active) CREB was suppressed by RAF-MEK inhibition but restored in relapsing tumours. Expression of transcription factors activated downstream of MAP kinase and cAMP pathways also conferred resistance, including c-FOS, NR4A1, NR4A2 and MITF. Combined treatment with MAPK-pathway and histone-deacetylase inhibitors suppressed MITF expression and cAMP-mediated resistance. Collectively, these data suggest that oncogenic dysregulation of a melanocyte lineage dependency can cause resistance to RAF-MEK-ERK inhibition, which may be overcome by combining signalling- and chromatin-directed therapeutics.
411 citations
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20 Jan 2020
TL;DR: An unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines in a manner predictable from the cell lines' molecular features.
Abstract: Anti-cancer uses of non-oncology drugs have occasionally been found, but such discoveries have been serendipitous. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. We used PRISM, a molecular barcoding method, to screen drugs against cell lines in pools. An unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines in a manner predictable from the cell lines' molecular features. Our findings include compounds that killed by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing depended on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which killed cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, which killed via the multi-drug resistance protein ABCB1. The PRISM drug repurposing resource (https://depmap.org/repurposing) is a starting point to develop new oncology therapeutics, and more rarely, for potential direct clinical translation.
347 citations
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TL;DR: Changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks are described.
Abstract: Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
3,253 citations
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TL;DR: The cellular ecosystem of tumors is begin to unravel and how single-cell genomics offers insights with implications for both targeted and immune therapies is unraveled.
Abstract: To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.
3,061 citations
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TL;DR: Structural-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci is described and the potential of Cas9-based activators as a powerful genetic perturbation technology is demonstrated.
Abstract: Systematic interrogation of gene function requires the ability to perturb gene expression in a robust and generalizable manner. Here we describe structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci. We used these engineered Cas9 activation complexes to investigate single-guide RNA (sgRNA) targeting rules for effective transcriptional activation, to demonstrate multiplexed activation of ten genes simultaneously, and to upregulate long intergenic non-coding RNA (lincRNA) transcripts. We also synthesized a library consisting of 70,290 guides targeting all human RefSeq coding isoforms to screen for genes that, upon activation, confer resistance to a BRAF inhibitor. The top hits included genes previously shown to be able to confer resistance, and novel candidates were validated using individual sgRNA and complementary DNA overexpression. A gene expression signature based on the top screening hits correlated with markers of BRAF inhibitor resistance in cell lines and patient-derived samples. These results collectively demonstrate the potential of Cas9-based activators as a powerful genetic perturbation technology.
2,186 citations
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TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that 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.
1,943 citations
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TL;DR: How these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems are described.
Abstract: Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.
1,843 citations