Zihan Liu

Bio: Zihan Liu is an academic researcher from Broad Institute. The author has contributed to research in topics: Proteomics & Drug repositioning. The author has an hindex of 5, co-authored 6 publications receiving 1711 citations.

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

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.

The Drug Repurposing Hub: a next-generation drug library and information resource

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

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations.

Abstract: Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the "connectivity" framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.

BioAssay Research Database (BARD): chemical biology and probe-development enabled by structured metadata and result types

Abstract: BARD, the BioAssay Research Database (https://bard.nih.gov/) is a public database and suite of tools developed to provide access to bioassay data produced by the NIH Molecular Libraries Program (MLP). Data from 631 MLP projects were migrated to a new structured vocabulary designed to capture bioassay data in a formalized manner, with particular emphasis placed on the description of assay protocols. New data can be submitted to BARD with a user-friendly set of tools that assist in the creation of appropriately formatted datasets and assay definitions. Data published through the BARD application program interface (API) can be accessed by researchers using web-based query tools or a desktop client. Third-party developers wishing to create new tools can use the API to produce stand-alone tools or new plug-ins that can be integrated into BARD. The entire BARD suite of tools therefore supports three classes of researcher: those who wish to publish data, those who wish to mine data for testable hypotheses, and those in the developer community who wish to build tools that leverage this carefully curated chemical biology resource.

Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells.

Abstract: The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.