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Journal ArticleDOI

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

30 Nov 2017-Cell (NIH Public Access)-Vol. 171, Iss: 6, pp 1437-1452

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.

AbstractWe 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.

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Citations
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01 Dec 2016
TL;DR: Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions, and posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation.
Abstract: Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes-such as transcriptional profiles-at scale. Here, we develop Perturb-seq, combining single-cell RNA sequencing (RNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbations to perform many such assays in a pool. We demonstrate Perturb-seq by analyzing 200,000 cells in immune cells and cell lines, focusing on transcription factors regulating the response of dendritic cells to lipopolysaccharide (LPS). Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions. We posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation. By decomposing many high content measurements into the effects of perturbations, their interactions, and diverse cell metadata, Perturb-seq dramatically increases the scope of pooled genomic assays.

539 citations


Journal ArticleDOI
05 Apr 2018-Cell
TL;DR: Novel stemness indices for assessing the degree of oncogenic dedifferentiation are provided and it is found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors.
Abstract: Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.

477 citations


Cites methods from "A Next Generation Connectivity Map:..."

  • ...To further investigate about mechanism of actions (MoA) and drug-target we performed specific analysis within Connectivity Map tools (https://clue.io/) (Subramanian et al., 2017)....

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  • ...Connectivity Map (CMap) was recently updated (September 2017) (Subramanian et al., 2017), providing the end-users new functionalities and new graphical interface as web-server, previous registration (https://clue.io/) allowing easily the extraction of druginteraction knowledge using as input a…...

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Journal ArticleDOI
01 Nov 2018-Cell
TL;DR: A resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion is identified, and this study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance.
Abstract: Immune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance.

460 citations


Cites background or methods from "A Next Generation Connectivity Map:..."

  • ...Moreover, there is a significant overlap between the perturbations that reverse the expression of the program’s repressed and induced components (p = 4.353 10 6, hypergeometric test), including the overexpression of IFN-g and IFN-b and the knockdown of MYC and CDK7 (Subramanian et al., 2017)....

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  • ...Three genes (CDKN2C/ p18, CDKN1B/p27, andCDKN1A/p21) that inhibit CDK4 repress the program when overexpressed (Subramanian et al., 2017) (STARMethods), and the program ismore pronounced in cycling cells (Figures 1E, 2C, and S2E), where CDK4/6 are active....

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  • ...The latter mirrors the significantly large number of Myc and CDK7 (direct) targets (Oki et al., 2018; Subramanian et al., 2005) in the program (p < 1 3 10 17, hypergeometric test)....

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  • ...Indeed, the programs are enriched for Myc targets, even after removing RP genes (p 7.18 3 10 10) and are predicted to be repressed byMYC knockdown according to the Connectivity Map (Subramanian et al., 2017)....

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  • ...353 10 (6), hypergeometric test), including the overexpression of IFN-g and IFN-b and the knockdown of MYC and CDK7 (Subramanian et al., 2017)....

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Journal ArticleDOI
08 Aug 2018-Nature
TL;DR: The extent, origins and consequences of genetic variation within human cell lines are studied, providing a framework for researchers to measure such variation in efforts to support maximally reproducible cancer research.
Abstract: Human cancer cell lines are the workhorse of cancer research. Although cell lines are known to evolve in culture, the extent of the resultant genetic and transcriptional heterogeneity and its functional consequences remain understudied. Here we use genomic analyses of 106 human cell lines grown in two laboratories to show extensive clonal diversity. Further comprehensive genomic characterization of 27 strains of the common breast cancer cell line MCF7 uncovered rapid genetic diversification. Similar results were obtained with multiple strains of 13 additional cell lines. Notably, genetic changes were associated with differential activation of gene expression programs and marked differences in cell morphology and proliferation. Barcoding experiments showed that cell line evolution occurs as a result of positive clonal selection that is highly sensitive to culture conditions. Analyses of single-cell-derived clones demonstrated that continuous instability quickly translates into heterogeneity of the cell line. When the 27 MCF7 strains were tested against 321 anti-cancer compounds, we uncovered considerably different drug responses: at least 75% of compounds that strongly inhibited some strains were completely inactive in others. This study documents the extent, origins and consequences of genetic variation within cell lines, and provides a framework for researchers to measure such variation in efforts to support maximally reproducible cancer research.

408 citations


Journal ArticleDOI
Alexandra B Keenan1, Sherry L. Jenkins1, Kathleen M. Jagodnik1, Simon Koplev1, Edward He1, Denis Torre1, Zichen Wang1, Anders B. Dohlman1, Moshe C. Silverstein1, Alexander Lachmann1, Maxim V. Kuleshov1, Avi Ma'ayan1, Vasileios Stathias2, Raymond Terryn2, Daniel J. Cooper2, Michele Forlin2, Amar Koleti2, Dusica Vidovic2, Caty Chung2, Stephan C. Schürer2, Jouzas Vasiliauskas3, Marcin Pilarczyk3, Behrouz Shamsaei3, Mehdi Fazel3, Yan Ren3, Wen Niu3, Nicholas A. Clark3, Shana White3, Naim Al Mahi3, Lixia Zhang3, Michal Kouril3, John F. Reichard3, Siva Sivaganesan3, Mario Medvedovic3, Jaroslaw Meller3, Rick J. Koch1, Marc R. Birtwistle1, Ravi Iyengar1, Eric A. Sobie1, Evren U. Azeloglu1, Julia A. Kaye4, Jeannette Osterloh4, Kelly Haston4, Jaslin Kalra4, Steve Finkbiener4, Jonathan Z. Li5, Pamela Milani5, Miriam Adam5, Renan Escalante-Chong5, Karen Sachs5, Alexander LeNail5, Divya Ramamoorthy5, Ernest Fraenkel5, Gavin Daigle6, Uzma Hussain6, Alyssa Coye6, Jeffrey D. Rothstein6, Dhruv Sareen7, Loren Ornelas7, Maria G. Banuelos7, Berhan Mandefro7, Ritchie Ho7, Clive N. Svendsen7, Ryan G. Lim8, Jennifer Stocksdale8, Malcolm Casale8, Terri G. Thompson8, Jie Wu8, Leslie M. Thompson8, Victoria Dardov7, Vidya Venkatraman7, Andrea Matlock7, Jennifer E. Van Eyk7, Jacob D. Jaffe9, Malvina Papanastasiou9, Aravind Subramanian9, Todd R. Golub, Sean D. Erickson10, Mohammad Fallahi-Sichani10, Marc Hafner10, Nathanael S. Gray10, Jia-Ren Lin10, Caitlin E. Mills10, Jeremy L. Muhlich10, Mario Niepel10, Caroline E. Shamu10, Elizabeth H. Williams10, David Wrobel10, Peter K. Sorger10, Laura M. Heiser11, Joe W. Gray11, James E. Korkola11, Gordon B. Mills12, Mark A. LaBarge13, Mark A. LaBarge14, Heidi S. Feiler11, Mark A. Dane11, Elmar Bucher11, Michel Nederlof11, Damir Sudar11, Sean M. Gross11, David Kilburn11, Rebecca Smith11, Kaylyn Devlin11, Ron Margolis, Leslie Derr, Albert Lee, Ajay Pillai 
TL;DR: The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders.
Abstract: The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

222 citations


References
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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.

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TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.

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TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

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Journal ArticleDOI
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
Abstract: Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences BLAT's speed stems from an index of all nonoverlapping K-mers in the genome This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly BLAT has several major stages It uses the index to find regions in the genome likely to be homologous to the query sequence It performs an alignment between homologous regions It stitches together these aligned regions (often exons) into larger alignments (typically genes) Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible This paper describes how BLAT was optimized Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications http://genomeucscedu hosts a web-based BLAT server for the human genome

7,686 citations


Journal ArticleDOI
Abstract: SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.

4,947 citations


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