A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
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.
About: This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.
Citations
More filters
Henry Ford Health System1, Harvard University2, Stanford University3, University of Hasselt4, University of Texas MD Anderson Cancer Center5, Nencki Institute of Experimental Biology6, École Polytechnique Fédérale de Lausanne7, Sage Bionetworks8, Université libre de Bruxelles9, Poznan University of Medical Sciences10, George Washington University11, Cold Spring Harbor Laboratory12, University of Kansas13, University of California, Santa Cruz14, University of North Carolina at Chapel Hill15, Van Andel Institute16
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.
1,099 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)....
[...]
...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…...
[...]
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.
794 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)....
[...]
...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....
[...]
...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)....
[...]
...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)....
[...]
...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)....
[...]
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.
601 citations
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
TL;DR: The current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects.
Abstract: Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
425 citations
References
More filters
TL;DR: It is demonstrated that HES1 directly downregulates the expression of BBC3, the gene encoding the PUMA BH3-only proapoptotic factor in T-cell acute lymphoblastic leukemia, and perhexiline, a small-molecule inhibitor of mitochondrial carnitine palmitoyltransferase-1, is identified as a H ES1-signature antagonist drug with robust antileukemic activity against NOTCH1-induced leukemias
39 citations
TL;DR: This work shows that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a Consensus Gene Signature (CGS), and compares RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 sgRNAs in 6 cells lines, and shows that the on-target efficacies are comparable.
Abstract: The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss of function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that miRNA-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a Consensus Gene Signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 sgRNAs in 6 cells lines, and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function.
35 citations
TL;DR: This work developed several algorithms to simultaneously select probes and impute missing values, and it is demonstrated that these 'probe selection for imputation' (PSI) algorithms can successfully reconstruct missing gene expression values in a wide variety of applications, as evaluated using multiple metrics of biological importance.
Abstract: The probe selection for imputation (PSI) approach accurately imputes global gene expression profiles from a small subset of probes that it chooses based on a training set of full profiles, allowing many more combinatorial experiments to be performed given the same resources. Measuring complete gene expression profiles for a large number of experiments is costly. We propose an approach in which a small subset of probes is selected based on a preliminary set of full expression profiles. In subsequent experiments, only the subset is measured, and the missing values are imputed. We developed several algorithms to simultaneously select probes and impute missing values, and we demonstrate that these 'probe selection for imputation' (PSI) algorithms can successfully reconstruct missing gene expression values in a wide variety of applications, as evaluated using multiple metrics of biological importance. We analyze the performance of PSI methods under varying conditions, provide guidelines for choosing the optimal method based on the experimental setting, and indicate how to estimate imputation accuracy. Finally, we apply our approach to a large-scale study of immune system variation.
30 citations
"A Next Generation Connectivity Map:..." refers background in this paper
...Alternative probe-selection methods, however, have been proposed (Donner et al., 2012)....
[...]
TL;DR: Evidence is reported that phosphatase and tensin homolog (PTEN) and phosphoinositide 3-kinase (PI-3K) play a crucial role in the propagation, survival and potential response to therapy in this CD15+ CSC/TPC-driven malignant disease and that a sequential combination of PI-3k inhibitors and chemotherapy will have augmented efficacy in the treatment of this disease.
Abstract: Sonic hedgehog (SHH) medulloblastoma (MB) subtype is driven by a proliferative CD15+ tumor propagating cell (TPC), also considered in the literature as a putative cancer stem cell (CSC). Despite considerable research, much of the biology of this TPC remains unknown. We report evidence that phosphatase and tensin homolog (PTEN) and phosphoinositide 3-kinase (PI-3K) play a crucial role in the propagation, survival and potential response to therapy in this CD15+ CSC/TPC-driven malignant disease. Using the ND2-SmoA1 transgenic mouse model for MB, mouse genetics and patient-derived xenografts (PDXs), we demonstrate that the CD15+TPCs are 1) obligately required for SmoA1Tg-driven tumorigenicity 2) regulated by PTEN and PI-3K signaling 3) selectively sensitive to the cytotoxic effects of pan PI-3K inhibitors in vitro and in vivo but resistant to chemotherapy 4) in the SmoA1Tg mouse model are genomically similar to the SHH human MB subgroup. The results provide the first evidence that PTEN plays a role in MB TPC signaling and biology and that PI-3K inhibitors target and suppress the survival and proliferation of cells within the mouse and human CD15+ cancer stem cell compartment. In contrast, CD15+ TPCs are resistant to cisplatinum, temozolomide and the SHH inhibitor, NVP-LDE-225, agents currently used in treatment of medulloblastoma. These studies validate the therapeutic efficacy of pan PI-3K inhibitors in the treatment of CD15+ TPC dependent medulloblastoma and suggest a sequential combination of PI-3K inhibitors and chemotherapy will have augmented efficacy in the treatment of this disease.
26 citations
TL;DR: Observations provide a rationale for the combined targeting of PKC and GSK3 signaling pathways in CTCL to enhance the therapeutic outcome.
19 citations
"A Next Generation Connectivity Map:..." refers background in this paper
...1444 Cell 171, 1437–1452, November 30, 2017 ported (Rovedo et al., 2011), and the biochemical profiling confirms that enzastaurin is indeed also a potent GSK3 inhibitor with a KD of 8 nM (Davis et al....
[...]