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.
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TL;DR: Wang et al. as discussed by the authors developed and validated an effective signature based on autophagy-, apoptosis-and necrosis-related genes for prognostic implications in Glioblastoma (GBM) patients.
Abstract: Glioblastoma (GBM) is considered the most malignant and devastating intracranial tumor without effective treatment. Autophagy, apoptosis, and necrosis, three classically known cell death pathways, can provide novel clinical and immunological insights, which may assist in designing personalized therapeutics. In this study, we developed and validated an effective signature based on autophagy-, apoptosis- and necrosis-related genes for prognostic implications in GBM patients.Variations in the expression of genes involved in autophagy, apoptosis and necrosis were explored in 518 GBM patients from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were performed to construct a combined prognostic signature. Kaplan-Meier survival, receiver-operating characteristic (ROC) curves and Cox regression analyses based on overall survival (OS) and progression-free survival (PFS) were conducted to estimate the independent prognostic performance of the gene signature. The Chinese Glioma Genome Atlas (CGGA) dataset was used for external validation. Finally, we investigated the differences in the immune microenvironment between different prognostic groups and predicted potential compounds targeting each group.A 16-gene cell death index (CDI) was established. Patients were clustered into either the high risk or the low risk groups according to the CDI score, and those in the low risk group presented significantly longer OS and PFS than the high CDI group. ROC curves demonstrated outstanding performance of the gene signature in both the training and validation groups. Furthermore, immune cell analysis identified higher infiltration of neutrophils, macrophages, Treg, T helper cells, and aDCs, and lower infiltration of B cells in the high CDI group. Interestingly, this group also showed lower expression levels of immune checkpoint molecules PDCD1 and CD200, and higher expression levels of PDCD1LG2, CD86, CD48 and IDO1.Our study proposes that the CDI signature can be utilized as a prognostic predictor and may guide patients' selection for preferential use of immunotherapy in GBM.
8 citations
TL;DR: The LINCS Microenvironment (ME) perturbation dataset as discussed by the authors is a dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2.
Abstract: The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.
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TL;DR: The dysfunction of fat metabolic pathways, the cell cycle, oxidation-reduction processes and viral carcinogenesis may serve critical roles in the occurrence of HBV-associated early stage HCC.
Abstract: Primary liver cancer is a rapidly progressing neoplasm with high morbidity and mortality rates. The present study aimed to identify potential diagnostic and prognostic biomarkers, and candidate targeted agents for hepatitis B virus (HBV)-associated early stage hepatocellular carcinoma (HCC). The gene expression profiles were extracted from the Gene Expression Omnibus database. Differentially expressed genes (DEGs), hub genes and the enrichment of signaling pathways were filtered out via a high-throughput sequencing method. The association between hub genes and the effects of the abnormal expression of hub genes on the rate of genetic variation, overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS) and disease-free survival (DSS) of patients with HCC, as well as pathological stage and grade, were analyzed using different databases. A total of 1,582 DEGs were identified. Gene Ontology analysis revealed that the DEGs were mainly involved in the 'oxidation-reduction process', 'steroid metabolic process', 'metabolic process' and 'fatty acid beta-oxidation'. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways revealed that the DEGs were mainly associated with 'metabolic pathways', 'PPAR signaling pathway', 'fatty acid degradation' and the 'cell cycle'. A total of 8 hub genes were extracted. Additionally, the abnormal expression levels of hub genes were closely associated with the OS, RFS, PFS and DSS of patients, the pathological stage and the grade. Furthermore, abnormal expression levels of the 8 hub genes were found in >30% of all samples. Several small molecular compounds that may reverse the altered DEGs were identified based on Connectivity Map analysis, including phenoxybenzamine, GW-8510, resveratrol, 0175029-0000 and daunorubicin. In conclusion, the dysfunction of fat metabolic pathways, the cell cycle, oxidation-reduction processes and viral carcinogenesis may serve critical roles in the occurrence of HBV-associated early stage HCC. The identified 8 hub genes may act as robust biomarkers for diagnosis and prognosis. Some small molecular compounds may be promising targeted agents against HBV-associated early stage HCC.
8 citations
Cites background from "A Next Generation Connectivity Map:..."
...org/cmap/) (31) is a collection of genome‐wide transcrip‐ tional expression data from cultured human cells treated with bioactive small molecules and simple pattern‐matching algorithms that together enable the discovery of functional connections between drugs, genes and diseases through the transitory feature of common gene expression changes....
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TL;DR: In this paper, the authors discuss recent therapeutic advances of RUNX1 targeting in acute myeloid leukemia (AML) and discuss the clinical basis for runx1 targeting and subdivide recent therapeutic approaches either by common biochemical pathways or by similar pharmacological targets.
Abstract: Introduction: RUNX1 is an essential transcription factor for normal and malignant hematopoiesis. RUNX1 forms a heterodimeric complex with CBFB. Germline mutations and somatic alterations (i.e. translocations, mutations and abnormal expression) are frequently associated with acute myeloid leukemia (AML) with RUNX1 mutations conferring unfavorable prognosis. Therefore, RUNX1 constitutes a potential innovative and interesting therapeutic target. In this review, we discuss recent therapeutic advances of RUNX1 targeting in AML.Areas covered: Firstly, we cover the clinical basis for RUNX1 targeting. We have subdivided recent therapeutic approaches either by common biochemical pathways or by similar pharmacological targets. Genome editing of RUNX1 induces anti-leukemic effects; however, off-target events prohibit clinical use. Several molecules inhibit the interaction between RUNX1/CBFB and control AML development and progression. BET protein antagonists target RUNX1 (i.e. specific BET inhibitors, BRD4 shRNRA, proteolysis targeting chimeras (PROTAC) or expression-mimickers). All these molecules improve survival in mutant RUNX1 AML preclinical models.Expert opinion: Some of these novel molecules have shown encouraging anti-leukemic potency at the preclinical stage. A better understanding of RUNX1 function in AML development and progression and its key downstream pathways, may result in more precise and more efficient RUNX1 targeting therapies.
8 citations
TL;DR: The results derived from the present study further advance the understanding of the complex regulatory mechanisms of MI and provide a potential MI theranostic signature with ovatodiolide as a therapeutic candidate.
Abstract: Myocardial infarction (MI) is a multifactorial global disease, recognized as one of the leading causes of cardiovascular morbidity and mortality. Timely and correct diagnoses and effective treatments could significantly reduce incidence of complications and improve patient prognoses. In this study, seven unconventional differentially expressed genes (DEGs) (MAN2A2, TNFRSF12A, SPP1, CSNK1D, PLAUR, PFKFB3, and CXCL16, collectively termed the MTSCPPC signature) were identified through integrating DEGs from six MI microarray datasets. The pathological and theranostic roles of the MTSCPPC signature in MI were subsequently analyzed. We evaluated interactions of the MTSCPPC signature with ovatodiolide, a bioactive compound isolated from Anisomeles indica (L.) Kuntze, using in silico molecular docking tools and compared it to specific inhibitors of the members of the MTSCPPC signature. Single-cell transcriptomic analysis of the public databases revealed high expression levels of the MTSCPPC signature in immune cells of adult human hearts during an MI event. The MTSCPPC signature was significantly associated with the cytokine–cytokine receptor interactions, chemokine signaling, immune and inflammatory responses, and metabolic dysregulation in MI. Analysis of a micro (mi)RNA regulatory network of the MTSCPPC signature suggested post-transcriptional activation and the roles of miRNAs in the pathology of MI. Our molecular docking analysis suggested a higher potential for ovatodiolide to target MAN2A2, CSNK1D, and TNFRSF12A. Collectively, the results derived from the present study further advance our understanding of the complex regulatory mechanisms of MI and provide a potential MI theranostic signature with ovatodiolide as a therapeutic candidate.
8 citations
References
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
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.
34,830 citations
Journal Article•
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.
30,124 citations
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.
10,968 citations
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
8,326 citations
TL;DR: This paper proposed 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.
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/.
6,319 citations