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1.
Cell ; 154(5): 1151-1161, 2013 Aug 29.
Artigo em Inglês | MEDLINE | ID: mdl-23993102

RESUMO

The high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene ß-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage.


Assuntos
Bases de Dados de Produtos Farmacêuticos , Descoberta de Drogas , Neoplasias/tratamento farmacológico , Antineoplásicos/química , Linhagem Celular Tumoral , Humanos , Neoplasias/genética
2.
Cell ; 144(2): 296-309, 2011 Jan 21.
Artigo em Inglês | MEDLINE | ID: mdl-21241896

RESUMO

Though many individual transcription factors are known to regulate hematopoietic differentiation, major aspects of the global architecture of hematopoiesis remain unknown. Here, we profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression and analysis of cis-elements in gene promoters to decipher the general organization of their regulatory circuitry. We identified modules of highly coexpressed genes, some of which are restricted to a single lineage but most of which are expressed at variable levels across multiple lineages. We found densely interconnected cis-regulatory circuits and a large number of transcription factors that are differentially expressed across hematopoietic states. These findings suggest a more complex regulatory system for hematopoiesis than previously assumed.


Assuntos
Regulação da Expressão Gênica , Redes Reguladoras de Genes , Hematopoese , Fatores de Transcrição/metabolismo , Perfilação da Expressão Gênica , Humanos
3.
Nat Methods ; 15(7): 543-546, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-29915188

RESUMO

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.


Assuntos
Genômica/métodos , Internet , Aprendizado de Máquina , DNA/genética , Bases de Dados de Ácidos Nucleicos , Técnicas de Amplificação de Ácido Nucleico , RNA/genética , Software
5.
Nat Methods ; 13(3): 245-247, 2016 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-26780094

RESUMO

Complex biomedical analyses require the use of multiple software tools in concert and remain challenging for much of the biomedical research community. We introduce GenomeSpace (http://www.genomespace.org), a cloud-based, cooperative community resource that currently supports the streamlined interaction of 20 bioinformatics tools and data resources. To facilitate integrative analysis by non-programmers, it offers a growing set of 'recipes', short workflows to guide investigators through high-utility analysis tasks.


Assuntos
Algoritmos , Mapeamento Cromossômico/métodos , Biologia Computacional/métodos , Bases de Dados Genéticas , Genoma Humano/genética , Software , Mineração de Dados , Humanos , Internet , Integração de Sistemas
6.
Nat Chem Biol ; 12(2): 109-16, 2016 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-26656090

RESUMO

Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with ∼19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.


Assuntos
Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Bibliotecas de Moléculas Pequenas/farmacologia , Aflatoxinas/química , Aflatoxinas/farmacologia , Western Blotting , Neoplasias da Mama/tratamento farmacológico , Linhagem Celular Tumoral , Simulação por Computador , Sistemas de Liberação de Medicamentos , Feminino , Humanos , Estrutura Molecular , Análise de Componente Principal , Reação em Cadeia da Polimerase em Tempo Real
7.
Nature ; 483(7391): 603-7, 2012 Mar 28.
Artigo em Inglês | MEDLINE | ID: mdl-22460905

RESUMO

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.


Assuntos
Bases de Dados Factuais , Ensaios de Seleção de Medicamentos Antitumorais/métodos , Enciclopédias como Assunto , Modelos Biológicos , Neoplasias/tratamento farmacológico , Neoplasias/patologia , Antineoplásicos/farmacologia , Linhagem Celular Tumoral , Linhagem da Célula , Cromossomos Humanos/genética , Ensaios Clínicos como Assunto/métodos , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Genes ras/genética , Genoma Humano/genética , Genômica , Humanos , Quinases de Proteína Quinase Ativadas por Mitógeno/antagonistas & inibidores , Quinases de Proteína Quinase Ativadas por Mitógeno/metabolismo , Neoplasias/genética , Neoplasias/metabolismo , Farmacogenética , Plasmócitos/citologia , Plasmócitos/efeitos dos fármacos , Plasmócitos/metabolismo , Medicina de Precisão/métodos , Receptor IGF Tipo 1/antagonistas & inibidores , Receptor IGF Tipo 1/metabolismo , Receptores de Hidrocarboneto Arílico/genética , Receptores de Hidrocarboneto Arílico/metabolismo , Análise de Sequência de DNA , Inibidores da Topoisomerase/farmacologia
8.
Nature ; 471(7339): 467-72, 2011 Mar 24.
Artigo em Inglês | MEDLINE | ID: mdl-21430775

RESUMO

Multiple myeloma is an incurable malignancy of plasma cells, and its pathogenesis is poorly understood. Here we report the massively parallel sequencing of 38 tumour genomes and their comparison to matched normal DNAs. Several new and unexpected oncogenic mechanisms were suggested by the pattern of somatic mutation across the data set. These include the mutation of genes involved in protein translation (seen in nearly half of the patients), genes involved in histone methylation, and genes involved in blood coagulation. In addition, a broader than anticipated role of NF-κB signalling was indicated by mutations in 11 members of the NF-κB pathway. Of potential immediate clinical relevance, activating mutations of the kinase BRAF were observed in 4% of patients, suggesting the evaluation of BRAF inhibitors in multiple myeloma clinical trials. These results indicate that cancer genome sequencing of large collections of samples will yield new insights into cancer not anticipated by existing knowledge.


Assuntos
Genoma Humano/genética , Mieloma Múltiplo/genética , Mutação/genética , Sequência de Aminoácidos , Coagulação Sanguínea/genética , Ilhas de CpG/genética , Análise Mutacional de DNA , Reparo do DNA/genética , Éxons/genética , Complexo Multienzimático de Ribonucleases do Exossomo , Genômica , Histonas/metabolismo , Proteínas de Homeodomínio/genética , Homeostase/genética , Humanos , Metilação , Modelos Moleculares , Dados de Sequência Molecular , Mieloma Múltiplo/tratamento farmacológico , Mieloma Múltiplo/enzimologia , Mieloma Múltiplo/metabolismo , NF-kappa B/metabolismo , Oncogenes/genética , Fases de Leitura Aberta/genética , Biossíntese de Proteínas/genética , Conformação Proteica , Proteínas Proto-Oncogênicas B-raf/antagonistas & inibidores , Proteínas Proto-Oncogênicas B-raf/genética , Proteínas Proto-Oncogênicas B-raf/metabolismo , Processamento Pós-Transcricional do RNA/genética , Ribonucleases/química , Ribonucleases/genética , Transdução de Sinais/genética , Transcrição Gênica/genética
9.
Nature ; 463(7283): 899-905, 2010 Feb 18.
Artigo em Inglês | MEDLINE | ID: mdl-20164920

RESUMO

A powerful way to discover key genes with causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here we present high-resolution analyses of somatic copy-number alterations (SCNAs) from 3,131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across several cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-kappaBeta pathway. We show that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in several cancer types.


Assuntos
Variações do Número de Cópias de DNA/genética , Dosagem de Genes/genética , Neoplasias/genética , Apoptose/genética , Linhagem Celular Tumoral , Sobrevivência Celular/genética , Amplificação de Genes/genética , Genômica , Humanos , Família Multigênica/genética , Proteína de Sequência 1 de Leucemia de Células Mieloides , Neoplasias/classificação , Neoplasias/patologia , Proteínas Proto-Oncogênicas c-bcl-2/genética , Transdução de Sinais , Proteína bcl-X/genética
10.
bioRxiv ; 2023 Jun 27.
Artigo em Inglês | MEDLINE | ID: mdl-37398372

RESUMO

Non-negative Matrix Factorization (NME) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using Cupy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePatten gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelnes on high performance computing (HPC) culsters that enable reproducible in silco research for non-programmers.

11.
J Bioinform Syst Biol ; 6(4): 379-383, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38390437

RESUMO

Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers.

12.
Nat Protoc ; 18(12): 3690-3731, 2023 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-37989764

RESUMO

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.


Assuntos
Algoritmos , Linguagens de Programação , Teorema de Bayes , Análise de Célula Única
13.
STAR Protoc ; 2(2): 100561, 2021 06 18.
Artigo em Inglês | MEDLINE | ID: mdl-34095869

RESUMO

Here, we describe a protocol combining functional metrics with genomic data to elucidate drivers of within-cell-type heterogeneity via the phenotype-to-genotype link. This technique involves using fluorescence tagging to label and isolate cells grown in 3D culture, enabling high-throughput enrichment of phenotypically defined cell subpopulations by fluorescence-activated cell sorting. We then perform a validated phenotypically supervised single-cell analysis pipeline to reveal unique functional cell states, including genes and pathways that contribute to cellular heterogeneity and were undetectable by unsupervised analysis. For complete details on the use and execution of this protocol, please refer to Chen et al. (2020).


Assuntos
Análise de Célula Única/métodos , Animais , Clonagem Molecular , Vetores Genéticos , Células HEK293 , Ensaios de Triagem em Larga Escala/métodos , Humanos , Lentivirus/genética , Mamíferos , Fenótipo , Análise de Sequência de RNA/métodos
14.
iScience ; 24(4): 102361, 2021 Apr 23.
Artigo em Inglês | MEDLINE | ID: mdl-33870146

RESUMO

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

16.
JCO Clin Cancer Inform ; 4: 421-435, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32383980

RESUMO

PURPOSE: The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data. METHODS: Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes. RESULTS: To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms. CONCLUSION: Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.


Assuntos
Glioblastoma , Genômica por Imageamento , Biomarcadores , Glioblastoma/diagnóstico por imagem , Glioblastoma/genética , Humanos , Radiografia , Software
17.
Cell Syst ; 5(2): 149-151.e1, 2017 08 23.
Artigo em Inglês | MEDLINE | ID: mdl-28822753

RESUMO

Interactive analysis notebook environments promise to streamline genomics research through interleaving text, multimedia, and executable code into unified, sharable, reproducible "research narratives." However, current notebook systems require programming knowledge, limiting their wider adoption by the research community. We have developed the GenePattern Notebook environment (http://www.genepattern-notebook.org), to our knowledge the first system to integrate the dynamic capabilities of notebook systems with an investigator-focused, easy-to-use interface that provides access to hundreds of genomic tools without the need to write code.


Assuntos
Biologia Computacional , Perfilação da Expressão Gênica/métodos , Software , Genômica , Interface Usuário-Computador
18.
Drug Discov Today ; 10(22): 1566-72, 2005 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-16257380

RESUMO

Since the Life Science Identifier (LSID) data identification and access standard made its official debut in late 2004, several organizations have begun to use LSIDs to simplify the methods used to uniquely name, reference and retrieve distributed data objects and concepts. In this review, the authors build on introductory work that describes the LSID standard by documenting how five early adopters have incorporated the standard into their technology infrastructure and by outlining several common misconceptions and difficulties related to LSID use, including the impact of the byte identity requirement for LSID-identified objects and the opacity recommendation for use of the LSID syntax. The review describes several shortcomings of the LSID standard, such as the lack of a specific metadata standard, along with solutions that could be addressed in future revisions of the specification.


Assuntos
Biologia Computacional/métodos , Bases de Dados Genéticas/normas , Armazenamento e Recuperação da Informação/métodos , Design de Software
19.
Cancer Discov ; 5(11): 1210-23, 2015 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-26482930

RESUMO

UNLABELLED: Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2). SIGNIFICANCE: We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses.


Assuntos
Biologia Computacional/métodos , Resistencia a Medicamentos Antineoplásicos/genética , Ensaios de Seleção de Medicamentos Antitumorais , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Neoplasias/genética , Bibliotecas de Moléculas Pequenas , Antineoplásicos/farmacologia , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Sobrevivência Celular/efeitos dos fármacos , Análise por Conglomerados , Conjuntos de Dados como Assunto , Relação Dose-Resposta a Droga , Sinergismo Farmacológico , Humanos , Mutação , Neoplasias/tratamento farmacológico , Inibidores de Proteínas Quinases/farmacologia
20.
F1000Res ; 3: 151, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25165537

RESUMO

Modern genomic analysis often requires workflows incorporating multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows between them. One such tool is Cytoscape 3, a popular application that enables analysis and visualization of graph-oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over 850 times since the release of its first version in September, 2013.

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