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1.
Cell Syst ; 12(2): 128-140.e4, 2021 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-33373583

RESUMO

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in a complex multidimensional space and mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework, implemented in TensorFlow. We tested the modeling framework on a perturbation-response dataset of a melanoma cell line after drug treatments. The models can be efficiently trained to describe cellular behavior accurately. Even though completely data driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The approach is readily applicable to various kinetic models of cell biology. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

2.
Nucleic Acids Res ; 2020 Nov 16.
Artigo em Inglês | MEDLINE | ID: mdl-33196823

RESUMO

CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.

3.
F1000Res ; 92020.
Artigo em Inglês | MEDLINE | ID: mdl-33123346

RESUMO

AlignmentViewer is a web-based tool to view and analyze multiple sequence alignments of protein families. The particular strengths of AlignmentViewer include flexible visualization at different scales as well as analysis of conservation patterns and of the distribution of proteins in sequence space. The tool is directly accessible in web browsers without the need for software installation. It can handle protein families with tens of thousands of sequences and is particularly suitable for evolutionary coupling analysis, e.g. via EVcouplings.org.

4.
PLoS One ; 15(11): e0234669, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33137091

RESUMO

SUMMARY: Large-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated high throughput sequencing and molecular profiling data sets, but it is still challenging to identify potentially causal changes in cellular processes in cancer as well as in other diseases in an automated fashion. We developed the netboxr package written in the R programming language, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules. The algorithm makes use of a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets. The method can combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology. AVAILABILITY AND IMPLEMENTATION: The netboxr package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html.

5.
Artigo em Inglês | MEDLINE | ID: mdl-33015524

RESUMO

PURPOSE: The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor. MATERIALS AND METHODS: To benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell-specific mRNA and microRNA profiles. RESULTS: We present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types. CONCLUSION: The choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers.

6.
JAMA Oncol ; 6(10): e202948, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32789511

RESUMO

Importance: Pancreatic cancer is the third-leading cause of cancer death in the United States; however, few high-risk groups have been identified to facilitate early diagnosis strategies. Objective: To evaluate the association of diabetes duration and recent weight change with subsequent risk of pancreatic cancer in the general population. Design, Setting, and Participants: This cohort study obtained data from female participants in the Nurses' Health Study and male participants in the Health Professionals Follow-Up Study, with repeated exposure assessments over 30 years. Incident cases of pancreatic cancer were identified from self-report or during follow-up of participant deaths. Deaths were ascertained through reports from the next of kin, the US Postal Service, or the National Death Index. Data collection was conducted from October 1, 2018, to December 31, 2018. Data analysis was performed from January 1, 2019, to June 30, 2019. Exposures: Duration of physician-diagnosed diabetes and recent weight change. Main Outcome and Measures: Hazard ratios (HRs) for subsequent development of pancreatic cancer. Results: Of the 112 818 women (with a mean [SD] age of 59.4 [11.7] years) and 46 207 men (with a mean [SD] age of 64.7 [10.8] years) included in the analysis, 1116 incident cases of pancreatic cancers were identified. Compared with participants with no diabetes, those with recent-onset diabetes had an age-adjusted HR for pancreatic cancer of 2.97 (95% CI, 2.31-3.82) and those with long-standing diabetes had an age-adjusted HR of 2.16 (95% CI, 1.78-2.60). Compared with those with no weight loss, participants who reported a 1- to 4-lb weight loss had an age-adjusted HR for pancreatic cancer of 1.25 (95% CI, 1.03-1.52), those with a 5- to 8-lb weight loss had an age-adjusted HR of 1.33 (95% CI, 1.06-1.66), and those with more than an 8-lb weight loss had an age-adjusted HR of 1.92 (95% CI, 1.58-2.32). Participants with recent-onset diabetes accompanied by weight loss of 1 to 8 lb (91 incident cases per 100 000 person-years [95% CI, 55-151]; HR, 3.61 [95% CI, 2.14-6.10]) or more than 8 lb (164 incident cases per 100 000 person-years [95% CI, 114-238]; HR, 6.75 [95% CI, 4.55-10.00]) had a substantially increased risk for pancreatic cancer compared with those with neither exposure (16 incident cases per 100 000 person-years; 95% CI, 14-17). Incidence rates were even higher among participants with recent-onset diabetes and weight loss with a body mass index of less than 25 before weight loss (400 incident cases per 100 000 person-years) or whose weight loss was not intentional judging from increased physical activity or healthier dietary choices (334 incident cases per 100 000 person-years). Conclusions and Relevance: This study demonstrates that recent-onset diabetes accompanied by weight loss is associated with a substantially increased risk for developing pancreatic cancer. Older age, previous healthy weight, and no intentional weight loss further elevate this risk.

7.
PLoS Comput Biol ; 16(7): e1007909, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32667922

RESUMO

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.


Assuntos
Antineoplásicos/farmacologia , Melanoma , Fosfoproteínas , Linhagem Celular Tumoral , Sobrevivência Celular/efeitos dos fármacos , Quimioterapia Combinada , Humanos , Modelos Biológicos , Fosfoproteínas/análise , Fosfoproteínas/metabolismo , Transdução de Sinais/efeitos dos fármacos , Biologia de Sistemas
8.
Cell Syst ; 10(1): 15-24.e5, 2020 01 22.
Artigo em Inglês | MEDLINE | ID: mdl-31838147

RESUMO

Natural evolution encodes rich information about the structure and function of biomolecules in the genetic record. Previously, statistical analysis of co-variation patterns in natural protein families has enabled the accurate computation of 3D structures. Here, we explored generating similar information by experimental evolution, starting from a single gene and performing multiple cycles of in vitro mutagenesis and functional selection in Escherichia coli. We evolved two antibiotic resistance proteins, ß-lactamase PSE1 and acetyltransferase AAC6, and obtained hundreds of thousands of diverse functional sequences. Using evolutionary coupling analysis, we inferred residue interaction constraints that were in agreement with contacts in known 3D structures, confirming genetic encoding of structural constraints in the selected sequences. Computational protein folding with interaction constraints then yielded 3D structures with the same fold as natural relatives. This work lays the foundation for a new experimental method (3Dseq) for protein structure determination, combining evolution experiments with inference of residue interactions from sequence information. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

9.
Nucleic Acids Res ; 48(D1): D489-D497, 2020 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-31647099

RESUMO

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.


Assuntos
Bases de Dados Factuais , Redes e Vias Metabólicas , Software , Genoma Humano , Genômica/métodos , Humanos , Metabolômica/métodos
10.
iScience ; 21: 664-680, 2019 Nov 22.
Artigo em Inglês | MEDLINE | ID: mdl-31733513

RESUMO

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, ß-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses.

11.
Proteins ; 87(12): 1315-1332, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31603581

RESUMO

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.


Assuntos
Espectroscopia de Ressonância Magnética/métodos , Modelos Moleculares , Conformação Proteica , Dobramento de Proteína , Proteínas/química , Algoritmos , Simulação por Computador , Cristalografia por Raios X , Reprodutibilidade dos Testes
12.
Nat Commun ; 10(1): 3682, 2019 08 15.
Artigo em Inglês | MEDLINE | ID: mdl-31417090

RESUMO

Somatic mutations in the RNase IIIb domain of DICER1 arise in cancer and disrupt the cleavage of 5' pre-miRNA arms. Here, we characterize an unstudied, recurrent, mutation (S1344L) in the DICER1 RNase IIIa domain in tumors from The Cancer Genome Atlas (TCGA) project and MSK-IMPACT profiling. RNase IIIa/b hotspots are absent from most cancers, but are notably enriched in uterine cancers. Systematic analysis of TCGA small RNA datasets show that DICER1 RNase IIIa-S1344L tumors deplete 5p-miRNAs, analogous to RNase IIIb hotspot samples. Structural and evolutionary coupling analyses reveal constrained proximity of RNase IIIa-S1344 to the RNase IIIb catalytic site, rationalizing why mutation of this site phenocopies known hotspot alterations. Finally, examination of DICER1 hotspot endometrial tumors reveals derepression of specific miRNA target signatures. In summary, comprehensive analyses of DICER1 somatic mutations and small RNA data reveal a mechanistic aspect of pre-miRNA processing that manifests in specific cancer settings.


Assuntos
RNA Helicases DEAD-box/genética , Neoplasias do Endométrio/genética , MicroRNAs/biossíntese , Ribonuclease III/genética , Bases de Dados Genéticas , Feminino , Humanos , MicroRNAs/genética , Mutação
13.
Nat Genet ; 51(7): 1170-1176, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31209393

RESUMO

We describe an experimental method of three-dimensional (3D) structure determination that exploits the increasing ease of high-throughput mutational scans. Inspired by the success of using natural, evolutionary sequence covariation to compute protein and RNA folds, we explored whether 'laboratory', synthetic sequence variation might also yield 3D structures. We analyzed five large-scale mutational scans and discovered that the pairs of residues with the largest positive epistasis in the experiments are sufficient to determine the 3D fold. We show that the strongest epistatic pairings from genetic screens of three proteins, a ribozyme and a protein interaction reveal 3D contacts within and between macromolecules. Using these experimental epistatic pairs, we compute ab initio folds for a GB1 domain (within 1.8 Å of the crystal structure) and a WW domain (2.1 Å). We propose strategies that reduce the number of mutants needed for contact prediction, suggesting that genomics-based techniques can efficiently predict 3D structure.


Assuntos
Proteínas Adaptadoras de Transdução de Sinal/química , Proteínas de Bactérias/química , Epistasia Genética , Mutação , Proteínas de Ligação a Poli(A)/química , Conformação Proteica , RNA Catalítico/química , Proteínas de Saccharomyces cerevisiae/química , Fatores de Transcrição/química , Proteínas Adaptadoras de Transdução de Sinal/genética , Proteínas de Bactérias/genética , Humanos , Proteínas de Ligação a Poli(A)/genética , Domínios Proteicos , Dobramento de Proteína , RNA Catalítico/genética , Proteínas de Saccharomyces cerevisiae/genética , Fatores de Transcrição/genética
14.
Nature ; 569(7755): 275-279, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-30996345

RESUMO

Drosophila Lgl and its mammalian homologues, LLGL1 and LLGL2, are scaffolding proteins that regulate the establishment of apical-basal polarity in epithelial cells1,2. Whereas Lgl functions as a tumour suppressor in Drosophila1, the roles of mammalian LLGL1 and LLGL2 in cancer are unclear. The majority (about 75%) of breast cancers express oestrogen receptors (ERs)3, and patients with these tumours receive endocrine treatment4. However, the development of resistance to endocrine therapy and metastatic progression are leading causes of death for patients with ER+ disease4. Here we report that, unlike LLGL1, LLGL2 is overexpressed in ER+ breast cancer and promotes cell proliferation under nutrient stress. LLGL2 regulates cell surface levels of a leucine transporter, SLC7A5, by forming a trimeric complex with SLC7A5 and a regulator of membrane fusion, YKT6, to promote leucine uptake and cell proliferation. The oestrogen receptor targets LLGL2 expression. Resistance to endocrine treatment in breast cancer cells was associated with SLC7A5- and LLGL2-dependent adaption to nutrient stress. SLC7A5 was necessary and sufficient to confer resistance to tamoxifen treatment, identifying SLC7A5 as a potential therapeutic target for overcoming resistance to endocrine treatments in breast cancer. Thus, LLGL2 functions as a promoter of tumour growth and not as a tumour suppressor in ER+ breast cancer. Beyond breast cancer, adaptation to nutrient stress is critically important5, and our findings identify an unexpected role for LLGL2 in this process.


Assuntos
Neoplasias da Mama/metabolismo , Proteínas do Citoesqueleto/metabolismo , Leucina/metabolismo , Receptores Estrogênicos/metabolismo , Animais , Neoplasias da Mama/patologia , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Estrogênios/farmacologia , Feminino , Humanos , Transportador 1 de Aminoácidos Neutros Grandes/metabolismo , Camundongos , Proteínas R-SNARE/metabolismo
15.
Elife ; 82019 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-30924768

RESUMO

While genomic sequencing routinely identifies oncogenic alterations for the majority of cancers, many tumors harbor no discernable driver lesion. Here, we describe the exceptional molecular phenotype of a genomically quiet kidney tumor, clear cell papillary renal cell carcinoma (CCPAP). In spite of a largely wild-type nuclear genome, CCPAP tumors exhibit severe depletion of mitochondrial DNA (mtDNA) and RNA and high levels of oxidative stress, reflecting a shift away from respiratory metabolism. Moreover, CCPAP tumors exhibit a distinct metabolic phenotype uniquely characterized by accumulation of the sugar alcohol sorbitol. Immunohistochemical staining of primary CCPAP tumor specimens recapitulates both the depletion of mtDNA-encoded proteins and a lipid-depleted metabolic phenotype, suggesting that the cytoplasmic clarity in CCPAP is primarily related to the presence of glycogen. These results argue for non-genetic profiling as a tool for the study of cancers of unknown driver.


Assuntos
Carcinoma de Células Renais/patologia , Respiração Celular , Neoplasias Renais/patologia , Aerobiose , Histocitoquímica , Humanos , Imuno-Histoquímica , Redes e Vias Metabólicas , Oxirredução
16.
Methods Enzymol ; 614: 363-392, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30611430

RESUMO

Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.


Assuntos
Algoritmos , Proteínas de Escherichia coli/química , Escherichia coli/química , Evolução Molecular , Ressonância Magnética Nuclear Biomolecular/métodos , Proteínas Periplásmicas de Ligação/química , Software , Análise de Variância , Sítios de Ligação , Cristalografia por Raios X , Bases de Dados de Proteínas , Deutério/química , Escherichia coli/metabolismo , Proteínas de Escherichia coli/metabolismo , Humanos , Marcação por Isótopo , Modelos Moleculares , Proteínas Periplásmicas de Ligação/metabolismo , Ligação Proteica , Conformação Proteica em alfa-Hélice , Conformação Proteica em Folha beta , Domínios e Motivos de Interação entre Proteínas , Homologia Estrutural de Proteína , Termodinâmica
17.
Bioinformatics ; 35(9): 1582-1584, 2019 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-30304492

RESUMO

SUMMARY: Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings.


Assuntos
Análise de Sequência , Software , Proteínas , RNA , Alinhamento de Sequência
18.
iScience ; 10: 247-264, 2018 Dec 21.
Artigo em Inglês | MEDLINE | ID: mdl-30553813

RESUMO

CellMinerCDB provides a web-based resource (https://discover.nci.nih.gov/cellminercdb/) for integrating multiple forms of pharmacological and genomic analyses, and unifying the richest cancer cell line datasets (the NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables data queries for genomics and gene regulatory network analyses, and exploration of pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs across databases to examine reproducibility and expand pathway analyses. We illustrate the value of CellMinerCDB for elucidating gene expression determinants, such as DNA methylation and copy number variations, and highlight complexities in assessing mutational burden. We demonstrate the value of CellMinerCDB in selecting drugs with reproducible activity, expand on the dominant role of SLFN11 for drug response, and present novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins. We also introduce LIX1L as a gene associated with mesenchymal signature and regulation of cellular migration and invasiveness.

19.
Cancer Cell ; 34(2): 211-224.e6, 2018 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-30078747

RESUMO

Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions ("neojunctions") in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders ("putative neoantigens").


Assuntos
Processamento Alternativo , Neoplasias/genética , Humanos , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Análise de Sequência de RNA , Sequenciamento Completo do Exoma
20.
NPJ Syst Biol Appl ; 4: 26, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29977602

RESUMO

In the United States alone one in five newly diagnosed cancers in men are prostate carcinomas (PCa). Androgen receptor (AR) status and the PI3K-AKT-mTOR signal transduction pathway are critical in PCa. After initial response to single drugs targeting these pathways resistance often emerges, indicating the need for combination therapy. Here, we address the question of efficacy of drug combinations and development of resistance mechanisms to targeted therapy by a systems pharmacology approach. We combine targeted perturbation with detailed observation of the molecular response by mass spectrometry. We hypothesize that the molecular short-term (24 h) response reveals details of how PCa cells adapt to counter the anti-proliferative drug effect. With focus on six drugs currently used in PCa treatment or targeting the PI3K-AKT-mTOR signal transduction pathway, we perturbed the LNCaP clone FGC cell line by a total of 21 treatment conditions using single and paired drug combinations. The molecular response was analyzed by the mass spectrometric quantification of 52 proteins. Analysis of the data revealed a pattern of strong responders, i.e., proteins that were consistently downregulated or upregulated across many of the perturbation conditions. The downregulated proteins, HN1, PAK1, and SPAG5, are potential early indicators of drug efficacy and point to previously less well-characterized response pathways in PCa cells. Some of the upregulated proteins such as 14-3-3 proteins and KLK2 may be useful early markers of adaptive response and indicate potential resistance pathways targetable as part of combination therapy to overcome drug resistance. The potential of 14-3-3ζ (YWHAZ) as a target is underscored by the independent observation, based on cancer genomics of surgical specimens, that its DNA copy number and transcript levels tend to increase with PCa disease progression. The combination of systematic drug perturbation combined with detailed observation of short-term molecular response using mass spectrometry is a potentially powerful tool to discover response markers and anti-resistance targets.

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