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1.
Cancer Discov ; 8(12): 1548-1565, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30322867

RESUMO

Malignant pleural mesothelioma (MPM) is a highly lethal cancer of the lining of the chest cavity. To expand our understanding of MPM, we conducted a comprehensive integrated genomic study, including the most detailed analysis of BAP1 alterations to date. We identified histology-independent molecular prognostic subsets, and defined a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity. We also report strong expression of the immune-checkpoint gene VISTA in epithelioid MPM, strikingly higher than in other solid cancers, with implications for the immune response to MPM and for its immunotherapy. Our findings highlight new avenues for further investigation of MPM biology and novel therapeutic options. SIGNIFICANCE: Through a comprehensive integrated genomic study of 74 MPMs, we provide a deeper understanding of histology-independent determinants of aggressive behavior, define a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity, and discovered strong expression of the immune-checkpoint gene VISTA in epithelioid MPM.See related commentary by Aggarwal and Albelda, p. 1508.This article is highlighted in the In This Issue feature, p. 1494.


Assuntos
Biomarcadores Tumorais/genética , Neoplasias Pulmonares/genética , Mesotelioma/genética , Mutação , Neoplasias Pleurais/genética , Idoso , Feminino , Histona-Lisina N-Metiltransferase , Humanos , Estimativa de Kaplan-Meier , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/terapia , Masculino , Mesotelioma/patologia , Mesotelioma/terapia , Pessoa de Meia-Idade , Neoplasias Pleurais/patologia , Neoplasias Pleurais/terapia , Prognóstico , Proteínas Metiltransferases/genética , Proteínas Supressoras de Tumor/genética , Ubiquitina Tiolesterase/genética
2.
Cell ; 161(5): 1215-1228, 2015 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-26000489

RESUMO

Toward development of a precision medicine framework for metastatic, castration-resistant prostate cancer (mCRPC), we established a multi-institutional clinical sequencing infrastructure to conduct prospective whole-exome and transcriptome sequencing of bone or soft tissue tumor biopsies from a cohort of 150 mCRPC affected individuals. Aberrations of AR, ETS genes, TP53, and PTEN were frequent (40%-60% of cases), with TP53 and AR alterations enriched in mCRPC compared to primary prostate cancer. We identified new genomic alterations in PIK3CA/B, R-spondin, BRAF/RAF1, APC, ß-catenin, and ZBTB16/PLZF. Moreover, aberrations of BRCA2, BRCA1, and ATM were observed at substantially higher frequencies (19.3% overall) compared to those in primary prostate cancers. 89% of affected individuals harbored a clinically actionable aberration, including 62.7% with aberrations in AR, 65% in other cancer-related genes, and 8% with actionable pathogenic germline alterations. This cohort study provides clinically actionable information that could impact treatment decisions for these affected individuals.


Assuntos
Perfilação da Expressão Gênica/métodos , Neoplasias de Próstata Resistentes à Castração/genética , Neoplasias de Próstata Resistentes à Castração/patologia , Estudos de Coortes , Humanos , Masculino , Mutação , Metástase Neoplásica/tratamento farmacológico , Metástase Neoplásica/genética , Metástase Neoplásica/patologia , Neoplasias de Próstata Resistentes à Castração/tratamento farmacológico
3.
Hum Mutat ; 36(4): E2423-9, 2015 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-25703262

RESUMO

Oncotator is a tool for annotating genomic point mutations and short nucleotide insertions/deletions (indels) with variant- and gene-centric information relevant to cancer researchers. This information is drawn from 14 different publicly available resources that have been pooled and indexed, and we provide an extensible framework to add additional data sources. Annotations linked to variants range from basic information, such as gene names and functional classification (e.g. missense), to cancer-specific data from resources such as the Catalogue of Somatic Mutations in Cancer (COSMIC), the Cancer Gene Census, and The Cancer Genome Atlas (TCGA). For local use, Oncotator is freely available as a python module hosted on Github (https://github.com/broadinstitute/oncotator). Furthermore, Oncotator is also available as a web service and web application at http://www.broadinstitute.org/oncotator/.


Assuntos
Bases de Dados Genéticas , Neoplasias/genética , Biologia Computacional/métodos , Variação Genética , Genômica/métodos , Humanos , Internet , Neoplasias/diagnóstico , Neoplasias/metabolismo , Navegador
6.
PLoS One ; 5(7): e11631, 2010 Jul 22.
Artigo em Inglês | MEDLINE | ID: mdl-20661478

RESUMO

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode 'skeletons' for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and 'skeletonizing' across a wide range of motility assays.


Assuntos
Caenorhabditis elegans/fisiologia , Locomoção/fisiologia , Modelos Estatísticos , Algoritmos , Animais
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