Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
BMC Bioinformatics ; 19(1): 430, 2018 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-30453881

RESUMO

BACKGROUND: Because driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral (passenger) mutations. However, mutation frequency alone is insufficient to identify cancer genes because mutability is influenced by many gene characteristics, such as size, nucleotide composition, etc. The goal of this study was to identify gene characteristics associated with the frequency of somatic mutations in the gene in tumor samples. RESULTS: We used data on somatic mutations detected by genome wide screens from the Catalog of Somatic Mutations in Cancer (COSMIC). Gene size, nucleotide composition, expression level of the gene, relative replication time in the cell cycle, level of evolutionary conservation and other gene characteristics (totaling 11) were used as predictors of the number of somatic mutations. We applied stepwise multiple linear regression to predict the number of mutations per gene. Because missense, nonsense, and frameshift mutations are associated with different sets of gene characteristics, they were modeled separately. Gene characteristics explain 88% of the variation in the number of missense, 40% of nonsense, and 23% of frameshift mutations. Comparisons of the observed and expected numbers of mutations identified genes with a higher than expected number of mutations- positive outliers. Many of these are known driver genes. A number of novel candidate driver genes was also identified. CONCLUSIONS: By comparing the observed and predicted number of mutations in a gene, we have identified known cancer-associated genes as well as 111 novel cancer associated genes. We also showed that adding the number of silent mutations per gene reported by genome/exome wide screens across all cancer type (COSMIC data) as a predictor substantially exceeds predicting accuracy of the most popular cancer gene predicting tool - MutsigCV.

2.
Clin Cancer Res ; 23(2): 399-406, 2017 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-27435399

RESUMO

PURPOSE: We have previously demonstrated that patients with metastatic colorectal cancer who exhibit immune responses to a dendritic cell (DC) vaccine have superior recurrence-free survival following surgery, compared with patients in whom responses do not occur. We sought to characterize the patterns of T-lymphocyte infiltration and somatic mutations in metastases that are associated with and predictive of response to the DC vaccine. EXPERIMENTAL DESIGN: Cytotoxic, memory, and regulatory T cells in resected metastases and surrounding normal liver tissue from 22 patients (11 responders and 11 nonresponders) were enumerated by immunohistochemistry prior to vaccine administration. In conjunction with tumor sequencing, the combined multivariate and collapsing method was used to identify gene mutations that are associated with vaccine response. We also derived a response prediction score for each patient using his/her tumor genotype data and variant association effect sizes computed from the other 21 patients; greater weighting was placed on gene products with cell membrane-related functions. RESULTS: There was no correlation between vaccine response and intratumor, peritumor, or hepatic densities of T-cell subpopulations. Associated genes were found to be enriched in the PI3K/Akt/mTOR signaling axis (P < 0.001). Applying a consistent prediction score cutoff over 22 rounds of leave-one-out cross-validation correctly inferred vaccine response in 21 of 22 patients (95%). CONCLUSIONS: Adjuvant DC vaccination has shown promise as a form of immunotherapy for patients with metastatic colorectal cancer. Its efficacy may be influenced by somatic mutations that affect pathways involving PI3K, Akt, and mTOR, as well as tumor surface proteins. Clin Cancer Res; 23(2); 399-406. ©2016 AACR.


Assuntos
Vacinas Anticâncer/administração & dosagem , Terapia Baseada em Transplante de Células e Tecidos , Neoplasias Colorretais/terapia , Imunoterapia , Proteínas de Membrana/sangue , Idoso , Vacinas Anticâncer/imunologia , Neoplasias Colorretais/sangue , Neoplasias Colorretais/genética , Neoplasias Colorretais/imunologia , Células Dendríticas/imunologia , Células Dendríticas/transplante , Resistencia a Medicamentos Antineoplásicos/imunologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mutação , Metástase Neoplásica , Fosfatidilinositol 3-Quinases/genética , Proteínas Proto-Oncogênicas c-akt/genética , Transdução de Sinais , Linfócitos T/imunologia , Linfócitos T/patologia , Linfócitos T Citotóxicos/imunologia , Linfócitos T Citotóxicos/patologia , Serina-Treonina Quinases TOR/genética
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA