RESUMO
In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.
Assuntos
Mapeamento Cromossômico/métodos , Genoma Humano/genética , Bases de Conhecimento , Modelos Genéticos , Análise de Sequência de DNA/métodos , Interface Usuário-Computador , Algoritmos , Simulação por Computador , Sistemas de Gerenciamento de Base de Dados , Bases de Dados Genéticas , Humanos , Alinhamento de Sequência/métodosRESUMO
To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.
Assuntos
Inibidores da Aromatase/uso terapêutico , Aromatase/metabolismo , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/genética , Genoma Humano/genética , Anastrozol , Androstadienos/farmacologia , Androstadienos/uso terapêutico , Antineoplásicos/farmacologia , Antineoplásicos/uso terapêutico , Neoplasias da Mama/metabolismo , Neoplasias da Mama/patologia , Reparo do DNA , Exoma/genética , Éxons/genética , Feminino , Variação Genética/genética , Humanos , Letrozol , MAP Quinase Quinase 4/genética , MAP Quinase Quinase Quinase 1/genética , Mutação/genética , Nitrilas/farmacologia , Nitrilas/uso terapêutico , Receptores de Estrogênio/metabolismo , Resultado do Tratamento , Triazóis/farmacologia , Triazóis/uso terapêuticoRESUMO
OBJECTIVE: We hypothesized that circulating leukocyte RNA profiles or "riboleukograms" detect ventilator-associated pneumonia after blunt trauma. SUMMARY BACKGROUND DATA: A pilot microarray study of 11 ventilator-associated pneumonia (VAP) patients suggested that 85 leukocyte genes can be used to diagnose VAP. Validation of this gene set to detect VAP was tested using data from an independent patient cohort. METHODS: A total of 158 intubated blunt trauma patients were enrolled at 5 centers, where 57 (36%) developed VAP. Patient age was 34.2 ± 11.1 years; 65% were male. Circulating leukocyte GeneChip U133 2.0 expression values were measured at time 0.5, 1, 4, 7, 14, 21, and 28 days after injury. DChip normalized leukocyte transcriptional profiles were analyzed using repeated measures logistic regression. A compound covariate model based on leukocyte gene transcriptional profiles in a training subset of patients was tested to determine predictive accuracy for VAP 4 days prior to clinical diagnosis in the test subset. RESULTS: Using gene expression values measured on each study day at an FDR <0.05, 27 (32%) of the 85 genes were associated with the diagnosis of VAP 1 to 4 days before diagnosis. However, the compound covariate model based on these 85-genes did not predict VAP in the test cohort better than chance (P = 0.27). In contrast, a compound covariate model based upon de novo transcriptional analysis of the 158 patients predicted VAP better than chance 4 days before diagnosis with a sensitivity of 57% and a specificity of 69%. CONCLUSION: Our results validate those described in a pilot study, confirming that riboleukograms are associated with the development of VAP days prior to clinical diagnosis. Similarly, a riboleukogram predictive model tested on a larger cohort of 158 patients was better than chance at predicting VAP days prior to clinical diagnosis.