47 research outputs found

    PTEN protein loss by immunostaining: Analytic validation and prognostic indicator for a high risk surgical cohort of prostate cancer patients

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    PURPOSE: Analytically validated assays to interrogate biomarker status in clinical samples are crucial for personalized medicine. PTEN is a tumor suppressor commonly inactivated in prostate cancer that has been mechanistically linked to disease aggressiveness. Though deletion of PTEN, as detected by cumbersome fluorescence in situ hybridization (FISH) spot counting assays, is associated with poor prognosis, few studies have validated immunohistochemical (IHC) assays to determine whether loss of PTEN protein is associated with unfavorable disease. EXPERIMENTAL DESIGN: PTEN IHC was validated by employing formalin fixed and paraffin embedded isogenic human cell lines containing or lacking intact PTEN alleles. PTEN IHC was 100% sensitive and 97.8% specific for detecting genomic alterations in 58 additional cell lines. PTEN protein loss was then assessed on 376 prostate tumor samples, and PTEN FISH or high resolution SNP microarray analysis was performed on a subset of these cases. RESULTS: PTEN protein loss, as assessed as a dichotomous IHC variable, was highly reproducible, correlated strongly with adverse pathologic features (e.g. Gleason score and pathological stage), detected between 75% and 86% of cases with PTEN genomic loss, and was found at times in the absence of apparent genomic loss. In a cohort of 217 high risk surgically treated patients, PTEN protein loss was associated with decreased time to metastasis. CONCLUSIONS: These studies validate a simple method to interrogate PTEN status in clinical specimens and support the utility of this test in future multi-center studies, clinical trials and ultimately perhaps for routine clinical care

    Genomic predictors of patterns of progression in glioblastoma and possible influences on radiation field design

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    We present a retrospective investigation of the role of genomics in the prediction of central versus marginal disease progression patterns for glioblastoma (GBM). Between August 2000 and May 2010, 41 patients with GBM and gene expression and methylation data available were treated with radiotherapy with or without concurrent temozolomide. Location of disease progression was categorized as within the high dose (60 Gy) or low dose (46 Gy) volume. Samples were grouped into previously described TCGA genomic groupings: Mesenchymal (m), classical (c), proneural (pn), and neural (n); and were also classified by MGMT-Methylation status and G-Cimp methylation phenotype. Genomic groupings and methylation status were investigated as a possible predictor of disease progression in the high dose region, progression in the low dose region, and time to progression. Based on TCGA category there was no difference in OS (p = 0.26), 60 Gy progression (PN: 71 %, N: 60 %, M: 89 %, C: 83 %, p = 0.19), 46 Gy progression (PN: 57 %, N: 40 %, M: 61 %, C: 50 %, p = 0.8) or time to progression (PN: 9 months, N:15 months, M: 9 months, C: 7 months, p = 0.58). MGMT methylation predicted for improved OS (median 25 vs. 13 months, p = 0.01), improved DFS (median 13 vs. 8 months, p = 0.007) and decreased 60 Gy (p = 0.003) and 46 Gy (p = 0.006) progression. There was a cohort of MGMT methylated patients with late marginal disease progression (4/22 patients, 18 %). TCGA groups demonstrated no difference in survival or progression patterns. MGMT methylation predicted for a statistically significant decrease in in-field and marginal disease progression. There was a cohort of MGMT methylated patients with late marginal progression. Validations of these findings would have implications that could affect radiation field size

    DNA alterations in the tumor genome and their associations with clinical outcome in prostate cancer

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    Although most prostate cancer (PCa) cases are not life-threatening, approximately 293 000 men worldwide die annually due to PCa. These lethal cases are thought to be caused by coordinated genomic alterations that accumulate over time. Recent genome-wide analyses of DNA from subjects with PCa have revealed most, if not all, genetic changes in both germline and PCa tumor genomes. In this article, I first review the major, somatically acquired genomic characteristics of various subtypes of PCa. I then recap key findings on the relationships between genomic alterations and clinical parameters, such as biochemical recurrence or clinical relapse, metastasis and cancer-specific mortality. Finally, I outline the need for, and challenges with, validation of recent findings in prospective studies for clinical utility. It is clearer now than ever before that the landscape of somatically acquired aberrations in PCa is highlighted by DNA copy number alterations (CNAs) and TMPRSS2-ERG fusion derived from complex rearrangements, numerous single nucleotide variations or mutations, tremendous heterogeneity, and continuously punctuated evolution. Genome-wide CNAs, PTEN loss, MYC gain in primary tumors, and TP53 loss/mutation and AR amplification/mutation in advanced metastatic PCa have consistently been associated with worse cancer prognosis. With this recently gained knowledge, it is now an opportune time to develop DNA-based tests that provide more accurate patient stratification for prediction of clinical outcome, which will ultimately lead to more personalized cancer care than is possible at present

    Translation of genomics and epigenomics in prostate cancer: progress and promising directions

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    During the last several years, exciting discoveries have been made in prostate cancer (PCa) as a result of significant advances in genomic technology and information. For example, using genome-wide association studies, more than 100 inherited genetic variants associated with PCa risk have been identified. Similarly, with the use of next-generation sequencing, various types of recurrent somatic DNA alterations in prostate tumors have been revealed. Some of these discoveries have potential clinical application to supplement existing tools for better decision-making regarding the need for screening, biopsy, and treatment of PCa. However, because of the complexity of these genomic findings and incomplete understanding of the genetics of this multifactorial disease, this potential has not yet been fully realized
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