Copy number and gene expression differences between African American and Caucasian American prostate cancer

<p>Abstract</p> <p>Background</p> <p>The goal of our study was to investigate the molecular underpinnings associated with the relatively aggressive clinical behavior of prostate cancer (PCa) in African American (AA) compared to Caucasian American (CA) patients using a genome-wide approach.</p> <p>Methods</p> <p>AA and CA patients treated with radical prostatectomy (RP) were frequency matched for age at RP, Gleason grade, and tumor stage. Array-CGH (BAC SpectralChip2600) was used to identify genomic regions with significantly different DNA copy number between the groups. Gene expression profiling of the same set of tumors was also evaluated using Affymetrix HG-U133 Plus 2.0 arrays. Concordance between copy number alteration and gene expression was examined. A second aCGH analysis was performed in a larger validation cohort using an oligo-based platform (Agilent 244K).</p> <p>Results</p> <p>BAC-based array identified 27 chromosomal regions with significantly different copy number changes between the AA and CA tumors in the first cohort (Fisher's exact test, P < 0.05). Copy number alterations in these 27 regions were also significantly associated with gene expression changes. aCGH performed in a larger, independent cohort of AA and CA tumors validated 4 of the 27 (15%) most significantly altered regions from the initial analysis (3q26, 5p15-p14, 14q32, and 16p11). Functional annotation of overlapping genes within the 4 validated regions of AA/CA DNA copy number changes revealed significant enrichment of genes related to immune response.</p> <p>Conclusions</p> <p>Our data reveal molecular alterations at the level of gene expression and DNA copy number that are specific to African American and Caucasian prostate cancer and may be related to underlying differences in immune response.</p

Predictors and long-term reproducibility of urinary phthalate metabolites in middle-aged men and women living in urban Shanghai

Phthalate esters are man-made chemicals commonly used as plasticizers and solvents, and humans may be exposed through ingestion, inhalation, and dermal absorption. Little is known about predictors of phthalate exposure, particularly in Asian countries. Because phthalates are rapidly metabolized and excreted from the body following exposure, it is important to evaluate whether phthalate metabolites measured at a single point in time can reliably rank exposures to phthalates over a period of time. We examined the concentrations and predictors of phthalate metabolite concentrations among 50 middle-aged women and 50 men from two Shanghai cohorts, enrolled in 1997-2000 and 2002-2006, respectively. We assessed the reproducibility of urinary concentrations of phthalate metabolites in three spot samples per participant taken several years apart (mean interval between first and third sample was 7.5 years [women] or 2.9 years [men]), using Spearman's rank correlation coefficients and intra-class correlation coefficients. We detected ten phthalate metabolites in at least 50% of individuals for two or more samples. Participant sex, age, menopausal status, education, income, body mass index, consumption of bottled water, recent intake of medication, and time of day of collection of the urine sample were associated with concentrations of certain phthalate metabolites. The reproducibility of an individual's urinary concentration of phthalate metabolites across several years was low, with all intra-class correlation coefficients and most Spearman rank correlation coefficients ≤ 0.3. Only mono(2-ethylhexyl) phthalate, a metabolite of di(2-ethylhexyl)phthalate, had a Spearman rank correlation coefficient ≥ 0.4 among men, suggesting moderate reproducibility. These findings suggest that a single spot urine sample is not sufficient to rank exposures to phthalates over several years in an adult urban Chinese population

Predictors and Variability of Repeat Measurements of Urinary Phenols and Parabens in a Cohort of Shanghai Women and Men

Background: Exposure to certain phenols is ubiquitous because of their use in many consumer and personal care products. However, predictors of exposure have not been well characterized in most populations

A combined analysis of outcome following breast cancer: differences in survival based on BRCA1/BRCA2 mutation status and administration of adjuvant treatment

BACKGROUND: The prognostic significance of germline mutations in BRCA1 and BRCA2 in women with breast cancer remains unclear. A combined analysis was performed to address this uncertainty. METHODS: Two retrospective cohorts of Ashkenazi Jewish women undergoing breast-conserving treatment for invasive cancer between 1980 and 1995 (n = 584) were established. Archived tissue blocks were used as the source of DNA for Ashkenazi Jewish BRCA1/BRCA2 founder mutation analysis. Paraffin-embedded tissue and follow-up information was available for 505 women. RESULTS: Genotyping was successful in 496 women, of whom 56 (11.3%) were found to carry a BRCA1/BRCA2 founder mutation. After a median follow-up period of 116 months, breast cancer specific survival was worse in women with BRCA1 mutations than in those without (62% at 10 years versus 86%; P < 0.0001), but not in women with the BRCA2 mutation (84% versus 86% at 10 years; P = 0.76). Germline BRCA1 mutations were an independent predictor of breast cancer mortality in multivariate analysis (hazard ratio 2.4, 95% confidence interval 1.2–4.8; P = 0.01). BRCA1 status predicted breast cancer mortality only among women who did not receive chemotherapy (hazard ratio 4.8, 95% confidence interval 2.0–11.7; P = 0.001). The risk for metachronous ipsilateral cancer was not greater in women with germline BRCA1/BRCA2 founder mutations than in those without mutations (P = 0.68). CONCLUSION: BRCA1 mutations, but not BRCA2 mutations, are associated with reduced survival in Ashkenazi women undergoing breast-conserving treatment for invasive breast cancer, but the poor prognosis associated with germline BRCA1 mutations is mitigated by adjuvant chemotherapy. The risk for metachronous ipsilateral disease does not appear to be increased for either BRCA1 or BRCA2 mutation carriers, at least up to 10 years of follow up

Normalization Method for Transcriptional Studies of Heterogeneous Samples - Simultaneous Array Normalization and Identification of Equivalent Expression

Normalization is an important step in the analysis of microarray data of transcription profiles as systematic non-biological variations often arise from the multiple steps involved in any transcription profiling experiment. Existing methods for data normalization often assume that there are few or symmetric differential expression, but this assumption does not always hold. Alternatively, non-differentially expressed genes may be used for array normalization. However, it is unknown at the outset which genes are non-differentially expressed. In this paper we propose a hierarchical mixture model framework to simultaneously identify non-differentially expressed genes and normalize arrays using these genes. The Fisher's information matrix corresponding to array effects is derived, which provides useful intuition for guiding the choice of array normalization method. The operating characteristics of the proposed method are evaluated using simulated data. The simulations conducted under a wide range of parametric configurations suggest that the proposed method provides a useful alternative for array normalization. For example, the proposed method has better sensitivity than median normalization under modest prevalence of differentially expressed genes and when the magnitudes of over-expression and under-expression are not the same. Further, the proposed method has properties similar to median normalization when the prevalence of differentially expressed genes is very small. Empirical illustration of the proposed method is provided using a liposarcoma study from MSKCC to identify genes differentially expressed between normal fat tissue versus liposarcoma tissue samples.

Estimating the number of quantitative trait loci via Bayesian model determination

this paper we use a Bayesian model determination approach via MCMC to estimate the number of loci by including the number of QTL as an unknown parameter following Green (1995)

Normalization Method for Transcriptional Studies of Heterogeneous Samples – Simultaneous Array Normalization and Identification of Equivalent Expression

Normalization is an important step in the analysis of microarray data of transcription profiles as systematic non-biological variations often arise from the multiple steps involved in any transcription profiling experiment. Existing methods for data normalization often assume that there are few or symmetric differential expression, but this assumption does not always hold. Alternatively, non-differentially expressed genes may be used for array normalization. However, it is unknown at the outset which genes are non-differentially expressed. In this paper we propose a hierarchical mixture model framework to simultaneously identify non-differentially expressed genes and normalize arrays using these genes. The Fisher"s information matrix corresponding to array effects is derived, which provides useful intuition for guiding the choice of array normalization method. The operating characteristics of the proposed method are evaluated using simulated data. The simulations conducted under a wide range of parametric configurations suggest that the proposed method provides a useful alternative for array normalization. For example, the proposed method has better sensitivity than median normalization under modest prevalence of differentially expressed genes and when the magnitudes of over-expression and under-expression are not the same. Further, the proposed method has properties similar to median normalization when the prevalence of differentially expressed genes is very small. Empirical illustration of the proposed method is provided using a liposarcoma study from MSKCC to identify genes differentially expressed between normal fat tissue versus liposarcoma tissue samples