Ovarian cancer is asymptomatic at early stages and most patients present with advanced levels of disease. Lack of cost-effective methods that can achieve frequent, simple and non-invasive testing hinders early detection and causes high mortality in ovarian cancer patients. Here, we report a simple and inexpensive microchip ELISA-based detection module that employs a portable detection system, i.e., a cell phone/charge-coupled device (CCD) to quantify an ovarian cancer biomarker, HE4, in urine. Integration of a mobile application with a cell phone enabled immediate processing of microchip ELISA results, which eliminated the need for a bulky, expensive spectrophotometer. The HE4 level detected by a cell phone or a lensless CCD system was significantly elevated in urine samples from cancer patients (n = 19) than normal healthy controls (n = 20) (p < 0.001). Receiver operating characteristic (ROC) analyses showed that the microchip ELISA coupled with a cell phone running an automated analysis application had a sensitivity of 89.5% at a specificity of 90%. Under the same specificity, the microchip ELISA coupled with a CCD had a sensitivity of 84.2%. In conclusion, integration of microchip ELISA with cell phone/CCD-based colorimetric measurement technology can be used to detect HE4 biomarker at the point-of-care (POC), paving the way to create bedside technologies for diagnostics and treatment monitoring.
Airway hyperresponsiveness (AHR), a primary characteristic of asthma, involves increased airway smooth muscle contractility in response to certain exposures. We sought to determine whether common genetic variants were associated with AHR severity.
A genome-wide association study (GWAS) of AHR, quantified as the natural log of the dosage of methacholine causing a 20% drop in FEV1, was performed with 994 non-Hispanic white asthmatic subjects from three drug clinical trials: CAMP, CARE, and ACRN. Genotyping was performed on Affymetrix 6.0 arrays, and imputed data based on HapMap Phase 2, was used to measure the association of SNPs with AHR using a linear regression model. Replication of primary findings was attempted in 650 white subjects from DAG, and 3,354 white subjects from LHS. Evidence that the top SNPs were eQTL of their respective genes was sought using expression data available for 419 white CAMP subjects.
The top primary GWAS associations were in rs848788 (P-value 7.2E-07) and rs6731443 (P-value 2.5E-06), located within the ITGB5 and AGFG1 genes, respectively. The AGFG1 result replicated at a nominally significant level in one independent population (LHS P-value 0.012), and the SNP had a nominally significant unadjusted P-value (0.0067) for being an eQTL of AGFG1.
Based on current knowledge of ITGB5 and AGFG1, our results suggest that variants within these genes may be involved in modulating AHR. Future functional studies are required to confirm that our associations represent true biologically significant findings.
Asthma; Airway hyperresponsiveness; Genome-wide association study; ITGB5; AGFG1
The impact of cigarette smoking can persist for extended periods following smoking cessation and may involve epigenetic reprogramming. Changes in DNA methylation associated with smoking may help to identify molecular pathways that contribute to the latency between exposure and disease onset. Cross-sectional cohort data from subjects in the International COPD Genetics Network (n = 1085) and the Boston Early-Onset COPD study (n = 369) were analyzed as the discovery and replication cohorts, respectively. Genome-wide methylation data on 27 578 CpG sites in 14 475 genes were obtained on DNA from peripheral blood leukocytes using the Illumina HumanMethylation27K Beadchip in both cohorts. We identified 15 sites significantly associated with current smoking, 2 sites associated with cumulative smoke exposure, and, within the subset of former smokers, 3 sites associated with time since quitting cigarettes. Two loci, factor II receptor-like 3 (F2RL3) and G-protein-coupled receptor 15 (GPR15), were significantly associated in all three analyses and were validated by pyrosequencing. These findings (i) identify a novel locus (GPR15) associated with cigarette smoking and (ii) suggest the existence of dynamic, site-specific methylation changes in response to smoking which may contribute to the extended risks associated with cigarette smoking that persist after cessation.
The response to treatment for asthma is characterized by wide interindividual variability, with a significant number of patients who have no response. We hypothesized that a genomewide association study would reveal novel pharmacogenetic determinants of the response to inhaled glucocorticoids.
We analyzed a small number of statistically powerful variants selected on the basis of a family-based screening algorithm from among 534,290 single-nucleotide polymorphisms (SNPs) to determine changes in lung function in response to inhaled glucocorticoids. A significant, replicated association was found, and we characterized its functional effects.
We identified a significant pharmacogenetic association at SNP rs37972, replicated in four independent populations totaling 935 persons (P = 0.0007), which maps to the glucocorticoid-induced transcript 1 gene (GLCCI1) and is in complete linkage disequilibrium (i.e., perfectly correlated) with rs37973. Both rs37972 and rs37973 are associated with decrements in GLCCI1 expression. In isolated cell systems, the rs37973 variant is associated with significantly decreased luciferase reporter activity. Pooled data from treatment trials indicate reduced lung function in response to inhaled glucocorticoids in subjects with the variant allele (P = 0.0007 for pooled data). Overall, the mean (± SE) increase in forced expiratory volume in 1 second in the treated subjects who were homozygous for the mutant rs37973 allele was only about one third of that seen in similarly treated subjects who were homozygous for the wild-type allele (3.2 ± 1.6% vs. 9.4 ± 1.1%), and their risk of a poor response was significantly higher (odds ratio, 2.36; 95% confidence interval, 1.27 to 4.41), with genotype accounting for about 6.6% of overall inhaled glucocorticoid response variability.
A functional GLCCI1 variant is associated with substantial decrements in the response to inhaled glucocorticoids in patients with asthma. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT00000575.)
Rationale: Chronic obstructive pulmonary disease (COPD) is associated with local (lung) and systemic (blood) inflammation and manifestations. DNA methylation is an important regulator of gene transcription, and global and specific gene methylation marks may vary with cigarette smoke exposure.
Objectives: To perform a comprehensive assessment of methylation marks in DNA from subjects well phenotyped for nonneoplastic lung disease.
Methods: We conducted array-based methylation screens, using a test-replication approach, in two family-based cohorts (n = 1,085 and 369 subjects).
Measurements and Main Results: We observed 349 CpG sites significantly associated with the presence and severity of COPD in both cohorts. Seventy percent of the associated CpG sites were outside of CpG islands, with the majority of CpG sites relatively hypomethylated. Gene ontology analysis based on these 349 CpGs (330 genes) suggested the involvement of a number of genes responsible for immune and inflammatory system pathways, responses to stress and external stimuli, as well as wound healing and coagulation cascades. Interestingly, our observations include significant, replicable associations between SERPINA1 hypomethylation and COPD and lower average lung function phenotypes (combined P values: COPD, 1.5 × 10−23; FEV1/FVC, 1.5 × 10−35; FEV1, 2.2 × 10−40).
Conclusions: Genetic and epigenetic pathways may both contribute to COPD. Many of the top associations between COPD and DNA methylation occur in biologically plausible pathways. This large-scale analysis suggests that DNA methylation may be a biomarker of COPD and may highlight new pathways of COPD pathogenesis.
chronic obstructive pulmonary disease; epigenetics; DNA methylation; smoking
Coronary artery bypass graft (CABG) surgery is the standard of care for the management of patients with severe three-vessel and left main coronary artery disease (CAD). However, the optimal strategy for management of patients with CAD and severe left ventricular (LV) dysfunction [ejection fraction (EF) ≤35%] is not clear. A meta-analysis of observational studies was performed to determine the operative mortality and long-term (5-year actuarial survival) outcomes among patients with severe LV dysfunction undergoing CABG.
Methods and results
A systematic computerized literature search was performed and observational studies consisting of patients undergoing isolated CABG for CAD and severe LV dysfunction were included. Studies that did not report operative mortality, long-term (≥1 year) survival data, or pre-operative EF and multiple studies from the same group were excluded. In total, 4119 patients from 26 observational clinical studies were included. The estimated mean age was 63.9 years and 82.4% of patients were men. The mean (estimate) pre-operative EF was 24.7% (95% CI 22.5–27.0%). The operative mortality among patients (26 studies, n= 3621) who underwent on-pump CABG was 5.4%, n= 189 (95% CI 4.5–6.4%). The 5-year actuarial survival among patients (13 studies, n= 1980) who underwent on-pump CABG was 73.4%, n= 1483 (95% CI 68.7–77.7%). Patients who underwent off-pump CABG (7 studies, n= 498) tended to have reduced operative mortality of 4.4%, n= 20 (95% CI 2.8–6.4%). The mean (estimate) post-operative EF was 35.19% (95% CI 31.95–38.43%).
The present meta-analysis demonstrates that based on data from available observational clinical studies, CABG can be performed with acceptable operative mortality and 5-year actuarial survival in patients with severe LV dysfunction.
Coronary artery bypass surgery; Revascularization; Coronary artery disease; Meta-analysis; Observational studies
Recent breakthroughs in next-generation sequencing technologies allow cost-effective methods for measuring a growing list of cellular properties, including DNA sequence and structural variation. Next-generation sequencing has the potential to revolutionize complex trait genetics by directly measuring common and rare genetic variants within a genome-wide context. Because for a given gene both rare and common causal variants can coexist and have independent effects on a trait, strategies that model the effects of both common and rare variants could enhance the power of identifying disease-associated genes. To date, little work has been done on integrating signals from common and rare variants into powerful statistics for finding disease genes in genome-wide association studies. In this analysis of the Genetic Analysis Workshop 17 data, we evaluate various strategies for association of rare, common, or a combination of both rare and common variants on quantitative phenotypes in unrelated individuals. We show that the analysis of common variants only using classical approaches can achieve higher power to detect causal genes than recently proposed rare variant methods and that strategies that combine association signals derived independently in rare and common variants can slightly increase the power compared to strategies that focus on the effect of either the rare variants or the common variants.
Previous expression quantitative trait loci (eQTL) studies have performed genetic association studies for gene expression, but most of these studies examined lymphoblastoid cell lines from non-diseased individuals. We examined the genetics of gene expression in a relevant disease tissue from chronic obstructive pulmonary disease (COPD) patients to identify functional effects of known susceptibility genes and to find novel disease genes. By combining gene expression profiling on induced sputum samples from 131 COPD cases from the ECLIPSE Study with genomewide single nucleotide polymorphism (SNP) data, we found 4315 significant cis-eQTL SNP-probe set associations (3309 unique SNPs). The 3309 SNPs were tested for association with COPD in a genomewide association study (GWAS) dataset, which included 2940 COPD cases and 1380 controls. Adjusting for 3309 tests (p<1.5e-5), the two SNPs which were significantly associated with COPD were located in two separate genes in a known COPD locus on chromosome 15: CHRNA5 and IREB2. Detailed analysis of chromosome 15 demonstrated additional eQTLs for IREB2 mapping to that gene. eQTL SNPs for CHRNA5 mapped to multiple linkage disequilibrium (LD) bins. The eQTLs for IREB2 and CHRNA5 were not in LD. Seventy-four additional eQTL SNPs were associated with COPD at p<0.01. These were genotyped in two COPD populations, finding replicated associations with a SNP in PSORS1C1, in the HLA-C region on chromosome 6. Integrative analysis of GWAS and gene expression data from relevant tissue from diseased subjects has located potential functional variants in two known COPD genes and has identified a novel COPD susceptibility locus.
Fatty acid synthase (FASN) regulates de novo lipogenesis, body weight, and tumor growth. We examined whether common germline single nucleotide polymorphisms (SNPs) in the FASN gene affect prostate cancer (PCa) risk or PCa-specific mortality and whether these effects vary by body mass index (BMI).
In a prospective nested case-control study of 1,331 white patients with PCa and 1,267 age-matched controls, we examined associations of five common SNPs within FASN (and 5 kb upstream/downstream, R2 > 0.8) with PCa incidence and, among patients, PCa-specific death and tested for an interaction with BMI. Survival analyses were repeated for tumor FASN expression (n = 909).
Four of the five SNPs were associated with lethal PCa. SNP rs1127678 was significantly related to higher BMI and interacted with BMI for both PCa risk (Pinteraction = .004) and PCa mortality (Pinteraction = .056). Among overweight men (BMI ≥ 25 kg/m2), but not leaner men, the homozygous variant allele carried a relative risk of advanced PCa of 2.49 (95% CI, 1.00 to 6.23) compared with lean men with the wild type. Overweight patients carrying the variant allele had a 2.04 (95% CI, 1.31 to 3.17) times higher risk of PCa mortality. Similarly, overweight patients with elevated tumor FASN expression had a 2.73 (95% CI, 1.05 to 7.08) times higher risk of lethal PCa (Pinteraction = .02).
FASN germline polymorphisms were significantly associated with risk of lethal PCa. Significant interactions of BMI with FASN polymorphisms and FASN tumor expression suggest FASN as a potential link between obesity and poor PCa outcome and raise the possibility that FASN inhibition could reduce PCa-specific mortality, particularly in overweight men.
The use of the cumulative average model to investigate the association between disease incidence and repeated measurements of exposures in medical follow-up studies can be dated back to the 1960s (Kahn and Dawber, J Chron Dis 19:611–620, 1966). This model takes advantage of all prior data and thus should provide a statistically more powerful test of disease-exposure associations. Measurement error in covariates is common for medical follow-up studies. Many methods have been proposed to correct for measurement error. To the best of our knowledge, no methods have been proposed yet to correct for measurement error in the cumulative average model. In this article, we propose a regression calibration approach to correct relative risk estimates for measurement error. The approach is illustrated with data from the Nurses’ Health Study relating incident breast cancer between 1980 and 2002 to time-dependent measures of calorie-adjusted saturated fat intake, controlling for total caloric intake, alcohol intake, and baseline age.
Measurement error; Regression calibration; Nutritional data
Adipocytokines may mediate the association between adiposity and lethal prostate cancer outcomes.
In the Physicians’ Health Study, we prospectively examined the association of prediagnostic plasma concentrations of adiponectin and leptin with risk of developing incident prostate cancer (654 case diagnosed 1982-2000 and 644 age-matched controls) and, among cases, risk of dying from prostate cancer by 2007.
Adiponectin concentrations were not associated with risk of overall prostate cancer. However, men with higher adiponectin concentrations had lower risk of developing high grade or lethal cancer (metastatic or fatal disease). The relative risk (95% confidence interval) comparing the highest to the lowest quintile (Q5 vs. Q1) was 0.25 (0.07-0.87; Ptrend=0.02) for lethal cancer. Among all the cases, higher adiponectin concentrations predicted lower prostate cancer-specific mortality (hazard ratio, HR Q5 vs. Q1=0.39; 0.17-0.85; Ptrend=0.02), independent of body mass index (BMI), plasma C-peptide (a marker of insulin secretion), leptin, clinical stage and tumor grade. This inverse association was apparent mainly among men whose BMI ≥25 kg/m2 (HR Q5 vs. Q1=0.10; 0.01-0.78; Ptrend=0.02), but not among men of normal weight (Ptrend=0.51). Although the correlation of leptin concentrations with BMI (r=0.58, P <0.001) was stronger than that of adiponectin (r=−0.17, P<0.001), leptin was unrelated to prostate cancer risk or mortality.
Higher prediagnostic adiponectin (but not leptin) concentrations predispose men to a lower risk of developing high grade prostate cancer and a lower risk of subsequently dying from the cancer, suggesting a mechanistic link between obesity and poor PCa outcome.
Adiponectin; obesity; prostate cancer; risk; survival
To examine consequences of deferred treatment (DT) as initial management of prostate cancer (PCa) in a contemporary, prospective cohort of American men diagnosed with PCa.
Participants and Methods
We evaluated deferred treatment for PCa in the Health Professionals Follow-up Study, a prospective study of 51,529 men. Cox proportional hazards models were used to calculate hazard ratios (HRs) for time to eventual treatment among men who deferred treatment for more than 1 year after diagnosis. HRs for time to metastasis or death as a result of PCa were compared between patients who deferred treatment and those who underwent immediate treatment within 1 year of diagnosis.
From among 3,331 cohort participants diagnosed with PCa from 1986 to 2007, 342 (10.3%) initially deferred treatment. Of these, 174 (51%) remained untreated throughout follow-up (mean 7.7 years); the remainder were treated an average of 3.9 years after diagnosis. Factors associated with progression to treatment among DT patients included younger age, higher clinical stage, higher Gleason score, and higher prostate-specific antigen at diagnosis. We observed similar rates for development of metastases (n = 20 and n = 199; 7.2 v 8.1 per 1,000 person-years; P = .68) and death as a result of PCa (n = 8 and n = 80; 2.4 v 2.6 per 1,000 person-years; P = .99) for DT and immediate treatment, respectively.
In this nationwide cohort, more than half the men who opted for DT remained without treatment for 7.7 years after diagnosis. Older men and men with lesser cancer severity at diagnosis were more likely to remain untreated. PCa mortality did not differ between DT and active treatment patients.
It is essential to understand the molecular basis of ovarian cancer etiology and tumor development to provide more effective preventive and therapeutic approaches to reduce mortality. Particularly, the molecular targets and pathways involved in early malignant transformation are still not clear. Pro-inflammatory lipids and pathways have been reported to play significant roles in ovarian cancer progression and metastasis. The major objective of this study was to explore and determine whether platelet activating factor (PAF) and receptor associated networking pathways might significantly induce malignant potential in BRCA1-mutant at-risk epithelial cells.
BRCA1-mutant ovarian epithelial cell lines including (HOSE-636, HOSE-642), BRCA1-mutant ovarian cancer cell (UWB1.289), wild type normal ovarian epithelial cell (HOSE-E6E7) and cancerous cell line (OVCA429), and the non-malignant BRCA1-mutant distal fallopian tube (fimbria) tissue specimens were used in this study. Mutation analysis, kinase microarray, western blot, immune staining, co-immune precipitation, cell cycle, apoptosis, proliferation and bioinformatic pathway analysis were applied.
We found that PAF, as a potent pro-inflammatory mediator, induced significant anti-apoptotic effect in BRCA1-mutant ovarian surface epithelial cells, but not in wild type HOSE cells. With kinase microarray technology and the specific immune approaches, we found that phosphor-STAT1 was activated by 100 nM PAF treatment only in BRCA1-mutant associated at-risk ovarian epithelial cells and ovarian cancer cells, but not in BRCA1-wild type normal (HOSE-E6E7) or malignant (OVCA429) ovarian epithelial cells. Co-immune precipitation revealed that elevated PAFR expression is associated with protein-protein interactions of PAFR-FAK and FAK-STAT1 in BRCA1-mutant ovarian epithelial cells, but not in the wild-type control cells.
Previous studies showed that potent inflammatory lipid mediators such as PAF and its receptor (PAFR) significantly contribute to cancer progression and metastasis. Our findings suggest that these potent inflammatory lipids and receptor pathways are significantly involved in the early malignant transformation through PAFR-FAK-STAT1 networking and to block apoptosis pathway in BRCA1 dysfunctional at-risk ovarian epithelium.
Excess body mass index (BMI) has been associated with adverse outcomes in prostate cancer, and hyperinsulinemia is a candidate mediator, but prospective data are sparse. We assessed the influence of prediagnostic BMI and plasma C-peptide (reflecting insulin secretion) on prostate cancer-specific mortality after diagnosis.
BMI was available at baseline (1982) and in 1990 among 2,546 men who developed prostate cancer (281 prostate cancer deaths). Baseline C-peptide concentration were available in 827 men (117 prostate cancer deaths). We used Cox proportional hazards regression models controlling for age, smoking, time between BMI measurement and prostate cancer diagnosis, and competing causes of death.
Compared with men of normal weight (BMI<25 kg/m2) at baseline, overweight men (BMI 25–29.9 kg/m2) and obese men (BMI≥30 kg/m2) had significantly higher risk of prostate cancer mortality; the proportional hazard ratio (HR)s (95% confidence interval, CI) were 1.47 (1.16–1.88) for overweight and 2.66 (1.62–4.39; Ptrend<0.0001) for obesity. The trend remained significant after controlling for clinical stage and Gleason grade and was stronger for prostate cancer diagnosed during the PSA screening era (1991–2007) or using BMI obtained in 1990. Men with C-peptide concentrations in the highest quartile (high), versus the lowest quartile (low), also had higher risk (HR=2.38; 1.31–4.30). Compared with men with BMI<25 kg/m2 and low C-peptide concentrations, those with BMI≥25 kg/m2 and high C-peptide concentration had a four times higher risk (HR=4.12; 1.97–8.61; Pinteraction=0.001) independent of clinical predictors.
Excess body weight and high plasma concentration of C-peptide each predispose men with a subsequent diagnosis of prostate cancer to increased likelihood of dying of this disease; those with both factors have the worst outcome.
Bayesian hierarchical models that characterize the distributions of (transformed) gene profiles have been proven very useful and flexible in selecting differentially expressed genes across different types of tissue samples (e.g. Lo and Gottardo, 2007). However, the marginal mean and variance of these models are assumed to be the same for different gene clusters and for different tissue types. Moreover, it is not easy to determine which of the many competing Bayesian hierarchical models provides the best fit for a specific microarray data set. To address these two issues, we propose a marginal mixture model that directly models the marginal distribution of transformed gene profiles. Specifically, we approximate the marginal distributions of transformed gene profiles via a mixture of three-component multivariate Normal distributions, each component of which has the same structures of marginal mean vector and covariance matrix as those for Bayesian hierarchical models, but the values can differ. Based on the proposed model, a method is derived to select genes differentially expressed across two types of tissue samples. The derived gene selection method performs well on a real microarray data set and consistently has the best performance (based on class agreement indices) compared with several other gene selection methods on simulated microarray data sets generated from three different mixture models.
Although there is now plenty of genomic data and no shortage of analysis methods for translational genomic research, many biologists do not have efficient and transparent access to the computational resources they need. No single data resource or analysis application is ever likely to efficiently address all aspects of any individual researcher’s needs, so most researchers are forced to manually integrate data and outputs from multiple resources. The inevitable heterogeneity of data formats and of command syntax between data resources and software applications presents a major obstacle, particularly to those biologists lacking practical informatics skills. We describe some design and implementation features of an open-source application that supports the integration of the best available third-party genomics software applications, data and annotation resources into a coherent framework, substantially overcoming many practical challenges associated with actually doing translational genomic research.
Calculation of the appropriate sample size in planning microarray studies is
important because sample collection can be expensive and time-consuming.
Sample-size calculation is also a challenging issue for microarray studies
because the number of genes is far larger than the number of samples so that
traditional methods of sample-size calculation cannot be directly applied. To
help investigators answer the question of how many samples are needed in their
microarray studies, we developed a user-friendly web-based calculator,
SPCalc, for calculating sample size and power for a variety of
commonly used experimental designs, including completely randomized
treatmentcontrol design, matched-pairs design, multiple-treatment design having
an isolated treatment effect, and randomized block design.
The web-based calculator SPCalc is publicly available at
gene expression; microarray; sample size; power calculation