High Goblet Cell Count Is Inversely Associated with Ploidy Abnormalities and Risk of Adenocarcinoma in Barrett's Esophagus.

PURPOSE: Goblet cells may represent a potentially successful adaptive response to acid and bile by producing a thick mucous barrier that protects against cancer development in Barrett's esophagus (BE). The aim of this study was to determine the relationship between goblet cells (GC) and risk of progression to adenocarcinoma, and DNA content flow cytometric abnormalities, in BE patients. EXPERIMENTAL DESIGN: Baseline mucosal biopsies (N=2988) from 213 patients, 32 of whom developed cancer during the follow up period, enrolled in a prospective dynamic cohort of BE patients were scored in a blinded fashion, for the total number (#) of GC, mean # of GC/crypt (GC density), # of crypts with ≥ 1 GC, and the proportion of crypts with ≥1 GC, in both dysplastic and non-dysplastic epithelium separately. The relationship between these four GC parameters and DNA content flow cytometric abnormalities and adenocarcinoma outcome was compared, after adjustment for age, gender, and BE segment length. RESULTS: High GC parameters were inversely associated with DNA content flow cytometric abnormalities, such as aneuploidy, ploidy >2.7N, and an elevated 4N fraction > 6%, and with risk of adenocarcinoma. However, a Kaplan-Meier analysis showed that the total # of GC and the total # crypts with ≥1 GC were the only significant GC parameters (p<0.001 and 0.003, respectively). CONCLUSIONS: The results of this study show, for the first time, an inverse relationship between high GC counts and flow cytometric abnormalities and risk of adenocarcinoma in BE. Further studies are needed to determine if GC depleted foci within esophageal columnar mucosa are more prone to neoplastic progression or whether loss of GC occurs secondary to underlying genetic abnormalities.

An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples.

BACKGROUND: Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing. RESULTS: We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low µg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples. CONCLUSIONS: We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.

Validation of a multiprotein plasma classifier to identify benign lung nodules.

INTRODUCTION: Indeterminate pulmonary nodules (IPNs) lack clinical or radiographic features of benign etiologies and often undergo invasive procedures unnecessarily, suggesting potential roles for diagnostic adjuncts using molecular biomarkers. The primary objective was to validate a multivariate classifier that identifies likely benign lung nodules by assaying plasma protein expression levels, yielding a range of probability estimates based on high negative predictive values (NPVs) for patients with 8 to 30 mm IPNs. METHODS: A retrospective, multicenter, case-control study was performed using multiple reaction monitoring mass spectrometry, a classifier comprising five diagnostic and six normalization proteins, and blinded analysis of an independent validation set of plasma samples. RESULTS: The classifier achieved validation on 141 lung nodule-associated plasma samples based on predefined statistical goals to optimize sensitivity. Using a population based nonsmall-cell lung cancer prevalence estimate of 23% for 8 to 30 mm IPNs, the classifier identified likely benign lung nodules with 90% negative predictive value and 26% positive predictive value, as shown in our prior work, at 92% sensitivity and 20% specificity, with the lower bound of the classifier's performance at 70% sensitivity and 48% specificity. Classifier scores for the overall cohort were statistically independent of patient age, tobacco use, nodule size, and chronic obstructive pulmonary disease diagnosis. The classifier also demonstrated incremental diagnostic performance in combination with a four-parameter clinical model. CONCLUSIONS: This proteomic classifier provides a range of probability estimates for the likelihood of a benign etiology that may serve as a noninvasive, diagnostic adjunct for clinical assessments of patients with IPNs.

A blood-based proteomic classifier for the molecular characterization of pulmonary nodules.

Each year, millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, we identified 371 protein candidates and developed a multiple reaction monitoring (MRM) assay for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancer matched for nodule size, age, gender, and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a negative predictive value (NPV) of 90%. Validation performance on samples from a nondiscovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis.

Multivariate linkage scan for metabolic syndrome traits in families with type 2 diabetes.

The purpose of this study was to evaluate evidence for linkage to interrelated quantitative features of the metabolic syndrome (MetS). Data on eight quantitative MetS traits (body weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein (HDL) cholesterol, triglycerides (TGs), and fasting glucose and insulin measurements) and a 10 cM genome scan were available for 78 white families (n = 532 subjects). These data were used to conduct multipoint, multivariate linkage analyses, including tests for coincident linkage and complete pleiotropy. The strongest evidence for linkage from the bivariate analyses was observed on chromosome 1 (1p22.2) (HDL-TG; univariate lod score equivalent (lod(eq) = 3.99)) with stronger results from the trivariate analysis at the same location (HDL-TG-Insulin; lod(eq) = 4.32). Seven additional susceptibility regions (lod(eq) scores >1.9) were observed (1p36, 1q23, 2q21.2, 8q23.3, 14q23.2, 14q32.11, and 20p11.21). The results from this study indicate that several correlated traits of the MetS are influenced by the same gene(s) that account for some of the clustering of the MetS features.

Association of diabetes susceptibility gene calpain-10 with pancreatic cancer among smokers.

OBJECTIVE: The objective of this study was to test the association between calpain-10 (CAPN10), a diabetes susceptibility gene, with risk of pancreatic cancer (PC). METHODS: DNA samples from 83 incident exocrine PC cases and 166 controls, all of whom were smokers, were genotyped for four markers of CAPN10 in a nested case-control study based on the Beta-Carotene and Retinol Efficacy Trial (CARET), a randomized chemoprevention trial of subjects at high risk of lung cancer. Controls were matched on sex, race, age, CARET intervention arm, duration of exposure to asbestos, and smoking history. Conditional logistic regression was used for statistical analyses. RESULTS: The minor allele of SNP-43 (rs3792267) in intron 3 was associated with increased risk of PC with an odds ratio of 1.57 (95%CI 1.03-2.38, p = 0.035) per allele. The three markers of the highest risk haplotype had an odds ratio of 1.98 (95%CI 1.12-3.49, p = 0.019) for risk of PC compared to the most common haplotype. There was no evidence of interaction between either of these associations by diabetes status. CONCLUSION: These results suggest that variation in CAPN10 may be associated with increased risk of PC among smokers. Thus, studies of genes associated with diabetes risk in PC are warranted in a larger population.

Mapping of a Hirschsprung's disease locus in 3p21.

Hirschsprung's disease (HSCR) is a congenital disorder in which ganglion cells are absent in variable portions of the lower digestive tract according to which patients are classified. The RET gene is the major HSCR gene, although reduced penetrance of RET mutations and variable expression of HSCR phenotype indicates that more than one gene is required. An unidentified RET-dependent modifier on 3p21 appears to be necessary for transmission of the short HSCR (S-HSCR) phenotype. We investigated 6 Mb of the 3p21 region on a quest for the HSCR-susceptibility locus. Fifty-eight S-HSCR case-parent trios were genotyped using Sequenom technology for 214 tag single nucleotide polymorphisms (SNPs) distributed along 6 Mb of the 3p21 region. A five-marker haplotype, spanning a 118 kb gene-rich region, was found to be overtransmitted to affected offspring. The associated haplotype encompasses three genes involved in neurological phenotypes. Importantly, this association was replicated in an independent sample of 172 S-HSCR cases and 153 unrelated controls. Ranking markers by proximity to candidate genes or by expected functional consequences could be used in follow-up studies to finally pinpoint this HSCR locus.

CLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs.

UNLABELLED: Cluster and set-cover algorithms are developed to obtain a set of tag single nucleotide polymorphisms (SNPs) that can represent all the known SNPs in a chromosomal region, subject to the constraint that all SNPs must have a squared correlation R2>C with at least one tag SNP, where C is specified by the user. AVAILABILITY: http://hkumath.hku.hk/web/link/CLUSTAG/CLUSTAG.html CONTACT: mng@maths.hku.hk.

Mcl-1 expression in gestational trophoblastic disease correlates with clinical outcome: a differential expression study.

BACKGROUND: Hydatidiform moles (HMs) are abnormal pregnancies with a propensity for developing persistent disease in the form of gestational trophoblastic neoplasia (GTN), which requires chemotherapy. In previous studies, the authors demonstrated that low apoptotic activity was correlated with the progression of HM to GTN, and they hypothesized that some apoptosis-related genes may determine this progression. METHODS: The differential expression of apoptotic genes in HMs that subsequently developed into GTN was compared with the same expression in HMs that spontaneously regressed using a human apoptosis array; then, the expression was evaluated with real-time quantitative polymerase chain reaction analysis and immunohistochemistry using 54 clinical samples from patients with HMs who had follow-up data available. RESULTS: Using an apoptosis array, greater expression of Mcl-1, which is an antiapoptotic gene, was detected in HMs that subsequently developed into GTN. It was confirmed that the levels of Mcl-1 RNA expression (P = 0.017) and Mcl-1 protein expression (P < 0.001) in HMs that developed into persistent disease and required chemotherapy were significantly greater compared with the levels in HMs that regressed. Moreover, Mcl-1 immunoreactivity, which was detected predominantly in cytotrophoblasts, was correlated with the apoptotic index, as assessed with M30 cytoDeath immunohistochemistry, which is a good indicator of apoptotic events in the early-stage disease. CONCLUSIONS: The current results demonstrated that Mcl-1, as identified by a cyclic DNA array, may play a role in the pathogenesis of HMs and may have potential as a useful marker for predicting the clinical behavior of HMs.