Xing Qiu

Are endoscopic therapies appropriate for superficial submucosal esophageal adenocarcinoma? An analysis of esophagectomy specimens.

BACKGROUND Endoscopic resection and ablation have advanced the treatment of intramucosal esophageal adenocarcinoma and have been promoted as definitive therapy for selected superficial submucosal tumors. Controversy exists regarding the prevalence of nodal metastases at various depths of mucosal and submucosal invasion. Our aim was to clarify this prevalence and identify predictors of nodal spread. STUDY DESIGN An expert gastrointestinal pathologist retrospectively reviewed 54 T1 adenocarcinomas from 258 esophagectomy specimens (2000 to 2008). Tumors were classified as intramucosal or submucosal, the latter being subclassified as SM1 (upper third), SM2 (middle third), or SM3 (lower third) based on the depth of tumor invasion. The depth of invasion was correlated with the prevalence of positive nodes. Fishers exact test and univariate and multivariate logistic regression were used to identify variables predicting nodal disease. RESULTS Nodal metastases were present in 0% (0 of 25) of intramucosal, 21% (3 of 14) of SM1, 36% (4 of 11) of SM2, and 50% (2 of 4) of SM3 tumors. The differences were significant between intramucosal and submucosal tumors (p < 0.0001), although not between the various subclassifications of submucosal tumors (p = 0.503). Univariate logistic regression identified poor differentiation (p = 0.024), lymphovascular invasion (p = 0.049), and number of harvested lymph nodes (p = 0.037) as significantly correlated with nodal disease. Multivariate logistic regression did not identify any of the tested variables as independent predictors of the prevalence of positive lymph nodes. CONCLUSIONS All depths of submucosal invasion of esophageal adenocarcinoma were associated with an unacceptably high prevalence of nodal metastases and a marked increase relative to intramucosal cancer. Accurate predictors of nodal spread, independent of tumor depth, are currently lacking and will be necessary before recommending endoscopic resection with or without concomitant ablation as curative treatment for even superficial submucosal neoplasia.

Control of the mean number of false discoveries, Bonferroni and stability of multiple testing

The Bonferroni multiple testing procedure is commonly perceived as being overly conservative in large-scale simultaneous testing situations such as those that arise in microarray data analysis. The objective of the present study is to show that this popular belief is due to overly stringent requirements that are typically imposed on the procedure rather than to its conservative nature. To get over its notorious conservatism, we advocate using the Bonferroni selection rule as a procedure that controls the per family error rate (PFER). The present paper reports the first study of stability properties of the Bonferroni and Benjamini--Hochberg procedures. The Bonferroni procedure shows a superior stability in terms of the variance of both the number of true discoveries and the total number of discoveries, a property that is especially important in the presence of correlations between individual

The effects of normalization on the correlation structure of microarray data.

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Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes

-values. Its stability and the ability to provide strong control of the PFER make the Bonferroni procedure an attractive choice in microarray studies.

Quantification of accuracy and precision of multi-center DTI measurements: A diffusion phantom and human brain study ☆

BackgroundStochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test-statistics across genes. It is frequently assumed that dependence between genes (or tests) is suffciently weak to justify the proposed methods of testing for differentially expressed genes. A potential impact of between-gene correlations on the performance of such methods has yet to be explored.ResultsThe paper presents a systematic study of correlation between the t-statistics associated with different genes. We report the effects of four different normalization methods using a large set of microarray data on childhood leukemia in addition to several sets of simulated data. Our findings help decipher the correlation structure of microarray data before and after the application of normalization procedures.ConclusionA long-range correlation in microarray data manifests itself in thousands of genes that are heavily correlated with a given gene in terms of the associated t-statistics. By using normalization methods it is possible to significantly reduce correlation between the t-statistics computed for different genes. Normalization procedures affect both the true correlation, stemming from gene interactions, and the spurious correlation induced by random noise. When analyzing real world biological data sets, normalization procedures are unable to completely remove correlation between the test statistics. The long-range correlation structure also persists in normalized data.

Assessing stability of gene selection in microarray data analysis.

Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely related methods employing a two-component mixture model, represent typical examples. It is frequently assumed that dependence between gene expressions (or associated test statistics) is sufficiently weak to justify the application of such methods for selecting differentially expressed genes. By applying resampling techniques to simulated and real biological data sets, we have studied a potential impact of the correlation between gene expression levels on the statistical inference based on the empirical Bayes methodology. We report evidence from these analyses that this impact may be quite strong, leading to a high variance of the number of differentially expressed genes. This study also pinpoints specific components of the empirical Bayes method where the reported effect manifests itself.

MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas

The inter-site and intra-site variability of system performance of MRI scanners (due to site-dependent and time-variant variations) can have significant adverse effects on the integration of multi-center DTI data. Measurement errors in accuracy and precision of each acquisition determine both the inter-site and intra-site variability. In this study, multiple scans of an identical isotropic diffusion phantom and of the brain of a traveling human volunteer were acquired at MRI scanners from the same vendor and with similar configurations at three sites. We assessed the feasibility of multi-center DTI studies by direct quantification of accuracy and precision of each dataset. Accuracy was quantified via comparison to carefully constructed gold standard datasets while precision (the within-scan variability) was estimated by wild bootstrap analysis. The results from both the phantom and human data suggest that the inter-site variation in system performance, although relatively small among scanners of the same vendor, significantly affects DTI measurement accuracy and precision and therefore the effectiveness for the integration of multi-center DTI measurements. Our results also highlight the value of a DTI-specific phantom in identifying and quantifying measurement errors due to site-dependent variations in the system performance, and its usefulness for quality assurance/quality control in multi-center DTI studies. In addition, we observed that the within-scan variability of each data acquisition, as assessed by wild bootstrap analysis, is of the same magnitude as the inter-site and intra-site variability. We propose that by weighing datasets based on their variability, as evaluated by wild bootstrap analysis, one can improve the quality of the dataset. This approach will provide a more effective integration of datasets from multi-center DTI studies.

HER2 amplification, overexpression and score criteria in esophageal adenocarcinoma

BackgroundThe number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data.ResultsUsing resampling techniques we have found that some genes are selected much less frequently (across sub-samples) than other genes with the same adjusted p-values. The extent to which this type of instability manifests itself can be assessed by a method introduced in this paper. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations.ConclusionResampling represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using resampling techniques it is also possible to assess variability of different performance indicators. Stability properties of several multiple testing procedures are described at length in the present paper.

Detecting intergene correlation changes in microarray analysis: a new approach to gene selection

We evaluate the value of MR diffusion tensor imaging (DTI) and dynamic susceptibility-weighted contrast material-enhanced perfusion-weighted imaging (PWI) in preoperative grading of supratentorial nonenhancing gliomas. This institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study involved 52 patients: 37 with low-grade gliomas (LGGs) and 15 with high-grade gliomas (HGGs). The mean trace apparent diffusion coefficient (ADC), minimal ADC, mean fractional anisotropy (FA), maximal FA, and maximal relative cerebral blood volume (rCBV) ratio of the lesions were measured and compared between LGG and HGG. The efficacy of the above parameters in grading supratentorial nonenhancing gliomas was evaluated. There was no significant difference in rCBV ratio, minimal ADC, and mean ADC between LGG and HGG (p > 0.05). The mean and maximal FA values of LGG were significantly lower than the values of HGG (p < 0.001). The receiver operating characteristic analysis showed that the mean FA with a cutoff value of 0.129 and the maximal FA with a cutoff value of 0.219 could differentiate between LGG and HGG with specificity of 69.2% and 76.9%, respectively, and sensitivity of 93.3% and 100.0%, respectively. The combination of mean FA and maximal FA based on the linear discriminant analysis improved the diagnostic accuracy with specificity of 92.3% and sensitivity of 86.7%. These findings were better than maximal rCBV ratio, mean ADC, and minimum ADC. The mean FA and maximal FA, used individually or combined, may be useful in preoperative grading of supratentorial nonenhancing gliomas.

Identical de novo Mutation in the Type 1 Ryanodine Receptor Gene Associated with Fatal, Stress-induced Malignant Hyperthermia in Two Unrelated Families

The HER2 oncogene was recently reported to be amplified and overexpressed in esophageal adenocarcinoma. However, the relationship of HER2 amplification in esophageal adenocarcinoma with prognosis has not been well defined. The scoring systems for clinically evaluating HER2 in esophageal adenocarcinoma are not established. The aims of the study were to establish a HER2 scoring system and comprehensively investigate HER2 amplification and overexpression in esophageal adenocarcinoma and its precursor lesion. Using a tissue microarray, containing 116 cases of esophageal adenocarcinoma, 34 cases of Barretts esophagus, 18 cases of low-grade dysplasia and 15 cases of high-grade dysplasia, HER2 amplification and overexpression were analyzed by HercepTest and chromogenic in situ hybridization methods. The amplification frequency in an independent series of 116 esophageal adenocarcinoma samples was also analyzed using Affymetrix SNP 6.0 microarrays. In our studies, we have found that HER2 amplification does not associate with poor prognosis in total 232 esophageal adenocarcinoma patients by chromogenic in situ hybridization and high-density microarrays. We further confirm the similar frequency of HER2 amplification by chromogenic in situ hybridization (18%; 21 out of 116) and SNP 6.0 microarrays (16%, 19 out of 116) in esophageal adenocarcinoma. HER2 protein overexpression was observed in 12% (14 out of 116) of esophageal adenocarcinoma and 7% (1 out of 15) of high-grade dysplasia. No HER2 amplification or overexpression was identified in Barretts esophagus or low-grade dysplasia. All HER2 protein overexpression cases showed HER2 gene amplification. Gene amplification was found to be more frequent by chromogenic in situ hybridization than protein overexpression in esophageal adenocarcinoma (18 vs 12%). A modified two-step model for esophageal adenocarcinoma HER2 testing is recommended for clinical esophageal adenocarcinoma HER2 trial.