HyungJun Cho

The Expression of Phospho-AKT, Phospho-mTOR, and PTEN in Extrahepatic Cholangiocarcinoma

Purpose: The protein kinase B (AKT) pathway plays a key role in the regulation of cellular survival, apoptosis, and protein translation, and has been shown to have prognostic significance in a number of cancers. We sought to define its role in extrahepatic cholangiocarcinoma. Experimental Design: Two hundred twenty-one extrahepatic cholangiocarcinoma patients with clinicopathologic data, including survival, were arrayed into tissue microarrays. Phosphorylated AKT (p-AKT), phosphorylated mammalian target of rapamycin (p-mTOR), and total phosphatase and tensin homolog deleted on chromosome 10 (PTEN) protein expressions were studied with multiplex tissue immunoblotting assay. Results: Expressions of p-AKT and p-mTOR were significantly increased in extrahepatic cholangiocarcinoma cases compared with normal and dysplastic bile duct epithelium (P < 0.05 both). Decreased PTEN expression was observed in patients with increasing depth of invasion (P < 0.05), T classification (P < 0.05), and stage grouping (P < 0.05), and the presence of invasion of the pancreas (P < 0.05) and duodenum (P < 0.05). Decreased PTEN expression (P = 0.004) as well as decreased PTEN/p-AKT (P = 0.003) and PTEN/p-mTOR (P = 0.009) expression showed shorter survival by univariate but not by multivariate analysis. Conclusions: The AKT pathway is activated in a subset of extrahepatic cholangiocarcinoma. Elevated PTEN expression correlates with longer survival. Quantitative data obtained by multiplex tissue immunoblotting may provide additional information than assessment of immunohistochemistry alone. Quantitative analysis of PTEN, PTEN/p-AKT and PTEN/p-mTOR shows differences in survival by univariate analysis.

Robust classification modeling on microarray data using misclassification penalized posterior

MOTIVATION Genome-wide microarray data are often used in challenging classification problems of clinically relevant subtypes of human diseases. However, the identification of a parsimonious robust prediction model that performs consistently well on future independent data has not been successful due to the biased model selection from an extremely large number of candidate models during the classification model search and construction. Furthermore, common criteria of prediction model performance, such as classification error rates, do not provide a sensitive measure for evaluating performance of such astronomic competing models. Also, even though several different classification approaches have been utilized to tackle such classification problems, no direct comparison on these methods have been made. RESULTS We introduce a novel measure for assessing the performance of a prediction model, the misclassification-penalized posterior (MiPP), the sum of the posterior classification probabilities penalized by the number of incorrectly classified samples. Using MiPP, we implement a forward step-wise cross-validated procedure to find our optimal prediction models with different numbers of features on a training set. Our final robust classification model and its dimension are determined based on a completely independent test dataset. This MiPP-based classification modeling approach enables us to identify the most parsimonious robust prediction models only with two or three features on well-known microarray datasets. These models show superior performance to other models in the literature that often have more than 40-100 features in their model construction. AVAILABILITY Our MiPP software program is available at the Bioconductor website (http://www.bioconductor.org).

The number of metastatic lymph nodes in extrahepatic bile duct carcinoma as a prognostic factor.

The number of lymph nodes with metastases is known to be an important prognostic factor in carcinomas of many organs. The insufficient sampling of lymph nodes has also been associated with worse outcome in several types of carcinoma. However, the prognostic significance of lymph node dissection is not well characterized in extrahepatic bile duct (EBD) carcinomas. For 209 patients with EBD carcinoma, the total number of retrieved lymph nodes and the number of metastatic lymph nodes were evaluated, and other clinicopathologic variables were correlated with patient survival. The number of retrieved lymph nodes was not significantly correlated with survival in this study. The presence of metastasis to lymph nodes significantly decreased survival of patients with EBD carcinoma. The patients with 5 or more metastatic lymph nodes had significantly worse survival than those with 4 or less metastatic lymph nodes. To evaluate the prognosis of the patients with EBD carcinomas more precisely, the number of metastatic lymph nodes as well as the status of metastasis to lymph nodes should be examined and reported. Based on the present data, we propose that nodal classification should be divided into N1 (metastasis in 1 to 4 regional lymph nodes) and N2 (metastasis in 5 or more regional lymph nodes).

Bayesian hierarchical error model for analysis of gene expression data

MOTIVATION Analysis of genome-wide microarray data requires the estimation of a large number of genetic parameters for individual genes and their interaction expression patterns under multiple biological conditions. The sources of microarray error variability comprises various biological and experimental factors, such as biological and individual replication, sample preparation, hybridization and image processing. Moreover, the same gene often shows quite heterogeneous error variability under different biological and experimental conditions, which must be estimated separately for evaluating the statistical significance of differential expression patterns. Widely used linear modeling approaches are limited because they do not allow simultaneous modeling and inference on the large number of these genetic parameters and heterogeneous error components on different genes, different biological and experimental conditions, and varying intensity ranges in microarray data. RESULTS We propose a Bayesian hierarchical error model (HEM) to overcome the above restrictions. HEM accounts for heterogeneous error variability in an oligonucleotide microarray experiment. The error variability is decomposed into two components (experimental and biological errors) when both biological and experimental replicates are available. Our HEM inference is based on Markov chain Monte Carlo to estimate a large number of parameters from a single-likelihood function for all genes. An F-like summary statistic is proposed to identify differentially expressed genes under multiple conditions based on the HEM estimation. The performance of HEM and its F-like statistic was examined with simulated data and two published microarray datasets-primate brain data and mouse B-cell development data. HEM was also compared with ANOVA using simulated data. AVAILABILITY The software for the HEM is available from the authors upon request.

Measurement of the Invasion Depth of Extrahepatic Bile Duct Carcinoma: An Alternative Method Overcoming the Current T Classification Problems of the Ajcc Staging System

Tumor staging of extrahepatic bile duct (EBD) carcinoma is problematic for a number of reasons, including definitional problems with the current T classification of the American Joint Committee on Cancer staging system and the common occurrence of severe desmoplastic stromal reaction around the advancing edges of these tumors. To address these problems we evaluated the depth of invasion in 222 cases of EBD carcinoma by measuring the distance between the basal lamina of the adjacent normal epithelium to the most deeply infiltrating tumor cells, and compared this evaluation to time of survival and other clinical and pathologic parameters. A complex pattern of survival time versus the depth of invasion was observed by censored local regression. The recursive-partitioning technique was coupled with the log-rank test to identify 2 significant cutoff points for the depth of invasion, 5 and 12 mm, which segregated patients into 3 groups with statistically significant decreasing length of median survival (<5 mm, 61 mo; 5 to 12 mm, 23 mo; >12 mm, 17 mo, P<0.001). On the basis of the present data, we propose that a measurement of the depth of invasion should be performed in cases of EBD carcinoma, and that the T classification of EBD carcinoma should be changed to incorporate this measurement: T1 (<5 mm), T2 (5 to 12 mm), and T3 (>12 mm).

Microarray data mining using landmark gene-guided clustering

BackgroundClustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of clusters independent of the biological context of the analysis. This is often inadequate to explore data from different biological perspectives and gain new insights. We propose a new clustering model that can generate multiple versions of different clusters from a single dataset, each of which highlights a different aspect of the given dataset.ResultsBy applying our SigCalc algorithm to three yeast Saccharomyces cerevisiae datasets we show two results. First, we show that different sets of clusters can be generated from the same dataset using different sets of landmark genes. Each set of clusters groups genes differently and reveals new biological associations between genes that were not apparent from clustering the original microarray expression data. Second, we show that many of these new found biological associations are common across datasets. These results also provide strong evidence of a link between the choice of landmark genes and the new biological associations found in gene clusters.ConclusionWe have used the SigCalc algorithm to project the microarray data onto a completely new subspace whose co-ordinates are genes (called landmark genes), known to belong to a Biological Process. The projected space is not a true vector space in mathematical terms. However, we use the term subspace to refer to one of virtually infinite numbers of projected spaces that our proposed method can produce. By changing the biological process and thus the landmark genes, we can change this subspace. We have shown how clustering on this subspace reveals new, biologically meaningful clusters which were not evident in the clusters generated by conventional methods. The R scripts (source code) are freely available under the GPL license. The source code is available [see Additional File 1] as additional material, and the latest version can be obtained at http://www4.ncsu.edu/~pchopra/landmarks.html. The code is under active development to incorporate new clustering methods and analysis.

Increased number of metastatic lymph nodes in adenocarcinoma of the ampulla of Vater as a prognostic factor: a proposal of new nodal classification.

BACKGROUND Although the number of metastatic lymph nodes in most gastrointestinal carcinomas is correlated inversely with prognosis, the prognostic value of the number of metastatic lymph nodes in ampullary adenocarcinoma has not been well characterized. METHODS Lymph node metastasis was assessed in the Surveillance, Epidemiology and End Results database in 1,057 ampullary adenocarcinomas that were operatively resected and for which at least 12 lymph nodes were examined. A complex pattern of survival versus extent of lymph node metastasis was captured by censored local regression. The impact of the extent of lymph nodes metastasis on survival was investigated by use of the K-adaptive partitioning algorithm to identify the most significant cut-off points of metastatic lymph nodes affecting survival. RESULTS Two significant cut-off points (0 and 2) for the metastatic lymph node segregated patients into 3 groups with clinically important differences in median survival: patients with no metastatic lymph node (477 cases) had a median survival of 91 months, patients with 1-2 metastatic lymph nodes (279 cases) had a median survival of 29 months, whereas patients with ≥3 metastatic Lymph nodes (301 cases) had a median survival of 19 months (P < .0001). These results were validated with additional single institution dataset (318 cases, P < .0001). CONCLUSION The present results suggest that the nodal classification of ampullary adenocarcinoma should be categorized N0 (no metastatic lymph node), N1 (1-2 metastatic lymph nodes), and N2 (≥3 metastatic lymph nodes).

Telomeres are shortened in acinar-to-ductal metaplasia lesions associated with pancreatic intraepithelial neoplasia but not in isolated acinar-to-ductal metaplasias

Telomeres protect against chromosomal breakage, fusion, and interchromosome bridges during cell division. Shortened telomeres have been observed in the lowest grade of pancreatic intraepithelial neoplasia (PanIN). Genetically engineered mouse models of pancreatic neoplasia develop acinar-to-ductal metaplasia prior to the development of PanIN, suggesting that acinar-to-ductal metaplasias can be an early precursor lesion to pancreatic cancer. Some human PanINs are associated with acinar-to-ductal metaplasias, and it has been suggested that these acinar-to-ductal metaplasias arise as a consequence of growth of adjacent PanINs. As the earliest known genetic lesions of PanINs is shortened telomeres, we compared the telomere lengths of acinar-to-ductal metaplasia lesions, PanINs, and adjacent normal cells of human pancreata to determine whether acinar-to-ductal metaplasias could be precursors to PanIN. We used quantitative fluorescent in situ hybridization to measure the telomere length of cells from pancreatic lesions and adjacent normal pancreata from 22 patients, including 20 isolated acinar-to-ductal metaplasias, 13 PanINs associated with acinar-to-ductal metaplasias, and 12 PanINs. Normalized mean telomere fluorescence was significantly different among the cell types analyzed; 12.6±10.2 units in normal acinar cells, 10.2±6.4 in ductal cells, 8.4±5.9 in fibroblasts, 9.4±7.3 in isolated acinar-to-ductal metaplasias, 4.1±2.9 in PanIN-associated acinar-to-ductal metaplasias, and 1.6±1.9 in PanINs, respectively (P<0.001, ANOVA with randomized block design). Telomeres were significantly shorter in PanIN-associated acinar-to-ductal metaplasias (P<0.05, post hoc Duncan test) and in PanINs (P<0.05), than in normal cells, or isolated acinar-to-ductal metaplasias. Thus, shortened telomeres are found in PanIN-associated acinar-to-ductal metaplasias, but not in isolated acinar-to-ductal metaplasia lesions. These results indicate that isolated acinar-to-ductal metaplasias are not a precursor to PanIN, and support the hypothesis that PanIN-associated acinar-to-ductal metaplasias arise secondary to PanIN lesions.

OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data

UNLABELLED It is important to preprocess high-throughput data generated from mass spectrometry experiments in order to obtain a successful proteomics analysis. Outlier detection is an important preprocessing step. A naive outlier detection approach may miss many true outliers and instead select many non-outliers because of the heterogeneity of the variability observed commonly in high-throughput data. Because of this issue, we developed a outlier detection software program accounting for the heterogeneous variability by utilizing linear, non-linear and non-parametric quantile regression techniques. Our program was developed using the R computer language. As a consequence, it can be used interactively and conveniently in the R environment. AVAILABILITY An R package, OutlierD, is available at the Bioconductor project at http://www.bioconductor.org

Survival effect of tumor size and extrapancreatic extension in surgically resected pancreatic cancer: proposal for improved T classification ☆,☆☆

The T classification for pancreatic cancer of the American Joint Committee on Cancer may be inaccurate owing to lack of consideration of tumor size in cases of extension beyond the pancreas. To examine the accuracy of American Joint Committee on Cancer staging and to determine the prognostic implication of combined tumor size and extrapancreatic extension, 6145 cases of pancreatic ductal adenocarcinomas from the Surveillance, Epidemiology, and End Results database were categorized according to tumor size and extension as follows: group 1 (G1, ≤2 cm and limited to the pancreas), G2 (>2 cm and limited to the pancreas), G3 (≤2 cm with extrapancreatic extension), and G4 (>2 cm with extrapancreatic extension). The median survival of G1, G2, G3, and G4 were 23, 15, 19, and 14 months, respectively (P < .001), and the survival time in G3 was closer to that of G2 than G4. To test the classification system for accuracy of prognosis, G3 was merged with G2. The survival discrimination of this new grouping was greater (overall comparison, P < .001; G1 versus G2 + G3, P < .001; G2 + G3 versus G4, P < .001; χ(2) = 92.043) than that of the current T-classification scheme (overall comparison, P < .001; G1 versus G2, P < .001; G2 versus G3 + G4, P = .048; χ(2) = 60.424). To better discriminate survival, patients with a tumor less than or equal to 2 cm extending beyond the pancreas should be downstaged from the current class T3 to class T2.