Specialized DNA Arrays for the Differentiation of Pancreatic Tumors

Buchholz, Malte; Kestler, Hans A.; Bauer, Andrea S.; Bock, W. J.; Rau, Bettina; Leder, Gerhard; Kratzer, Wolfgang; Bommer, Martin; Scarpa, Aldo; Schilling, Martin K.; Adler, Guido; Hoheisel, Jörg D.; Gress, Thomas M.

doi:10.1158/1078-0432.ccr-05-1274

Cited by 40 publications

(28 citation statements)

References 23 publications

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“…Annexin A2-Overexpression of annexin A2 in pancreatic cancer has been reported previously (4,7,12,21,22). Our data in this study revealed that annexin A2 is not overexpressed in the primary chronic pancreatitis: the expression ratio of annexin A2 in chronic pancreatitis compared with normal pancreas is 1.1-fold.…”

Section: Figsupporting

confidence: 72%

Quantitative Proteomics Analysis Reveals That Proteins Differentially Expressed in Chronic Pancreatitis Are Also Frequently Involved in Pancreatic Cancer

Chen

Brentnall

Pan

et al. 2007

Molecular & Cellular Proteomics

134

121

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The effective treatment of pancreatic cancer relies on the diagnosis of the disease at an early stage, a difficult challenge. One major obstacle in the development of diagnostic biomarkers of early pancreatic cancer has been the dual expression of potential biomarkers in both chronic pancreatitis and cancer. To better understand the limitations of potential protein biomarkers, we used ICAT technology and tandem mass spectrometry-based proteomics to systematically study protein expression in chronic pancreatitis. Among the 116 differentially expressed proteins identified in chronic pancreatitis, most biological processes were responses to wounding and inflammation, a finding consistent with the underlining inflammation and tissue repair associated with chronic pancreatitis. Furthermore 40% of the differentially expressed proteins identified in chronic pancreatitis have been implicated previously in pancreatic cancer, suggesting some commonality in protein expression between these two diseases. Biological network analysis further identified c-MYC as a common prominent regulatory protein in pancreatic cancer and chronic pancreatitis. Lastly five proteins were selected for validation by Western blot and immunohistochemistry. Annexin A2 and insulin-like growth factor-binding protein 2 were overexpressed in cancer but not in chronic pancreatitis, making them promising biomarker candidates for pancreatic cancer. In addition, our study validated that cathepsin D, integrin ␤1, and plasminogen were overexpressed in both pancreatic cancer and chronic pancreatitis. The positive involvement of these proteins in chronic pancreatitis and pancreatic cancer will potentially lower the specificity of these proteins as biomarker candidates for pancreatic cancer. Altogether our study provides some insights into the molecular events in chronic pancreatitis that may lead to diverse strategies for diagnosis and treatment of these diseases. Molecular & Cellular Proteomics 6:1331-1342, 2007.Pancreatitis is an inflammatory condition of the pancreas that shares many molecular features with pancreatic cancer. Many of the abnormally expressed proteins present in the setting of pancreatic cancer are also abnormally expressed in chronic pancreatitis, providing an unacceptably low level of specificity for use as protein biomarkers and cancer screening. Thus, a major obstacle for the development of biomarkers for early diagnosis of pancreatic cancer has been the dual expression of potential biomarkers in the neoplastic and non-neoplastic setting. It is therefore important to understand the proteins expressed in pancreatitis because they could be a source of false positive biomarkers for pancreatic cancer. Moreover chronic pancreatitis is a risk factor for eventual neoplastic progression. Patients with chronic pancreatitis have a 2-fold increased risk of pancreatic cancer. Understanding the molecular events involved in both diseases may lead to a better understanding of the mechanisms that link them.The DNA and gene expression profile of pancreat...

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Section: Figsupporting

confidence: 72%

Quantitative Proteomics Analysis Reveals That Proteins Differentially Expressed in Chronic Pancreatitis Are Also Frequently Involved in Pancreatic Cancer

Chen

Brentnall

Pan

et al. 2007

Molecular & Cellular Proteomics

134

121

View full text Add to dashboard Cite

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“…In order to circumvent both of these problems, we have previously designed a specialized 588-feature cDNA array for pancreatic cancer differential diagnosis. In a proof-of-principle study with 62 pancreatic tissue samples, including 16 FNAB samples, we demonstrated that a 169-gene expression signature obtained with this array in conjunction with a specifically developed classification algorithm is suitable to distinguish between PDAC and nonmalignant processes with 1 95% diagnostic accuracy [86] ( fig. 2 ).…”

Section: Multiclass Diagnosismentioning

confidence: 96%

“…The classifier was then evaluated using the independent 20-sample test set, demonstrating 6 95% diagnostic accuracy (right panel: one benign control sample is falsely classified as PDAC). Modified from Buchholz et al [86] . However, the use of large scale ('whole genome') arrays is extremely costly and generates vast amounts of data which are difficult to analyze in a routine diagnostic setting.…”

Section: Multiclass Diagnosismentioning

confidence: 99%

Differential Diagnosis of Pancreatic Tumors by Molecular Analysis of Clinical Specimens

2008

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“…Our second data set comes from gene expression profiles from the pancreas (see Buchholz et al [12]). This data set provides two classes (pancreas cancer vs. pancreatitis/normal) for 62 samples.…”

Section: Data Setsmentioning

confidence: 99%

On the Effects of Constraints in Semi-supervised Hierarchical Clustering

Kestler

Kraus

Palm

et al. 2006

Artificial Neural Networks in Pattern Recognition

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Abstract.We explore the use of constraints with divisive hierarchical clustering. We mention some considerations on the effects of the inclusion of constraints into the hierarchical clustering process. Furthermore, we introduce an implementation of a semi-supervised divisive hierarchical clustering algorithm and show the influence of including constraints into the divisive hierarchical clustering process. In this task our main interest lies in building stable dendrograms when clustering with different subsets of data. BackgroundThe aim of cluster analysis is to explore a collection of data items and to group similar objects together. Similarity can be measured in many different ways, for example by the Euclidean distance. Every information used in an unsupervised learning technique comes from the data themselve, which means no external teaching signal guides the algorithm. During the past years several modifications were proposed to incorporate additional background knowledge into clustering algorithms [1][2][3][4][5][6][7][8][9]. The main idea is that in some tasks prior information about a small amount of data is known and should support the clustering process. This background information is usually provided as a set of constraints between pairs of data items [2]. Real class labels seem not feasible in clustering because of the inherent discrepancy between labels and clusters. Pairwise constraints are typically encoded as must-links and cannot-links [2] either indicating two points belong to the same cluster or to different clusters.Up to now different clustering algorithms were adapted to make use of semisupervised clustering. Constraint-based approaches modify the cluster search, either by including constraints into the objective function [1] or by initalizing and constraining during the clustering process [10, 3,9]. Metric-based approaches first train the cluster metric to satisfy the constraints using shortest-path algorithm Many real-world unsupervised learning tasks may profit from an inclusion of background knowledge, e.g. image processing, text mining or even lane finding using GPS data. Another important field for data mining using semi-supervised clustering is the functional grouping of genes. For this problem we are not only interested in one particular clustering as built by partitioning clustering algorithms, such as K-means or SOM, but in getting a hierarchy of partitions (dendrogram).Our main emphasis lies on building robust hierarchical clusterings for DNA microarray data. Here we are interested in the resulting dendrogram and focus on the question of creating stable dendrograms. In this setting one of the problems that frequently arise is the change of the branching structure when computing several runs with different data subsets. Since the inclusion of background knowledge seems to enhance the accuracy of clustering algorithms, we studied its effect on the dendrogram stability. In Section 2 we explain some basic annotations on the effects of constraints in semi-supervised hierarchical cluste...

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Specialized DNA Arrays for the Differentiation of Pancreatic Tumors

Cited by 40 publications

References 23 publications

Quantitative Proteomics Analysis Reveals That Proteins Differentially Expressed in Chronic Pancreatitis Are Also Frequently Involved in Pancreatic Cancer

Quantitative Proteomics Analysis Reveals That Proteins Differentially Expressed in Chronic Pancreatitis Are Also Frequently Involved in Pancreatic Cancer

Differential Diagnosis of Pancreatic Tumors by Molecular Analysis of Clinical Specimens

On the Effects of Constraints in Semi-supervised Hierarchical Clustering

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