Purpose: Malignant tumors of the pancreas are frequently indistinguishable from inflammatory tumors arising in the context of a chronic pancreatitis with the use of conventional imaging techniques. Thus, cytologic analysis of cells obtained by abdominal ultrasound, computed tomography, or endoscopic ultrasound–guided fine needle aspiration biopsy is required for diagnosis. However, the reliability of cytologic analyses of pancreatic fine needle aspirates remains unsatisfactory, with a diagnostic accuracy of ≤80%. The purpose of the current study was therefore to develop a novel diagnostic approach based on expression profiling of biopsy material using a specialized diagnostic cDNA array.

Experimental Design: Previous gene expression profiling studies were reevaluated to design a 558-feature diagnostic array. Minimal amounts of residual material from pancreatic cytology samples as well as surgically resected tumor and control tissue specimens were analyzed using the diagnostic array and a newly developed statistical classification system.

Results and Conclusions: Our diagnostic approach resulted in 95% accurate differentiation between ductal adenocarcinomas and nonmalignant tumors of the pancreas. The diagnostic array, in conjunction with conventional diagnostic procedures, is thus suitable to significantly improve the reliability of pancreatic cancer diagnostics and can be expected to become a valuable new tool in the routine workup of suspect masses in the pancreas.

Pancreatic cancer is the fifth leading cause of cancer-related deaths in industrialized countries. With a 5-year survival rate of <5% and a median survival of <6 months, pancreatic cancer carries the most dismal prognosis of all solid tumors. Chronic pancreatitis is a persistent inflammatory disease of the pancreas most often caused by alcohol abuse. In the course of chronic pancreatitis, inflammatory tumors may develop in the pancreas causing the same signs and symptoms as malignant pancreatic tumors. Malignant and inflammatory tumors are frequently indistinguishable with the use of conventional imaging modalities such as computed tomography, abdominal, or endoscopic ultrasound, thus requiring cytologic analysis of cells obtained by abdominal ultrasound, computed tomography-, or endoscopic ultrasound–guided fine needle aspiration biopsy (FNAB). However, the reliability of the largely morphology-based cytologic analyses of fine needle aspirates of pancreatic tumors remains unsatisfactory with a diagnostic accuracy between 60% and 80% (15). Well-differentiated carcinomas may escape recognition because of the minimal cytologic atypia they display. Conversely, chronic pancreatitis may give rise to atypical cells that can be mistaken for neoplastic cells. For both malignant and benign tumors, diagnosis is extremely difficult when intact cells in the aspirate are rare or completely missing (Fig. 1).

Fig. 1.

Typical examples of a clearly malignant (A) and a nondiagnostic (B) finding illustrating the limitations of cytologic analyses. Both samples were obtained by FNAB of suspect pancreatic masses and analyzed within the course of this study. A, sample from “Biopsy_3” (see Table 1): malignant cells (arrow) showing nuclear overlapping, irregular contours, high nuclear/cytoplasmic ratios and anisocaryosis are readily detectable. B, sample from “Biopsy_13” (see Table 2): the specimen contains large amounts of mucus and cellular debris with few or no intact cells; original magnification, ×200.

Fig. 1.

Typical examples of a clearly malignant (A) and a nondiagnostic (B) finding illustrating the limitations of cytologic analyses. Both samples were obtained by FNAB of suspect pancreatic masses and analyzed within the course of this study. A, sample from “Biopsy_3” (see Table 1): malignant cells (arrow) showing nuclear overlapping, irregular contours, high nuclear/cytoplasmic ratios and anisocaryosis are readily detectable. B, sample from “Biopsy_13” (see Table 2): the specimen contains large amounts of mucus and cellular debris with few or no intact cells; original magnification, ×200.

Close modal

It is well known that the process of carcinogenesis in the pancreas is associated with the accumulation of characteristic genetic changes within the cells of origin. Among the hallmark features of pancreatic ductal adenocarcinoma, which accounts for >90% of all malignant tumors in the pancreas, are mutations in the K-ras and HER2/neu oncogenes as well as the p53, p16INK4a, and SMAD4/DPC4 tumor suppressor genes (for an overview, see ref. 6). Based on these observations, several attempts have been made to improve the accuracy of preoperative diagnostics by analyzing molecular markers in pancreatic juice (7, 8), brush cytologies (7, 9), or FNAB's (1012) by means of RNA, DNA, or protein analysis. Most of these studies were aimed at detecting mutant K-ras in the biopsy samples because this is the gene most frequently affected by mutations (>85% of cases) in pancreatic ductal adenocarcinoma. However, K-ras-mutations were also detected in up to 25% of samples from chronic pancreatitis patients (7, 13), severely compromising the specificity of the test. Analyses of other single markers, including p53, CA19.9, SMAD4/DPC4, or Mucin expression, have likewise shown either low specificity, low sensitivity or, in the case of immunocytologic analyses, dependency on the presence of significant numbers of intact tumor cells.

Analysis of single molecular markers is therefore not sufficient to provide for accurate diagnosis of suspect pancreatic masses. DNA arrays with their potential to survey the expression levels of many genes simultaneously represent ideal tools to circumvent this problem. Several expression profiling analyses using different technological platforms (1419) have shown the existence of distinct gene expression signatures characteristic of pancreatic cancer. 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. Both drawbacks can be circumvented by designing dedicated arrays with limited numbers of genes specifically selected for diagnostic purposes. The aim of this study was therefore to develop specialized cDNA arrays specifically designed for the differential diagnosis of pancreatic tumors based on expression profiling of fine needle aspiration biopsies. Because >90% of all malignant pancreatic tumors represent ductal adenocarcinomas (20), we focused on this tumor type for the present study.

Tissue and biopsy samples. For cytologic analysis of FNAB samples, aspirated material was expelled onto microscope slides and smeared. The slides were air-dried, stained with May-Grunwald-Giemsa stain, and evaluated by an expert cytologist. Samples were classified as “malignant” if cell aggregates showing clear signs of malignancy were detected on at least two different slides from the same biopsy, “benign” if evaluable material without any signs of malignancy were detected, or “nondiagnostic” if the results were inconclusive (e.g., the cellularity of the sample was too low).

Surgically resected pancreatic adenocarcinoma and chronic pancreatitis tissues were provided by the surgery departments at the Universities of Ulm and Homburg/Saar. Normal pancreas samples were obtained from healthy areas at the borders of chronic pancreatitis resectates. Informed consent was obtained from all patients prior to using tissue or biopsy samples. The study was approved by the local ethics committees at the Universities of Ulm (Germany), Homburg/Saar (Germany), and Verona (Italy).

RNA isolation and linear amplification. Snap-frozen surgical samples were ground on dry ice with a mortar and pestle, suspended in RLT buffer and total RNA were isolated using the RNEasy Mini Kit (Qiagen, Hilden, Germany). Fine needle biopsy in the routine diagnostic workup of pancreatic tumors was done with transabdominal ultrasound or endoscopic ultrasound guidance. FNAB sample material was recovered by flushing the needle and syringe with RLT buffer after material for cytologic analysis had been removed. Total RNA was then isolated using the RNEasy Mini Kit (Qiagen). In both cases, the total RNA was finally dissolved in water and quality-checked on a BioAnalyzer Lab-on-a-Chip system (Agilent, Waldbronn, Germany). In order to obtain sufficient material for hybridization, the complete FNAB RNA samples were subjected to one round of T7 RNA polymerase-based linear amplification using the MessageAmp Kit (Ambion, Huntingdon, Great Britain). To avoid data bias, all surgical samples were treated likewise by linearly amplifying 0.5 μg of total RNA prior to hybridization.

Array production and hybridization. cDNA fragments were PCR-amplified using vector primers and contact-printed in duplicate on nylon membranes (Nytran N+, Schleicher and Schuell, Germany). For radioactive labeling, the complete amplified RNA samples were labeled with 33P-dATP using the StripEZ-RT Kit (Ambion) and hybridized overnight to nylon membrane arrays in ULTRArray hybridization buffer (Ambion) at 50°C. Radioactive signals were detected using a STORM phosphorimaging system (Amersham Biosciences, Feiburg, Germany) and quantified with the ArrayVision software (InterFocus, Haverhill, Great Britain). Signal intensities were normalized to the mean signal intensity of all features on an individual array.

Construction of the classifier. All equations used are listed in Panel 1. Details of the analysis and complete data sets are available as part of the Supplementary Data.8

The nylon array data set was filtered to include only featfures (genes) for which normalized intensities exceeded a value of 0.8 in at least 10 samples to remove uniformly low (and thus uninformative) signals. Control spots were excluded as well. The remaining 169 features were used in the construction of a linear classifier based on the analysis of the 42-sample training set (Table 1).

Table 1.

Composition of the 42-sample training set

SampleDiagnosisTumor-node-metastasis classificationCytologic diagnosisFinal diagnosis (follow up)
Biopsy_12 — — benign pseudocyst
Biopsy_13 — — nondiagnostic chronic pancreatitis
Biopsy_14 — — nondiagnostic chronic pancreatitis
Biopsy_16 — — benign pseudocyst
Infl/Norm_1 chronic pancreatitis — — —
Infl/Norm_3 chronic pancreatitis — — —
Infl/Norm_4 chronic pancreatitis — — —
Infl/Norm_6 chronic pancreatitis — — —
Infl/Norm_7 chronic pancreatitis — — —
Infl/Norm_9 chronic pancreatitis — — —
Infl/Norm_10 normal pancreas — — —
Infl/Norm_12 chronic pancreatitis — — —
Infl/Norm_13 normal pancreas — — —
Infl/Norm_16 chronic pancreatitis — — —
Infl/Norm_17 normal pancreas — — —
Infl/Norm_18 chronic pancreatitis — — —
Infl/Norm_19 chronic pancreatitis — — —
SampleDiagnosisTumor-node-metastasis classificationCytologic diagnosisFinal diagnosis (follow up)
Biopsy_12 — — benign pseudocyst
Biopsy_13 — — nondiagnostic chronic pancreatitis
Biopsy_14 — — nondiagnostic chronic pancreatitis
Biopsy_16 — — benign pseudocyst
Infl/Norm_1 chronic pancreatitis — — —
Infl/Norm_3 chronic pancreatitis — — —
Infl/Norm_4 chronic pancreatitis — — —
Infl/Norm_6 chronic pancreatitis — — —
Infl/Norm_7 chronic pancreatitis — — —
Infl/Norm_9 chronic pancreatitis — — —
Infl/Norm_10 normal pancreas — — —
Infl/Norm_12 chronic pancreatitis — — —
Infl/Norm_13 normal pancreas — — —
Infl/Norm_16 chronic pancreatitis — — —
Infl/Norm_17 normal pancreas — — —
Infl/Norm_18 chronic pancreatitis — — —
Infl/Norm_19 chronic pancreatitis — — —

During the first step of the analysis, a principal component analysis (21) was done on the training data set. The first 30 principal components of the training data set, which represented 99.9% of the total variation within the data, were then used for a linear discriminant analysis to search for combinations of principal components facilitating complete separation of the tumor from the control tissue samples in the training set. All possible combinations of up to 7 out of the 30 principal components were tested for their performance in the linear separation of the diagnostic classes. Evaluation of the feature set combinations was done by measuring the area under the receiver operating characteristic curve (2224). All combinations producing an area under the receiver operating characteristic curve of ≥0.95 were subjected to a stochastic search to add additional discriminative principal components until perfect separation of the diagnostic classes was achieved. Out of all combinations producing perfect linear separation, we selected the set that resulted in the greatest margin between tumor and control samples when plotting the samples according to their relative distances to the separating hyperplane. The resulting linear classifier was then evaluated using the independent 20-sample test set (Table 2).

Table 2.

Composition of the 20-sample test set

SampleDiagnosisTumor-node-metastasis classificationCytologic diagnosisFinal diagnosis (follow up)
Biopsy_11 — — benign pseudocyst
Biopsy_15 — — nondiagnostic chronic pancreatitis
Infl/Norm_2 chronic pancreatitis — — —
Infl/Norm_5 normal pancreas — — —
Infl/Norm_8 chronic pancreatitis — — —
Infl/Norm_11 normal pancreas — — —
Infl/Norm_14 normal pancreas — — —
Infl/Norm_15 chronic pancreatitis — — —
SampleDiagnosisTumor-node-metastasis classificationCytologic diagnosisFinal diagnosis (follow up)
Biopsy_11 — — benign pseudocyst
Biopsy_15 — — nondiagnostic chronic pancreatitis
Infl/Norm_2 chronic pancreatitis — — —
Infl/Norm_5 normal pancreas — — —
Infl/Norm_8 chronic pancreatitis — — —
Infl/Norm_11 normal pancreas — — —
Infl/Norm_14 normal pancreas — — —
Infl/Norm_15 chronic pancreatitis — — —

In order to develop the pancreatic cancer diagnostic cDNA array, we extensively analyzed the results of various studies on differential gene expression in pancreatic cancer done in our own group (25, 26) as well as information obtained from the SAGE (http://www.ncbi.nlm.nih.gov/SAGE/) and Digital Differential Display (http://www.ncbi.nlm.nih.gov/UniGene/info_ddd.shtml) gene expression databases and reports from the literature to identify genes with the potential to differentiate between malignant and nonmalignant tumors of the pancreas. In order to allow for robust normalization of the hybridization results, we have designed the array to comprise a sufficiently high total number of features (558), including balanced numbers of up- and down-regulated genes. In addition, important genes were represented by multiple cDNA clones and control spots of mixed cDNA clones were included to facilitate grid alignment (for a complete list of features, see Supplementary Information).

In the present study, 16 FNAB samples of pancreatic adenocarcinoma and benign pancreatic tumors for which clinical patient follow up with a definitive diagnosis was available were analyzed both by conventional cytology and diagnostic array hybridization. Cytologic analysis of the 16 FNAB samples correctly identified a malignant process in 9 out of 10 adenocarcinoma cases (90%) and a benign process in 3 out of 6 chronic pancreatitis and pseudocyst cases (50%). The remaining adenocarcinoma case as well as the three benign cases were nondiagnostic due to the absence of evaluable intact cells. The resulting overall diagnostic accuracy of 75% is well in agreement with the numbers reported in the literature (14).

Residual material from the same biopsy samples which were used for cytologic analyses were subjected to expression profiling analyses using the diagnostic arrays. In order to ensure an adequate representation of different tumor stages in the data sets used for the development and evaluation of the classification procedure, we analyzed an additional 27 samples of histopathologically well-defined surgically resected ductal adenocarcinomas as well as 19 surgically resected control samples of chronic pancreatitis or normal pancreas. The samples were arbitrarily divided into a 42-sample training set (Table 1) and a 20-sample test set (Table 2), such that both sets contained equal proportions of malignant and benign samples as well as FNAB's. The training set was subsequently used to develop the classification system for the distinction between malignant and benign samples (see below), which was independently evaluated using the test sample set. The complete process is schematically outlined in Fig. 2.

Fig. 2.

Flowchart outlining the process of construction and evaluation of the linear classifier using the independent training and test sample sets.

Fig. 2.

Flowchart outlining the process of construction and evaluation of the linear classifier using the independent training and test sample sets.

Close modal

Even though the number of genes featured on the diagnostic array was low compared with large-scale arrays, it still far exceeded the number of tissue and biopsy samples available for training of the classifier in the training set. It was therefore of paramount importance to first reduce the number of features used for classification and thus the dimensionality of the data set in order to avoid overadaption of the classifier to this specific set of data (27). Instead of omitting individual genes from the analysis to achieve this purpose, we opted to apply principal component analysis (21) to the data, resulting in a reduced set of combined features (principal components) representing weighted combinations of all genes in the data set. Tissue or biopsy samples can be mapped to the principal components (or a subset thereof), effectively creating a coordinate system of uncorrelated variables which replace the high dimensional space that individual gene expression values fall into. Principal component analyses thus serves to greatly reduce the dimensionality of the data whereas preserving its general structure, resulting in reduced sensitivity to outliers or hybridization artifacts in individual diagnostic samples.

For the construction of the classifier, we opted to perform linear discriminant analysis using the first 30 principal components of the training data set, which represented 99.9% of the total variation within the data. Because linear discriminant analysis assumes a relatively simple model of sample distribution, it is far less prone to overadaption to a specific data set than nonlinear methods, again increasing the robustness of the classification procedure. We identified a total of 429,917 different combinations of principal components producing perfect linear separation of tumor and control samples in the training data set. Out of these, a set of 23 principal components which provided the maximum margin between tumor and control samples was selected to define the linear classifier (Fig. 3A; see also Supplementary Information).

Fig. 3.

Separation of the adenocarcinoma (red circles) and control (blue circles) samples in the training and test data sets using the selected combination of 23 principal components. The samples are plotted according to their relative distances to the separating hyperplane. Dotted lines, cutoff calculated by linear discriminant analysis. FNAB samples are identified by triangles next to the corresponding circles. A, perfect linear separation of the diagnostic classes (including surgically resected tissue as well as FNAB samples) in the training set. B, 95% diagnostic accuracy using the linear classifier on the independent test set. One surgically resected chronic pancreatitis sample is misclassified; all FNAB samples are correctly classified.

Fig. 3.

Separation of the adenocarcinoma (red circles) and control (blue circles) samples in the training and test data sets using the selected combination of 23 principal components. The samples are plotted according to their relative distances to the separating hyperplane. Dotted lines, cutoff calculated by linear discriminant analysis. FNAB samples are identified by triangles next to the corresponding circles. A, perfect linear separation of the diagnostic classes (including surgically resected tissue as well as FNAB samples) in the training set. B, 95% diagnostic accuracy using the linear classifier on the independent test set. One surgically resected chronic pancreatitis sample is misclassified; all FNAB samples are correctly classified.

Close modal

The predictive performance of the linear classifier was then evaluated by assessing its performance on the independent 20-sample test set. Our system correctly classified 19 out of the total 20 test samples, resulting in an overall diagnostic accuracy of 95% (Fig. 3B). Only one surgically resected chronic pancreatitis sample (“Infl/Norm_15”; Table 2) was misclassified, producing one false-positive result. Of note, all FNAB samples were correctly classified regardless of the outcome of the cytologic analysis. Moreover, all FNAB samples were placed well clear of the separating cutoff by the classifier (Fig. 3B).

We were thus able to show that expression profiling analyses of surgically resected tumor specimens and FNAB samples using specialized diagnostic arrays with limited numbers of highly selected genes produce reliable, reproducible, and informative results. Supplementing conventional cytologic analyses of aspiration biopsies with diagnostic array profiling thus promises to significantly improve the accuracy of preoperative diagnostics of suspect masses in the pancreas. The large number of different combinations of principal components yielding perfect linear separation of the diagnostic classes in the training set, as well as the convincing performance of the classifier on the independent test set, serve to show both the expedience of the diagnostic gene collection as well as the validity of the analytic approach. Our results confirm conclusions drawn from earlier expression profiling studies using large-scale arrays, which have shown that the number of informative genes for the classification of different types and subtypes of cancer is usually <100 (2830), suggesting that dedicated diagnostic arrays should perform as well as whole-genome arrays in defined diagnostic settings.

Due to the use of residual material from biopsy needles for the analysis of the FNAB samples, the amount of starting material available for expression profiling analysis was extremely limited, so that we initially produced the arrays in the nylon membrane format to take advantage of the superior sensitivity of radioactive labeling and detection. Parallel hybridizations of a subset of samples to diagnostic arrays produced in the glass microarray format (see Supplementary Material), however, showed that the concept and design of the diagnostic array can readily be transferred to the glass microarray/fluorescent labeling platform as well, which may be better suited for routine clinical settings.

In the present study, we have focused on the distinction between pancreatic ductal adenocarcinoma and nonmalignant diseases of the pancreas, because pancreatic ductal adenocarcinoma is by far the most frequent malignant tumor arising in the pancreas and thus poses the clinically most relevant diagnostic problem (20). We are currently in the process of analyzing additional tumor entities, such as acinar and neuroendocrine tumors, using both the diagnostic array as well as large-scale arrays, in order to develop a multiclass classification system for the comprehensive diagnosis of different malignancies in the pancreas. In addition, we expect further development of the array in combination with careful analysis of clinical patient data to result in the recognition of distinct prognostic gene expression signatures predicting important clinical variables such as stage of disease, response to therapy, or prognosis, thus setting the stage for therapeutic regimens custom tailored to the individual patient.

Given is a set of n d-dimensional samples x1, …, xn, n1 in the subset D1 and n2 in the subset D2.

The sample mean μ̂i is estimated by:

$\mathit{\mathbf{{\hat{{\mu}}}}_{i}}\ =\ \frac{1}{\mathit{n_{i}}}{{\sum}_{\mathit{x}{\in}\mathit{D_{i}}}}\mathbf{\mathit{x}}$

The scatter matrices Si and Sw are:

$\mathit{S_{i}}\ =\ {{\sum}_{\mathit{x}{\in}\mathit{D_{i}}}}(\mathbf{\mathit{x}}\ {-}\ \mathit{\mathbf{{\mu}}_{i}})\ (\mathbf{\mathit{x}}\ {-}\ \mathit{\mathbf{{\mu}}_{i}})\mathit{^{T}},\mathit{S}\mathrm{_{W}}\ =\ \mathit{S}_{1}\ +\ \mathit{S}_{2}$

The projection vector w for Fisher's linear discriminant is given by:

$\mathbf{\mathrm{w}}\ =\ \mathrm{S_{W}}^{{-}1}(\mathbf{\mathit{{\mu}}}_{1}\ {-}\ \mathbf{\mathit{{\mu}}}_{2})$

The area under the receiver operating characteristic curve is estimated by:

$\mathit{{\hat{A}}}\ =\ {{\sum}_{\mathit{j}\ =\ 1}^{\mathit{n}_{2}}}(\mathit{{\Xi}_{j}}\ {-}\ \mathit{j})/\mathit{n}_{1}\mathit{n}_{2}$

where n2 is the number of pancreatic carcinoma patients, and Ξj (j = 1, …, n2) are the ranks of these cases obtained by ranking all n = n1 + n2 values of wTx.

The margin m for = 1 is calculated by:

where i = 1, …, n1, j = 1, …, n2, and the threshold is centered in the margin.

Grant support: German Cancer Research Foundation (Deutsche Krebshilfe/Mildred Scheel-Stiftung) grant no. 10-1473-Gr2 (T.M. Gress and J.D. Hoheisel) and supported in part by grants from the Deutsche Forschungsgemeinschaft (SFB 518 project B1, T.M. Gress), NGFN (01GR 0101, J.D. Hoheisel), Stifterverband für die Deutsche Wissenschaft (T.M. Gress and H.A. Kestler), Assocazione Italiana Ricerca Cancro (A. Scarpa), Ministero Università e Ricerca (A. Scarpa), Fondazione Giorgio Zanotto (A. Scarpa), and the European Community (QLG1-CT-2002-01196, T.M. Gress and J.D. Hoheisel).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: M. Buchholz and H.A. Kestler contributed equally to this study and should both be considered first authors.

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