Applying proteomic-based biomarker tools for the accurate diagnosis of pancreatic cancer

Soluble stroma-related biomarkers of pancreatic cancer

The clinical management of pancreatic ductal adenocarcinoma (PDAC) is hampered by the lack of reliable biomarkers. This study investigated the value of soluble stroma‐related molecules as PDAC biomarkers. In the first exploratory phase, 12 out of 38 molecules were associated with PDAC in a cohort of 25 PDAC patients and 16 healthy subjects. A second confirmatory phase on an independent cohort of 131 PDAC patients, 30 chronic pancreatitis patients, and 131 healthy subjects confirmed the PDAC association for MMP7, CCN2, IGFBP2, TSP2, sICAM1, TIMP1, and PLG. Multivariable logistic regression model identified biomarker panels discriminating respectively PDAC versus healthy subjects (MMP7 + CA19.9, AUC = 0.99, 99% CI = 0.98–1.00) (CCN2 + CA19.9, AUC = 0.96, 99% CI = 0.92–0.99) and PDAC versus chronic pancreatitis (CCN2 + PLG+FN+Col4 + CA19.9, AUC = 0.94, 99% CI = 0.88–0.99). Five molecules were associated with PanIN development in two GEM models of PDAC (PdxCre/LSL‐Kras^G12D and PdxCre/LSL‐Kras^G12D/+/LSL‐Trp53^R172H/+), suggesting their potential for detecting early disease. These markers were also elevated in patient‐derived orthotopic PDAC xenografts and associated with response to chemotherapy. The identified stroma‐related soluble biomarkers represent potential tools for PDAC diagnosis and for monitoring treatment response of PDAC patients.

Identification of candidate biomarker mass (m/z) ranges in serous ovarian adenocarcinoma using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling

OBJECTIVE

To differentiate plasma from ovarian cancer and healthy individuals using MALDI-TOF mass spectroscopy.

MATERIALS AND METHODS

MALDI-TOF was used to generate profiles of immuno-depleted plasma samples (89 cancers and 199 healthy individuals) that were fractionated using three types of magnetic beads (HIC8, WCX and IMAC-Cu).

RESULTS

Differentially expressed mass ranges showing >1.5-2-fold change in expression from HIC8 (30), WCX (12) and IMAC-Cu (6) fractions were identified. Cross validation and recognition capability scores for the models indicated discrimination between the classes.

CONCLUSIONS

Spectral profiles can differentiate plasma samples of ovarian cancer patients from healthy individuals.

Biomarkers for pancreatic cancer: Recent achievements in proteomics and genomics through classical and multivariate statistical methods

Pancreatic cancer (PC) is one of the most aggressive and lethal neoplastic diseases. A valid alternative to the usual invasive diagnostic tools would certainly be the determination of biomarkers in peripheral fluids to provide less invasive tools for early diagnosis. Nowadays, biomarkers are generally investigated mainly in peripheral blood and tissues through high-throughput omics techniques comparing control vs pathological samples. The results can be evaluated by two main strategies: (1) classical methods in which the identification of significant biomarkers is accomplished by monovariate statistical tests where each biomarker is considered as independent from the others; and (2) multivariate methods, taking into consideration the correlations existing among the biomarkers themselves. This last approach is very powerful since it allows the identification of pools of biomarkers with diagnostic and prognostic performances which are superior to single markers in terms of sensitivity, specificity and robustness. Multivariate techniques are usually applied with variable selection procedures to provide a restricted set of biomarkers with the best predictive ability; however, standard selection methods are usually aimed at the identification of the smallest set of variables with the best predictive ability and exhaustivity is usually neglected. The exhaustive search for biomarkers is instead an important alternative to standard variable selection since it can provide information about the etiology of the pathology by producing a comprehensive set of markers. In this review, the most recent applications of the omics techniques (proteomics, genomics and metabolomics) to the identification of exploratory biomarkers for PC will be presented with particular regard to the statistical methods adopted for their identification. The basic theory related to classical and multivariate methods for identification of biomarkers is presented and then, the most recent applications in this field are discussed.

The outcome of cryoablation in treating advanced pancreatic cancer: a comparison with palliative bypass surgery alone

OBJECTIVE

This study aimed to investigate the efficacy and safety of palliative bypass surgery combined with cryoablation in treating patients with advanced pancreatic cancer and compare this combination therapy with palliative bypass surgery alone.

METHODS

Medical records of 118 patients with advanced pancreatic cancer who received palliative bypass surgery combined with cryoablation (the combination treatment group) or bypass surgery alone (the bypass surgery alone group) at the Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital (Tianjin, China) were retrospectively reviewed. Their baseline and peri-operative parameters were collected and compared.

RESULTS

In both groups abdominal distension and pain was significantly ameliorated after treatment. Preoperative jaundice was more common in the bypass surgery group while backache was more frequent in the combination treatment group, which were both relieved by treatment. The pre-operative serum bilirubin level was higher in the bypass surgery group and was decreased significantly after treatment. However, a significant reduction in tumor size and serum carbohydrate antigen 19-9 level was found only in the combination treatment group. There was no significant difference in the incidence of postoperative complications and prognosis between the two groups.

CONCLUSIONS

Cryoablation can reduce tumor size and relieve the patients' symptoms and signs such as abdominal discomfort and backache, although it could not improve the patients' prognosis significantly. It is a safe and efficient modality when combined with bypass surgery for patients with advanced pancreatic cancer.

The effects of frozen tissue storage conditions on the integrity of RNA and protein

Unfixed tissue specimens most frequently are stored for long term research uses at either -80° C or in vapor phase liquid nitrogen (VPLN). There is little information concerning the effects such long term storage on tissue RNA or protein available for extraction. Aliquots of 49 specimens were stored for 5-12 years at -80° C or in VPLN. Twelve additional paired specimens were stored for 1 year under identical conditions. RNA was isolated from all tissues and assessed for RNA yield, total RNA integrity and mRNA integrity. Protein stability was analyzed by surface-enhanced or matrix-assisted laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS, MALDI-TOF-MS) and nano-liquid chromatography electrospray ionization tandem mass spectrometry (nLC-ESI-MS/MS). RNA yield and total RNA integrity showed significantly better results for -80° C storage compared to VPLN storage; the transcripts that were preferentially degraded during VPLN storage were these involved in antigen presentation and processing. No consistent differences were found in the SELDI-TOF-MS, MALDI-TOF-MS or nLC-ESI-MS/MS analyses of specimens stored for more than 8 years at -80° C compared to those stored in VPLN. Long term storage of human research tissues at -80° C provides at least the same quality of RNA and protein as storage in VPLN.

Serum proteomics for gastric cancer

According to the World Health Organization, 800,000 cancer-related deaths are caused by gastric cancer each year globally, hence making it the second leading cause of cancer-related deaths in the world. Gastric cancer is often either asymptomatic or causing only nonspecific symptoms in its early stages. By the time the symptoms occur, the cancer has usually reached an advanced stage, which is one of the main reasons for its relatively poor prognosis. Therefore, early diagnosis and early treatment are very crucial. The differential analysis of serum protein between cancer patients and healthy controls can be performed using proteomics techniques and can hence be adopted as tumor biomarkers for the early diagnosis of cancer. So far, several serum tumor biomarkers have been identified for gastric cancer. However due to their poor specificity and sensitivity, they have proven to be insufficient for the reliable diagnosis of gastric cancer. Thus, using modern advanced proteomics techniques to find some new and reliable serum tumor biomarkers for earlier and reliable diagnosis of gastric cancer is a must. Nowadays, proteomic-based techniques, such as SELDI and HCLP, are available to discover biomarkers in gastric cancer. Numerous novel serum tumor biomarkers such as SAA, plasminogen and C9c, have been discovered through serological proteomics strategies. This review mainly focuses on the serum proteomics techniques and their application in the research of gastric cancer.

Identification of kininogen-1 as a serum biomarker for the early detection of advanced colorectal adenoma and colorectal cancer

Background

Serum markers represent potential tools for the detection of colorectal cancer (CRC). The aim of this study was to obtain proteomic expression profiles and identify serum markers for the early detection of CRC.

Methods

Proteomic profiles of serum samples collected from 35 healthy volunteers, 35 patients with advanced colorectal adenoma (ACA), and 40 patients with CRC were compared using Clinprot technology. Using enzyme-linked immunosorbent assays (ELISAs), 366 sera samples were additionally analyzed, and immunohistochemistry studies of 400 tissues were used to verify the expression of kininogen-1 and its value in the early detection of CRC.

Results

Predicting models were established among the three groups, and kininogen-1 was identified as a potential marker for CRC using Clinprot technology. ELISAs also detected significantly higher serum kininogen-1 levels in ACA and CRC patients compared to controls (P<0.05). Furthermore, the area under the receiver operating characteristic curve (AUC) for serum kininogen-1 in the diagnosis of ACA was 0.635 (P = 0.003), and for serum carcinoembryonic antigen (CEA) was 0.453 (P = 0.358). The sensitivity, specificity, and accuracy of serum kininogen-1 for diagnosing Duke’s stage A and B CRC was 70.13%, 65.88%, and 67.90%, respectively, whereas serum CEA was 38.96%, 85.88%, and 63.58%, respectively. Moreover, immunohistochemistry showed that expression of kininogen-1 was significantly higher in CRC and ACA tissues than in normal mucosa (48.39% vs. 15.58% vs. 0%, P<0.05).

Conclusions

These results suggest that Clinprot technology provides a useful tool for the diagnosis of CRC, and kininogen-1 is a potential serum biomarker for the early detection of advanced colorectal adenoma and CRC.

Usefulness of MALDI-TOF/MS identification of low-MW fragments in sera for the differential diagnosis of pancreatic cancer

OBJECTIVES

To identify new biomarkers of pancreatic cancer (PaCa), we performed MALDI-TOF/MS analysis of sera from 22 controls, 51 PaCa, 37 chronic pancreatitis, 24 type II diabetes mellitus (DM), 29 gastric cancer (GC), and 24 chronic gastritis (CG).

METHODS

Sera were purified by Sep-Pak C18 before MALDI-TOF/MS Anchorchip analysis.

RESULTS

Features present in at least 5% of all spectra were selected (n = 160, m/z range, 1200-5000). At univariate analysis, 2 features (m/z 2049 and 2305) correlated with PaCa, 3 (m/z 1449, 1605, and 2006) with DM. No feature characterized gastric cancer or chronic gastritis. Ten-fold cross-validation binary recursive partitioning trees were obtained for patients' classification. The tree (CA 19-9, age, m/z 2006, 2599, 2753, and 4997), built considering only patients with diabetes, allowed a distinction between DM [area under the receiver operating characteristic curve (AUC), 0.997], chronic pancreatitis (AUC, 0.968), and PaCa (AUC, 0.980), with an overall correct classification rate of 89%. The tree including CA 19-9, 1550, and 2937 m/z features, achieved an AUC of 0.970 in distinguishing localized from advanced PaCa. MALDI-TOF-TOF analysis revealed the 1550 feature as a fragment of Apo-A1, which was determined as whole protein and demonstrated to be closely correlated with PaCa.

CONCLUSIONS

The findings made demonstrate a role for serum peptides identified using MALDI-TOF/MS for addressing PaCa diagnosis.

Serum biomarkers identification by mass spectrometry in high-mortality tumors

Cancer affects millions of people worldwide. Tumor mortality is substantially due to diagnosis at stages that are too late for therapies to be effective. Advances in screening methods have improved the early diagnosis, prognosis, and survival for some cancers. Several validated biomarkers are currently used to diagnose and monitor the progression of cancer, but none of them shows adequate specificity, sensitivity, and predictive value for population screening. So, there is an urgent need to isolate novel sensitive, specific biomarkers to detect the disease early and improve prognosis, especially in high-mortality tumors. Proteomic techniques are powerful tools to help in diagnosis and monitoring of treatment and progression of the disease. During the last decade, mass spectrometry has assumed a key role in most of the proteomic analyses that are focused on identifying cancer biomarkers in human serum, making it possible to identify and characterize at the molecular level many proteins or peptides differentially expressed. In this paper we summarize the results of mass spectrometry serum profiling and biomarker identification in high mortality tumors, such as ovarian, liver, lung, and pancreatic cancer.

Proteome-based biomarkers in pancreatic cancer

Pancreatic cancer, as a highly malignant cancer and the fourth cause of cancer-related death in world, is characterized by dismal prognosis, due to rapid disease progression, highly invasive tumour phenotype, and resistance to chemotherapy. Despite significant advances in treatment of the disease during the past decade, the survival rate is little improved. A contributory factor to the poor outcome is the lack of appropriate sensitive and specific biomarkers for early diagnosis. Furthermore, biomarkers for targeting, directing and assessing therapeutic intervention, as well as for detection of residual or recurrent cancer are also needed. Thus, the identification of adequate biomarkers in pancreatic cancer is of extreme importance. Recently, accompanying the development of proteomic technology and devices, more and more potential biomarkers have appeared and are being reported. In this review, we provide an overview of the role of proteome-based biomarkers in pancreatic cancer, including tissue, serum, juice, urine and cell lines. We also discuss the possible mechanism and prospects in the future. That information hopefully might be helpful for further research in the field.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Proteomics in pancreatic cancer research

Pancreatic cancer is a highly aggressive malignancy with a poor prognosis and deeply affects the life of people. Therefore, the earlier diagnosis and better treatments are urgently needed. In recent years, the proteomic technologies are well established and growing rapidly and have been widely applied in clinical applications, especially in pancreatic cancer research. In this paper, we attempt to discuss the development of current proteomic technologies and the application of proteomics to the field of pancreatic cancer research. This will explore the potential perspective in revealing pathogenesis, making the diagnosis earlier and treatment.

Analysis of synovial fluid in knee joint of osteoarthritis:5 proteome patterns of joint inflammation based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

PURPOSE

The purpose of this study was to use matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in osteoarthritis research. Our aim was to find differentially expressed disease-related and condition-specific peptide in synovial fluid in the knee joint of patients suffering from osteoarthritis (OA), and to develop and validate the peptide classification model for OA diagnosis.

METHODS

Based on the American College of Rheumatology criteria, 30 OA cases and ten healthy donors were enrolled and underwent analysis. Magnetic beads-based weak cation exchange chromatography (MB-WCX) was performed for sample processing, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was conducted for peptide profile. ClinProt software 2.2 was used for data analysis and a genetic algorithm was created for class prediction.

RESULTS

Two peptide peaks were found which may be characterised as the potential diagnostic markers for OA. Two other significantly different peptide peaks were found in OA patients at a medium stage compared to the early and late stages. A genetic algorithm (GA) was used to establish differential diagnosis models of OA. As a result, the algorithm models marked 100% of OA, and of 97.92% of medium-stage OA.

CONCLUSION

This study demonstrated that use of proteomics methods to identify potential biomarkers of OA is possible, and the identified potential biomarkers may be potential markers for diagnosis and monitoring the progression of OA.

New lipidomic approaches in cystic fibrosis

Lipid analysis has been a crucial source of information in cystic fibrosis (CF). New methodologies for qualitative and quantitative lipidomics allow evaluation of a large number of samples, of special interest in patient screening for diagnostic and prognostic biological markers, as well as in cell physiology. In this chapter, two new complementary approaches are described: matrix-assisted laser desorption coupled to time of flight (MALDI-TOF-ClinProTools™) and liquid chromatography coupled to ion trap mass spectrometry (LC-MS( n )). MALDI-TOF-ClinProTools™ offers a large unbiased screening for the discovery of potential lipid alterations in diseased patients. LC-MS( n ) represents a state-of-the-art lipidomic tool for the identification and quantification of such alterations. The combination of both may open new perspectives in the quest for lipids participating in CF pathogenesis, therapy targets, and biomarkers.

Patients with ovarian carcinoma excrete different altered levels of urine CD59, kininogen-1 and fragments of inter-alpha-trypsin inhibitor heavy chain H4 and albumin

Background

Diagnosis of ovarian carcinoma is in urgent need for new complementary biomarkers for early stage detection. Proteins that are aberrantly excreted in the urine of cancer patients are excellent biomarker candidates for development of new noninvasive protocol for early diagnosis and screening purposes. In the present study, urine samples from patients with ovarian carcinoma were analysed by two-dimensional gel electrophoresis and the profiles generated were compared to those similarly obtained from age-matched cancer negative women.

Results

Significant reduced levels of CD59, kininogen-1 and a 39 kDa fragment of inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), and enhanced excretion of a 19 kDa fragment of albumin, were detected in the urine of patients with ovarian carcinoma compared to the control subjects. The different altered levels of the proteins were confirmed by Western blotting using antisera and a lectin that bind to the respective proteins.

Conclusion

CD59, kininogen-1 and fragments of ITIH4 and albumin may be used as complementary biomarkers in the development of new noninvasive protocols for diagnosis and screening of ovarian carcinoma.

Standardized peptidome profiling of human serum for the detection of pancreatic cancer

OBJECTIVE

To establish new biomarkers for accurate diagnosis of pancreatic cancer (PC) using a standardized serum peptidome profiling and compare the results with those from the tumor marker, CA 19-9.

METHODS

Serum samples from 102 patients (55 with chronic pancreatitis and 47 with PC) and 56 healthy controls were collected and analyzed following a protocol that was rigorously designed to prevent preanalytical variation. Serum peptides were extracted using immobilized copper ion chromatography on a robotic platform. Mass spectra were acquired by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry on an Autoflex II spectrometer (Bruker Daltonics, Bremen, Germany). Statistical analysis was performed using the Clinprotools 2.2 software (Bruker Daltonics) and the SPSS 15.0 software (SPSS Inc, Chicago, Ill).

RESULTS

Standardized peptidome profiling showed a median coefficient of variation of 11.6% calculated using all the extracted peptides and negligible influence of sex and age on peptidome profiles. The diagnostic sensitivity was 89.9%, and the diagnostic specificity was 92.7%, using 2 serum features and CA 19-9 serum concentration. Healthy controls were differentiated from patients with PC and chronic pancreatitis, with the use of 3 features of the peptidome (diagnostic sensitivity, 98.2%; diagnostic specificity, 97.1%).

CONCLUSIONS

Standardized serum peptidome profiling could be a useful tool to improve biochemical diagnosis of PC in combination with the classic tumor marker, CA 19-9.

A magnetic bead-based serum proteomic fingerprinting method for parallel analytical analysis and micropreparative purification

ProteinChip surface-enhanced laser desorption/ionization technology and magnetic beads-based ClinProt system are commonly used for semi-quantitative profiling of plasma proteome in biomarker discovery. Unfortunately, the proteins/peptides detected by MS are non-recoverable. To obtain the protein identity of a MS peak, additional time-consuming and material-consuming purification steps have to be done. In this study, we developed a magnetic beads-based proteomic fingerprinting method that allowed semi-quantitative proteomic profiling and micropreparative purification of the profiled proteins in parallel. The use of different chromatographic magnetic beads allowed us to obtain different proteomic profiles, which were comparable to those obtained by the ProteinChip surface-enhanced laser desorption/ionization technology. Our assays were semi-quantitative. The normalized peak intensity was proportional to concentration measured by immunoassay. Both intra-assay and inter-assay coefficients of variation of the normalized peak intensities were in the range of 4-30%. Our method only required 2 microL of serum or plasma for generating enough proteins for semi-quantitative profiling by MALDI-TOF-MS as well as for gel electrophoresis and subsequent protein identification. The protein peaks and corresponding gel spots could be easily matched by comparing their intensities and masses. Because of its high efficiency and reproducibility, our method has great potentials in clinical research, especially in biomarker discovery.

Serum peptidome patterns of human systemic lupus erythematosus based on magnetic bead separation and MALDI-TOF mass spectrometry analysis

OBJECTIVES

Currently few studies have been reported to utilize matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in rheumatic disease, especially in systemic lupus erythematosus (SLE). Our aim was to find differentially expressed disease-related and condition-specific peptides in sera from patients with SLE, as well as to develop and validate the peptide classification model for the diagnosis of SLE.

METHODS

Based on the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) 2000, 50 SLE patients were divided into two subgroups: 25 were defined as stable SLE (SLEDAI < or = 8) and 25 as active SLE (SLEDAI > 8). Twenty-five patients with rheumatoid arthritis (RA) and 24 healthy donors were also included and underwent analysis. We performed magnetic beads-based weak cation exchange chromatography (MB-WCX) for sample processing and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for peptide profiling. ClinProt software 2.1 was used for data analysis and a genetic algorithm was modelled for class prediction.

RESULTS

A series of significant short peptides was detected. Classification models were developed to classify samples across normal controls, active SLE patients, and stable SLE patients, and achieved high capability of prediction and cross-validation. Blinded verification of the classification model showed 91.7% sensitivity in active SLE, 83.3% sensitivity in stable SLE, and 86.7% specificity in normal controls.

CONCLUSION

We have completed a preliminary study to describe the serum peptide profile of SLE and improve the diagnosis of SLE from an integrated perspective of peptide mass patterns.

Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles

Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.

Pancreatic cancer biomarkers discovery by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry

BACKGROUND

Surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF/MS), a laboratory-friendly technique, is used to identify biomarkers for cancer. The aim of the present study was to explore the application of SELDI proteomic patterns in serum for distinguishing between cases of pancreatic cancer, chronic pancreatitis, type 2 diabetes mellitus and healthy controls.

METHODS

Sera from 12 healthy controls, 24 patients with type 2 diabetes mellitus, 126 with pancreatic cancer, including 84 with diabetes, and 61 with chronic pancreatitis, 32 of which were diabetics, were analyzed using SELDI-TOF/MS. Spectra (IMAC-30) were clustered and classified using Biomarker Wizard and Biomarker Pattern software.

RESULTS

Two decision tree classification algorithms, one with and one without CA 19-9, were constructed. In the absence of CA 19-9, the splitting protein peaks were: m/z 1526, 1211, and 3519; when CA 19-9 was used in the analysis, it replaced the m/z 3519 splitter. The two algorithms performed equally for classifying patients. A classification tree that considered diabetic patients only was constructed; the main splitters were: 1211, CA 19-9, 7903, 3359, 1802. With this algorithm, 100% of patients with type 2 diabetes mellitus, 97% with chronic pancreatitis and 77% of patients with pancreatic cancer were correctly classified. SELDI-TOF/MS features improved the diagnostic accuracy of CA 19-9 (AUC = 0.883 for CA 19-9; AUC = 0.935 for CA 19-9 and SELDI-TOF/MS features combined).

CONCLUSIONS

SELDI-TOF/MS allows identification of new peptides which, in addition to CA 19-9, enable the correct classification of the vast majority of patients with pancreatic cancer, which can be distinguished from patients with chronic pancreatitis or type 2 diabetes mellitus.