Identification of potential markers for the detection of pancreatic cancer through comparative serum protein expression profiling

Potential serum biomarkers for glioblastoma diagnostic assessed by proteomic approaches

BACKGROUND

The rapid progress of proteomics over the past years has allowed the discovery of a large number of potential biomarker candidates to improve early tumor diagnosis and therapeutic response, thus being further integrated into clinical environment. High grade gliomas represent one of the most aggressive and treatment-resistant types of human brain cancer, with approximately 9-12 months median survival rate for patients with grade IV glioma (glioblastoma). Using state-of-the-art proteomics technologies, we have investigated the proteome profile for glioblastoma patients in order to identify a novel protein biomarker panel that could discriminate glioblastoma patients from controls and increase diagnostic accuracy.

RESULTS

In this study, SELDI-ToF MS technology was used to screen potential protein patterns in glioblastoma patients serum; furthermore, LC-MS/MS technology was applied to identify the candidate biomarkers peaks. Through these proteomic approaches, three proteins S100A8, S100A9 and CXCL4 were selected as putative biomarkers and confirmed by ELISA. Next step was to validate the above mentioned molecules as biomarkers through identification of protein expression by Western blot in tumoral versus peritumoral tissue.

CONCLUSIONS

Proteomic technologies have been used to investigate the protein profile of glioblastoma patients and established several potential diagnostic biomarkers. While it is unlikely for a single biomarker to be highly effective for glioblastoma diagnostic, our data proposed an alternative and efficient approach by using a novel combination of multiple biomarkers.

Serum protein signatures differentiating autoimmune pancreatitis versus pancreatic cancer

Autoimmune pancreatitis (AIP) is defined by characteristic lymphoplasmacytic infiltrate, ductal strictures and a pancreatic enlargement or mass that can mimic pancreatic cancer (PaCa). The distinction between this benign disease and pancreatic cancer can be challenging. However, an accurate diagnosis may pre-empt the misdiagnosis of cancer, allowing the appropriate medical treatment of AIP and, consequently, decreasing the number of unnecessary pancreatic resections.

Mass spectrometry (MS) and two-dimensional differential gel electrophoresis (2D-DIGE) have been applied to analyse serum protein alterations associated with AIP and PaCa, and to identify protein signatures indicative of the diseases. Patients' sera were immunodepleted from the 20 most prominent serum proteins prior to further 2D-DIGE and image analysis. The identity of the most-discriminatory proteins detected, was performed by MS and ELISAs were applied to confirm their expression. Serum profiling data analysis with 2D-DIGE revealed 39 protein peaks able to discriminate between AIP and PaCa. Proteins were purified and further analysed by MALDI-TOF-MS. Peptide mass fingerprinting led to identification of eleven proteins. Among them apolipoprotein A-I, apolipoprotein A-II, transthyretin, and tetranectin were identified and found as 3.0-, 3.5-, 2-, and 1.6-fold decreased in PaCa sera, respectively, whereas haptoglobin and apolipoprotein E were found to be 3.8- and 1.6-fold elevated in PaCa sera. With the exception of haptoglobin the ELISA results of the identified proteins confirmed the 2D-DIGE image analysis characteristics. Integration of the identified serum proteins as AIP markers may have considerable potential to provide additional information for the diagnosis of AIP to choose the appropriate treatment.

Detection of pancreatic cancer using serum protein profiling

BACKGROUND

Currently, no suitable biomarkers for the early detection of pancreatic cancer (PC) are available. Proteins present in the serum could reflect a state of the disease. In this study, these profiles as a diagnostic marker for PC were evaluated.

METHODS

Serum samples were obtained from PC patients (n = 50 calibration set, n = 39 validation set) and healthy volunteers (n = 110 and n = 75 respectively) according to a uniform standardized collection and processing protocol. For peptide and protein isolation, automated solid-phase extraction (SPE) with Weak Cation Exchange (WCX) magnetic beads (MB) was performed using a 96-channel liquid handling platform. Protein profiles were obtained by mass spectrometry (MS) and evaluated by linear discriminant analysis with double cross-validation.

RESULTS

A discriminating profile for PC has been identified, with a sensitivity of 78% and a specificity of 89% in the calibration set with an area under the curve (AUC) of 90%. These results were validated with a sensitivity of 74% and a specificity of 91% (AUC 90%).

CONCLUSION

Serum profiles of healthy controls and PC can be discrimated between. Further research is warranted to evaluate specificity and whether this biosignature can be used for early detection in a high risk population.

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.

Identification and verification of transthyretin as a potential biomarker for pancreatic ductal adenocarcinoma

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers worldwide and is difficult to detect at its early stages when treatment is most effective. Therefore, we performed a comparative proteomic study to identify new biomarkers for the detection of PDAC.

METHODS

Serum samples from patients with PDAC, chronic pancreatitis and normal controls were compared using two-dimensional difference gel electrophoresis (2D-DIGE). Differentially expressed separated proteins were subsequently identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/TOF-MS). Then, transthyretin (TTR), one of the differentially expressed proteins, was validated through real-time PCR, western blot and immunohistochemistry. Finally, enzyme-linked immunosorbent assays (ELISA) were employed to confirm the levels of transthyretin in the sera.

RESULTS

A total of 21 protein spots showed greater than 1.5-fold changes in expression level in the sera from PDAC patients compared with the normal controls. Among the identified proteins, validation experiments verified the differential expression of transthyretin in PDAC tissue, confirming the proteomic data showing that transthyretin was significantly elevated in patients with PDAC. The ELISA results revealed that the sensitivity and specificity for TTR and CA19-9 in distinguishing PDAC patients from normal individuals were 90.5, 47.6, 66.7 and 85.7 %, respectively, and 81.0 and 85.7 % for their combination.

CONCLUSIONS

These results suggest that the level of transthyretin is elevated in patients with PDAC. In combination with CA19-9, transthyretin may provide additional information for the detection of PDAC and should be further investigated.

The clinical utility of CA 19-9 in pancreatic adenocarcinoma: diagnostic and prognostic updates

CA 19-9 and CEA are the most commonly used biomarkers for diagnosis and management of patients with pancreatic cancer. Since the original compendium by Steinberg in 1990, numerous studies have reported the use of CA 19-9 and, to a lesser extent, CEA in the diagnosis of pancreatic cancer. Here we update an evaluation of the accuracy of CA 19-9 and CEA, and, unlike previous reviews, focus on discrimination between malignant and benign disease instead of normal controls. In 57 studies involving 3,285 pancreatic carcinoma cases, the combined sensitivity of CA 19-9 was 78.2% and in 37 studies involving 1,882 cases with benign pancreatic disease the specificity of CA 19-9 was 82.8%. From the combined analysis of studies reporting CEA, the sensitivity was 44.2% (1,324 cases) and the specificity was 84.8% (656 cases). These measurements more appropriately reflect the expected biomarker accuracy in the differential diagnosis of patients with periampullary diseases. We also present a summary of the use of CA 19-9 as a prognostic tool and evaluate CA 19-9 diagnostic and prognostic utility in a 10-year, single institution experience.

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.

Preoperative CEA and CA 19-9 are prognostic markers for survival after curative resection for ductal adenocarcinoma of the pancreas - a retrospective tumor marker prognostic study

BACKGROUND

The prognosis for patients with ductal adenocarcinoma of the pancreas (PDAC) remains poor even after curative resection. Carbohydrate antigen 19-9 (CA 19-9) and the carcinoembryonic antigen (CEA) are the most widely used serum-based tumor markers for the diagnosis and follow up of pancreatic cancer. In our analysis we aim to assess the prognostic value of a combination of both tumor markers in patients with pancreatic ductal adenocarcinoma (PDAC).

PATIENTS AND METHODS

Between 01/1995 and 08/2012 we performed a total of 264 pancreatic resections due to PDAC. Patients were stratified into 3 groups in regard to their preoperative tumor marker levels. Survival was compared between the groups using Kaplan Meier analysis and log rank test. Univariate subgroup analysis and multivariate analysis were performed.

RESULTS

For 259 cases complete follow up could be obtained. In patients with low preoperative CEA and CA 19-9 levels (group 1 n = 91) the mean survival was 33.3 month (CI 95% 25.1-41.5). If one of the analyzed tumor markers (CEA/CA19-9) was preoperatively elevated above the cut-off level (group 2 n = 106) mean survival was 28.5 month (CI 95% 22.1-35.1). 62 patients showed preoperative elevation of both, CEA and CA 19-9 (group 3); mean survival in this group was 23.9 month (CI 95% 13.9-33.9), p > 0.01. Multivariate analysis confirmed preoperative CEA/CA 19-9 level as independent prognostic factor (HR 1.299).

CONCLUSION

Preoperative CEA and CA 19-9 levels correlate with patient prognosis after curative pancreatic resection due to PDAC. This is especially true for the most frequently pT 3/4 stages of PDAC. Even if CEA and CA 19-9 might not be appropriate for screening, its serum levels should therefore be determined prior to operation and taken into account when resectability or operability is doubtful.

Proteomic approaches to the discovery of cancer biomarkers for early detection and personalized medicine

Cancer biomarkers for the early detection of malignancies and selection of therapeutic strategies have been requested in the clinical field. Accurate and informative cancer biomarkers hold significant promise for improvements in the early detection of disease and in the selection of the most effective therapeutic strategies. Recently, significant progress in the comprehensive analysis of the human genome, epigenome, transcriptome, proteome and metabolome has led to revolutionary changes in the discovery of cancer biomarkers. The Human Proteome Organization has launched a global Human Proteome Project to map the entire human protein set. The Human Proteome Project research group has focused on three working proteomic pillars-mass spectrometry-based, antibody-based and knowledge-based proteomics-and each of these technologies is advancing rapidly. In this review, we introduce the proteomic platforms that are currently being used for cancer biomarker discovery, and describe examples of novel cancer biomarkers that were identified with each proteomic technology.

Comparative proteomic profiling of human bile reveals SSP411 as a novel biomarker of cholangiocarcinoma

Background

Cholangiocarcinoma (CC) is an intractable cancer, arising from biliary epithelial cells, which has a poor prognosis and is increasing in incidence. Early diagnosis of CC is essential as surgical resection remains the only effective therapy. The purpose of this study was to identify improved biomarkers to facilitate early diagnosis and prognostication in CC.

Methods

A comparative expression profile of human bile samples from patients with cholangitis and CC was constructed using a classic 2D/MS/MS strategy and the expression of selected proteins was confirmed by Western blotting. Immunohistochemistry was performed to determine the expression levels of selected candidate biomarkers in CC and matched normal tissues. Finally, spermatogenesis associated 20 (SSP411; also named SPATA20) was quantified in serum samples using an ELISA.

Results

We identified 97 differentially expressed protein spots, corresponding to 49 different genes, of which 38 were upregulated in bile from CC patients. Western blotting confirmed that phosphoglycerate mutase 1 (brain) (PGAM-1), protein disulfide isomerase family A, member 3 (PDIA3), heat shock 60 kDa protein 1 (chaperonin) (HSPD1) and SSP411 were significantly upregulated in individual bile samples from CC patients. Immunohistochemistry demonstrated these proteins were also overexpressed in CC, relative to normal tissues. SSP411 displayed value as a potential serum diagnostic biomarker for CC, with a sensitivity of 90.0% and specificity of 83.3% at a cutoff value of 0.63.

Conclusions

We successfully constructed a proteomic profile of CC bile proteins, providing a valuable pool novel of candidate biomarkers. SSP411 has potential as a biomarker for the diagnosis of CC.

Variant Ciz1 is a circulating biomarker for early-stage lung cancer

There is an unmet need for circulating biomarkers that can detect early-stage lung cancer. Here we show that a variant form of the nuclear matrix-associated DNA replication factor Ciz1 is present in 34/35 lung tumors but not in adjacent tissue, giving rise to stable protein quantifiable by Western blot in less than a microliter of plasma from lung cancer patients. In two independent sets, with 170 and 160 samples, respectively, variant Ciz1 correctly identified patients who had stage 1 lung cancer with clinically useful accuracy. For set 1, mean variant Ciz1 level in individuals without diagnosed tumors established a threshold that correctly classified 98% of small cell lung cancers (SCLC) and non-SCLC patients [receiver operator characteristic area under the curve (AUC) 0.958]. Within set 2, comparison of patients with stage 1 non-SCLC with asymptomatic age-matched smokers or individuals with benign lung nodules correctly classified 95% of patients (AUCs 0.913 and 0.905), with overall specificity of 76% and 71%, respectively. Moreover, using the mean of controls in set 1, we achieved 95% sensitivity among patients with stage 1 non-SCLC patients in set 2 with 74% specificity, demonstrating the robustness of the classification. RNAi-mediated selective depletion of variant Ciz1 is sufficient to restrain the growth of tumor cells that express it, identifying variant Ciz1 as a functionally relevant driver of cell proliferation in vitro and in vivo. The data show that variant Ciz1 is a strong candidate for a cancer-specific single marker capable of identifying early-stage lung cancer within at-risk groups without resort to invasive procedures.

Altered plasma apolipoprotein modifications in patients with pancreatic cancer: protein characterization and multi-institutional validation

Background

Among the more common human malignancies, invasive ductal carcinoma of the pancreas has the worst prognosis. The poor outcome seems to be attributable to difficulty in early detection.

Methods

We compared the plasma protein profiles of 112 pancreatic cancer patients with those of 103 sex- and age-matched healthy controls (Cohort 1) using a newly developed matrix-assisted laser desorption/ionization (oMALDI) QqTOF (quadrupole time-of-flight) mass spectrometry (MS) system.

Results

We found that hemi-truncated apolipoprotein AII dimer (ApoAII-2; 17252 m/z), unglycosylated apolipoprotein CIII (ApoCIII-0; 8766 m/z), and their summed value were significantly decreased in the pancreatic cancer patients [P = 1.36×10⁻²¹, P = 4.35×10⁻¹⁴, and P = 1.83×10⁻²⁴ (Mann-Whitney U-test); area-under-curve values of 0.877, 0.798, and 0.903, respectively]. The significance was further validated in a total of 1099 plasma/serum samples, consisting of 2 retrospective cohorts [Cohort 2 (n = 103) and Cohort 3 (n = 163)] and a prospective cohort [Cohort 4 (n = 833)] collected from 8 medical institutions in Japan and Germany.

Conclusions

We have constructed a robust quantitative MS profiling system and used it to validate alterations of modified apolipoproteins in multiple cohorts of patients with pancreatic cancer.

Discovery of potential bladder cancer biomarkers by comparative urine proteomics and analysis

OBJECTIVE

We searched for bladder tumor markers by analyzing urine samples from patients with bladder cancer and from normal controls.

METHODS

Proteins in urine samples of patients with bladder cancer and with normal controls were systematically examined by 2-dimensional electrophoresis combined with matrix-assisted laser desorption ionization time-of-flight mass spectrometry. The expression of the protein apolipoprotein A-I (apoA-I) was confirmed by Western blot analysis and further evaluated.

RESULTS

We successfully obtained the 2-dimensional electrophoresis gel maps of urinary proteins in patients with bladder cancer and in normal controls. Thirty differentially expressed protein spots were successfully matched by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Combined with the SWISS-PROT database, only 14 proteins (beta-2-microglobulin, fatty acid-binding protein adipocyte, gelsolin, isoform 1 of gelsolin, myoglobin, isoform 2 of fibrinogen alpha chain, apoA-I, prostaglandin D(2) synthase 21 kDa [brain], protein AMBP, transthyretin, keratin type II cytoskeletal 1, type II cytoskeletal 8, putative uncharacterized protein ALB, putative uncharacterized protein MASP2 [fragment]) were identified, including 2 putative proteins. Furthermore, apoA-I was confirmed by Western blot analysis, and the high level of apoA-I was found in urine samples from patients with bladder tumors compared with normal controls.

CONCLUSIONS

Analysis of urinary proteome may be a feasible, noninvasive, and efficient strategy for searching for potential bladder tumor biomarkers. A significant relationship of expressed apoA-I was established between bladder cancer and normal controls. We concluded that 14 differential spots included the apoA-I and would be potential urinary biomarkers for the diagnosis and surveillance of bladder cancer.

Evaluation of serum diagnosis of pancreatic cancer by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Proteomic methods have been widely used in disease marker discovery research. The aim of this study was to discover potential biomarkers for pancreatic cancer (PCa) using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Crude serum samples from 132 patients with PCa and 67 healthy controls (HCs) were analyzed in duplicate using SELDI. Support vector machine (SVM) analysis of the spectra was used to generate a predictive algorithm based on proteins that were maximally differentially expressed between patients with PCa and the HCs in the training cohort. This algorithm was tested using leave-one-out cross-validation in the test cohort. From the 4 significant peaks in the training cohort, a classifier for separating patients with PCa from HCs was developed. The classifier was challenged with all samples achieving 96.67% sensitivity and 100% specificity in the training cohort and 93.1% sensitivity and 78.57% specificity in the test cohort. Additionally, the classifier correctly classified 12/12 stage Ia and 13/16 stage IIa PCa cases. The combination of the SELDI panel and CA19-9 was superior to CA19-9 alone in distinguishing individuals with PCa from the healthy subject group. These results suggest that high-throughput proteomic profiling has the capacity to provide new biomarkers for the early detection and diagnosis of PCa.

The orosomucoid 1 protein (α1 acid glycoprotein) is overexpressed in odontogenic myxoma

BACKGROUND

Odontogenic myxoma (OM) is a benign, but locally invasive, neoplasm occurring in the jaws. However, the molecules implicated in its development are unknown. OM as well as Dental Follicle (DF), an odontogenic tissue surrounding the enamel organ, is derived from ectomesenchymal/mesencyhmal elements. To identify some protein that could participate in the development of this neoplasm, total proteins from OM were separated by two-dimensional electrophoresis and the profiles were compared with those obtained from DF, used as a control.

RESULTS

We identified eight proteins with differential expression; two of them were downregulated and six upregulated in OM. A spot consistently overexpressed in odontogenic myxoma, with a molecular weight of 44-kDa and a pI of 3.5 was identified as the orosomucoid 1 protein. Western blot experiments confirmed the overexpression of this protein in odontogenic myxoma and immunohistochemical assays showed that this protein was mainly located in the cytoplasm of stellate and spindle-shaped cells of this neoplasm.

CONCLUSION

Orosomucoid 1, which belongs to a group of acute-phase proteins, may play a role in the modulation of the immune system and possibly it influences the development of OM.

Seven-signal proteomic signature for detection of operable pancreatic ductal adenocarcinoma and their discrimination from autoimmune pancreatitis

There is urgent need for biomarkers that provide early detection of pancreatic ductal adenocarcinoma (PDAC) as well as discrimination of autoimmune pancreatitis, as current clinical approaches are not suitably accurate for precise diagnosis. We used mass spectrometry to analyze protein profiles of more than 300 plasma specimens obtained from PDAC, noncancerous pancreatic diseases including autoimmune pancreatitis patients and healthy subjects. We obtained 1063 proteomic signals from 160 plasma samples in the training cohort. A proteomic signature consisting of 7 mass spectrometry signals was used for construction of a proteomic model for detection of PDAC patients. Using the test cohort, we confirmed that this proteomic model had discrimination power equal to that observed with the training cohort. The overall sensitivity and specificity for detection of cancer patients were 82.6% and 90.9%, respectively. Notably, 62.5% of the stage I and II cases were detected by our proteomic model. We also found that 100% of autoimmune pancreatitis patients were correctly assigned as noncancerous individuals. In the present paper, we developed a proteomic model that was shown able to detect early-stage PDAC patients. In addition, our model appeared capable of discriminating patients with autoimmune pancreatitis from those with PDAC.

Prioritization of biomarker targets in human umbilical cord blood: identification of proteins in infant blood serving as validated biomarkers in adults

BACKGROUND

Early diagnosis represents one of the best lines of defense in the fight against a wide array of human diseases. Umbilical cord blood (UCB) is one of the first easily available diagnostic biofluids and can inform about the health status of newborns. However, compared with adult blood, its diagnostic potential remains largely untapped.

OBJECTIVES

Our goal was to accelerate biomarker research on UCB by exploring its detectable protein content and providing a priority list of potential biomarkers based on known proteins involved in disease pathways.

METHODS

We explored cord blood serum proteins by profiling a UCB pool of 12 neonates with different backgrounds using a combination of isoelectric focusing and liquid chromatography coupled with matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS) and by comparing results with information contained in metabolic and disease databases available for adult blood.

RESULTS

A total of 1,210 UCB proteins were identified with a protein-level false discovery rate of ~ 5% as estimated by naïve target-decoy and MAYU approaches, signifying a 6-fold increase in the number of UCB proteins described to date. Identified proteins correspond to 138 different metabolic and disease pathways and provide a platform of mechanistically linked biomarker candidates for tracking disruptions in cellular processes. Moreover, among the identified proteins, 38 were found to be approved biomarkers for adult blood.

CONCLUSIONS

The results of this study advance current knowledge of the human cord blood serum proteome. They showcase the potential of UCB as a diagnostic medium for assessing infant health by detection and identification of candidate biomarkers for known disease pathways using a global, nontargeted approach. These biomarkers may inform about mechanisms of exposure-disease relationships. Furthermore, biomarkers approved by the U.S. Food and Drug Administration for screening in adult blood were detected in UCB and represent high-priority targets for immediate validation.

The association of a distinct plasma proteomic profile with the cervical high-grade squamous intraepithelial lesion of Uyghur women: a 2D liquid-phase chromatography/mass spectrometry study

OBJECTIVE

To identify plasma protein biomarkers of cervical high-grade squamous intraepithelial lesion (HSIL) of Uyghur women by proteomics approach.

METHODS

Plasma protein samples of Uyghur women with HSIL and chronic cervicitis were analyzed with 2D HPLC followed by detection of target proteins with Linear Trap Quadrupole Mass Spectrometer (LTQ MS/MS).

RESULTS

We detected three upregulated and one downregulated protein peaks representing protein constituents distinguishing HSIL from controls by 2D HPLC, identified 31 target proteins by LTQ MS/MS. Further confirmed analysis with online software IPA® 8.7 and ELISA assay showed APOA1 and mTOR as potential biomarkers.

CONCLUSIONS

A distinct plasma proteomic profile may be associated with HSIL of Uyghur women.

Pancreatic cancer screening

Accumulating data indicate that clinically available abdominal imaging tests such as EUS and MRI/MRCP can detect asymptomatic precursor benign (IPMN, PanIN) and invasive malignant pancreatic neoplasms, such as ductal adenocarcinoma, in individuals with an inherited predisposition. These asymptomatic FPCs detected have been more likely to be resectable, compared to symptomatic tumors. The most challenging part of screening high-risk individuals is the selection of individuals with high-grade precursor neoplasms for preventive treatment (ie, surgical resection before development of invasive cancer). Ongoing and future research should focus on formulating and validating a model for FPC risk and neoplastic progression using patient characteristics, imaging, and biomarkers. The comparative cost and effectiveness of various approaches for screening and surveillance of high-risk individuals also deserves study. For now, screening is best performed in high-risk individuals within the research protocols in academic centers with multidisciplinary teams with expertise in genetics, gastroenterology, radiology, surgery, and pathology.

Discovery of diagnostic biomarkers for pancreatic cancer in immunodepleted serum by SELDI-TOF MS

MOTIVATION

Reports of serum pancreatic cancer (PC) biomarkers using SELDI-TOF MS have been inconsistent because different chip surfaces and interference with high-abundant proteins. This study examines the influence of these factors on the detection of discriminating diagnostic biomarkers.

METHODS

Serum from fourteen from patients with PC, disease controls (DC, n = 14) and healthy volunteers (HV, n = 14) were evaluated by SELDI using H50, IMAC, Q10 and CM10 chips. A further evaluation was undertaken after depletion of seven high-abundant proteins using spin cartridges.

RESULTS

More protein peaks were detected in whole serum than in depleted serum for IMAC, H50 and Q10 chips: 60 vs 39, 56 vs 48 and 69 vs 65, respectively, while the CM10 found less peaks in serum (27 vs 47 peaks). However, there were more differentially expressed peaks in the depleted serum samples for PC vs DC and PC vs HV samples using the H50, Q10 and CM10 ProteinChip arrays, whereas for IMAC arrays, more discriminating peaks were seen in non-depleted serum. The highly significant peaks observed on Q10, CM10 and H50 are consistent with the previous finding of ApoA-I (m/z 27,910-28000) and ApoA-II (m/z 8758 and 17,240). In addition, a number of new discriminating protein peaks were found on different ProteinChip arrays, notably peaks at m/z 4280 and 7763 on IMAC arrays.

CONCLUSION

This study confirms the diagnostic value of ApoA-I&II and identifies further potential diagnostic biomarkers for pancreatic cancer when multiple chip surfaces are used with depletion of the most highly-abundant proteins.

Advances in biomarker research for pancreatic cancer

Pancreatic cancer (PC) is a leading cause of cancer related deaths in United States. The lack of early symptoms results in latestage detection and a high mortality rate. Currently, the only potentially curative approach for PC is surgical resection, which is often unsuccessful because the invasive and metastatic nature of the tumor masses makes their complete removal difficult. Consequently, patients suffer relapses from remaining cancer stem cells or drug resistance that eventually lead to death. To improve the survival rate, the early detection of PC is critical. Current biomarker research in PC indicates that a serum carbohydrate antigen, CA 19-9, is the only available biomarker with approximately 90% specificity to PC. However, the efficacy of CA 19-9 for assessing prognosis and monitoring patients with PC remains contentious. Thus, advances in technology and the detection of new biomarkers with high specificity to PC are needed to reduce the mortality rate of pancreatic cancer.

Identification of two portal vein tumor thrombosis associated proteins in hepatocellular carcinoma: protein disulfide-isomerase A6 and apolipoprotein A-I

BACKGROUND AND AIM

Portal vein tumor thrombus (PVTT) is one of the factors that can affect prognosis and survival of hepatocellular carcinoma (HCC). In the present study, we aimed to find out some biomarkers associated with vascular invasion features of HCC with the method of comparative proteomic analysis.

METHODS

The proteins were extracted from a pair of HCC tissues with PVTT and without PVTT, and then separated by two-dimensional polyacrylamide gel electrophoresis. Differentially expressed protein spots were identified by matrix assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Further analysis of two proteins were completed using real-time fluorescence quantitative polymerase chain reaction and western-blot in 40 HCC tissues with PVTT (n = 20) and without PVTT (n = 20).

RESULTS

Among 465 protein spots displayed on the gels, 33 unique proteins (> twofold change, P < 0.01) were identified, including 24 upregulated in HCC tissue without PVTT and nine upregulated in HCC tissue with PVTT. The real-time fluorescence quantitative PCR showed no statistically significant difference between HCC tissues with PVTT and without PVTT for mRNA expressions of protein disulfide-isomerase, A6 (PDI A6) (P = 0.137) and apolipoprotein A-I (Apo A-I) (P = 0.718). However, compared with HCC tissues without PVTT, protein expression of PDI A6 was higher in HCC tissues with PVTT (P < 0.001), while protein expression of Apo A-I was lower in HCC tissues with PVTT (P = 0.012).

CONCLUSIONS

PDI A6 and Apo A-I are closely related to vascular invasion feature of HCC.

An integrated proteomic approach to identifying circulating biomarkers in high-risk neuroblastoma and their potential in relapse monitoring

PURPOSE

Despite intensive treatment regimens, overall survival for high-risk neuroblastoma (HRNB) is still poor. This is in part due to an inability to cure the disease once a patient has reached clinical relapse. Identifying plasma biomarkers of active disease may provide a way of relapse monitoring in HRNB.

EXPERIMENTAL DESIGN

In this study, we developed an integrated proteomic approach to identify plasma biomarkers for HRNB.

RESULTS

We identified seven candidate biomarkers (SAA, APOA1, IL-6, EGF, MDC, sCD40L and Eotaxin) for HRNB. These biomarkers were then used to create a multivariate classifier of HRNB, which showed a specificity of 90% (95% confidence interval (CI), 73%, 98%), and a sensitivity of 81% (95%CI, 54%, 96%) for classifying HRNB in a training set. When evaluated on independent test samples, the classifier exhibited 86% accuracy (95% CI, 42%, 100%) of identifying diagnostic samples, and 86% accuracy (95% CI, 70%, 100%) of detecting post-diagnosis longitudinal samples that having active disease.

CONCLUSION AND CLINICAL RELEVANCE

Further validation of these biomarkers may improve patients' outcomes by developing a simple blood test for the detection of relapse prior to the development of clinically evident disease. Understanding the role of these biomarkers in immune surveillance of neuroblastoma may also provide a new direction of therapeutic strategies.

Low levels of apolipoprotein A-I and HDL are associated with risk of prostate cancer in the Swedish AMORIS study

BACKGROUND

A detailed analysis of lipid profiles, using apolipoproteins, has not yet been conducted for prostate cancer (PCa). Since several etiological pathways have been proposed for PCa and lipids, we aimed to study this in a large Swedish cohort with 1,469 primary prostate cancers.

METHODS

A cohort (n = 69,735) of all men aged 35 years or older, whose levels of triglycerides (TG) (mmol/L), total cholesterol (mmol/L), glucose (mmol/L), LDL (mmol/L), HDL (mmol/L), apoB (g/L), and apoA-I (g/L) were measured at baseline, was selected from the Apolipoprotein MOrtality RISk (AMORIS) database. About 2,008 men developed PCa. Multivariate Cox proportional hazard models were used to analyze associations between lipid components and PCa.

RESULTS

ApoA-I and HDL were inversely associated with PCa risk (e.g., HR for HDL: 0.93 (95% CI: 0.81-1.07), 0.88 (0.76-1.01), 0.81 (0.70-0.94), for second, third, and fourth quartiles compared with the first quartile; with p for trend: 0.004; HR for apoA-I: 1.00 (0.88-1.13), 0.93 (0.82-1.05), 0.88 (0.77-0.99),), for second, third, and fourth quartiles compared with the first quartile; with p for trend: 0.022). ApoB, LDL, and non-HDL were not associated with PCa risk.

CONCLUSIONS

Our results show that low HDL and ApoA-I as well as increased lipid ratios are related to increased PCa risk. Experimental studies are required to tease out the underlying biological mechanisms linking these lipid components to PCa.

Identification of Apo-A1 as a biomarker for early diagnosis of bladder transitional cell carcinoma

Background

Bladder transitional cell carcinoma (BTCC) is the fourth most frequent neoplasia in men, clinically characterized by high recurrent rates and poor prognosis. Availability of urinary tumor biomarkers represents a convenient alternative for early detection and disease surveillance because of its direct contact with the tumor and sample accessibility.

Results

We tested urine samples from healthy volunteers and patients with low malignant or aggressive BTCC to identify potential biomarkers for early detection of BTCC by two-dimensional electrophoresis (2-DE) coupled with mass spectrometry (MS) and bioinformatics analysis. We observed increased expression of five proteins, including fibrinogen (Fb), lactate dehydrogenase B (LDHB), apolipoprotein-A1 (Apo-A1), clusterin (CLU) and haptoglobin (Hp), which were increased in urine samples of patients with low malignant or aggressive bladder cancer. Further analysis of urine samples of aggressive BTCC showed significant increase in Apo-A1 expression compared to low malignant BTCC. Apo-A1 level was measured quantitatively using enzyme-linked immunosorbent assay (ELISA) and was suggested to provide diagnostic utility to distinguish patients with bladder cancer from controls at 18.22 ng/ml, and distinguish patients with low malignant BTCC from patients with aggressive BTCC in two-tie grading system at 29.86 ng/ml respectively. Further validation assay showed that Apo-A1 could be used as a biomarker to diagnosis BTCC with a sensitivity and specificity of 91.6% and 85.7% respectively, and classify BTCC in two-tie grading system with a sensitivity and specificity of 83.7% and 89.7% respectively.

Conclusion

Taken together, our findings suggest Apo-A1 could be a potential biomarker related with early diagnosis and classification in two-tie grading system for bladder cancer.

Serum biomarkers for improved diagnostic of pancreatic cancer: a current overview

PURPOSE

Complete resection constitutes the only curative approach in pancreatic cancer but is possible only in a minority of patients due to advanced stages upon diagnosis. Consequently, early detection is crucial for curative treatment. Clinical routine still lacks efficient, non-invasive screening assays, and 80-90% of pancreatic carcinomas are detected at unresectable stages. A wide range of serum proteins have been in the focus of intensive search for biomarkers specific for pancreatic cancer. This article will give an overview on serum biomarkers with screening potential for pancreatic malignancy.

DESIGN AND METHODS

PUBMED database was searched for articles, and 43 manuscripts were selected that provided data regarding biomarkers used, type of assay, study population, sample cohort quality and diagnostic performance.

RESULTS

Superior values for diagnostic performance were shown for MIC-1, PAM4, OPN, HSP27, TPS, TSGF, and CAM17.1 as individual markers. Panels of biomarkers comprised CA 19-9, MCSF, CEA, SAA, Haptoglobin, TSGF, CA 242, and HSP27. Individually or in concerted form, sensitivity and specificity ranged from 77 to 100% and 84-100%, respectively.

CONCLUSIONS

While the above named markers show high screening potential for pancreatic cancer, standardized validation studies using multiplex assays are required to pave the way for clinical routine application.

Proteome profiling reveals gender differences in the composition of human serum

Proteome analysis using human serum is a technological advancement that will enable the discovery of novel biomarkers and biomarker patterns of various human diseases. Although proteome analysis using serum has potential in disease prevention, early diagnosis and treatment of diseases, and evaluation of pharmacotherapies, this technology is still in its infancy. Thus, we sought to develop an advanced method of conducting proteome analysis on human serum. In this study, we report the development of the semi-comprehensive protein analytical technique, which involves the systematic use of iTRAQ labeling, HPLC, nano-LC and MS. We compared the composition of the serum proteome in males and females using this technique and detected gender-based differences in serum protein composition. This technology will enable the generation of databases that may ultimately lead to the discovery of specific biomarkers or biomarker patterns of various diseases.

Identification of serum proteins involved in pancreatic cancer cachexia

AIMS

Treatment of cachexia requires pharmacological intervention which, in turn, requires knowledge of the mediators and processes. Cachexia markers that are specifically expressed in pancreatic cancer and secreted into the blood circulation have yet to be identified. The aim of our study was to investigate the serum protein profiles and protein alterations associated with cachexia and to identify potential disease protein biomarkers indicative for this syndrome.

MAIN METHODS

Serum samples from cachectic and non-cachectic patients undergoing pancreatic cancer (PaCa) surgery and controls were investigated by Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). The identity of detected discriminatory markers was determined by a combination of protein fractionation, chromatographic purification steps, gel electrophoresis, and mass spectrometry.

KEY FINDINGS

Using Cu-IMAC array and CM-10 array based SELDI-TOF-MS. we identified eleven up- and four down-regulated proteins associated with cachexia. CiphergenExpress analysis revealed four disease-associated protein features (38559Da, 9138Da, 8925Da and 3358Da) that were elevated by a factor of 2.3, 1.7, 1.4 and 1.4, respectively. Zinc-α2-glycoprotein (ZAG), apolipoproteins apo C-II and apo C-III and glucagon-like peptide-1 (GLP-1) were identified as markers for PaCa-associated cachexia syndrome. ZAG levels were additionally evaluated in serum and tissue samples by ELISA and immunohistochemistry and the obtained data confirmed the SELDI-TOF-MS results.

SIGNIFICANCE

The identified proteins could be routinely and reliably measured in the serum of patients and provide an elegant non-invasive approach for early diagnosis of cachectic pancreatic cancer patients. Controlling ZAG and GLP-1 activity could be beneficial in the management of cancers and cachexia-induced conditions.

Apolipoprotein A-I (apoA-I) and apoA-I mimetic peptides inhibit tumor development in a mouse model of ovarian cancer

We examined whether reduced levels of Apolipoprotein A-I (apoA-I) in ovarian cancer patients are causal in ovarian cancer in a mouse model. Mice expressing a human apoA-I transgene had (i) increased survival (P < 0.0001) and (ii) decreased tumor development (P < 0.01), when compared with littermates, following injection of mouse ovarian epithelial papillary serous adenocarcinoma cells (ID-8 cells). ApoA-I mimetic peptides reduced viability and proliferation of ID8 cells and cis-platinum-resistant human ovarian cancer cells, and decreased ID-8 cell-mediated tumor burden in C57BL/6J mice when administered subcutaneously or orally. Serum levels of lysophosphatidic acid, a well-characterized modulator of tumor cell proliferation, were significantly reduced (>50% compared with control mice, P < 0.05) in mice that received apoA-I mimetic peptides (administered either subcutaneously or orally), suggesting that binding and removal of lysophosphatidic acid is a potential mechanism for the inhibition of tumor development by apoA-I mimetic peptides, which may serve as a previously unexplored class of anticancer agents.

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.

New pre-analytical approach for the deep proteome analysis of sera from pancreatitis and pancreas cancer patients

The presence of high-abundance proteins and the wide dynamic range of protein-distribution complicates the proteome analysis of crude serum. The aim was to establish a new preanalytical protocol for analysis of the deep serum-proteome for biomarker discovery.

METHODS

We investigated the stability and functionality of ProteoMiner™ and tested the new protocol by SELDI-TOF-MS profiling with serum samples obtained from patients with different pancreatic diseases.

RESULTS

We developed a high-throughput protocol and proved the convenience of ProteoMiner™ in the 96-well format to provide insights into the deep serum proteome and facilitate the detection of novel serum biomarkers. Serum samples spiked with defined amounts of insulin, processed with ProteoMiner™ and analyzed by SELDI-TOF-MS revealed that the concentration of the spiked insulin was not altered by ProteoMiner™ treatment.

CONCLUSION

ProteoMiner™ technology is robust preanalytical step and can be used in a high-throughput format for analysis of low-abundant proteins in serum.

Discovery of novel bladder cancer biomarkers by comparative urine proteomics using iTRAQ technology

A urine sample preparation workflow for the iTRAQ (isobaric tag for relative and absolute quantitation) technique was established. The reproducibility of this platform was evaluated and applied to discover proteins with differential levels between pooled urine samples from nontumor controls and three bladder cancer patient subgroups with different grades/stages (a total of 14 controls and 23 cancer cases in two multiplex iTRAQ runs). Combining the results of two independent clinical sample sets, a total of 638 urine proteins were identified. Among them, 55 proteins consistently showed >2-fold differences in both sample sets. Western blot analyses of individual urine samples confirmed that the levels of apolipoprotein A-I (APOA1), apolipoprotein A-II, heparin cofactor 2 precursor and peroxiredoxin-2 were significantly elevated in bladder cancer urine specimens (n = 25-74). Finally, we quantified APOA1 in a number of urine samples using a commercial ELISA and confirmed again its potential value for diagnosis (n = 126, 94.6% sensitivity and 92.0% specificity at a cutoff value of 11.16 ng/mL) and early detection (n = 71, 83.8% sensitivity and 94.0% specificity). Collectively, our results provide the first iTRAQ-based quantitative profile of bladder cancer urine proteins and represent a valuable resource for the discovery of bladder cancer markers.

Discovery of serum biomarkers for pancreatic adenocarcinoma using proteomic analysis

Background and aims:

The serum/plasma proteome was explored for biomarkers to improve the diagnostic ability of CA19-9 in pancreatic adenocarcinoma (PC).

Methods:

A Training Set of serum samples from 20 resectable and 18 stage IV PC patients, 54 disease controls (DCs) and 68 healthy volunteers (HVs) were analysed by surface-enhanced laser desorption and ionisation time-of-flight mass spectrometry (SELDI-TOF MS). The resulting protein panel was validated on 40 resectable PC, 21 DC and 19 HV plasma samples (Validation-1 Set) and further by ELISA on 33 resectable PC, 28 DC and 18 HV serum samples (Validation-2 Set). Diagnostic panels were derived using binary logistic regression incorporating internal cross-validation followed by receiver operating characteristic (ROC) analysis.

Results:

A seven-protein panel from the training set PC vs DC and from PC vs HV samples gave the ROC area under the curve (AUC) of 0.90 and 0.90 compared with 0.87 and 0.91 for CA19-9. The AUC was greater (0.97 and 0.99, P<0.05) when CA19-9 was added to the panels and confirmed on the validation-1 samples. A simplified panel of apolipoprotein C-I (ApoC-I), apolipoprotein A-II (ApoA-II) and CA19-9 was tested on the validation-2 set by ELISA, in which the ROC AUC was greater than that of CA19-9 alone for PC vs DC (0.90 vs 0.84) and for PC vs HV (0.96 vs 0.90).

Conclusions:

A simplified diagnostic panel of CA19-9, ApoC-I and ApoA-II improves the diagnostic ability of CA19-9 alone and may have clinical utility.

Detection of oesophageal cancer biomarkers by plasma proteomic profiling of human cell line xenografts in response to chemotherapy

Background:

The incidence of oesophageal adenocarcinoma is increasing worldwide but survival remains poor. Neoadjuvant chemotherapy may improve survival, but targeting treatment to patients who respond to chemotherapy could be improved by the availability of markers of response. This study sought proteomic markers of therapeutic response using an adenocarcinoma xenograft model.

Methods:

Epirubicin, cisplatin or 5-fluorouracil was administered to severe combined immune-deficient mice bearing OE19 oesophageal adenocarcinoma xenografts. Murine plasma samples from treated and untreated xenografts were analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectroscopy, and panels of peaks were found using class prediction models that distinguished treatment groups. Proteins in these peaks were identified by mass spectroscopy in tryptic digests of purified fractions. Five paired samples from oesophageal cancer patients before and after chemotherapy were analysed using the same methodology.

Results:

Plasma protein peaks were identified that differed significantly (P<0.05, ANOVA) between the treated xenograft and control groups. Marker panels predicted treated vs untreated xenografts with sensitivities of 100%, specificities of 86–100% and test efficiencies of 89–100%. Three of the proteins identified in these panels, apolipoprotein A-I, serum amyloid A and transthyretin were confirmed in the clinical samples.

Conclusion:

Plasma protein markers can be detected in response to chemotherapy in oesophageal adenocarcinoma xenografts and in clinical samples, and have the potential to monitor response and guide chemotherapy in oesophageal adenocarcinoma.

Seromic profiling of ovarian and pancreatic cancer

Autoantibodies, a hallmark of both autoimmunity and cancer, represent an easily accessible surrogate for measuring adaptive immune responses to cancer. Sera can now be assayed for reactivity against thousands of proteins using microarrays, but there is no agreed-upon standard to analyze results. We developed a set of tailored quality control and normalization procedures based on ELISA validation to allow patient comparisons and determination of individual cutoffs for specificity and sensitivity. Sera from 60 patients with pancreatic cancer, 51 patients with ovarian cancer, and 53 age-matched healthy donors were used to assess the binding of IgG antibodies against a panel of >8000 human antigens using protein microarrays and fluorescence detection. The resulting data interpretation led to the definition and ranking of proteins with preferred recognition by the sera from cancer patients in comparison with healthy donors, both by frequency and strength of signal. We found that 202 proteins were preferentially immunogenic in ovarian cancer sera compared to 29 in pancreatic cancer, with few overlaps. Correlates of autoantibody signatures with known tumor expression of corresponding antigens, functional pathways, clinical stage, and outcome were examined. Serological analysis of arrays displaying the complete human proteome (seromics) represents a new era in cancer immunology, opening the way to defining the repertoire of the humoral immune response to cancer.

Advances in pancreatic cancer detection

Pancreatic cancer represents a major challenge for research studies and clinical management. No specific tumor marker for the diagnosis of pancreatic cancer exists. Therefore, extensive genomic, transcriptomic, and proteomic studies are being developed to identify candidate markers for use in high-throughput systems capable of large cohort screening. Understandably, the complex pathophysiology of pancreatic cancer requires sensitive and specific biomarkers that can improve both early diagnosis and therapeutic monitoring. The lack of a single diagnostic marker makes it likely that only a panel of biomarkers is capable of providing the appropriate combination of high sensitivity and specificity. Biomarker discovery using novel technology can improve prognostic upgrading and pinpoint new molecular targets for innovative therapy.

Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective

Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.

Cancer-specific MALDI-TOF profiles of blood serum and plasma: biological meaning and perspectives

MALDI-TOF mass-spectrometry has become a popular tool of cancer research during the last decade. High throughput and relative simplicity of this technology have made it attractive for biomarker discovery and validation across various platforms in blood serum/plasma. Many technical approaches have been developed for plasma/serum profiling including protein-chip based SELDI-TOF mass-spectrometry, purification of serum on magnetic beads, analysis of carrier-associated fraction and mass-spectrometric immunoassays. Extensive data about the identity of differential features detected on mass-spectra up to now makes it possible to draw conclusions about potency and perspectives of MALDI-TOF mass-spectrometry in this field. A great majority of identified differentially expressed proteins are either house-keeping or inflammatory proteins as well as their modifications or fragments. Discriminating ability of mass-spectra is likely to be based on differential modification and fragmentation patterns of abundant serum proteins reflecting activity of enzymes including proteases and their inhibitors.

Identification of potential serum markers for endometrial cancer using protein expression profiling

OBJECTIVES

Screening method of endometrial cancer (EC) has not been established yet. Our study was to explore serum biomarkers of EC patients using surface-enhanced laser desorption and ionization-time-of-flight mass spectrometry (SELDI-TOF MS).

METHODS

Serum samples from 65 EC patients and 40 controls were analyzed by SELDI-TOF MS (training set). Single- and multi-variant analyses were performed to compare protein profiles in serum of EC patients and healthy controls. Subsequently, blind test set including 40 EC patients and 40 controls were analyzed for validation.

RESULTS

A panel of four biomarker candidates were selected in training set analysis. These markers could also distinguish stage I patients from controls. Among them, two biomarkers were purified and identified as apolipoprotein A1 and a modified form of apolipoprotein C1. Screening for blind test set using dual-biomarker analysis yielded a sensitivity of 82% and a specificity of 86%.

CONCLUSIONS

Involvement of apolipoproteins with EC is first suggested in this study. In addition to possibility of screening method for EC, findings of these new biomarkers might be related with carcinogenesis or predisposition to EC.

Biomarkers in the diagnosis and early detection of pancreatic cancer

BACKGROUND

Pancreatic cancer, owing to its raising incidence and aggressiveness, is a major challenge, both for research and for clinical management. As pancreatic cancer has a complex pathophysiology, in addition to improving the methods of early diagnosis, sensitive and specific biomarkers are a prerequisite.

OBJECTIVE

As there is no specific tumor marker for pancreatic cancer diagnosis, extensive genomics/transcriptomics and proteomics studies have been developed with the aim of finding candidate markers and contributing to high-throughput systems for large cohort screening.

METHODS

A literature review was done to study these biomarkers in relation to diagnosis, prognosis and therapy targets in pancreatic cancer.

RESULTS/CONCLUSION

For early diagnosis improvement, only a panel of soluble biomarkers could provide the appropriate combination between high sensitivity and specificity. Prognostic upgrading would benefit from biomarker discovery and validation performed on tumor tissue. New technology could delineate molecular targets for innovative therapy in pancreatic cancer.

Pancreatic cancer: proteomic approaches to a challenging disease

PURPOSE OF REVIEW

To describe progress in the application of proteomic approaches to advance our understanding of the biology of pancreatic cancer as well as contribute potential protein biomarkers for this disease.

RECENT FINDINGS

Here we review proteomic studies relating to pancreatic cancer that have been published in the past 12 months. We describe novel techniques for the simplification of complex protein samples, focusing particularly on emerging methods for reducing the complexity of blood. We provide examples, where possible, of the application of these novel technologies to pancreatic cancer research.

SUMMARY

Both the range of proteomic-based approaches and their sensitivities for the detection of low-abundance proteins has increased. This provides promise that further research will yield insight into pancreatic cancer, including valuable information on proteins that may ultimately serve as biomarkers for pancreatic cancer.

Large-scale quantitative clinical proteomics by label-free liquid chromatography and mass spectrometry

We previously reported the development of an integrated proteome platform, namely 2-Dimensional Image Converted Analysis of Liquid chromatography and mass spectrometry (2DICAL), for quantitative comparison of large peptide datasets generated by nano-flow liquid chromatography (LC) and mass spectrometry (MS). The key technology of 2DICAL was the precise adjustment of the retention time of LC by dynamic programming. In order to apply 2DICAL to clinical studies that require comparison of a large number of patient samples we further refined the calculation algorithm and increased the accuracy and speed of the peptide peak alignment using a greedy algorithm, which had been used for fast DNA sequence alignment. The peptide peaks of each sample with the same m/z were extracted every 1 m/z and displayed with along the horizontal axis. Here we report a precise comparison of more than 150,000 typtic peptide ion peaks derived from 70 serum samples (40 patients with uterine endometrial cancer and 30 controls). The levels of 49 MS peaks were found to differ significantly between cancer patients and controls (P < 0.01, Welch's t-test and interquartile range [IQR] of >40), and the differential expression and identification of selected three proteins was validated by immunoblotting. 2DICAL was is highly advantageous for large-scale clinical proteomics because of its simple procedure, high throughput, and quantification accuracy.

Confounding effect of obstructive jaundice in the interpretation of proteomic plasma profiling data for pancreatic cancer

It is well established that variation in sampling, processing and storage protocols can alter the levels of potential biomarkers in serum and plasma. Here, using pancreatic cancer as an example, we demonstrate that consideration of clinical parameters related to the patient's illness is equally important when seeking cancer-specific biomarkers. Bile duct-obstruction is a feature of pancreatic disease that can cause jaundice. Comparing patients with pancreatic cancer, chronic pancreatitis or biliary duct obstruction, we observed that the plasma levels of apolipoprotein A1, transthyretin, and apolipoprotein E, when examined in isolation, were each associated with pancreatic cancer. However, when the effect of bile duct obstruction was considered, only transthyretin levels were independently associated with cancer likelihood. Our results demonstrate the importance of accounting for disease-related confounding factors when analyzing data for the detection of cancer biomarkers.

Examining the significance of fingerprint-based classifiers

Background

Experimental examinations of biofluids to measure concentrations of proteins or their fragments or metabolites are being explored as a means of early disease detection, distinguishing diseases with similar symptoms, and drug treatment efficacy. Many studies have produced classifiers with a high sensitivity and specificity, and it has been argued that accurate results necessarily imply some underlying biology-based features in the classifier. The simplest test of this conjecture is to examine datasets designed to contain no information with classifiers used in many published studies.

Results

The classification accuracy of two fingerprint-based classifiers, a decision tree (DT) algorithm and a medoid classification algorithm (MCA), are examined. These methods are used to examine 30 artificial datasets that contain random concentration levels for 300 biomolecules. Each dataset contains between 30 and 300 Cases and Controls, and since the 300 observed concentrations are randomly generated, these datasets are constructed to contain no biological information. A modest search of decision trees containing at most seven decision nodes finds a large number of unique decision trees with an average sensitivity and specificity above 85% for datasets containing 60 Cases and 60 Controls or less, and for datasets with 90 Cases and 90 Controls many DTs have an average sensitivity and specificity above 80%. For even the largest dataset (300 Cases and 300 Controls) the MCA procedure finds several unique classifiers that have an average sensitivity and specificity above 88% using only six or seven features.

Conclusion

While it has been argued that accurate classification results must imply some biological basis for the separation of Cases from Controls, our results show that this is not necessarily true. The DT and MCA classifiers are sufficiently flexible and can produce good results from datasets that are specifically constructed to contain no information. This means that a chance fitting to the data is possible. All datasets used in this investigation are available on the web.

This work is funded by NCI Contract N01-CO-12400.