Diagnosis of pancreatic cancer using serum proteomic profiling

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

OBJECTIVE

Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible.

METHODS AND PROCEDURES

In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses.

RESULTS

The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection.

CONCLUSION

Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure.

CLINICAL IMPACT

With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Comparative Proteomic Profiling of Divergent Phenotypes for Water Holding Capacity across the Post Mortem Ageing Period in Porcine Muscle Exudate

Two dimensional Difference Gel Electrophoresis (2-D DIGE) and mass spectrometry were applied to investigate the changes in metabolic proteins that occur over a seven day (day 1, 3 and 7) post mortem ageing period in porcine centrifugal exudate from divergent meat quality phenotypes. The objectives of the research were to enhance our understanding of the phenotype (water holding capacity) and search for biomarkers of this economically significant pork quality attribute. Major changes in protein abundance across nine phenotype-by-time conditions were observed. Proteomic patterns were dominated by post mortem ageing timepoint. Using a machine learning algorithm (l1-regularized logistic regression), a model was derived with the ability to discriminate between high drip and low drip phenotypes using a subset of 25 proteins with an accuracy of 63%. Models discriminating between divergent phenotypes with accuracy of 72% and 73% were also derived comparing respectively, high drip plus intermediate phenotype (considered as one phenotype) versus low drip and comparing low drip plus intermediate phenotype (considered as one phenotype) versus high drip. In all comparisons, the general classes of discriminatory proteins identified include metabolic enzymes, stress response, transport and structural proteins. In this research we have enhanced our understanding of the protein related processes underpinning this phenotype and provided strong data to work toward development of protein biomarkers for water holding capacity.

Classification of Samples with Order-Restricted Discriminant Rules

In recent years, mass spectrometry techniques have helped proteomics to become a powerful tool for the early diagnosis of cancer, as they help to discover protein profiles specific to each pathological state. One of the questions where proteomics is giving useful practical results is that of classifying patients into one of the possible severity levels of an illness, based on some features measured on the patient. This classification is usually made using one of the many discrimination procedures available in statistical literature. We present in this chapter recently developed restricted discriminant rules that use additional information in terms of orderings on the means, and we illustrate how to apply them to mass spectrometry data using R package dawai. Specifically, we use proteomic prostate cancer data, and we describe all steps needed, including data preprocessing and feature extraction, to build a discriminant rule that classifies samples in one of several disease stages, thus helping diagnosis. The restricted discriminant rules are compared with some standard classifiers that do not take into account the additional information, showing better performance in terms of error rates.

Novel Use of Proteomic Profiles in a Convex-Hull Ensemble Classifier to Predict Gynecological Cancer Patients' Susceptibility to Gastrointestinal Mucositis as Side Effect of Radiation Therapy

Background

Whole-pelvis radiation therapy is common practice in the post-surgical treatment of cervical and endometrial cancer. Gastrointestinal mucositis is an adverse side effect of radiation therapy, and is a primary concern in patient management. We investigate whether proteomic information obtained from blood samples drawn from patients scheduled to receive radiation therapy for gynecological cancers could be used to predict which patients are most susceptible to radiation-induced gastrointestinal mucositis, in order to improve the individualization of radiation therapy.

Methods

We use 132 proteins measured on 17 gynecological cancer patients in a convex-hull-based, selective-voting ensemble classifier to classify each patient into one of two classes: patients who would not (class 1) or would (class 2) develop gastrointestinal mucositis. We employ 20 repetitions of 10-fold cross-validation to measure classification accuracy.

Results

We achieved a 95% confidence interval on average prediction accuracy of (0.711, 0.771) using pre-radiation proteomic profiles to predict which patients would experience gastrointestinal mucositis. Pathway analysis of the 12 most prominent proteins indicated that they could be assembled into a single interaction network with direct associations. The function associated with the highest number of these 12 proteins was cell-to-cell signaling and interaction.

Conclusions

Pre-radiation proteomic profiles have the potential to classify cervical/endometrial cancer patients with high accuracy as to their susceptibility to gastrointestinal mucositis following radiation therapy. Further study of the network of 12 identified proteins is warranted with a larger patient sample to confirm that these proteins are predictive of gastrointestinal mucositis in this patient population.

Strategies for early detection of resectable pancreatic cancer

Pancreatic cancer is difficult to diagnose at an early stage and generally has a poor prognosis. Surgical resection is the only potentially curative treatment for pancreatic carcinoma. To improve the prognosis of this disease, it is essential to detect tumors at early stages, when they are resectable. The optimal approach to screening for early pancreatic neoplasia has not been established. The International Cancer of the Pancreas Screening Consortium has recently finalized several recommendations regarding the management of patients who are at an increased risk of familial pancreatic cancer. In addition, there have been notable advances in research on serum markers, tissue markers, gene signatures, and genomic targets of pancreatic cancer. To date, however, no biomarkers have been established in the clinical setting. Advancements in imaging modalities touch all aspects of the clinical management of pancreatic diseases, including the early detection of pancreatic masses, their characterization, and evaluations of tumor resectability. This article reviews strategies for screening high-risk groups, biomarkers, and current advances in imaging modalities for the early detection of resectable pancreatic cancer.

Sweating the small stuff: microRNAs and genetic changes define pancreatic cancer

MicroRNAs (miRNAs) are 18- to 22-nucleotide-long, single-stranded, noncoding RNAs that regulate important biological processes including differentiation, proliferation, and response to cellular stressors such as hypoxia, nutrient depletion, and traversion of the cell cycle by controlling protein expression within the cell. Many investigators have profiled cancer tissue and serum miRNAs to identify potential therapeutic targets, understand the pathways involved in tumorigenesis, and identify diagnostic tumor signatures. In the setting of pancreatic cancer, obtaining pancreatic tissue is invasive and impractical for early diagnosis. Several groups have profiled miRNAs that are present in the blood as a means to diagnose tumor progression and predict prognosis/survival or drug resistance. Several miRNA signatures found in pancreatic tissue and the peripheral blood, as well as the pathways that are associated with pancreatic cancer, are reviewed here in detail. Three miRNA biomarkers (miR-21, miR-155, and miR-200) have been repetitively identified in both pancreatic cancer tissue and patients' blood. Those miRNAs regulate and are regulated by the central genetic and epigenetic changes observed in pancreatic cancer including p53, transforming growth factor β, p16(INK4A), BRCA1/2, and Kras. These miRNAs are involved in DNA repair, cell cycle, and cell invasion and also play important roles in promoting metastases.

Identification of PTHrP(12-48) as a plasma biomarker associated with breast cancer bone metastasis

BACKGROUND

Breast cancer bone metastasis is a complication that significantly compromises patient survival due, in part, to the lack of disease-specific biomarkers that allow early and accurate diagnosis.

METHODS

Using mass spectrometry protein profiling, plasma samples were screened from three independent breast cancer patient cohorts with and without clinical evidence of bone metastasis.

RESULTS

The results identified 13 biomarkers that classified all 110 patients with a sensitivity of 91% and specificity of 93% [receiver operating characteristics area under the curve (AUC = 1.00)]. The most discriminatory protein was subsequently identified as a unique 12-48aa peptide fragment of parathyroid hormone-related protein (PTHrP). PTHrP(12-48) was significantly increased in plasma of patients with bone metastasis compared with patients without bone metastasis (P < 0.0001). Logistic regression models were used to evaluate the diagnostic potential of PTHrP(12-48) as a single biomarker or in combination with the measurement of the clinical marker N-telopeptide of type I collagen (NTx). The PTHrP(12-48) and NTx logistic regression models were not significantly different and classified the patient groups with high accuracy (AUC = 0.85 and 0.95), respectively. Interestingly, in combination with serum NTx, the plasma concentration of PTHrP(12-48) increased diagnostic specificity and accuracy (AUC = 0.99).

CONCLUSIONS

These data show that PTHrP(12-48) circulates in plasma of patient with breast cancer and is a novel and predictive biomarker of breast cancer bone metastasis. Importantly, the clinical measurement of PTHrP(12-48) in combination with NTx improves the detection of breast cancer bone metastasis.

IMPACT

In summary, we present the first validated, plasma biomarker signature for diagnosis of breast cancer bone metastasis that may improve the early diagnosis of high-risk individuals.

Tumour markers: An overview

Tumor Markers comprise a wide spectrum of biomacromolecules synthesized in excess concentration by a wide variety of neoplastic cells. The markers could be endogenous products of highly active metabolic malignant cells or the products of newly switched on genes, which remained unexprssed in early life or newly acquired antigens at cellular and sub-cellular levels. The appearance of tumor marker and their concentration are related to the genesis and growth of malignant tumors in patients. An ideal tumor marker should be highly sensitive, specific, reliable with high prognostic value, organ specificity and it should correlate with tumor stages. However, none of the tumor markers reported to date has all these characteristics. Inspite of these limitations, many tumor markers have shown excellent clinical relevance in monitoring efficacy of different modes of therapies during entire course of illness in cancer patients. Additionally, determination of markers also helps in early detection of cancer recurrence and in prognostication.

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.

A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma

Purpose

Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.

Results

The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.

Conclusions and Clinical Relevance

The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.

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.

Detection of pancreatic adenocarcinoma using circulating fragments of fibrinogen

BACKGROUND

There is an urgent need for biomarkers to detect pancreatic cancer in the early, potentially curable, stages.

METHODS

We have used SELDI profiling to analyze serum from 75 patients with pancreatic cancer and 61 patients with nonmalignant pancreaticobiliary diseases.

RESULTS

A peak in the SELDI spectra corresponding to a 53 residue fragment of the α-chain of fibrinogen is remarkably elevated in approximately 50% of the cancer patients. In addition, fibrinogen degradation products were measured using the DR-70 assay. The areas under the receiver operating characteristic curves for the SELDI-detected fibrinogen fragment, DR-70 and CA19-9 were 0.65, 0.75 and 0.86, respectively. Class prediction models using combinations of these markers did not increase the area under the receiver operating characteristic curve compared with CA19-9. The novel fibrinogen fragment was not elevated to the same extent in other malignancies but was elevated in some patients with benign pancreatic disease.

CONCLUSION

Both the SELDI-detected fragment of fibrinogen and DR-70 are significantly elevated in the serum of pancreatic cancer patients. However, they do not seem to improve pancreatic cancer detection over CA19-9 alone.

Current status of molecular markers for early detection of sporadic pancreatic cancer

Pancreatic cancer (PC) is a highly lethal malignancy with near 100% mortality. This is in part due to the fact that most patients present with metastatic or locally advanced disease at the time of diagnosis. Significantly, in nearly 95% of PC patients there is neither an associated family history of PC nor of diseases known to be associated with an increased risk of PC. These groups of patients who comprise the bulk of PC cases are termed as "sporadic PC" in contrast to the familial PC cases that comprise only about 5% of all PCs. Given the insidious onset of the malignancy and its extreme resistance to chemo and radiotherapy, an abundance of research in recent years has focused on identifying biomarkers for the early detection of PC, specifically aiming at the sporadic PC cohort. However, while several studies have established that asymptomatic individuals with a positive family history of PC and those with certain heritable syndromes are candidates for PC screening, the role of screening in identifying sporadic PC is still an unsettled question. The present review attempts to assess this critical question by investigating the recent advances made in molecular markers with potential use in the early diagnosis of sporadic PC - the largest cohort of PC cases worldwide. It also outlines a novel yet simple risk factor based stratification system that could be potentially employed by clinicians to identify those individuals who are at an elevated risk for the development of sporadic PC and therefore candidates for screening.

Discovery of putative pancreatic cancer biomarkers using subcellular proteomics

Pancreatic cancer (PC) is a highly aggressive disease that frequently remains undetected until it has progressed to an advanced, systemic stage. Successful treatment of PC is hindered by the lack of early detection. The application of proteomic analysis to PC combined with subcellular fractionation has introduced new possibilities in the field of biomarker discovery. We utilized matched pairs of pancreas tumor and non-tumor pancreas from patients undergoing tumor resection. The tissues were treated to obtain cellular protein fractions corresponding to cytosol, membrane, nucleus and cytoskeleton. The fractions were then separated by molecular weight and digested with trypsin, followed by liquid chromatography and tandem mass spectrometry. The spectra obtained were searched using Sequest engine and combined into a single analysis file to obtain a semi-quantitative number, spectral count, using Scaffold software. We identified 2393 unique proteins in non-tumor and cancer pancreas. Utilizing PLGEM statistical analysis we determined 104 proteins were significantly changed in cancer. From these, we further validated four secreted proteins that are up-regulated in cancer and have potential for development as minimally-invasive diagnostic markers. We conclude that subcellular fractionation followed by gel electrophoresis and tandem mass spectrometry is a powerful strategy for identification of differentially expressed proteins in pancreatic cancer.

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.

The promise of bone cancer proteomics

Mass spectrometric analysis of the low-molecular-weight (LMW) range of the serum/plasma proteome is revealing the existence of large numbers of previously unknown peptides and protein fragments, predicted to be derived from circulating low-abundance proteins. While genomics and proteomics are the primary discovery research tool, recent innovations in high-throughput proteomics are now standard practice for biomarker and target discovery. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) is the current mainstay for serum or plasma analysis, although other methods are emerging as alternative high-throughput approaches. From a proteomics perspective, the bone cancers, such as myeloma, breast and prostate cancer bony metastases, and osteosarcoma, are likely among the least studied. As recent advances in proteomic technology have thrust the bone cancer field into the era of proteomics, a review of the current status of the proteome as it relates to the skeletal consequences of malignancy seems reasonable.

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.

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.

Analytical considerations for mass spectrometry profiling in serum biomarker discovery

The potential of using mass spectrometry profiling as a diagnostic tool has been demonstrated for a wide variety of diseases. Various cancers and cancer-related diseases have been the focus of much of this work because of both the paucity of good diagnostic markers and the knowledge that early diagnosis is the most powerful weapon in treating cancer. The implementation of mass spectrometry as a routine diagnostic tool has proved to be difficult, however, primarily because of the stringent controls that are required for the method to be reproducible. The method is evolving as a powerful guide to the discovery of biomarkers that could, in turn, be used either individually or in an array or panel of tests for early disease detection. Using proteomic patterns to guide biomarker discovery and the possibility of deployment in the clinical laboratory environment on current instrumentation or in a hybrid technology has the possibility of being the early diagnosis tool that is needed.

Proteomic and metabolic prediction of response to therapy in gastrointestinal cancers

Despite substantial improvements in the diagnosis and treatment of many gastrointestinal cancers, particularly colorectal cancer, numerous patients are only diagnosed in advanced stages of disease, which can preclude curative treatment. Screening and early diagnosis of high-risk individuals might be the most promising approach to improve prognosis; however, molecular biomarkers for early diagnosis of most gastrointestinal cancers are not yet available. The prognosis of patients with advanced gastrointestinal cancers has improved through the development of multimodal treatments and the introduction of targeted therapies. Nonetheless, not all patients benefit equally from these treatment approaches, and toxicity can be substantial. The ability to predict whether a patient will respond to therapy early in their treatment for gastrointestinal cancer may be of particular value to stratify and individualize patient treatment strategies. Despite improvement in the understanding of cancer pathogenesis and progression at the molecular level, the molecular changes that underlie treatment response and/or drug resistance are still largely unknown. PET is the first technique to show promise in prediction of response to therapy, and has resulted in promising advancements, particularly in esophageal and gastric cancers. Tissue-based and blood-based molecular biomarkers are still subject to validation. Prediction of response to treatment could ultimately lead to an overall improvement in prognosis.

Diagnosis of pancreatic adenocarcinoma using protein chip technology

BACKGROUND

To develop a serum-specific protein fingerprint which is capable of differentiating samples from patients with pancreatic cancer and those with other pancreatic conditions.

METHODS

We used SELDI-TOF-MS coupled with CM10 chips and bioinformatics tools to analyze a total of 118 serum samples in this study; 78 serum samples were analyzed to establish the diagnostic models and the other 40 samples were analyzed on the second day as an independent test set.

RESULTS

The analysis of this independent test set yielded a specificity of 91.6% and a sensitivity of 91.6% for pattern 1, which distinguished pancreatic adenocarcinoma (PC) from healthy individuals and a specificity of 80.0% and a sensitivity of 90.9% for pattern 2, which distinguished PC from chronic pancreatitis.

CONCLUSION

This study indicated that the SELDI-TOF-MS technique can facilitate the discovery of better serum tumor biomarkers and a combination of specific models is more accurate than a single model in diagnosis of PC.

Clinical biomarkers of angiogenesis inhibition

INTRODUCTION

An expanding understanding of the importance of angiogenesis in oncology and the development of numerous angiogenesis inhibitors are driving the search for biomarkers of angiogenesis. We review currently available candidate biomarkers and surrogate markers of anti-angiogenic agent effect.

DISCUSSION

A number of invasive, minimally invasive, and non-invasive tools are described with their potential benefits and limitations. Diverse markers can evaluate tumor tissue or biological fluids, or specialized imaging modalities.

CONCLUSIONS

The inclusion of these markers into clinical trials may provide insight into appropriate dosing for desired biological effects, appropriate timing of additional therapy, prediction of individual response to an agent, insight into the interaction of chemotherapy and radiation following exposure to these agents, and perhaps most importantly, a better understanding of the complex nature of angiogenesis in human tumors. While many markers have potential for clinical use, it is not yet clear which marker or combination of markers will prove most useful.

Serum biomarker profile associated with high bone turnover and BMD in postmenopausal women

Early diagnosis of the onset of osteoporosis is key to the delivery of effective therapy. Biochemical markers of bone turnover provide a means of evaluating skeletal dynamics that complements static measurements of BMD by DXA. Conventional clinical measurements of bone turnover, primarily the estimation of collagen and its breakdown products in the blood or urine, lack both sensitivity and specificity as a reliable diagnostic tool. As a result, improved tests are needed to augment the use of BMD measurements as the principle diagnostic modality. In this study, the serum proteome of 58 postmenopausal women with high or low/normal bone turnover (training set) was analyzed by surface enhanced laser-desorption/ionization time-of-flight mass spectrometry, and a diagnostic fingerprint was identified using a variety of statistical and machine learning tools. The diagnostic fingerprint was validated in a separate distinct test set, consisting of serum samples from an additional 59 postmenopausal women obtained from the same Mayo cohort, with a gap of 2 yr. Specific protein peaks that discriminate between postmenopausal patients with high or low/normal bone turnover were identified and validated. Multiple supervised learning approaches were able to classify the level of bone turnover in the training set with 80% sensitivity and 100% specificity. In addition, the individual protein peaks were also significantly correlated with BMD measurements in these patients. Four of the major discriminatory peaks in the diagnostic profile were identified as fragments of interalpha-trypsin-inhibitor heavy chain H4 precursor (ITIH4), a plasma kallikrein-sensitive glycoprotein that is a component of the host response system. These data suggest that these serum protein fragments are the serum-borne reflection of the increased osteoclast activity, leading to the increased bone turnover that is associated with decreasing BMD and presumably an increased risk of fracture. In conjunction with the identification of the individual proteins, this protein fingerprint may provide a novel approach to evaluate high bone turnover states.

Proteomics of pancreatic cancer

Pancreatic cancer is a devastating disease, with a mortality rate almost identical with its incidence. Late diagnosis and limited therapeutic options make early detection of pancreatic cancer a pressing clinical problem. In this context, the investigation of the pancreatic cancer proteome has recently gained considerable attention because profiles of proteins may be able to more accurately identify disease states, such as cancer. Recent pancreatic cancer proteome studies may be categorized into basic studies cataloguing the pancreatic proteome, studies investigating differential protein expression patterns, and studies searching for proteome-based biomarkers for early cancer detection and differentiation. Although these studies clearly demonstrate that a range of biological samples are suitable for proteomic analyses, comparison of different studies is problematic due to the diversity of methodologies, sample sources, and characterization of patient populations. Reproducibility between studies has rarely been investigated, and no investigation has compared the different methods of proteomic research. The results of this review have shown that more stringent requirements concerning the design and the analysis of future studies should be implemented. These include an adequate patient number, obligatory histological examination of tissues, appropriate control groups, identification of proteins and peaks, validation of differential expression using independent cohorts and/or a second methodology, and, finally, demonstration of result reproducibility. This will hopefully lead to the discovery of prognostic and predictive biomarkers that help to improve prognosis of pancreatic cancer patients.

Diagnostic and prognostic biomarkers in pancreatic carcinoma

Pancreatic ductal carcinoma, one of the leading causes of cancer mortality, is typically diagnosed at an advanced stage, which significantly contributes to its high mortality rates. Studies have demonstrated that resection of small pancreatic tumors and tumors at lower stages correlates with improved survival. Detection of pancreatic carcinoma at an early, surgically resectable stage is the key to decreasing mortality and improving survival. Identification of sensitive diagnostic biomarkers as screening tools is crucial in detecting preinvasive pancreatic neoplasms. Numerous new DNA-, RNA- and protein-based biomarkers have been extensively investigated. This review aims to provide an update on these molecular markers, including biomarkers from blood, tissue as well as pancreatic juice and cystic fluid. These biomarkers hold potential utility in early diagnosis and prognostification of pancreatic ductal carcinoma, though many of which need to be validated in large-scale prospective studies before they can be used in clinical settings.

Molecular markers of pancreatic cancer: development and clinical relevance

BACKGROUND

The prognosis of pancreatic cancer remains poor, mainly because of its aggressive biological behaviour and late clinical diagnosis, which precludes the application of appropriate curative therapies. Therefore, one of the major goals in clinical pancreatology is to find molecular markers, specific and sensitive enough to make an early and correct diagnosis of pancreatic cancer, before it has disseminated and become untreatable.

OBJECTIVE

This overview article explores the potential utility of current molecular markers for the diagnosis of pancreatic cancer.

RESULTS

There is a wide array of serum-based and tissue-based markers for pancreatic cancer. Serum-based molecular markers include CA 19-9, CA 125, M2-PK and secreted proteins. A tissue can be used to test genetic mutations such as K-ras, inactivation of tumour suppressor genes (e.g. p16, p53), mucins, telomerase activity, growth factors, DNA methylation, and global gene expression of cDNA microarrays, mitochondrial mutations and proteomics. None of these markers is currently useful for the detection of early pancreatic cancer. In clinical practice, the most commonly accepted use of CA 19-9 is to assess the prognosis and monitor the response to therapy.

CONCLUSIONS

Many molecular markers have been proposed for the early diagnosis of PC, but most are not ready to be included as part of the routine diagnostic algorithm because they still lack sensitivity, specificity or reproducibility. CA 19-9 remains the most useful molecular marker for the diagnosis and follow-up of clinically and radiological evident pancreatic cancer.

Identifying molecular markers for the early detection of pancreatic neoplasia

Pancreatic cancer is the fourth most common cause of cancer death in the United States. There is a great need for better diagnostic markers of pancreatic neoplasia. Better markers would improve the early diagnosis of pancreatic cancer and allow more patients to undergo curative surgical resection. Identifying individuals at high risk of developing pancreatic cancer and applying markers that could identify precancerous lesions of the pancreas in these individuals could allow such lesions to be resected before the development of pancreatic cancer. As we continue to characterize the genetic, epigenetic, and proteomics alterations that occur in pancreatic cancers and their percursors, better diagnostic markers of pancreatic cancer are expected to follow.

[Proteome analysis--basis for individualized pancreatic carcinoma therapy?]

Ductal pancreatic adenocarcinoma is a dismal disease, having the worst prognosis of all solid tumors. While genomics and transcriptomics have provided a wealth of data, no contribution has been made to clinical medicine in terms of diagnostic or prognostic markers. Hope lies in yet another novel technology, proteomics. Conceptually, proteomics bears the advantage of incorporating both posttranslational modifications as well as host factors. This is thought to be important in factors influencing survival such as chemoresistance. This tutorial review discusses the state of the art in pancreatic cancer proteomics in light of technical developments. At this moment, proteomics is still at the beginning in clinical application. First results, however, suggest some hope for the development of a new understanding of the molecular biology in pancreatic cancer yielding into very specific markers of disease or allowing a rational and individualized therapy.

Technology insight: the application of proteomics in gastrointestinal disease

Analysis of the human genome has increased our knowledge of the genes that are associated with disease. At the same time, however, it has become clear that having complete DNA sequences alone is not sufficient to elucidate the biological functions of the proteins that they encode. For this reason, proteomics-the analysis of proteins-has become increasingly attractive, because the proteome reflects both the intrinsic genetic programming of a cell and the impact of its immediate environment. The principal goals of clinical proteomics are to identify biomarkers for the early diagnosis of disease and potential targets for therapeutic intervention. Other goals include the identification of biomarkers for the early detection of disease recurrence (relapse) and how they might be combined with diagnostic imaging techniques to improve the sensitivity for detecting disease. This Review describes conventional proteomic technologies, their strengths and limitations, and demonstrates their application to clinical practice, with specific reference to their use in the gastroenterology field.

Proteomic analysis of bone cancer: a review of current and future developments

The ability of sophisticated proteomic approaches to scrutinize the dynamic nature of protein expression, cellular and subcellular protein distribution, post-translational modifications, and protein-protein interactions has culminated in the identification of many potential new therapeutic targets and an abundance of cancer-related biomarkers. From a proteomics perspective, amongst the most under-studied diseases are bone cancers, such as myeloma, osteosarcoma and breast and prostate cancer bony metastases. This review focuses on the recent advances in proteomic technology that have thrust the skeletal cancer field into this exciting age of proteomics, and highlights the future work that is required to adapt this technology to specifically interrogate the skeletal consequences of malignancy.

Sample preparation for serum/plasma profiling and biomarker identification by mass spectrometry

In this article, we present an overview of the different strategies for sample preparation for identification by mass spectrometry (MS) of biomarkers from serum and/or plasma. We consider the effects of the variables involved in sample collection, handling and storage, and describe different approaches for removal of high abundance proteins and serum/plasma fractionation. We review the advantages and disadvantages of such techniques as centrifugal ultrafiltration, different formats for solid phase extraction, organic solvent extraction, gel and capillary electrophoresis, and liquid chromatography. We also discuss a variety of current proteomic methods and their main applications for biomarker-related studies.

Research progress in screening biomarkers of pancreatic cancer by proteomic techniques

Pancreatic cancer is one kind of devastating diseases. Those patients without nonspecific symptoms at early stage had mostly lost the opporunity of surgical therapy when pancreatic cancer was detected at advanced stage. Rapid growth of proteomic technologies provides possibilities to study etiopathogenesis, and screen early diagnostic and prognosis biomarkers of pancreatic cancer. In this paper, the application of proteomic techniques in cell lines, tissues, serum and pancreatic juice from patients with pancreatic cancer is reviewed briefly.

CEACAM1, a novel serum biomarker for pancreatic cancer

OBJECTIVES

Serum biomarkers for early diagnosis of pancreatic adenocarcinoma are not currently available. We recently observed elevated expression of CEACAM1 in pancreatic adenocarcinomas and sought to determine whether serum CEACAM1 levels were elevated in pancreatic cancer patients.

METHODS

CEACAM1 messenger RNA levels were measured in pancreatic tissue samples using quantitative reverse transcription-polymerase chain reaction. CEACAM1 was localized by immunohistochemistry in adenocarcinomas and in pancreatic intraductal neoplasia lesions. CEACAM1 serum levels were assessed by a double determinant enzyme-linked immunosorbent assay and compared with serum levels of CA19-9.

RESULTS

CEACAM1 had higher expression levels in pancreatic adenocarcinomas compared with noncancerous pancreas (P < 0.0001) and was localized to neoplastic cells (95% (45/47) of adenocarcinomas and 85% (17/20) of pancreatic intraductal neoplasia 3 lesions. CEACAM1 was expressed in the sera of 91% (74/81) of pancreatic cancer patients, 24% (15/61) of normal patients, and 66% (35/53) of patients with chronic pancreatitis, with a sensitivity and specificity superior to CA19-9. The combination of CEACAM1 and CA19-9 had significantly higher diagnostic accuracy than CA19-9.

CONCLUSIONS

CEACAM1 is expressed in pancreatic adenocarcinoma, and serum levels of CEACAM1 serve as a useful indicator for the presence of pancreatic cancer. Additional validation studies on the use of serum CEACAM1 as a diagnostic marker in pancreatic cancer are warranted.

Posttranslational modifications of transthyretin are serum markers in patients with mycosis fungoides

Cutaneous T-cell lymphomas (CTCLs) are characterized by the recruitment of malignant T-cell clones, predominantly of the CD4(+) T-helper subpopulation, into the skin. Mycosis fungoides (MF) is the most common type of CTCL and accounts for almost 50% of all primary cutaneous lymphomas. The ProteinChip technology surface-enhanced laser desorption/ionization time of flight/mass spectrometry (SELDI-TOF-MS) was used to detect biomarkers in sera from MF patients (n = 25) and healthy controls (n = 26). Therefore, diluted sera were applied to IMAC30 ProteinChip arrays, and the resulting protein profiles were bioinformatically analyzed. A protein set that distinguishes MF patients from healthy controls with a sensitivity of 82.6% and a specificity of 100% was identified. Four significant peaks were identified by two-dimensional gel electrophoresis, immunodepletion, and SELDI-TOF-MS as transthyretin (TTR) and three TTR modifications. A subsequent enzyme-linked immunosorbent assay confirmed these findings. The ability to detect and identify proteins and protein modifications using SELDI-TOF-MS might reveal a better insight on this kind of disease and may lead to a better understanding and earlier detection of MF patients.

Guilt-by-association feature selection: identifying biomarkers from proteomic profiles

In recent years, proteomic profiling by mass spectrometry has opened up a new realm of methods for identifying potential biomarkers. Mass spectrometry data, like other proteomic and genomic data, are challenging to analyze because of their high dimensionality and the availability of few samples. Hence, feature selection is extremely important because it directly provides a list of potential biomarkers by choosing a subset of effective features that separate diseased samples from healthy ones. The rule of thumb for feature selection is that features must be discriminant and independent for the best separation of the two groups. However, in general, existing feature selection algorithms only take into account the discrimination ability of features. In this paper, we present a novel method for feature selection, guilt-by-association feature selection (GBA-FS). The algorithm makes it possible to select features that are independent as well as discriminant. After measuring similarities between features, the algorithm groups together similar features using a clustering algorithm, and selects the best representative feature from each group. As a result, it produces a list of discriminant and independent features. The efficacy of GBA-FS was extensively tested on two real-world SELDI TOF data sets. The experimental results demonstrate that GBA-FS assists in selecting more independent features as compared to a common filter type feature selection method, the t test. The results also show that GBA-FS can be used to deconvolve multiply charged states of the same protein molecules. As GBA-FS successfully identifies feature groups with similar mass values, it can also be employed as an alternative to peak detection for preprocessing the mass spectrometry data.

Plasma proteomic analysis of pancreatic cancer by 2-dimensional gel electrophoresis

OBJECTIVE

Late diagnosis is the major reason for poor prognosis of pancreatic cancer (PC). Developing new biomarkers of early stage detection is critical.

METHODS

Proteomic analysis with 2-dimensional gel electrophoresis was used to identify differentially expressed proteins in plasma of PC patients. The 2-dimensional gel electrophoresis plasma protein profiles of 11 PC patients (preoperative and postoperative) were compared with those of 10 patients with chronic pancreatitis (CP) and 11 healthy controls.

RESULTS

Five proteins were found to be constantly changed. Haptoglobin (Hp) beta chain and leucine-rich alpha2 glycoprotein up-regulated slightly in PC plasma. Pancreatic cancer had a higher frequency of Hp2-2 phenotype but lacked Hp1-1 phenotype. Hemoglobin was increased significantly in plasma samples of PC and CP. Alpha1 antitrypsin gradually increased its expression level in healthy control, PC, and CP. Immunoglobin J chain was elevated in CP plasma samples. Haptoglobin, alpha1 antitrypsin, and leucine-rich alpha2-glycoprotein were all greatly elevated after tumor resection in PC patients.

CONCLUSIONS

Proteomic analysis can simultaneously detect changes of multiproteins in plasma of PC, but detected proteins are abundant and common plasma proteins and their diagnostic value may be limited.

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.

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

OBJECTIVE

Early detection is the only promising approach to significantly improve the survival of patients with pancreatic cancer (PCa). Noninvasive tools for the diagnosis, prognosis, and monitoring of this disease are of urgent need. The purpose of this study was to identify and validate new biomarkers in PCa patient serum samples.

METHODS

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry has been applied to analyze serum protein alterations associated with PCa and to identify sets of potential biomarkers indicative for this disease. A cohort of 96 serum samples from patients undergoing PCa surgery was compared with sera from 96 healthy volunteers as controls. The sera were fractionated by anion exchange chromatography, and 3 of 6 fractions were analyzed onto 2 different chromatographic arrays.

RESULTS

Data analysis revealed 24 differentially expressed protein peaks (P < 0.001), of which 21 were downregulated in the PCa samples. The best single marker can predict 92% of the controls and 89% of the cancer samples correctly. In addition, multivariate pattern analysis was performed. The best pattern model using a set of 3 markers was obtained using fraction 6 on immobilized metal affinity capture, loaded with Cu-Cu arrays. With this pattern model, a sensitivity of 100% and a specificity of 98% for the training data set and a sensitivity of 83% and specificity of 77% for the test data set were achieved with the PCa group set as true positive. Several of protein peaks, including the best single marker at 17.27 kd and other proteins from the pattern models, were purified and identified by peptide mapping and postsource decay-matrix-assisted laser desorption ionization-time-of-flight mass spectrometry. Apolipoprotein A-II, transthyretin, and apolipoprotein A-I were identified as markers, and these identified proteins were decreased at least 2-fold in PCa serum when compared with the control group.

CONCLUSIONS

PCa is associated with a specific decrease of distinct serum proteins, which allows a reliable differentiation between pancreatic cancer and healthy controls.

Plasma proteomics of pancreatic cancer patients by multi-dimensional liquid chromatography and two-dimensional difference gel electrophoresis (2D-DIGE): up-regulation of leucine-rich alpha-2-glycoprotein in pancreatic cancer

We investigated the aberrant expression of plasma proteins in patients with pancreatic cancer. High-abundance plasma proteins (albumin, transferrin, haptoglobin, alpha-1-antitrypsin, IgG and IgA) were depleted by use of an immuno-affinity column, and low-abundance ones were separated into five fractions by anion-exchange chromatography. The fractionated plasma proteins were subjected to 2D-DIGE with highly sensitive fluorescent dyes. The quantitative protein expression profiles obtained by 2D-DIGE were compared between two plasma protein mixtures: one from five non-cancer bearing healthy donors and the other from five patients with pancreatic cancer. Among 1200 protein spots, we found that 33 protein spots were differently expressed between the two mixtures; 27 of these were up-regulated and six were down-regulated in cancer. Mass spectrometry and database searching allowed the identification of the proteins corresponding to the gel spots. Up-regulation of leucine-rich alpha-2-glycoprotein (LRG), which has not previously been implicated in pancreatic cancer, was observed. Western blotting with an anti-LRG antibody validated the up-regulation of LRG in an independent series of plasma samples from healthy controls, patients with chronic pancreatitis, and patients with pancreatic cancer. Our results demonstrate the application of a combination of multi-dimensional liquid chromatography with 2D-DIGE for plasma proteomics and suggest the clinical utility of LRG plasma level measurement.

A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality

Clinicians and health service researchers are frequently interested in predicting patient-specific probabilities of adverse events (e.g. death, disease recurrence, post-operative complications, hospital readmission). There is an increasing interest in the use of classification and regression trees (CART) for predicting outcomes in clinical studies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting mortality after hospitalization with an acute myocardial infarction (AMI). We also examined the predictive ability of two other types of data-driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 9484 patients admitted to hospital with an AMI in Ontario. We used repeated split-sample validation: the data were randomly divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using the area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1000 times-the initial data set was randomly divided into derivation and validation samples 1000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1000 derivation samples was 0.762, while the mean ROC curve area of a simple logistic regression model was 0.845. The mean ROC curve areas for the other methods ranged from a low of 0.831 to a high of 0.851. Our study shows that regression trees do not perform as well as logistic regression for predicting mortality following AMI. However, the logistic regression model had performance comparable to that of more flexible, data-driven models such as GAMs and MARS.

Human body fluid proteome analysis

The focus of this article is to review the recent advances in proteome analysis of human body fluids, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, and amniotic fluid, as well as its applications to human disease biomarker discovery. We aim to summarize the proteomics technologies currently used for global identification and quantification of body fluid proteins, and elaborate the putative biomarkers discovered for a variety of human diseases through human body fluid proteome (HBFP) analysis. Some critical concerns and perspectives in this emerging field are also discussed. With the advances made in proteomics technologies, the impact of HBFP analysis in the search for clinically relevant disease biomarkers would be realized in the future.