Proteomic patterns of nipple aspirate fluids obtained by SELDI-TOF: potential for new biomarkers to aid in the diagnosis of breast cancer

Proteomics in Diagnostic Evaluation and Treatment of Breast Cancer: A Scoping Review

Objectives: The aim of this scoping review was to delineate the current role and possible applications of proteomics in personalized breast cancer diagnostic evaluation and treatment. Methods: A comprehensive search in PubMed/MEDLINE and Scopus/EMBASE was conducted, according to the PRISMA-ScR guidelines. Inclusion criteria: proteomic studies of specimens from breast cancer patients, clinically relevant studies and clinical studies. Exclusion criteria: in silico, in vitro and studies in animal models, review articles, case reports, case series, comments, editorials, and articles in language other than English. The study protocol was registered in the Open Science Framework. Results: In total, 1093 records were identified, 170 papers were retrieved and 140 studies were selected for data extraction. Data analysis and synthesis of evidence showed that most proteomic analyses were conducted in breast tumor specimens (n = 77), followed by blood samples (n = 48), and less frequently in other biologic material taken from breast cancer patients (n = 19). The most commonly used methods were liquid chromatography-tandem mass spectrometry (LC-MS/MS), followed by Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF), Surface-Enhanced Laser Desorption/Ionization Time-of-Flight (SELDI-TOF) and Reverse Phase Protein Arrays (RPPA). Conclusions: The present review provides a thorough map of the published literature reporting clinically relevant results yielded from proteomic studies in various biological samples from different subgroups of breast cancer patients. This analysis shows that, although proteomic methods are not currently used in everyday practice to guide clinical decision-making, nevertheless numerous proteins identified by proteomics could be used as biomarkers for personalized diagnostic evaluation and treatment of breast cancer patients.

Early detection of breast cancer through the diagnosis of Nipple Aspirate Fluid (NAF)

The development of breast cancer has been mainly reported in women who have reached the post-menopausal stage; therefore, it is the primary factor responsible for death amongst postmenopausal women. However, if treated on time it has shown a survival rate of 20 years in about two-thirds of women. Cases of breast cancer have also been reported in younger women and the leading cause in them is their lifestyle pattern or they may be carriers of high penetrance mutated genes. Premenopausal women who have breast cancer have been diagnosed with aggressive build-up of tumors and are therefore at more risk of loss of life. Mammography is an effective way to test for breast cancer in women after menopause but is not so effective for premenopausal women or younger females. Imaging techniques like contrast-enhanced MRI can up to some extent indicate the presence of a tumor but it cannot adequately differentiate between benign and malignant tumors. Although the 'omics' strategies continuing for the last 20 years have been helpful at the molecular level in enabling the characteristics and proper understanding of such tumors over long-term longitudinal monitoring. Classification, diagnosis, and prediction of the outcomes have been made through tissue and serum biomarkers but these also fail to diagnose the disease at an early stage. Considerably there is no adequate detection technique present globally that can help early detection and provide adequate specificity, safety, sensitivity, and convenience for the younger and premenopausal women, thereby it becomes necessary to take early measures and build efficient tools and techniques for the same. Through biopsies of nipple aspirate fluid (NAF) biomarker profiling can be performed. It is a naturally secreted fluid from the cells of epithelium found in the breast. Nowadays, home-based liquid biopsy collection kits are also available through which a routine check on breast health can be performed with the help of NAF. Herein, we will review the biomarker screening liquid biopsy, and the new emerging technologies for the examination of cancer at an early stage, especially in premenopausal women.

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Nipple Aspirate Fluid at a Glance

Simple Summary

Nipple aspirate fluid (NAF) is a promising source of markers for detection of breast cancer. NAF can be acquired via the nipple by aspiration using a suction device, which is well tolerated by women. Future possible applications of biomarkers for breast cancer derived from NAF could be (1) as a detection tool to identify the initiation of the cancer development process, (2) as an additional tool next to imaging (mammography and breast magnetic resonance imaging) or (3) as a replacement tool for when imaging is not advisable for women, such as during pregnancy and breastfeeding. With this paper, we present a narrative review and perspectives of NAF research at a glance.

Abstract

Nipple aspirate fluid (NAF) is an intraductal mammary fluid that, because of its close proximity to and origin from the tissue from which breast cancer originates, is a promising source of biomarkers for early breast cancer detection. NAF can be non-invasively acquired via the nipple by aspiration using a suction device; using oxytocin nasal spray helps increase yield and tolerability. The aspiration procedure is generally experienced as more tolerable than the currently used breast imaging techniques mammography and breast magnetic resonance imaging. Future applications of NAF-derived biomarkers include their use as a tool in the detection of breast carcinogenesis at its earliest stage (before a tumor mass can be seen by imaging), or as a supporting diagnostic tool for imaging, such as when imaging is less reliable (to rule out false positives from imaging) or when imaging is not advisable (such as during pregnancy and breastfeeding). Ongoing clinical studies using NAF samples will likely shed light on NAF’s content and clinical potential. Here, we present a narrative review and perspectives of NAF research at a glance.

Proteomics and its applications in breast cancer

Breast cancer is an individually unique, multi-faceted and chameleonic disease, an eternal challenge for the new era of high-integrated precision diagnostic and personalized oncomedicine. Besides traditional single-omics fields (such as genomics, epigenomics, transcriptomics and metabolomics) and multi-omics contributions (proteogenomics, proteotranscriptomics or reproductomics), several new "-omics" approaches and exciting proteomics subfields are contributing to basic and advanced understanding of these "multiple diseases termed breast cancer": phenomics/cellomics, connectomics and interactomics, secretomics, matrisomics, exosomics, angiomics, chaperomics and epichaperomics, phosphoproteomics, ubiquitinomics, metalloproteomics, terminomics, degradomics and metadegradomics, adhesomics, stressomics, microbiomics, immunomics, salivaomics, materiomics and other biomics. Throughout the extremely complex neoplastic process, a Breast Cancer Cell Continuum Concept (BCCCC) has been modeled in this review as a spatio-temporal and holistic approach, as long as the breast cancer represents a complex cascade comprising successively integrated populations of heterogeneous tumor and cancer-associated cells, that reflect the carcinoma's progression from a "driving mutation" and formation of the breast primary tumor, toward the distant secondary tumors in different tissues and organs, via circulating tumor cell populations. This BCCCC is widely sustained by a Breast Cancer Proteomic Continuum Concept (BCPCC), where each phenotype of neoplastic and tumor-associated cells is characterized by a changing and adaptive proteomic profile detected in solid and liquid minimal invasive biopsies by complex proteomics approaches. Such a profile is created, beginning with the proteomic landscape of different neoplastic cell populations and cancer-associated cells, followed by subsequent analysis of protein biomarkers involved in epithelial-mesenchymal transition and intravasation, circulating tumor cell proteomics, and, finally, by protein biomarkers that highlight the extravasation and distant metastatic invasion. Proteomics technologies are producing important data in breast cancer diagnostic, prognostic, and predictive biomarkers discovery and validation, are detecting genetic aberrations at the proteome level, describing functional and regulatory pathways and emphasizing specific protein and peptide profiles in human tissues, biological fluids, cell lines and animal models. Also, proteomics can identify different breast cancer subtypes and specific protein and proteoform expression, can assess the efficacy of cancer therapies at cellular and tissular level and can even identify new therapeutic target proteins in clinical studies.

Proteomics analysis of human breast milk to assess breast cancer risk

Detection of breast cancer (BC) in young women is challenging because mammography, the most common tool for detecting BC, is not effective on the dense breast tissue characteristic of young women. In addition to the limited means for detecting their BC, young women face a transient increased risk of pregnancy-associated BC. As a consequence, reproductively active women could benefit significantly from a tool that provides them with accurate risk assessment and early detection of BC. One potential method for detection of BC is biochemical monitoring of proteins and other molecules in bodily fluids such as serum, nipple aspirate, ductal lavage, tear, urine, saliva and breast milk. Of all these fluids, only breast milk provides access to a large volume of breast tissue, in the form of exfoliated epithelial cells, and to the local breast environment, in the form of molecules in the milk. Thus, analysis of breast milk is a non-invasive method with significant potential for assessing BC risk. Here we analyzed human breast milk by mass spectrometry (MS)-based proteomics to build a biomarker signature for early detection of BC. Ten milk samples from eight women provided five paired-groups (cancer versus control) for analysis of dysregulatedproteins: two within woman comparisons (milk from a diseased breast versus a healthy breast of the same woman) and three across women comparisons (milk from a woman with cancer versus a woman without cancer). Despite a wide range in the time between milk donation and cancer diagnosis (cancer diagnosis occurred from 1 month before to 24 months after milk donation), the levels of some proteins differed significantly between cancer and control in several of the five comparison groups. These pilot data are supportive of the idea that molecular analysis of breast milk will identify proteins informative for early detection and accurate assessment of BC risk, and warrant further research. Data are available via ProteomeXchange with identifier PXD007066.

Evaluation of nipple aspirate fluid as a diagnostic tool for early detection of breast cancer

There has been tremendous progress in detection of breast cancer in postmenopausal women, resulting in two-thirds of women surviving more than 20 years after treatment. However, breast cancer remains the leading cause of cancer-related deaths in premenopausal women. Breast cancer is increasing in younger women due to changes in life-style as well as those at high risk as carriers of mutations in high-penetrance genes. Premenopausal women with breast cancer are more likely to be diagnosed with aggressive tumours and therefore have a lower survival rate. Mammography plays an important role in detecting breast cancer in postmenopausal women, but is considerably less sensitive in younger women. Imaging techniques, such as contrast-enhanced MRI improve sensitivity, but as with all imaging approaches, cannot differentiate between benign and malignant growths. Hence, current well-established detection methods are falling short of providing adequate safety, convenience, sensitivity and specificity for premenopausal women on a global level, necessitating the exploration of new methods. In order to detect and prevent the disease in high risk women as early as possible, methods that require more frequent monitoring need to be developed. The emergence of "omics" strategies over the last 20 years, enabling the characterisation and understanding of breast cancer at the molecular level, are providing the potential for long term, longitudinal monitoring of the disease. Tissue and serum biomarkers for breast cancer stratification, diagnosis and predictive outcome have emerged, but have not successfully translated into clinical screening for early detection of the disease. The use of breast-specific liquid biopsies, such as nipple aspirate fluid (NAF), a natural secretion produced by breast epithelial cells, can be collected non-invasively for biomarker profiling. As we move towards an age of active surveillance, home-based liquid biopsy collection kits are increasingly being applied and these could provide a paradigm shift where NAF biomarker profiling is used for routine breast health monitoring. The current status of established and newly emerging imaging techniques for early detection of breast cancer and the potential for alternative biomarker screening of liquid biopsies, particularly those applied to high-risk, premenopausal women, will be reviewed.

Gender proteomics II. Which proteins in sexual organs

In continuity with the review dealing with differences by gender in non-sexual organs [1], this review collects data on the proteomes of the sexual organs as involved in human reproduction, under both physiological and pathological conditions. It also collects data on the tissue structures and biological fluids typical of pregnancy, such as placenta and amniotic fluid, as well as what may be tested on preimplantation embryos during medically assisted reproduction. The review includes as well mention to all fluids and secretions connected with sex organs and/or reproduction, including sperm and milk, to exemplify two distinctive items in male and female physiology.

SIGNIFICANCE

The causes of infertility are only incompletely understood; the same holds for the causes, and even the early markers, of the most frequent complications of pregnancy. To these established medical challenges, present day practice adds new issues connected with medically assisted reproduction. Omics approaches, including proteomics, are building the database for basic knowledge to possibly translate into clinical testing and eventually into medical routine in this critical branch of health care.

Understanding the Characteristics of Mass Spectrometry Data through the use of Simulation

Background

Mass spectrometry is actively being used to discover disease-related proteomic patterns in complex mixtures of proteins derived from tissue samples or from easily obtained biological fluids. The potential importance of these clinical applications has made the development of better methods for processing and analyzing the data an active area of research. It is, however, difficult to determine which methods are better without knowing the true biochemical composition of the samples used in the experiments.

Methods

We developed a mathematical model based on the physics of a simple MALDI-TOF mass spectrometer with time-lag focusing. Using this model, we implemented a statistical simulation of mass spectra. We used the simulation to explore some of the basic operating characteristics of MALDI or SELDI instruments.

Results

The simulation reproduced several characteristics of actual instruments. We found that the relative mass error is affected by the time discretization of the detector (about 0.01%) and the spread of initial velocities (about 0.1%). The accuracy of calibration based on external standards decays rapidly outside the range spanned by the calibrants. Natural isotope distributions play a major role in broadening peaks associated with individual proteins. The area of a peak is a more accurate measure of its size than the height.

Conclusions

The model described here is capable of simulating realistic mass spectra. The simulation should become a useful tool for generating spectra where the true inputs are known, allowing researchers to evaluate the performance of new methods for processing and analyzing mass spectra.

Availability

http://bioinformatics.mdanderson.org/cromwell.html

The Need for Review and Understanding of SELDI/MALDI Mass Spectroscopy Data Prior to Analysis

Multiple studies have reported that surface enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS) is useful in the early detection of disease based on the analysis of bodily fluids. Use of any multiplex mass spectroscopy based approach as in the analysis of bodily fluids to detect disease must be analyzed with great care due to the susceptibility of multiplex and mass spectroscopy methods to biases introduced via experimental design, patient samples, and/or methodology. Specific biases include those related to experimental design, patients, samples, protein chips, chip reader and spectral analysis. Contributions to biases based on patients include demographics (e.g., age, race, ethnicity, sex), homeostasis (e.g., fasting, medications, stress, time of sampling), and site of analysis (hospital, clinic, other). Biases in samples include conditions of sampling (type of sample container, time of processing, time to storage), conditions of storage, (time and temperature of storage), and prior sample manipulation (freeze thaw cycles). Also, there are many potential biases in methodology which can be avoided by careful experimental design including ensuring that cases and controls are analyzed randomly. All the above forms of biases affect any system based on analyzing multiple analytes and especially all mass spectroscopy based methods, not just SELDI-TOF-MS. Also, all current mass spectroscopy systems have relatively low sensitivity compared with immunoassays (e.g., ELISA). There are several problems which may be unique to the SELDI-TOF-MS system marketed by Ciphergen®. Of these, the most important is a relatively low resolution (±0.2%) of the bundled mass spectrometer which may cause problems with analysis of data. Foremost, this low resolution results in difficulties in determining what constitutes a “peak” if a peak matching approach is used in analysis. Also, once peaks are selected, the peaks may represent multiple proteins. In addition, because peaks may vary slightly in location due to instrumental drift, long term identification of the same peaks may prove to be a challenge. Finally, the Ciphergen® system has some “noise” of the baseline which results from the accumulation of charge in the detector system. Thus, we must be very aware of the factors that may affect the use of proteomics in the early detection of disease, in determining aggressive subsets of cancers, in risk assessment and in monitoring the effectiveness of novel therapies.

Plasma Protein Profiles Differ between Women Diagnosed with Cervical Intraepithelial Neoplasia (CIN) 1 and 3

Early detection of precancerous cells in the cervix and their clinical management is the main purpose of cervical cancer prevention and treatment programs. Cytological findings or testing for high risk (HR)-human papillomavirus (HPV) are inadequately sensitive for use in triage of women at high risk for cervical cancer. The current study is an exploratory study to identify candidate surface-enhanced laser desorption/ionization (SELDI) time of flight (TOF) mass spectrometry (MS) protein profiles in plasma that may distinguish cervical intraepithelial neoplasia (CIN 3) from CIN 1 among women infected with HR-HPV. We evaluated the SELDI-TOF-MS plasma protein profiles of HR-HPV positive 32 women with CIN 3 (cases) and 28 women with CIN1 (controls). Case-control status was kept blinded and triplicates of each sample and quality control plasma samples were randomized and after robotic sample preparations were run on WCX2 chips. After alignment of mass/charge (m-z values), an iterative method was used to develop a classifier on a training data set that had 28 cases and 22 controls. The classifier developed was used to classify the subjects in a test data set that has six cases and six controls. The classifier separated the cases from controls in the test set with 100% sensitivity and 100% specificity suggesting the possibility of using plasma SELDI protein profiles to identify women who are likely to have CIN 3 lesions.

New horizon for breast cancer biomarker discoveries: What might the liquid biopsy of nipple aspirate fluid hold?

The existence of cellular, molecular and biochemical heterogeneity of human breast cancers reveals the intricacy of biomarkers complexity, stimulating studies on new approaches (like "liquid biopsies") for the improvements in precision medicine. Breast cancer is recognized as a leading cause of morbidity and mortality worldwide with tumors significantly diverse and containing many types of cells showing different genetic and epigenetic profiles. In this field, the technology of liquid biopsy (applied to a fluid produced by breast gland, named nipple aspirate fluids, NAF) highlights the power of combining basic and clinical research. NAF is the mirror of the entire ductal/alveolar breast tree providing almost complete proteomic profile and a valuable source for biomarker discovery, in non-invasive manner than tissue biopsies. The liquid biopsy technology using NAF may represent the outstanding breakthrough of proteomic cancer research revealing novel diagnostic and prognostic applications. In conjunction to metabolomic and degradome profiling, the use of NAF as liquid biopsy approach will improve the detection of changes in the cellular microenvironment of the breast tumors, understanding molecular and biochemical mechanisms which drive breast tumor initiation, maintenance and progression, and finally enhancing the development of novel drug targets and new treatment strategies.

Protein profiling of plasma proteins in dairy cows with subclinical hypocalcaemia

Subclinical hypocalcaemia (SH) is an important metabolic disease in dairy cows that has a serious impact on production performance. The objective of this study was to investigate novel aspects of pathogenesis using proteomics technology to identify proteins that are differentially expressed in diseased and healthy animals. Dairy cows were divided into an SH group (T, n = 10) and a control group (C, n = 10) based on plasma calcium concentration. A total of 398 differentially expressed proteins were identified, of which 265 proteins were overlapped in the two parallel experiments. Of these, 24 differentially expressed proteins were statistically significant. Gene Ontology analysis yielded 74 annotations, including 7 cellular component, 55 biological process and 12 molecular function categories. Bioinformatics analysis indicated that calcium regulation, immune and inflammatory response, blood coagulation and complement pathway were all related to SH. Our iTRAQ/LC-MS/MS (isobaric tags for relative and absolute quantification/liquid chromatography-mass spectrometry/mass spectrometry) approach proved highly effective for plasma protein profiling of dairy cows with SH, and the results pave the way for further studies in this area.

A Quantitative Proteomics Approach to Clinical Research with Non-Traditional Samples

The proper handling of samples to be analyzed by mass spectrometry (MS) can guarantee excellent results and a greater depth of analysis when working in quantitative proteomics. This is critical when trying to assess non-traditional sources such as ear wax, saliva, vitreous humor, aqueous humor, tears, nipple aspirate fluid, breast milk/colostrum, cervical-vaginal fluid, nasal secretions, bronco-alveolar lavage fluid, and stools. We intend to provide the investigator with relevant aspects of quantitative proteomics and to recognize the most recent clinical research work conducted with atypical samples and analyzed by quantitative proteomics. Having as reference the most recent and different approaches used with non-traditional sources allows us to compare new strategies in the development of novel experimental models. On the other hand, these references help us to contribute significantly to the understanding of the proportions of proteins in different proteomes of clinical interest and may lead to potential advances in the emerging field of precision medicine.

Proteomic study of benign and malignant pleural effusion

BACKGROUND

Lung adenocarcinoma can easily cause malignant pleural effusion which was difficult to discriminate from benign pleural effusion. Now there was no biomarker with high sensitivity and specificity for the malignant pleural effusion.

PURPOSE

This study used proteomics technology to acquire and analyze the protein profiles of the benign and malignant pleural effusion, to seek useful protein biomarkers with diagnostic value and to establish the diagnostic model.

METHODS

We chose the weak cationic-exchanger magnetic bead (WCX-MB) to purify peptides in the pleural effusion, used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to obtain peptide expression profiles from the benign and malignant pleural effusion samples, established and validated the diagnostic model through a genetic algorithm (GA) and finally identified the most promising protein biomarker.

RESULTS

A GA diagnostic model was established with spectra of 3930.9 and 2942.8 m/z in the training set including 25 malignant pleural effusion and 26 benign pleural effusion samples, yielding both 100 % sensitivity and 100 % specificity. The accuracy of diagnostic prediction was validated in the independent testing set with 58 malignant pleural effusion and 34 benign pleural effusion samples. Blind evaluation was as follows: the sensitivity was 89.6 %, specificity 88.2 %, PPV 92.8 %, NPV 83.3 % and accuracy 89.1 % in the independent testing set. The most promising peptide biomarker was identified successfully: Isoform 1 of caspase recruitment domain-containing protein 9 (CARD9), with 3930.9 m/z, was decreased in the malignant pleural effusion.

CONCLUSIONS

This model is suitable to discriminate benign and malignant pleural effusion and CARD9 can be used as a new peptide biomarker.

Statistical Aspects in Proteomic Biomarker Discovery

In the pursuit of a personalized medicine, i.e., the individual treatment of a patient, many medical decision problems are desired to be supported by biomarkers that can help to make a diagnosis, prediction, or prognosis. Proteomic biomarkers are of special interest since they can not only be detected in tissue samples but can also often be easily detected in diverse body fluids. Statistical methods play an important role in the discovery and validation of proteomic biomarkers. They are necessary in the planning of experiments, in the processing of raw signals, and in the final data analysis. This review provides an overview on the most frequent experimental settings including sample size considerations, and focuses on exploratory data analysis and classifier development.

Set-Based Test Procedures for the Functional Analysis of Protein Lists from Differential Analysis

The analysis of most high-throughput proteomics experiments involves the selection of differentially expressed proteins or peptides between two different sets of samples, e.g., from two experimental groups. As a result, a large list of selected features is reported, typically sorted by a measure for the expression fold change and a p-value from a statistical test. The biological interpretation of such a list is usually difficult since the features can typically be assigned to a large variety of biological classes. To facilitate the biological interpretation, set-based procedures focus on the analysis of feature subsets that all belong to the same biological class (e.g., same cellular component, biological process, molecular function, or pathway). Set-based procedures can roughly be divided into "enrichment methods" and "global test procedures," where the first involve all features of an experiment and the second only those features of a particular set. In this chapter we detail the working principle of these kind of statistical methods and describe how features can be classified into molecular subsets. We illustrate the use of the methods on a data example from a proteomics Parkinson study.

Raman microspectroscopy of noncancerous and cancerous human breast tissues. Identification and phase transitions of linoleic and oleic acids by Raman low-temperature studies

We present the results of Raman studies in the temperature range of 293-77 K on vibrational properties of linoleic and oleic acids and Raman microspectroscopy of human breast tissues at room temperature. Our results confirmed the significant role of unsaturated fatty acids in differentiation of noncancerous and cancerous breast tissues and the role of vibrational spectroscopy in phase transition identification. We have found that vibrational properties are very sensitive indicators to specify phases and phase transitions typical of unsaturated fatty acids at the molecular level. Using Raman spectroscopy we have identified high-temperature, middle-temperature and low-temperature phases of linoleic acid. Results obtained for linoleic acid were compared with parameters characteristic of α and γ phases of oleic acid - the parent compound of polyunsaturated fatty acids.

Biomarker Signature Discovery from Mass Spectrometry Data

Mass spectrometry based high throughput proteomics are used for protein analysis and clinical diagnosis. Many machine learning methods have been used to construct classifiers based on mass spectrometry data, for discrimination between cancer stages. However, the classifiers generated by machine learning such as SVM techniques typically lack biological interpretability. We present an innovative technique for automated discovery of signatures optimized to characterize various cancer stages. We validate our signature discovery algorithm on one new colorectal cancer MALDI-TOF data set, and two well-known ovarian cancer SELDI-TOF data sets. In all of these cases, our signature based classifiers performed either better or at least as well as four benchmark machine learning algorithms including SVM and KNN. Moreover, our optimized signatures automatically select smaller sets of key biomarkers than the black-boxes generated by machine learning, and are much easier to interpret.

Analysis of nipple aspirate fluid for diagnosis of breast cancer: an alternative to invasive biopsy

Over 40,000 women in the USA will die this year of breast cancer. Current generally accepted techniques to detect breast cancer are limited to breast examination and mammography. Abnormalities found by these techniques require an invasive needle or surgical biopsy to determine if cancer is present. The author's ultimate goal is to determine if a woman has breast cancer without the need for invasive biopsies, and do this before the abnormality is detectable by standard screening techniques. Herein, the technology is reviewed as it was, as it is today, and its future potential is discussed.

Tissue proteomics using capillary isoelectric focusing-based multidimensional separations

The capabilities of capillary isoelectric focusing-based multidimensional separations for performing proteome analysis from minute samples create new opportunities in the pursuit of biomarker discovery using enriched and selected cell populations procured from tissue specimens. In this article, recent advances in online integration of capillary isoelectric focusing with nano-reversed phase liquid chromatography for achieving high-resolution peptide and protein separations prior to mass spectrometry analysis are reviewed, along with its potential application to tissue proteomics. These proteome technological advances combined with recently developed tissue microdissection techniques, provide powerful tools for those seeking to gain a greater understanding at the global level of the cellular machinery associated with human diseases such as cancer.

Novel proteomic approaches for tissue analysis

Proteomics, the global study of protein expression and characteristics, has recently emerged as a key component in the field of molecular analysis. The dynamic nature of proteins, from ion channels to chaperones, presents a challenge, yet the understanding of these molecules provides a rich source of information. When applying proteomic analysis directly to human tissue samples, additional difficulties arise. The following article presents an overview of the current proteomic tools used in the analysis of tissues, beginning with conventional methods such as western blot analysis and 2D polyacrylamide gel electrophoresis. The most current high-throughput techniques being used today are also reviewed. These include protein arrays, reverse-phase protein lysate arrays, matrix-assisted laser desorption/ionization, surface-enhanced laser desorption/ionization and layered expression scanning. In addition, bioinformatics as well as issues regarding tissue preservation and microdissection to obtain pure cell populations are included. Finally, future directions of the tissue proteomics field are discussed.

Using breast milk to assess breast cancer risk: the role of mass spectrometry-based proteomics

Although mammography and treatment advances have led to declines in breast cancer mortality in the United States, breast cancer remains a major cause of morbidity and mortality. Breast cancer in young women is associated with increased mortality and current methods of detecting breast cancers in this group of women have known limitations. Tools for accurately assessing personal breast cancer risk in young women are needed to identify those women who would benefit the most from earlier intervention. Proteomic analysis of breast milk could identify biomarkers of breast cancer risk and provide a tool for identifying women at increased risk. A preliminary analysis of milk from four women provides a proof of concept for using breast milk to assess breast cancer risk.

The differential diagnostic model for serous peptidomics in HBV carriers established by MALDI-TOF-MS analysis

OBJECTIVES

Hepatitis B virus (HBV) can result in asymptomatic carrier (AsC) state or chronic inflammation of liver, which depends on the host immunity. We therefore investigated the peptidomic profiling in the process of HBV infection.

DESIGN AND METHODS

In this study, serum from 116 HBV infected (AsC and chronic hepatitis), 60 HBV-immunized and 70 normal subjects was treated with MB-WCX (weak cation exchange based magnetic beads) kits and analyzed by the Clinprot/Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS) techniques. Purified serous proteins were subjected to FT-ICR-MS analysis, and Western blot further confirmed the results.

RESULTS

The specific model comprised of two peptides m/z 2882.89 and 4476.12 could distinguish HBV infected from healthy (HBV-immunized and normal) group and showed 95.5% of the sensitivity and 95.4% of the specificity by cross-validation analysis. 40/56 HBV infected and 43/50 healthy subjects could be correctly classified by the model. The area under the receiving operating curves (AUROC) of m/z 2882.89 and 4476.12, identified as subunits of fibrinogen beta chain (FBG) Bβ10-42 and nucleophosmin (NPM) respectively, were both up to 0.88 when discriminating AsC from the healthy group. The expression of Bβ10-42 and NPM decreased significantly in the plasma of HBV infected individuals by Western blot analysis.

CONCLUSIONS

There were specific serum peptide profilings for host responses to HBV infection, and m/z 2882.89 and 4476.12 could be valuable follow-up and prognostic tools for HBV infection.

Clinical neuroproteomics and biomarkers: from basic research to clinical decision making

Clinical neuroproteomics aims to advance our understanding of disease and injury affecting the central and peripheral nervous systems through the study of protein expression and the discovery of protein biomarkers to facilitate diagnosis and treatment. The general premise of the biomarker field is that in vivo factors present in either tissue or circulating biofluids, reflect pathological changes, and can be identified and analyzed. This approach offers an opportunity to illuminate changes occurring at both the population and patient levels toward the realization of personalized medicine. This review is intended to provide research-driven clinicians with an overview of protein biomarkers of disease and injury for clinical use and to highlight methodology and potential pitfalls. We examine the neuroproteomic biomarker field and discuss the hallmarks and the challenges of clinically relevant biomarker discovery relating to central nervous system pathology. We discuss the issues in the maturation of potential biomarkers from discovery to Food and Drug Administration approval and review several platforms for protein biomarker discovery, including protein microarray and mass spectrometry-based proteomics. We describe the application of microfluidic technologies to the evolution of a robust clinical test. Finally, we highlight several biomarkers currently in use for cancer, ischemia, and injury in the central nervous system. Future efforts using these technologies will result in the maturation of existing and the identification of de novo biomarkers that could guide clinical decision making and advance diagnostic and therapeutic options for the treatment of neurological disease and injury.

Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach

Noninvasive biomarkers are urgently needed for early detection of breast cancer since the risk of recurrence, morbidity and mortality are closely related to disease stage at the time of primary surgery. In the past decade, many proteomics-based approaches were developed that utilize the protein profiling of human body fluids or identification of putative biomarkers to obtain more knowledge on the effects of cancer emergence and progression. Herein, we report on an analysis of proteins in the tear fluid from breast carcinoma patients and healthy women using a de novo proteomic approach and 25 mixed samples from each group. This study included 25 patients with primary invasive breast carcinoma and 25 age-matched healthy controls. We performed a MALDI-TOF-TOF-driven semi-quantitative comparison of tear protein levels in cancer (CA) and control (CTRL) using a de novo approach in pooled samples. Over 150 proteins in the tear fluid of CTRL and CA were identified. Using an in-house-developed algorithm we found more than 20 proteins distinctly upregulated or downregulated in the CTRL and CA groups. We identified several proteins that had modified expression in breast cancer patients. These proteins are involved in host immune system pathways (e.g., C1Q1 or S100A8) and different metabolic cascades (ALDH3A or TPI). Further validation of the results in an independent population combined with individual protein profiling of participants is needed to confirm the specificity of our findings and may lead to a better understanding of the pathological mechanism of breast cancer.

Proteomic biomarkers in second trimester amniotic fluid that identify women who are destined to develop preeclampsia

OBJECTIVES

This study investigated whether proteomic analysis of amniotic fluid (AF) in the early second trimester can be used to predict the development of preeclampsia.

METHODS

Amniotic fluid samples were collected at the time of genetic amniocentesis (15-19 weeks of gestation) from women who subsequently developed preeclampsia and from gestational age-matched normotensive controls (n = 10 for each). Amniotic fluid samples were subjected to proteomic analysis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, sodium dodecyl sulfate polyacrylamide gel coupled with in-gel tryptic digestion, electrospray ionization tandem mass spectrometry (MS/MS), immunodepletion assays, and enzyme-linke immunosorbent assay.

RESULTS

Five proteomic biomarkers were identified, which were differentially expressed in women who subsequently developed preeclampsia compared with those women who did not; four of these peaks were significantly upregulated (mass-to-charge ratio of 9080 [P = .006], 14 045 [P = .010], 14 345 [P = .049], and 28 087 [P = .006]) and one was significantly downregulated (mass-to-charge ratio of 4679 [P = .014]) in women who subsequently developed preeclampsia. Using electrospray ionization MS/MS and immunodepletion assays, two protein peaks were identified as albumin fragment and apolipoprotein A-I.

CONCLUSIONS

Using proteomic technology, this study identified protein biomarkers that are differentially expressed in the early second trimester AF from women who subsequently develop preeclampsia compared with women who remained normotensive. Early identification of women at risk of developing preeclampsia will allow clinicians to better optimize maternal and perinatal outcomes.

Validation of tumor-associated macrophage ferritin light chain as a prognostic biomarker in node-negative breast cancer tumors: A multicentric 2004 national PHRC study

Novel prognostic biomarkers are imperatively needed to help direct treatment decisions by typing subgroups of node-negative breast cancer patients. Large screening of different biological compartments, such as the proteome, by means of high throughput techniques may greatly help scientists to find such markers. The present retrospective multicentric study included 268 node-negative breast cancer patients. We used a proteomic approach of SELDI-TOF-MS screening to identify differentially expressed cytosolic proteins with prognostic impact. The screening cohort was composed of 198 patients. Seventy supplementary patients were included for validation. Immunohistochemistry (IHC) and immunoassay (IA) were run to confirm the prognostic role of the marker identified by SELDI-TOF-MS screening. IHC was also used to explore links between selected marker and epithelial-mesenchymal transition (EMT)-like, proliferation and macrophage markers. Ferritin light chain (FTL) was identified as an independent prognostic marker (HR = 1.30-95% CI: 1.10-1.50, p = 0.001). Validation step by means of IHC and IA confirmed the prognostic value of FTL level. CD68 IHC showed that FTL was stored in tumor-associated macrophages (TAM), which exhibit an M2-like phenotype. We report here, first, the validation of FTL as a breast tumor prognostic biomarker in node-negative patients, and second, the fact that FTL is stored in TAM.

Protein biomarkers for the early detection of breast cancer

Advances in breast cancer control will be greatly aided by early detection so as to diagnose and treat breast cancer in its preinvasive state prior to metastasis. For breast cancer, the second leading cause of cancer-related death among women in the United States, early detection does allow for increased treatment options, including surgical resection, with a corresponding better patient response. Unfortunately, however, many patients' tumors are diagnosed following metastasis, thus making it more difficult to successfully treat the malignancy. There are, at present, no existing validated plasma/serum biomarkers for breast cancer. Only a few biomarkers (such as HER-2/neu, estrogen receptor, and progesterone receptor) have utility for diagnosis and prognosis. Thus, there is a great need for new biomarkers for breast cancer. This paper will focus on the identification of new serum protein biomarkers with utility for the early detection of breast cancer.

Characterization of discriminatory urinary proteomic biomarkers for severe preeclampsia using SELDI-TOF mass spectrometry

OBJECTIVE

To analyze the proteomic pattern in urine for distinguishing severe preeclampsia from mild preeclampsia and normotensive controls using surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS).

STUDY DESIGN

Urine samples were collected from women with severe preeclampsia (n=11 [sPE]), mild preeclampsia (n=7 [mPE]), and normotensive controls (n=8) and analyzed by SELDI-TOF-MS to identify discriminatory protein peaks in the sPE cohort. A scoring system was constructed--designated as Preeclampsia Proteomic Score of Urine (PPSU)--to differentiate sPE from mPE and normotensive controls.

RESULTS

Four discriminatory protein peaks were identified (m/z ratio: 4155, 6044, 6663, and 7971), all of which were down-regulated in women with sPE. PPSU scores in women with sPE were significantly lower than that in both mPE and controls (sPE 0 [0-4] vs. mPE 3 [0-4] vs. controls 4 [2-4]; median [range]; P<0.05). PPSU<2 had a sensitivity of 90.9% and specificity of 93.3% in discriminating patients with sPE from mPE and controls.

CONCLUSION

Proteomic analysis of urine can accurately distinguish sPE from mPE and normotensive controls.

Different protein profile in amniotic fluid with nervous system malformations by surface-enhanced laser desorption-ionization/time-of-flight mass spectrometry (SELDI-TOF-MS) technology

AIM

To detect the distinct proteins in amniotic fluid (AF) between nervous system malformations fetuses and normal fetuses.

MATERIAL AND METHODS

Surface-enhanced laser desorption-ionization/time-of-flight mass spectrometry was used to characterize AF peptides in AF between nervous system malformations fetuses and normal fetuses. WCX2 protein chips were used to characterize AF peptides in AF. Protein chips were examined in a PBSIIC protein reader, the protein profiling was collected by ProteinChip software version 3.1 (Ciphergen Biosystems, Fremont, CA, USA) and analyzed by Biomarker Wizard software (Ciphergen Biosystems). Nine distinct proteins were identified in AF between nervous system malformations fetuses and normal fetuses.

RESULTS

Compared with the control group, three proteins with m/z 4967.5 Da, 5258.0 Da, and 11,717.0 Da were down-regulated, and six proteins with m/z 2540.4 Da, 3107.1 Da, 3396.8 Da, 4590.965 Da, 5589.2 Da and 6429.4 Da up-regulated in nervous system malformations fetuses.

CONCLUSION

The results suggest that there are distinct proteins in protein profiling of AF between nervous system malformations fetuses and normal fetuses.

The value of serum biomarkers (Bc1, Bc2, Bc3) in the diagnosis of early breast cancer

BACKGROUND

Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) is an approach to biomarker discovery that combines chromatography and mass spectrometry. We aimed to consider the efficacy of Bc1, Bc2, and Bc3 serum biomarkers on early detection of breast cancer (BC) in this study.

STUDY DESIGN

In this prospective study, 91 patients who were admitted to our hospital between January 2007 and July 2008 were included. Serum samples from 91 women were stored at -80 °C until use. The cancer group included 27 cases of BC. The benign breast disease group included 24 women with benign breast diseases and control group 37 age-matched apparently healthy women. The data obtained for these three groups of patients was worked out for each serum biomarker (Bc1, Bc2, and Bc3) by using SELDI-TOF individually and compared with each other separately and evaluated statistically.

RESULTS

Bc2 possesses the highest individual diagnostic power. Bc2 was statistically significant in comparison between the malignant disease group, control group and benign disease group. Bc1 was statistically significant in the malignant disease group compared to control group as well as in the benign disease group compared to control group. Thus Bc1, rather than showing malignant progression, it shows tumoral progression or inflammatory process. Bc3 was found upregulated in all malignant cases; however, it was not statistically significant compared to the benign disease group or the control group.

CONCLUSIONS

It has been shown that Bc2 profiles might be useful in clinical practice to improve BC diagnosis. However none of the proteomics reach reasonable AUC values for the discrimination of the BC. Additional confirmation in larger and similarly-designed prospective studies is needed to consider of the efficacy of Bc1 and Bc2 in early diagnosis of the BC.

Reverse phase protein microarrays advance to use in clinical trials

Individualizing cancer therapy for molecular targeted inhibitors requires a new class of molecular profiling technology that can map the functional state of the cancer cell signal pathways containing the drug targets. Reverse phase protein microarrays (RPMA) are a technology platform designed for quantitative, multiplexed analysis of specific phosphorylated, cleaved, or total (phosphorylated and non-phosphorylated) forms of cellular proteins from a limited amount of sample. This class of microarray can be used to interrogate tissue samples, cells, serum, or body fluids. RPMA were previously a research tool; now this technology has graduated to use in research clinical trials with clinical grade sensitivity and precision. In this review we describe the application of RPMA for multiplexed signal pathway analysis in therapeutic monitoring, biomarker discovery, and evaluation of pharmaceutical targets, and conclude with a summary of the technical aspects of RPMA construction and analysis.

Protein expression profiles distinguish between experimental invasive pulmonary aspergillosis and Pseudomonas pneumonia

We hypothesized that invasive pulmonary aspergillosis (IPA) may generate a distinctive proteomic signature in plasma and bronchoalveolar lavage (BAL). Proteins in plasma and BAL from two neutropenic rabbit models of IPA and Pseudomonas pneumonia were analyzed by SELDI-TOF MS. Hierarchical clustering analysis of plasma time course spectra demonstrated two clusters of peaks that were differentially regulated between IPA and Pseudomonas pneumonia (57 and 34 peaks, respectively, p<0.001). PCA of plasma proteins demonstrated a time-dependent separation of the two infections. A random forest analysis that ranked the top 30 spectral points distinguished between late Aspergillus and Pseudomonas pneumonias with 100% sensitivity and specificity. Based on spectral data analysis, three proteins were identified using SDS-PAGE and LC/MS and quantified using reverse phase arrays. Differences in the temporal sequence of plasma haptoglobin (p<0.001), apolipoprotein A1 (p<0.001) and transthyretin (p<0.038) were observed between IPA and Pseudomonas pneumonia, as was C-reactive protein (p<0.001). In summary, proteomic analysis of plasma and BAL proteins of experimental Aspergillus and Pseudomonas pneumonias demonstrates unique protein profiles with principal components and spectral regions that are shared in early infection and diverge at later stages of infection. Haptoglobin, apolipoprotein A1, transthyretin, and C-reactive protein are differentially expressed in these infections suggesting important contributions to host defense against IPA.

Advances in proximal fluid proteomics for disease biomarker discovery

Although serum/plasma has been the preferred source for identification of disease biomarkers, these efforts have been met with little success, in large part due the relatively small number of highly abundant proteins that render the reliable detection of low abundant disease-related proteins challenging due to the expansive dynamic range of concentration of proteins in this sample. Proximal fluid, the fluid derived from the extracellular milieu of tissues, contains a large repertoire of shed and secreted proteins that are likely to be present at higher concentrations relative to plasma/serum. It is hypothesized that many, if not all, proximal fluid proteins exchange with peripheral circulation, which has provided significant motivation for utilizing proximal fluids as a primary sample source for protein biomarker discovery. The present review highlights recent advances in proximal fluid proteomics, including the various protocols utilized to harvest proximal fluids along with detailing the results from mass spectrometry- and antibody-based analyses.

Clinical proteomics of breast cancer

Despite the lifetimes that increased in breast cancers due to the the early screening programs and new therapeutic strategies, many cases still are being lost due to the metastatic relapses. For this reason, new approaches such as the proteomic techniques have currently become the prime objectives of breast cancer researches. Various omic-based techniques have been applied with increasing success to the molecular characterisation of breast tumours, which have resulted in a more detailed classification scheme and have produced clinical diagnostic tests that have been applied to both the prognosis and the prediction of outcome to the treatment. Implementation of the proteomics-based techniques is also seen as crucial if we are to develop a systems biology approach in the discovery of biomarkers of the early diagnosis, prognosis and prediction of the outcome of the breast cancer therapies. In this review, we discuss the studies that have been conducted thus far, for the discovery of diagnostic, prognostic and predictive biomarkers, and evaluate the potential of the discriminating proteins identified in this research for clinical use as breast cancer biomarkers.

Selective release of microRNA species from normal and malignant mammary epithelial cells

MicroRNAs (miRNAs) in body fluids are candidate diagnostics for a variety of conditions and diseases, including breast cancer. One premise for using extracellular miRNAs to diagnose disease is the notion that the abundance of the miRNAs in body fluids reflects their abundance in the abnormal cells causing the disease. As a result, the search for such diagnostics in body fluids has focused on miRNAs that are abundant in the cells of origin. Here we report that released miRNAs do not necessarily reflect the abundance of miRNA in the cell of origin. We find that release of miRNAs from cells into blood, milk and ductal fluids is selective and that the selection of released miRNAs may correlate with malignancy. In particular, the bulk of miR-451 and miR-1246 produced by malignant mammary epithelial cells was released, but the majority of these miRNAs produced by non-malignant mammary epithelial cells was retained. Our findings suggest the existence of a cellular selection mechanism for miRNA release and indicate that the extracellular and cellular miRNA profiles differ. This selective release of miRNAs is an important consideration for the identification of circulating miRNAs as biomarkers of disease.

Identification of esophageal carcinoma-associated proteins by proteomics in Han, Uygur and Kazakh patients with esophageal carcinoma in Xinjiang, China

AIM: To conduct a serum protein profile analysis in Han, Uygur and Kazakh patients with esophageal carcinoma (EC) in Xinjiang, China.

METHODS: Serum samples from patients with EC (43 Han, 43 Uygur and 41 Kazakh subjects) were detected by weak cation exchange (CM10) protein chip assay using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology to screen differentially expressed serum markers for EC.

RESULTS: The peaks at the mass to charge ratios (M/Z) 4 310.0109, 8 713.0142 and 7 993.0223 were significantly different between Han and Uygur EC patients (P < 0.05). The peaks at M/Z 4 310.0184, 8 167.9277, 8 158.1117, 13 789.4864, 8 067.7056, 4 611.9098, 7 993.4422 and 16 146.8706 were significantly different between Han and Kazakh EC patients (P < 0.05). The peaks at M/Z 9 161.7944, 4 611.6342, 6 649.6163 and 4 979.3807 were significantly different between Uygur and Kazakh EC patients (P < 0.05). The peat at M/Z 4 310.0109 was highly expressed in Uygur and Kazakh patients but lowly expressed in Han patients.

CONCLUSION: The protein fingerprints are significantly different among Han, Uygur and Kazakh EC patients in Xinjiang, China, which can be used to build a diagnostic model of EC.

Proteomic analysis of breast tissue and nipple aspirate fluid for breast cancer detection

Clinical proteomics has great promise, but many hurdles. Proteomic approaches may prove to be the answer to decades of frustrating searches for markers that are specific and selective for breast cancer. In order to achieve this goal, the challenge arises that breast cancer is a heterogeneous disease and individual variations are enormous. To identify sensitive and specific breast cancer markers will require standardization of every method, from tissue collection through statistical analysis and diligence. Small pilot studies of tumors with varied characteristics will not suffice; large, homogeneous sample sets will be the most efficient route to identify breast cancer markers. Proteomic analysis of nipple aspirate fluid may identify markers that can be used for screening with this noninvasive technique. Proteomic analysis of breast biopsies will allow more detailed characterization of the individual's cancer, with therapy tailored to the disease based on this information.

A Targeted Proteomics Approach for Biomarker Discovery Using Bilateral Matched Nipple Aspiration Fluids

Introduction

The objective of this study was to identify cancer-associated protein expression patterns in bilateral matched nipple aspiration fluids using nanoscale reciprocal Cy3/Cy5 labeling and high-content antibody microarrays. This novel platform allows the pair-wise comparisons of the relative abundance of 512 different antigens using minimal NAF sample containing 1 µg of total protein.

Material and Methods

Matched NAF samples from two normal controls and 18 patients with early stage breast cancer (stage I/II, 13; DCIS, 2) or precancerous lesions (ADH, 3) were studied. Self-to-self and left-to-right comparisons of the normal controls were performed to determine antigen variations due to normal experimental and biological variability. Based on these two experiments, a stringency filter of 15% was applied to eliminate noise. Antigens were considered differentially expressed if there were a consistent >15% change on relative Cy3/Cy5 signals on reciprocal slides.

Results and Discussion

The number of differentially expressed antigens varied between 10 and 72 in tumor associated NAF samples, and no single antigen can be used as a “universal” marker to identify all patients. Antigens that are elevated in at least four patients were selected for further evaluations, including NME1, PTK2B, ARRB1, MRIP, GFRA1, APC, HSPD1, and SLP76. The validity of the antibody array findings was affirmed by single immunoassay on western blot; elevated expression of four of the selected markers in NAF is supported by published immunohistochemistry studies on breast cancer tissues.

Conclusions

Nipple aspiration fluid is a rich source of breast cancer biomarkers. This targeted proteomics approach for biomarker discovery is proven effective for clinical samples with limited protein content.

Nipple Aspirate Fluid Proteome of Healthy Females and Patients with Breast Cancer

Background: The ductal/alveolar system of the female breast constantly secretes and reabsorbs fluid in nonpregnant/nonlactating women. This fluid, referred to as nipple aspirate fluid (NAF), can be obtained by a noninvasive procedure and it is part of the microenvironment where more than 95% of breast cancers arise.

Methods: Using an Orbitrap® mass analyzer coupled to a linear ion trap, we performed an in-depth proteomic analysis of NAF samples obtained from 3 healthy individuals and 3 patients with breast cancer. Multiple fractionation methods such as size-exclusion and anion-exchange chromatography were applied for protein separation before mass spectrometric analysis.

Results: We identified more than 800 unique proteins in total, generating the most extensive NAF proteome thus far. Using gene ontology, we classified the identified proteins by their subcellular localization and found that more than 50% were extracellular or plasma membrane proteins. By searching against the Plasma Proteome Database, we confirmed that 40% of the proteins were also found in the plasma. Unigene database searching for transcripts of the proteins not found in the plasma revealed that the vast majority were expressed in the mammary gland.

Conclusions: Our extensive proteome database for NAF may be helpful in the identification of novel cancer biomarkers.

The cancer cell secretome: a good source for discovering biomarkers?

Cancer is a leading cause of death. Early detection is usually associated with better clinical outcomes. Recent advances in genomics and proteomics raised hopes that new biomarkers for diagnosis, prognosis or monitoring therapeutic response will soon be discovered. Proteins secreted by cancer cells, referred also as "the cancer cell secretome", is a promising source for biomarker discovery. In this review we will summarize recent advances in cancer cell secretome analysis, focusing on the five most fatal cancers (lung, breast, prostate, colorectal, and pancreatic). For each cancer type we will describe the proteomic approaches utilized for the identification of novel biomarkers. Despite progress, identification of markers that are superior to those currently used has proven to be a difficult task and very few, if any, newly discovered biomarker has entered the clinic the last 10 years.

Enhanced detection of early hepatocellular carcinoma by serum SELDI-TOF proteomic signature combined with alpha-fetoprotein marker

Background

Biomarkers for accurate diagnosis of early hepatocellular carcinoma (HCC) are limited in number and clinical validation. We applied SELDI-TOF-MS ProteinChip technology to identify serum profile for distinguishing HCC and liver cirrhosis (LC) and to compare the accuracy of SELDI-TOF-MS profile and alpha-fetoprotein (AFP) level in HCC diagnosis.

Patients and Methods

Serum samples were obtained from 120 HCC and 120 LC patients for biomarker discovery and validation studies. ProteinChip technology was employed for generating SELDI-TOF proteomic features and analyzing serum proteins/peptides.

Results

A diagnostic model was established by CART algorithm, which is based on 5 proteomic peaks with m/z values at 3324, 3994, 4665, 4795, and 5152. In the training set, the CART algorithm could differentiate HCC from LC subjects with a sensitivity and specificity of 98% and 95%, respectively. The results were assessed in blind validation using separate cohorts of 60 HCC and 60 LC patients, with an accuracy of 83% for HCC and 92% for LC patients. The diagnostic odd ratio (DOR) indicated that SELDI-TOF proteomic signature could achieve better diagnostic performance than serum AFP level at a cutoff of 20 ng/mL (AFP₂₀) (92.72 vs 9.11), particularly superior for early-stage HCC (87% vs 54%). Importantly, a combined use of both tests could enhance the detection of HCC (sensitivity, 95%; specificity, 98%; DOR, 931).

Conclusion

Serum SELDI-TOF proteomic signature, alone or in combination with AFP marker, promises to be a good tool for early diagnosis and/screening of HCC in at-risk population with liver cirrhosis.

Comparative proteomic analysis of normal and tumor stromal cells by tissue on chip based mass spectrometry (toc-MS)

In carcinoma tissues, genetic and metabolic changes not only occur at the tumor cell level, but also in the surrounding stroma. This carcinoma-reactive stromal tissue is heterogeneous and consists e.g. of non-epithelial cells such as fibroblasts or fibrocytes, inflammatory cells and vasculature-related cells, which promote carcinoma growth and progression of carcinomas. Nevertheless, there is just little knowledge about the proteomic changes from normal connective tissue to tumor stroma. In the present study, we acquired and analysed specific protein patterns of small stromal sections surrounding head and neck cell complexes in comparison to normal subepithelial connective tissue. To gain defined stromal areas we used laser-based tissue microdissection. Because these stromal areas are limited in size we established the highly sensitive 'tissue on chip based mass spectrometry' (toc-MS). Therefore, the dissected areas were directly transferred to chromatographic arrays and the proteomic profiles were subsequently analysed with mass spectrometry. At least 100 cells were needed for an adequate spectrum. The locating of differentially expressed proteins enables a precise separation of normal and tumor stroma. The newly described toc-MS technology allows an initial insight into proteomic differences between small numbers of exactly defined cells from normal and tumor stroma.