Plasma Protein Profiling for Diagnosis of Pancreatic Cancer Reveals the Presence of Host Response Proteins

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Nanotechnology Meets Oncology: A Perspective on the Role of the Personalized Nanoparticle-Protein Corona in the Development of Technologies for Pancreatic Cancer Detection

In recent years nanotechnology has opened exciting opportunities in the struggle against cancer. In 2007 Dawson and coworkers demonstrated that nanomaterials exposed to biological fluids are coated with plasma proteins that form the so-called “protein corona”. A few years later our joint research team made of physicists, chemists, biotechnologists, surgeons, oncologists, and bioinformaticians introduced the concept of “personalized protein corona” and demonstrated that it is unique for each human condition. This concept paved the way for the development of nano-enabled blood (NEB) tests for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). These studies gave an impetus to serious work in the field that came to maturity in the late 2010s. In this special issue, we provide the reader with a comprehensive overview of the most significant discoveries of our research team in the field of PDAC detection. We focus on the main achievements with an emphasis on the fundamental aspects of this arena and how they shaped the integration of different scientific backgrounds towards the development of advanced diagnostic technologies. We conclude the review by outlining future perspectives and opportunities to transform the NEB tests into a reliable clinical diagnostic technology for early diagnosis, follow-up, and management of PDAC patients.

Comparative Proteomic Analysis Reveals Novel Biomarkers for Gastric Cancer in South Indian Tamil Population

BACKGROUND

Gastric cancer (GC) remains a major global health problem due to a poor understanding of its progression at the molecular level and a lack of early detection or diagnosis. Early detection is highly crucial for improving prognosis. The incidence of GC is very high in countries like India due to the limitations among the established biomarkers for GC owing to poor sensitivity and specificity.

OBJECTIVE

To identify the novel biomarkers from serum samples obtained from GC patients when compared with healthy subjects.

METHODS

Serum samples from GC patients were analyzed by two-dimensional gel electrophoresis (2DGE) coupled with tandem mass spectrometry (MS), including both matrix-assisted laser desorption/ionization-time of flight (MALDI-ToF) and liquid chromatography-MS (LC-MS/MS) analysis. Identified proteins were further analyzed by gene ontology and protein interaction studies.

RESULTS

A total of 73 protein spots were detected in 2DGE image analysis. Among them, seven differentially-expressed proteins were identified using MS analyses, which included serotransferrin/transferrin, albumin, ceruloplasmin, C-reactive protein (CRP), fibrinogen γ-chain (FGG), and two unreported novel proteins, immunoglobulin kappa constant (IgκC) region and Homo sapiens zinc finger protein 28 (ZNF28) homolog. Among these proteins, serotransferrin, albumin, ceruloplasmin, FGG, and ZNF28 were down-regulated in GC samples (p < 0.05), while IgκC region and CRP were up-regulated significantly.

CONCLUSION

Most of the differentially expressed proteins were involved in angiogenesis, plasminogen-activating cascade, and blood coagulation pathways which are known to play a critical role in gastric tumorigenesis. Our current results provide a panel of candidate biomarkers for GC with novel biomarkers which were not reported earlier.

FUNCTION-ON-SCALAR QUANTILE REGRESSION WITH APPLICATION TO MASS SPECTROMETRY PROTEOMICS DATA

Mass spectrometry proteomics, characterized by spiky, spatially heterogeneous functional data, can be used to identify potential cancer biomarkers. Existing mass spectrometry analyses utilize mean regression to detect spectral regions that are differentially expressed across groups. However, given the inter-patient heterogeneity that is a key hallmark of cancer, many biomarkers are only present at aberrant levels for a subset of, not all, cancer samples. Differences in these biomarkers can easily be missed by mean regression, but might be more easily detected by quantile-based approaches. Thus, we propose a unified Bayesian framework to perform quantile regression on functional responses. Our approach utilizes an asymmetric Laplace working likelihood, represents the functional coefficients with basis representations which enable borrowing of strength from nearby locations, and places a global-local shrinkage prior on the basis coefficients to achieve adaptive regularization. Different types of basis transform and continuous shrinkage priors can be used in our framework. A scalable Gibbs sampler is developed to generate posterior samples that can be used to perform Bayesian estimation and inference while accounting for multiple testing. Our framework performs quantile regression and coefficient regularization in a unified manner, allowing them to inform each other and leading to improvement in performance over competing methods as demonstrated by simulation studies. We also introduce an adjustment procedure to the model to improve its frequentist properties of posterior inference. We apply our model to identify proteomic biomarkers of pancreatic cancer that are differentially expressed for a subset of cancer patients compared to the normal controls, which were missed by previous mean-regression based approaches. Supplementary materials for this article are available online.

Identification of diagnostic upper gastrointestinal cancer tissue type-specific urinary biomarkers

Several potential urinary biomarkers exhibiting an association with upper gastrointestinal tumour growth have been previously identified, of which S100A6, S100A9, rabenosyn-5 and programmed cell death 6-interacting protein (PDCD6IP) were further validated and found to be upregulated in malignant tumours. The cancer cohort from our previous study was subclassified to assess whether distinct molecular markers can be identified for each individual cancer type using a similar approach. Urine samples from patients with cancers of the stomach, oesophagus, oesophagogastric junction or pancreas were analysed by surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry using both CM10 and IMAC30 (Cu²⁺-complexed) chip types and LC-MS/MS-based mass spectrometry after chromatographic enrichment. This was followed by protein identification, pattern matching and validation by western blotting. We found 8 m/z peaks with statistical significance for the four cancer types investigated, of which m/z 2447 and 2577 were identified by pattern matching as fragments of cathepsin-B (CTSB) and cystatin-B (CSTB); both molecules are indicative of pancreatic cancer. Additionally, we observed a potential association of upregulated α-1-antichymotrypsin with pancreatic and gastric cancers, of PDCD6IP, vitelline membrane outer layer protein 1 homolog (VMO1) and triosephosphate isomerase (TPI1) with oesophagogastric junctional cancers, and of complement C4-A, prostatic acid phosphatase, azurocidin and histone-H1 with oesophageal cancer. Furthermore, the potential pancreatic cancer biomarkers CSTB and CTSB were validated independently by western blotting. Therefore, the present study identified two new potential urinary biomarkers that appear to be associated with pancreatic cancer. This may provide a simple, non-invasive screening test for use in the clinical setting.

Serum amyloid A predisposes inflammatory tumor microenvironment in triple negative breast cancer

Acute-phase proteins (APPs) are associated with a variety of disorders such as infection, inflammatory diseases, and cancers. The signature profile of APPs in breast cancer (BC) is poorly understood. Here, we identified serum amyloid A (SAA) for proinflammatory predisposition in BC through the signature profiles of APPs, interleukin (IL) and tumor necrosis factor (TNF) superfamily using publicly available datasets of tumor samples and cell lines. Triple-negative breast cancer (TNBC) subtype highly expressed SAA1/2 compared to HER2, luminal A (LA) and luminal B (LB) subtypes. IL1A, IL1B, IL8/CXCL8, IL32 and IL27RA in IL superfamily and CD70, TNFSF9 and TNFRSF21 in TNF superfamily were highly expressed in TNBC compared to other subtypes. SAA is restrictedly regulated by nuclear factor (NF)-κB and IL-1β, an NF-κB activator highly expressed in TNBC, increased the promoter activity of SAA1 in human TNBC MDA-MB231 cells. Interestingly, two κB-sites contained in SAA1 promoter were involved, and the proximal region (-96/-87) was more critical than the distal site (-288/-279) in regulating IL-1β-induced SAA1. Among the SAA receptors, TLR1 and TLR2 were highly expressed in TNBC. Cu-CPT22, TLR1/2 antagonist, abrogated IL-1β-induced SAA1 promoter activity. In addition, SAA1 induced IL8/CXCL8 promoter activity, which was partially reduced by Cu-CPT22. Notably, SAA1/2, TLR2 and IL8/CXCL8 were associated with a poor overall survival in mesenchymal-like TNBC. Taken together, IL-1-induced SAA via NF-κB-mediated signaling could potentiate an inflammatory burden, leading to cancer progression and high mortality in TNBC patients.

Can the Acute-Phase Reactant Proteins be Used as Cancer Biomarkers?

The association between the acute-phase reactant proteins (APRPs) and cancer has long been established. There have been numerous reports correlating altered levels of various APRPs with different types of cancers. However, researchers are often quick to dismiss the use of these APRPs as potential biomarkers for the diagnosis and monitoring of cancer because alterations in APRP concentrations are observed in a wide range of diseases. Recent progress in proteomics studies which profiled the serum proteins of cancer patients and those of normal individuals indicated that the altered APRP expressions were different for distinct types, subtypes, and even stages of cancer. Interestingly, these data are in agreement with those observed earlier using immunochemical and biochemical assays. In view of this compelling association of different patterns of APRPs with various types of cancers and in an apparent shift of paradigm, we present in this review some indications that APRP fingerprinting may be used as complementary cancer biomarkers.

Exploring the expression bar code of SAA variants for gastric cancer detection

We reported an integrated platform to explore serum protein variant pattern in cancer and its utility as a new class of biomarker panel for diagnosis. On the model study of serum amyloid A (SAA), we employed nanoprobe-based affinity mass spectrometry for enrichment, identification and quantitation of SAA variants from serum of 105 gastric cancer patients in comparison with 54 gastritis patients, 54 controls, and 120 patients from other cancer. The result revealed surprisingly heterogeneous and most comprehensive SAA bar code to date, which comprises 24 SAA variants including SAA1- and SAA2-encoded products, polymorphic isoforms, N-terminal-truncated forms, and three novel SAA oxidized isotypes, in which the variant-specific peptide sequence were also confirmed by LC-MS/MS. A diagnostic model was developed for dimension reduction and computational classification of the 24 SAA-variant bar code, providing good discrimination (AUC = 0.85 ± 3.2E-3) for differentiating gastric cancer group from gastritis and normal groups (sensitivity, 0.76; specificity, 0.81) and was validated with external validation cohort (sensitivity, 0.71; specificity, 0.74). Our platform not only shed light on the occurrence and modification extent of under-represented serum protein variants in cancer, but also suggested a new concept of diagnostic platform by serum protein variant profile.

Treponema denticola chymotrypsin-like proteinase may contribute to orodigestive carcinogenesis through immunomodulation

Background:

Periodontal pathogens have been linked to oral and gastrointestinal (orodigestive) carcinogenesis. However, the exact mechanisms remain unknown. Treponema denticola (Td) is associated with severe periodontitis, a chronic inflammatory disease leading to tooth loss. The anaerobic spirochete Td is an invasive bacteria due to its major virulence factor chymotrypsin-like proteinase. Here we aimed to investigate the presence of Td chymotrypsin-like proteinase (Td-CTLP) in major orodigestive tumours and to elucidate potential mechanisms for Td to contribute to carcinogenesis.

Methods:

The presence of Td-CTLP within orodigestive tumour tissues was examined using immunohistochemistry. Oral, tonsillar, and oesophageal squamous cell carcinomas, alongside gastric, pancreatic, and colon adenocarcinomas were stained with a Td-CTLP-specific antibody. Gingival tissue from periodontitis patients served as positive controls. SDS–PAGE and immunoblot were used to analyse the immumodulatory activity of Td-CTLP in vitro.

Results:

Td-CTLP was present in majority of orodigestive tumour samples. Td-CTLP was found to convert pro MMP-8 and -9 into their active forms. In addition, Td-CTLP was able to degrade the proteinase inhibitors TIMP-1, TIMP-2, and α-1-antichymotrypsin, as well as complement C1q.

Conclusions:

Because of its presence within tumours and regulatory activity on proteins critical for the regulation of tumour microenvironment and inflammation, the Td-CTLP may contribute to orodigestive carcinogenesis.

Differential overexpression of SERPINA3 in human prion diseases

Prion diseases are fatal neurodegenerative disorders with sporadic, genetic or acquired etiologies. The molecular alterations leading to the onset and the spreading of these diseases are still unknown. In a previous work we identified a five-gene signature able to distinguish intracranially BSE-infected macaques from healthy ones, with SERPINA3 showing the most prominent dysregulation. We analyzed 128 suitable frontal cortex samples, from prion-affected patients (variant Creutzfeldt-Jakob disease (vCJD) n = 20, iatrogenic CJD (iCJD) n = 11, sporadic CJD (sCJD) n = 23, familial CJD (gCJD) n = 17, fatal familial insomnia (FFI) n = 9, Gerstmann-Sträussler-Scheinker syndrome (GSS)) n = 4), patients with Alzheimer disease (AD, n = 14) and age-matched controls (n = 30). Real Time-quantitative PCR was performed for SERPINA3 transcript, and ACTB, RPL19, GAPDH and B2M were used as reference genes. We report SERPINA3 to be strongly up-regulated in the brain of all human prion diseases, with only a mild up-regulation in AD. We show that this striking up-regulation, both at the mRNA and at the protein level, is present in all types of human prion diseases analyzed, although to a different extent for each specific disorder. Our data suggest that SERPINA3 may be involved in the pathogenesis and the progression of prion diseases, representing a valid tool for distinguishing different forms of these disorders in humans.

The Peptidome Comes of Age: Mass Spectrometry-Based Characterization of the Circulating Cancer Peptidome

Peptides play a seminal role in most physiological processes acting as neurotransmitters, hormones, antibiotics, and immune regulation. In the context of tumor biology, it is hypothesized that endogenous peptides, hormones, cytokines, growth factors, and aberrant degradation of select protein networks (e.g., enzymatic activities, protein shedding, and extracellular matrix remodeling) are fundamental in mediating cancer progression. Analysis of peptides in biological fluids by mass spectrometry holds promise of providing sensitive and specific diagnostic and prognostic information for cancer and other diseases. The identification of circulating peptides in the context of disease constitutes a hitherto source of new clinical biomarkers. The field of peptidomics can be defined as the identification and comprehensive analysis of physiological and pathological peptides. Like proteomics, peptidomics has been advanced by the development of new separation strategies, analytical detection methods such as mass spectrometry, and bioinformatic technologies. Unlike proteomics, peptidomics is targeted toward identifying endogenous protein and peptide fragments, defining proteolytic enzyme substrate specificity, as well as protease cleavage recognition (degradome). Peptidomics employs "top-down proteomics" strategies where mass spectrometry is applied at the proteoform level to analyze intact proteins and large endogenous peptide fragments. With recent advances in prefractionation workflows for separating peptides, mass spectrometry instrumentation, and informatics, peptidomics is an important field that promises to impact on translational medicine. This review covers the current advances in peptidomics, including top-down and imaging mass spectrometry, comprehensive quantitative peptidome analyses (developments in reproducibility and coverage), peptide prefractionation and enrichment workflows, peptidomic data analyses, and informatic tools. The application of peptidomics in cancer biomarker discovery will be discussed.

Challenges in the Analysis of Mass-Throughput Data: A Technical Commentary from the Statistical Machine Learning Perspective

Sound data analysis is critical to the success of modern molecular medicine research that involves collection and interpretation of mass-throughput data. The novel nature and high-dimensionality in such datasets pose a series of non-trivial data analysis problems. This technical commentary discusses the problems of over-fitting, error estimation, curse of dimensionality, causal versus predictive modeling, integration of heterogeneous types of data, and lack of standard protocols for data analysis. We attempt to shed light on the nature and causes of these problems and to outline viable methodological approaches to overcome them.

Comparative proteomic profiling of the serum differentiates pancreatic cancer from chronic pancreatitis

Finland ranks sixth among the countries having highest incidence rate of pancreatic cancer with mortality roughly equaling incidence. The average age of diagnosis for pancreatic cancer is 69 years in Nordic males, whereas the average age of diagnosis of chronic pancreatitis is 40–50 years, however, many cases overlap in age. By radiology, the evaluation of a pancreatic mass, that is, the differential diagnosis between chronic pancreatitis and pancreatic cancer is often difficult. Preoperative needle biopsies are difficult to obtain and are demanding to interpret. New blood based biomarkers are needed. The accuracy of the only established biomarker for pancreatic cancer, CA 19‐9 is rather poor in differentiating between benign and malignant mass of the pancreas. In this study, we have performed mass spectrometry analysis (High Definition MS^E) of serum samples from patients with chronic pancreatitis (13) and pancreatic cancer (22). We have quantified 291 proteins and performed detailed statistical analysis such as principal component analysis, orthogonal partial least square discriminant analysis and receiver operating curve analysis. The proteomic signature of chronic pancreatitis versus pancreatic cancer samples was able to separate the two groups by multiple statistical techniques. Some of the enriched pathways in the proteomic dataset were LXR/RXR activation, complement and coagulation systems and inflammatory response. We propose that multiple high‐confidence biomarker candidates in our pilot study including Inter‐alpha‐trypsin inhibitor heavy chain H2 (Area under the curve, AUC: 0.947), protein AMBP (AUC: 0.951) and prothrombin (AUC: 0.917), which should be further evaluated in larger patient series as potential new biomarkers for differential diagnosis.

Proteomics as a Tool for Clinically Relevant Biomarker Discovery and Validation

The excitement associated with clinical applications of proteomics was initially focused on its potential to serve as a vehicle for both biomarker discovery and drug discovery and routine clinical sample analysis. Some approaches were thought to be able to “identify” mass spectral characteristics that distinguished between control and disease samples, and thereafter it was believed that the same tool could be employed to screen samples in a high-throughput clinical setting. However, this has been difficult to achieve, and the early promise is yet to be fully realized. While we see an important place for mass spectrometry in drug and biomarker discovery, we believe that alternative strategies will prove more fruitful for routine analysis. Here we discuss the power and versatility of 2D gels and mass spectrometry in the discovery phase of biomarker work but argue that it is better to rely on immunochemical methods for high-throughput validation and routine assay applications.

Biomarkers of inflammation and innate immunity in atrophic nonunion fracture

BACKGROUND

Nonunion is a failure of healing following a bone fracture. Its physiopathology remains partially unclear and the discovery of new mediators could promote the understanding of bone healing.

METHODS

Thirty-three atrophic nonunion (NU) patients that failed to demonstrate any radiographic improvement for 6 consecutive months were recruited for providing serum samples. Thirty-five healthy volunteers (HV) served as the control group. Proteomics studies were performed using SELDI-TOF-MS and 2D-DIGE approaches, associated or not with Proteominer® preprocessing, to highlight biomarkers specific to atrophic nonunion pathology. Peak intensities were analyzed by two statistical approaches, a nonparametric Mann-Whitney U tests (univariate approach) and a machine-learning algorithm called extra-trees (multivariate approach). Validation of highlighted biomarkers was performed by alternative approaches such as microfluidic LC-MS/MS, nephelometry, western blotting or ELISA assays.

RESULTS

From the 35 HV and 33 NU crude serum samples and Proteominer® eluates, 136 spectra were collected by SELDI-TOF-MS using CM10 and IMAC-Cu(2+) ProteinChip arrays, and 665 peaks were integrated for extra-trees multivariate analysis. Accordingly, seven biomarkers and several variants were identified as potential NU biomarkers. Their levels of expression were found to be down- or up-regulated in serum of HV vs NU. These biomarkers are inter-α-trypsin inhibitor H4, hepcidin, S100A8, S100A9, glycated hemoglobin β subunit, PACAP related peptide, complement C3 α-chain. 2D-DIGE experiment allowed to detect 14 biomarkers as being down- or up-regulated in serum of HV vs NU including a cleaved fragment of apolipoprotein A-IV, apolipoprotein E, complement C3 and C6. Several biomarkers such as hepcidin, complement C6, S100A9, apolipoprotein E, complement C3 and C4 were confirmed by an alternative approach as being up-regulated in serum of NU patients compared to HV controls.

CONCLUSION

Two proteomics approaches were used to identify new biomarkers up- or down-regulated in the nonunion pathology, which are involved in bone turn-over, inflammation, innate immunity, glycation and lipid metabolisms. High expression of hepcidin or S100A8/S100A9 by myeloid cells and the presence of advanced glycation end products and complement factors could be the result of a longstanding inflammatory process. Blocking macrophage activation and/or TLR4 receptor could accelerate healing of fractured bone in at-risk patients.

A re-examination of the role of the acute phase protein response in innate cancer defence

Anti-cancer host defense mechanisms are traditionally considered to consist of tumor suppressor genes and immune surveillance by cells of the innate and adaptive immune systems. However, there is mounting evidence that components of the acute phase protein response (APPR), and, in particular, certain cationic host defense peptides (HDPs), also contribute to anti-cancer host defense. In a number of in vitro studies, certain HDPs have been shown to be cytotoxic to tumor cells either directly through cancer cell membrane destabilization and lysis or through the initiation of apoptosis in the cancer cell. In addition, many cancer cells elaborate the pro-inflammatory cytokine interleukin-6, which in turn produces an APPR that involves the release of HDPs. It is therefore possible that the release of pro-inflammatory cytokines by cancer cells initiates a poorly understood anti-tumor response by the host that involves HDP induction. We hypothesize that the APPR may form an important anti-cancer host defense response. This may be an important consideration in light of cancer treatments designed to decrease systemic inflammation. Blunting of the anti-cancer effect of the APPR may also contribute to the increased cancer rates seen in chronic immunosuppressive states.

A novel approach using MALDI-TOF/TOF mass spectrometry and prestructured sample supports (AnchorChip Technology) for proteomic profiling and protein identification

Mass spectrometry (MS)-based proteomic profiling and protein identification has become a powerful tool for the discovery of new disease biomarkers. Among the MS platforms, matrix-assisted laser desorption/ionization time-of-flight/time-of-flight (MALDI-TOF/TOF) MS offers high sample throughput and the flexibility to couple with different off-line sample fractionation techniques. Here, we present a strategy using MALDI-TOF/TOF MS to analyze fractionated human serum samples for proteomic profiling and then identify serum peptides from these proteomic profiles. We achieve the profiling analyses by using different functionalized magnetic beads to enrich specific subsets of serum proteins/peptides based on their absorption to these beads. This step is followed by elution, transfer onto prestructured sample supports (AnchorChip targets), and analysis in a MALDI-TOF/TOF mass spectrometer. Selected serum peptides are then analyzed in the tandem MS (TOF/TOF) mode to generate fragment ions for determination of their amino acid sequences. We have demonstrated that using this approach, proteomic profiling and protein identification can be done in a single MS instrument.

Current status and prospects of clinical proteomics studies on detection of colorectal cancer: Hopes and fears

Colorectal adenocarcinoma (CRC) is the third most common type of cancer and the fourth most frequent cause of death due to cancer worldwide. Given the natural history of CRC, early diagnosis appears to be the most appropriate tool to reduce disease-related mortality. A field of recent interest is clinical proteomics, which was reported to lead to high sensitivity and specificities for early detection of several solid tumors. This emerging field uses mass spectrometry-based protein profiles/patterns of easy accessible body fluids to distinguish cancer from non-cancer patients. These discrepancies may be a result of: (1) proteins being abnormally produced or shed and added to the serum proteome, (2) proteins clipped or modified as a consequence of the disease process, or (3) proteins subtracted from the proteome owing to disease-related proteolytic degradation pathways. Therefore, protein pattern diagnostics would provide easy and reliable tools for detection of cancer. This paper focuses on the current status of clinical proteomics research in oncology and in colorectal cancer especially, and will reflect on pitfalls and fears in this relatively new area of clinical medicine, which are reproducibility issues and pre-analytical factors, statistical issues, and identification and nature of discriminating proteins/peptides.