Identification of serum amyloid A as a biomarker to distinguish prostate cancer patients with bone lesions

Inhibition of Pancreatic Cancer Cells by Different Amyloid Proteins Reveals an Inverse Relationship between Neurodegenerative Diseases and Cancer

Neurodegenerative diseases and cancers are considered to be two families of diseases caused by completely opposite cell-death mechanisms: the former caused by premature cell death, with the latter due to the increased resistance to cell death. Growing epidemiologic evidence appear to suggest an inverse correlation between neurodegenerative diseases and cancers. However, pathological links, particularly from a protein-cell interaction perspective, between these two families of diseases remains to be proven. Here, a fundamental study investigates the effects of three amyloid proteins of Aβ (associated with AD), hIAPP (associated with T2D), and hCT (associated with MTC) on pancreatic cancer (PANC-1) cells. Collective results demonstrate a general inhibitory activity of all of three amyloid proteins on cancer cell proliferation, but inhibition efficiencies are strongly dependent on amyloid sequence (Aβ, hIAPP, hCT), concentration (IC25, IC50, IC75), and aggregation states (monomers, oligomers). Amyloid proteins exhibit two pathways against cancer cells: amyloid monomer-induced ROS production to inhibit cell growth and amyloid oligomer-induced membrane disruption to kill cells. Collectively, the results demonstrate a general inhibition function of amyloid proteins to induce cancer cell death by preventing cell proliferation, suppressing cell migration, promoting reactive oxygen species production, and disrupting cell membranes.

AA Amyloidosis associated with cancers

Systemic AA amyloidosis is associated with systemic inflammatory processes such as autoimmune disorders or chronic infections. In addition, AA amyloidosis can develop in a localized or systemic form in patients with malignant neoplastic disorders, and usually involves kidneys impacting renal function. Among solid tumors, renal cell carcinoma (RCC) appears to be responsible for one-quarter to half of all cancers associated with amyloidosis. Among other solid cancers, various clinical presentation and pathological types of lung cancer and basal cell carcinoma skin were reported with AA amyloidosis more often than isolated case reports on other cancers with AA amyloidosis. Symptoms from kidney involvement rather than from the tumor per se were the presenting manifestations in cases of RCC associated with AA amyloidosis. Among hematological malignancies, clonal B cell/plasma cell dyscrasias such as monoclonal gammopathy and lymphoma were noted to be associated with AA amyloidosis. In addition, AA amyloidosis was reported in a substantial number of cases treated with immune checkpoint inhibitors such as pembrolizumab and nivolumab. The mechanism of association of cancer and AA amyloidosis seems to be mediated by the immune response exacerbated from the tumor and its microenvironment or immune therapy. The mainstay of treatment consists of therapy directed against the underlying malignancy or careful withdrawal of the offending agent. This review will discuss this rare but highly morbid clinical condition.

Systemic Amyloidosis in a Patient With Familial Mediterranean Fever and Hodgkin Lymphoma: A Case Report

Systemic amyloidosis is a clinical manifestation of the accumulation of amyloid fibrils in tissues because of persistent acute phase elevation and chronic inflammation. Its most common causes are inflammatory diseases and malignancies. Here, we present a 12-year-old girl diagnosed with systemic amyloidosis and Hodgkin lymphoma (HL) who was also previously diagnosed with familial Mediterranean fever (FMF). Despite colchicine treatment for FMF, the patient had a persistent elevation of acute phase reactants and AA-type amyloid deposits were observed in a kidney biopsy. Anakinra, an interleukin-1 antagonist, was added to the treatment. Shortly after the diagnosis of amyloidosis, mediastinal lymphadenopathy was recognized, and she was also diagnosed with HL. A chemotherapy protocol of doxorubicin, bleomycin, vinblastine, and dacarbazine was initiated. After 6 cycles of the chemotherapy and 8 months of the anakinra treatment, no recurrence or residual malignancy was observed and proteinuria was decreased. To the authors' knowledge, this is the first reported case of systemic amyloidosis in the literature associated with both FMF and HL.

Immune checkpoint inhibitor–associated renal amyloid A amyloidosis: A case series and review of the literature

Immune checkpoint inhibitors are a cornerstone in the management of many oncological disorders, and their indications continue to grow. However, as with any therapy we must remain vigilant of the possible adverse effects. Although interstitial nephritis is a reported cause of immune checkpoint inhibitor–related kidney injury, immune-mediated glomerular disease has rarely been described. Here, we present three patients being treated with checkpoint inhibitors for colon cancer, metastatic squamous cell carcinoma of the lung, and melanoma, who developed biopsy-proven amyloid A amyloidosis. In all three cases, the malignancies were in remission, yet continued inflammation and amyloid deposition occurred, pointing toward a primary role of the immune checkpoint inhibitor. Treatment generally remains a challenge due to a paucity of reported cases, thus further study of cytokine profile is prudent. In one case, the patient was given tocilizumab in the setting of elevated interleukin-6 levels; unfortunately no appreciable renal benefit was noted and the patient became dialysis dependent. In the other two cases, the patients were treated with colchicine and steroids. One patient had a substantial decrease in proteinuria and inflammatory markers while no significant response was noted in the other. Knowledge of immune checkpoint inhibitor–associated amyloid A amyloidosis is important for the oncologist and the nephrologists.

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.

Early detection of lung cancer biomarkers through biosensor technology: A review

Lung cancer is undoubtedly one of the most serious health issues of the 21 st century. It is the second leading cause of cancer-related deaths in both men and women　worldwide, accounting for about 1.5 million deaths annually. Despite advances in the treatment of lung cancer with new pharmaceutical products and technological improvements, morbidity and mortality rates remains a significant challenge for the cancer biologists and oncologists. The vast majority of lung cancer patients present with advanced-stage of pathological process that ultimately leads to poor prognosis and a five-year survival rate less than 20%. Early and accurate screening and analysis using cost-effective means are urgently needed to effectively diagnose the disease, improve the survival rate or to reduce mortality and morbidity associated with lung cancer patients. Thus, the only hope for early recognition of risk factors and timely diagnosis and treatment of lung cancer is biosensors technology. Novel biosensing based diagnostics approaches for predicting metastatic risks are likely to have significant therapeutic and clinical impact in the near future. This article systematically provides a brief overview of various biosensing platforms for identification of lung cancer disease biomarkers, with a specific focus on recent advancements in electrochemical and optical biosensors, analytical performances of different biosensors, challenges and further research opportunities for routine clinical analysis.

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.

Identification of αB-Crystallin, a Biomarker of Renal Cell Carcinoma by SELDI-TOF MS

Spectrometric-based surface-enhanced laser desorption/ionization ProteinChip (SELDI-TOF) facilitates rapid and easy analysis of protein mixtures and is often exploited to define potential diagnostic markers from sera. However, SELDI-TOF is a relatively insensitive technique and unable to detect circulating proteins at low levels even if they are differentially expressed in cancer patients. Therefore, we applied this technology to study tissues from renal cell carcinomas (RCC) in comparison to healthy controls. We found that different biomarkers are identified from tissues than those previously identified in serum, and that serum markers are often not produced by the tumors themselves at detectable levels, reflecting the nonspecific nature of many circulating biomarkers. We detected and characterized αB-crystallin as an overexpressed protein in RCC tissues and showed differential expression by immunohistochemistry. We conclude that SELDI-TOF is more useful for the identification of biomarkers that are synthesized by diseased tissues than for the identification of serum biomarkers and identifies a separate set of markers. We suggest that SELDI-TOF should be used to screen human cancer tissues to identify potential tissue-specific proteins and simpler and more sensitive techniques can then be applied to determine their validity as biomarkers in biological fluids.

Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study

BACKGROUND

Prostate cancer (PCa) is the most common male cancer in the United Kingdom and we aimed to identify clinically relevant biomarkers corresponding to stage progression of the disease.

METHODS

We used enhanced proteomic profiling of PCa progression using iTRAQ 3D LC mass spectrometry on high-quality serum samples to identify biomarkers of PCa.

RESULTS

We identified >1000 proteins. Following specific inclusion/exclusion criteria we targeted seven proteins of which two were validated by ELISA and six potentially interacted forming an 'interactome' with only a single protein linking each marker. This network also includes accepted cancer markers, such as TNF, STAT3, NF-κB and IL6.

CONCLUSIONS

Our linked and interrelated biomarker network highlights the potential utility of six of our seven markers as a panel for diagnosing PCa and, critically, in determining the stage of the disease. Our validation analysis of the MS-identified proteins found that SAA alongside KLK3 may improve categorisation of PCa than by KLK3 alone, and that TSR1, although not significant in this model, might also be a clinically relevant biomarker.

Acute phase serum amyloid A induces proinflammatory cytokines and mineralization via toll-like receptor 4 in mesenchymal stem cells

The role of serum amyloid A (SAA) proteins, which are ligands for toll-like receptors, was analyzed in human bone marrow-derived mesenchymal stem cells (hMSCs) and their osteogenic offspring with a focus on senescence, differentiation and mineralization. In vitro aged hMSC developed a senescence-associated secretory phenotype (SASP), resulting in enhanced SAA1/2, TLR2/4 and proinflammatory cytokine (IL6, IL8, IL1β, CXCL1, CXCL2) expression before entering replicative senescence. Recombinant human SAA1 (rhSAA1) induced SASP-related genes and proteins in MSC, which could be abolished by cotreatment with the TLR4-inhibitor CLI-095. The same pattern of SASP-resembling genes was stimulated upon induction of osteogenic differentiation, which is accompanied by autocrine SAA1/2 expression. In this context additional rhSAA1 enhanced the SASP-like phenotype, accelerated the proinflammatory phase of osteogenic differentiation and enhanced mineralization. Autocrine/paracrine and rhSAA1 via TLR4 stimulate a proinflammatory phenotype that is both part of the early phase of osteogenic differentiation and the development of senescence. This signaling cascade is tightly involved in bone formation and mineralization, but may also propagate pathological extraosseous calcification conditions such as calcifying inflammation and atherosclerosis.

Loss of MyD88 leads to more aggressive TRAMP prostate cancer and influences tumor infiltrating lymphocytes

BACKGROUND

The influence of pattern recognition receptor (PRR) signaling in the prostate tumor microenvironment remains unclear. Although there may be a role for PRR agonists as adjuvants to therapy, prior evidence suggests tumor promoting as well as tumor inhibiting mechanisms. The purpose of this study is to examine the role of the key Toll-like receptor (TLR) signaling adaptor protein myeloid differentiation primary response gene 88 (MyD88) in prostate cancer development.

METHODS

MyD88(-/-) mice in a C57Bl6 background were crossed with transgenic adenocarcinomas of the mouse prostate (TRAMP) mice to create MyD88(-/-) TRAMP(Tg+/-) animals, which were compared to MyD88(+/+) TRAMP(Tg+/-) animals and their non-transgenic counterparts at 30 weeks. Prostates were examined histologically, by immunohistochemistry and immunofluorescence staining, and by qPCR, to characterize tumor-infiltrating immune populations as well as activation of the downstream NF-κB pathway and androgen receptor (AR) expression. Splenocytes were examined for development of distinct immune cell populations.

RESULTS

Absence of MyD88 led to increased prostatic intraepithelial neoplasm (PIN) and areas of well-differentiated adenocarcinoma in TRAMP transgenic mice. Analysis of infiltrating immune populations revealed an increase in CD11b(+) Gr1(+) myeloid-derived suppressor cells (MDSCs), as evidenced by increased expression of prostatic arginase-1 and iNOS as well as the cytokine IL-10, and a deficiency in NK cells in prostates from MyD88(-/-) TRAMP(Tg+/-) compared to MyD88(+/+) TRAMP(Tg+/-) mice, whereas a decrease in splenocytic NK cell differentiation was observed in MyD88(-/-) mice. Prostate tumors revealed no significant differences in NF-κB or AR expression in MyD88(+/+) TRAMP(Tg+/-) compared to MyD88(-/-) TRAMP(Tg+/-) mice.

CONCLUSIONS

During prostate cancer development in the TRAMP model, MyD88 may play a role in limiting prostate tumorigenesis by altering tumor-infiltrating immune populations. This suggests that in the context of specific cancers, distinct PRRs and signaling pathways of innate immune signaling may influence the tumor microenvironment and represent a novel therapeutic strategy.

Study of treadmill exercise effect on rats with osteoarthritis using proteomic analysis

[Purpose] To investigate the correlation between the effect treadmill exercise and change in serum proteins in rats with osteoarthritis, a study of proteins was carried out using a mass spectrometer. [Subjects and Methods] Rats were randomly divided into five groups. After 4 weeks of treadmill training, serum from each rat was analyzed by Liquid chromatography-electrospray ionization tandem mass spectrometry. Complementary component 9 (C9) was discovered to be downregulated in the serum of the exercise groups, and this was validated by Western blot. [Results] Seventeen proteins were discovered to be elevated in the monosodium iodoacetate injection osteoarthritis group samples by more than 1.5 fold compared with the control group. One of the proteins upregulated, C9 protein, was validated, and it was found to decrease in the middle-intensity exercise group. [Conclusion] We showed that the serum level of C9, an inflammatory-related protein, decreased after treadmill exercise. Therefore, treadmill exercise with an appropriate intensity might be recommended for OA patients.

Peptidomics analysis of human blood specimens for biomarker discovery

This review addresses the concepts, limitations and perspectives for the application of peptidomics science and technologies to discover putative biomarkers in blood specimens. Peptidomics can be defined as the comprehensive multiplex analysis of endogenous peptides contained within a biological sample under defined conditions to describe the multitude of native peptides in a biological compartment. In addition to the discovery of disease associated biomarkers, an emerging field in peptidomics is the analysis of peptides to describe in vivo effects of protease inhibitors. The development and application of peptidomics technologies represent an arena of biomarker research that has the potential for adding significant clinical value.

Medical technologies for the diagnosis of prostate cancer

Prostate cancer is an extremely prevalent problem, especially in our aging population. The prostate-specific antigen test has revolutionized prostate cancer screening. Significant advances have been made in the usage of prostate-specific antigen and its derivatives, biomarkers, diagnostic imaging techniques, biopsy strategy, biopsy needle design and anesthetic agents. Further improvement in prostate cancer detection hinges on the development of an imaging technique that is tumor specific and sensitive to biological aggressiveness.

Population proteomics: addressing protein diversity in humans

In the past several years, proteomics and its subdiscipline clinical proteomics have been engaged in the discovery of the next generation protein of biomarkers. As the effort and the intensive debate it has sparked continue, it is becoming apparent that a paradigm shift is needed in proteomics in order to truly comprehend the complexity of the human proteome and assess its subtle variations among individuals. This review introduces the concept of population proteomics as a future direction in proteomics research. Population proteomics is the study of protein diversity in human populations. High-throughput, top-down mass spectrometric approaches are employed to investigate, define and understand protein diversity and modulations across and within populations. Population proteomics is a discovery-oriented endeavor with a goal of establishing the incidence of protein structural variations and quantitative regulation of these modifications. Assessing human protein variations among and within populations is viewed as a paramount undertaking that can facilitate clinical proteomics' effort in discovery and validation of protein features that can be used as markers for early diagnosis of disease, monitoring of disease progression and assessment of therapy. This review outlines the growing need for analyzing individuals' proteomes and describes the approaches that are likely to be applied in such a population proteomics endeavor.

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.

Molecular diagnosis of prostate cancer: are we up to age?

Prostate cancer (PCa), a highly heterogeneous disease, is the one of the leading cause of morbidity and mortality in the developed countries. Historically used biomarkers such as prostatic acid phosphatase (PAP), serum prostate-specific antigen (PSA), and its precursor have not stood the challenge of sensitivity and specificity. At present, there is need to re-evaluate the approach to diagnose and monitor PCa. To this end, molecular markers that can accurately identify men with PCa at an early stage, and those who would benefit from early therapeutic intervention, are the need of the hour. There has been unprecedented progress in the development of new PCa biomarkers through advancements in proteomics, tissue DNA and protein/RNA microarray, identification of microRNA, isolation of circulating tumor cells, and tumor immunohistochemistry. This review will examine the current status of prostate cancer biomarkers with emphasis on emerging biomarkers by evaluating their diagnostic and prognostic potentials.

MALDI mass spectrometry in prostate cancer biomarker discovery

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) is a highly versatile and sensitive analytical technique, which is known for its soft ionisation of biomolecules such as peptides and proteins. Generally, MALDI MS analysis requires little sample preparation, and in some cases like MS profiling it can be automated through the use of robotic liquid-handling systems. For more than a decade now, MALDI MS has been extensively utilised in the search for biomarkers that could aid clinicians in diagnosis, prognosis, and treatment decision making. This review examines the various MALDI-based MS techniques like MS imaging, MS profiling and proteomics in-depth analysis where MALDI MS follows fractionation and separation methods such as gel electrophoresis, and how these have contributed to prostate cancer biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Identification of serum CCL15 in hepatocellular carcinoma

Background:

Early serum detection is of critical importance to improve the therapy for hepatocellular carcinoma (HCC), one of the most deadly cancers. Hepatitis infection is a leading cause of HCC.

Methods:

In the present study, we collected total serum samples with informed consent from 80 HCC patients with HBV (+)/cirrhosis (+), 80 patients with benign diseases (50 liver cirrhosis patients and 30 HBV-infected patients) and 60 healthy controls. Analysis was by using surface-enhanced laser desorption/ionisation-time-of-flight mass spectroscopy (SELDI-TOF-MS) to find new serum markers of HCC. SELDI peaks were isolated by SDS–PAGE, identified by LC-MS/MS and validated by immunohistochemistry (IHC) in liver tissues. Migration and invasion assay were performed to test the ability of cell migration and invasion in vitro.

Results:

SELDI-TOF-MS revealed a band at 7777 M/Z in the serum samples from HCC patients but not from healthy controls or patients with benign diseases. The protein (7777.27 M/Z) in the proteomic signature was identified as C-C motif chemokine 15 (CCL15) by peptide mass fingerprinting. A significant increase in serum CCL15 was detected in HCC patients. Functional analysis showed that HCC cell expressed CCL15, which in turn promoted HCC cell migration and invasion.

Conclusion:

CCL15 may be a specific proteomic biomarker of HCC, which has an important role in tumorigenesis and tumour invasion.

Systemic AA amyloidosis in a patient with lung metastasis from renal cell carcinoma

AA amyloidosis occurs in patients with high levels of serum amyloid A protein (SAA), which is produced by liver cells in response to signals from several pro-inflammatory cytokines. Chronic inflammatory disease is a major cause of AA amyloidosis; however, malignant neoplasms are rarely reported to be associated with AA amyloidosis. We report herein a case of a solitary lung metastasis of renal cell carcinoma associated with systemic AA amyloidosis. Pathological specimens of the resected lung tumor demonstrated renal cell carcinoma, and the presence of IL-1β, IL-6, and TNF-α in the lymphocytes and plasma cells surrounding the tumor cells, and AA amyloid in the vascular area, but not in metastatic clear cells. Four weeks after surgery, serum IL-6, SAA, and CRP levels normalized. Although this case is very rare, it is full of interesting suggestions about the pathogenesis of malignancy-related systemic amyloidosis.

MALDI-MS-Based Profiling of Serum Proteome: Detection of Changes Related to Progression of Cancer and Response to Anticancer Treatment

Mass spectrometry-based analyses of the low-molecular-weight fraction of serum proteome allow identifying proteome profiles (signatures) that are potentially useful in detection and classification of cancer. Several published studies have shown that multipeptide signatures selected in numerical tests have potential values for diagnostics of different types of cancer. However due to apparent problems with standardization of methodological details, both experimental and computational, none of the proposed peptide signatures analyzed directly by MALDI/SELDI-ToF spectrometry has been approved for routine diagnostics. Noteworthy, several components of proposed cancer signatures, especially those characteristic for advanced cancer, were identified as fragments of blood proteins involved in the acute phase and inflammatory response. This indicated that among cancer biomarker candidates to be possibly identified by serum proteome profiling were rather those reflecting overall influence of a disease (and the therapy) upon the human organism, than products of cancer-specific genes. Current paper focuses on changes in serum proteome that are related to response of patient's organism to progressing malignancy and toxicity of anticancer treatment. In addition, several methodological issues that affect robustness and interlaboratory reproducibility of MS-based serum proteome profiling are discussed.

Large scale phosphoproteome analysis of LNCaP human prostate cancer cells

Prostate cancer is the most frequently diagnosed cancer among men in the western world. The androgen receptor, a phosphoprotein, is suspected to be involved in all stages of the prostate cancer. Androgen receptor activity can be modulated by various kinases such as PKA, MAPK, AKT, and Src. Phosphorylation is an important post-translational modification and serves as a molecular on-off switch to regulate signaling. Disruptions of cellular phosphorylation are associated with various diseases such as cancer and kinases provide important drug targets. Here we present an analysis of the phosphoproteome in LNCaP human prostate cancer cells. The analytical strategy employed here used proteomics based methodologies with a combination of detergents and chaotropic reagents during trypsin digestion followed by titanium dioxide enrichment of phosphopeptides. Over the course of multiple analyses by mass spectrometry we identified a total of 746 phosphorylation sites in 540 phosphopeptides corresponding to 116 phosphoproteins, of which 56 had not been previously reported. Phosphoproteins identified included transcription factors, co-regulators of the androgen receptor, and cancer-related proteins that include β-catenin, USP10, and histone deacetylase-2. The information of signaling pathways, motifs of phosphorylated peptides, biological processes, molecular functions, cellular components, and protein interactions from the identified phosphoproteins established a map of phosphoproteome and signaling pathways in LNCaP cells.

Identification of serum amyloid A in the serum of gastric cancer patients by protein expression profiling

The purpose of this study was to screen serum samples from gastric carcinoma patients and to determine whether serum amyloid A protein (SAA) served as a biomarker. SELDI technology was used to screen for changes in SAA levels in the serum samples. A mass cluster with a mass/charge (m/z) value between 11.1 and 11.9 kDa was identified in the serum samples from gastric carcinoma patients which was much higher than that of the control group. Furthermore, the increase in this m/z peak correlated with the severity of the cancer. High-performance liquid chromatography (HPLC) analysis confirmed that the peak was SAA1. In conclusion, this increase in SAA may be used as a potential biomarker for gastric cancer.

Allelic transcripts dosage effect in morphologically normal ovarian cells from heterozygous carriers of a BRCA1/2 French Canadian founder mutation

We hypothesized that the transcriptome of primary cultures of morphologically normal ovarian surface epithelial cells could be altered by the presence of a heterozygous BRCA1 or BRCA2 mutation. We aimed to discover early events associated with ovarian carcinogenesis, which could represent putative targets for preventive strategies of this silent killer tumor. We identified the first molecular signature associated with French Canadian BRCA1 or BRCA2 founder mutations in morphologically normal ovarian epithelial cells. We discovered that wild-type and mutated BRCA2 allelic transcripts were expressed not only in morphologically normal but also in tumor cells from BRCA2-8765delAG carriers. Further analysis of morphologically normal ovarian and tumor cells from BRCA1-4446C>T carriers lead to the same observation. Our data support the idea that one single hit in BRCA1 or BRCA2 is sufficient to alter the transcriptome of phenotypically normal ovarian epithelial cells. The highest level of BRCA2-mutated allele transcript expression was measured in cells originating from the most aggressive ovarian tumor. The penetrance of the mutation and the aggressiveness of the related tumor could depend on a dosage effect of the mutated allele transcript.

iTRAQ identification of candidate serum biomarkers associated with metastatic progression of human prostate cancer

A major challenge in the management of patients with prostate cancer is identifying those individuals at risk of developing metastatic disease, as in most cases the disease will remain indolent. We analyzed pooled serum samples from 4 groups of patients (n = 5 samples/group), collected prospectively and actively monitored for a minimum of 5 yrs. Patients groups were (i) histological diagnosis of benign prostatic hyperplasia with no evidence of cancer 'BPH', (ii) localised cancer with no evidence of progression, 'non-progressing' (iii) localised cancer with evidence of biochemical progression, 'progressing', and (iv) bone metastasis at presentation 'metastatic'. Pooled samples were immuno-depleted of the 14 most highly abundant proteins and analysed using a 4-plex iTRAQ approach. Overall 122 proteins were identified and relatively quantified. Comparisons of progressing versus non-progressing groups identified the significant differential expression of 25 proteins (p<0.001). Comparisons of metastatic versus progressing groups identified the significant differential expression of 23 proteins. Mapping the differentially expressed proteins onto the prostate cancer progression pathway revealed the dysregulated expression of individual proteins, pairs of proteins and 'panels' of proteins to be associated with particular stages of disease development and progression. The median immunostaining intensity of eukaryotic translation elongation factor 1 alpha 1 (eEF1A1), one of the candidates identified, was significantly higher in osteoblasts in close proximity to metastatic tumour cells compared with osteoblasts in control bone (p = 0.0353, Mann Whitney U). Our proteomic approach has identified leads for potentially useful serum biomarkers associated with the metastatic progression of prostate cancer. The panels identified, including eEF1A1 warrant further investigation and validation.

Serum amyloid A: A new potential serum marker correlated with the stage of breast cancer

Previous studies reported that serum amyloid A (SAA) is elevated in patients with tumors, including breast cancer, compared to healthy controls. In addition, the levels of SAA increase gradually with tumor progression. In this study, we investigated the blood SAA level of breast cancer patients, and evaluated its potential as a serum biomarker for the early diagnosis of breast cancer and as a staging estimate. SAA protein was determined by enzyme-linked immunosorbent assay in serum samples from 30 healthy women, 21 women with benign diseases and 118 breast cancer patients who were subdivided into 4 groups based on their clinical characteristics. SAA levels were not statistically different in stage I breast cancer patients compared with the healthy controls and benign breast disease patients. SAA concentrations had medians of 0.63 µg/ml in normal healthy women, 0.76 µg/ml in patients with benign disease (p>0.05) and 0.82 µg/ml in stage I breast cancer patients (p>0.05). By contrast, SAA values in stage Ⅱ, Ⅲ and Ⅳ patients had a significantly higher median compared to those of the healthy, benign breast diseases and stage I groups (p<0.05). Breast cancer patients with lymph node (LN) metastasis or distant metastasis were found to have significantly higher SAA concentrations than those without metastases. SAA is not a suitable marker for early breast cancer diagnosis, but its level is correlated with the stage of breast cancer. Thus, it may be a good candidate marker for the staging and prognosis of breast cancer.

Purification, identification and profiling of serum amyloid A proteins from sera of advanced-stage cancer patients

Surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) is a powerful tool for screening potential biomarkers of various pathological conditions. However, low resolution and mass accuracy of SELDI-TOF-MS remain a major obstacle for determination of biological identities of potential protein biomarkers. We report here a refined workflow that combines ZipTip desalting, acetonitrile precipitation, high-performance liquid chromatography (HPLC) separation and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) analysis for the profiling, purification and identification of the targeted serum proteins found by SELDI-TOF-MS. By using this workflow, we purified ten targeted proteins from the sera of patients with various types of advanced stage (stage III–IV) cancers. These proteins were identified as isoforms of the human serum amyloid protein A (SAA) family with or without truncations at their N-terminals. This was confirmed by Western blot analysis. Different SAA expression patterns were observed by MALDI-TOF-MS profiling. SAA has long been reported as a biomarker for various cancer types such as lung cancer, ovarian cancer, and breast cancer. However, in this study we found increased SAA expression in the sera of advanced-stage cancer patients with different cancer types. Our results suggest that maybe SAA should not be used alone as a biomarker for any specific cancer type.

Profiling of antibody production against xenograft-released proteins by protein microarrays discovers prostate cancer markers

This study describes a novel xenograft-based biomarker discovery platform and proves its usefulness in the discovery of serum markers for prostate cancer. By immunizing immuno-competent mice with serum from nude mice bearing prostate cancer xenografts, an antibody response against xenograft-derived antigens was elicited. By probing protein microarrays with serum from immunized mice, several prostate cancer-derived antigens were identified, of which a subset was successfully retrieved in serum from mice bearing prostate cancer xenografts and prevalidated in human serum samples of prostate cancer patients. Among the discovered and validated proteins were the members of the TAM receptor family (TYRO3, AXL, and MERTK), ACY1, and PSMA1. In conclusion, this novel method allows for the identification of low abundant cancer-derived serum proteins, circumventing dynamic range and host-response issues in standard patient cohort proteomics comparisons.

Serum amyloid a protein in clinical cancer diagnosis

The serum amyloid A (SAA) protein is an acute phase protein that is synthesized under the regulation of inflammatory cytokines during both acute and chronic inflammation. It is suggested that the SAA increases correlate with many types of carcinogenesis and neoplastic diseases. Th changes in SAA in serum could therefore indicate the progress and malignancy of the disease, as well as the host responses. The present paper reviewed the rationale of using SAA as potential cancer biomarker in clinical diagnosis, including the contribution and involvement of SAA in cancer growth and development. Then we discussed the current applications of SAA in diagnosis and tracing of different types of cancers. Finally the proteomics techniques, especially the SELDI-TOF MS to identify SAA in serum from patients were appreciated as an important manner in clinical diagnosis.

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

PURPOSE

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

EXPERIMENTAL DESIGN

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

RESULTS

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

CONCLUSION AND CLINICAL RELEVANCE

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

Evolving role of bone biomarkers in castration-resistant prostate cancer

The preferential metastasis of prostate cancer cells to bone disrupts the process of bone remodeling and results in lesions that cause significant pain and patient morbidity. Although prostate-specific antigen (PSA) is an established biomarker in prostate cancer, it provides only limited information relating to bone metastases and the treatment of metastatic bone disease with bisphosphonates or novel noncytotoxic targeted or biological agents that may provide clinical benefits without affecting PSA levels. As bone metastases develop, factors derived from bone metabolism are released into blood and urine, including N- and C-terminal peptide fragments of type 1 collagen and bone-specific alkaline phosphatase, which represent potentially useful biomarkers for monitoring metastatic bone disease. A number of clinical trials have investigated these bone biomarkers with respect to their diagnostic, prognostic, and predictive values. Results suggest that higher levels of bone biomarkers are associated with an increased risk of skeletal-related events and/or death. As a result of these findings, bone biomarkers are now being increasingly used as study end points, particularly in studies investigating novel agents with putative bone effects. Data from prospective clinical trials are needed to validate the use of bone biomarkers and to confirm that marker levels provide additional information beyond traditional methods of response evaluation for patients with metastatic prostate cancer.

Acute-phase proteins: As diagnostic tool

The varied reactions of the host to infection, inflammation, or trauma are collectively known as the acute-phase response and encompass a wide range of pathophysiological responses such as pyrexia, leukocytosis, hormone alterations, and muscle protein depletion combining to minimize tissue damage while enhancing the repair process. The mechanism for stimulation of hepatic production of acute-phase proteins is by proinflammatory cytokines. The functions of positive acute-phase proteins (APP) are regarded as important in optimization and trapping of microorganism and their products, in activating the complement system, in binding cellular remnants like nuclear fractions, in neutralizing enzymes, scavenging free hemoglobin and radicals, and in modulating the host's immune response. APP can be used as diagnostic tool in many diseases like bovine respiratory syncytial virus, prostate cancer, bronchopneumonia, multiple myeloma, mastitis, Streptococcus suis infection, starvation, or lymphatic neoplasia. Thus, acute-phase proteins may provide an alternative means of monitoring animal health.

The promise of bone cancer proteomics

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

Elevated levels of the acute-phase serum amyloid are associated with heightened lung cancer risk

BACKGROUND

The authors investigated whether early stage lung cancer could be identified by proteomic analyses of plasma.

METHODS

For the first case-control study, plasma samples from 52 patients with lung cancer and from a group of 51 controls were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. In a second case-control study, a classifier of 4 markers (mass-to-charge ratio, 11,681, 6843, 5607, and 8762) also was tested for validation on plasma from 16 consecutive patients with screen-detected cancer versus 406 healthy individuals. The most relevant marker was identified, and an enzyme-linked immunosorbent assay-based analysis revealed that signal intensity was correlated with concentration.

RESULTS

The classifier had a sensitivity of 94.23% and a specificity of 76.47% in the first study but lost predictive value in the second study. Nevertheless, the 11,681 cluster, which was identified as serum amyloid protein A (SAA), resulted in a multiple logistic regression model that indicated a strong association with lung cancer. When both studies were considered as a together, the odds ratio (OR) for an SAA intensity > or =0.5 was 10.27 (95% confidence interval [CI], 4.64-22.74), whereas an analysis restricted to stage I cancers (TNM classification) revealed an OR of 8.45 (95% CI, 2.76-25.83) for T1 lung cancer and 21.22 (95% CI, 5.62-80.14) for T2 lung cancer.

CONCLUSIONS

SAA levels were predictive of an elevated risk of lung cancer, supporting the general view that inflammation is implicated in lung cancer development.

Proteomic assays for the detection of urothelial cancer

Bladder cancer is one of the most expensive cancers from diagnosis to death of the patient due to life-long surveillance involving upper tract imaging, urinary cytology, and cystoscopy. Cytology has been historically used in conjunction with cystoscopy to help detect disease that may be missed by routine cystoscopy (e.g., carcinoma in situ and upper tract disease). Urine cytology is highly cytopathologist dependent and has reasonable sensitivity for detecting high grade disease. However, its sensitivity drops precipitously with regard to well-differentiated low grade cancers. Intensive investigations have been undertaken using proteomics to find an alternative to cystoscopy and cytology. Urine proteomic markers currently evaluated critically in the literature include bladder tumor antigen, nuclear matrix protein 22, BLCA-4, hyaluronic acid, hyaluronidase, cytokeratin 8, cytokeratin 18, cytokeratin 19, tissue polypeptide antigen, and tissue polypeptide-specific antigen. Markers used as alternatives to cystoscopy must be accurate with high sensitivity and specificity, cost effective for life-long surveillance, and minimally invasive to minimize the burden to the patient. To date, no proteomic marker has been developed that can replace cystoscopy for the detection of bladder cancer. However, several urinary markers appear to have higher sensitivity albeit lower specificity than cytology and can be used to supplement cystoscopy. Some of those markers are herein described in this chapter. By defining and characterizing the current state of the art in protein based markers, we are poised to evaluate and benchmark newly discovered protein biomarkers that will be isolated through new proteomics based investigations of urine.

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

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

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

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

SELDI-TOF-MS-based serum proteomic screening in combination with CT scan distinguishes renal cell carcinoma from benign renal tumors and healthy persons

In order to identify the proteomic differences between renal cell carcinoma (RCC) and benign renal tumors, we analyzed 168 serum samples from 65 RCC patients, 34 patients with benign renal tumors, and 69 healthy persons using the IMAC-Cu2+ ProteinChip system by surface enhanced laser desorption/ionization mass spectrometry technology. Two decision trees were generated by Biomarker Pattern software to distinguish between RCC versus healthy and RCC versus patients with benign tumors, respectively. Although the sensitivity and specificity of the RCC vs. healthy decision tree were 97.6% and 95.7%, respectively, it could not be used to distinguish RCC from benign renal tumors. The sensitivity of a blind test process using RCC, benign tumors and healthy persons were 92%. The specificity of the test process was 35.3% for benign tumors and 95.5% for healthy persons. The sensitivity and specificity of the RCC-Benign tumors decision tree were 85.7% and 95.5%, respectively. The blind test process using RCC, benign tumors and healthy persons also showed significant results. The sensitivity was 90.0%. The specificity was 95.7% for healthy persons and 90.0% for benign tumors. Combining these data with the results of CT scanning, the sensitivity can be improved over the use of either CT and decision tree analysis and the specificity may reach 100%. Two peaks with molecular masses of 3887.11 Da and 11079.8 Da were detected that are potentially useful for the diagnosis or screening of RCC. It was found that these two peaks can be used, not only to distinguish the RCC vs. healthy cases, but also to distinguish RCC from benign renal tumors. In combination with CT scanning, the sensitivity and specificity of the diagnosis of renal tumors can be improved. However, the decision tree constructed for RCC and healthy persons may not present good specificity for use in distinguishing malignant from benign renal tumors.

Plasma biomarkers in a mouse model of preterm labor

Preterm labor (PTL) is frequently associated with inflammation. We hypothesized that biomarkers during pregnancy can identify pregnancies most at risk for development of PTL. An inflammation-induced mouse model of PTL was used. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry was used to analyze and compare the plasma protein (PP) profile between CD-1 mice injected intrauterine with either lipopolysaccharide (LPS) or PBS on d 14.5 of gestation. The median differences of normalized PP peaks between the two groups were determined using the Mann-Whitney U test and the false discovery rate. In a second series of experiments, both groups of mice were injected with a lower dose of LPS. A total of 1665 peaks were detected. Thirty peaks were highly differentially expressed (p < 0.0001) between the groups. Two 11 kDa protein peaks were identified by MALDI-TOF/TOF-MS and confirmed to be mouse serum amyloid A (SAA) 1 and 2. Plasma SAA2 levels were increased in LPS-treated animals compared with controls and in LPS-treated animals that delivered preterm vs. those that delivered at term. SAA2 has the potential to be a plasma biomarker that can identify pregnancies at risk for development of PTL.

Serum amyloid A: an acute-phase protein involved in tumour pathogenesis

The synthesis of acute-phase protein serum amyloid A (SAA) is largely regulated by inflammation- associated cytokines and a high concentration of circulating SAA may represent an ideal marker for acute and chronic inflammatory diseases. However, SAA is also synthesized in extrahepatic tissues, e.g. human carcinoma metastases and cancer cell lines. An increasing body of in vitro data supports the concept of involvement of SAA in carcinogenesis and neoplastic diseases. Accumulating evidence suggests that SAA might be included in a group of biomarkers to detect a pattern of physiological events that reflect the growth of malignancy and host response. This review is meant to provide a broad overview of the many ways that SAA could contribute to tumour development, and accelerate tumour progression and metastasis, and to gain a better understanding of this acute-phase reactant as a possible link between chronic inflammation and neoplasia.

Biomarker rediscovery in diagnostics

BACKGROUND

Proteomics has evolved into a large-scale biomarker discovery program; however, these initiatives are viewed as failing owing to a lack of successful implementation of new protein biomarkers in the diagnostic arena. New approaches to proteomics biomarker discovery and validation may be the key to boosting clinical proteomics into diagnostics.

OBJECTIVE

To review the technologies and the mindsets behind proteomic biomarker discovery and discuss suitable methods for the detection of protein variants and their use as potential biomarkers of disease states.

METHODS

A literature review of recent research on proteomic biomarkers and through experience with biomarker discovery research was surveyed and described. Emphasis was placed on top-down proteomics approaches for the discovery and routine screening of protein variation.

CONCLUSION

Protein variation is an untapped resource in the biomarker space, but only a selected few forms of proteomics applications are suitable for their analysis. Such variation could have a significant impact in disease diagnostics and therapeutic intervention.

Expression of serum amyloid A transcripts in human bone tissues, differentiated osteoblast-like stem cells and human osteosarcoma cell lines

Although the liver is the primary site of cytokine-mediated expression of acute-phase serum amyloid A (SAA) protein, extrahepatic production has also been reported. Besides its role in amyloidosis and lipid homeostasis during the acute-phase, SAA has recently been assumed to contribute to bone and cartilage destruction. However, expression of SAA in human osteogenic tissue has not been studied. Therefore, we first show that SAA1 (coding for the major SAA isoform) but not SAA2 transcripts are expressed in human trabecular and cortical bone fractions and bone marrow. Next, we show expression of (i) IL-1, IL-6, and TNF receptor transcripts; (ii) the human homolog of SAA-activating factor-1 (SAF-1, a transcription factor involved in cytokine-mediated induction of SAA genes); and (iii) SAA1/2 transcripts in non-differentiated and, to a higher extent, in osteoblast-like differentiated human mesenchymal stem cells. Third, we provide evidence that human osteoblast-like cells of tumor origin (MG-63 and SAOS-2) express SAF-1 under basal conditions. SAA1/2 transcripts are expressed under basal conditions (SAOS-2) and cytokine-mediated conditions (MG-63 and SAOS-2). RT-PCR, Western blot analysis, and immunofluorescence technique confirmed cytokine-mediated expression of SAA on RNA and protein level in osteosarcoma cell lines while SAA4, a protein of unknown function, is constitutively expressed in all osteogenic tissues investigated.