Detection and identification of potential biomarkers of breast cancer

The Yin Yang of Complement and Cancer

Cancer-related inflammation is a crucial component of the tumor microenvironment (TME). Complement activation occurs in cancer and supports the development of an inflammatory microenvironment. Complement has traditionally been considered a mechanism of immune resistance against cancer, and its activation is known to contribute to the cytolytic effects of antibody-based immunotherapeutic treatments. However, several studies have recently revealed that complement activation may exert protumoral functions by sustaining cancer-related inflammation and immunosuppression through different molecular mechanisms, targeting both the TME and cancer cells. These new discoveries have revealed that complement manipulation can be considered a new strategy for cancer therapies. Here we summarize our current understanding of the mechanisms by which the different elements of the complement system exert antitumor or protumor functions, both in preclinical studies and in human tumorigenesis. Complement components can serve as disease biomarkers for cancer stratification and prognosis and be exploited for tumor treatment.

Expression of lncRNAs AK058003 and APOC1P1 in breast cancer patients

In the current study, the expression levels of two important lncRNAs, i.e., AK058003 and APOC1P1, in breast tumors were compared with adjacent non-tumor tissues to evaluate their diagnostic potential in a panel of 121 patients. Total RNA was extracted, cDNA was synthesized and expression of AK058003 and APOC1P1 was assessed using qRT-PCR. A significant overexpression and positive correlation between these two lncRNAs were observed in tumor tissues compared to marginal healthy tissues. In conclusion, the examined lncRNAs were overexpressed in tumor tissues, suggesting their significant diagnostic value in breast cancer.

Breast cancer in the era of integrating “Omics” approaches

Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput “omics” technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of “omics” strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different “omics” datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

Whole Transcriptomic Analysis of Apigenin on TNFα Immuno-activated MDA-MB-231 Breast Cancer Cells

BACKGROUND

Triple-negative breast cancer is categorized by a lack of hormone receptors, inefficacy of anti-estrogen or aromatase inhibitor chemotherapies and greater mortality rates in African American populations. Advanced-stage breast tumors have a high concentration of tumor necrosis factor-α (TNFα) throughout the tumor/stroma milieu, prompting sustained release of diverse chemokines (i.e. C-C motif chemokine ligand 2 (CCL2)/CCL5). These potent chemokines can subsequently direct mass infiltration of leukocyte sub-populations to lodge within the tumor, triggering a loss of tumor immune surveillance and subsequent rapid tumor growth. Previously, we demonstrated that in the MDA-MB-231 TNBC cell line, TNFα evoked a rise in immune signaling proteins: CCL2, granulocyte macrophage colony-stimulating factor, interleukin (IL)1α, IL6 and inhibitor of nuclear factor kappa-B kinase subunit epsilon (IKBKε) all of which were attenuated by apigenin, a dietary flavonoid found in chamomile and parsley.

MATERIALS AND METHODS

The present work elucidates changes evoked by TNFα in the presence or absence of apigenin by examining the entire transcriptome for mRNA and long intergenic non-coding RNA with Affymetrix Hugene-2.1_ST human microarrays. Differential gene-expression analysis was conducted on 48,226 genes.

RESULTS

TNFα caused up-regulation of 75 genes and down-regulation of 10. Of these, apigenin effectively down-regulated 35 of the 75 genes which were up-regulated by TNFα. These findings confirm our previous work, specifically for the TNFα-evoked spike in IL1A vs. untreated controls [+21-fold change (FC), p<0.0001] being attenuated by apigenin in the presence of TNFa (-15 FC vs. TNFα, p<0.0001). Similar trends were seen for apigenin-mediated down-regulation of TNFα-up-regulated transcripts: IKBKE (TNFα: 4.55 FC vs. control, p<0.001; and TNFα plus apigenin: -4.92 FC, p<0.001), CCL2 (2.19 FC, p<0.002; and -2.12 FC, p<0.003), IL6 (3.25 FC, p<0.020; and -2.85 FC, p<0.043) and CSF2 (TNFα +6.04 FC, p<0.001; and -2.36 FC, p<0.007). In addition, these data further establish more than a 65% reduction by apigenin for the following transcripts which were also up-regulated by TNFα: cathepsin S (CTSS), complement C3 (C3), laminin subunit gamma 2 (LAMC2), (TLR2), toll-like receptor 2 G protein-coupled receptor class C group 5 member B (GPRC5B), contactin-associated protein 1 (CNTNAP1), claudin 1 (CLDN1), nuclear factor of activated T-cells 2 (NFATC2), C-X-C motif chemokine ligand 10 (CXCL10), CXCL11, interleukin 1 receptor-associated kinase 3 (IRAK3), nuclear receptor subfamily 3 group C member 2 (NR3C2), interleukin 32 (IL32), IL24, slit guidance ligand 2 (SLIT2), transmembrane protein 132A (TMEM132A), TMEM171, signal transducing adaptor family member 2 (STAP2), mixed lineage kinase domain-like pseudokinase (MLKL), kinase insert domain receptor (KDR), BMP-binding endothelial regulator (BMPER), and kelch-like family member 36 (KLHL36).

CONCLUSION

There is a possible therapeutic role for apigenin in down-regulating diverse genes associated with tumorigenic leukocyte sub-population infiltration by triple-negative breast cancer. The data have been deposited into the Gene Expression Omnibus for public analysis at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120550.

The decrease of some serum free amino acids can predict breast cancer diagnosis and progression

This study was targeted on a metabolomic approach to compare the blood serum free amino acid profiles and concentration of confirmed breast cancer (stages I-III) patients to healthy controls in order to establish reliable biomarkers of early detection and prediction of breast cancer. The ultra-high-performance liquid chromatography coupled with mass spectrometry using positive ionization electrospray was applied for the picoline-derivatized serum free amino acids using the EZ:faastTM kit. Multivariate statistical analysis principal component analysis, partial least squares discrimination analysis and univariate analysis were applied in order to discriminate between patient groups and putative amino acid biomarkers for breast cancer. A significant decrease of amino acid concentrations between the breast cancer group and the control group was positively correlated with breast cancer progression. Arginine, Alanine, Isoleucine, Tyrosine and Tryptophan were identified as being good potential discriminants (AUROC ≥0.85) and suitable candidates to diagnose and predict the breast cancer progression.

Complement anaphylatoxins C3a and C5a: Emerging roles in cancer progression and treatment

Recent insights into the role of complement anaphylatoxins C3a and C5a in cancer provide new opportunities for the development of innovative biomarkers and therapeutic strategies. These two complement activation products can maintain chronic inflammation, promote an immunosuppressive microenvironment, induce angiogenesis, and increase the motility and metastatic potential of cancer cells. Still, the diverse heterogeneity of responses mediated by these peptides poses a challenge both to our understanding of the role played by these molecules in cancer progression and to the development of effective treatments. This review attempts to summarize the evidence surrounding the involvement of anaphylatoxins in the biological contexts associated with tumor progression. We also describe the recent developments that support the inhibition of anaphylatoxins, or their cognate receptors C3aR and C5aR1, as a treatment option for maximizing the clinical efficacy of current immunotherapies that target the PD-1/PD-L1 immune checkpoint.

Identification of differentially expressed inflammatory factors in Wilms tumors and their association with patient outcomes

The present study aimed to identify differentially expressed inflammatory factors observed in Wilms tumors (WT), and to investigate the association of these factors with clinical stage, pathological type, lymph node metastasis and vascular involvement of WT. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry was performed to screen differentially expressed proteins among WT and normal tissue pairs. Upregulated proteins in WT were separated and purified by solid phase extraction and Tricine SDS-PAGE, respectively. Following in-gel digestion, the peptide mixture was subjected to liquid chromatography mass spectrometry to identify proteins on the basis of their amino acid sequences. Immunohistochemistry was used to confirm the expression of differentially expressed inflammatory proteins. Of the proteins that were upregulated in WT, two proteins with mass/charge (m/z) ratio of 12,138 and 13,462 were identified as macrophage migration inhibitory factor (MIF) and C-X-C motif ligand 7 (CXCL7) chemokine, respectively. The expression of these two proteins was increased in WT compared with adjacent normal tissues and normal renal tissues, and increased with increasing clinical stage. In addition, their expression was significantly increased in patients with unfavorable pathological type, lymph node metastasis and vascular involvement compared with the groups with favorable type, and without lymph node metastasis or vascular involvement (P<0.05). Increased pro-inflammatory MIF and CXCL7 expression in WT is closely associated with the clinical stage, pathological type, lymph node metastasis and vascular involvement, and may represent biomarkers for the clinical diagnosis of WT.

Diagnostic and prognostic significance of serum apolipoprotein C-I in triple-negative breast cancer based on mass spectrometry

Women with triple-negative breast cancer (TNBC) have poor prognosis because of the aggressive nature of the tumor, delayed diagnosis and non-specific symptoms in the early stages. Identification of novel specific TNBC serum biomarkers for screening and therapeutic purposes therefore remains an urgent clinical requirement.We obtained serum samples from a total of 380 recruited individuals split into mining and testing sets, with the aim of screening for reliable protein biomarkers from TNBC and non-TNBC (NTNBC) sera. Samples were assessed using mass spectrometry, followed by receiver operating characteristic (ROC), survival and hazard function curve as well as multivariate Cox regression analyses to ascertain the potential of the protein constituents as diagnostic and prognostic biomarkers for TNBC.We identified upregulated apolipoprotein C-I (apoC-I) with a validated positive effect on TNBC tumorigenesis, with confirmation in an independent test set and minimization of systematic bias by pre-analytical parameters. The apoC-I protein had superior diagnostic ability in distinguishing between TNBC and NTNBC cases. Moreover, the protein presented a more robust potential prognostic factor for TNBC than NTNBC. The apoC-I protein identified in this study presents an effective novel diagnostic and prognostic biomarker for TNBC, indicating that measurement of the peak intensity at 7785 Da in serum samples could facilitate improved early detection and estimation of postoperative survival prognosis for TNBC.

Long intergenic non-coding RNA APOC1P1-3 inhibits apoptosis by decreasing α-tubulin acetylation in breast cancer

Increasing evidence indicates that long non-coding RNAs (lncRNAs) act as important regulatory factors in tumor progression. However, their roles in breast cancer remain largely unknown. In present studies, we identified aberrantly expressed long intergenic non-coding RNA APOC1P1-3 (lincRNA-APOC1P1-3) in breast cancer by microarray, verified it by quantitative real-time PCR, and assessed methylation status in the promoter region by pyrosequencing. We also investigated the biological functions with plasmid transfection and siRNA silencing experiments, and further explored their mechanisms by RNA pull-down and RNA immunoprecipitation to identify binding proteins. We found that 224 lncRNAs were upregulated in breast cancer, whereas 324 were downregulated. The lincRNA-APOC1P1-3 was overexpressed in breast cancer, which was related to tumor size and hypomethylation in its promoter region. We also found that APOC1P1-3 could directly bind to tubulin to decrease α-tubulin acetylation, to inactivate caspase-3, and to inhibit apoptosis. This study demonstrates that overexpression of APOC1P1-3 can inhibit breast cancer apoptosis.

Identification of Apolipoprotein C-I Peptides as a Potential Biomarker and its Biological Roles in Breast Cancer

Background

Breast cancer (BC) is one of the most common cancers and is among the main causes of death in females around the world. Although several serum biomarkers have been identified for breast cancer, due to lack of adequate sensitivity and specificity they do not adequately distinguish BC from confounding conditions. New approaches are urgently needed to improve BC detection and treatment.

Material/Methods

Eighty serum samples from 20 healthy individuals and 60 patients with BC (22 triple-negative breast cancer, TNBC; 38 non-triple-negative breast cancer, NTNBC) were included. Protein profiling of serum samples was analyzed using surface-enhanced laser desorption/ionization time-of-flight mass spectroscopy (SELDI-TOF-MS). Candidate biomarkers were purified by SDS-PAGE electrophoresis and identified by MALDI-TOF/TOF.

Results

The candidate biomarker positioned at 6447.9 m/z was significantly decreased in BC patients. Moreover, the expression intensity of the candidate biomarker was weaker in the TNBC and pre-surgery group compared with the NTNBC and post-surgery group. We ultimately identified the biomarker as apolipoprotein C-I (ApoC-I). Furthermore, we found that ApoC-I peptides inhibited proliferation of human breast cancer cells in vitro and suppressed tumor growth in vivo.

Conclusions

These results suggest that ApoC-I peptides may be a potential diagnostic biomarker and therapeutic approach for BC.

Diagnostic and prognostic role of serum protein peak at 6449 m/z in gastric adenocarcinoma based on mass spectrometry

Background:

Gastric cancer (GC) is a highly aggressive cancer type associated with significant mortality owing to delayed diagnosis and non-specific symptoms observed in the early stages. Therefore, identification of novel specific GC serum biomarkers for screening purposes is an urgent clinical requirement.

Methods:

This study recruited a total of 432 serum samples from 296 GC patients split into the mining and testing sets. We aimed to screen for reliable protein biomarkers from matched serum samples based on mass spectrometry, followed by comparison with three representative conventional markers using receiver operating characteristic and survival curve analyses to ascertain their potential values as diagnostic and prognostic biomarkers for GC.

Results:

We identified an apoC-III fragment with confirmation in an independent test set from a second hospital. We found that the diagnostic ability of this fragment performed better than current standard GC diagnostic biomarkers both individually and in combination in distinguishing patients with GC from healthy individuals. Moreover, we found that this apoC-III protein fragment represents a more robust potential prognostic factor for GC than the three conventional markers.

Conclusions:

In view of these findings, we suggest that apoC-III protein fragment is a novel diagnostic and prognostic biomarker, a complement to conventional biomarkers in detecting GC.

Implementation of proteomics for cancer research: past, present, and future

Cancer is the leading cause of the death, accounts for about 13% of all annual deaths worldwide. Many different fields of science are collaborating together studying cancer to improve our knowledge of this lethal disease, and find better solutions for diagnosis and treatment. Proteomics is one of the most recent and rapidly growing areas in molecular biology that helps understanding cancer from an omics data analysis point of view. The human proteome project was officially initiated in 2008. Proteomics enables the scientists to interrogate a variety of biospecimens for their protein contents and measure the concentrations of these proteins. Current necessary equipment and technologies for cancer proteomics are mass spectrometry, protein microarrays, nanotechnology and bioinformatics. In this paper, we provide a brief review on proteomics and its application in cancer research. After a brief introduction including its definition, we summarize the history of major previous work conducted by researchers, followed by an overview on the role of proteomics in cancer studies. We also provide a list of different utilities in cancer proteomics and investigate their advantages and shortcomings from theoretical and practical angles. Finally, we explore some of the main challenges and conclude the paper with future directions in this field.

Identification of apolipoprotein C-I as a potential Wilms' tumor marker after excluding inflammatory factors

Wilms' tumor is one of the most common malignant tumors observed in children, and its early diagnosis is important for late-stage treatment and prognosis. We previously screened and identified protein markers for Wilms' tumor; however, these markers lacked specificity, and some were associated with inflammation. In the current study, serum samples from children with Wilms' tumors were compared with those of healthy controls and patients with systemic inflammatory response syndrome (SIRS). After exclusion of factors associated with inflammation, specific protein markers for Wilms' tumors were identified. After comparing the protein peak values obtained from all three groups, a protein with a m/z of 6438 Da was specified. Purification and identification of the target protein using high-pressure liquid chromatography (HPLC) and two-dimensional liquid chromatography-linearion trap mass spectrometry(2D-LC-LTQ-MS) mass spectrometry, respectively, revealed that it was apolipoprotein C-I (APO C-I). Thus, APO C-I is a specific protein marker for Wilms' tumor.

SELDI-TOF-MS proteomic profiling of serum, urine, and amniotic fluid in neural tube defects

Neural tube defects (NTDs) are common birth defects, whose specific biomarkers are needed. The purpose of this pilot study is to determine whether protein profiling in NTD-mothers differ from normal controls using SELDI-TOF-MS. ProteinChip Biomarker System was used to evaluate 82 maternal serum samples, 78 urine samples and 76 amniotic fluid samples. The validity of classification tree was then challenged with a blind test set including another 20 NTD-mothers and 18 controls in serum samples, and another 19 NTD-mothers and 17 controls in urine samples, and another 20 NTD-mothers and 17 controls in amniotic fluid samples. Eight proteins detected in serum samples were up-regulated and four proteins were down-regulated in the NTD group. Four proteins detected in urine samples were up-regulated and one protein was down-regulated in the NTD group. Six proteins detected in amniotic fluid samples were up-regulated and one protein was down-regulated in the NTD group. The classification tree for serum samples separated NTDs from healthy individuals, achieving a sensitivity of 91% and a specificity of 97% in the training set, and achieving a sensitivity of 90% and a specificity of 97% and a positive predictive value of 95% in the test set. The classification tree for urine samples separated NTDs from controls, achieving a sensitivity of 95% and a specificity of 94% in the training set, and achieving a sensitivity of 89% and a specificity of 82% and a positive predictive value of 85% in the test set. The classification tree for amniotic fluid samples separated NTDs from controls, achieving a sensitivity of 93% and a specificity of 89% in the training set, and achieving a sensitivity of 90% and a specificity of 88% and a positive predictive value of 90% in the test set. These suggest that SELDI-TOF-MS is an additional method for NTDs pregnancies detection.

Proteomic characterization of pediatric craniopharyngioma intracystic fluid by LC-MS top-down/bottom-up integrated approaches

The combination of top-down and bottom-up platforms was utilized for the LC-MS proteomic characterization of the intracystic fluid of adamantinomatous craniopharyngioma pediatric brain tumor disease. Proteins and peptides characterization was achieved by high-resolution LC-ESI-LTQ-Orbitrap-MS analysis while low-resolution LC-ESI-IT-MS was applied for the complete screening of the samples and the evaluation of the protein distribution within patients. Top-down analyses were applied to liquid/liquid extracted samples while bottom-up analyses were performed after trypsin digestion of both untreated and pretreated samples. The two proteomic approaches were complementary for the characterization of the proteome of craniopharyngioma intracystic fluid. Proteins and peptides involved in inflammation, mineralization processes and lipid transport were identified, in agreement with the calcium flecks, cholesterol granules and bone residues characteristic of this fluid. Apolipoprotein A-I, A-II, C-I and J, hemoglobin fragments, ubiquitin, α-2-HS-glycoprotein or fetuin A, α-1-antichymotrypsin, vitamin D binding protein, and α-1-acid glycoprotein were characterized. These data could be relevant for the comprehension of the processes involved in the pathogenesis of the disease and the development of the cyst and could contribute to the individuation of therapeutic targets for the reduction of the cyst volume delaying and/or avoiding invasive surgical treatments.

Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer

Introduction

Serum profiling using proteomic techniques has great potential to detect biomarkers that might improve diagnosis and predict outcome for breast cancer patients (BC). This study used surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) to identify differentially expressed proteins in sera from BC and healthy volunteers (HV), with the goal of developing a new prognostic biomarker panel.

Methods

Training set serum samples from 99 BC and 51 HV subjects were applied to four adsorptive chip surfaces (anion-exchange, cation-exchange, hydrophobic, and metal affinity) and analyzed by time-of-flight MS. For validation, 100 independent BC serum samples and 70 HV samples were analyzed similarly. Cluster analysis of protein spectra was performed to identify protein patterns related to BC and HV groups. Univariate and multivariate statistical analyses were used to develop a protein panel to distinguish breast cancer sera from healthy sera, and its prognostic potential was evaluated.

Results

From 51 protein peaks that were significantly up- or downregulated in BC patients by univariate analysis, binary logistic regression yielded five protein peaks that together classified BC and HV with a receiver operating characteristic (ROC) area-under-the-curve value of 0.961. Validation on an independent patient cohort confirmed the five-protein parameter (ROC value 0.939). The five-protein parameter showed positive association with large tumor size (P = 0.018) and lymph node involvement (P = 0.016). By matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, immunoprecipitation and western blotting the proteins were identified as a fragment of apolipoprotein H (ApoH), ApoCI, complement C3a, transthyretin, and ApoAI. Kaplan-Meier analysis on 181 subjects after median follow-up of >5 years demonstrated that the panel significantly predicted disease-free survival (P = 0.005), its efficacy apparently greater in women with estrogen receptor (ER)-negative tumors (n = 50, P = 0.003) compared to ER-positive (n = 131, P = 0.161), although the influence of ER status needs to be confirmed after longer follow-up.

Conclusions

Protein mass profiling by MS has revealed five serum proteins which, in combination, can distinguish between serum from women with breast cancer and healthy control subjects with high sensitivity and specificity. The five-protein panel significantly predicts recurrence-free survival in women with ER-negative tumors and may have value in the management of these patients.

Serum-based protein biomarkers for detection of lung cancer

Lung cancer is one of the most common cancers in terms of both incidence and mortality.The major reasons for the increasing number of deaths from lung cancer are late detection and lack of effective therapies. To improve our understanding of lung cancer biology, there is urgent need for blood-based, non-invasive molecular tests to assist in its detection in a cost-effective manner at an early stage when curative interventions are still possible. Recent advances in proteomic technology have provided extensive, high throughput analytical tools for identification, characterization and functional studies of proteomes. Changes in protein expression patterns in response to stimuli can serve as indicators or biomarkers of biological and pathological processes as well as physiological and pharmacological responses to drug treatment, thus aiding in early diagnosis and prognosis of disease. However, only a few biomarkers have been approved by the FDA to date for screening and diagnostic purposes. This review provides a brief overview of currently available proteomic techniques, their applications and limitations and the current state of knowledge about important serum biomarkers in lung cancer and their potential value as prognostic and diagnostic tools.

Identification of osteoarthritis biomarkers by proteomic analysis of synovial fluid

OBJECTIVE

To use proteomic analysis to identify novel candidate biomarker proteins in synovial fluid for the differential diagnosis of osteoarthritis and rheumatoid arthritis.

METHODS

Synovial fluid samples were analysed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Data were used to generate an artificial neural network (ANN). The identification of one protein peak was confirmed via Western blotting.

RESULTS

Fluid samples were analysed from 36 patients with osteoarthritis and 24 with rheumatoid arthritis. In total, three protein peaks (mass-to-charge ratio [m/z] 3893, 10,576 and 14,175 Da) were identified as potential biomarkers for osteoarthritis. The ANN differentiated between osteoarthritis and rheumatoid arthritis with a sensitivity of 89.4% and a specificity of 91.2%. The protein peak at m/z 10 576 was identified as S100 calcium binding protein A12 (S100A12).

CONCLUSIONS

A combination of SELDI-TOF-MS and ANN identified osteoarthritis biomarkers. SELDI-TOF-MS may be a useful tool in the screening of synovial fluid for osteoarthritis diagnosis.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.

Personalized medicine in screening for malignant disease: a review of methods and applications

Personalized medicine (PM) is currently a hot topic in the professional world. It is often called the medicine of the future and has already achieved resounding success in the area of targeted therapy. Nevertheless, integration of the concepts of PM into routine clinical practice is slow. This review is intended to give an overview of current and potential applications of PM in oncology. PM could soon play a decisive role, especially in screening. The relevance of PM in screening was examined in the case of four common cancers (colorectal cancer, lung cancer, breast cancer, and prostate cancer). A literature search was performed. This showed that biomarkers in particular play a crucial role in screening. In summary, it can be emphasized that there are already numerous known promising biomarkers in malignant disease. This results in several possibilities for individualizing and revolutionizing screening.

Availability of MudPIT data for classification of biological samples

Background

Mass spectrometry is an important analytical tool for clinical proteomics. Primarily employed for biomarker discovery, it is increasingly used for developing methods which may help to provide unambiguous diagnosis of biological samples. In this context, we investigated the classification of phenotypes by applying support vector machine (SVM) on experimental data obtained by MudPIT approach. In particular, we compared the performance capabilities of SVM by using two independent collection of complex samples and different data-types, such as mass spectra (m/z), peptides and proteins.

Results

Globally, protein and peptide data allowed a better discriminant informative content than experimental mass spectra (overall accuracy higher than 87% in both collection 1 and 2). These results indicate that sequencing of peptides and proteins reduces the experimental noise affecting the raw mass spectra, and allows the extraction of more informative features available for the effective classification of samples. In addition, proteins and peptides features selected by SVM matched for 80% with the differentially expressed proteins identified by the MAProMa software.

Conclusions

These findings confirm the availability of the most label-free quantitative methods based on processing of spectral count and SEQUEST-based SCORE values. On the other hand, it stresses the usefulness of MudPIT data for a correct grouping of sample phenotypes, by applying both supervised and unsupervised learning algorithms. This capacity permit the evaluation of actual samples and it is a good starting point to translate proteomic methodology to clinical application.

Identification of potential complementary serum biomarkers to differentiate prostate cancer from benign prostatic hyperplasia using gel- and lectin-based proteomics analyses

Diagnosis of prostate cancer (PCa) is currently much reliant on the invasive and time-consuming transrectal ultrasound-guided biopsy of the prostate gland, particularly in light of the inefficient use of prostate-specific antigen as its biomarker. In the present study, we have profiled the sera of patients with PCa and benign prostatic hyperplasia (BPH) using the gel- and lectin-based proteomics methods and demonstrated the significant differential expression of apolipoprotein AII, complement C3 beta chain fragment, inter-alpha-trypsin inhibitor heavy chain 4 fragment, transthyretin, alpha-1-antitrypsin, and high molecular weight kininogen (light chain) between the two groups of patients' samples. Our data are suggestive of the potential use of the serum proteins as complementary biomarkers to effectively discriminate PCa from BPH, although this requires further extensive validation on clinically representative populations.

Whole-cell MALDI-TOF MS: a new tool to assess the multifaceted activation of macrophages

Whole-cell MALDI-TOF MS is routinely used to identify bacterial species in clinical samples. This technique has also proven to allow identification of intact mammalian cells, including macrophages. Here, we wondered whether this approach enabled the assessment human macrophages plasticity. The whole-cell MALDI-TOF spectra of macrophages stimulated with IFN-γ and IL-4, two inducers of M1 and M2 macrophage polarisation, consisted of peaks ranging from 2 to 12 kDa. The spectra of unstimulated and stimulated macrophages were clearly different. The fingerprints induced by the M1 agonists, IFN-γ, TNF, LPS and LPS+IFN-γ, and the M2 agonists, IL-4, TGF-β1 and IL-10, were specific and readily identifiable. Thus, whole-cell MALDI-TOF MS was able to characterise M1 and M2 macrophage subtypes. In addition, the fingerprints induced by extracellular (group B Streptococcus, Staphylococcus aureus) or intracellular (BCG, Orientia tsutsugamushi, Coxiella burnetii) bacteria were bacterium-specific. The whole-cell MALDI-TOF MS fingerprints therefore revealed the multifaceted activation of human macrophages. This approach opened a new avenue of studies to assess the immune response in the clinical setting, by monitoring the various activation patterns of immune cells in pathological conditions.

Improved classification of breast cancer peptide and protein profiles by combining two serum workup procedures

Purpose

Detection of breast cancer at early stage increases patient’s survival. Mass spectrometry-based protein analysis of serum samples is a promising approach to obtain biomarker profiles for early detection. A combination of commonly applied solid-phase extraction procedures for clean-up may increase the number of detectable peptides and proteins. In this study, we have evaluated whether the classification performance of breast cancer profiles improves by using two serum workup procedures.

Methods

Serum samples from 105 breast cancer patients and 202 healthy volunteers were processed according to a standardized protocol implemented on a high-end liquid-handling robot. Peptide and protein enrichments were carried out using weak-cation exchange (WCX) and reversed-phase (RP) C18 magnetic beads. Profiles were acquired on a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometer. In this way, two different biomarker profiles were obtained for each serum sample, yielding a WCX- and RPC18-dataset.

Results

The profiles were statistically evaluated with double cross-validation. Classification results of WCX- and RPC18-datasets were determined for each set separately and for the combination of both sets. Sensitivity and specificity were 82 and 87 % (WCX) and 73 and 93 % (RPC18) for the individual workup procedures. These values increased up to 84 and 95 %, respectively, upon combining the data.

Conclusion

It was found that MALDI-TOF peptide and protein profiles can be used for classification of breast cancer with high sensitivity and specificity. The classification performance even improved when two workup procedures were applied, since these provide a greater number of features (proteins).

Electronic supplementary material

The online version of this article (doi:10.1007/s00432-012-1273-4) contains supplementary material, which is available to authorized users.

Screening and identification of serum proteomic biomarkers for gastric adenocarcinoma

The aim of the present study was to screen for possible serum biomarkers for gastric adenocarcinoma. Surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) was used to screen serum samples from 109 cases of gastric adenocarcinoma and 106 control subjects (60 healthy subjects, 30 patients with chronic superficial gastritis and 16 cases of chronic atrophic gastritis). The differentially expressed protein peaks were selected and isolated using high performance liquid chromatography (HPLC) and processed with enzyme prior to liquid chromatography-mass spectrometry tandem mass spectrometry (LC-MS/MS) analysis and data mining with software XCalibur program components BioWorks 3.2. Among the gastric cancer cases, three differentially expressed protein peaks were selected as potential serum biomarkers: the m/z peaks at 5,906.5 showed increased expression (8.53±4.33 in the cancer group, and 0.88±0.31 in the control group); the m/z peaks at 6,635.7 and 8,716.3 showed decreased expression (6.54±2.44 and 0.93±0.29, respectively, in the cancer group and 17.56±4.43 and 2.16±0.98, respectively, in the control group) (P<0.01). The m/z peaks at 5,906.5, 6,635.7 and 8,716.3, were identified as fibrinogen α-chain, apolipo-protein A-II and apolipoprotein C-I. The combined use of the three biomarkers distinguished the cancer group patients from the control group samples at a sensitivity of 93.85% (61/65) and a specificity of 94.34% (50/53). In conclusion, fibrinogen α-chain, apolipoprotein A-II and apolipoprotein C-I were identified as potential markers for gastric cancer and appear to have diagnostic value for clinical applications.

Multi-biomarker pattern for tumor identification and prognosis

In last decades, the basic, clinical, and translational research efforts have been directed to the identification of standard biomarkers associated with the degree of malignancy. There is an increasingly public health concern for earlier detection of cancer development at stages in which successful treatments can be achieved. To meet this urgent clinical demand, early stage cancer biomarkers supported by reliable and robust experimental data that can be readily applicable in the clinical practice, are required. In the current standard protocols, when one or two of the canonical proliferating index biomarkers are analyzed, contradictory results are frequently reached leading to incorrect cancer diagnostic and unsuccessful therapies. Therefore, the identification of other cellular characteristics or signatures present in the tumor cells either alone or in combination with the well-established proliferation markers emerge as an alternative strategy in the improvement of cancer diagnosis and treatment. Because it is well known that several pathways and processes are altered in tumor cells, the concept of "single marker" in cancer results incorrect. Therefore, this review aims to analyze and discuss the proposal that the molecular profile of different genes or proteins in different altered tumor pathways must be established to provide a better global clinical pattern for cancer detection and prognosis.

New serum biomarkers for detection of endometriosis using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

This study used proteomic fingerprint technology, combining nano-sized magnetic beads with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), to screen for potential protein biomarkers for the diagnosis of endometriosis. Serum proteins from 126 patients with endometriosis and 120 healthy controls were profiled and compared. Biomarker pattern software identified 46 discriminating mass-to-charge m/z ratio peaks that were related to endometriosis. The model constructed by the software, based on three of these peaks (m/z 5988.7, 7185.3 and 8929.8), generated excellent separation between the endometriosis and control groups. The sensitivity was 91.4% and the specificity 95.0%. Blind testing on a second series of serum samples from patients with endometriosis and healthy controls indicated a sensitivity of 89.3% and a specificity of 90.0%. Biomarkers for endometriosis can be discovered in serum by MALDI-TOF-MS in combination with nano-sized magnetic beads. The pattern of combined markers provides a powerful and reliable diagnostic method for endometriosis, with high sensitivity and specificity.

Diagnosis of gallbladder cancer using matrix-assisted laser desorption/ionization time-of-flight profiling

Proteomic fingerprint technology combining magnetic beads with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used to profile and compare the serum proteins from 45 patients with gallbladder cancer and 50 healthy blood donors. The proteomic patterns were identified; the tree model of biomarkers was constructed and evaluated using the Biomarker Patterns Software. The model tree was constructed based on the 3 biomarkers (5913 Da, 6181 Da and 13,752 Da), which generated excellent separation between the gallbladder cancer and control groups. The sensitivity was 86.7% and the specificity was 93.3%. The blind test data showed a sensitivity of 80% and a specificity of 90%. Taken together, our studies suggested that biomarkers for gallbladder cancer could be discovered in serum by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with the use of magnetic beads. The pattern of combined markers would provide a powerful and reliable diagnostic method for gallbladder cancer with high sensitivity and specificity.

Conducting molecular biomarker discovery studies in plants

Molecular biomarkers are molecules whose concentrations in a biological system inform about the current phenotypical state and, more importantly, may also be predictive of future phenotypic trait endpoints. The identification of biomarkers has gained much attention in targeted plant breeding since technologies have become available that measure many molecules across different levels of molecular organization and at decreasing costs. In this chapter, we outline the general strategy and workflow of conducting biomarker discovery studies. Critical aspects of study design as well as the statistical data analysis and model building will be highlighted.

Early diagnostic protein biomarkers for breast cancer: how far have we come?

Many studies have used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry or matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to search for blood-based proteins that are related to the presence of breast cancer. We review the biomarkers discovered or targeted measured by these methods and discuss the strengths and weaknesses of these studies. We highlight two proteins that were most often related to breast cancer: C3a des-arginine anaphylatoxin (C3adesArg) (molecular weight: 8,938 Da) and fragments of inter-alpha trypsin inhibitor heavy chain H4 (ITIH4). In addition, we elaborate on three important methodological aspects related to these studies: protein identification, specificity of the markers, and disease heterogeneity. Finally, we propose some points to be addressed in future studies. These include the use of other analytical measurement techniques, need of protein identification, the importance of identical sample handling protocols for cases and controls, and the stratification of the results according to molecular subtypes and stages of breast cancer. Ultimately this may lead to the discovery of new and valid breast cancer specific biomarkers.

Discrimination analysis of mass spectrometry proteomics for cervical cancer detection

To study the serum protein fingerprint of patients with cervical cancer and to screen for protein molecules closely related to cervical cancer during the onset and progression of the disease using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Serum samples from 85 patients with cervical cancer and 80 healthy volunteers. Weak cation exchange (WCX) magnetic beads and PBSII-C protein chips reader (Ciphergen Biosystems Ins.) were used.The protein fingerprint expression of all the serum samples and the resulting profiles between cancer and normal were analyzed with Biomarker Wizard system. A group of proteomic peaks were detected. Three differently expressed potential biomarkers were identified with the relative molecular weights of 3974 Da, 4175 Da, 5906 Da. This diagnostic model can distinguish cervical cancer from healthy controls with a sensitivity of 93.3% and a specificity of 95%. Blind test data indicated a sensitivity of 87.5% and a specificity of 90%. MALDI technology can be used to screen significant proteins of differential expression in the serum of cervical cancer patients. These different proteins could be specific biomarkers of the patients with cervical cancer in the serum and have the potential value of further investigation.

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.

Prediction of metastasis and recurrence in colorectal cancer based on gene expression analysis: ready for the clinic?

Cancers of the colon and rectum, which rank among the most frequent human tumors, are currently treated by surgical resection in locally restricted tumor stages. However, disease recurrence and formation of local and distant metastasis frequently occur even in cases with successful curative resection of the primary tumor (R0). Recent technological advances in molecular diagnostic analysis have led to a wealth of knowledge about the changes in gene transcription in all stages of colorectal tumors. Differential gene expression, or transcriptome analysis, has been proposed by many groups to predict disease recurrence, clinical outcome, and also response to therapy, in addition to the well-established clinico-pathological factors. However, the clinical usability of gene expression profiling as a reliable and robust prognostic tool that allows evidence-based clinical decisions is currently under debate. In this review, we will discuss the most recent data on the prognostic significance and potential clinical application of genome wide expression analysis in colorectal cancer.

Identification of vitronectin as a novel serum marker for early breast cancer detection using a new proteomic approach

PURPOSE

Breast cancer is the most frequent malignancy in women. However, no useful serum markers with high sensitivity and specificity for the detection of early breast cancer have been identified. The search for biological markers of early breast cancer is of continual interest in experimental and clinical breast cancer research. We recently described a simple and highly reproducible three-step proteome analysis for identifying potential disease-marker candidates among the low-abundance serum proteins.

METHODS

Serum samples from breast ductal carcinoma in situ (DCIS) patients and normal controls were subjected to a three-step serum proteome analysis. The steps were the following: first, immunodepletion of most abundant proteins; second, fractionation using reverse-phase high-performance liquid chromatography; and third, separation using two-dimensional electrophoresis (2-DE). Differences revealed by protein staining were further confirmed by Western blotting, immunohistochemical staining, and enzyme-linked immunosorbent assays (ELISA).

RESULTS

Twenty-two upregulated and 26 downregulated spots were detected on the 2-DE gels, and a total of 33 proteins were identified by liquid chromatography and tandem mass spectrometry. Western blotting confirmed that the level of vitronectin was significantly increased in DCIS patients compared with that of normal controls. Immunohistochemical staining of vitronectin in breast cancer tissue revealed high expression in small vessel walls surrounding cancer cells and the extracellular matrix of stroma. Moreover, vitronectin serum concentrations, as measured by ELISA, were significantly increased in patients with DCIS or more advanced breast cancer compared with those of normal controls.

CONCLUSIONS

Vitronectin could serve as a promising serum marker for the detection of primary breast cancer.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.

Reproducibility of SELDI Spectra Across Time and Laboratories

This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.The reproducibility of mass spectrometry (MS) data collected using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) has been questioned. This investigation was designed to test the reproducibility of SELDI data collected over time by multiple users and instruments. Five laboratories prepared arrays once every week for six weeks. Spectra were collected on separate instruments in the individual laboratories. Additionally, all of the arrays produced each week were rescanned on a single instrument in one laboratory. Lab-to-lab and array-to-array variability in alignment parameters were larger than the variability attributable to running samples during different weeks. The coefficient of variance (CV) in spectrum intensity ranged from 25% at baseline, to 80% in the matrix noise region, to about 50% during the exponential drop from the maximum matrix noise. Before normalization, the median CV of the peak heights was 72% and reduced to about 20% after normalization. Additionally, for the spectra from a common instrument, the CV ranged from 5% at baseline, to 50% in the matrix noise region, to 20% during the drop from the maximum matrix noise. Normalization reduced the variability in peak heights to about 18%. With proper processing methods, SELDI instruments produce spectra containing large numbers of reproducibly located peaks, with consistent heights.

Statistical issues in quality control of proteomic analyses: good experimental design and planning

Quality control is becoming increasingly important in proteomic investigations as experiments become more multivariate and quantitative. Quality control applies to all stages of an investigation and statistics can play a key role. In this review, the role of statistical ideas in the design and planning of an investigation is described. This involves the design of unbiased experiments using key concepts from statistical experimental design, the understanding of the biological and analytical variation in a system using variance components analysis and the determination of a required sample size to perform a statistically powerful investigation. These concepts are described through simple examples and an example data set from a 2-D DIGE pilot experiment. Each of these concepts can prove useful in producing better and more reproducible data.

Strategy for purification and mass spectrometry identification of SELDI peaks corresponding to low-abundance plasma and serum proteins

Analysis by SELDI-TOF-MS of low abundance proteins makes it possible to select peaks as candidate biomarkers. Our aim was to define a purification strategy to optimise identification by MS of peaks detected by SELDI-TOF-MS from plasma or serum, regardless of any treatment by a combinatorial peptide ligand library (CPLL). We describe 2 principal steps in purification. First, choosing the appropriate sample containing the selected peak requires setting up a databank that records all the m/z peaks detected from samples in different conditions. Second, the specific purification process must be chosen: separation was achieved with either chromatographic columns or liquid-phase isoelectric focusing, both combined when appropriate with reverse-phase chromatography. After purification, peaks were separated by gel electrophoresis and the candidate proteins were analyzed by nano-liquid-chromatography-MS/MS. We chose 4m/z peaks (9400, 13,571, 13,800 and 15,557) selected for their differential expression between two conditions, as examples to explain the different strategies of purification, and we successfully identified 3 of them. Despite some limitations, our strategy to purify and identify peaks selected from SELDI-TOF-MS analysis was effective.

Proteomic serum biomarkers and their potential application in cancer screening programs

Early diagnosis of cancer is of pivotal importance to reduce disease-related mortality. There is great need for non-invasive screening methods, yet current screening protocols have limited sensitivity and specificity. The use of serum biomarkers to discriminate cancer patients from healthy persons might be a tool to improve screening programs. Mass spectrometry based proteomics is widely applied as a technology for mapping and identifying peptides and proteins in body fluids. One commonly used approach in proteomics is peptide and protein profiling. Here, we present an overview of profiling methods that have the potential for implementation in a clinical setting and in national screening programs.

Non-invasive proteomics-thinking about personalized breast cancer screening and treatment

The early diagnosis of breast cancer in potentially curable stages improves prognosis and consecutively reduces mortality of breast cancer patients. Established screening programs have an unfavorable connotation due to significant rates of false negative as well as false positive results leading to overdiagnosis and overtherapy. The combination of a non-invasive breast-cancer-suspectability-biomarker with established clinical diagnostics could help to increase the acceptance of population based breast cancer screening programs by creating an individual risk profile, which is irrespective of mammography quality and interpretation. Recently, non-invasive proteomic biomarkers obtained from blood, saliva or nipple aspiration fluid have been extensively investigated and might play a future role in the personalized management of breast cancer screening. A simple, robust and inexpensive, non-invasive test for screening and diagnosis could easily be performed in every medical practice leading to an affordable, high-throughput instrument. This review describes recently investigated proteomic screening biomarkers that could improve the early diagnosis of breast cancer in the following years.