Postoperative serum proteomic profiles may predict metastatic relapse in high-risk primary breast cancer patients receiving adjuvant chemotherapy

Oncolytic adenovirus encoding apolipoprotein A1 suppresses metastasis of triple-negative breast cancer in mice

BACKGROUND

Dysregulation of cholesterol metabolism is associated with the metastasis of triple-negative breast cancer (TNBC). Apolipoprotein A1 (ApoA1) is widely recognized for its pivotal role in regulating cholesterol efflux and maintaining cellular cholesterol homeostasis. However, further exploration is needed to determine whether it inhibits TNBC metastasis by affecting cholesterol metabolism. Additionally, it is necessary to investigate whether ApoA1-based oncolytic virus therapy can be used to treat TNBC.

METHODS

In vitro experiments and mouse breast cancer models were utilized to evaluate the molecular mechanism of ApoA1 in regulating cholesterol efflux and inhibiting breast cancer progression and metastasis. The gene encoding ApoA1 was inserted into the adenovirus genome to construct a recombinant adenovirus (ADV-ApoA1). Subsequently, the efficacy of ADV-ApoA1 in inhibiting the growth and metastasis of TNBC was evaluated in several mouse models, including orthotopic breast cancer, spontaneous breast cancer, and human xenografts. In addition, a comprehensive safety assessment of Syrian hamsters and rhesus monkeys injected with oncolytic adenovirus was conducted.

RESULTS

This study found that dysregulation of cholesterol homeostasis is critical for the progression and metastasis of TNBC. In a mouse orthotopic model of TNBC, a high-cholesterol diet promoted lung and liver metastasis, which was associated with keratin 14 (KRT14), a protein responsible for TNBC metastasis. Furthermore, studies have shown that ApoA1, a cholesterol reverse transporter, inhibits TNBC metastasis by regulating the cholesterol/IKBKB/FOXO3a/KRT14 axis. Moreover, ADV-ApoA1 was found to promote cholesterol efflux, inhibit tumor growth, reduce lung metastasis, and prolonged the survival of mice with TNBC. Importantly, high doses of ADV-ApoA1 administered intravenously and subcutaneously were well tolerated in rhesus monkeys and Syrian hamsters.

CONCLUSIONS

This study provides a promising oncolytic virus treatment strategy for TNBC based on targeting dysregulated cholesterol metabolism. It also establishes a basis for subsequent clinical trials of ADV-ApoA1 in the treatment of TNBC.

Circulating Proteins Associated with Response and Resistance to Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer

Simple Summary

The goal of this study was to find circulating proteins that can be easily sampled and incorporated into a clinical setting to improve predictive treatment response in HER2-positive breast cancer patients receiving neoadjuvant chemotherapy. We looked for potential biomarkers in serum, which we identified using two proteomics techniques: qualitative LC-MS/MS and a quantitative assay that assessed protein expression between responders and non-responders HER2-positive breast cancer patients to neoadjuvant chemotherapy.

Abstract

Despite the increasing use of neoadjuvant chemotherapy (NAC) in HER2-positive breast cancer (BC) patients, the clinical problem of predicting individual treatment response remains unanswered. Furthermore, the use of ineffective chemotherapeutic regimens should be avoided. Serum biomarker levels are being studied more and more for their ability to predict therapy response and aid in the development of personalized treatment regimens. This study aims to identify effective protein networks and biomarkers to predict response to NAC in HER2-positive BC patients through an exhaustive large-scale LC-MS/MS-based qualitative and quantitative proteomic profiling of serum samples from responders and non-responders. Serum samples from HER2-positive BC patients were collected before NAC and were processed by three methods (with and without nanoparticles). The qualitative analysis revealed differences in the proteomic profiles between responders and non-responders, mainly in proteins implicated in the complement and coagulation cascades and apolipoproteins. Qualitative analysis confirmed that three proteins (AFM, SERPINA1, APOD) were correlated with NAC resistance. In this study, we show that serum biomarker profiles can predict treatment response and outcome in the neoadjuvant setting. If these findings are further developed, they will be of significant clinical utility in the design of treatment regimens for individual BC patients.

ERα promotes transcription of tumor suppressor gene ApoA-I by establishing H3K27ac-enriched chromatin microenvironment in breast cancer cells

Apolipoprotein A-I (ApoA-I), the main protein component of high-density lipoprotein (HDL), plays a pivotal role in reverse cholesterol transport (RCT). Previous studies indicated a reduction of serum ApoA-I levels in various types of cancer, suggesting ApoA-I as a potential cancer biomarker. Herein, ectopically overexpressed ApoA-I in MDA-MB-231 breast cancer cells was observed to have antitumor effects, inhibiting cell proliferation and migration. Subsequent studies on the mechanism of expression regulation revealed that estradiol (E2)/estrogen receptor α (ERα) signaling activates ApoA-I gene transcription in breast cancer cells. Mechanistically, our ChIP-seq data showed that ERα directly binds to the estrogen response element (ERE) site within the ApoA-I gene and establishes an acetylation of histone 3 lysine 27 (H3K27ac)‍-enriched chromatin microenvironment. Conversely, Fulvestrant (ICI 182780) treatment blocked ERα binding to ERE within the ApoA-I gene and downregulated the H3K27ac level on the ApoA-I gene. Treatment with p300 inhibitor also significantly decreased the ApoA-I messenger RNA (mRNA) level in MCF7 cells. Furthermore, the analysis of data from The Cancer Genome Atlas (TCGA) revealed a positive correlation between ERα and ApoA-I expression in breast cancer tissues. Taken together, our study not only revealed the antitumor potential of ApoA-I at the cellular level, but also found that ERα promotes the transcription of ApoA-I gene through direct genomic effects, and p300 may act as a co-activator of ERα in this process.

Absolute quantification of senescence mediators in cells using multiple reaction monitoring liquid chromatography-Tandem mass spectrometry

BACKGROUND

The identification of unique senescence markers remains challenging. Current hallmarks of senescent cells, including increased senescence-associated β-galactosidase activity, increased levels of cell cycle regulators such as p16^INK4a, p27, and p53, and altered levels of sirtuins and lamins, are detected commonly by Western blot and immunohistochemistry methods. Mass spectrometry outperforms these conventional quantification methods in terms of high throughput, specificity, and reproducibility.

OBJECTIVES

To develop multiple reaction monitoring-based tandem mass spectrometric senescence assay for simultaneous measuring of p16^INK4a, p27, p53, p53-β, the seven proteins of the sirtuins family and the four transcript variants of lamins proteins in aging cell model and cancerous cell lines.

METHODOLOGY

Multiple reaction monitoring-tandem mass transitions per protein were developed for each signature peptide(s) and stable isotope-labeled internal standard. The developed assay was validated in a matrix using breast cancer MCF7 cell lines according to the US-FDA guidelines for bioanalytical assays.

RESULTS

The analytes chromatographic peaks were baseline separated and showed linear behavior in a wide dynamic range with r² ≥ 0.98. The method for all proteins has passed the inter/intra-day precision and accuracy validation using three levels of quality control samples. The accuracy and the precision for most analytes were 80-120% and ≤20%, respectively. The method's sensitivity for the panels' signature peptides ranged from 1 ng μL^-1 to 1 μg mL^-1. Extraction recovery assessed in two quality control levels was >60% for most analytes. This LC-MS-MS validated senescence assay showed reduced lamin A, lamin A△10, lamin A△50, SIRT1, SIRT3, SIRT5, p53, and p16^INK4a, as well as p53-β induction, are implicated in replicative senescence. Meanwhile, increased lamin C: lamin A ratio was evident and can diagnose breast carcinogenesis. Moreover, in breast cancer metastasis, reduced SIRT2 and p27 and elevated levels of lamin A△50, SIRT5, SIRT7, and p53-β are evident.

CONCLUSION

LC-MS/MS is a potent alternative tool to the currently available assays. The high throughput method established can study senescence's role in different pathophysiological processes.

Proteomic Alterations in Multiple Myeloma: A Comprehensive Study Using Bone Marrow Interstitial Fluid and Serum Samples

Multiple myeloma (MM) is a plasma cell-associated cancer and exists as the second most common hematological malignancy worldwide. Although researchers have been working on MM, a comprehensive quantitative Bone Marrow Interstitial Fluid (BMIF) and serum proteomic analysis from the same patients’ samples is not yet reported. The present study involves the investigation of alterations in the BMIF and serum proteome of MM patients compared to controls using multipronged quantitative proteomic approaches viz., 2D-DIGE, iTRAQ, and SWATH-MS. A total of 279 non-redundant statistically significant differentially abundant proteins were identified by the combination of three proteomic approaches in MM BMIF, while in the case of serum 116 such differentially abundant proteins were identified. The biological context of these dysregulated proteins was deciphered using various bioinformatic tools. Verification experiments were performed in a fresh independent cohort of samples using immunoblotting and mass spectrometry based SRM assays. Thorough data evaluation led to the identification of a panel of five proteins viz., haptoglobin, kininogen 1, transferrin, and apolipoprotein A1 along with albumin that was validated using ELISA in a larger cohort of serum samples. This panel of proteins could serve as a useful tool in the diagnosis and understanding of the pathophysiology of MM in the future.

Serum proteomics analysis of feline mammary carcinoma based on label-free and PRM techniques

Background

Feline mammary carcinoma is the third most common cancer that affects female cats.

Objectives

The purpose of this study was to screen differential serum proteins in feline and clarify the relationship between them and the occurrence of feline mammary carcinoma.

Methods

Chinese pastoral cats were used as experimental animals. Six serum samples from cats with mammary carcinoma (group T) and six serum samples from healthy cats (group C) were selected. Differential protein analysis was performed using a Label-free technique, while parallel reaction monitoring (PRM) was performed to verify the screened differential proteins.

Results

A total of 82 differential proteins were detected between group T and group C, of which 55 proteins were down regulated and 27 proteins were up regulated. Apolipoprotein A-I, Apolipoprotein A-II (ApoA-II), Apolipoprotein B (ApoB), Apolipoprotein C-III (ApoC-III), coagulation factor V, coagulation factor X, C1q, albumen (ALB) were all associated with the occurrence of feline mammary carcinoma. Differential proteins were involved in a total of 40 signaling pathways, among which the metabolic pathways associated with feline mammary carcinoma were the complement and coagulation cascade and cholesterol metabolism. According to the Label-free results, ApoB, ApoC-III, ApoA-II, FN1, an uncharacterized protein, and ALB were selected for PRM target verification. The results were consistent with the trend of the label-free.

Conclusions

This experimen is the first to confirm ApoA-II and ApoB maybe new feline mammary carcinoma biomarkers and to analyze their mechanisms in the development of such carcinoma in feline.

Platelet Factor 4 as a Novel Exosome Marker in MALDI-MS Analysis of Exosomes from Human Serum

Exosomes are nanosized vesicles commonly found in biological fluids as a result of a secretion process involving endosomes and multivesicular bodies. The isolation and analysis of exosomes can be useful for noninvasive clinical diagnosis of a variety of human diseases. We investigated the utility of analyzing exosomal proteins, using matrix-assisted laser desorption/ionization combined with Fourier-transform ion cyclotron resonance mass spectrometry (MALDI-FTICR-MS), as a means of determining the presence of exosomes. MALDI-FTICR-MS analyses of exosomes enriched from human serum via centrifugation in a mass range of m/z 1000-20 000 yielded a distinctive protein around m/z 7766. The high mass accuracy and resolution of MALDI-FTICR-MS allowed for reliable comparisons against a protein database, through which the protein was identified as platelet factor 4 (PLF4), whose singly charged protein peak has an elemental composition of C₃₄₁H₅₇₇N₉₆O₁₀₁S₄⁺, with a theoretical most abundant isotopic peak at m/z 7765.194 and a theoretical average peak at m/z 7766. The MALDI-TOF MS analysis of exosomes from the serum of 27 patients with different states of liver diseases provided the most abundant PLF4 peak for each mass spectrum, along with several additional minor peaks. In conclusion, MALDI-MS is suitable as an alternative exosome detection method, serving as a valuable confirmation tool, greatly decreasing the time and workload associated with exosome identification.

Apolipoprotein A-I (ApoA-I), Immunity, Inflammation and Cancer

Apolipoprotein A-I (ApoA-I), the major protein component of high-density lipoproteins (HDL) is a multifunctional protein, involved in cholesterol traffic and inflammatory and immune response regulation. Many studies revealing alterations of ApoA-I during the development and progression of various types of cancer suggest that serum ApoA-I levels may represent a useful biomarker contributing to better estimation of cancer risk, early cancer diagnosis, follow up, and prognosis stratification of cancer patients. In addition, recent in vitro and animal studies disclose a more direct, tumor suppressive role of ApoA-I in cancer pathogenesis, which involves anti-inflammatory and immune-modulatory mechanisms. Herein, we review recent epidemiologic, clinicopathologic, and mechanistic studies investigating the role of ApoA-I in cancer biology, which suggest that enhancing the tumor suppressive activity of ApoA-I may contribute to better cancer prevention and treatment.

Blood-based protein biomarkers in breast cancer

Breast cancer (BCa) is a significant healthcare problem on women worldwide. Thus, early detection is very important to reduce mortality. Furthermore, better BCa prognosis could improve selection of patients eligible for adjuvant therapy. New markers for early diagnosis, accurate prognosis and prediction of response to treatment are necessary to improve BCa care. The present review summarizes important aspects of the potential usefulness of modern technologies, strategies, and scientific findings in proteomic research for discovery of breast cancer-associated blood-based protein biomarkers in the clinic.

Sex hormone-binding globulin (SHBG) is a potential early diagnostic biomarker for gastric cancer

The use of blood plasma biomarkers in gastric cancer (GC) management is limited due to a lack of reliable biomarkers. An LC-MS/MS assay and a bioinformatic analysis were performed to identify blood plasma biomarkers in a GC discovery cohort. The data obtained were verified and validated by western blotting and an ELISA in an independent study cohort. A label-free quantification analysis of the MS data using PEAKS7 software found that four plasma proteins of apolipoprotein C-1, gelsolin, sex hormone-binding globulin (SHBG), and complement component C4-A were significantly overexpressed in GC patients. A western blot assay of these plasma proteins showed that only SHBG was consistently overexpressed in the patient group. ELISA measurement of SHBG blood plasma levels confirmed that the patient group had significantly higher SHBG levels than the control group. SHBG levels in the patient group remained significantly higher after being stratified by gender, age, and disease stage. These findings show that LC-MS/MS is powerful and highly sensitive for plasma biomarker discovery, and SHBG could be a potential plasma biomarker for GC management.

Identification of novel serum peptides biomarkers for female breast cancer patients in Western China

This study aimed to identify novel serum peptides biomarkers for female breast cancer (BC) patients. We analyzed the serum proteomic profiling of 247 serum samples from 96 BC patients, 48 additional paired pre- and postoperative BC patients, 39 fibroadenoma patients as benign disease controls, and 64 healthy controls, using magnetic-bead-based separation followed by MALDI-TOF MS. ClinProTools software identified 78 m/z peaks that differed among all analyzed groups, ten peaks were significantly different (P < 0.0001), with Peaks 1-6 upregulated and Peaks 7-10 downregulated in BC. Moreover, three peaks of ten (Peak 1, m/z: 2660.11; Peak 2, m/z: 1061.09; Peak 10, m/z: 1041.25) showed a tendency to return to healthy control values after surgery. And these three peptide biomarkers were identified as FGA605-629, ITIH4 347-356, and APOA2 43-52. Methods used in this study could generate serum peptidome profiles of BC, and provide a new approach to identify potential biomarkers for diagnosis as well as prognosis of this malignancy.

Proteomic Analysis of Urine to Identify Breast Cancer Biomarker Candidates Using a Label-Free LC-MS/MS Approach

INTRODUCTION

Breast cancer is a complex heterogeneous disease and is a leading cause of death in women. Early diagnosis and monitoring progression of breast cancer are important for improving prognosis. The aim of this study was to identify protein biomarkers in urine for early screening detection and monitoring invasive breast cancer progression.

METHOD

We performed a comparative proteomic analysis using ion count relative quantification label free LC-MS/MS analysis of urine from breast cancer patients (n = 20) and healthy control women (n = 20).

RESULTS

Unbiased label free LC-MS/MS-based proteomics was used to provide a profile of abundant proteins in the biological system of breast cancer patients. Data analysis revealed 59 urinary proteins that were significantly different in breast cancer patients compared to the normal control subjects (p<0.05, fold change >3). Thirty-six urinary proteins were exclusively found in specific breast cancer stages, with 24 increasing and 12 decreasing in their abundance. Amongst the 59 significant urinary proteins identified, a list of 13 novel up-regulated proteins were revealed that may be used to detect breast cancer. These include stage specific markers associated with pre-invasive breast cancer in the ductal carcinoma in-situ (DCIS) samples (Leucine LRC36, MAST4 and Uncharacterized protein CI131), early invasive breast cancer (DYH8, HBA, PEPA, uncharacterized protein C4orf14 (CD014), filaggrin and MMRN2) and metastatic breast cancer (AGRIN, NEGR1, FIBA and Keratin KIC10). Preliminary validation of 3 potential markers (ECM1, MAST4 and filaggrin) identified was performed in breast cancer cell lines by Western blotting. One potential marker MAST4 was further validated in human breast cancer tissues as well as individual human breast cancer urine samples with immunohistochemistry and Western blotting, respectively.

CONCLUSIONS

Our results indicate that urine is a useful non-invasive source of biomarkers and the profile patterns (biomarkers) identified, have potential for clinical use in the detection of BC. Validation with a larger independent cohort of patients is required in the following study.

Prediagnostic serum inflammatory markers in relation to breast cancer risk, severity at diagnosis and survival in breast cancer patients

Inflammation has been linked to cancer but its role in breast cancer is unclear. We investigated common serum markers of inflammation: C-reactive protein (CRP), albumin, haptoglobin and white blood cells (WBC) in relation to breast cancer incidence, severity and survival. A total of 155179 women aged 20 and older without any history of cancer were selected from a large Swedish cohort. Hazard ratios (HRs) for breast cancer were estimated with Cox regression, adjusting for potential confounders. Ordered and binomial logistic regression models were used to assess the associations of serum inflammatory markers with breast cancer severity and oestrogen receptor (ER) positivity at diagnosis, on the other. Cumulative incidence functions by levels of inflammatory markers were assessed for early death from breast cancer and all causes. During a mean follow-up of 18.3 years, 6606 women were diagnosed with breast cancer, of whom 1474 died. A positive association with incident breast cancer was seen for haptoglobin ≥ 1.4g/l [HR 1.09; 95% confidence interval (CI): 1.00-1.18] compared to lower levels. No association was observed between inflammatory markers and breast cancer severity or ER positivity. Higher haptoglobin was linked to risk of early death from breast cancer (HR: 1.27, 95% CI: 1.02-1.59), whereas higher risk of early death from all causes was additionally found with CRP ≥ 10mg/l (HR: 1.19, 95% CI: 1.04-1.36) and WBC ≥ 10×10(9)/l (HR: 1.57, 1.14-2.16). Our findings indicate that prediagnostic serum inflammatory markers were weakly linked to incident breast cancer but corresponded to worse survival after diagnosis.

Matrix-assisted laser desorption/ionisation (MALDI) TOF analysis identifies serum angiotensin II concentrations as a strong predictor of all-cause and breast cancer (BCa)-specific mortality following breast surgery

MALDI-TOF MS was used to recognise serum peptidome profiles predictive of mortality in women affected by early BCa. Mortality was analysed based on signal profiling, and appropriate statistics were used. The results indicate that four signals were increased in deceased patients compared with living patients. Three of the four signals were individually associated with all-cause mortality, but only one having mass/charge ratio (m/z) 1,046.49 was associated with BCa-specific mortality and was the only peak to maintain an independent prognostic role after multivariate analysis. Two groups exhibiting different mortality probabilities were identified after clustering patients based on the expression of the four peptides, but m/z 1,046.49 was exclusively expressed in the cluster exhibiting the worst mortality outcome, thus confirming the crucial value of this peptide. The specific role of this peak was confirmed by competing risk analysis. MS findings were validated by ELISA analysis after demonstrating that m/z 1,046.49 structurally corresponded to Angiotensin II (ATII). In fact, mortality results obtained after arbitrarily dividing patients according to an ATII serum value of 255 pg/ml (which corresponds to the 66(th) percentile value) were approximately comparable to those previously demonstrated when the same patients were analysed according to the expression of signal m/z 1,046.49. Similarly, ATII levels were specifically correlated with BCa-related deaths after competing risk analysis. In conclusion, ATII levels were increased in women who exhibited worse mortality outcomes, reinforcing the evidence that this peptide potentially significantly affects the natural history of early BCa. Our findings also confirm that MALDI-TOF MS is an efficient screening tool to identify novel tumour markers and that MS findings can be rapidly validated through less complex techniques, such as ELISA.

Outcomes of palliative weekly low-dose gemcitabine-Cisplatin chemotherapy in anthracycline- and taxane- pretreated metastatic breast cancer patients

Purpose

The combination of gemcitabine and cisplatin (GP) has been shown to be safe and efficacious for patients with metastatic breast cancer (MBC), pretreated with anthracyclines and taxanes. We assessed the efficacy and safety of weekly low-dose GP in patients with MBC.

Methods

We collected clinicopathological data from MBC patients who had been treated with gemcitabine, 800 mg/m² plus cisplatin, 30 mg/m² intravenously, on days 1 and 8 every 3 weeks, between January 2001 and November 2011 in Korea.

Results

The analysis included 294 patients previously treated anthracycline-xand taxane-based chemotherapies prior to GP (median age, 48 years [range, 28-78 years]; median follow-up duration, 63.9 months). Seventeen patients (5.8%) discontinued GP because of toxicities. The median progression-free survival (PFS) was 3.9 months (95% confidence interval [CI], 3.394.4 months) and the median overall survival (OS) was 27.7 months (95% CI, 17.6-37.8 months) months. Statistically significant factors for PFS were performance status (Eastern Cooperative Oncology Group, ≥2 vs. <2; hazard ratio [HR], 1.37; 95% CI, 1.02-1.85; p=0.037), distant disease-free interval (DDFI; ≤2 years vs. >2 years; HR, 1.66; 95% CI, 1.28-1.95, p<0.001), time interval from the diagnosis of metastasis to GP therapy (≤1 year vs. >1 year; HR, 1.48; 95% CI, 1.13-1.95, p<0.001), and presence of brain metastasis (HR, 1.47; 95% CI, 1.03-2.10; p=0.031). Similarly, DDFI (≤2 years vs. >2 years; HR, 2.07; 95% CI, 1.36-3.14; p<0.001) and the presence of brain metastasis (HR, 2.14; 95% CI, 1.27-3.61; p=0.004) were important factors for OS after GP treatment.

Conclusion

Weekly low-dose GP chemotherapy appears safe and effective for heavily pretreated MBC patients.

Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for lung cancer: A marker phase I trial

BACKGROUND

Tumor cells continuously evolve over time in response to host pressures. However, explanations as to how tumor cells are influenced by the inflammatory tumor microenvironment over time are, to date, poorly defined. We hypothesized that prognostic biomarkers could be obtained by exploring the expression of inflammation-associated genes between early and late stage lung cancer tumor samples.

METHODS

Candidate inflammation-associated genes, apolipoprotein C-1 (APOC1), MMP1, KMO)1, CXCL5, CXCL)7, IL-1α, IL-1β, TNF-α and IL-6 were verified by real-time quantitative polymerase chain reaction. Gene expression profiles and immunofluorescence staining of 30 lung cancer tissues were compared.

RESULTS

Expressions of APOC1 and IL-6 mRNA on tumor tissues in late stage disease were significantly higher than in early stage lung cancer samples. Immunofluorescence staining of tumor samples showed that the expression of APOC1 gradually increased from early to late stage in lung cancer patients. The expression levels of IL-6 and APOC1 in tumor samples were positively correlated; however, no prognostic value of APOC1 can be identified in serum samples.

CONCLUSIONS

We found that the level of tumor APOC1 was highly expressed in late stage lung cancer. Further research is warranted to determine the molecular mechanisms underlying the cross talk of APOC1 and IL-6 in tumor progression. An expanded sample size marker phase II study may lead to the discovery of new lung cancer therapeutics targeting APOC1.

Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies

INTRODUCTION

Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation.

METHODS

PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data.

RESULTS

Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 ± 1.06 months and 4.6 ± 1.60 months, respectively; median overall survival was also clinically significant at 4.01 ± 1.60 months and 10.52 ± 3.49 months, respectively.

CONCLUSION

Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted.

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.

Modern approach of breast cancer diagnostics

In the review have been classified literature data concerning modern instrumental, microscopic and molecular (metabolomics, proteomics, genetics and epigenetics) approaches for early breast cancer diagnostics. The analytical performance and perspectives of their application in clinical practice also have been evaluated.

Induction of a tumor-metastasis-receptive microenvironment as an unwanted and underestimated side effect of treatment by chemotherapy or radiotherapy

There are well-known side effects of chemotherapy and radiotherapy that are mainly related to the toxicity and impaired function of vital organs; however, the induction by these therapies of expression of several pro-metastatic factors in various tissues and organs that in toto create a pro-metastatic microenvironment is still, surprisingly, not widely acknowledged. In this review, we support the novel concept that toxic damage in various organs leads to upregulation in “bystander” tissues of several factors such as chemokines, growth factors, alarmines, and bioactive phosphosphingolipids, which attract circulating normal stem cells for regeneration but unfortunately also provide chemotactic signals to cancer cells that survived the initial treatment. We propose that this mechanism plays an important role in the metastasis of cancer cells to organs such as bones, lungs, and liver, which are highly susceptible to chemotherapeutic agents as well as ionizing irradiation. This problem indicates the need to develop efficient anti-metastatic drugs that will work in combination with, or follow, standard therapies in order to prevent the possibility of therapy-induced spread of tumor cells.

Overview of current standpoints in profiling of circulating tumor cells

The goal of this review is summarizing current technologies developed as the in vitro prognostic/diagnostic systems that can rapidly separate and detect circulating tumor cells (CTCs) from cancer patient's blood (1-10 CTCs of 1 billion red blood cells) by labeled and non-labeled method. The review is focused on three major areas of CTC research (1) Summary of previous research on capturing of CTCs, (2) New development of the in vitro prognostic diagnosis system of cancer that is capable of rapid separation of CTCs, (3) Future direction on development of new technologies for CTC profiling. Current CTC researches have helped on identifying patients who may benefit from chemotherapy before treatment, patients who may benefit from continued chemotherapy, and leading to clinical development of CTC-guided chemotherapy strategies. We analyze the feasibility of clinical application of these current CTC research for the ultimate goal of increasing the survivability of cancer patient. The biomolecular assays of viable CTCs from cancer patient may elucidate the mechanism of metastasis and tumor initiating cells and also may have high impact on the development of personalized medicine to overcome the incurable diseases.

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.

Proteomics: methodologies and applications to the study of human diseases

Proteomic approach has allowed large-scale studies of protein expression in different tissues and body fluids in discrete conditions and/or time points. Recent advances of methodologies in this field have opened new opportunities to obtain relevant information on normal and abnormal processes occurring in the human body. In the current report, the main proteomics techniques and their application to human disease study are reviewed.

Plasma and tissue proteomic prognostic factors of response in primary breast cancer patients receiving neoadjuvant chemotherapy

A pathological complete response (pCR) after neoadjuvant chemotherapy is observed in approximately 20% of breast cancer patients. A proteomic analysis was performed on plasma and tumor tissue before treatment to evaluate its potential impact on the prediction of response. One hundred and forty-nine breast cancer patients eligible for neoadjuvant chemotherapy were included in the study between February 2004 and January 2009 at three centers. The proteomic analysis was performed using SELDI Technology (ProteinChip CM10 pH4, IMAC-Cu and H50). Three acquisition protocols were used according to the mass range. Plasma and tumor proteomic signatures were generated using generalized ROC criteria and cross-validation. Twenty-eight (18.8%) patients out of 149 experienced a pCR according to Sataloff criteria. In the cytosol analysis, respectively 4, 2 and 8 proteins had significantly different levels of expression in the responders and non-responders using IMAC-Cu, H50 and CM10 pH4. Among the 8 proteins of interest on CM10 pH4, 2 (C1 and C7) were selected and were validated in 95.0 and 85.6% of the models. In the plasma analysis, respectively 12, 6 and 2 proteins had different levels of expression using the same proteinchips. Among the 12 plasma proteins of interest on IMAC-Cu, 2 (P1 and P7) were selected and were validated in 94.8 and 97.6% of the models. A combined proteomic signature was generated, which remained statistically significant when adjusted for hormone receptor status and Ki-67. Our results show that proteomic analysis can differentiate complete pathological responders in breast cancer patients after neoadjuvant chemotherapy.

Intrinsic subtype-associated changes in the plasma proteome in breast cancer

Breast cancers are classified into five intrinsic subtypes: Luminal subtype A, Luminal subtype B, HER2+, Basal, and Normal-like. In this study, we compared the plasma proteome of patients with Luminal A, Luminal B, HER2+, and Basal subtype with plasma from healthy individuals. Protein changes were considered significant if q-value (false discovery rate) was less than 5%. The highest number of changes in the plasma proteome was observed in patients with Luminal type B followed by Basal type breast cancers. The plasma proteome of Luminal A and HER2+ breast cancer patients did not differ significantly from healthy individuals. In Basal breast cancer, a significant number of plasma proteins were downregulated compared with healthy individuals. Acute phase-response proteins α-glycoprotein orosomucoid 1 and serum amyloid protein P were specifically upregulated in the plasma of Luminal B breast cancer patients, suggesting prevalence of low-grade inflammation. Proteins involved in immune response and free radical scavenging were downregulated in the plasma of Luminal B patients, which is in agreement with defective immune system observed in cancer patients. These results reveal intrinsic subtype specific changes in the plasma proteome that may influence tumor progression as well as the systemic effects of cancer.

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.

Postoperative serum proteomic profiles may predict recurrence-free survival in high-risk primary breast cancer

PURPOSE

Better breast cancer prognostication may improve selection of patients for adjuvant therapy. We conducted a retrospective longitudinal study in which we investigated sera of high-risk primary breast cancer patients, to search for proteins predictive of recurrence-free survival.

METHODS

Sera of 82 breast cancer patients obtained after surgery, but prior to the administration of adjuvant therapy, were fractionated using anion-exchange chromatography, to facilitate the detection of the low-abundant serum peptides. Selected fractions were subsequently analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS), and the resulting protein profiles were searched for prognostic markers by appropriate bioinformatics tools.

RESULTS

Four peak clusters (i.e. m/z 3073, m/z 3274, m/z 4405 and m/z 7973) were found to bear significant prognostic value (P ≤ 0.01). The m/z 3274 candidate marker was structurally identified as inter-alpha-trypsin inhibitor heavy chain 4 fragment(658-688) in serum. Except for the m/z 7973 peak cluster, these peaks remained independently associated with recurrence-free survival upon multivariate Cox regression analysis, including clinical parameters of known prognostic value in this study population.

CONCLUSION

Investigation of the postoperative serum proteome by, e.g., anion-exchange fractionation followed by SELDI-TOF MS analysis is promising for the detection of novel prognostic factors. However, regarding the rather limited study population, validation of these results by analysis of independent study populations is warranted to assess the true clinical applicability of discovered prognostic markers. In addition, structural identification of the other markers will aid in elucidation of their role in breast cancer prognosis, as well as enable development of absolute quantitative assays.

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.

Searching for early breast cancer biomarkers by serum protein profiling of pre-diagnostic serum; a nested case-control study

Background

Serum protein profiles have been investigated frequently to discover early biomarkers for breast cancer. So far, these studies used biological samples collected at or after diagnosis. This may limit these studies' value in the search for cancer biomarkers because of the often advanced tumor stage, and consequently risk of reverse causality. We present for the first time pre-diagnostic serum protein profiles in relation to breast cancer, using the Prospect-EPIC (European Prospective Investigation into Cancer and nutrition) cohort.

Methods

In a nested case-control design we compared 68 women diagnosed with breast cancer within three years after enrollment, with 68 matched controls for differences in serum protein profiles. All samples were analyzed with SELDI-TOF MS (surface enhanced laser desorption/ionization time-of-flight mass spectrometry). In a subset of 20 case-control pairs, the serum proteome was identified and relatively quantified using isobaric Tags for Relative and Absolute Quantification (iTRAQ) and online two-dimensional nano-liquid chromatography coupled with tandem MS (2D-nanoLC-MS/MS).

Results

Two SELDI-TOF MS peaks with m/z 3323 and 8939, which probably represent doubly charged apolipoprotein C-I and C3a des-arginine anaphylatoxin (C3a_desArg), were higher in pre-diagnostic breast cancer serum (p = 0.02 and p = 0.06, respectively). With 2D-nanoLC-MS/MS, afamin, apolipoprotein E and isoform 1 of inter-alpha trypsin inhibitor heavy chain H4 (ITIH4) were found to be higher in pre-diagnostic breast cancer (p < 0.05), while alpha-2-macroglobulin and ceruloplasmin were lower (p < 0.05). C3a_desArg and ITIH4 have previously been related to the presence of symptomatic and/or mammographically detectable breast cancer.

Conclusions

We show that serum protein profiles are already altered up to three years before breast cancer detection.

Radiation-related changes in serum proteome profiles detected by mass spectrometry in blood of patients treated with radiotherapy due to larynx cancer

The study aimed to detect features of human serum proteome that were associated with exposure to ionizing radiation. The analyzed group consisted of 46 patients treated with radical radiotherapy for larynx cancer; patients were irradiated with total doses in a range from 51 to 72 Gy. Three consecutive blood samples were collected from each patient: before the start, 2 weeks after the start, and 4-6 weeks after the end of radiotherapy. The low-molecular-weight fraction of the serum proteome (2,000-13,000 Da) was analyzed by the MALDI-ToF mass spectrometry. Proteome profiles of serum samples collected before the start of radiotherapy and during the early stage of the treatment were similar. In marked contrast, mass profiles of serum samples collected several weeks after the end of the treatment revealed clear changes. We found that 41 out of 312 registered peptide ions changed their abundance significantly when serum samples collected after the final irradiation were compared with samples collected at the two earlier time points. We also found that abundances of certain serum peptides were associated with total doses of radiation received by patients. The results of this pilot study indicate that features of serum proteome analyzed by mass spectrometry have potential applicability as a retrospective marker of exposure to ionizing radiation.

Identification of serum protein markers for breast cancer relapse with SELDI-TOF MS

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to screen serum samples to identify protein markers for early breast cancer relapse. We collected 67 serum samples from patients with breast cancer (24 preoperative; 23 postoperative without breast cancer relapse; 20 postoperative with breast cancer relapse). Eight protein peaks varied between the presurgical group and the postsurgical group without breast cancer relapse; 4 protein peaks were differentially expressed between the postsurgical patients without relapse and patients with relapse. The peak at 3964 m/z dropped after surgery and rebounded after relapse (P < 0.01). These results indicate that there are differences in serum protein expression among the three different groups of patients. SELDI-TOF MS could be used to screen blood samples for the early detection of relapse in primary breast cancer patients. Specifically, protein peak at 3964 m/z is a potential biomarker for the detection of early breast cancer relapse.

Metastasis: new perspectives on an old problem

Many hypotheses have been postulated to explain the intricate nature of the metastatic process, but none of them completely accounted for the actual biological and clinical observations. Consequently, metastasis still remains an open issue with only few metastasis-inducing proteins experimentally validated so far. Recently proposed novel metastatic model, where serial and parallel metastatic processes are adequately integrated, might help to bridge the current gap between experimental results and clinical observations. In addition, the identification, isolation and molecular characterization of cancer stem cells, a population of the cells within the tumour mass able to proliferate, self-renew and induce tumorigenesis, will shed new light on the complex molecular events mediating metastasis, invasion and resistance to therapy. Understanding the molecular basis of these tumour characteristics will usher in a new age of individualized cancer therapy. In this review article, we will provide a current overview of molecular mechanisms underpinning metastasis, and discuss recent findings in this field obtained by global molecular profiling strategies such as proteomics.

[The therapeutic contribution of proteomic approaches in cancers]

Up to now, there are no protein tumor markers with a specificity and sensitivity sufficient to have a utility in prognosis and early diagnosis of cancer. Recent advances in proteomics approaches have led to the identification of novel tumor markers of cancer that may have a utility in screening strategies and treatment. The purpose of the current review is to describe the major advances in cancer proteomics, especially those related to the study of serum biomarkers, immune-related responses (autoantibodies) and alterations in cellular proteins.

Evaluation of changes in serum protein profiles during neoadjuvant chemotherapy in HER2-positive breast cancer using an LC-MALDI-TOF/MS procedure

Comparison of protein profiles of sera acquired before and after preoperative chemotherapy for breast cancer may reveal tumor markers that could be used to monitor tumor response. In this study, we analyzed pre- and post-chemotherapy protein profiles of sera from 39 HER2-postive breast cancer patients (n=78 samples) who received 6 months of preoperative chemotherapy using LC-MALDI-TOF/MS technology. We detected qualitative and quantitative differences in pair-wise comparison of pre- and post chemotherapy samples that were different in patients who achieved pathological complete response (pCR, n=21) compared with those with residual disease (n=18). We identified 2329 and 3152 peaks as differentially expressed in the pre-chemotherapy samples of the responders and non-responders. Comparison of matching pre- and post-chemotherapy samples identified 34 (32 decreased, two increased) and 304 peaks (157 decreased, 147 increased) that significantly changed (p<0.01, false discovery rate ≤ 20%) after treatment in responders and non-responders, respectively. The top 11 most significantly altered peptide peaks with the greatest change in intensity were positively identified. These corresponded to eight proteins including α-2-macroglobulin, complement 3, hemopexin, and serum amyloid P in the responder group and chains C and A of apolipoprotein A-I, hemopexin precursor, complement C, and amyloid P component in the non-responding groups. All proteins decreased after therapy, except chain C apolipoprotein A and hemopexin precursor that increased. These results suggest that changes in serum protein levels occur in response to chemotherapy and these changes partly appear different in patients who are highly sensitive to chemotherapy compared with those with lesser response.

Clinical proteomics of breast cancer

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

Modulation of plasma complement by the initial dose of epirubicin/docetaxel therapy in breast cancer and its predictive value

BACKGROUND

Despite the widespread use of neoadjuvant chemotherapy in breast cancer patients, prediction of individual response to treatment remains an unsolved clinical problem. Particularly, administration of an inefficient chemotherapeutic regimen should be avoided. Therefore, a better understanding of the molecular mechanisms underlying response to neoadjuvant chemotherapy is of particular clinical interest. Aim of the present study was to test whether neoadjuvant chemotherapy with epirubicin/docetaxel induces early changes in the plasma proteome of breast cancer patients and whether such changes correlate with response to therapy.

METHODS

Plasma samples of 25 breast cancer patients obtained before and 24 h after initiation of epirubicin/docetaxel-based neoadjuvant chemotherapy were analysed using two-dimensional differential gel electrophoresis (2D-DIGE). Protein spots found to be differentially expressed were identified using mass spectrometry and then correlated with the pathological response after six cycles of therapy. Markers identified in a discovery set of patients (n=12) were confirmed in an independent validation set (n=13).

RESULTS

2D-DIGE revealed 33 protein spots to be differentially expressed in response to chemotherapy, including the complement factors C1, C3 and C4, inter-α-trypsin inhibitor, α-1-antichymotrypsin and α-2-Heremans-Schmid glycoprotein (AHSG). With respect to cytokines, only interleukin (IL)-6, IL-10 and soluble intracellular adgesion molecule 3 (sICAM3) were minimally modulated. Moreover, two protein spots within the complement component C3 significantly correlated with response to therapy.

CONCLUSION

We have identified acute phase proteins and the complement system as part of the early host response to epirubicin/docetaxel chemotherapy. As complement C3 cleavage correlates with the efficacy of docetaxel/epirubicin-based chemotherapy, it has the potential as an easily accessible predictive biomarker.

Mass spectrometry-based analysis of therapy-related changes in serum proteome patterns of patients with early-stage breast cancer

Background

The proteomics approach termed proteome pattern analysis has been shown previously to have potential in the detection and classification of breast cancer. Here we aimed to identify changes in serum proteome patterns related to therapy of breast cancer patients.

Methods

Blood samples were collected before the start of therapy, after the surgical resection of tumors and one year after the end of therapy in a group of 70 patients diagnosed at early stages of the disease. Patients were treated with surgery either independently (26) or in combination with neoadjuvant chemotherapy (5) or adjuvant radio/chemotherapy (39). The low-molecular-weight fraction of serum proteome was examined using MALDI-ToF mass spectrometry, and then changes in intensities of peptide ions registered in a mass range between 2,000 and 14,000 Da were identified and correlated with clinical data.

Results

We found that surgical resection of tumors did not have an immediate effect on the mass profiles of the serum proteome. On the other hand, significant long-term effects were observed in serum proteome patterns one year after the end of basic treatment (we found that about 20 peptides exhibited significant changes in their abundances). Moreover, the significant differences were found primarily in the subgroup of patients treated with adjuvant therapy, but not in the subgroup subjected only to surgery. This suggests that the observed changes reflect overall responses of the patients to the toxic effects of adjuvant radio/chemotherapy. In line with this hypothesis we detected two serum peptides (registered m/z values 2,184 and 5,403 Da) whose changes correlated significantly with the type of treatment employed (their abundances decreased after adjuvant therapy, but increased in patients treated only with surgery). On the other hand, no significant correlation was found between changes in the abundance of any spectral component or clinical features of patients, including staging and grading of tumors.

Conclusions

The study establishes a high potential of MALDI-ToF-based analyses for the detection of dynamic changes in the serum proteome related to therapy of breast cancer patients, which revealed the potential applicability of serum proteome patterns analyses in monitoring the toxicity of therapy.

Discovery of serum biomarkers for pancreatic adenocarcinoma using proteomic analysis

Background and aims:

The serum/plasma proteome was explored for biomarkers to improve the diagnostic ability of CA19-9 in pancreatic adenocarcinoma (PC).

Methods:

A Training Set of serum samples from 20 resectable and 18 stage IV PC patients, 54 disease controls (DCs) and 68 healthy volunteers (HVs) were analysed by surface-enhanced laser desorption and ionisation time-of-flight mass spectrometry (SELDI-TOF MS). The resulting protein panel was validated on 40 resectable PC, 21 DC and 19 HV plasma samples (Validation-1 Set) and further by ELISA on 33 resectable PC, 28 DC and 18 HV serum samples (Validation-2 Set). Diagnostic panels were derived using binary logistic regression incorporating internal cross-validation followed by receiver operating characteristic (ROC) analysis.

Results:

A seven-protein panel from the training set PC vs DC and from PC vs HV samples gave the ROC area under the curve (AUC) of 0.90 and 0.90 compared with 0.87 and 0.91 for CA19-9. The AUC was greater (0.97 and 0.99, P<0.05) when CA19-9 was added to the panels and confirmed on the validation-1 samples. A simplified panel of apolipoprotein C-I (ApoC-I), apolipoprotein A-II (ApoA-II) and CA19-9 was tested on the validation-2 set by ELISA, in which the ROC AUC was greater than that of CA19-9 alone for PC vs DC (0.90 vs 0.84) and for PC vs HV (0.96 vs 0.90).

Conclusions:

A simplified diagnostic panel of CA19-9, ApoC-I and ApoA-II improves the diagnostic ability of CA19-9 alone and may have clinical utility.

Classification across gene expression microarray studies

BACKGROUND

The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.

RESULTS

For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios.

CONCLUSIONS

We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.

Stratification and monitoring of juvenile idiopathic arthritis patients by synovial proteome analysis

Juvenile idiopathic arthritis (JIA) comprises a poorly understood group of chronic, childhood onset, autoimmune diseases with variable clinical outcomes. We investigated whether profiling of the synovial fluid (SF) proteome by a fluorescent dye based, two-dimensional gel (DIGE) approach could distinguish patients in whom inflammation extends to affect a large number of joints, early in the disease process. SF samples from 22 JIA patients were analyzed: 10 with oligoarticular arthritis, 5 extended oligoarticular and 7 polyarticular disease. SF samples were labeled with Cy dyes and separated by two-dimensional electrophoresis. Multivariate analyses were used to isolate a panel of proteins which distinguish patient subgroups. Proteins were identified using MALDI-TOF mass spectrometry with expression further verified by Western immunoblotting and immunohistochemistry. Hierarchical clustering based on the expression levels of a set of 40 proteins segregated the extended oligoarticular from the oligoarticular patients (p < 0.05). Expression patterns of the isolated protein panel have also been observed over time, as disease spreads to multiple joints. The data indicates that synovial fluid proteome profiles could be used to stratify patients based on risk of disease extension. These protein profiles may also assist in monitoring therapeutic responses over time and help predict joint damage.

Protein profiling of pleural effusions to identify malignant pleural mesothelioma using SELDI-TOF MS

Diagnosis of malignant pleural mesothelioma (MM) is limited. Novel proteomic techno_logies can be utilized to discover changes in expression of pleural proteins that might have diagnostic value. The objective of this study was to detect protein profiles that could be used to identify malignant pleural mesothelioma with surface enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS). Pleural effusions were collected from patients with confirmed mesothelioma (n = 41) and from patients with effusions due to other causes ([n = 48] cancerous and non-cancerous). Samples were fractionated using anion exchange chromatography and bound to different types of ProteinChip array surfaces. All samples were also subjected to other commercially available immunoassays (human epididymes protein 4 [HE4], osteopontin [OPN], soluble mesothelin-related proteins [SMRP], and the cytokeratin 19 fragment [CYFRA 21-1]). Peak intensity data obtained by SELDI-TOF were subjected to classification algorithms in order to identify potential classifier peaks. A protein peak at m/z 6614 was characterized as apolipoprotein (Apo) CI. In this setting, the sensitivity and specificity of this potential biomarker was 76 % and 69 %, respectively. The area under the receiver operating characteristic curve (AUC) for Apo CI was 0.755, thereby outperforming OPN, HE4, and CYFRA 21-1. SMRP performed best with an AUC of 0.860 with a sensitivity of 83% and specificity of 74%. Our study validates the use of SMRP as a diagnostic marker for pleural mesothelioma and furthermore suggests that Apo CI levels could be used in the future to discriminate pleural mesothelioma from other causes of exudates.

Serum protein signature may improve detection of ductal carcinoma in situ of the breast

Ductal carcinoma in situ (DCIS) of the breast is part of a spectrum of preinvasive lesions that originate within normal breast tissue and progress to invasive breast cancer. The detection of DCIS is important for the reduction of mortality from breast cancer, but the diagnosis of preinvasive breast tumors is hampered by the lack of an adequate detection method. To identify the changes in protein expression during the initial stage of tumorigenesis and to identify the presence of new DCIS markers, we analysed serum from 60 patients with breast cancer and 60 normal controls using mass spectrometry. A 23-protein index was generated that correctly distinguishes the DCIS and control groups with sensitivities and specificities in excess of 80% in two independent cohorts. Two candidate peptides were purified and identified as platelet factor 4 (PF-4) and complement C3a(desArg) anaphylatoxin (C3a(desArg)) using liquid chromatography-tandem mass spectrometry (LC-MS/MS). In an independent serum set of 165 patients, PF-4 and C3a(desArg) were significantly upregulated in DCIS compared with non-cancerous controls, as validated using western blot and enzyme-linked immunosorbent assay. We conclude that our serum protein-based test, used in conjunction with image-based screening practices, could improve the sensitivity and specificity of breast cancer detection.

Influence of sample storage duration on serum protein profiles assessed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS)

BACKGROUND

Issues have been raised concerning the robustness and validity of alleged serum markers discovered by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). Pre-analytical variables have been shown to exert a profound effect on protein profiles, irrespective of true biological variation. However, little is known about the possible effects of sample storage duration on protein profiles. We, therefore, aimed to investigate the effects of extended storage duration on the serum protein profile.

METHODS

Archival sera from 140 breast cancer patients, stored at -30 degrees C for 1-11 years, were profiled by SELDI-TOF MS using immobilised metal affinity capture (IMAC) arrays, a condition applied in the majority of breast cancer biomarker discovery studies.

RESULTS

Fourteen peak clusters, structurally identified as C3a des-arginine anaphylatoxin and multiple fragments of albumin and fibrinogen, were found to be significantly associated with sample storage duration by five distinct patterns. These proteins have been described previously as potential cancer markers, rendering them specific to both disease and sample handling issues.

CONCLUSIONS

To prevent experimental variation being interpreted erroneously as disease associated variation, assessment of potential confounding pre-analytical parameters is a pre-requisite in biomarker discovery and validation studies. In addition, with respect to the different (non-)linear patterns observed in the current study, simply performing linear corrections to account for sample storage duration will not necessarily suffice.