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

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

Testing breast cancer serum biomarkers for early detection and prognosis in pre-diagnosis samples

Background:

Breast cancer is a leading cause of morbidity and mortality worldwide. Although mammography screening is available, there is an ongoing interest in improved early detection and prognosis. Herein, we have analysed a combination of serological biomarkers in a case–control cohort of sera taken before diagnosis.

Methods:

This nested case–control study within the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) used serum samples from 239 women who subsequently developed breast cancer and 239 matched cancer-free controls. Sera were screened by ELISA for 9 candidate markers. Univariate and multivariate analyses were performed to examine associations with clinico-pathological features and between case controls in different time groups before diagnosis.

Results:

Significant associations with clinico-pathological features related to prognosis were found for several candidates (CA15-3, HSP90A and PAI-1). However, there were no consistent differences between cases and controls for any candidate in the lead up to diagnosis. Whilst combination models outperformed single markers, there was no increase in performance towards diagnosis.

Conclusions:

This study using unique pre-diagnosis samples shows that CA15-3, HSP90A and PAI-1 have potential as early prognostic markers and warrant further investigation. However, none of the candidates or combinations would be useful for screening.

From genome to proteome: Looking beyond DNA and RNA in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) remains the most common leukemia in the Western world. Whilst its disease course is extremely heterogeneous (ranging from indolent to aggressive), current methods are unable to accurately predict the clinical journey of each patient. There is clearly a pressing need for both improved prognostication and treatment options for patients with this disease. Whilst molecular studies have analyzed both genetic mutations and gene expression profiles of these malignant B-cells, and as a result have shed light on the pathogenesis of CLL, proteomic studies have been largely overlooked to date. This review summarizes our current knowledge of the proteomics of CLL, and discusses some of the issues in CLL proteomic research, such as reproducibility and data interpretation. In addition, we look ahead to how proteomics may significantly help in the development of a successful treatment for this currently incurable disease.

MRE11 and ATM Expression Levels Predict Rectal Cancer Survival and Their Association with Radiotherapy Response

Background

Aberrant expression of DNA repair proteins is associated with poor survival in cancer patients. We investigated the combined expression of MRE11 and ATM as a predictive marker of response to radiotherapy in rectal cancer.

Methods

MRE11 and ATM expression were examined in tumor samples from 262 rectal cancer patients who underwent surgery for rectal cancer, including a sub-cohort of 54 patients who were treated with neoadjuvant radiotherapy. The relationship between expression of the two-protein panel and tumor regression grade (TRG) was assessed by Mann–Whitney U test and receiver operating characteristics area under curve (ROC-AUC) analysis. The association between expression of the two-protein panel and clinicopathologic variables and survival was examined by Kaplan-Meier methods and Cox regression analysis.

Results

A high score for two-protein combined expression in the tumor center (TC) was significantly associated with worse disease-free survival (DFS) (P = 0.035) and overall survival (OS) (P = 0.003) in the whole cohort, and with DFS (P = 0.028) and OS (P = 0.024) in the neoadjuvant subgroup (n = 54). In multivariate analysis, the two-protein combination panel (HR = 2.178, 95% CI 1.115–4.256, P = 0.023) and perineural invasion (HR = 2.183, 95% CI 1.222–3.899, P = 0.008) were significantly associated with DFS. Using ROC-AUC analysis of good versus poor histological tumor response among patients treated preoperatively with radiotherapy, the average ROC-AUC was 0.745 for the combined panel, 0.618 for ATM alone, and 0.711 for MRE11 alone.

Conclusions

The MRE11/ATM two-protein panel developed in this study may have clinical value as a predictive marker of tumor response to neoadjuvant radiotherapy, and a prognostic marker for disease-free and overall survival.

'Omics Approaches in Breast Cancer Research and Clinical Practice

Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care. How can the data be applied to predict response to molecular-targeted therapies? When should treatment decisions be based on tumor genetics rather than histology? And with the sudden explosion of "big data" from large 'omics consortia and new precision clinical trials, how do we now negotiate evidence-based pathways to clinical translation through this apparent sea of opportunity? The aim of this review is to provide a broad overview of 'omics technologies used in breast cancer research today, the current state-of-play in terms of applying this new knowledge in the clinic, and the practical and ethical issues that will be central to the public discussion on the future of precision cancer care.

Metastatic Tissue Proteomic Profiling Predicts 5-Year Outcomes in Patients with Colorectal Liver Metastases

Colorectal cancer (CRC) is one of the most common cancers in the developed countries, and nearly 70% of patients with CRC develop colorectal liver metastases (CRLMs). During the last decades, several scores have been proposed to predict recurrence after CRLM resection. However, these risk scoring systems do not accurately reflect the prognosis of these patients. Therefore, this investigation was designed to identify a proteomic profile in human hepatic tumor samples to classify patients with CRLM as “mild” or “severe” based on the 5-year survival. The study was performed on 85 CRLM tumor samples. Firstly, to evaluate any distinct tumor proteomic signatures between mild and severe CRLM patients, a training group of 57 CRLM tumor samples was characterized by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, and a classification and regression tree (CART) analysis was subsequently performed. Finally, 28 CRLM tumor samples were used to confirm and validate the results obtained. Based on all the protein peaks detected in the training group, the CART analysis was generated, and four peaks were considered to be the most relevant to construct a diagnostic algorithm. Indeed, the multivariate model yielded a sensitivity of 85.7% and a specificity of 86.1%, respectively. In addition, the receiver operating characteristic (ROC) curve showed an excellent diagnostic accuracy to discriminate mild from severe CRLM patients (area under the ROC: 0.903). Finally, the validation process yielded a sensitivity and specificity of 68.8% and 83.3%, respectively. We identified a proteomic profile potentially useful to determine the prognosis of CRLM patients based on the 5-year survival.

Novel and simple electrochemical biosensor monitoring attomolar levels of miRNA-155 in breast cancer

This work, describes for the first time, a simple biosensing design to yield an ultrasensitive electrochemical biosensor for a cancer biomarker detection, miRNA-155, with linear response down to the attomolar range. MiRNA-155 was selected for being overexpressed in breast cancer. The biosensor was assembled in two stages: (1) the immobilization of the anti-miRNA-155 that was thiol modified on an Au-screen printed electrode (Au-SPE), followed by (2) blocking the areas of non-specific binding with mercaptosuccinic acid. Atomic force microscopy (AFM) and electrochemical techniques including cyclic voltammetry (CV), impedance spectroscopy (EIS) and square wave voltammetry (SWV) confirmed the surface modification of these devices and their ability to hybridize successfully and stably with miRNA-155. The final biosensor provided a sensitive detection of miRNA-155 from 10 aM to 1.0 nM with a low detection limit (LOD) of 5.7 aM in real human serum samples. Good results were obtained in terms of selectivity towards breast cancer antigen CA-15.3 and bovine serum albumin (BSA). Raw fluid extracts from cell-lines of melanoma did not affect the biosensor response (no significant change of the blank), while raw extracts from breast cancer yielded a positive signal against miRNA-155. This simple and sensitive strategy is a promising alternative for simultaneous quantitative analysis of multiple miRNA in physiological fluids for biomedical research and point-of-care (POC) diagnosis.

Elevated Preoperative Serum Alanine Aminotransferase/Aspartate Aminotransferase (ALT/AST) Ratio Is Associated with Better Prognosis in Patients Undergoing Curative Treatment for Gastric Adenocarcinoma

The level of anine aminotransferase/aspartate aminotransferase (ALT/AST) ratio in the serum was often used to assess liver injury. Whether the ALT/AST ratio (LSR) was associated with prognosis for gastric adenocarcinoma (GA) has not been reported in the literature. Our aim was to investigate the prognostic value of the preoperative LSR in patients with GA. A retrospective study was performed in 231 patients with GA undergoing curative resection. The medical records collected include clinical information and laboratory results. We investigated the correlations between the preoperative LSR and overall survival (OS). Survival analysis was conducted with the Kaplan–Meier method, and Cox regression analysis was used to determine significant independent prognostic factors for predicting survival. A p value of <0.05 was considered to be statistically significant. A total of 231 patients were finally enrolled. The median overall survival was 47 months. Multivariate analysis indicated that preoperative LSR was an independent prognostic factor in GA. Patients with LSR ≤ 0.80 had a greater risk of death than those with LSR > 0.80. The LSR was independently associated with OS in patients with GA (hazard ratio: 0.610; 95% confidence interval: 0.388–0.958; p = 0.032), along with tumor stages (hazard ratio: 3.118; 95% confidence interval: 2.044–4.756; p < 0.001) and distant metastases (hazard ratio: 1.957; 95% confidence interval: 1.119–3.422; p = 0.019). Our study first established a connection between the preoperative LSR and patients undergoing curative resection for GA, suggesting that LSR was a simple, inexpensive, and easily measurable marker as a prognostic factor, and may help to identify high-risk patients for treatment decisions.

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.

Identification of functional metabolic biomarkers from lung cancer patient serum using PEP technology

BACKGROUND

Reprogrammed metabolism is a new hallmark of cancer. In many types of cancer, most of the genes in the glycolytic pathway are overexpressed, reflecting an essential shift of metabolism during cancer development. The reprogrammed metabolism contributes to cancer development in multiple ways, from supplying the elevated energy requirement to creating a microenvironment suitable for tumor growth and suppressing the human immune surveillance system.

METHOD

In this study, a functional proteomics top-down approach was used to systematically monitor metabolic enzyme activities in resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP.

RESULTS

We found that the enrichment of low abundance proteins with a bead based product called AlbuVoid™(,) is important to increase the number of observable features and to increase the level of signal achievable from the assay used. From our methods, significant metabolic enzyme activities were detected in both normal and lung cancer patient sera in many fractions after the elution of the 2-D gel separated proteins to the Protein Elution Plate (PEP). Eighteen fractions with the most dramatic metabolic enzyme activity difference between the normal and lung cancer patient sera were submitted for mass spectrometry protein identification. Proteins from the glycolytic metabolic pathway, such as GAPDH along with other proteins not previously annotated to the glycolytic pathway were identified. Further verification with commercially purified GAPDH showed that the addition of purified GAPDH to the metabolic enzyme assay system employed enhanced the enzyme activity, demonstrating that proteins identified from the PEP technology and mass spectrometry could be further verified with biological assay.

CONCLUSION

This study identified several potential functional enzyme biomarkers from lung cancer patient serum, it provides an alternative and complementary approach to sequence annotation for the discovery of biomarkers in human diseases.

Variation in serum biomarkers with sex and female hormonal status: implications for clinical tests

Few serum biomarker tests are implemented in clinical practice and recent reports raise concerns about poor reproducibility of biomarker studies. Here, we investigated the potential role of sex and female hormonal status in this widespread irreproducibility. We examined 171 serum proteins and small molecules measured in 1,676 participants from the Netherlands Study of Depression and Anxiety. Concentrations of 96 molecules varied with sex and 66 molecules varied between oral contraceptive pill users, postmenopausal females, and females in the follicular and luteal phases of the menstrual cycle (FDR-adjusted p-value <0.05). Simulations of biomarker studies yielded up to 40% false discoveries when patient and control groups were not matched for sex and up to 41% false discoveries when premenopausal females were not matched for oral contraceptive pill use. High accuracy (over 90%) classification tools were developed to label samples with sex and female hormonal status where this information was not collected.

Serum uPAR as Biomarker in Breast Cancer Recurrence: A Mathematical Model

There are currently over 2.5 million breast cancer survivors in the United States and, according to the American Cancer Society, 10 to 20 percent of these women will develop recurrent breast cancer. Early detection of recurrence can avoid unnecessary radical treatment. However, self-examination or mammography screening may not discover a recurring cancer if the number of surviving cancer cells is small, while biopsy is too invasive and cannot be frequently repeated. It is therefore important to identify non-invasive biomarkers that can detect early recurrence. The present paper develops a mathematical model of cancer recurrence. The model, based on a system of partial differential equations, focuses on tissue biomarkers that include the plasminogen system. Among them, only uPAR is known to have significant correlation to its concentration in serum and could therefore be a good candidate for serum biomarker. The model includes uPAR and other associated cytokines and cells. It is assumed that the residual cancer cells that survived primary cancer therapy are concentrated in the same location within a region with a very small diameter. Model simulations establish a quantitative relation between the diameter of the growing cancer and the total uPAR mass in the cancer. This relation is used to identify uPAR as a potential serum biomarker for breast cancer recurrence.

Lipoprotein subfractions by nuclear magnetic resonance are associated with tumor characteristics in breast cancer

Background

High-Density Lipoprotein (HDL)-cholesterol, has been associated with breast cancer development, but the association is under debate, and whether lipoprotein subfractions is associated with breast tumor characteristics remains unclear.

Methods

Among 56 women with newly diagnosed invasive breast cancer stage I/II, aged 35–75 years, pre-surgery overnight fasting serum concentrations of lipids were assessed, and body mass index (BMI) was measured. All breast tumors were immunohistochemically examined in the surgical specimen. Serum metabolomics of lipoprotein subfractions and their contents of cholesterol, free cholesterol, phospholipids, apolipoprotein-A1 and apolipoprotein-A2, were assessed using nuclear magnetic resonance. Principal component analysis, partial least square analysis, and uni- and multivariable linear regression models were used to study whether lipoprotein subfractions were associated with breast cancer tumor characteristics.

Results

The breast cancer patients had following means: age at diagnosis: 55.1 years; BMI: 25.1 kg/m²; total-Cholesterol: 5.74 mmol/L; HDL-Cholesterol: 1.78 mmol/L; Low-Density Lipoprotein (LDL)-Cholesterol: 3.45 mmol/L; triglycerides: 1.18 mmol/L. The mean tumor size was 16.4 mm, and the mean Ki67 hotspot index was 26.5 %. Most (93 %) of the patients had estrogen receptor (ER) positive tumors (≥1 % ER+), and 82 % had progesterone receptor (PgR) positive tumors (≥10 % PgR+). Several HDL subfraction contents were strongly associated with PgR expression: Apolipoprotein-A1 (β 0.46, CI 0.22–0.69, p < 0.001), HDL cholesterol (β 0.95, CI 0.51–1.39, p < 0.001), HDL free cholesterol (β 2.88, CI 1.28–4.48, p = 0.001), HDL phospholipids (β 0.70, CI 0.36–1.04, p < 0.001). Similar results were observed for the subfractions of HDL1-3. We observed inverse associations between HDL phospholipids and Ki67 (β -0.25, p = 0.008), and in particular between HDL1’s contents of cholesterol, phospholipids, apolipoprotein-A1, apolipoprotein-A2 and Ki67. No association was observed between lipoproteins and ER expression.

Conclusion

Our findings hypothesize associations between different lipoprotein subfractions, and PgR expression, and Ki 67 % in breast tumors. These findings may have clinical implications, but require confirmation in larger studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12944-016-0225-4) contains supplementary material, which is available to authorized users.

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.

Identification of cysteinylated transthyretin, a predictive biomarker of treatment response to partially hydrolyzed guar gum in type 2 diabetes rats, by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Recent evidence has indicated that total fiber intake is inversely related to type 2 diabetes risk. The present study aimed to investigate the effects of chronic administration of partially hydrolyzed guar gum (PHGG), a water-soluble dietary fiber, on the occurrence of diabetes and its complications, fatty liver and nephropathy. We also identified predictive serum biomarkers of treatment response to PHGG by mass spectroscopy-based proteomic analysis using Otsuka Long-Evans Tokushima Fatty (OLETF) rats, a good model of human non-insulin-dependent diabetes mellitus. In this study, at 5 weeks of age, OLETF rats and control strain Long-Evans Tokushima Otsuka (LETO) rats were fed a control diet or a high-fiber diet (5% PHGG) for 57 weeks. Body weight, food intake, oral glucose tolerance test, plasma insulin levels, and urine glucose and protein levels were regularly measured. Oral glucose tolerance tests (OGTT) and storage of serum in a deep freezer were conducted at the beginning of the experiment and every 4 weeks after overnight fasting during the experiments. PHGG treatment affected neither meal patterns nor the body weight of OLETF and LETO rats. Repeated measure analysis of variance revealed significant differences in fasting plasma glucose and plasma glucose at 2 h after OGTT between control OLETF (OLETF-C) rats and OLETF rats treated with PHGG (OLETF-F). The glucose response determined by the area under the curve of OGTT was significantly greater in OLETF-C rats than that in OLETF-F rats at 25 weeks of age. HOMA-IR, an index of insulin resistance, increased at 25 weeks of age in OLETF-C rats, while this increase was significantly inhibited in OLETF-F rats. At 62 weeks of age, PHGG treatment significantly improved hepatic steatosis as well as renal mesangial matrix accumulation in OLETF rats. To identify the risk marker for diabetes mellitus by SELDI-TOF MS, we collected sera from 21-week-old individuals. Among the 12 specific peaks that were risk marker candidates for diabetes mellitus, the m/z 13,720 peak was identified as that of cysteinylated transthyretin by sequencing of four tryptic peptides using tandem mass spectrometry and peak distribution around the m/z 13,720 peak in the SELDI-TOF spectra. In conclusion, we found that chronic treatment with PHGG improved insulin resistance, delayed the onset of diabetes, and inhibited the development of diabetic complications, as well as identified cysteinylated transthyretin as a predictive biomarker of treatment response to PHGG in OLETF rats.

Quantitative Profiling of Post-translational Modifications by Immunoaffinity Enrichment and LC-MS/MS in Cancer Serum without Immunodepletion

A robust method was developed and optimized for enrichment and quantitative analysis of posttranslational modifications (PTMs) in serum/plasma samples by combining immunoaffinity purification and LC-MS/MS without depletion of abundant proteins. The method was used to survey serum samples of patients with acute myeloid leukemia (AML), breast cancer (BC), and nonsmall cell lung cancer (NSCLC). Peptides were identified from serum samples containing phosphorylation, acetylation, lysine methylation, and arginine methylation. Of the PTMs identified, lysine acetylation (AcK) and arginine mono-methylation (Rme) were more prevalent than other PTMs. Label-free quantitative analysis of AcK and Rme peptides was performed for sera from AML, BC, and NSCLC patients. Several AcK and Rme sites showed distinct abundance distribution patterns across the three cancer types. The identification and quantification of posttranslationally modified peptides in serum samples reported here can be used for patient profiling and biomarker discovery research.

A novel truncated form of S100P predicts disease-free survival in patients with lymph node positive breast cancer

The calcium-binding protein S100P is overexpressed in various cancers and may contribute to the oncogenic phenotype. This study used mass spectrometry to characterize a novel 9.2-kDa C-terminally truncated form of S100P (t-S100P), and to investigate its potential prognostic value in breast cancer. Univariate analysis demonstrated the association between breast tissue t-S100P levels (n = 148) and conventional pathological markers. Across all tumor samples, high t-S100P was strongly prognostic for poor disease-free survival (P = 0.005), its efficacy confined to lymph node-positive tumors (n = 74, P = 0.007). Matrix-assisted laser desorption/ionization imaging mass spectrometry confirmed differential t-S100P abundance between breast cancer and unaffected adjacent tissue. t-S100P was exclusively located in the cell nucleus of breast cancer tissue, and full-length S100P was essentially undetectable by mass spectrometry. We conclude that t-S100P is the predominant form of S100P in breast cancer tissue and is strongly prognostic for disease-free survival in women with lymph node-positive disease.

Harvesting Low Molecular Weight Biomarkers Using Gold Nanoparticles

We developed and characterized a platform based on gold (Au) nanoparticles (NPs) coated with poly(acrylic acid) (PAA) for harvesting positively charged, low molecular weight (LMW) proteins. The particles are synthesized using a layer by layer (LbL) procedure: first the gold NPs are coated with positively charged polyethylenimine (PEI) and subsequently with PAA. This simple procedure produces stable PAA-PEI-Au (PPAu) NPs with high selectivity and specificity. PPAu NPs successfully harvested, separated, and detected various LMW proteins and peptides from serum containing a complex mixture of abundant high molecular weight (HMW) proteins, including bovine serum albumin (BSA) and Immunoglobulin G (IgG). In addition, PPAu NPs selectively harvested and separated LMW proteins from serum in the presence of another positively charged competing protein. Furthermore, PPAu NPs successfully harvested a LMW biomarker in a mock diseased state. This system can be applied in various biomedical applications where selective harvesting and identifying of LMW proteins is required. A particularly useful application for this system can be found in early cancer diagnosis as described hereinafter.

Reverse Phase Protein Arrays-Quantitative Assessment of Multiple Biomarkers in Biopsies for Clinical Use

Reverse Phase Protein Arrays (RPPA) represent a very promising sensitive and precise high-throughput technology for the quantitative measurement of hundreds of signaling proteins in biological and clinical samples. This array format allows quantification of one protein or phosphoprotein in multiple samples under the same experimental conditions at the same time. Moreover, it is suited for signal transduction profiling of small numbers of cultured cells or cells isolated from human biopsies, including formalin fixed and paraffin embedded (FFPE) tissues. Owing to the much easier sample preparation, as compared to mass spectrometry based technologies, and the extraordinary sensitivity for the detection of low-abundance signaling proteins over a large linear range, RPPA have the potential for characterization of deregulated interconnecting protein pathways and networks in limited amounts of sample material in clinical routine settings. Current aspects of RPPA technology, including dilution curves, spotting, controls, signal detection, antibody validation, and calculation of protein levels are addressed.

An easy way to realize SPR aptasensor: A multimode plastic optical fiber platform for cancer biomarkers detection

The introduction of new compact systems for sensitive, fast and simplified analysis is currently playing a substantial role in the development of point-of-care solutions aimed to assist both prognosis and diagnosis. Here we report a simple and low cost biosensor based on Surface Plasmon Resonance (SPR) taking advantage of a plastic optical fiber (POF) for the detection of Vascular endothelial growth factor (VEGF), selected as a circulating protein potentially associated with cancer. Our system is based onto two crucial aspects. By one hand, the functional layer which allows the transduction signal is based on DNA aptamers, short oligonucleotide sequences that bind to non-nucleic acid targets with high affinity and specificity. By the other hand, the light guiding structure is based on a POF with a planar gold layer as the sensing region, which is particularly suitable for bioreceptors implementation. The sensor revealed to be really useful in the interface characterization. The developed system is relatively easy to realize and could well address the development of a rapid, portable and low cost diagnostic platform, with a sensitivity in the nanomolar range.

Comparative proteomic analysis of serum diagnosis patterns of sputum smear-positive pulmonary tuberculosis based on magnetic bead separation and mass spectrometry analysis

A major challenge in pulmonary tuberculosis (TB) control is early and accurate diagnosis of sputum smear negative pulmonary TB (SSN-PTB). The patients with SSN-PTB have to wait for a longer period of time before receiving proper treatment than sputum smear positive pulmonary TB (SSP-PTB) patients due to delay in diagnosis. The purpose of this study is to discover potential serum protein biomarkers for SSN-PTB. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF MS) combined with weak cation exchange (WCX) magnetic beads was used to screen serum samples from SSN-PTB patients (N = 66), SSP-PTB patients (N = 49), and healthy volunteers (N = 80). The serum protein profiles were analyzed with Biomarker Wizard system. A classification model was established using Biomarker Pattern Software (BPS). Fifty-eight protein peaks were identified to exhibit significant differences between SSN-PTB, SSP-PTB and healthy control groups (P < 0.05), among which 6 peaks were found to be down-regulated, while 10 peaks were up-regulated gradually in the healthy control, SSN-PTB, and SSP-PTB groups. Twenty-three discriminating m/z peaks were detected between SSN-PTB patients and healthy controls (P < 0.01, Fold ≥ 1.5). The classification tree combined with three protein peaks (2747.0, 4480.0, and 9410.1 Da) could distinguish SSN-PTB patients from healthy controls with a sensitivity of 83.33% and a specificity of 82.50%. Early diagnosis of SSN-PTB disease is critical in order to reduce morbidity and mortality associated with TB. The study will help to clarify the role of differential proteins in the pathogenesis of TB.

SPAD aptasensor for the detection of circulating protein biomarkers

The need for decentralized clinical tests together with the concept of time and cost saving are pushing the development of portable, miniaturized, compact biosensors with diagnostic and prognostic purpose. Here, we propose an innovative detection system based on a Single Photon Avalanche Diode (SPAD) with high sensitivity and low noise, crucial features for an efficient chemiluminescence biosensor. The SPAD detector, having 60 µm diameter, has a Photon Detection Efficiency higher than 55% at 425 nm and a Dark Count Rate lower than 100 Hz at room temperature. Our design allows a good optical coupling efficiency between sample and detector. A specific biofunctional surface was implemented taking advantage of aptamers, short DNA sequences having high selectivity and affinity toward their targets. We successfully detected physiological levels of Vascular Endothelial Growth Factor (VEGF), a circulating protein biomarker highly correlated with cancer. The SPAD aptasensor showed a Limit of Detection (LoD) in the pM range, stability (up to 42 days) and re-usability (up to seven cycles). This compact biosensor is therefore a promising step toward the actual use of portable microdevices in diagnostics.

Introducing biomarker panel in esophageal, gastric, and colon cancers; a proteomic approach

Cancer research is an attractive field in molecular biology and medicine. By applying large-scale tools such as advanced genomics and proteomics, cancer diagnosis and treatment have been improved greatly. Cancers of esophagus, gastric, and colon accounted for major health problem globally. Biomarker panel could bring out the accuracy for cancer evaluation tests as it can suggest a group of candidate molecules specified to particular malignancy in a way that distinguishing malignant tumors from benign, differentiating from other diseases, and identifying each stages with high specificity and sensitivity. In this review, a systematic search of unique protein markers reported by several proteomic literatures are classified in their specific cancer type group as novel panels for feasible accurate malignancy diagnosis and treatment. About thousands of introduced proteins were studied; however, a small number of them belonged to a specific kind of malignancy. In conclusion, despite the fact that combinatorial biomarkers appear to be hopeful, more evaluation of them is crucial to achieve the suitable biomarker panel for clinical application. This effort needs more investigations and researches for finding a specific and sensitive panel.