Do serum biomarkers really measure breast cancer?

A method of separating extracellular vesicles from blood shows potential clinical translation, and reveals extracellular vesicle cargo gremlin-1 as a diagnostic biomarker

Extracellular vesicles (EVs) have potential as minimally invasive biomarkers. However, the methods most commonly used for EV retrieval rely on ultracentrifugation, are time-consuming, and unrealistic to translate to standard-of-care. We sought a method suitable for EV separation from blood that could be used in patient care. Sera from breast cancer patients and age-matched controls (n = 27 patients; n = 36 controls) were analysed to compare 6 proposed EV separation methods. The EVs were then characterised on 8 parameters. The selected method was subsequently applied to independent cohorts of sera (n = 20 patients; n = 20 controls), as proof-of-principle, investigating EVs' gremlin-1 cargo. Three independent runs with each method were very reproducible, within each given method. All isolates contained EVs, although they varied in quantity and purity. Methods that require ultracentrifugation were not superior for low volumes of sera typically available in routine standard-of-care. A CD63/CD81/CD9-coated immunobead-based method was most suitable based on EV markers' detection and minimal albumin and lipoprotein contamination. Applying this method to independent sera cohorts, EVs and their gremlin-1 cargo were at significantly higher amounts for breast cancer patients compared to controls. In conclusion, CD63/CD81/CD9-coated immunobeads may enable clinical utility of blood-based EVs as biomarkers.

Serum Analysis of Women with Early-Stage Breast Cancer Using a Mini-Array of Tumor-Associated Antigens

Background: Several studies have shown that patients with cancer have antibodies in serum that react with cellular autoantigens, known as Tumor-Associated Antigens (TAA). The present work aimed to determine whether a mini-array comprising four recombinant TAA increases the detection of specific serum antibodies for the diagnosis of early-stage breast cancer. Methods: The mini-array included Alpha 1-AntiTrypsin (A1AT), TriosePhosphate Isomerase 1 (TPI1), Peptidyl-Prolyl cis-trans Isomerase A (PPIA), and PeroxiReDoXin 2 (PRDX2) full-length recombinant proteins. The proteins were produced after gene cloning, expression, and purification, and were verified by Western blot assays. Then, Dot-Blot was performed to find antibodies against the four TAA in 12 sera from women with early-stage breast cancer (stage II) and 12 sera from healthy women. Results: Antibody detection against individual TAA in early-stage breast cancer sera ranged from 58.3% to 83.3%. However, evaluation of the four TAA showed that there was a positive antibody reaction reaching a sensitivity of 100% and a specificity of 85% in early-stage breast cancer, suggesting that this mini-array must be evaluated as a clinical diagnostic tool for early-stage breast cancer in a larger sample size. Conclusion: Our results suggest that TAA mini-arrays may provide a promising and powerful method for improving the detection of breast cancer in Mexican women.

Serum Health Biomarkers in African and Asian Elephants: Value Ranges and Clinical Values Indicative of the Immune Response

Simple Summary

Biomarkers are biological molecules found in the blood or other fluids or tissues that can indicate normal or abnormal processes or disease. Developing tools to measure biomarkers that indicate immune function and establishing concentrations observed within a species is an important first step in their use for managing health and understanding disease processes. Here we report assays, observed value ranges, and concentrations during illness or injury for seven immune biomarkers measured in the serum of African and Asian elephants under human care. Concentrations were variable in both clinical and non-clinical samples, but all seven biomarkers were elevated in at least one case and most increased in response to routine vaccination in a single Asian elephant. These tools provide an exciting avenue for monitoring health status and helping diagnose and treat health problems in wildlife species, like elephants.

Abstract

Serum biomarkers indicative of inflammation and disease can provide useful information regarding host immune processes, responses to treatment and prognosis. The aims of this study were to assess the use of commercially available anti-equine reagents for the quantification of cytokines (tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), interleukins (IL) 2, 6, and 10) in African (Loxodonta africana, n = 125) and Asian (Elephas maximus, n = 104) elephants, and alongside previously validated anti-human reagents for acute-phase proteins (serum amyloid A and haptoglobin), calculate species-specific biomarker value ranges. In addition, we used opportunistically collected samples to investigate the concentrations of each biomarker during identified clinical cases of illness or injury, as a first step to understanding what biomarkers may be useful to managing elephant health. Immune biomarkers were each elevated above the calculated species-specific value ranges in at least one clinical case, but due to variability in both clinical and non-clinical samples, only serum amyloid A was significantly higher in clinical compared to non-clinical paired samples, with tendencies for higher TNF-α and IL-10. We also detected increased secretion of serum amyloid A and all five cytokines following routine vaccination of a single Asian elephant, indicating that these biomarkers can be beneficial for studying normal immune processes as well as pathology. This study indicates that assays developed with commercial reagents can be used to quantify health biomarkers in wildlife species and identifies several that warrant further investigation to elucidate immune responses to various pathologies.

Prognostic value of biomarkers EpCAM and αB-crystallin associated with lymphatic metastasis in breast cancer by iTRAQ analysis

Background

Metastasis is responsible for the majority of deaths in a variety of cancer types, including breast cancer. Although several factors or biomarkers have been identified to predict the outcome of patients with breast cancer, few studies have been conducted to identify metastasis-associated biomarkers.

Methods

Quantitative iTRAQ proteomics analysis was used to detect differentially expressed proteins between lymph node metastases and their paired primary tumor tissues from 23 patients with metastatic breast cancer. Immunohistochemistry was performed to validate the expression of two upregulated (EpCAM, FADD) and two downregulated (NDRG1, αB-crystallin) proteins in 190 paraffin-embedded tissue samples. These four proteins were further analyzed for their correlation with clinicopathological features in 190 breast cancer patients.

Results

We identified 637 differentially regulated proteins (397 upregulated and 240 downregulated) in lymph node metastases compared with their paired primary tumor tissues. Data are available via ProteomeXchange with identifier PXD013931. Furthermore, bioinformatics analysis using GEO profiling confirmed the difference in the expression of EpCAM between metastases and primary tumors tissues. Two upregulated (EpCAM, FADD) and two downregulated (NDRG1, αB-crystallin) proteins were associated with the progression of breast cancer. Obviously, EpCAM plays a role in the metastasis of breast cancer cells to the lymph node. We further identified αB-crystallin as an independent biomarker to predict lymph node metastasis and the outcome of breast cancer patients.

Conclusion

We have identified that EpCAM plays a role in the metastasis of breast cancer cells to the lymph node. αB-crystallin, a stress-related protein that has recently been shown to be important for cell invasion and survival, was identified as a potential prognostic biomarker to predict the outcome of breast cancer patients.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-6016-3) contains supplementary material, which is available to authorized users.

Albumin and hemoglobin adducts of estrogen quinone as biomarkers for early detection of breast cancer

Cumulative estrogen concentration is an important determinant of the risk of developing breast cancer. Estrogen carcinogenesis is attributed to the combination of receptor-driven mitogenesis and DNA damage induced by quinonoid metabolites of estrogen. The present study was focused on developing an improved breast cancer prediction model using estrogen quinone-protein adduct concentrations. Blood samples from 152 breast cancer patients and 71 healthy women were collected, and albumin (Alb) and hemoglobin (Hb) adducts of estrogen-3,4-quinone and estrogen-2,3-quinone were extracted and evaluated as potential biomarkers of breast cancer. A multilayer perceptron (MLP) was used as the predictor model and the resultant prediction of breast cancer was more accurate than other existing detection methods. A MLP using the logarithm of the concentrations of the estrogen quinone-derived adducts (four input nodes, 10 hidden nodes, and one output node) was used to predict breast cancer risk with accuracy close to 100% and area under curve (AUC) close to one. The AUC value of one showed that both data sets were separable. We conclude that Alb and Hb adducts of estrogen quinones are promising biomarkers for the early detection of breast cancer.

Emerging proteomics biomarkers and prostate cancer burden in Africa

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers.

Myeloperoxidase as an Active Disease Biomarker: Recent Biochemical and Pathological Perspectives

Myeloperoxidase (MPO) belongs to the family of heme-containing peroxidases, produced mostly from polymorphonuclear neutrophils. The active enzyme (150 kDa) is the product of the MPO gene located on long arm of chromosome 17. The primary gene product undergoes several modifications, such as the removal of introns and signal peptides, and leads to the formation of enzymatically inactive glycosylated apoproMPO which complexes with chaperons, producing inactive proMPO by the insertion of a heme moiety. The active enzyme is a homodimer of heavy and light chain protomers. This enzyme is released into the extracellular fluid after oxidative stress and different inflammatory responses. Myeloperoxidase is the only type of peroxidase that uses H₂O₂ to oxidize several halides and pseudohalides to form different hypohalous acids. So, the antibacterial activities of MPO involve the production of reactive oxygen and reactive nitrogen species. Controlled MPO release at the site of infection is of prime importance for its efficient activities. Any uncontrolled degranulation exaggerates the inflammation and can also lead to tissue damage even in absence of inflammation. Several types of tissue injuries and the pathogenesis of several other major chronic diseases such as rheumatoid arthritis, cardiovascular diseases, liver diseases, diabetes, and cancer have been reported to be linked with MPO-derived oxidants. Thus, the enhanced level of MPO activity is one of the best diagnostic tools of inflammatory and oxidative stress biomarkers among these commonly-occurring diseases.

A Noninvasive Blood-based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years

BACKGROUND

Despite significant advances in breast imaging, the ability to detect breast cancer (BC) remains a challenge. To address the unmet needs of the current BC detection paradigm, 2 prospective clinical trials were conducted to develop a blood-based combinatorial proteomic biomarker assay (Videssa Breast) to accurately detect BC and reduce false positives (FPs) from suspicious imaging findings.

PATIENTS AND METHODS

Provista-001 and Provista-002 (cohort one) enrolled Breast Imaging Reporting and Data System 3 or 4 women aged under 50 years. Serum was evaluated for 11 serum protein biomarkers and 33 tumor-associated autoantibodies. Individual biomarker expression, demographics, and clinical characteristics data from Provista-001 were combined to develop a logistic regression model to detect BC. The performance was tested using Provista-002 cohort one (validation set).

RESULTS

The training model had a sensitivity and specificity of 92.3% and 85.3% (BC prevalence, 7.7%), respectively. In the validation set (BC prevalence, 2.9%), the sensitivity and specificity were 66.7% and 81.5%, respectively. The negative predictive value was high in both sets (99.3% and 98.8%, respectively). Videssa Breast performance in the combined training and validation set was 99.1% negative predictive value, 87.5% sensitivity, 83.8% specificity, and 25.2% positive predictive value (BC prevalence, 5.87%). Overall, imaging resulted in 341 participants receiving follow-up procedures to detect 30 cancers (90.6% FP rate). Videssa Breast would have recommended 111 participants for follow-up, a 67% reduction in FPs (P < .00001).

CONCLUSIONS

Videssa Breast can effectively detect BC when used in conjunction with imaging and can substantially reduce unnecessary medical procedures, as well as provide assurance to women that they likely do not have BC.

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.

Macrophage migration inhibitory factor involvement in breast cancer (Review)

Macrophage migration inhibitory factor (MIF) is a pleiotropic inflammatory cytokine involved in many cellular processes and in particular carcinogenesis. Here, we review the experimental and clinical published data on MIF and its pathways in breast cancer. Experimental data show that MIF is overexpressed in breast cancer cells (BCC) due, at least partly, to its stabilization by HSP90 and upregulation by HIF-1α. MIF interacts with its main receptor CD74 and its co-receptor CXCR-4, both overexpressed, promoting cell survival by PI3K/Akt activation, a possible link with EGFR and HER2 pathways and inhibition of autophagy. Besides these auto- and paracrine effects on BCC, MIF interacts with BCC micro-environment by several mechanisms: immunomodulation by increasing the prevalence of immune suppressive cells, neo-angiogenesis by its link to HIF-1, and finally BCC transendothelial migration. Clinical studies show higher levels of MIF in breast cancer patients serum compared to healthy volunteers but without obvious clinical significance. In breast cancer tissue, MIF and CD74 are overexpressed in the cancer cells and in the stroma but correlations with classical prognostic factors or survival are elusive. However, an inverse correlation with the tumor size for stromal MIF and a positive correlation with the triple receptor negative tumor status for stromal CD74 seem to be showed. This set of experimental and clinical data shows the involvement of MIF pathways in breast carcinogenesis. Several anti-MIF targeted strategies are being explored in therapeutic goals and should merit further investigations.

Machine learning for biomarker identification in cancer research - developments toward its clinical application

The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.

Early detection of breast cancer using total biochemical analysis of peripheral blood components: a preliminary study

Background

Most of the blood tests aiming for breast cancer screening rely on quantification of a single or few biomarkers. The aim of this study was to evaluate the feasibility of detecting breast cancer by analyzing the total biochemical composition of plasma as well as peripheral blood mononuclear cells (PBMCs) using infrared spectroscopy.

Methods

Blood was collected from 29 patients with confirmed breast cancer and 30 controls with benign or no breast tumors, undergoing screening for breast cancer. PBMCs and plasma were isolated and dried on a zinc selenide slide and measured under a Fourier transform infrared (FTIR) microscope to obtain their infrared absorption spectra. Differences in the spectra of PBMCs and plasma between the groups were analyzed as well as the specific influence of the relevant pathological characteristics of the cancer patients.

Results

Several bands in the FTIR spectra of both blood components significantly distinguished patients with and without cancer. Employing feature extraction with quadratic discriminant analysis, a sensitivity of ~90 % and a specificity of ~80 % for breast cancer detection was achieved. These results were confirmed by Monte Carlo cross-validation. Further analysis of the cancer group revealed an influence of several clinical parameters, such as the involvement of lymph nodes, on the infrared spectra, with each blood component affected by different parameters.

Conclusion

The present preliminary study suggests that FTIR spectroscopy of PBMCs and plasma is a potentially feasible and efficient tool for the early detection of breast neoplasms. An important application of our study is the distinction between benign lesions (considered as part of the non-cancer group) and malignant tumors thus reducing false positive results at screening. Furthermore, the correlation of specific spectral changes with clinical parameters of cancer patients indicates for possible contribution to diagnosis and prognosis.

Construction and analysis of the NCI-EDRN breast cancer reference set for circulating markers of disease

BACKGROUND

Many circulating biomarkers have been reported for the diagnosis of breast cancer, but few, if any, have undergone rigorous credentialing using prospective cohorts and blinded evaluation.

METHODS

The NCI Early Detection Research Network (EDRN) has created a prospective, multicenter collection of plasma and serum samples from 832 subjects designed to evaluate circulating biomarkers for the detection and diagnosis of breast cancer. These samples are available to investigators who wish to evaluate their biomarkers using a set of blinded samples. The breast cancer reference set is composed of blood samples collected using a standard operating procedure at four U.S. medical centers from 2008 to 2010 from women undergoing either tissue diagnosis for breast cancer or routine screening mammography. The reference set contains samples from women with incident invasive cancer (n = 190), carcinoma in situ (n = 55), benign pathology with atypia (n = 63), benign disease with no atypia (n = 231), and women with no evidence of breast disease by screening mammography (BI-RADS 1 or 2, n = 276). Using a subset of plasma samples (n = 505) from the reference set, we analyzed 90 proteins by multiplexed immunoassays for their potential utility as diagnostic markers.

RESULTS

We found that none of these markers is useful for distinguishing cancer from benign controls. However, elevated CA-125 does appear to be a candidate marker for estrogen receptor-negative cancers.

CONCLUSIONS

Markers that can distinguish benign breast conditions from invasive cancer have not yet been found.

IMPACT

Availability of prospectively collected samples should improve future validation efforts.

Involvement of macrophage migration inhibitory factor and its receptor (CD74) in human breast cancer

Macrophage migration inhibitory factor (MIF) and its receptor CD74 appear to be involved in tumorigenesis. We evaluated, by immunohistochemical staining, the tissue expression and distribution of MIF and CD74 in serial sections of human invasive breast cancer tumor specimens. The serum MIF level was also determined in breast cancer patients. We showed a significant increase in serum MIF average levels in breast cancer patients compared to healthy individuals. MIF tissue expression, quantified by a modified Allred score, was strongly increased in carcinoma compared to tumor-free specimens, in the cancer cells and in the peritumoral stroma, with fibroblasts the most intensely stained. We did not find any significant correlation with histoprognostic factors, except for a significant inverse correlation between tumor size and MIF stromal positivity. CD74 staining was heterogeneous and significantly decreased in cancer cells but increased in the surrounding stroma, namely in lymphocytes, macrophages and vessel endothelium. There was no significant variation according to classical histoprognostic factors, except that CD74 stromal expression was significantly correlated with triple-negative receptor (TRN) status and the absence of estrogen receptors. In conclusion, our data support the concept of a functional role of MIF in human breast cancer. In addition to auto- and paracrine effects on cancer cells, MIF could contribute to shape the tumor microenvironment leading to immunomodulation and angiogenesis. Interfering with MIF effects in breast tumors in a therapeutic perspective remains an attractive but complex challenge. Level of co-expression of MIF and CD74 could be a surrogate marker for efficacy of anti-angiogenic drugs, particularly in TRN breast cancer tumor.

Phosphoprotein secretome of tumor cells as a source of candidates for breast cancer biomarkers in plasma

Breast cancer is a heterogeneous disease whose molecular diversity is not well reflected in clinical and pathological markers used for prognosis and treatment selection. As tumor cells secrete proteins into the extracellular environment, some of these proteins reach circulation and could become suitable biomarkers for improving diagnosis or monitoring response to treatment. As many signaling pathways and interaction networks are altered in cancerous tissues by protein phosphorylation, changes in the secretory phosphoproteome of cancer tissues could reflect both disease progression and subtype. To test this hypothesis, we compared the phosphopeptide-enriched fractions obtained from proteins secreted into conditioned media (CM) derived from five luminal and five basal type breast cancer cell lines using label-free quantitative mass spectrometry. Altogether over 5000 phosphosites derived from 1756 phosphoproteins were identified, several of which have the potential to qualify as phosphopeptide plasma biomarker candidates for the more aggressive basal and also the luminal-type breast cancers. The analysis of phosphopeptides from breast cancer patient plasma and controls allowed us to construct a discovery list of phosphosites under rigorous collection conditions, and second to qualify discovery candidates generated from the CM studies. Indeed, a set of basal-specific phosphorylation CM site candidates derived from IBP3, CD44, OPN, FSTL3, LAMB1, and STC2, and luminal-specific candidates derived from CYTC and IBP5 were selected and, based on their presence in plasma, quantified across all cell line CM samples using Skyline MS1 intensity data. Together, this approach allowed us to assemble a set of novel cancer subtype specific phosphopeptide candidates for subsequent biomarker verification and clinical validation.

N-acetylglucosaminidase, myeloperoxidase and vascular endothelial growth factor serum levels in breast cancer patients

Inflammatory cells surround breast carcinomas and may act promoting tumor development or stimulating anti-tumor immunity. N-acetylglucosaminidase (NAG) has been employed to detect macrophage accumulation/activation. Myeloperoxidase (MPO) is considered a marker for neutrophils activity/accumulation. Vascular Endothelial Growth Factor (VEGF) is as strong pro-angiogenic cytokine. The aim of this study was to measure the systemic inflammatory response by measuring serum levels of NAG, MPO and VEGF in women diagnosed with breast cancer and associate this response to the peritumoral inflammatory infiltrate and to prognostic factors. Serum samples obtained from women with no evidence of disease (n=31) and with breast cancer (n=68) were analyzed for the activities of NAG, MPO and VEGF by enzymatic assay. Serum levels of NAG and VEGF were higher in healthy volunteers (P<0.0001) and serum levels of MPO were higher in patients with breast cancer (P=0.002). Serum levels of NAG were positively correlated to serum levels of MPO and VEGF (P<0.0001 and P=0.0012, respectively) and MPO and VEGF serum levels had also a positive correlation (P=0.0018). The inflammatory infiltrate was not associated to serum levels of the inflammatory markers, and higher levels of MPO were associated to lymphovascular invasion negativity (P=0.0175).

Assessment of macrophage migration inhibitory factor in humans: protocol for accurate and reproducible levels

The analytical validation of a possible biomarker is the first step in the long translational process from basic science to clinical routine. Although the chemokine-like cytokine macrophage migration inhibitory factor (MIF) has been investigated intensively in experimental approaches to various disease conditions, its transition into clinical research is just at the very beginning. Because of its presence in preformed storage pools, MIF is the first cytokine to be released under various stimulation conditions. In the first proof-of-concept studies, MIF levels correlated with the severity and outcome of various disease states. In a recent small study with acute coronary syndrome patients, elevation of MIF was described as a new factor for risk assessment. When these studies are compared, not only MIF levels in diseased patients differ, but also MIF levels in healthy control groups are inconsistent. Blood MIF concentrations in control groups vary between 0.56 and 95.6 ng/ml, corresponding to a 170-fold difference. MIF concentrations in blood were analyzed by ELISA. Other than the influence of this approach due to method-based variations, the impact of preanalytical processing on MIF concentrations is unclear and has not been systematically studied yet. Before large randomized studies are performed to determine the impact of circulating MIF on prognosis and outcome and before MIF is characterized as a diagnostic marker, an accurate protocol for the determination of reproducible MIF levels needs to be validated. In this study, the measurement of MIF in the blood of healthy volunteers was investigated focusing on the potential influence of critical preanalytical factors such as anticoagulants, storage conditions, freeze/thaw stability, hemolysis, and dilution. We show how to avoid pitfalls in the measurement of MIF and that MIF concentrations are highly susceptible to preanalytical factors. MIF serum concentrations are higher than plasma concentrations and show broader ranges. MIF concentrations are higher in samples processed with latency than in those processed directly and strongly correlate with hemoglobin in plasma. Neither storage temperature nor storage length or dilution or repeated freezing and thawing influenced MIF concentrations in plasma. Preanalytical validation of MIF is essential. In summary, we suggest using plasma and not serum samples when determining circulating MIF and avoiding hemolysis by processing samples immediately after blood drawing.

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.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

Detection of cancer using advanced computerized analysis of infrared spectra of peripheral blood

We have developed a novel approach for detection of cancer based on biochemical analysis of peripheral blood plasma using Fourier transform infrared spectroscopy. This approach has proven to be quick, safe, minimal invasive, and effective. Our approach recognizes any signs of solid tumor presence, regardless of location in the body or cancer type by measuring a spectrum that gives information regarding the total molecular composition and structure of the peripheral blood samples. The analysis includes clinically relevant preprocessing and feature extraction with principal component analysis, and uses Fisher's linear discriminant analysis to classify between cancer patients and healthy controls. We evaluated our method with leave-one-out cross validation and were able to establish sensitivity of 93.33%, specificity of 87.8%, and overall accuracy of 90.7%. Using our method for cancer detection should result in fewer unnecessary invasive procedures and yield fast detection of solid tumors.

Identification of a mimotope for circulating anti-cytokeratin 8/18 antibody and its usage for the diagnosis of breast cancer

A novel circulating tumor-associated autoantibody, K94, obtained from a hepatocellular carcinoma (HCC) mouse model was characterized. The target antigen of K94 autoanti-body was expressed in various tumor cell lines including liver cancer, and its secretion was detectable using MCF-7 breast carcinoma cells. Proteomic analysis revealed that the protein bands reactive to K94 included cytokeratin (CK) 8 and 18, which are known to be related to tumorigenesis and form a heterotypic complex with each other. However, K94 showed no activity toward CK8 or CK18 separately. The epitope of the K94 antibody was only presented by a complex between CK8 and CK18, which was confirmed by analysis using recombinant CK8 and CK18 proteins. To formulate an assay for anti-CK8/18 complex autoantibody, a mimotope peptide reactive to K94 was selected from loop-constrained heptapeptide (-CX₇C-) display phage library, of which sequence was CISPDAHSC (K94p1). A mimotope enzyme-linked immunosorbent assay (ELISA) using phage-displayed K94p1 peptide as a coating antigen was able to discriminate breast cancer (n=30) patients from normal subjects (n=30) with a sensitivity of 50% and a specificity of 82.61%. CA15.3 was detected at very low levels in the same breast cancer subjects and did not discriminate breast cancer patients from normal subjects, although it is a conventional biomarker of breast cancer. These results suggest that a mimotope ELISA composed of K94p1 peptide may be useful for the diagnosis of breast cancer.

Orally bioavailable small-molecule inhibitor of transcription factor Stat3 regresses human breast and lung cancer xenografts

Computer-aided lead optimization derives a unique, orally bioavailable inhibitor of the signal transducer and activator of transcription (Stat)3 Src homology 2 domain. BP-1-102 binds Stat3 with an affinity (K(D)) of 504 nM, blocks Stat3-phospho-tyrosine (pTyr) peptide interactions and Stat3 activation at 4-6.8 μM, and selectively inhibits growth, survival, migration, and invasion of Stat3-dependent tumor cells. BP-1-102-mediated inhibition of aberrantly active Stat3 in tumor cells suppresses the expression of c-Myc, Cyclin D1, Bcl-xL, Survivin, VEGF, and Krüppel-like factor 8, which is identified as a Stat3 target gene that promotes Stat3-mediated breast tumor cell migration and invasion. Treatment of breast cancer cells with BP-1-102 further blocks Stat3-NF-κB cross-talk, the release of granulocyte colony-stimulating factor, soluble intercellular adhesion molecule 1, macrophage migration-inhibitory factor/glycosylation-inhibiting factor, interleukin 1 receptor antagonist, and serine protease inhibitor protein 1, and the phosphorylation of focal adhesion kinase and paxillin, while enhancing E-cadherin expression. Intravenous or oral gavage delivery of BP-1-102 furnishes micromolar or microgram levels in tumor tissues and inhibits growth of human breast and lung tumor xenografts.

-The advancement of biomarker-based diagnostic tools for ovarian, breast, and pancreatic cancer through the use of urine as an analytical biofluid

Despite considerable advancements, the development of effective cancer screening tools based on serum biomarker measurements has thus far failed to achieve a meaningful clinical impact. The incremental progress observed over the course of serum biomarker development suggests that further refinements based on novel approaches may yet result in a breakthrough. The use of urine as an analytical biofluid for biomarker development may represent such an approach. The unique characteristics of urine including a high level of stability, ease of sampling, and an inactive and low-complexity testing matrix offer several potential advantages over the use of serum. A number of recent reports have demonstrated the utility of urine in the identification of novel cancer biomarkers and also the improved performance of biomarkers previously evaluated in serum. In this review, advancements related to the use of urine biomarkers within the settings of ovarian, breast, and pancreatic cancer are presented and discussed. Findings regarding the identification of specific urine biomarkers for each disease are highlighted along with comparative analyses of urine and serum biomarkers as diagnostic tools.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

An HLA-presented fragment of macrophage migration inhibitory factor is a therapeutic target for invasive breast cancer

This report describes a novel HLA/peptide complex with potential prognostic and therapeutic roles for invasive breast cancer. Macrophage migration inhibitory factor (MIF) mediates inflammation and immunity, and MIF overexpression is observed in breast cancer. We hypothesized that the HLA class I of cancerous breast epithelial cells would present MIF-derived peptides. Consistent with this hypothesis, the peptide FLSELTQQL (MIF(19-27)) was eluted from the HLA-A*0201 (HLA-A2) of breast cancer cell lines. We posited that if this MIF(19-27)/HLA-A2 complex was exclusively found in invasive breast cancer, it could be a useful prognostic indicator. To assess the presentation of MIF peptides by the HLA of various cells and tissues, mice were immunized with the MIF(19-27)/HLA-A2 complex. The resulting mAb (RL21A) stained invasive ductal carcinoma (IDC) but not ductal carcinoma in situ, fibroadenoma, or normal breast tissues. RL21A did not stain WBCs (total WBCs) or normal tissues from deceased HLA-A2 donors, substantiating the tumor-specific nature of this MIF/HLA complex. As this MIF/HLA complex appeared specific to the surface of IDC, RL21A was tested as an immunotherapeutic for breast cancer in vitro and in vivo. In vitro, RL21A killed the MDA-MB-231 cell line via complement and induction of apoptosis. In an in vivo orthotopic mouse model, administration of RL21A reduced MDA-MB-231 and BT-20 tumor burden by 5-fold and by >2-fold, respectively. In summary, HLA-presented MIF peptides show promise as prognostic cell surface indicators for IDC and as targets for immunotherapeutic intervention.

Breast cancer genomics: normal tissue and cancer markers

Mammography is a powerful screening tool for early detection of breast cancer, but it has limitations in terms of both specificity and sensitivity. Imaging tools such as MRI that complement mammography are too costly to serve as first-line screens. Recently, progress has been made on blood markers, particularly microRNAs and proteins. There are new methods for protein marker discovery directly in blood, but they are limited in the number of patients that can be examined. An alternative is to discover markers as transcripts in tissues, followed by development of blood protein tests for those that perform best. To identify genes that are overexpressed in malignancy it is paramount to include normal control tissues from healthy individuals. Here we report the identification of potential breast cancer markers, including some that are overexpressed in aggressive disease.

Macrophage migration inhibitory factor (MIF): a promising biomarker

Macrophage migration inhibitory factor (MIF) is an immunoregulatory cytokine, the effect of which on arresting random immune cell movement was recognized several decades ago. Despite its historic name, MIF also has a direct chemokine-like function and promotes cell recruitment. Multiple clinical studies have indicated the utility of MIF as a biomarker for different diseases that have an inflammatory component; these include systemic infections and sepsis, autoimmune diseases, cancer, and metabolic disorders such as type 2 diabetes and obesity. The identification of functional promoter polymorphisms in the MIF gene (MIF) and their association with the susceptibility or severity of different diseases has not only served to validate MIF's role in disease development but also opened the possibility of using MIF genotype information to better predict risk and outcome. In this article, we review the clinical data of MIF and discuss its potential as a biomarker for different disease applications.