Autoantibody microarrays for biomarker discovery

Identification of autoantigens and the detection of autoantibody reactivity are useful in biomarker discovery and for explaining the role of important biochemical pathways in disease. Despite all of their potential advantages, the main challenge to working with autoantibodies is their sensitivity. Nevertheless, proteomics may hold the key to overcoming this limitation by providing the means to multiplex. Clearly, the ability to detect multiple autoantigens using a platform such as a high-density antigen microarray would improve sensitivity and specificity of detection for autoantibody profiling. The aims of this review are to: briefly describe the current status of antigen–autoantibody microarrays; provide examples of their use in biomarker discoveries; address current limitations; and provide examples and strategies to facilitate their implementation in the clinical setting.     

Autoantibody microarrays for biomarker discovery

Identification of autoantigens and the detection of autoantibody reactivity are useful in biomarker discovery and for explaining the role of important biochemical pathways in disease. Despite all of their potential advantages, the main challenge to working with autoantibodies is their sensitivity. Nevertheless, proteomics may hold the key to overcoming this limitation by providing the means to multiplex. Clearly, the ability to detect multiple autoantigens using a platform such as a high-density antigen microarray would improve sensitivity and specificity of detection for autoantibody profiling. The aims of this review are to: briefly describe the current status of antigen-autoantibody microarrays; provide examples of their use in biomarker discoveries; address current limitations; and provide examples and strategies to facilitate their implementation in the clinical setting.     

Immune checkpoint blockade opens a new way to cancer immunotherapy

Among the main promising systems to triggering therapeutic antitumor immunity is the blockade of immune checkpoints. Immune checkpoint pathways regulate the control and eradication of infections, malignancies, and resistance against a host of autoantigens. Initiation point of the immune response is T cells, which have a critical role in this pathway. As several immune checkpoints are initiated by ligand–receptor interactions, they can be freely blocked by antibodies or modulated by recombinant forms of ligands or receptors. Antibodies against cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) were the first immunotherapeutics that achieved the US Food and Drug Administration approval. Preliminary clinical results with the blockers of additional immune checkpoint proteins, such as programmed cell death protein 1 (PD-1) indicate extensive and different chances to boost antitumor immunity with the objective of conferring permanent clinical effects. This study provides an overview of the immune checkpoint pathways, including CTLA-4, PD-1, lymphocyte activation gene 3, T-cell immunoglobulin and mucin domain 3, B7-H3, and diacylglycerol kinase α and implications of their inhibition in the cancer therapy.     

Legumain: a biomarker for diagnosis and prognosis of human ovarian cancer

Legumain is a member of the asparaginyl endopeptidase family that is over‐expressed in response to hypoxic stress on mammary adenocarcinoma, colorectal cancer, proliferating endothelial cells, and tumor‐associated macrophages (TAMs). Here, we demonstrate that elevated expression of legumain in ovarian cancer by a proteomic approach using isobaric tags for relative and absolute quantification (iTRAQ) followed by liquid chromatography–mass spectrometry (LC–MS/MS). To investigate the relationship between legumain expression and ovarian cancer development, we tested legumain expression in malignant human ovarian tumors (n = 60), borderline ovarian tumors (n = 20), benign ovarian tumors (n = 20), and normal ovary samples (n = 20) using immunohistochemical assay (IHC). A correlation between legumain expression, and clinocopathologic and biological variables was also established. Importantly, increased legumain expression was validated by real‐time PCR and Western blots, correlated positively with an increased malignancy of ovarian tumors (P < 0.01). In fact, patients with strong legumain expression had a worse prognosis (P = 0.03). In addition, results of in vitro experiments revealed that over‐expression of legumain correlates with increased cell migration and invasion of ovarian cancer cells. Although legumain's functional role and clinical utility remain to be established, our results indicated that a sensitive assay for early expression of legumain may serve as both a potential biomarker and a molecular target for treatment of ovarian cancer. J. Cell. Biochem. 113: 2679–2686, 2012. © 2012 Wiley Periodicals, Inc.     

Circular RNA; a new biomarker for breast cancer: A systematic review

Circular RNAs (circRNAs) were recently discovered as a looped subset of competing endogenous RNAs, with an ability to regulate gene expression by microRNA sponging. There are several studies on their potential roles in cancer development, such as colorectal cancer and basal cell carcinoma. However, there is still a significant gap in the knowledge about circRNA functions in breast cancer (BC) progression. The current study systematically reviewed circRNA biogenesis and their potential roles as a novel biomarker in BC on published studies of the MEDLINE®/PubMed, Cochrane®, and Scopus® databases. The obtained results showed a general dysregulation of circRNAs expression in BC cells with a cell-type and stage-specific manner. The potential connection between circRNAs and BC cell proliferation, apoptosis, metastasis, and chemotherapy sensitivity and resistance were discussed.     

Proteomic biomarker identification for diagnosis of early relapse in ovarian cancer

Ovarian cancer recurs at the rate of 75% within a few months or several years later after therapy. Early recurrence, though responding better to treatment, is difficult to detect. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry has showed the potential to accurately identify disease biomarkers to help early diagnosis. A major challenge in the interpretation of SELDI-TOF data is the high dimensionality of the feature space. To tackle this problem, we have developed a multi-step data processing method composed of t-test, binning and backward feature selection. A new algorithm, support vector machine-Markov blanket/recursive feature elimination (SVM-MB/RFE) is presented for the backward feature selection. This method is an integration of minimum weight feature elimination by SVM-RFE and information theory based redundant/irrelevant feature removal by Markov Blanket. Subsequently, SVM was used for classification. We conducted the biomarker selection algorithm on 113 serum samples to identify early relapse from ovarian cancer patients after primary therapy. To validate the performance of the proposed algorithm, experiments were carried out in comparison with several other feature selection and classification algorithms.     

Comprehensive serum N-glycan profiling identifies a biomarker panel for early diagnosis of non-small-cell lung cancer

Aberrant serum N-glycan profiles have been observed in multiple cancers including non-small-cell lung cancer (NSCLC), yet the potential of N-glycans in the early diagnosis of NSCLC remains to be determined. In this study, serum N-glycan profiles of 275 NSCLC patients and 309 healthy controls were characterized by MALDI-TOF-MS. The levels of serum N-glycans and N-glycosylation patterns were compared between NSCLC and control groups. In addition, a panel of N-glycan biomarkers for NSCLC diagnosis was established and validated using machine learning algorithms. As a result, a total of 54 N-glycan structures were identified in human serum. Compared with healthy controls, 29 serum N-glycans were increased or decreased in NSCLC patients. N-glycan abundance in different histological types or clinical stages of NSCLC presented differentiated changes. Furthermore, an optimal biomarker panel of eight N-glycans was constructed based on logistic regression, with an AUC of 0.86 in the validation set. Notably, this model also showed a desirable capacity in distinguishing early-stage patients from healthy controls (AUC = 0.88). In conclusion, our work highlights the abnormal N-glycan profiles in NSCLC and provides supports potential application of N-glycan biomarker panel in clinical NSCLC detection.     

Methylated promoters of genes encoding protocadherins as a new cancer biomarker family

Protocadherins are a major subfamily of the cadherin superfamily. Their functions and intracellular signal transduction are poorly understood, although some have been explored in nervous system development. However, recent researches have shown that protocadherins frequently act as tumor suppressor genes (TSGs) and inactivation of these genes through promoter methylation is closely correlated with tumor development. Furthermore, these methylated protocadherins may serve as tumor biomarkers in various body fluids, stool and scrapings not only for early cancer diagnosis, but also for assessing prognosis and monitoring response to therapy. Thus, methylated promoters of genes encoding protocadherins show promise as a new cancer biomarker family.     

Carcinogen-DNA adducts as a biomarker for cancer risk

Carcinogen-DNA adducts are thought to be a useful biomarker in epidemiologic studies seeking to show that environmental exposures to xenobiotics cause cancer. This paper reviews the literature in this field from an epidemiologic perspective and identifies several common problems in the epidemiologic design and analysis of these studies. Carcinogen-DNA adducts have been used in studies attempting to link xenobiotic exposures to hepatocellular carcinoma, smoking related cancers and breast cancer. Adduct measurements have been useful in further implicating aflatoxin exposure in the etiology of hepatocellular carcinoma. For smoking related cancers, associations with carcinogen-DNA adducts are commonly seen in current smokers but less so in ex- or non-smokers. In breast cancer the associations have been inconsistent and weak and there is little evidence that carcinogen-DNA adducts implicate xenobiotic exposures in the etiology of breast cancer. Methodological issues common to these studies are the use of target versus surrogate tissues and how this choice impacts control selection, disease effects on adduct levels, the time period reflected by adduct levels, the use of inappropriate statistical analyses and small sample sizes. It is unclear whether the lack of association between carcinogen-DNA adducts and cancer reflects a lack of association between the xenobiotic exposure of interest and cancer or the effects of these methodological issues. A greater focus needs to be placed on designs that allow measurements of adduct levels in tissues collected years prior to cancer diagnosis, there is little need for further hospital based case-control studies in which adducts are measured at the time of or after diagnosis. New designs that address these issues are suggested in the paper.     

Polymersomes: a new multi-functional tool for cancer diagnosis and therapy

Nanoparticles are being developed as delivery vehicles for therapeutic pharmaceuticals and contrast imaging agents. Polymersomes (mesoscopic polymer vesicles) possess a number of attractive biomaterial properties that make them ideal for these applications. Synthetic control over block copolymer chemistry enables tunable design of polymersome material properties. The polymersome architecture, with its large hydrophilic reservoir and its thick hydrophobic lamellar membrane, provides significant storage capacity for both water soluble and insoluble substances (such as drugs and imaging probes). Further, the brush-like architecture of the polymersome outer shell can potentially increase biocompatibility and blood circulation times. A further recent advance is the development of multi-functional polymersomes that carry pharmaceuticals and imaging agents simultaneously. The ability to conjugate biologically active ligands to the brush surface provides a further means for targeted therapy and imaging. Hence, polymersomes hold enormous potential as nanostructured biomaterials for future in vivo drug delivery and diagnostic imaging applications.     

Calreticulin as a potential diagnostic biomarker for lung cancer

Calreticulin (CRT) is an endoplasmic reticulum luminal Ca(2+)-binding chaperone protein. By immunizing mice with recombinant fragment (rCRT/39-272), six clones of monoclonal antibodies (mAbs) were generated and characterized. Based on these mAbs, a microplate chemiluminescent enzyme immunoassay (CLEIA) system with a measured limit of detection of 0.09?ng/ml was developed. Using this CLEIA system, it was found that soluble CRT (sCRT) level in serum samples from 58 lung cancer patients was significantly higher than that from 40 healthy individuals (only 9 were detectable, P?相似文献   

pHLIP and acidity as a universal biomarker for cancer

Of great importance to clinical cancer diagnosis is the use of organic biomarkers. The detection of RNA, DNA, and protein antigen are all established methods for identifying specific cancer types and instrumental in promoting greater survivorship of the patient. Despite many decades of intense cancer research, we have yet to identify a "universal" protein or nucleic acid that allows us to diagnose more than a small subset of cancers at a time. In this review, we examine the use of localized cellular acidity as a universal marker for solid tumors, outlining some successes with a small peptide we call pHLIP, a pH-sensitive biosensor that allows us to label tumor tissue in live mice.