Serum Glycoprotein Biomarker Discovery and Qualification Pipeline Reveals Novel Diagnostic Biomarker Candidates for Esophageal Adenocarcinoma

We report an integrated pipeline for efficient serum glycoprotein biomarker candidate discovery and qualification that may be used to facilitate cancer diagnosis and management. The discovery phase used semi-automated lectin magnetic bead array (LeMBA)-coupled tandem mass spectrometry with a dedicated data-housing and analysis pipeline; GlycoSelector (http://glycoselector.di.uq.edu.au). The qualification phase used lectin magnetic bead array-multiple reaction monitoring-mass spectrometry incorporating an interactive web-interface, Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny), for univariate and multivariate statistical analysis. Relative quantitation was performed by referencing to a spiked-in glycoprotein, chicken ovalbumin. We applied this workflow to identify diagnostic biomarkers for esophageal adenocarcinoma (EAC), a life threatening malignancy with poor prognosis in the advanced setting. EAC develops from metaplastic condition Barrett''s esophagus (BE). Currently diagnosis and monitoring of at-risk patients is through endoscopy and biopsy, which is expensive and requires hospital admission. Hence there is a clinical need for a noninvasive diagnostic biomarker of EAC. In total 89 patient samples from healthy controls, and patients with BE or EAC were screened in discovery and qualification stages. Of the 246 glycoforms measured in the qualification stage, 40 glycoforms (as measured by lectin affinity) qualified as candidate serum markers. The top candidate for distinguishing healthy from BE patients'' group was Narcissus pseudonarcissus lectin (NPL)-reactive Apolipoprotein B-100 (p value = 0.0231; AUROC = 0.71); BE versus EAC, Aleuria aurantia lectin (AAL)-reactive complement component C9 (p value = 0.0001; AUROC = 0.85); healthy versus EAC, Erythroagglutinin Phaseolus vulgaris (EPHA)-reactive gelsolin (p value = 0.0014; AUROC = 0.80). A panel of 8 glycoforms showed an improved AUROC of 0.94 to discriminate EAC from BE. Two biomarker candidates were independently verified by lectin magnetic bead array-immunoblotting, confirming the validity of the relative quantitation approach. Thus, we have identified candidate biomarkers, which, following large-scale clinical evaluation, can be developed into diagnostic blood tests. A key feature of the pipeline is the potential for rapid translation of the candidate biomarkers to lectin-immunoassays.Biomarkers play a central role in health care by enabling accurate diagnosis and prognosis; hence there is extensive research on the identification and development of novel biomarkers. However, despite numerous biomarker publications over the years (1), only a handful of new cancer biomarkers have successfully completed the journey from discovery, qualification, to verification and validation (2–4). One possible way to overcome this challenge is to develop an integrated biomarker pipeline that facilitates the smooth and successful transition from discovery to validation (5–10). The first and foremost consideration in an integrated pipeline is the sample source. In general, most of the proteomics based workflows use tissues or proximal fluids during the discovery phase, with the goal of extending the findings to plasma. Although this approach avoid the high complexity serum/plasma proteome and the associated requisite multi-dimensional sample separation in discovery stages, it often leads to failure when the candidates are not detected in plasma because of the limited sensitivity of the available analytical methods, or the absence of candidates in the plasma (11). To overcome this pitfall, we have developed an integrated glycoprotein biomarker pipeline, which can simply and rapidly isolate glycosylated proteins from serum to enable high throughput analysis of differentially glycosylated proteins in discovery and qualification stages.The workflow utilizes naturally occurring glycan binding proteins, lectins, in a semi-automated high throughput workflow called lectin magnetic bead array-tandem mass spectrometry (LeMBA-MS/MS)¹ (12, 13). Although lectins have been well-utilized in glycobiology and biomarker discovery (14–17), the LeMBA-MS/MS workflow demonstrates several unique features. First, serum glycoproteins are isolated in a single-step using 20 individual lectin-coated magnetic beads in microplate format. Second, we have optimized the concentrations of salts and detergents for sample denaturation to avoid co-isolation of protein complexes without adversely affecting lectin pull-down efficiency. Third, a liquid handler is used for sample processing to facilitate high-throughput screening and increase reproducibility. In addition, we have optimized on-bead trypsin digestion and incorporated lectin-exclusion lists during nano-LC-MS/MS to identify nonglycosylated peptides from the isolated glycoproteins. With these innovations, LeMBA-MS/MS demonstrates nanomolar sensitivity and linearity, and applicability across species (12). Compared with existing single, serial or multi-lectin affinity chromatography (18, 19), LeMBA-MS/MS offers the capability to simultaneously screen 20 lectins in a semi-automated, high throughput manner. On the other hand, because LeMBA-MS/MS identifies the nonglycosylated peptides, it cannot be used for glycan site assignment and glycan structure elucidation (20–23). However, the main advantage of LeMBA, we believe, is as a part of an integrated translational biomarker pipeline leading to lectin immunoassays. The lack of glycan structure details is not critical for clinical translation, as exemplified by the alpha-fetoprotein-L3 (AFP-L3) test, which measures the Lens culinaris agglutinin (LCA) binding fraction of serum alpha-fetoprotein (24, 25), and has been approved by the U.S. Food and Drug Administration for detection of hepatocellular carcinoma.In this study, we report the extension of the glycoprotein biomarker pipeline to the qualification phase with LeMBA-MRM-MS, and introduce statistical analysis pipelines GlycoSelector (http://glycoselector.di.uq.edu.au/) and Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny) for the discovery and qualification phases, respectively. The utility of this integrated serum glycoprotein biomarker pipeline is demonstrated using esophageal adenocarcinoma (EAC) with unmet clinical need for an in vitro diagnostic test. EAC is a lethal malignancy of the lower esophagus with very poor 5-year survival rate of less than 25% (26). EAC is becoming increasingly common and its incidence is associated with the prevalent precursor metaplastic condition Barrett''s esophagus (BE), but with a low annual conversion rate of up to 1% (27). A common set of risk factors are described for BE and EAC, include gastroesophageal reflux disease (GERD), obesity, male gender, and smoking (28, 29). The current endoscopy-biopsy based diagnosis is invasive and costly, leading to an ineffective surveillance program. A blood test employing serum biomarkers that can distinguish patients with EAC from those with either BE or healthy tissue would, potentially, change the paradigm for the way in which BE and EAC are managed in the population (30). Serum glycan profiling studies have shown differential expression of glycan structures between healthy, BE, early dysplastic and EAC patients (31–35). However, diagnostic serum glycoproteins showing differential glycosylation hence differential lectin binding remain to be discovered, making it a suitable disease model for this study.     

Biomarker signatures of aging

Because people age differently, age is not a sufficient marker of susceptibility to disabilities, morbidities, and mortality. We measured nineteen blood biomarkers that include constituents of standard hematological measures, lipid biomarkers, and markers of inflammation and frailty in 4704 participants of the Long Life Family Study (LLFS), age range 30–110 years, and used an agglomerative algorithm to group LLFS participants into clusters thus yielding 26 different biomarker signatures. To test whether these signatures were associated with differences in biological aging, we correlated them with longitudinal changes in physiological functions and incident risk of cancer, cardiovascular disease, type 2 diabetes, and mortality using longitudinal data collected in the LLFS. Signature 2 was associated with significantly lower mortality, morbidity, and better physical function relative to the most common biomarker signature in LLFS, while nine other signatures were associated with less successful aging, characterized by higher risks for frailty, morbidity, and mortality. The predictive values of seven signatures were replicated in an independent data set from the Framingham Heart Study with comparable significant effects, and an additional three signatures showed consistent effects. This analysis shows that various biomarker signatures exist, and their significant associations with physical function, morbidity, and mortality suggest that these patterns represent differences in biological aging. The signatures show that dysregulation of a single biomarker can change with patterns of other biomarkers, and age‐related changes of individual biomarkers alone do not necessarily indicate disease or functional decline.     

Biomarker discovery in biological fluids

Discovery of novel protein biomarkers is essential for successful drug discovery and development. These novel protein biomarkers may aid accelerated drug efficacy, response, or toxicity decision making based on their enhanced sensitivity and/or specificity. These biomarkers, if necessary, could eventually be converted into novel diagnostic marker assays. Proteomic platforms developed over the past few years have given us the ability to rapidly identify novel protein biomarkers in various biological matrices from cell cultures (lysates, supernatants) to human clinical samples (serum, plasma, and urine). In this article, we delineate an approach to biomarker discovery. This approach is divided into three steps, (i) identification of markers, (ii) prioritization of identified markers, and (iii) preliminary validation (qualification) of prioritized markers. Using drug-induced idiosyncratic hepatotoxicity as a case study, the article elaborates methods and techniques utilized during the three steps of biomarker discovery process. The first step involves identification of markers using multi-dimensional protein identification technology. The second step involves prioritization of a subset of marker candidates based on several criteria such as availability of reagent set for assay development and literature association to disease biology. The last step of biomarker discovery involves development of preliminary assays to confirm the bio-analytical measurements from the first step, as well as qualify the marker(s) in pre-clinical models, to initiate future marker validation and development.     

Targeted and Untargeted Proteomics Approaches in Biomarker Development

An enormous amount of research effort has been devoted to biomarker discovery and validation. With the completion of the human genome, proteomics is now playing an increasing role in this search for new and better biomarkers. Here, what leads to successful biomarker development is reviewed and how these features may be applied in the context of proteomic biomarker research is considered. The “fit‐for‐purpose” approach to biomarker development suggests that untargeted proteomic approaches may be better suited for early stages of biomarker discovery, while targeted approaches are preferred for validation and implementation. A systematic screening of published biomarker articles using MS‐based proteomics reveals that while both targeted and untargeted technologies are used in proteomic biomarker development, most researchers do not combine these approaches. i) The reasons for this discrepancy, (ii) how proteomic technologies can overcome technical challenges that seem to limit their translation into the clinic, and (iii) how MS can improve, complement, or replace existing clinically important assays in the future are discussed.     

Biomarker discovery and validation: technologies and integrative approaches

The emerging field of biomarkers has applications in the diagnosis, staging, prognosis and monitoring of disease progression, as well as in the monitoring of clinical responses to a therapeutic intervention and the development and delivery of personalized treatments to reduce attrition in clinical trials. Moreover, biomarkers have a positive impact on health economics. The word "biomarker" has been used extensively across therapeutic areas and many disciplines, and its nature takes into consideration clinical, physiological, biochemical, developmental, morphological and molecular measures. In drug trials, biomarkers have been proposed for use in efficacy determination and patient population stratification, in deducing pharmacokinetic-pharmacodynamic relationships and in safety monitoring. The interfacing and integration of different technologies for data collection and analysis are pivotal to biomarker identification, characterization, validation and application. "Integrative functional informatics" represents a novel direction in such technology integration.     

Plasma Biomarker of Dietary Phytosterol Intake

BackgroundDietary phytosterols, plant sterols structurally similar to cholesterol, reduce intestinal cholesterol absorption and have many other potentially beneficial biological effects in humans. Due to limited information on phytosterol levels in foods, however, it is difficult to quantify habitual dietary phytosterol intake (DPI). Therefore, we sought to identify a plasma biomarker of DPI.ConclusionThe ratio of plasma campesterol to the coordinately regulated endogenous cholesterol metabolite 5-α-cholestanol is a biomarker of dietary phytosterol intake. Conversely, plasma phytosterol levels alone are not ideal biomarkers of DPI because they are confounded by large inter-individual variation in absorption and turnover of non-cholesterol sterols. Further work is needed to assess the relation between non-cholesterol sterol metabolism and associated cholesterol transport in the genesis of coronary heart disease.     

肿瘤诊断新的标记物--甲基化谱

肿瘤的形成与有关基因的异常有一定的关系,但是目前还没有可以用于检测与肿瘤有关的基因异常系统。通过对肿瘤的不同基因甲基化检测,认为甲基化谱可以达到上述目的。对十几种基因在十几种肿瘤中的甲基化情况分析,发现不同基因和肿瘤甲基化的特点。由于启动子甲基化改变在肿瘤初期即可发生,有利于肿瘤的早期诊断。     

尿液蛋白质组作为下一代生物标志物的潜力

生物标志物是与机体生理及病理生理状态相关的可监测到变化的生化指标,尿液不属于内环境,没有稳态机制,能够积累并反映机体生理状态的早期变化,有潜力辅助疾病的早期诊断和预后监测。得益于非侵入性的收集方式,尿液可以被连续、大量、重复收集并便捷、稳定地保存,且组分相对简单,易于分析,是理想的标志物研究样本。但临床尿液样本蛋白质组可能会受到生活习惯、用药情况等多种混杂因素的影响,而动物模型方便控制变量,可以最大程度减少混杂因素的干扰,并使得在疾病发生、发展极早期采集样本成为可能;此外,患者的疾病分期、分型、用药情况等信息不能被忽视,现有样本策略和分析方式有待优化,例如,对同一个人不同时期、不同状态(例如患病前后)的尿液样本进行前后对照是一种理想的分析方式,这种方式能够消除个体间差异性的影响,符合个性化、精准化医疗的趋势;在无自身对照样本的情况下,一对多的分析方法能够更好地体现个体与健康群体的差别,辅助未知疾病的诊断和鉴别。尿液大分子的膜保存方式使得临床样本的保存更加简单经济。尿液生物标志物领域研究的进步需要政策和伦理的支持、资金和人力长期持续的投入以及大样本、大数据的辅助。本文综述了尿液生物标志物...     

Alzheimer 病生物标志物表达的研究进展

目前,对阿尔茨海默病（AD）的诊断和治疗还存在一定的局限性和困难,临床上筛选出敏感性和特异性高的生物标志物有助于对AD的诊断和鉴别诊断,本文就与淀粉样变过程、胆固醇代谢过程及氧化应激过程的标志物的研究现状及其对临床的价值作一综述。     

Biomarker discovery in urine by proteomics

A hypothesis was formed that it would be possible to isolate an adequate amount of protein from a patient, having normal renal function, to identify biological markers of a particular disease state using a variety of proteomics techniques. To support this hypothesis, three samples of urine were collected from a volunteer: first when healthy, later when experiencing acute inflammation due to a pilonidal abcess, and again later still after successful recovery from the condition. The urine from these samples was processed by solid-phase extraction to concentrate and desalt the endogenous proteins and peptides. The proteins and peptides from these urine samples were analyzed in three different experiments: (1) traditional two-dimensional gel electrophoresis followed by proteolysis and mass spectrometric identification of various protein spots, (2) whole mixture proteolysis followed by one-dimensional packed capillary liquid chromatography and tandem mass spectrometry, (3) whole mixture proteolysis followed by two-dimensional capillary liquid chromatography and tandem mass spectrometry. In all three cases, a set of proteins was identified representing putative biomarkers. Each of these proteins was then found to have been previously linked in the scientific literature to inflammation. One acute phase reactant in particular, orosomucoid, was readily observed in all three experiments to dramatically increase in abundance, thereby supporting the hypothesis.     

Acute Kidney Injury Urinary Biomarker Time-Courses

Factors which modify the excretion profiles of acute kidney injury biomarkers are difficult to measure. To facilitate biomarker choice and interpretation we modelled key modifying factors: extent of hyperfiltration or reduced glomerular filtration rate, structural damage, and reduced nephron number. The time-courses of pre-formed, induced (upregulated), and filtered biomarker concentrations were modelled in single nephrons, then combined to construct three multiple-nephron models: a healthy kidney with normal nephron number, a non-diabetic hyperfiltering kidney with reduced nephron number but maintained total glomerular filtration rate, and a chronic kidney disease kidney with reduced nephron number and reduced glomerular filtration rate. Time-courses for each model were derived for acute kidney injury scenarios of structural damage and/or reduced nephron number. The model predicted that pre-formed biomarkers would respond quickest to injury with a brief period of elevation, which would be easily missed in clinical scenarios. Induced biomarker time-courses would be influenced by biomarker-specific physiology and the balance between insult severity (which increased single nephron excretion), the number of remaining nephrons (reduced total excretion), and the extent of glomerular filtration rate reduction (increased concentration). Filtered biomarkers have the longest time-course because plasma levels increased following glomerular filtration rate decrease. Peak concentration and profile depended on the extent of damage to the reabsorption mechanism and recovery rate. Rapid recovery may be detected through a rapid reduction in urinary concentration. For all biomarkers, impaired hyperfiltration substantially increased concentration, especially with chronic kidney disease. For clinical validation of these model-derived predictions the clinical biomarker of choice will depend on timing in relation to renal insult and interpretation will require the pre-insult nephron number (renal mass) and detection of hyperfiltration.     

Stability of Bivariate GWAS Biomarker Detection

Given the difficulty and effort required to confirm candidate causal SNPs detected in genome-wide association studies (GWAS), there is no practical way to definitively filter false positives. Recent advances in algorithmics and statistics have enabled repeated exhaustive search for bivariate features in a practical amount of time using standard computational resources, allowing us to use cross-validation to evaluate the stability. We performed 10 trials of 2-fold cross-validation of exhaustive bivariate analysis on seven Wellcome–Trust Case–Control Consortium GWAS datasets, comparing the traditional test for association, the high-performance GBOOST method and the recently proposed GSS statistic (Available at http://bioinformatics.research.nicta.com.au/software/gwis/). We use Spearman''s correlation to measure the similarity between the folds of cross validation. To compare incomplete lists of ranks we propose an extension to Spearman''s correlation. The extension allows us to consider a natural threshold for feature selection where the correlation is zero.This is the first reported cross-validation study of exhaustive bivariate GWAS feature selection. We found that stability between ranked lists from different cross-validation folds was higher for GSS in the majority of diseases. A thorough analysis of the correlation between SNP-frequency and univariate score demonstrated that the test for association is highly confounded by main effects: SNPs with high univariate significance replicably dominate the ranked results. We show that removal of the univariately significant SNPs improves replicability but risks filtering pairs involving SNPs with univariate effects. We empirically confirm that the stability of GSS and GBOOST were not affected by removal of univariately significant SNPs.These results suggest that the GSS and GBOOST tests are successfully targeting bivariate association with phenotype and that GSS is able to reliably detect a larger set of SNP-pairs than GBOOST in the majority of the data we analysed. However, the test for association was confounded by main effects.     

肿瘤免疫治疗新药研发及生物标记物研究

由于免疫学和肿瘤学发展的不断深入以及交叉渗透,肿瘤免疫学渐渐成为肿瘤治疗的新热点,为肿瘤治疗带来了新的希望。肿瘤免疫疗法主要是通过激活或正常化机体免疫系统,例如T细胞,NK细胞等,对肿瘤细胞进行杀伤,以期达到缓解或治愈的目的。随着肿瘤免疫研究的不断深入,多种肿瘤免疫新药已成功获批,并展现出了前所未有的多癌种普适性,但提高患者响应率仍是肿瘤免疫治疗领域之重要议题。从肿瘤免疫新靶点的研究,联合治疗的探索及生物标记物的应用三个方面浅析肿瘤免疫发展过程中的机遇与挑战。