Clinical features and predictive biomarkers for bladder cancer in patients with type 2 diabetes presenting with haematuria.

AIMS: To identify clinical features and protein biomarkers associated with bladder cancer (BC) in individuals with type 2 diabetes mellitus presenting with haematuria. MATERIALS AND METHODS: Data collected from the Haematuria Biomarker (HaBio) study was used in this analysis. A matched sub-cohort of patients with type 2 diabetes and patients without diabetes was created based on age, sex, and BC diagnosis, using approximately a 1:2 fixed ratio. Randox Biochip Array Technology and ELISA were applied for measurement of 66 candidate serum and urine protein biomarkers. Hazard ratios and 95% confidence intervals were estimated by chi-squared and Wilcoxon rank sum test for clinical features and candidate protein biomarkers. Diagnostic protein biomarker models were identified using Lasso-based binominal regression analysis. RESULTS: There was no difference in BC grade, stage, and severity between individuals with type 2 diabetes and matched controls. Incidence of chronic kidney disease (CKD) was significantly higher in patients with type 2 diabetes (p = 0.008), and CKD was significantly associated with BC in patients with type 2 diabetes (p = 0.032). A biomarker model, incorporating two serum (monocyte chemoattractant protein 1 and vascular endothelial growth factor) and three urine (interleukin 6, cytokeratin 18, and cytokeratin 8) proteins, predicted incidence of BC with an Area Under the Curve (AUC) of 0.84 in individuals with type 2 diabetes. In people without diabetes, the AUC was 0.66. CONCLUSIONS: We demonstrate the potential clinical utility of a biomarker panel, which includes proteins related to BC pathogenesis and type 2 diabetes, for monitoring risk of BC in patients with type 2 diabetes. Earlier urology referral of patients with type 2 diabetes will improve outcomes for these patients. TRIAL REGISTRATION: http://www.isrctn.com/ISRCTN25823942.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management.

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990's, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3-5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

Proteomes, Their Compositions and Their Sources.

Biological samples of human and animal origin are utilized in research for many purposes and in a variety of scientific fields, including mass spectrometry-based proteomics. Various types of samples, including organs, tissues, cells, body fluids such as blood, plasma, cerebrospinal fluid, saliva and semen, can be collected from humans or animals and processed for proteomics analysis. Depending on the physiological state and sample origin, collected samples are used in research and diagnostics for different purposes. In mass spectrometry-based proteomics, body fluids and tissues are commonly used in discovery experiments to search for specific protein markers that can distinguish physiological from pathophysiological states, which in turn offer new diagnosis strategies and help developing new drugs to prevent disease more efficiently. Cell lines in combination with technologies such as Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) have broader application and are used frequently to investigate the mechanism of a disease or to investigate for the mechanism of a drug function. All of these are important components for defining the mechanisms of disease, discovering new pharmaceutical treatments and finally testing side effects of newly discovered drugs.

Discovery and Longitudinal Evaluation of Candidate Protein Biomarkers for Disease Recurrence in Prostate Cancer.

When compared with hormonal therapy alone, treatment with combined hormone and radiation therapy (CHRT) gives improved disease-specific survival outcomes for patients with prostate cancer; however, a significant number of CHRT patients still succumb to recurrent disease. The purpose of this study was to use longitudinal patient samples obtained as part of an ongoing noninterventional clinical trial (ICORG06-15) to identify and evaluate a potential serum protein signature of disease recurrence. Label-free LC-MS/MS based protein discovery was undertaken on depleted serum samples from CHRT patients who showed evidence of disease recurrence (n = 3) and time-matched patient controls (n = 3). A total of 104 proteins showed a significant change between these two groups. Multiple reaction monitoring (MRM) assays were designed for a subset of these proteins as part of a panel of putative prostate cancer biomarkers (41 proteins) for evaluation in longitudinal serum samples. These data revealed significant interpatient variability in individual protein expression between time of diagnosis, disease recurrence, and beyond and serve to highlight the importance of longitudinal patient samples for evaluating the use of candidate protein biomarkers in disease monitoring.

Proteomic Characterisation of Heart Failure Reveals a Unique Molecular Phenotype for Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a disease, which is difficult to diagnose at an early stage and for which there is a pressing need for more effective treatment options. The purpose of this study was to compare the molecular profile of HCM to that of ischaemic cardiomyopathy (ISCM) and dilated cardiomyopathy (DCM) for identification of protein and pathway targets that could support the development of better diagnostic and treatment options for HCM. A high-throughput mass spectrometry workflow was applied to achieve deep quantitative coverage of left ventricular tissue from HCM, DCM, ISCM and non-heart-failure control patients. HCM had a diverse proteomic profile compared to that of DCM and ISCM. Differentially expressed proteins unique to HCM were identified based on an observed fold change of ≥1.5 or ≤0.67 and q-value ≤ 0.05. Candidate proteins of interest were found to be significantly associated with clinical features of HCM. The significant association between these proteins and HCM was validated in an independent dataset. This represents one of the largest and deepest proteomic datasets for myocardial tissue reported to date. The dataset highlights the diverse proteomic profile of HCM, relative to other cardiomyopathies, and reveals disease-relevant pathways and promising biomarker candidates that are uniquely associated with HCM.

What do patients want from a digital device that supports heart failure self-management?

OBJECTIVE: To assess patients' needs and the factors determining their acceptance of an e-Health intervention. METHODS: Purposive sample of patients with heart failure, attending an outpatient consultation were invited to complete a questionnaire designed to assess their needs and acceptance of an e-Health intervention. Data analysis was performed using Chi-square tests with post-hoc corrections. RESULTS: Response rate was 61 % (n = 101), with most patients (>70 %) indicating they could see themselves using an e-Health intervention to manage their heart failure. Participants with a healthy lifestyle (i.e., non-smoker and consumed alcohol < once/week) were more likely to have a positive attitude towards technology (p adj.=0.013). Those willing to use an e-Health intervention were convinced of its advantages by other patients with heart failure (p adj.=0.013). Advanced age, education level, employment or marital status did not influence patient attitudes toward health technology. CONCLUSIONS & PRACTICE IMPLICATIONS: Results indicate patients have a positive attitude towards the use of e-Health interventions to enable their self-management of heart failure. These findings will inform further development and delivery strategies of e-Health interventions.

Single-Cell RNA Sequencing Reveals Cardiac Fibroblast-Specific Transcriptomic Changes in Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is the most common cause of heart failure, with a complex aetiology involving multiple cell types. We aimed to detect cell-specific transcriptomic alterations in DCM through analysis that leveraged recent advancements in single-cell analytical tools. Single-cell RNA sequencing (scRNA-seq) data from human DCM cardiac tissue were subjected to an updated bioinformatic workflow in which unsupervised clustering was paired with reference label transfer to more comprehensively annotate the dataset. Differential gene expression was detected primarily in the cardiac fibroblast population. Bulk RNA sequencing was performed on an independent cohort of human cardiac tissue and compared with scRNA-seq gene alterations to generate a stratified list of higher-confidence, fibroblast-specific expression candidates for further validation. Concordant gene dysregulation was confirmed in TGFß-induced fibroblasts. Functional assessment of gene candidates showed that AEBP1 may play a significant role in fibroblast activation. This unbiased approach enabled improved resolution of cardiac cell-type-specific transcriptomic alterations in DCM.

Circulating biomarkers for management of cancer therapeutics-related cardiac dysfunction.

Cancer therapeutics-related cardiac dysfunction (CTRCD) has emerged as a major cause of morbidity and mortality in cancer survivors. Effective clinical management of CTRCD is impeded by a lack of sensitive diagnostic and prognostic strategies. Circulating molecular markers could potentially address this need as they are often indicative of cardiac stress before cardiac damage can be detected clinically. A growing understanding of the underlying physiological mechanisms for CTRCD has inspired research efforts to identify novel pathophysiologically relevant biomarkers that may also guide development of cardio-protective therapeutic approaches. The purpose of this review is to evaluate current circulating biomarkers of cardiac stress and their potential role in diagnosis and management of CTRCD. We also discuss some emerging avenues for CTRCD-focused biomarker investigations.

Serum HCoV-spike specific antibodies do not protect against subsequent SARS-CoV-2 infection in children and adolescents.

SARS-CoV-2 infections in children are generally asymptomatic or mild and rarely progress to severe disease and hospitalization. Why this is so remains unclear. Here we explore the potential for protection due to pre-existing cross-reactive seasonal coronavirus antibodies and compare the rate of antibody decline for nucleocapsid and spike protein in serum and oral fluid against SARS-CoV-2 within the pediatric population. No differences in seasonal coronaviruses antibody concentrations were found at baseline between cases and controls, suggesting no protective effect from pre-existing immunity against seasonal coronaviruses. Antibodies against seasonal betacoronaviruses were boosted in response to SARS-CoV-2 infection. In serum, anti-nucleocapsid antibodies fell below the threshold of positivity more quickly than anti-spike protein antibodies. These findings add to our understanding of protection against infection with SARS-CoV-2 within the pediatric population, which is important when considering pediatric SARS-CoV-2 immunization policies.

Multiplexed measurement of candidate blood protein biomarkers of heart failure.

AIMS: There is a critical need for better biomarkers so that heart failure can be diagnosed at an earlier stage and with greater accuracy. The purpose of this study was to design a robust mass spectrometry (MS)-based assay for the simultaneous measurement of a panel of 35 candidate protein biomarkers of heart failure, in blood. The overall aim was to evaluate the potential clinical utility of this biomarker panel for prediction of heart failure in a cohort of 500 patients. METHODS AND RESULTS: Multiple reaction monitoring (MRM) MS assays were designed with Skyline and Spectrum Mill PeptideSelector software and developed using nanoflow reverse phase C18 chromatographic Chip Cube-based separation, coupled to a 6460 triple quadrupole mass spectrometer. Optimized MRM assays were applied, in a sample-blinded manner, to serum samples from a cohort of 500 patients with heart failure and non-heart failure (non-HF) controls who had cardiovascular risk factors. Both heart failure with reduced ejection fraction (HFrEF) patients and heart failure with preserved ejection fraction (HFpEF) patients were included in the study. Peptides for the Apolipoprotein AI (APOA1) protein were the most significantly differentially expressed between non-HF and heart failure patients (P = 0.013 and P = 0.046). Four proteins were significantly differentially expressed between non-HF and the specific subtypes of HF (HFrEF and HFpEF); Leucine-rich-alpha-2-glycoprotein (LRG1, P < 0.001), zinc-alpha-2-glycoprotein (P = 0.005), serum paraoxanse/arylesterase (P = 0.013), and APOA1 (P = 0.038). A statistical model found that combined measurements of the candidate biomarkers in addition to BNP were capable of correctly predicting heart failure with 83.17% accuracy and an area under the curve (AUC) of 0.90. This was a notable improvement on predictive capacity of BNP measurements alone, which achieved 77.1% accuracy and an AUC of 0.86 (P = 0.005). The protein peptides for LRG1, which contributed most significantly to model performance, were significantly associated with future new onset HF in the non-HF cohort [Peptide 1: odds ratio (OR) 2.345 95% confidence interval (CI) (1.456-3.775) P = 0.000; peptide 2: OR 2.264 95% CI (1.422-3.605), P = 0.001]. CONCLUSIONS: This study has highlighted a number of promising candidate biomarkers for (i) diagnosis of heart failure and subtypes of heart failure and (ii) prediction of future new onset heart failure in patients with cardiovascular risk factors. Furthermore, this study demonstrates that multiplexed measurement of a combined biomarker signature that includes BNP is a more accurate predictor of heart failure than BNP alone.

Seroprevalence of SARS-CoV-2 antibodies in children: a prospective multicentre cohort study.

BACKGROUND: Studies based on molecular testing of oral/nasal swabs underestimate SARS-CoV-2 infection due to issues with test sensitivity, test timing and selection bias. The objective of this study was to report the presence of SARS-CoV-2 antibodies, consistent with previous infection. DESIGN: This multicentre observational cohort study, conducted between 16 April to 3 July 2020 at 5 UK sites, recruited children of healthcare workers, aged 2-15 years. Participants provided blood samples for SARS-CoV-2 antibody testing and data were gathered regarding unwell contacts and symptoms. RESULTS: 1007 participants were enrolled, and 992 were included in the final analysis. The median age of participants was 10·1 years. There were 68 (6.9%) participants with positive SARS-CoV-2 antibody tests indicative of previous SARS-CoV-2 infection. Of these, 34/68 (50%) reported no symptoms prior to testing. The presence of antibodies and the mean antibody titre was not influenced by age. Following multivariable analysis four independent variables were identified as significantly associated with SARS-CoV-2 seropositivity: known infected household contact OR=10.9 (95% CI 6.1 to 19.6); fatigue OR=16.8 (95% CI 5.5 to 51.9); gastrointestinal symptoms OR=6.6 (95% CI 3.0 to 13.8); and changes in sense of smell or taste OR=10.0 (95% CI 2.4 to 11.4). DISCUSSION: Children demonstrated similar antibody titres in response to SARS-CoV-2 irrespective of age. Fatigue, gastrointestinal symptoms and changes in sense of smell or taste were the symptoms most strongly associated with SARS-CoV-2 antibody positivity. TRIAL REGISTRATION NUMBER: NCT0434740.

Tetranectin, a potential novel diagnostic biomarker of heart failure, is expressed within the myocardium and associates with cardiac fibrosis.

Heart failure (HF) screening strategies require biomarkers to predict disease manifestation to aid HF surveillance and management programmes. The aim of this study was to validate a previous proteomics discovery programme that identified Tetranectin as a potential HF biomarker candidate based on expression level changes in asymptomatic patients at future risk for HF development. The initial study consisted of 132 patients, comprising of HF (n = 40), no-HF controls (n = 60), and cardiac surgery patients (n = 32). Serum samples were quantified for circulating levels of Tetranectin and a panel of circulating fibro-inflammatory markers. Cardiac tissue served as a resource to investigate the relationship between cardiac Tetranectin levels and fibrosis and inflammation within the myocardium. An independent cohort of 224 patients with or without HF was used to validate serum Tetranectin levels. Results show that circulating Tetranectin levels are significantly reduced in HF patients (p < 0.0001), and are associated with HF more closely than B-type natriuretic peptide (AUC = 0.97 versus 0.84, p = 0.011). Serum Tetranectin negatively correlated with circulating fibrosis markers, whereas cardiac tissue Tetranectin correlated positively with fibrotic genes and protein within the myocardium. In conclusion, we report for the first time that Tetranectin is a promising HF biomarker candidate linked with fibrotic processes within the myocardium.

Probing the prostate tumour microenvironment I: impact of glucose deprivation on a cell model of prostate cancer progression.

In the developed world, prostate cancer is the most common cancer diagnosis in men. Although prostate cancer initially presents as a non life-threatening disease, 90% of patients will develop castration resistant prostate cancer (CRPC), which preludes distant metastasis and is largely accountable for prostate cancer associated deaths. This is because as yet, there are no viable molecular therapeutic targets for effective treatment of CRPC. It is now widely accepted that cancer cells can alter their metabolic profile during the course of tumourgenesis and metastasis such that they are able to survive in oxygen and nutrient-poor environments. This work was aimed towards gaining greater mechanistic understanding of how such 'stresses' in the tumour microenvironment impact on both androgen sensitive (LNCaP) and androgen independent (LNCaP-abl and LNCaP-abl-Hof) prostate cancer cell lines. Here we have applied technically robust and reproducible label-free liquid chromatography mass spectrometry analysis for comprehensive proteomic profiling of prostate cancer cell lines under nutrient deficient (low glucose) conditions. This led to the identification of approximately 4,000 proteins - one of the largest protein datasets for prostate cancer cell lines established to date. The biological and clinical significance of proteins showing a significant change in expression as result of low glucose conditions was established. Novel, intuitive workflows were subsequently implemented to ensure the verification of selected proteins of interest in a robust, reproducible and high throughput manner. Overall, these data suggest that this strategy supports identification of protein biomarkers of prostate cancer progression and potential therapeutic targets for CRPC.

Probing the prostate tumour microenvironment II: Impact of hypoxia on a cell model of prostate cancer progression.

Approximately one in six men are diagnosed with Prostate Cancer every year in the Western world. Although it can be well managed and non-life threatening in the early stages, over time many patients cease to respond to treatment and develop castrate resistant prostate cancer (CRPC). CRPC represents a clinically challenging and lethal form of prostate cancer. Progression of CRPC is, in part, driven by the ability of cancer cells to alter their metabolic profile during the course of tumourgenesis and metastasis so that they can survive in oxygen and nutrient-poor environments and even withstand treatment. This work was carried out as a continuation of a study aimed towards gaining greater mechanistic understanding of how conditions within the tumour microenvironment impact on both androgen sensitive (LNCaP) and androgen independent (LNCaP-abl and LNCaP-abl-Hof) prostate cancer cell lines. Here we have applied technically robust and reproducible label-free liquid chromatography mass spectrometry analysis for comprehensive proteomic profiling of prostate cancer cell lines under hypoxic conditions. This led to the identification of over 4,000 proteins - one of the largest protein datasets for prostate cancer cell lines established to date. The biological and clinical significance of proteins showing a significant change in expression as result of hypoxic conditions was established. Novel, intuitive workflows were subsequently implemented to enable robust, reproducible and high throughput verification of selected proteins of interest. Overall, these data suggest that this strategy supports identification of protein biomarkers of prostate cancer progression and potential therapeutic targets for CRPC.

Pathology-Driven Comprehensive Proteomic Profiling of the Prostate Cancer Tumor Microenvironment.

Prostate cancer is the second most common cancer in men worldwide. Gleason grading is an important predictor of prostate cancer outcomes and is influential in determining patient treatment options. Clinical decisions based on a Gleason score of 7 are difficult as the prognosis for individuals diagnosed with Gleason 4+3 cancer is much worse than for those diagnosed with Gleason 3+4 cancer. Laser capture microdissection (LCM) is a highly precise method to isolate specific cell populations or discrete microregions from tissues. This report undertook a detailed molecular characterization of the tumor microenvironment in prostate cancer to define the proteome in the epithelial and stromal regions from tumor foci of Gleason grades 3 and 4. Tissue regions of interest were isolated from several Gleason 3+3 and Gleason 4+4 tumors using telepathology to leverage specialized pathology expertise to support LCM. Over 2,000 proteins were identified following liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of all regions of interest. Statistical analysis revealed significant differences in protein expression (>100 proteins) between Gleason 3 and Gleason 4 regions-in both stromal and epithelial compartments. A subset of these proteins has had prior strong association with prostate cancer, thereby providing evidence for the authenticity of the approach. Finally, validation of these proteins by immunohistochemistry has been obtained using an independent cohort of prostate cancer tumor specimens.Implications: This unbiased strategy provides a strong foundation for the development of biomarker protein panels with significant diagnostic and prognostic potential. Mol Cancer Res; 15(3); 281-93. ©2017 AACR.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer.

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.