Samantha Larkin

Identification of markers of prostate cancer progression using candidate gene expression

Background:Metastatic prostate cancer (PCa) has no curative treatment options. Some forms of PCa are indolent and slow growing, while others metastasise quickly and may prove fatal within a very short time. The basis of this variable prognosis is poorly understood, despite considerable research. The aim of this study was to identify markers associated with the progression of PCa.Methods:Artificial neuronal network analysis combined with data from literature and previous work produced a panel of putative PCa progression markers, which were used in a transcriptomic analysis of 29 radical prostatectomy samples and correlated with clinical outcome.Results:Statistical analysis yielded seven putative markers of PCa progression, ANPEP, ABL1, PSCA, EFNA1, HSPB1, INMT and TRIP13. Two data transformation methods were utilised with only markers that were significant in both selected for further analysis. ANPEP and EFNA1 were significantly correlated with Gleason score. Models of progression co-utilising markers ANPEP and ABL1 or ANPEP and PSCA had the ability to correctly predict indolent or aggressive disease, based on Gleason score, in 89.7% and 86.2% of cases, respectively. Another model of TRIP13 expression in combination with preoperative PSA level and Gleason score was able to correctly predict recurrence in 85.7% of cases.Conclusion:This proof of principle study demonstrates a novel association of carcinogenic and tumourigenic gene expression with PCa stage and prognosis.

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.

Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study

Background:Prostate cancer (PCa) is the most common male cancer in the United Kingdom and we aimed to identify clinically relevant biomarkers corresponding to stage progression of the disease.Methods:We used enhanced proteomic profiling of PCa progression using iTRAQ 3D LC mass spectrometry on high-quality serum samples to identify biomarkers of PCa.Results:We identified >1000 proteins. Following specific inclusion/exclusion criteria we targeted seven proteins of which two were validated by ELISA and six potentially interacted forming an ‘interactome’ with only a single protein linking each marker. This network also includes accepted cancer markers, such as TNF, STAT3, NF-κB and IL6.Conclusions:Our linked and interrelated biomarker network highlights the potential utility of six of our seven markers as a panel for diagnosing PCa and, critically, in determining the stage of the disease. Our validation analysis of the MS-identified proteins found that SAA alongside KLK3 may improve categorisation of PCa than by KLK3 alone, and that TSR1, although not significant in this model, might also be a clinically relevant biomarker.

Banking the brain: Addressing the ethical challenges of a mental‐health biobank

Psychiatric disorders are increasingly regarded as diseases of the brain, as opposed to diseases of the mind; they are alterations in neurobiological brain circuits that are influenced by genetic and environmental factors [1]. Nonetheless, diagnostic classifications are still largely based on clinical observation and symptom reports by patients, rather than biological evidence. Consequently, treatment is aimed at reducing and managing observable symptoms. This inability to target the causes of disease results in suboptimal response rates and adverse effects of medication. For example, 30–40% of patients with depression do not respond to appropriate drug therapy and treatment, and approximately one‐third of those with schizophrenia do not respond to standard treatments [2,3,4,5]. Understanding the real biological causes of psychiatric disorders would therefore help to improve diagnostics and treatment efficiency, and should reduce adverse side effects. It would also help to decrease the health and economic burden of psychiatric diseases, which costs the National Health Service (NHS) in England approximately £22.5 billion per year [6]. Advances in clinical neuroscience, genomics and neuroimaging are increasing our knowledge of brain function and promise to elucidate aetiological factors of neuropsychiatric disorders. Detecting associations between genetic or environmental factors and a given disease, however, requires studies with access to many samples. A biobank that collects biological samples from patients with psychiatric disorders, along with their clinical records and neuroimaging data could, therefore, become an important component for basic and clinical research. It would allow a more precise identification of disease mechanisms, contribute to devising specific and personalized therapies that target relevant neural processes effectively, help to identify individuals for whom particular treatments will be harmful, and hopefully bring us closer to identifying factors that can either prevent or minimize the occurrence of neuropsychiatric disorders. However, …

Increased circulating resistin levels in early-onset breast cancer patients of normal body mass index correlate with lymph node negative involvement and longer disease free survival: a multi-center POSH cohort serum proteomics study

BackgroundEarly-onset breast cancer (EOBC) affects about one in 300 women aged 40 years or younger and is associated with worse outcomes than later onset breast cancer. This study explored novel serum proteins as surrogate markers of prognosis in patients with EOBC.MethodsSerum samples from EOBC patients (stages 1–3) were analysed using agnostic high-precision quantitative proteomics. Patients received anthracycline-based chemotherapy. The discovery cohort (n = 399) either had more than 5-year disease-free survival (DFS) (good outcome group, n = 203) or DFS of less than 2 years (poor outcome group, n = 196). Expressed proteins were assessed for differential expression between the two groups. Bioinformatics pathway and network analysis in combination with literature research were used to determine clinically relevant proteins. ELISA analysis against an independent sample set from the Prospective study of Outcomes in Sporadic versus Hereditary breast cancer (POSH) cohort (n = 181) was used to validate expression levels of the selected target. Linear and generalized linear modelling was applied to determine the effect of target markers, body mass index (BMI), lymph node involvement (LN), oestrogen receptor (ER), progesterone receptor and human epidermal growth factor receptor 2 status on patients’ outcome.ResultsA total of 5346 unique proteins were analysed (peptide FDR p ≤ 0.05). Of these, 812 were differentially expressed in the good vs poor outcome groups and showed significant enrichment for the insulin signalling (p = 0.01) and the glycolysis/gluconeogenesis (p = 0.01) pathways. These proteins further correlated with interaction networks involving glucose and fatty acid metabolism. A consistent nodal protein to these metabolic networks was resistin (upregulated in the good outcome group, p = 0.009). ELISA validation demonstrated resistin to be upregulated in the good outcome group (p = 0.04), irrespective of BMI and ER status. LN involvement was the only covariate with a significant association with resistin measurements (p = 0.004). An ancillary in-silico observation was the induction of the inflammatory response, leucocyte infiltration, lymphocyte migration and recruitment of phagocytes (p < 0.0001, z-score > 2). Survival analysis showed that resistin overexpression was associated with improved DFS.ConclusionsHigher circulating resistin correlated with node-negative patients and longer DFS independent of BMI and ER status in women with EOBC. Overexpression of serum resistin in EOBC may be a surrogate indicator of improved prognosis.

Proteomic evaluation of cancer cells: identification of cell surface proteins.

The plasma membrane proteome can be defined as the entire complement of proteins present in the plasma membrane at a specific time. The process of carcinogenesis leads to changes in the array of proteins present in the plasma membrane proteome. Analysis of differential expression of such proteins in cancer is extremely important; due to their position on the cell surface they have a potential for use as diagnostic and/or prognostic markers and therapeutic targets. Biotin labelling followed by avidin chromatography can be used to obtain membrane protein enriched lysates from cell lines, which can then be resolved using SDS-PAGE, coomassie staining and mass spectrometry.

Proteomics in Prostate Cancer Research

Early detection of disease possible through the detection of biological markers (biomarkers) is critical for the healthcare journey of all patients within the twenty-first century. Biomarkers are defined by the National Institutes of Health as ‘characteristics that are objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention’. Therefore, to reduce mortality rates, it is imperative new disease-linked biomarkers are discovered in order to develop future diagnostics and the therapeutics. Once identified, it may for instance enable diagnoses, staging and treatment of haematological cancers (such as leukaemia) and solid tumours (such as prostate). Identification of these disease-linked biomarkers can be a slow and arduous process, although the market will be worth an estimate

070 HIGH RESOLUTION MULTIDIMENSIONAL PROTEOMICS DETECTS CANDIDATE ARRHYTHMIA BIOMARKERS

45.5 billion by 2020. To date, a considerable amount of this research results in failure to identify any novel, clinically relevant markers. Biomarker discovery fails, mainly due to poor study design, sample procurement and the inability to actually identify applicable markers, either due low concentration or inability to resolve two similar markers. This if further compounded by the fact that even if identification is possible, current diagnostic and therapeutic assays rely on antibodies to be raised to the target, which is not always possible. Here we describe modern approaches to detecting prostate cancer (PCa) and normal prostate-associated biomarkers utilising mass spectrometry as our tool for detection and identification.

Association of common variations of 8q24 with the risk of prostate cancer in Koreans and a review of the Asian population

Introduction The ability to predict arrhythmia risk in patients with LVSD is important, as the clinical and cost-effectiveness of implantable cardioverter defibrillator (ICD) therapy depends on its use in appropriately selected patient populations. Individual biomarkers can be powerful predictors of prognosis, but studies are limited to small marker panels, chosen a priori. Mass spectroscopy based proteomic techniques can powerfully and simultaneously demonstrate differential candidate proteins in an unbiased fashion but resolving power can be limited by high serum abundant proteins. Depletion methods can overcome this, but often result in coremoval of potential biomarkers too. The aim of the study was to utilise a novel wide spectrum technique to identify candidate biomarkers from the whole proteome associated with arrhythmia outcomes. Table 1 Proteins differentially expressed in arrhythmia outcomes. Values represent ratio of protein expression in clinical group compared to control (no VT/VF). Protein Death VT/VF No VT/VF Semaphorin-6B 0.728 7.064 1 Tropomyosin α-3 chain 1.354 3.367 1 Heat shock cognate 1.849 3.048 1 Ubiquitin carboxyl-terminal hydrolase 1.686 2.175 1 F-box only protein 36 1.751 2.132 1 Apolipoprotein C-III 1.629 2.095 1 Histone H2A type 1-H 1.928 2.091 1 Hepatocyte nuclear factor 6 1.179 0.333 1 Proteasome subunit α type-1 0.859 0.344 1 Dynein heavy chain 17 0.726 0.355 1 Keratin, type I cytoskeletal 1.981 0.437 1 Collagen α-1(XVIII) chain 1.741 0.440 1 Ig heavy chain V-II region OU 0.706 0.489 1 DnaJ homologue subfamily C member 1 1.177 0.496 1 TIR domain-containing adapter molecule 1 0.811 0.498 1 Natriuretic peptides B 4.374 1.764 1 Methods Consecutive patients attending device follow up clinic were recruited to the study. Serum was collected and stored at −80°C. The occurrence of prespecified arrhythmia end points and death was recorded. Sera were then pooled according to clinical categories occurring during follow up. Grouped sera underwent stable isotope iTRAC labelling, and following protein fractionation and immunodepletion, samples underwent simultaneous tandem mass spectroscopy followed by data processing and identification of differentially expressed protein peaks p<0.05. Technical validation of the technique was achieved through detection of B type natriuretic peptide using mass spectroscopy and in unfractionated serum using standard ELISA techniques. Results 243 patients (54% male, age 71±6) were included in the analysis. During follow up of 40 months, there were eight cardiovascular deaths. 25 experienced VT>182 bpm or VF, whilst 48 never experienced VT at any rate/VF (controls). 634 proteins were identified by this method. When compared to the control group, proteins had significant differential expression if twofold up- or down-regulated. Overall, 94 proteins were differentially expressed in those who died or experienced VT>182 or VF. 15 proteins were associated with the arrhythmia endpoint but not death (table 1). BNP was detected by both MS and ELISA, and had greater up-regulation in patients who died, but was not discriminative for arrhythmia occurrence. Conclusions This study provides proof of principle that proteomic techniques can identify candidate proteins for use as biomarkers of arrhythmia risk. Further investigation is needed to select proteins with potential for clinical application before testing in a prospective setting.

Discovery of serum protein biomarkers for prostate cancer progression by proteomic analysis

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