A pilot study of a nurse-led integrated care review (the INCLUDE review) for people with inflammatory rheumatological conditions in primary care: feasibility study findings.

BACKGROUND: People with inflammatory rheumatological conditions such as rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, polymyalgia rheumatica and giant cell arteritis are at an increased risk of common comorbidities including cardiovascular disease, osteoporosis and mood problems, leading to increased morbidity and mortality. Identifying and treating these problems could lead to improved patient quality of life and outcomes. Despite these risks being well-established, patients currently are not systematically targeted for management interventions for these morbidities. This study aimed to assess the feasibility of conducting a randomised controlled trial (RCT) of a nurse-led integrated care review in primary care to identify and manage these morbidities. METHODS: A pilot cluster RCT was delivered across four UK general practices. Patients with a diagnostic Read code for one of the inflammatory rheumatological conditions of interest were recruited by post. In intervention practices (n = 2), eligible patients were invited to attend the INCLUDE review. Outcome measures included health-related quality of life (EQ-5D-5L), patient activation, self-efficacy and treatment burden. A sample (n = 24) of INCLUDE review consultations were audio-recorded and assessed against a fidelity checklist. RESULTS: 453/789 (57%) patients responded to the invitation, although 114/453 (25%) were excluded as they either did not fulfil eligibility criteria or failed to provide full written consent. In the intervention practices, uptake of the INCLUDE review was high at 72%. Retention at 3 and 6 months both reached pre-specified success criteria. Participants in intervention practices had more primary care contacts than controls (mean 29 vs 22) over the 12 months, with higher prescribing of all relevant medication classes in participants in intervention practices, particularly so for osteoporosis medication (baseline 29% vs 12 month 46%). The intervention was delivered with fidelity, although potential areas for improvement were identified. CONCLUSIONS: The findings of this pilot study suggest it is feasible to deliver an RCT of the nurse-led integrated care (INCLUDE) review in primary care. A significant morbidity burden was identified. Early results suggest the INCLUDE review was associated with changes in practice. Lessons have been learnt around Read codes for patient identification and refining the nurse training. TRIAL REGISTRATION: ISRCTN, ISRCTN12765345.

Quantitative proteomics investigation of pancreatic intraepithelial neoplasia.

Patients with pancreatic cancer are usually diagnosed at late stages, when the disease is incurable. Pancreatic intraepithelial neoplasia (PanIN) 3 is believed to be the immediate precursor lesion of pancreatic adenocarcinoma, and would be an ideal stage to diagnose patients, when intervention and cure are possible and patients are curable. In this study, we used quantitative proteomics to identify dysregulated proteins in PanIN 3 lesions. Altogether, over 200 dysregulated proteins were identified in the PanIN 3 tissues, with a minimum of a 1.75-fold change compared with the proteins in normal pancreas. These dysregulated PanIN 3 proteins play roles in cell motility, the inflammatory response, the blood clotting cascade, the cell cycle and its regulation, and protein degradation. Further network analysis of the proteins identified c-MYC as an important regulatory protein in PanIN 3 lesions. Finally, three of the overexpressed proteins, laminin beta-1, galectin-1, and actinin-4 were validated by immunohistochemistry analysis. All three of these proteins were overexpressed in the stroma or ductal epithelial cells of advanced PanIN lesions as well as in pancreatic cancer tissue. Our findings suggest that these three proteins may be useful as biomarkers for advanced PanIN and pancreatic cancer if further validated. The dysregulated proteins identified in this study may assist in the selection of candidates for future development of biomarkers for detecting early and curable pancreatic neoplasia.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.

BACKGROUND: Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. RESULTS: We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. CONCLUSION: The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

The INCLUDE study: INtegrating and improving Care for patients with infLammatory rheUmatological DisordErs in the community; identifying multimorbidity: Protocol for a pilot randomized controlled trial.

BACKGROUND: Patients with inflammatory rheumatic conditions such as rheumatoid arthritis, polymyalgia rheumatica and ankylosing spondylitis are at increased risk of common comorbidities such as cardiovascular disease, osteoporosis and anxiety and depression which lead to increased morbidity and mortality. These associated morbidities are often un-recognized and under-treated. While patients with other long-term conditions such as diabetes are invited for routine reviews in primary care, which may include identification and management of co-morbidities, at present this does not occur for patients with inflammatory conditions, and thus, opportunities to diagnose and optimally manage these comorbidities are missed. OBJECTIVE: To evaluate the feasibility and acceptability of a nurse-led integrated care review (the INtegrating and improving Care for patients with infLammatory rheUmatological DisordErs in the community (INCLUDE) review) for people with inflammatory rheumatological conditions in primary care. DESIGN: A pilot cluster randomized controlled trial will be undertaken to test the feasibility and acceptability of a nurse-led integrated primary care review for identification, assessment and initial management of common comorbidities including cardiovascular disease, osteoporosis and anxiety and depression. A process evaluation will be undertaken using a mixed methods approach including participant self-reported questionnaires, a medical record review, an INCLUDE EMIS template, intervention fidelity checking using audio-recordings of the INCLUDE review consultation and qualitative interviews with patient participants, study nurses and study general practitioners (GPs). DISCUSSION: Success of the pilot study will be measured against the engagement, recruitment and study retention rates of both general practices and participants. Acceptability of the INCLUDE review to patients and practitioners and treatment fidelity will be explored using a parallel process evaluation. TRIAL REGISTRATION: ISRCTN12765345.

The Road of Mentorship.

Mentorship can be the cornerstone of professional development and career satisfaction. There is literature to support that mentorship not only improves job satisfaction, but also improves productivity, facilitates personal growth, and can rekindle our passion while lessening the risk of compassion fatigue. Mentorship is a developmental relationship that changes as the relationship evolves. There are two broad categories of mentorship: traditional and transformational. There are four subtypes within each of those areas: formal, informal, spot, or peer. Mentorship is critical to the professional development of junior colleagues. Good mentorship is guiding and steering younger partners and other colleagues toward paths of success. As a mentor, one should be looking for opportunities for formal professional development and engagement of mentees. Self-motivation is the hallmark of the successful mentee. The mentee should be able to set his or her own goals, strive to actively seek feedback, ask questions, and keep an accurate record of progress. Although the onus is on the mentee to reach out, mentorship has bidirectional value directly related to the efforts of both parties. There are many benefits to mentorship, such as the promotion of learning, personal development, improved job satisfaction, and improved job performance. Barriers exist, including the rapidly changing landscape of oncology, time constraints, lack of self-awareness, and generational differences. Through a career, mentoring needs will change, as will mentors.

Proteins That Underlie Neoplastic Progression of Ulcerative Colitis.

Patients with ulcerative colitis (UC) have an increased risk for developing colorectal cancer. Because UC tumorigenesis is associated with genomic field defects that can extend throughout the entire colon, including the non-dysplastic mucosa; we hypothesized that the same field defect will include abnormally expressed proteins. Here we applied proteomics to study the protein expression of UC neoplastic progression. The protein profiles of colonic epithelium were compared from 1) UC patients without dysplasia (non-progressors); 2) none-dysplastic colonic tissue from UC patient with high-grade dysplasia or cancer (progressors); 3) high-grade dysplastic tissue from UC progressors and 4) normal colon. We identified protein differential expression associated with UC neoplastic progression. Proteins relating to mitochondria, oxidative activity, calcium-binding proteins were some of interesting classes of these proteins. Network analysis discovered that Sp1 and c-myc proteins may play roles in UC early and late stages of neoplastic progression, respectively. Two over-expressed proteins in the non-dysplastic tissue of UC progressors, CPS1 and S100P, were further confirmed by IHC analysis. Our study provides insight into the molecular events associated with UC neoplastic progression, which could be exploited for the development of protein biomarkers in fields of non-dysplastic mucosa that identify a patient's risk for UC dysplasia.

Comparison of pancreas juice proteins from cancer versus pancreatitis using quantitative proteomic analysis.

OBJECTIVES: Pancreatitis is an inflammatory condition of the pancreas. However, it often shares many molecular features with pancreatic cancer. Biomarkers present in pancreatic cancer frequently occur in the setting of pancreatitis. The efforts to develop diagnostic biomarkers for pancreatic cancer have thus been complicated by the false-positive involvement of pancreatitis. METHODS: In an attempt to develop protein biomarkers for pancreatic cancer, we previously use quantitative proteomics to identify and quantify the proteins from pancreatic cancer juice. Pancreatic juice is a rich source of proteins that are shed by the pancreatic ductal cells. In this study, we used a similar approach to identify and quantify proteins from pancreatitis juice. RESULTS: In total, 72 proteins were identified and quantified in the comparison of pancreatic juice from pancreatitis patients versus pooled normal control juice. Nineteen of the juice proteins were overexpressed, and 8 were underexpressed in pancreatitis juice by at least 2-fold compared with normal pancreatic juice. Of these 27 differentially expressed proteins in pancreatitis, 9 proteins were also differentially expressed in the pancreatic juice from pancreatic cancer patient. CONCLUSIONS: Identification of these differentially expressed proteins from pancreatitis juice provides useful information for future study of specific pancreatitis-associated proteins and to eliminate potential false-positive biomarkers for pancreatic cancer.

Quantitative proteomics analysis reveals that proteins differentially expressed in chronic pancreatitis are also frequently involved in pancreatic cancer.

The effective treatment of pancreatic cancer relies on the diagnosis of the disease at an early stage, a difficult challenge. One major obstacle in the development of diagnostic biomarkers of early pancreatic cancer has been the dual expression of potential biomarkers in both chronic pancreatitis and cancer. To better understand the limitations of potential protein biomarkers, we used ICAT technology and tandem mass spectrometry-based proteomics to systematically study protein expression in chronic pancreatitis. Among the 116 differentially expressed proteins identified in chronic pancreatitis, most biological processes were responses to wounding and inflammation, a finding consistent with the underlining inflammation and tissue repair associated with chronic pancreatitis. Furthermore 40% of the differentially expressed proteins identified in chronic pancreatitis have been implicated previously in pancreatic cancer, suggesting some commonality in protein expression between these two diseases. Biological network analysis further identified c-MYC as a common prominent regulatory protein in pancreatic cancer and chronic pancreatitis. Lastly five proteins were selected for validation by Western blot and immunohistochemistry. Annexin A2 and insulin-like growth factor-binding protein 2 were overexpressed in cancer but not in chronic pancreatitis, making them promising biomarker candidates for pancreatic cancer. In addition, our study validated that cathepsin D, integrin beta1, and plasminogen were overexpressed in both pancreatic cancer and chronic pancreatitis. The positive involvement of these proteins in chronic pancreatitis and pancreatic cancer will potentially lower the specificity of these proteins as biomarker candidates for pancreatic cancer. Altogether our study provides some insights into the molecular events in chronic pancreatitis that may lead to diverse strategies for diagnosis and treatment of these diseases.

Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics.

Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Recently much emphasis has been placed upon producing highly reliable data for quantitative profiling for which highly reproducible methodologies are indispensable. The main problems that affect experimental reproducibility stem from variations introduced by sample collection, preparation, and storage protocols and LC-MS settings and conditions. On the basis of a formally precise and quantitative definition of similarity between LC-MS experiments, we have developed Chaorder, a fully automatic software tool that can assess experimental reproducibility of sets of large scale LC-MS experiments. By visualizing the similarity relationships within a set of experiments, this tool can form the basis of systematic quality control and thus help assess the comparability of mass spectrometry data over time, across different laboratories, and between instruments. Applying Chaorder to data from multiple laboratories and a range of instruments, experimental protocols, and sample complexities revealed biases introduced by the sample processing steps, experimental protocols, and instrument choices. Moreover we show that reducing bias by correcting for just a few steps, for example randomizing the run order, does not provide much gain in statistical power for biomarker discovery.

Head-to-head comparison of serum fractionation techniques.

Multiple approaches for simplifying the serum proteome have been described. These techniques are generally developed across different laboratories, samples, mass spectrometry platforms, and analysis tools. Hence, comparing the available schemes is impossible from the existing literature because of confounding variables. We describe a head-to-head comparison of several serum fractionation schemes, including N-linked glycopeptide enrichment, cysteinyl-peptide enrichment, magnetic bead separation (C3, C8, and WCX), size fractionation, protein A/G depletion, and immunoaffinity column depletion of abundant serum proteins. Each technique was compared to results obtained from unfractionated human serum. The results show immunoaffinity subtraction is the most effective means for simplifying the serum proteome while maintaining reasonable sample throughput. The reported dataset is publicly available and provides a standard against which emergent technologies can be compared and evaluated for their contribution to serum-based biomarker discovery.

UniPep--a database for human N-linked glycosites: a resource for biomarker discovery.

There has been considerable recent interest in proteomic analyses of plasma for the purpose of discovering biomarkers. Profiling N-linked glycopeptides is a particularly promising method because the population of N-linked glycosites represents the proteomes of plasma, the cell surface, and secreted proteins at very low redundancy and provides a compelling link between the tissue and plasma proteomes. Here, we describe UniPep http://www.unipep.org--a database of human N-linked glycosites--as a resource for biomarker discovery.

Increased quantitative proteome coverage with (13)C/(12)C-based, acid-cleavable isotope-coded affinity tag reagent and modified data acquisition scheme.

Quantitative protein profiling using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS) enables the pair-wise comparison of protein expression levels in biological samples. A new version of the ICAT reagent with an acid-cleavable bond, which allows removal of the biotin moiety prior to MS and which utilizes (13)C substitution for (12)C in the heavy-ICAT reagent rather than (2)H (for (1)H) as in the original reagent, was investigated. We developed and validated an MS data acquisition strategy using this new reagent that results in an increased number of protein identifications per experiment, without losing the accuracy of protein quantification. This was achieved by following a single survey (precursor) ion scan and serial collision induced dissociations (CIDs) of four different precursor ions observed in the prior survey scan. This strategy is common to many high-performance liquid chromatography-electrospray ionization (HPLC-ESI)-MS shotgun proteomic strategies, but heretofore not to ICAT experiments. This advance is possible because the new ICAT reagent uses (13)C as the "heavy" element rather than (2)H, thus, eliminating the slight delay in retention time of ICAT-labeled "light" peptides on a C18-based HPLC separation that occurs with (2)H and (1)H. Analyses using this new scheme of an ICAT-labeled trypsin-digested six protein mixture as well as a tryptic digest of a total yeast lysate, indicated that about two times more proteins were identified in a single analysis, and that there was no loss in accuracy of quantification.

Pancreatic cancer proteome: the proteins that underlie invasion, metastasis, and immunologic escape.

BACKGROUND & AIMS: Pancreatic cancer is a highly lethal disease that has seen little headway in diagnosis and treatment for the past few decades. The effective treatment of pancreatic cancer is critically relying on the diagnosis of the disease at an early stage, which still remains challenging. New experimental approaches, such as quantitative proteomics, have shown great potential for the study of cancer and have opened new opportunities to investigate crucial events underlying pancreatic tumorigenesis and to exploit this knowledge for early detection and better intervention. METHODS: To systematically study protein expression in pancreatic cancer, we used isotope-coded affinity tag technology and tandem mass spectrometry to perform quantitative proteomic profiling of pancreatic cancer tissues and normal pancreas. RESULTS: A total of 656 proteins were identified and quantified in 2 pancreatic cancer samples, of which 151 were differentially expressed in cancer by at least 2-fold. This study revealed numerous proteins that are newly discovered to be associated with pancreatic cancer, providing candidates for future early diagnosis biomarkers and targets for therapy. Several differentially expressed proteins were further validated by tissue microarray immunohistochemistry. Many of the differentially expressed proteins identified are involved in protein-driven interactions between the ductal epithelium and the extracellular matrix that orchestrate tumor growth, migration, angiogenesis, invasion, metastasis, and immunologic escape. CONCLUSIONS: Our study is the first application of isotope-coded affinity tag technology for proteomic analysis of human cancer tissue and has shown the value of this technology in identifying differentially expressed proteins in cancer.

Controlled cell killing by a recombinant nonsegmented negative-strand RNA virus.

In most tissue culture cell lines tested, infection with the paramyxovirus simian virus 5 (SV5) results in very little cell death. To determine if SV5 could be used as a vector for controlled killing of tumor cells, a recombinant SV5 (rSV5-TK) was constructed to encode the herpes simplex virus thymidine kinase (TK) gene. MDBK cells infected with rSV5-TK showed a time-dependent loss of viability when infected cells were cultured in the presence of the prodrug acyclovir (ACV) or ganciclovir (GCV) while no significant toxicity was observed in the absence of prodrug. Cells infected with a control rSV5 expressing GFP and cultured with prodrug showed only a slight reduction in growth rate and little cell death. Time-lapse video microscopy of rSV5-TK-infected MDBK cells that were cultured in the presence of ACV showed an accumulation of cells with morphological effects characteristic of apoptotic cell death. An MDBK cell line persistently infected with rSV5-TK retained long-term expression of TK and sensitivity to prodrug-mediated cell killing that were comparable to those found in an acute infection. Titration experiments showed that the rSV5-TK plus GCV combination resulted in cell death for all mouse and human cell lines tested, although the kinetics and efficiency of cell death varied between cell types. Our results demonstrating controlled cell killing by a recombinant paramyxovirus support the use of negative-strand RNA viruses as therapeutic vectors for targeted killing of cancer cells.