Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse.

To evaluate the role of oncogenic RAS mutations in pancreatic tumorigenesis, we directed endogenous expression of KRAS(G12D) to progenitor cells of the mouse pancreas. We find that physiological levels of Kras(G12D) induce ductal lesions that recapitulate the full spectrum of human pancreatic intraepithelial neoplasias (PanINs), putative precursors to invasive pancreatic cancer. The PanINs are highly proliferative, show evidence of histological progression, and activate signaling pathways normally quiescent in ductal epithelium, suggesting potential therapeutic and chemopreventive targets for the cognate human condition. At low frequency, these lesions also progress spontaneously to invasive and metastatic adenocarcinomas, establishing PanINs as definitive precursors to the invasive disease. Finally, mice with PanINs have an identifiable serum proteomic signature, suggesting a means of detecting the preinvasive state in patients.

Learning from the GP-consultant exchange scheme: a qualitative evaluation.

Collaborative working across primary and secondary care is crucial to providing high quality patient care. There is still a lack of communication and understanding between primary and secondary care, which can impede collaborative working. The experience of observing colleagues in a different speciality can prompt insight, improve morale and promote collaborative working. The GP-Consultant Exchange Scheme aimed to improve professional understanding, foster deeper partnerships, and ignite opportunities for innovation and/or quality improvement (QI) with co-owned local solutions. This paper gives an overview of how the scheme works and sets out some of the outcomes reported by some 200 Consultants and GPs participants to date. Overall, the participants found the scheme an enjoyable way to reconnect clinicians and allowed them to learn about the challenges faced in different areas within the NHS. This low-cost intervention needs motivated individuals to drive the project forward and make it sustainable, but it can be replicated within any organisation or profession in the NHS.

Discovering clinical biomarkers of ionizing radiation exposure with serum proteomic analysis.

In this study, we sought to explore the merit of proteomic profiling strategies in patients with cancer before and during radiotherapy in an effort to discover clinical biomarkers of radiation exposure. Patients with a diagnosis of cancer provided informed consent for enrollment on a study permitting the collection of serum immediately before and during a course of radiation therapy. High-resolution surface-enhanced laser desorption and ionization-time of flight (SELDI-TOF) mass spectrometry (MS) was used to generate high-throughput proteomic profiles of unfractionated serum samples using an immobilized metal ion-affinity chromatography nickel-affinity chip surface. Resultant proteomic profiles were analyzed for unique biomarker signatures using supervised classification techniques. MS-based protein identification was then done on pooled sera in an effort to begin to identify specific protein fragments that are altered with radiation exposure. Sixty-eight patients with a wide range of diagnoses and radiation treatment plans provided serum samples both before and during ionizing radiation exposure. Computer-based analyses of the SELDI protein spectra could distinguish unexposed from radiation-exposed patient samples with 91% to 100% sensitivity and 97% to 100% specificity using various classifier models. The method also showed an ability to distinguish high from low dose-volume levels of exposure with a sensitivity of 83% to 100% and specificity of 91% to 100%. Using direct identity techniques of albumin-bound peptides, known to underpin the SELDI-TOF fingerprints, 23 protein fragments/peptides were uniquely detected in the radiation exposure group, including an interleukin-6 precursor protein. The composition of proteins in serum seems to change with ionizing radiation exposure. Proteomic analysis for the discovery of clinical biomarkers of radiation exposure warrants further study.

Biomarker amplification by serum carrier protein binding.

Mass spectroscopic analysis of the low molecular mass (LMM) range of the serum/plasma proteome is a rapidly emerging frontier for biomarker discovery. This study examined the proportion of LMM biomarkers, which are bound to circulating carrier proteins. Mass spectroscopic analysis of human serum following molecular mass fractionation, demonstrated that the majority of LMM biomarkers exist bound to carrier proteins. Moreover, the pattern of LMM biomarkers bound specifically to albumin is distinct from those bound to non-albumin carriers. Prominent SELDI-TOF ionic species (m/z 6631.7043) identified to correlate with the presence of ovarian cancer were amplified by albumin capture. Several insights emerged: a) Accumulation of LMM biomarkers on circulating carrier proteins greatly amplifies the total serum/plasma concentration of the measurable biomarker, b) The total serum/plasma biomarker concentration is largely determined by the carrier protein clearance rate, not the unbound biomarker clearance rate itself, and c) Examination of the LMM species bound to a specific carrier protein may contain important diagnostic information. These findings shift the focus of biomarker detection to the carrier protein and its biomarker content.

Novel approaches to visualization and data mining reveals diagnostic information in the low amplitude region of serum mass spectra from ovarian cancer patients.

The ability to identify patterns of diagnostic signatures in proteomic data generated by high throughput mass spectrometry (MS) based serum analysis has recently generated much excitement and interest from the scientific community. These data sets can be very large, with high-resolution MS instrumentation producing 1-2 million data points per sample. Approaches to analyze mass spectral data using unsupervised and supervised data mining operations would greatly benefit from tools that effectively allow for data reduction without losing important diagnostic information. In the past, investigators have proposed approaches where data reduction is performed by a priori "peak picking" and alignment/warping/smoothing components using rule-based signal-to-noise measurements. Unfortunately, while this type of system has been employed for gene microarray analysis, it is unclear whether it will be effective in the analysis of mass spectral data, which unlike microarray data, is comprised of continuous measurement operations. Moreover, it is unclear where true signal begins and noise ends. Therefore, we have developed an approach to MS data analysis using new types of data visualization and mining operations in which data reduction is accomplished by culling via the intensity of the peaks themselves instead of by location. Applying this new analysis method on a large study set of high resolution mass spectra from healthy and ovarian cancer patients, shows that all of the diagnostic information is contained within the very lowest amplitude regions of the mass spectra. This region can then be selected and studied to identify the exact location and amplitude of the diagnostic biomarkers.

Proteomic technologies to study diseases of the lymphatic vascular system.

Now that the human genome has been mapped, a new challenge has emerged: deciphering the various products of individual genes. Consequently, new proteomic technologies are being developed to monitor and identify protein function and interactions responsible for the total activities of the cell. The application of these new proteomic technologies to study cellular activities, will lead to a faster sample throughput and increased sensitivity for the detection of individual proteins, thus providing major opportunities for the discovery of new biomarkers for the early detection of protein alterations associated with the progression of the disease state.

Clinical proteomics and biomarker discovery.

Early detection of disease generally provides much-improved outcomes by a definitive medical procedure or through lifestyle modification along with specific medical management strategies. For serum biomarkers, which are central to the diagnosis of many diseases, to become truly useful sentinels of pathogenesis, their sensitivity and specificity in both early detection and recurrence monitoring must be improved. Currently, the detection and monitoring of disease markers is based on solitary proteins, and this approach is not always reliable. New classes of biomarkers derived from mass spectroscopy analysis of the low molecular weight proteome have shown improved abilities in the early detection of disease and hence in patient risk stratification and outcome. The development of a modular platform technology with sufficient flexibility and design abstractions allowing for concurrent experimentation, test, and refinement will help speed the progress of mass spectroscopy-derived proteomic pattern-based diagnostics from the scientific laboratory to the medical clinic. For acceptance by scientists, physicians, and regulatory personnel, new bioinformatic tools are essential system components for data management, analysis, and intuitive display of these new and complex data. Clinically engineered mass spectroscopy systems are essential for the further development and validation of multiplexed biomarkers that have shown tremendous promise for the early detection of disease.

"It's cheesy when they smile:" what girl athletes prefer in images of female college athletes.

Building on previous research in which we provided an opportunity for female college athletes to construct their own photographic portrayals, this study explored young female athletes' perceptions of the college athlete photographs. Fifty-two girls participated in focus group interviews where they viewed and discussed the images. The young athletes particularly liked images they perceived to show authentic athletes (e.g, in athletic settings, with appropriate sport attire), images they could relate to due to personal experiences, and images that reflected competent and passionate sportswomen. Images perceived as revealing a lack of motivation, poor sporting attitudes, and nonathletic poses generally were disliked. Images depicting multiple social identities (e.g., an athlete in a dress) were controversial and generated much discussion.

Fractionation of serum components using nanoporous substrates.

Numerous previously uncharacterized molecules resident within the low molecular weight circulatory proteome may provide a picture of the ongoing pathophysiology of an organism. Recently, proteomic signatures composed of low molecular weight molecules have been identified using mass spectrometry combined with bioinformatic algorithms. Attempts to sequence and identify the molecules that underpin the fingerprints are currently underway. The finding that many of these low molecular weight molecules may exist bound to circulating carrier proteins affords a new opportunity for fractionation and separation techniques prior to mass spectrometry-based analysis. In this study we demonstrate a method whereby nanoporous substrates may be used for the facile and reproducible fractionation and selective binding of the serum-based biomarker material, including subcellular proteins found within the serum. Aminopropyl-coated nanoporous silicon, when exposed to serum, can deplete serum of proteins and yield a serum with a distinct, altered MS profile. Additionally, aminopropyl-coated, nanoporous controlled-pore glass beads are able to bind a subset of serum proteins and release them with stringent elution. The eluted proteins have distinct MS profiles, gel electrophoresis profiles, and differential peptide sequence identities, which vary based on the size of the nanopores. These material surfaces could be employed in strategies for the harvesting and preservation of labile and carrier-protein-bound molecules in the blood.

A serum proteomic approach to gauging the state of remission in Wegener's granulomatosis.

OBJECTIVE: To identify serum ion patterns that distinguish remission from active disease in patients with Wegener's granulomatosis (WG). METHODS: Using sera collected in the WG Etanercept Trial, we selected samples from patients who either were undergoing a period of extended disease remission or had recent flares of active WG. Unfractionated samples were randomized into sets for training and testing, such that remission sera and active disease sera could be analyzed without batch bias. Molecular species within the sera were ionized by high-resolution, matrix-assisted laser desorption ionization time-of-flight mass spectrometry. We then used a bioinformatics pattern-recognition tool to identify optimal combinations of ions. During the training stage, the clinical data (remission versus active disease) were provided in association with the spectral data from each sample. In the testing stage, we performed blinded testing on a previously unexamined set of samples. RESULTS: The most robust model, trained on a total of 82 samples (42 remission, 40 active disease), included 7 key ions with mass:charge ratios of 803.239, 2,171.672, 2,790.574, 3,085.237, 5,051.726, 5,833.989, and 6,630.465. The combined relative amplitudes of these 7 ions identified 5 distinct clusters of either remission or active disease samples during the training stage. In the testing stage, this model segregated 72 samples into the same 5 clusters, including 1 large remission cluster (n = 28) and another large active disease cluster (n = 32). Three smaller clusters of active disease or remission samples were also identified, with remission clusters populated by 2 samples in one cluster and 8 in another, and an active disease cluster populated by 2 samples. The model categorized 35 of 37 remission samples correctly (sensitivity 95%, 95% confidence interval [95% CI] 82.1-99.4) and 32 of 35 active disease samples correctly (specificity 91%, 95% CI 78.1-98.1). CONCLUSION: This serum proteomic profiling approach appears to be useful in distinguishing between states of stable clinical remission and active disease. Further validation and refinement of this strategy may help clinicians apply immunosuppressive therapies more judiciously among their patients, thereby avoiding morbidity and mortality from excessive treatment. Identification of the most robust and clinically useful combinations of ions will permit the rational selection of molecules for sequencing and analysis.

Proteomic analysis of lymph.

This report provides the first proteomic analysis of normal ovine lymph. By establishing the fact that lymph is more than an ultrafiltrate of blood plasma, it documents that the lymph proteome contains an array of proteins that differentiates it from plasma. The protein chip technology, surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS), two-dimensional gel electrophoresis (2-D PAGE) and MS, were employed to examine the protein expression profiles of ovine lymph. Using a weak cation exchange chip surface to assay lymph and plasma samples by SELDI-TOF-MS showed that the analysis of peak maps from lymph contained three protein peaks that were found only in lymph, while analysis of peak maps from plasma samples showed that five protein peaks were found only in plasma. Lymph and plasma samples showed eight peaks that were common to both. There were also more ions present in plasma than in lymph, which is consistent with the 2-D PAGE analysis. MS analysis of a large number of protein spots from 2-D PAGE gels of lymph produced MS/MS sequences for 18 proteins that were identified by searching against a comprehensive protein sequence database. As in plasma, large protein spots of albumin dominated the protein pattern in lymph. Other major proteins identified in 2-D PAGE gels of lymph included, fibrinogen alpha- and beta-chains, immunoglobulin G (IgG) heavy chain, serotransferrin precursor, lactoferrin, and apolipoprotein A-1. Two proteins that were identified and were differentially expressed in lymph were glial fibrillary astrocyte acidic protein and neutrophil cytosol factor-1. By bringing the technologies of proteomics to bear on the analysis of lymph, it is possible to detect proteins in lymph that are quantitatively and qualitatively differentially expressed from those of plasma.

Serum proteomic profiling can discriminate prostate cancer from benign prostates in men with total prostate specific antigen levels between 2.5 and 15.0 ng/ml.

PURPOSE: Artificial intelligence based pattern recognition algorithms have been developed and successfully used to analyze complex serum proteomic data streams generated by surface enhanced, laser desorption ionization time-of-flight mass spectroscopy. In the current study we used a high performance, hybrid quadrupole time-of-flight mass spectrometer to generate discriminatory serum proteomic profiles to determine if this technology could be used to determine the need for prostate biopsy in men with elevated prostate specific antigen (PSA). MATERIALS AND METHODS: Serum samples were collected from 154 men with serum PSA 2.5 to 15.0 ng/ml and/or abnormal digital rectal examination prior to transrectal ultrasound guided biopsy. Serum samples were applied to WCX2 (weak cation exchange protein chip) Protein Arrays (Ciphergen Biosystems, Fremont, California) by a Biomek 2000 robotic liquid handler (Beckman-Coulter, Chaska, Minnesota) and low molecular weight (less than 20 kDa) proteomic patterns were generated with an API QSTAR Pulsar i LC/MS/MS System (Applied Biosystems, Framingham, Massachusetts). High resolution mass spectra were analyzed with a pattern recognition bioinformatics tool, that is Proteome Quest beta version 1.0 (Correlogic Systems, Inc., Bethesda, Maryland), in an attempt to identify and discover key discriminating ion signatures. Serum samples from 63 men (2 or more negative prostate biopsies in 23, 1 negative biopsy in 10 and biopsy detected prostate cancer [CaP] in 30) were used to train the diagnostic algorithm. The remaining 91 samples, including 28 of prostate cancer and 63 of 1 or more negative biopsies, were analyzed in blinded fashion. RESULTS: The most discriminatory model was found using the WCX2 chip. Testing the remaining 91 men with this model yielded 100% sensitivity and 67% specificity. In other words, if the proteomic pattern had been used to determine the need for prostate biopsy in this cohort of men with PSA between 2.5 and 15.0 ng/ml, 67% (42 of 63) with negative biopsies would have avoided unnecessary biopsy, while no cancers would have been missed. CONCLUSIONS: Our data demonstrate that high resolution mass spectroscopy can generate serum proteomic patterns that discriminate men with elevated PSA due to benign processes from men with CaP even when PSA is within the diagnostic gray zone. We are currently expanding the testing set to determine the reliability of this new technology to decrease unnecessary prostate biopsies without compromising the detection of curable CaP.

Toxicoproteomics: serum proteomic pattern diagnostics for early detection of drug induced cardiac toxicities and cardioprotection.

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry which communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity based processes as cascades of reinforcing information percolate through the system and become reflected in changing proteomic information content of the circulation. Serum Proteomic Pattern Diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. While this approach has shown tremendous promise in early detection of cancers, detection of drug-induced toxicity may also be possible with this same technology. Analysis of serum from rat models of anthracycline and anthracenedione induced cardiotoxicity indicate the potential clinical utility of diagnostic proteomic patterns where low molecular weight peptides and protein fragments may have higher accuracy than traditional biomarkers of cardiotoxicity such as troponins. These fragments may one day be harvested by circulating nanoparticles designed to absorb, enrich and amplify the diagnostic biomarker repertoire generated even at the critical initial stages of toxicity.