Multiplex detection of disease biomarkers using SERS molecular sentinel-on-chip

Developing techniques for multiplex detection of disease biomarkers is important for clinical diagnosis. In this work, we have demonstrated for the first time the feasibility of multiplex detection of genetic disease biomarkers using the surface-enhanced Raman scattering (SERS)-based molecular sentinel-on-chip (MSC) diagnostic technology. The molecular sentinel (MS) sensing mechanism is based upon the decrease of SERS intensity when Raman labels tagged at 3'-ends of MS nanoprobes are physically displaced from the nanowave chip's surface upon DNA hybridization. The use of bimetallic layer (silver and gold) for the nanowave fabrication was investigated. SERS measurements were performed immediately following a single hybridization reaction between the target single-stranded DNA sequences and the complementary MS nanoprobes immobilized on the nanowave chip without requiring target labeling (i.e., label-free), secondary hybridization, or post-hybridization washing, thus shortening the assay time and reducing cost. Two nucleic acid transcripts, interferon alpha-inducible protein 27 and interferon-induced protein 44-like, are used as model systems for the multiplex detection concept demonstration. These two genes are well known for their critical role in host immune response to viral infection and can be used as molecular signature for viral infection diagnosis. The results indicate the potential of the MSC technology for nucleic acid biomarker multiplex detection.

Impact of delivery models on understanding genomic risk for type 2 diabetes

BACKGROUND

Genetic information, typically communicated in-person by genetic counselors, can be challenging to comprehend; delivery of this information online--as is becoming more common--has the potential of increasing these challenges.

METHODS

To address the impact of the mode of delivery of genomic risk information, 300 individuals were recruited from the general public and randomized to receive genomic risk information for type 2 diabetes mellitus in-person from a board-certified genetic counselor or online through the testing company's website.

RESULTS

Participants were asked to indicate their genomic risk and overall lifetime risk as reported on their test report as well as to interpret their genomic risk (increased, decreased, or same as population). For each question, 59% of participants correctly indicated their risk. Participants who received their results in-person were more likely than those who reviewed their results on-line to correctly interpret their genomic risk (72 vs. 47%, p = 0.0002) and report their actual genomic risk (69 vs. 49%, p = 0.002).

CONCLUSIONS

The delivery of personal genomic risk through a trained health professional resulted in significantly higher comprehension. Therefore, if the online delivery of genomic test results is to become more widespread, further evaluation of this method of communication may be needed to ensure the effective presentation of results to promote comprehension.

Quality of family history collection with use of a patient facing family history assessment tool

Background

Studies have shown that the quality of family health history (FHH) collection in primary care is inadequate to assess disease risk. To use FHH for risk assessment, collected data must have adequate detail. To address this issue, we developed a patient facing FHH assessment tool, MeTree. In this paper we report the content and quality of the FHH collected using MeTree.

Methods

Design: A hybrid implementation-effectiveness study. Patients were recruited from 2009 to 2012. Setting: Two community primary care clinics in Greensboro, NC. Participants: All non-adopted adult English speaking patients with upcoming appointments were invited to participate. Intervention: Education about and collection of FHH with entry into MeTree. Measures: We report the proportion of pedigrees that were high-quality. High-quality pedigrees are defined as having all the following criteria: (1) three generations of relatives, (2) relatives’ lineage, (3) relatives’ gender, (4) an up-to-date FHH, (5) pertinent negatives noted, (6) age of disease onset in affected relatives, and for deceased relatives, (7) the age and (8) cause of death (Prim Care31:479–495, 2004.).

Results

Enrollment: 1,184. Participant demographics: age range 18-92 (mean 58.8, SD 11.79), 56% male, and 75% white. The median pedigree size was 21 (range 8-71) and the FHH entered into MeTree resulted in a database of 27,406 individuals. FHHs collected by MeTree were found to be high quality in 99.8% (N = 1,182/1,184) as compared to <4% at baseline. An average of 1.9 relatives per pedigree (range 0-50, SD 4.14) had no data reported. For pedigrees where at least one relative has no data (N = 497/1,184), 4.97 relatives per pedigree (range 1-50, SD 5.44) had no data. Talking with family members before using MeTree significantly decreased the proportion of relatives with no data reported (4.98% if you talked to your relative vs. 10.85% if you did not, p-value < 0.001.).

Conclusion

Using MeTree improves the quantity and quality of the FHH data that is collected and talking with relatives prior to the collection of FHH significantly improves the quantity and quality of the data provided. This allows more patients to be accurately risk stratified and offered appropriate preventive care guided by their risk level.

Trial number

NCT01372553

A novel diagnostic approach may reduce inappropriate antibiotic use for acute respiratory infections

Respiratory infections can be due to a multitude of etiologies and are common throughout the world. Most are viral and self-limited, yet these infections are commonly treated with antibiotics thus contributing to the increase in resistance. Historically, infectious disease diagnostics have focused on identification of the microbial culprit at the site of infection but the specificity of host response as measured by the host transcriptome, now enables us to classify the etiology of infection agnostic to pathogen class. The ability to rapidly determine whether a similar set of symptoms is due to a virus, bacteria, or other agent from a common specimen (blood) will have far-reaching public health benefits, and further research is warranted to transfer this technology into the clinical setting.

Genomic medicine: a decade of successes, challenges, and opportunities

Genomic medicine--an aspirational term 10 years ago--is gaining momentum across the entire clinical continuum from risk assessment in healthy individuals to genome-guided treatment in patients with complex diseases. We review the latest achievements in genome research and their impact on medicine, primarily in the past decade. In most cases, genomic medicine tools remain in the realm of research, but some tools are crossing over into clinical application, where they have the potential to markedly alter the clinical care of patients. In this State of the Art Review, we highlight notable examples including the use of next-generation sequencing in cancer pharmacogenomics, in the diagnosis of rare disorders, and in the tracking of infectious disease outbreaks. We also discuss progress in dissecting the molecular basis of common diseases, the role of the host microbiome, the identification of drug response biomarkers, and the repurposing of drugs. The significant challenges of implementing genomic medicine are examined, along with the innovative solutions being sought. These challenges include the difficulty in establishing clinical validity and utility of tests, how to increase awareness and promote their uptake by clinicians, a changing regulatory and coverage landscape, the need for education, and addressing the ethical aspects of genomics for patients and society. Finally, we consider the future of genomics in medicine and offer a glimpse of the forces shaping genomic medicine, such as fundamental shifts in how we define disease, how medicine is delivered to patients, and how consumers are managing their own health and affecting change.

Health coaching and genomics-potential avenues to elicit behavior change in those at risk for chronic disease: protocol for personalized medicine effectiveness study in air force primary care

Background:

Type 2 diabetes (T2D) and coronary heart disease (CHD) are prevalent chronic diseases from which military personnel are not exempt. While many genetic markers for these diseases have been identified, the clinical utility of genetic risk testing for multifactorial diseases such as these has not been established. The need for a behavioral intervention such as health coaching following a risk counseling intervention for T2D or CHD also has not been explored. Here we present the rationale, design, and protocol for evaluating the clinical utility of genetic risk testing and health coaching for active duty US Air Force (AF) retirees and beneficiaries.

Primary Study Objectives:

Determine the direct and interactive effects of health coaching and providing genetic risk information when added to standard risk counseling for CHD and T2D on health behaviors and clinical risk markers.

Design:

Four-group (2 X 2 factorial) randomized controlled trial.

Setting:

Two AF primary care clinical settings on the west coast of the United States.

Participants:

Adult AF primary care patients.

Intervention:

All participants will have a risk counseling visit with a clinic provider to discuss personal risk factors for T2D and CHD. Half of the participants (two groups) will also learn of their genetic risk testing results for T2D and CHD in this risk counseling session. Participants randomized to the two groups receiving health coaching will then receive telephonic health coaching over 6 months.

Main Outcome Measures:

Behavioral measures (self-reported dietary intake, physical activity, smoking cessation, medication adherence); clinical outcomes (AF composite fitness scores, weight, waist circumference, blood pressure, fasting glucose, lipids, T2D/CHD risk scores) and psychosocial measures (self-efficacy, worry, perceived risk) will be collected at baseline and 6 weeks, and 3, 6, and 12 months.

Conclusion:

This study tests novel strategies deployed within existing AF primary care to increase adherence to evidence-based diet, physical activity, smoking cessation, and medication recommendations for CHD and T2D risk reduction through methods of patient engagement and self-management support.

Patient and primary care provider experience using a family health history collection, risk stratification, and clinical decision support tool: a type 2 hybrid controlled implementation-effectiveness trial

BACKGROUND

Family health history (FHH) is the single strongest predictor of disease risk and yet is significantly underutilized in primary care. We developed a patient facing FHH collection tool, MeTree, that uses risk stratification to generate clinical decision support for breast cancer, colorectal cancer, ovarian cancer, hereditary cancer syndromes, and thrombosis. Here we present data on the experience of patients and providers after integration of MeTree into 2 primary care practices.

METHODS

This was a Type 2 hybrid controlled implementation-effectiveness study in 3 community-based primary care clinics in Greensboro, NC. All non-adopted adult English speaking patients with upcoming routine appointments were invited. Patients were recruited from December 2009 to the present and followed for one year. Ease of integration of MeTree into clinical practice at the two intervention clinics was evaluated through patient surveys after their appointment and at 3 months post-visit, and physician surveys 3 months after tool integration.

RESULTS

Total enrollment =1,184. Average time to complete MeTree = 27 minutes. Patients found MeTree: easy to use (93%), easy to understand (97%), useful (98%), raised awareness of disease risk (85%), and changed how they think about their health (86%). Of the 26% (N = 311) asking for assistance to complete the tool, age (65 sd 9.4 vs. 57 sd 11.8, p-value < 0.00) and large pedigree size (24.4 sd 9.81 vs. 22.2 sd 8.30, p-value < 0.00) were the only significant factors; 77% of those requiring assistance were over the age of 60. Providers (N = 14) found MeTree: improved their practice (86%), improved their understanding of FHH (64%), made practice easier (79%), and worthy of recommending to their peers (93%).

CONCLUSIONS

Our study shows that MeTree has broad acceptance and support from both patients and providers and can be implemented without disruption to workflow.

Clinical utility of genetic risk testing in primary care: the example of Type 2 diabetes

Genetic advances in Type 2 diabetes (T2D) have led to the discovery and validation of multiple markers for this complex disease. Despite low predictive value of current T2D markers beyond clinical risk factors and family history, researchers are exploring the clinical utility and outcomes of implementation in practice, and testing is available via direct-to-consumer markets. Clinical utility research demonstrates high hypothetical utility to patients for motivating behavior change and potentially reducing risk. However, trials to date have not demonstrated improvements in behavioral and clinical outcomes over and above counseling based on traditional risk factors. Ongoing research in T2D genetics and associated risk-prediction models is necessary to refine genetic risk pathways, algorithms for risk prediction and use of this information in clinical care. Further research is also needed to explore care models and support interventions that address the needs of personalized risk information and sustainable preventive behaviors to reduce the rising prevalence of T2D.

An integrated clinico-metabolomic model improves prediction of death in sepsis

Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and β-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.

Development and validation of a primary care-based family health history and decision support program (MeTree)

INTRODUCTION

Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care.

METHODS

We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics.

RESULTS

Stakeholder feedback resulted in changes to MeTree's interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree's strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers' needs.

LIMITATIONS

The tool was validated in a small cohort.

CONCLUSION

MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines.

Collection of family health history for assessment of chronic disease risk in primary care

BACKGROUND

Family health history can predict a patient's risk for common complex diseases. This project assessed the completeness of family health history data in medical charts and evaluated the utility of these data for performing risk assessments in primary care.

METHODS

Family health history data were collected and analyzed to determine the presence of quality indicators that are necessary for effective and accurate assessment of disease risk.

RESULTS

More than 99% of the 390 paper charts analyzed contained information about family health history, which was usually scattered throughout the chart. Information on the health of the patient's parents was collected more often than information on the health of other relatives. Key information that was often not collected included age of disease onset, affected side of the family, and second-degree relatives affected. Less than 4% of patient charts included family health histories that were informative enough to accurately assess risk for common complex diseases.

LIMITATIONS

Limitations of this study include the small number of charts reviewed per provider, the fact that the sample consisted of primary care providers in a single geographic location, and the inability to assess ethnicity, consanguinity, and other indicators of the informativeness of family health history.

CONCLUSIONS

The family health histories collected in primary care are usually not complete enough to assess the patient's risk for common complex diseases. This situation could be improved with use of tools that analyze the family health history information collected and provide risk-stratified decision support recommendations for primary care.

Comparing influenza and RSV viral and disease dynamics in experimentally infected adults predicts clinical effectiveness of RSV antivirals

BACKGROUND

Antivirals reduce influenza viral replication and illness measures, particularly if initiated early, within 48 h of symptom onset. Whether experimental antivirals that reduce respiratory syncytial virus (RSV) load would also reduce disease is unknown. This study compares viral and disease dynamics in humans experimentally infected with influenza or RSV.

METHODS

Clinical strains of RSV-A and influenza A were inoculated intranasally into 20 and 17 healthy volunteers, respectively, on day 0. Symptom scores and nasal washes were performed twice daily, and daily mucus weights were collected. Viral loads in nasal washes were quantified by culture (plaque assay in HEp-2 cells for RSV and by end point dilution in Madin-Darby canine kidney cells for influenza).

RESULTS

After influenza inoculation, influenza viral load and illness markers increased simultaneously until day 2. Within individual subjects, peak influenza load occurred 0.4 days (95% CI -0.4, 1.3) before peak symptoms. Influenza viral load and disease declined thereafter. After RSV inoculation, a longer incubation period occurred prior to viral detection and symptom onset. RSV load and disease increased together until day 5. Within individual subjects, peak RSV loads occurred 0.2 days (95% CI -0.7, 1.05) before peak symptoms, after which both illness measures and viral load declined together.

CONCLUSIONS

Viral and disease dynamics in experimental human infections suggest that reducing RSV load, if timed similarly to clinically-effective influenza antivirals, might be expected to have a similar or greater window of opportunity for reducing clinical RSV disease.

Comparative effectiveness research in cancer genomics and precision medicine: current landscape and future prospects

A major promise of genomic research is information that can transform health care and public health through earlier diagnosis, more effective prevention and treatment of disease, and avoidance of drug side effects. Although there is interest in the early adoption of emerging genomic applications in cancer prevention and treatment, there are substantial evidence gaps that are further compounded by the difficulties of designing adequately powered studies to generate this evidence, thus limiting the uptake of these tools into clinical practice. Comparative effectiveness research (CER) is intended to generate evidence on the "real-world" effectiveness compared with existing standards of care so informed decisions can be made to improve health care. Capitalizing on funding opportunities from the American Recovery and Reinvestment Act of 2009, the National Cancer Institute funded seven research teams to conduct CER in genomic and precision medicine and sponsored a workshop on CER on May 30, 2012, in Bethesda, Maryland. This report highlights research findings from those research teams, challenges to conducting CER, the barriers to implementation in clinical practice, and research priorities and opportunities in CER in genomic and precision medicine. Workshop participants strongly emphasized the need for conducting CER for promising molecularly targeted therapies, developing and supporting an integrated clinical network for open-access resources, supporting bioinformatics and computer science research, providing training and education programs in CER, and conducting research in economic and decision modeling.

The genomic medicine model: an integrated approach to implementation of family health history in primary care

As an essential tool for risk stratification, family health history (FHH) is a central component of personalized medicine; yet, despite its widespread acceptance among professional societies and its established place in the medical interview, its widespread adoption is hindered by three major barriers: quality of FHH collection, risk stratification capabilities and interpretation of risk stratification for clinical care. To overcome these barriers and bring FHH to the forefront of the personalized medicine effort, we developed the genomic medicine model (GMM) for primary care. The GMM, founded upon the principles of the Health Belief Model, Adult Learning Theory and the implementation sciences, shifts responsibility for FHH onto the patient, uses information technology (MeTree©) for risk stratification and interpretation, and provides education across multiple levels for each stakeholder, freeing up the clinical encounter for discussion around personalized preventive healthcare plans. The GMM has been implemented and optimized as part of an implementation-effectiveness hybrid pilot study for breast/ovarian cancer, colon cancer and thrombosis, and risk for hereditary cancer syndromes in two primary care clinics in NC, USA. This paper describes the conceptual development of the model and key findings relevant for broader uptake and sustainability in the primary care community.

Public knowledge of and attitudes toward genetics and genetic testing

BACKGROUND

Variable health literacy and genetic knowledge may pose significant challenges to engaging the general public in personal genomics, specifically with respect to promoting risk comprehension and healthy behaviors.

METHODS

We are conducting a multistage study of individual responses to genomic risk information for Type 2 diabetes mellitus. A total of 300 individuals were recruited from the general public in Durham, North Carolina: 60% self-identified as White; 70% female; and 65% have a college degree. As part of the baseline survey, we assessed genetic knowledge and attitudes toward genetic testing.

RESULTS

Scores of factual knowledge of genetics ranged from 50% to 100% (average=84%), with significant differences in relation to racial groups, the education level, and age. Scores were significantly higher on questions pertaining to the inheritance and causes of disease (mean score 90%) compared to scientific questions (mean score 77.4%). Scores on the knowledge survey were significantly higher than scores from European populations. Participants' perceived knowledge of the social consequences of genetic testing was significantly lower than their perceived knowledge of the medical uses of testing. More than half agreed with the statement that testing may affect a person's ability to obtain health insurance (51.3%) and 16% were worried about the consequences of testing for chances of finding a job.

CONCLUSIONS

Despite the relatively high educational status and genetic knowledge of the study population, we find an imbalance of knowledge between scientific and medical concepts related to genetics as well as between the medical applications and societal consequences of testing, suggesting that more effort is needed to present the benefits, risks, and limitations of genetic testing, particularly, at the social and personal levels, to ensure informed decision making.

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background

This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters.

Results

Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here.

Conclusions

The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor.

Transforming epidemiology for 21st century medicine and public health

In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving toward more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical, and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating "big data" science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy, and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology, in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.

Personalized medicine in women's obesity prevention and treatment: implications for research, policy and practice

The prevalence of obesity in America has reached epidemic proportions, and obesity among women is particularly concerning. Severe obesity (body mass index ≥35 kg m(-2) ) is more prevalent in women than men. Further, women have sex-specific risk factors that must be considered when developing preventive and therapeutic interventions. This review presents personalized medicine as a dynamic approach to obesity prevention, management and treatment for women. First, we review obesity as a complex health issue, with contributing sex-specific, demographic, psychosocial, behavioural, environmental, epigenetic and genetic/genomic risk factors. Second, we present personalized medicine as a rapidly advancing field of health care that seeks to quantify these complex risk factors to develop more targeted and effective strategies that can improve disease management and/or better minimize an individual's likelihood of developing obesity. Third, we discuss how personalized medicine can be applied in a clinical setting with current and emerging tools, including health risk assessments, personalized health plans, and strategies for increasing patient engagement. Finally, we discuss the need for additional research, training and policy that can enhance the practice of personalized medicine in women's obesity, including further advancements in the '-omics' sciences, physician training in personalized medicine, and additional development and standardization of innovative targeted therapies and clinical tools.

Implementing genomic medicine in the clinic: the future is here

Although the potential for genomics to contribute to clinical care has long been anticipated, the pace of defining the risks and benefits of incorporating genomic findings into medical practice has been relatively slow. Several institutions have recently begun genomic medicine programs, encountering many of the same obstacles and developing the same solutions, often independently. Recognizing that successful early experiences can inform subsequent efforts, the National Human Genome Research Institute brought together a number of these groups to describe their ongoing projects and challenges, identify common infrastructure and research needs, and outline an implementation framework for investigating and introducing similar programs elsewhere. Chief among the challenges were limited evidence and consensus on which genomic variants were medically relevant; lack of reimbursement for genomically driven interventions; and burden to patients and clinicians of assaying, reporting, intervening, and following up genomic findings. Key infrastructure needs included an openly accessible knowledge base capturing sequence variants and their phenotypic associations and a framework for defining and cataloging clinically actionable variants. Multiple institutions are actively engaged in using genomic information in clinical care. Much of this work is being done in isolation and would benefit from more structured collaboration and sharing of best practices.

Genet Med 2013:15(4):258–267

A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2

There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent.

Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

Abstract P3-06-07: Ki67 as a Predictive Marker of Response to Neoadjuvant Chemotherapy in Patients with Early-Stage Breast Cancer (ESBC): A Systematic Review and Evidence Summary

Background: Immunohistochemical (IHC) assessment of the proportion of cells staining for the KI67 nuclear antigen is being increasing utilized in the management of patients with early-stage breast cancer (ESBC). A comprehensive systematic review and evidence synthesis of biomarkers potentially predictive of response to systemic therapy was initiated as a part of an NCI-funded comparative effectiveness research program.

Methods: Studies of chemotherapy response prediction based on baseline IHC assessment of Ki67 in patients with ESBC receiving neoadjuvant systemic therapy were identified. Response was specified as pathologic complete response (pCR) or clinical response (ClinR). Assay predictive performance for response was assessed on the basis of sensitivity, specificity, predictive value and predictive odds ratio (POR±95%CLs) utilizing mixed effects models. Study results were fitted in an ROC analysis based on the method of DerSimonian and Laird. Publication bias was evaluated on the basis of funnel plot asymmetry assessed by Egger's regression intercept and Begg and Mazumdar's rank correlation.

Results: Of 469 potentially eligible studies, dual blind full text review identified 42 eligible studies reporting 44 independent cohorts with 6,716 patients (21–979). While Ki67 cutpoints varied considerably, they were most commonly between 10%–30% (median 20%, range 1–50%). The analysis prsented here is limited to the 30 studies of ESBC patients (N = 3,343) receiving neoadjuvant therapy of which 14 reported fewer than 100 patients. The proportion of patients with elevated Ki67 across studies ranged from 0.20–0.92 (median = 0.54). Sensitivity and specificity for treatment response in patients with high vs. low baseline Ki67 was 0.65 [0.61, 0.68] and 0.52 [0.50, 0.54], respectively. Estimated response rates across studies in patients with high vs. low Ki67 were 31% [29%, 34%] and 19% [17%, 21%], respectively. The estimated POR for response across studies was 2.82 [2.14, 3.72; P &lt; .001].

The host response to infection: advancing a novel diagnostic paradigm

Capturing the host response by using genomic technologies such as transcriptional profiling provides a new paradigm for classifying and diagnosing infectious disease and for potentially distinguishing infection from other causes of serious respiratory illness. This strategy has been used to define a blood-based RNA signature as a classifier for pandemic H1N1 influenza infection that is distinct from bacterial pneumonia and other inflammatory causes of respiratory disease. To realize the full potential of this approach as a diagnostic test will require additional independent validation of the results and studies to examine the specificity of this signature for viral versus bacterial infection or co-infection.

Comparative effectiveness research, genomics-enabled personalized medicine, and rapid learning health care: a common bond

Despite stunning advances in our understanding of the genetics and the molecular basis for cancer, many patients with cancer are not yet receiving therapy tailored specifically to their tumor biology. The translation of these advances into clinical practice has been hindered, in part, by the lack of evidence for biomarkers supporting the personalized medicine approach. Most stakeholders agree that the translation of biomarkers into clinical care requires evidence of clinical utility. The highest level of evidence comes from randomized controlled clinical trials (RCTs). However, in many instances, there may be no RCTs that are feasible for assessing the clinical utility of potentially valuable genomic biomarkers. In the absence of RCTs, evidence generation will require well-designed cohort studies for comparative effectiveness research (CER) that link detailed clinical information to tumor biology and genomic data. CER also uses systematic reviews, evidence-quality appraisal, and health outcomes research to provide a methodologic framework for assessing biologic patient subgroups. Rapid learning health care (RLHC) is a model in which diverse data are made available, ideally in a robust and real-time fashion, potentially facilitating CER and personalized medicine. Nonetheless, to realize the full potential of personalized care using RLHC requires advances in CER and biostatistics methodology and the development of interoperable informatics systems, which has been recognized by the National Cancer Institute's program for CER and personalized medicine. The integration of CER methodology and genomics linked to RLHC should enhance, expedite, and expand the evidence generation required for fully realizing personalized cancer care.

The Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study Community Registry and Biorepository

Current understanding of chronic diseases is based on crude clinical characterization, imaging studies, and laboratory testing that has evolved over decades. The Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study is a multi-tiered, longitudinal study designed to enable classification of chronic diseases using clinically annotated biospecimen collections, -omic technologies, electronic health records, and standard epidemiological methods. We expect that detailed molecular classification will improve mechanistic understanding of chronic diseases, augmenting discovery and testing of new treatments, and allowing refined selection of prevention and treatment strategies. The MURDOCK Study Community Registry and Biorepository will serve as a bridge for validation of initial exploratory studies, a platform for future prospective studies in targeted populations, and a resource of both data (analytical and clinical) and samples for cross-registry meta-analyses and comparative population studies. Participation of local health care providers and the Cabarrus County/Kannapolis, NC, community will facilitate future medical research and provide the opportunity to educate and inform the public about genomic research, actively engaging them in shaping the future of medical discovery and treatment of chronic diseases. We present the rationale and study design for the MURDOCK Community Registry and Biorepository and baseline characteristics of the first 6000 participants.

Primary care physicians' knowledge of and experience with pharmacogenetic testing

It is anticipated that as the range of drugs for which pharmacogenetic testing becomes available expands, primary care physicians (PCPs) will become major users of these tests. To assess their training, familiarity, and attitudes toward pharmacogenetic testing in order to identify barriers to uptake that may be addressed at this early stage of test use, we conducted a national survey of a sample of PCPs. Respondents were mostly white (79%), based primarily in community-based primary care (81%) and almost evenly divided between family medicine and internal medicine. The majority of respondents had heard of PGx testing and anticipated that these tests are or would soon become a valuable tool to inform drug response. However, only a minority of respondents (13%) indicated they felt comfortable ordering PGx tests and almost a quarter reported not having any education about pharmacogenetics. Our results indicate that primary care practitioners envision a major role for themselves in the delivery of PGx testing but recognize their lack of adequate knowledge and experience about these tests. Development of effective tools for guiding PCPs in the use of PGx tests should be a high priority.

The MURDOCK Study: a long-term initiative for disease reclassification through advanced biomarker discovery and integration with electronic health records

BACKGROUND

Facing critically low return per dollar invested on clinical research and clinical care, the American biomedical enterprise is in need of a significant transformation. A confluence of high-throughput "omic" technologies and increasing adoption of the electronic health record has fueled excitement for a new paradigm for biomedical research and practice. The ability to simultaneously measure thousands of molecular variables and assess their relationships with clinical data collected during the course of care could enable reclassification of disease not only by gross phenotypic observation but according to underlying molecular mechanism and influence of social determinants.In turn, this reclassification could enable development of targeted therapeutic interventions as well as disease prevention strategies at the individual and population levels.

METHODS/DESIGN

The MURDOCK Study consists of distinct project "horizons" or stages. Horizon 1 entailed the generation and analysis of molecular data for existing large,clinically well-annotated cohorts in four disease areas. Horizon 1.5 involves creating and maintaining a 50,000-person,community volunteer registry for biomarker signature validation and prospective studies, including integration of environmental and social data. Horizon 2 leverages and prospectively recruits Horizon 1.5 volunteers, and extends the study to additional disease areas of interest. Horizon 3 will expand the study through regional, national,and international partnerships.

DISCUSSION

The MURDOCK Study embodies a new model of team science investigation and represents a significant resource for translational research. The study team invites inquiries to form new collaborations to exploit the rich resources provided by these biospecimens and associated study data.

Conference Scene: Is personalized medicine ready for prime time?

This article provides a meeting report from the Duke Center for Personalized Medicine 2012 Symposium, which took place in Durham, NC, USA, on 29 March 2012. The event titled ‘At the Interface of Clinical Research and Clinical Medicine’, focused on many of the issues that arise as personalized medicine becomes integrated into clinical care. In particular, we summarize presentations on various topics: the future of genomic medicine, opportunities in pharmacogenomics and genetic testing; challenges in the clinical implementation of personalized medicine; systems medicine and biomedical informatics; the policy and education strategies for adopting personalized medicine; the common bond between comparative effectiveness and personalized medicine; and the value of personalized medicine.

Academic medical centers: ripe for rapid-learning personalized health care

In an attempt to reduce the lengthy process of translating scientific findings into clinical practice, the United States and several European governments are making substantial investments in health information technology, comparative effectiveness research, and increased access to quality health care. New technologies--genomics in particular--are expected to usher in more cost-effective personalized health care. Academic medical centers can play a central role in this transformation through the development of rapid learning environments, evidence generation, implementation research, and education of health professionals and the public.

Effect of genetic testing for risk of type 2 diabetes mellitus on health behaviors and outcomes: study rationale, development and design

Abstract

Trial Registration

ClinicalTrials.gov: NCT00849563

P3-14-04: Assessment of Genomic Prognostic Signatures as Predictors of Response to Neoadjuvant Chemotherapy in Patients with Early Stage Breast Cancer

Background: Based on results from randomized clinical trials, adjuvant and neoadjuvant chemotherapy (NCT) strategies in early stage breast cancer patients (ESBC) achieve comparable long term results. Recently, a number of genomic signatures have been reported, distinguishing patients with low versus high risk of recurrence. While developed primarily as prognostic assays, these classifiers have also been proposed to be predictive of benefit from systemic chemotherapy. Neoadjuvant studies provide an opportunity to evaluate their predictive value for response to NCT.

Methods: A systematic review of gene expression profile studies in ESBC patients receiving chemotherapy was conducted. Medline search of original research articles of human studies published between January 2000 and February 2011 was based on key words and MeSH heading terms. Publications presenting outcomes for chemotherapy treated patients in groups stratified by multi-gene array signatures and utilizing a new independent cohort of patients compared to the original development cohort were selected. Information from eligible studies was extracted by dual abstraction. Reported results were synthesized into combined diagnostic odds ratio (DOR) using method of Mantel-Haenszel. This analysis is restricted to neoadjuvant studies investigating the association of genomic signature prognostic categories with objective tumor response to chemotherapy. Results: A total of 42 articles were eligible for data abstraction. Out of these, 6 publications evaluated response to NCT in good (low risk of recurrence) versus poor prognosis groups based on genomic prediction. Since two of the studies analyzed the same signature on a cohort with large overlap, only 5 studies were included in the final analysis, accounting for n=918 patients. Response consisted of pathologic complete response (pCR) in 3 studies, pCR or minimal residual disease (1 study), and clinical complete response (1 study). Prognostic genomic assays included Oncotype DX (1), MammaPrint (1), Genomic Grade Index (2) and PAM50 Risk of Relapse Score (1). Eight different chemotherapy regimens were utilized. The most common drugs were cyclophosphamide, anthracyclines, taxanes, and 5-fluorouracil. Across all genomic signatures, good prognosis patients, as defined by gene expression data, demonstrated consistently low rates of response to chemotherapy (median 3%, range 0–12%) compared to patients with less favorable prognosis (median 32%, range 19–43%). Odds ratio for response in poor versus good prognosis patients ranged from 3.9 to 21.7 with combined DOR= 6.6 (95% CI 3.9−11.3, P&lt;0.0001). No heterogeneity was determined across studies (P=0.4). The C-statistic estimating assay discriminatory ability was reported in 3 studies ranged from 0.72 to 0.78.

Conclusions: Across all genomic prognostic signatures reported, only a very small proportion of patients with signature predicted good prognosis achieved complete response to NCT. This suggests low sensitivity to chemotherapy among good prognosis patients, as determined by the prognostic genomic signatures. This further confirms the association between poor prognosis tumors and higher responsiveness to chemotherapy.

Funding: NCI: UC2CA14041-01

Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-14-04.

Predicting Viral Infection From High-Dimensional Biomarker Trajectories

There is often interest in predicting an individual's latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dynamically starting at the latent initiation time of infection. Using a nonparametric cure rate model for the latent initiation times, we allow selection of the genes in the viral response pathway, variability among individuals in infection times, and a subset of individuals who are not infected. As we demonstrate using held-out data, this statistical framework allows accurate predictions of infected individuals in advance of the development of clinical symptoms, without labeled data and even when the number of biomarkers vastly exceeds the number of individuals under study. Biological interpretation of several of the inferred pathways (factors) is provided.

A hub for bench-to-bedside pharmacogenomic-based research

In 2003 Duke University (Durham, NC, USA) launched the Institute for Genome Sciences & Policy (IGSP) as an interdisciplinary network of centers comprised of scientists, engineers and physicians, as well as experts in law, business, economics public policy and ethics. Within this environment, the IGSP and its Center for Genomic Medicine form the hub for pharmacogenomic research discovery initiatives through collaborations with other scientific and clinical units at the Duke University Medical Center. The Center for Genomic Medicine specifically focuses on developing strategies for translating and implementing pharmacogenomic discoveries into the clinical arena; therefore, by harnessing the resources of the IGSP as well as other complementary centers on campus, Duke University is poised to accelerate the development of novel pharmacgenomic paradigms for the prevention and treatment of disease. These new treatment paradigms can, potentially, ensure that the right dose of the right drug is prescribed to the right individual - an often stated goal of personalized medicine and pharmacogenomics.

Personalized medicine: progress and promise

Personalized medicine is a broad and rapidly advancing field of health care that is informed by each person's unique clinical, genetic, genomic, and environmental information. Personalized medicine depends on multidisciplinary health care teams and integrated technologies (e.g., clinical decision support) to utilize our molecular understanding of disease in order to optimize preventive health care strategies. Human genome information now allows providers to create optimized care plans at every stage of a disease, shifting the focus from reactive to preventive health care. The further integration of personalized medicine into the clinical workflow requires overcoming several barriers in education, accessibility, regulation, and reimbursement. This review focuses on providing a comprehensive understanding of personalized medicine, from scientific discovery at the laboratory bench to integration of these novel ways of understanding human biology at the bedside.

H3N2 influenza infection elicits more cross-reactive and less clonally expanded anti-hemagglutinin antibodies than influenza vaccination

Background

During the recent H1N1 influenza pandemic, excess morbidity and mortality was seen in young but not older adults suggesting that prior infection with influenza strains may have protected older subjects. In contrast, a history of recent seasonal trivalent vaccine in younger adults was not associated with protection.

Methods and Findings

To study hemagglutinin (HA) antibody responses in influenza immunization and infection, we have studied the day 7 plasma cell repertoires of subjects immunized with seasonal trivalent inactivated influenza vaccine (TIV) and compared them to the plasma cell repertoires of subjects experimentally infected (EI) with influenza H3N2 A/Wisconsin/67/2005. The majority of circulating plasma cells after TIV produced influenza-specific antibodies, while most plasma cells after EI produced antibodies that did not react with influenza HA. While anti-HA antibodies from TIV subjects were primarily reactive with single or few HA strains, anti-HA antibodies from EI subjects were isolated that reacted with multiple HA strains. Plasma cell-derived anti-HA antibodies from TIV subjects showed more evidence of clonal expansion compared with antibodies from EI subjects. From an H3N2-infected subject, we isolated a 4-member clonal lineage of broadly cross-reactive antibodies that bound to multiple HA subtypes and neutralized both H1N1 and H3N2 viruses. This broad reactivity was not detected in post-infection plasma suggesting this broadly reactive clonal lineage was not immunodominant in this subject.

Conclusion

The presence of broadly reactive subdominant antibody responses in some EI subjects suggests that improved vaccine designs that make broadly reactive antibody responses immunodominant could protect against novel influenza strains.

Consideration of patient preferences and challenges in storage and access of pharmacogenetic test results

PURPOSE

Pharmacogenetic testing is one of the primary drivers of personalized medicine. The use of pharmacogenetic testing may provide a lifetime of benefits through tailoring drug dosing and selection of multiple medications to improve therapeutic outcomes and reduce adverse responses. We aimed to assess public interest and concerns regarding sharing and storage of pharmacogenetic test results that would facilitate the reuse of pharmacogenetic data across a lifetime of care.

METHODS

We conducted a random-digit-dial phone survey of a sample of the US public.

RESULTS

We achieved an overall response rate of 42% (n = 1139). Most respondents indicated that they were extremely or somewhat comfortable allowing their pharmacogenetic test results to be shared with other doctors involved in their care management (90% ± 2.18%); significantly fewer respondents (74% ± 3.27%) indicated that they were extremely or somewhat comfortable sharing results with their pharmacist (P < 0.0001).

CONCLUSION

Patients, pharmacists, and physicians will all be critical players in the pharmacotherapy process. Patients are supportive of sharing pharmacogenetic test results with physicians and pharmacists and personally maintaining their test results. However, further study is needed to understand which options are needed for sharing, appropriate storage, and patient education about the relevance of pharmacogenetic test results to promote consideration of this information by other prescribing practitioners.

The oral microbiome in health and disease and the potential impact on personalized dental medicine

Every human body contains a personalized microbiome that is essential to maintaining health but capable of eliciting disease. The oral microbiome is particularly imperative to health because it can cause both oral and systemic disease. The oral microbiome rests within biofilms throughout the oral cavity, forming an ecosystem that maintains health when in equilibrium. However, certain ecological shifts in the microbiome allow pathogens to manifest and cause disease. Severe forms of oral disease may result in systemic disease at different body sites. Microbiomics and metagenomics are two fields of research that have emerged to identify the presence of specific microbes in the body and understand the nature of the microbiome activity during both health and disease. The analysis of the microbiome and its genomes will pave the way for more effective therapeutic and diagnostic techniques and, ultimately, contribute to the development of personalized medicine and personalized dental medicine.

Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection

Exposure to influenza viruses is necessary, but not sufficient, for healthy human hosts to develop symptomatic illness. The host response is an important determinant of disease progression. In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection, we inoculated 17 healthy adults with live influenza (H3N2/Wisconsin) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours. Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections. We show that symptomatic hosts invoke, simultaneously, multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress. In contrast, asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses. We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection. Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature, suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza.

The transcriptional responses of human hosts towards influenza viral pathogens are important for understanding virus-mediated immunopathology. Despite great advances gained through studies using model organisms, the complete temporal host transcriptional responses in a natural human system are poorly understood. In a human challenge study using live influenza (H3N2/Wisconsin) viruses, we conducted a clinically uninformed (unsupervised) factor analysis on gene expression profiles and established an ab initio molecular signature that strongly correlates to symptomatic clinical disease. This is followed by the identification of 42 biomarkers whose expression patterns best differentiate early from late phases of infection. In parallel, a clinically informed (supervised) analysis revealed over-stimulation of multiple viral sensing pathways in symptomatic hosts and linked their temporal trajectory with development of diverse clinical signs and symptoms. The resultant inflammatory cytokine profiles were shown to contribute to the pathogenesis because their significant increase preceded disease manifestation by 36 hours. In subclinical asymptomatic hosts, we discovered strong transcriptional regulation of genes involved in inflammasome activation, genes encoding virus interacting proteins, and evidence of active anti-oxidant and cell-mediated innate immune response. Taken together, our findings offer insights into influenza virus-induced pathogenesis and provide a valuable tool for disease monitoring and management in natural environments.

Whole blood gene expression analyses in patients with single versus recurrent venous thromboembolism

INTRODUCTION

Venous thromboembolism may recur in up to 30% of patients with a spontaneous venous thromboembolism after a standard course of anticoagulation. Identification of patients at risk for recurrent venous thromboembolism would facilitate decisions concerning the duration of anticoagulant therapy.

OBJECTIVES

In this exploratory study, we investigated whether whole blood gene expression data could distinguish subjects with single venous thromboembolism from subjects with recurrent venous thromboembolism.

METHODS

40 adults with venous thromboembolism (23 with single event and 17 with recurrent events) on warfarin were recruited. Individuals with antiphospholipid syndrome or cancer were excluded. Plasma and serum samples were collected for biomarker testing, and PAXgene tubes were used to collect whole blood RNA samples.

RESULTS

D-dimer levels were significantly higher in patients with recurrent venous thromboembolism, but P-selectin and thrombin-antithrombin complex levels were similar in the two groups. Comparison of gene expression data from the two groups provided us with a 50 gene probe model that distinguished these two groups with good receiver operating curve characteristics (AUC 0.75). This model includes genes involved in mRNA splicing and platelet aggregation. Pathway analysis between subjects with single and recurrent venous thromboembolism revealed that the Akt pathway was up-regulated in the recurrent venous thromboembolism group compared to the single venous thromboembolism group.

CONCLUSIONS

In this exploratory study, gene expression profiles of whole blood appear to be a useful strategy to distinguish subjects with single venous thromboembolism from those with recurrent venous thromboembolism. Prospective studies with additional patients are needed to validate these results.

Discovery of biomarker candidates for coronary artery disease from an APOE-knock out mouse model using iTRAQ-based multiplex quantitative proteomics

Due to the lack of precise markers indicative of its occurrence and progression, coronary artery disease (CAD), the most common type of heart diseases, is currently associated with high mortality in the United States. To systemically identify novel protein biomarkers associated with CAD progression for early diagnosis and possible therapeutic intervention, we employed an iTRAQ-based quantitative proteomic approach to analyze the proteome changes in the plasma collected from a pair of wild-type versus apolipoprotein E knockout (APOE(-/-) ) mice which were fed with a high fat diet. In a multiplex manner, iTRAQ serves as the quantitative 'in-spectra' marker for 'cross-sample' comparisons to determine the differentially expressed/secreted proteins caused by APOE knock-out. To obtain the most comprehensive proteomic data sets from this CAD-associated mouse model, we applied both MALDI and ESI-based mass spectrometric (MS) platforms coupled with two different schemes of multidimensional liquid chromatography (2-D LC) separation. We then comparatively analyzed a series of the plasma samples collected at 6 and 12 wk of age after the mice were fed with fat diets, where the 6- or 12-wk time point represents the early or intermediate phase of the fat-induced CAD, respectively. We then categorized those proteins showing abundance changes in accordance with APOE depletion. Several proteins such as the γ and β chains of fibrinogen, apolipoprotein B, apolipoprotein C-I, and thrombospondin-4 were among the previously known CAD markers identified by other methods. Our results suggested that these unbiased proteomic methods are both feasible and a practical means of discovering potential biomarkers associated with CAD progression.