High predictive accuracy of an unbiased proteomic profile for sustained virologic response in chronic hepatitis C patients

Chronic hepatitis C (CHC) infection is a leading cause of endstage liver disease. Current standard-of-care (SOC) interferon-based therapy results in sustained virological response (SVR) in only one-half of patients, and is associated with significant side effects. Accurate host predictors of virologic response are needed to individualize treatment regimens. We applied a label-free liquid chromatography mass spectrometry (LC-MS)-based proteomics discovery platform to pretreatment sera from a well-characterized and matched training cohort of 55 CHC patients, and an independent validation set of 41 CHC genotype 1 patients with characterized IL28B genotype. Accurate mass and retention time methods aligned samples to generate quantitative peptide data, with predictive modeling using Bayesian sparse latent factor regression. We identified 105 proteins of interest with two or more peptides, and a total of 3,768 peptides. Regression modeling selected three identified metaproteins, vitamin D binding protein, alpha 2 HS glycoprotein, and Complement C5, with a high predictive area under the receiver operator characteristic curve (AUROC) of 0.90 for SVR in the training cohort. A model averaging approach for identified peptides resulted in an AUROC of 0.86 in the validation cohort, and correctly identified virologic response in 71% of patients without the favorable IL28B "responder" genotype.

CONCLUSION

Our preliminary data indicate that a serum-based protein signature can accurately predict treatment response to current SOC in most CHC patients.

Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients

BACKGROUND

Alterations in gene expression in peripheral blood cells have been shown to be sensitive to the presence and extent of coronary artery disease (CAD). A non-invasive blood test that could reliably assess obstructive CAD likelihood would have diagnostic utility.

RESULTS

Microarray analysis of RNA samples from a 195 patient Duke CATHGEN registry case:control cohort yielded 2,438 genes with significant CAD association (p < 0.05), and identified the clinical/demographic factors with the largest effects on gene expression as age, sex, and diabetic status. RT-PCR analysis of 88 CAD classifier genes confirmed that diabetic status was the largest clinical factor affecting CAD associated gene expression changes. A second microarray cohort analysis limited to non-diabetics from the multi-center PREDICT study (198 patients; 99 case: control pairs matched for age and sex) evaluated gene expression, clinical, and cell population predictors of CAD and yielded 5,935 CAD genes (p < 0.05) with an intersection of 655 genes with the CATHGEN results. Biological pathway (gene ontology and literature) and statistical analyses (hierarchical clustering and logistic regression) were used in combination to select 113 genes for RT-PCR analysis including CAD classifiers, cell-type specific markers, and normalization genes.RT-PCR analysis of these 113 genes in a PREDICT cohort of 640 non-diabetic subject samples was used for algorithm development. Gene expression correlations identified clusters of CAD classifier genes which were reduced to meta-genes using LASSO. The final classifier for assessment of obstructive CAD was derived by Ridge Regression and contained sex-specific age functions and 6 meta-gene terms, comprising 23 genes. This algorithm showed a cross-validated estimated AUC = 0.77 (95% CI 0.73-0.81) in ROC analysis.

CONCLUSIONS

We have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography.

CLINICAL TRIAL REGISTRATION INFORMATION

PREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov, NCT00500617.

Retraction: A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med 2006;355:570-80

To the Editor: We would like to retract our article, "A Genomic Strategy to Refine Prognosis in Early-Stage Non-Small-Cell Lung Cancer,"(1) which was published in the Journal on August 10, 2006. Using a sample set from a study by the American College of Surgeons Oncology Group (ACOSOG) and a collection of samples from a study by the Cancer and Leukemia Group B (CALGB), we have tried and failed to reproduce results supporting the validation of the lung metagene model described in the article. We deeply regret the effect of this action on the work of other investigators.

Cancer pharmacogenomics and pharmacoepidemiology: setting a research agenda to accelerate translation

Recent advances in genomic research have demonstrated a substantial role for genomic factors in predicting response to cancer therapies. Researchers in the fields of cancer pharmacogenomics and pharmacoepidemiology seek to understand why individuals respond differently to drug therapy, in terms of both adverse effects and treatment efficacy. To identify research priorities as well as the resources and infrastructure needed to advance these fields, the National Cancer Institute (NCI) sponsored a workshop titled "Cancer Pharmacogenomics: Setting a Research Agenda to Accelerate Translation" on July 21, 2009, in Bethesda, MD. In this commentary, we summarize and discuss five science-based recommendations and four infrastructure-based recommendations that were identified as a result of discussions held during this workshop. Key recommendations include 1) supporting the routine collection of germline and tumor biospecimens in NCI-sponsored clinical trials and in some observational and population-based studies; 2) incorporating pharmacogenomic markers into clinical trials; 3) addressing the ethical, legal, social, and biospecimen- and data-sharing implications of pharmacogenomic and pharmacoepidemiologic research; and 4) establishing partnerships across NCI, with other federal agencies, and with industry. Together, these recommendations will facilitate the discovery and validation of clinical, sociodemographic, lifestyle, and genomic markers related to cancer treatment response and adverse events, and they will improve both the speed and efficiency by which new pharmacogenomic and pharmacoepidemiologic information is translated into clinical practice.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

Background

Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis.

Results

Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches.

Conclusions

Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Blood gene expression signatures predict invasive candidiasis

Candidemia is the fourth most common bloodstream infection, with Candida albicans being the most common causative species. Success in reducing the associated morbidity and mortality has been limited by the inadequacy and time delay of currently available diagnostic modalities. Focusing on host response to infection, we used a murine model to develop a blood gene expression signature that accurately classified mice with candidemia and distinguished candidemia from Staphylococcus aureus bacteremia. Validation of the signature was achieved in an independent cohort of mice. Genes represented in the signature have known associations with host defense against Candida and other microorganisms. Our results demonstrate a temporal pattern of host molecular responses that distinguish candidemia from S. aureus-induced bacteremia and establish a novel paradigm for infectious disease diagnosis.

Detection of viruses via statistical gene expression analysis

We develop a new bayesian construction of the elastic net (ENet), with variational bayesian analysis. This modeling framework is motivated by analysis of gene expression data for viruses, with a focus on H3N2 and H1N1 influenza, as well as Rhino virus and RSV (respiratory syncytial virus). Our objective is to understand the biological pathways responsible for the host response to such viruses, with the ultimate objective of developing a clinical test to distinguish subjects infected by such viruses from subjects with other symptom causes (e.g., bacteria). In addition to analyzing these new datasets, we provide a detailed analysis of the bayesian ENet and compare it to related models.

Genome-wide association study of Lp-PLA(2) activity and mass in the Framingham Heart Study

Lipoprotein-associated phospholipase A₂ (Lp-PLA₂) is an emerging risk factor and therapeutic target for cardiovascular disease. The activity and mass of this enzyme are heritable traits, but major genetic determinants have not been explored in a systematic, genome-wide fashion. We carried out a genome-wide association study of Lp-PLA₂ activity and mass in 6,668 Caucasian subjects from the population-based Framingham Heart Study. Clinical data and genotypes from the Affymetrix 550K SNP array were obtained from the open-access Framingham SHARe project. Each polymorphism that passed quality control was tested for associations with Lp-PLA₂ activity and mass using linear mixed models implemented in the R statistical package, accounting for familial correlations, and controlling for age, sex, smoking, lipid-lowering-medication use, and cohort. For Lp-PLA₂ activity, polymorphisms at four independent loci reached genome-wide significance, including the APOE/APOC1 region on chromosome 19 (p = 6×10⁻²⁴); CELSR2/PSRC1 on chromosome 1 (p = 3×10⁻¹⁵); SCARB1 on chromosome 12 (p = 1×10⁻⁸) and ZNF259/BUD13 in the APOA5/APOA1 gene region on chromosome 11 (p = 4×10⁻⁸). All of these remained significant after accounting for associations with LDL cholesterol, HDL cholesterol, or triglycerides. For Lp-PLA₂ mass, 12 SNPs achieved genome-wide significance, all clustering in a region on chromosome 6p12.3 near the PLA2G7 gene. Our analyses demonstrate that genetic polymorphisms may contribute to inter-individual variation in Lp-PLA₂ activity and mass.

Blood levels of lipoprotein-associated phospholipase A₂ (Lp-PLA₂) show a strong association with atherosclerosis in humans. This enzyme is made by certain cells of the immune system, associates with lipoproteins (HDL and LDL), and is thought to be involved in inflammation. Studies have shown that Lp-PLA₂ is a good predictor of cardiovascular disease, independent of HDL and LDL cholesterol levels. This has led to the development of drugs aimed at inhibiting Lp-PLA₂ as a way to treat or prevent cardiovascular disease. The activity and mass of Lp-PLA₂ are heritable traits, but major genetic determinants have not been explored in a systematic fashion. We examined genetic variants across the human genome to identify genes influencing Lp-PLA₂ activity and mass. We studied 6,668 Caucasian subjects from the population-based Framingham Heart Study. Clinical data and genetic data on 550,000 genetic variants were available for association analysis. There was no overlap in the most significantly associated SNPs for activity and mass. We identified four distinct gene regions showing highly significant associations with Lp-PLA₂ activity, all of which are known to include genes involved in cholesterol metabolism. The only locus associated with Lp-PLA₂ mass was a region harboring PLA2G7, the gene that encodes lipoprotein-associated phospholipase A2.

Intratumor heterogeneity and precision of microarray-based predictors of breast cancer biology and clinical outcome

PURPOSE

Identifying sources of variation in expression microarray data and the effect of variance in gene expression measurements on complex predictive and diagnostic models is essential when translating microarray-based experimental approaches into clinical assays. The technical reproducibility of microarray platforms is well established. Here, we investigate the additional impact of intratumor heterogeneity, a largely unstudied component of variance, on the performance of several microarray-based assays in breast cancer.

PATIENTS AND METHODS

Genome-wide expression profiling was performed on 50 core needle biopsies from 18 breast cancer patients using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Array-based measures of estrogen receptor (ER) and progesterone receptor (PR) status were compared with immunohistochemistry. The precision of genomic predictors of ER pathway status, recurrence risk, and sensitivity to chemotherapeutics was evaluated by interclass correlation.

RESULTS

Global patterns of gene expression demonstrated that intratumor variation was substantially less than the total variation observed across the patient population. Nevertheless, a fraction of genes exhibited significant intratumor heterogeneity in expression. A high degree of reproducibility was observed in single-gene predictors of ER (intraclass correlation coefficient [ICC] = 0.94) and PR expression (ICC = 0.90), and in a multigene predictor of ER pathway activation (ICC = 0.98) with high concordance with immunohistochemistry. Substantial agreement was also observed for multigene signatures of cancer recurrence (ICC = 0.71) and chemotherapeutic sensitivity (ICC = 0.72 and 0.64).

CONCLUSION

Intratumor heterogeneity, although present at the level of individual gene expression, does not preclude precise microarray-based predictions of tumor behavior or clinical outcome in breast cancer patients.

Association of a Peripheral Blood Metabolic Profile With Coronary Artery Disease and Risk of Subsequent Cardiovascular Events

Background— Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events.

Methods and Results— We performed mass–spectrometry–based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls (“initial”), and 140 CAD cases and 140 controls (“replication”). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined (“event” group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled (“event-replication” group; n=63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis–derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial P =0.002, replication P =0.01), and 1 comprising urea cycle metabolites (factor 9, initial P =0.0004, replication P =0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; P =0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; P =0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; P =0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; P =0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; P =0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; P =0.01).

Conclusions— Metabolite profiles are associated with CAD and subsequent cardiovascular events.

Impact of gene variants on sex-specific regulation of human Scavenger receptor class B type 1 (SR-BI) expression in liver and association with lipid levels in a population-based study

BACKGROUND

Several studies have noted that genetic variants of SCARB1, a lipoprotein receptor involved in reverse cholesterol transport, are associated with serum lipid levels in a sex-dependent fashion. However, the mechanism underlying this gene by sex interaction has not been explored.

METHODS

We utilized both epidemiological and molecular methods to study how estrogen and gene variants interact to influence SCARB1 expression and lipid levels. Interaction between 35 SCARB1 haplotype-tagged polymorphisms and endogenous estradiol levels was assessed in 498 postmenopausal Caucasian women from the population-based Rancho Bernardo Study. We further examined associated variants with overall and SCARB1 splice variant (SR-BI and SR-BII) expression in 91 human liver tissues using quantitative real-time PCR.

RESULTS

Several variants on a haplotype block spanning intron 11 to intron 12 of SCARB1 showed significant gene by estradiol interaction affecting serum lipid levels, the strongest for rs838895 with HDL-cholesterol (p=9.2x10(-4)) and triglycerides (p=1.3x10(-3)) and the triglyceride:HDL cholesterol ratio (p=2.7x10(-4)). These same variants were associated with expression of the SR-BI isoform in a sex-specific fashion, with the strongest association found among liver tissue from 52 young women<45 years old (p=0.002).

CONCLUSIONS

Estrogen and SCARB1 genotype may act synergistically to regulate expression of SCARB1 isoforms and impact serum levels of HDL cholesterol and triglycerides. This work highlights the importance of considering sex-dependent effects of gene variants on serum lipid levels.

Genomic and personalized medicine: foundations and applications

The last decade has witnessed a steady embrace of genomic and personalized medicine by senior government officials, industry leadership, health care providers, and the public. Genomic medicine, which is the use of information from genomes and their derivatives (RNA, proteins, and metabolites) to guide medical decision making-is a key component of personalized medicine, which is a rapidly advancing field of health care that is informed by each person's unique clinical, genetic, genomic, and environmental information. As medicine begins to embrace genomic tools that enable more precise prediction and treatment disease, which include "whole genome" interrogation of sequence variation, transcription, proteins, and metabolites, the fundamentals of genomic and personalized medicine will require the development, standardization, and integration of several important tools into health systems and clinical workflows. These tools include health risk assessment, family health history, and clinical decision support for complex risk and predictive information. Together with genomic information, these tools will enable a paradigm shift to a comprehensive approach that will identify individual risks and guide clinical management and decision making, all of which form the basis for a more informed and effective approach to patient care. DNA-based risk assessment for common complex disease, molecular signatures for cancer diagnosis and prognosis, and genome-guided therapy and dose selection are just among the few important examples for which genome information has already enabled personalized health care along the continuum from health to disease. In addition, information from individual genomes, which is a fast-moving area of technological development, is spawning a social and information revolution among consumers that will undoubtedly affect health care decision making. Although these and other scientific findings are making their way from the genome to the clinic, the full application of genomic and personalized medicine in health care will require dramatic changes in regulatory and reimbursement policies as well as legislative protections for privacy for system-wide adoption. Thus, there are challenges from both a scientific and a policy perspective to personalized health care; however, they will be confronted and solved with the certainty that the science behind genomic medicine is sound and the practice of medicine that it informs is evidence based.

Evaluation of the PharmGKB knowledge base as a resource for efficiently assessing the clinical validity and utility of pharmacogenetic assays

Prior to clinical use, pharmacogenetic tests should be systematically evaluated for their clinical validity and utility. Here, we evaluated whether the publicly available, online Pharmacogenomics Knowledge Base (PharmGKB) could facilitate such assessments by efficiently identifying relevant peer-reviewed manuscripts. The search targets were 55 manuscripts regarding clinical validity and utility included in systematic reviews of warfarin, antidepressant, and irinotecan pharmacogenetics. When direct inclusion in PharmGKB was the search criterion, recall was 33% and precision was 16%. However, recall increased to 78% when citation within a PharmGKB-identified manuscript was added as a search criterion. These recalled manuscripts accounted for 87% of the study subjects, and domain experts determined that the omission of the remaining manuscripts was unlikely to have changed the conclusions of the reviews. Thus, we conclude that PharmGKB can facilitate the systematic assessment of pharmacogenetic assays through the efficient identification of relevant peer-reviewed manuscripts.

Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease

Systemic and local inflammation plays a prominent role in the pathogenesis of atherosclerotic coronary artery disease, but the relationship of whole blood gene expression changes with coronary disease remains unclear. We have investigated whether gene expression patterns in peripheral blood correlate with the severity of coronary disease and whether these patterns correlate with the extent of atherosclerosis in the vascular wall.

Patients were selected according to their coronary artery disease index (CADi), a validated angiographical measure of the extent of coronary atherosclerosis that correlates with outcome. RNA was extracted from blood of 120 patients with at least a stenosis greater than 50% (CADi≥23) and from 121 controls without evidence of coronary stenosis (CADi = 0).

160 individual genes were found to correlate with CADi (rho>0.2, P<0.003). Prominent differential expression was observed especially in genes involved in cell growth, apoptosis and inflammation. Using these 160 genes, a partial least squares multivariate regression model resulted in a highly predictive model (r² = 0.776, P<0.0001). The expression pattern of these 160 genes in aortic tissue also predicted the severity of atherosclerosis in human aortas, showing that peripheral blood gene expression associated with coronary atherosclerosis mirrors gene expression changes in atherosclerotic arteries.

In conclusion, the simultaneous expression pattern of 160 genes in whole blood correlates with the severity of coronary artery disease and mirrors expression changes in the atherosclerotic vascular wall.

Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans

Acute respiratory infections (ARIs) are a common reason for seeking medical attention, and the threat of pandemic influenza will likely add to these numbers. Using human viral challenge studies with live rhinovirus, respiratory syncytial virus, and influenza A, we developed peripheral blood gene expression signatures that distinguish individuals with symptomatic ARIs from uninfected individuals with >95% accuracy. We validated this "acute respiratory viral" signature-encompassing genes with a known role in host defense against viral infections-across each viral challenge. We also validated the signature in an independently acquired data set for influenza A and classified infected individuals from healthy controls with 100% accuracy. In the same data set, we could also distinguish viral from bacterial ARIs (93% accuracy). These results demonstrate that ARIs induce changes in human peripheral blood gene expression that can be used to diagnose a viral etiology of respiratory infection and triage symptomatic individuals.

The Scientific Foundation for personal genomics: recommendations from a National Institutes of Health-Centers for Disease Control and Prevention multidisciplinary workshop

The increasing availability of personal genomic tests has led to discussions about the validity and utility of such tests and the balance of benefits and harms. A multidisciplinary workshop was convened by the National Institutes of Health and the Centers for Disease Control and Prevention to review the scientific foundation for using personal genomics in risk assessment and disease prevention and to develop recommendations for targeted research. The clinical validity and utility of personal genomics is a moving target with rapidly developing discoveries but little translation research to close the gap between discoveries and health impact. Workshop participants made recommendations in five domains: (1) developing and applying scientific standards for assessing personal genomic tests; (2) developing and applying a multidisciplinary research agenda, including observational studies and clinical trials to fill knowledge gaps in clinical validity and utility; (3) enhancing credible knowledge synthesis and information dissemination to clinicians and consumers; (4) linking scientific findings to evidence-based recommendations for use of personal genomics; and (5) assessing how the concept of personal utility can affect health benefits, costs, and risks by developing appropriate metrics for evaluation. To fulfill the promise of personal genomics, a rigorous multidisciplinary research agenda is needed.

The Center for Genomic Medicine at the Duke Institute for Genome Sciences & Policy: propelling genomics into clinical practice

Until now, advances in the genomic sciences have had a minor impact on the practice of medicine. But genomic medicine has the potential to radically alter healthcare in the USA and around the world. By understanding the predictive power of patients’ genomes, it should be possible to identify individuals at risk of disease and to create smarter, more effective treatments for those who are already ill. It is the mission of the Center for Genomic Medicine at the Duke Institute for Genome Sciences & Policy (NC, USA) to harness information from the human genome in order to optimize efficiency, effectiveness and success in bringing the right therapy to the right patient at the right time. In doing so, the Center has focused on the development and implementation of translational models for delivery of genomic healthcare, exploring the nature and breadth of genomic tests, novel approaches to pre- and post‑analytical care, and the design of an optimal healthcare delivery team.

High heritability of metabolomic profiles in families burdened with premature cardiovascular disease

Integration of genetic and metabolic profiling holds promise for providing insight into human disease. Coronary artery disease (CAD) is strongly heritable, but the heritability of metabolomic profiles has not been evaluated in humans. We performed quantitative mass spectrometry-based metabolic profiling in 117 individuals within eight multiplex families from the GENECARD study of premature CAD. Heritabilities were calculated using variance components. We found high heritabilities for amino acids (arginine, ornithine, alanine, proline, leucine/isoleucine, valine, glutamate/glutamine, phenylalanine and glycine; h(2)=0.33-0.80, P=0.005-1.9 x 10(-16)), free fatty acids (arachidonic, palmitic, linoleic; h(2)=0.48-0.59, P=0.002-0.00005) and acylcarnitines (h(2)=0.23-0.79, P=0.05-0.0000002). Principal components analysis was used to identify metabolite clusters. Reflecting individual metabolites, several components were heritable, including components comprised of ketones, beta-hydroxybutyrate and C2-acylcarnitine (h(2)=0.61); short- and medium-chain acylcarnitines (h(2)=0.39); amino acids (h(2)=0.44); long-chain acylcarnitines (h(2)=0.39) and branched-chain amino acids (h(2)=0.27). We report a novel finding of high heritabilities of metabolites in premature CAD, establishing a possible genetic basis for these profiles. These results have implications for understanding CAD pathophysiology and genetics.

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

BACKGROUND

In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.

DISCUSSION

Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the Roadmap for National Action on Clinical Decision Support commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.

SUMMARY

A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.

Transforming the practice of medicine using genomics

Recent studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in complex diseases. Such studies herald the future for genomic medicine and the opportunity for personalized prognosis in a variety of clinical contexts that utilize genomescale molecular information. Several key areas represent logical and critical next steps in the use of complex genomic profiling data towards the goal of personalized medicine. First, analyses should be geared toward the development of molecular profiles that predict future events - such as major clinical events or the response, resistance, or adverse reaction to therapy. Secondly, these must move into actual clinical practice by forming the basis for the next generation of clinical trials that will employ these methodologies to stratify patients. Lastly, there remain formidable challenges is in the translation of genomic technologies into clinical medicine that will need to be addressed: professional and public education, health outcomes research, reimbursement, regulatory oversight and privacy protection.

Pharmacogenetic predictors of statin-mediated low-density lipoprotein cholesterol reduction and dose response

BACKGROUND

There is interindividual variation in low-density lipoprotein cholesterol (LDLc) lowering by statins and limited study into the genetic associations of the dose dependant LDLc lowering by statins.

METHODS AND RESULTS

Five hundred nine patients with hyperlipidemia were randomly assigned atorvastatin 10 mg, simvastatin 20 mg, or pravastatin 10 mg (low-dose phase) followed by 80 mg, 80 mg, and 40 mg (high-dose phase), respectively. Thirty-one genes in statin, cholesterol, and lipoprotein metabolism were sequenced and 489 single nucleotide polymorphisms with minor allele frequencies >2% were tested for associations with percentage LDLc lowering at low doses using multivariable adjusted general linear regression. Significant associations from the analysis at low dose were then repeated at high-dose statins. At low doses, only 1 single nucleotide polymorphism met our experiment-wide significance level, ABCA1 rs12003906. Twenty-six subjects carried the minor allele of rs12003906, which was associated with an attenuated LDLc reduction (LDLc reduction in carriers versus noncarriers -24.1+/-2.6% versus -32.2+/-1.5%; P=0.0001). In addition, we replicated the association with the APOE epsilon3 allele and a reduced LDLc reduction. At high doses, carriers of the minor allele of ABCA1 rs12003906 and the APOE epsilon3 allele improved their LDLc reduction but continued to have a diminished LDLc reduction compared with noncarriers (-30.5+/-4.0% versus -42.0+/-2.4%; P=0.005) and (-38.5+/-1.9% versus -45.3+/-2.8%; P=0.009), respectively.

CONCLUSIONS

An intronic single nucleotide polymorphism in ABCA1 and the APOE epsilon3 allele are associated with reduced LDLc lowering by statins and identify individuals who may be resistant to maximal LDLc lowering by statins.

Correlation of Peripheral-Blood Gene Expression With the Extent of Coronary Artery Stenosis

Background— The molecular pathophysiology of coronary artery disease (CAD) includes cytokine release and a localized inflammatory response within the vessel wall. The extent to which CAD and its severity is reflected by gene expression in circulating cells is unknown.

Methods and Results— From an initial coronary catheterization cohort we identified 41 patients, comprising 27 cases with angiographically significant CAD and 14 controls without coronary stenosis. Whole-genome microarray analysis performed on peripheral-blood mononuclear cells yielded 526 genes with >1.3-fold differential expression ( P <0.05) between cases and controls. Real-time polymerase chain reaction on 106 genes (the 50 most significant microarray genes and 56 additional literature genes) in an independent subset of 95 patients (63 cases, 32 controls) from the same cohort yielded 14 genes ( P <0.05) that independently discriminated CAD state in a multivariable analysis that included clinical and demographic factors. From an independent second catheterization cohort, 215 patients were selected for real-time polymerase chain reaction–based replication. A case:control subset of 107 patients (86 cases, 21 controls) replicated 11 of the 14 multivariably significant genes from the first cohort. An analysis of the 14 genes in the entire set of 215 patients demonstrated that gene expression was proportional to maximal coronary artery stenosis ( P <0.001 by ANOVA).

Conclusions— Gene expression in peripheral-blood cells reflects the presence and extent of CAD in patients undergoing angiography.

Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer

CONTEXT

Gene expression profiling may be useful for prognostic and therapeutic strategies in breast carcinoma.

OBJECTIVES

To demonstrate the value in integrating genomic information with clinical and pathological risk factors, to refine prognosis, and to improve therapeutic strategies for early stage breast cancer.

DESIGN, SETTING, AND PATIENTS

Retrospective study of patients with early stage breast carcinoma who were candidates for adjuvant chemotherapy; 964 clinically annotated breast tumor samples (573 in the initial discovery set and 391 in the validation cohort) with corresponding microarray data were used. All patients were assigned relapse risk scores based on their respective clinicopathological features. Signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of deregulation that correspond with relapse risk scores to refine prognosis with the clinicopathological prognostic model alone. Predictors of chemotherapeutic response were also applied to further characterize clinically relevant heterogeneity in early stage breast cancer.

MAIN OUTCOME MEASURES

Gene expression signatures and clinicopathological variables in early stage breast cancer to determine a refined estimation of relapse-free survival and sensitivity to chemotherapy.

RESULTS

In the initial data set of 573 patients, prognostically significant clusters representing patterns of oncogenic pathway activation and tumor biology/microenvironment states were identified within the low-risk (log-rank P = .004), intermediate-risk (log-rank P = .01), and high-risk (log-rank P = .003) model cohorts, representing clinically important genomic subphenotypes of breast cancer. As an example, in the low-risk cohort, of 6 prognostically significant clusters, patients in cluster 4 had an inferior relapse-free survival vs patients in cluster 1 (log-rank P = .004) and cluster 5 (log-rank P = .03). Median relapse-free survival for patients in cluster 4 was 16 months less than for patients in cluster 1 (95% CI, 7.5-24.5 months) and 19 months less than for patients in cluster 5 (95% CI, 10.5-27.5 months). Multivariate analyses confirmed the independent prognostic value of the genomic clusters (low risk, P = .05; high risk, P = .02). The reproducibility and validity of these patterns of pathway deregulation in predicting relapse risk was established using related but not identical clusters in the independent validation cohort. The prognostic clinicogenomic clusters also have unique sensitivity patterns to commonly used cytotoxic therapies.

CONCLUSIONS

These results provide preliminary evidence that incorporation of gene expression signatures into clinical risk stratification can refine prognosis. Prospective studies are needed to determine the value of this approach for individualizing therapeutic strategies.

Peripheral blood gene expression profiling for cardiovascular disease assessment

Whole blood gene expression profiling has the potential to be informative about dynamic changes in disease states and to provide information on underlying disease mechanisms. Having demonstrated proof of concept in animal models, a number of studies have now tried to tackle the complexity of cardiovascular disease in human hosts to develop better diagnostic and prognostic indicators. These studies show that genomic signatures are capable of classifying patients with cardiovascular diseases into finer categories based on the molecular architecture of a patient's disease and more accurately predict the likelihood of a cardiovascular event than current techniques. To highlight the spectrum of potential applications of whole blood gene expression profiling approach in cardiovascular science, we have chosen to review the findings in a number of complex cardiovascular diseases such as atherosclerosis, hypertension and myocardial infarction as well as thromboembolism, aortic aneurysm, and heart transplant.

Genetic profiling and tailored therapy in asthma: are we there yet?

Asthma is characterized by reversible bronchial hyper-responsiveness and airway inflammation, and encompasses a wide variety of patients with different clinical phenotypes that display variable responses to therapy. The definition of genomic variation presented in the Human Genome Project has facilitated the development of genetic-guided therapy in various diseases, including asthma. Tailored therapy is a reality in many types of malignancies where specific gene mutations or molecular profiles are identified and used to make critical therapeutic decisions. Despite the identification of beta-adrenergic receptor polymorphisms by Liggett and colleagues during the 1990s, the pharmacogenetics of asthma is still in its infancy. There have been great advances in asthma pharmacogenetics and pharmacotherapy with the completion of several large trials highlighting the effects of genotype on response to asthma therapy. This review focuses on research articles that serve to emphasize the potential role of using genotyping as a tool to develop individualized patient treatment regimens for asthma, thus improving outcomes and limiting adverse effects of certain therapies.

Peripheral blood expression of nuclear factor-kappab-regulated genes is associated with rheumatoid arthritis disease activity and responds differentially to anti-tumor necrosis factor-alpha versus methotrexate

OBJECTIVE

To evaluate peripheral blood expression of genes regulated by nuclear factor-kappaB (NF-kappaB), a key mediator of tumor necrosis factor-alpha (TNF-alpha) signaling, in patients with rheumatoid arthritis (RA) before and during treatment with anti-TNF-alpha or methotrexate (MTX). We analyzed association of gene expression with disease activity, rheumatoid factor (RF), age, sex, disease duration, treatment modality, and clinical response.

METHODS

Sixty patients consented for RNA analysis at baseline and after 2 and 6 weeks of treatment. Disease activity was quantified using Disease Activity Score (DAS28) and C-reactive protein (CRP). Expression of 67 TNF-alpha-responsive, NF-kappaB-regulated genes was measured using Affymetrix arrays and RT-PCR.

RESULTS

Expression of 34 genes was associated with DAS28-CRP, notably S100A12/calgranulin C, IL7R, and aquaporin 3. No association was observed with age, sex, RF, or disease duration. Expression of 16 genes changed in a manner that differed significantly between treatment groups. Eleven were reduced in anti-TNF-alpha-treated patients relative to MTX, while 5 were increased. The majority of these observations were confirmed using RT-PCR. Gene expression was not associated significantly with change in disease activity.

CONCLUSION

NF-kappaB-dependent gene expression in peripheral leukocytes is highly correlated with RA activity as measured by DAS28-CRP. Expression of many genes responds differentially to anti-TNF-alpha versus MTX, suggesting fundamentally different effects on the NF-kappaB pathway. This peripheral blood expression signature provides candidate markers that could lead to development of a simple, minimally invasive pharmacodynamic assay for RA treatments directed at the NF-kappaB pathway. Combination of gene expression data with clinical scores and serum markers may provide more sensitive and predictive measures of RA disease activity.

Taking cardiovascular genetic association studies to the next level

Genetic information is beginning to have a direct impact on patient care and it is important that cardiologists appreciate the value and approaches to associating genetic variation and health outcomes. Genetic associations should be based on compelling genetic and biological hypotheses and should be statistically sound so as to reduce the possibility of "false discovery" in the setting of testing multiple hypotheses. Study designs should clearly define cases and controls and measurement of phenotypes. Finally, findings should be replicated in at least 1 independent cohort. Consideration of these principles should provide insight into disease biology based on genetic findings and encourage their meaningful adoption into clinical practice.

Gene expression signatures that predict radiation exposure in mice and humans

BACKGROUND

The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation.

METHODS AND FINDINGS

We have made use of gene expression analysis of peripheral blood (PB) mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans.

CONCLUSIONS

We suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.

An Integrated Genomic-Based Approach to Individualized Treatment of Patients With Advanced-Stage Ovarian Cancer

Purpose

The purpose of this study was to develop an integrated genomic-based approach to personalized treatment of patients with advanced-stage ovarian cancer. We have used gene expression profiles to identify patients likely to be resistant to primary platinum-based chemotherapy and also to identify alternate targeted therapeutic options for patients with de novo platinum-resistant disease.

Patients and Methods

A gene expression model that predicts response to platinum-based therapy was developed using a training set of 83 advanced-stage serous ovarian cancers and tested on a 36-sample external validation set. In parallel, expression signatures that define the status of oncogenic signaling pathways were evaluated in 119 primary ovarian cancers and 12 ovarian cancer cell lines. In an effort to increase chemotherapy sensitivity, pathways shown to be activated in platinum-resistant cancers were subject to targeted therapy in ovarian cancer cell lines.

Results

Gene expression profiles identified patients with ovarian cancer likely to be resistant to primary platinum-based chemotherapy with greater than 80% accuracy. In patients with platinum-resistant disease, we identified expression signatures consistent with activation of Src and Rb/E2F pathways, components of which were successfully targeted to increase response in ovarian cancer cell lines.

Conclusion

We have defined a strategy for treatment of patients with advanced-stage ovarian cancer that uses therapeutic stratification based on predictions of response to chemotherapy, coupled with prediction of oncogenic pathway deregulation, as a method to direct the use of targeted agents.

Challenges in the phenotypic characterisation of patients in genetic studies of coronary artery disease

Coronary artery disease and acute myocardial infarction are complex traits in which there has been recent research to identify the principal genes that engender susceptibility or provide protection. Although there has been exceptional progress in the technology, which now allows genotyping of hundreds of thousands of single-nucleotide polymorphisms in each individual, there remains a pattern of inconsistency in the studies performed to date, in part owing to the difficulties in defining cases and controls. In this paper, salient issues to facilitate research in this important field are reviewed.

A framework to evaluate the economic impact of pharmacogenomics

INTRODUCTION

Pharmacogenomics and personalized medicine promise to improve healthcare by increasing drug efficacy and minimizing side effects. There may also be substantial savings realized by eliminating costs associated with failed treatment. This paper describes a framework using health claims data for analyzing the potential value of pharmacogenomic testing in clinical practice.

METHODS

We evaluated a model of alternate clinical strategies using asthma patients' data from a retrospective health claims database to determine a potential cost offset. We estimated the likely cost impact of using a hypothetical pharmacogenomic test to determine a preferred initial therapy. We compared the annualized per patient costs distributions under two clinical strategies: testing all patients for a nonresponse genotype prior to treating and testing none.

RESULTS

In the Test All strategy, more patients fall into lower cost ranges of the distribution. In our base case (15% phenotype prevalence, 200 US dollars test, 74% overall first-line treatment efficacy and 60% second-line therapy efficacy) the cost savings per patient for a typical run of the testing strategy simulation ranged from 200 US dollars to 767 US dollars (5th and 95th percentile). Genetic variant prevalence, test cost and the cost of choosing the wrong treatment are key parameters in the economic viability of pharmacogenomics in clinical practice.

CONCLUSIONS

A general tool for predicting the impact of pharmacogenomic-based diagnostic tests on healthcare costs in asthma patients suggests that upfront testing costs are likely offset by avoided nonresponse costs. We suggest that similar analyses for decision making could be undertaken using claims data in which a population can be stratified by response to a drug.

Genomic signatures to guide the use of chemotherapeutics

Using in vitro drug sensitivity data coupled with Affymetrix microarray data, we developed gene expression signatures that predict sensitivity to individual chemotherapeutic drugs. Each signature was validated with response data from an independent set of cell line studies. We further show that many of these signatures can accurately predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, could also predict response to multidrug regimens. Finally, we integrated the chemotherapy response signatures with signatures of oncogenic pathway deregulation to identify new therapeutic strategies that make use of all available drugs. The development of gene expression profiles that can predict response to commonly used cytotoxic agents provides opportunities to better use these drugs, including using them in combination with existing targeted therapies.

A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer

BACKGROUND

Clinical trials have indicated a benefit of adjuvant chemotherapy for patients with stage IB, II, or IIIA--but not stage IA--non-small-cell lung cancer (NSCLC). This classification scheme is probably an imprecise predictor of the prognosis of an individual patient. Indeed, approximately 25 percent of patients with stage IA disease have a recurrence after surgery, suggesting the need to identify patients in this subgroup for more effective therapy.

METHODS

We identified gene-expression profiles that predicted the risk of recurrence in a cohort of 89 patients with early-stage NSCLC (the lung metagene model). We evaluated the predictor in two independent groups of 25 patients from the American College of Surgeons Oncology Group (ACOSOG) Z0030 study and 84 patients from the Cancer and Leukemia Group B (CALGB) 9761 study.

RESULTS

The lung metagene model predicted recurrence for individual patients significantly better than did clinical prognostic factors and was consistent across all early stages of NSCLC. Applied to the cohorts from the ACOSOG Z0030 trial and the CALGB 9761 trial, the lung metagene model had an overall predictive accuracy of 72 percent and 79 percent, respectively. The predictor also identified a subgroup of patients with stage IA disease who were at high risk for recurrence and who might be best treated by adjuvant chemotherapy.

CONCLUSIONS

The lung metagene model provides a potential mechanism to refine the estimation of a patient's risk of disease recurrence and, in principle, to alter decisions regarding the use of adjuvant chemotherapy in early-stage NSCLC.

Translating genomic biomarkers into clinically useful diagnostics

The landmark sequencing of the human genome has ushered in a new field of large-scale research. Advances in understanding the molecular basis of disease have opened up new opportunities to develop genomics-based tools to diagnose, predict disease onset or recurrence, tailor treatment options, and assess treatment response. Although still in the early stages of research and development, genomic biomarker research has the capability of providing a comprehensive insight into pathophysiological processes as well as more precise predictors of outcome not previously attainable with traditional biomarkers. Before genomic biomarkers are incorporated into clinical practice, several issues will need to be addressed in order to generate the necessary levels of evidence to demonstrate analytical and clinical validity and utility. In addition, efforts will be needed to educate health professionals and the public about genomics-based tools, revise regulatory oversight mechanisms, and ensure privacy safeguards of the information generated from these new tests.

Gene expression analysis of cardiovascular diseases: novel insights into biology and clinical applications

Although the contribution of genetics to complex cardiovascular diseases such as atherosclerosis has been accepted for quite some time, full and detailed knowledge of the individual causative genes has been elusive. With the advent of genomic technologies and methods, the necessary tools are now available to begin pinpointing the genes that contribute to disease susceptibility and progression. One approach being applied extensively in candidate gene discovery is gene expression analysis of human and animal tissues using microarrays. The genes identified by these genomic studies provide valuable insight into disease biology and represent the initial steps toward the development of diagnostic tests and therapeutic strategies that will substantially improve human health. This paper highlights the progress that has been made in using gene expression analysis cardiovascular genomic research and the potential for applying these findings in clinical medicine.

Embracing the complexity of genomic data for personalized medicine

Numerous recent studies have demonstrated the use of genomic data, particularly gene expression signatures, as clinical prognostic factors in cancer and other complex diseases. Such studies herald the future of genomic medicine and the opportunity for personalized prognosis in a variety of clinical contexts that utilizes genome-scale molecular information. The scale, complexity, and information content of high-throughput gene expression data, as one example of complex genomic information, is often under-appreciated as many analyses continue to focus on defining individual rather than multiplex biomarkers for patient stratification. Indeed, this complexity of genomic data is often--rather paradoxically--viewed as a barrier to its utility. To the contrary, the complexity and scale of global genomic data, as representing the many dimensions of biology, must be embraced for the development of more precise clinical prognostics. The need is for integrated analyses--approaches that embrace the complexity of genomic data, including multiple forms of genomic data, and aim to explore and understand multiple, interacting, and potentially conflicting predictors of risk, rather than continuing on the current and traditional path that oversimplifies and ignores the information content in the complexity. All forms of potentially relevant data should be examined, with particular emphasis on understanding the interactions, complementarities, and possible conflicts among gene expression, genetic, and clinical markers of risk.

Prescribing BiDil: is it black and white?

The approval of BiDil as an adjunct treatment in self-identified blacks with heart failure raises questions regarding the underlying etiology of drug response in this target population and the ability to accurately identify patients who are most likely to benefit. Preliminary data have indicated that differences in nitric oxide synthesis between groups may account for differences in response to BiDil and genetic studies have begun to elucidate the mechanism of these differences. Until more accurate selection criteria are developed to identify patients who are most likely to benefit, both clinicians and the general public will need to consider the unique issues raised by BiDil.

The future may be closer than you think: a response from the Personalized Medicine Coalition to the Royal Society's report on personalized medicine

A recent report from the British Royal Society on the prospects for personalized medicine provides a sobering assessment of the field and its prospects. The report contends that pharmacogenetics has little clinical relevance at the moment and will only progress with the completion of large, cumbersome clinical trials. The report goes on to note that the regulatory infrastructure, medical education initiatives and public deliberation necessary to make personalized medicine a reality are essentially nonexistent, at least so far. In our view, personalized medicine is much more than a hypothetical protocol designed to correlate genotypes with prescriptions. We argue that the development of personalized medicine is a broader phenomenon that is already being practiced in one form or another in many contexts. Both academic medicine and the pharmaceutical industry have a huge stake in bringing pharmacogenetic-based personalized medicine to fruition; we expect both entities to act as drivers of what will be a long-term, iterative process.