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Geoffrey S Ginsburg

Publications and source records attributed to Geoffrey S Ginsburg.

At least 19 recordsLinked to original sources
PubMed2006

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.

Adolescent↗
PubMed2006

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.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2006

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.

Adult↗
PubMed2006

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.

Animals↗
PubMed2006

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.

Angiotensin-Converting Enzyme Inhibitors↗
PubMed2006

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.

Breast Neoplasms↗
PubMed2006

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.

Biomarkers↗
PubMed2006

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.

Anti-Asthmatic Agents↗
PubMed2005

Implications of pharmacogenomics for drug development and clinical practice.

Pharmacogenomics is likely to be among the first clinical applications of the Human Genome Project and is certain to have an enormous impact on the clinical practice of medicine. Herein, we discuss the potential implications of pharmacogenomics on the drug development process, including drug safety, productivity, market segmentation, market expansion, differentiation, and personalized health care. We also review 3 challenges facing the translation of pharmacogenomics into clinical practice: dependence on information technology, limited health care financing, and the scientific uncertainty surrounding validation of specific applications of the technology. To our knowledge, there is currently no formal agenda to promote and cultivate innovation, to develop progressive information technology, or to obtain the financing that would be required to advance the use of pharmacogenomic technologies in patient care. Although the potential of these technologies is driving change in the development of clinical sciences, it remains to be seen which health care systems level needs will be addressed.

Animals↗
PubMed2005

Prospects for personalized cardiovascular medicine: the impact of genomics.

Sequencing of the human genome has ushered in prospects for individualizing cardiovascular health care. There is growing evidence that the practice of cardiovascular medicine might soon have a new toolbox to predict and treat disease more effectively. The Human Genome Project has spawned several important "omic" technologies that allow "whole genome" interrogation of sequence variation (re-sequencing, genotyping, comparative genome hybridization), transcription (expression profiling, tissue arrays), proteins (gas or liquid chromatography and tandem mass spectroscopy [MS]), and metabolites (MS or nuclear magnetic resonance profiling); deoxyribonucleic acid, ribonucleic acid, protein, and metabolic approaches all provide more exacting detail of cardiovascular disease mechanisms and, in some cases, are redefining its taxonomy. Pharmacogenomic approaches are emerging across broad classes of cardiovascular therapeutics to assist practitioners in making more precise decisions about which drugs to give to which patients to optimize the benefit-to-risk ratio. Molecular imaging is developing chemical and biological probes that can sense molecular pathway mechanisms that will allow us to monitor health and disease. Together, these tools will enable a paradigm shift from genetic medicine--on the basis of the study of individual inherited characteristics, most often single genes--to genomic medicine, which by its nature is comprehensive and focuses on the functions and interactions of multiple genes and gene products, among themselves and with their environment. The information gained from such analyses, in combination with clinical data, is now allowing us to assess individual risks and guide clinical management and decision-making, all of which form the basis for cardiovascular genomic medicine.

Cardiology↗
PubMed2005

Genomic medicine: genetic variation and its impact on the future of health care.

Advances in genome technology and other fruits of the Human Genome Project are playing a growing role in the delivery of health care. With the development of new technologies and opportunities for large-scale analysis of the genome, transcriptome, proteome and metabolome, the genome sciences are poised to have a profound impact on clinical medicine. Cancer prognostics will be among the first major test cases for a genomic medicine paradigm, given that all cancer is caused by genomic instability, and microarrays allow assessment of patients' entire expressed genomes. Analysis of breast cancer patients' expression patterns can already be highly correlated with recurrence risks. By integrating clinical data with gene expression profiles, imaging, metabolomic profiles and proteomic data, the prospect for developing truly individualized care becomes ever more real. Notwithstanding these promises, daunting challenges remain for genomic medicine. Success will require planning robust prospective trials, analysing health care economic and outcome data, assuaging insurance and privacy concerns, developing health delivery models that are commercially viable and scaling up to meet the needs of the whole population.

Delivery of Health Care↗
PubMed2005

Genomic medicine: bringing biomarkers to clinical medicine.

An important by-product of sequencing the human genome has been the development of a novel 'toolbox' for biomarker discovery and development. Genomic medicine is an emerging discipline in the genome sciences that integrates these tools to interrogate genomic variation in well-defined populations in order to develop predictors of disease susceptibility, progression and drug response. Several important classes of biomarkers result from these analyses which, when translated to clinical medicine and drug development, will have an important impact on human health and disease. This review highlights both the opportunities and challenges in bringing biomarkers into clinical medicine.

Clinical Medicine↗
PubMed2005

The Personalized Medicine Coalition: goals and strategies.

The concept of personalized medicine--that medical care can be tailored to the genomic and molecular profile of the individual--has repercussions that extend far beyond the technology that makes it possible. The adoption of personalized medicine will require changes in healthcare infrastructure, diagnostics and therapeutics business models, reimbursement policy from government and private payers, and a different approach to regulatory oversight. Personalized medicine will shift medical practices upstream from the reactive treatment of disease, to proactive healthcare management including screening, early treatment, and prevention, and will alter the roles of both physician and patient. It will create a greater reliance on electronic medical records and decision support systems in an industry that has a long history of resistance to information technology. Personalized medicine requires a systems approach to implementation. But in a healthcare economy that is highly decentralized and market driven, it is incumbent upon the stakeholders themselves to advocate for a consistent set of policies and legislation that pave the way for the adoption of personalized medicine. To address this need, the Personalized Medicine Coalition (PMC) was formed as a nonprofit umbrella organization of pharmaceutical, biotechnology, diagnostic, and information technology companies, healthcare providers and payers, patient advocacy groups, industry policy organizations, major academic institutions, and government agencies. The PMC provides a structure for achieving consensus positions among these stakeholders on crucial public policy issues, a role which will be vital to translating personalized medicine into widespread clinical practice. In this article, we outline the goals of the PMC, and the strategies it will take to foster communication, debate, and consensus on issues such as genetic discrimination, the reimbursement structures for pharmacogenomic drugs and diagnostics, regulation, physician training and medical school curricula, and public education.

Communication↗
PubMed2004

Targeted therapies for cancer 2004.

The regulatory agency approvals in the United States and Europe of imatinib mesylate (Gleevec) for patients with bcr/abl-positive chronic myelogenous leukemia, cetuximab (Erbitux) for patients with epidermal growth factor receptor overexpressing metastatic colorectal cancer, the antiangiogenesis agent bevacizumab (Avastin), and the proteasome inhibitor bortezomib (Velcade)--and the considerable public interest in new anticancer drugs that take advantage of specific genetic defects that render the malignant cells more likely to respond to specific treatment--are driving a new era of integrated diagnostics and therapeutics. The recent discovery of a drug response predicting activating mutation in the epidermal growth factor receptor gene for patients with non-small cell lung cancer treated with gefitinib (Iressa) has intensified this interest. In this review, the history of targeted anticancer therapies is highlighted, with focus on the development of molecular diagnostics for hematologic malignancies and the emergence of trastuzumab (Herceptin), an antibody-based targeted therapy for HER-2/neu overexpressing metastatic breast cancer: The potential of pharmacogenomic strategies and the use of high-density genomic microarrays to classify and select therapy for cancer are briefly considered. This review also considers the widely held view that, in the next 5 to 10 years, the clinical application of molecular diagnostics will further revolutionize the drug discovery and development process; customize the selection, dosing, route of administration of existing and new therapeutic agents; and truly personalize medical care for cancer patients.

Aminoglycosides↗