domingo, 19 de mayo de 2013

Diseases of the Week

Genomics|Update|Current

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Diseases of the Week

May is Cystic Fibrosis Awareness Month

Cystic Fibrosis Awareness ToolKit, Cystic Fibrosis Foundation
CDC facts: What is cystic fibrosis? [PDF 298.67 KB]
Cystic fibrosis is included in the universal panel for newborn screening in the United States. Find out more about cystic fibrosis.

Association of the Period3 clock gene ... [Neuro Endocrinol Lett. 2013] - PubMed - NCBI

2013;34(1):27-37.

Association of the Period3 clock gene length polymorphism with salivary cortisol secretion among police officers.

Wirth M, Burch J, Violanti J, Burchfiel C, Fekedulegn D, Andrew M, Zhang H, Miller DB, Youngstedt SD, Hébert JR, Vena JE.

Source

Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA. wirthm@mailbox.sc.edu

Abstract

OBJECTIVE:

This study evaluated whether measures of waking or diurnal cortisol secretion, or self-reported psychological disturbances differed among police officers with a Period3 (PER3) clock gene length polymorphism.

METHODS:

The cortisol awakening response was characterized via the area under the salivary cortisol curve with respect to the increase (AUCI) or total waking cortisol (AUCG). Diurnal cortisol measures included the slope of diurnal cortisol and the diurnal AUCG. Psychological disturbances were characterized using the Center for Epidemiologic Studies Depression Scale, Impact of Events Scale, and Life Events Scale.

RESULTS:

Officers with a 4/5 or 5/5 genotype had higher awakening AUCG and greater diurnal cortisol AUCG levels compared to officers with the 4/4 genotype. Among those working more afternoon or night shifts, waking AUCI and AUCG were greater among officers with a 4/5 or 5/5 genotype compared to the 4/4 referents.

CONCLUSION:

Cortisol secretion was modified among police officers with different PER3 VNTR clock gene variants.

PMID:: 23524621; [PubMed - in process]
PMCID:: PMC3655703

Comparative effectiveness research, genomics-en... [J Clin Oncol. 2012] - PubMed - NCBI

2012 Dec 1;30(34):4233-42. doi: 10.1200/JCO.2012.42.6114. Epub 2012 Oct 15.

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

Ginsburg GS, Kuderer NM.

Source

Duke University Medical Center, Duke Center for Personalized Medicine, Institute for Genome Sciences and Policy, Durham, NC 27708, USA. Geoffrey.Ginsburg@duke.edu

Abstract

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.

Comment in

Special series on comparative effectiveness research: challenges to real-world solutions to quality improvement in personalized medicine. [J Clin Oncol. 2012]

PMID:: 23071236; [PubMed - indexed for MEDLINE]
PMCID:: PMC3504328; [Available on 2013/12/1]

Realizing the opportunities of genomics in health care. [JAMA. 2013] - PubMed - NCBI

2013 Apr 10;309(14):1463-4. doi: 10.1001/jama.2013.1465.

Realizing the opportunities of genomics in health care.

Ginsburg GS.

Source

Duke University, Institute for Genome Sciences & Policy, Center for Personalized Medicine, 101 Science Dr, Durham, NC 27708, USA. geoffrey.ginsburg@duke.edu

PMID:: 23571581; [PubMed - indexed for MEDLINE]

Stakeholder assessment of the evidence for cancer ... [Genet Med. 2012] - PubMed - NCBI

2012 Jul;14(7):656-62.

Stakeholder assessment of the evidence for cancer genomic tests: insights from three case studies.

Deverka PA, Schully SD, Ishibe N, Carlson JJ, Freedman A, Goddard KA, Khoury MJ, Ramsey SD.

Source

Center for Medical Technology Policy, Baltimore, MD, USA.

Erratum in

Genet Med. 2013 Jan;15(1):91.

Abstract

PURPOSE:

Insufficient evidence on the net benefits and harms of genomic tests in real-world settings is a translational barrier for genomic medicine. Understanding stakeholders' assessment of the current evidence base for clinical practice and coverage decisions should be a critical step in influencing research, policy, and practice.

METHODS:

Twenty-two stakeholders participated in a workshop exploring the evidence of genomic tests for clinical and coverage decision making. Stakeholders completed a survey prior to and during the meeting. They also discussed if they would recommend for or against current clinical use of each test.

RESULTS:

At baseline, the level of confidence in the clinical validity and clinical utility of each test varied, although the group expressed greater confidence for epidermal growth factor receptor mutation and Lynch syndrome testing than for Oncotype DX. Following the discussion, survey results reflected even less confidence for Oncotype DX, intermediate levels of confidence for [corrected] epidermal growth factor receptor mutation testing and stable levels of confidence [corrected] for Lynch syndrome testing. The majority of stakeholders would consider clinical use for all three tests, but under the conditions of additional research or a shared clinical decision-making approach.

CONCLUSION:

Stakeholder engagement in unbiased settings is necessary to understand various perspectives about evidentiary thresholds in genomic medicine. Participants recommended the use of various methods for evidence generation and synthesis.

PMID:: 22481130; [PubMed - indexed for MEDLINE]

Building the evidence base for decision making in ... [Genet Med. 2012] - PubMed - NCBI

2012 Jul;14(7):633-42.

Building the evidence base for decision making in cancer genomic medicine using comparative effectiveness research.

Goddard KA, Knaus WA, Whitlock E, Lyman GH, Feigelson HS, Schully SD, Ramsey S, Tunis S, Freedman AN, Khoury MJ, Veenstra DL.

Source

Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA. katrina.ab.goddard@kpchr.org

Abstract

The clinical utility is uncertain for many cancer genomic applications. Comparative effectiveness research (CER) can provide evidence to clarify this uncertainty. The aim of this study was to identify approaches to help stakeholders make evidence-based decisions and to describe potential challenges and opportunities in using CER to produce evidence-based guidance. We identified general CER approaches for genomic applications through literature review, the authors' experiences, and lessons learned from a recent, seven-site CER initiative in cancer genomic medicine. Case studies illustrate the use of CER approaches. Evidence generation and synthesis approaches used in CER include comparative observational and randomized trials, patient-reported outcomes, decision modeling, and economic analysis. Significant challenges to conducting CER in cancer genomics include the rapid pace of innovation, lack of regulation, and variable definitions and evidence thresholds for clinical and personal utility. Opportunities to capitalize on CER methods in cancer genomics include improvements in the conduct of evidence synthesis, stakeholder engagement, increasing the number of comparative studies, and developing approaches to inform clinical guidelines and research prioritization. CER offers a variety of methodological approaches that can address stakeholders' needs and help ensure an effective translation of genomic discoveries.

PMID:: 22516979; [PubMed - indexed for MEDLINE]
PMCID:: PMC3632438

Free PMC Article

Comparative Effectiveness Research in Cancer Genomics and Precision Medicine: Current Landscape and Future Prospects

+ Author Affiliations

Affiliations of authors: Division of Cancer Control and Population Science (DCCPS), National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD (NIS, MJK, SDS, ANF); Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA (MJK); Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (KA); Division of Biomedical Informatics, Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA (WK); Biomedical Informatics Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL (DF); Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, NC (GSG); Center for Health Research, Kaiser Permanente Northwest, Portland, OR (KABG); Applied Genomics Research Institute, Center for Clinical and Research Informatics, North Shore University Health System, Evanston, IL (WK); Department of Medicine, Division of Medical Oncology, Duke University School of Medicine and the Duke Cancer Institute, Durham, NC (GHL); Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA (SDR); Center for Cancer Genomics, Wake Forest School of Medicine, Winston-Salem, NC (JX).

Correspondence to: Naoko I. Simonds, Clinical and Translational Epidemiology Branch, DCCPS, 9609 Medical Center Dr, Rm 4E228, Bethesda, MD 20892 (e-mail: naoko.simonds@nih.gov).

Received January 8, 2013.
Revision received March 20, 2013.
Accepted March 21, 2013.

Abstract

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.