Personalized medicine: new genomics, old lessons

Personalized medicine uses traditional, as well as emerging concepts of the genetic and environmental basis of disease to individualize prevention, diagnosis and treatment. Personalized genomics plays a vital, but not exclusive role in this evolving model of personalized medicine. The distinctions between genetic and genomic medicine are more quantitative than qualitative. Personalized genomics builds on principles established by the integration of genetics into medical practice. Principles shared by genetic and genomic aspects of medicine, include the use of variants as markers for diagnosis, prognosis, prevention, as well as targets for treatment, the use of clinically validated variants that may not be functionally characterized, the segregation of these variants in non-Mendelian as well as Mendelian patterns, the role of gene–environment interactions, the dependence on evidence for clinical utility, the critical translational role of behavioral science, and common ethical considerations. During the current period of transition from investigation to practice, consumers should be protected from harms of premature translation of research findings, while encouraging the innovative and cost-effective application of those genomic discoveries that improve personalized medical care

The promises of genomic screening: building a governance infrastructure. Special issue: genetics and democracy

New screening possibilities become available at a high rate, both useful and unsound possibilities. All screening programmes do harm, and only few have more advantages than disadvantages at reasonable cost. Horizon scanning is needed to identify those few possibilities with more pros than cons. Attunement is needed between actors involved: scientists developing new high-throughput screening techniques and treatment, health care workers, patients and consumers and governmental agencies. The product of a process of attunement may be a quality mark as a norm for professional conduct, rather than legal measures, as the field is moving fast. As actors may have varying perspectives, a governance structure is needed to develop an agenda that is agreed upon by all or most actors involved. A standing committee might oversee the evaluation of benefits and disadvantages in an integrated approach, taking evidence, economics and ethics into account. A proactive role of governmental agencies is needed to facilitate agenda setting and attunement. Policy making has to be transparent and open to stakeholder engagement

The EGAPP initiative: lessons learned.

The Evaluation of Genomic Applications in Practice and Prevention Working Group was first convened in 2005 to develop and test evidence-based methods for the evaluation of genomic tests in transition from research to clinical and public health practice. Over the ensuing years, the Working Group has met 26 times, publishing eight recommendation statements, two methods papers, and one outcomes paper, as well as planning and serving as technical experts on numerous associated systematic reviews. Evaluation of Genomic Applications in Practice and Prevention methods have evolved to address implications of the proliferation of genome-wide association studies and are currently expanding to face challenges expected from clinical implementation of whole-genome sequencing tests. In this article, we review the work of the Evaluation of Genomic Applications in Practice and Prevention Working Group over the first 8 years of its existence with an emphasis on lessons learned throughout the process. It is hoped that in addition to the published methods of the Working Group, the lessons we have learned along the way will be informative to others who are producers and consumers of evidence-based guidelines in the field of genomic medicine

The EGAPP initiative: lessons learned.

The Evaluation of Genomic Applications in Practice and Prevention Working Group was first convened in 2005 to develop and test evidence-based methods for the evaluation of genomic tests in transition from research to clinical and public health practice. Over the ensuing years, the Working Group has met 26 times, publishing eight recommendation statements, two methods papers, and one outcomes paper, as well as planning and serving as technical experts on numerous associated systematic reviews. Evaluation of Genomic Applications in Practice and Prevention methods have evolved to address implications of the proliferation of genome-wide association studies and are currently expanding to face challenges expected from clinical implementation of whole-genome sequencing tests. In this article, we review the work of the Evaluation of Genomic Applications in Practice and Prevention Working Group over the first 8 years of its existence with an emphasis on lessons learned throughout the process. It is hoped that in addition to the published methods of the Working Group, the lessons we have learned along the way will be informative to others who are producers and consumers of evidence-based guidelines in the field of genomic medicine

Theoretical vs. empirical discriminability:the application of ROC methods to eyewitness identification

Abstract ᅟ Receiver operating characteristic (ROC) analysis was introduced to the field of eyewitness identification 5 years ago. Since that time, it has been both influential and controversial, and the debate has raised an issue about measuring discriminability that is rarely considered. The issue concerns the distinction between empirical discriminability (measured by area under the ROC curve) vs. underlying/theoretical discriminability (measured by d’ or variants of it). Under most circumstances, the two measures will agree about a difference between two conditions in terms of discriminability. However, it is possible for them to disagree, and that fact can lead to confusion about which condition actually yields higher discriminability. For example, if the two conditions have implications for real-world practice (e.g., a comparison of competing lineup formats), should a policymaker rely on the area-under-the-curve measure or the theory-based measure? Here, we illustrate the fact that a given empirical ROC yields as many underlying discriminability measures as there are theories that one is willing to take seriously. No matter which theory is correct, for practical purposes, the singular area-under-the-curve measure best identifies the diagnostically superior procedure. For that reason, area under the ROC curve informs policy in a way that underlying theoretical discriminability never can. At the same time, theoretical measures of discriminability are equally important, but for a different reason. Without an adequate theoretical understanding of the relevant task, the field will be in no position to enhance empirical discriminability

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