Comparative Effectiveness Research, Genomics-Enabled Personalized Medicine, and Rapid Learning Health Care: A Common Bond
and Nicole M. Kuderer, Duke Cancer Institute, Duke University Medical Center, Duke University, Durham, NC.Journal of Clinical Oncology (Impact Factor: 18.43). 10/2012; 30(34). DOI: 10.1200/JCO.2012.42.6114
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.
- Journal of Clinical Oncology 10/2012; 30(34). DOI:10.1200/JCO.2012.45.9792 · 18.43 Impact Factor
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ABSTRACT: The laudatory visions of McShane and Hayes1 and Ginsburg and Kuderer2 for improvements in the quality and reporting of predictive/prognostic biomarker research - so crucial to the future of personalized medicine - sets an important agenda, but more effort is required to address real-world challenges for implementation. The QI effort for genomic medicine is itself challenged by real-world barriers to adoption of standards. To accelerate the quality agenda in genomic medicine we need to add to CER the insights and methodologies of health services research and the emerging fields of implementation science and knowledge translation. To this I would add the imperative of including policy analysis research to broaden our perspectives about how societal trends, attitudes, and changing values reflected in the notion of personalized medicine can influence the acceptability to all stakeholders of more rigorous evaluation standards and their consequences, especially if more rigorous evaluation is perceived by the public and by clinicians as counter to the availability of innovation earlier in the development cycle of an intervention.Journal of Clinical Oncology 10/2012; 30(34). DOI:10.1200/JCO.2012.44.8225 · 18.43 Impact Factor
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ABSTRACT: Cancer is a complex disease that causes the alterations in the levels of gene, RNA, protein and metabolite. With the development of genomics, transcriptomics, proteomics and metabolomic techniques, the characterization of key mutations and molecular pathways responsible for tumour progression has led to the identification of a large number of potential targets. The increasing understanding of molecular carcinogenesis has begun to change paradigms in oncology from traditional single-factor strategy to multi-parameter systematic strategy. The therapeutic model of cancer has changed from adopting the general radiotherapy and chemotherapy to personalised strategy. The development of predictive, preventive and personalised medicine (PPPM) will allow prediction of response with substantially increased accuracy, stratification of particular patient groups and eventual personalization of medicine. The PPPM will change the approach to tumour diseases from a systematic and comprehensive point of view in the future. Patients will be treated according to the specific molecular profiles that are found in the individual tumour tissue and preferentially with targeted substances, if available.01/2013; 4(1):2. DOI:10.1186/1878-5085-4-2
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