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J. Pers. Med., Volume 7, Issue 3 (September 2017)

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Research

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Open AccessArticle The Clinical and Economic Impact of Inaccurate EGFR Mutation Tests in the Treatment of Metastatic Non-Small Cell Lung Cancer
J. Pers. Med. 2017, 7(3), 5; doi:10.3390/jpm7030005
Received: 4 April 2017 / Accepted: 26 June 2017 / Published: 28 June 2017
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Abstract
Advances in personalized medicine are supported by companion diagnostic molecular tests. Testing accuracy is critical for selecting patients for optimal therapy and reducing treatment-related toxicity. We assessed the clinical and economic impact of inaccurate test results between laboratory developed tests (LDTs) and a
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Advances in personalized medicine are supported by companion diagnostic molecular tests. Testing accuracy is critical for selecting patients for optimal therapy and reducing treatment-related toxicity. We assessed the clinical and economic impact of inaccurate test results between laboratory developed tests (LDTs) and a US Food and Drug Administration (FDA)-approved test for detection of epidermal growth factor receptor (EGFR) mutations. Using a hypothetical US cohort of newly diagnosed metastatic non-small cell lung cancer (NSCLC) patients and EURTAC (erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer) clinical trial data, we developed a decision analytic model to estimate the probability of misclassification with LDTs compared to a FDA-approved test. We estimated the clinical and economic impact of inaccurate test results by quantifying progression-free and quality-adjusted progression-free life years (PFLYs, QAPFLYs) lost, and costs due to incorrect treatment. The base-case analysis estimated 2.3% (n = 1422) of 60,502 newly diagnosed metastatic NSCLC patients would be misclassified with LDTs compared to 1% (n = 577) with a FDA-approved test. An average of 477 and 194 PFLYs were lost among the misclassified patients tested with LDTs compared to the FDA-approved test, respectively. Aggregate treatment costs for patients tested with LDTs were approximately $7.3 million more than with the FDA-approved test, due to higher drug and adverse event costs among patients incorrectly treated with targeted therapy or chemotherapy, respectively. Invalid tests contributed to greater probability of patient misclassification and incorrect therapy. In conclusion, risks associated with inaccurate EGFR mutation tests pose marked clinical and economic consequences to society. Utilization of molecular diagnostic tests with demonstrated accuracy could help to maximize the potential of personalized medicine. Full article
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Open AccessArticle Dairy Product Consumption Interacts with Glucokinase (GCK) Gene Polymorphisms Associated with Insulin Resistance
J. Pers. Med. 2017, 7(3), 8; doi:10.3390/jpm7030008
Received: 18 May 2017 / Revised: 31 July 2017 / Accepted: 21 August 2017 / Published: 30 August 2017
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Abstract
Dairy product intake and a person’s genetic background have been reported to be associated with the risk of type 2 diabetes (T2D). The objective of this study was to examine the interaction between dairy products and genes related to T2D on glucose-insulin homeostasis
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Dairy product intake and a person’s genetic background have been reported to be associated with the risk of type 2 diabetes (T2D). The objective of this study was to examine the interaction between dairy products and genes related to T2D on glucose-insulin homeostasis parameters. A validated food frequency questionnaire, fasting blood samples, and glucokinase (GCK) genotypes were analyzed in 210 healthy participants. An interaction between rs1799884 in GCK and dairy intake on the homeostasis model assessment of insulin resistance was identified. Secondly, human hepatocellular carcinoma cells (HepG2) were grown in a high-glucose medium and incubated with either 1-dairy proteins: whey, caseins, and a mixture of whey and casein; and 2-four amino acids (AA) or mixtures of AA. The expression of GCK-related genes insulin receptor substrate-1 (IRS-1) and fatty acid synthase (FASN) was increased with whey protein isolate or hydrolysate. Individually, leucine increased IRS-1 expression, whereas isoleucine and valine decreased FASN expression. A branched-chain AA mixture decreased IRS-1 and FASN expression. In conclusion, carriers of the A allele for rs1799884 in the GCK gene may benefit from a higher intake of dairy products to maintain optimal insulin sensitivity. Moreover, the results show that whey proteins affect the expression of genes related to glucose metabolism. Full article
(This article belongs to the Special Issue Nutrigenomics)
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Review

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Open AccessReview Empowering Mayo Clinic Individualized Medicine with Genomic Data Warehousing
J. Pers. Med. 2017, 7(3), 7; doi:10.3390/jpm7030007
Received: 13 June 2017 / Revised: 28 July 2017 / Accepted: 2 August 2017 / Published: 22 August 2017
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Abstract
Individualized medicine enables better diagnoses and treatment decisions for patients and promotes research in understanding the molecular underpinnings of disease. Linking individual patient’s genomic and molecular information with their clinical phenotypes is crucial to these efforts. To address this need, the Center for
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Individualized medicine enables better diagnoses and treatment decisions for patients and promotes research in understanding the molecular underpinnings of disease. Linking individual patient’s genomic and molecular information with their clinical phenotypes is crucial to these efforts. To address this need, the Center for Individualized Medicine at Mayo Clinic has implemented a genomic data warehouse and a workflow management system to bring data from institutional electronic health records and genomic sequencing data from both clinical and research bioinformatics sources into the warehouse. The system is the foundation for Mayo Clinic to build a suite of tools and interfaces to support various clinical and research use cases. The genomic data warehouse is positioned to play a key role in enhancing the research capabilities and advancing individualized patient care at Mayo Clinic. Full article
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Open AccessReview Value-Based Pricing and Reimbursement in Personalised Healthcare: Introduction to the Basic Health Economics
J. Pers. Med. 2017, 7(3), 10; doi:10.3390/jpm7030010
Received: 20 July 2017 / Revised: 24 August 2017 / Accepted: 28 August 2017 / Published: 4 September 2017
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Abstract
‘Value-based’ outcomes, pricing, and reimbursement are widely discussed as health sector reforms these days. In this paper, we discuss their meaning and relationship in the context of personalized healthcare, defined as receipt of care conditional on the results of a biomarker-based diagnostic test.
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‘Value-based’ outcomes, pricing, and reimbursement are widely discussed as health sector reforms these days. In this paper, we discuss their meaning and relationship in the context of personalized healthcare, defined as receipt of care conditional on the results of a biomarker-based diagnostic test. We address the question: “What kinds of pricing and reimbursement models should be applied in personalized healthcare?” The simple answer is that competing innovators and technology adopters should have incentives that promote long-term dynamic efficiency. We argue that—to meet this social objective of optimal innovation in personalized healthcare—payers, as agents of their plan participants, should aim to send clear signals to their suppliers about what they value. We begin by revisiting the concept of value from an economic perspective, and argue that a broader concept of value is needed in the context of personalized healthcare. We discuss the market for personalized healthcare and the interplay between price and reimbursement. We close by emphasizing the potential barrier posed by inflexible or cost-based reimbursement systems, especially for biomarker-based predictive tests, and how these personalized technologies have global public goods characteristics that require global value-based differential pricing to achieve dynamic efficiency in terms of the optimal rate of innovation and adoption. Full article
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Other

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Open AccessCommentary Personalized Computational Models as Biomarkers
J. Pers. Med. 2017, 7(3), 9; doi:10.3390/jpm7030009
Received: 20 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
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Abstract
Biomarkers are cornerstones of clinical medicine, and personalized medicine, in particular, is highly dependent on reliable and highly accurate biomarkers for individualized diagnosis and treatment choice. Modern omics technologies, such as genome sequencing, allow molecular profiling of individual patients with unprecedented resolution, but
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Biomarkers are cornerstones of clinical medicine, and personalized medicine, in particular, is highly dependent on reliable and highly accurate biomarkers for individualized diagnosis and treatment choice. Modern omics technologies, such as genome sequencing, allow molecular profiling of individual patients with unprecedented resolution, but biomarkers based on these technologies often lack the dynamic element to follow the progression of a disease or response to therapy. Here, we discuss computational models as a new conceptual approach to biomarker discovery and design. Being able to integrate a large amount of information, including dynamic information, computational models can simulate disease evolution and response to therapy with high sensitivity and specificity. By populating these models with personal data, they can be highly individualized and will provide a powerful new tool in the armory of personalized medicine. Full article
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