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      Jumping on the Train of Personalized Medicine: A Primer for Non- Geneticist Clinicians: Part 3. Clinical Applications in the Personalized Medicine Area

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          Abstract

          The rapid decline of sequencing costs brings hope that personal genome sequencing will become a common feature of medical practice. This series of three reviews aim to help non-geneticist clinicians to jump into the fast-moving field of personalized genetic medicine. In the first two articles, we covered the fundamental concepts of molecular genetics and the methodologies used in genetic epidemiology. In this third article, we discuss the evolution of personalized medicine and illustrate the most recent success in the fields of Mendelian and complex human diseases. We also address the challenges that currently limit the use of personalized medicine to its full potential.

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          J A Hanley, B J McNeil, Marnix van Holsbeeck (1982)
          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Use and misuse of the receiver operating characteristic curve in risk prediction.

            Nancy R. Cook (2007)
            The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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              Gene-expression signatures in breast cancer.

              Christos Sotiriou, Lajos Pusztai (2009)
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                Author and article information

                Journal
                Journal ID (nlm-ta): Curr Psychiatry Rev
                Journal ID (iso-abbrev): Curr Psychiatry Rev
                Journal ID (publisher-id): CPSR
                Title: Current Psychiatry Reviews
                Publisher: Bentham Science Publishers
                ISSN (Print): 1573-4005
                ISSN (Electronic): 1875-6441
                Publication date (Electronic): May 2014
                Publication date (Print): May 2014
                Volume: 10
                Issue: 2
                Pages: 118-132
                Affiliations
                Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON L8N 3Z5, Canada
                Author notes
                [* ]Address correspondence to this author at the McMaster University, Michael DeGroote Centre for Learning & Discovery, Room 3205, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Tel: 905-525-9140 Ext. 26802; Fax: 905-528-2814; E-mail: meyred@ 123456mcmaster.ca
                Article
                Publisher ID: CPSR-10-118
                DOI: 10.2174/1573400510666140630170549
                PMC ID: 4287884
                PubMed ID: 25598768
                SO-VID: 6a73f5df-e65d-4554-b506-8d23aa9b32e8
                Copyright © © 2014 Bentham Science Publishers
                License:

                This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

                History
                Date received : 29 April 2014
                Date revision received : 27 May 2014
                Date accepted : 29 May 2014
                Categories
                Subject: Article

                ScienceOpen disciplines: Clinical Psychology & Psychiatry
                Keywords: clinical utility,ethics,next generation sequencing,pharmacogenetics,prediction,personalized medicine.
                Data availability:
                ScienceOpen disciplines: Clinical Psychology & Psychiatry
                Keywords: clinical utility, ethics, next generation sequencing, pharmacogenetics, prediction, personalized medicine.

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