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Slide 1-

Challenges in and Principles for Conducting Systematic Reviews of Genetic Tests Used as Predictive Indicators Prepared for: The Agency for Healthcare Research and Quality (AHRQ) Training Modules for Medical Test Reviews Methods Guide www.ahrq.gov
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Describe challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. Recognize the utility of organizing frameworks and analytic frameworks. Assess the importance of preanalytic, analytic, and postanalytic factors used for evaluating genetic tests. Understand important statistical and bias issues involved with predictive genetic tests. Learning Objectives Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Background on Genetic Tests Challenges in Reviews of Genetic Tests Principles for Addressing the Challenges Illustrative Examples General Overview Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Background on Genetic Tests Challenges in Reviews of Genetic Tests Principles for Addressing the Challenges Illustrative Examples General Overview: Section 1 Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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This module focuses on genetic tests that: Determine risk in asymptomatic individuals For example, BRCA1/BRCA2 hereditary mutations for breast and ovarian cancer Reveal prognostic information to guide future clinical management and treatment in those with a condition For example, the Oncotype Dx® test of acquired tumor genome mutations for breast cancer recurrence Predict response, using hereditary genetic makeup, to treatments or environmental factors including: Diet (nutrigenomics) Drugs (pharmacogenomics) Infectious agents Chemicals Physical agents Behavioral factors Background: Genetic Tests (1 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Key factors specifically affecting hereditary genetic tests need to be taken into account. These are: Penetrance of the disease Variable expressivity Pleiotropy Time lag to outcomes The first three terms (penetrance, variable expressivity, and pleiotropy) usually refer to single-gene disorders. Single-gene disorder: Occurs as the result of a single mutated gene; is inherited in well-described ways Polygenic disorder: Occurs as the result of combined action of more than one gene; is not inherited in simple Mendelian patterns Background: Genetic Tests (2 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Background on Genetic Tests Challenges in Reviews of Genetic Tests Principles for Addressing the Challenges Illustrative Examples General Overview: Section 2 Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Penetrance Penetrance is the proportion of people with a particular genetic change who exhibit signs and symptoms of a disorder. It is essential in determining the future risk of developing a disease and for assessing clinical utility of genetic tests. Sufficient data may be lacking to estimate penetrance. Challenges in Reviews of Genetic Tests (1 of 6) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Lister Hill National Center for Biomedical Communications. Genetics Home Reference: What are reduced penetrance and variable expressivity. Available at www.ghr.nlm.nih.gov/handbook/inheritance/penetranceexpressivity.
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Variable Expressivity Variable expressivity is the range of severity of symptoms among different people with the same condition. Example: Hemochromatosis manifests into a wide spectrum of symptoms that vary in severity from mild to life threatening. The degree of expressivity should be considered in evaluating genetic tests. Challenges in Reviews of Genetic Tests (2 of 6) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Lister Hill National Center for Biomedical Communications. Genetics Home Reference: What are reduced penetrance and variable expressivity. Available at www.ghr.nlm.nih.gov/handbook/inheritance/penetranceexpressivity.
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Pleiotropy This term refers to a single gene’s influence on multiple phenotypic traits. For example, the genetic mutation causing Marfan’s syndrome results in cardiovascular, skeletal, and ophthalmologic abnormalities. BRCA1 and BRCA2 mutations can influence the risk for multiple cancer types including breast, ovarian, prostate, and skin (i.e., melanoma). Challenges in Reviews of Genetic Tests (3 of 6) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Time Lag This term refers to the time difference between genetic testing and clinically important events. Sufficient duration of studies is important in evaluating studies of genetic tests. It should be determined if subjects have reached the age beyond which clinical expression would be likely. Challenges in Reviews of Genetic Tests (4 of 6) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Other Challenges Direct evidence for the impact of genetic test results on health outcomes is often lacking. The evidence base is too limited in scope. It is difficult to find published information on various aspects of genetic tests, particularly analytic validity. There are several technical issues that are relevant to assessment of analytic validity. These issues: Differ according to the type of test May influence interpretation of results May differ, depending on the specimen being tested (e.g., different considerations when assessing tumor genomes vs. human genomes) Challenges in Reviews of Genetic Tests (5 of 6) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Other Challenges (Continued) Difficulties can arise when determining susceptibility or risk in asymptomatic individuals. Utility of a test may depend on the ability of individuals to report and identify certain clinical factors. Statistical issues must also be taken into account. Genetic test results often are derived from analytically complex studies that have undergone a very large number of statistical tests. Challenges in Reviews of Genetic Tests (6 of 6) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Background on Genetic Tests Challenges in Reviews of Genetic Tests Principles for Addressing the Challenges Illustrative Examples General Overview: Section 3 Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Use an organizing framework appropriate for genetic tests. Develop analytic frameworks to reflect the predictive nature of genetic tests and incorporate appropriate outcomes. Search databases appropriate for genetic tests. Consult with experts to determine which technical issues are important to address in assessing genetic tests. Distinguish between functional assays and DNA-based assays to determine important technical issues. Evaluate case-control studies carefully for potential selection bias. Determine the added value of the genetic test over existing risk assessment approaches. Understand statistical issues of particular relevance to genetic tests. Principles for Addressing the Challenges Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Organizing frameworks for genetic tests have been developed by the USPSTF, the CDC, and the EGAPP Working Group. The ACCE model is endorsed by the EGAPP Working Group. The criteria of the model are as follows: Analytic validity (technical accuracy and reliability) Clinical validity (ability to detect or predict an outcome, disorder, or phenotype) Clinical utility (whether test ultimately leads to improved patient outcomes) Ethical, legal, and social implications The ACCE model has also developed a series of 44 questions following these criteria. The questions help define a review’s scope and critically appraise studies. Principle 1: Use an Organizing Framework Appropriate for Genetic Tests Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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ACCE Model Questions for Reviews of Genetic Tests (1 of 3) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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ACCE Model Questions for Reviews of Genetic Tests (2 of 3) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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ACCE Model Questions for Reviews of Genetic Tests (3 of 3) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Clearly define the Key Questions and understand the needs of decisionmakers and the test context. Consider whether test is used to: Determine the future risk of disease in asymptomatic individuals Establish prognostic information that will influence treatment decisions Predict response to treatments (either effectiveness or harms) Clarify the specimens (e.g., patient or tumor genome) to be tested. Clearly describe the PICOTS typology. Doing so will inform development of the analytic framework and vice versa. Principle 2: Develop Analytic Frameworks To Reflect the Predictive Nature of Genetic Tests and Incorporate Appropriate Outcomes (1 of 5) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Principle 2: Develop Analytic Frameworks To Reflect the Predictive Nature of Genetic Tests and Incorporate Appropriate Outcomes (2 of 5) A Generic Analytic Framework for Evaluating Predictive Genetic Tests Considering preanalytic, analytic, and postanalytic factors, as well as outcomes, in analytic frameworks may be useful. Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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The adverse effects of genetic testing are: Potential effects on family members Psychological distress Potential stigmatization or discrimination Harms particularly occur with the prediction of high disease probability, but no preventative or ameliorative measures exist. For these harms, take into account: Whether testing is for inherited or acquired mutations/ variations Whether the test condition is multifactorial or follows Mendelian inheritance patterns Principle 2: Develop Analytic Frameworks To Reflect the Predictive Nature of Genetic Tests and Incorporate Appropriate Outcomes (3 of 5) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Other outcomes include but are not limited to: Cost Quality of life Long-term morbidity Indirect impact Genetic tests may impact decisions that are difficult to measure yet are very important (e.g., decisions regarding pregnancy). The impact of genetic testing on family members may be important depending on context Particularly in cases involving testing for heritable conditions Principle 2: Develop Analytic Frameworks To Reflect the Predictive Nature of Genetic Tests and Incorporate Appropriate Outcomes (4 of 5) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Principle 2: Develop Analytic Frameworks To Reflect the Predictive Nature of Genetic Tests and Incorporate Appropriate Outcomes (5 of 5) Generic Analytic Framework for Evaluating Predictive Genetic Tests When the Impact on Family Members Is Important Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Human Genome Epidemiology Network (HuGE Net) HuGE Net is a good supplement to main literature databases (e.g., PubMed®). It contains meta-analyses of genetic association studies. The HuGE Navigator can identify all types of available studies related to a genetic test. Gray Literature Package inserts for genetic tests approved by the U.S. Food and Drug Administration (FDA) These inserts include summaries of analytic validity data that were instrumental during FDA approval. They are available on the FDA Web site and on that of the test manufacturers. Genetic test data are also available from the FDA Center for Devices and Radiological Health Web site. Principle 3: Search Databases Appropriate for Genetic Tests (1 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Gray Literature (Continued) American College of Medical Genetics and College of American Pathologists joint external proficiency testing program Useful when no published data on analytic validity exist Gives degree of lab-to-lab variability and sense of reproducibility Professional society conference publications GeneTests Web site sponsored by the Genetic Alliance Association for Molecular Pathology Web site Centers for Disease Control and Prevention programs (e.g., the Genetic Testing Reference Materials Coordination Program) International proficiency testing programs LexisNexis® database and Cambridge Healthtech Institute Use to identify genetic tests in development for cancer Principle 3: Search Databases Appropriate for Genetic Tests (2 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Consult with an expert familiar with a genetic test to determine which of the following technical issues are pertinent: Preanalytic Factors Obtaining, preserving, and storing samples before analysis Analytic Factors Type of assay, sample, methods being used, et cetera Postanalytic Factors Complexity of interpreting the genetic test result, lab-to-lab variability, and quality control Principle 4: Consult With Experts To Determine Which Technical Issues Are Important in Assessing Genetic Tests Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Questions for Assessing Preanalytic, Analytic, and Postanalytic Factors for Evaluating Predictive Genetic Tests *Portions of this table were adapted from Burke et al. (2002) and Little et al. (2002). Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Burke W, Atkins D, Gwinn M, et al. Am J Epidemiol 2002 Aug 15;156(4):300-10. PMID: 12181100. Little J, Bradley L, Bray MS, et al. Am J Epidemiol 2002 Aug 15;156(4):300-10. PMID: 12181099.
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Functional Assays Are used to measure the substrate/metabolic product affected by a genetic polymorphism Have the advantage of showing functional significance of an underlying genetic polymorphism Can be affected by factors unrelated to a polymorphism: Unmeasured environmental factors Other genetic polymorphisms Various disease states Assays that measure enzyme activity: Are evaluated at a single point in time Yield activities that vary over time Principle 5: Distinguish Between Functional Assays and DNA-Based Assays To Determine Important Technical Issues (1 of 3) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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DNA-Based Assays Analyze the DNA itself Use a variety of sample sources (e.g., blood, cheek swab, hair) All samples should result in identical genotypic results. Some sources more difficult to isolate DNA from (e.g., formalin-fixed tissue). Studies sometimes use different sources of DNA, which can introduce measurement bias because of differences in ease of technique and test accuracy. Principle 5: Distinguish Between Functional Assays and DNA-Based Assays To Determine Important Technical Issues (2 of 3) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Extracting DNA from tumor samples raises additional issues: Quantity of tissue Admixture of normal and cancerous tissue Amount of necrosis Timing of collection Storage technique (e.g., fresh, frozen, paraffin, formalin) Complexity of test method, lab-to-lab variability, and quality control should be evaluated. Principle 5: Distinguish Between Functional Assays and DNA-Based Assays To Determine Important Technical Issues (3 of 3) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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It is essential for cases and controls to be selected from the same source population. Matching cases and controls for ethnicity/race is preferred over stating the geographic location of a source population. This is because frequencies of DNA polymorphisms vary from population to population. Generally, only nested case-control studies have a potential for selection bias that is low enough to provide reliable information. Principle 6: Evaluate Case-Control Studies Carefully for Potential Selection Bias Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Many clinical factors associated with risk assessment may already be well characterized. Reviewers should assess the added value of using genetic testing along with known factors when compared with using known factors alone. Example: Cardiovascular Disease Age, sex, smoking, hypertension, diabetes, and cholesterol are known risk factors. Assessment of newly identified polymorphisms (such as on chromosome 9p21) should be evaluated in context with these known factors to determine added value. Principle 7: Determine the Added Value of the Genetic Test Over Existing Risk Assessment Approaches Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Hardy-Weinberg Equilibrium (HWE) Most allele distributions follow a usual distribution, the HWE. Distributions deviate from HWE due to: New mutations Selection Migration Genetic drift Inbreeding Chance (applies when many polymorphisms are tested) Principle 8: Understand Statistical Issues of Particular Relevance to Genetic Tests (1 of 4) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Attia J, Ioannidis JP, Thakkinstian A, et al. JAMA 2009 Jan 7;301(1):74-81. PMID: 19126812. Attia J, Ioannidis JP, Thakkinstian A, et al. JAMA 2009 Jan 14;301(2):191-7. PMID: 19141767.
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Sample Size Calculations It is important to assess whether sample size calculations appropriately accounted for: The number of variant alleles The prevalence of variants in the study population Pharmacogenomic studies often enroll an insufficient number of subjects to account for these factors. Principle 8: Understand Statistical Issues of Particular Relevance to Genetic Tests (2 of 4) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Williams JA, Johnson K, Paulauskis J, et al. J Clin Pharmacol 2006 Mar;46(3):258-64. PMID: 16490801.
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Genetic Association Studies Genetic test results are sometimes derived from complex studies with a large number of statistical tests. For example, genome-wide association studies that search for associations between a huge number of genetic polymorphisms and health conditions These studies should generally be regarded as hypothesis generating rather than hypothesis testing or confirmatory of cause-effect relationships. Associations should be validated in multiple studies to minimize potential confounding and publication bias. Evaluating causality may be difficult because of factors that influence associations (e.g., environmental exposures, behaviors, other genes). Principle 8: Understand Statistical Issues of Particular Relevance to Genetic Tests (3 of 4) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Overlapping Data Sets Publications may sometimes have overlapping data sets regarding prevalence of genetic variants. Genome-wide association studies or other large collaborative efforts may pool patients who were previously included in other published studies. To the extent possible, double-counting should be avoided by investigators. Assessing Tumor Genetics Tumor genomes may be in a dynamic state. Tumor specimens often contain normal patient cells. Specimen characteristics affect the operation and sensitivity of the test. Principle 8: Understand Statistical Issues of Particular Relevance to Genetic Tests (4 of 4) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Background on Genetic Tests Challenges in Reviews of Genetic Tests Principles for Addressing the Challenges Illustrative Examples General Overview: Section 4 Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Adults with depression who are treated with selective serotonin reuptake inhibitors (SSRIs) undergo genetic testing for cytochrome P450 polymorphisms. This is an example of a well-defined predictive genetic testing scenario. The investigators developed an analytic framework and five key questions to explore the chain of evidence leading to intermediate outcomes. The investigators found no prospective studies of the use of genotyping to guide treatment that measured clinical outcomes The investigators evaluated the links between genotype and metabolism of SSRIs (phenotype), metabolism and SSRI efficacy, and metabolism and adverse drug reactions to SSRIs. Illustrative Example 1: Cytochrome P450 Testing (1 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Matchar DB, Thakur MD, Grossman I, et al. Evidence Report/Technology Assessment No. 146. Available at www.archive.ahrq.gov/downloads/pub/evidence/pdf/cyp450/cyp450.pdf.
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Analytic Framework for Gathering Evidence on CYP450 Genotyping of Adults Receiving SSRI Treatment for Nonpsychotic Depression Illustrative Example 1: Cytochrome P450 Testing (2 of 2) *The circled numbers refer to the Key Questions addressed. SSRI = selective serotonin reuptake inhibitors * Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Matchar DB, Thakur MD, Grossman I, et al. Evidence Report/Technology Assessment No. 146. Available at www.archive.ahrq.gov/downloads/pub/evidence/pdf/cyp450/cyp450.pdf.
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Assessment of human epidermal growth factor receptor 2 (HER2) status is used to manage patients with breast cancer and other solid tumors. HER2 testing is challenging because of substantial heterogeneity and the lack of information related to test methods. Very recently, certain aspects of the HER2 test were standardized. Before standardization, the effects of widely varying test methods could not be isolated. The challenges of HER2 testing were targeted in narrative review with this Key Question: What is the evidence of concordance and discrepancy rates for methods (e.g., fluorescence in situ hybridization, immunohistochemistry, etc.) used to analyze HER2 status in breast tumor tissue? Illustrative Example 2: HER2 Testing Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Seidenfeld J, Samson DJ, Rothenberg BM, et al. Evidence Report/Technology Assessment No. 172. Available at www.ahrq.gov/research/findings/evidence-based-reports/her2-evidence-report.pdf
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Testing for the BRCA mutation is used to predict risk of breast and ovarian cancer in high-risk women. The need for such testing exemplifies the importance of obtaining an accurate family history. Population-based BRCA testing is currently not appropriate. All contributing genes are not known. Mutation detection methods are not perfect. Family history is used to determine risk categories instead. It is the key preanalytic factor with influence on analytic validity. Family history is a genetic/genomics tool used to: Identify people with possible inherited susceptibilities Guide genetic testing Help interpret genetic test results Assess disease risk Illustrative Example 3: BRCA Mutation Testing (1 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Nelson HD, Huffman LH, Fu R, et al. Ann Intern Med 2005 Sep 6;143(5):362-79. PMID: 16144895. U.S. Preventive Services Task Force. Ann Intern Med 2005 Sep 6;143(5):355-61. PMID: 16144894.
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The ability of providers to accurately determine a family history that indicates increased risk is needed for BRCA mutation tests and other predictive genetic tests to be useful. Sometimes it is difficult for people to accurately recall whether their relatives have a medical condition. The sensitivity and specificity of family history self-reports are important in determining overall usefulness of predictive genetic testing. Illustrative Example 3: BRCA Mutation Testing (2 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Nelson HD, Huffman LH, Fu R, et al. Ann Intern Med 2005 Sep 6;143(5):362-79. PMID: 16144895.
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The general principles for evaluating genetic tests are similar to prognostic or predictive tests, but differences exist in their application and relevance. A clearly defined clinical scenario and analytic framework are important for evaluating any test. Organizing frameworks—such as the ACCE model (analytic validity, clinical validity, clinical utility, and ethical, legal, and social implications)—and analytic frameworks are useful for evaluating genetic tests. Preanalytic, analytic, and postanalytic factors should be considered when constructing an analytic framework. Predictive genetic tests generally have a time lag between testing and clinically important events. Key Messages (1 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Published information on the analytic validity of genetic tests may be difficult to locate—Web sites and gray literature may be important sources. When clinical factors associated with risk are well known, genetic tests should be evaluated for added value. For genome-wide association studies, it should be determined whether the association has been validated in multiple studies to minimize confounding and publication bias and whether appropriate adjustments for multiple comparisons were used. Key Messages (2 of 2) Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Methods guide for medical test reviews. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm.
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Reviewers should assess the added value of using genetic testing along with which of the following: Known risk factors Age, sex, hypertension, etc. Functional assays Choices: i only i and ii i and iii i, ii, and iii Practice Question 1 (1 of 2)
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Explanation for Question 1: The correct answer is b. Reviewers should assess the added value of using genetic testing technology along with known risk factors. Age, sex, and hypertension are examples of known risk factors that may apply to the disease being studied, and genetic tests should be evaluated in context with known risk factors such as these. Practice Question 1 (2 of 2)
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Analytic frameworks for evaluating predictive genetic tests only focus on the preanalytic, analytic, and postanalytic factors that affect the test. True False Practice Question 2 (1 of 2)
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Explanation for Question 2: The statement is false. Analytic frameworks for evaluating predictive genetic tests do include considerations of preanalytic, analytic, and postanalytic factors; however, other factors are also included, such as outcomes, benefits, harms, and the intended use of the test. Practice Question 2 (2 of 2)
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The “E” in the ACCE model refers to which of the following: Expressivity Effectiveness Efficacy Ethical Environmental Practice Question 3 (1 of 2)
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Explanation for Question 3: The correct answer is d. The “E” stands for ethical. ACCE is an acronym for analytic validity, clinical validity, clinical utility, and ethical, legal, and social implications. Practice Question 3 (2 of 2)
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This presentation was prepared by Brooke Heidenfelder, Rachael Posey, Lorraine Sease, Remy Coeytaux, Gillian Sanders, and Alex Vaz, members of the Duke University Evidence-based Practice Center. The module is based on Jonas DE, Wilt TJ, Taylor BC, et al. Challenges in and principles for conducting systematic reviews of genetic tests used as predictive indicators. In: Chang SM and Matchar DB, eds. Methods guide for medical test reviews. Rockville, MD: Agency for Healthcare Research and Quality; June 2012. p. 11.1-17. AHRQ Publication No. 12-EHC017. Available at www.effectivehealthcare.ahrq.gov/medtestsguide.cfm. Authors
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Caporaso N. Selection of candidate genes for population studies. IARC Sci Publ 1999;(148):23-36. PMID: 10493246. Centers for Disease Control and Prevention. Public Health Genomics. Genomic Testing. ACCE Model Process for Evaluating Genetic Tests. Available at www.cdc.gov/genomics/gtesting/ACCE/index.htm. Accessed August 22, 2011. Chin KM, Wessler B, Chew P, et al. Genetic Tests for Cancer. Technology Assessment (Prepared by the Tufts–New England Medical Center Evidence-based Practice Center under Contract No. 290-02-0022). Rockville, MD: Agency for Healthcare Research and Quality; January 2006. Available at www.archive.ahrq.gov/clinic/ta/ gentests/gentests.pdf. Cotton SC, Sharp L, Little J, et al. Glutathione S-transferase polymorphisms and colorectal cancer: a HuGE review. Am J Epidemiol 2000 Jan 1;151(1):7-32. PMID: 10625170. References (2 of 11)
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d'Errico A, Malats N, Vineis P, et al. Review of studies of selected metabolic polymorphisms and cancer. IARC Sci Publ 1999;(148):323-93. PMID: 10493265. Dorman JS, Bunker CH. HLA-DQ locus of the human leukocyte antigen complex and type 1 diabetes mellitus: a HuGE review. Epidemiol Rev 2000;22(2):218-27. PMID: 11218373. Gale KB, Ford AM, Repp R, et al. Backtracking leukemia to birth: identification of clonotypic gene fusion sequences in neonatal blood spots. Proc Natl Acad Sci USA 1997 Dec;94(25):13950-4. PMID: 9391133. Garcia-Closas M, Egan KM, Abruzzo J, et al. Collection of genomic DNA from adults in epidemiological studies by buccal cytobrush and mouthwash. Cancer Epidemiol Biomarkers Prev 2001 Jun;10(6):687-96. PMID: 11401920. References (3 of 11)
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Khoury MJ, Dorman JS. The Human Genome Epidemiology Network. Am J Epidemiol 1998 Jul 1;148(1):1-3. PMID: 9663396. Lister Hill National Center for Biomedical Communications. Genetics Home Reference: What are reduced penetrance and variable expressivity? Available at www.ghr.nlm.nih.gov/ handbook/inheritance/penetranceexpressivity. Accessed August 22, 2011. Little J, Bradley L, Bray MS, et al. Reporting, appraising, and integrating data on genotype prevalence and gene-disease associations. Am J Epidemiol 2002 Aug 15;156(4):300-10. PMID: 12181099. References (5 of 11)
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Matchar DB, Thakur ME, Grossman I, et al. Testing for Cytochrome P450 Polymorphisms in Adults With Non-psychotic Depression Treated With Selective Serotonin Reuptake Inhibitors (SSRIs). Evidence Report/Technology Assessment No. 146 (Prepared by the Duke Evidence-based Practice Center under Contract No. 290-02-0025). Rockville, MD: Agency for Healthcare Research and Quality; January 2007. AHRQ Publication No. 07-E002. Available at www.archive.ahrq.gov/downloads/pub/ evidence/pdf/cyp450/cyp450.pdf. McCarthy MI, Abecasis GR, Cardon LR, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008 May;9(5):356-69. PMID: 18398418. References (6 of 11)
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National Cholesterol Education Program Expert Panel on Detection, Evaluations, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002 Dec 17;106(24):3143-421. PMID: 12485966. Nelson HD, Huffman LH, Fu R, et al. Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med 2005 Sep 6;143(5):362-79. PMID: 16144895. Palomaki GE, Bradley LA, Richards CS, et al. Analytic validity of cystic fibrosis testing: a preliminary estimate. Genet Med 2003 Jan-Feb;5(1):15-20. PMID: 12544471. References (7 of 11)
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Palomaki GE, Haddow JE, Bradley LA, et al. Updated assessment of cystic fibrosis mutation frequencies in non-Hispanic Caucasians. Genet Med 2002 Mar-Apr;4(2):90-4. PMID: 11882786. Palomaki GE, Haddow JE, Bradley LA, et al. Estimated analytic validity of HFE C282Y mutation testing in population screening: the potential value of confirmatory testing. Genet Med 2003 Nov-Dec;5(6):440-3. PMID: 14614395. Schunkert H, Gotz A, Braund P, et al; Cardiogenics Consortium. Repeated replication and a prospective meta-analysis of the association between chromosome 9p21.3 and coronary artery disease. Circulation 2008 Apr 1;117(13):1675-84. PMID: 18362232. References (8 of 11)
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Seidenfeld J, Samsom DJ, Rothenberg BM, et al. HER2 Testing To Manage Patients With Breast Cancer or Other Solid Tumors. Evidence Report/Technology Assessment No. 172 (Prepared by the Blue Cross and Blue Shield Association Technology Evaluation Center Evidence-based Practice Center under Contract No. 290-02-0026). Rockville, MD: Agency for Healthcare Research and Quality; November 2008. AHRQ Publication No. 09-E001. Available at www.ahrq.gov/research/ findings/evidence-based-reports/her2-evidence-report.pdf. Sun F, Bruening W, Erinoff E, et al. Evaluation Frameworks, Analytic Validity and Quality Rating of Genetic Tests. Methods Research Report (Prepared by the ECRI Institute Evidence-based Practice Center under Contract No. HHSA 290-2007-10063-I). Rockville, MD: Agency for Healthcare Research and Quality; June 2011. AHRQ Publication No. 11-EHC048-EF. Available at www.effectivehealthcare.ahrq.gov/ehc/products/105/704/Genetic-Test-Evaluation_Final-Report_20110615.pdf. References (9 of 11)
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Teutsch SM, Bradley LA, Palomaki GE, et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet Med 2009 Jan;11(1):3-14. PMID: 18813139. U.S. Preventive Services Task Force. Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: recommendation statement. Ann Intern Med 2005 Sep 6;143(5):355-61. PMID: 16144894. Walker AH, Najarian D, White DL, et al. Collection of genomic DNA by buccal swabs for polymerase chain reaction-based biomarker assays. Environ Health Perspect 1999 Jul;107(7):517-20. PMID: 10378997. Whitlock EP, Garlitz BA, Harris EL, et al. Screening for hereditary hemochromatosis: a systematic review for the U.S. Preventive Services Task Force. Ann Intern Med 2006 Aug 1;145(3):209-23. PMID: 16880463. References (10 of 11)
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Williams JA, Johnson K, Paulauskis J, et al. So many studies, too few subjects: establishing functional relevance of genetic polymorphisms on pharmacokinetics. J Clin Pharmacol 2006 Mar;46(3):258-64. PMID: 16490801. Yang M, Hendrie HC, Hall KS, et al. Improved procedure for eluting DNA from dried blood spots. Clin Chem 1996 Jul;42(7):1115-6. PMID: 8674202. References (11 of 11)