Challenges in and Principles for Conducting Systematic Reviews of Genetic Tests Used as Predictive Indicators Prepared for: The Agency for Healthcare Research

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

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.

Background on Genetic Tests Challenges in Reviews of Genetic Tests Principles for Addressing the Challenges Illustrative Examples

General Overview



General Overview: Section 1


This module focuses on genetic tests that:1. Determine risk in asymptomatic individuals

For example, BRCA1/BRCA2 hereditary mutations for breast and ovarian cancer

2. 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

3. 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)


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)





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 variableexpressivity. Available at www.ghr.nlm.nih.gov/handbook/inheritance/penetranceexpressivity.

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.


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 variableexpressivity. Available at www.ghr.nlm.nih.gov/handbook/inheritance/penetranceexpressivity.

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).



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.



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)



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.






1. Use an organizing framework appropriate for genetic tests.

2. Develop analytic frameworks to reflect the predictive nature of genetic tests and incorporate appropriate outcomes.

3. Search databases appropriate for genetic tests.

4. Consult with experts to determine which technical issues are important to address in assessing genetic tests.

5. Distinguish between functional assays and DNA-based assays to determine important technical issues.

6. Evaluate case-control studies carefully for potential selection bias.

7. Determine the added value of the genetic test over existing risk assessment approaches.

8. Understand statistical issues of particular relevance to genetic tests.

Principles for Addressing the Challenges


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:1. Analytic validity (technical accuracy and reliability)

2. Clinical validity (ability to detect or predict an outcome, disorder, or phenotype)

3. Clinical utility (whether test ultimately leads to improved patient outcomes)

4. 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


ACCE Model Questions for Reviews of Genetic Tests (1 of 3)

Element Questions Disorder/setting

1. What is the specific clinical disorder to be studied? 2. What are the clinical findings defining this disorder? 3. What is the clinical setting in which the test is to be performed? 4. What DNA test(s) are associated with this disorder?* 5. Are preliminary screening questions employed? 6. Is it a stand-alone test or is it one of a series of tests? 7. If it is part of a series of screening tests, are all tests performed

in all instances (parallel) or are only some tests performed on the basis of other

results (series)? Analytic validity

8. Is the test qualitative or quantitative? 9. How often is the test positive when a mutation is present?* 10. How often is the test negative when a mutation is not present?* 11. Is an internal quality control program defined and externally

monitored? 12. Have repeated measurements been made on specimens? 13. What is the within-and between-laboratory precision? 14. If appropriate, how is confirmatory testing performed to resolve

false positive results in a timely manner? 15. What range of patient specimens have been tested? 16. How often does the test fail to give a usable result? 17. How similar are results obtained in multiple laboratories using

the same, or different technology?



Clinical validity

18. How often is the test positive when the disorder is present? 19. How often is the test negative when a disorder is not

present? 20. Are there methods to resolve clinical false positive results in

a timely manner?21. What is the prevalence of the disorder in this setting? 22. Has the test been adequately validated on all populations to

which it may be offered? 23. What are the positive and negative predictive values? 24. What are the genotype/phenotype relationships?* 25. What are the genetic, environmental or other modifiers?*

Clinical utility

(Continued)

26. What is the natural history of the disorder? 27. What is the impact of a positive (or negative) test on patient

care? 28. If applicable, are medical tests available? 29. Is there an effective remedy, acceptable action, or other

measurable benefit? 30. Is there general access to that remedy or action? 31. Is the test being offered to a socially vulnerable population? 32. What quality assurance measures are in place? 33. What are the results of pilot trials? 34. What health risks can be identified for follow-up testing

and/or intervention?



Clinical utility(Continued)

35. What are the financial costs associated with testing? 36. What are the economic benefits associated with actions resulting

from testing? 37. What facilities/personnel are available or easily put in place? 38. What educational materials have been developed and validated

and which of these are available?* 39. Are there informed consent requirements?* 40. What methods exist for long term monitoring? 41. What guidelines have been developed for evaluating program

performance? Ethical, legal, and social implications

42. What is known about stigmatization, discrimination, privacy/confidentiality and

personal/family social issues?* 43. Are there legal issues regarding consent, ownership of data

and/or samples, patents, licensing, proprietary testing, obligation to disclose, or

reporting requirements?* 44. What safeguards have been described and are these safeguards

in place and effective?* 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.

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)



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.


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



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




Generic Analytic Framework for Evaluating Predictive Genetic Tests When the Impact on Family Members Is

Important


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)


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)


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


Questions for Assessing Preanalytic, Analytic, and Postanalytic Factors for Evaluating Predictive Genetic Tests

Element Questions*Preanalytic • What patient characteristics are relevant to the analytic validity of the test

(e.g., age, sex, ethnicity, race, ancestry, parental history of consanguinity, family health history)?

• What types of samples were used? • How were samples obtained? • How were samples handled and stored prior to analysis?

Analytic • What type of assay was used? What is the reliability of the assay? • What specific analyte was investigated (e.g., specification of which alleles, genes, or biochemical analytes were evaluated)?

• For DNA-based tests, what is the definition of the genotype investigated? • Did the study test for all potentially relevant alleles? • For DNA-based tests, what genotyping methods were used? • When were samples analyzed (compared to when they were collected)? • Was the timing of analysis equal for both study groups (if applicable)? • How often does the test give a usable result (what is the “call rate”)?

Postanalytic

• How are the test results interpreted and applied? How complex is interpretation and application?

• What quality control measures were used? Were repeated measurements made on specimens?

• How reproducible is the test over time? How reproducible is the test when repeated in the same patient multiple times? How reproducible is the test from laboratory to laboratory?

*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.

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)


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.



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.



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


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


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.

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.


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.

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).



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.






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.

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.

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 atwww.ahrq.gov/research/findings/evidence-based-reports/her2-evidence-report.pdf

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.

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.

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)


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)


1. Reviewers should assess the added value of using genetic testing along with which of the following:

i. Known risk factors

ii. Age, sex, hypertension, etc.

iii. Functional assays

Choices:

a. i only

b. i and ii

c. i and iii

d. i, ii, and iii

Practice Question 1 (1 of 2)

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.


2. Analytic frameworks for evaluating predictive genetic tests only focus on the preanalytic, analytic, and postanalytic factors that affect the test.

a. True

b. False



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.


3. The “E” in the ACCE model refers to which of the following:

a. Expressivity

b. Effectiveness

c. Efficacy

d. Ethical

e. Environmental



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.


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

Attia J, Ioannidis JP, Thakkinstian A, et al. How to use an article about genetic association: A: Background concepts. JAMA 2009 Jan 7;301(1):74-81. PMID: 19126812.

Attia J, Ioannidis JP, Thakkinstian A, et al. How to use an article about genetic association: B: Are the results of the study valid? JAMA 2009 Jan 14;301(2):191-7. PMID: 19141767.

Botto LD, Yang Q. 5,10-Methylenetetrahydrofolate reductase gene variants and congenital anomalies: a HuGE review. Am J Epidemiol 2000 May 1;151(9):862-77. PMID: 10791559.

Brockton N, Little J, Sharp L, et al. N-acetyltransferase polymorphisms and colorectal cancer: a HuGE review. Am J Epidemiol 2000 May 1;151(9):846-61. PMID: 10791558.

Burke W, Atkins D, Gwinn M, et al. Genetic test evaluation: information needs of clinicians, policy makers, and the public. Am J Epidemiol 2002 Aug 15;156(4):311-8. PMID: 12181100.

References (1 of 11)

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.


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.


Harris RP, Helfand M, Woolf SH, et al; Methods Work Group, Third US Preventive Services Task Force. Current methods of the US Preventive Services Task Force: a review of the process. Am J Prev Med 2001 Apr;20(3 Suppl):21-35. PMID: 11306229.

Harty LC, Shields PG, Winn DM, et al. Self-collection of oral epithelial cell DNA under instruction from epidemiologic interviewers. Am J Epidemiol 2000 Jan 15;151(2):199-205. PMID: 10645823.

Hoggart CJ, Clark TG, De Iorio M, et al. Genome-wide significance for dense SNP and resequencing data. Genet Epidemiol 2008 Feb;32(2): 179-85. PMID: 18200594.

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.


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.


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.


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.


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.


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.


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.


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.


Documents

Challenges in and Principles for Conducting Systematic Reviews of Genetic Tests Used as Predictive Indicators Prepared for: The Agency for Healthcare Research