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Suggested Citation:"6 Addressing Challenges." Institute of Medicine. 2014. Assessing Genomic Sequencing Information for Health Care Decision Making: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18799.
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Suggested Citation:"6 Addressing Challenges." Institute of Medicine. 2014. Assessing Genomic Sequencing Information for Health Care Decision Making: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18799.
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Suggested Citation:"6 Addressing Challenges." Institute of Medicine. 2014. Assessing Genomic Sequencing Information for Health Care Decision Making: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18799.
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Suggested Citation:"6 Addressing Challenges." Institute of Medicine. 2014. Assessing Genomic Sequencing Information for Health Care Decision Making: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18799.
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Suggested Citation:"6 Addressing Challenges." Institute of Medicine. 2014. Assessing Genomic Sequencing Information for Health Care Decision Making: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18799.
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Suggested Citation:"6 Addressing Challenges." Institute of Medicine. 2014. Assessing Genomic Sequencing Information for Health Care Decision Making: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18799.
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Page 66

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6 Addressing Challenges During the final session of the workshop, a panel of discussants each sought to identify the top challenges and areas that could be pursued for evaluating genomic information in the era of next-generation sequencing. The group addressed such issues as a framework for reimbursement of genetic testing; understanding the clinical context in which testing in- formation is used, or “evidence fit for purpose,” as David Veenstra, the workshop chair, said; forming data resource collaborations, such as ClinGen; and population-based studies for evidence generation. Box 6-1 lists the major themes that emerged during the workshop. Box 6-2 contains suggestions and proposals from individual workshop speakers for assessing genomic sequencing information. BOX 6-1 Topics That Were Addressed During the Workshop David Veenstra, the workshop chair, listed several points that had been mentioned repeatedly during the course of discussion about evaluating evidence: • Greater consensus, or at least consistency, in the ways that genomic data are gathered, analyzed, graded, reimbursed, and used to shape practice guidelines could greatly advance their application in the clinic. • The context in which genomic information is to be used can be a major influence on that use. 61

62 GENOME SEQUENCE INFORMATION IN HEALTH CARE DECISIONS • Collaborations among researchers and clinicians may be useful in generating and applying genomic data effectively. • Patient preferences and financial costs are likely to be im- portant factors in the application of genomic data to medicine. BOX 6-2 Proposals Made By Individual Speakers • Establishing the minimum amount of data that is needed to include a gene on a test panel would reduce the variation in the genes evaluated for the same condition. (Rehm) • Strategies and terminology for classifying sequence variants vary by society and research group, and it will take work within the genomics community to agree on a common clas- sification system that is easily understood by all users. (Rehm) • A searchable, international database that contains large- scale sequencing data along with phenotypic information will be useful for identifying phenotype-related genetic common- alities that would otherwise be unknown. (Rehm) • There is a need for prospective follow-up studies of individu- als who have been found to have germline sequence vari- ants in genes that are thought to be associated with disease risk. Partnerships with both academic and commercial test- ing laboratories will be an effective way to identify such indi- viduals. (Robson) • There must be an evidentiary basis for incorporating a vari- ant into clinical care or reporting it to physicians or patients. (Berg) • Collecting more population-based data in a central location from clinical or research studies could be a solution for ex- trapolating to larger populations instead of relying on what is currently used for this purpose—high-risk population data. (Goddard) • Establishing central repositories for clinically relevant vari- ants and phenotypes and encouraging laboratories to con- tribute to it would provide resources with a standardized format for studying the clinical validity of gene–phenotype associations. (Berg) • Working collectively on assessing variants for clinical use would be a more efficient process as it would help alleviate the time required by individual groups. (Hegde, Kulkarni)

ADDRESSING CHALLENGES 63 • Electronic health records with the capacity to handle ge- nomic information are needed so that the data can be ac- cessed throughout the course of care, but decisions need to be made about what specific genomic information should be in- cluded and where it will reside. (Berg) • Taking an individualized approach to returning results to pa- tients allows for the consideration of patient preferences and the ability to contextualize the information as it relates to the patient’s condition and family history. (Everett) • Discussing the content of laboratory testing consent forms can be valuable for delivering results to patients. (Gambello) • A more efficient but still rigorous practice guideline develop- ment process is needed because current methods are time consuming and additional practice guidelines would be use- ful for the field of genomics. (Lyman) • Medical schools and residency programs need to expand their genetics and genomics curricula so that physicians and other practitioners are better prepared to handle this type of information in clinical practice. (Saal) • Evidence quality is more important than expert opinion for assessing new genomic tests and determining coverage. If stakeholders can agree on an evidentiary framework for making these assessments, it will bring predictability to the coverage determination process. (McDonough) A FRAMEWORK FOR REIMBURSEMENT Robert McDonough of Aetna said that the biggest challenge is com- ing up with a logical, pragmatic framework for reimbursement. “We need to be able to try to come to some consensus and have some con- sistency around what type of genomic testing is useful,” he said. With regard to reimbursement, Shashikant Kulkarni, director of cy- togenomics and molecular pathology at the Washington University School of Medicine, observed that work is under way to identify genetic tests with established clinical utility so that reimbursement makes sense. But a lack of information about next-generation sequencing hinders re- imbursement decisions. For example, amplicon-based tests cost much less than large-scale sequencing but are not equivalent to whole gene panels. “When it comes to reimbursement, the payers should take into consideration these differences in approaches.”

64 GENOME SEQUENCE INFORMATION IN HEALTH CARE DECISIONS A LACK OF EVIDENCE The most significant difficulty is the lack of evidence, said Robert Green of Brigham and Women’s Hospital and Harvard Medical School. “Lacking evidence is not something that is entirely new to doctors. Doc- tors have been practicing medicine without evidence for a long time and continue to do so in lots of domains. [But more evidence is] definitely something we need.” In particular, Green called for more coordinated sharing of genotype–phenotype correlations over the next 5 to 10 years. The ClinGen collaboration is a good first step, he said, but even that “is probably underfunded for what is going to happen.” Jonathan Berg of the University of North Carolina at Chapel Hill agreed that projects like ClinGen provide an opportunity to share data in a common format and language but that a clinically relevant resource is needed for mining vari- ants from different sources. “There is a significant body of data out there which we and others are mining: the Cancer Genome Atlas and the International Consortium of Cancer Genomics, which is beginning to produce an enormous amount of data,” Kulkarni said. “Still, it’s a huge amount of data which has to be mined.” The data analysis and interpretation is time consuming, so even with a significant amount of information, he said, the field of oncology suffers from a similar lack of evidence for the majority of ge- netic variants. Sequencing Standards Establishing quality standards for sequencing studies—and also for how to report on such studies—would be valuable, said Katrina Goddard of the Kaiser Permanente Northwest Center for Health Research. For example, sometimes a study is rated as being of poor quality because the information needed to assess the quality of the study is not included in the literature. Green added that this idea could be implemented if jour- nals led the way. Standards have been established for both conducting and reporting on randomized clinical trials, he observed, and something similar could be done for gene association studies. Kulkarni also called attention to the lack of sequencing standards for such parameters as sensitivity and specificity. For example, some groups are using 200 nanograms of DNA for detecting 10 percent of the tumor cells, he said, while others claim that only 5 or 10 nanograms provides sufficient sensitivity. These issues are even more pressing in cancer,

ADDRESSING CHALLENGES 65 where the frequency of alleles and composition of cells within a tumor can differ. “We need to address standards to understand what types of minimal requirements are essential,” he said. POPULATION-BASED STUDIES Because of the concern that extrapolated data from high-risk popula- tions may not be generalizable to the larger population, there is a need for collecting data from large population-based studies, Goddard said. Information from clinical studies and from research studies must be combined in order to arrive at valid conclusions at the population level, she said. “By combining across different efforts, you may be able to get a sufficient sample size,” she said. Green noted that longitudinally collect- ing such information would be very expensive, to which Goddard re- sponded that simply starting with unselected populations would be a step in the right direction. Goddard pointed to initiatives that are using EHRs as a source of re- search data. Green also pointed to the need to look beyond single patients to entire families. Given there will be HIPAA challenges, it will be use- ful to use EHRs that contain phenotype data and link that information with genotypes and phenotypes from other family members, he said. Ge- netics does this at an individual level, but it has not made the transition to a macro level. Jessica Everett of the University of Michigan Comprehen- sive Cancer Center noted, however, that the sequencing of family mem- bers is typically not reimbursed, even when the information would be extremely useful in understanding a condition. Large-scale genome sequencing efforts are now under way in the United States,1 the United Kingdom, and Saudi Arabia,2 Green said (Callaway, 2013). Berg observed, however, that the challenge is doing the phenotyping. “It’s trivial to sequence a million genomes compared to phenotyping a million people,” he said. Until enough people with rare variants are phenotyped, the penetrance of those variants will be largely unknown, he added. A million genomes may not even be close to the sample size that is needed for generating the evidence, Veenstra said. 1 Regeneron and Geisinger Health System Announce Major Human Genetics Research Collaboration, http://investor.regeneron.com/releasedetail.cfm?ReleaseID=818844 (accessed May 15, 2014). 2 Saudi Human Genome Program, http://rc.kfshrc.edu.sa/sgp (accessed May 15, 2014).

66 GENOME SEQUENCE INFORMATION IN HEALTH CARE DECISIONS CONCLUDING REMARKS “Next-generation sequencing is a disruptive technology,” Veenstra said. In fact, it is likely also disruptive to the process of evidence-based medicine, especially with the issues related to many possible causative variants and secondary findings or incidental findings. The way these issues can be addressed, he said, is by continuing to increase our under- standing of how policy and treatment decisions are made in an era of limited evidence and a large volume of information.

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Rapid advances in technology have lowered the cost of sequencing an individual's genome from the several billion dollars that it cost a decade ago to just a few thousand dollars today and have correspondingly greatly expanded the use of genomic information in medicine. Because of the lack of evidence available for assessing variants, evaluation bodies have made only a few recommendations for the use of genetic tests in health care. For example, organizations, such as the Evaluation of Genomic Applications in Practice and Prevention working group, have sought to set standards for the kinds of evaluations needed to make population-level health decisions. However, due to insufficient evidence, it has been challenging to recommend the use of a genetic test. An additional challenge to using large-scale sequencing in the clinic is that it may uncover "secondary," or "incidental," findings - genetic variants that have been associated with a disease but that are not necessarily related to the conditions that led to the decision to use genomic testing. Furthermore, as more genetic variants are associated with diseases, new information becomes available about genomic tests performed previously, which raises issues about how and whether to return this information to physicians and patients and also about who is responsible for the information.

To help develop a better understanding of how genomic information is used for healthcare decision making, the Roundtable on Translating Genomic-Based Research for Health of the Institute of Medicine held a workshop in Washington,DC in February 2014. Stakeholders, including clinicians, researchers, patients, and government officials, discussed the issues related to the use of genomic information in medical practice. Assessing Genomic Sequencing Information for Health Care Decision Making is the summary of that workshop. This report compares and contrasts evidence evaluation processes for different clinical indications and discusses key challenges in the evidence evaluation process.

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