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Summary The clinical application of genetics and genomics to advance precision health is one of the most dynamic and promising areas of medicine. In 2020, building on nearly 15 years of work, the Roundtable on Genomics and Precision Health of the National Academies of Sciences, Engineering, and Medicine undertook a strategic planning process to assess its strengths, consider the current challenges facing the field, and set out new goals for its future work. As a result, the Roundtable has updated its vision and mission and prioritized four major areas of inquiry—innovation, dialogue, equity, and adoption—while keeping true to its founding goal of providing a neutral convening space for the diversity of stakeholders in genomics and precision health. The Roundtable is unique for its breadth of membership and is committed to fostering a new era for precision health built on decades of expanding knowledge and the emergence of new technologies. To achieve its goals, the Roundtable seeks to broaden its membership’s diversity and to engage with new audiences. Roundtable members explore how evidence-based discoveries in genomics could be adopted and used in innovative ways to better serve human health, how equitable access to genomic and precision health technologies can be ensured, and how the Roundtable and broader genomics and precision health community can communicate more effectively to inform the public regarding genomics and precision health. As a first principle, the Roundtable is working to support the overall goal that all people benefit from genomics for precision health.

IMPORTANCE: Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection. OBJECTIVE: To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017. EXPOSURES: Germline variant detection using standard or deep learning methods. MAIN OUTCOMES AND MEASURES: The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach. RESULTS: The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, –1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, –2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]). CONCLUSIONS AND RELEVANCE: Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.

Persons with a family history (FH) of colorectal cancer (CRC) or adenomas that are not due to known hereditary syndromes have an increased risk for CRC. An understanding of these risks, screening recommendations, and screening behaviors can inform strategies for reducing the CRC burden in these families. A comprehensive review of the literature published within the past 10 years has been performed to assess what is known about cancer risk, screening guidelines, adherence and barriers to screening, and effective interventions in persons with an FH of CRC and to identify FH tools used to identify these individuals and inform care. Existing data show that having 1 affected first-degree relative (FDR) increases the CRC risk 2-fold, and the risk increases with multiple affected FDRs and a younger age at diagnosis. There is variability in screening recommendations across consensus guidelines. Screening adherence is

This study examines the analytic validity of a software tool designed to provide individuals with risk assessments for colorectal cancer based on personal health and family history information. The software is compatible with the US Surgeon General's My Family Health Portrait (MFHP).An algorithm for risk assessment was created using accepted colorectal risk assessment guidelines and programmed into a software tool (MFHP). Risk assessments derived from 150 pedigrees using the MFHP tool were compared with "gold standard" risk assessments developed by three expert cancer genetic counselors.Genetic counselor risk assessments showed substantial, but not perfect, agreement. MFHP risk assessments for colorectal cancer yielded a sensitivity for colorectal cancer risk of 81% (95% confidence interval: 54-96%) and specificity of 90% (95% confidence interval: 83-94%), as compared with genetic counselor pedigree review. The positive predictive value for risk for MFHP was 48% (95% confidence interval: 29-68%), whereas the negative predictive value was 98% (95% confidence interval: 93-99%). Agreement between MFHP and genetic counselor pedigree review was moderate (κ = 0.54).The analytic validity of the MFHP colorectal cancer risk assessment software is similar to those of other types of screening tools used in primary care. Future investigations should explore the clinical validity and utility of the software in diverse population groups.Genet Med 17 9, 753-756.

The US health care system has seen significant changes over the last decade resulting from diverse factors including the widespread adoption of electronic health records, the opioid crisis, and the roll-out of the Affordable Care Act. Such changes have buffeted the day-to-day work of physicians and other health care practitioners, leaving many finding it challenging to address demands placed on them. Looking forward in 2017, it seems that the future will hold more turbulence.1 In this milieu, it is easily possible to overlook that medicine has been undergoing a much more gradual and deeper transformation. This shift is inexorably moving medicine from an endeavor in which care for individual patients is driven by trial and error informed by studies designed to measure population outcomes to one in which care is selected based on a deep understanding of health and disease attributes unique to each individual. Accelerated by the completion of the Human Genome Project, this transformation has been variably called genomic medicine, genomic health care, personalized medicine, precision medicine, and precision health. The extent to which it ultimately alters medicine remains unclear.

Newborn screening (NBS) programs have been successful in identifying infants with rare, treatable, congenital conditions. While current programs rely largely on biochemical analysis, some predict that in the future, genome sequencing may be used as an adjunct. The purpose of this exploratory pilot study was to begin to characterize genetics professionals' opinions of the use of whole-genome sequencing (WGS) in NBS. We surveyed members of the American College of Medical Genetics and Genomics (ACMG) via an electronic survey distributed through email. The survey included questions about results disclosure, the current NBS paradigm, and the current criteria for adding a condition to the screening panel. The response rate was 7.3 % (n = 113/1549). The majority of respondents (85 %, n = 96/113) felt that WGS should not be currently used in NBS, and that if it were used, it should not be mandatory (86.5 %, n = 96/111). However, 75.7 % (n = 84/111) foresee it as a future use of WGS. Respondents felt that accurate interpretation of results (86.5 %, n = 83/96), a more extensive consent process (72.6 %, n = 69/95), pre- (79.2 %, n = 76/96) and post-test (91.6 %, n = 87/95) counseling, and comparable costs (70.8 %, n = 68/96) and turn-around-times (64.6 %, n = 62/96) to current NBS would be important for using WGS in NBS. Participants were in favor of disclosing most types of results at some point in the lifetime. However, the majority (87.3 %, n = 96/110) also indicated that parents should be able to choose what results are disclosed. Overall, respondents foresee NBS as a future use of WGS, but indicated that WGS should not occur within the framework of traditional NBS. They agreed with the current criteria for including a condition on the recommended uniform screening panel (RUSP). Further discussion about these criteria is needed in order to better understand how they could be utilized if WGS is incorporated into NBS.

Persons with a family history (FH) of colorectal cancer (CRC) or adenomas that are not due to known hereditary syndromes have an increased risk for CRC. An understanding of these risks, screening recommendations, and screening behaviors can inform strategies for reducing the CRC burden in these families. A comprehensive review of the literature published within the past 10 years has been performed to assess what is known about cancer risk, screening guidelines, adherence and barriers to screening, and effective interventions in persons with an FH of CRC and to identify FH tools used to identify these individuals and inform care. Existing data show that having 1 affected first-degree relative (FDR) increases the CRC risk 2-fold, and the risk increases with multiple affected FDRs and a younger age at diagnosis. There is variability in screening recommendations across consensus guidelines. Screening adherence is50% and is lower in persons under the age of 50 years. A provider's recommendation, multiple affected relatives, and family encouragement facilitate screening; insufficient collection of FH, low knowledge of guidelines, and poor family communication are important barriers. Effective interventions incorporate strategies for overcoming barriers, but these have not been broadly tested in clinical settings. Four strategies for reducing CRC in persons with familial risk are suggested: 1) improving the collection and utilization of the FH of cancer, 2) establishing a consensus for screening guidelines by FH, 3) enhancing provider-patient knowledge of guidelines and communication about CRC risk, and 4) encouraging survivors to promote screening within their families and partnering with existing screening programs to expand their reach to high-risk groups. Cancer 2016. © 2016 American Cancer Society. Cancer 2016;122:2633-2645. © 2016 American Cancer Society.

Implementation of genomic discoveries into health care optimally includes evaluation of outcomes for recipients of care, providers, payers, and health care systems. However, the influence of specific aspects of the implementation process on observed outcomes may be missed if assessment of implementation success is not built into the implementation design. The intersection of implementation science with genomics may provide new insights on how to maximize the benefits of emerging genomic technologies in health care. In this summary, members of the Roundtable on Genomics and Precision Health, formerly the Roundtable on Translating Genomic-Based Research for Health, of the National Academies of Sciences, Engineering, and Medicine and the American Academy of Nursing explore challenges and opportunities for nurses to participate in implementing genomic discoveries into their practice informed by the principles of implementation science. Implementation requires collaboration across disciplines. Nurses can take leadership roles in engaging key stakeholders in health care organizations, assuring that communications regarding implementation are consistent with genomic literacy for each group of stakeholders, and planning for evaluation of data to assess how each component of the implementation process affected the overall outcome for health care.

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.

Genomic research has generated much new knowledge into mechanisms of human disease, with the potential to catalyze novel drug discovery and development, prenatal and neonatal screening, clinical pharmacogenomics, more sensitive risk prediction, and enhanced diagnostics. Genomic medicine, however, has been limited by critical evidence gaps, especially those related to clinical utility and applicability to diverse populations. Genomic medicine may have the greatest impact on health care if it is integrated into primary care, where most health care is received and where evidence supports the value of personalized medicine grounded in continuous healing relationships. Redesigned primary care is the most relevant setting for clinically useful genomic medicine research. Taking insights gained from the activities of the Institute of Medicine (IOM) Roundtable on Translating Genomic-Based Research for Health, we apply lessons learned from the patient-centered medical home national experience to implement genomic medicine in a patient-centered, learning health care system.

This Viewpoint discusses the integration of precision medicine discoveries with the learning health care system via implementation science.