Management of Incidental Findings in Clinical Genomic Sequencing

Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

The rate of secondary genomic findings in the Saudi population

Secondary findings (SF) are defined as genetic conditions discovered unintentionally during an evaluation of raw data for another disease. We aimed to identify the rate of secondary genetic findings in the Saudi population in the 59 genes of the American College of Medical Genetics and Genomics (ACMG) list. In our study, the raw data of 1254 individuals, generated from exome sequencing for clinical purposes, were studied. Variants detected in the 59 genes on the ACMG list of secondary findings were investigated. Pathogenicity classifications were assigned to those variants based on the ACMG scoring system. We identified 2409 variants in the 59 gene list, 45 variants were classified as pathogenic/likely pathogenic variants according to the ACMG classification. The LDLR gene had the greatest number of pathogenic/likely pathogenic variants 12%. Cardiovascular genetic diseases had the highest frequency of disorders detected as secondary findings. In this study, the overall rate of positive cases identified with secondary findings in the Saudi population was 8%. The different in our current study and the previous studies in Saudi Arabia can be explained by the differences between the sequencing method, the criteria used for variant classification, the availability of newer evidence at the time of the publication, and the fact that we identified Saudi novel variants never reported in other populations.

Anticipated responses of early adopter genetic specialists and nongenetic specialists to unsolicited genomic secondary findings

PURPOSE

Secondary findings from genomic sequencing are becoming more common. We compared how health-care providers with and without specialized genetics training anticipated responding to different types of secondary findings.

METHODS

Providers with genomic sequencing experience reviewed five secondary-findings reports and reported attitudes and potential clinical follow-up. Analyses compared genetic specialists and physicians without specialized genetics training, and examined how responses varied by secondary finding.

RESULTS

Genetic specialists scored higher than other providers on four-point scales assessing understandings of reports (3.89 vs. 3.42, p = 0.0002), and lower on scales assessing reporting obligations (2.60 vs. 3.51, p < 0.0001) and burdens of responding (1.73 vs. 2.70, p < 0.0001). Nearly all attitudes differed between findings, although genetic specialists were more likely to assert that laboratories had no obligations when findings had less-established actionability (p < 0.0001 in interaction tests). The importance of reviewing personal and family histories, documenting findings, learning more about the variant, and recommending familial discussions also varied according to finding (all p < 0.0001).

CONCLUSION

Genetic specialists felt better prepared to respond to secondary findings than providers without specialized genetics training, but perceived fewer obligations for laboratories to report them, and the two groups anticipated similar clinical responses. Findings may inform development of targeted education and support.

A Comparison of Whole Genome Sequencing to Multigene Panel Testing in Hypertrophic Cardiomyopathy Patients

BACKGROUND

As DNA sequencing costs decline, genetic testing options have expanded. Whole exome sequencing and whole genome sequencing (WGS) are entering clinical use, posing questions about their incremental value compared with disease-specific multigene panels that have been the cornerstone of genetic testing.

METHODS AND RESULTS

Forty-one patients with hypertrophic cardiomyopathy who had undergone targeted hypertrophic cardiomyopathy genetic testing (either multigene panel or familial variant test) were recruited into the MedSeq Project, a clinical trial of WGS. Results from panel genetic testing and WGS were compared. In 20 of 41 participants, panel genetic testing identified variants classified as pathogenic, likely pathogenic, or uncertain significance. WGS identified 19 of these 20 variants, but the variant detection algorithm missed a pathogenic 18 bp duplication in myosin binding protein C (MYBPC3) because of low coverage. In 3 individuals, WGS identified variants in genes implicated in cardiomyopathy but not included in prior panel testing: a pathogenic protein tyrosine phosphatase, non-receptor type 11 (PTPN11) variant and variants of uncertain significance in integrin-linked kinase (ILK) and filamin-C (FLNC). WGS also identified 84 secondary findings (mean=2 per person, range=0-6), which mostly defined carrier status for recessive conditions.

CONCLUSIONS

WGS detected nearly all variants identified on panel testing, provided 1 new diagnostic finding, and allowed interrogation of posited disease genes. Several variants of uncertain clinical use and numerous secondary genetic findings were also identified. Whereas panel testing and WGS provided similar diagnostic yield, WGS offers the advantage of reanalysis over time to incorporate advances in knowledge, but requires expertise in genomic interpretation to appropriately incorporate WGS into clinical care.

CLINICAL TRIAL REGISTRATION

URL: https://clinicaltrials.gov. Unique identifier: NCT01736566.

A Comparison of Whole Genome Sequencing to Multigene Panel Testing in Hypertrophic Cardiomyopathy Patients

BACKGROUND

As DNA sequencing costs decline, genetic testing options have expanded. Whole exome sequencing and whole genome sequencing (WGS) are entering clinical use, posing questions about their incremental value compared with disease-specific multigene panels that have been the cornerstone of genetic testing.

METHODS AND RESULTS

Forty-one patients with hypertrophic cardiomyopathy who had undergone targeted hypertrophic cardiomyopathy genetic testing (either multigene panel or familial variant test) were recruited into the MedSeq Project, a clinical trial of WGS. Results from panel genetic testing and WGS were compared. In 20 of 41 participants, panel genetic testing identified variants classified as pathogenic, likely pathogenic, or uncertain significance. WGS identified 19 of these 20 variants, but the variant detection algorithm missed a pathogenic 18 bp duplication in myosin binding protein C (MYBPC3) because of low coverage. In 3 individuals, WGS identified variants in genes implicated in cardiomyopathy but not included in prior panel testing: a pathogenic protein tyrosine phosphatase, non-receptor type 11 (PTPN11) variant and variants of uncertain significance in integrin-linked kinase (ILK) and filamin-C (FLNC). WGS also identified 84 secondary findings (mean=2 per person, range=0-6), which mostly defined carrier status for recessive conditions.

CONCLUSIONS

WGS detected nearly all variants identified on panel testing, provided 1 new diagnostic finding, and allowed interrogation of posited disease genes. Several variants of uncertain clinical use and numerous secondary genetic findings were also identified. Whereas panel testing and WGS provided similar diagnostic yield, WGS offers the advantage of reanalysis over time to incorporate advances in knowledge, but requires expertise in genomic interpretation to appropriately incorporate WGS into clinical care.

CLINICAL TRIAL REGISTRATION

URL: https://clinicaltrials.gov. Unique identifier: NCT01736566.

Incidentalome from Genomic Sequencing: A Barrier to Personalized Medicine?

BACKGROUND

In Western cohorts, the prevalence of incidental findings (IFs) or incidentalome, referring to variants in genes that are unrelated to the patient's primary condition, is between 0.86% and 8.8%. However, data on prevalence and type of IFs in Asian population is lacking.

METHODS

In 2 cohorts of individuals with genomic sequencing performed in Singapore (total n = 377), we extracted and annotated variants in the 56 ACMG-recommended genes and filtered these variants based on the level of pathogenicity. We then analyzed the precise distribution of IFs, class of genes, related medical conditions, and potential clinical impact.

RESULTS

We found a total of 41,607 variants in the 56 genes in our cohort of 377 individuals. After filtering for rare and coding variants, we identified 14 potential variants. After reviewing primary literature, only 4 out of the 14 variants were classified to be pathogenic, while an additional two variants were classified as likely pathogenic. Overall, the cumulative prevalence of IFs (pathogenic and likely pathogenic variants) in our cohort was 1.6%.

CONCLUSION

The cumulative prevalence of IFs through genomic sequencing is low and the incidentalome may not be a significant barrier to implementation of genomics for personalized medicine.