Kapplinger, Jamie D.

Radioiodine ablation therapy is commonly used for patients with well-differentiated thyroid cancer. False-positive radioiodine uptake in benign cystic lesions on post-therapy SPECT/CT presents a recognized diagnostic challenge. Here, we present the case of a 33-year-old woman who underwent total thyroidectomy, followed by I-131 radioiodine therapy. Her post–I-131 therapy SPECT/CT revealed incidental radioiodine uptake in a diaphragmatic mesothelial cyst, prompting further investigation. She subsequently underwent a dedicated liver MRI and excision biopsy, both of which were negative for thyroid cancer metastasis. This case highlights the importance of identifying benign sources of radioiodine uptake to avoid unnecessary interventions.

Background: Next-Generation Sequencing (NGS) allows for sequencing of a many genes simultaneously, leading genetic testing companies to provide sequencing of all genes associated with a particular disease in large gene test panels. For example, there are over 90 distinct genes associated with premature sudden arrhythmic death. Previous publiclations have shown a background rate of rare non-synonymous single nucleotide variants (nsSNVs) in many of these genes which may be compounded when included in large gene panels. Therefore, we hoped to determine this background rate of nsSNVs in 93 genes that comprise various sudden death genetic heart disease genetic test panels. Methods: The exomes from the 1000 Genomes Project (1kG) and the NHLBI Exome Sequencing Project (ESP) were genotyped in-silico for nsSNVs in 93 genes indentified on clinically available genetic tests available for HCM, DCM, ARVC, LQTS, SQTS, BrS, and CPVT. The number of subjects hosting an exceedingly rare and unique nsSNV was determined for each gene. Results: Strikingly, 40.8% of the 1 kG cohort (12.5% hosted > 2 unique nsSNVs) and 41.9% of the ESP cohort possessed > 1 rare nsSNV within the entire 93 gene panel, while the yield of rare nsSNVs in individual genes only ranged from 0.02% for PLN to 2.3% for DMD. This background rate directly correlated with the number of genes on the panel (r=0.97). When limited to only those genes that account for >10% of the disease (HCM - MYBPC3, MYH7; LQTS - KCNQ1, KCNH2; ARVC - PKP2, DSG2; CPVT - RYR2; BrS - SCN5A), at least 7% of the subjects in both cohorts had at least one nsSNV. Thus, nearly 90% of the background rate is contained within the minor disease-susceptibility genes. Conclusions: Despite not being enriched for the presence of genetic channelopathies and cardiomyopathies, two out of every 5 subjects within these cohorts hosted at least 1 ultra-rare nsSNV within the 93 known channelopathy/cardiomyopathy-susceptibility genes. Despite the anticipated transition from disease-specific genetic testing to whole exome genetic testing, great caution must be exercised to glean the true pathogenic mutation amidst this very daunting burden of background genetic noise. If handled poorly, false positive genetic test results and misguided cascade screens will increase.

Background: Pathogenic RYR2 variants account for ≈60% of clinically definite cases of catecholaminergic polymorphic ventricular tachycardia. However, the rate of rare benign RYR2 variants identified in the general population remains a challenge for genetic test interpretation. Therefore, we examined the results of the RYR2 genetic test among patients referred for commercial genetic testing and examined factors impacting variant interpretability. Methods: Frequency and location comparisons were made for RYR2 variants identified among 1355 total patients of varying clinical certainty and 60 706 Exome Aggregation Consortium controls. The impact of the clinical phenotype on the yield of RYR2 variants was examined. Six in silico tools were assessed using patient- and control-derived variants. Results: A total of 18.2% (218/1200) of patients referred for commercial testing hosted rare RYR2 variants, statistically less than the 59% (46/78) yield among clinically definite cases, resulting in a much higher potential genetic false discovery rate among referrals considering the 3.2% background rate of rare, benign RYR2 variants. Exclusion of clearly putative pathogenic variants further complicates the interpretation of the next novel RYR2 variant. Exonic/topologic analyses revealed overrepresentation of patient variants in exons covering only one third of the protein. In silico tools largely failed to show evidence toward enhancement of variant interpretation. Conclusions: Current expert recommendations have resulted in increased use of RYR2 genetic testing in patients with questionable clinical phenotypes. Using the largest to date catecholaminergic polymorphic ventricular tachycardia patient versus control comparison, this study highlights important variables in the interpretation of variants to overcome the 3.2% background rate that confounds RYR2 variant interpretation.

Background Variable expressivity and incomplete penetrance between individuals with identical long QT syndrome (LQTS) causative mutations largely remain unexplained. Founder populations provide a unique opportunity to explore modifying genetic effects. We examined the role of a novel synonymous KCNQ1 p.L353L variant on the splicing of exon 8 and on heart rate corrected QT interval (QTc) in a population known to have a pathogenic LQTS type 1 (LQTS1) causative mutation, p.V205M, in KCNQ1-encoded Kv7.1. Methods 419 adults were genotyped for p.V205M, p.L353L and a previously described QTc modifier (KCNH2-p.K897T). Adjusted linear regression determined the effect of each variant on QTc, alone and in combination. In addition, peripheral blood RNA was extracted from three controls and three p.L353L-positive individuals. The mutant transcript levels were assessed via qPCR and normalised to overall KCNQ1 transcript levels to assess the effect on splicing. Results For women and men, respectively, p.L353L alone conferred a 10.0 (p=0.064) ms and 14.0 (p=0.014) ms increase in QTc and in men only a significant interaction effect in combination with the p.V205M (34.6 ms, p=0.003) resulting in a QTc of ∼500 ms. The mechanism of p.L353L's effect was attributed to approximately threefold increase in exon 8 exclusion resulting in ∼25% mutant transcripts of the total KCNQ1 transcript levels. Conclusions Our results provide the first evidence that synonymous variants outside the canonical splice sites in KCNQ1 can alter splicing and clinically impact phenotype. Through this mechanism, we identified that p.L353L can precipitate QT prolongation by itself and produce a clinically relevant interactive effect in conjunction with other LQTS variants.

Background— A 2% to 5% background rate of rare SCN5A nonsynonymous single nucleotide variants (nsSNVs) among healthy individuals confounds clinical genetic testing. Therefore, the purpose of this study was to enhance interpretation of SCN5A nsSNVs for clinical genetic testing using estimated predictive values derived from protein-topology and 7 in silico tools. Methods and Results— Seven in silico tools were used to assign pathogenic/benign status to nsSNVs from 2888 long-QT syndrome cases, 2111 Brugada syndrome cases, and 8975 controls. Estimated predictive values were determined for each tool across the entire SCN5A -encoded Na v 1.5 channel as well as for specific topographical regions. In addition, the in silico tools were assessed for their ability to correlate with cellular electrophysiology studies. In long-QT syndrome, transmembrane segments S3–S5+S6 and the DIII/DIV linker region were associated with high probability of pathogenicity. For Brugada syndrome, only the transmembrane spanning domains had a high probability of pathogenicity. Although individual tools distinguished case- and control-derived SCN5A nsSNVs, the composite use of multiple tools resulted in the greatest enhancement of interpretation. The use of the composite score allowed for enhanced interpretation for nsSNVs outside of the topological regions that intrinsically had a high probability of pathogenicity, as well as within the transmembrane spanning domains for Brugada syndrome nsSNVs. Conclusions— We have used a large case/control study to identify regions of Na v 1.5 associated with a high probability of pathogenicity. Although topology alone would leave the variants outside these identified regions in genetic purgatory, the synergistic use of multiple in silico tools may help promote or demote a variant’s pathogenic status.

Background— Hundreds of nonsynonymous single nucleotide variants (nsSNVs) have been identified in the 2 most common long-QT syndrome-susceptibility genes ( KCNQ1 and KCNH2 ). Unfortunately, an ≈3% background rate of rare KCNQ1 and KCNH2 nsSNVs amongst healthy individuals complicates the ability to distinguish rare pathogenic mutations from similarly rare yet presumably innocuous variants. Methods and Results— In this study, 4 tools [(1) conservation across species, (2) Grantham values, (3) sorting intolerant from tolerant, and (4) polymorphism phenotyping] were used to predict pathogenic or benign status for nsSNVs identified across 388 clinically definite long-QT syndrome cases and 1344 ostensibly healthy controls. From these data, estimated predictive values were determined for each tool independently, in concert with previously published protein topology–derived estimated predictive values, and synergistically when ≥3 tools were in agreement. Overall, all 4 tools displayed a statistically significant ability to distinguish between case-derived and control-derived nsSNVs in KCNQ1 , whereas each tool, except Grantham values, displayed a similar ability to differentiate KCNH2 nsSNVs. Collectively, when at least 3 of the 4 tools agreed on the pathogenic status of C-terminal nsSNVs located outside the KCNH2/Kv11.1 cyclic nucleotide−binding domain, the topology-specific estimated predictive value improved from 56% to 91%. Conclusions— Although in silico prediction tools should not be used to predict independently the pathogenicity of a novel, rare nSNV, our results support the potential clinical use of the synergistic utility of these tools to enhance the classification of nsSNVs, particularly for Kv11.1’s difficult to interpret C-terminal region.