Multiplexed Assays of Variant Effect and Automated Patch-clamping Improve KCNH2-LQTS Variant Classification and Cardiac Event Risk Stratification

Abstract

Background: Long QT syndrome (LQTS) is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by KCNH2. Variant classification is difficult, often owing to lack of functional data. Moreover, variant-based risk stratification is also complicated by heterogenous clinical data and incomplete penetrance. Here, we sought to test whether variant-specific information, primarily from high-throughput functional assays, could improve both classification and cardiac event risk stratification in a large, harmonized cohort of KCNH2 missense variant heterozygotes.

Methods: We quantified cell-surface trafficking of 18,796 variants in KCNH2 using a Multiplexed Assay of Variant Effect (MAVE). We recorded KCNH2 current density for 533 variants by automated patch clamping (APC). We calibrated the strength of evidence of MAVE data according to ClinGen guidelines. We deeply phenotyped 1,458 patients with KCNH2 missense variants, including QTc, cardiac event history, and mortality. We correlated variant functional data and Bayesian LQTS penetrance estimates with cohort phenotypes and assessed hazard ratios for cardiac events.

Results: Variant MAVE trafficking scores and APC peak tail currents were highly correlated (Spearman Rank-order ρ = 0.69). The MAVE data were found to provide up to pathogenic very strong evidence for severe loss-of-function variants. In the cohort, both functional assays and Bayesian LQTS penetrance estimates were significantly predictive of cardiac events when independently modeled with patient sex and adjusted QT interval (QTc); however, MAVE data became non-significant when peak-tail current and penetrance estimates were also available. The area under the ROC for 20-year event outcomes based on patient-specific sex and QTc (AUC 0.80 [0.76-0.83]) was improved with prospectively available penetrance scores conditioned on MAVE (AUC 0.86 [0.83-0.89]) or attainable APC peak tail current data (AUC 0.84 [0.81-0.88]).

Conclusion: High throughput KCNH2 variant MAVE data meaningfully contribute to variant classification at scale while LQTS penetrance estimates and APC peak tail current measurements meaningfully contribute to risk stratification of cardiac events in patients with heterozygous KCNH2 missense variants.

Keywords: LQTS; arrhythmias; automated patch-clamping; multiplexed assay of variant effect; risk stratification.

Figure 1.. Project Overview.

Integration of two high-throughput functional assays with clinical deep phenotyping, quantitative penetrance estimates, and prospective risk prediction models.

Figure 2.. Results of a KCNH2 Multiplexed Assay of Variant Effect.

A) Schematic of MAVE assay. We employed a barcode abundance-based MAVE of cell-surface KCNH2 variant expression to quantify variant trafficking, the primary mechanism of KCNH2 variant loss-of-function. B) Distribution of WT-normalized variant trafficking scores among missense, synonymous, and nonsense variants. C) Heatmap depicting trafficking scores across the coding region of KCNH2. Dark orange indicated less than WT trafficking, white similar trafficking to WT, and blue increased trafficking. Missing data are depicted in gray.

Figure 3.. Classification and Diagnostic Value of MAVE Trafficking Assay.

A) Distribution of trafficking scores among control variants for assay calibration. Controls selected from those described by Thomson et al. B) 2×2 table of assay result vs variant classification in control group. An abnormal assay result was defined as z-score < −2, arising from the distribution of B/LB variant scores as previously described. C) Receiver operator characteristic curve of MAVE data applied across all readily available ClinVar B/LB and P/LP annotations. D-E) Violin plots showing trafficking scores of VUS and Conflicting Interpretation variants in ClinVar. Dotted lines reflect OddsPath thresholds (D), and Log-likelihood ratios (E).

Figure 4.. Results of a KCNH2 Automated Patch Clamp assay.

A) Example APC peak tail currents recorded at −50 mV showing different levels of function. Y-axis is 500 pA and X-axis is 500ms. B) KCNH2 peak-tail current densities for 533 variants (n=38,772 recordings) across 6 domains of the protein observed in our clinical cohort, gnomAD, and previous literature reports. Benign variant controls from gnomAD are shown as white circles. Blue range depicts variants with ‘normal function’, as defined by a ±2 z-score window from the mean current density for B/LB variants. C) Matrix of z-score determined normal and abnormal variants studied by both functional assays. D) Visual correlation of functional assays by residue position.

Figure 5.. Descriptive Correlations of Functional Data with Missense Heterozygote Cohort Clinical Features.

A-C) Correlations between participant QTc and functional scores for all available cohort members with each descriptor (Spearman rho). D-F) Stratification of each descriptor by cohort members experiencing cardiac event (Mann-Whitney U-test).

Figure 6.. Clinical Risk Models and Applications of Variant-specific and Patient-Specific features.

A-C) Royston-Parmar Hazard Ratios (RP HR) for first 20-year cardiac event with baseline patient-specific features of sex and adjusted QTc, and variant-specific data of MAVE, LQTS penetrance, and APC. D) Royston-Parmar Hazard Ratios (RP HR) for first 20-year cardiac event cardiac event with all patient-specific and variant-specific data. E) ROCs/AUCs for three different models for all cardiac events through 20 years of age.