Genetic Diagnostic Testing for Inherited Cardiomyopathies

Accepted Manuscript Genetic Diagnostic Testing for Inherited Cardiomyopathies: Considerations for Offering Multi-Gene Tests in a Health Care Setting Hussein Daoud, Mahdi Ghani, Landry Nfonsam, Ryan Potter, Shelley Ordorica, Virginia Haslett, Nathaniel Santos, Heather Derksen, Donelda Lahey, Martha McGill, Vanessa Trudel, Brittany Antoniuk, Nasim Vasli, Caitlin Chisholm, Gabrielle Mettler, Elizabeth Sinclair-Bourque, Jean McGowan-Jordan, Amanda Smith, Robert Roberts, Olga Jarinova PII:

S1525-1578(18)30177-6

DOI:

https://doi.org/10.1016/j.jmoldx.2019.01.004

Reference:

JMDI 767

To appear in:

The Journal of Molecular Diagnostics

Received Date: 25 April 2018 Revised Date:

16 November 2018

Accepted Date: 3 January 2019

Please cite this article as: Daoud H, Ghani M, Nfonsam L, Potter R, Ordorica S, Haslett V, Santos N, Derksen H, Lahey D, McGill M, Trudel V, Antoniuk B, Vasli N, Chisholm C, Mettler G, Sinclair-Bourque E, McGowan-Jordan J, Smith A, Roberts R, Jarinova O, Genetic Diagnostic Testing for Inherited Cardiomyopathies: Considerations for Offering Multi-Gene Tests in a Health Care Setting, The Journal of Molecular Diagnostics (2019), doi: https://doi.org/10.1016/j.jmoldx.2019.01.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Genetic Diagnostic Testing for Inherited Cardiomyopathies: Considerations for Offering Multi-Gene Tests in a Health Care Setting

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Hussein Daoud,* Mahdi Ghani,* Landry Nfonsam,* Ryan Potter,* Shelley Ordorica,* Virginia Haslett,* Nathaniel Santos,* Heather Derksen,* Donelda Lahey,* Martha McGill,* Vanessa Trudel,* Brittany Antoniuk,* Nasim Vasli,* Caitlin Chisholm,* Gabrielle

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Mettler,* Elizabeth Sinclair-Bourque,* Jean McGowan-Jordan,*† Amanda Smith,*†

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Robert Roberts,‡ and Olga Jarinova*†

From the Department of Genetics,* Children’s Hospital of Eastern Ontario, Ottawa, Ontario, Canada; the Department of Pathology and Laboratory Medicine,† University of Ottawa, Ottawa, Ontario, Canada; and the University of Arizona College of Medicine,‡

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Tucson, Arizona

Address correspondence to Drs. Hussein Daoud and Olga Jarinova: Department of

7600,

Fax:

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Genetics, CHEO, 401 Smyth Road, Ottawa, K1H 8L1, Ontario, Canada. Tel: (613) 737(613)

738-4814,

Email:

[email protected]

or

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[email protected]

Funding: Supported by the Innovation Fund of the Alternative Funding Plan for the Academic Health Sciences Centers of Ontario.

Disclosures: None declared.

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Footnote: Portions of this work were presented in the Science Hot Line session at the 2017 European Society of Cardiologists Congress held 26-30 August 2017, in

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Barcelona, Spain.

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Running Title: Cardiomyopathy Testing in a Diagnostic Setting

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Abstract

Inherited cardiomyopathies (ICs) are a major cause of heart disease. Given their

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marked clinical and genetic heterogeneity, the content and clinical utility of IC multigene panels has been the topic of continuous debate. Our genetics diagnostic laboratory has been providing clinical diagnostic testing for ICs since 2012. We began

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by testing nine genes, and expanded our panel by 5-fold in 2015. Here, we describe the implementation of a cost-effective next-generation sequencing (NGS)-based assay for

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testing of IC genes, including a protocol that minimizes the amount of Sanger sequencing required to confirm variants identified via NGS, which reduces the cost and time of testing. The NGS assay was developed for the simultaneous analysis of 45 IC genes, and assessed for the impact of panel expansion on variant detection, turn-

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around-time (TAT), and cost of testing in a cohort of 993 patients. The assay led to a considerable reduction in test cost and TAT. However, only a marginal increase was observed in the diagnostic yield, whereas the rate of inconclusive findings increased

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considerably. These findings suggest that the ongoing evaluation of gene content and monitoring of clinical utility for multi-gene tests are essential to achieve maximum

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clinical utility of multi-gene tests in a publicly-funded health care setting.

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Introduction

Inherited cardiomyopathies (ICs) are heterogeneous disorders of the myocardium,

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associated with cardiac dysfunction. ICs are classified into two major groups depending on the predominant organ involvement: primary cardiomyopathies which affect primarily the heart muscle, and secondary cardiomyopathies, where the cardiac involvement is

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secondary to generalized systemic disorders1. Primary cardiomyopathies include hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic

catecholaminergic

polymorphic

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right ventricular cardiomyopathy (ARVC), left ventricular non-compaction (LVNC), ventricular

tachycardia

(CPVT),

and

restrictive

cardiomyopathy (RCM). ICs have a substantial genetic component, and commonly exhibit autosomal dominant inheritance with reduced penetrance and variable

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expressivity2. These disorders often lead to progressive heart failure, and are the leading cause of sudden cardiac death in young adults and competitive athletes3-5. Consequently, genetic testing is used to confirm a diagnosis, guide management, and

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provide disease prognosis6-7. As there are now more than 50 genes implicated in ICs, genetic testing by Sanger-based technologies has become complex and prohibitively

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expensive8.

The advent of next-generation sequencing (NGS) technologies has led to a dramatic increase in data generation, enabling the simultaneous analysis of thousands of genes in a rapid, accurate, and cost-effective manner9-10. The availability of a wide variety of NGS platforms, and their adoption by diagnostic laboratories, has led to a dramatic change in the genetic testing landscape11. 4

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Currently, the three main NGS approaches used clinically include whole genome sequencing (WGS), whole exome sequencing (WES), and targeted sequencing (TS)12. WGS and WES are more comprehensive than TS, but the associated high costs and

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computational and data storage requirements have limited their widespread adoption by diagnostic laboratories for all clinical testing. TS has been implemented successfully for the diagnosis of a wide range of disorders, due to its increased coverage of genes of

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interest, improved analytical performance, and reduced number of variants identified

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requiring interpretation, compared to WGS and WES13-19.

TS assays are often employed for the molecular diagnosis of ICs. However, the gene content of these panels used for different phenotypic subtypes has been the subject of continuous debate. Several recent studies have compared the diagnostic performance

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of expanded versus focused IC panels; however, these studies have either focused on a specific cardiomyopathy subtype20 or analyzed results of a relatively small number of

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individuals21.

In this study, we describe the implementation of an NGS-based clinical assay for the

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simultaneous analysis of 45 IC genes, and examine the impact of panel expansion on variant detection, turn-around-time (TAT), and relative cost in a cohort of 993 patients.

Materials and Methods

Sample Selection and Experimental Design

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Samples from patients with a clinical diagnosis or suspicion of HCM or ARVC, referred for testing at the CHEO Genetics Diagnostic Laboratory, were selected for the validation of our NGS-based multi-gene panel. In the development phase, the NGS assay was

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performed on a cohort of 12 healthy individuals as well as 24 unrelated patients (14 with HCM and 10 with ARVC) who had previously underwent molecular genetic testing via Sanger Sequencing (SS) for five HCM genes (MYBPC3, MYH7, TNNI3, TNNT2, and

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TPM1) or four ARVC genes (DSG2, DSP, PKP2, and the TMEM43 Newfoundland founder mutation p.Ser358Leu22). This cohort of 36 individuals was used to i) optimize

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assay conditions and analysis settings, ii) assess the sequencing quality and coverage across all targeted genes, and iii) establish the performance of the variant-calling pipeline by comparing NGS variant calls to SS data from the previously tested HCM and

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ARVC genes. Hereafter, this cohort will be referred to as the “training cohort”.

In the validation phase, a separate cohort of 36 unrelated patients (27 with HCM, nine with ARVC) with no identified molecular diagnosis were analyzed using both the NGS

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procedure established during the development phase (Figure 1), and the validated diagnostic SS procedure; note that only the nine routinely tested HCM or ARVC genes

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listed above were analyzed in both procedures). Hereafter, this cohort will be referred to as the “validation cohort”. The sensitivity and specificity of the NGS assay was determined using data generated during the development and validation phases. Confidence intervals were calculated using the PEDro tool (http://www.pedro.org.au; last accessed April 25, 2018). The reproducibility of the NGS assay was assessed by repeating the entire NGS procedure for the same 24 patients from the training cohort. 6

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DNA Extraction, Quality Control, and Quantification Genomic DNA (gDNA) from each individual was extracted from whole blood using either

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the Qiagen Puregene kit or the QIAsymphony automated blood 200 protocol, according to the manufacturer’s instructions (Qiagen, Hilden, Germany). gDNA quality was assessed

by

estimating

the

A260/A280

ratio

using

the

NanoDrop

2000

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spectrophotometer (Thermo Scientific, Wilmington, DE). gDNA was quantified using the Qubit dsDNA BR assay kit and the Qubit 2.0 Fluorometer (Life Technologies, Carlsbad,

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CA), and normalized in Tris-HCl 10 mM, pH 8.5 to a final concentration of 5 ng/uL.

Targeted Enrichment

The TruSight Cardiomyopathy panel (Illumina, San Diego, CA), was used to target 46

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genes known to be involved in ICs8 including HCM, DCM, ARVC, and CPVT (Supplemental Table S1). These genes were selected by genetic experts in ICs, based on a careful literature review8. The panel includes ~2600 80-mer oligonucleotide probes

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designed to enrich 1,020 exons spanning the 46 targeted genes (Supplemental Table S1), with a cumulative target region size of 246 Kb. One gene (DTNA) was excluded

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from analysis by our laboratory due to its primary involvement in LVNC8, a condition that is not a primary target of our testing. After the commercially available Trusight Cardiomyopathy (CM) panel was validated and implemented in our laboratory, Illumina replaced it with the more comprehensive TruSight Cardio Sequencing Kit that targets 174 genes associated with inherited cardiac conditions. Because the implementation of new diagnostic tests is associated with a high validation burden, the validated CM panel, which is now provided as a custom-designed panel, was used. Although TruSight 7

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Cardiomyopathy kit is no longer available as an “off-the-shelf” product, manifest files for

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this panel are still on file and it is now provided by Illumina as a custom panel.

Library Preparation and Sequencing

Using Nextera transposomes, 50 ng of gDNA was enzymatically fragmented and tagged

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with adapters. Fragmented DNA was purified, indexed, and common adapters required for cluster generation and sequencing, added via PCR. The size distribution of DNA

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libraries was assessed using the Agilent DNA 1000 chip or high-sensitivity chip on the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). DNA libraries were quantified with the Qubit 2.0 Fluorometer using the Qubit dsDNA BR or dsDNA HS assay kit, and 12 libraries pooled in equimolar proportion (12-plex pool). Pooled libraries target

enriched

via

hybridization-based

chemistry

using

biotin-labeled

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were

oligonucleotide probes from the TruSight Cardiomyopathy panel, captured using streptavidin beads, washed, eluted from beads, purified, and PCR amplified. The final

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post-captured library was again quantified and the concentration in nM calculated using

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the following formula:

[concentration in ng/uL * 106) / (660 g/mol * average library size)]

The average library size was obtained from the Agilent Bioanalyzer. The final captured library was denatured using 0.2N NaOH and diluted to a final concentration of 12 pM in chilled HT1 buffer with a 1% PhiX control spike-in. Finally, 600 uL of the spiked library 8

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was loaded on a MiSeq cartridge and sequenced with a MiSeq version 2 reagent kit, using dual index paired-end 151 bp reads.

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NGS Data Analysis

The Sequencing Analysis Viewer program (Illumina) was used to assess the quality metrics of the sequencing runs. The MiSeq Reporter Software v.2.6.2 (Illumina, San

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Diego, CA) was used for adapter trimming, sample demultiplexing, and fastq file generation. Data from the following public databases were imported into NextGENe

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software v.2.4.1 (SoftGenetics, State College, PA) using the track manager tool and used for variant annotation: the 1000 Genomes Project, the NHLBI Exome Sequencing Project (ESP), the Exome Aggregation Consortium (ExAC), the database for nonsynonymous SNPs' functional predictions (dbNSFP), and ClinVar.

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Unique fasta files produced from MiSeq-generated Fastq files were aligned to the reference human genome (build hg19) using the NextGene software. Single nucleotide variants (SNVs), insertions/deletions (InDels), and homopolymer InDels were called

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using the default mutation filter settings (Mutation percentage ≤ 20%, SNP allele count ≤ 3, Total coverage count ≤ 5X, balance ratios and frequency unchecked). A “.bed” file

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(Illumina, Diego, CA) that contains genomic coordinates of all targeted coding regions ± 10 bp adjacent to each exon was used to automatically generate three reports: i) coverage report that includes all targeted nucleotides with low coverage (≤ 20X) as well as target region statistics (total and aligned reads, minimum and average coverage, etc.), ii) expression report that includes the minimum, average, and maximum coverage per targeted region, and iii) mutation report that include all variants within the target 9

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coding sequences ± 10 bp. A second mutation report was generated using a different “.bed” file to determine the presence of two clinically relevant intronic variants (GLA: c.640-801G>A and MYBPC3: c.3628-41_3628-17del) in individuals tested for HCM,

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DCM, or by a comprehensive Pan Cardiomyopathy panel (PanCM). Variants were described in accordance with the Human Genome Variation Society (HGVS version 2)

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recommendations (http://varnomen.hgvs.org/; last accessed April 25, 2018).

Variant Filtering and Interpretation

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Mutation and coverage reports generated by the NextGENe software were imported into individual worksheets of a Microsoft 2010 Excel template (Microsoft Corporation, Redmond, WA) which underwent diagnostic grade in-house validation to identify sequence variants as well as regions with low coverage in the genes of interest, based

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on the disease panel requested by the referring provider (Supplemental Table S1). Variants with a Minor Allele Frequency (MAF) ≥ 1% in ESP as well as either ExAC or the 1000 Genomes Project were considered to be common variants, and automatically

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annotated as benign polymorphisms. Variants with a MAF < 1% were assessed using the Alamut Visual software v.2.9.0 (Interactive Biosoftware, Rouen, France) in

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accordance with the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines for sequence variant interpretation, and subsequent classification23-24. Our diagnostic laboratory routinely submits variant interpretation data to ClinVar.

Sanger Sequencing of NGS Variants and Regions with Low Coverage 10

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Bidirectional SS was performed using the Big-Dye Terminator version 3.1 cycle sequencing kit on ABI 3730xl/ABI 3130xl genetic analyzers (Applied Biosystems, Carlsbad, CA), and results analyzed using Mutation Surveyor v.5.0.1 (SoftGenetics). All

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targeted nucleotides with NGS coverage <20 reads underwent SS if they also met one of the following criteria: i) gene contributes > 1% of the known mutations for the tested type of IC and coverage is < 10 reads, or ii) gene contributes < 1% of the known

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mutations for the tested type of IC, coverage is < 10 reads and a variant is seen in > 20% of the reads, or iii) coverage is between 10 and 20 reads, and a variant is identified

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in > 20% of the reads. For variants identified by NGS, SS was confirmed for pathogenic, likely pathogenic, and variants of uncertain significance (VUSs). To reduce the burden of SS confirmation, criteria for waiving SS was validated and implemented if the variant met all of the following criteria: i) variant is a single nucleotide substitution, ii) coverage

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is > 100 reads, iii) mutant allele frequency ≥ 35%, and iv) ≥ 10 single nucleotide substitutions have previously been confirmed via SS in that gene. These criteria were based on the New York State guidelines (July 2015). However, these criteria were not

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applied for variants in regions with high sequence homology (eg, MYH6, MYH7, some exons of TTN) that pose technical challenges for correct NGS read alignment, and

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therefore are prone to false negative and false positive results25; all pathogenic, likely pathogenic, and VUS variants in these regions were confirmed via SS.

Results

Sequencing Quality and Coverage Analysis

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In the test development phase, three MiSeq sequencing runs with 12 samples each (36 total) were performed. An overview of the sequencing metrics for these three runs is provided in Table 1. On average, 5.4 Gb of data were generated in each run, with

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89.27% of the sequenced bases meeting or exceeding the Q30 quality score. The read distribution across the 12 samples pooled in each run was proportional, resulting in an average coefficient of variation equal to 0.24. The training cohort data showed that the

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performance of the targeted capture of the 45 cardiomyopathy genes was highly efficient, with 79.56% of the NGS reads mapped to the targeted genes (Table 1).

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The average read depth across the targeted genes was 521, with 99.92% of targeted regions covered at a read depth of ≥ 10 reads, 99.78% covered at ≥ 20 reads, and 99.62% at ≥ 30 reads. The average read depth for each of the 45 targeted genes across the 36 samples is shown in Figure 2. Based on coverage data obtained from the training

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cohort, an average of 17 out of 996 (1.7%) targeted regions per sample had at least one nucleotide covered at ≤ 20 reads (Figure 3). When the coverage data of these regions were visually inspected, the average number of targets that needed to be filled-in by SS

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per the criteria described above was reduced to 3 per sample (0.3%). Among these regions, two exons with high GC content located in CTF1 (exon 2) and LDB3 (exon 1)

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had consistently low coverage, and frequently required SS fill-in when a variant was suspected to be present.

Performance of the NextGENe Analysis Pipeline The 24 patients sequenced in the training cohort were used to evaluate the performance of the NextGENe analysis pipeline. These patients carried known rare 12

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disease-causing variants (Supplemental Table S2) and/or common variants of various types such as single nucleotide variants (SNVs) and insertions or deletions (InDels), some of which were in homopolymer regions that are known to be technically

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challenging to sequence. A total of 254 variants (218 SNVs, 36 InDels) previously identified by SS were all detected by the NextGENe analysis pipeline in their correct zygosity, resulting in no false negatives (FN) (Table 2). In addition, no false positives

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(FP) were identified, leading to a 100% concordance rate between the NGS and SS

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assays.

Analytical Sensitivity and Specificity

A summary of pathogenic, likely pathogenic, and VUSs detected in our validation cohort are listed in Supplemental Table S2. All these variants had adequate coverage (> 40

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reads). Variant allele frequencies (VAF) for heterozygous calls ranged from 28.12% to 62.69% for SNVs, and from 26.21% to 52.38% for InDels (Supplemental Table S2). In total, 250 SNVs and 21 InDels of various sizes were identified in the 36 patients in the

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validation cohort. The InDels included one 6 bp deletion and 4 bp insertion in the PKP2 gene (c.2443_2448delins), as well as a deep intronic 25 bp deletion in the MYBPC3

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gene (c.3628-41_3628-17) (Supplemental Table S2). All of these variants were validated by SS, and no FN or FP variants were detected, resulting in full concordance between the two assays. A total of 468 SNVs and 57 InDels were detected by the NGS assay in the training and validation cohorts, and were all validated by SS, achieving an overall analytical sensitivity of 100% (95% CI, 99.27 to 100) (Table 2). The analytical specificity was calculated by evaluating the total number of bases in the five HCM and 13

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four ARVC genes previously analyzed by SS in the training and validation cohorts (reference calls or true negatives). No FP variants were detected across 893,597 sites within the nine genes, achieving an overall analytical specificity of 100% (95% CI, 100

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to 100) (Table 2).

Reproducibility

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To assess the inter-run variability, 24 patient samples from the training cohort underwent a duplicate enrichment process, and the four libraries (12 samples/library)

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were sequenced on different runs. Variant calls in the 45 targeted genes were compared between duplicates, and the number of discordant variant calls was calculated for each sample. In total, 5,651 variants were detected across the 48 samples. Only 31 out of the 5,651 variants were discordant between the 24 duplicated

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samples, resulting in a concordance rate of 99.45%. Assessment of the 31 discordant variants revealed that discrepancy in 11 variants was due to the location of these variants in regions with low coverage, which was resolved when these regions were

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filled-in by SS. The remaining 20 discordant variants had low VAF (<30%) and were ruled out by SS. Five unique variants located in four genes (EMD, LMNA, MYH6, TAZ)

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were detected in five samples, and three recurrent variants located in DSG2, MYH6, and TTN were detected in 15 samples, which were considered as technical artifacts or common FP variants. Considering the number of sequenced bases in the 24 duplicated samples, the FP rate was 0.00058%, demonstrating a robust performance of the NGS assay.

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Diagnostic Yield, Performance, and Relative Cost In July 2015, our laboratory expanded the genetic testing panel for HCM and ARVC, to incorporate three new IC sub-panels (DCM, CPVT, and a comprehensive Pan

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Cardiomyopathy panel (PanCM) to the test menu (Supplemental Table S1). From July 2015 through September 2016, 993 patients with a diagnosis or suspicion of an IC were referred to our laboratory for genetic testing, from genetics professionals and

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cardiologists. The distribution of patients across the different sub-panels is represented in Supplemental Figure S1. The most requested sub-panel was HCM (n=580; 58%),

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followed by PanCM (n=263; 27%), DCM (n=66; 7%), ARVC (n=61; 6%), and CPVT (n=23; 2%). All 993 patients were sequenced using the same NGS-based assay, and only genes included in the requested sub-panel were analyzed. Following variant interpretation and classification, patients were divided into three different categories: i)

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positive, when at least one likely pathogenic or pathogenic variant was identified, ii) inconclusive, when one or more VUSs were identified and no likely pathogenic or pathogenic variants were identified, and iii) negative, when only likely benign or benign

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variants were identified. The diagnostic yield for all five disease sub-panels is represented in Figure 4. The highest diagnostic yield was obtained for DCM (22.7%),

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and the lowest yield for CPVT (4.3%). By expanding the gene panel for HCM and ARVC, the diagnostic yield slightly increased for HCM from 14.66% to 16.55%, but remained unchanged for ARVC (4.9%). The diagnostic yield of PanCM (16.7%) was lower than that of DCM (22.7%). Notably, PanCM and DCM sub-panels yielded the highest proportion of inconclusive results (68.1% and 60.6%, respectively). The target TAT for IC panel testing in our laboratory is 10 weeks. 15

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Although the target TAT for HCM and ARVC has increased from four to six weeks using SS to 10 weeks using NGS, it is important to consider that in our estimation, testing the increased number of genes included in our expanded panel using SS would take longer

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than 20 weeks, particularly because some of the newly added genes have more than 100 exons (eg, RYR2).

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Discussion

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The increasing availability of DNA sequencing behooves clinical laboratories to develop a panel that is rapid to perform, has appropriate diagnostic sensitivity and specificity, while remaining reasonably cost effective. Focused and expanded cardiomyopathy panels were evaluated and comparative analysis of their sensitivity, specificity, and cost, was performed. HCM and ARVC IC testing was studied in a cohort of 993

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individuals with clinical diagnosis or suspicion of diverse ICs. The expanded panel showed sensitivity of 100%, reproducibility of 99.45%, and a 45% reduction in cost compared to the focused panel. We anticipate that in the future, the content and nature

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of our expanded panel will undergo further changes, as continuous gene evaluation and

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test optimization are necessary to achieve optimal clinical validity while maintaining rapid turnaround time with the least expensive panel.

Assay Development and Validation We described the clinical validation and implementation of our NGS-based assay for the rapid and cost-effective molecular diagnosis of diverse ICs, and reported its performance characteristics according to the NGS standards provided by the ACMG12. 16

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This NGS assay achieved an overall analytical sensitivity of 100% with a reproducibility of 99.45%, thus demonstrating a robust performance of the expanded panel.

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In addition to detecting variants in the coding and splice site regions, the assay was optimized to detect known disease-causing SNVs and InDels in deep intronic regions. This included a 25 bp deep intronic deletion in the MYBPC3 gene (c.3628-41_3628-

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17del; data not shown), which is highly prevalent in the South Asian population (~3%), and is associated with mild hypertrophy in heterozygotes and severe early-onset

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disease in homozygotes26. This deletion is of particular interest as it is a clinically relevant variant that is not systematically analyzed by diagnostic laboratories due to its location >10 bp into an intron, and was recently recommended to be included in the development of clinical NGS tests for HCM27. Another deep intronic variant detected is

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located in intron 4 of the GLA gene (c.640-801G>A; data not shown), which leads to abnormal splicing and a truncated protein, and has been reported in multiple individuals with Fabry disease and HCM, all of whom exhibited reduced GLA enzyme activity

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levels28.

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In validating this NGS assay, the minimum coverage threshold (MCT) required to identify regions of low coverage in which a variant cannot be reliably called was set. As this parameter depends on several aspects of the assay design, sequencing technology, and bioinformatics pipeline, no recommendations had been made regarding a specific MCT12. Other diagnostic laboratories have used MCTs ranging from 15 reads29 to 30 reads16. For the NGS assay, a MCT of 20 reads was used for all targeted 17

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nucleotides. An average of 17 out of 996 targeted regions per sample were found to have at least one nucleotide that does not meet the 20 reads threshold, which would traditionally require re-sequencing using SS. To limit the number of regions requiring SS

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fill-in, the reads in these regions were visually inspected as recommended by others30, and SS was performed according to specific criteria that are mainly based on the contribution of each gene to the tested disorder as described above. Following these

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criteria, the number of regions that require SS fill-in was shown to be reduced to an

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average of 3 regions per sample.

NGS-based assays are rapidly displacing SS as the gold standard in molecular diagnostics. Indeed, recent studies have suggested that SS confirmation is unnecessary for SNVs that meet appropriate quality and/or coverage thresholds, but is still required

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for InDels and SNVs with low quality scores16,31-34. However, extensive experience with the targeted capture and NGS technologies, as well as the variant detection pipeline, is needed before SS confirmation can be eliminated or reduced in a diagnostic setting12.

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Consequently, an in-depth analysis was performed for more than 300 unique variants detected by the NGS-based assay for which SS data were also available (data not

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shown). Data on depth of coverage, quality score, read balance, and VAF were collected for each variant, and criteria for waiving SS confirmation were developed based on the New York State guidelines (July 2015) for laboratories using NGS-based tests. The analysis showed that SS confirmation can be waived for SNVs meeting specific criteria (≥100 read coverage and ≥35% VAF) in genes in which more than 10 NGS variants were previously confirmed by SS in other patients. SS was performed for 18

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all InDels, as well as variants located in regions, and genes, with high sequence homology (eg, MYH6, MYH7, and some exons of TTN) that can lead to NGS read misalignment and subsequently erroneous variant calls25. Although a VAF of

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approximately 50% is expected for germline heterozygous variants, a few pathogenic, likely pathogenic, and variants of uncertain significance with low VAF (<35%) were observed. This low VAF may be due to low coverage, in rare circumstances somatic

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mosaicism, and/or the preferential amplification/capture of the normal allele, particularly in cases of InDels following loss of sequence homology35. This is supported by the fact

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that most of the detected low VAF variants (5/7 with VAF <35%) were InDels. Concerns for potential incorrect calling of variants with lower than expected VAF were minimized by performing SS.

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Using the validation approach described above, successful transition was made from SS to an NGS-based assay in July 2015. Overall, the implementation of this NGSbased assay described above led to a 45% decrease in the cost of both HCM and

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ARVC sub-panels, as compared to the previous smaller SS-based assays for these

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disorders (data not shown).

Implementing these SS waiving criteria in the laboratory resulted in a further decrease of the test cost (approximately $200/sample; data not shown) and TAT, without compromising patient care. In summary, this NGS-based assay allowed expanding the number of IC genes tested, compared to SS, while achieving reduction in the cost of our tests. 19

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As a proof of principle, if the current SS waiving criteria were applied to the 300 variants identified in the cohort used for the assay validation, a total of 77 Sanger confirmations

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would have to be performed comparing to 300 confirmations required without the “SS waiving” criteria. This amounts to an estimated savings of $21000 CAD, in staffing and

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reagent costs.

Risks, Costs, and Benefits of the IC Panel Expansion

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These results showed that the expansion of the HCM sub-panel from five to 19 genes led to only a marginal increase (~2.2%) in diagnostic yield, from 14.7% to 16.6%. This marginal increase is unsurprising, given that 77% of the clinically relevant variants were located in genes that were already included in the focused panel, MYBPC3 (52%) and

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MYH7 (25%), and is consistent with recent studies36. The diagnostic yield for ARVC remained unchanged (4.9%) following the expansion of the ARVC sub-panel from four to nine genes, which can partially be explained by the challenging clinical diagnosis of

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ARVC. This is evidenced by the fact that 19 of the 61 ARVC patients tested by our NGS assay did not meet the revised task force criteria for ARVC37 and were only suspected

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to have a diagnosis of ARVC; as genetic testing becomes more commonplace, and testing is offered to patients who would have a lower pre-test probability of carrying a mutation, diagnostic yields may continue to remain constant despite increasing number of genes sequenced.

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Although our results suggest that the expanded cardiomyopathy gene panel offers a slightly higher diagnostic yield, there is a concurrent increase in the rate of inconclusive results (Figure 4). For example, the relatively high diagnostic yield of our larger DCM

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and PanCM sub-panels (22.7% and 16.7%, respectively) is tempered by a high rate of inconclusive results (60.6% and 68.1%, respectively). The complexity of the interplay between detection rate and rate of inconclusive results has been previously shown for

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DCM20. The higher rate of inconclusive results for DCM and PanCM sub-panels (60.6% and 68.1%) compared to the HCM sub-panel (27.4%) is at least partially attributable to

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the inclusion of TTN, and the previously stipulated challenges of interpreting TTN variants20. In particular, 32 rare synonymous TTN variants, including three variants predicted to have a significant effect on splicing, were classified by our and other diagnostic laboratories as VUSs at the time the variants were interpreted.

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It was questioned if the high rate of inconclusive results across the cardiomyopathy subpanels could be at least partially explained by i) usage of overly conservative allele frequency thresholds and/or ii) inclusion of genes of uncertain significance (GUSs) in

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the expanded panel. The presence of large reference datasets from ExAC and gnomAD provided a means for establishing gene-specific allele frequency thresholds that are “too

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common” for the tested disorder. Recently, ClinGen’s Inherited Cardiomyopathy Expert Panel published a new framework for interpreting variants in the MYH7 gene38. To determine if the MYH7-specific allele frequency thresholds38 can help “resolve” variants classified as VUSs based on the general ACMG recommendations for variant interpretation12, filtering allele frequency (FAF) was applied to previously classified MYH7 variants. The results showed that the revised strong (FAF>0.02%) criteria for 21

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benign classification can be applied to 21% (10/48) of previously interpreted MYH7 variants (Supplemental Table S3). Further, the possibility that genes without clinically actionable variants that are included

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in the expanded panel are more likely to be genes of uncertain significance (GUSs) was explored. Because the variant interpretation pipeline is stepwise and evidence-based, inadequate and/or conflicting data for clinical association of some of the more recently

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added genes would be expected to result in classification of variants as having

uncertain significance, thereby contributing to inconclusive results. Consistent with this

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notion, comparative analysis of CinGen gene-disease curation status39 showed that while clinical validity has been definitively established for most genes included in the focused panel, many genes included in the expanded panel have not yet been curated and/or have limited or moderate evidence to support disease causality (Supplemental

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Table S4). Consequently, suspicious variants such as a rare loss of function variant in ANKRD1 (p.Glu130*) and a potentially deleterious novel variant in FHL2 (p.Arg199Ser) were classified as VUSs, as a variant cannot be considered disease-causing if it is

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present in a GUS, whose clinical validity has not yet been definitively established. These results suggest that usage of disease-specific allele frequency thresholds and

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inclusion of genes with established validity for the tested disorders are expected to reduce the rate of inconclusive findings.

Though NGS-based technologies are equated with cutting-edge healthcare, recent studies highlight important limitations of NGS technologies and emphasize the need for continuous test development in a diagnostic setting40-41. In addition, it is recognized that 22

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careful evaluation of system impacts are needed before these new technologies can safely replace older validated technologies in health-care setting42. Because health care expenditures outpace economic growth in many countries (including Canada), there is a

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growing need to justify the use of scarce healthcare resources for funding of new and/or expanded genetic tests. Although NGS technologies dramatically reduce the per-base cost of sequencing, these results demonstrate that gene panel expansions result in a

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corresponding increase in rate of inconclusive findings, which is expected to heighten the need for genetic counseling resources, familial cascade testing, and may have a

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serious psychosocial impact on individuals and their families43.

Importantly, in our cohort of 993 individuals with a personal and/or family history of ICs, clinically actionable variants were identified only in 21 out of the 45 genes included in

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the PanCM sub-panel (Supplemental Table S4). It is important to consider that some of the remaining 24 genes tested by our diagnostic laboratory as part of the contract with the Ontario Ministry of Health and Long-Term Care currently have limited or conflicting

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evidence of association with cardiomyopathy and/or their disease mechanism is currently uncertain (eg, FHL2 and CTF1), whereas other genes have strong or definitive

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evidence of association, but their rate of clinically actionable variants is relatively low in our patient population (eg, TMEM43). These results further highlight the fact that continuous evaluation of strength of evidence and relative disease contribution of tested genes are necessary to achieve maximum benefits of multi-gene tests, and that genes that are no longer thought to be clinically relevant should be removed from testing panels. 23

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Finding the right balance between the diagnostic yield and the rate of inconclusive findings remains one of the major challenges in the field of clinical genetics. Importantly, our findings suggest that strategic oversight and ongoing evaluation of system impacts

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are required to optimize performance of multi-gene tests in a healthcare setting. Such efforts are expected to result in better test performance, improved treatments, and more effective prevention strategies. We hope that this study will contribute to the

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development of expert recommendations for offering multi-gene tests in a publicly-

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funded health care setting.

Acknowledgements

We thank the patients without whom this work would not be possible; Janna Poapst, April Doyle, Alicia Storey, Faye Sullivan, and Claudia Platero for DNA extraction and

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manuscript.

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quality control; and Dr. Lucas Bronicki for his comments and suggestions on the

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Figure legends

Figure 1: Flowchart illustrating an overview of the cardiomyopathy next-generation

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sequencing (NGS) workflow.

Figure 2: Box-and-Whisker plot showing the average read depth across the 45

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cardiomyopathy genes.

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Figure 3: Average read depth from 36 different samples of 996 targeted regions spanning the 45 cardiomyopathy genes.

Figure 4: Diagnostic yield of all cardiomyopathy sub-panels from July 2015 through

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September 2016.

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Run 2

Run 3

Average

12 (6 HCM, 6 ARVC)

12 controls

12

1,215 76.00% 5.3 87.60% 21,877,017 21,285,898 15,830,236

1,241 77.98% 5.5 87.80% 18,063,733 17,554,485 14,094,285

74.37% 512 99.97% 99.95% 99.93%

80.29% 484 99.94% 99.79% 99.63%

1,033 88.00% 5.4 92.40% 20,285,991 19,784,644 16,624,023

1,163 80.66% 5.4 89.27% 20,075,580 19,541,676 15,516,181

84.02% 568 99.84% 99.59% 99.31%

79.56% 521 99.92% 99.78% 99.62%

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Samples Cluster Density (K/mm2) PF Clusters Yield Total (Gb) Bases ≥ Q30 Total Reads Aligned Reads* Reads on Target* Read enrichment (%)*† Average Read Depth* % ROI with ≥ 10X*‡ % ROI with ≥ 20X*‡ % ROI with ≥ 30X*‡

Run 1 12 patients (8 HCM, 4 ARVC)

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Table 1: Overview of the sequencing metrics for the original runs.

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*Metrics were calculated with PCR duplicates removed. †Values calculated with padding (±10 bp). ‡%ROI values include all genes on the panel. ARVC, arrhythmogenic right ventricular cardiomyopathy; Gb, gigabase; HCM, hypetrophic cardiomyotpathy; PF, passing filter; ROI, region of interest.

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Table 2: Analytical performance of the cardiomyopathy NGS test. Training cohort

Validation cohort

SNVs

Indels

SNVs

Indels

True Positive

218

36

250

21

False Positive

0

0

0

0

False Negative

0

0

0

0

360950

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(95% CI)

57

0

0

0

0

532647

Performance Metrics, %

Indels

468

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True Negative

SNVs

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Call, n

All

893597

100 (98.27

100 (90.36

100 (98.49

100 (84.54

100 (99.19

100 (93.69

Sensitivity (TP/TP+FN)

- 100)

- 100)

- 100)

- 100)

- 100)

- 100)

Specificity (TN/TN+FP)

100 (100-100)

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100 (100-100)

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100 (100-100)

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