- Email: [email protected]
Clinical Next-Generation Sequencing Pipeline Outperforms a Combined Approach Using Sanger Sequencing and Multiplex Ligation-Dependent Probe Amplification in Targeted Gene Panel Analysis
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TECHNICAL ADVANCE Clinical Next-Generation Sequencing Pipeline Outperforms a Combined Approach Using Sanger Sequencing and Multiplex Ligation-Dependent Probe Amplification in Targeted Gene Panel Analysis Q14
Laila C. Schenkel,* Jennifer Kerkhof,y Alan Stuart,y Jack Reilly,y Barry Eng,z Crystal Woodside,z Alexander Levstik,y Christopher J. Howlett,* Anthony C. Rupar,*x Joan H.M. Knoll,*y Peter Ainsworth,*y John S. Waye,z and Bekim Sadikovic*y From the Department of Pathology and Laboratory Medicine,* Western University, London, Ontario; the Molecular Genetics Laboratory,y Molecular Diagnostics Division, London Health Sciences Centre, Children’s Health Research Institute, London, Ontario; the Department of Pathology and Laboratory Medicine,z Hamilton Regional Laboratory Medicine Program, Hamilton Health Sciences, Hamilton, Ontario; and the Biochemical Genetics Laboratory,x Molecular Diagnostics Division, London Health Sciences Centre, London, Ontario, Canada Accepted for publication April 19, 2016. Address correspondence to Bekim Sadikovic, Ph.D., DABMGG, FACMG, Department of Pathology and Laboratory Medicine, Victoria Hospital, London Health Sciences Centre, 800 Commissioner’s Rd. E, B10104, London, ON N6A 5W, Canada. E-mail: bekim. [email protected].
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Advances in next-generation sequencing (NGS) have facilitated parallel analysis of multiple genes enabling the implementation of cost-effective, rapid, and high-throughput methods for the molecular diagnosis of multiple genetic conditions, including the identification of BRCA1 and BRCA2 mutations in high-risk patients for hereditary breast and ovarian cancer. We clinically validated a NGS pipeline designed to replace Sanger sequencing and multiplex ligation-dependent probe amplification analysis and to facilitate highly sensitive and specific detection of sequence and copy number alterations in a single test focusing on a BRCA1/BRCA2 gene analysis panel. Our custom capture library covers 46 exons, including BRCA1 exons 2, 3, and 5 to 24 and BRCA2 exons 2 to 27, with 20 nucleotides of intronic regions both 50 and 30 of each exon. We analyzed 402 retrospective patients, with previous Sanger sequencing and multiplex ligation-dependent probe amplification results, and 240 clinical prospective patients. One-hundred eighty-three unique variants, including sequence and copy number variants, were detected in the retrospective (n Z 95) and prospective (n Z 88) cohorts. This standardized NGS pipeline demonstrated 100% sensitivity and 100% specificity, uniformity, and high-depth nucleotide coverage per sample (approximately 7000 reads per nucleotide). Subsequently, the NGS pipeline was applied to the analysis of larger gene panels, which have shown similar uniformity, sample-to-sample reproducibility in coverage distribution, and sensitivity and specificity for detection of sequence and copy number variants. (J Mol Diagn 2016, -: 1e11; http://dx.doi.org/10.1016/j.jmoldx.2016.04.002)
Advances in the next-generation sequencing (NGS) technology have revolutionized clinical molecular diagnostics. Clinical utilization of massively parallel sequencing enables simultaneous assessment of multiple genes, gene panels, and entire exomes or genomes using a limited quantity of biological samples. Multiplexing of patient samples combined with broad genomic coverage results in significantly decreased cost and turnaround times (TATs).1 Mutations of highly penetrant cancer susceptibility genes, BRCA1 and BRCA2, are the strongest genetic predisposition factors for hereditary breast and ovarian cancer (HBOC),
increasing the lifetime risk of developing these cancers to as high as 80%.2 Moreover, BRCA1 and BRCA2 mutations are also associated with earlier disease onset and with other types of cancers, including fallopian tube, gastric, pancreatic, and prostate cancers. The identification of BRCA mutations is important for individual and family genetic counseling, as well as for early implementation of Supported by the London Health Sciences and Hamilton Health Sciences Molecular Genetics Laboratories operating budget. Disclosures: None declared.
Copyright ª 2016 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jmoldx.2016.04.002
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Schenkel et al personalized pharmacotherapy using poly (ADP-ribose) polymerase inhibitors.3 Thus, the efficient identification of BRCA1/2 mutation carriers among women affected with breast and/or ovarian cancers may be crucial for determining optimum therapeutic strategies for these patients and their affected family members. Because of the relatively large size of the BRCA1/2 genes and the allelic heterogeneity of the pathogenic mutations, clinical testing of these genes is technically complex and labor intensive and, as such, is currently restricted to screening high-risk patients as determined by their family cancer history or ethnic origin in the case of identified founder mutations.4 Methods for mutation screening of the BRCA1 and BRCA2 genes have evolved since these genes were first characterized and testing was initiated in the mid1990s.5 The earliest approaches to BRCA1/2 gene analysis involved indirect methods of mutation identification, such as protein truncation analysis which did not detect potentially damaging missense mutations or structural gene rearrangements. Subsequent improvements in mutation scanning approaches included denaturing high-performance liquid chromatography, and other methods such as multiplex ligation-dependent probe amplification (MLPA),6,7 to help identify the small, although significant, proportion of potentially damaging genomic rearrangements in the BRCA1 and BRCA2 genes. Clinical molecular testing continued to evolve to Sanger sequencing8 in conjunction with MLPA (currently considered the gold standard for sequencing assays), an approach that is highly sensitive but relatively expensive and labor intensive. Recent improvements to NGS technologies that facilitate massively parallel analysis of multiple genes have enabled the implementation of more cost-effective, rapid, and high-throughput methods to allow precise identification of BRCA1 and BRCA2 mutations in these high-risk patients, as well as testing of more comprehensive gene panels for cancer predisposition syndromes.4,9,10 NGS is also capable of sensitive detection of sequence variants and may also be used for detection of copy number variants (CNVs), such as exon deletions and duplications that currently require use of MLPA or other copy number technologies. Here, we describe the clinical validation of an NGS pipeline designed to replace Sanger sequencing and MLPA analysis and to facilitate highly sensitive and specific detection of sequence and copy number alterations in a single assay, using the BRCA1/BRCA2 gene analysis panel as a test example, and to demonstrate application of this approach for analysis of other larger clinical gene panels.
Science Centre Molecular Genetics Laboratory, Molecular Diagnostics Division, and 298 HBOC patient samples, previously tested at the Hamilton Health Sciences Molecular Diagnostic Laboratory. We also analyzed a prospective cohort of the first 240 HBOC patients whose samples were initially tested with the NGS method at Hamilton Health Sciences; for those samples in which pathogenic, likely pathogenic, and variants of unknown clinical significance were identified, the findings were confirmed by Sanger sequencing and/or MLPA. NGS was performed at both institutions for their respective patients. All patients gave consent and were counseled for testing as part of their clinical HBOC screening.
Materials and Methods
The clinical validation cohort (retrospective) comprised 104 HBOC patient specimens, previously tested using Sanger sequencing and MLPA method at the London Health
Custom sequence capture probes were designed using the SeqCap EZ Choice Library system (Roche NimbleGen, Inc., Madison, WI). The design included enrichment for all coding exons as well as 20 bp of the 50 and 30 flanking intronic regions on each side. This design can include up to 2.1 million different probes that are designed to massively overlap each other across the target sequence, thereby
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Sample Collection
DNA Isolation Genomic DNA from each sample was isolated by standard protocols using the MagNA Pure system (Roche Diagnostics, Laval, QC, Canada) at London Health Sciences Centre, or the Qiagen Puregene method or QIAcube automated method (Qiagen, Hilden, Germany) at Hamilton Health Sciences. DNA was quantified by measuring absorbance with a DTX 880 Multimode Detector (Beckman Coulter, Brea, CA).
CNV Analysis Using MLPA Genomic DNA (100 ng) was amplified according to the manufacturer’s recommendations using a SALSA MLPA kit (P002-D1 and/or P087-C1 for BRCA1, P090-A4 and/or P077-A2 for BRCA2; MRC Holland, Amsterdam, the Netherlands). PCR products were separated by capillary electrophoresis on an ABI 3730 (Life Technologies, Thermo Fisher Scientific, Waltham, MA). Copy number alterations were analyzed with Coffalyser.Net software version 131211.1524 (MRC Holland).
Sanger-Based Sequencing Coding regions and the flanking intronic regions (20 bp to 10 bp) from the genes of interest were PCR-amplified and sequenced with the BigDye Terminator version 1.1 cycle sequencing kit (Life Technologies). Sequencing products were separated by capillary electrophoresis on an ABI 3730 (Life Technologies) and analyzed with Mutation Surveyor version 4.0.7 software (SoftGenetics, LLC, State College, PA).
NGS Library Design
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Figure 1 BRCA1/2 gene panel. A: Coverage plot for a 24-patient sample batch (each line represents one patient); y axis is sequence read depth; gene and exon locations are indicated. B: Sequence coverage depth for three representative BRCA1/2 screens; gene and exon locations are indicated; red lines indicate exon boundaries; x axis indicates nucleotide positions on corresponding genes and exons; y axis indicates depth of sequence coverage (approximate mean coverage for BRACA1/2 Z 7000); y axis is on a linear scale so for a sample with a mean coverage of 2000, reduction in coverage to the half equates to approximately 1000 times coverage at that locus. C and D: Normalized copy number plot demonstrating deletion detection (arrows, C) and gene duplication (arrow, D). Gene and exon locations are indicated (x axis); blue lines indicate exon boundaries; y axis represents quantile normalized copy number data (for autosomal genes 0.5 Z one copy, 1 Z two copies, 1.5 Z three copies).
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Clinical NGS Pipeline in Targeted Gene Panel Analysis Q1
introducing significant redundancy and ability to capture complex, CG-rich, and polymorphic genomic regions. The SeqCap EZ Choice Library are proprietary designs involving Nimblegen-designed bioinformatically targeted probe coverage of the region of interest, that normally involves high-density tiling of the targeted region. If needed, each specific design can be adjusted for probe concentrations on empirical assessment on patient samples to ensure uniform depth of coverage.
Library Preparation and Target Capture Sequencing Libraries were prepared with 100 ng of genomic DNA fragmented to 180 to 220 bp using a Covaris E220 Series Focused-ultrasonicator (Covaris, Inc., Woburn, MA). After fragment quantification and size distribution assessment
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with the Qubit fluorometer (Life Technologies) and 2200 TapeStation (Agilent Technologies, Santa Clara, CA), respectively, each sample library was ligated with a specific barcode index according to the manufacturer’s protocol (Roche NimbleGen, Inc.). DNA libraries were then pooled and captured using the SeqCap EZ Choice Library system (Roche NimbleGen, Inc.). Captured libraries underwent appropriate quality control analysis and were diluted to a concentration of 4 nmol/L to process for sequencing according to the manufacturer’s instructions (Illumina, San Diego, CA). The final captured library concentration for sequencing was 8 pmol/L with a 1% PhiX spike-in. Q7 Libraries were sequenced using the MiSeq version 2 reagent kit to generate 2 150 bp paired-end reads using the MiSeq fastq generation mode (Illumina), with 24 different patient samples multiplexed per run.
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373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434
Schenkel et al Table 1
BRCA Mutations Identified in the Retrospective Cohort (n Z 402)
Gene
Patients, n
Variant type
cDNA change
Protein change
Homozygous/ heterozygous
BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2
1 1 1 23 1 1 1 1 1 1 1 23 102 101 1 106 1 1 1 1 101 8 1 102 3 1 101 1 1 1 102 1 1 1 1 6 1 2 1 1 1 1 1 1 1 1 104 12 1 1 4 2 12 1 1 12
Deletion Duplication Duplication Missense Frame-shift Frame-shift Missense Frame-shift Nonsense Frame-shift Missense Missense Synonymous Synonymous Missense Missense Synonymous Missense Missense Missense Missense Missense Frame-shift Missense Missense Synonymous Synonymous Synonymous Frame-shift Missense Missense Nonsense Missense Missense Frame-shift Missense Missense Frame-shift Synonymous Synonymous Deletion Deletion Deletion Synonymous Frame-shift Missense Missense Synonymous Missense Missense NA Synonymous Synonymous Synonymous Synonymous Missense
BRCA1:Ex1_24del BRCA1:Ex13dup BRCA1:Ex18_20dup c.1067A>G c.1174_1213del c.1386_1387insG c.1390A>G c.1500_1504del c.1687C>T c.1961delA c.2050C>T c.2077G>A c.2082C>T c.2311T>C c.2566T>C c.2612C>T c.2733A>G c.2791G>T c.3024G>A c.3092T>G c.3113A>G c.3119G>A c.3479_3489del c.3548A>G c.4039A>G c.4050C>T c.4308T>C c.4416T>G c.4417delT c.4535G>T c.4837A>G c.4945A>T c.4946G>T c.4947A>T c.4947delAins c.4956G>A c.5207T>C c.5266_5267ins c.591C>T c.981A>G BRCA1:Ex24del BRCA2: Ex8_10del, 12_13del BRCA2:Ex19_20del c.10095C>G c.10096_10097ins c.10234A>G c.1114A>C c.1365A>G c.1487C>T c.1762A>G c.1909þ22_1909þ23ins c.1938C>T c.2229T>C c.231T>G c.2883G>A c.2971A>G
NA NA NA p.Gln356Arg p.Leu392Glnfs p.Lys463Glufs p.Thr464Ala p.Leu502Alafs p.Gln563Ter p.Lys654Serfs p.Pro684Ser p.Asp693Asn p.Ser694Z p.Leu771Z p.Tyr856His p.Pro871Leu p.Gly911Z p.Val931Leu p.Met1008Ile p.Ile1031Ser p.Glu1038Gly p.Ser1040Asn p.Glu1161Phefs p.Lys1183Arg p.Arg1347Gly p.Gly1350Z p.Ser1436Z p.Leu1472Z p.Ser1473Leufs p.Ser1512Ile p.Ser1613Gly p.Arg1649Ter p.Arg1649Ile p.Arg1649Ser p.Met1650Tyrfs p.Met1652Ile p.Val1736Ala p.Gln1756Profs p.Cys197Z p.Thr327Z NA NA NA p.Val3365Z p.Ser3366Asnfs p.Ile3412Val p.Asn372His p.Ser455Z p.Ser496Phe p.Asn588Asp NA p.Ser646Z p.His743Z p.Thr77Z p.Gln961Z p.Asn991Asp
0/1 0/1 0/1 0/23 0/1 0/1 0/1 0/1 0/1 0/1 0/1 1/22 22/80 20/81 0/1 25/81 0/1 0/1 0/1 0/1 20/81 0/8 0/1 20/82 0/3 0/1 20/81 0/1 0/1 0/1 20/82 0/1 0/1 0/1 0/1 0/6 0/1 0/2 0/1 0/1 0/1 0/1 0/1 0/1 0/1 0/1 16/88 0/12 0/1 0/1 3/1 0/2 0/12 0/1 0/1 0/12 (table continues)
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497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
Clinical NGS Pipeline in Targeted Gene Panel Analysis Table 1
(continued )
Gene
Patients, n
Variant type
cDNA change
Protein change
Homozygous/ heterozygous
BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2
1 92 3 62 2 1 1 1 1 1 3 191 3 6 1 1 1 1 1 191 1 2 1 1 70 191 2 143 1 1 12 1 1 1 1 3 1 1 3
Missense Synonymous Synonymous Synonymous Synonymous Missense In-frame Missense Frame-shift Missense Missense Synonymous Synonymous Missense Frame-shift Missense Missense Missense Frame-shift Synonymous Missense NA Missense Missense Synonymous Missense Missense NA Missense Missense Missense Missense Synonymous Frame-shift Synonymous NA Frame-shift NA Nonsense
c.3262C>T c.3396A>G c.3516G>A c.3807T>C c.4068G>A c.4094G>A c.4131_4132ins c.4163C>A c.4169delT c.4189G>A c.4258G>T c.4563A>G c.5199C>T c.5744C>T c.5775_5860del c.5869A>G c.5945G>C c.6100C>T c.6402_6406del c.6513G>C c.6796A>C c.68-7T>A c.7007G>A c.7010C>T c.7242A>G c.7397T>C c.7469T>C c.7806-14T>C c.8149G>T c.8182G>A c.865A>C c.8567A>C c.8460A>C c.8537_8538del c.9234C>T c.9257-16T>C c.9403delC c.9649-19G>A c.9976A>T
p.Pro1088Ser p.Lys1132Z p.Ser1172Z p.Val1269Z p.Leu1356Z p.Cys1365Tyr p.Thr1378Ter p.Thr1388Asn p.Leu1390Trpfs p.Glu1397Lys p.Asp1420Tyr p.Leu1521Z p.Ser1733Z p.Thr1915Met p.Gln1925Hisfs p.Ile1957Val p.Ser1982Thr p.Arg2034Cys p.Asn2135Lysfs p.Val2171Z p.Asn2266His NA p.Arg2336His p.Thr2337Ile p.Ser2414Z p.Val2466Ala p.Ile2490Thr NA p.Ala2717Ser p.Val2728Ile p.Asn289His p.Glu2856Ala p.Val2820Z p.Glu2846Glyfs p.Val3078Z NA p.Leu3135Phefs NA p.Lys3326Ter
0/1 17/75 0/3 8/54 0/2 0/1 0/1 0/1 0/1 0/1 0/3 190/1 0/3 0/6 0/1 0/1 0/1 0/1 0/1 190/1 0/1 0/2 0/1 0/1 7/63 190/1 0/2 50/93 0/1 0/1 0/12 0/1 0/1 0/1 0/1 0/3 0/1 0/1 0/3
NA, not applicable.
NGS Analysis for Sequence Variants
NGS Analysis of CNVs
Sequence analysis for variant identification, alignment, and coverage distribution was performed with NextGene software version 2.4.1 (SoftGenetics, LLC) using standard alignment settings (allowable mismatch bases: 1; allowable ambiguous alignments: 50; seeds bases: 30; move step bases: 5; allowable alignments: 100; matching base percentage >85%). BAM and VCF files were imported into Geneticist Assistant version 1.1.5 (SoftGenetics, LLC) for quality control assessment (minimum base coverage; mean exon coverage) and for data basing. Variants were analyzed and interpreted by a clinical molecular geneticist and were classified for pathogenicity using the American College of Medical Genetics guidelines.11
Reports for base coverage distribution were generated using NextGene software version 2.4.1 (SoftGenetics, LLC). Single nucleotide coverage for each patient was normalized. Briefly, the sum of all 24 sample sequencing reads divided by the number of patients equals the total mean coverage per patient (mean of sums). The normalization factor (NF) is then calculated by dividing the sum of reads for each patient by the mean of sums. Finally, each read per nucleotide per patient is divided by the NF and by the average read of each nucleotide in the 24 samples, resulting in the normalized reads per nucleotide per patient (NR). The normalized data were then presented in a graph allowing visualization of
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benign variants was available for all patients and was compared with the NGS data. Briefly, we identified 95 different variants (40 in BRCA1, 55 in BRCA2), resulting in a total of 1964 variant occurrences. To further characterize the variant frequency distribution, they (both sequence and CNVs) were classified into seven groups: intronic heterozygous single nucleotide polymorphisms (SNPs), intronic homozygous SNPs, coding heterozygous SNPs, coding homozygous SNPs, deletions, insertions, and exon deletions/ duplications. We observed 1022 (52%) heterozygous SNPs and 763 (39%) homozygous SNPs in coding regions, as well as 153 (7.8%) intronic SNPs. In addition, among the 1964 variants, 26 (1.32%) were CNVs that included 20 deletions and insertions and six exon del/dups (Figure 2A). All pre- ½F2 viously observed variants were confirmed, demonstrating 100% sensitivity of the NGS technology. Five additional variants outside of the region included in the original Sanger sequencing were also detected, and these variants were subsequently confirmed by Sanger sequencing. The prospective cohort included select patients in whom pathogenic variants or VUSs were detected by the NGS assay and were subsequently confirmed by Sanger sequencing and MLPA, as well as normal controls. In the prospective cohort of 240 HBOC patients tested by NGS,
A
10
6
10
53
Intron heterozygous SNP Intron homozygous SNP
100
Coding heterozygous SNP 763
Coding homozygous SNP Deletions 1022
Insertions Exon del/dups
B 12
3
7
Intron heterozygous SNP Coding heterozygous SNP Deletions Insertions
79
Figure 2 A: BRCA1 and BRCA2 variants identified by NGS in the retrospective cohort. SNPs were classified according to zygosity (homozygous/ heterozygous) and location (intron or exon). B: Type of BRCA1 and BRCA2 variants identified by NGS in the prospective patient cohort. The number of deletions, insertions, and exon deletion/duplications identified are shown in each panel. n Z 402 (A); n Z 240 (B). del, deletion; dup, duplication; NGS, next-generation sequencing; SNP, single nucleotide polymorphism.
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621 CNVs (ie, deletions and duplications) at exon and subexon 622 levels (Excel version 14.0.6129.5000; Microsoft Corpora623 tion, Redmond, WA). Deletions were defined by a mean 624 ratio of 0.65 and duplications were defined by a ratio 625 of 1.35. 626 P Reads 627 ZMean of Sums 628 #Patients P 629 Reads per individual patient ZNF 630 Mean of Sums 631 Reads per nucleotide per patient 632 ZNR NF Average Reads per nucleotide 633 634 635 636 Results 637 Sequencing Coverage and Normalization 638 639 Our custom capture library covers 46 exons, including 640 641 BRCA1 exons 2, 3, and 5 to 24 and BRCA2 exons 2 to 27, 642 with 20 nucleotides of intronic regions both 50 and 30 of 643 each exon, with a total of 17,769 nucleotides (89% in 644 coding regions and 11% in flanking regions). Sequencing 645 BRCA1 and BRCA2 in the retrospective cohort generated a 646 mean of approximately 6500 reads per nucleotide per pa647 tient, with 24 patient samples batched per library enrichment 648 and sequencing run. The raw sequence alignment of 24 649 patients in a single run is shown in Figure 1A, demon½F1 650 strating deep and uniform coverage across all nucleotides 651 analyzed. In addition, the high level of uniformity of 652 653 coverage among patients is evident at the subexon resolu654 tion (Figure 1B). Taking advantage of the reproducibility 655 and uniformity of the coverage distribution, we applied a 656 quantile normalization algorithm to the coverage distribu657 tion reports from the NextGene alignment output for each 658 patient to assess copy number alterations. Figure 1C shows 659 an example of a quantile normalized copy number graph for 660 a 24-patient batch that included a patient with a BRCA1 661 exon 24 deletion and another patient with a complex BRCA2 662 deletion involving exons 8 to 10 and 12 to 13, with normal 663 exon 11 copy number. Similarly, two patients’ samples with 664 665 a BRCA1 duplication involving exon 13 demonstrate an 666 easily detectable, distinct abnormality on the copy number 667 plot (Figure 1D). 668 669 BRCA1 and BRCA2 Variants 670 671 NGS analysis for BRCA1 and BRCA2 was performed in a 672 retrospective cohort of 402 HBOC individuals that had 673 previously been analyzed by Sanger sequencing and MLPA 674 techniques. A summary of the sequence variants is shown in 675 Table 1. The retrospective cohort are patients who were ½T1 676 previously tested by the Sanger sequencing and MLPA 677 method and included preselected patients with the wide 678 679 range of the types of mutations found in these genes, as well 680 as select normal controls. The information for pathogenic, 681 variants of unknown clinical significance (VUSs), and 682
683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744
745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806
Clinical NGS Pipeline in Targeted Gene Panel Analysis Table 2 BRCA Mutations Identified in the Prospective Cohort (n Z 240) Gene
Patients, n Variant type cDNA change
Protein change
Zygosity
BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1
1 1 1 1 1 2 1 1 1
p.Lys355Z p.Trp385Ter p.Arg496His p.Thr582Met p.Cys61Gly p.Gln657Z p.Tyr856His p.Arg866Cys p.Lys894Thrfs
HZ HZ HZ HZ HZ HZ HZ HZ HZ
p.Ser945Thr p.Ile946Glnfs
HZ HZ
p.Arg1028Cys p.Gln1090Z p.Glu1134Ter p.Ser1187Asn p.Gln1200His p.Arg1203Gln p.Ser1262Pro p.Glu1494Ter p.Gly1591Ser p.Gln1604Z p.Trp1712Ter p.Gln172Asnfs p.Val1719Z p.Gln1756Profs
HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ
p.Met1775Arg N/A p.Cys197Z N/A p.Trp321Ter p.Arg3370Z p.Thr3371Ala p.Ser384Phe p.Tyr42Cys p.Lys454Z p.Glu510Ter p.Leu61Z N/A
HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ
p.Ala75Pro p.Ser846Z p.Pro9Glnfs p.Asp935Asn p.Ser973Z p.Ser976Thr p.Ser976Phe p.Leu1019Val p.Lys1057Thrfs
HZ HZ HZ HZ HZ HZ HZ HZ HZ
p.Pro1088Z p.Met1149Val p.Leu1356Z p.Leu1478Z p.Gly1529Arg p.His1561Asn p.Gln1562Z p.Tyr1655Ter p.Glu1806Z p.Ile1851Ser
HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ
Synonymous Nonsense Missense Missense Missense Synonymous Missense Missense Frame-shift
BRCA1 1 BRCA1 1
Missense Frame-shift
BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1
1 1 1 1 1 1 1 1 1 1 1 1 1 2
Missense Synonymous Nonsense Missense Missense Missense Missense Nonsense Missense Synonymous Nonsense Frame-shift Synonymous Frame-shift
BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 4022
1 1 1 2 1 1 1 2 1 1 1 1 1
Missense N/A Synonymous N/A Nonsense Synonymous Missense Missense Missense Synonymous Nonsense Synonymous N/A
BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2
1 1 1 1 1 2 2 1 1
Missense Synonymous Frame-shift Missense Synonymous Missense Missense Missense Frame-shift
BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2
1 1 2 1 1 1 2 1 1 1
Synonymous Missense Synonymous Synonymous Missense Missense Synonymous Nonsense Synonymous Missense
c.1065G>A c.1154G>A c.1487G>A c.1745C>T c.181T>G c.1971A>G c.2566T>C c.2596C>T c.2681_ 2682del c.2834G>C c.2835_ 2836del c.3082C>T c.3270A>G c.3400G>T c.3560G>A c.3600G>C c.3608G>A c.3784T>C c.4480G>T c.4771G>A c.4812A>G c.5136G>A c.514delC c.5157G>T c.5266_ 5267ins c.5324T>G c.5332+13G>T c.591C>T c.81-14C>T c.962G>A c.10110G>A c.10111A>G c.1151C>T c.125A>G c.1362A>G c.1528G>T c.183A>G c.1909+22_ 1909+23ins c.223G>C c.2538A>C c.26delC c.2803G>A c.2919G>A c.2926T>A c.2927C>T c.3055C>G c.3170_ 3174del c.3264T>C c.3445A>G c.4068G>A c.4434A>G c.4585G>A c.4681C>A c.4686A>G c.4965C>G c.5418A>G c.5552T>G
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807 808 809 Patients, 810 Gene n Variant type cDNA change Protein change Zygosity 811 BRCA2 2 Missense c.5704G>A p.Asp1902Asn HZ 812 BRCA2 1 Frame-shift c.5722_ p.Leu1908Argfs HZ 813 5723del 814 BRCA2 1 Nonsense c.5857G>T p.Glu1953Ter HZ 815 BRCA2 1 Missense c.6322C>T p.Arg2108Cys HZ BRCA2 3 Missense c.6323G>A p.Arg2108His HZ 816 BRCA2 1 Missense c.6325G>A p.Val2109Ile HZ 817 BRCA2 1 Missense c.6412G>T p.Val2138Phe HZ 818 BRCA2 1 Frame-shift c.6486_ p.Lys2162Asnfs HZ 819 6489del 820 BRCA2 1 Frame-shift c.658_ p.Val220Ilefs HZ 821 659delGT 822 BRCA2 1 N/A c.68-7T>A N/A HZ 823 BRCA2 1 Synonymous c.6987G>A p.Pro2329Z HZ BRCA2 1 Missense c.7017G>C p.Lys2339Asn HZ 824 BRCA2 1 Frame-shift c.7069_ p.Leu2357Valfs HZ 825 7070del 826 BRCA2 1 Missense c.7319A>G p.His2440Arg HZ 827 BRCA2 1 Missense c.7544C>T p.Thr2515Ile HZ 828 BRCA2 1 Frame-shift c.7577delC p.Ala2526Glufs HZ 829 BRCA2 1 Frame-shift c.7580delT p.Val2527Glufs HZ 830 BRCA2 1 Frame-shift c.7583delG p.Gly2528Glufs HZ 831 BRCA2 1 N/A c.7805+6C>G N/A HZ BRCA2 1 Missense c.7916C>T p.Pro2639Leu HZ 832 BRCA2 1 Missense c.8057T>G p.Leu2686Arg HZ 833 BRCA2 1 Missense c.825A>T p.Lys275Asn HZ 834 BRCA2 1 Missense c.8356G>A p.Ala2786Thr HZ 835 BRCA2 1 N/A c.8487+3A>G N/A HZ 836 BRCA2 3 Missense c.8567A>C p.Glu2856Ala HZ 837 BRCA2 1 N/A c.8633-16C>G N/A HZ 838 BRCA2 1 Missense c.9032T>C p.Leu3011Pro HZ 839 BRCA2 1 Missense c.9038C>T p.Thr3013Ile HZ BRCA2 1 Missense c.9076C>G p.Gln3026Glu HZ 840 BRCA2 1 Missense c.9271G>A p.Val3091Ile HZ 841 BRCA2 1 Missense c.9730G>A p.Val3244Ile HZ 842 843 HZ, heterozygous. 844 845 88 different sequence variants (30 in BRCA1 and 58 in 846 BRCA2) (Table 2, Figure 2B) classified as pathogenic, likely ½T2 847 pathogenic, or VUSs were observed. One hundred one 848 849 BRCA1 and BRCA2 variant occurrences were observed, of 850 which 93 (79 sequence variants, 12 deletions, and 2 inser851 tion) were in coding regions and eight (seven sequence 852 variants and one insertion) were intronic. All variants were 853 confirmed by Sanger sequencing and MLPA analysis, 854 demonstrating 100% specificity for the NGS analysis. 855 856 857 Discussion 858 859 In this study, we present clinical validation of a NGS 860 pipeline designed to replace conventional Sanger 861 sequencing and MLPA for detection of sequence and CNVs, 862 respectively. NGS-based analysis of the BRCA1 and BRCA2 863 genes were previously reported, including PCR-based 864 865 library preparation on the Roche sequencer,10 PCR-based 866 on Ion-Torrent sequencer,4,12 PCR-based on SOLiD and 867 IoneTorrent sequencers,13 and RainDance microdroplet 868 Table 2
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Figure 3 Sequence coverage depth for three representative patient screens for CMT panel (17 genes: MFN2, LMNA, MPZ, RAB7A, SH3TC2, FIG4, GARS, HSPB1, NEFL, GDAP1, EGR2, TRPV4, HSPB8, LITAF, PMP22, PRX, GJB1) (A); CANcer (25 genes: MUTYH, EPCAM, MSH2, MSH6, BARD1, MLH1, APC, PMS2, NBN, CDKN2A, BMPR1A, PTEN, ATM, CDK4, BRCA2, PALB2, CDH1, TP53, RAD51D, BRCA1, RAD51C, BRIP1, SMAD4, STK11, CHEK2) (B); and MtDNA panel (37 genes: ATP6, ATP8, COX1, COX2, COX3, CYTB, ND1, ND2, ND3, ND4, ND4L, ND5, ND6, TRNA, TRNC, TRND, TRNE, TRNF, TRNG, TRNH, TRNI, TRNK, TRNL1, TRNL2, TRNM, TRNN, TRNP, TRNQ, TRNR, TRNS1, TRNS2, TRNT, TRNV, TRNW, TRNY, RNR1, RNR2) (C). Red lines indicate exon boundaries. The x axis indicates nucleotide positions on corresponding genes and exons; y axis indicates depth of sequence coverage (approximate mean coverage for CMT, 2000; CANcer panel, 4000, and MtDNA, 5000 and 20,000 for blood and muscle, respectively). The y axis is on a linear scale so for a sample with a mean coverage of 2000, reduction in coverage to the half equates to approximately 1000 times coverage at that locus. CANcer, hereditary cancer; CMT, Charcot-Marie Tooth; MtDNA, mitochondrial genome sequencing.
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PCR-based sequencing.14 Although many of the approaches describe ability to sensitively detect sequence-level variation (SNPs and small insertions and deletions), all of these assays require parallel methods for the exon-level copy number detection. Our method is unique in its ability to use NGS data to sensitively detect both sequence level and copy number alterations in the same assay. The key variables to be assessed when validating a clinical assay are sensitivity and specificity. We conducted NGS analysis of samples from 402 individuals, in which Sanger sequencing and MLPA testing had been conducted as part of the routine clinical workup. A total of 1964 variants, including coding and intronic SNPs, deletions, insertions, and exon level copy number alterations (Figure 2A, Table 1), were validated with 100% sensitivity. As part of the clinical laboratory standard operating procedures, reportable pathogenic variants and VUSs were confirmed from the source DNA specimen using an alternate technique
(Sanger sequencing, MLPA, long-range PCR, etc.) to confirm the mutation and sample fidelity. Each of the 88 variants detected in the first 240 clinical NGS analyses was verified by confirmatory testing, demonstrating 100% specificity of the NGS assay. One of the limitations of both Sanger sequencing and MLPA analysis relates to allele dropout, which pertains to the inability of a PCR primer, sequencing primer, or an MLPA probe to bind and subsequently amplify one of the two alleles that may contain a sequence variant or a polymorphism,15,16 thereby resulting in the reduced sensitivity. Allele dropout is effectively abolished in this NGS pipeline due to the redundancy that is introduced by the Roche/ Nimblegen library preparation protocol, including random genomic fragmentation and densely overlapped DNA library capture probes (up to two million unique probes per design). In fact, one of the patient samples in the prospective cohort with an MLPA-detected exon copy number loss,
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Clinical NGS Pipeline in Targeted Gene Panel Analysis 993 which subsequently could not be confirmed by NGS anal994 ysis, was demonstrated to exhibit a SNP at the MLPA probe 995 binding site, resulting in a false-positive MLPA result. Thus, 996 the results presented herein demonstrate that this NGS 997 pipeline outperforms at all levels the current gold standard 998 of combined Sanger sequencing and MLPA. 999 A potential limitation of NGS-based assays can be a 1000 reduced ability to detect CNVs and complex sequence 1001 variants.17 Our targeted NGS analytical pipeline takes 1002 1003 advantage of the high depth of sequence coverage (mean 1004 depth of 2000 to 10,000 times, depending on the panel and 1005 number of patients) and intersample coverage uniformity 1006 ½F3 (Figures 1 and 3) to address those limitations. Unlike off1007 the-shelf generic panel design, or even exome-slices where 1008 larger gene sets are sequenced and only genes of interest are 1009 analyzed and reported, targeted panel designs provide a 1010 significantly greater depth of coverage. With the use of a 1011 single nucleotide resolution depth of coverage information 1012 from the coverage distribution files generated by the Next1013 Gene alignment software and a quantile normalization 1014 1015 algorithm, we are able to detect with 100% sensitivity and 1016 specificity the copy number alterations for each individual 1017 exon and all genes in our previously defined retrospective 1018 Q8 samples in every panel examined (see below description of 1019 other panels). In addition to replacing the MLPA screening 1020 in the BRCA1/2 panel, we no longer perform MLPA testing 1021 as a primary screen for any gene panel. 1022 Another aspect related to a specifically targeted panel 1023 design is scalability. Depending on the sample volumes and 1024 the required TAT, there is significant opportunity for scaling 1025 up both patient sample number and gene number. The 1026 1027 abundance and redundancy in our probe designs makes it 1028 relatively easy to introduce additional genes to a design 1029 without significantly compromising the depth of coverage 1030 and increasing the possibility of sequence dropouts. 1031 Targeted gene panel can be designed to yield complete 1032 coverage for every single nucleotide in every gene with 1033 minimal sample-to-sample variability (Figure 1B and 1034 Figure 3), eliminating the necessity for Sanger sequence 1035 back-filling, a common complication with clinical NGS tests 1036 with lower levels of coverage, or PCR-based enrichment 1037 designs. Therefore, this NGS pipeline allows for extensive 1038 1039 scalability both in patient volume and gene number. 1040 Although providing significant improvements in test 1041 quality, sensitivity, and specificity compared with standard 1042 NGS approaches as well as the gold standard Sanger 1043 sequencing/MLPA, this NGS pipeline also provides signif1044 icant cost advantages through both reduced reagent costs 1045 and labor time. With the use of the simplest BRCA1/2 two1046 gene panel with 24 patients per MiSeq run compared with 1047 the classic Sanger sequencing and MLPA testing, we can 1048 achieve approximately 70% reduction in total test costs for 1049 reagents, labor, and significant reduction in TAT (including 1050 1051 service contracts and institutional overhead). Cost1052 effectiveness can be enhanced further through increased 1053 automation and scalability (100 patients per run instead of 1054
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1055 24). If, however, a laboratory does not have the sufficient 1056 volumes to batch 24 or more samples per run, it is possible 1057 to combine multiple lower volume test panels on the 1058 same enrichment library design. This would ensure cost1059 effectiveness while enabling low TATs. Similar advan1060 tages are achieved in expanded, comprehensive gene panels 1061 (eg, CANcer panel, described below), enabling significant Q9 1062 improvements in efficiency of delivery of patient care and 1063 genetic services with a net positive financial impact on the 1064 1065 health care system. 1066 In addition to the BRCA1/2 panel, this NGS pipeline has 1067 been applied to the analysis of other larger gene panels, 1068 including Charcot Marie Tooth (CMT) syndrome panel, 1069 hereditary cancer syndrome panel, and mitochondrial 1070 genome sequencing panel (Figure 3; Table 3) with similar ½T3 1071 results. One hundred percent sensitivity and specificity for 1072 detection of CNVs was also achieved for these panels 1073 (Figure 4). For example, our hereditary cancer panel design ½F4 1074 totaling 25 genes and approximately 100 kb of targeted 1075 sequence, when performed in a batch of 24 patients on a 1076 1077 single MiSeq (2 150 chip) run, achieves a mean of 1078 4000 times depth of coverage, with no single nucleotide Table 3 Genes Included in Charcot Marie Tooth Panel (CMT; 17 Genes) Hereditary Cancer Panel (CANcer; 25 Genes), and Mitochondrial Genome Sequencing Panel (MtDNA; 37 Genes) Panel
Genes
CMT
MFN2 LMNA MPZ RAB7A SH3TC2 FIG4
GDAP1 EGR2 TRPV4 GARS HSPB1 NEFL
HSPB8 LITAF PMP22 PRX GJB1
CANcer
MUTYH EPCAM MSH2 MSH6 BARD1 MLH1 APC PMS2 CHEK2
CDK4 BRCA2 PALB2 CDH1 TP53 RAD51D BRCA1 RAD51C
NBN CDKN2A BMPR1A PTEN ATM BRIP1 SMAD4 STK11
Genes
tRNAs
ATP6 ATP8 COX1 COX2 COX3 CYTB ND1 ND2 ND3 ND4 ND4L ND5 ND6
TRNA TRNC TRND TRNE TRNF TRNG TRNH TRNI TRNK TRNL1 TRNL2 TRNM TRNN
MtDNA
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Figure 4 A: Normalized copy number plots for CMT; top image shows the comprehensive CMT panel; bottom image is a zoom in of PMP22 gene showing detection of one deletion and one duplication (indicated by arrows). Red lines indicate exon boundaries. B: CANcer panel; arrow indicates a patient with complex multi-exon deletion in BRCA2 (same patient as in Figure 1C). Red lines indicate exon boundaries. C: MtDNA panel; identification of patients with KSS, with approximately 10% heteroplasmy (in blue and pink) and approximately 50% heteroplasmy (in green). *Hypervariable D-loop region (m.16,024 to m.576). AeC: The x axis indicates gene/exon positions; y axis represents quantile normalized copy number data (for autosomal genes 0.5 Z one copy, 1 Z two copies, 1.5 Z three copies). CANcer, hereditary cancer; CMT, Charcot-Marie Tooth; KSS, Kearns-Sayre syndrome; MtDNA, mitochondrial genome sequencing.
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covered <500 times (Figure 3B). This approach has also enabled detection of the most common mutations in CMT (a whole gene deletion or duplication of the PMP22 gene resulting in the hereditary neuropathy with pressure palsies or CMT1, respectively), which previously required standalone MLPA prescreening of all patients with suspected CMT or hereditary neuropathy with pressure palsies before the sequencing analysis (Figure 4A). Furthermore, our mitochondrial genome sequencing panel demonstrates the ability to sensitively detect the common 5-kb deletion associated with the Kearns-Sayre syndrome in peripheral blood samples of patients with as little as 10% heteroplasmy (Figure 4C), along with sensitive detection of sequence variants down to 2% heteroplasmy. Importantly, this NGS pipeline design ensures that any copy number alterations and/or complex sequence alterations that result in disrupted
NGS sequence alignment and loss of sequence coverage would be detectable on the copy number plot, triggering further investigation and confirmation, thereby ensuring absolute sensitivity for complex sequence and CNVs. Although screening entire genomes using NGS technology may be warranted for some clinical indications18,19 and for genetic research and gene discovery, targeted gene panel sequencing provides the optimal way to deliver clinically significant, high-quality, and cost-effective genetic diagnostic. Such targeted panel-based genetic testing, in contrast to the higher cost and lower sensitivity/specificity of genome screening or exome-slicing, remains the preferred choice for polygenic conditions in which identification of a genetic defect affects patient management and clinical care. Here, we have described a clinical NGS pipeline that outperforms the current gold standard Sanger sequencing and
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Clinical NGS Pipeline in Targeted Gene Panel Analysis MLPA. Given the promise of the NGS technology to further expand the ability and capacity to discover and test an ever expanding number of genes, it is important to ensure that the application of NGS in a clinical setting does not happen at the cost of lowering the clinical standards of test delivery. Rather, when displacing a gold standard technology, we should aim to do it with a platinum standard one, while keeping the focus on clinical utility, patient care, and fiscal responsibility.
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References 1. Cottrell CE, Al-Kateb H, Bredemeyer AJ, Duncavage EJ, Spencer DH, Abel HJ, Lockwood CM, Hagemann IS, O’Guin SM, Burcea LC, Sawyer CS, Oschwald DM, Stratman JL, Sher DA, Johnson MR, Brown JT, Cliften PF, George B, McIntosh LD, Shrivastava S, Nguyen TT, Payton JE, Watson MA, Crosby SD, Head RD, Mitra RD, Nagarajan R, Kulkarni S, Seibert K, Virgin HW IV, Milbrandt J, Pfeifer JD: Validation of a next-generation sequencing assay for clinical molecular oncology. J Mol Diagn 2014, 16:89e105 2. Roy R, Chun J, Powell SN: BRCA1 and BRCA2: different roles in a common pathway of genome protection. Nat Rev Cancer 2012, 12: 68e78 3. Benafif S, Hall M: An update on PARP inhibitors for the treatment of cancer. Onco Targets Ther 2015, 8:519e528 4. Trujillano D, Weiss ME, Schneider J, Koster J, Papachristos EB, Saviouk V, Zakharkina T, Nahavandi N, Kovacevic L, Rolfs A: Nextgeneration sequencing of the BRCA1 and BRCA2 genes for the genetic diagnostics of hereditary breast and/or ovarian cancer. J Mol Diagn 2015, 17:162e170 5. Heisey RE, Carroll JC, Warner E, McCready DR, Goel V: Hereditary breast cancer. Identifying and managing BRCA1 and BRCA2 carriers. Can Fam Physician 1999, 45:114e124 6. Wagner T, Stoppa-Lyonnet D, Fleischmann E, Muhr D, Pages S, Sandberg T, Caux V, Moeslinger R, Langbauer G, Borg A, Oefner P: Denaturing high-performance liquid chromatography detects reliably BRCA1 and BRCA2 mutations. Genomics 1999, 62:369e376 7. Hogervorst FB, Nederlof PM, Gille JJ, McElgunn CJ, Grippeling M, Pruntel R, Regnerus R, van Welsem T, van Spaendonk R, Menko FH, Kluijt I, Dommering C, Verhoef S, Schouten JP, van’t Veer LJ, Pals G: Large genomic deletions and duplications in the BRCA1 gene identified by a novel quantitative method. Cancer Res 2003, 63: 1449e1453 8. Sanger F, Nicklen S, Coulson AR: DNA sequencing with chainterminating inhibitors. Proc Natl Acad Sci U S A 1977, 74:5463e5467 9. Feliubadalo L, Lopez-Doriga A, Castellsague E, del Valle J, Menendez M, Tornero E, Montes E, Cuesta R, Gomez C, Campos O,
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Pineda M, Gonzalez S, Moreno V, Brunet J, Blanco I, Serra E, Capella G, Lazaro C: Next-generation sequencing meets genetic diagnostics: development of a comprehensive workflow for the analysis of BRCA1 and BRCA2 genes. Eur J Hum Genet 2013, 21:864e870 D’Argenio V, Esposito MV, Telese A, Precone V, Starnone F, Nunziato M, Cantiello P, Iorio M, Evangelista E, D’Aiuto M, Calabrese A, Frisso G, D’Aiuto G, Salvatore F: The molecular analysis of BRCA1 and BRCA2: next-generation sequencing supersedes conventional approaches. Clin Chim Acta 2015, 446:221e225 Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL; ACMG Laboratory Quality Assurance Committee: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015, 17:405e424 Dacheva D, Dodova R, Popov I, Goranova T, Mitkova A, Mitev V, Kaneva R: Validation of an NGS approach for diagnostic BRCA1/BRCA2 mutation testing. Mol Diagn Ther 2015, 19:119e130 Chan M, Ji SM, Yeo ZX, Gan L, Yap E, Yap YS, Ng R, Tan PH, Ho GH, Ang P, Lee AS: Development of a next-generation sequencing method for BRCA mutation screening: a comparison between a highthroughput and a benchtop platform. J Mol Diagn 2012, 14:602e612 Judkins T, Leclair B, Bowles K, Gutin N, Trost J, McCulloch J, Bhatnagar S, Murray A, Craft J, Wardell B, Bastian M, Mitchell J, Chen J, Tran T, Williams D, Potter J, Jammulapati S, Perry M, Morris B, Roa B, Timms K: Development and analytical validation of a 25-gene next generation sequencing panel that includes the BRCA1 and BRCA2 genes to assess hereditary cancer risk. BMC Cancer 2015, 15:215 Chou LS, Lyon E, Mao R: Molecular diagnosis utility of multiplex ligation-dependent probe amplification. Expert Opin Med Diagn 2008, 2:373e385 Chong HK, Wang T, Lu HM, Seidler S, Lu H, Keiles S, Chao EC, Stuenkel AJ, Li X, Elliott AM: The validation and clinical implementation of BRCAplus: a comprehensive high-risk breast cancer diagnostic assay. PLoS One 2014, 9:e97408 Lohmann K, Klein C: Next generation sequencing and the future of genetic diagnosis. Neurotherapeutics 2014, 11:699e707 Boycott K, Hartley T, Adam S, Bernier F, Chong K, Fernandez BA, Friedman JM, Geraghty MT, Hume S, Knoppers BM, Laberge AM, Majewski J, Mendoza-Londono R, Meyn MS, Michaud JL, Nelson TN, Richer J, Sadikovic B, Skidmore DL, Stockley T, Taylor S, van Karnebeek C, Zawati MH, Lauzon J, Armour CM; Canadian College of Medical Geneticists: The clinical application of genome-wide sequencing for monogenic diseases in Canada: Position Statement of the Canadian College of Medical Geneticists. J Med Genet 2015, 52:431e437 ACMG Board of Directors: Points to consider in the clinical application of genomic sequencing. Genet Med 2012, 14:759e761
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TECHNICAL ADVANCE Clinical Next-Generation Sequencing Pipeline Outperforms a Combined Approach Using Sanger Sequencing and Multiplex Ligation-Dependent Probe Amplification in Targeted Gene Panel Analysis Q14
Laila C. Schenkel,* Jennifer Kerkhof,y Alan Stuart,y Jack Reilly,y Barry Eng,z Crystal Woodside,z Alexander Levstik,y Christopher J. Howlett,* Anthony C. Rupar,*x Joan H.M. Knoll,*y Peter Ainsworth,*y John S. Waye,z and Bekim Sadikovic*y From the Department of Pathology and Laboratory Medicine,* Western University, London, Ontario; the Molecular Genetics Laboratory,y Molecular Diagnostics Division, London Health Sciences Centre, Children’s Health Research Institute, London, Ontario; the Department of Pathology and Laboratory Medicine,z Hamilton Regional Laboratory Medicine Program, Hamilton Health Sciences, Hamilton, Ontario; and the Biochemical Genetics Laboratory,x Molecular Diagnostics Division, London Health Sciences Centre, London, Ontario, Canada Accepted for publication April 19, 2016. Address correspondence to Bekim Sadikovic, Ph.D., DABMGG, FACMG, Department of Pathology and Laboratory Medicine, Victoria Hospital, London Health Sciences Centre, 800 Commissioner’s Rd. E, B10104, London, ON N6A 5W, Canada. E-mail: bekim. [email protected].
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Advances in next-generation sequencing (NGS) have facilitated parallel analysis of multiple genes enabling the implementation of cost-effective, rapid, and high-throughput methods for the molecular diagnosis of multiple genetic conditions, including the identification of BRCA1 and BRCA2 mutations in high-risk patients for hereditary breast and ovarian cancer. We clinically validated a NGS pipeline designed to replace Sanger sequencing and multiplex ligation-dependent probe amplification analysis and to facilitate highly sensitive and specific detection of sequence and copy number alterations in a single test focusing on a BRCA1/BRCA2 gene analysis panel. Our custom capture library covers 46 exons, including BRCA1 exons 2, 3, and 5 to 24 and BRCA2 exons 2 to 27, with 20 nucleotides of intronic regions both 50 and 30 of each exon. We analyzed 402 retrospective patients, with previous Sanger sequencing and multiplex ligation-dependent probe amplification results, and 240 clinical prospective patients. One-hundred eighty-three unique variants, including sequence and copy number variants, were detected in the retrospective (n Z 95) and prospective (n Z 88) cohorts. This standardized NGS pipeline demonstrated 100% sensitivity and 100% specificity, uniformity, and high-depth nucleotide coverage per sample (approximately 7000 reads per nucleotide). Subsequently, the NGS pipeline was applied to the analysis of larger gene panels, which have shown similar uniformity, sample-to-sample reproducibility in coverage distribution, and sensitivity and specificity for detection of sequence and copy number variants. (J Mol Diagn 2016, -: 1e11; http://dx.doi.org/10.1016/j.jmoldx.2016.04.002)
Advances in the next-generation sequencing (NGS) technology have revolutionized clinical molecular diagnostics. Clinical utilization of massively parallel sequencing enables simultaneous assessment of multiple genes, gene panels, and entire exomes or genomes using a limited quantity of biological samples. Multiplexing of patient samples combined with broad genomic coverage results in significantly decreased cost and turnaround times (TATs).1 Mutations of highly penetrant cancer susceptibility genes, BRCA1 and BRCA2, are the strongest genetic predisposition factors for hereditary breast and ovarian cancer (HBOC),
increasing the lifetime risk of developing these cancers to as high as 80%.2 Moreover, BRCA1 and BRCA2 mutations are also associated with earlier disease onset and with other types of cancers, including fallopian tube, gastric, pancreatic, and prostate cancers. The identification of BRCA mutations is important for individual and family genetic counseling, as well as for early implementation of Supported by the London Health Sciences and Hamilton Health Sciences Molecular Genetics Laboratories operating budget. Disclosures: None declared.
Copyright ª 2016 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jmoldx.2016.04.002
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63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 Q4 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 Q2 Q3 123 124
125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186
Schenkel et al personalized pharmacotherapy using poly (ADP-ribose) polymerase inhibitors.3 Thus, the efficient identification of BRCA1/2 mutation carriers among women affected with breast and/or ovarian cancers may be crucial for determining optimum therapeutic strategies for these patients and their affected family members. Because of the relatively large size of the BRCA1/2 genes and the allelic heterogeneity of the pathogenic mutations, clinical testing of these genes is technically complex and labor intensive and, as such, is currently restricted to screening high-risk patients as determined by their family cancer history or ethnic origin in the case of identified founder mutations.4 Methods for mutation screening of the BRCA1 and BRCA2 genes have evolved since these genes were first characterized and testing was initiated in the mid1990s.5 The earliest approaches to BRCA1/2 gene analysis involved indirect methods of mutation identification, such as protein truncation analysis which did not detect potentially damaging missense mutations or structural gene rearrangements. Subsequent improvements in mutation scanning approaches included denaturing high-performance liquid chromatography, and other methods such as multiplex ligation-dependent probe amplification (MLPA),6,7 to help identify the small, although significant, proportion of potentially damaging genomic rearrangements in the BRCA1 and BRCA2 genes. Clinical molecular testing continued to evolve to Sanger sequencing8 in conjunction with MLPA (currently considered the gold standard for sequencing assays), an approach that is highly sensitive but relatively expensive and labor intensive. Recent improvements to NGS technologies that facilitate massively parallel analysis of multiple genes have enabled the implementation of more cost-effective, rapid, and high-throughput methods to allow precise identification of BRCA1 and BRCA2 mutations in these high-risk patients, as well as testing of more comprehensive gene panels for cancer predisposition syndromes.4,9,10 NGS is also capable of sensitive detection of sequence variants and may also be used for detection of copy number variants (CNVs), such as exon deletions and duplications that currently require use of MLPA or other copy number technologies. Here, we describe the clinical validation of an NGS pipeline designed to replace Sanger sequencing and MLPA analysis and to facilitate highly sensitive and specific detection of sequence and copy number alterations in a single assay, using the BRCA1/BRCA2 gene analysis panel as a test example, and to demonstrate application of this approach for analysis of other larger clinical gene panels.
Science Centre Molecular Genetics Laboratory, Molecular Diagnostics Division, and 298 HBOC patient samples, previously tested at the Hamilton Health Sciences Molecular Diagnostic Laboratory. We also analyzed a prospective cohort of the first 240 HBOC patients whose samples were initially tested with the NGS method at Hamilton Health Sciences; for those samples in which pathogenic, likely pathogenic, and variants of unknown clinical significance were identified, the findings were confirmed by Sanger sequencing and/or MLPA. NGS was performed at both institutions for their respective patients. All patients gave consent and were counseled for testing as part of their clinical HBOC screening.
Materials and Methods
The clinical validation cohort (retrospective) comprised 104 HBOC patient specimens, previously tested using Sanger sequencing and MLPA method at the London Health
Custom sequence capture probes were designed using the SeqCap EZ Choice Library system (Roche NimbleGen, Inc., Madison, WI). The design included enrichment for all coding exons as well as 20 bp of the 50 and 30 flanking intronic regions on each side. This design can include up to 2.1 million different probes that are designed to massively overlap each other across the target sequence, thereby
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Sample Collection
DNA Isolation Genomic DNA from each sample was isolated by standard protocols using the MagNA Pure system (Roche Diagnostics, Laval, QC, Canada) at London Health Sciences Centre, or the Qiagen Puregene method or QIAcube automated method (Qiagen, Hilden, Germany) at Hamilton Health Sciences. DNA was quantified by measuring absorbance with a DTX 880 Multimode Detector (Beckman Coulter, Brea, CA).
CNV Analysis Using MLPA Genomic DNA (100 ng) was amplified according to the manufacturer’s recommendations using a SALSA MLPA kit (P002-D1 and/or P087-C1 for BRCA1, P090-A4 and/or P077-A2 for BRCA2; MRC Holland, Amsterdam, the Netherlands). PCR products were separated by capillary electrophoresis on an ABI 3730 (Life Technologies, Thermo Fisher Scientific, Waltham, MA). Copy number alterations were analyzed with Coffalyser.Net software version 131211.1524 (MRC Holland).
Sanger-Based Sequencing Coding regions and the flanking intronic regions (20 bp to 10 bp) from the genes of interest were PCR-amplified and sequenced with the BigDye Terminator version 1.1 cycle sequencing kit (Life Technologies). Sequencing products were separated by capillary electrophoresis on an ABI 3730 (Life Technologies) and analyzed with Mutation Surveyor version 4.0.7 software (SoftGenetics, LLC, State College, PA).
NGS Library Design
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Figure 1 BRCA1/2 gene panel. A: Coverage plot for a 24-patient sample batch (each line represents one patient); y axis is sequence read depth; gene and exon locations are indicated. B: Sequence coverage depth for three representative BRCA1/2 screens; gene and exon locations are indicated; red lines indicate exon boundaries; x axis indicates nucleotide positions on corresponding genes and exons; y axis indicates depth of sequence coverage (approximate mean coverage for BRACA1/2 Z 7000); y axis is on a linear scale so for a sample with a mean coverage of 2000, reduction in coverage to the half equates to approximately 1000 times coverage at that locus. C and D: Normalized copy number plot demonstrating deletion detection (arrows, C) and gene duplication (arrow, D). Gene and exon locations are indicated (x axis); blue lines indicate exon boundaries; y axis represents quantile normalized copy number data (for autosomal genes 0.5 Z one copy, 1 Z two copies, 1.5 Z three copies).
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249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310
Clinical NGS Pipeline in Targeted Gene Panel Analysis Q1
introducing significant redundancy and ability to capture complex, CG-rich, and polymorphic genomic regions. The SeqCap EZ Choice Library are proprietary designs involving Nimblegen-designed bioinformatically targeted probe coverage of the region of interest, that normally involves high-density tiling of the targeted region. If needed, each specific design can be adjusted for probe concentrations on empirical assessment on patient samples to ensure uniform depth of coverage.
Library Preparation and Target Capture Sequencing Libraries were prepared with 100 ng of genomic DNA fragmented to 180 to 220 bp using a Covaris E220 Series Focused-ultrasonicator (Covaris, Inc., Woburn, MA). After fragment quantification and size distribution assessment
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with the Qubit fluorometer (Life Technologies) and 2200 TapeStation (Agilent Technologies, Santa Clara, CA), respectively, each sample library was ligated with a specific barcode index according to the manufacturer’s protocol (Roche NimbleGen, Inc.). DNA libraries were then pooled and captured using the SeqCap EZ Choice Library system (Roche NimbleGen, Inc.). Captured libraries underwent appropriate quality control analysis and were diluted to a concentration of 4 nmol/L to process for sequencing according to the manufacturer’s instructions (Illumina, San Diego, CA). The final captured library concentration for sequencing was 8 pmol/L with a 1% PhiX spike-in. Q7 Libraries were sequenced using the MiSeq version 2 reagent kit to generate 2 150 bp paired-end reads using the MiSeq fastq generation mode (Illumina), with 24 different patient samples multiplexed per run.
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311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372
373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434
Schenkel et al Table 1
BRCA Mutations Identified in the Retrospective Cohort (n Z 402)
Gene
Patients, n
Variant type
cDNA change
Protein change
Homozygous/ heterozygous
BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2
1 1 1 23 1 1 1 1 1 1 1 23 102 101 1 106 1 1 1 1 101 8 1 102 3 1 101 1 1 1 102 1 1 1 1 6 1 2 1 1 1 1 1 1 1 1 104 12 1 1 4 2 12 1 1 12
Deletion Duplication Duplication Missense Frame-shift Frame-shift Missense Frame-shift Nonsense Frame-shift Missense Missense Synonymous Synonymous Missense Missense Synonymous Missense Missense Missense Missense Missense Frame-shift Missense Missense Synonymous Synonymous Synonymous Frame-shift Missense Missense Nonsense Missense Missense Frame-shift Missense Missense Frame-shift Synonymous Synonymous Deletion Deletion Deletion Synonymous Frame-shift Missense Missense Synonymous Missense Missense NA Synonymous Synonymous Synonymous Synonymous Missense
BRCA1:Ex1_24del BRCA1:Ex13dup BRCA1:Ex18_20dup c.1067A>G c.1174_1213del c.1386_1387insG c.1390A>G c.1500_1504del c.1687C>T c.1961delA c.2050C>T c.2077G>A c.2082C>T c.2311T>C c.2566T>C c.2612C>T c.2733A>G c.2791G>T c.3024G>A c.3092T>G c.3113A>G c.3119G>A c.3479_3489del c.3548A>G c.4039A>G c.4050C>T c.4308T>C c.4416T>G c.4417delT c.4535G>T c.4837A>G c.4945A>T c.4946G>T c.4947A>T c.4947delAins c.4956G>A c.5207T>C c.5266_5267ins c.591C>T c.981A>G BRCA1:Ex24del BRCA2: Ex8_10del, 12_13del BRCA2:Ex19_20del c.10095C>G c.10096_10097ins c.10234A>G c.1114A>C c.1365A>G c.1487C>T c.1762A>G c.1909þ22_1909þ23ins c.1938C>T c.2229T>C c.231T>G c.2883G>A c.2971A>G
NA NA NA p.Gln356Arg p.Leu392Glnfs p.Lys463Glufs p.Thr464Ala p.Leu502Alafs p.Gln563Ter p.Lys654Serfs p.Pro684Ser p.Asp693Asn p.Ser694Z p.Leu771Z p.Tyr856His p.Pro871Leu p.Gly911Z p.Val931Leu p.Met1008Ile p.Ile1031Ser p.Glu1038Gly p.Ser1040Asn p.Glu1161Phefs p.Lys1183Arg p.Arg1347Gly p.Gly1350Z p.Ser1436Z p.Leu1472Z p.Ser1473Leufs p.Ser1512Ile p.Ser1613Gly p.Arg1649Ter p.Arg1649Ile p.Arg1649Ser p.Met1650Tyrfs p.Met1652Ile p.Val1736Ala p.Gln1756Profs p.Cys197Z p.Thr327Z NA NA NA p.Val3365Z p.Ser3366Asnfs p.Ile3412Val p.Asn372His p.Ser455Z p.Ser496Phe p.Asn588Asp NA p.Ser646Z p.His743Z p.Thr77Z p.Gln961Z p.Asn991Asp
0/1 0/1 0/1 0/23 0/1 0/1 0/1 0/1 0/1 0/1 0/1 1/22 22/80 20/81 0/1 25/81 0/1 0/1 0/1 0/1 20/81 0/8 0/1 20/82 0/3 0/1 20/81 0/1 0/1 0/1 20/82 0/1 0/1 0/1 0/1 0/6 0/1 0/2 0/1 0/1 0/1 0/1 0/1 0/1 0/1 0/1 16/88 0/12 0/1 0/1 3/1 0/2 0/12 0/1 0/1 0/12 (table continues)
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435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
Clinical NGS Pipeline in Targeted Gene Panel Analysis Table 1
(continued )
Gene
Patients, n
Variant type
cDNA change
Protein change
Homozygous/ heterozygous
BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2
1 92 3 62 2 1 1 1 1 1 3 191 3 6 1 1 1 1 1 191 1 2 1 1 70 191 2 143 1 1 12 1 1 1 1 3 1 1 3
Missense Synonymous Synonymous Synonymous Synonymous Missense In-frame Missense Frame-shift Missense Missense Synonymous Synonymous Missense Frame-shift Missense Missense Missense Frame-shift Synonymous Missense NA Missense Missense Synonymous Missense Missense NA Missense Missense Missense Missense Synonymous Frame-shift Synonymous NA Frame-shift NA Nonsense
c.3262C>T c.3396A>G c.3516G>A c.3807T>C c.4068G>A c.4094G>A c.4131_4132ins c.4163C>A c.4169delT c.4189G>A c.4258G>T c.4563A>G c.5199C>T c.5744C>T c.5775_5860del c.5869A>G c.5945G>C c.6100C>T c.6402_6406del c.6513G>C c.6796A>C c.68-7T>A c.7007G>A c.7010C>T c.7242A>G c.7397T>C c.7469T>C c.7806-14T>C c.8149G>T c.8182G>A c.865A>C c.8567A>C c.8460A>C c.8537_8538del c.9234C>T c.9257-16T>C c.9403delC c.9649-19G>A c.9976A>T
p.Pro1088Ser p.Lys1132Z p.Ser1172Z p.Val1269Z p.Leu1356Z p.Cys1365Tyr p.Thr1378Ter p.Thr1388Asn p.Leu1390Trpfs p.Glu1397Lys p.Asp1420Tyr p.Leu1521Z p.Ser1733Z p.Thr1915Met p.Gln1925Hisfs p.Ile1957Val p.Ser1982Thr p.Arg2034Cys p.Asn2135Lysfs p.Val2171Z p.Asn2266His NA p.Arg2336His p.Thr2337Ile p.Ser2414Z p.Val2466Ala p.Ile2490Thr NA p.Ala2717Ser p.Val2728Ile p.Asn289His p.Glu2856Ala p.Val2820Z p.Glu2846Glyfs p.Val3078Z NA p.Leu3135Phefs NA p.Lys3326Ter
0/1 17/75 0/3 8/54 0/2 0/1 0/1 0/1 0/1 0/1 0/3 190/1 0/3 0/6 0/1 0/1 0/1 0/1 0/1 190/1 0/1 0/2 0/1 0/1 7/63 190/1 0/2 50/93 0/1 0/1 0/12 0/1 0/1 0/1 0/1 0/3 0/1 0/1 0/3
NA, not applicable.
NGS Analysis for Sequence Variants
NGS Analysis of CNVs
Sequence analysis for variant identification, alignment, and coverage distribution was performed with NextGene software version 2.4.1 (SoftGenetics, LLC) using standard alignment settings (allowable mismatch bases: 1; allowable ambiguous alignments: 50; seeds bases: 30; move step bases: 5; allowable alignments: 100; matching base percentage >85%). BAM and VCF files were imported into Geneticist Assistant version 1.1.5 (SoftGenetics, LLC) for quality control assessment (minimum base coverage; mean exon coverage) and for data basing. Variants were analyzed and interpreted by a clinical molecular geneticist and were classified for pathogenicity using the American College of Medical Genetics guidelines.11
Reports for base coverage distribution were generated using NextGene software version 2.4.1 (SoftGenetics, LLC). Single nucleotide coverage for each patient was normalized. Briefly, the sum of all 24 sample sequencing reads divided by the number of patients equals the total mean coverage per patient (mean of sums). The normalization factor (NF) is then calculated by dividing the sum of reads for each patient by the mean of sums. Finally, each read per nucleotide per patient is divided by the NF and by the average read of each nucleotide in the 24 samples, resulting in the normalized reads per nucleotide per patient (NR). The normalized data were then presented in a graph allowing visualization of
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559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620
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6
benign variants was available for all patients and was compared with the NGS data. Briefly, we identified 95 different variants (40 in BRCA1, 55 in BRCA2), resulting in a total of 1964 variant occurrences. To further characterize the variant frequency distribution, they (both sequence and CNVs) were classified into seven groups: intronic heterozygous single nucleotide polymorphisms (SNPs), intronic homozygous SNPs, coding heterozygous SNPs, coding homozygous SNPs, deletions, insertions, and exon deletions/ duplications. We observed 1022 (52%) heterozygous SNPs and 763 (39%) homozygous SNPs in coding regions, as well as 153 (7.8%) intronic SNPs. In addition, among the 1964 variants, 26 (1.32%) were CNVs that included 20 deletions and insertions and six exon del/dups (Figure 2A). All pre- ½F2 viously observed variants were confirmed, demonstrating 100% sensitivity of the NGS technology. Five additional variants outside of the region included in the original Sanger sequencing were also detected, and these variants were subsequently confirmed by Sanger sequencing. The prospective cohort included select patients in whom pathogenic variants or VUSs were detected by the NGS assay and were subsequently confirmed by Sanger sequencing and MLPA, as well as normal controls. In the prospective cohort of 240 HBOC patients tested by NGS,
A
10
6
10
53
Intron heterozygous SNP Intron homozygous SNP
100
Coding heterozygous SNP 763
Coding homozygous SNP Deletions 1022
Insertions Exon del/dups
B 12
3
7
Intron heterozygous SNP Coding heterozygous SNP Deletions Insertions
79
Figure 2 A: BRCA1 and BRCA2 variants identified by NGS in the retrospective cohort. SNPs were classified according to zygosity (homozygous/ heterozygous) and location (intron or exon). B: Type of BRCA1 and BRCA2 variants identified by NGS in the prospective patient cohort. The number of deletions, insertions, and exon deletion/duplications identified are shown in each panel. n Z 402 (A); n Z 240 (B). del, deletion; dup, duplication; NGS, next-generation sequencing; SNP, single nucleotide polymorphism.
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621 CNVs (ie, deletions and duplications) at exon and subexon 622 levels (Excel version 14.0.6129.5000; Microsoft Corpora623 tion, Redmond, WA). Deletions were defined by a mean 624 ratio of 0.65 and duplications were defined by a ratio 625 of 1.35. 626 P Reads 627 ZMean of Sums 628 #Patients P 629 Reads per individual patient ZNF 630 Mean of Sums 631 Reads per nucleotide per patient 632 ZNR NF Average Reads per nucleotide 633 634 635 636 Results 637 Sequencing Coverage and Normalization 638 639 Our custom capture library covers 46 exons, including 640 641 BRCA1 exons 2, 3, and 5 to 24 and BRCA2 exons 2 to 27, 642 with 20 nucleotides of intronic regions both 50 and 30 of 643 each exon, with a total of 17,769 nucleotides (89% in 644 coding regions and 11% in flanking regions). Sequencing 645 BRCA1 and BRCA2 in the retrospective cohort generated a 646 mean of approximately 6500 reads per nucleotide per pa647 tient, with 24 patient samples batched per library enrichment 648 and sequencing run. The raw sequence alignment of 24 649 patients in a single run is shown in Figure 1A, demon½F1 650 strating deep and uniform coverage across all nucleotides 651 analyzed. In addition, the high level of uniformity of 652 653 coverage among patients is evident at the subexon resolu654 tion (Figure 1B). Taking advantage of the reproducibility 655 and uniformity of the coverage distribution, we applied a 656 quantile normalization algorithm to the coverage distribu657 tion reports from the NextGene alignment output for each 658 patient to assess copy number alterations. Figure 1C shows 659 an example of a quantile normalized copy number graph for 660 a 24-patient batch that included a patient with a BRCA1 661 exon 24 deletion and another patient with a complex BRCA2 662 deletion involving exons 8 to 10 and 12 to 13, with normal 663 exon 11 copy number. Similarly, two patients’ samples with 664 665 a BRCA1 duplication involving exon 13 demonstrate an 666 easily detectable, distinct abnormality on the copy number 667 plot (Figure 1D). 668 669 BRCA1 and BRCA2 Variants 670 671 NGS analysis for BRCA1 and BRCA2 was performed in a 672 retrospective cohort of 402 HBOC individuals that had 673 previously been analyzed by Sanger sequencing and MLPA 674 techniques. A summary of the sequence variants is shown in 675 Table 1. The retrospective cohort are patients who were ½T1 676 previously tested by the Sanger sequencing and MLPA 677 method and included preselected patients with the wide 678 679 range of the types of mutations found in these genes, as well 680 as select normal controls. The information for pathogenic, 681 variants of unknown clinical significance (VUSs), and 682
683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744
745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806
Clinical NGS Pipeline in Targeted Gene Panel Analysis Table 2 BRCA Mutations Identified in the Prospective Cohort (n Z 240) Gene
Patients, n Variant type cDNA change
Protein change
Zygosity
BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1
1 1 1 1 1 2 1 1 1
p.Lys355Z p.Trp385Ter p.Arg496His p.Thr582Met p.Cys61Gly p.Gln657Z p.Tyr856His p.Arg866Cys p.Lys894Thrfs
HZ HZ HZ HZ HZ HZ HZ HZ HZ
p.Ser945Thr p.Ile946Glnfs
HZ HZ
p.Arg1028Cys p.Gln1090Z p.Glu1134Ter p.Ser1187Asn p.Gln1200His p.Arg1203Gln p.Ser1262Pro p.Glu1494Ter p.Gly1591Ser p.Gln1604Z p.Trp1712Ter p.Gln172Asnfs p.Val1719Z p.Gln1756Profs
HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ
p.Met1775Arg N/A p.Cys197Z N/A p.Trp321Ter p.Arg3370Z p.Thr3371Ala p.Ser384Phe p.Tyr42Cys p.Lys454Z p.Glu510Ter p.Leu61Z N/A
HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ
p.Ala75Pro p.Ser846Z p.Pro9Glnfs p.Asp935Asn p.Ser973Z p.Ser976Thr p.Ser976Phe p.Leu1019Val p.Lys1057Thrfs
HZ HZ HZ HZ HZ HZ HZ HZ HZ
p.Pro1088Z p.Met1149Val p.Leu1356Z p.Leu1478Z p.Gly1529Arg p.His1561Asn p.Gln1562Z p.Tyr1655Ter p.Glu1806Z p.Ile1851Ser
HZ HZ HZ HZ HZ HZ HZ HZ HZ HZ
Synonymous Nonsense Missense Missense Missense Synonymous Missense Missense Frame-shift
BRCA1 1 BRCA1 1
Missense Frame-shift
BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA1
1 1 1 1 1 1 1 1 1 1 1 1 1 2
Missense Synonymous Nonsense Missense Missense Missense Missense Nonsense Missense Synonymous Nonsense Frame-shift Synonymous Frame-shift
BRCA1 BRCA1 BRCA1 BRCA1 BRCA1 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 4022
1 1 1 2 1 1 1 2 1 1 1 1 1
Missense N/A Synonymous N/A Nonsense Synonymous Missense Missense Missense Synonymous Nonsense Synonymous N/A
BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2
1 1 1 1 1 2 2 1 1
Missense Synonymous Frame-shift Missense Synonymous Missense Missense Missense Frame-shift
BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2
1 1 2 1 1 1 2 1 1 1
Synonymous Missense Synonymous Synonymous Missense Missense Synonymous Nonsense Synonymous Missense
c.1065G>A c.1154G>A c.1487G>A c.1745C>T c.181T>G c.1971A>G c.2566T>C c.2596C>T c.2681_ 2682del c.2834G>C c.2835_ 2836del c.3082C>T c.3270A>G c.3400G>T c.3560G>A c.3600G>C c.3608G>A c.3784T>C c.4480G>T c.4771G>A c.4812A>G c.5136G>A c.514delC c.5157G>T c.5266_ 5267ins c.5324T>G c.5332+13G>T c.591C>T c.81-14C>T c.962G>A c.10110G>A c.10111A>G c.1151C>T c.125A>G c.1362A>G c.1528G>T c.183A>G c.1909+22_ 1909+23ins c.223G>C c.2538A>C c.26delC c.2803G>A c.2919G>A c.2926T>A c.2927C>T c.3055C>G c.3170_ 3174del c.3264T>C c.3445A>G c.4068G>A c.4434A>G c.4585G>A c.4681C>A c.4686A>G c.4965C>G c.5418A>G c.5552T>G
(table continues)
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807 808 809 Patients, 810 Gene n Variant type cDNA change Protein change Zygosity 811 BRCA2 2 Missense c.5704G>A p.Asp1902Asn HZ 812 BRCA2 1 Frame-shift c.5722_ p.Leu1908Argfs HZ 813 5723del 814 BRCA2 1 Nonsense c.5857G>T p.Glu1953Ter HZ 815 BRCA2 1 Missense c.6322C>T p.Arg2108Cys HZ BRCA2 3 Missense c.6323G>A p.Arg2108His HZ 816 BRCA2 1 Missense c.6325G>A p.Val2109Ile HZ 817 BRCA2 1 Missense c.6412G>T p.Val2138Phe HZ 818 BRCA2 1 Frame-shift c.6486_ p.Lys2162Asnfs HZ 819 6489del 820 BRCA2 1 Frame-shift c.658_ p.Val220Ilefs HZ 821 659delGT 822 BRCA2 1 N/A c.68-7T>A N/A HZ 823 BRCA2 1 Synonymous c.6987G>A p.Pro2329Z HZ BRCA2 1 Missense c.7017G>C p.Lys2339Asn HZ 824 BRCA2 1 Frame-shift c.7069_ p.Leu2357Valfs HZ 825 7070del 826 BRCA2 1 Missense c.7319A>G p.His2440Arg HZ 827 BRCA2 1 Missense c.7544C>T p.Thr2515Ile HZ 828 BRCA2 1 Frame-shift c.7577delC p.Ala2526Glufs HZ 829 BRCA2 1 Frame-shift c.7580delT p.Val2527Glufs HZ 830 BRCA2 1 Frame-shift c.7583delG p.Gly2528Glufs HZ 831 BRCA2 1 N/A c.7805+6C>G N/A HZ BRCA2 1 Missense c.7916C>T p.Pro2639Leu HZ 832 BRCA2 1 Missense c.8057T>G p.Leu2686Arg HZ 833 BRCA2 1 Missense c.825A>T p.Lys275Asn HZ 834 BRCA2 1 Missense c.8356G>A p.Ala2786Thr HZ 835 BRCA2 1 N/A c.8487+3A>G N/A HZ 836 BRCA2 3 Missense c.8567A>C p.Glu2856Ala HZ 837 BRCA2 1 N/A c.8633-16C>G N/A HZ 838 BRCA2 1 Missense c.9032T>C p.Leu3011Pro HZ 839 BRCA2 1 Missense c.9038C>T p.Thr3013Ile HZ BRCA2 1 Missense c.9076C>G p.Gln3026Glu HZ 840 BRCA2 1 Missense c.9271G>A p.Val3091Ile HZ 841 BRCA2 1 Missense c.9730G>A p.Val3244Ile HZ 842 843 HZ, heterozygous. 844 845 88 different sequence variants (30 in BRCA1 and 58 in 846 BRCA2) (Table 2, Figure 2B) classified as pathogenic, likely ½T2 847 pathogenic, or VUSs were observed. One hundred one 848 849 BRCA1 and BRCA2 variant occurrences were observed, of 850 which 93 (79 sequence variants, 12 deletions, and 2 inser851 tion) were in coding regions and eight (seven sequence 852 variants and one insertion) were intronic. All variants were 853 confirmed by Sanger sequencing and MLPA analysis, 854 demonstrating 100% specificity for the NGS analysis. 855 856 857 Discussion 858 859 In this study, we present clinical validation of a NGS 860 pipeline designed to replace conventional Sanger 861 sequencing and MLPA for detection of sequence and CNVs, 862 respectively. NGS-based analysis of the BRCA1 and BRCA2 863 genes were previously reported, including PCR-based 864 865 library preparation on the Roche sequencer,10 PCR-based 866 on Ion-Torrent sequencer,4,12 PCR-based on SOLiD and 867 IoneTorrent sequencers,13 and RainDance microdroplet 868 Table 2
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Figure 3 Sequence coverage depth for three representative patient screens for CMT panel (17 genes: MFN2, LMNA, MPZ, RAB7A, SH3TC2, FIG4, GARS, HSPB1, NEFL, GDAP1, EGR2, TRPV4, HSPB8, LITAF, PMP22, PRX, GJB1) (A); CANcer (25 genes: MUTYH, EPCAM, MSH2, MSH6, BARD1, MLH1, APC, PMS2, NBN, CDKN2A, BMPR1A, PTEN, ATM, CDK4, BRCA2, PALB2, CDH1, TP53, RAD51D, BRCA1, RAD51C, BRIP1, SMAD4, STK11, CHEK2) (B); and MtDNA panel (37 genes: ATP6, ATP8, COX1, COX2, COX3, CYTB, ND1, ND2, ND3, ND4, ND4L, ND5, ND6, TRNA, TRNC, TRND, TRNE, TRNF, TRNG, TRNH, TRNI, TRNK, TRNL1, TRNL2, TRNM, TRNN, TRNP, TRNQ, TRNR, TRNS1, TRNS2, TRNT, TRNV, TRNW, TRNY, RNR1, RNR2) (C). Red lines indicate exon boundaries. The x axis indicates nucleotide positions on corresponding genes and exons; y axis indicates depth of sequence coverage (approximate mean coverage for CMT, 2000; CANcer panel, 4000, and MtDNA, 5000 and 20,000 for blood and muscle, respectively). The y axis is on a linear scale so for a sample with a mean coverage of 2000, reduction in coverage to the half equates to approximately 1000 times coverage at that locus. CANcer, hereditary cancer; CMT, Charcot-Marie Tooth; MtDNA, mitochondrial genome sequencing.
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PCR-based sequencing.14 Although many of the approaches describe ability to sensitively detect sequence-level variation (SNPs and small insertions and deletions), all of these assays require parallel methods for the exon-level copy number detection. Our method is unique in its ability to use NGS data to sensitively detect both sequence level and copy number alterations in the same assay. The key variables to be assessed when validating a clinical assay are sensitivity and specificity. We conducted NGS analysis of samples from 402 individuals, in which Sanger sequencing and MLPA testing had been conducted as part of the routine clinical workup. A total of 1964 variants, including coding and intronic SNPs, deletions, insertions, and exon level copy number alterations (Figure 2A, Table 1), were validated with 100% sensitivity. As part of the clinical laboratory standard operating procedures, reportable pathogenic variants and VUSs were confirmed from the source DNA specimen using an alternate technique
(Sanger sequencing, MLPA, long-range PCR, etc.) to confirm the mutation and sample fidelity. Each of the 88 variants detected in the first 240 clinical NGS analyses was verified by confirmatory testing, demonstrating 100% specificity of the NGS assay. One of the limitations of both Sanger sequencing and MLPA analysis relates to allele dropout, which pertains to the inability of a PCR primer, sequencing primer, or an MLPA probe to bind and subsequently amplify one of the two alleles that may contain a sequence variant or a polymorphism,15,16 thereby resulting in the reduced sensitivity. Allele dropout is effectively abolished in this NGS pipeline due to the redundancy that is introduced by the Roche/ Nimblegen library preparation protocol, including random genomic fragmentation and densely overlapped DNA library capture probes (up to two million unique probes per design). In fact, one of the patient samples in the prospective cohort with an MLPA-detected exon copy number loss,
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Clinical NGS Pipeline in Targeted Gene Panel Analysis 993 which subsequently could not be confirmed by NGS anal994 ysis, was demonstrated to exhibit a SNP at the MLPA probe 995 binding site, resulting in a false-positive MLPA result. Thus, 996 the results presented herein demonstrate that this NGS 997 pipeline outperforms at all levels the current gold standard 998 of combined Sanger sequencing and MLPA. 999 A potential limitation of NGS-based assays can be a 1000 reduced ability to detect CNVs and complex sequence 1001 variants.17 Our targeted NGS analytical pipeline takes 1002 1003 advantage of the high depth of sequence coverage (mean 1004 depth of 2000 to 10,000 times, depending on the panel and 1005 number of patients) and intersample coverage uniformity 1006 ½F3 (Figures 1 and 3) to address those limitations. Unlike off1007 the-shelf generic panel design, or even exome-slices where 1008 larger gene sets are sequenced and only genes of interest are 1009 analyzed and reported, targeted panel designs provide a 1010 significantly greater depth of coverage. With the use of a 1011 single nucleotide resolution depth of coverage information 1012 from the coverage distribution files generated by the Next1013 Gene alignment software and a quantile normalization 1014 1015 algorithm, we are able to detect with 100% sensitivity and 1016 specificity the copy number alterations for each individual 1017 exon and all genes in our previously defined retrospective 1018 Q8 samples in every panel examined (see below description of 1019 other panels). In addition to replacing the MLPA screening 1020 in the BRCA1/2 panel, we no longer perform MLPA testing 1021 as a primary screen for any gene panel. 1022 Another aspect related to a specifically targeted panel 1023 design is scalability. Depending on the sample volumes and 1024 the required TAT, there is significant opportunity for scaling 1025 up both patient sample number and gene number. The 1026 1027 abundance and redundancy in our probe designs makes it 1028 relatively easy to introduce additional genes to a design 1029 without significantly compromising the depth of coverage 1030 and increasing the possibility of sequence dropouts. 1031 Targeted gene panel can be designed to yield complete 1032 coverage for every single nucleotide in every gene with 1033 minimal sample-to-sample variability (Figure 1B and 1034 Figure 3), eliminating the necessity for Sanger sequence 1035 back-filling, a common complication with clinical NGS tests 1036 with lower levels of coverage, or PCR-based enrichment 1037 designs. Therefore, this NGS pipeline allows for extensive 1038 1039 scalability both in patient volume and gene number. 1040 Although providing significant improvements in test 1041 quality, sensitivity, and specificity compared with standard 1042 NGS approaches as well as the gold standard Sanger 1043 sequencing/MLPA, this NGS pipeline also provides signif1044 icant cost advantages through both reduced reagent costs 1045 and labor time. With the use of the simplest BRCA1/2 two1046 gene panel with 24 patients per MiSeq run compared with 1047 the classic Sanger sequencing and MLPA testing, we can 1048 achieve approximately 70% reduction in total test costs for 1049 reagents, labor, and significant reduction in TAT (including 1050 1051 service contracts and institutional overhead). Cost1052 effectiveness can be enhanced further through increased 1053 automation and scalability (100 patients per run instead of 1054
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1055 24). If, however, a laboratory does not have the sufficient 1056 volumes to batch 24 or more samples per run, it is possible 1057 to combine multiple lower volume test panels on the 1058 same enrichment library design. This would ensure cost1059 effectiveness while enabling low TATs. Similar advan1060 tages are achieved in expanded, comprehensive gene panels 1061 (eg, CANcer panel, described below), enabling significant Q9 1062 improvements in efficiency of delivery of patient care and 1063 genetic services with a net positive financial impact on the 1064 1065 health care system. 1066 In addition to the BRCA1/2 panel, this NGS pipeline has 1067 been applied to the analysis of other larger gene panels, 1068 including Charcot Marie Tooth (CMT) syndrome panel, 1069 hereditary cancer syndrome panel, and mitochondrial 1070 genome sequencing panel (Figure 3; Table 3) with similar ½T3 1071 results. One hundred percent sensitivity and specificity for 1072 detection of CNVs was also achieved for these panels 1073 (Figure 4). For example, our hereditary cancer panel design ½F4 1074 totaling 25 genes and approximately 100 kb of targeted 1075 sequence, when performed in a batch of 24 patients on a 1076 1077 single MiSeq (2 150 chip) run, achieves a mean of 1078 4000 times depth of coverage, with no single nucleotide Table 3 Genes Included in Charcot Marie Tooth Panel (CMT; 17 Genes) Hereditary Cancer Panel (CANcer; 25 Genes), and Mitochondrial Genome Sequencing Panel (MtDNA; 37 Genes) Panel
Genes
CMT
MFN2 LMNA MPZ RAB7A SH3TC2 FIG4
GDAP1 EGR2 TRPV4 GARS HSPB1 NEFL
HSPB8 LITAF PMP22 PRX GJB1
CANcer
MUTYH EPCAM MSH2 MSH6 BARD1 MLH1 APC PMS2 CHEK2
CDK4 BRCA2 PALB2 CDH1 TP53 RAD51D BRCA1 RAD51C
NBN CDKN2A BMPR1A PTEN ATM BRIP1 SMAD4 STK11
Genes
tRNAs
ATP6 ATP8 COX1 COX2 COX3 CYTB ND1 ND2 ND3 ND4 ND4L ND5 ND6
TRNA TRNC TRND TRNE TRNF TRNG TRNH TRNI TRNK TRNL1 TRNL2 TRNM TRNN
MtDNA
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Figure 4 A: Normalized copy number plots for CMT; top image shows the comprehensive CMT panel; bottom image is a zoom in of PMP22 gene showing detection of one deletion and one duplication (indicated by arrows). Red lines indicate exon boundaries. B: CANcer panel; arrow indicates a patient with complex multi-exon deletion in BRCA2 (same patient as in Figure 1C). Red lines indicate exon boundaries. C: MtDNA panel; identification of patients with KSS, with approximately 10% heteroplasmy (in blue and pink) and approximately 50% heteroplasmy (in green). *Hypervariable D-loop region (m.16,024 to m.576). AeC: The x axis indicates gene/exon positions; y axis represents quantile normalized copy number data (for autosomal genes 0.5 Z one copy, 1 Z two copies, 1.5 Z three copies). CANcer, hereditary cancer; CMT, Charcot-Marie Tooth; KSS, Kearns-Sayre syndrome; MtDNA, mitochondrial genome sequencing.
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covered <500 times (Figure 3B). This approach has also enabled detection of the most common mutations in CMT (a whole gene deletion or duplication of the PMP22 gene resulting in the hereditary neuropathy with pressure palsies or CMT1, respectively), which previously required standalone MLPA prescreening of all patients with suspected CMT or hereditary neuropathy with pressure palsies before the sequencing analysis (Figure 4A). Furthermore, our mitochondrial genome sequencing panel demonstrates the ability to sensitively detect the common 5-kb deletion associated with the Kearns-Sayre syndrome in peripheral blood samples of patients with as little as 10% heteroplasmy (Figure 4C), along with sensitive detection of sequence variants down to 2% heteroplasmy. Importantly, this NGS pipeline design ensures that any copy number alterations and/or complex sequence alterations that result in disrupted
NGS sequence alignment and loss of sequence coverage would be detectable on the copy number plot, triggering further investigation and confirmation, thereby ensuring absolute sensitivity for complex sequence and CNVs. Although screening entire genomes using NGS technology may be warranted for some clinical indications18,19 and for genetic research and gene discovery, targeted gene panel sequencing provides the optimal way to deliver clinically significant, high-quality, and cost-effective genetic diagnostic. Such targeted panel-based genetic testing, in contrast to the higher cost and lower sensitivity/specificity of genome screening or exome-slicing, remains the preferred choice for polygenic conditions in which identification of a genetic defect affects patient management and clinical care. Here, we have described a clinical NGS pipeline that outperforms the current gold standard Sanger sequencing and
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Clinical NGS Pipeline in Targeted Gene Panel Analysis MLPA. Given the promise of the NGS technology to further expand the ability and capacity to discover and test an ever expanding number of genes, it is important to ensure that the application of NGS in a clinical setting does not happen at the cost of lowering the clinical standards of test delivery. Rather, when displacing a gold standard technology, we should aim to do it with a platinum standard one, while keeping the focus on clinical utility, patient care, and fiscal responsibility.
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