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BRCA Testing by Single-Molecule Molecular Inversion Probes.

The growing influence of genetic data on clinical management and therapy demands improvements in both speed and comprehensiveness of genetic testing. Recent examples that reflect this trend include the impact of exome sequencing on therapeutic decisions in neonatal care (1) and BRCA1 [9] and BRCA2 gene analyses for surgical choices and potential treatment with poly (ADP-ribose) polymerase inhibitors (2, 3). Combined with finite resources to cover growing numbers of diagnostic requests, there is a need to revise existing laboratory practices. Therefore, we set out to develop an automated laboratory work flow that was fast, stable, and flexible with respect to the increasing numbers of requested tests and samples, while also delivering comprehensive high-quality data. We hypothesized that molecular inversion probes (MIPs) [10] would serve these needs for target enrichment.

First described in 1994 as padlock probes for multiplex sequence detection (4) or genotyping (5), MIPs were subsequently adapted to enable multiplex targeted sequence capture in the context of next generation sequencing (NGS) (6, 7). Over the years, protocols for sequence capture with MIPs have been improved, e.g., by adding a single-molecule tag that enables the differentiation between PCR-duplicated and truly independent sequencing reads [single-molecule MIPs (smMIPs)] (8, 9).

There are many potential advantages of the smMIP technology for clinical sequencing, including the fact that it is flexible and easily optimized (e.g., single gene vs panels; adding new genes when needed; adjusting probe concentrations or designs to improve performance), automatable (targeted capture and sample barcoding through a small number of enzymatic reactions; crucial for laboratories dealing with high numbers of diagnostic requests), reproducible [to be used for single nucleotide variant (SNV) as well as copy number variant (CNV) analysis], and inexpensive relative to commercial enrichment kits. However, smMIPs have primarily been used in an academic setting such as targeted sequencing of candidate disease genes in large cohorts.

We here describe a fully automated clinical-sequencing work flow based on smMIP enrichment (9), in combination with 2-color NGS (NextSeq500, Illumina). As a proof of principle, we focused on BRCA1 and BRCA2. Since the discovery that pathogenic variants in BRCA1 and BRCA2 genes predispose to hereditary breast and ovarian cancer, variant analysis of these genes has been offered to women with a concerning family history, and even population screening is suggested (10). For breast cancer, the availability of results within days is important because the variant status may affect the choice of surgical strategy and chemotherapy (11-13). Furthermore, poly (ADP-ribose) polymerase inhibitors are available for women with ovarian cancer and germline or somatic pathogenic variants in BRCA1 or BRCA2 and are evaluated for other cancer types like breast, pancreatic, and prostate cancer (2, 3, 14). Current tests include Sanger sequencing (15, 16) and several NGS-based tests (17-20). Here we present a new approach that offers several advantages.

Methods

GENOMIC DNA SAMPLES

All individuals gave written informed consent for BRCA1 and BRCA2 testing. Human genomic DNA samples were isolated from EDTA blood. The study cohort consisted of patients who visited the outpatient clinic of the department of Human Genetics of the Radboud University Medical Center in Nijmegen, the Netherlands, or the Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway. All samples were blinded but had a known variant status as determined by either Sanger sequencing or ion semiconductor sequencing after AmpliSeq enrichment.

The MIP design and protocol are based on previous methods (8). Modifications of this protocol and its implementation to an automated work flow are described below.

DESIGN OF smMIPs

smMIPs for BRCA1, BRCA2, and checkpoint kinase 2 (CHEK2) (GenBank reference sequences BRCA1: NM_007294.3, BRCA2: NM_00059.3, and CHEK2: NM_007194.3) were designed using the MIPgen pipeline (21). For details of the design see Supplemental Methods and Supplemental Table 1 in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol63/issue2.

PHOSPHORYLATION AND DILUTION OF smMIPs

Phosphorylation was performed after pooling of all smMIPs. The phosphorylation mix included: 50 [micro]L pooled smMIPs after rebalancing, 2 [micro]L [T.sub.4] polynucleotide kinase (New England Biolabs), 2 [micro]L [H.sub.2]O, 6.0 [micro]L 10X T4 DNA ligase buffer with 10 mmol/L ATP (New England Biolabs) (total volume: 60 [micro]L). The PCR program used the following conditions: 45 min 37 [degrees]C, 20 min 65 [degrees]C, storage at 4 [degrees]C. Phosphorylated smMIP pools were diluted to reach a ratio of 800:1 in the final capture reaction (smMIPs:DNA molecules). This dilution was variable, depending on the number of smMIPs present in the pools.

DNA ISOLATION

Genomic DNA isolation was performed as described previously (22).

AUTOMATED smMIP LIBRARY PREPARATION

A Hamilton Microlab Star Plus robot was used for pipetting the capture mastermix at 4 [degrees]C, which contained per reaction: 2.5 [micro]L 10X Ampligase buffer (Epicentre/ Illumina), 0.03 [micro]L 0.25 mmol/L dNTPs (deoxynucleotide triphosphates), 0.32 [micro]L HemoKlentaq (New England Biolabs, 10 U/[micro]L), 0.01 [micro]L Ampligase (Epicentre/ Illumina, 100 U/[micro]L), 12.44 [micro]L [H.sub.2]O, patient's genomic DNA (6.7 [micro]L, 15 ng/[micro]L), and 3.0 [micro]L smMIP dilution (2 [micro]L smMIP pool + 88 [micro]L [H.sub.2O]). smMIP capture was performed for 18 h overnight (10 min 95 [degrees]C, 18 h 60 [degrees]C, storage at 4 [degrees]C), followed by exonuclease treatment [0.5 [micro]L EXO I (New England Biolabs), 0.5 [micro]L EXO III (New England Biolabs), 0.2 [micro]L 10X Ampligase buffer (Epicentre/Illumina), 0.8 [micro]L [H.sub.2]O] using the following program: 45 min 37 [degrees]C, 2 min 95 [degrees]C, storage at 4 [degrees]C. The exonuclease treatment was performed manually at a distinct location to prevent exonuclease contamination in pre- and post-PCR environments. Following exonuclease treatment, postcapture PCR including barcodes was performed on a MicroLab Starlet Replicator Robot (Hamilton). The PCR mixture was: 25 [micro]L 2X iProof (Bio-Rad Laboratories), 2.5 [micro]L 5 [micro]mol/L smMIP forward primer (Integrated DNA Technologies), 2.5 [micro]L 5 [micro]mol/L smMIP reverse primer (including barcode, Integrated DNA Technologies), 20 [micro]L Exonuclease-treated smMIP sample (custom PCR barcoded primers were used as described by (8)). The PCR program was: 30 s 98 [degrees]C, 24X (10 s 98 [degrees]C, 30 s 60 [degrees]C, 30 s 72 [degrees]C), 2 min 72 [degrees]C, storage at 4 [degrees]C. The mapping from DNA sample to barcode sequence was logged in an associated database; the respective sample sheet was created automatically. After PCR, all samples from one 96-well plate were pooled using the MicroLab Starlet Replicator Robot (Hamilton), and 2 [micro]L PCR-product per sample. The final volume was added up to 200 [micro]L using 0.1 X TrisEDTA buffer. Each pool was independently purified using 0.8 X Agencourt AMPure XP magnetic beads (Beckman Coulter Genomics Inc.), and concentrations were measured using a Qubit 2.0 Fluorometer (Invitrogen). If more than 1 pool was present, pools were combined to 1 megapool.

SEQUENCING

The final pooled smMIP libraries were denaturated and diluted to a sequencing concentration of 1.2 pmol/L. Sequencing was performed on a NextSeq500 (Illumina) according to manufacturer's instructions [300 cycles Mid-Output sequencing kit (Norwegian data set: 300 cycles High-Output kit)]. smMIP libraries required spike-in of custom primers as described previously (8).

VARIANT NOMENCLATURE

The variant nomenclature follows the guidelines indicated by the Human Genome Variation Society v2.0.

DATA ANALYSIS

By using the generated sample sheet, BCL-to-FASTQ conversion and demultiplexing were performed without manual intervention (bcl2fastqv2.14, Illumina). Demultiplexed FASTQ files were uploaded to a server running the analysis software SeqNext (Sequence Pilot[TM], version 4.2.2 Build 502, JSI medical systems). Within SeqNext, sequencing reads were mapped to region of interest and primer sequences (MIP extension and ligation arms), and variant calling (for SNVs and small indels) was performed (excluding MIP extension and ligation arms). A minimal coverage of 20 unique reads, i.e., unique patient molecules, was required. Since the smMIPs were sequenced in both directions, this corresponded to 40X total reads. This enabled the detection of variants with at least 35% variant reads with >95% confidence. Variants were called at >5% variant reads if present in >3 unique reads. These settings were established for the detection of germline mutations and enabled exclusion of sequencing errors using the random tag of each smMIP. Single molecules, or unique reads, were detected using the filter settings available in the software. Consensus reads were selected on their random 5N tag. The consensus nucleotide was called when present in >50% of the contributing duplicate reads. If >30% of the duplicate reads contributing to a consensus read deviated from the consensus read, the consensus read was ignored.

CNV ANALYSIS

CNV read-depth analysis was performed to identify deletions and duplications. The FASTQ files were mapped using BWA-MEM (default settings to hg19) (23). BAM files were filtered for Q20 reads and read depth per target calculated. Only unique reads were taken into account. Read depth ratios were calculated using a self-referencing batch and normalizing for the total number of reads in the sample and the mean coverage per exonic target within the sequencing run. Three test runs containing positive controls were analyzed (see online Supplemental Table 2), followed by a blinded set containing 36 samples run in duplo. Segmentation was performed on the test vs reference ratios using 3 states representing a duplication (0.3), no copy number change (0) and a deletion (-0.5), requiring a positive call in both duplo samples.

Results

VALIDATION OF smMIPs

For validation of smMIPs for clinical BRCA1 and BRCA2 sequencing, 166 human genomic DNA samples were analyzed, 90 samples carrying a pathogenic variant in BRCA1, and 77 samples carrying a pathogenic variant in BRCA2. One sample had both a pathogenic BRCA1 and BRCA2 variant. The 166 samples contained 110 unique variants, most of them pathogenic variants identified in our laboratory between January 2010 and July 2014. smMIP capture was performed manually and in duplicate. Sequencing was completed in 3 independent sequencing runs (runs 1-3). The mean (SD) coverage per smMIP (unique reads only) was 359X (159X) for BRCA1 and 289X (137X) for BRCA2 (Fig. 1, Table 1). On average, 0.4% of the individual BRCA1 smMIPs and 2.4% of the individual BRCA2 smMIPs had a coverage <40X (in total 17 smMIPs). Still, the "horizontal" sequence coverage per gene was 100%, with all nucleotides showing >40X coverage due to overlapping smMIPs that redundantly covered the same coordinates (Fig. 1, Table 1). Variant calling resulted in correct identification of all 110 pathogenic variants (Fig. 2, online Supplemental Tables 3-6). Analytical sensitivity and specificity were calculated to be 100%, based on all variants present in a subset of 152 samples that were previously sequenced for BRCA1 and BRCA2 either by Sanger or semiconductor sequencing (Table 2). Three variants were identified that had been missed previously either by Sanger (2 variants) or by semiconductor sequencing (1 variant). Two of them were benign variants not detected earlier due to adjacent frameshift-causing variant; the other one was a benign variant not present in the data due to allelic dropout, introduced by the pathogenic variant located underneath the overlapping PCR primer (see online Supplemental Table 7).

IMPLEMENTATION AND VALIDATION OF AN AUTOMATED smMIP SEQUENCING WORK FLOW

Despite 100% horizontal coverage of both genes, 35 smMIPs were redesigned, covering the regions of the 17 poorly performing smMIPs for BRCA1 and BRCA2, and adding smMIPs for CHEK2_c.1100del. We also included additional smMIPs for which single nucleotide polymorphisms (SNPs) in one of the hybridization arms were identified. These SNP-smMIPs only differ at the position of the SNP, thus allowing both alleles to be captured and minimizing allelic dropout. All smMIPs for BRCA1, BRCA2, and CHEK2 were pooled in a single pool. The new pool was validated using 22 known samples, carrying 10 pathogenic BRCA1 variants, 10 pathogenic BRCA2 variants, and 2 pathogenic CHEK2 variants (see online Supplemental Table 8). For these experiments, smMIP capture and library construction was performed using a newly developed generic automated smMIP work flow (Fig. 3). Three independent test runs (library preparation plus sequencing) were performed. FASTQ files were automatically imported into the SeqNext module (JSI medical systems). All variants were identified correctly (see online Supplemental Table 8).

PROCESS OPTIMIZATION

Several scenarios regarding implementation of the new work flow (from available isolated DNA until availability of data) were simulated to achieve the best possible outcome concerning fast and stable processing with low flow times and costs. On the basis of these simulations, we elected to execute the new work flow twice per week, with every sample being tested in duplo.

Running the process twice per week was a consequence of simulations of turnaround times (TATs) when sequencing daily, or once, twice, or 3 times a week. With sequencing once per week, the waiting time for a sample until a processing was started had an upper limit of 6 days (see online Supplemental Fig. 2A). For sequencing twice per week, the waiting time was at most 3 days. Sequencing more often would only reduce TATs when weekends were included as working days (see online Supplemental Fig. 2A). We now initiate smMIP capture twice a week, on Mondays and Thursdays, and sequencing data are available on Thursdays and Mondays, respectively. Samples arriving just after a process has started need to wait, with a maximum of 3 days. The practical work flow is therefore approximately 4 days for up to 400 samples a week, with 2 runs per week (and no weekend shift) (see online Supplemental Fig. 2B).

Capturing and sequencing samples in duplo was beneficial to obviate potential inefficiencies in the work flow (see online Supplemental Fig. 3). In case of potential (technical) failure of a sample, duplicate samples likely produce useful data. In case data from both duplicates are available but coverage is critical (usually due to poor DNA quality or quantity), data from duplicates can be combined to reach sufficient sequencing depth. In the more likely scenario where both duplicates perform well and a variant is identified, the duplicate can be used as confirmation, thereby excluding sample swaps and obviating the need for a serial run to provide validation and thereby extending the TAT.

DOUBLE-BLIND SEQUENCING OF ROUTINE BRCA SAMPLES

Diagnostic testing was performed using the work flow as described above, in parallel to the routine method (ion semiconductor sequencing after AmpliSeq enrichment, in combination with Sanger sequencing) for 65 different samples (see online Supplemental Table 9). All tests were completed independently by different employees, and results were compared afterward. Data analyses showed that all variants identified by semiconductor sequencing or Sanger sequencing were also detected in the smMIP-based data, including 13 pathogenic variants (see online Supplemental Table 9).

INTERLABORATORY VALIDATION

The BRCA smMIP work flow was independently validated using 267 Sanger-sequenced BRCA-samples of Norwegian origin. Six samples (2.2%) did not contain any sequencing data. These samples were excluded from further analysis. For the residual samples, the variant tables were analyzed blinded. A total of 3692 variants, 61 pathogenic and 3631 benign variants, were identified by the smMIP approach, all previously seen by Sanger sequencing, resulting in a 100% analytical sensitivity and 100% analytical specificity. The overall false-positive rate was 0% (see online Supplemental Table 10).

ANALYSIS OF BRCA1 CNVs

We next assessed the ability to detect BRCA1 CNVs in the smMIP data by including 5 positive control samples, an exon 1-8 deletion, an exon 3-20 deletion, an exon 11-12 deletion, an exon 13 deletion, and an exon 22 deletion, in duplo in 3 different sequencing runs which also included diagnostic samples (see online Supplemental Table 2 and online Supplemental Fig. 4). This resulted in an analytical sensitivity of 100% and an analytical specificity of 88%. Subsequent application of the CNV analysis identified an exon 22 deletion in a patient sample in a blinded fashion (see online Supplemental Fig. 4).

TURNAROUND TIME

TATs for BRCA1 and BRCA2 testing in our laboratory in Nijmegen have varied over the years, depending on the respective test performed (denaturing gradient gel electrophoresis in combination with protein truncation test; Sanger sequencing; AmpliSeq/IonTorrent PGM, smMIP/NextSeq500). Looking at monthly intervals over the last 3 years (2013-2015), the mean TAT at our center was 18-32 days, measured from acceptance of sample to reporting (see online Supplemental Fig. 5A). smMIP BRCA testing was started in July 2015, resulting in a steep decrease of TATs to a current mean of 11-18 days. Interestingly, numbers of requested BRCA tests rose simultaneously (currently approximately 150 samples/ month; see online Supplemental Fig. 5B).

Discussion

Results of clinical genetic tests, such as BRCA1 and BRCA2, increasingly influence clinical management and therapeutic decision-making. Therefore, completeness, accuracy, and reproducibility of clinical sequencing data are becoming more important than ever. Here we describe a newly developed diagnostic sequencing work flow, based on smMIP enrichment technology, that reached 100% analytical sensitivity and specificity for the breast-cancer genes BRCA1 and BRCA2.

BRCA1 and BRCA2 genetic testing is performed in many laboratories and often is still Sanger-based sequencing (14, 15, 24). Recently, studies have shown that Sanger sequencing can be readily transferred to NGS (22, 25), concluding that NGS is as good or even better (24, 26). Accordingly, new technologies to perform BRCA1 and BRCA2 testing have been described, including in-solution capture with complementary RNA probes and sequencing on an Illumina Genome Analyzer (17), long-range PCR in combination with Genome Analyzer (27) or GS Junior 454 (28) and multiplex PCR strategies for the 454 GS-FLX (19, 29) and the IonTorrent PGM (20). Reported analytical sensitivities were close to 100%, whereas analytical specificities ranged from 94.4-97.5% (30-32), still leaving room for improvement. Besides completeness, accuracy, and reproducibility, increasing numbers of BRCA1 and BRCA2 tests require a high throughput, and fast and predictable TATs at acceptable costs (33, 34).

In an attempt to satisfy these needs, we implemented a new NGS-based work flow for analyses of single genes and small gene panels. The TAT of this new wet laboratory process is 4 days (excluding DNA isolation and reporting). The resulting data are complete and accurate. The new work flow is based on smMIP technology (6-9, 21). smMIPs have been shown to reach 99% analytical sensitivity and 98% positive predictive value for single nucleotide variants at well-covered positions, i.e., 92%-98% of targeted bases, at relatively low reagent costs (8). Recently, another study reported both analytical sensitivity and specificity >99% for smMIP targeted enrichment (35).

A major advantage of the smMIP technology is that smMIPs, if well designed, do not interfere with each other, enabling smMIPs to be combined to test for complete genes or even several genes in a single pool (in a single well). Solely for this reason, the number of reactions is reduced substantially compared to most conventional approaches, decreasing handling time and costs. Further, the availability of column-synthesized individual smMIPs enables rebalancing of individual probes, giving the user opportunities to optimize uniformity of sequencing depth independent of any supplying company. High reproducibility and even coverage then allows both SNV and CNV calling in a single experiment. smMIPs for newly discovered variants or genes could easily be added to an existing pool. The quantities of each smMIP oligonucleotide obtained are sufficient to support resequencing from millions of human DNA samples. Furthermore, smMIP-based enrichment is platform-independent, and has been described for Illumina (36, 37) and IonTorrent sequencing (38). Using the smMIP approach per se is therefore already beneficial for genetic analyses.

The excellent performance and predictability of our work flow is achieved by small but important adjustments to the originally described implementations of MIPs for sequencing capture, namely single-molecule tags, double tiling/capturing, and sequencing, combined with complete automation of both wet laboratory and subsequent data transfer and analyses.

Single-molecule tags are random tags of(in our case) 5 nucleotides in length that are incorporated adjacent to 1 of the hybridization arms (9, 21). The complexity of 1024 (5N = [5.sup.4]) tagged single molecules enables tracing of individual molecules throughout capture and sequencing. If needed, the 5N tag can be adapted to a larger N tag, e.g., 10N, if larger numbers of unique reads are required (9). Following sequencing, this random tag can be used to distinguish whether reads are coming from the same molecule, or represent unique reads from independent molecules. Reads showing the same tag are combined to a single consensus read, whereby random errors incorporated during PCR or sequencing can be removed (9, 39).

Double-tiling enrichment indicates that each single nucleotide of the gene/panel of interest is targeted by at least 2 independent smMIPs. In our approach, we aimed to cover the complete region of interest with overlapping smMIPs, for both DNA strands independent of each other. Double tiling creates an intrinsic validation, since every variant is detected with at least 2 independent probes. An additional advantage of maintaining strand information throughout the experiments is that this can be used to exclude strand-specific errors or biases, e.g., formalin-fixed paraffin-embedded deamination. Finally, differences in variant percentages for the same nucleotide position hint to potential allelic dropout, meaning that allelic dropouts are likely to be uncovered when using double tiling.

In our work flow, each sample is captured and sequenced twice. Experience and simulation has shown that duration of wet laboratory analysis often depends on the number of failed experiments that need to be repeated, e.g., a single amplicon failure that needs to be repeated to have a complete gene sequenced. Sequencing each sample in duplo reduces the rework and leads to shorter TATs.

The newly implemented work flow has been extensively tested for altogether more than 500 samples from 2 different laboratories. Concerning SNV analysis, the 3 features--single molecule tags, double tiling, and double enrichment/sequencing--enabled an analytical sensitivity and specificity of 100%, a value not reached before by any other technology used in our laboratory. Preliminary tests concerning CNV analysis of 5 well-defined BRCA1 deletions gave 100% analytical sensitivity, with an analytical specificity of approximately 88%. Since the numbers were low, there is still room for improvement. Due to the small insert size, smMIP-based sequencing also enables the analysis of BRCA1, BRCA2, and other genes on DNA isolated from formalin-fixed, paraffin-embedded material (14, 39), using the same automated work flow. Absolute coverage correlates with input amounts of DNA. In another study variants were still reliably detected with an input amount as low as 19 ng of DNA (39). This will not only enable genetic analyses on deceased patients, but also the analysis of tumor-specific variants for therapeutic interventions. An assay as suggested here might enable population-based screening of BRCA1 and BRCA2 (10, 40, 41).

Altogether, the smMIP-enrichment technology turns out to be highly competitive compared to previous technologies. The combination of smMIP enrichment and subsequent NGS delivers sequencing data that are of outstanding quality. With smMIP enrichment, we are able to achieve 100% analytical sensitivity and specificity, and substantially reduced TATs. Therefore, we believe that smMIPs are not only beneficial for research purposes, but also provide remarkable value for clinics.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: M.J.L. Ligtenberg, Astra-Zeneca.

Stock Ownership: None declared.

Honoraria: A.R. Mensenkamp and M.J.L. Ligtenberg, Astra-Zeneca.

Research Funding: None declared.

Expert Testimony: None declared.

Patents: E. Boyle, patent no. 20160055293; J. Shendure, patent no. WO2014160736A1.

Other Remuneration: M.J.L. Ligtenberg, Astra-Zeneca.

Role of Sponsor: No sponsor was declared.

Acknowledgments: We are grateful: to the Eichler and Shendure labs (University of Washington), in particular to Evan Eichler and Bradley Coe, for assistance with the initial MIP design and protocol; to the Genome Technology Center, Laboratory of Tumor Genetics, Department of Pathology and Service-Team Genetics (Radboudumc Nijmegen); to Paal Borge for assistance and JSI medical systems for collaboration on smMIP analysis.

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Kornelia Neveling, [1] [dagger]] Arjen R. Mensenkamp, [1] [dagger]] Ronny Derks, [1] Michael Kwint, [1] Hicham Ouchene, [1] Marloes Steehouwer, [1] Bart van Lier, [1] Ermanno Bosgoed, [1] Alwin Rikken, [1] Marloes Tychon, [1] Dimitra Zafeiropoulou, [1] Steven Castelein, [1] Jayne Hehir-Kwa, [1] Djie Tjwan Thung, [1] Tom Hofste, [1] Stefan H. Lelieveld, [1] Stijn M.M. Bertens, [2] Ivo B.J.F. Adan, [2] Astrid Eijkelenboom, [3] Bastiaan B. Tops, [3] Helger Yntema, [1] Tomasz Stokowy, [4,5] Per M. Knappskog, [4] Hildegunn Hoberg-Vetti, [4,5] Vidar M. Steen, [4,6] Evan Boyle, [7] Beth Martin, [7] Marjolijn J.L. Ligtenberg, [1,3] Jay Shendure, [7] [dagger]] Marcel R. Nelen, [1] * [dagger]] and Alexander Hoischen [1,8] [dagger]]

[1] Department of Human Genetics, Radboud university medical center, Nijmegen, the Netherlands; [2] Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; [3] Department of Pathology, Radboud university medical center, Nijmegen, the Netherlands; [4] Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway; [5] Western Norway Familial Cancer Center, Haukeland University Hospital, Bergen, Norway; [6] Department of Clinical Science, University of Bergen, Bergen, Norway; [7] Department of Genome Sciences, University of Washington, Seattle, WA; [8] Donders Centre for Neuroscience, Radboud University Nijmegen, Nijmegen, the Netherlands.

* Address correspondence to this author at: Department of Human Genetics, Radboud university medical center, PO Box 9101, Nijmegen 6500 HB, the Netherlands. Fax +31 24-36-16658; e-mail marcel.nelen@radboudumc.nl.

[[dagger]] Kornelia Neveling and Arjen R. Mensenkamp contributed equally to this work, and should be considered as first authors. Jay Shendure, Marcel R. Nelen, and Alexander Hoischen contributed equally to this work, and should be considered as last authors.

Received July 26,2016; accepted September 29,2016.

Previously published online at DOI: 10.1373/clinchem.2016.263897

[9] Human genes: BRCA1; BRCA2; CHEK2, checkpoint kinase 2.

[10] Nonstandard abbreviations: MIP, molecular inversion probe; NGS, next-generation sequencing; smMIP, single-molecule MIP; SNV, single nucleotide variant; CNV, copy number variant; SNP, single nucleotide polymorphism; TAT, turnaround time.

Caption: Fig. 1. Coverage plots of BRCA1 and BRCA2. The unique number of reads per smMIP for BRCA1 and BRCA2. x Axis: unique smMIPs used to enrich BRCA1 and BRCA2, respectively, sorted per exon. BRCAI : exon 1-24 (exon 4 is not existing), BRCA2: exon 1-25 (exons 5 and 6, and 23 and 24 are combined, respectively). y Axis: mean coverage (unique reads) per smMIP.

Caption: Fig. 2. Schematic overview of the BRCA1 and BRCA2 proteins, with all variants identified. Vertical bars indicate BRCA1 and BRCA2 variants. Red: deletions, green: insertions, black: substitutions, numbers in brackets: number of cases with identical variants. In total 110 unique variants from 166 samples are shown, most of them being pathogenic. Protein domains and regions are based on UniProt (www.uniprot.org). RING, RING (really interesting new gene) domain; PALB2, partner and localizer of BRCA2; BRCT, BRCA1 c-terminus domain; NPM1, nucleophosmin 1; POLH, DNA polymerase eta; FANCD2, Fanconi anemia complementation group D2; SHFM1/DSS, 26S proteasome complex subunit protein DSS (deleted in split hand/split foot), encoded by the gene SHFM1; BRC repeats, breast cancer repeats; NES, nuclear export signal.

Caption: Fig. 3. Automated work flow for smMIP analysis. Prehybridization reactions were performed on a pre-PCR robot. Hybridization, extension, and ligation occurred overnight. Exonuclease treatment was performed manually the next day. The post-PCR robot pipetted the posthybridization reaction, including a PCR to incorporate barcodes. Following PCR, pooling was performed per plate. Purification occurred automatically per pool. Pools were subsequently combined, diluted, and denaturated for sequencing. Exo, exonuclease treatment; BC-PCR, barcode PCR.
Table 1. Validation of BRCA smMIPs.

                            Run 1        Run 2        Run 3

Cluster density per run   78 000 K/    110000 K/    60 000 K/
                          [mm.sup.2]   [mm.sup.2]   [mm.sup.2]

Mean (SD) coverage         416(129)     360(195)    302 (153)
(unique reads) for
smMIPs BRCA1

Mean (SD) coverage         297(99)      296(170)     276(141)
(unique reads) for
smMIPs BRCA2

% BRCA1 smMIPs with          100%         100%        98.7%
>40 unique reads

% BRCA2 smMIPs with         97.6%        97.6%        97.6%
>40 unique reads

% BRCA1 smMIPs with         96.0%         100%        95.3%
>100 unique reads

% BRCA2 smMIPs with         95.2%        94.8%        92.3%
>100 unique reads

Horizontal coverage          100%         100%         100%
BRCA1 (-20..+20)
>40 unique reads

Horizontal coverage          100%         100%         100%
BRCA2 (-20..+20)
>40 unique reads

% BRCA1 nucleotides          100%         100%        98.0%
covered >2 smMIPs

% BRCA2 nucleotides         97.6%        97.6%        97.6%
covered >2 smMIPs

Table 2. Analytical sensitivity and specificity of
manual runs.

Number of     152
samples

Total of      2688728
sequenced
bases

  TP (a)      1 821       Variant calls: pathogenic +
                          benign variants

  FP          0           False-positive calls
  TN          2 686 907   Bases identical to reference
  FN          0           Missed variants
Sensitivity
  TP rate     100%        = TP/(TP + FN)
  FP rate     0%          = FP/(FP + TN)
  Accuracy    100%        = (TP + TN)/(TP + TN + FP + FN)
  Precision   100%        = TP/(TP + FP)
Specificity
  TN rate     100%        = TN/(FP + TN)

(a) TP, true positives; FP, false positives; TN, true negatives; FN,
false negatives. TP/(TP + FN) = 1 821/(1 821 + 0) = 1, FP/(FP + TN) =
0/(0 + 2686907) = 0, (TP + TN)/(TP + TN + FP + FN) = (1 821 + 2 686
907)/(1 821 + 2 686 907 + 0 + 0) = 1, TP/(TP + FP) = 1 821/(1 821 +
0) = 1, TN/(FP + TN) = 2 686 907/(0 + 2 686 907) = 1.
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Title Annotation:Molecular Diagnostics and Genetics
Author:Neveling, Kornelia; Mensenkamp, Arjen R.; Derks, Ronny; Kwint, Michael; Ouchene, Hicham; Steehouwer,
Publication:Clinical Chemistry
Article Type:Report
Date:Feb 1, 2017
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