Original Article Detection of TERT Promoter Mutations Using Targeted

CANCER RESEARCH AND TREATMENT (CRT)

Original Article

Detection of TERT Promoter Mutations Using Targeted Next-Generation Sequencing:

Overcoming GC Bias through Trial and Error

Hyunwoo Lee1, Boram Lee2,3, Deok Geun Kim3,4, Yoon Ah Cho5, Jung-Sun Kim1,3, Yeon-Lim

Suh1

1Department of Pathology and Translational Genomics, Samsung Medical Center,

Sungkyunkwan University School of Medicine, Seoul, 2Samsung Genome Institute, Samsung

Medical Center, Sungkyunkwan University School of Medicine, Seoul, 3Department of Digital

Health, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan

University School of Medicine, Seoul, 4Department of Clinical Genomic Center, Samsung

Article5 Medical Center, Sungkyunkwan University School of Medicine, Seoul, Department of

Pathology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine,

Anyang, Korea

Running title: Next-generation Sequencing and TERT Mutation

Correspondence: Yeon-Lim Suh

Department of Pathology and Translational Genomics, Samsung Medical Center, SungkyunkwanAccepted University School of Medicine, 81 Irwon -ro, Gangnam-gu, Seoul 06351, Korea Tel: 82-2-3410-2761 Fax: 82-2-3410-0025 E-mail: [email protected]

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi:10.4143/crt.2021.107

Abstract

Purpose

Detection of telomerase reverse transcriptase (TERT) promoter mutations is a crucial process in the integrated diagnosis of glioblastomas. However, the TERT promoter region is difficult to amplify because of its high guanine-cytosine (GC) content (> 80%). This study aimed to analyze the capturing of TERT mutations by targeted next-generation sequencing (NGS) using formalin-fixed paraffin-embedded tissues.

Materials and Methods

We compared the detection rate of TERT mutations between targeted NGS and Sanger sequencing in 25 cases of isocitrate dehydrgenase (IDH)-wildtype glioblastomas and 10 cases of non-neoplastic gastric tissues. Our customized panel consistedArticle of 232 essential glioma- associated genes.

Results

Sanger sequencing detected TERT mutations in 17 out of 25 glioblastomas, but all TERT mutations were missed by targeted NGS. After the manual visualization of the NGS data using an integrative genomics viewer, 16 cases showed a TERT mutation with a very low read depth

(mean, 21.59; median, 25), which revealed false-negative results using auto-filtering. We optimized our customized panel by extending the length of oligonucleotide baits and increasing the number of baits spanning the coverage of the TERT promoter, which did not amplify well due to the Acceptedhigh GC content. Conclusion

Our study confirmed that it is crucial to consider the recognition of molecular bias and to carefully interpret NGS data.

Key Words Next-generation sequencing, TERT, Glioblastoma, GC-rich sequence

Introduction

Telomerase reverse transcriptase (TERT) is a catalytic subunit of telomerase [1]. In normal tissues, the activity of telomerase is silenced, but when a single nucleotide variant occurs in the two major hotspots [2] of the promoter region, telomerase is activated, which leads to telomere lengthening [3,4]. This series of processes is known to play a critical role in tumorigenesis in solid cancers [1,2,4], including gliomas [5,6].

In the 2016 World Health Organization (WHO) classification of central nervous system tumors, molecular parameters, including the status of the TERT promoter, are important for the integrated diagnosis of brain tumors [7]. When astrocytic gliomas harbor one of the following genetic variations: TERT promoter mutations, epidermal growth factor receptor (EGFR) amplification, or chromosome 7 gain/chromosome 10 loss,Article even though there is a lack of necrosis or microvascular proliferation, this tumor can be diagnosed as an isocitrate dehydrgenase (IDH)-wildtype glioblastoma, WHO grade IV [8]. Detecting the above molecular alterations in a very limited tumor sample would be the benefit the patients for important prognostic and potentially therapeutic information.

Next-generation sequencing (NGS) is one of the ideal methods for comprehensive molecular profiling of brain tumor. The NGS study can detect molecular alterations in wide genomic regions with relatively fast turnaround time, and low cost. However, the NGS study could yield plenteous information including inaccurate results, the appropriate approaches for extracting Acceptedaccurate information from NGS data would be a crucial step. The promoter area of TERT has a high guanine-cytosine (GC) content (> 80%) and easily forms a secondary structure, such as a hairpin structure [9], resulting in a poor amplification.

Therefore, many studies have suggested several ways to more accurately detect mutations in the TERT promoter region [10,11]. However, in the case of targeted NGS, which targets

multiple genes, including TERT, the detection of variants in the TERT promoter region is often difficult due to poor amplification. In this regard, we planned to compare the results of NGS and Sanger sequencing of TERT and sought to find ways to accurately detect variants of the

TERT promoter region using targeted NGS.

Materials and Methods

1. Case selection

We retrospectively selected 25 cases of IDH-wildtype glioblastoma from the NGS data in the pathology file of the Samsung Medical Center. All patients underwent brain tumor surgery.

All patients provided informed consent for NGS. All tumors were histologically confirmed as glioblastomas, showing microvascular proliferation (n=25)Article or necrosis (n=22). Clinical information, such as sex, age, and tumor location, was collected. Tumor location was confirmed using preoperative magnetic resonance imaging. The mean age of the 25 patients was 63.64 years (range, 41 to 83 years; median, 65 years), and the male-to-female ratio was 14:11. The tumors were located in the cerebrum in 22 cases (frontal lobe in 10 cases, temporal in 6, parietal in 3, and frontoparietal in 3) and in the cerebellum in three cases. We also collected 10 anonymized non-neoplastic formalin-fixed paraffin-embedded (FFPE) stomach tissue samples for control.

2. DNA extractionAccepted DNA was extracted from 5-µm-thick FFPE tissues using a QIAamp DSP DNA FFPE

Tissue Kit (Qiagen, Hilden, Germany), and the extracted DNA was quantified using the QUBIT dsDNA BR Assay kit (Thermo Fisher Scientific, Waltham, MA).

3. Library preparation and targeted hybrid capture-based NGS

Target capture was performed according to the manufacturer's protocol using the

SureSelect XT automation reagent kit (Agilent, Santa Clara, CA), and a paired-end sequencing library was prepared using a barcode. The size and quality of the genomic DNA were validated using the Genomic DNA Analysis ScreenTape and Genomic DNA Reagent together with the

Agilent 4200 Tape station (Agilent). Libraries were sequenced using the TG NextSeq 500/550

High Output Kit v2 (Illumina, Inc., San Diego, CA) and TG NextSeq 500/550 Mid Output Kit v2 (Illumina, Inc.).

4. Panel design and data analysis We used a targeted sequencing panel pipeline named BrainTumorSCANArticle for data analysis, which was designed to cover 232 target genes at the Samsung Genome Institute. The list of 232 target genes is listed in S1 Table.

Paired-end reads were aligned to the human reference genome (GRCh37/hg19) using

BWA-MEM v0.7.5 [12], SAMTOOLS v0.1.18 [13], GATK v3.1-1 [14], and Picard v1.93

(http://broadinstitute.github.io/picard/). Single nucleotide variants (SNVs) were detected using

MuTect version 1.1.4 [15], Lofreq version 0.6.1 [16], and VarDict version 1.06 [17] software.

All of SNVs detected by each software were collected and merged. After collecting and merging, sequencing errors were filtered out through an in-house algorithm [18] and we also excluded Accepted variants which had been reported as benign or likely benign in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/). Small insertions and deletions (Indels) < 30 bp in size were detected using Pindel v0.2.5a4 [19]. We used ANNOVAR for annotation of predicted

SNVs [18]. Among all variants, genetic alterations with a variant of allele frequency (VAF) lower than 1%, a total read depth (TD) value lower than 50, or a variant read count (VC) value

lower than 4 were considered as spurious variants and were excluded. To identify the somatic copy number alterations (CNAs), we calculated the mean read depth of each exon and normalized it according to the depth of the target regions in that sample. This normalized read depth was further standardized by dividing it by the expected read depth of a normal population.

The expected read depth at each exon was taken from the median value of the read depth at that exon across a set of normal individual samples. Then, the amplitude of copy numbers was calibrated based on the calculated purity of the tumor cells in the sample for ratiocinating accurate copy numbers. If the calibrated amplitude of the copy number was greater than 4, it was considered as amplification, and if it was lower than 1, it was considered a deletion.

Additionally, in case of Log2 scale of the adjusted copy number fold change in chromosomal short or long arm entirely is greater than 1 or lower than -Article1, it is considered as chromosomal short arm, long arm, or whole chromosomal gain or loss, respectively.

5. Sanger sequencing for TERT

Sanger sequencing for the analysis of TERT promoter mutation was performed using the following pair of primers as previously reported [20]. Amplification of the genomic DNA was performed using the GeneAmp PCR System 9700 (Applied Biosystems, Foster City, CA) and

C1000 Touch Thermal Cycler kit (Bio-Rad, Hercules, CA), according to the manufacturer’s instructions. Sanger sequencing was performed using the BigDye Terminator v1.1 Cycle SequencingAccepted Kit (Applied Biosystems). The sequencing reaction was performed both in the forward and reverse direction, and the result was confirmed only when the forward-primer and reverse-primer results were consistent with each other.

6. O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation analysis

A total of 2 µg of DNA was denatured using sodium hydroxide and modified using sodium bisulfite. Primer pairs specific for methylated, 5′-TTTCGACGTTCGTTCGTAGGTTTTCGC-

3′ (sense) and 5′-GCACTCTTCCGAAAACGAAACG-3′ (antisense), and unmethylated

MGMT promoter, 5′-TTTGTGTTTTGATGTTTGTAGGTTTTTGT-3′ (sense) and 5′-

AACTCCACACTCTTCCAAAAACAAAACA-3′ (antisense), were prepared. Control DNA for methylated and unmethylated samples was obtained from QIAGEN control DNA (Qiagen).

The PCR conditions used were as follows: 95°C for 10 minutes for 40 cycles, and 94°C for 15 seconds, 58°C for 30 seconds, 72°C for 30 seconds (35 cycles), and 72°C for 5 minutes.

Electrophoresis for each PCR product was performed using an 8% acrylamide gel and ethidium bromide staining, and the gels were visualized under ultravioletArticle illumination.

7. Statistical analysis

All statistical analyses were performed using the SPSS software (ver. 24.0, IBM Corp.,

Armonk, NY). The general characteristics and demographic parameters were compared using

χ2 and Fisher's exact tests, and other quantitative data were analyzed using paired t-tests. For comparisons between non-normally distributed variables, we used Shapiro-Wilk test and Mann-

Whitney U test. A p-value lower than 0.05 was considered statistically significant.

Results Accepted 1. Detection of TERT promoter mutations using targeted NGS and Sanger sequencing

We reviewed the targeted NGS results for 25 glioblastomas. The overall sequencing quality and recommended guideline quality thresholds are summarized in S2 Table adopted from previous study [18]. The average Q30 values of the reads were 92.9% (range, 91.5% to 93.2%)

and the on-target rate was 94.2% (range, 93.1% to 95.5%). The average TD value was

852.87±91.16 (range, 736.75 to 1,051.0; median, 839.58) and the average rate of uniformity was 78.0% (range, 74% to 81%).

None of the tumors showed any mutations in the TERT promoter using targeted NGS, while

TERT promoter mutations were detected in 17 out of 25 cases (68%) using Sanger sequencing

(Table 1). To explore the different detection rates of mutations in the TERT promoter using NGS and Sanger sequencing, the aligned reads were manually checked using an integrative genomics viewer (IGV) (http://software.broadinstitute.org/software/igv/), considering the possible filtering because of the unsuccessful amplification of the TERT promoter. When reviewing the results of the analysis using an IGV (Fig. 1), we found TERT mutations in 16 of 25 cases (64%), and their TD, VC, and VAF values are summarized in TableArticle 1. One case (case 7) showed a TERT promoter mutation in Chr 5: 1,295,228 using Sanger sequencing, but the TERT promoter region could not be amplified using targeted NGS. As shown in Table 1, this tumor showed a very low read depth (2) in that position. The average TD value of the TERT promoter region was 21.59 (range, 2 to 47; median, 25) and was significantly lower than the average TD values of other genes (mean, 885.40; range, 736.8 to 1,051.0; median, 839.6) (p < 0.001) (Fig. 2). The average VC value of the TERT promoter mutation was 7.18 ± 6.5 (range, 0 to 28; median, 6), and the average VAF value was 32.2±18.4 (range, 0 to 75; median, 28.6). All cases were excluded for the TERT promoter mutation in the original NGS study because of the auto- filtering criteria.Accepted

2. Modification of Baits Design to detect TERT mutations

To improve the TD in the TERT promoter area, we corrected the design of the capture probe, the so-called baits, which were the single-stranded oligonucleotides used for

hybridization with the DNA or RNA in the target capture process. We increased the number of baits around the TERT promoter region, especially in two hotspot areas, for increasing the coverage of these baits, and extended the area of the overlapped regions covered by the baits.

Then, targeted NGS was performed on 10 cases of non-neoplastic gastric tissues. The results of the analysis of these cases using the IGV showed a change in the distribution of baits around the TERT promoter region (Fig. 3). The mean read depth of the two hotspots (Chr 5: 1,295,228 and Chr 5: 1,295,250) where mutations occur predominantly in the TERT promoter region were originally 12.3 and 13.1, respectively. After the modification of the baits in the TERT promoter region, the mean read depth of the two hotspots increased significantly to 124.89 and 99.2 (p <

0.001), respectively (Fig. 4). The mean Phred quality score (before modification, 33.95; after modification, 31.7) were not significantly different (p > 0.05)Article and the mapping quality rate score was 60 in all cases, before and after.

3. Genetic landscape of glioblastomas

The NGS mutational analysis and Sanger sequencing detected 328 genetic alterations, including 201 non-synonymous SNVs, 36 truncating variants (stop-gain, frameshift, or splice- disrupting variant), seven in-frame indels, 82 CNAs, and two fusions (S3 Table).

Seventeen out of twenty-five cases (68%) harbored a TERT promoter mutation. Whole chromosome 7 gain was detected in 19 cases (76%), and whole chromosome 10 loss was detected inAccepted 14 cases (64%). MGMT promoter methylation was found in 56% of the cases. Six out of 25 cases (24%) showed EGFR amplification. The other genetic alterations observed are shown in Fig. 5. The landscape of glioblastomas evaluated in our study was not significantly different from those of other studies.

Discussion

As the diagnostic criteria for brain tumors have been more detailed and molecular techniques for their diagnosis have been developed, various genes related to their prognosis have come into the spotlight, such as TERT [10,12] or EGFR [21]. The detection of alterations in these genes can be used to predict a worse prognosis and to choose an appropriate treatment strategy and increase the survival of patients with gliomas.

In particular, recurrent mutations in two sites of the TERT promoter region have been found in Chr 5:1295228 and Chr 5:1295250, which are referred to as C228T and C250T, respectively.

About 72%-90% of glioblastomas [3,6] and 95% of oligodendrogliomas [21,22] show a mutation of the TERT promoter in these two hotspots. Among histologically proven WHO grade II or III IDH-wildtype diffuse astrocytic gliomas, tumorsArticle with genetic alterations (EGFR amplification, +7/‒10, TERT promoter mutations) are associated with an aggressive clinical behavior and show DNA-methylation profiles similar to those of IDH-wildtype glioblastomas

[21,22]. In line with this, cIMPACT-NOW announced that for IDH-wildtype gliomas with

TERT promoter mutations, EGFR amplification or whole chromosome 7 gain/10 loss was predicted to show a prognosis similar to that of glioblastomas and could be classified as a glioblastoma [8]. Therefore, the evaluation of TERT promoter mutations can be a critical step in the diagnosis of high-grade astrocytic tumors.

However, the TERT promoter region contains a GC-rich region [10] and forms a secondary structure, Acceptedsuch as a hairpin structure, during PCR. It is usually amplified unsuccessfully and may show false-negative results [9]. Therefore, numerous approaches have been attempted for the appropriate amplification of genes having GC-rich regions: modification of the cycling environment, such as the temperature or cooling time [23,24]; the addition of various organic molecules, including dimethylsulfoxide [25]; or increasing the concentration of chemicals

important to the amplification process, such as MgCl2 [26].

In high-throughput sequencing, the read depth of the GC-rich region is significantly lower than that of other regions [27], and has been a chronic problem in NGS data analysis. The unimodal GC curve shows that the read depth of GC-poor or GC-rich fragments is significantly lower than the average read depth [27]. Hybrid capture-based NGS is expected to be more vulnerable to GC bias due to an unexpected behavior in the solution-hybridization process [28].

In the present study, TERT mutations were initially not detected using targeted NGS, but were detected using Sanger sequencing, and to explore the different detection rates of mutations in the TERT promoter using NGS and Sanger sequencing, we checked the filtering guidelines of our targeted NGS. We found variants, but they showed TD values lower than 50 or VCs lower than 4. These variants were removed by our filtering Articleguidelines to exclude fake variants caused by sequencing errors. These results were similar to those of a previous study by Sahm et al. [29]. They also reported that calls from the TERT promoter position were filtered because the average read depth of the hotspot of the TERT promoter was low. The present study demonstrated that a manual review of the aligned reads using the IGV showed a TERT promoter mutation in 68% of the 25 cases. Therefore, given that a poor read depth due to GC bias in the

GC-rich region is a problem, it is necessary to manually check the reads directly without relying on filtering. Besides a manual review of the aligned reads, there are other methods that can be used to minimize the GC bias. To compensate for the poor amplification of GC-rich sequences, we attemptedAccepted to expand the target area of the TERT promoter region captured through baits. During the process of target capture, the baits hybridize with genomic targets of interest and select target sequences from DNA libraries for amplification. Therefore, by designing baits of the TERT promoter region more tightly and increasing the number of baits targeting the TERT promoter region, the probability of the amplification of the targeted region was increased to

reach a meaningful quality for amplification.

In addition to the GC content, several factors may be associated with biases in the NGS analysis. Chen et al. reported that DNA synthesis bias and PCR stochasticity bias impacted the

NGS bias to a greater extent than the GC bias [30]. They also introduced a computational model predicting the molecular bias produced by the DNA synthesis and PCR sequencing retrieval process [30].

This study is signifying because it proposes an enforceable solution different from those presented in previous studies, such as increasing the amount of sequencing data or maintaining the ‘fragment length’ of DNA data [27]. It will also benefit many institutions that are preparing to design customized NGS panels for patients with brain tumors. In addition to TERT, the first exon of genes and the 5′ untranslated region or promoter Articleregion containing GC-rich regions [31] can provide important clinical information for patients.

In conclusion, we reaffirmed the harmful influence of GC bias in the interpretation of NGS data and provided a strategy for optimizing the NGS data analysis. Although high-throughput

NGS provides abundant data, the underestimation of molecular bias can lead to the obsolescence of major portions of these data. Therefore, it is crucial to keep in mind the importance of molecular bias and to carefully interpret NGS data.

Accepted

Ethical Statement

Our study was approved by the Institutional Review Board of Samsung Medical Center (IRB

No. 2020-03-155). Informed written consent from patients was waived by the Institutional

Review Board of Samsung Medical Center because of the retrospective study design.

Author Contributions

Conceived and designed the analysis: Lee H, Suh YL.

Collected the data: Lee H, Lee B, Kim DG, Cho YA, Kim JS, Suh YL.

Contributed data or analysis tools: Lee H, Lee B, Kim DG, Cho YA, Kim JS, Suh YL.

Performed the analysis: Lee H, Suh YL. Wrote the paper: Lee H, Suh YL. Article

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Accepted

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Accepted

Table 1. The result of NGS and Sanger sequencing for TERT promoter area

Case WHO NGS TERT promoter NGS TERT promoter TERT TERT TERT Surgical diagnosis Sanger sequencing No. grade (initial) (after reviewing IGV) TD VC VAF (%) 1 Glioblastoma, IDH-wildtype IV Wild type Not detected Wild type 13 0 0 2 Glioblastoma, IDH-wildtype IV Wild type c.1-146C>T (C250T) c.1-146C>T (C250T) 7 2 28.6 3 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 8 6 75 4 Glioblastoma, IDH-wildtype IV Wild type Not detected Wild type 9 0 0 5 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 16 7 43.8 6 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 25 5 20 7 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) Wild type 2 0 0 8 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 9 2 22.2 9 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 14 2 14.3 10 Glioblastoma, IDH-wildtype IV Wild type c.1-146C>T (C250T)Article c.1-146C>T (C250T) 7 3 42.9 11 Glioblastoma, IDH-wildtype IV Wild type Not detected Wild type 24 0 0 12 Glioblastoma, IDH-wildtype IV Wild type Not detected Wild type 26 0 0 13 Glioblastoma, IDH-wildtype IV Wild type Not detected Wild type 25 0 0 14 Glioblastoma, IDH-wildtype IV Wild type c.1-146C>T (C250T) c.1-146C>T (C250T) 28 6 21.4 15 Glioblastoma, IDH-wildtype IV Wild type Not detected Wild type 17 0 0 16 Glioblastoma, IDH-wildtype IV Wild type c.1-146C>T (C250T) c.1-146C>T (C250T) 27 13 48.2 17 Glioblastoma, IDH-wildtype IV Wild type Not detected Wild type 28 0 0 18 Glioblastoma, IDH-wildtype IV Wild type c.1-146C>T (C250T) c.1-146C>T (C250T) 37 10 27 19 Glioblastoma, IDH-wildtype IV Wild type c.1-146C>T (C250T) c.1-146C>T (C250T) 32 11 34.4 20 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 40 6 15 21 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 47 28 59.6 22 Glioblastoma, IDH-wildtype IV Wild type c.1-146C>T (C250T) c.1-146C>T (C250T) 30 6 20 23 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 12 4 33.3 24 Glioblastoma, IDH-wildtype IV Wild type c.1-124C>T (C228T) c.1-124C>T (C228T) 26 11 42.3 25 Glioblastoma, IDH-wildtype IV Wild type Not detected Wild type 28 0 0 IDH, isocitrate dehydrogenase; IGV, integrative genomics viewer; NGS, next-generation sequencing; TD, total read depth; TERT, telomerase reverse transcriptase; VAF, variant allele frequency; VC, variant read count; WHO, World Health Organization. Accepted 18 Korean Cancer Association This article is protected by copyright. All rights reserved. CANCER RESEARCH AND TREATMENT (CRT)

Fig. 1. The result of integrative genomics viewer (IGV). Cases 3 (A) and 8 (B) reveal telomerase reverse transcriptase (TERT) promoter mutation Articlein Chr5: 1,295,228 (C228T). Cases

2 (C) and 10 (D) reveal TERT promoter mutation in Chr5: 1,295,250 (C250T).

Accepted

Article

Fig. 2. The comparison of total read depth between telomerase reverse transcriptase (TERT) promoter region and other region. The total read depth of TERT promoter region is significantly lower than that of other region (paired t-test, p < 0.001).

Accepted

Article

Fig. 3. Optimization of sequence of baits around the telomerase reverse transcriptase (TERT) promoter area. After optimization, the number of baits are increased and the length of oligonucleotideAccepted sequence of baits are elongated.

Article

Fig. 4. The mean total read depth of two hotspots (A, Chr 5:1,295,228; B, Chr 5:1,295,250) of telomerase reverse transcriptase (TERT) promoter area before and after optimization of baits.

After optimization, the mean total read depth of two hotspots are significantly increased (Mann-

Whitney U test, p < 0.001). Accepted

Article

Fig. 5. Genetic and histological landscape of glioblastomas. We excluded variants which had been reported as benign or likely benign in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/).

EGFR, epidermal growth factor receptor; PDGFRA, platelet-derived growth factor receptor A;

FGFR3, fibroblast growth factor receptor 3; CDKN2A/B, cyclin-dependent kinase inhibitor 2A/B; CDKAccepted4, cyclin-dependent kinase 4; NF1, neurofibromin 1; IDH1/2, isocitrate dehydrogenase 1/2; MGMT, O-6-methylguanine-DNA-methyltransferase; NGS, next- generation sequencing; PI3K, phosphoinositide 3-kinase; RB, retinoblastoma; RTK, receptor tyrosine kinase; TERT, telomerase reverse transcriptase.

S1 Table. Targeted gene list of BraintumorSCAN panel

Number SNV Gene Chr Position Size (bp) of regions ABL1 9 133,589,243-133,761,095 SNV InDel CNV 12 4,949 ABL2 1 179,076,828-179,198,844 SNV InDel CNV 14 4,802 ACVR1 2 158,594,018-158,732,399 SNV InDel CNV 18 3,698 AKT1 14 105,235,898-105,262,113 SNV InDel CNV 17 4,674 AKT2 19 40,739,754-40,791,468 SNV InDel CNV 29 5,862 AKT3 1 243,663,020-244,014,406 SNV InDel CNV 17 2,607 ALK 2 29,416,065-30,144,502 SNV InDel CNV 31 7,620 APC 5 112,043,170-112,198,258 SNV InDel CNV 22 11,391 ARID1A 1 27,022,497-27,107,693 SNV InDel CNV 21 9,315 ARID1B 6 157,099,038-157,529,050 SNV InDel CNV 23 12,772 ARID2 12 46,123,423-46,301,848 SNV InDel CNV 24 13,259 ATM 11 108,093,186-108,236,786 SNV InDel CNV 63 21,810 ATRX X 76,763,804-77,041,780 SNV InDel CNV 36 9,611 B9D2 19 41,860,580-41,870,103 SNV InDel CNV 5 1,093 BAX 19 49,458,047-49,465,080 SNV InDel CNV 4 2,584 BCAM 19 45,312,291-45,324,698 SNV InDel CNV 13 5,400 BCL2 18 60,795,833-60,987,386 SNV InDel CNV 2 2,315 BCOR X 39,909,144-40,036,607 SNV InDel CNV 19 7,204 BIRC5 17 76,210,242-76,219,962 SNV InDel CNV 5 1,966 BLNK 10 97,951,704-98,031,369 SNV InDel CNV 19 2,596 BMX X 15,482,344-15,574,295 SNV InDel CNV 21 3,532 BRAF 7 140,426,269-140,624,589 SNV InDel CNV Fusion 21 3,490 BRCA1 17 41,197,670-41,277,525 SNV InDel CNVArticle 26 7,454 BRCA2 13 32,889,586-32,972,932 SNV InDel CNV 29 12,219 BSND 1 55,464,581-55,474,326 SNV InDel CNV 4 1,417 C19ORF33 19 38,794,776-38,795,671 SNV InDel CNV 3 764 CABP5 19 48,533,789-48,547,336 SNV InDel CNV 6 954 CADM1 11 115,047,169-115,375,700 SNV InDel CNV 17 2,907 CARD11 7 2,946,247-3,083,604 SNV InDel CNV 26 5,230 CBL 11 119,076,727-119,170,516 SNV InDel CNV 16 3,897 CCND1 11 69,455,830-69,466,200 SNV InDel CNV 5 1,916 CCND2 12 4,382,877-4,409,200 SNV InDel CNV 7 2,184 CCND3 6 41,903,564-42,018,120 SNV InDel CNV 8 3,580 CCNE2 8 95,893,835-95,908,931 SNV InDel CNV 14 2,481 CD44 11 35,160,392-35,251,018 SNV InDel CNV 21 6,819 CD79B 17 62,006,561-62,009,739 SNV InDel CNV 6 1,086 CDH1 16 68,771,103-68,867,512 SNV InDel CNV 19 5,140 CDH8 16 61,681,656-62,070,964 SNV InDel CNV 16 4,644 CDK4 12 58,142,283-58,149,821 SNV InDel CNV 10 2,323 CDK6 7 92,244,429-92,465,966 SNV InDel CNV 9 1,993 CDKN1B 12 12,867,967-12,874,166 SNV InDel CNV 5 1,897 CDKN2A 9 21,968,183-21,995,325 SNV InDel CNV 8 2,849 CDKN2B 9 22,005,961-22,009,387 SNV InDel CNV 2 1,008 Accepted CDKN2C 1 51,426,392-51,439,967 SNV InDel CNV 3 3,149 CHEK1 11 125,495,006-125,546,175 SNV InDel CNV 14 7,266 CHEK2 22 29,083,860-29,138,435 SNV InDel CNV 21 3,955 CIC 19 42,772,664-42,799,368 SNV InDel CNV Fusion 24 10,088 CSF1R 5 149,433,607-149,492,960 SNV InDel CNV 23 4,478 CSK 15 75,074,373-75,094,963 SNV InDel CNV 9 4,455 CTDNEP1 17 7,147,245-7,155,835 SNV InDel CNV 11 2,033 CTNNB1 3 41,236,303-41,301,612 SNV InDel CNV 22 6,615 CTU1 19 51,601,833-51,611,672 SNV InDel CNV 3 1,262 CXCR4 2 136,872,414-136,875,760 SNV InDel CNV 2 1,291

DDR1 6 30,844,173-30,867,884 SNV InDel CNV 25 9,584 DDR2 1 162,601,138-162,750,061 SNV InDel CNV 21 4,417 DDX3X X 41,192,536-41,223,750 SNV InDel CNV 21 5,595 DICER1 14 95,556,810-95,624,372 SNV InDel CNV 33 8,322 EED 11 85,955,401-85,989,806 SNV InDel CNV 11 7,249 EGFR 7 55,086,689-55,273,335 SNV InDel CNV 34 7,229 EPHA3 3 89,156,649-89,528,677 SNV InDel CNV 17 4,053 ERBB2 17 37,844,142-37,886,704 SNV InDel CNV 31 11,869 ERBB3 12 56,473,616-56,495,864 SNV InDel CNV 25 8,147 ERBB4 2 212,248,315-213,403,590 SNV InDel CNV 30 5,872 ERCC2 19 45,854,862-45,874,201 SNV InDel CNV 23 4,167 ERVV-2 19 53,547,966-53,554,405 SNV InDel CNV 2 2,582 EWSR1 22 29,663,973-29,696,540 SNV InDel CNV Fusion 15 11,078 EZH2 7 148,504,713-148,581,466 SNV InDel CNV 23 4,216 EZR 6 159,187,921-159,240,481 SNV InDel CNV 14 2,784 FAM167B 1 32,712,793-32,714,227 SNV InDel CNV 2 797 FGFR1 8 38,271,121-38,326,377 SNV InDel CNV Fusion 26 6,022 FGFR2 10 123,239,070-123,357,997 SNV InDel CNV 28 6,105 FGFR3 4 1,795,009-1,809,040 SNV InDel CNV Fusion 18 4,150 FGFR4 5 176,513,848-176,524,790 SNV InDel CNV 19 5,413 FGR 1 27,939,400-27,961,813 SNV InDel CNV 15 3,093 FIZ1 19 56,103,791-56,113,361 SNV InDel CNV 3 5,861 FLT1 13 28,877,279-29,069,290 SNV InDel CNV 35 6,621 FLT3 13 28,578,164-28,674,754 SNV InDel CNV 25 4,336 FLT4 5 180,030,167-180,076,649 SNV InDel CNV 32 5,806 FUBP1 1 78,413,174-78,444,914 SNV InDel CNV 23 3,430 GFI1 1 92,941,561-92,952,458 SNV InDel CNVArticle 9 2,247 GFI1B 9 135,820,907-135,866,462 SNV InDel CNV 11 2,371 GLI2 2 121,493,174-121,748,276 SNV InDel CNV 16 6,163 GLTSCR1 19 48,111,428-48,206,558 SNV InDel CNV 16 7,947 GNA11 19 3,094,383-3,121,477 SNV InDel CNV 7 4,042 GNAQ 9 80,336,214-80,646,399 SNV InDel CNV 9 1,952 GPBP1L1 1 46,093,903-46,152,327 SNV InDel CNV 13 3,436 GPR88 1 101,003,668-101,007,608 SNV InDel CNV 2 3,600 GSK3B 3 119,545,610-119,813,289 SNV InDel CNV 12 2,885 H3F3A 1 226,249,527-226,259,205 SNV InDel CNV 5 1,504 H3F3B 17 73,774,647-73,781,592 SNV InDel CNV 4 1,318 HDAC2 6 114,262,197-114,332,497 SNV InDel CNV 19 3,779 HDAC9 7 18,126,547-19,036,947 SNV InDel CNV 43 11,491 HGF 7 81,331,872-81,399,779 SNV InDel CNV 21 3,829 HIST1H3B 6 26,031,853-26,032,313 SNV InDel CNV 1 461 HIST1H3C 6 26,045,614-26,046,074 SNV InDel CNV 1 461 HNF1A 12 121,416,321-121,439,020 SNV InDel CNV 8 3,213 HPCAL4 1 40,148,183-40,157,407 SNV InDel CNV 5 1,345 ID2 2 8,818,950-8,824,186 SNV InDel CNV 5 1,932 IDH1 2 209,101,778-209,130,823 SNV InDel CNV 12 3,169 IDH2 15 90,627,473-90,645,811 SNV InDel CNV 12 2,243 Accepted INPP5D 2 233,924,652-234,115,428 SNV InDel CNV 34 7,552 ITGB4 17 73,717,383-73,753,811 SNV InDel CNV 43 8,634 JAK1 1 65,300,220-65,432,212 SNV InDel CNV 25 4,964 KDM4D 11 94,706,820-94,732,707 SNV InDel CNV 3 3,128 KDM5A 12 394,597-498,646 SNV InDel CNV 31 7,357 KDM5B 1 202,698,139-202,778,623 SNV InDel CNV 30 7,847 KDM5C X 53,221,901-53,254,629 SNV InDel CNV 29 6,907 KDM6A X 44,732,396-44,971,882 SNV InDel CNV 30 8,653 KDM6B 17 7,743,197-7,756,864 SNV InDel CNV 24 7,183 KDR 4 55,946,083-55,991,787 SNV InDel CNV 30 5,873

KEL 7 142,638,314-142,659,793 SNV InDel CNV 20 3,708 KIT 4 55,524,060-55,604,748 SNV InDel CNV 21 4,455 KLF4 9 110,248,007-110,252,788 SNV InDel CNV 5 2,768 KLHL21 1 6,653,400-6,674,692 SNV InDel CNV 7 2,676 KMT2B 19 36,208,896-36,229,804 SNV InDel CNV 35 12,030 KMT2C 7 151,833,892-152,133,115 SNV InDel CNV 61 20,730 KMT2D 12 49,415,538-49,453,582 SNV InDel CNV 56 19,661 KRAS 12 25,362,704-25,403,895 SNV InDel CNV 7 1,255 LPHN2 1 81,771,820-82,458,145 SNV InDel CNV 37 9,576 LRIG2 1 113,615,767-113,669,847 SNV InDel CNV 19 8,385 LRP1B 2 140,990,730-142,889,295 SNV InDel CNV 92 19,486 LZTR1 22 21,333,726-21,353,346 SNV InDel CNV 20 8,482 MALT1 18 56,338,593-56,415,288 SNV InDel CNV 17 5,254 MAP2K1 15 66,679,130-66,782,978 SNV InDel CNV 12 2,662 MAX 14 65,472,897-65,569,438 SNV InDel CNV 9 1,791 MDM2 12 69,201,927-69,238,000 SNV InDel CNV 17 4,046 MDM4 1 204,485,482-204,527,834 SNV InDel CNV 14 7,708 MEG3 14 101,297,758-101,327,360 SNV InDel CNV 1 29,603 MET 7 116,312,223-116,436,203 SNV InDel CNV 25 6,362 MLH1 3 37,034,798-37,107,135 SNV InDel CNV 24 4,413 MRTO4 1 19,578,008-19,585,349 SNV InDel CNV 8 1,924 MSH2 2 47,630,083-47,789,475 SNV InDel CNV 19 4,423 MSH3 5 79,950,442-80,171,706 SNV InDel CNV 26 5,411 MSH5 6 31,707,700-31,730,478 SNV InDel CNV 24 6,926 MSH6 2 47,922,644-48,034,024 SNV InDel CNV 18 8,464 MTOR 1 11,167,517-11,322,633 SNV InDel CNV 58 11,647 MYB 6 135,502,428-135,540,334 SNV InDel CNVArticle 22 5,685 MYBL1 8 67,476,907-67,525,554 SNV InDel CNV 16 3,515 MYC 8 128,747,655-128,753,229 SNV InDel CNV 3 2,675 MYCL 1 40,363,019-40,367,953 SNV InDel CNV 3 2,067 MYCN 2 16,080,535-16,086,244 SNV InDel CNV 3 2,278 MYD88 3 38,179,944-38,184,538 SNV InDel CNV 4 4,189 MZF1 19 59,073,414-59,084,967 SNV InDel CNV 7 3,350 NDRG2 14 21,485,013-21,539,056 SNV InDel CNV 22 5,713 NF1 17 29,421,920-29,709,159 SNV InDel CNV 62 24,596 NF2 22 29,999,520-30,090,816 SNV InDel CNV 17 3,117 NFKBIA 14 35,871,194-35,873,985 SNV InDel CNV 6 1,706 NOTCH1 9 139,390,498-139,440,339 SNV InDel CNV 34 9,444 NOTCH2 1 120,457,904-120,612,342 SNV InDel CNV 37 10,092 NOTCH3 19 15,271,448-15,311,817 SNV InDel CNV 33 8,797 NOTCH4 6 32,163,189-32,191,869 SNV InDel CNV 30 7,651 NRAS 1 115,251,131-115,259,540 SNV InDel CNV 5 1,074 NTRK1 1 156,785,407-156,851,667 SNV InDel CNV 20 5,356 NTRK2 9 87,283,348-87,636,377 SNV InDel CNV 24 4,900 NTRK3 15 88,420,141-88,800,003 SNV InDel CNV Fusion 28 5,014 OTX2 14 57,268,428-57,277,222 SNV InDel CNV 5 1,692 PARP2 14 20,811,716-20,825,981 SNV InDel CNV 15 4,625 Accepted PDGFA 7 538,176-559,958 SNV InDel CNV 9 2,376 PDGFRA 4 55,095,239-5,5161,464 SNV InDel CNV 30 6,913 PDGFRB 5 149,495,301-149,535,448 SNV InDel CNV 24 5,063 PIK3C2A 11 17,111,260-17,229,555 SNV InDel CNV 34 6,993 PIK3C2B 1 204,393,955-204,463,877 SNV InDel CNV 36 8,207 PIK3C2G 12 18,400,523-18,800,987 SNV InDel CNV 36 7,139 PIK3CA 3 178,865,877-178,952,177 SNV InDel CNV 23 4,832 PIK3CB 3 138,374,206-138,553,805 SNV InDel CNV 25 5,681 PIK3CD 1 9,711,765-9,789,002 SNV InDel CNV 29 6,899 PIK3CG 7 106,505,698-106,545,857 SNV InDel CNV 13 4,822

PIK3R1 5 67,511,523-67,594,458 SNV InDel CNV 18 8,562 PIK3R2 19 18,263,903-18,281,368 SNV InDel CNV 16 5,496 PMS1 2 190,648,786-190,742,380 SNV InDel CNV 17 5,206 PMS2 7 6,013,005-6,048,781 SNV InDel CNV 15 3,445 POLD1 19 50,887,436-50,921,300 SNV InDel CNV 27 6,208 POLE 12 133,201,258-133,264,135 SNV InDel CNV 47 12,784 POT1 7 124,462,557-124,570,062 SNV InDel CNV 22 4,277 PPM1D 17 58,677,519-58,740,938 SNV InDel CNV 8 2,638 PRDM2 1 14,026,668-14,149,704 SNV InDel CNV 15 8,226 PRKAR1A 17 66,409,739-66,547,290 SNV InDel CNV 16 4,344 PRKCA 17 64,298,729-64,800,180 SNV InDel CNV 20 3,796 PRKCD 3 53,190,000-53,226,307 SNV InDel CNV 23 4,095 PTCH1 9 98,209,169-98,279,364 SNV InDel CNV 27 7,372 PTCH2 1 45,286,336-45,308,760 SNV InDel CNV 24 5,022 PTEN 10 89,622,845-89,725,254 SNV InDel CNV 11 4,299 PTPN11 12 112,856,130-112,942,593 SNV InDel CNV 17 3,275 PTPRD 9 8,317,849-10,612,748 SNV InDel CNV 53 9,568 QKI 6 163,835,007-163,992,509 SNV InDel CNV Fusion 15 4,864 RAF1 3 12,625,988-12,705,750 SNV InDel CNV 18 3,347 RB1 13 48,877,858-49,054,808 SNV InDel CNV 28 6,234 RELA 11 65,421,824-65,430,590 SNV InDel CNV 10 3,172 RET 10 43,572,450-43,623,742 SNV InDel CNV 20 4,829 ROS1 6 117,609,630-117,747,043 SNV InDel CNV 45 9,527 RPS6KB1 17 57,970,382-58,027,806 SNV InDel CNV 21 6,198 SAMD11 1 860,235-879,558 SNV InDel CNV 14 3,483 SAMD5 6 147,829,803-148,058,609 SNV InDel CNV Fusion 3 1,041 SDHA 5 218,313-256,822 SNV InDel CNVArticle 16 9,350 SDHAF2 11 61,197,489-61,213,568 SNV InDel CNV 6 1,206 SDHB 1 17,345,351-17,380,690 SNV InDel CNV 8 1,394 SDHC 1 161,284,022-161,334,560 SNV InDel CNV 8 3,965 SDHD 11 111,957,472-112,064,553 SNV InDel CNV 13 3,110 SETD2 3 47,058,558-47,205,492 SNV InDel CNV 24 9,142 SHH 7 155,592,709-155,604,992 SNV InDel CNV 7 2,446 SIGLEC11 19 50,453,202-50,464,454 SNV InDel CNV 11 2,738 SLC30A2 1 26,365,626-26,372,654 SNV InDel CNV 8 1,761 SMARCA4 19 11,071,573-11,175,902 SNV InDel CNV 43 12,904 SMARCB1 22 24,129,125-24,176,392 SNV InDel CNV 9 2,595 SMARCE1 17 38,785,012-38,804,785 SNV InDel CNV 15 3,187 SMO 7 128,828,688-128,852,317 SNV InDel CNV 12 3,704 SNCAIP 5 121,647,024-121,799,939 SNV InDel CNV 20 8,329 SOX2 3 181,429,687-181,432,249 SNV InDel CNV 1 2,563 SPTA1 1 158,581,029-158,656,531 SNV InDel CNV 52 10,059 STAT1 2 191,835,404-191,885,711 SNV InDel CNV 29 4,798 STAT3 17 40,467,738-40,540,611 SNV InDel CNV 25 4,024 STAT5A 17 40,439,540-40,462,712 SNV InDel CNV 20 5,311 STAT6 12 57,490,330-57,525,947 SNV InDel CNV Fusion 26 5,162 SUFU 10 104,263,694-104,389,937 SNV InDel CNV 14 2,738 Accepted SUZ12 17 30,264,012-30,326,420 SNV InDel CNV 17 6,871 T 6 166,571,778-166,582,213 SNV InDel CNV 9 2,420 TCF12 15 57,210,796-57,579,168 SNV InDel CNV 32 7,786 TEK 9 27,109,114-27,229,255 SNV InDel CNV 23 4,975 TENM3 4 183,065,115-18,372,1529 SNV InDel CNV 32 10,932 TERT 5 1,294,900-1,296,162 SNV InDel CNV 1 1,263 (Promoter) TMEM127 2 96,919,521-96,931,776 SNV InDel CNV 5 1,344 TOP2A 17 38,545,746-38,574,227 SNV InDel CNV 36 6,677 TP53 17 7,565,232-7,590,893 SNV InDel CNV 14 2,804

TRAF7 16 2,205,674-2,226,600 SNV InDel CNV 22 4,034 TSC1 9 135,771,597-135,820,045 SNV InDel CNV 23 4,954 TSC2 16 2,097,441-2,138,737 SNV InDel CNV 44 13,312 VHL 3 10,182,667-10,191,854 SNV InDel CNV 4 2,338 WWTR1 3 149,238,567-149,421,085 SNV InDel CNV 13 3,292 YAP1 11 101,981,167-102,101,258 SNV InDel CNV 11 3,999 ZMYM3 X 70,460,741-70,475,072 SNV InDel CNV 26 6,346 ZNF599 19 35,249,914-35,264,159 SNV InDel CNV 5 2,460 ZNF865 19 56,116,746-5,6129,932 SNV InDel CNV 2 5,377

Chr, chromosome; SNV, single nucleotide variant; InDel, insertion and deletion; CNV, copy number variant

Article

Accepted

S2 Table. Ovaerall quality and recommended guideline threshold of BraintumorSCAN Variable Value* Guideline Total DNA amount (μg) 3.63 (0.6-9.8) ≥ 0.5 Inserted median size (bp) 4,011.92 (2,066-18,637) ≥ 350 Mean coverage depth 852.87 (736.8-1051.0) ≥ 300X Q30 value (%) 92.89 (91.5-93.8) ≥ 90.0 On-target rate (%) 94.20 (93.1-95.5) ≥ 90.0 Uniformity (%) 77.96 (74-81) ≥ 70.0

*Data are presented as mean (range).

Article

Accepted

S3 Table. Genetic alterations detected by next-generation sequencing and Sanger sequencing

ABL1 ABL2 ADGRL2 AKT1 ARID1A ARID1B ARID2 ATM B9D2 BCAM BCL2 BCOR Case 1 ------MISSENSE - - MISSENSE - - (R558H) (R582W) Case 2 ------Case 3 ------MISSENSE - - - - - (G936C) Case 4 ------MISSENSE AMP - (V126M) Case 5 MISSENSE ------(P58S) Case 6 ------Case 7 - - - - MISSENSE ------(P1651L) Case 8 ------Article- - - - - Case 9 ------Case 10 - - - - MISSENSE ------(N865S, Q1579R) Case 11 ------HOMDEL - Case 12 ------Case 13 ------HOMDEL - - Case 14 ------HOMDEL - Case 15 - - - - - MISSENSE - - - - HOMDEL - (S49F) Case 16 - - - - - INFRAME - - AMP - - - (G219dup) Case 17 ------Case 18 ------Case 19 ------Case 20 - - - MISSENSE - - - MISSENSE - - - - (I75M) (R13C) Case 21 - MISSENSE ------Accepted 30 Korean Cancer Association This article is protected by copyright. All rights reserved. CANCER RESEARCH AND TREATMENT (CRT)

(R63H) Case 22 ------MISSENSE - - - - TRUNC (V103I) (K1039Rfs*39) Case 23 ------TRUNC (D801Efs*6) Case 24 ------Case 25 - - TRUNC - - - - MISSENSE - - - - (L926*) (H2480R) Abbreviation: MISSENSE, missense mutation; INFRAME, in-frame insertion or deletion; AMP, amplification; HOMDEL, homogenous deletion; TRUNC, truncation mutation

BICRA BLNK BRCA1 BRCA2 BSND CBL CCNE2 CDK4 CDKN2A CDKN2B CDKN2C CIC Case 1 ------Case 2 - - MISSENSE - MISSENSE - - - HOMDEL HOMDEL - - (L52F) (P224S) Case 3 ------Case 4 - - - - - MISSENSE - Article- HOMDEL HOMDEL - - (W408C) Case 5 ------Case 6 ------HOMDEL HOMDEL - - Case 7 ------HOMDEL HOMDEL - - Case 8 ------Case 9 - - MISSENSE - - - - - HOMDEL HOMDEL - - (L52F) Case 10 MISSENSE - - - - MISSENSE - - HOMDEL HOMDEL - - (A997V) (L405R) Case 11 ------HOMDEL HOMDEL - - Case 12 ------Case 13 HOMDEL ------HOMDEL HOMDEL HOMDEL HOMDEL Case 14 - MISSENSE ------HOMDEL - - - (S129C) Case 15 ------Case 16 ------Case 17 - - - MISSENSE - - - - HOMDEL HOMDEL - - (E984G) Accepted 31 Korean Cancer Association This article is protected by copyright. All rights reserved. CANCER RESEARCH AND TREATMENT (CRT)

Case 18 ------TRUNC - HOMDEL - - - (Y203*) Case 19 ------AMP - - - - Case 20 ------HOMDEL HOMDEL - - Case 21 ------HOMDEL HOMDEL - - Case 22 ------Case 23 - - - MISSENSE ------(V208G) Case 24 HOMDEL ------Case 25 ------AMP - - - -

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