Oral Diseases (2015) 21, 872–878 doi:10.1111/odi.12357 © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd All rights reserved www.wiley.com

ORIGINAL ARTICLE

Molecular characterization of oral squamous cell carcinoma using targeted next-generation sequencing Tze-Kiong Er1,2, Yen-Yun Wang3, Chih-Chieh Chen4,5,6, Marta Herreros-Villanueva7, Ta-Chih Liu1,8,9, Shyng-Shiou F. Yuan3,5,10,11 1

Division of Molecular Diagnostics, Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Medical Laboratory Science and Biotechnology, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan; 3Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; 4Center for Lipid Biosciences, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; 5Center for Lipid and Glycomedicine Research, Kaohsiung Medical University, Kaohsiung, Taiwan; 6Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan; 7Department of Gastroenterology, Hospital Donostia/Instituto Biodonostia, Centro de Investigaci on Biomedica en Red de Enfermedades Hep aticas y Digestivas (CIBERehd), Universidad del Paıs Vasco UPV/EHU, San Sebasti an, Spain; 8Division of Hematology and Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; 9Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; 10Department of Obstetrics and Gynecology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; 11Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan 2

OBJECTIVES: Many genetic factors play an important role in the development of oral squamous cell carcinoma. The aim of this study was to assess the mutational profile in oral squamous cell carcinoma using formalin-fixed, paraffin-embedded tumors from a Taiwanese population by performing targeted sequencing of 26 cancer-associated genes that are frequently mutated in solid tumors. METHODS: Next-generation sequencing was performed in 50 formalin-fixed, paraffin-embedded tumor specimens obtained from patients with oral squamous cell carcinoma. Genetic alterations in the 26 cancer-associated genes were detected using a deep sequencing (>1000X) approach. RESULTS: TP53, PIK3CA, MET, APC, CDH1, and FBXW7 were most frequently mutated genes. Most remarkably, TP53 mutations and PIK3CA mutations, which accounted for 68% and 18% of tumors, respectively, were more prevalent in a Taiwanese population. Other genes including MET (4%), APC (4%), CDH1 (2%), and FBXW7 (2%) were identified in our population. CONCLUSIONS: In summary, our study shows the feasibility of performing targeted sequencing using formalin-fixed, paraffin-embedded samples. Additionally, this study also reports the mutational landscape of oral squamous cell carcinoma in the Taiwanese population. We believe that this study will shed new light on fundaCorrespondence: Shyng-Shiou F. Yuan, MD, PhD, Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100 Shih-Chuan 1st Rd., Kaohsiung, Taiwan. Tel: +8867-3121101#2557, Fax: +886-7-3121101#2555, E-mail: yuanssf@ms33. hinet.net Tze-Kiong Er and Yen-Yun Wang contributed equally to this article. Received 27 May 2015; revised 28 June 2015; accepted 1 July 2015

mental aspects in understanding the molecular pathogenesis of oral squamous cell carcinoma and may aid in the development of new targeted therapies. Oral Diseases (2015) 21, 872–878 Keywords: Oral squamous cell carcinoma; next-generation sequencing; TP53 mutation; PIK3CA mutation

Introduction Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease including malignant squamous lesions arising in the nasal cavity, oral cavity, larynx, and pharynx (Rizzo et al, 2015). Oral squamous cell carcinoma (OSCC) accounts for more than 50% of all HNSCC (Song et al, 2014). In Taiwan, OSCC is the fourth common cause of cancer mortality in the male Taiwanese population (Chen et al, 2012). A study showed that the incidence of OSCC in Taiwan is the highest in the world (Chen et al, 2012). Risk factors for OSCC include smoking, alcohol consumption, betel quid, and human papillomavirus infection (Chang et al, 1989). Cancer is a genetic disease caused by DNA mutations and epigenetic alterations controlling gene expression. Applying the next-generation sequencing (NGS) platform will have a significant impact on cancer diagnosis, management, and treatment (Meldrum et al, 2011). NGS technology is a feasible and reliable method with which to detect novel and rare somatic mutations. In addition, NGS has been successfully employed to identify novel mutations in a variety of cancers. Recently, several studies have applied NGS for the personalized treatment of can-

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cer. For example, NGS has been applied in the treatment of pancreatic cancer (Mardis, 2012). In addition, NGS has also been used in the detection of epidermal growth factor receptor (EGFR) deletions in non-small cell lung cancer (NSCLC), which has shown important pathogenetic and clinical implications for patients with NSCLC (Marchetti et al, 2012). A recent study also compared the sensitivity of PCR-enriched NGS, Sanger sequencing, and pyrosequencing in the detection of EGFR and KRAS mutations in lung cancer specimens. The sensitivity of NGS is superior to the other two methods and is able to detect a 100% response to an EGFR inhibitor (Querings et al, 2011). A recent study demonstrated the feasibility of performing indepth DNA analyses using FFPE tissue and has brought new insight into understanding the genomic landscape of the advanced prostate cancer (Beltran et al, 2013). In addition, NGS has the potential to use in developing precision and targeted treatment strategies after understanding the mutational landscape of HNSCC (Rizzo et al, 2015). Overall, these studies indicated the potential of NGS in the application of molecular diagnostics and personalized cancer treatment. The mechanisms underlying the tumorigenesis of OSCC remain to be elucidated. Therefore, identification of the precise disease course and the risk factors of OSCC is urgently needed. This identification is fundamental in understanding the molecular pathogenesis of cancer as well as providing the rationale for the development of personalized diagnostic tests and therapies in the near future. In this study, we performed deep sequencing to detect the mutational status in 26 cancer-associated genes (Illumina TruSightTM Tumor Panel) using formalin-fixed paraffin-embedded (FFPE) samples in 50 Taiwanese patients with OSCC.

Materials and methods Patients Between October 2010 and July 2014, patients with OSCC were recruited from the Department of Oral and Maxillofacial Surgery, Kaohsiung Medical University Hospital in Southern Taiwan. All participants signed the written informed consent forms, and the protocols were written and conducted in accordance with institutional guidelines and the Declaration of Helsinki and were approved by the Institutional Review Board of Kaohsiung Medical University Hospital (KMUH-IRB-20130300). The clinical and pathological characteristics of the 50 patients are summarized in Table S1.

Samples Fifty formalin-fixed, paraffin-embedded (FFPE) specimens from patients with OSCC were included in this study. Patients with OSCC were independently diagnosed by two dental pathologists, and the diagnoses were confirmed using clinical and histological data. Each patient’s clinical cancer [TNM (tumor–node–metastasis)] stage was determined according to the 1992 criteria of the American Joint Cancer Committee/Union International Cancer Control (AJCC/UICC) (Verschuur et al, 1999). The cancerous tissue samples were collected from newly diagnosed patients with OSCC who had not undergone any radiation therapy, chemotherapy, or other types of medical treatment before surgery.

DNA extraction Representative sections were stained with hematoxylin and eosin (H&E) and reviewed by pathologists (percentage of tumor cells over 50%) before DNA extraction. Five 10-lm FFPE tissue sections from each tis-

sue block were deparaffinized, and DNA was extracted using a QIAamp DNA mini kit (Qiagen, Heidelberg, Germany), following the manufacturer’s protocol. The quality and quantity of the nucleic acids were analyzed using a Qubit Fluorometer (Invitrogen, Carlsbad, CA, USA) and Nano-200 Nucleic Acid Analyzer MEDCLUB (Tainan, Taiwan), respectively.

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DNA quality assessment A qPCR was performed to determine the amplifiability of the FFPE-extracted gDNA samples. The primers for QCT target highly repetitive regions found throughout the human genomic DNA (refer to the manufacturer’s instructions for additional details). By comparing the amplifiability of FFPE DNA relative to that of the non-FFPE reference gDNA (quality–control–template, OCT), a DCq value was calculated for each sample and used to predict its performance in the TruSightTM Tumor Sample Preparation Assay (Illumina, San Diego, USA). The exact amount of FFPE DNA input varied according to the quality of the extracted DNA determined by qPCR.

Targeted NGS (TruSightTM Tumor Panel, Illumina) A total of 26 cancer-associated genes were analyzed, namely AKT1, ALK, APC, BRAF, CDH1, CTNNB1, EGFR, ERBB2, FBXW7, FGFR2, FOXL2, GNAQ, GNAS, KIT, KRAS, MAP2K1, MET, MSH6, NRAS, PDGFRA, PIK3CA, PTEN, SMAD4, SRC, STK11, and TP53. The genes involved with solid tumors were selected for this panel, which was composed of 26 genes and 174 amplicons from relevant content from the College of American Pathologists (CAP) and National Comprehensive Cancer Network (NCCN) guidelines, relevant publications, and late-phase pharmaceutical clinical trials.

Preparation of libraries Libraries were prepared with the TruSightTM Tumor Kit (Illumina) according to the manufacturer’s instructions. Briefly, the FFPE genomic DNA, 50 ll in total, was hybridized with an oligo pool. Then, unbound oligos were discarded, and extension and ligation of the bound oligos were followed by PCR amplification. The PCR products were then cleaned and checked for quality using the Caliper LabChip XT DNA Assay. The size of the PCR product was required to be approximately 300–330 bp. Before sequencing, the libraries were normalized using the preparing libraries for sequencing on the MiSeq platform (Illumina), followed by the sequence run on the MiSeq system.

Sequencing and bioinformatics analysis with the MiSeq platform As each library possessed a specific primer index combination, the two libraries of each sample were pooled in the sequencing run (FPA and FPB libraries), and each sample was identified by its specific index combination. Libraries were paired-end sequenced with 2 9 151 bp cycles. Data from sequencing runs using the Illumina MiSeq platform were automatically transferred to the bioinformatics analysis pipeline built into the sequencer; this process is automatically executed for sequence alignments, coverage evaluation, and variant detection. The identified variants are exported in the VCF file format for further analysis using sequenceraccompanied software (Variant Studio, Illumina) to predict the variants with damage potential.

Read mapping, variant calling, and filtering of the called variants The MiSeqâ system provides fully integrated on-instrument data analysis software. MiSeq Reporter software performs secondary analysis on the base calls and Phred-like quality score (Qscore) generated by Real Time Analysis (RTA) software during the sequencing run. The amplicon workflow in MiSeq Reporter evaluates short regions of amplified DNA (amplicons) for variants through the alignment of reads against a ‘manifest file’ provided by Illumina which contains all of the information on the TruSight Tumor Assay. The amplicon workflow requires the reference genome specified in the manifest file (Homo sapiens, hg19, build 37.2). The reference genome provides variant annotations and sets the chromosome sizes in the BAM file output. The TSCA workflow performs demultiplexing of indexed reads, generates FASTQ files, aligns the reads to a refer-

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ence, identifies variants, and writes output files to the Alignment folder. SNPs and short indels are identified using the somatic variant caller (Lepri et al, 2014).

Results Mutation status of the Illumina TruSightTM Tumor Panel in OSCC We sequenced each sample on the Illumina MiSeq platform to an average sequencing depth of 1000X. The list of mutations detected by the Illumina TruSightTM Tumor Panel after applying the relevant filters is shown in Table 1. Notably, mutations were identified in 6 genes, including TP53, PIK3CA, APC, MET, CDH1, and FBXW7, but not in the remaining 20 genes. Further study using OncoPrint provides an overview of genetic alterations in particular genes (rows) that affect particular individual OSCC samples (columns) (Figure 1). Of the 50 OSCC samples, 68% harbored TP53 mutations (Figure 1). The spectrum of TP53 mutations included missense, nonsense, and frameshift mutations. The majority of TP53 mutations were missense mutations (45.5%) including p.S37Y, p.C135F, p.C141Y, p.P142F, p.R158H, p.A159V, p.R174S, p.C176F, p.R248Q, p.R273C, p.R273H, p.R273L, p.R282W, and p.R337G (Table 1). Nevertheless, approximately 33.3% and 21.2% of TP53 mutations were nonsense mutations and frameshift mutations, respectively (Table 1). Mutations in the PIK3CA gene were detected in 18% of OSCC specimens (Figure 1), with mutations at p.Y1021, p.Y1021C, p.M1043V, p.N1044S, p.H1047L, and p.H1047R identified in our OSCC samples (Table 1). Both MET mutations (missense and frameshift) and the APC mutation at p.V1125A were detected in 4% of OSCC samples (Table 1). In addition, mutations in both CDH1 (p.G382V) and FBXW7 (p.S462F) genes were detected in 2% of OSCC specimens (Figure 1). The results between the Illumina TruSightTM Tumor Panel and direct sequencing were concordant (data not shown).

Discussion In the current study, we provide a survey of the mutational status of 26 cancer-associated genes in OSCC using the Illumina TruSightTM Tumor Panel on the Illumina MiSeq. We obtained an average sequencing depth of >1000X. Overall, 68% of OSCCs harbored mutations in the tumor suppressor gene (TP53). Mutations in catalytic subunit alpha of the phosphatidylinositol-4,5-bisphosphate 3-kinase gene (PIK3CA) were observed in 18% of samples, including p.Y1021C/H, p.M1043V, and p.N1044S, which had not been previously reported in OSCC. Mutations were also found in other genes, including the MET proto-oncogene, receptor tyrosine kinase (MET) (4%), the adenomatous polyposis coli gene (APC) (4%), the cadherin 1 E-cadherin (epithelial) gene (CDH1) (2%), and the F-box and WD repeat domain containing 7 E3 ubiquitin protein ligase gene (FBXW7) (2%). The genetic alteration of TP53 through point mutations, deletions, and insertions occurs in more than half of tumors, including 25–75% of oral cancers (Leemans et al, 2011; Rothenberg and Ellisen, 2012). Whole-exOral Diseases

ome sequencing analysis was previously performed to investigate the mutational landscape of HNSCC, and the results showed that >80% of tumors contain TP53 mutations (Agrawal et al, 2011; Stransky et al, 2011). Most TP53 mutations are missense mutations, in the DNAbinding domain, which suppress p53 transcriptional activity via its incapability in binding p53 responsive elements (Brachmann et al, 1996). The ‘hotspot’ mutations of p53 protein are R175, G245, R248, R249, R273, and R282 (Brachmann et al, 1996). Indeed, mutations at residues such as R248, R273, and R282 were identified in the current study. Previously, Hsieh et al (2001) showed that approximately 48.7% of OSCC cases presented TP53 gene mutations in a series of Taiwanese patients with OSCC, which was associated with alcohol consumption. A recent study on the Taiwanese population showed that TP53 mutations were identified in 32.91% of patients with OSCC and were significantly correlated with poor survival (Chang et al, 2014). A recent study using ultra-deep targeted sequencing on the Taiwanese population showed that the most frequently mutated genes were TP53 (65%) in patients with OSCC (Chen et al, 2015). Similarly, our results showed the frequency of TP53 mutations was 68% using targeted sequencing. Moreover, Zanaruddin et al (2013) and Ara et al (2014) showed that the mutations in TP53 were observed in 27.7% and 23%, respectively, of the OSCC samples determined by NGS in patients of Asian origin. The discrepancy in the TP53 gene mutational status is because the OncoCarta Panel only screens for the hotspot driver mutations. PIK3CA mutations have been reported in HNSCC, oropharyngeal squamous cell carcinoma, salivary duct carcinoma, and OSCC. Moreover, discrepancies in the PIK3CA gene mutational status have been reported in several studies, and the mutation rates range from 0% to 21% in OSCC samples (Qiu et al, 2008; Kostakis et al, 2010; Cohen et al, 2011; Zanaruddin et al, 2013; Chang et al, 2014). Zanaruddin et al (2013) demonstrated that hotspot PIK3CA mutations were observed in 5.7% of OSCC specimens, which was similar to previous findings from related studies (Kozaki et al, 2006; Murugan et al, 2008) in Asian populations. However, we showed that 18% of patients with OSCCs harbor PIK3CA mutations, which is similar to the report of Chang et al (2014) that demonstrated a 13. 92% prevalence of PIK3CA mutations in 79 patients with OSCC using high-resolution melting analysis and the report of Chen et al (2015) that demonstrated a 16.8% prevalence of PIK3CA mutations in 276 patients with OSCC using ultra-deep targeted sequencing. In our study, three previously reported mutations, p.Y1021C/H, p.M1043V, and p.N1044S, were identified in OSCC for the first time and are worthy of further biological and pharmacological considerations. Recently, Ong et al (2014), Abe et al (2013), and Gallia et al (2006) identified a PIK3CA mutation p.N1044S in patients with invasive ductal carcinoma of the breast, ovarian clear cell carcinoma, and glioblastoma multiforme, respectively. Our data corroborate the PIK3CA mutational profile that has been described by others (Cohen et al, 2011; Chen et al,

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Table 1 List of mutations in OSCC samples detected by next-generation sequencing after applying the relevant filters

Gene

Reference/Variant

CDS Position

Protein Position

3374 1145 1385 439

1125 382 462 147

V/A G/V S/F

632 3061 3062 3127 3131 3140 3140 107

211 1021 1021 1043 1044 1047 1047 36

L/W Y/H Y/C M/V N/S H/L H/R

COSM10827 COSM1180853

110 184 267

37 62 89

S/Y E/*

COSM44492 COSM10647 COSM131470

273 404 422

COSM43744 COSM45896

466 466

91 135 141 142 156 156

W/* C/F C/Y P/F R/G

NM_000546.5 NM_000546.5 NM_000546.5

COSM10690 COSM11148

473 476

158 159 163

R/H A/V

NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5

COSM1172488 COSM11508 COSM44518 COSM10645

493 497 522 527

165 166 174 176 189

Q/* S/* R/S C/F

NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5

COSM10733 COSM10705 COSM10654

574 586 637

192 196 213 217

Q/* R/* R/*

NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5

COSM10662 COSM10659 COSM10660 COSM10660 COSM10704 COSM10663 COSM11071 COSM11073 COSM1522201

743 817 818 818 844 916 1009 1024 1036

248 273 273 273 282 306 337 342 346

R/Q R/C R/H R/L R/W R/* R/G R/* E/* WVDS/C

Chr

Consequence

Transcript

missense_variant missense_variant missense_variant frameshift_variant, feature_truncation missense_variant missense_variant missense_variant missense_variant missense_variant missense_variant missense_variant frameshift_variant, feature_truncation missense_variant stop_gained frameshift_variant, feature_truncation stop_gained missense_variant missense_variant missense_variant missense_variant frameshift_variant, feature_truncation missense_variant missense_variant frameshift_variant, feature_elongation stop_gained stop_gained missense_variant missense_variant frameshift_variant, feature_elongation stop_gained stop_gained stop_gained frameshift_variant, feature_elongation missense_variant missense_variant missense_variant missense_variant missense_variant stop_gained missense_variant stop_gained stop_gained missense_variant, feature_truncation frameshift_variant, feature_truncation

NM_000038.5 NM_004360.3 NM_033632.3 NM_001127500.1

APC CDH1 FBXW7 MET

T>C G>T G>A TC>T

5 16 4 7

MET PIK3CA PIK3CA PIK3CA PIK3CA PIK3CA PIK3CA TP53

T>G T>C A>G A>G A>G A>T A>G CG>C

7 3 3 3 3 3 3 17

TP53 TP53 TP53

G>T C>A AG>A

17 17 17

TP53 TP53 TP53 TP53 TP53 TP53

C>T C>A C>T GG>AA G>C CG>C

17 17 17 17 17 17

TP53 TP53 TP53

C>T G>A G>GTAGA

17 17 17

TP53 TP53 TP53 TP53 TP53

G>A G>C C>A C>A G> GGCCAGACCTAAGAGCAATCA

17 17 17 17 17

TP53 TP53 TP53 TP53

G>A G>A G>A A>AC

17 17 17 17

TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53

C>T G>A C>T C>A G>A G>A G>C G>A C>A GGAATCAACC>G

17 17 17 17 17 17 17 17 17 17

TP53

GGCTGGTGCAGGGGC>G

17

NM_001127500.1 NM_006218.2 NM_006218.2 NM_006218.2 NM_006218.2 NM_006218.2 NM_006218.2 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5 NM_000546.5

COSMIC ID

COSM1177864

COSM17444 COSM12461 COSM12591 COSM1421001 COSM249874 COSM249874

Amino Acids

NM_000546.5

Figure 1 The OncoPrint provides an overview of genetic alterations in particular genes (rows) affecting particular individual OSCC samples (columns)

2015), with a range of mutations found in approximately 20% of patients with OSCC. PIK3CA mutations in our study are in the higher rank (18%) for two pos-

sible reasons. First, we utilized a sensitive method, NGS, to conduct deep sequencing. Second, the prevalence of the PIK3CA mutational profile may be higher Oral Diseases

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in the Taiwanese compared with some other Asian countries, which needs to be corroborated in larger scale studies. The MET proto-oncogene encodes the tyrosine kinase receptor for hepatocyte growth factor (HGF). After binding to its ligand, the C-terminal tyrosine residues of MET are phosphorylated. This phosphorylation is followed by a cascade of intracellular signals, resulting in the activation of MAPK and/or PI3K/Akt pathways, which are involved in cell proliferation and cell survival (Zhang and Liu, 2002; Neklason et al, 2011). Improper signaling through MET receptor promotes pleiotropic effects, including growth, survival, invasion, migration, angiogenesis, and metastasis (Organ and Tsao, 2011). Additionally, amplification of the MET gene is associated with advanced stages and worse prognosis in colorectal cancer (Zeng et al, 2008). Intriguingly, we identified an OSCC patient (stage IV) that harbored two MET mutations. One mutation (p.L211W) has been previously reported (Tan et al, 2010), and the other is a novel mutation (p.His148ThrfsTer19). However, the roles of these mutations remain unclear. Mutations in APC gene can be identified in many colorectal cancers, and these mutations are likely to be the initiating events in tumorigenesis (Kinzler and Vogelstein, 1996; Rowan et al, 2000). Previously, the APC gene and it derivatives have been described in the carcinogenicity pathway of WNT-1, identifying its role as a tumor suppressor gene in OSCC (Perez-Sayans et al, 2012). In the current study, we identified one reported missense mutation, p.V1125A, in the Taiwanese population (Chen et al, 2006). This mutation may result in disruption of the putative cell signaling function of the APC protein, which needs to be experimentally elucidated. CDH1 encodes a calcium-dependent cell–cell adhesion glycoprotein E-cadherin that comprises five extracellular cadherin repeats, a transmembrane region, and a highly conserved cytoplasmic tail. Several types of cancers have mutations in this gene. The loss of E-cadherin function contributes to increasing invasion, proliferation, and metastasis in cancer (Semb and Christofori, 1998). In OSCC, E-cadherin is correlated with invasion and metastasis (Mattijssen et al, 1993). However, mutations of the CDH1 gene that encodes E-cadherin are uncommon or absent, and CDH1 mutations that yield the non-adhesive function of E-cadherin have only been observed in human gastric carcinoma cell lines, lobular breast cancer, and familial gastric cancer (Kudo et al, 2004). In this study, we identified a novel mutation p.G382V in patients with OSCC, and this mutation may carry a detrimental effect on E-cadherin function and play a role in the pathogenesis of oral cancer. However, the mechanism remains to be determined in more detail. FBXW7 is a tumor suppressor gene that is mutated in multiple types of human tumors (Welcker and Clurman, 2008). F-box protein, which is encoded by FBXW7, is responsible for the ubiquitination and turnover of several oncoproteins, and the loss of its function has been correlated with genetic instability and tumor growth (Mao et al, 2004; Wang et al, 2012). Recently, Jardim DL et al demonstrated that somatic mutations in FBXW7 usually occur simultaneously with other molecular alter-

Oral Diseases

ations in patients with advanced cancers (Jardim et al, 2014). Recently, Aydin et al (2014) showed that FBXW7 is a critical tumor suppressor that is mutated and inactivated in melanoma, resulting in sustained NOTCH1 activation and rendering the inhibition of NOTCH signaling a promising therapeutic strategy. The disruption of NOTCH1 signaling was also observed in head and neck cancer (Agrawal et al, 2011), and a recent study demonstrated that NOTCH1 mutations are drivers of oral tumorigenesis (Izumchenko et al, 2015). FBXW7 mutations have been reported in 5% of HNSCC (Agrawal et al, 2011). The authors hypothesize that FBXW7 mutations modulate the Notch pathway, although FBXW7 also targets other cancer-related proteins for degradation, including cyclin E and c-myc. In the current study, we identified a novel mutation, p.S462F, in patients with OSCC. It is necessary to screen large populations to estimate the frequency of FBXW7 mutations in OSCC especially in high-incidence countries. Most importantly, the effect of FBXW7 mutations in OSCC must be studied further in the near future. In conclusion, our study shows the feasibility of performing targeted sequencing using FFPE samples. Importantly, targeted sequencing can be applied to routine practice after it is strictly verified and validated. Moreover, the use of NGS in clinical practice may revolutionize the management of patients with OSCC in the near future. A limitation of this study is that the gene content was focused on the 26 known cancer-associated genes (TruSightTM Tumor Sequencing Panel). Additional genes will be added as relevant genes are identified and verified (e.g., NOTCH1, CDKN2A, CCND1, FGFR1, and HRAS), and as their relevance to OSCC is determined in the near future. Additionally, we identified that TP53 mutations were the most common genetic alterations in OSCC, which is consistent with previous studies. On the other hand, we also observed frequent PIK3CA mutations in OSCC, which are consistent with recent data from Chang et al (2014) and Chen et al (2015), which showed the mutational rates of 13.92% and 16.8%, respectively, in Taiwanese patients with OSCC. Moreover, the presence of three reported PIK3CA mutations was found in our population. Notably, other mutations including MET, APC, CDH1, and FBXW7 were also identified in the current study. We are confident that this information will shed new light on fundamental aspects to understanding the molecular pathogenesis of OSCC especially in areas that have a high incidence of OSCC.

Author contributions TKE, YYW, CCC, MHV, TCL, and SSY participated in the concept and design of the experiments. TKE, YYW, and CCC performed the experiments. TKE, YYW, CCC, MHV, TCL, and SSY participated in the data analysis and discussion. TCL and SSY contributed reagents/materials/ analysis tools. YYW, TCL, and SSY provided clinical information. TKE, YYW, CCC, MHV, TCL, and SSY participated in the manuscript preparation. All authors read and approved the final manuscript.

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Acknowledgements We thank the NCKU Center for Genomic Medicine for their support in performing next-generation sequencing experiments and the NCKU Bioinformatics Center for primary data analysis. We also thank ACT Genomics Co., Ltd. for their assistance with bioinformatics analysis. We are also grateful to Professor ChungHo Chen for his expertise in the field of OSCC and for providing us with the OSCC samples for analysis. This study was supported by grants from Kaohsiung Medical University Hospital (KMUH102-M207 and KMUH103-3R64) and the Ministry of Health and Welfare (MOHW103-TD-B-111-05, MOHW104TDU-B-212-124-003, and MOHW103-TDU-212-114007).

Conflict of interest The authors report no conflict of interest.

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Supporting Information Additional Supporting Information may be found in the online version of this article: Table S1 The clinical and pathological characteristics of the 50 OSCC patients.