Figures
Abstract
Background
Cancers of the oral cavity are primarily oral squamous cell carcinomas (OSCCs). Many of the OSCCs present at late stages with an exceptionally poor prognosis. A probable limitation in management of patients with OSCC lies in the insufficient knowledge pertaining to the linkage between copy number alterations in OSCC and oral tumourigenesis thereby resulting in an inability to deliver targeted therapy.
Objectives
The current study aimed to identify copy number alterations (CNAs) in OSCC using array comparative genomic hybridization (array CGH) and to correlate the CNAs with clinico-pathologic parameters and clinical outcomes.
Materials and methods
Using array CGH, genome-wide profiling was performed on 75 OSCCs. Selected genes that were harboured in the frequently amplified and deleted regions were validated using quantitative polymerase chain reaction (qPCR). Thereafter, pathway and network functional analysis were carried out using Ingenuity Pathway Analysis (IPA) software.
Results
Multiple chromosomal regions including 3q, 5p, 7p, 8q, 9p, 10p, 11q were frequently amplified, while 3p and 8p chromosomal regions were frequently deleted. These findings were in confirmation with our previous study using ultra-dense array CGH. In addition, amplification of 8q, 11q, 7p and 9p and deletion of 8p chromosomal regions showed a significant correlation with clinico-pathologic parameters such as the size of the tumour, metastatic lymph nodes and pathological staging. Co-amplification of 7p, 8q, 9p and 11q regions that harbored amplified genes namely CCND1, EGFR, TPM2 and LRP12 respectively, when combined, continues to be an independent prognostic factor in OSCC.
Conclusion
Amplification of 3q, 5p, 7p, 8q, 9p, 10p, 11q and deletion of 3p and 8p chromosomal regions were recurrent among OSCC patients. Co-alteration of 7p, 8q, 9p and 11q was found to be associated with clinico-pathologic parameters and poor survival. These regions contain genes that play critical roles in tumourigenesis pathways.
Citation: Vincent-Chong VK, Salahshourifar I, Woo KM, Anwar A, Razali R, Gudimella R, et al. (2017) Genome wide profiling in oral squamous cell carcinoma identifies a four genetic marker signature of prognostic significance. PLoS ONE 12(4): e0174865. https://doi.org/10.1371/journal.pone.0174865
Editor: Xuefeng Liu, Georgetown University, UNITED STATES
Received: July 24, 2016; Accepted: March 16, 2017; Published: April 6, 2017
Copyright: © 2017 Vincent-Chong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All microarray data files are available from the Gene Expression Omnibus (GEO) database (accession number GSE89924).
Funding: This study was supported by High Impact Research MoE Grant UM.C/625/1/HIR/DENT/08 from the Ministry of Education Malaysia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The following authors Vincent-Chong VK, Salahshourifar I, Anwar A, Razali R, Zain RB are named as inventors for the patent application (PI 2015703972 filed on 4/11/2015) entitled “A Novel Signature For Molecular Sub Classification Of Oral Squamous Cell Carcinoma Based Upon Four Genomic Amplification Discovered Using Array Based Comparative Genomic Hybridization ”. The authors would like to declare that the authors (Arif Anwar, Rozami Razali and Ranganath Gudimella) who were under the affiliation to Sengenics Sdn Bhd have no competing interests relating to employment, consultancy, patents, products in development or marketed products etc. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials and does not provide a conflict of interest.
Introduction
Globally, oral and oropharyngeal cancer is ranked as the sixth most common cancer with an estimated 300,000 new cases being reported in 2012 [1]. Although globally oral cancer is a male-predominant disease [2], incidence of oral cancer in Malaysia varies according to gender and ethnicity [3]. According to the National Cancer Registry Statistics in Malaysia, there was a higher incidence of oral cancer reported in Indian and Malay females. In contrast, for the Chinese population, there was a high oral cancer incidence in males. The higher prevalence of oral cancer in Indian female population may be related to the predominant lifestyle habits such as betel quid chewing [4] among this group. Despite the advances in diagnosis and therapeutic approaches, the mortality and morbidity rates have not improved over the past decades [5]. Copy number alterations promote genetic instability in cancer and lack of improvement in the clinical outcomes most probably reflects the paucity in the knowledge that explains how genetic instabilities in oral cancer contribute in oral carcinogenesis [6, 7]. Moreover, molecular heterogeneity is another issue that should be kept in mind [8].
Oral carcinogenesis is a complex process, resulting from a multistep pathway with accumulation of genetic alterations [7]. Copy number alterations (CNAs) that include amplifications and deletions result in activation of proto-oncogenes and inactivation of tumour suppressor genes, respectively [9]. Several recurrent CNAs have been reported in OSCC by many authors [10–13], but how these CNAs play a role in the pathogenesis of OSCC has not been thus far elucidated. Profiling of CNAs using high-throughput methods provides advanced tools to discover potential biomarkers that could be used for predictive, prognostic and diagnostic approaches [14, 15].
The pattern of CNAs as biomarkers have remarkable significance due to their great impact related with diseases outcome and personalized medicine [15]. Therefore, the identification of the effective biomarkers for prognosis and diagnosis is an early step in the plan for molecular sub-classification that underlies the pathophysiology of the disease. These specific molecular classifications may have the potential to predict early disease and in deciding the patient’s treatment including personalized medicine (targeted gene therapy) [15]. Cervical lymph node metastasis (LNM) is a predictor of poor prognosis in OSCC [16, 17]. The ability to accurately predict lymph node metastases in OSCC patients will enable the clinician to plan the appropriate treatment. However, the CNAs in OSCC and its association with clinico-pathological parameters and clinical outcome remain undetermined. Hence, we aimed to identify recurrent CNAs and their clinical and prognostic impacts in OSCC using high-resolution array CGH. High-resolution array CGH could aid in the identification of candidate genes/regions that may drive the development of oral cancer.
Materials and methods
In this study, tumour samples selected were SCCs derived from within the oral cavity consisting of the anterior two-thirds of the tongue, the buccal mucosa, alveolar ridge, lip, floor of the mouth and hard palate (C00, 02–06) and excluded the base of tongue (C01) and other head and neck sites such as oropharynx, hypopharynx and larynx due to their varied etiologic, genetic, clinical characteristics and prognosis [18, 19]. For example, the principal etiological factors for oral cancer are tobacco smoking, alcohol drinking and betel quid chewing whereas majority of the oropharyngeal cancers are HPV-related. Apart from that, Chung et al. [20] observed that OSCC is more heterogeneous in terms of their genetic and molecular expression as compared to squamous cell carcinomas derived from other head and neck regions (oropharynx, hypopharynx, and larynx). In view of this, we have grouped OSCC of all these sub-sites (C00, C02-06) as a single oral cancer site in this current study.
Tumour samples
A total of 75 OSCC fresh-frozen tissue samples were included for the genome wide array CGH analysis. Sixty-six overlapping OSCC samples were used for validation of the CNAs that resulted from array CGH data using quantitative real-time PCR analysis. Fresh-frozen OSCC tissue samples and the related socio-demographic (risk habits, gender, age group) and clinico-pathologic data (site of lesion, tumour size, lymph node status and tumour staging) were acquired from the Malaysian Oral Cancer Database and Tissues Bank System (MOCDTBS) coordinated by the Oral Cancer Research and Coordinating Centre, University of Malaya [21]. All the OSCC samples recruited in this study had been tested for infection with Human papillomavirus (HPV) 16 and 18 using the HPV GenoArray (Hybribio Ltd, Hong Kong) and all were found to be negative for both types (unpublished data). The socio-demographic and clinico-pathologic parameters of the OSCC samples are listed in Table 1. The International Classification of Disease (ICD-10), developed by World Health Organization (WHO) was used to categorize the OSCC samples according to the anatomical subsites. Tumour staging was done according to the criteria by The American Joint Committee on cancer staging [22]. All the OSCC samples that were included in this study were histologically confirmed by oral pathologists. Approval for this study was granted by the Medical Ethics Committee (MEC), Faculty of Dentistry, University of Malaya vide MEC code no: DF0306/ 001/(L) and DF OS1007/0048(P). All the methodology employed in this study was in accordance with International Conference on Harmonisation–Good Clinical Practice (ICH-GCP) guideline for good clinical practice and the Declaration of Helsinki.
Histopathology and array CGH analysis
Histological assessment was carried out on haematoxylin and eosin (H&E) stained frozen tissue specimens mounted in optimal cutting temperature (OCT) compound. The sections were analysed to determine percentage of tumour content. Tissues that did not contain 70% tumour were macro-dissected to gain areas with ≥ 70% of tumour content for DNA extraction. Extraction of DNA was carried out using DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions.
A customized array CGH platform was designed for OSCC based on our previous research [11, 23] and previously reported candidate regions/genes for OSCC. This customized oligonucleotide array CGH (8x60k) was manufactured by Agilent Technologies, CA, USA. Thereafter, genome-wide profiling was completed based on the manufacturer's instructions (version 5.0, June 2007) by Oxford Gene Technology, Oxford, UK. Genomic DNA of tumour samples was fragmented by enzymatic digestion. Subsequent steps included sample labelling, probe purification, microarray hybridization, washing and scanning. For each array CGH profiling, 1.5 μg of DNA (gDNA) from each of the test samples and commercially obtained gender matched pooled blood gDNA sample (Promega Corporation, WI, USA) were obtained and labelled with fluorescence Cy5 and Cy3 dyes in dye-swap protocol using the CytoSure Genomic DNA labelling kit (Oxford Gene Technology, Oxford, UK), respectively. Probe purification was done using Microcon YM-30 filters (Merck Millipore, MA, USA). This was followed by probe denaturation and pre-annealing with Cot-1 DNA. Constant rotation at 20 rpm (65°C for 40 hours) completed the hybridization process. Slides were washed after the hybridization process conforming to the manufacturer’s instructions and scanned using a DNA Microarray Scanner (Agilent Technologies, CA, USA). Feature Extraction software, version 10.7.3.1 (Agilent Technologies, CA, USA) was used to generate the signal intensities in the text file per array. The data from the text file was segmented using a modified Circular Binary Segmentation (CBS) algorithm [24]. The CNAs were recognized using CytoSure Interpret software version 4.2.5 (Oxford Gene Technology, Oxford, UK) based on the application of log2 intensity ratios of sample to reference (Cy3/Cy5: log2-ratios above 0.3 for amplifications and below -0.6 for deletions). CNAs genomic positions (start and end) along with list of cytobands were annotated based on the human genome assembly version GRCh37/hg19. The microarray data have been deposited in the Gene Expression Omnibus (GEO) database with the accession number of GSE89924. CNAs were classified irrespective of the sizes which may have gene-rich regions with possibility of being pathogenic. CNAs with closest overlap regions and their redundancies with in each cytoband were considered to calculate frequency of amplification and/or deletions. Overall percentages were calculated by dividing the frequency of amplification and/or deletion in each cytoband by total number of CNAs identified. Cytobands were ranked according to the percentage, a cutoff of 8% was applied to highlight significant CNAs and also focus on highly altered genes in CNA. Significant CNAs identified by above cutoff were compared to data from The Cancer Genome Atlas (TCGA) [25] and International Cancer Genome Consortium (ICGC) [26].
Pathway and network analysis
The annotated genes within the copy number altered regions that had a frequency of 8% were subjected to gene pathway/network and biological function (diseases, molecular and cellular functions) analysis using Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, CA, USA). The default setting from the software was used to map the CNA associated genes to the reference set of direct and indirect relationships. Next, relevant input to the gene list such as the molecular networks and biological functions were generated by the software algorithm. The significance of the gene annotation with a p-value less than 0.05 was determined with right-tailed Fisher’s exact test.
Copy number analysis by the TaqMan PCR assay
Copy number analysis was done on 66 OSCCs using TaqMan Copy Number Assay: LRP12 (Hs01987319_cn), FSCN1 (Hs03631914_cn), EGFR (Hs02309320_cn), CCND1 (Hs02226007_cn), CHL1 (Hs02163529_cn), TPM2 (Hs01060645_cn), CLPTM1L (Hs01133209_cn), CSMD1 (Hs03683117_cn) (Applied Biosystems, Foster City, CA, USA). The commercially male and female-pooled blood gDNA samples (Promega Corporation, WI, USA) served as calibrator controls. PCR was done in a total volume of 20 μl consisting of 4 μl of genomic DNA(5 ng/μl), 10 μl of 2× TaqMan® Genotyping Master Mix (Applied Biosystems, CA, USA), 1 μl of 20X TaqMan Copy number assay, 1 μl of 20X TaqMan copy number reference assay (RNAse P) and 4 μl of nuclease free water. Quantitative PCR was performed on an ABI 7500 Fast Real Time PCR System (Applied Biosystems, CA, USA) using the manufacturer’s PCR conditions as follows: initial denaturation at 95°C for 10 minutes followed by 40 cycles of denaturation for 15 seconds at 95°C and annealing for 60 seconds at 60°C.
The values of copy number for each sample were normalized using RNAase P as a reference control with 2 copies in the human genome. Copy number was quantified using the equation 2 x (2−ΔΔCt), comparative CT (ΔΔCT) relative quantitation method [27]. Target and reference assays that were used for copy number calculation were derived from the mean of triplicate, RNase P and the calibrator samples. The calculated relative quantity was multiplied by a base copy number of 2 to obtain the copy number value. The copy number of selected CNA associated genes were then classified into three groups, deletion (< 1.0), amplification (> 2.0 copies) and no change (> 1.0 and ≤ 2.0 copies) [28, 29].
Selection of the cut-off point for LRP12, FSCN1, EGFR, CCND1, CHL1, TPM2, CLPTM1L, CSMD1 genes
The clinico-pathologic data was first dichotomized based on the survival status of the OSCC patients (alive vs dead). Receiver operating characteristic (ROC) curve analysis was used to determine the best cut-off score for LRP12, FSCN1, EGFR, CCND1, CHL1, TPM2, CLPTM1L and CSMD1 genes copy number to survival status using 0, 1 criterion [30]. For copy number alterations (scores) of the LRP12, FSCN1, EGFR, CCND1, CHL1, TPM2, CLPTM1L and CSMD1 genes, the sensitivity and specificity of each score was plotted to generate various area under the ROC curves (AUC) against survival status (alive vs dead). The score that was closest to the point with maximum sensitivity and specificity was selected as the cut-off value. The copy number alteration scores were divided into amplifications/deletions and no change where no change was the score below or equal to the cut-off value, while amplifications/deletions were the scores above the cut-off value.
Statistical analysis
The chi-square (or Fisher exact where appropriate) statistic was used to test the associations between the selected CNAs (amplification of chromosome 3q, 8q, 7p, 9p, 11q and deletion of 3p and 8p) and clinico-pathologic parameters. The Mann-Whitney U test was used to compare copy number changes of the candidate genes (LRP12, FSCN1, EGFR, CCND1, CHL1, TPM2, CLPTM1L, CSMD1) between OSCC and non-cancer tissues. The Kaplan-Meier analysis was used to ascertain the prognostic significance of these CNAs and candidate genes of the chromosomes studied. In order to further test whether any of the selected CNAs and the associated genes which showed significant association from the Kaplan-Meier analysis, the Multivariate Cox Regression analysis was further employed. All statistical analyses were performed using the SPSS statistical package (SPSS version 12.0, IL, USA) and the p-values < 0.05 was considered significant.
Results
Copy number alterations
The regions with a frequency of copy number alterations that was ≥ 8% were reported in this study. In array CGH analysis, 26 amplified and 3 deleted chromosomal regions were found (Table 2 and Fig 1). The number of occurrences, size of the start genome position and end genome position of the CNAs are illustrated in Table 2. In the whole genome wide profiling dataset, amplifications outnumbered deletions. Amplifications in 3q, 5p, 7p, 8q, 9p, 10p, 11q and deletions in 3p and 8p chromosomal regions were recurrent. Amplification in 8q22.3-q23.1 and deletion in 3p21.31 were the most common findings, accounting for 18.7% and 9.3% of all samples, respectively (Table 2). Chromosomal regions 3q, 8q and 11q depicted the largest number of CNAs (Table 2 and Fig 1). There were 11 and 21 CNAs identified from the current study that shared similarities with the TGCA of the oral cancer array CGH OSCC study and the International Cancer Genome Consortium (ICGC) respectively (Fig 2 and S1 Table).
Association of the copy number alterations with clinico-pathologic parameters
Amplification of the chromosome 7p was significantly associated with both the tumour size (T1-T2: 31.1% vs T3-T4: 70%, p = 0.001) and staging (early stages: 26.9% vs advanced stages: 57.1%, p = 0.013). Amplification in the long arm of chromosome 8 (early stage: 15.4% vs advanced stages: 51.1%, p = 0.003), and 11 (early stages: 7.7% vs advanced stages: 28.6%, p = 0.036) was associated with staging. In addition, deletion in the short arm of the chromosome 8 was found to be significantly associated with pathologic staging, (early stages: 11.5% vs advanced stages: 38.8%, p = 0.014). Amplification in the short arm of chromosome 9 was significantly associated with lymph node metastasis (LNM negative: 5.3% vs LNM positive: 24.3%, p = 0.02) (Table 3). The association of amplification of chromosomes 7p, 8q, 9p and 11q with the size of the tumour, metastatic lymph nodes and pathological staging prompted us to combine these CNAs as a genetic signature to increase clinical significance in OSCC patients. Co-amplification of ≥ 1 of these CNAs within the genetic signature were found to be associated with tumour sizes (T1-T2: 55.6% vs T3-T4: 83.3%, p = 0.012), lymph node metastasis (LNM negative: 52.6% vs LNM positive: 81.1%, p = 0.009) and pathologic staging (early stages: 42.3% vs advanced stages: 79.6%, p = 0.001) (Table 3).
Association of the copy number alterations with clinical outcomes
Three-year survival rates for amplification and non-amplification of all chromosomes are summarized in S2 Table. Using Kaplan Meier analysis (Fig 3), amplification of chromosomes 7p, 8q, 9p, 11q and deletion of 8p was significantly associated with poor prognosis. However, after using multivariate analysis and controlling for other confounders (after adjustment for selected socio-demographic and clinico-pathologic data), all associations obtained in the Kaplan Meier analysis were not significant except for chromosome 11 (S3 Table). Thus, only chromosome 11q can be accepted as an independent prognostic marker based on the Multivariate analysis (S3 Table).
When amplification of any 1 or more (≥ 1) of the chromosomes 7p, 8q, 9p, 11q was considered, both Kaplan-Meier (Fig 3) and Multivariate Cox regression analysis (S3 Table) revealed that co-amplification of ≥ 1 of these CNAs within the genetic signature was found to be associated with poor prognosis (HRR = 3.554, 95% C1 1.161–10.886, p = 0.026) after adjustment for selected socio-demographic and clinico-pathologic data of OSCC (S2 Table). This result showed that in addition to amplification of chromosome 11, co-amplification of ≥ 1 of these CNAs within the genetic signature is also an independent prognostic marker.
TaqMan copy number assay of LRP12, TPM2, EGFR, FSCN1, CCND1, CLPTM1L, CHL1 and CSMD1
Several candidate genes within the chromosomal regions that showed changes in the copy number were validated using qPCR analysis (Fig 4). Out of 26 samples that showed amplification of chromosome 8q22.3 (LRP12) in array CGH, 13 (50%) samples showed amplification in the qPCR copy number assay validation. Approximately 50% (7/14), 54.5% (6/11), 59.4%, 71.9% and 75% of the samples that showed amplification in array CGH analysis for CCND1 (chromosome 11q13.3), TPM2 (chromosome 9p13.3), FSCN, EGFR (chromosome 7p11.2) and CLPTM1L (chromosome 5p15.33) respectively were validated in qPCR copy number analysis. As for the deletion CNAs, approximately 33.3% (7/21) and 36.8% (7/19) of the samples that showed deletions in array CGH analysis for CSMD1 (chromosome 8p23.2) and CHL1 (chromosome 3p26.3) respectively were validated in qPCR copy number analysis (Fig 4).
The chi-square analysis results showed that LRP12, TPM2, EGFR, FSCN1, CCND1, CLPTM1L, CHL1 and CSMD1 genes were not associated with socio-demographic and clinico-pathologic parameters. Additionally, Kaplan-Meier survival analysis did not show statistical significance between LRP12, TPM2, FSCN1, CCND1, CLPTM1L, CHL1 and CSMD1 genes and poor prognosis. Only amplification of the EGFR showed a trend towards association with poor prognosis (p = 0.060). Moreover, the combination of four candidate genes namely EGFR, LRP12, TPM2 and CCND1 located on 7p, 8q, 9p and 11q respectively were subjected to the statistical analysis. The combined AUC for all the markers was 0.621 which is higher than the single genetic markers. The OSCC patients were divided into two groups (Group 1 & 2). Group 1 included those patients who had a cumulative score of 0 markers while those with a cumulative score of 1, 2, 3, or 4 markers were placed under group 2. A significant difference between groups 1 and 2 (p = 0.045) (Fig 5) was observed in the Kaplan-Meier survival curves. It was also observed that the 4 combined genetic markers remained as an independent prognostic factor with a hazard risk ratio (HRR = 2.34) towards death in patients with amplification of 1 or more markers after adjustment for socio-demographic and clinico-pathologic parameters (Table 4) using Cox regression multivariate model.
Pathway and network analysis
The top significant signaling pathway revealed by IPA analysis was identified as integrin-linked kinase signaling pathway (Table 5). Five different groups of molecular and cellular functions were identified and included cell death and survival, cellular function and maintenance, cellular development, cellular growth and proliferation, and cellular movement. Table 6 shows the top three molecular and cellular functions that were associated with amplified and deleted genes. They were cell death and survival, cellular function and maintenance that were mostly associated with colony survival of cells (p = 8.72E-05) through contributions of ATR, CA9, CCND1, FANCG, RAD21, RB1CC1, TERC and TNFSF10 genes. IPA analysis also revealed that there were 26 genes associated with head and neck SCC. These 26 genes were MALAT1, MRC1, POLQ, CCND1, SOX17, LIFR, FGF4, mir-15, FGF3, SHANK2, RAD21, EGFR, FGF19, PPFIA1, TPCN2, MECOM, ANO1, ORAOV1, FADD, DDX58, EPPK1, LYN, ATR, SETD2, MYEOV and CTTN. Network analysis on 1427 genes linked to CNA identified correlation in the most remarkable network with cell death and survival, cellular movement and cellular development (Table 7). This significant network harbored 73 genes and between them, the major centers (cores) like CCND1, RELA, TP63 and EGFR formed interconnected auto-regulatory and feed forward circuitry in the network (S1 Fig). The main function of this network involved tumour growth and proliferation by evasion of apoptosis signals thereby promoting cell survival and metastasis.
Discussion
It has been previously noted that the TCGA consortium [19] had conducted the largest genome-wide profiling study on 172 OSCC samples including oral tongue, buccal mucosa, alveolar ridge, lip, floor of mouth and hard palate using array CGH technology. In order to validate the CNAs derived from this OSCC cohort, we selected samples from within the oral cavity and excluded oropharynx, hypopharynx, and larynx. We identified 26 amplifications and 3 deletions with a frequency of ≥ 8%. Amplifications outnumbered deletions and were noted in chromosomes 3q, 5p, 7p, 8q, 9p, 10p and 11q while deletions were observed in chromosomes 3p and 8p. This study confirms and adds to the earlier evidence of frequent CNAs among OSCCs that have been reported in the TGCA and the International Cancer Genome Consortium (ICGC) [19,20]. As depicted in Fig 2, evidence of replicating recurrent CNAs in the present research was in agreement with those from ICGC and TGCA projects that could provide new insights into oral cancer biology.
To the best of our knowledge this is the first study that have identified the presence of one or more of a group of CNAs (gain 7p, 8q, 11q, and 9p) which function as the novel CNA signature from array CGH analysis. Interestingly, this CNA signature could serve as a clue to determine which OSCC patients have a high risk for lymph node metastasis and therefore an advanced tumor stage. Apart from that, the Kaplan Meier survival curve analysis revealed that presence of one or more of a group of this CNA signature was significantly associated with poor prognosis (p < 0.050). Additionally, Multivariate Cox regression model analysis revealed that this CNA signature group remained as an independent prognostic marker (HRR = 3.455, 95% C1 1.125–10.615, p = 0.026) after adjustment for selected sociodemographic (age, gender, and risk habits) and clinico-pathological parameters (tumor subsite, tumor differentiation, tumor size, lymph node status and tumor staging). The qPCR method was used to further validate the CNAs identified from array CGH. Copy number analysis on LRP12, TPM2, EGFR, FSCN1, CCND1, CLPTM1L, CHL1 and CSMD1 genes were carried out using qPCR. The rationale of choosing these CNA associated genes was basically due to LRP12, TPM2, EGFR, CCND1 being matched with ICGC and TCGA databases whereas FSCN1, CLPTM1L, CHL1 and CSMD1 had been found to be associated with oral cancer [12, 31–33]. This study attempted to elucidate whether these genes were the driver genes in the CNA regions which is 7p, 5p, 3p and 8p. Thus, the validation of these CNA-associated genes resulted in the identification of a “genetic signature” marker. This genetic signature marker contains LRP12, CCND1, EGFR and TPM2 genes that could predict clinical outcomes and facilitate selection of therapeutic strategies in oral cancer management that are tailor-made for patients.
CNAs have the tendency to disrupt proto-oncogenes or tumour suppressor genes, and are known to be major contributors to poor prognosis of oral cancer [7,9,10]. In this current study, we have identified highly frequent copy number alterations in chromosomes 3p, 3q, 8p, 8q and 11q, as described in previous studies that are capable of leading to poor clinical outcome in OSCC [10, 13, 23][10,25,26]. Amplification of 8q22.3-q23.1 was the most frequent event in the current study, and was seen in 18.70% (n = 75) of all OSCCs. Genomic alterations in chromosome 8q, especially amplification of 8q22.3-q23.1, have been commonly noted in OSCC [27]. In this current study, amplification of chromosome 8q was found to be significantly related to advanced pathologic stages in OSCC patients. The plausible reason behind this observation could be attributed by the presence of the putative oncogene known as LRP12 gene which belongs to the LDLR superfamily. According to Garnis et al. [34] suggest that this gene plays a role in oral tumourigenesis and over-expression of this gene is associated with oral cancer.
Losses in 3p26.3-3p26.1 and 3p21.31 were frequently detected and remained as a prognosticator in OSCC cases. These regions harbour the tumour-suppressor gene known as Cell Adhesion Molecule L1 (CHL1), which has been recently shown to contribute in oral tumourigenesis [10]. Loss of this gene able to arrest both in vitro and in vivo proliferation and invasion of tumour cells in breast tumourigenesis [28][29].
Both arms of chromosomes 3 and 8 contain several tumour related genes that are involved in the genesis and progression of oral cancer [8]. Of these, loss in 8p23.32 has been reported more frequently [8]. In this study, deletion of chromosome 8p was found to be associated with advanced pathologic stage of the tumour. The focal area of 8p23.32 that contained the CUB and sushi multiple domain protein 1 (CSMD1) genes could be of interest in future investigations. Deletion and expression loss of this gene have been reported in association with poor survival, lymph node metastasis and advanced pathologic staging in several cancers [30].
Amplification in 11q13.3 was associated with advanced stage of the tumour in OSCC. It has been well-established that chromosome 11q has cancer-related genes that play an important role in tumourigenesis [31,32]. Cancer-related genes including ANO1, CCND1, CTTN, FADD and ORAOV1 are involved in tumour cell proliferation, evasion of apoptosis, invasion, and migration [33–37]. The amplification of 11q13 has been related with poor clinical outcome [38] and metastasis in head and neck cancer [39]. This data reflects the great value of this region as being a valuable biomarker in the prognosis and treatment planning of oral cancers.
Amplification in 7p12-22 has been identified in almost 30% of OSCCs and more than 40% of HNSCC samples from TCGA [25, 35]. We found that amplification in 7p is associated with tumour size (T3-T4) and advanced pathologic staging among OSCCs. Among the candidate oncogenes harbored in this region such as EGFR, TWIST1, and HOXA genes, EGFR has shown a high level of amplification in the OSCC samples of the TCGA project [25]. In this study, amplification in 9p21.1–13.3 was identified in 14.7% of OSCC samples.
Amplification of 9p, especially 9p13, has been frequently reported in OSCC, contributing to early stages of oral tumourigenesis [10, 36]. This current study also revealed that amplification of 9p was significantly associated with lymph node metastasis that might be driven by proto-oncogenes such as CA9, VCP, DCTN3, and STOML2. Towle et al. [36] demonstrated that the inhibition of these putative genes in OSCC cell lines suppressed tumour cell proliferation, suggesting that amplification of 9p13 is more likely to contribute in the aggressiveness of multiple oncogenes in oral tumourigenesis.
Various studies have employed pathway and network analyses to filter the driver genes in signaling pathways and cancer-related gene networks from the robust cancer data sets [37–39]. In this study, several oncogenic signaling pathways were identified using the IPA pathway analysis. The most significant signaling pathway was associated with Integrin-linked kinase (ILK) signaling. Integrin-linked kinase, a candidate oncogene, acts as a multifunctional serine/threonine kinase. Activation of this gene results in cell proliferation, evasion of apoptotic signals and metastasis [40]. Recently, Que et al. [40] showed that targeting the ILK signalling pathway would suppress tumour cell proliferation, the adhesion and invasion ability in oral tumourigenesis and inhibit tumour growth, invasion, and metastasis in the in vivo model. These findings highlight that the ILK signalling pathway plays a novel role in oral tumourigenesis by regulating EMT associated genes and other downstream targets in this pathway. The IPA analysis also showed that the biological functions related to the CNAs associated genes included cell death and survival, cellular function and maintenance, cellular development, cellular growth and proliferation, and cellular movement. All these hallmark cancer-associated functions are well-documented by Douglas Hanahan and Robert Weinberg in their review paper [41]. The identification of the CCND1, an amplified gene harboured in 11q13.3, was involved in all of the findings and implies that its over-expression would lead to the characteristics of cancers by promoting the proliferation, migration, and invasion of tumour cells and the evasion of apoptosis signals in oral tumourigenesis.
The IPA network analysis revealed that a a novel network was related to cell death and survival, cellular movement and cellular development. Within this network, several genes were identified namely CCND1, RELA, TP63, and EGFR as being major contributors to tumour cell proliferation, immortalization, and metastasis in oral tumourigenesis. The interaction network between these four candidate oncogenes namely CCND1, RELA, TP63, and EGFR have been associated with several oncogenic pathways which are the PI3 Kinase/Akt signalling, NF-κB signalling, cell cycle control signalling, and MAPK/Erk ingrowth and differentiation signalling pathways. Overall, these oncogenic pathways cross-regulate each other and are regulated by EGFR, ERK, and Akt phosphorylation, forming an important network that enhances tumor cell activities such as evasion of apoptosis, immortalization, proliferation and metastasis in tumorigenesis.
In summary, this study has recognized several CNAs that are associated with oral tumorigenesis. This study also demonstrated the significant association between amplification of chromosome 8q, 11q, 7p and 9p and deletion of 8p with clinico-pathologic parameters such as the size of the tumour, metastatic lymph nodes and pathological stage in OSCC. Furthermore, co-amplification of the four chromosomes 7p, 8q, 9p, 11q that function as a CNA signature and the genetic markers (CCND1, EGFR, LRP12 and TPM2) remained as independent prognosticators in OSCC for predicting disease outcome. Apart from that, this research detected several copy number changes that are related with pivotal biological networks which often disrupt oral tumorigenesis in different oncogenes associated with CNAs. This research will provide an enhanced appreciation of the CNAs in OSCC and will add to the growing body of knowledge that these pathways/networks play in oral tumorigenesis.
Supporting information
S1 Table. Detailed list of CNAs identified from the current study that shared similarities with the TGCA and ICGC of the oral cancer array CGH OSCC study.
https://doi.org/10.1371/journal.pone.0174865.s001
(XLSX)
S2 Table. Three-year survival rates for the amplification and non-amplification of the selected CNAs.
https://doi.org/10.1371/journal.pone.0174865.s002
(DOCX)
S3 Table. Table of multivariate cox regression model analysis of chromosome 7p, 8q, 9p, 11q and genetic signature in OSCC overall survival.
https://doi.org/10.1371/journal.pone.0174865.s003
(DOCX)
S1 Fig. IPA network analysis.
There are total 86 CNA associated genes in top significant network which relate cell death and survival, cellular movement, cellular development. EGFR, RELA, CCND1, TP63 are the main four gene hubs in the network that highlighted in red circle. Among the 86 genes, these genes are known for performing the biological functions by interconnecting and auto-regulating with the four gene hubs including cell proliferation (CCND1, EGFR, MEK, ERK 1/2, AP-1, p85, p38 MAPK, GSK3), metastasis which relate to migration or invasion (RELA, VIM, VCP, FSCN1, SNAI2, FAK, RHOA, RAC, RAC1, ROCK), apoptosis (TP63, RELA, caspase 3/7, BAD, PI3K, ATR, 14-3-3) and angiogenesis (VEGF).
https://doi.org/10.1371/journal.pone.0174865.s004
(TIF)
Acknowledgments
The authors acknowledged the Oral Cancer Research and Coordinating Centre (OCRCC), University of Malaya (UM) for providing tissue and data from the Malaysian Oral Cancer Database & Tissue Bank System (MOCDTBS). The authors also acknowledged Oxford Gene Technology for their technical support. We also thank the clinicians and pathologists from Ministry of Health Malaysia for their expertise.
Author Contributions
- Conceptualization: VKV-C IS RBZ.
- Data curation: VKV-C IS AA RR RG.
- Formal analysis: VKV-C IS AA RR RG.
- Funding acquisition: RBZ.
- Investigation: VKV-C IS KMW.
- Methodology: VKV-C IS RBZ.
- Project administration: VKV-C IS RBZ.
- Resources: VKV-C ZAAR SMI TGK AR RR RG WMWM MTA KKT.
- Software: VKV-C IS AA RR RG.
- Supervision: VKV-C IS RBZ.
- Validation: VKV-C IS RBZ.
- Visualization: VKV-C IS TGK RBZ.
- Writing – original draft: VKV-C.
- Writing – review & editing: VKV-C KMW IS TGK AR RR RG RBZ.
References
- 1. Warnakulasuriya S. Global epidemiology of oral and oropharyngeal cancer. Oral Oncol. 2009;45(4–5):309–16. pmid:18804401
- 2. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer. 2015;136(5):E359–86. Epub 2014/09/16. pmid:25220842
- 3.
Ariffin OZ, Saleha IN. NCR report 2007. Ministry of Health, Malaysia. 2011.
- 4. Ghani WM, Razak IA, Yang YH, Talib NA, Ikeda N, Axell T, et al. Factors affecting commencement and cessation of betel quid chewing behaviour in Malaysian adults. BMC public health. 2011;11:82. pmid:21294919
- 5. Silverman S Jr. Demographics and occurrence of oral and pharyngeal cancers. The outcomes, the trends, the challenge. J Am Dent Assoc. 2001;132 Suppl:7S–11S. Epub 2002/01/24.
- 6. Viet CT, Schmidt BL. Understanding oral cancer in the genome era. Head Neck. 2010;32(9):1246–68. Epub 2010/02/27. pmid:20187013
- 7. Choi S, Myers JN. Molecular pathogenesis of oral squamous cell carcinoma: implications for therapy. J Dent Res. 2008;87(1):14–32. Epub 2007/12/22. pmid:18096889
- 8. Mendez E, Houck JR, Doody DR, Fan W, Lohavanichbutr P, Rue TC, et al. A genetic expression profile associated with oral cancer identifies a group of patients at high risk of poor survival. Clinical cancer research: an official journal of the American Association for Cancer Research. 2009;15(4):1353–61. Epub 2009/02/21.
- 9. Pinkel D, Albertson DG. Array comparative genomic hybridization and its applications in cancer. Nat Genet. 2005;37 Suppl:S11–7. Epub 2005/05/28.
- 10. Ambatipudi S, Gerstung M, Gowda R, Pai P, Borges AM, Schaffer AA, et al. Genomic profiling of advanced-stage oral cancers reveals chromosome 11q alterations as markers of poor clinical outcome. PloS one. 2011;6(2):e17250. Epub 2011/03/10. pmid:21386901
- 11. Vincent-Chong VK, Salahshourifar I, Razali R, Anwar A, Zain RB. Immortalization of epithelial cells in oral carcinogenesis as revealed by genome-wide array comparative genomic hybridization: A meta-analysis. Head Neck. 2015. Epub 2015/04/29.
- 12. Uchida K, Oga A, Nakao M, Mano T, Mihara M, Kawauchi S, et al. Loss of 3p26.3 is an independent prognostic factor in patients with oral squamous cell carcinoma. Oncol Rep. 2011;26(2):463–9. Epub 2011/05/28. pmid:21617881
- 13. Chen C, Zhang Y, Loomis MM, Upton MP, Lohavanichbutr P, Houck JR, et al. Genome-Wide Loss of Heterozygosity and DNA Copy Number Aberration in HPV-Negative Oral Squamous Cell Carcinoma and Their Associations with Disease-Specific Survival. PloS one. 2015;10(8):e0135074. Epub 2015/08/08. pmid:26247464
- 14. Dienstmann R, Jang IS, Bot B, Friend S, Guinney J. Database of genomic biomarkers for cancer drugs and clinical targetability in solid tumors. Cancer Discov. 2015;5(2):118–23. Epub 2015/02/07. pmid:25656898
- 15. Ludwig JA, Weinstein JN. Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer. 2005;5(11):845–56. Epub 2005/10/22. pmid:16239904
- 16. Shah JP. Cervical lymph node metastases—diagnostic, therapeutic, and prognostic implications. Oncology (Williston Park). 1990;4(10):61–9; discussion 72, 6.
- 17. Woolgar JA. Histopathological prognosticators in oral and oropharyngeal squamous cell carcinoma. Oral Oncol. 2006;42(3):229–39. pmid:16150633
- 18. Deschler DG, Richmon JD, Khariwala SS, Ferris RL, Wang MB. The "new" head and neck cancer patient-young, nonsmoker, nondrinker, and HPV positive: evaluation. Otolaryngology—head and neck surgery: official journal of American Academy of Otolaryngology-Head and Neck Surgery. 2014;151(3):375–80.
- 19. Chi AC, Day TA, Neville BW. Oral cavity and oropharyngeal squamous cell carcinoma—an update. CA: a cancer journal for clinicians. 2015;65(5):401–21.
- 20. Chung CH, Parker JS, Karaca G, Wu J, Funkhouser WK, Moore D, et al. Molecular classification of head and neck squamous cell carcinomas using patterns of gene expression. Cancer cell. 2004;5(5):489–500. pmid:15144956
- 21. Zain RB, Athirajan V, Ghani WM, Razak IA, Raja Latifah RJ, Ismail SM, et al. An oral cancer biobank initiative: a platform for multidisciplinary research in a developing country. Cell Tissue Bank. 2013;14(1):45–52. Epub 2012/03/01. pmid:22373599
- 22.
Sobin LH, Gospodarowicz MK, Wittekind C. TNM classification of malignant tumours: John Wiley & Sons; 2011.
- 23. Vincent-Chong VK, Anwar A, Karen-Ng LP, Cheong SC, Yang YH, Pradeep PJ, et al. Genome wide analysis of chromosomal alterations in oral squamous cell carcinomas revealed over expression of MGAM and ADAM9. PloS one. 2013;8(2):e54705. pmid:23405089
- 24. Venkatraman ES, Olshen AB. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics. 2007;23(6):657–63. pmid:17234643
- 25. TCGA. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517(7536):576–82. Epub 2015/01/30. pmid:25631445
- 26. ICGC. Mutational landscape of gingivo-buccal oral squamous cell carcinoma reveals new recurrently-mutated genes and molecular subgroups. Nat Commun. 2013;4:2873. Epub 2013/12/03. pmid:24292195
- 27. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–8. pmid:11846609
- 28. Bronstad I, Wolff AS, Lovas K, Knappskog PM, Husebye ES. Genome-wide copy number variation (CNV) in patients with autoimmune Addison's disease. BMC Med Genet. 2011;12:111. pmid:21851588
- 29. Lopez-Nieva P, Vaquero C, Fernandez-Navarro P, Gonzalez-Sanchez L, Villa-Morales M, Santos J, et al. EPHA7, a new target gene for 6q deletion in T-cell lymphoblastic lymphomas. Carcinogenesis. 2012;33(2):452–8. pmid:22114070
- 30. Zlobec I, Steele R, Terracciano L, Jass JR, Lugli A. Selecting immunohistochemical cut-off scores for novel biomarkers of progression and survival in colorectal cancer. Journal of clinical pathology. 2007;60(10):1112–6. pmid:17182662
- 31. Alam H, Bhate AV, Gangadaran P, Sawant SS, Salot S, Sehgal L, et al. Fascin overexpression promotes neoplastic progression in oral squamous cell carcinoma. BMC cancer. 2012;12:32. pmid:22264292
- 32. Liu Z, Li G, Wei S, Niu J, Wang LE, Sturgis EM, et al. Genetic variations in TERT-CLPTM1L genes and risk of squamous cell carcinoma of the head and neck. Carcinogenesis. 2010;31(11):1977–81. pmid:20802237
- 33. Toomes C, Jackson A, Maguire K, Wood J, Gollin S, Ishwad C, et al. The presence of multiple regions of homozygous deletion at the CSMD1 locus in oral squamous cell carcinoma question the role of CSMD1 in head and neck carcinogenesis. Genes, chromosomes & cancer. 2003;37(2):132–40.
- 34. Garnis C, Coe BP, Zhang L, Rosin MP, Lam WL. Overexpression of LRP12, a gene contained within an 8q22 amplicon identified by high-resolution array CGH analysis of oral squamous cell carcinomas. Oncogene. 2004;23(14):2582–6. Epub 2003/12/17. pmid:14676824
- 35. Sheu JJ, Hua CH, Wan L, Lin YJ, Lai MT, Tseng HC, et al. Functional genomic analysis identified epidermal growth factor receptor activation as the most common genetic event in oral squamous cell carcinoma. Cancer research. 2009;69(6):2568–76. Epub 2009/03/12. pmid:19276369
- 36. Towle R, Tsui IF, Zhu Y, MacLellan S, Poh CF, Garnis C. Recurring DNA copy number gain at chromosome 9p13 plays a role in the activation of multiple candidate oncogenes in progressing oral premalignant lesions. Cancer Med. 2014;3(5):1170–84. Epub 2014/07/26. pmid:25060540
- 37. Li Y, Rouhi O, Chen H, Ramirez R, Borgia JA, Deng Y. RNA-Seq and Network Analysis Revealed Interacting Pathways in TGF-beta-Treated Lung Cancer Cell Lines. Cancer Inform. 2014;13(Suppl 5):129–40. pmid:25991908
- 38. Mutation Consequences and Pathway Analysis working group of the International Cancer Genome C. Pathway and network analysis of cancer genomes. Nat Methods. 2015;12(7):615–21. pmid:26125594
- 39. Xu C, Liu Y, Wang P, Fan W, Rue TC, Upton MP, et al. Integrative analysis of DNA copy number and gene expression in metastatic oral squamous cell carcinoma identifies genes associated with poor survival. Mol Cancer. 2010;9:143. pmid:20537188
- 40. Que L, Zhao D, Tang XF, Liu JY, Zhang XY, Zhan YH, et al. Effects of lentivirus-mediated shRNA targeting integrin-linked kinase on oral squamous cell carcinoma in vitro and in vivo. Oncol Rep. 2016;35(1):89–98. Epub 2015/11/05. pmid:26531674
- 41. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. pmid:21376230