Biomedical Research

Research Article - Biomedical Research (2017) Volume 28, Issue 18

Identification of mutations in cancer predisposition genes in radiosensitive and radioresistant patients with nasopharyngeal carcinoma

Xingwen Wang1, Yunyan Wu2, Qiang Li1, Dongxiao Lv1, Junqing Han1* and Ping Liu3*

1Department of Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, PR China

2Department of Ophthalmology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, PR China

3Department of Pharmacy, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, PR China

*Corresponding Authors:
Junqing Han
Department of Tumor Research and Therapy Center
Shandong Provincial Hospital Affiliated to Shandong, University, PR China

 
Ping Liu
Department of Pharmacy
Shandong Provincial Hospital Affiliated to Shandong, University, PR China
 

Accepted on August 30, 2017

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Abstract

The objective of the present study was to determine whether the alterations of cancer predisposition genes were different between radiosensitive and radioresistant patients with Nasopharyngeal Carcinoma (NPC). A total of 21 patients with nasopharyngeal carcinoma were included in this study. All patients were treated with standardized radiotherapy. Sixteen of the tumors were clinically radiosensitive and 5 were radioresistant. Genomic DNA, extracted from Formalin-Fixed Paraffin-Embedded (FFPE) tumor specimens obtained prior to treatment, was subjected to amplicon-based Next-Generation Sequencing (NGS) with primer sets targeting 50 critical human tumor suppressor genes and oncogenes. We identified 18 nonsynonymous mutations, 1 nonframeshift deletion and 1 frameshift mutation distributed across 15 genes, including 11 mutations have been reported in COSMIC (the Catalogue of Somatic Mutations in Cancer) or dbSNP database (database of single nucleotide polymorphisms), and 9 novel mutations. Most of these mutations have not been reported in NPC. More importantly, 5 radiosensitivespecific mutations targeting AKT1, PIK3CA, MET, TP53 and STK11 were observed, suggesting the genetic alterations of PI3K/AKT and p53 pathways were involved in the response to radiotherapy. Collectively, genetic mutations may differentiate tumor radiosensitivity and radioresistance, although validation of such mutations using a large sample size cohort is necessary before a solid conclusion can be reached.

Keywords

Mutations, Amplicon, Next-generation sequencing, Nasopharyngeal carcinoma.

Introduction

Nasopharyngeal Carcinoma (NPC) is extremely common in southern China, particularly in Guangdong, accounting for 18% of all cancers in China [1]. Epstein-Barr virus infection, heredity and environmental influences are major causes of NPC [2-4]. Most NPC patients are sensitive to radiotherapy, and combined radiochemotherapy may improve the survival of these patients [5,6]. However, radioresistance remains a serious problem affecting the treatment of NPC in many cases. If patients who tend to be resistant to radiotherapy can be identified before treatment and receive a higher dose of therapy without increased the rate of complications, the outcome of radiotherapy or combined radiochemotherapy may be significantly improved.

Microarrays and proteomics have been applied to identify radioresistance associated genes or proteins in multiple types of cancer, including NPC [7-15]. A number of studies have focused on the association between the radiosensitivity of various cancers and genetic changes of oncogenes and tumor suppressor genes, such as p53 [16-18], ATM (19), Epidermal Growth Factor Receptor (EGFR) [20,21]. Although mutations of RB2/p130 [22], p53 [23,24], PIK3CA [25-27] and EGFR [28] have been investigated in NPC, little is known about radioresistance-associated mutations in NPC.

With the advances in Next-Generation Sequencing (NGS) techniques, a large number of mutations have been identified in cancers [29]. Multiplex amplicon-based NGS, sequencing only the Regions of Interest (ROI), has several advantages: requires relatively small amounts of DNA, is relatively low cost and is compatible with Formalin-Fixed Paraffin-Embedded (FFPE)- DNA [30]. In the current study, genomic DNA was extracted from FFPE tissue samples from radiosensitive and radioresistant patients with NPC, and then the hotspot regions of 50 critical human tumor suppressor genes and oncogenes were amplified. Amplicon-based NGS was carried out to investigate any potential correlation of mutational hot spots with radiosensitivity.

Materials and Methods

Collection of NPC specimens

A total of 21 Formalin-Fixed Paraffin-Embedded (FFPE) samples were taken form Shandong Provincial Hospital Affiliated to Shandong University, China after approval by the local ethics committee. All patients were given written informed content. All patients were treated with two cycles of radiotherapy (50-70 Gy total dose) after cisplatin-docetaxel induction chemotherapy. Four response categories are proposed: complete response (CR, which means the disappearance of all detectable tumor), partial response (PR, which is defined as more than 50% decrease in tumor size), stable disease (SD, which means tumor remains the same size), and Progressive Disease (PD). Radiosensitive patients are those reached CR 2 to 4 w after completion of radiotherapy, and radioresistant patients are clarified as those of PR or SD or even with PD 2 to 4 w after completion of radiotherapy. FFPE specimens were obtained prior to treatment, and 16 patients were found radiosensitive (Samples 1-16) and 5 patients (Samples 17-21) were found radioresistant during follow-up.

DNA isolation and amplicon library preparation

Genomic DNA was extracted separately from FFPE samples using QIAamp DNA FFPE Tissue Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s protocol. The concentrations of isolated FFPE-DNA samples were determined on a Qubit 2.0 Fluorometer (Life Technologies).

The amplicon library was prepared using DNA Seq Library Preparation Kit for Amplicon Sequencing-Illumina Compatible (Gnomegen, San Diego, CA), which contains 207 primer pairs and targets 50 proto-oncogenes and tumor suppressor genes (Table 1). Target sequences were amplified with 10 ng FEPEDNA. Subsequently, end repair, barcode ligation and a simple PCR amplification were performed following the manufacturer’s instructions with several purification steps (Gnomegen). The barcode sequences were unique for each sample. Size selection was then performed to obtain clean library products. Following Quality Control, 21 purified and quantified libraries were pooled to form a library product.

ABL1 AKT1 ALK APC ATM BRAF CDH1 CDKN2A CSF1R EZH2
CTNNB1 EGFR ERBB2 ERBB4 FBXW7 FGFR1 FGFR2 FGFR3 FLT3 GNA11
GNAS GNAQ HNF1A HRAS IDH1 IDH2 JAK2 JAK3 KDR KIT
KRAS MET MLH1 MPL NOTCH1 NPM1 NRAS PDGFRA PIK3CA PTEN
PTPN11 RB1 RET SMAD4 SMARCB1 SMO SRC STK11 TP53 VHL

Table 1. The panel of 50 proto-oncogenes and tumor suppressor genes.

Illumina sequencing and sequence assembly

Sequencing was performed on an Illumina Hiseq2000 sequencing platform (Illumina, San Diego, CA) with 150 bp pair reads. After sequencing, the raw data were separately trimmed to remove 5’- and 3’- adapters and low quality regions (<Q20). After discarding reads shorter than 50 bp, reads were mapped against a reference genome using Burrows-Wheeler Alignment tool (BWA). Variances were identified by VarScan software (http://varscan.sourceforge.net) with the following parameters: minimum base quality of 20, minimum coverage of 30X, minimum variation frequency of 3%, no strand bias, and a P value of less than 0.05.

Results

Sequencing data

We applied NGS to investigate mutations of 50 protooncogenes and tumor suppressor genes on 5 radioresistant (Samples 17-21) and 16 radiosensitive (Samples 1-16) patients. We generated 1.3-2.4 million sequencing reads per sample (Q20>96% and Q30>93%). As shown in Table 2, the align rate was >98%, on target rate was >93%, the average read depth (the number of times a nucleotide is sequenced) per sample was 5000X, and coverage uniformity was >90%. Thus, the sequencing data allowed the detection of variances.

Sample Aligned reads (M) Align rate On target reads (M) On target rate Mean depth (1000x) Specificity Coverage uniformity
1 1.929 99.34% 1.889 97.91% 8.847 97.13% 91.41%
2 1.678 99.31% 1.637 97.54% 7.661 96.79% 90.55%
3 1.322 99.40% 1.301 98.47% 6.128 97.84% 89.10%
4 1.568 99.28% 1.528 97.44% 6.994 96.40% 90.85%
5 1.556 99.41% 1.504 96.67% 7.149 96.12% 78.11%
6 2.423 99.30% 2.362 97.48% 11.123 96.67% 90.52%
7 1.568 99.16% 1.514 96.59% 7.105 95.75% 81.89%
8 1.966 99.11% 1.894 96.38% 8.898 95.50% 82.63%
9 1.71 99.27% 1.678 98.13% 7.893 97.41% 93.80%
10 1.67 98.78% 1.636 97.98% 7.605 96.69% 87.29%
11 1.68 99.27% 1.645 97.92% 7.762 97.15% 86.70%
12 2.012 99.24% 1.967 97.74% 9.192 96.97% 85.00%
13 1.674 98.69% 1.646 98.34% 7.574 96.71% 89.75%
14 2.273 99.95% 2.249 98.91% 3.076 89.14% 74.06%
15 1.962 99.92% 1.936 98.67% 3.239 86.72% 76.50%
16 1.844 99.97% 1.808 98.08% 3.229 88.53% 77.91%
17 2.017 99.98% 1.978 98.04% 3.333 88.10% 78.40%
18 1.602 99.97% 1.573 98.19% 3.051 88.14% 79.51%
19 1.925 99.98% 1.902 98.82% 3.187 85.83% 75.21%
20 1.971 99.11% 1.918 97.30% 9.112 96.43% 93.22%
21 1.854 99.36% 1.73 93.29% 8.248 92.60% 87.56%

Table 2. Quality control of sequencing data.

Frequency and distribution of variances

After data processing, 67 variances were detected in the 21 samples (Table S1). Then, we excluded 27 variances within Untranslated Region (UTR) and intron, as well as 20 synonymous mutations (no amino acid change occurred), and obtained 20 distinct variants (18 nonsynonymous, 1 nonframeshift deletion and 1 frameshift) of 15 genes (PTEN, HRAS, AKT1, IDH2, CDH1, TP53, SMAD4, STK11, PIK3CA, FGFR3, KDR, APC, MET, FGFR1 and NOTCH1) (Table 3). Of the 20 distinct variants, 10 were found in COSMIC database (the Catalogue of Somatic Mutations in Cancer) [31] and 8 variants were found in dbSNP database (database of single nucleotide polymorphisms) [32]. Nine novel variants, which were not found in COSMIC database or dbSNP, were then subjected to Polyphen2 (http://genetics.bwh.harvard.edu/ pph2/). The results showed that 8 novel variants were predicted to be “probably damaging” or “possibly damaging”.

Screening for radioresistance-associated variances

By comparing the 20 variances occurred in radiosensitive and radioresistant samples, we found that 5 variants occurred only in radiosensitive samples, but not in radioresistant samples. These 5 variants may be associated with radioresistance (Table 3). No obvious difference was observed in other 15 variants between the two groups of samples. Table 1.

  Gene Accession Chromosome Mutations Cosmic dbSNP Polyphen-2
Radiosensitive AKT1 NM_001014431 chr14 E17K COSM33765 rs121434592  
TP53 NM_001126118 chr17 S51fs COSM131026, COSM131025, COSM131024    
STK11 NM_000455 chr19 F354L COSM21360 rs59912467  
PIK3CA NM_006218 chr3 R108H COSM27497, COSM342716    
MET NM_000245 chr7 N375S COSM710 rs33917957  
Others PTEN NM_000314 chr10 Q110E no entry   0.001 (benign)
HRAS NM_001130442 chr11 G13D COSM490 rs104894226  
IDH2 NM_001290114 chr15 P32H no entry   0.998 (probably)
CDH1 NM_004360 chr16 P372H no entry   0.999 (probably)
TP53 NM_001126118 chr17 P33R COSM250061 rs1042522  
SMAD4 NM_005359 chr18 D424N no entry   0.575 (possibly)
PIK3CA NM_006218 chr3 Y68C no entry   1 (probably)
PIK3CA NM_006218 chr3 E545A COSM297145, COSM12458 rs121913274  
PIK3CA NM_006218 chr3 L719V no entry   0.849 (possibly)
FGFR3 NM_000142 chr4 V266M COSM29434    
KDR NM_002253 chr4 Q472H COSM149673 rs1870377  
APC NM_000038 chr5 A1582T no entry   1 (probably)
APC NM_000038 chr5 A1582P no entry   1 (probably)
FGFR1 NM_001174066 chr8 43_44del no entry rs138489552  
NOTCH1 NM_017617 chr9 E1567D no entry   0.549 (possibly)

Table 3. Identified distinct variants.

Discussion

Radioresistance remains a major obstacle to the treatment of NPC. Genetic changes have been reported to be associated with the radiosensitivity of various cancers. Identification of NPC radioresistance-associated mutations will help finding biomarkers to predict the response to radiotherapy and explaining the molecular mechanisms of NPC radioresistance. As far as we known, this study represents the first sequencing study of multiple oncogenes and tumor suppresser genes in radiosensitive and radiotolerant NPC patients.

In this study, NPC samples were tested with a 50-gene hotspot panel, of which, mutations in PIK3CA, NRAS, KIT, PDGFRA, ABL, HRAS, BRAF and EGFR [24,26-28,33,34] have been reported in NPC. Here, we identified 18 nonsynonymous mutations, 1 nonframeshift deletion and 1 frameshift mutation distributed across 15 oncogenes and tumor suppresser genes, of which 8 mutations were previously observed in dbSNP and 10 mutations, were already known Cancer-Associated Mutations (COSMIC). By analyzing with Polyphen-2 Software, 8 of the 9 novel mutations were predicted to be damaging. These results suggested that we have identified novel mutations which may be associated with tumorigenesis. Alterations in most of the 15 genes have not been reported in NPC, except PI3CA and TP53. Our study also provided novel information regarding gene alterations during NPC tumorigenesis.

The PI3K/AKT pathway is known to regulating radioresistance in cancer cells [35-37] and experimental animals [38]. p53 is a well-known regulator of radiosensitivity in tumor cells [39,40]. By comparing radioresistant and radiosensitive samples, 5 radiosensitive-specific mutations, AKT1 (COSM33765), PIK3CA (COSM27497, COSM342716), MET (COSM710), TP53 (COSM131026, COSM131025 and COSM131024) and STK11 (COSM21360), were identified, suggesting the genetic alterations of PI3K/AKT and p53 pathways were involved in the response to radiotherapy. However, these mutations should be validated by large sample size cohort. In vitro and in vivo experiments with these mutations may further elucidate the underlying mechanisms.

In summary, the present study has identified several candidate variants in NPC patients that may be used to predict increased radiosensitivity although the sample size was relatively small. Our findings reported may be used to distinguish radiosensitive and radioresistant patients before treatment, thus driving personalized therapeutic strategies and improving the therapy outcome.

Acknowledgements

This work was supported by the grant from the Science and Technology Development Plan Project of Shandong Province (No. 2012G0021822).

Competing Interests

No conflict of interest to declare.

References