Mutational landscape of head and neck squamous cell carcinomas in a South Asian population

Abstract

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer type globally and contributes significantly to burden of disease in South Asia. In Pakistan, HNSCC is among the most commonly diagnosed cancer in males and females. The increasing regional burden of HNSCC along with a unique set of risk factors merited a deeper investigation of the disease at the genomic level. Whole exome sequencing of HNSCC samples and matched normal genomic DNA analysis (n=7) was performed. Significant somatic single nucleotide variants (SNVs) were identified and pathway analysis performed to determine frequently affected signaling pathways. We identified significant, novel recurrent mutations in ASNS (asparagine synthetase) that may affect substrate binding, and variants in driver genes including TP53, PIK3CA, FGFR2, ARID2, MLL3, MYC and ALK. Using the IntOGen platform, we identified MAP kinase, cell cycle, actin cytoskeleton regulation, PI3K-Akt signaling and other pathways in cancer as affected in the samples. This data is the first of its kind from the Pakistani population. The results of this study can guide a better mechanistic understanding of HNSCC in the population, ultimately contributing new, rational therapeutic targets for the treatment of the disease.

Keywords:
Head and neck squamous cell carcinoma (HNSCC); whole exome sequencing; driver mutation; novel mutation; Pakistani population

Introduction

Head and neck squamous cell carcinomas (HNSCC), which include tumours of the oral cavity, oropharynx, hypopharynx and larynx, are the sixth most common cancer worldwide with a global incidence of ~600,000 cases (Jemal et al., 2005, 2011; Hayat et al., 2007; Murar and Forastiere, 2008; Ferlay et al., 2011). In Pakistan, a developing country in South Asia, HNSCC is among the most commonly diagnosed cancers in both males and females (Bhurgri et al., 2006; Masood et al., 2015). The major risk factors for HNSCC include tobacco use, alcohol consumption, and human papilloma virus (HPV) infection (Leemans et al., 2018).

HPV-negative disease accounts for ~80% of the HNSCC cases (Leemans et al., 2011). Unlike developed countries, the incidence of HPV-negative disease has steadily increased in developing countries (Leemans et al., 2011). The increased incidence in both males and females in Pakistan can be attributed to the prevalence of traditional risk factor such as smoking. The use of smokeless tobacco, betel nut, gutka (a preparation of crushed areca nut, tobacco, slaked lime and other flavorings) and betel quid or paan (a preparation of betel leaf, areca nut and occasionally tobacco) along with its related products are additional risk factors in this part of the world (Gupta and Johnson, 2014; Khan et al., 2014; Li et al., 2014).

HNSCC is associated with considerable disease-related mortality and treatment-related morbidity (Forastiere et al., 2001) and is a major public health concern for Pakistan (Bhurgri et al., 2002; Bhurgri 2004, 2005; Warnakulasuriya 2009; Bray et al., 2012) and worldwide. Despite the advances in all the major treatments for HNSCC including surgery, radiotherapy and chemotherapy, the mortality rate is ~50% (Laramore et al., 1992; Leemans et al., 2018). The existing literature focuses primarily on HNSCC in North American and European populations. There is a dearth of information specific for the South Asian population. The unique set of population-specific risk factors, germline variability and molecular heterogeneity of HNSCC demands a thorough molecular profiling of these tumours in this population in order to understand tumour progression, and identify actionable targets for therapy, leading to improved patient care. The aim of the study described here was to identify the global genetic aberrations underlying HPV-negative HNSCC in the South Asian (Pakistani) population.

Materials and Methods

Ethics approval and consent to participate

The Aga Khan University Ethics Review Committee approved the procedures used in collecting and processing of participant material and information (reference #: 1003-Sur/ERC-08). Written informed consent to participate was obtained from all subjects.

Sample collection

Fresh tumour tissue and matched blood were obtained from treatment-naïve patients undergoing surgical resection of HNSCC primary tumour at the Aga Khan University Hospital in Karachi, Pakistan. Patients with confirmed histological diagnosis of HNSCC were included in this study. At the time of resection, fresh tumour tissue away from the necrotic core measuring at least 0.5 cm² was collected and stored in RNAlater^® solution (Thermo Fisher Scientific) at -80 °C till further processing. Formalin-fixed tumour tissue samples were assessed by a histopathologist for tumour content and cellularity based on hematoxylin and eosin (H&E) staining. Seven tumour samples negative for HPV with at least 70% cancer cells and 1 μg (50 ng/μl) of extracted DNA (both tumour as well as genomic DNA) were utilized for whole exome sequencing.

DNA extraction

Genomic and tumour DNA was extracted in-house using TRIzol® Reagent (Invitrogen, USA) according to manufacturer’s instructions. Tumour DNA was extracted from at least 50 mg of tissue and genomic DNA was extracted from 3-5 mL of peripheral blood samples obtained before patients underwent surgical procedure. DNA yield and quality was assessed both in-house using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) and by Macrogen Inc. (Seoul, South Korea) using PicoGreen® Assay (Invitrogen, USA).

Tumour HPV status

Formalin-fixed paraffin embedded (FFPE) tumour blocks were retrieved and DNA was extracted for assessing HPV status. PCR detection was performed using two sets of general HPV primers (GP5/GP6) (Baay et al., 1996; Khan et al., 2007). Additionally, HPV in situ hybridization (ISH) was performed on FFPE blocks using GenPoint assay according to the manufacturer’s instructions (Dako, Denmark). Dako assay can detect HPV-DNA from 13 high-risk genotypes.

Whole Exome Sequencing (WES)

WES was performed by Macrogen Inc. (Seoul, South Korea). 1-2 μg of tumour and genomic DNA was fragmented by nebulization. DNA libraries were prepared from each sample using TruSeq DNA Sample Prep Kit using the manufacturer’s protocol (Illumina, USA). Unique molecular indices were used for each sample. Exome enrichment was performed using the TruSeq Exome Enrichment kit (Illumina, USA). Paired-end sequencing was performed on Illumina HiSeq 2000 instrument. Each read was of 100 bp size.

Availability of data and materials

The data sets supporting the results of this article are included within the article and its supplementary files. The raw sequencing data of those patients that consented to deposition of data in a public database (4 out of 7 total) have been deposited in NCBI’s Sequence Read Archive and are accessible through accession number SRP083063.

Data analysis

Paired-end sequence reads from Illumina were mapped against UCSC Human Genome (hg) 19 using BWA (Li and Durbin 2009). Local realignment was performed using Genome Analysis Tool Kit (GATK) to improve mapping quality (McKenna et al., 2010). Single nucleotide variants (SNVs) were identified in both somatic and germline DNA using MuTect (high-confidence mode) with default settings. Somatic variants were defined as those SNVs which were only identified in the somatic DNA and not seen in germline DNA. Variants marked REJECT were excluded from downstream analysis. Tumour mutational burden was calculated as previously described by others (Chalmers et al., 2017). All mutations were annotated and prioritized using Variant Effect Predictor (VEP) and ANNOVAR. Further characterization of SNVs into missense, nonsense, frameshift, stop loss and stop gain variants was done using wANNOVAR, SNPEFF, SIFT and Polyphen. All somatic missense mutations were analysed for their likely tumourigenic impact based on CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations) (Carter et al., 2009; Wong et al., 2011) and the IntOGen-mutations platform (Gonzalez-Perez et al., 2013). A cut-off score threshold of ≤ 0.2 for FDR with a p-value of ≤ 0.05 was applied. The annotation ranked the SNVs for somatic driver mutations for specific cancer tissue types, predicted protein functional impact, allele frequencies from the 1000 Genomes Project and ESP6500 populations, and previous cancer association of the gene harbouring the variants. CHASM training set is composed of a positive class of driver mutations from the COSMIC database and VEST training set comprising a positive class of disease mutations from the Human Gene Mutation Database 66 and a negative class of variants detected in the ESP6500 population and 1000 Genomes Project cohort with an allele frequency of >1%. SNPeff (Cingolani et al., 2012) and CHASM were used to identify stop-gain, start-loss and splice site variants in nonsynonymous coding region. Those SNVs identified by both tools were selected as significant. Mutations in non-coding regions were annotated using CADD and a cut-off threshold score of ≥15 with p<10^–5 applied to predict benign and deleterious variants (Kircher et al., 2014). Pathway analysis was carried out using the IntOGen-Mutations platform (Gonzalez-Perez et al., 2013) and significantly (p≤0.05) affected pathways in the cohort and genes within identified.

ASNS protein modeling

The homology model of human asparagine synthetase was constructed using crystal structural coordinates of the enzyme from Escherichia coli (PDB id 1CT9). The Modeller program (Fiser and Sali, 2003) was used to build the asparagine synthetase model.

Results

Clinical characteristics and HPV-status of HNSCC patients

Primary tumour samples from 7 treatment-naïve HNSCC patients (Figure S1), along with their matched genomic DNA, were used for this study. The detailed demographics and clinical characteristics of these patients are provided in Table 1. The samples were taken from five male and two female patients, who had an average age at diagnosis of 54 years (SD = 13.24). Two patients reported a family history of cancer; one patient had a personal history of smoking (110 pack years), two of oral tobacco use, one of alcohol and oral tobacco use, and four reported use of betel nut/quid. All samples were negative for human papilloma virus (Figure 1).

Table 1
Clinical characteristics of HNSCC patients. Data that is unavailable is indicated with a dash (-).

Sample ID	Gender	Age at diagnosis	Family history of cancer (type)	Smoking history	Oral tobacco use	Betel nut/quid use	Alcohol use	TNM	Stage	Tumour site
NM-02	M	67	Yes (brain)	Yes	No	-	No	pT4N0M0	IV	Left buccal mucosa
				(110 pack years)
NM-08	M	35	No	No	Yes	Yes	No	pT3N0M0	III	Right buccal mucosa
NM-11	M	57	No	No	No	No	No	pT1N0M0	I	Right tongue
NM-13	F	40	No	No	Yes	Yes	No	pT1N1M0	III	Left tongue
M-11	M	71	Yes (-)	No	Yes	Yes	Yes	pT4N2bM0	IV	Right pyriform fossa
M-12	F	49	No	No	No	Yes	No	T2N2bM0	IV	Lower mandible alveolus
M-14	M	56	No	No	No	No	No	pT3N1M0	III	Right tongue

Figure 1
Human papilloma virus (HPV) detection. PCR (left) for HPV detection using GP5/GP6 primers (expected product ~150bp). HPV in situ hybridization (right) using GenPoint in a representative HPV-negative HNSCC sample at a magnification of 40 x 10X; inset at magnification of 4 x 10X shows control HPV-positive nuclei stained brown.

Summary of exome capture and sequencing results

Paired-end whole exome sequencing (WES) of all seven HNSCC samples and matched genomic DNA was performed on Illumina HiSeq 2000 platform. Each read was of 100 bp size. Additional details of the sequencing, including coverage and depth, are summarized in Table S1. Whole exome sequencing revealed a total of 3,959 single nucleotide variants across all 7 HNSCC samples, of which 2,547 are novel (Figure 2; Table 2, left panel). Nonsynonymous mutation rates ranged from 2.11 to 5.02 mutations per megabase (mean = 3.07) (Table 2, right panel). Several mutations recurred in more than one sample in both coding (Figure 3; Table 3) and non-coding regions (Table S2). Nonsense and splice site variants were also identified in all samples (Table S3).

Figure 2
Mutational landscape of HNSCC tumours. Left panel: Number of mutations (known and novel) in HNSCC patients Middle panel: significant somatic nucleotide variants (synonymous, nonsynonymous missense) Right panel: Rate of synonymous, nonsynonymous and other (3’ UTR, 3’ flank, 5’ UTR, 5’ flank, intron, splice site) mutations expressed in mutations per megabase of covered target sequence.

Figure 3
Somatic coding single nucleotide variants (SNV) found in ≥ 2 HNSCC patients and dbSNP database. The variant allele frequency (VAF) on the x-axis indicates the proportion of reads with the variant allele within individual samples.

Table 2
Number of somatic single nucleotide variants (SNVs) in HNSCC patients; total and (novel).

Sample ID	NM-02	NM-08	NM-11	NM-13	M-11	M-12	M-14
Nonsynonymous
Missense	151 (106)	91 (55)	145 (111)	122 (90)	221 (61)	101 (67)	95 (65)
Nonsense	14 (11)	4 (4)	7 (6)	1 (1)	5 (4)	4 (4)	7 (5)
Synonymous	85 (49)	35 (14)	91 (61)	69 (35)	196 (30)	93 (45)	52 (20)
3’ UTR	199 (179)	131 (117)	176 (155)	156 (145)	477 (138)	206 (193)	200 (185)
3’ Flank	34 (32)	23 (20)	52 (49)	24 (23)	78 (25)	35 (31)	40 (37)
5’ UTR	22 (21)	14 (10)	15 (10)	14 (10)	32 (11)	17 (15)	17 (15)
5’ Flank	8 (4)	6 (5)	24 (18)	9 (8)	23 (8)	9 (6)	5 (5)
Intron	33 (32)	23 (17)	74 (56)	35 (33)	79 (20)	29 (26)	34 (30)
Splice site	4 (3)	2 (1)	2 (2)	2 (2)	1 (1)	3 (3)	3 (2)
Total (novel)	550 (437)	329 (243)	586 (468)	432 (347)	1112 (298)	497 (390)	453 (364)

Table 3
Somatic coding single nucleotide variants (SNVs) found in ≥2 HNSCC patients. NS: nonsynonymous; S: synonymous. The variant allele frequency (VAF) indicates the proportion of reads with the variant allele within individual samples.

Gene	Chr	Position	Base change	Amino acid change	Variant type	Variant Allele Frequency (VAF)							Freq	COSMIC ID	rsID
						NM02	NM08	NM11	NM13	M11	M12	M14
CLMN	chr14	95669509	A>G	L726P	NS	0.128	0.118	-	0.145	-	0.079	0.111	5/7	COSM1293528	-
CHEK2	chr22	29091840	T>C	K152E	NS	0.158	-	-	-	0.1	-	0.211	3/7	COSM42871	rs142470496
SFTPA1	chr10	81373600	G>A	G160S	NS	0.179	-	-	-	0.195	-	-	2/7	-	rs368889920
DRD5	chr4	9784542	A>C	T297P	NS	-	0.429	-	-	-	-	0.5	2/7	COSM1431796	rs2227851
NT5C3A	chr7	33054388	T>C	D283G	NS	0.3	-	-	-	-	-	0.333	2/7	COSM222478	rs79747830
KRTAP13-3	chr21	31797833	C>T	R133K	NS	-	-	0.167	-	-	-	0.089	2/7	-	-
PAK2	chr3	196509577	C>G	Q101H	NS	-	0.8	-	0.086	-	-	-	2/7	COSM1422035	rs201465227
ST6GALNAC4	chr9	130674582	G>A	-	S	0.294	-	-	0.1	0.222	0.286	0.436	5/7	-	rs148599736
PPA1	chr10	71969413	A>G	-	S	0.118	0.143	-	0.109	0.108	-	0.16	5/7	-	-
KRT83	chr12	52709871	G>A	-	S	0.173	-	-	0.136	0.147	0.176	0.375	5/7	-	-
MYLK	chr3	123419183	G>A	-	S	0.104	0.17	-	0.122	-	0.067	0.064	5/7	-	rs58176285
OR8I2	chr11	55861593	G>A	-	S	0.393	0.221	-	0.239	-	0.218	0.261	5/7	-	-
HIST1H2BL	chr6	27775319	G>A	-	S	0.078	-	-	0.077	0.086	-	0.132	4/7	-	rs141178835
ST6GALNAC4	chr9	130674558	C>T	-	S	-	0.3	-		0.182	0.279	0.488	4/7	-	-
PPA1	chr10	71969401	T>C	-	S	0.111	0.143	-	0.106	0.125	-	-	4/7	-	rs150430650
KRT83	chr12	52709724	A>G	-	S	0.101	0.097	-	-	0.092	0.093	-	4/7	-	-
KRT83	chr12	52709895	G>A	-	S	0.24	-	-	-	0.192	0.245	0.429	4/7	-	-
OR1M1	chr19	9204157	T>C	-	S	0.129	-	-	0.071	0.176	0.231	-	4/7	-	-
OR1M1	chr19	9204184	G>A	-	S	0.325	-	-	0.113	0.089	0.296	-	4/7	-	-
HHIPL2	chr1	222715425	A>G	-	S	0.244	-	-	0.17	-	0.17	0.111	4/7	-	-
MYLK	chr3	123419189	C>T	-	S	0.132	0.163	-	0.111	-	-	0.065	4/7	-	-
ASNS	chr7	97498451	C>G	-	S	0.16	-	-	-	0.219	0.096	-	3/7	-	rs76996735
IFITM1	chr11	315009	C>T	-	S	0.32	-	-	-	0.316	0.093	-	3/7	-	rs3197137
KRT83	chr12	52709883	T>C	-	S	-	-	-	0.161	0.167	-	0.375	3/7	-	rs2248473
OR7D4	chr19	9324989	C>T	-	S	0.088	-	-	-	0.089	0.057	-	3/7	-	rs111293642
OR7D4	chr19	9324995	C>T	-	S	0.068	-	-	-	0.072	0.05	-	3/7	-	rs201732443
CHEK2	chr22	29091841	G>A	-	S	0.167	-	-	-	0.1	-	0.222	3/7	-	rs146546850
OR10G7	chr11	123908827	T>C	-	S	0.17	-	-	0.074	-	0.069	-	3/7	-	-
KRT86	chr12	52699041	G>A	-	S	-	-	-	0.141	-	0.127	0.114	3/7	-	-
KRT86	chr12	52699545	G>A	-	S	-	-	-	0.103	-	0.127	0.19	3/7	-	rs374471358
KRT83	chr12	52710279	T>C	-	S	0.118	-	-	-	-	0.137	0.267	3/7	-	rs202206430
KRTAP4-8	chr17	39254154	C>T	-	S	0.167	-	-	0.173	-	0.102	-	3/7	-	-
DHX40	chr17	57663568	A>G	-	S	-	-	0.104	0.065	-	-	0.062	3/7	-	rs2697395
LCE1E	chr1	152759892	A>T	-	S	-	0.25	-	-	0.263	-	-	2/7	-	-
FOLH1	chr11	49204790	A>G	-	S	0.292	-	-	-	0.139	-	-	2/7	-	rs76509850
KRT81	chr12	52681089	G>A	-	S	-	-	-	0.167	0.214	-	-	2/7	-	-
KRT81	chr12	52681092	C>T	-	S	-	-	-	0.167	0.214	-	-	2/7	-	-
KRT83	chr12	52710790	T>C	-	S	0.216	-	-	-	-	0.25	-	2/7	-	rs143202217
KRT83	chr12	52714757	T>C	-	S	-	-	-	0.082	-	0.227	-	2/7	-	-
KRT83	chr12	52710798	T>C	-	S	0.235	-	-	-	-	0.268	-	2/7	-	-
KRT83	chr12	52713122	C>T	-	S	-	-	-	0.179	-	0.113	-	2/7	-	-
SEH1L	chr18	12955467	T>C	-	S	-	-	-	-	0.086	0.128	-	2/7	-	-
KRTAP10-7	chr21	46020536	T>C	-	S	0.143	-	-	-	0.118	-	-	2/7	-	rs512211
KRTAP10-7	chr21	46020542	T>C	-	S	0.13	-	-	-	0.118	-	-	2/7	-	rs512214
HIST2H2AC	chr1	149858563	C>T	-	S	-	-	-	-	-	0.096	0.093	2/7	-	-
HIST2H2AC	chr1	149858593	C>T	-	S	-	-	-	-	-	0.125	0.071	2/7	-	-
KIAA1549	chr7	138601891	A>T	-	S	0.063	-	-	-	-	0.098	-	2/7	-	-
GIMAP5	chr7	150439323	A>G	-	S	-	-	-	-	-	0.115	0.278	2/7	-	-
KRTAP5-11	chr11	71293458	G>A	-	S	-	-	-	0.093	-	0.095	-	2/7	-	-
OR10G8	chr11	123901199	G>A	-	S	-	-	-	0.118	-	0.074	-	2/7	-	-
OR10G8	chr11	123901211	G>A	-	S	-	-	-	0.136	-	0.12	-	2/7	-	-
DUOX1	chr15	45433188	T>C	-	S	0.071	-	-	-	-	0.081	-	2/7	-	-
KRTAP4-11	chr17	39274373	G>A	-	S	-	-	-	-	-	0.049	0.111	2/7	-	-
KRTAP4-12	chr17	39280045	G>A	-	S	-	-	-	0.14	-	0.145	-	2/7	-	-
KRTAP10-4	chr21	45994676	A>G	-	S	0.103	-	-	-	-	0.065	-	2/7	-	-
HIST2H2AB	chr1	149859383	C>T	-	S	-	-	-	0.118	-	-	0.071	2/7	-	-
DRD5	chr4	9784550	G>A	-	S	0.375	-	-	-	-	-	0.5	2/7	-	rs2227844
KRTAP5-5	chr11	1651760	C>T	-	S	0.11	-	-	-	-	-	0.077	2/7	-	rs183750160
DPY19L2	chr12	63964600	G>A	-	S	-	-	-	0.103	-	-	0.105	2/7	-	-
SETD8	chr12	123875311	C>T	-	S	0.385	-	-	0.171	-	-	-	2/7	-	rs74356260
POTEE	chr2	132021452	C>A	-	S	-	0.176	-	0.094	-	-	-	2/7	-	-
CBWD1	chr9	163985	A>G	-	S	-	0.065	0.062	-	-	-	-	2/7	-	-
ZNF814	chr19	58385762	C>G	-	S	-	-	0.263	0.353	-	-	-	2/7	-	rs199732634

Mutational landscape in HNSCC patients

On average, 227 coding mutations were identified per tumour, 39% of which are synonymous. The majority of the mutations identified were nonsynonymous missense mutations and mutations in the 3’ UTR region (Table 2). Filtering for driver and other significant variants using CHASM revealed alterations in genes that have been implicated in HNSCC or other cancers (Figure 2, middle panel; Table 4). Driver missense mutations in FGFR2 (Fibroblast Growth Factor Receptor 2), SETBP1 (SET Binding Protein 1), PIK3CA (Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha), IGF2BP3 (Insulin Like Growth Factor 2 MRNA Binding Protein 3), TP53 (Tumour Protein P53), PTPN11 (Protein Tyrosine Phosphatase, Non-Receptor Type 11) and NF2 (Neurofibromin 2) were identified. Significant missense mutations were also identified in ASNS (Asparagine Synthetase (Glutamine-Hydrolyzing)) in four of the seven samples. Other genes that exhibited recurrent mutations included the CLMN (Calmin) gene (5/7), CHEK2 (Checkpoint Kinase 2) (3/7), and DRD5 (Dopamine Receptor D5) and PAK2 (P21 (RAC1) Activated Kinase 2) (2/7) (Table 3). These recurrent mutation sites have not been reported as hotspots in previous HNSCC sequencing studies.

Table 4
Somatic single nucleotide variants (SNVs) in HNSCC patients in coding regions. NS MS: nonsynonymous missense; S: synonymous. Driver missense variants are in bold text and synonymous variants in possible driver genes are marked with an asterisk (*).The variant allele frequency (VAF) indicates the proportion of reads with the variant allele within individual samples. The minor allele frequency (MAF) signifies prevalence of the known variants in the global population as per the ExAc dataset.

Sample ID	Gene	Chr	Position	Variant type	Base change	Amino acid change	Variant allele frequency	Minor allele frequency	rsID	COSMIC ID
NM-02	FGFR2	chr10	123256128	NS MS	G>T	P595H	0.273	-	-	-
	SETBP1	chr18	42530740	NS MS	G>T	G479C	0.13	-	-	-
	ASNS	chr7	97498395	NS MS	G>A	A25V	0.225	-	-	-
		chr7	97498404	NS MS	A>G	M22T	0.243	-	-	-
	KIF21B	chr1	200954042	NS MS	G-T	R1250S	0.143	-	-	-
	UBA7	chr3	49848502	NS MS	G>T	P382H	0.132	-	-	-
	KDM3B	chr5	137735569	NS MS	G>T	A1023S	0.158	-	-	-
	EXOSC1	chr10	99196233	NS MS	G>T	A186D	0.3	-	-	-
	DENND5A	chr11	9166573	NS MS	C>A	V1031F	0.273	-	-	-
	DGKZ	chr11	46394214	NS MS	G>T	G541V	0.5	-	-
	FOLH1	chr11	49186320	NS MS	G>C	N459K	0.227	0.00003	rs201724751	-
		chr11	49204779	NS MS	C>T	R281H	0.3	0.0351	rs116795343	-
	FAT3	chr11	92087697	NS MS	G>T	G807C	0.211	-	-	-
	SLC7A7	chr14	23282391	NS MS	G>T	L73M	1	-	-	-
	CEP128	chr14	81244269	NS MS	A>T	L778Q	0.174	-	-	-
	EML2	chr19	46130008	NS MS	C>A	W433C	0.3	-	-	-
	CHRNA4	chr20	61981122	NS MS	C>A	K547N	0.375	-	-	-
	GART	chr21	34889834	NS MS	C>T	R595Q	0.2	0.0003	rs202015633	-
	CHEK2	chr22	29091840	NS MS	T>C	K416E	0.158	0.0259	rs74751600	-
	ARID2*	chr12	46245344	S	G>T	S1146S	0.333	-	-	-
NM-08	ASNS	chr7	97498378	NS MS	C>T	A31T	0.167	-	-	-
	ZFHX4	chr8	77618158	NS MS	G>T	G612V	0.231	-	-	COSM73358
	CKAP5	chr11	46819413	NS MS	C>G	C427S	0.12	-	-	-
	SF1	chr11	64537028	NS MS	C>A	R303L	0.097	-	-	-
	HECTD4	chr12	112669460	NS MS	C>G	K1885N	0.158	-	-	-
	FBN1	chr15	48744840	NS MS	C>T	A1822T	0.273	0.00003	rs777539060	-
	HELZ	chr17	65144830	NS MS	G>T	L826I	0.2	-	-	-
NM-11	RALGPS2	chr1	178855145	NS MS	C>T	T361M	0.125	-	-	-
	DYNC1I2	chr2	172584439	NS MS	C>A	P369T	0.125	-	-	-
	NFE2L2	chr2	178098966	NS MS	C>A	D27Y	0.188	-	-	-
	POSTN	chr13	38154051	NS MS	G>T	P536Q	0.111	-	-	-
	INO80	chr15	41377611	NS MS	G>A	R277C	0.158	-	-	-
	CDH16	chr16	66949240	NS MS	G>T	P156T	0.364	-	-	-
	PRPSAP2	chr17	18785908	NS MS	T>C	L147S	0.098	-	-	-
NM-13	ASNS	chr7	97498378	NS MS	C>T	A31T	0.125	-	-	-
	ASNS	chr7	97498395	NS MS	G>A	A25V	0.105	-	-	-
	GIGYF2	chr2	233684687	NS MS	C>T	R862C	0.138	0.00002	rs561616045	-
	CBLB	chr3	105464767	NS MS	G>T	P280H	0.097	-	-	-
	LAP3	chr4	17598708	NS MS	C>A	A343D	0.214	-	-	-
	TRIM7	chr5	180625732	NS MS	G>A	L316F	0.156	-	-	-
	ABCB8	chr7	150733032	NS MS	G>A	A331T	0.227	0.00004	rs777741819	-
	ESRP1	chr8	95674755	NS MS	G>C	V206L	0.079	-	-	-
	ADCY6	chr12	49176793	NS MS	C>A	R142L	0.375	-	-	-
	NFATC4	chr14	24843541	NS MS	C>T	S581L	0.25	-	-	COSM3793625
	EML5	chr14	89124732	NS MS	C>A	G1226W	0.143	-	-	-
	ANKFY1	chr17	4086708	NS MS	G>T	A688E	0.176	-	-	-
	UQCRFS1	chr19	29698630	NS MS	C>A	C217F	0.25	-	-	-
	ITSN1	chr21	35237479	NS MS	G>T	M1305I	0.667	-	-	-
	ABCB7	chrX	74332770	NS MS	C>G	C96S	0.111	-	-	-
	HDX	chrX	83730396	NS MS	G>C	R4G	0.231	-	-	-
M-11	PIK3CA	chr3	178936091	NS MS	G>A	E545K	0.278	0.000008	rs104886003	COSM763
	IGF2BP3	chr7	23353160	NS MS	A>G	I503T	0.211	0.0040	rs79900450	-
	TP53	chr17	7577106	NS MS	G>C	P278A	0.647	-	-	COSM10814
	SLC8A1	chr2	40656504	NS MS	C>T	G306D	0.234	-	-	-
	MITF	chr3	70008494	NS MS	C>A	Q362K	0.333	-	-	-
	VEPH1	chr3	157034861	NS MS	A>G	L622P	0.139	-	-	-
		chr3	157099046	NS MS	C>G	L342F	0.208	-	-	-
	GNGT1	chr7	93536114	NS MS	T>C	V19A	0.133	-	-	-
	ARHGEF10	chr8	1824752	NS MS	A>G	D232G	0.214	-	-	-
	NEBL	chr10	21098782	NS MS	T>A	D855V	0.339	-	-	-
	NAALAD2	chr11	89891404	NS MS	A>C	L296F	0.375	-	-	-
	SMG8	chr17	57290439	NS MS	A>T	H752L	0.25	-	-
	RBM39	chr20	34302295	NS MS	C>A	C303F	0.15	-	-	-
M-12	ASNS	chr7	97498378	NS MS	C>T	A31T	0.214	-	-	-
	GRK7	chr3	141499490	NS MS	A>C	Y296S	0.273	-	-	-
	TIPARP	chr3	156413805	NS MS	C>A	P413Q	0.121	-	-	-
	RGS3	chr9	116346401	NS MS	C>A	S903R	1	-	-	-
	SPTBN2	chr11	66472616	NS MS	C>A	G711C	1	-	-	-
	UBE4A	chr11	118253450	NS MS	C>A	A726E	0.15	-	-	-
	TP53	chr17	7577538	NS MS	C>T	R248Q	0.286	0.00006	rs11540652	COSM10662
	CDC27	chr17	45229257	NS MS	T>C	T335A	0.167	0.00002	rs199890121	-
	HELZ	chr17	65163619	NS MS	C>A	C575F	0.667	-	-	-
	ALK*	chr2	29474099	S	C>A	G692G	1	-	-	-
M-14	PTPN11	chr12	112892407	NS MS	T>G	S189A	0.167	0.0027	rs79068130	-
	NF2	chr22	30090766	NS MS	G>T	R588L	1	-	-	-
	MLL3*	chr7	151962176	S	T>A	P377P	0.084	0.4554	rs62478356	COSM4162022
	MYC*	chr8	128750817	S	C>A	T118T	0.5	-	-	-

Synonymous variants in previously identified driver genes ARID2 (AT-Rich Interaction Domain 2), ALK (Anaplastic Lymphoma Receptor Tyrosine Kinase), MLL3 [Myeloid/Lymphoid Or Mixed-Lineage Leukemia 3, also known as KMT2C (Lysine Methyltransferase 2C)] and MYC (V-Myc Avian Myelocytomatosis Viral Oncogene Homolog), were also identified (Figure 2, middle panel; Table 4). The ASNS gene was found to have a synonymous mutation in three samples, and recurrent synonymous mutations were also observed in CHEK2 and DRD5 genes (Table 3). Splice site variants in FCGR2A (Fc Fragment of IgG, Low Affinity IIa, Receptor (CD32)) and two genes involved in eukaryotic translation initiation [EIF4B (Eukaryotic Translation Initiation Factor 4B) and EIF4A3 (Eukaryotic Translation Initiation Factor 4A3)] were seen in two of the seven samples (Table S3). Significant non-coding mutations were filtered using CADD (Table S4). In the 3’UTR region, mutations in IGF1R (Insulin Like Growth Factor 1 Receptor) and ERBB4 (Erb-B2 Receptor Tyrosine Kinase 4) were identified as significant. Another eukaryotic translation initiation factor, EIF2B4 (Eukaryotic Translation Initiation Factor 2B Subunit Delta), exhibited significant splice site variance. IntOGen pathway analysis revealed that the MAP kinase pathway was the most significantly affected pathway in all samples tested. In addition, cell cycle, actin cytoskeleton regulation, PI3K-Akt signaling and other pathways in cancer were among those significantly enriched for exomic alterations in all samples (Table 5). Genes with driver mutations implicated in multiple pathways included FGFR2, PIK3CA, and TP53. Significant mutations in the pathway genes were all deleterious with respect to protein function as predicted by SIFT and PolyPhen.

Table 5
Significantly involved pathways (p ≤ 0.05) identified by IntOGen-Mutations platform. Driver mutations in each pathway are in bold text and marked with an asterisk (*).

Pathway ID	KEGG annotation	Total genes in pathway	Number of genes affected	Pathway genes with significant/ driver (*) mutations
hsa04010	MAPK signaling pathway	257	46	FGFR2*
				PIK3CA*
				TP53*
hsa04110	Cell cycle	124	35	TP53*
				CHEK2
				CDC27
hsa05166	HTLV-1 infection	260	60	PIK3CA*
				TP53*
				CHEK2
				CDC27
				ADCY6
				NFATC4
hsa05200	Pathways in cancer	326	71	FGFR2*
				PIK3CA*
				TP53*
				CBLB
				ADCY6
				ADCY6
				GNGT1
				MITF
hsa04810	Regulation of actin cytoskeleton	213	46	FGFR2*
				PIK3CA*
hsa04151	PI3K-Akt signaling pathway	338	80	FGFR2
				PIK3CA*
				TP53*
				GNGT1

Asparagine synthetase protein modeling

The ASNS gene codes for asparagine synthetase, which catalyzes the formation of asparagine from glutamine, aspartate and ATP. Protein modeling of the effect of the three novel, recurrent mutations in ASNS identified in this cohort revealed that the mutated amino acids (p.A13T, p.A25V and p.M22T) are located in the vicinity (within 10 Å distance) of the glutamine binding pocket (Figure 4).

Figure 4
Homology model of the N-terminal domain of human asparagine synthetase (ASNS) complexed with glutamine (Gln). Amino acid changes due to nonsynonymous mutations in ASNS are indicated.

Discussion

This is the first study reported in the literature to describe the mutational landscape of Pakistani HNSCC patients. We performed exome sequencing of a small set of HPV-negative HNSCC patients from Pakistan. We identified a total of ~4000 somatic variants (novel and known). Previous studies have reported greater number of mutations in HPV-negative as compared to HPV-positive HNSCC tumours (Riaz et al., 2014; Beck and Golemis, 2016). As a comparison, Stransky et al. (2011) on average found 130 coding mutations per tumour (25% synonymous), while in the current cohort an average of 227 coding mutations per tumour (39% synonymous) were identified.

Several variants were found in more than one sample and in genes that have been previously identified to play a role in HNSCC carcinogenesis. Next generation sequencing studies in other populations have identified mutations in the tumour suppressor gene TP53, which is associated with smoking-related disease, and the oncogene PIK3CA, at a mutation rate of 40-60% and 6-8%, respectively (Agrawal et al., 2011; Stransky et al., 2011; Loyo et al., 2013). The TCGA study, with the largest cohort to date, reported a TP53 mutation rate of 72% and PIK3CA mutation rate of 18-21% (TCGA 2015). Mutations in TP53 gene were detected in two of the seven cases in the current study, and in PIK3CA in one patient. In a comparative genomic analysis of HPV-positive and HPV-negative tumours, the former showed mutations in FGFR2 and MLL3, among others. The mutational spectrum in HPV-negative tumours closely resembled lung and esophageal squamous cell carcinomas, with mutations identified in genes including TP53, MLL2/3, NOTCH1, PIK3CA and DDR2 (Seiwert et al., 2015). The HPV-negative cohort in the current study exhibited a nonsense variant (p.Y223X) in DDR2 in a single sample. A different nonsense mutation (p.R709X) and missense mutations (p.I474M; p.I724M) have been previously identified exclusively in HNSCC recurrences (Hedberg et al., 2015). DDR2 and FGFR2, which was identified as having a potential missense driver mutation in one sample in the current study, are both genes that code for receptor tyrosine kinases and are potentially targetable for therapeutics. In addition, an SNV was identified in MLL3 in a sample that also exhibited an SNV in the driver gene MYC. MLL genes encode histone lysine methyltransferases that are involved in chromatin remodeling. Recurrent mutations in MLL genes have been identified in several other cancers, including lung squamous cell carcinoma, and been associated with poor clinical outcomes (Morin et al., 2011; Grasso et al., 2012; Jones et al., 2012; Kim et al., 2014; Seiwert et al., 2015). The oncogene MYC is most often altered in HPV-negative HNSCC tumours (TCGA 2015).

Additionally, we discovered recurrent significant missense mutations in ASNS (asparagine synthetase) gene in 4 out of 7 samples. These SNVs in ASNS have not previously been reported in the literature as significant in HNSCC pathogenesis. The ASNS gene codes for a ubiquitously expressed, ATP-dependent enzyme that converts aspartate and glutamine to asparagine and glutamate (Balasubramanian et al., 2013). The protein folds into two distinct domains, where the N-terminal domain contains two layers of antiparallel beta-sheets. The active site responsible for the binding and hydrolysis of glutamine is situated between these layers and important, evolutionarily conserved side chains involved in glutamine binding within the substrate binding pocket include Arg 49, Asn 74, Glu 76, and Asp 98 (Van Heeke and Schuster, 1989). While the amino acids mutated as a result of the novel and recurrent mutations in ASNS identified in this cohort are not part of the glutamine binding pocket, protein modeling revealed their proximity to the region. Therefore, these mutations may affect glutamine binding during catalysis. Elevated levels of ASNS play a role in drug resistance in acute lymphoblastic leukemias and have been implicated in solid tumour adaptation to nutrient deprivation and hypoxia (Balasubramanian et al., 2013). ASNS expression has also been shown to be an independent factor affecting survival in hepatocellular carcinoma and low ASNS levels are correlated with poorer surgical outcomes (Zhang et al., 2013). In HNSCC, deregulation of miR-183-5p and its target gene ASNS has been documented in a radiochemotherapy cell culture model of primary HNSCC cells and is a potential prognostic marker for radiochemotherapy outcome (Summerer et al., 2015). Two recent reports have further elucidated the role of ASNS in carcinogenesis. One showed that ASNS expression in primary tumours is correlated with metastatic relapse and bioavailability of asparagine regulates metastatic potential and progression in breast cancer cells, potentially by affecting the epithelial-to-mesenchymal transition (Knott et al., 2018). ASNS was also identified as a key target of the KRAS-ATF4 axis in non-small-cell lung cancer. Oncogenic KRAS regulates amino acid homeostasis and cellular response to nutrient stress via the ATF4 target ASNS, which subsequently contributes to inhibition of apoptosis and increase in proliferation of cancer cells (Gwinn et al., 2018). While KRAS mutations are uncommon in HNSCC, particularly as compared to HRAS (Rothenberg and Ellisen, 2012), mutations in ASNS could effectively have the same functional consequences. Given the role of ASNS in cellular stress and the unfolded protein response, it is an intriguing target for further study in HNSCC pathogenesis.

The current analysis also revealed significant low-frequency driver mutations in SETBP1, IGF2BP3, PTPN11 and NF2. SETBP1 was identified in a patient who also had a driver mutation in FGFR2. SETBP1 encodes a nuclear protein and its overexpression results in inhibition of the tumour-suppressor PP2A serine-threonine phosphatase activity (Cristobal et al., 2010). Mutations in SETBP1 resulting in overexpression or gain of function have been documented previously in hematological malignancies (Ciccone et al., 2015).

An IGF2BP3 mutation was found in a sample that also had driver mutations in PIK3CA and TP53. The protein product of IGF2BP3 is an RNA-binding factor that promotes cancer invasion by binding to transcripts that encode proteins, such as CD44, for functions related to cell migration, proliferation and adhesion (Ennajdaoui et al., 2016). IGF2BP3 mutations and copy number variations have been reported previously in HNSCC (Lin et al., 2011; Clauditz et al., 2013; Jimenez et al., 2015), and its role in cell invasiveness and metastasis in several other cancers has been documented in the literature (Schaeffer et al., 2010; Lin et al., 2011; Taniuchi et al., 2014; Hsu et al., 2015; Shantha Kumara et al., 2015; Belharazem et al., 2016; de Lint et al., 2016; Ennajdaoui et al., 2016).

Mutations in PTPN11 and NF2 genes were found in the same sample. The protein encoded by the proto-oncogene PTPN11 is a cytoplasmic tyrosine phosphatase, which is widely expressed in most tissues and known to play a regulatory role in normal hematopoiesis, and in mitogenic activation, metabolic control, transcription regulation, and cell migration signaling pathways (Chan and Feng, 2007). Somatic PTPN11 mutations have been detected in juvenile myelomonocytic leukemia, myelodysplastic syndromes and acute myeloid leukemia (Tartaglia et al., 2003; Chan and Feng, 2007). While PTPN11 mutations have not been reported previously in HNSCC, this gene has been identified as a target of the tumour-suppressive microRNA miR-489. Knockdown of PTPN11 in HNSCC cell lines resulted in the inhibition of cell proliferation (Kikkawa et al., 2010). Neurofibromatosis type 2 (NF2) is a tumour suppressor gene on chromosome 22q12 that encodes for merlin, a membrane-cytoskeleton scaffolding protein that inhibits key signaling pathways crucial to cell proliferation, such as the PI3K pathway. Somatic NF2 mutations have been reported in a number of different cancers (Schroeder et al., 2014). In HNSCC, chromosome 22q is a frequent site of allele loss. Merlin and the cytoplasmic tail of CD44, which is regulated at the transcript level by IGF2BP3 gene product as mentioned above, create a molecular switch complex that is responsible for either cell growth or proliferation (Morrison et al., 2001).

In addition to non-synonymous mutations, synonymous mutations are known to frequently act as driver mutations in cancers (Supek et al., 2014). We identified SNVs in MLL3, ARID2 and ALK. Mutations in MLL and ARID gene families have been previously documented for HNSCC (India Project Team of the International Cancer Genome Consortium, 2013; Martin et al., 2014). The ALK gene encodes yet another receptor tyrosine kinase, which has been found to be aberrantly expressed in several tumours, including anaplastic large cell lymphomas (Chiarle et al., 2008; Salaverria et al., 2008), neuroblastoma (Lasorsa et al., 2016; Theruvath et al., 2016; Ueda et al., 2016) and non-small cell lung cancer (Soda et al., 2007; Quere et al., 2016).

A study of gingivo-buccal oral squamous cell carcinoma (OSCC-GB), an HNSCC clinical sub-type, in the Indian population revealed frequently altered genes that are specific to OSCC-GB and others that are also affected in HNSCC (India Project Team of the International Cancer Genome Consortium, 2013). Altered genes that are common between the OSCC-GB study and the current study in the Pakistani population are ARID2 and TP53. MLL family member MLL4 was also identified as a frequently altered gene specific to OSCC-GB. Other genes identified in the study in the Indian population, such as CASP8, HRAS and NOTCH1, are also altered in HNSCC in other populations (albeit at different frequencies and with varying significance) (Agrawal et al., 2011; Stransky et al., 2011; Seiwert et al., 2015; The Cancer Genome Atylas Network, 2015; Al-Hebshi et al., 2016), but were not identified in this study.

The small sample size is a limitation of this study, which may explain low frequency of commonly mutated genes and why some of the commonly occurring HNSCC mutations such as NOTCH1 and HRAS were not identified in this small cohort. However, given limited resources, it was deemed important to establish preliminary data prior to a larger scale study. The approach of using a smaller discovery cohort followed by validation of identified mutations in a larger cohort has been proposed and taken by others and reported in the literature (Bacchetti et al., 2011; Nichols et al., 2012; Romero Arenas et al., 2014; Hedberg et al., 2015). It is also possible that given the heterogeneous nature of this disease and unique set of risk factors compared to Western countries, the predominant driver gene mutations may vary among populations. Previous studies in East and South Asian populations with oral squamous cell carcinoma have highlighted that the pattern of genetic mutations is significantly different from tumour profiles in other studies largely conducted in Caucasian populations (Vettore et al., 2015; Su et al., 2017). Population-based differences in mutational profile have also been documented for other cancer types. In lung cancers, several studies have highlighted the geographic variations in genes such as EGFR and LKBI between Asian (Chinese, Japanese, Korean) and Caucasian populations (Koivunen et al., 2008; Mitsudomi, 2014; Li et al., 2015).

This is the first report describing the mutational spectrum of Pakistani HNSCC patients. In addition to reporting known HNSCC mutations, we have identified novel, recurrent mutations in ASNS and other genes in the Pakistani population. It has been well established that a complex interplay of genetic and environmental factors results in varying risk of cancer development and treatment outcomes across different ethnicities and geographic regions (Ma et al., 2010). Such diversity among different populations can be explained by the type and frequency of variations in both germline and somatic genomes (Wang and Wheeler, 2014). Therefore, this study is an important step towards gaining a better mechanistic understanding of the complex nature of HNSCC. Future studies will be undertaken to confirm and validate the findings from this study in a larger cohort. Additionally, functional analysis of mutations and correlation with clinical outcomes will be performed.

Acknowledgments

This work was supported by the Higher Education Commission of Pakistan (20-1224-R&D/2009 to K.G. and M.J.K). The funding source had no involvement in the study design and conduct or preparation of the article. The authors gratefully acknowledge Aisha Nazir for her role in pre-sequencing sample processing, Muhammed Murtaza and Faiz Gani for their input and support in the initial stages of this project, and Faizan Saleem for his help with data analysis. HPV primers (GP5/GP6) were graciously provided by Dr SH Ali (then at Aga Khan University, Pakistan).

Conflict of Interest

The authors have no conflicts to declare.

Author contributions

KG was involved in conceiving and designing the study, data analysis and interpretation, and drafted the manuscript; SSR conceived and designed the study, processed the samples prior to sequencing, analyzed and interpreted the data and contributed in writing the manuscript; SAR and MKA were involved in data analysis and interpretation, and contributed to the manuscript; SM contributed to data acquisition and manuscript writing; RA performed the histopathological examination of HNSCC samples and contributed to the manuscript; MJK was involved in conceiving and designing the study and was responsible for sample resection and acquisition. All authors read and approved the final version of the manuscript.

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Supplementary material

The following online material is available for this article

Figure S1 HNSCC samples.

Table S1 Whole exome sequencing data summary.

Table S2 Somatic non-coding single nucleotide variants (SNVs) found in ≥ 2 HNSCC patients.

Table S3 Genes with somatic mutations in HNSCC patients resulting in nonsense or splice site variants.

Table S4 Significant single nucleotide variant (SNV) mutations in HNSCC patients in non-coding regions.

Associate Editor: Ricardo G. Correa

Publication Dates

• Publication in this collection
  14 Nov 2019
• Date of issue
  Jul-Sep 2019

History

• Received
  10 Jan 2018
• Accepted
  28 Nov 2018