• Users Online: 1131
  • Print this page
  • Email this page

Table of Contents
Year : 2022  |  Volume : 2  |  Issue : 1  |  Page : 25-32

Multigene profiling to identify clinically relevant actionable mutations in head and neck cancers: An Indian study

1 Centre for Academic Research, Health Care Global Cancer Centre, Bengaluru, Karnataka, India
2 Strand Life Sciences, HealthCare Global (HCG) Cancer Centre, Bengaluru, Karnataka, India

Date of Submission18-Sep-2021
Date of Decision08-Mar-2022
Date of Acceptance11-Mar-2022
Date of Web Publication03-May-2022

Correspondence Address:
Dr. Shalini Thakur
Centre for Academic Research, HealthCare Global Cancer Centre, Bengaluru, Karnataka
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpo.jpo_3_22

Rights and Permissions

Background: Head and neck squamous cell carcinoma (HNSCC) represents approximately 5%–10% of malignancies worldwide. The most appropriate treatment approach for HNSCC varies with the disease stage and disease site in the head and neck (H&N). Radiotherapy (RT) combined with chemotherapy has become the standard of care for patients having locally advanced tumors. However, there is significant morbidity associated with these treatments, and recurrent or metastatic diseases will occur in 50%–60% of patients. Moreover, the detection of residual viable tumor at the end of therapy remains an important issue. It is therefore an unmet need to improve the outcome of therapy by identifying predictive (prognostic) indicators at the molecular level and radioresistance that will enable the clinicians to select the logical treatment modality.
Materials and Methods: Fifty H&N cancer patients aged 27 to 85 years diagnosed at HCG between April 2015 and 2017 were screened using Illumina's TSCAP panel and MiSeq technology for hotspot mutations in 48 cancer-related genes. All the cases had histopathological reviews and comprised tumors from the following sites – oral, nasopharynx, throat, hypopharynx, larynx, thyroid, or nasal cavity and paranasal sinuses. The average coverage across 220 hotspots was >1000X. Data were processed using Strand Avadis NGS™. Mutations identified in the tumor were assessed for “actionability,” i.e., response to therapy and impact on prognosis.
Results: Somatic variants were detected in 65% of cases with direct impact on therapy and/or prognosis. Genetic aberrations were identified in major RAS/RAF signaling pathways in nearly 15% of H&N cancers, out of which HRAS activating mutations were the most common (n = 5). HRAS was also found to be co-mutated with phosphatidylinositol 3-kinase (n = 3) and PTEN deletions (n = 3). In contrast to the MAPK signaling pathways, mutant HRAS is able to signal exclusively through PI3K-AKT, reducing the response to cetuximab and increasing the response to MEK inhibitors including selutinib and tramatinib. Based on the results, cetuximab was discontinued in two patients who had presented with metastatic HNSCC. Other targetable mutations included PIK3CA (n = 3), EGFR (n = 1), cKIT (n = 1), RB1 (n = 1), and PTEN (n = 3) were reported. Further, disruptive and nondisruptive mutations in TP53 alone were found in 45% of H&N cancers, varying widely among different histologies, indicating a poor response to cisplatin- and 5FU-based chemotherapy. Interestingly, all metastatic/recurrent patients treated with cisplatin presented with very short progression-free survival of 9–12 months were found to have TP53. TP53 was also found to be co-mutated with ATM gene (n = 1), an important prognostic marker indicating poor response to chemotherapy and RT.
Conclusion: This study validates the utility of multigene profiling in H&N cancer patients, both early diagnosed and advanced cases, to stratify based on their molecular profile that could potentially benefit/not benefit from targeted therapy and chemoradiation. Few ongoing prospective studies and randomized clinical trials may help us confirm the independent prognostic and therapeutic value of the mutations in a larger cohort of Indian population.

Keywords: Actionable mutations, head and neck squamous cell carcinoma, multigene profiling

How to cite this article:
Kunigal SS, Thakur S, Shivkumar Y, Sheela M L, Krishna C R, Kundu A, Jain J, Bahadhur U, Gopinath K S, Arakeri G, Ghosh M, Vishal Rao U S, Ajaikumar B S. Multigene profiling to identify clinically relevant actionable mutations in head and neck cancers: An Indian study. J Precis Oncol 2022;2:25-32

How to cite this URL:
Kunigal SS, Thakur S, Shivkumar Y, Sheela M L, Krishna C R, Kundu A, Jain J, Bahadhur U, Gopinath K S, Arakeri G, Ghosh M, Vishal Rao U S, Ajaikumar B S. Multigene profiling to identify clinically relevant actionable mutations in head and neck cancers: An Indian study. J Precis Oncol [serial online] 2022 [cited 2023 Feb 6];2:25-32. Available from: https://www.jprecisiononcology.com//text.asp?2022/2/1/25/344536

  Introduction Top

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide.[1] The Indian subcontinent has more than 48% of total reported cases in the world. The most commonly associated risk factors responsible for the majority of HNSCC in the oral cavity, pharynx, and larynx are smoking and alcohol consumption.[2],[3] Another risk factor well documented, especially in oropharyngeal cancer, is the human papillomavirus (HPV).[4]

A major issue in pathogenesis of HNSCC is that it starts as a preneoplastic field in the mucosal epithelium, which harbors mutated cells clonally related and often extends into the surgical margins postexcision probably resulting in recurrence.[5]

Recent genetic mutational analysis reveals that HNSCC has difficult to identify cancer-causing genes, which is unusually heterogeneous, resulting in poor outcome and resistance to treatment.[6] Many of the tumor-suppressor genes causing head and neck (H&N) cancers are candidates in cell proliferation and cell cycle control, cell survival with epigenetic regulations, and also Wnt/β-catenin signaling pathway.[6],[7] HNSCC is divided into three genetically well-defined major subgroups: HPV-positive tumors, HPV-negative tumors with many copy number alterations (CNAs), and HPV-negative tumors with no copy number changes (CNA silent). This shows that it is not only heterogeneous at molecular level but also heterogeneous in its clinical behavior.[8],[9]

There have been numerous studies conducted to identify patients at risk for HNSCC, identify optimal treatments, and prevent recurrence. However, over 60% of patients with locally advanced HNSCC remain disease-free despite multimodal treatment combining surgery and/or chemoradiation. Patients with recurrent/metastatic disease who are not amenable to radiotherapy (RT) or surgery have a median survival of 10–12 months.[10]

To date, the personalized treatment for HNSCC patients remains as a challenge. Cetuximab is the only targeted biological agent approved for use in HNSCC, and neither EGFR copy number nor level of EGFR expression is known to predict its response. Expanding the treatment options, Food and Drug Administration approved, in 2016, the anti-PD-1 antibodies, i.e., nivolumab and pembrolizumab, for treating HNSCC. Further dissecting out the genomics, landscape of HNSCC may throw light on the development of new therapeutic strategies to support the improvement of personalized therapies in HNSCC patients.

Radioresistance is a major challenge in HNSCC, resulting in poor survival rates. The understanding of underlying molecular mechanism of radioresistance will also help improve patient survival rates in HNSCC.

In this study, we have studied the mutational landscape of a cohort of H&N patients, identified actionable mutations, and correlated our findings with the demography, risk factors, stage at presentation, influence of mutations on standard treatment, and 2-year overall survival (OS).

Somatic mutations in head and neck squamous cell carcinoma

The Cancer Genome Atlas (TCGA) performed a comprehensive genomic study which has analyzed 528 HNSCC cases, which included a dataset of multigenome analysis of somatic mutations, expression, and methylation of genes and also miRNA profiling, which are clinically and pathologically characterized. The data analysis has given insight into the landscape of somatic genomic alterations and has recognized major molecular signaling pathways involved in HNSCC progression. Explicitly, HNSCC is marked by TP53 mutation, whole-genome duplications, and recurrent multiple chromosomal gains and losses, which leads to genomic alterations affecting cell cycle checkpoints, PI3K-AKT pathway, PTEN inactivation, HRAS mutation, EGFR mutation, and CDH1 gene mutations and amplification. The poor prognosis is attributed to increased rates of CNAs across the tumor genome. The knowledge of genes shows how diversified genomic patterns present among cases lead to the identification of various subgroups of tumors having specific relationships with histological subtypes, habits, HPV status, and OS.[11]

TP53 is the most commonly mutated tumor suppressor in HNSCC, and almost all HNSCCs displayed a dysregulated cell cycle followed by NOTCH1 (11%–19%).[12],[13] Mutations in BRCA1, BRCA2, and ATR are seen in 6%, 7%, and 4%–10% of HNSCC. ATM mutations are also seen in 1%–16% of HPV-positive HNSCC across studies.[14],[15]

Further, CDKN2A expression is downregulated in almost all HPV-negative HNSCC which may be silenced by promoter hypermethylation. On the other hand, TP53 and CDKN2A gene alterations are infrequent in HPV-positive tumors.[16],[17],[18],[19] RTK and MAPK pathways are shown to be mutated in HPV-positive and HPV-negative HNSCC at different frequencies.[20],[21] While alterations in EGFR, FGFR1, and IGF1R are predominantly seen in HPV-negative tumors, FGFR2 and FGFR3 alterations including FGFR3 fusions are more frequent in HPV-positive tumors.[8],[22],[23] ERBB2 alterations are seen in both subtypes at a low frequency (3%–4%).[24]

There is an ongoing effort to catalog the genes that are mutated and implicated in cancer, particularly HNSCC by the Cancer Gene Census. The Census has attempted to explain how the mutated genes dysfunction and drives the development of the cancer (COSMIC, https://cancer.sanger.ac.UK/census). The Census has divided the genes into Tier I to IV. The classification is based on the degree of the clinical significance of variants of the gene (Pieriandx).

  Materials and Methods Top

The study has been approved by the Human Research Ethics Committee of the institute (HCG Central Ethics Committee/EC Registration No: ECR/386/Inst/KA/2013/RR-19, a tertiary comprehensive cancer care hospital in Bangalore, India). The institutional ethics committee recommended the need for consent for formalin-fixed, paraffin-embedded (FFPE) tumor samples obtained from the tumor tissue bank at the hospital's department of pathology. All samples and medical data used in this study have been irreversibly anonymized.

Patient/study subjects and sample preparation

One hundred patients with HNSCC (early diagnosed and/or advanced/metastatic) aged 26–75 years (median age 50.5 years) diagnosed at HCG from April 2013 to 18 were counseled/consulted to be profiled by targeted deep sequencing for hotspot mutations in 48 cancer-related genes. FFPE tumor samples were obtained from the tumor tissue bank at the hospital's department of pathology. All the cases had pathology review for stage, histological type, hormonal status, and Ki67. The H and E slides of the FFPE samples were examined for the presence of viable tumor cells and scored for percentage of tumor nuclei by a pathologist who selected the areas of neoplastic cells. The specimen with estimated tumor nuclei ≥30% in circled areas was considered for the study.

DNA preparation

FFPE tissue samples were first deparaffinized in xylene, 3–5-μm-thick sections were extracted, after deparaffinization with xylene and 100% ethanol, and DNA was isolated using the QIAamp DNA Mini Kit (QIAGEN) as per the manufacturer's instructions. FFPE DNA was quantified using Qubit and was qualified using the Illumina Infinium assay kit (Illumina, San Diego, CA, USA).

Library preparation and sequencing

Amplicon-based library was prepared from 250 ng of DNA samples using TruSeq® Amplicon-Cancer Panel (Illumina, San Diego, CA, USA) that provides predesigned, optimized oligonucleotide probes for sequencing mutational hotspots in >35 kilobases of the target genome sequence. Forty-eight genes (ABL1, AKT1, ALK, APC, ATM, BRAF, CDH1, CDKN2A, CSF1R, CTNNB1, EGFR, ERBB2, ERBB4, FBXW7, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, JAK2, JAK3, KDR, KIT, KRAS, MET, MLH1, MPL, NOTCH1, NPM1, NRAS, PDGFRA, PIK3CA, PTEN, PTPN11, RB1, RET, SMAD4, SMARCB1, SMO, SRC, STK11, TP53, and VHL) were targeted with 212 amplicons in a highly multiplexed, single-tube reaction. Briefly, genomic DNA was initially hybridized with pairs of oligonucleotide probes specific to the targeted regions and subsequently washed to remove the unbound probes. The pairs of oligonucleotide probes were extended and ligated to form templates, which was followed by polymerase chain reaction (PCR) amplification using primers that add adaptors and index tags for multiplex sequencing. The PCR products were then purified using Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA). The quality of the DNA libraries was assessed with an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The normalized libraries were pooled and sequenced using the Illumina MiSeq system in 151-base-pair paired-end reads.


Paired end NGS reads obtained from the Illumina MiSeq sequencer were aligned against the hg19 reference genome using the MiSeq Reporter Software from Illumina, and the aligned files were imported into Avadis NGS 1.5 for QC and downstream analysis. Reads with average quality below Q20 were filtered. The Avadis NGS variant caller was used to identify single base variants and multibase variants at all locations with coverage of at least 10X. Variants with Phred confidence above 50 were annotated using the db single-nucleotide polymorphism (SNP) 137 database. The SNP effect analysis feature of Avadis NGS was used to identify the functional effects of the variants on RefSeq transcripts of the genes. This test namely somatic 48 gene test detects somatic alterations in the hotspot regions of 48 genes and interprets those with possible therapeutics, clinical, or prognostic implications.

IHC/HP for P16 or human papillomavirus detection

P16 staining is done on Intellisite IHC equipment using G175-405 clone from BioGenex. Sections from FFPE are taken on positive charged slides and kept in the hot air oven at 35° overnight. These are then deparaffinized in several changes of xylene and rehydrated in various grades of alcohol (from 100% to 70%). The last step is in distilled water. Antigen retrieval is done in a decloaking chamber with ethylenediaminetetraacetic acid at pH 8.0 for antigen retrieval.

Antigen retrieval is done by placing the slides in the AR solution container in the decloaking chamber. The program is set at 110° for 30 min. The slides are then cooled in DI water and kept in buffer solution for 5 min. Further processing for staining is done with autostainer Intellipath from Biocare as per the steps given in the [Table 1].
Table 1: Staining procedure using autostainer Intellipath from Biocare

Click here to view

Collection of clinical data

The clinical data of all the patients was collected retrospectively from medical records and case files. Detailed analysis of the clinical stage at presentation, subsite, habit history, treatment given, and their current disease status were collected. The patient's survival status at 2 years was recorded using telephonic follow-up or the latest follow-up. The survival status was recorded as alive and well, alive with disease, died of disease, and died due to other causes. The collected data were collated and analyzed.

  Results Top

According to the above given [Figure 1], pie chart shows, out of the 100 samples of FFPE that were subjected to mutational analysis, 64 patients displayed the presence of somatic mutation, four patients had a germ line mutation, while 32 patients had no somatic or germ line mutations.
Figure 1: Pie Chart showing the mutational frequency of genes in HNSCC patient samples obtained by NGS

Click here to view

On sequencing with the 48 gene panel using the Illumina platform, a list of genes such as PTEN, TP53, ATM, RB1, PIK3CA, HRAS, EGFR, RET, STK11, and cKIT with mutations were seen in our cohort of patient; it was found that TP53 was most frequently mutated accounting for 46% of the patient samples followed by HRAS (9%), PTEN (3%), PIK3 (5%), and BRAF, RET, EGFR, cKIT, RB1, STK11, and ATM (1% each).

Based on the classification of the genes as per the clinical evidence from Tier I to Tier IV (as shown in the given [Table 2]), only EGFR mutation where cetuximab is prescribed belonged to Tier I. All other mutations observed in our cohort belonged to the Tier II and III.
Table 2: Tiers of the Cancer Gene

Click here to view

Complete clinical data were available only for 58 patients. Of these 58, 35 patients had either one or multiple actionable somatic mutation while one patient had a germ line mutation. 22 of the 58 patients did not display any germ line or somatic mutations.

Correlation of clinical parameters with the presence or absence of mutation

The study population had a male-to-female ratio of 4.8:1, ranging in age between 17 and 80 years. Histologically, 89% of the studied specimens were SCC followed by salivary gland tumors (7%) and thyroid or others (2%–4%). Majority of the tumors were seen in the oral cavity (77%), followed by oropharynx (10%), nasopharynx (3.5), larynx (1.5%), hypopharynx (3.5), nasal cavity and peripheral nervous system (1.5%), and others (1.5%). Presence or absence of somatic mutations was not significantly associated with gender, age, or subsite.

Correlation of influence of known risk factors such as smoking, smokeless tobacco, HPV, on mutational burden revealed that out of 47 patients with habits (tobacco, alcohol, or both), 70% had mutation in one gene or multiple genes. This was significantly higher (P < 0.05) than the patients with no habits (only 30% had mutations). An individual having any one, multiple, or all of the habits was 5.13 times more odds of having a somatic mutation and 4.95 times more odds of having a TP53 mutation when compared with the person who had no habits. Smoking was found to be the most significant contributory risk factor for the presence of somatic mutation in our population. None of our patients had an associated HPV infection.

Clinical stage at presentation had no influence on the presence or absence of somatic mutation.

Among the 26 patients who presented at advanced stage of disease (stage IV), 57% had somatic mutations in one or multiple genes.

When we looked at the survival status, 35 patients (67%) were alive after treatment without disease, while three patients (5%) were alive with disease. Totally, 17 (29%) deaths without disease were recorded and two (3.5%) patients died with disease. Above all in 17 patients, the disease recurred with 11 patients having mutations.

Person's Chi-square test was used to test the association between the variables. The magnitude of the association was measured using odds ratio. Descriptive statistics were used to find out the variable which has stronger association than its paired variable.

There is a statistically significant (P < 0.05) association between the habits and any mutation [Table 3]. Person having an either one of the habits is 5.13 times more odds of having a mutation when compared with the person who does not have any.
Table 3: Mutational load compared with habits

Click here to view

There is a statistically significant (P < 0.05) association between the habits and TP53 mutation [Table 4]. Person having an either one of the habits is 4.95 times more odds of having a TP53 mutation when compared with the person who does not have any.
Table 4: TP 53 Mutations and Habits

Click here to view

There was no (P > 0.05) association between the current status and any mutation [Table 5]. The frequency table indicated that there was greater proportion of people alive when there is no mutation than any other conditions.
Table 5: Relationship between Mutations and death of the patient

Click here to view

  Discussion Top

Despite developments in diagnostic and treatment protocols, prognosis barely improved over the past few decades. This is probably due to the high likelihood of recurrence (25%–50%) and the lack of specific detection methods for recurrent disease. Early detection of recurrences using biomarkers might contribute to improving disease outcome. Targeted therapy is being used only for advanced cancers to prolong OS without much benefit due to a lack of in depth understanding of underlying mechanisms. Gaining a better understanding of molecular and genetic mechanism may help device novel therapeutic interventions. Over the last 6 years, landmark advancements in whole-exome sequencing and gene copy number analyses in primary patient tumors have revealed an extensive network of molecular changes underlying HNSCC.

TCGA, published in 2015, offers robust insights into the mutational landscape of HNSCC patients. In addition to reporting some of the most frequently mutated genes, the TCGA study also extensively analyzed key factors likely to contribute to increased tumorigenicity including somatic copy number alterations (SCNAs), differential gene and protein expression profiles, and epigenetic markers. Most frequent mutational burden in HNSCC was associated with the TP53 gene responsible for cellular survival and proliferation and the CDKN2A gene responsible for cell cycle control. The only exceptions to these were HPV-positive tumors (these patients were excluded from our study). A study of 420 HNSCC patients suggested that decreased OS rates postsurgical intervention were associated with disruptive TP53 mutations.[19]

In addition to the above-discussed cellular survival and cell cycle control pathways, increased incidence of mutations in cellular differentiation (NOTCH) and adhesion/invasion signaling pathways (FAT1) also have implications in the tumorigenesis of HNSCC.[25]

In our study, we found that more than 40% of the patients showed mutation in P53. Patients with malignancy in larynx and hypopharynx showed maximum P53 mutations (80%) followed by tongue, oral cavity, and oropharynx. Both gain-of-function (GOF) and loss-of-function (LOF) mutations of P53 were reported in the patients with majority having LOF. GOF of P53 R273H mutation is seen in majority of the patients.

A recent study by Meucci et al.[26] evaluated SNPs in somatic cell-DNA of HNSCC patients revealed that mutational burden in patient DNA significantly increased with age. This occurred as a result of genetic and environmental factors such as aging-related genomic instability and prolonged tobacco exposure. Interestingly, it was found that the reported increased mutational burden in older patients did not follow distinct mutational patterns and was rather an accumulation of mutations in specific pathways such as the “notch signaling pathway.” The mutations identified within these distinct cellular signaling and senescence pathways in older HNSCC patients suggested that reduced invasive and metastatic tumor activity, which was underlined by the tumor staging at diagnosis. HPV-negative tumors, in particular, were more broadly distributed across different anatomic sites and presented with unique DNA/RNA structural aberrations. Interestingly, unlike other identified mutations, the most frequent ones such as TP53 and CDKN2A were differentially mutated across a spectrum of anatomic sites. Inactivating, nonsynonymous somatic mutations were also abundant in these genes. These findings highlight the possibility of delivering more tailored/precise treatments to older HNSCC patients on account of their distinct mutational background.

HNSCC genomes were also found to display significantly high levels of genomic instability on account of specific chromosomal patterns of SCNA that were consistent with those previously recognized in lung squamous cell carcinomas. SCNAs serve as vital predictive markers of HNSCC clinical outcomes and prognosis. Several studies have reported that HNSCC patients with decreased SCNAs in their genome present with better survival rates. Moreover, the most frequent genetic changes, which enable immune evasion and acquisition of cellular immortality by tumor cells, are of consistent SCNAs involving chromosomal regions enriched for genes in the above discussed cancer-related pathways.

A recent study also found that these SCNAs correlated with genetic alterations that were reported in HNSCC patients with a history of smoking and alcohol consumption as well as those with advanced-stage tumor biomarkers.[27] More recent studies have identified higher incidence of mutations in tumor samples from patients with a history of smoking, tobacco, and alcohol consumption. The incidence of HPV-negative tumors showed a positive correlation with increased alcohol and tobacco consumption.

In our study, we found that smokers (50%), tobacco chewing (73%), and tobacco with alcohol (71%) had somatic mutation, whereas only around 31% of the patient whom did not have any habits had mutations.

SCNAs and other pathological mutations associated with a relatively small set of genes reported above such as TP53, CDKN2A, and NOTCH1 and certain predicted cancer drivers (PABPC3, NR4A2, and MACF1) were discovered to arise early on in tumorigenesis and were often present in potentially premalignant lesions in HNSCC patients. Further analysis of the mutational and genomic landscape of these patients revealed novel and relevant stage-specific changes associated with immortalization and malignancy. Not only do these findings shed light on potential druggable alterations in HNSCC but also reveal vital correlations between developmental events governing changes in mutational landscape with disease progression, which if effectively exploited can result in better control of premalignancy and prevention of progression to frank malignancy in HNSCC patients.[28]

A key challenge that is posed by this high degree of genomic heterogeneity of HNSCCs is in the ability to effectively differentiate between driver and passenger alterations from aberrant regions.[29] Previous studies have indicated that specific types of SCNAs, namely broad and focal CNAs correlate with distinct characteristics of tumor cells and cellular signaling and immune pathways. Broad CNAs are known to play a role in immune evasion and suppression, while focal alterations are known to interfere with the activity of oncogenes and tumor-suppressor genes offering the tumor cells with a proliferative advantage. Thus, effectively categorizing SCNAs is vital when choosing potential therapeutic targets and designing respective interventions.


Accurate analysis of mutational burden and distinct molecular signatures in HNSCC, such as those outlines in our study, plays a crucial role in serving as effective therapeutic targets, resulting in improved clinical outcomes and prognosis. Increased knowledge of the immunological characteristics of these mutations would help devise targeted immunotherapeutic interventions based on specific HNSCC and patient subtypes.

Ethical approval

The study has been approved by the Human Research Ethics Committee of the institute (HCG Central Ethics Committee/EC Registration No: ECR/386/Inst/KA/2013/RR-19).

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Jin Y, Yang Y. Bioinformatics-based discovery of PYGM and TNNC2 as potential biomarkers of head and neck squamous cell carcinoma. Biosci Rep 2019;39:BSR20191612.  Back to cited text no. 1
Dua B, Chufal KS, Bhatnagar A, Thakwani A. Nodal volume as a prognostic factor in locally advanced head and neck cancer: Identifying candidates for elective neck dissection after chemoradiation with IGRT from a single institutional prospective series from the Indian subcontinent. Oral Oncol 2018;87:179-85.  Back to cited text no. 2
Delagranda A, Leterme G, Chirpaz E, Ferdynus C, Fernandez C, Rubin F. Epidemiological features of cancers of the oral cavity, oropharynx, hypopharynx and larynx cancer in Réunion Island. Eur Ann Otorhinolaryngol Head Neck Dis 2018;135:175-81.  Back to cited text no. 3
Timbang MR, Sim MW, Bewley AF, Farwell DG, Mantravadi A, Moore MG. HPV-related oropharyngeal cancer: A review on burden of the disease and opportunities for prevention and early detection. Hum Vaccin Immunother 2019;15:1920-8.  Back to cited text no. 4
Rothenberg SM, Ellisen LW. The molecular pathogenesis of head and neck squamous cell carcinoma. J Clin Invest 2012;122:1951-7.  Back to cited text no. 5
Leemans CR, Snijders PJ, Brakenhoff RH. The molecular landscape of head and neck cancer. Nat Rev Cancer 2018;18:269-82.  Back to cited text no. 6
Tabor MP, Brakenhoff RH, van Houten VM, Kummer JA, Snel MH, Snijders PJ, et al. Persistence of genetically altered fields in head and neck cancer patients: Biological and clinical implications. Clin Cancer Res 2001;7:1523-32.  Back to cited text no. 7
Seiwert TY, Zuo Z, Keck MK, Khattri A, Pedamallu CS, Stricker T, et al. Integrative and comparative genomic analysis of HPV-positive and HPV-negative head and neck squamous cell carcinomas. Clin Cancer Res 2015;21:632-41.  Back to cited text no. 8
Aggarwal N, Yadav J, Thakur K, Bibban R, Chhokar A, Tripathi T, et al. Human papillomavirus infection in head and neck squamous cell carcinomas: Transcriptional triggers and changed disease patterns. Front Cell Infect Microbiol 2020;10:537650.  Back to cited text no. 9
Mantovani G, Maccio A, Massa E, Mulas C, Mudu MC, Massidda S, et al. Phase II study of induction chemotherapy followed by concomitant chemoradiotherapy in advanced head and neck cancer: Clinical outcome, toxicity and organ/function preservation. Int J Oncol 2000;16:1227-33.  Back to cited text no. 10
Pérez Sayáns M, Chamorro Petronacci CM, Lorenzo Pouso AI, Padín Iruegas E, Blanco Carrión A, Suárez Peñaranda JM, et al. Comprehensive genomic review of TCGA head and neck squamous cell carcinomas (HNSCC). J Clin Med 2019;8:1896.  Back to cited text no. 11
Fukusumi T, Califano JA. The NOTCH pathway in head and neck squamous cell carcinoma. J Dent Res 2018;97:645-53.  Back to cited text no. 12
Li VD, Li KH, Li JT. TP53 mutations as potential prognostic markers for specific cancers: Analysis of data from the cancer genome atlas and the international agency for research on cancer TP53 database. J Cancer Res Clin Oncol 2019;145:625-36.  Back to cited text no. 13
Stransky N, Egloff AM, Tward AD, Kostic AD, Cibulskis K, Sivachenko A, et al. The mutational landscape of head and neck squamous cell carcinoma. Science 2011;333:1157-60.  Back to cited text no. 14
Yang J, Zhou L, Zhang Y, Zheng J, Zhou J, Wei Z, et al. DIAPH 1 is upregulated and inhibits cell apoptosis through ATR/p53/caspase-3 signaling pathway in laryngeal squamous cell carcinoma. Dis Markers 2019;2019:6716472.  Back to cited text no. 15
Solomon B, Young RJ, Rischin D. Head and neck squamous cell carcinoma: Genomics and emerging biomarkers for immunomodulatory cancer treatments. Semin Cancer Biol 2018;52:228-40.  Back to cited text no. 16
Beck TN, Golemis EA. Genomic insights into head and neck cancer. Cancers Head Neck 2016;1:1.  Back to cited text no. 17
Chung CH, Guthrie VB, Masica DL, Tokheim C, Kang H, Richmon J, et al. Genomic alterations in head and neck squamous cell carcinoma determined by cancer gene-targeted sequencing. Ann Oncol 2015;26:1216-23.  Back to cited text no. 18
Poeta ML, Manola J, Goldwasser MA, Forastiere A, Benoit N, Califano JA, et al. TP53 mutations and survival in squamous-cell carcinoma of the head and neck. N Engl J Med 2007;357:2552-61.  Back to cited text no. 19
Liu C, Sadat SH, Ebisumoto K, Sakai A, Panuganti BA, Ren S, et al. Cannabinoids promote progression of HPV-positive head and neck squamous cell carcinoma via p38 MAPK activation. Clin Cancer Res 2020;26:2693-703.  Back to cited text no. 20
Brand TM, Hartmann S, Bhola NE, Peyser ND, Li H, Zeng Y, et al. Correction: Human papillomavirus regulates HER3 expression in head and neck cancer: Implications for targeted HER3 therapy in HPV+patients. Clin Cancer Res 2021;27:4129.  Back to cited text no. 21
Agarwal V, Subash A, Nayar RC, Rao V. Is EGFR really a therapeutic target in head and neck cancers? J Surg Oncol 2019;119:685-6.  Back to cited text no. 22
Koole K, Brunen D, van Kempen PM, Noorlag R, de Bree R, Lieftink C, et al. FGFR1 is a potential prognostic biomarker and therapeutic target in head and neck squamous cell carcinoma. Clin Cancer Res 2016;22:3884-93.  Back to cited text no. 23
Birkeland AC, Yanik M, Tillman BN, Scott MV, Foltin SK, Mann JE, et al. Identification of targetable ERBB2 aberrations in head and neck squamous cell carcinoma. JAMA Otolaryngol Head Neck Surg 2016;142:559-67.  Back to cited text no. 24
Mountzios G, Rampias T, Psyrri A. The mutational spectrum of squamous-cell carcinoma of the head and neck: Targetable genetic events and clinical impact. Ann Oncol 2014;25:1889-900.  Back to cited text no. 25
Meucci S, Keilholz U, Tinhofer I, Ebner OA. Mutational load and mutational patterns in relation to age in head and neck cancer. Oncotarget 2016;7:69188-99.  Back to cited text no. 26
Perdomo S, Anantharaman D, Foll M, Abedi-Ardekani B, Durand G, Reis Rosa LA, et al. Genomic analysis of head and neck cancer cases from two high incidence regions. PLoS One 2018;13:e0191701.  Back to cited text no. 27
Veeramachaneni R, Walker T, Revil T, Weck A, Badescu D, O'Sullivan J, et al. Analysis of head and neck carcinoma progression reveals novel and relevant stage-specific changes associated with immortalisation and malignancy. Sci Rep 2019;9:11992.  Back to cited text no. 28
Yang J, Chen Y, Luo H, Cai H. The landscape of somatic copy number alterations in head and neck squamous cell carcinoma. Front Oncol 2020;10:321.  Back to cited text no. 29


  [Figure 1]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  In this article
Materials and Me...
Article Figures
Article Tables

 Article Access Statistics
    PDF Downloaded47    
    Comments [Add]    

Recommend this journal