WO2017046714A1

WO2017046714A1 - Methylation signature in squamous cell carcinoma of head and neck (hnscc) and applications thereof

Info

Publication number: WO2017046714A1
Application number: PCT/IB2016/055469
Authority: WO
Inventors: Binay PANDA; Neeraja M KRISHNAN
Original assignee: Genomics Applications And Informatics Technology (Ganit) Labs
Priority date: 2015-09-14
Filing date: 2016-09-14
Publication date: 2017-03-23

Abstract

The present disclosure relates to indicators/predictors/biomarkers of head and neck squamous cell carcinomas (HNSCC). In particular, the present disclosure relates to methylation signatures in head and neck squamous cell carcinomas (HNSCC), specifically oral tongue squamous cell carcinoma (OTSCC) which serve as biomarkers for such carcinomas and associated methods and products.

Description

METHYLATION SIGNATURE IN SQUAMOUS CELL CARCINOMA OF HEAD AND NECK (HNSCC) AND APPLICATIONS THEREOF"

TECHNICAL FIELD

The present disclosure relates to the field of Oncology, Molecular Biology, Genomics and Bioinformatics. The present disclosure relates to indicators/predictors/biomarkers of head and neck squamous cell carcinomas (HNSCC). In particular, the present disclosure relates to DNA methylation signatures in head and neck squamous cell carcinomas (HNSCC), specifically oral tongue squamous cell carcinoma (OTSCC) which serve as biomarkers for such carcinomas and associated methods and products.

BACKGROUND OF THE DISCLOSURE

The squamous cell carcinoma of head and neck (HNSCC) is heterogeneous in nature with different incidences, mortalities and prognosis for different subsites. HNSCC are the sixth leading cause of cancer worldwide and account for almost 30% of all cancer cases in India. Oral cancer is the most common subtype of head and neck cancers, with a worldwide incidence of greater than 300,000 cases. The disease is an important cause of morbidity and mortality, with a 5-year survival of less than 50%. Unlike other subsites of oral cavity like gingivo-buccal, tumors originating in the anterior part of tongue or oral tongue (OT) have an increased association with younger patients, spread early to lymph nodes and have a higher regional failure. Tobacco and alcohol are common risk factors for this group of cancer. Unlike oropharyngeal tumors where the role of human papilloma virus (HPV) is clearly understood in disease prognosis, the role of HPV as an etiological agent and/or a prognostic marker in Oral tongue squamous cell carcinoma (OTSCC) is not yet established. In the last four years, studies have been performed on somatic mutations, insertions, deletions and amplifications, copy number alterations, loss of heterozygosity and gene expression changes across the tumor genomes.

Epigenetic changes are known as responsible factors for cancer initiation and progression. DNA methylation at cytosine residues (5-methylcytosine or 5mC) is one of such epigenetic changes. Studies on epigenetic changes and cancer (HNSCC) development have been performed previously. However, the drawbacks of such prior art studies/methods are that they were not comprehensive and genome-wide. Therefore, the earlier studies might have missed some of the critical loci possible to identify using the comprehensive assays that can interogate higher number of genome-wide DNA methylation loci and their relationship with altered gene expressions playing an important and functional role in these cancers. An additional drawback of earlier studies is that the methylated loci were not correlated with various epidemeological and clinical factors.

Therefore, there exists a need for providing improved/reliable indicators/molecular biomarkers based on epigenetic changes in squamous cell carcinomas of head and neck, in particular OTSCC, and employ such biomarkers for understanding and practical management of HNSCC. The present disclosure tries to address the above-mentioned drawbacks of prior art.

BRIEF DESCRIPTION OF THE ACCOMPANYING FIGURES

In order that the disclosure may be readily understood and put into practical effect, reference will now be made to exemplary embodiments as illustrated with reference to the accompanying figures. The figures together with a detailed description below, are incorporated in and form part of the specification, and serve to further illustrate the embodiments and explain various principles and advantages, in accordance with the present disclosure where: Figure 1 shows Analysis workflow scheme. The figure illustrates the tools, Bioconductor packages and functions in R used for pre-processing, differential expression and methylation analyses, correlation between them, identifying a minimal methylation signature predictive of tumor status, and associated clinical factors for the dataset derived from the 450K genome-wide methylation arrays and genome expression arrays. Additionally, the scheme shows the pipeline used for overall data analysis and validation.

Figure 2 shows differentially Methylated Region (DMR) statistics. A. DMR (q <= 0.05) numbers compared between in house oral tongue tumors (OTSCC) and data from the TCGA cohort (TCGA_OralTongue: data for oral tongue subsites and TCGA_HNSCC: data for all tumor subsites); B. Region-wise characterization thereof; C. Distribution of magnitude of differential methylation in CpG islands and TSS 1500; D. Distribution of DMRs in early and late stage tumors; and E. Circular representation using CIRCOS using the summary of all molecular changes (somatic mutations, Indels, Copy number variations, Loss of heterozygosity, Expression and Methylation) in OTSCC samples used in the current study.

Figure 3 shows Region-wise correlation between expression and methylation. Pre- processed differential methylation (tumor MINUS normal) values are plotted for probes from specific regions such as gene bodies, CpG islands, TSS 1500, N Shore, S Shore, N Shelf, S Shelf, 5' UTR, 3' UTR, first exon and promoters, versus the log₂FC (tumor/normal fold change) of the corresponding gene for the same patient samples. Figure 4 shows discovery of predictive DMPs and DMRs. A-C. A minimal differential methylation profiles of methylation loci from random forest analyses using three different training sets (see Methods), and other DMPs located in the same DMR as the predictive DMP, represented as a bees warm plot of the Δβ values; and D. Quantitative methyl- specific PCR (qMSP) based validation of these DMRs.

Figure 5 shows validation of specific DMPs and DMRs using various training sets, in the TCGA HNSCC data (A-C).

Figure 6 shows linking methylation in MiR-lOB, and associated minimal methylation set, to expression of downstream target genes. A-B depicts correlation with differential expression (log₂FoldChange) of MiR-lOB target genes, NR4A3 and BCL2L11. C-D depicts correlation of log₂FoldChange of NR4A3 and BCL2L11 with disease-free survival. Differential expression and methylation profiles of regions flanking MiR-lOB, and two genes from the same minimal methylation set, ENAH and EMX20S. The green line represents differential expression and the red line represents differential methylation.

Figure 7 shows discovery of epidemiology predictive DMPs and DMRs. Differential methylation profiles of epidemiology (HPV, Habits, Node, Prognosis and Stage; A-E) predicting DMPs and DMRs from random forest analyses using Δβ values as the training set, and other DMPs located in the same DMR as the predictive DMP, represented as a beeswarm plot of the Δβ values.

SUMMARY OF THE DISCLOSURE

Accordingly, the present disclosure relates to a method of detecting head and neck squamous cell carcinoma (HNSCC) in a sample having or suspected of having the HNSCC, said method comprising step of detecting aberration in at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6, optionally along with detecting aberration in the MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof.

In an embodiment, the disclosure relates to a method of detecting HNSCC, said method comprises detecting the aberration in:

OR2T6 and GPER1;

RHPN1 and TTLL8;

OR2T6, GPER1, RHPN1 and TTLL8;

MiR-lOB and ENAH;

GPRASP1, EMX20S and ENAH;

MiR-lOB, EMX20S and ENAH;

ORDF3, CENPVL1, RARRES2 and DEFA1; or

BTK, ORDF3, RHPN1, SP6, SHF, CENPVL1, Cl lorf53, COL9A1,

RARRES2 and DEFA1.

In another embodiment, the disclosure relates to a method of detecting epidemiological parameter or clinical parameter, or a combination thereof in a subject having or suspected of having HNSCC by detecting aberration in gene selected from a group comprising TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 and C10rfl86, or any combination thereof. In another embodiment, the disclosure relates to aberration of gene selected from group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, SP6, GPRASP1, TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578, and C10rfl86, or any combination thereof, optionally along with aberration in the MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof.

In another embodiment, the disclosure relates to use of aberration of at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6, optionally along with aberration of MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, for detecting HNSCC in a sample having or suspected of having the HNSCC. In another embodiment, the disclosure relates to use of aberration of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 or C10rfl86, or any combination thereof for detecting epidemiological parameter or clinical parameter, or a combination thereof selected from a group comprising HPV infection, tumor nodal status, risk habits, tumor stage and tumor recurrence status, in a subject having or suspected of having HNSCC.

In another embodiment, the disclosure relates to a kit for detecting HNSCC in a sample having or suspected of having the HNSCC, wherein the kit comprises agent for detecting aberration in at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6, optionally along with aberration in the MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof. In another embodiment, the disclosure relates to a kit for detecting epidemiological parameter or clinical parameter, or a combination thereof in a subject having or suspected of having HNSCC, wherein the kit comprises agent for detecting aberration in gene selected from a group comprising TMEM179, ARX, TSPAN7, FUT3, TREVI5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 and C10rfl86, or any combination thereof.

In another embodiment, the disclosure relates to agent for use in detecting aberration of GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1, SP6, TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578, C10rfl86, NR4A3 (Nor-1) or BCL2L11, or any combination thereof.

In another embodiment, the disclosure relates to a method for predicting disease-free survival in a subject having or suspected of having HNSCC, said method comprise detecting aberration in MiR-lOB and its associated gene selected from a group comprising NR4A3 (Nor- 1 ) and BCL2L11 , or a combination thereof In another embodiment, the disclosure relates to a kit comprising an agent for predicting disease-free survival in a subject having or suspected of having HNSCC, wherein the agent detects aberration in MiR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, thereby predicting disease-free survival in the subject.

In another embodiment, the disclosure relates to aberration of MirR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, wherein the aberration predicts disease-free survival in a subject having or suspected of having HNSCC.

In another embodiment, the disclosure relates to use of aberration of MirR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, wherein the use predicts disease-free survival in a subject having or suspected of having HNSCC.

DESCRIPTION OF THE DISCLOSURE

The present disclosure relates to a method of detecting head and neck squamous cell carcinoma (HNSCC) in a sample having or suspected of having the HNSCC, said method comprising step of detecting aberration in at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6, optionally along with detecting aberration in the MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof.

In an embodiment of the present disclosure, the method comprises detecting the aberration in:

OR2T6 and GPER1;

RHPN1 and TTLL8;

OR2T6, GPER1, RHPN1 and TTLL8;

MiR-lOB and ENAH;

GPRASP1, EMX20S and ENAH;

MiR-lOB, EMX20S and ENAH;

ORDF3, CENPVL1, RARRES2 and DEFA1; or BTK, ORDF3, RHPN1, SP6, SHF, CENPVL1, Cl lorf53, COL9A1, RARRES2 and DEFA1.

In another embodiment of the present disclosure, detecting the aberration comprises:

carrying out genome-wide methylation profiling of tumor sample and normal sample;

carrying out genome-wide gene expression profiling of tumor sample and normal sample; and

comparing the methylation profile and the gene expression profile, followed by determining differential methylation in the gene by correlating with differential gene expression to detect the aberration and thereby detecting the HNSCC.

In yet another embodiment of the present disclosure, detecting the aberration comprises:

carrying out genome-wide methylation profiling of tumor sample and normal sample employing kit selected from a group comprising Infinium Human methylation 450K beadchip, 27K beadchip_quantitative methylation- specific polymerase chain reaction (qMSP), MSP, microarrays, and sequencing method or any combination thereof;

pre-processing and analysing differential methylation using tools selected from a group comprising genome studio methylation module v 1.9.0, wateRmelon, minfi and comBat, or any combination thereof;

carrying out genome- wide gene expression profiling of tumor sample and normal sample employing kit selected from a group comprising illumine humaHT-12 v4 expression Beadchip, quantitiatve polymerase chain reaction (qPCR) PCR, microarrays and sequencing method, or any combination thereof; pre-processing and analysing differential expression using tools selected from a group comprising Genomestudio, lumi, comBat and limma, or any combination thereof;

determining differential methylation in the gene by comparing methylation data and gene expression data using tool selected from a group comprising Ensemble v75 database and annotation database, or a combination thereof;

identifying differentially methylated region by comparing differentially methylated loci in tumor genome and gene expression of downstream gene or microRNA targets or both; and analysing the differential DNA methylation to detect the aberration and thereby detecting HNSCC, wherein the analysis is carried by employing tool selected from a group comprising random forest analysis, supervised machine learning, unsupervised machine learning and semi- supervised machine learning, transduction learning methods and reinforcement learning methods, or any combination thereof.

In still another embodiment of the present disclosure, the method further comprises detecting epidemiological parameter or clinical parameter, or a combination thereof in a subject having or suspected of having HNSCC by detecting the aberration in TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 or C10rfl86, or any combination thereof.

The present disclosure further relates to a method of detecting epidemiological parameter or clinical parameter, or a combination thereof in a subject having or suspected of having HNSCC by detecting aberration in gene selected from a group comprising TMEM179, ARX, TSPAN7, FUT3, TREVI5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 and C10rfl86, or any combination thereof. In an embodiment of the present disclosure, the epidemiological parameter or clinical parameter is selected from a group comprising HPV infection, tumor nodal status, risk habits, tumor stage and tumor recurrence status.

In another embodiment of the present disclosure, the aberration in TMEM179 and ARX detects HPV infection; aberration in TSPAN7, AY660578 and C10rfl86 detects tumor nodal status; aberration in FUT3 and TRIM5 detects risk habits; aberration in SLC9A9 and NPAS3 detects tumor stage; and aberration in RPS6KA2 and MAP3K8 detects tumor recurrence status. The present disclosure further relates to aberration of gene selected from group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, SP6, GPRASP1, TMEM179, ARX, TSPAN7, FUT3, TREVI5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578, and C10rfl86, or any combination thereof, optionally along with aberration in the MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof.

The present disclosure further relates to use of aberration of at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6, optionally along with aberration of MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, for detecting HNSCC in a sample having or suspected of having the HNSCC.

The present disclosure further relates to use of aberration of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 or C10rfl86, or any combination thereof for detecting epidemiological parameter or clinical parameter, or a combination thereof selected from a group comprising HPV infection, tumor nodal status, risk habits, tumor stage and tumor recurrence status, in a subject having or suspected of having HNSCC.

The present disclosure further relates to a kit for detecting HNSCC in a sample having or suspected of having the HNSCC, wherein the kit comprises agent for detecting aberration in at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6, optionally along with aberration in the MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof.

In an embodiment of the present disclosure, the kit further comprises agent for detecting aberration in TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 or C10rfl86, or any combination thereof, thereby detecting the epidemiological parameter or clinical parameter, or a combination thereof.

The present disclosure further relates to a kit for detecting epidemiological parameter or clinical parameter, or a combination thereof in a subject having or suspected of having HNSCC, wherein the kit comprises agent for detecting aberration in gene selected from a group comprising TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 and C10rfl86, or any combination thereof. In an embodiment of the present disclosure, the epidemiological parameter or clinical parameter is selected from a group comprising HPV infection, tumor nodal status, risk habits, tumor stage and tumor recurrence status.

In an embodiment of the present disclosure, the aberration in TMEM179 and ARX detects HPV infection; aberration in TSPAN7, AY660578 and C10rfl86 detects tumor nodal status; aberration in FUT3 and TREVI5 detects risk habits; aberration in SLC9A9 and NPAS3 detects tumor stage; and aberration in RPS6KA2 and MAP3K8 detects tumor recurrence status.

The present disclosure further relates to agent for use in detecting aberration of GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1, SP6, TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578, C10rfl86, NR4A3 (Nor-1) or BCL2L11, or any combination thereof.

In an embodiment of the present disclosure, the HNSCC is selected from a group comprising cancer of oral cavity, nasopharyngeal cancer, oropharyngeal squamous cell carcinomas, cancer of hypopharynx, laryngeal cancer and cancer of trachea.

In another embodiment of the present disclosure, the aberration is selected from a group comprising methylation, over expression, under expression and histone modification, or any combination.

In yet another embodiment of the present disclosure, the aberration is methylation selected from group comprising hypomethylation and hypermethylation; wherein the gene selected from a group comprising MiR-lOB, EMX20S, CENPVL1, RARRES2, SHF and SP6, or any combination thereof is hypermethylated and the gene selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, BTK, Cl lorf53, COL9A1, DEFA1, and ORDF3, or any combination thereof is hypomethylated; and wherein the gene ENAH is hypermethylated or hypomethylated.

In still another embodiment of the present disclosure, the methylation selected from a group comprising hypomethylation and hypermethylation is ranging from about 5% to 50%. The present disclosure further relates to a method for predicting disease-free survival in a subject having or suspected of having HNSCC, said method comprise detecting aberration in MiR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof.

The present disclosure further relates to a kit comprising an agent for predicting disease- free survival in a subject having or suspected of having HNSCC, wherein the agent detects aberration in MiR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, thereby predicting disease-free survival in the subject.

The present disclosure further relates to aberration of MirR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, wherein the aberration predicts disease-free survival in a subject having or suspected of having HNSCC.

The present disclosure further relates to use of aberration of MirR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, wherein the use predicts disease-free survival in a subject having or suspected of having HNSCC.

In an embodiment of the present disclosure, the aberration is selected from a group comprising methylation, over expression, under expression and histone modification, or any combination, preferably methylation selected from a group comprising hypermethylation and hypomethylation.

The present disclosure further relates to use of NR4A3 (Nor-1) or BCL2L11, or a combination thereof, as target for therapeutics in HNSCC. The present disclosure relates to indicators/ biomarkers of head and neck squamous cell carcinomas (HNSCC). Since there is a need of molecular biomarkers of head and neck squamous cell carcinoma (HNSCC), the present disclosure studies 'epigenetic changes' as biomarkers of HNSCC. DNA methylation is a process by which methyl groups are added to DNA. Methylation modifies the function of DNA, typically regulating gene expression. The role of altered DNA methylation (epigenetic change) in HNSCC is analysed in the present disclosure.

Accordingly, the present disclosure relates to a method of analysing the role of epigenetic changes, in particular, DNA methylation in HNSCC, said method comprising steps of: a) carrying out genome-wide methylation profiling of tumor and normal samples; b) carrying out genome-wide gene expression profiling of tumor and normal samples; c) comparing methylation data obtained in step (a) and gene expression data obtained in step (b), and determining the extent of differential methylation in the sub-regions of various genes to be correlated with levels of differential gene expression; and d) employing machine learning algorithm to the results obtained in step (c) to analyse the role of epigenetic changes, in particular, DNA methylation, and determining minimal signatures based on significant differential DNA methylation, in HNSCC.

In an embodiment of the present disclosure, analysing the role of epigenetic changes/alterations, in particular, differential DNA methylation, in HNSCC involves both qualitative and quantitative analysis. In another embodiment of the present disclosure, the above method determines minimal DNA methylation signature as indicator/biomarker in HNSCC, more particularly, OTSCC. In a preferred embodiment, the above method analyses aberration/alteration in DNA methylation and its correlation with gene expression to determine the role of epigenetic changes in HNSCC.

In another exemplary embodiment, the above method in addition to determining minimal tumor- specific methylation signature in HNSCC, also determines differentially methylated regions of genes as predictors of various clinical and epidemiological parameters in HNSCC. Accordingly, the present method provides DNA methylation signatures as indicators/predictors/biomarkers of clinical and epidemiological parameters such as but not limited to risk habits, nodal status, tumor staging, prognosis and HPV infection.

In another exemplary embodiment of the present disclosure, the above method also provides aberration of DNA methylation and associated aberrant gene expression of their target(s), and their correlation with disease-free survival (DFS). In particular, the above method provides differential DNA methylation and associated aberration in gene expression as indicators/predictors/bio markers of DFS.

The present method performs genome- wide methylation profiling using about 485,512 probes/loci for about 52 pairs of OTSCC in order to identify and quantify genome-wide methylation signatures. The functional impact of altered methylation is studied by comparing methylation data with gene expression profiling and the extent of differential methylation in the sub-regions of various genes is determined to be correlated with levels of differential gene expression. Aberrant methylation is determined wherein a total of about 27,276 genes are hypermethylated, and about 21,231 genes are hypomethylated with an average Δβ (differential methylation) of at least 0.2, in OTSCC. Further, machine learning based algorithm is employed to determine genes showing significant differential methylation and thus arriving at specific methylation signatures in OTSCC.

Thus, in an exemplary embodiment, the above method determines a minimal DNA methylation signature in HNSCC, particularly OTSCC, wherein said methylation signature comprises differential/aberrant methylation in one or more genes consisting of GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF and SP6.

The aforementioned method further provides DNA methylation signature as indicators/predictors/biomarkers of risk habits, nodal status, tumor staging, prognosis, recurrence and HPV infection, in HNSCC, particularly OTSCC wherein, said signature comprises differential/aberrant methylation in one or more genes selected from a group consisting of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8. As used in the present disclosure, head and neck squamous cell carcinomas (HNSCC) refers to cancers including but not limiting to cancers of oral cavity including the inner lip, tongue, floor of mouth, gingivae, and hard palate, nasopharyngeal cancer, oropharyngeal squamous cell carcinomas (OSCC), cancer of hypopharynx, laryngeal cancer and cancer of trachea. The said terms/phrases are used interchangeably in the present disclosure and should be construed accordingly. In an exemplary embodiment of the present disclosure, the HNSCC is oral tongue squamous cell carcinoma (OTSCC).

In an exemplary embodiment, the aforementioned method of analysing the role of epigenetic changes, in particular, DNA methylation in HNSCC, specifically involves the following steps:

a) carrying out genome-wide methylation profiling of tumor and normal samples using kit Infinium HumanMethylation 450K beadchip kit, or any other kit capable of performing methylation profiling;

b) data pre-processing and differential methylation analyses using tools selected from a group comprising GenomeStudio Methylation Module v 1.9.0, R Bioconductor packages such as wateRmelon, minfi, ComBat, any other batch correction tool and any other tool capable of performing data pre-processing and differential methylation analyses, or any combination thereof;

c) carrying out genome-wide gene expression profiling of tumor and normal samples using whole-genome gene expression microarrays Illumina HumanHT-12 v4 expression BeadChip, or any other kit capable of performing gene expression profiling;

d) data pre-processing and differential expression analyses using tools selected from a group comprising GenomeStudio, R Bioconductor packages such as lumi, ComBat, limma and any other tool capable of performing data pre-processing and differential expression analyses, or any combination thereof;

e) comparing methylation data obtained in step (a) and gene expression data obtained in step (b) using tools selected from a group comprising Ensemble v75 database, any other annotation database, or a combination thereof to determine the extent of differential methylation in the sub-regions of various genes to be correlated with levels of differential gene expression.

f) taking individual differentially methylated loci in the tumor genome and combining with the gene expression data for downstream genes and/or microRNA targets to define regions that are functionally important called as differentially methylated region or DMR that is linked with gene expression changes obtained in (a), (b), (c), and (d); and

g) employing machine learning algorithm to the results obtained in steps (e) and (f) and analysing the role of epigenetic changes, in particular, differential DNA methylation, and determining minimal signatures based on significant differential DNA methylation, in HNSCC; wherein the machine learning algorithm selected from a group comprising random forest analysis, or any machine learning supervised, unsupervised and semi-supervised leaning and classification-based methods and transduction and reinforcement learning methods, or any combination thereof.

In an embodiment, the above method optionally comprises validating the data using quantitative methylation- specific PCR (qMSP) and TCGA project. Accordingly, the present disclosure specifically provides minimal DNA methylation signature as indicators/ biomarkers of HNSCC. In an embodiment of the present disclosure, the signature is one or more genes selected from a group of 16-genes consisting of GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVL1, COL9A1, DEFAl, ORDF3, RARRES2, SHF and SP6 which show significantly aberrant/differential methylation in HNSCC and thus serve as biomarkers of HNSCC. In exemplary embodiments, methylation signature is selected from a group comprising (a) GPER1, OR2T6, RHPN1 and TTLL8; (b) EMX20S, ENAH and MiR-lOB; (c) BTK, CENPVL1, DEFAl, RARRES2, SHF, Cllorf53, COL9A1, ODF3, RHPN1 and SP6. In another exemplary embodiment, the methylation signature is a 16-gene combination viz. GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVL1, COL9A1, DEFAl, ORDF3, RARRES2, SHF and SP6. In yet another exemplary embodiment, the methylation signature is any combination of genes selected from a group consisting of GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVL1, COL9A1, DEFAl, ORDF3, RARRES2, SHF and SP6. In an embodiment of the aforesaid methylation signature, differential/aberrant methylation refers to hypermethylation, hypomethylation, or a combination thereof. In an exemplary embodiment, MiR-lOB, EMX20S, CENPVL1, RARRES2, SHF and SP6 genes are hypermethylated. In another exemplary embodiment, GPER1, TTLL8, RHPN1, OR2T6, BTK, Cllorf53, COL9A1, DEFAl, and ORDF3 genes are hypomethylated. In yet another exemplary embodiment, ENAH shows sample specific differential methylation. In one embodiment, ENAH is hypermethylated. In another embodiment, ENAH is hypomethylated . The present method also provides minimal DNA methylation signatures as indicators/predictors/biomarkers of clinical and epidemiological and clinical parameters selected from a group comprising but not limiting to risk habits, nodal status, tumor staging, prognosis, recurrence and HPV infection in HNSCC, particularly OTSCC. One or more genes selected from a group consisting of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8 act as signatures wherein said genes which show significantly aberrant/differential methylation and thus serve as indicators/predictors/biomarkers of clinical and epidemiological parameters in HNSCC, particularly OTSCC. In exemplary embodiments, differential/aberrant methylation in TMEM179 and ARX serve as predictor of HPV infection status; differential/aberrant methylation in TSPAN7, AY660578 and C10rfl86 serve as predictor of tumor nodal status; differential/aberrant methylation in FUT3 and TRIM5 serve as predictor of risk habits; differential/aberrant methylation in SLC9A9 and NPAS3 serve as predictor of tumor stage; and differential/aberrant methylation in RPS6KA2 and MAP3K8 serve as predictor of tumor recurrence status.

The present method also provides differential/aberrant methylation in a micro-RNA, MiR- lOB and associated aberrant expression of its target genes NR4A3 (Nor-1) and BCL2L11 as indicators/predictors/biomarkers of disease-free survival (DFS). In an embodiment, differential/aberrant methylation in MiR-lOB is inversely correlated to the expression of its target genes NR4A3 (Nor-1) and BCL2L11 respectively. In an exemplary embodiment, the above method provides aberrant methylation in MiR-lOB and the associated gene expression changes in two of its validated gene targets NR4A3 and BCL2L11 correlates to better DFS. In one embodiment, aberrant methylation in MiR-lOB refers to hypermethylation in MiR-lOB and gene expression changes in NR4A3 and BCL2L11 refers to downregulation of NR4A3 and BCL2L11.

The present disclosure further provides NR4A3 (Nor-1) and BCL2L11 as drug targets in HNSCC, particularly OTSCC. The disclosure helps in finding novel drug candidates and/or lead to using existing drugs, for example, compounds against the nuclear receptors, for HNSCC based on gene expression changes in NR4A3 (Nor-1), BCL2L11 or a combination thereof. Thus, in a preferred embodiment, the present disclosure determines expression changes in NR4A3 (Nor-1), BCL2L11 or a combination thereof, as potential drug targets and methylation changes in MiR-lOB as a linkage to said gene expression changes.

The method of the present disclosure also provides pathways altered in HNSCC, in particular OTSCC. In an embodiment, the altered pathways in OTSCC are determined by integrating the loss-of-heterozygosity (LOH), somatic copy number alterations (CNAs) and the gene expression data in OTSCC with the differentially methylated genes described as above. In an exemplary embodiment, the pathways altered in OTSCC are selected from a group comprising but not limiting to skeletal muscle signalling, olfactory signalling, metabolism, actin nucleation, regulation of chromatin assembly and gene expression, and also specific processes and gene clusters such as matrix metalloproteinases, signalling by NOTCH1 PEST domain mutations, toll like receptor 7/8 cascade, loss of Smad2/3 function, TCR signalling, CDK-mediated phosphorylation and removal of CDC6, and FGFR signalling, cancer-specific signalling pathways such as apoptosis and influence of proteoglycans.

As used in the present disclosure, the term "aberration" includes but is not limiting to alteration in expression including up-regulation/over expression or down-regulation/under expression, epigenetic changes including DNA methylation, histone modification and microRNA- (miRNA) and non-coding RNA (ncRNA)-associated gene silencing or any combination of aberrations thereof. In specific embodiments of the present disclosure, aberrations include DNA hypermethylation or hypomethylation in genes forming signatures as per the instant disclosure. In other specific embodiments, the aberrations include up-regulation or down-regulation of target genes of the genes forming tumor- specific DNA signatures as per the instant disclosure.

The present disclosure relates to a method of detecting HNSCC in a sample having or suspected of having HNSCC, wherein said method comprises determining aberration(s) in one or more genes selected from a group consisting of GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllor 53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF and SP6. In an embodiment of the present disclosure, determination of aberration(s) in said genes include analysing DNA methylation of the genes. In a specific embodiment, hypermethylation and hypomethylation of said genes of the 16-gene methylation signature is determined to detect the HNSCC in a sample having or suspected of having HNSCC. In an exemplary embodiment of the present disclosure, the method of detecting HNSCC in a sample having or suspected of having HNSCC comprises acts of:

(a) contacting the sample with an agent or performing steps of a biomarker detection technique to determine aberration in one or more genes of the 16-gene signature viz. GPER1, TTLL8, RHPNl, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVLl, COL9A1, DEFA1, ORDF3, RARRES2, SHF and SP6; and

(b) detecting the HNSCC based on step (a) wherein aberration in said one or more genes correlate to the presence of HNSCC in said sample or vice- versa.

In an embodiment of the method as described above, the aberration(s) in the 16-gene signature relates to hypermethylation or hypomethylation of genes GPER1, TTLL8, RHPNl, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVLl, COL9A1, DEFA1, ORDF3, RARRES2, SHF and SP6. In specific embodiments of the above method, aberration is determined in gene combination selected from a group comprising (a) GPER1, OR2T6, RHPNl and TTLL8; (b) EMX20S, ENAH and MiR-lOB; and (c) BTK, CENPVLl, DEFA1, RARRES2, SHF, Cllorf53, COL9A1, ODF3, RHPNl and SP6. In an exemplary embodiment, the aberration is determined for the 16-gene combination viz. GPER1, TTLL8, RHPNl, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVLl, COL9A1, DEFA1, ORDF3, RARRES2, SHF and SP6. In yet another exemplary embodiment, the aberration is determined for any combination of genes selected from a group consisting of GPER1, TTLL8, RHPNl, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVLl, COL9A1, DEFA1, ORDF3, RARRES2, SHF and SP6. The present disclosure also relates to a method of detecting clinical and/or epidemiological parameters in a sample having or suspected of having HNSCC, wherein said method comprises determining aberration(s) in one or more genes consisting of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8. In an embodiment of the present disclosure, determination of aberration(s) in said genes include analysing DNA methylation of the genes. In a specific embodiment, hypermethylation and hypomethylation of said genes is determined to detect the clinical and/or epidemiological parameters in a sample having or suspected of having HNSCC. In an exemplary embodiment, clinical and epidemiological parameters are selected from a group comprising but not limiting to risk habits, nodal status, tumor staging, prognosis, recurrence and HPV infection. In specific embodiments, aberrant methylation in TMEM179 and ARX are indicators of HPV infection status; differential/aberrant methylation in TSPAN7, AY660578 and C10rfl86 are indicators of tumor nodal status; differential/aberrant methylation in FUT3 and TRIM 5 are indicators of risk habits; differential/aberrant methylation in SLC9A9 and NPAS3 are indicators of tumor stage; and differential/aberrant methylation in RPS6KA2 and MAP3K8 are indicators of tumor recurrence status.

In an exemplary embodiment of the present disclosure, the method of detecting clinical and/or epidemiological parameters in a sample having or suspected of having HNSCC comprises acts of:

(a) contacting the sample with an agent or performing steps of a biomarker detection technique to determine aberration in one or more genes selected from a group consisting of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8 genes ; and

(b) detecting the clinical and/or epidemiological parameters based on step (a) wherein aberration(s) in one or more genes correlates to the clinical and/or epidemiological parameters in HNSCC. In an embodiment of the method as described above, clinical and epidemiological parameters are selected from a group comprising but not limiting to risk habits, nodal status, tumor staging, prognosis, recurrence and HPV infection.

In another embodiment of the method as described above, the aberration in one or more genes comprises hypermethylation or hypomethylation of genes.

In yet another embodiment of the present disclosure, aberration in the gene methylation signatures as described above to detect HNSCC and/or clinical and epidemiological parameters in HNSCC is determined with the help of an agent selected from a group comprising primer, probe, antibody, nanoparticles and a suitable interacting protein/biological agent capable of detecting aberrant methylation in genes of said methylation signature, or any combination thereof. In another preferred embodiment, said agent is employed for determining aberrations in one of more genes selected from a group consisting of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8 genes.

In another embodiment of the present disclosure, aberration(s) in the gene methylation signatures as described above is detected by employing techniques like assaying the bisulphite converted DNA that can distinguish methylated from unmethylated cytosine residues in DNA, selected from a group comprising but not limiting to solution- based assays and solid-support based assays, or a combination thereof. In yet another embodiment of the present disclosure, gene aberration is determined by employing techniques selected from a group comprising but not limiting to Sequencing, Reporter gene technique, PCR, Northern Blotting, fluorescence- based assays, luminescence/chemiluminescence-based assays, in-situ hybridization, Serial analysis of gene expression (SAGE), microarrays, tiling array, RNA Sequencing /Whole Transcriptome Shotgun Sequencing (WTSS) and electrochemical assays, or any combination of techniques thereof.

In still an embodiment of the present disclosure, HNSCC is selected from group comprising cancers of oral cavity including the inner lip, tongue, floor of mouth, gingivae, and hard palate, nasopharyngeal cancer, oropharyngeal squamous cell carcinomas (OSCC), cancer of hypopharynx, laryngeal cancer and cancer of trachea. In an exemplary embodiment of the present disclosure, the cancer is OTSCC.

As used herein, the term 'sample' refers to any biological material/fluid/cell having or suspected of having tumor/cancer, or a biological material/fluid/cell which is not affected with tumor/cancer. Further, a sample may be derived from humans and/or mammals, or the sample may be any biological fluid prepared/obtained in a laboratory.

The present disclosure further provides a method of prognosis or predicting survival in a subject having HNSCC, said method comprising determining genetic aberration in the subject. In an embodiment of the present disclosure, said determination of gene aberration includes analysing DNA methylation, gene expression, or a combination thereof. In a specific embodiment, said determination of gene aberrations includes analysing DNA methylation in MiR-lOB, gene expression in NR4A3 and/or BCL211, or a combination thereof. In an exemplary embodiment of the above method, alteration in methylation of MiR-lOB, gene expression changes of NR4A3 and/or BCL211, or a combination thereof associates with better disease free survival in HNSCC subjects.

In another exemplary embodiment of the present disclosure, the aforesaid method of prognosis or predicting survival in a HNSCC subject comprises acts of:

(a) contacting a sample obtained from the HNSCC subject with an agent or performing steps of a biomarker detection technique to determine aberration selected from a group comprising methylation changes of MiR-lOB, expression changes of NR4A3 and BCL211, or any combination thereof; and

(b) predicting survival in HNSCC based on step (a) wherein one or more aberration correlates to better survivability in the HNSCC subject.

The present disclosure further relates to a kit for detecting aberration in genes in a sample having or suspected of having said aberrated genes, wherein the gene is selected from a group comprising (1) one or more genes selected from 16-genes consisting of GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVL1, COL9A1, DEFA1, ORDF3, RARRES2, SHF and SP6 , (2) one or more genes selected from a group consisting of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8, and (3) one or more genes selected from a group consisting of MiR-lOB, NR4A3 and BCL211. In an embodiment, said kit comprises suitable agent(s) to detect aberration in one or more genes from the 16-genes, wherein the aberration is DNA methylation, more specifically hypermethylation, hypomethylation, or a combination thereof; and an instruction manual thereof. In another embodiment, the kit comprises suitable agent(s) to detect hypermethylation, hypomethylation, or a combination thereof in one or more genes selected from a group consisting of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8; and an instruction manual thereof. In yet another embodiment, said kit comprises suitable agent(s) to detect methylation changes in MiR-lOB, gene expression changes in NR4A3 and/or BCL211, or a combination thereof; and an instruction manual thereof. In still another embodiment, the agent is selected from a group comprising primer, probe, antibody, nanoparticle, suitable interacting protein/biological agent capable of interacting and detecting aberration in said genes, or any combination thereof. The present disclosure further relates to a kit for detecting HNSCC in a sample having or suspected of having HNSCC, wherein said kit comprises suitable agent(s) to detect aberration in one or more genes selected from a group consisting of GPER1, TTLL8, RHPNl, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cllorf53, CENPVLl, COL9A1, DEFA1 , ORDF3, RARRES2, SHF and SP6; and an instruction manual thereof.

The present disclosure also provides a kit for detecting clinical and epidemiological parameters in a sample having or suspected of having HNSCC, wherein said kit comprises suitable agent(s) to detect aberration in one or more genes selected from a group consisting of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8; and an instruction manual thereof.

The present disclosure also provides a kit for prognosis or predicting survival in a subject having HNSCC wherein, said kit comprises suitable agent(s) to detect one or more aberrations selected from a group comprising methylation changes of MiR-lOB, gene expression changes in NR4A3 and/or BCL211, or a combination thereof; and an instruction manual thereof.

In an embodiment, the aforesaid agents of the kit are selected from a group comprising primer, probe, antibody, nanoparticle, suitable interacting protein/biological agent capable of interacting and detecting aberration in said genes, or any combination thereof.

Accordingly, the present disclosure also relates to the aforementioned agents such as primer, probe, antibody and nanoparticles, suitable interacting protein/biological agent capable of interacting with above mentioned genes, or any combination thereof.

The technology of the instant application is further elaborated with the help of following examples, tables and figures. However, the examples, tables and figures should not be construed to limit the scope of the present disclosure.

EXAMPLES:

MATERIALS AND METHODS

Informed consent, ethics approval and patient samples used in the study Informed consent was obtained voluntarily from each patient and ethics approval was obtained from the Institutional Ethics Committees of the Mazumdar Shaw Medical Centre. Tumor and matched control (blood and/or adjacent normal tissue) specimens were collected and used in the study. Only those patients, where the histological sections 5 confirmed the presence of squamous cell carcinoma with at least 70% tumor cells in the specimen, were used in the current study. At the time of admission, patients were asked about the habits (chewing, smoking and/or alcohol consumption). Fifty-two treatment- naive patients who underwent staging according to AJCC criteria, and curative intent treatment as per NCCN guideline involving surgery with or without post-operative 10 adjuvant radiation or chemo-radiation at the Mazumdar Shaw Medical Centre were accrued for the study (Table 1).

Table 1: Patient details TRisk Habits (N = None, S = Smoking, A = Alcohol, C = Chewing), Clinical Staging (T = tumor stage, N = nodal status, M = metastasis), 15 Grade(SCC=Squamous Cell Carcinoma, WD=Well differentiated, MD=moderately differentiated PD=Poorly differentiated), and Treatment (CT = Chemotherapy, RT =

Radiotherapy), DFS (disease free survival)!

EXAMPLE 1

Whole-genome methylation assay, data pre-processing and differential methylation analyses

Genomic DNA was extracted from tissues using DNeasy Blood and Tissue kit (Qiagen, 5 Valencia, CA) as per the manufacturer's specifications. Genomic DNA quality was assessed by agarose gel electrophoresis and samples with intact, high molecular weight DNA bands were used for bisulfite conversion. Five hundred nanograms of genomic DNA was used for bisulphite conversion using the Zymo EZ DNA methylation kit (Zymo Research Corp., USA) as per the manufacturer' s instructions and eluted in 12 μΐ of elution buffer. Four microliter of the bisulphite converted DNA was used as template for target preparation to hybridize on the beadchip and was processed as per the manufacturer's instructions following Infinium HD and Infinium protocol for HumanMethylation450K BeadChip (Illumina, San Diego, CA) respectively. Data was collected using the HiScan (Illumina) reader and analyzed with GenomeStudio V2011.1 methylation module 1.1.1 (Illumina).

Methylation450 BeadChip probe alignment and removal of ambiguous probes

The probe sequences were aligned using bowtie2, while allowing up to 2 mismatches, to the human reference hgl9 sequence to detect and exclude probes mapping ambiguously to multiple locations in the reference genome. 17,649 probes were found to be ambiguously mapping in this manner. These probes were mapped to multiple locations, ranging from 2 to 6265.

Data pre-processing and differential methylation analyses

Raw data for 52 tumonmatched control sample pairs obtained from the Illumina Infinium Methylation450 BeadChip scanned using the HiScan system were analyzed in GenomeStudio Methylation Module v 1.9.0 (Illumina) for assay controls, before importing the beta ( )-values from GenomeStudio for analysis in R. For each CpG site, ratio of fluorescent signal was measured by that of a methylated probe relative to the sum of the methylated and unmethylated probes, termed as β value. Beta-values, ranging from 0 (no methylation) to 1.0 (100% methylation of both alleles), were recorded for each probe on the array. The beta-value (β) is calculated using the below formula: β = ^Pm

P + P

where P_m is the signal intensity of the methylation detection probe and P_um is the signal intensity of the non-methylation detection probe. Infinium methylation assay has built-in sample-dependent and -independent controls, which are used to test the overall efficiency of the assay for each sample. Raw signal intensities were exported as Adat files, pre- processed (transformed and normalized) and analyzed using R Bioconductor packages, watermelon, minfi and ComBat, to estimate differential methylation. The Illumina 450K data was preprocessed using a functional normalization procedure implemented by the 'preprocessFunnorm' function within the R Bioconductor package 'minfi'. Post functional normalization, differentially methylated probes (DMPs) were identified using 'bumphunter' function in minfi, retaining significant ones with p value <= 0.05 and fwer <= 0.05. Dasen normalization was also performed using wateRmelon to obtain differentially methylated loci using minfi. Density plots show a clear difference before and after normalization. The normalized data was further batch-corrected using ComBat using Empirical Bayes methods. Dasen-normalized DMPs corresponding to these DMRs were extracted. Postprocessing of the differentially methylated data for visualization was performed.

EXAMPLE 2

Whole- genome gene expression assay

Whole-genome gene expression profiling was carried out with Illumina HumanHT-12 v4 expression BeadChip (Illumina, San Diego, CA) with 21 tumors and their matched adjacent normal tissues. Total RNA was extracted using PureLink RNA mini kit (Invitrogen) and RNA quality was checked on the bioanalyzer using RNA Nano6000 chip (Agilent). RNA samples with poor RIN numbers (<7) on the bioanalyzer chip, indicating partial degradation of RNA were processed using Illumina WGDASL assay and the ones with good RIN numbers (>9) were labelled using Illumina TotalPrep RNA Amplification kit (Ambion) as per the manufacturer's recommendations. Targets were used to hybridize arrays and arrays were processed according to the manufacturer's recommendations. Arrays were scanned using HiScan, Illumina, and the data collected were analyzed with GenomeStudio V2011.1 Gene Expression module 1.9.0 (Illumina) to check for the assay quality control probes. Raw signal intensities were exported from GenomeStudio for transformation, normalization and differential expression analyses using R. Data pre-processing and differential expression analyses

Whole-genome gene expression data were analyzed using final reports exported from Genome Studio, using the R Bioconductor packages, lumi. The VST transformation and LOESS normalization methods in lumi, performed best in pre-processing data from the DASL and WG assays. They were chosen based on their highest mean IACs (Inter-array correlation coefficients), among various combinations of three types of transformations (VST, log₂ and cubicRoot) and six types of normalizations (SSN, Quantile, RSN, VSN, Ranklnvariant and LOESS). The pre-processed data was further batch-corrected using ComBat, since the experiments had been performed in multiple batches. The batch- corrected gene expression data was then analyzed for differential expression using limma. This method of transformation, normalization and batch-correction was done as described previously.

Results of Examples 1 and 2:

Analysis workflow and differentially methylated loci in OTSCC

The analytical pipeline for genome-wide methylation, gene expression data along with linking methylation with gene expression and validation is given in Figure 1. The analysed data was normalized by multiple methods, batch corrected and then used for the identification of the differentially methylated probes (DMPs) and regions (DMRs) in the tumors.

10% excess hyper-methylated DMRs than hypo-methylated DMRs were found in OTSCC patient samples (Figure 2A). The data was consistent with that from the TCGA cohort for the oral tongue subsite and was stronger (greater than thrice) for the entire HNSCC patient samples from TCGA, comprising multiple subsites, including that of oral tongue, for which there was 20% more DMPs in the hyper- methylated fraction (Figure 2A). The fraction of hyper-methylated probes located within the CpG islands, TSS 1500 and promoter sub-regions, combined, was higher, whereas hypo-methylated probes were more frequent in the gene bodies (Figure 2B) for the OTSCC cancer type. These trends were more pronounced for the TCGA HNSCC patient samples. The average Δβ of island DMPs had a narrow distribution for the OTSCC and HNSCC (all and oral tongue subsites from the TCGA cohort), with medians as positive values, around 0.30 and 0.40, respectively, indicative of greater hyper-methylation events (Figure 2C). The inter-quartile (IQR) ranges for average delta beta distributions of TSS 1500 DMRs span both hyper- and hypo- methylation ranges, for the OTSCC and HNSCC cancer types, respectively. The median, however, was more indicative of hyper-methylation in the OTSCC, and hypo-methylation in the HNSCC. The average Δβ distributions were identical for island DMRs between the OTSCC and the TCGA HNSCC cohort for early and late stage tumors, with their medians suggestive of hyper-methylation (Figure 2D). The delta beta distribution for TSS1500 DMRs in OTSCC was much wider in the late stage patient samples, as compared to those in early stages of the disease and their medians (both -0.3-0.4) were representative of hyper-methylation. Chromosome-wise comprehensive representation of variants

The comprehensive representation of variants in a circular form using CIRCOS indicated that there is an avoidance of loss-of-heterozygosity (LOH) events in chromosomes 1 and 2 (Figure 2E). Overall, hyper- methylation is more commonly observed than hypo- methylation, where chromosomes 3, 4, 7, 8, 9, 10, 11, 14, 15, 18, 19 and 21 showed only hyper-methylation events, while chromosomes 12 and 22 showed only hypo-methylation, and chromosomes 13, X and Y had no major methylation events. Up-regulation of genes was more commonly observed with chromosomes 14, 18, 19, 21 and X harboring the events. Chromosome X had no major expression changes.

EXAMPLE 3

Correlation of methylation and expression data

All probes were annotated using the Ensemble v75 database, and classified based on their locations to genie sub-regions such as 5' and 3' UTRs, north and south shelves and shores, TSS 1500, promoters, CpG islands, gene bodies and first exons. The relationship between differential expression of genes and differential methylation of genie sub-regions were visualized.

Results

Gene level information was extracted using probes spanning hyper- or hypo-methylated regions that were up- or down-regulated respectively for the same patient sample pairs. This was done separately for 450K probe sets falling into genie sub-regions such as gene bodies, CpG islands, 5' and 3' UTRs, north and south shelves and shores, promoters, 1.5 kilo bases flanking the transcription start sites (TSS 1500) and first exons. A maximum number of 27 genes were found to be satisfying these criteria in the CpG islands and gene bodies, and the next highest number of 22 genes was in the first exon and 5' UTR (Figure 3). The magnitude of correlation between expression and methylation varied widely across genes for various sub-regions (Figure 3). The island region harbored the largest fraction of hyper- methylated and down-regulated genes, while the promoter and TSS 1500 sub regions displayed a strong inverse relationship between differential expression and differential methylation. This observation formed a basis for considering DMRs housed in these three sub-regions, as one of our training sets. EXAMPLE 4

Predicting minimal differential methylated loci using machine-learning algorithm

Sample-wise dasen-normalized and batch-corrected intensities of probes corresponding to differentially methylated regions, identified post functional normalization using minfi, along with the tumonnormal tissue type for each sample was used as a training set for the random forest analyses. Using the varSelRF (package in R), least important 0.2% of the probes were eliminated iteratively until the OOB error rate did not exceed in comparison to its value in the first or previous iteration. Five hundred of such iterations were performed and each of the iterations was permuted across 3000 random forest trees. A score was computed for predictive parameter sets across all iterations, for each sample, which factored in the prediction accuracy of the tissue type of that sample, the repeatability (number of instances out of 500) of the predictive parameter set, presence of other predictive parameter sets and the parameter set size. The first two weighed the score positively, and the latter two, negatively.

The random forest algorithm was also implemented to predict tumor- specific minimal methylation signature, this time training with a categorical input for the probes, instead of the actual intensity values. This training set was created based on the normalized Δβ (tumor-matched control β) values falling into predefined categories. Following the Δβ density distributions across probes, for -0.2 < Δβ < 0.2, bins were determined at an interval of 0.032, and for Δβ > 0.2, the binning frequency was reduced to 0.08. For e.g. Δβ = 0.02 for a probe corresponding to a tumonmatched control sample pair results in categories A for the tumor and B for the matched control, while Δβ = -0.02, would result in reversal of the categories, i.e. B for the tumor and A for the matched control. In addition to the above two DMP training sets, another categorical set was created, based on hypo- or hyper- methylation of DMRs housing only CpG island, TSS 1500 or promoter- associated probes. In this scenario, DMRs housing heterogenously methylated probes (hyper- and hypo-) were discarded, and only those with all hypo- or all hyper-methylated probes were considered.

0.632+ bootstrapping method

0.632+ bootstrapping was performed for the tissue type predicting random forest analyses using 100 replicates, and the errors were reported for prediction of matched control, tumor and both tissues. Results:

Tumor-specific DNA methylation probes (DMPs) and DNA methylation regions (DMRs) Using variable selection by elimination analyses, using the first training set implemented by the random forest approach, two sets of two probes (corresponding to GPER1 and OR2T6, TTLL8 and RHPN1 genes) we found, each (Figure 4A), to be the best predictors of matched control and tumor tissue types among the OTSCC patient samples. These probes along with others housed within the same DMRs showed a hypo-methylation signature for most tumor samples when compared to the normal samples (Figure 4A). The signature was strongest for the predicted probe within each DMR. Using the categorical training set based on binning Δβ of paired tumor and matched control samples, we further identified a minimal methylation signature with three DMPs, corresponding to MiR-lOB, ENAH and EMX20S genes (Figure 4B). MiR-lOB and EMX20S DMPs and the DMRs housing them, were distinctly hyper-methylated for the majority of tumors, while ENAH DMP exhibited a mixed trend. The DMR-derived categorical training set identified 11 DMR minimal signature with the highest sensitivity and specificity (Figure 4C). This signature also harbored RHPN1, along with COL9A1, Cl lorf53, BTK, ODF3, CENPVL1, SP6, DEFA1, SHF and RARRES2 genes. The DMRs corresponding to BTK, Cl lorf53, COL9A1, DEFA1, ODF3 along with RHPN1, were hypo-methylated, while the remaining were relatively hyper-methylated. 0.632+ bootstrapping errors were low, with a median error of -0.025, for predictions of normal, tumor or either tissues.

EXAMPLE 5

Validation of methylation data

Validation of methylation data by quantitative methyl-Specific PCR (qMSP)

For the validation experiment, qMSP was used. The gene for β-actin was used as an internal control gene. Fully methylated and un-methylated bisulfite-converted human DNA standard from Zymo Research were used as control to test the efficiency of bisulphite conversion. Bisulphite converted DNA was used with primers designed using MethPrimer, to bind to methylated and unmethylated DNA separately. Bisulphite converted genome DNA using the EZ DNA Methylation Kit (Zymo Research) was used according to the manufacturer's instructions for a total of 26 sample pairs, and universal methylated and unmethylated human DNA standards D5011 (Zymo Research). Alternate overnight incubation conditions were used for bisulphite conversion (Illumina Infinium Methylation Assay: 95°C for 30 sec, 50°C for 60 min.) x 16 cycles, then "hold" at 4°C. One microliter of converted DNA was used for each PCR with Platinum Taq DNA Polymerase Kit (Invitrogen) and a final concentration of 4.5mM MgCk. The thermocycling condition was 3 min at 95°C for heat activation, and 40 cycles of 3 sec at 94°C, 20 sec at 54°C and 1 min at 72°C, followed by a dissociation curve. The bisulphite converted DNA was eluted in a volume of 20μ1. One microliter of bisulphite DNA elute was used for each reaction in the SYBR Q-PCR. The ACT of each sample for test gene was normalised with its respective Beta-actin gene ACT. Post normalization, the AACT was calculated for each paired sample in order to report the level of differential methylation for different gene in a given sample. The AACT values of the methylated/unmethylated Bisulphite Human DNA standard from Zymo were used as a control to for the experiment.

Validation of minimal methylation signature in TCGA HNSCC data

The best-scoring signatures identified from the random forest analyses on the OTSCC data, using three training sets, were examined in either 24 tumor: matched control pairs (oral tongue subsite only) or 50 tumonmatched control pairs (all subsites in head and neck region) from the TCGA cohort.

Results: Following the identification, biological validation of a few regions that were differentially methylated using quantitative MSPs (qMSP) of bi-sulphite converted tumor and matched normal DNAs was performed. The results were interpreted as tumor- specific hyper- methylation for negative AACt (ACt_tUmor-ACtnormai) values, and tumor- specific hypo- methylation for positive AACt values. Following this expectation, the differential methylation trend observed in the qMSP amplified regions in terms of AACt, was experimentally validated in 50-60% of the 26 tumors used for validation, in the case of DMRs spanning GPER1 and OR2T6, but only in 15% of the samples for RHPN1 (Figure 4D). The differential methylation trends observed in the tissue type predictive DMPs and DMRs derived using various training sets, also held true for the oral tongue and other subsite tumors from the TCGA HNSCC cohort (Figure 5A-C). We observed complete hypo-methylation in the case of ENAH gene, as opposed to the mixed trend seen in the OTSCC. Additionally, we also looked at the cell line data from ENCODE consortium that revealed DNAse I hyper-sensitivity and/or CpG methylation in these genes, including specific locations within the DMRs housing the tissue type predictive probes. EXAMPLE 6:

Minimal methylation signature for clinical parameters

Prediction of minimal methylation signature for various clinical parameters

Various epidemiological and clinical parameters were used for predictive analyses using the importance-based variable elimination by the random forest approach, where the Δβ of DMPs corresponding to DMRs were used as the training set. Minimal differential methylation signatures with the best scores were visualized for patients with different states for the selected epidemiological and clinical parameters. Results:

DMPs predicting various epidemiological and clinical parameters

Differential methylation (Δβ) for all sample pairs used as a training set along with five parameters, namely HPV infection, nodal status, risk habits, tumor stage and prognosis, resulted in best scoring DMPs corresponding to TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2 and MAP3K8 genes. The set of predictive DMPs displayed contrasting patterns of differential methylation among the various categories of each parameter (Figure 7).

EXAMPLE 7:

Role of MiR-lOB and target genes in disease free survival

Hyper-methylation in MiR-lOB and down-regulation of target gene expression linked differential methylation with disease free survival

Negative correlations were observed between the differential methylation of DMPs within the DMR spanning MiR-lOB gene, and the differential expression of two of its validated gene targets, NR4A3 and BCL2L11 (P = 0.0125 and 0.014; Figures 6A and 6B). NR4A3 was down-regulated in all except one tumor studied. The tumor- specific differential expression in NR4A3 and BCL2L11 genes was inversely correlated with disease-free survival (P = 0.0008 and 0.001; Figures 6C and 6D). Methylation in DMRs corresponding to the two other genes that came up in the above described minimal methylation signature, ENAH and EMX20S were also inversely correlated with the differential expression of their downstream targets (Figure 6E). EXAMPLE 8:

Pathway analyses

Visualization of variants

All the variants and the corresponding genes were visualized using Circos v0.66. LOHs, somatic SNPs and indels, copy number insertions and deletions with a >= 10% frequency of patients bearing them, genes undergoing significant expression and methylation changes were visualized using the circular genomic representation. All DNAsel hypersensitivity tracks, methylation, histone and chromatin modifications and TFBS tracks were loaded and visualized using the UCSC Genome Browser. Genome-wide profiles of differential expression and differential methylation of DMRS housing island/TSS 1500/promoter probes was visualized using GenomeGraphs, a tool in the UCSC Genome Browser.

Results:

A comprehensive pathway analyses of changes in expression and methylation, LOHs and copy number variations, reveal perturbations in a variety of basal housekeeping functions such as skeletal muscle signaling, olfactory signaling, metabolism, actin nucleation, regulation of chromatin assembly and gene expression, and also specific processes and gene clusters such as matrix metalloproteinases, signaling by NOTCH1 PEST domain mutations, toll like receptor 7/8 cascade, loss of Smad2/3 function, TCR signaling, CDK- mediated phosphorylation and removal of CDC6, and FGFR signaling, but also cancer- specific signaling pathways such as apoptosis and influence of proteoglycans.

Thus in the present method, about >482, 000 probes were used across the genome to profile genome-wide DNA methylation patterns in oral tongue tumors and compared the quantitative methylation differences between tumors and control tissues. The goal was also to provide distinct functional methylation signatures specific to the tongue tumors that are linked with gene expression changes. The results show the presence of higher number of hypo-methylated DMRs in OTSCC and from the HNSCC study from TCGA (Figure 2A). The overall hyper-methylation trend observed in CpG islands in present method (Figure 2B, C) is supportive of the basic theory that CpGs located within islands in normal cells are un-methylated, whereas those located outside of islands are methylated, with the an reverse pattern occurring in tumor cells. The shift from a global hyper-methylation trend in TSS 1500 regions, to that of a relatively hypo-methylation trend in the OTSCC, from early to advanced stages of the disease is observed from the present data/results (Figure 2D). A high level of correlation between methylation and associated gene expression levels is observed indicating the functional significance. Understandably, the numbers of genes showing such correlation were highest in the CpG islands and gene bodies and least for the shelves, shores and 3' UTR sub regions (Figure 3). Even within a given sub-region, it was observed that the nature of correlations vary. For example; within S shore, PLAU and PTHLH were hypo-methylated and over-expressed, NFIX is hyper-methylated and under- expressed, and COX7A1 displays an intermediate trend. The present study suggests the existence of changes in the extent of methylation among oral tongue tumors of different patients and that some of these changes are correlated with the expression of genes. Such inverse trends can also be observed at a genome-wide scale. Further, the TCGA HNSCC tumor subsites displayed a hyper-methylation trend for the MiR-lOB DMP and associated DMR, in a majority of samples (Figure 4C). The present analyses further picked TMEM179, a trans-membrane protein-coding gene, and ARX, a transcription factor, as the top genes, predictive of HPV infection status. Methylation probes corresponding to genes TSPAN7, AY660578 and C10rfl86 were predictive of tumor nodal status (Figure 6). Methylation in FUT3 and TRIM5 genes were predictive of risk habits. Differentially methylated probes in solute carrier family member, SLC9A9 and NPAS3 were predictive of tumor stage in our analysis. Methylation of probes in the kinases, RPS6KA2 and MAP3K8, were observed to be predictive of tumour recurrence status.

Further, the present study identified a signature containing 16 DMPs using a machine- learning algorithm (Figure 4A). Quantitative MSP within CpG islands spanning these probes locations further validated this observation (Figure 4).

NR4A3 (Nor-1) and BCL2L11 are targets of MiR-lOB. Gene expression changes in NR4A3 and hence BCL2L11, explained their association with improved disease-free survival. Nor- 1 is an orphan nuclear receptor, a member of the nuclear receptor superfamily, is one of the primary classes of therapeutic drug targets. The fact that Nor-1 was down-regulated in all tumors except one in our data is significant and opens possible doors to study it as a therapeutic molecule in NHSCC. In conclusion, a set of differential methylation signature with functional relevance in HNSCC, particularly, oral tongue tumors are provided by the present disclosure. The association between the differentially methylated signature with clinical and epidemiological parameters points to their potential application in diagnosis and disease stratification. Additionally, the data on Nor-1 presents potential avenues of exploiting the molecule as a target for therapeutic intervention in head and neck cancer.

Claims

A method of detecting head and neck squamous cell carcinoma (HNSCC) in a sample having or suspected of having the HNSCC, said method comprising step of detecting aberration in at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVLl, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6, optionally along with detecting aberration in the MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof.

The method as claimed in claim 1, wherein the method comprises detecting the aberration in:

OR2T6 and GPER1;

RHPN1 and TTLL8;

OR2T6, GPER1, RHPN1 and TTLL8;

MiR-lOB and ENAH;

GPRASP1, EMX20S and ENAH;

MiR-lOB, EMX20S and ENAH;

ORDF3, CENPVLl, RARRES2 and DEFA1; or

BTK, ORDF3, RHPN1, SP6, SHF, CENPVLl, Cl lorf53, COL9A1, RARRES2 and DEFA1.

The method as claimed in claim 1, wherein detecting the aberration comprises: carrying out genome-wide methylation profiling of tumor sample and normal sample;

The method as claimed in claim 3, wherein detecting the aberration comprises: carrying out genome-wide methylation profiling of tumor sample and normal sample employing kit selected from a group comprising Infinium Human methylation 450K beadchip, 27K beadchip quantitative methylation-specific polymerase chain reaction (qMSP), MSP, microarrays, and sequencing method or any combination thereof;

pre-processing and analysing differential methylation using tools selected from a group comprising genome studio methylation module vl .9.0, wateRmelon, minfi and comBat, or any combination thereof;

carrying out genome-wide gene expression profiling of tumor sample and normal sample employing kit selected from a group comprising illumine humaHT-12 v4 expression Beadchip, quantitiatve polymerase chain reaction (qPCR) PCR, microarrays and sequencing method, or any combination thereof; pre-processing and analysing differential expression using tools selected from a group comprising Genomestudio, lumi, comBat and limma, or any combination thereof;

identifying differentially methylated region by comparing differentially methylated loci in tumor genome and gene expression of downstream gene or microRNA targets or both; and

analysing the differential DNA methylation to detect the aberration and thereby detecting HNSCC, wherein the analysis is carried by employing tool selected from a group comprising random forest analysis, supervised machine learning, unsupervised machine learning and semi-supervised machine learning, transduction learning methods and reinforcement learning methods, or any combination thereof.

The method as claimed in claim 1, wherein the method further comprises detecting epidemiological parameter or clinical parameter, or a combination thereof in a subject having or suspected of having HNSCC by detecting the aberration in TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 or C10rfl86, or any combination thereof.

A method of detecting epidemiological parameter or clinical parameter, or a combination thereof in a subject having or suspected of having HNSCC by detecting aberration in gene selected from a group comprising TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, PAS3, RPS6KA2, MAP3K8, AY660578 and C10rfl86, or any combination thereof.

7. The method as claimed in claim 5 or claim 6, wherein the epidemiological parameter or clinical parameter is selected from a group comprising HPV infection, tumor nodal status, risk habits, tumor stage and tumor recurrence status.

8. The method as claimed in claim 7, wherein the aberration in TMEM179 and ARX detects HPV infection; aberration in TSPAN7, AY660578 and C10rfl86 detects tumor nodal status; aberration in FUT3 and TRFM5 detects risk habits; aberration in

SLC9A9 and PAS3 detects tumor stage; and aberration in RPS6KA2 and MAP3K8 detects tumor recurrence status.

9. Aberration of gene selected from group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CE PVLl, COL9A1,

DEFA1, ORDF3, RARRES2, SHF, SP6, GPRASP1, TMEM179, ARX, TSPAN7, FUT3, TRFM5, SLC9A9, PAS3, RPS6KA2, MAP3K8, AY660578, and C10rfl86, or any combination thereof, optionally along with aberration in the MiR-lOB associated gene selected from a group comprising R4A3 (Nor-1) and BCL2L11, or a combination thereof.

10. Use of aberration of at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVLl, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6, optionally along with aberration of MiR-lOB associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, for detecting HNSCC in a sample having or suspected of having the HNSCC.

11. Use of aberration of TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578 or C10rfl86, or any combination thereof for detecting epidemiological parameter or clinical parameter, or a combination thereof selected from a group comprising HPV infection, tumor nodal status, risk habits, tumor stage and tumor recurrence status, in a subject having or suspected of having HNSCC.

12. A kit for detecting HNSCC in a sample having or suspected of having the HNSCC, wherein the kit comprises agent for detecting aberration in at least two genes selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, MiR-lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVLl, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1 and SP6,

optionally along with aberration in the MiR-lOB associated gene selected from a group comprising R4A3 (Nor-1) and BCL2L11, or a combination thereof.

13. The kit as claimed in claim 12, wherein the kit further comprises agent for detecting aberration in TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, PAS3, RPS6KA2, MAP3K8, AY660578 or C10rfl86, or any combination thereof, thereby detecting the epidemiological parameter or clinical parameter, or a combination thereof.

14. A kit for detecting epidemiological parameter or clinical parameter, or a combination thereof in a subject having or suspected of having FINSCC, wherein the kit comprises agent for detecting aberration in gene selected from a group comprising TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, PAS3, RPS6KA2, MAP3K8, AY660578 and C10rfl86, or any combination thereof.

15. The kit as claimed in claim 13 or claim 14, wherein the epidemiological parameter or clinical parameter is selected from a group comprising FIPV infection, tumor nodal status, risk habits, tumor stage and tumor recurrence status.

16. The kit as claimed in claim 15, wherein the aberration in TMEM179 and ARX detects HPV infection; aberration in TSPAN7, AY660578 and C10rfl86 detects tumor nodal status; aberration in FUT3 and TREVI5 detects risk habits; aberration in SLC9A9 and NPAS3 detects tumor stage; and aberration in RPS6KA2 and MAP3K8 detects tumor recurrence status.

17. Agent for use in detecting aberration of GPER1, TTLL8, RHPN1, OR2T6, MiR- lOB, ENAH, EMX20S, BTK, Cl lorf53, CENPVLl, COL9A1, DEFA1, ORDF3, RARRES2, SHF, GPRASP1, SP6, TMEM179, ARX, TSPAN7, FUT3, TRIM5, SLC9A9, NPAS3, RPS6KA2, MAP3K8, AY660578, C10rfl86, NR4A3 (Nor-1) or BCL2L11, or any combination thereof.

18. The method as claimed in claim 1, or the aberration as claimed in claim 9, or the use as claimed in 10, or the use as claimed in 11, or the kit as claimed in claim 12, or the kit as claimed in claim 14, or the agent as claimed in claim 17, wherein the HNSCC is selected from a group comprising cancer of oral cavity, nasopharyngeal cancer, oropharyngeal squamous cell carcinomas, cancer of hypopharynx, laryngeal cancer and cancer of trachea.

19. The method as claimed in claim 1, or the aberration as claimed in claim 9, or the use as claimed in 10, or the use as claimed in 11, or the kit as claimed in claim 12, or the kit as claimed in claim 14, or the agent as claimed in claim 17, wherein the aberration is selected from a group comprising methylation, over expression, under expression and histone modification, or any combination.

20. The method as claimed in claim 1, or the aberration as claimed in claim 9, or the use as claimed in 10, or the use as claimed in 11, or the kit as claimed in claim 12, or the kit as claimed in claim 14, or the agent as claimed in claim 17, wherein the aberration is methylation selected from group comprising hypomethylation and hypermethylation; wherein the gene selected from a group comprising MiR-lOB, EMX20S, CE PVLl, RARRES2, SHF and SP6, or any combination thereof is hypermethylated and the gene selected from a group comprising GPER1, TTLL8, RHPN1, OR2T6, BTK, Cl lorf53, COL9A1, DEFA1, and ORDF3, or any combination thereof is hypomethylated; and wherein the gene ENAH is hypermethylated or hypomethylated.

21. The method or the aberration or the use or the kit or the agent as claimed in claim 20, wherein the methylation selected from a group comprising hypomethylation and hypermethylation is ranging from about 5% to 50%.

22. A method for predicting disease-free survival in a subject having or suspected of having HNSCC, said method comprise detecting aberration in MiR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof.

23. A kit comprising an agent for predicting disease-free survival in a subject having or suspected of having HNSCC, wherein the agent detects aberration in MiR-lOB and its associated gene selected from a group comprising R4A3 (Nor-1) and BCL2L11, or a combination thereof, thereby predicting disease-free survival in the subject.

24. Aberration of MirR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, wherein the aberration predicts disease-free survival in a subject having or suspected of having HNSCC.

25. Use of aberration of MirR-lOB and its associated gene selected from a group comprising NR4A3 (Nor-1) and BCL2L11, or a combination thereof, wherein the use predicts disease-free survival in a subject having or suspected of having HNSCC.

26. The method as claimed in claim 22, or the kit as claimed in 23, or the aberration as claimed in claim 24, or the use as claimed in claim 25, wherein the aberration is selected from a group comprising methylation, over expression, under expression and histone modification, or any combination, preferably methylation selected from a group comprising hypermethylation and hypomethylation.

27. The method as claimed in claim 22, or the kit as claimed in 23, or the aberration as claimed in claim 24, or the use as claimed in claim 25, wherein the HNSCC is selected from a group comprising cancer of oral cavity, nasopharyngeal cancer, oropharyngeal squamous cell carcinomas, cancer of hypopharynx, laryngeal cancer and cancer of trachea.

28. Use of NR4A3 (Nor-1) or BCL2L11, or a combination thereof, as target for therapeutics in HNSCC.