Novel Insight into Mutational Landscape of Head and Neck Squamous Cell Carcinoma

<div><p>Development of head and neck squamous cell carcinoma (HNSCC) is characterized by accumulation of mutations in several oncogenes and tumor suppressor genes. We have formerly described the mutation pattern of HNSCC and described NOTCH signaling pathway alterations. Given the complexity of the HNSCC, here we extend the previous study to understand the overall HNSCC mutation context and to discover additional genetic alterations. We performed high depth targeted exon sequencing of 51 highly actionable cancer-related genes with a high frequency of mutation across many cancer types, including head and neck. DNA from primary tumor tissues and matched normal tissues was analyzed for 37 HNSCC patients. We identified 26 non-synonymous or stop-gained mutations targeting 11 of 51 selected genes. These genes were mutated in 17 out of 37 (46%) studied HNSCC patients. Smokers harbored 3.2-fold more mutations than non-smokers. Importantly, TP53 was mutated in 30%, NOTCH1 in 8% and FGFR3 in 5% of HNSCC. HPV negative patients harbored 4-fold more TP53 mutations than HPV positive patients. These data confirm prior reports of the HNSCC mutational profile. Additionally, we detected mutations in two new genes, CEBPA and FES, which have not been previously reported in HNSCC. These data extend the spectrum of HNSCC mutations and define novel mutation targets in HNSCC carcinogenesis, especially for smokers and HNSCC without HPV infection.</p></div

Outlier Analysis Defines Zinc Finger Gene Family DNA Methylation in Tumors and Saliva of Head and Neck Cancer Patients

<div><p>Head and Neck Squamous Cell Carcinoma (HNSCC) is the fifth most common cancer, annually affecting over half a million people worldwide. Presently, there are no accepted biomarkers for clinical detection and surveillance of HNSCC. In this work, a comprehensive genome-wide analysis of epigenetic alterations in primary HNSCC tumors was employed in conjunction with cancer-specific outlier statistics to define novel biomarker genes which are differentially methylated in HNSCC. The 37 identified biomarker candidates were top-scoring outlier genes with prominent differential methylation in tumors, but with no signal in normal tissues. These putative candidates were validated in independent HNSCC cohorts from our institution and TCGA (The Cancer Genome Atlas). Using the top candidates, <i>ZNF14</i>, <i>ZNF160</i>, and <i>ZNF420</i>, an assay was developed for detection of HNSCC cancer in primary tissue and saliva samples with 100% specificity when compared to normal control samples. Given the high detection specificity, the analysis of ZNF DNA methylation in combination with other DNA methylation biomarkers may be useful in the clinical setting for HNSCC detection and surveillance, particularly in high-risk patients. Several additional candidates identified through this work can be further investigated toward future development of a multi-gene panel of biomarkers for the surveillance and detection of HNSCC.</p></div

Integrative methylation screening strategy.

<p>Schematic outline of the integrative approach utilized in this study, which combines high-throughput screening of DNA methylation and gene expression for the discovery cohort of HNSCC: employment of DNA methylation array data with 27,578 probes total; normalization of the data in R, 14,477 genes total; outlier analysis and cut-off to receive approximately 50 top genes (13.2 outlier score; 37 top ranked genes passed, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142148#sec002" target="_blank">Methods</a> for details); Integration of the normalized data from the expression assay (22,011 genes); Spearman’s correlation coefficient calculations (24 genes passed); 7 ZNFs bisulfite sequencing validation; qRT-PCR, 5 ZNFs gene expression validation; Validation of 3 ZNF QMSP detection in saliva and tumor samples in different cohorts.</p

Complete list of point mutations among the 11 cancer genes sequenced in the 37 HNSCC tumor tissues.

<p>Complete list of point mutations among the 11 cancer genes sequenced in the 37 HNSCC tumor tissues.</p

The 51 cancer genes selected for targeted or whole exon sequencing.

<p>The 51 cancer genes selected for targeted or whole exon sequencing.</p

Promoter DNA hypermethylation detection in salivary rinses of HNSCC and non-cancerous patients from the validation cohort.

<p>Promoter DNA hypermethylation detection in salivary rinses of HNSCC and non-cancerous patients from the validation cohort.</p

Genetic alterations in 37 HNSCC tumors.

<p>Heat-map representation of individual mutations present in a series of 37 HNSCC tumors, presented in columns. Mutation events are represented by black color. Left, Mutated genes, asterisks indicate genes characterized by whole-exon sequencing. Novel genes mutated in HNSCC are labeled by bold font. Right, mutation rate for each gene. Genes are ranked by mutation rate. Bottom, number of mutations per tumor sample. Note, that two patients have two mutations in the same gene TP53 gene: X16 and X27 (with number 2 on the heat-map). The smoking status of tumor patients is identified as: S – for smokers, NS – for never smoked patients, ND – not determined. The HPV status of tumor patients is identified as: “+” for HPV-positive patients and “-” for HPV-negative patients.</p

Correlation, concordance and agreement of ZNF DNA methylation signal in salivary rinses and in primary tissues from the validation cohort.

<p>Significant values are in bold.</p><p>Correlation, concordance and agreement of ZNF DNA methylation signal in salivary rinses and in primary tissues from the validation cohort.</p

Promoter DNA hypermethylation of prospective tumor suppressor genes.

<p>Bisulfite sequencing results are shown in 5 HNSCC tumor samples and 5 normal tissues from the original discovery cohort for the 24 top-scoring candidate genes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142148#pone.0142148.t001" target="_blank">Table 1</a>). Shaded black boxes represent completely methylated promoters, gray boxes represent hemimethylated promoters, and white boxes represent unmethylated promoters.</p

Twenty four DNA methylation biomarker candidates.

<p>The genes are ranked by outlier score. Spearman's coefficient was calculated to evaluate DNA methylation and gene expression correlation. P-values for testing the DNA methylation differences between tumor and normal groups were calculated using t-test</p><p>Twenty four DNA methylation biomarker candidates.</p