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

Gaykalova DA, Vatapalli R, Wei Y, Tsai HL, Wang H, Zhang C, Hennessey PT, Guo T, Tan M, Li R, Ahn J, Khan Z, Westra WH, Bishop JA, Zaboli D, Koch WM, Khan T, Ochs MF, Califano JA - PLoS ONE (2015)

Bottom Line: Head and Neck Squamous Cell Carcinoma (HNSCC) is the fifth most common cancer, annually affecting over half a million people worldwide.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins Medical Institutions, Baltimore, Maryland, United States of America.

ABSTRACT
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, ZNF14, ZNF160, and ZNF420, 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.

No MeSH data available.


Related in: MedlinePlus

Integrative methylation screening strategy.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 Methods 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.
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pone.0142148.g001: Integrative methylation screening strategy.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 Methods 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.

Mentions: From genome-wide differential DNA methylation analysis of 44 primary tumors and 25 normal tissue controls, biomarker candidates were selected based on the scheme shown in Fig 1. Based on the number of outlier samples and the relative signal intensity, 37 of the top ranking candidates were chosen for further analysis (S4 Table). Correlation of the expression and methylation array data allowed for the discovery of 24 candidate genes (out of 37 initial genes) with biologically relevant negative correlation between DNA methylation and gene expression (Table 1). Notably, all 24 candidate genes showed hypermethylation and decreased expression in tumor samples. Furthermore, all candidates showed minimal DNA methylation signal in normal tissues, with maximal mean of β-value for normal samples of 0.058, or 6% methylation (S1 Fig and S5 Table). The standard t-test demonstrated that 23 out of 24 genes (96%) had statistically significant difference in DNA methylation between normal and all tumor samples and between normal and HPV- tumor samples. One gene, CCND2, did not reach statistical significance based on a t-test, however this did demonstrate a difference between tumor and normal samples by a Fisher Exact test based on presence of hypermethylated outliers in tumor samples.


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

Gaykalova DA, Vatapalli R, Wei Y, Tsai HL, Wang H, Zhang C, Hennessey PT, Guo T, Tan M, Li R, Ahn J, Khan Z, Westra WH, Bishop JA, Zaboli D, Koch WM, Khan T, Ochs MF, Califano JA - PLoS ONE (2015)

Integrative methylation screening strategy.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 Methods 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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636259&req=5

pone.0142148.g001: Integrative methylation screening strategy.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 Methods 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.
Mentions: From genome-wide differential DNA methylation analysis of 44 primary tumors and 25 normal tissue controls, biomarker candidates were selected based on the scheme shown in Fig 1. Based on the number of outlier samples and the relative signal intensity, 37 of the top ranking candidates were chosen for further analysis (S4 Table). Correlation of the expression and methylation array data allowed for the discovery of 24 candidate genes (out of 37 initial genes) with biologically relevant negative correlation between DNA methylation and gene expression (Table 1). Notably, all 24 candidate genes showed hypermethylation and decreased expression in tumor samples. Furthermore, all candidates showed minimal DNA methylation signal in normal tissues, with maximal mean of β-value for normal samples of 0.058, or 6% methylation (S1 Fig and S5 Table). The standard t-test demonstrated that 23 out of 24 genes (96%) had statistically significant difference in DNA methylation between normal and all tumor samples and between normal and HPV- tumor samples. One gene, CCND2, did not reach statistical significance based on a t-test, however this did demonstrate a difference between tumor and normal samples by a Fisher Exact test based on presence of hypermethylated outliers in tumor samples.

Bottom Line: Head and Neck Squamous Cell Carcinoma (HNSCC) is the fifth most common cancer, annually affecting over half a million people worldwide.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins Medical Institutions, Baltimore, Maryland, United States of America.

ABSTRACT
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, ZNF14, ZNF160, and ZNF420, 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.

No MeSH data available.


Related in: MedlinePlus