Copy number profiling of tumor suppressor genes in head and neck cancer

Head & Neck ◽  
2016 ◽  
Vol 39 (2) ◽  
pp. 341-346 ◽  
Author(s):  
Zubeyde Yalniz ◽  
Semra Demokan ◽  
Burak Karabulut ◽  
Murat Ulusan ◽  
Yusufhan Suoglu ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Tumor Suppressor ◽  
Head And Neck ◽  
Neck Cancer ◽  
Tumor Suppressor Genes ◽  
Copy Number ◽  
Suppressor Genes

Abstract 2482: Key tumor suppressor genes inactivated by promoter methylation and somatic mutations in head and neck cancer

2014 ◽  
Author(s):  
Rafael Guerrero-Preston ◽  
Christina Michailidi ◽  
Luigi Marchionni ◽  
Curtis Pickering ◽  
Mitchell Frederick ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Tumor Suppressor ◽  
Head And Neck ◽  
Neck Cancer ◽  
Promoter Methylation ◽  
Tumor Suppressor Genes ◽  
Somatic Mutations ◽  
Suppressor Genes

Simultaneous Methylation Profiling of Tumor Suppressor Genes in Head and Neck Cancer

2011 ◽  
Vol 30 (1) ◽  
pp. 17-24 ◽  
Author(s):  
Zubeyde Yalniz ◽  
Semra Demokan ◽  
Yusufhan Suoglu ◽  
Murat Ulusan ◽  
Nejat Dalay
Keyword(s):  
Head And Neck Cancer ◽  
Tumor Suppressor ◽  
Head And Neck ◽  
Neck Cancer ◽  
Tumor Suppressor Genes ◽  
Suppressor Genes ◽  
Methylation Profiling

scholarly journals Key tumor suppressor genes inactivated by “greater promoter” methylation and somatic mutations in head and neck cancer

Epigenetics ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. 1031-1046 ◽  
Author(s):  
Rafael Guerrero-Preston ◽  
Christina Michailidi ◽  
Luigi Marchionni ◽  
Curtis R Pickering ◽  
Mitchell J Frederick ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Tumor Suppressor ◽  
Head And Neck ◽  
Neck Cancer ◽  
Promoter Methylation ◽  
Tumor Suppressor Genes ◽  
Somatic Mutations ◽  
Suppressor Genes

scholarly journals Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations

2018 ◽  
Author(s):  
An-Shun Tai ◽  
Chien-Hua Peng ◽  
Shih-Chi Peng ◽  
Wen-Ping Hsieh
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Tumor Evolution ◽  
Depth Information ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Copy Number Aberrations

AbstractMultistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.

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