Dataset Information

Integrative molecular characterization of head and neck cancer cell model genomes.

ABSTRACT: Cell lines are invaluable model systems for the investigation of cancer. Knowledge of the molecular alterations that exist within cell models is required to define the mechanisms governing cellular phenotypes.Five tongue squamous cell carcinomas cell lines and 1 submaxillary salivary gland epidermoid carcinoma cell line were analyzed for copy number and mRNA expression by tiling-path DNA microarrays and Agilent Whole Human Genome Oligoarrays, respectively.Integrative analysis of genetic and expression alterations revealed the molecular landscape of each cell line. Molecular results for individual cell lines and across all samples have been summarized and made available for easy reference.Our integrative genomic analyses have defined the DNA and RNA alterations for each individual line. These data will be useful to anyone modeling oral cancer behavior, providing a molecular context that will be useful for deciphering cell phenotypes.

SUBMITTER: Tsui IF

PROVIDER: S-EPMC2930055 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Integrative molecular characterization of head and neck cancer cell model genomes.

Tsui Ivy F L IF Garnis Cathie C

Head & neck 20100901 9

<h4>Background</h4>Cell lines are invaluable model systems for the investigation of cancer. Knowledge of the molecular alterations that exist within cell models is required to define the mechanisms governing cellular phenotypes.<h4>Methods</h4>Five tongue squamous cell carcinomas cell lines and 1 submaxillary salivary gland epidermoid carcinoma cell line were analyzed for copy number and mRNA expression by tiling-path DNA microarrays and Agilent Whole Human Genome Oligoarrays, respectively.<h4>R ...[more]

PMID: 20014447

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Project description:Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults. Decades of investigations and the recent effort of the Cancer Genome Atlas (TCGA) project have mapped many molecular alterations in GBM cells. Alterations on DNAs may dysregulate gene expressions and drive malignancy of tumors. It is thus important to uncover causal and statistical dependency between 'effector' molecular aberrations and 'target' gene expressions in GBMs. A rich collection of prior studies attempted to combine copy number variation (CNV) and mRNA expression data. However, systematic methods to integrate multiple types of cancer genomic data-gene mutations, single nucleotide polymorphisms, CNVs, DNA methylations, mRNA and microRNA expressions and clinical information-are relatively scarce. We proposed an algorithm to build 'association modules' linking effector molecular aberrations and target gene expressions and applied the module-finding algorithm to the integrated TCGA GBM data sets. The inferred association modules were validated by six tests using external information and datasets of central nervous system tumors: (i) indication of prognostic effects among patients; (ii) coherence of target gene expressions; (iii) retention of effector-target associations in external data sets; (iv) recurrence of effector molecular aberrations in GBM; (v) functional enrichment of target genes; and (vi) co-citations between effectors and targets. Modules associated with well-known molecular aberrations of GBM-such as chromosome 7 amplifications, chromosome 10 deletions, EGFR and NF1 mutations-passed the majority of the validation tests. Furthermore, several modules associated with less well-reported molecular aberrations-such as chromosome 11 CNVs, CD40, PLXNB1 and GSTM1 methylations, and mir-21 expressions-were also validated by external information. In particular, modules constituting trans-acting effects with chromosome 11 CNVs and cis-acting effects with chromosome 10 CNVs manifested strong negative and positive associations with survival times in brain tumors. By aligning the information of association modules with the established GBM subclasses based on transcription or methylation levels, we found each subclass possessed multiple concurrent molecular aberrations. Furthermore, the joint molecular characteristics derived from 16 association modules had prognostic power not explained away by the strong biomarker of CpG island methylator phenotypes. Functional and survival analyses indicated that immune/inflammatory responses and epithelial-mesenchymal transitions were among the most important determining processes of prognosis. Finally, we demonstrated that certain molecular aberrations uniquely recurred in GBM but were relatively rare in non-GBM glioma cells. These results justify the utility of an integrative analysis on cancer genomes and provide testable characterizations of driver aberration events in GBM.

Dataset Information

Integrative molecular characterization of head and neck cancer cell model genomes.

Publications

Integrative molecular characterization of head and neck cancer cell model genomes.

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