Integrated genomics approach to identify biologically relevant alterations in fewer samples.

Background: Several statistical tools have been developed to identify genes mutated at rates significantly higher than background, indicative of positive selection, involving large sample cohort studies. However, studies involving smaller sample sizes are inherently restrictive due to their limited statistical power to identify low frequency genetic variations.

Results: We performed an integrated characterization of copy number, mutation and expression analyses of four head and neck cancer cell lines - NT8e, OT9, AW13516 and AW8507 - by applying a filtering strategy to prioritize for genes affected by two or more alterations within or across the cell lines.