Fig. 1
An integrated deep-sequencing approach identifies novel variant features underlying OSCC of a unique demographic origin. Summary data for the 50 OSCC-Taiwan cases. The four blocks correspond to the different types of data attributes. They represent, from top to bottom: a Mutation analyses in a series of 50 OSCC samples. The y-axis shows the number of mutation events and the omics data (DNA exome sequencing, RNA RNA-Seq, CCP comprehensive cancer panel), whereas the x-axis indicates the samples of the individual patients. b Heatmap representation of individual genes exhibiting somatic mutations in the 50 OSCC samples. The q-values (false discovery rates) represent the significance of each mutated gene, as determined using MutSigCV. c Heatmap representation of the copy number variations, compared with those from TCGA and India. SNVs were identified with Mutect, which applies a Bayesian classifier to detect mutations with allelic fractions of 0.1 or less ( < 10%). For the number of mutation events, the mutation types are broken down by the indicated sequence features. For the mutation b and copy number analyses c, the tables on the right show the percentages of patients with the respective somatic sequence variation or amplification/deletion, as found in the OSCC-Taiwan (TW), OSCC-TCGA (TCGA), and OSCC-India (India) cohorts. NA data not available. d The risk exposure and A3B deletion genotypes. OSCC patients with the habits of alcohol, betel nut or cigarette are individually marked. For 3 APOBEC3B genotypes, A3B −/−, A3B +/−, and A3B +/+ are shown with full, half and empty squares, respectively
