Fig 1.
Characterization of somatic mutations in PTCL.
(A) Bar graph shows percent of specified transitions and transversions resulting in non-synonymous somatic mutations identified by Mutect and VarScan in PTCL samples. (B) Bar graph shows percentage of each type of somatic mutation identified in PTCL samples.
Fig 2.
Functional algorithms, MutationAssessor, PolyPhen2, and PROVEAN, predict the majority of somatic mutations identified to significantly impact protein function.
(A) Bar graph shows percent of non-synonymous somatic mutations and their probability to impact protein function, as predicted by MutationAssessor, PolyPhen2, and PROVEAN. (B) Venn diagram shows number of non-synonymous somatic mutations predicted to significantly impact protein function by each algorithm or combination of algorithms: MutationAssessor, PolyPhen2, and PROVEAN. Mutations were considered significant if selected as “high” or “medium”, “probably damaging” or “possibly damaging,” and “deleterious,” respectively.
Table 1.
104 somatic mutations predicted to be cancer drivers that significantly alter protein function by all four algorithms.
Fig 3.
70 genes contain somatic mutations in more than one PTCL sample.
(A) Venn diagram shows which algorithms predicted the mutations in genes somatically mutated in multiple PTCL cases to significantly alter protein function. Genes listed in red contain significant potential cancer driver mutations by CHASM (p≤0.05). Genes listed in blue contain identical SNVs in both samples. Genes were found to contain mutations in two cases, unless indicated otherwise with a number in parentheses. (B) Grid shows which PTCL subtypes (columns) contain the somatic mutations identified in genes mutated in more than one sample (rows) via grey shaded boxes. Gold shading indicates identical repeated SNVs identified in multiple cases/subtypes. Numbers in boxes indicate the number of samples of the indicated subtype containing a mutation in the indicated gene, if greater than one.
Fig 4.
Somatic mutations in ATM, identified in PTCL patients, involve highly conserved residues.
(A) Schematic representation of ATM protein domains showing location of somatic mutations in ATM from 5 different PTCL samples. (B) Multiple sequence alignment across species around the 6 mutations in ATM found in 5 samples from patients with PTCL. Conserved mutated residue highlighted in black, other conserved residues highlighted in grey.
Fig 5.
γc cytokine signal transducers contain mutations in highly conserved amino acid residues.
(A) Schematic representation of JAK3 protein domains showing somatic mutation in identified in PTCL sample. (B) Sequence alignment of JAK3 M511 across 7 species. Conserved mutated residue highlighted in black, other conserved residues in grey. (C) Schematic representation of STAT5B protein domains showing somatic mutation identified in PTCL sample. (D) Sequence alignment of STAT5B N642 across 6 species. Conserved mutated residue highlighted in black, other conserved residues in grey. (E) Schematic representation of γc protein domains showing somatic mutation identified in PTCL sample. (F) Sequence alignment of γc K315 across 6 species. Conserved mutated residue highlighted in black, other conserved residues in grey.