Figure 2.
Differential AICDA activity in BL. (A) Left-hand panel shows for each tumor the density of noncoding mutations as mutations per kilobase (mut./kbp) in noncoding mutation peaks annotated with the nearest transcription start site (relative position in parentheses) or regulatory element. Peaks overlapping IG loci are shown separately. Red points indicate discordant cases, which we define as EBV-negative eBLs and EBV-positive sBLs. Right-hand panel compares the mutation prevalence for each peak in EBV-positive and EBV-negative tumors. Significance brackets: *Q < 0.1; **Q < 0.001; ***Q < 0.00001 (Fisher’s exact test). (B) AICDA expression in BL tumor samples stratified on clinical variant status or tumor EBV status (N = 117). Discordant cases, including additional ones from the validation cohort, are highlighted as red points. Significance brackets: ***P < .00001 (Mann-Whitney U test). (C) Linear regression of AICDA expression as a function of tumor EBV status and clinical variant status. This linear model was also bootstrapped 10 000 times to calculate bootstrap 95% confidence intervals (CI). Ref, reference level.

Differential AICDA activity in BL. (A) Left-hand panel shows for each tumor the density of noncoding mutations as mutations per kilobase (mut./kbp) in noncoding mutation peaks annotated with the nearest transcription start site (relative position in parentheses) or regulatory element. Peaks overlapping IG loci are shown separately. Red points indicate discordant cases, which we define as EBV-negative eBLs and EBV-positive sBLs. Right-hand panel compares the mutation prevalence for each peak in EBV-positive and EBV-negative tumors. Significance brackets: *Q < 0.1; **Q < 0.001; ***Q < 0.00001 (Fisher’s exact test). (B) AICDA expression in BL tumor samples stratified on clinical variant status or tumor EBV status (N = 117). Discordant cases, including additional ones from the validation cohort, are highlighted as red points. Significance brackets: ***P < .00001 (Mann-Whitney U test). (C) Linear regression of AICDA expression as a function of tumor EBV status and clinical variant status. This linear model was also bootstrapped 10 000 times to calculate bootstrap 95% confidence intervals (CI). Ref, reference level.

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