Figure 1.
Mutation landscape of lymphoma-related genes in 135 rrDLBCL cases. Exonic mutations affecting the top 50 most recurrently mutated genes in our cohort of 135 rrDLBCL samples representing 5 different cohorts (“Methods”). The inferred effect of each mutation is indicated by color. Noncoding mutations are suppressed with the exception of NFKBIZ, which includes 3′ UTR mutations that have been previously described as driver mutations. The 2 covariate tracks on the bottom show COO information (where available) and the source cohort for each sample. Bar plots above and to the right of the plot indicate number of mutations per patient and number of patients with a mutation in that gene, respectively. Although the mutation landscape closely resembles untreated DLBCL, there are some notable differences. For example, approximately half of all rrDLBCLs harbored mutations in either TP53 (51%) or the histone methyltransferase KMT2D (50%) with 31% of cases harboring mutations in both genes.

Mutation landscape of lymphoma-related genes in 135 rrDLBCL cases. Exonic mutations affecting the top 50 most recurrently mutated genes in our cohort of 135 rrDLBCL samples representing 5 different cohorts (“Methods”). The inferred effect of each mutation is indicated by color. Noncoding mutations are suppressed with the exception of NFKBIZ, which includes 3′ UTR mutations that have been previously described as driver mutations. The 2 covariate tracks on the bottom show COO information (where available) and the source cohort for each sample. Bar plots above and to the right of the plot indicate number of mutations per patient and number of patients with a mutation in that gene, respectively. Although the mutation landscape closely resembles untreated DLBCL, there are some notable differences. For example, approximately half of all rrDLBCLs harbored mutations in either TP53 (51%) or the histone methyltransferase KMT2D (50%) with 31% of cases harboring mutations in both genes.

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