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A. Agathangelidis et al.
MBL: 61 of 73 (83.7%); ultra-stable CLL: 50 of 59 (84.7%)], whereas the remainder concerned either non- sense mutations or frameshift indels (Figure 3B and Online Supplementary Table S5). Forty-nine of the 186 mutations (26.3%) had a VAF more than 50%. Concerning the 7 PMN samples harboring mutations, only a single mutation (6.7%) had a VAF more than 50% (Figure 3C) (Online Supplementary Table S6). The most commonly mutated gene was IGLL5, in accordance with a recently reported study,6 carrying mutations in 5 different samples (2 LC- MBL, 2 HC-MBL and 1 CLL samples), likely introduced by the somatic hypermutation (SHM) process. Only 6 of 186 mutations (1.6%) detected in the MBL/CLL samples con- cerned putative CLL driver genes, according to 2 recently reported lists.7,9 In detail, 3 were identified in individuals with HC-MBL: i) a single NOTCH1 p.P2514Rfs*4 deletion (VAF 20%), a known hotspot mutation in CLL10,28,34-36 in HC-MBL_4; ii) a single FBXW7 p.W307S mutation (VAF 26%) in HC-MBL_2; and iii) a single KIAA0947 p.L2093X (VAF 43%) in HC-MBL_5. Two mutations concerned indi- viduals with LC-MBL: i) a KLHL6 p.A91D mutation (VAF 45%) in LC-MBL_5; and ii) a single CD79A p.E200G mutation (VAF 53%) in LC-MBL_6. Finally, a CD79B p.N68S mutation (VAF 41%) was identified in a single CLL sample (CLL_5). Although most of these exact muta- tions have not previously been reported in CLL, functional
prediction using Polyphen-2 classified all but the CD79B mutation as probably damaging. No CLL driver gene mutations were found in the PMN samples.
To assess whether the non-synonymous mutations identified here might be potentially relevant to CLL, we compared our findings to the variants reported by Puente et al.9 and the International Cancer Genome Consortium (ICGC) database.37 Overall, the vast majority of genes car- rying mutations in our series were also reported as mutat- ed in either or both datasets: 94% in LC-MBL, 89% in HC-MBL, and 97% in CLL.
We extended our analysis by performing targeted re- sequencing of 11 putative CLL driver genes in 8 LC-MBL, 13 HC-MBL and 7 ultra-stable CLL samples as well as 24 corresponding PMN samples. All but one LC-MBL case (LC-MBL_4) subjected to WGS were included in this analysis (Online Supplementary Table S7). In total, 5 variants were detected in 3 different HC-MBL samples, including 4 missense variants and 1 frameshift deletion. Two variants (targeting the NOTCH1 and FBXW7 genes) had been already identified by WGS, whereas the remaining 3 con- cerned the POT1 (n=2) and SF3B1 (n=1) genes. In detail, a single HC-MBL case (HC-MBL_5) harbored an SF3B1 mutation (p.K700E; VAF 1.1%), a known hotspot muta- tion in CLL,18,29,38,39 and a POT1 mutation (p.M1V; 4.3%), while the other POT1 mutation (p.S38R; 6.7%) was found
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Figure 2. Detailed analysis of mutation types. (A) Transition to transversion (Ti/Tv) ratios were comparable in all monoclonal B-cell lymphocytosis (MBL)/chronic lym- phocytic leukemia (CLL) samples and somewhat lower in the polymorphonuclear (PMN) cell samples. (B) Similar distribution of mutations among the 6 mutation classes for each MBL/CLL entity and PMN samples (average values ± Standard Deviation). Similar profiles were evident for all entities with the G>A mutation pre- dominating in all cases. (C) Mutational signatures that contribute to the somatic mutations observed in the MBL/CLL samples. (D) Mutational signatures that dom- inate in the PMN samples.
haematologica | 2018; 103(5)