Risk Stratification In Chronic Lymphocytic Leukemia Patients With IGHV3-21 Gene Usage According To Presence Of Stereotypy and Mutations In SF3B1

Abstract

IntroductionChronic Lymphocytic Leukemia (CLL) patients with monoclonal IGHV3-21 gene rearrangements have been described to have adverse prognosis independent of mutational status. Heterogeneous data exists whether only patients with a stereotyped motif in the junctional region (designated as subset #2, Stamatopoulos K. et al., Blood 2007) suffer from worse prognosis. Furthermore, it was recently suggested that co-occurrence of subset #2 and mutations (mut) in SF3B1 are indicative of a shorter time to treatment (TTT). Aims1. Determine the prognostic impact of IGHV3-21 and subset #2 rearrangements. 2. Evaluate the association with SF3B1mut and its prognostic impact. Patients and MethodsIGHV3-21 positive (n=213) and independently 1,094 unselected CLL patients without prior treatment were analyzed. The whole cohort comprised 63.9% (835/1,307) males and 36.1% (472/1,307) females with a median age of 66.8 years (range: 27.5 – 90.5 years). In all cases IGHV mutation status was analyzed. IGHV unmutated (unmut) status was present in 38.6% (504/1,307) and mutated status in 61.4% (803/1,307). Stereotypy of IGHV3-21 was classified according to published criteria (Agathangelidis A. et al., Blood 2012). SF3B1 was analyzed in all and TP53 in 1,262 cases for mutations. For all patients data on immunophenotype was available. Cases were further analyzed by FISH using probes for del(17p) (n=1,305), del(11q) (n=1,303), trisomy 12 (n=1,303) and del(13q) (n=1,305). Clinical follow-up data was available in 1,040 patients with a median follow-up of 4.4 years (IGHV3-21: n=160, 4.2 years). ResultsOf 213 IGHV3-21 positive patients, 111 (52.1%) cases were classified as subset #2 B-cell receptor. The frequency of IGHVmut was significantly higher in subset #2 vs. non-subset #2 (78/111, 70.3% vs. 49/102, 48.0%, p=0.001). IGHV3-21 was highly associated with SF3B1mut (52/213, 24.4% vs. 92/1,094, 8.4%, p<0.001), which were particularly frequent in subset #2 cases (38/111, 34.2% vs. 14/102, 13.7%, p=0.001). Furthermore, IGHV3-21 was associated with del(11q) (35/210, 16.7% vs. 122/1,093, 11.2%, p=0.028) and was rare in patients with trisomy 12 (8/210, 3.8% vs. 168/1,093, 15.4%, p<0.001). Accordingly, del(11q) was particularly frequent in subset #2 patients (25/110, 22.7% vs. 10/100, 10.0%, p=0.016), whereas trisomy 12 (1/110, 0.9% vs. 7/100, 7.0%, p=0.029) and del(17p) (1/111, 0.9% vs. 8/101, 7.9%, p=0.015) were nearly absent. Kaplan-Meier analysis revealed no significant difference in TTT between IGHV3-21mut vs. unmutated cases. However, IGHV3-21mut cases had slightly longer TTT compared to IGHVunmut (5.3 years vs. 3.4 years, p=0.039). Taking stereotypy into account, subset #2 patients showed nearly identical TTT compared to IGHVunmut patients (3.5 vs. 3.4 years). Further stratification according to IGHV mutational status presented mutated non-subset #2 patients with a similar TTT compared to IGHVmut cases (9.2 vs. 10.2 years), whereas all other subgroups assorted together with IGHVunmut (Fig. 1A). Additionally, there was a trend to a shorter TTT in subset #2 in combination with SF3B1mut vs. SF3B1wt (1.2 vs. 4.4 years, p=0.056) (Fig. 1B). In univariate Cox regression analysis, following parameters were analyzed and showed significant impact on TTT: IGHVmut (p<0.001, HR 0.33), IGHV3-21 (p=0.002, HR 1.51), subset #2 (p=0.005, HR 2.04), SF3B1mut (p<0.001, HR 2.06). A multivariate analysis including IGHV3-21, IGHVmut and SF3B1mut revealed independent impact on TTT only for the latter two parameters: IGHVmut (p<0.001, HR 0.35) and SF3B1mut (p=0.001, HR 1.59). In contrast, analyzing subset #2, IGHVmut and SF3B1mut in a multivariate model, only subset #2 (p=0.011, HR 1.93) and SF3B1mut (p=0.023, HR 1.82) retained their prognostic effect, whereas IGHV mutational status had no independent impact. [Display omitted] Conclusions1. Our data suggests to prognostically stratify IGHV3-21 patients according to the presence of stereotypy, since only subset #2 patients showed shorter TTT, whereas mutated non-subset #2 cases had a TTT similar to IGHVmut cases. 2. Mutation status of SF3B1 further refines the risk stratification of subset #2 patients, as co-occurrence of subset #2 with SF3B1mut leads to shorter TTT compared to subset #2/SF3B1wt cases. Disclosures:Jeromin: MLL Munich Leukemia Laboratory: Employment. Dicker:MLL Munich Leukemia Laboratory: Employment. Bayer:MLL Munich Leukemia Laboratory: Employment. Weissmann:MLL Munich Leukemia Laboratory: Employment. Eder:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Alpermann:MLL Munich Leukemia Laboratory: Employment. Kohlmann:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.