Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Chronic Lymphocytic Leukemia

SF3B1 mutations correlated to cytogenetics and mutations in NOTCH1, FBXW7, MYD88, XPO1 and TP53 in 1160 untreated CLL patients

Abstract

We analyzed a large cohort of 1160 untreated CLL patients for novel genetic markers (SF3B1, NOTCH1, FBXW7, MYD88, XPO1) in the context of molecular, immunophenotypic and cytogenetic data. NOTCH1 mutations (mut) (12.3%), SF3B1mut (9.0%) and TP53mut (7.1%) were more frequent than XPO1mut (3.4%), FBXW7mut (2.5%) and MYD88mut (1.5%). SF3B1mut, NOTCH1mut, TP53mut and XPO1mut were highly correlated to unmutated, whereas MYD88mut were associated with mutated IGHV status. Associations of diverse cytogenetic aberrations and mutations emerged: (1) SF3B1mut with del(11q), (2) NOTCH1mut and FBXW7mut with trisomy 12 and nearly exclusiveness of SF3B1mut, (3) MYD88mut with del(13q) sole and low frequencies of SF3B1mut, NOTCH1mut and FBXW7mut. In patients with normal karyotype only SF3B1mut were frequent, whereas NOTCH1mut rarely occurred. An adverse prognostic impact on time to treatment (TTT) and overall survival (OS) was observed for SF3B1mut, NOTCH1mut and TP53 disruption. In multivariate analyses SF3B1mut, IGHV mutational status and del(11q) were the only independent genetic markers for TTT, whereas for OS SF3B1mut, IGHV mutational status and TP53 disruption presented with significant impact. Finally, our data suggest that analysis of gene mutations refines the risk stratification of cytogenetic prognostic subgroups and confirms data of a recently proposed model integrating molecular and cytogenetic data.

Introduction

Mutational status of immunoglobulin heavy-chain variable region (IGHV) and of the tumor protein p53 (TP53) gene have been the most important prognostic molecular markers in chronic lymphocytic leukemia (CLL) for a long time.1, 2, 3, 4 However, recently promising novel candidate genes have been described in CLL patients as detected by whole-exome and/or whole-genome sequencing.5, 6, 7, 8 Besides TP53, the two most extensively studied genes are SF3B1 (splicing factor 3b, subunit 1) and NOTCH1. The SF3B1 protein is part of the spliceosome machinery. SF3B1mut occur with a frequency of 3–10% in newly diagnosed CLL patients and in up to 20% in relapsed and fludarabine-refractory patients.6, 7, 9, 10, 11, 12 Data suggest that SF3B1mut are associated with a more aggressive course of disease, with shorter TTT and OS independent of other prognosticators.6, 7, 9, 10, 11, 12 Mutations in NOTCH1 have been described in 5–10% of newly diagnosed CLL patients with increasing frequencies in advanced disease stages.5, 7, 8, 10, 13, 14, 15 Patients with NOTCH1mut have shorter TTT and OS independent of other prognostic factors.5, 8, 10, 11, 13 Various other recurrent mutations have been reported at frequencies below 10% in genes like MYD88 (myeloid differentiation primary response 88), FBXW7 (F-Box and WD40 domain protein 7) and XPO1 (exportin 1). The impact of these gene mutations remains elusive, as the data in CLL patients are currently limited.5, 12 The corresponding proteins have different roles in the cell and are part of diverse pathways: FBXW7 in NOTCH signaling,16 MYD88 in immune response,17 XPO1 in nucleocytoplasmic transport.18

Our aim was to study the frequency, associations and impact of SF3B1mut in a large cohort of 1160 untreated CLL patients in a comprehensive study taking other gene mutations, cytogenetic and immunophenotypic data into account.

Patients and methods

Patients

A total of 1160 patients without prior treatment were included in our analysis. Samples were referred to our laboratory between August 2005 and August 2010 for diagnostic purpose. Diagnosis of CLL was carried out on peripheral blood or bone marrow samples according to WHO criteria.19 A total of 958/1160 (82.6%) were evaluated at diagnosis. The median age was 67 years. Patient characteristics are detailed in Table 1. Various treatment schemes were applied to patients treated in the course of the disease. All patients gave their written informed consent for scientific evaluations. The study was approved by the Internal Review Board and adhered to the tenets of the Declaration of Helsinki.

Table 1 Patient characteristics and molecular mutations

Immunophenotyping and cytomorphology

Immunophenotyping and calculation of ZAP70 and CD38 expression was performed as described previously.20, 21 Data on CD38 expression were available for 1148 and on ZAP70 expression for 1156 patients. About 5/1160 cases with t(11;14)(q13;q32) and 28/1160 cases with t(14;18)(q32;q21) all displayed an immunophenotype yielding a score of 4 or 5 according to Matutes et al.22 and hence fulfilled the standard criteria of CLL. In 191 patients, the concentration of cells with CLL immunophenotype amounted to less than 5 × 109/l. These cases were diagnosed with CLL and not monoclonal B-cell lymphocytosis due to peripheral blood cytopenias and/or lymph node involvement. A total of 102/1160 (8.8%) patients had CLL with 10–55% prolymphocytes (CLL/prolymphocytes) according to cytomorphology and/or a corresponding immunophenotype.23

Molecular analyses

In 691 cases, peripheral blood and in 469 cases bone marrow mononuclear cells were used for the molecular analyses. Direct Sanger sequencing was performed using BigDye Term v1.1 cycle sequencing chemistry (Applied Biosystems, Weiterstadt, Germany). Amplicon next-generation deep-sequencing (NGS) was performed either with massively parallel Titanium amplicon NGS technology (454 Life Sciences, Branford, CT, USA) or using the MiSeq Instrument (Illumina, San Diego, CA, USA). Screening for SF3B1 mutations was performed by Sanger sequencing of exons 10–16. Mutations in the hotspot regions of MYD88 and XPO1 were analyzed either by Sanger sequencing or NGS. NOTCH1 and FBXW7 were analyzed using NGS technology. Part of the data on NOTCH1 mutations (852/908 cases) were published previously.24 TP53 mutation analysis was performed either using a TP53 resequencing research microarray, denaturing high-performance liquid chromatography in combination with direct DNA sequencing or NGS. The sensitivities for the individual methods were as follows: Sanger sequencing about 10% (for SF3B1 5–10%,25 as confirmed by NGS); denaturing high-performance liquid chromatography and microarray about 3%;3 NGS about 2%, depending on the number of generated reads and on the type of mutation.26, 27 For detailed information, see Supplementary Information and Supplementary Table S1. Mutational status of IGHV was analyzed as published previously.28 Unmutated status was defined by sequence identity 98%.

Cytogenetics

Chromosome banding analysis was performed as described previously28, 29 and reported according to the International System for Human Cytogenetic nomenclature.30 Interphase Fluorescence in situ hybridization (FISH) analyses included probes for the detection of deletions of 6q21, 11q22.3 (ATM), 13q14 (D13S25 and D13S319), 17p13 (TP53), trisomy 12 and IGH rearrangements.28, 29

Statistical analysis

SPSS (version 19.0.0) software (IBM Corporation, Armonk, NY, USA) was used for statistical analysis. Dichotomous variables were compared using the χ2-test and continuous variables using Student’s t-test. Survival curves were calculated for TTT or OS according to Kaplan–Meier and compared using the two-sided log rank test. Cox regression analysis was performed for TTT and OS with different parameters as covariates. Parameters that were significant in univariate analyses were included into multivariate analyses. All reported P-values are two-sided and were considered significant at P0.05. OS was measured from the date of diagnosis until last follow-up or death. TTT was evaluated from the date of diagnosis until the date of initial treatment.

Results

Frequency and characterization of SF3B1 mutations

For the whole cohort of 1160 CLL patients, SF3B1mut were analyzed. SF3B1mut were detected in 104/1160 patients (9.0%). Overall, 113 mutations were found in 104 patients. Seven patients had two and one patient had three different mutations. Nearly all of the mutations were missense (111/113, 98.2%) and only two in-frame alterations. The most frequent mutation was p.Lys700Asn/Glu (50/113, 44.2%) (Figure 1, Supplementary Table S2). Mutations were detected with a median mutation load of 35% (range: 5–60%). 12/113 (10.6%) occurred with a mutation load 10%. In addition, in 5/1160 cases (0.4%) a synonymous variant p.Gln473Gln (rs35493573) was detected, which was considered as polymorphism.

Figure 1
figure1

Localization and frequencies of mutations in (a) SF3B1, (b) NOTCH1, (c) FBXW7, (d) XPO1, (e) MYD88 and (f) TP53. Mutations are indicated on amino-acid level and each detected alteration is represented by a dot.

Frequency and characterization of NOTCH1 and FBXW7 mutations

In a subset of 908 patients, the C-terminus of NOTCH1 was analyzed. 112/908 (12.3%) patients showed mutations in this region. Ten patients had two mutations and thus 122 mutations were detected in total, with p.Pro2514Argfs*4 being the most frequent (80/122, 65.6%, Figure 1, Supplementary Table S2). Mutations other than p.Pro2514Argfs*4 were either out-of-frame (9/122, 7.4%), nonsense (19/122, 15.6%) or missense mutations (14/122, 11.5%). The median mutation load was 32% (range: 2–71%). 32/112 (28.6%) alterations had a mutation load 10%. In the same 908 cases, FBXW7 was analyzed. 23/908 (2.5%) patients showed FBXW7mut with 24 mutations in total. The most frequent FBXW7mut was p.Arg465Cys/His/Leu (7/24, 29.2%) (Figure 1, Supplementary Table S2). Nearly all FBXW7mut were missense mutations (22/24, 91.7%). The remaining two mutations were one in-frame and one out-of-frame alteration each. The median mutation load was 21% (range: 4–47%). A high number of cases (7/23, 30.4%) carried FBXW7 mutations with a ratio10%.

Frequency and characterization of MYD88 and XPO1 mutations

Furthermore, the hotspot regions of MYD88 and XPO1 were sequenced in a subset of 969 patients. MYD88mut were found in 15/969 cases (1.5%). The most frequent mutation was p.Leu265Pro (11/15, 73.3%, Figure 1, Supplementary Table S2). All mutations were missense mutations and the median mutation load was 40% (range: 3–67%). Only one mutation had a load of 3%, whereas all other ratios were in the range of 30–67%. The frequency of XPO1mut cases was 3.4% (33/969) with 34 mutations in total. The only patient with two XPO1mut showed a supposedly biallelic mutation (c.1711G>C, p.Glu571Gln and c.1711G>A, p.Glu571Lys). The most frequent mutation were p.Glu571Ala/Gln/Gly/Ile/Lys/Val (31/34, 91.2%; Figure 1, Supplementary Table S2). Nearly all XPO1mut were missense mutations (33/34, 97.1%), except for one in-frame alteration (c.1711_1712delinsAT, p.Glu571Ile). The median mutation load was 36% (range: 4–50%) with four cases carrying a mutation with a load10%.

Frequency and characterization of IGHV mutational status and TP53 mutations

For all 1160 patients, IGHV and for 1151, TP53 mutational status was available. An unmutated IGHV status was detected in 445/1160 (38.4%) and a mutated status in 715/1160 (61.6%). 82/1151 (7.1%) had at least one TP53mut. Overall, 93 mutations were detected. Nine patients had two and one had three different TP53mut. Most were missense mutations (78/93, 83.9%). The remaining were out-of-frame (10/93, 10.8%), in-frame (3/93, 3.2%) and splice-site alterations (2/93, 2.2%). p.Arg175Gly/His and p.Arg248Gln were the two most common mutations with a frequency of 5/93 (5.4%) each (Figure 1, Supplementary Table S2). Most of the mutations were localized in the DNA-binding domain (79/93, 84.9%) and the median mutation load was 41% (range: 3–100%). 20/93 (21.5%) mutations occurred with a mutation load10%.

Association with clinical and biological parameters

First, associations of different gene mutations with patient characteristics were analyzed (Table 1). SF3B1mut were more frequent in males vs females (10.3 vs 6.6%, P=0.041) and patients with these mutations had higher white blood cell (WBC) counts (mean: 51.0 vs 37.1 × 109/l, P=0.008), and higher percentages of cells with a CLL phenotype (mean: 68.3 vs 54.6%, P<0.001). Likewise, patients with NOTCH1mut (mean: 61.0 vs 56.5%, P=0.025) or XPO1mut (mean: 65.0 vs 56.5%, P=0.002) had higher percentages of cells with a CLL phenotype. TP53mut was significantly associated with higher WBC counts (mean: 53.0 vs 37.5 × 109/l, P=0.021) and higher percentages of cells with a CLL phenotype (mean: 64.3 vs 55.2%, P<0.001).

In 580 patients, data on Binet stage were available. SF3B1mut showed increasing frequencies with advanced Binet stages: A: 5.0% (22/438), B: 10.6% (12/113), C: 17.2% (5/29) (A vs B: P=0.045, A vs C: P=0.020). NOTCH1mut were particularly frequent in Binet B: 19.6% (19/97) compared with A: 11.0% (38/347) (P=0.038) and C: 8.0% (2/25) (P=0.025).

Association with immunophenotype and cytomorphology

SF3B1mut were more frequent in patients with CD38 expression 30% (56/384, 14.6% vs 48/764, 6.3%, P<0.001), so were NOTCH1mut (76/304, 25.0% vs 35/593, 5.9%, P<0.001), FBXW7mut (14/304, 4.6% vs 8/593, 1.3%, P=0.005) and XPO1mut (22/322, 6.8% vs 11/636, 1.7%, P<0.001; Table 1). In contrast, MYD88mut were less frequent in patients with CD38 expression 30% (1/322, 0.3% vs 14/636, 2.2%, P=0.026). For ZAP70 expression 20%, a positive association was found only for NOTCH1mut (73/403, 18.1% vs 38/502, 7.6%, P<0.001) and XPO1mut (21/447, 4.7% vs 12/519, 2.3%, P=0.050; Table 1). MYD88mut were significantly more frequent in CLL/prolymphocytes vs CLL (4/91, 4.4% vs 11/878, 1.3%, P=0.044). This was also the case for FBXW7mut (7/88, 8.0% vs 16/820, 2.0%, P=0.004).

Association with cytogenetics

The frequency of aberrations detected by FISH were as follows: del(17p) (48/1158, 4.1%), del(11q) (133/1158, 11.5%), trisomy 12 (171/1158, 14.8%), normal karyotype (NK) according to FISH (254/1150, 22.1%), del(13q) (697/1160, 60.1%) occurring as sole abnormality in 74.3% (518/697) and del(6q) 41/1148 (3.6%). IGH alterations (translocations or deletions) were detected in 57/1160 (4.9%) patients.

SF3B1mut were more frequent in patients with NK according to FISH (38/254, 15.0% vs 65/896, 7.3%, P<0.001). In the group with chromosomal aberrations SF3B1mut were associated with del(11q) (27/133, 20.3% vs 77/1 025, 7.5%, P<0.001), less frequent in del(13q) (52/697, 7.5% vs 52/463, 11.2%, P=0.035), particularly when occurring as sole abnormality (33/518, 6.4% vs 70/632, 11.1%, P=0.007) and were nearly mutually exclusive of trisomy 12 (2/171, 1.2% vs 102/987, 10.3%, P<0.001). SF3B1mut occurred rarely in combination with IGH alterations (3/57). Interestingly, these three patients with IGH alterations showed no t(11;14)(q13;q32) or t(14;18)(q32;q21), but one IGH deletion, one IGH–MYC rearrangement and one IGH rearrangement with unknown partner. NOTCH1mut were especially frequent in trisomy 12 patients (43/144, 29.9% vs 69/762, 9.1%, P<0.001) and showed negative association with NK according to FISH (13/186, 7.0% vs 98/715, 13.7%, P=0.012). They were less frequent in del(13q) (49/539, 9.1% vs 63/369, 17.1%, P<0.001) or del(13q) as sole abnormality (33/391, 8.4% vs 78/510, 15.3%, P=0.002). Of note, 3/10 patients with IGH deletions showed NOTCH1mut. Similar to NOTCH1mut, FBXW7mut were significantly associated with trisomy 12 (12/144, 8.3% vs 11/762, 1.4%, P<0.001) and were less frequent in del(13q) sole patients (4/391, 1.0% vs 19/510, 3.7%, P=0.010). MYD88mut were only found in the cytogenetic subgroups of del(13q) sole (10/15), NK according to FISH (4/15) and in one patient with IGH deletion and concomitant del(13q) (1/15). The distribution of gene mutations in combination with cytogenetic aberrations is depicted in Figure 2 and Supplementary Table S3.

Figure 2
figure2

Frequencies and distribution of cytogenetic aberrations, SF3B1, NOTCH1, FBXW7, TP53, MYD88, XPO1 mutations and IGHV mutational status. Rows correspond to the depicted lesions and columns represent individual patients. Cases which presented with a cytogenetic aberration, a mutation or IGHV mutated status are colored in black. del(13q) sole patients are highlighted in middle grey. Wild type or IGHV unmutated status are depicted in light grey. White means not analyzed.

Associations among molecular mutations and VH gene usage

SF3B1mut did not show any significant association with other gene mutations. However, a strong correlation of SF3B1mut with IGHVunmut was observed (68/444, 15.3% vs 36/716, 5.0%, P<0.001, Supplementary Figure S1) and SF3B1mut presented to be mutually exclusive of IGHV1-2 (0/42, 0% vs 101/1084, 9.3%, P=0.027). Furthermore, they were more frequent in patients with IGHV1-69 (27/142, 19.0% vs 74/984, 7.5%, P<0.001) and IGHV3-21 (12/65, 18.5% vs 89/1061, 8.4%, P=0.012).

NOTCH1mut showed strong correlation with XPO1mut (16/33, 48.5% vs 96/875, 11.0%, P<0.001) and TP53mut (17/79, 21.5% vs 95/829, 11.5%, P=0.018; Figure 2). They were associated with IGHVunmut (88/368, 23.9% vs 24/540, 4.4%, P<0.001; Figure 2, Supplementary Figure S1) and with IGHV1-69 (31/122, 25.4% vs 78/762, 10.2%, P<0.001). For FBXW7mut, no association could be found, but the only gene that was concomitantly mutated was NOTCH1 (4/23, 17.4%). MYD88mut were more frequent in patients with IGHVmut (14/577, 2.4 vs 1/392, 0.3%, P=0.006), whereas XPO1mut were more frequent in IGHVunmut (30/392, 7.7% vs 3/577, 0.5%, P<0.001; Supplementary Figure S1).

In total, 908 patients were analyzed for all six genes: SF3B1, NOTCH1, FBXW7, MYD88, XPO1 and TP53. 605/908 (66.6%) cases showed no mutation. 246/908 (27.1%) had single, 51/908 (5.6%) two and 6/908 (0.7%) three different mutations (Supplementary Figure S2). The comparison of mutation loads of co-occurring mutations allowed no definite hierarchical model (data not shown). No difference existed in the frequency of SF3B1mut between patients with isolated or at least two gene mutations. In contrast, NOTCH1mut were detected more frequently in combination with other mutations vs isolated (44/57, 77.2% vs 68/246, 27.6%, P<0.001). This was also the case for TP53mut (23/57, 40.4% vs 56/246, 22.8%, P=0.011) and XPO1mut (21/57, 36.8% vs 12/246, 4.9%, P<0.001). These three mutations in different combinations accounted for about half of all cases with more than one mutation (29/57, 50.9%).

Prognostic relevance of molecular mutations

Overall, data were available for TTT in 921 cases and for OS in 935 cases with a median follow-up of 4.6 years. In 1150 patients, FISH categories could be defined according to Döhner et al.31 (Supplementary Table S4). Patients with SF3B1mut had shorter TTT (median: 3.8 vs 8.0 years, P<0.001) and 5-year OS (64.7 vs 86.7%, P<0.001; Figure 3). Interestingly, del(11q) patients segregated into two different groups according to the presence of SF3B1mut for 5-year OS (37.9 vs 77.5%, P=0.024; Figure 4). Furthermore, SF3B1mut had adverse impact in patients with del(13q) sole (median TTT: 1.8 vs 9.1 years, P<0.001; 5-year OS: 69.7 vs 90.7%, P=0.002; Figure 4). Cases with NOTCH1mut had shorter TTT (median: 3.5 vs 7.6 years, P<0.001) and 5-year OS (75.7 vs 85.1%, P=0.016; Figure 3). This impact was especially strong in trisomy 12 patients regarding TTT (median: 1.7 vs 7.0 years, P=0.002) but not 5-year OS (Figure 4). TP53mut conferred shorter TTT (median: 4.8 vs 7.5 years, P=0.022) and 5-year OS (60.9 vs 86.8%, P<0.001; Figure 3). TP53mut cases also showed shorter 5-year OS in favorable subgroups of NK according to FISH (62.2 vs 90.0%, P=0.044) and del(13q) sole (73.3 vs 89.8%, P=0.004). The frequencies of mutations in FBXW7, MYD88 and XPO1 were too low to obtain reliable statistics (Supplementary Figure S3).

Figure 3
figure3

Kaplan–Meier plots of (ac) TTT and (df) OS from diagnosis for patients analyzed for (a) and (d) SF3B1, (b) and (e) NOTCH1 and (c) and (f) TP53 mutations. Mutated cases are represented by dotted lines. Number of patients with available follow-up data is given in brackets.

Figure 4
figure4

Kaplan–Meier plots of TTT and OS from diagnosis for patients analyzed for SF3B1 and NOTCH1 mutations in cytogenetic subgroups. del(11q) (panels a, d), del(13q) sole (panels b, e) and trisomy 12 (panels c, f) were classified according to Döhner et al.31 Mutated cases are represented by dotted lines. Numbers of patients with available follow-up data are given in brackets.

Diverse parameters were tested in Cox regression analyses for impact on TTT and OS: mutational status of SF3B1, NOTCH1, FBXW7, MYD88, XPO1, TP53, IGHV, cytogenetic subgroups31 (Supplementary Table S4), gender, age, WBC count, Hb level, platelet count, percentage of cells with a CLL phenotype, CD38 expression 30% and ZAP70 expression 20%. For these analyses, TP53mut (65/912 for TTT, 71/926 for OS) and del(17p) (39/920 for TTT, 44/934 for OS) were combined as TP53 disruption group (72/920 for TTT, 78/934 for OS). Furthermore, cases with mutated IGHV3-21 gene (37 follow-up data for TTT and OS each) were assigned together with IGHVunmut,32, 33 resulting in an IGHV unfavorable vs favorable (IGHVfav) group. An adverse impact on TTT was detected for SF3B1mut, NOTCH1mut, TP53 disruption, del(11q), trisomy 12, higher age, higher WBC count, higher percentage of cells with a CLL phenotype, CD38 expression 30% and ZAP70 expression 20%. Whereas, a favorable impact was shown for IGHVfav, del(13q) sole, higher Hb level and higher platelet count (Table 2, Supplementary Table S5). Univariate analysis for OS showed significant adverse impact of SF3B1mut, NOTCH1mut, TP53 disruption, del(11q), male gender, higher age, higher WBC count, higher percentage of cells with a CLL phenotype and CD38 expression 30%. A positive impact was shown for IGHVfav, NK, del(13q) sole, higher Hb level and higher platelet count (Table 2, Supplementary Table S5). To avoid overfitting of the multivariate model WBC count, Hb level, platelet count and percentage of cells with a CLL phenotype were not included. The multivariate analysis for TTT (available cases: 720, events: 273) presented SF3B1mut (P=0.023), IGHVfav (P<0.001) and del(11q) (P=0.033) as independent prognostic parameters. For OS (available cases: 719, events: 117) SF3B1mut (P=0.005), IGHVfav (P<0.001), TP53 disruption (P=0.001), male gender (0.003) and higher age (P<0.001) showed independent relevance (Table 2).

Table 2 Univariate and multivariate Cox regression analyses on TTT and OS

Integration of mutational and cytogenetical data

Recently, a novel hierarchical classification was published by Rossi et al.12 integrating mutational and cytogenetic data: (1) high-risk patients with TP53 and/or BIRC3 (not analyzed in this study) abnormalities, (2) intermediate-risk harboring NOTCH1mut and/or SF3B1mut and/or del(11q), (3) low-risk including trisomy 12 or NK, (4) very low-risk presented by del(13q) sole patients. According to this model we classified 930 patients from our cohort (Supplementary Table S4). Part of the patients (n=230) could not be classified as data on mutational status of NOTCH1, TP53 and/or FISH analysis were missing. Overall, subgroups according to Rossi et al.12 showed refined prognostication compared with the cytogenetic classification according to Döhner et al.31 (Figure 5). However, in our cohort for TTT, the high- and intermediate-risk groups did not differ significantly. In contrast, for OS, the high- and intermediate-risk groups separated significantly, whereas the low- and very low-risk groups did not. In total 178/1100 (16.2%) patients grouped according to Döhner et al.31 were now reclassified into a higher risk-group according to Rossi et al.12

Figure 5
figure5

Kaplan–Meier plots for TTT and OS for subgroups classified on cytogenetic data only and on integration of cytogenetic data and molecular mutations. Subgroups were classified according to Döhner et al.31 (panels a, c) and Rossi et al.12 (panels b, d). Numbers of patients with available follow-up data are given in brackets. Only significant P-values are listed.

Discussion

As the prognostic impact of many currently detected gene mutations is still under investigation the aim of this study was to analyze a large cohort of untreated CLL patients for mutations in SF3B1, NOTCH1, FBXW7, MYD88, XPO1 and TP53 in a comprehensive approach including immunophenotypic, molecular and cytogenetic data.

A total of 27.1% patients had isolated mutations and in 6.3% at least two mutations in different genes were detected. NOTCH1mut occurred more frequently in combination with other mutations, particularly with TP53mut and/or XPO1mut, than isolated.

The contribution of these different gene mutations to the pathogenesis of CLL remains elusive so far. SF3B1mut are supposed to lead to a modified function of SF3B1 possibly due to altered interaction with other proteins.6, 7, 34 In contrast, disruption of the C-terminal PEST domain of NOTCH1 is suggested to result in accumulation of a more stable and activated protein5, 35 and a constitutively active NOTCH1 was shown to increase cell survival and apoptosis resistance in CLL cells.36 The most frequent FBXW7mut in our cohort affected three highly conserved arginine residues that are essential for protein function.37 As FBXW7 targets activated NOTCH1 for degradation,38, 39 FBXW7mut might be a second mechanism leading to activated NOTCH signaling as shown for acute lymphoblastic leukemia.16, 40 XPO1mut are mainly localized in a region that is part of the domain needed for nuclear export.18 The most prominent mutation in MYD88 (p.Leu265Pro) has also been detected in patients with Morbus Waldenström or diffuse large B-cell lymphoma and presents a gain-of-function driver mutation that leads to a selective advantage during tumor evolution.41, 42

For the different mutations, diverse correlations were detected. SF3B1mut were associated with male sex, higher WBC count and higher percentages of cells with a CLL phenotype. Moreover, SF3B1mut were associated with CD38 positivity, advanced disease stage and IGHVunmut, as published previously.6, 7, 10, 43 They were particularly frequent in patients with IGHV3-21 or IGHV1-69 gene usage and were mutually exclusive of IGHV1-2. Recently, two studies were published that detected strong association of SF3B1mut with stereotyped IGHV3-21.44, 45 To our knowledge, an association of SF3B1mut with IGHV1-69 and mutual exclusiveness with IGHV1-2 were not shown before. However, the high frequency of SF3B1mut in patients with IGHV1-69 should be interpreted with care, as the fraction with an IGHVunmut was very high in the IGHV1-69 subgroup (125/142, 88.0%). Of special interest was also the distinct distribution of SF3B1mut in cytogenetic subgroups. They were particularly frequent in patients with NK and del(11q), whereas mutations were less frequent in cases with del(13q) and nearly mutually exclusive of trisomy 12. These associations were occasionally seen also in other studies.6, 10, 46

NOTCH1mut were associated with higher percentages of cells with a CLL phenotype and were particularly frequent in Binet B patients. They were associated with CD38/ZAP70 positivity, trisomy 12 and IGHVunmut and were particularly rare in del(13q) patients confirming data presented in previous studies.6, 8, 10, 13, 14, 15, 47, 48 NOTCH1mut showed an association with IGHV1-69 gene, which might be again due to the high ratio of patients with unmutated IGHV in this subgroup. FBXW7mut were associated with CD38 positivity and CLL/prolymphocytes. They were more frequent in patients with trisomy 12, similar to previous findings,6 and rare in del(13q) sole. This is of special interest as NOTCH1 and FBXW7 are part of the same signaling pathway. Our results imply that activated NOTCH signaling, trisomy 12 and maybe yet unknown alterations might be one way of CLL pathogenesis.

Data on XPO1mut in CLL are rare.5, 14 In our cohort based on 33 XPO1mut cases, mutations were associated with higher percentages of cells with a CLL phenotype, CD38 and ZAP70 expression, NOTCH1mut and IGHVunmut. MYD88mut were found frequently in del(13q) sole and IGHVmut patients5, 6 and were negatively associated with CD38 expression. Altogether, in our cohort, MYD88mut seemed to occur in patients with more indolent CLL. In contrast, MYD88mut were also found to be associated with CLL/prolymphocytes and presented with advanced clinical stage in a previous publication.5

Overall, different subgroups according to co-occurrence of diverse genetic alterations emerged in our cohort: (1) SF3B1mut showed frequently del(11q), (2) NOTCH1mut and FBXW7mut were associated with trisomy 12, whereas SF3B1mut were nearly mutually exclusive (3) MYD88mut was mainly found in combination with del(13q) sole, in contrast NOTCH1mut, FBXW7mut and SF3B1mut were rarely found in this cytogenetic subgroup. Moreover, only SF3B1mut showed an association with NK. The combination of molecular markers with discrete cytogenetic subgroups may hint at distinct ways of CLL pathogenesis and may explain the clinical heterogeneity of the disease.

In line with previous studies,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 48 we here show that SF3B1, NOTCH1 and TP53 mutated patients had significantly shorter TTT and OS. In contrast, for FBXW7mut, MYD88mut and XPO1mut, we could not find any impact on prognosis, although this might be partly due to the low frequencies of these gene mutations in untreated CLL patients. However, for MYD88mut also others5, 12 could not detect any impact on survival. In a multivariate analysis SF3B1mut, IGHV mutational status and del(11q) were the only independent parameters for TTT. For OS, a significant impact was detected only for SF3B1mut, IGHV mutational status and TP53 disruption besides age and gender. Similar results were published also by others, even though most of them detected independent impact also for NOTCH1mut besides SF3B1mut and TP53 disruption.10, 11, 12 However, parameters tested in multivariate analyses differed between studies. For example IGHV mutational status was not always included, which was a strong parameter in our analysis.

In addition to the prognostic value of cytogenetic aberrations, gene mutations allow extended risk stratification. Rossi et al.12 for the first time have developed an integrated prognostic model of cytogenetic and molecular markers. Applying this model to our cohort, about 16% of the patients were reclassified and resulted in a refinement of risk stratification. However, in the analysis for TTT high- and intermediate-risk groups did not differ significantly. This might be due to missing data on BIRC3 disruption in our analysis. In contrast, low- and very low-risk groups did not separate in the analysis of OS. Besides diversity of the two cohorts, the reason might be the detection of NOTCH1 and TP53 mutations with low allele frequencies due to the usage of very sensitive methods in our study. Nevertheless, our results support the integration of molecular markers in known prognostic risk groups, particularly for SF3B1mut in patients with del(13q) sole or del(11q).

Of note, mutations in SF3B1, NOTCH1 and TP53 can also emerge during the clinical course of CLL.12 Thus, it might be useful to repeat testing for new genetic lesions at critical time-points as has already been recommended for TP53.49 Future studies will clarify how this might influence treatment strategies, as data suggest that therapy may disrupt the interclonal equilibrium and lead to evolution of subclones with driver mutations like SF3B1mut, NOTCH1mut or TP53mut.50 However, TP53mut is the only molecular marker that has impact on therapy decision at present.51 Data on novel molecular markers are preliminary till now and new drugs that might enable an individualized treatment regimen based on the genetic profile are just under investigation. Nevertheless, the identification of gene mutations leads to a refined risk stratification and might identify patients that need closer follow-up.51

In conclusion, in our cohort of untreated CLL patients TP53, SF3B1 and NOTCH1 showed frequencies of 7–12%. In comprehensive multivariate analyses SF3B1mut, IGHV mutational status and del(11q) were the only independent markers for TTT, whereas for OS SF3B1mut, IGHV mutational status and TP53 disruption presented with significant impact. Moreover, the analysis of mutations contributes to the risk stratification of patients in addition to cytogenetic prognostic subgroups. Our data prompt the additional analysis of at least SF3B1 besides TP53 and the IGHV mutational status in the standard molecular screening of CLL patients.

References

  1. 1

    Ghia P, Stamatopoulos K, Belessi C, Moreno C, Stilgenbauer S, Stevenson F et al. ERIC recommendations on IGHV gene mutational status analysis in chronic lymphocytic leukemia. Leukemia 2007; 21: 1–3.

    CAS  Article  Google Scholar 

  2. 2

    Zenz T, Krober A, Scherer K, Habe S, Buhler A, Benner A et al. Monoallelic TP53 inactivation is associated with poor prognosis in chronic lymphocytic leukemia: results from a detailed genetic characterization with long-term follow-up. Blood 2008; 112: 3322–3329.

    CAS  Article  Google Scholar 

  3. 3

    Dicker F, Herholz H, Schnittger S, Nakao A, Patten N, Wu L et al. The detection of TP53 mutations in chronic lymphocytic leukemia independently predicts rapid disease progression and is highly correlated with a complex aberrant karyotype. Leukemia 2009; 23: 117–124.

    CAS  Article  PubMed  Google Scholar 

  4. 4

    Rossi D, Cerri M, Deambrogi C, Sozzi E, Cresta S, Rasi S et al. The prognostic value of TP53 mutations in chronic lymphocytic leukemia is independent of Del17p13: implications for overall survival and chemorefractoriness. Clin Cancer Res 2009; 15: 995–1004.

    CAS  Article  Google Scholar 

  5. 5

    Puente XS, Pinyol M, Quesada V, Conde L, Ordonez GR, Villamor N et al. Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. Nature 2011; 475: 101–105.

    CAS  Article  PubMed  Google Scholar 

  6. 6

    Wang L, Lawrence MS, Wan Y, Stojanov P, Sougnez C, Stevenson K et al. SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N Engl J Med 2011; 365: 2497–2506.

    CAS  Article  PubMed  Google Scholar 

  7. 7

    Quesada V, Conde L, Villamor N, Ordonez GR, Jares P, Bassaganyas L et al. Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nat Genet 2011; 44: 47–52.

    Article  PubMed  Google Scholar 

  8. 8

    Fabbri G, Rasi S, Rossi D, Trifonov V, Khiabanian H, Ma J et al. Analysis of the chronic lymphocytic leukemia coding genome: role of NOTCH1 mutational activation. J Exp Med 2011; 208: 1389–1401.

    CAS  Article  PubMed  Google Scholar 

  9. 9

    Rossi D, Bruscaggin A, Spina V, Rasi S, Khiabanian H, Messina M et al. Mutations of the SF3B1 splicing factor in chronic lymphocytic leukemia: association with progression and fludarabine-refractoriness. Blood 2011; 118: 6904–6908.

    CAS  Article  PubMed  Google Scholar 

  10. 10

    Oscier DG, Rose-Zerilli MJ, Winkelmann N, Gonzalez De Castro D, Gomez B, Forster J et al. The clinical significance of NOTCH1 and SF3B1 mutations in the UK LRF CLL4 trial. Blood 2013; 121: 468–475.

    CAS  Article  PubMed  Google Scholar 

  11. 11

    Mansouri L, Cahill N, Gunnarsson R, Smedby KE, Tjonnfjord E, Hjalgrim H et al. NOTCH1 and SF3B1 mutations can be added to the hierarchical prognostic classification in chronic lymphocytic leukemia. Leukemia 2013; 27: 512–514.

    CAS  Article  PubMed  Google Scholar 

  12. 12

    Rossi D, Rasi S, Spina V, Bruscaggin A, Monti S, Ciardullo C et al. Integrated mutational and cytogenetic analysis identifies new prognostic subgroups in chronic lymphocytic leukemia. Blood 2013; 121: 1403–1412.

    CAS  Article  PubMed  Google Scholar 

  13. 13

    Rossi D, Rasi S, Fabbri G, Spina V, Fangazio M, Forconi F et al. Mutations of NOTCH1 are an independent predictor of survival in chronic lymphocytic leukemia. Blood 2012; 119: 521–529.

    CAS  Article  PubMed  Google Scholar 

  14. 14

    Balatti V, Bottoni A, Palamarchuk A, Alder H, Rassenti LZ, Kipps TJ et al. NOTCH1 mutations in CLL associated with trisomy 12. Blood 2012; 119: 329–331.

    CAS  Article  PubMed  Google Scholar 

  15. 15

    Del Giudice I, Rossi D, Chiaretti S, Marinelli M, Tavolaro S, Gabrielli S et al. NOTCH1 mutations in +12 chronic lymphocytic leukemia (CLL) confer an unfavorable prognosis, induce a distinctive transcriptional profiling and refine the intermediate prognosis of +12 CLL. Haematologica 2012; 97: 437–441.

    CAS  Article  PubMed  Google Scholar 

  16. 16

    O'Neil J, Grim J, Strack P, Rao S, Tibbitts D, Winter C et al. FBW7 mutations in leukemic cells mediate NOTCH pathway activation and resistance to gamma-secretase inhibitors. J Exp Med 2007; 204: 1813–1824.

    CAS  Article  PubMed  Google Scholar 

  17. 17

    O'Neill LA, Bowie AG . The family of five: TIR-domain-containing adaptors in Toll-like receptor signalling. Nat Rev Immunol 2007; 7: 353–364.

    CAS  Article  PubMed  Google Scholar 

  18. 18

    Dong X, Biswas A, Suel KE, Jackson LK, Martinez R, Gu H et al. Structural basis for leucine-rich nuclear export signal recognition by CRM1. Nature 2009; 458: 1136–1141.

    CAS  Article  PubMed  Google Scholar 

  19. 19

    Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H et al WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues 4th ed. International Agency for Research on Cancer (IARC): Lyon, 2008.

    Google Scholar 

  20. 20

    Kern W, Dicker F, Schnittger S, Haferlach C, Haferlach T . Correlation of flow cytometrically determined expression of ZAP-70 using the SBZAP antibody with IgVH mutation status and cytogenetics in 1,229 patients with chronic lymphocytic leukemia. Cytometry B Clin Cytom 2009; 76: 385–393.

    Article  PubMed  Google Scholar 

  21. 21

    Kern W, Bacher U, Haferlach C, Alpermann T, Dicker F, Schnittger S et al. Frequency and prognostic impact of the aberrant CD8 expression in 5,523 patients with chronic lymphocytic leukemia. Cytometry B Clin Cytom 2012; 82: 145–150.

    Article  PubMed  Google Scholar 

  22. 22

    Matutes E, Owusu-Ankomah K, Morilla R, Garcia MJ, Houlihan A, Que TH et al. The immunological profile of B-cell disorders and proposal of a scoring system for the diagnosis of CLL. Leukemia 1994; 8: 1640–1645.

    CAS  Google Scholar 

  23. 23

    Matutes E, Attygalle A, Wotherspoon A, Catovsky D . Diagnostic issues in chronic lymphocytic leukaemia (CLL). Best Pract Res Clin Haematol 2010; 23: 3–20.

    CAS  Article  PubMed  Google Scholar 

  24. 24

    Weissmann S, Roller A, Jeromin S, Hernandez M, Abaigar M, Hernandez-Rivas JM et al. Prognostic impact and landscape of NOTCH1 mutations in chronic lymphocytic leukemia (CLL): a study on 852 patients. Leukemia 2013.

  25. 25

    Jeromin S, Haferlach T, Grossmann V, Alpermann T, Kowarsch A, Haferlach C et al. High frequencies of SF3B1 and JAK2 mutations in refractory anemia with ring sideroblasts associated with marked thrombocytosis strengthen the assignment to the category of myelodysplastic/myeloproliferative neoplasms. Haematologica 2013; 98: e15–e17.

    Article  PubMed  Google Scholar 

  26. 26

    Kohlmann A, Klein HU, Weissmann S, Bresolin S, Chaplin T, Cuppens H et al. The Interlaboratory RObustness of Next-generation sequencing (IRON) study: a deep sequencing investigation of TET2, CBL and KRAS mutations by an international consortium involving 10 laboratories. Leukemia 2011; 25: 1840–1848.

    CAS  Article  PubMed  Google Scholar 

  27. 27

    Grossmann V, Roller A, Klein HU, Weissmann S, Kern W, Haferlach C et al. Robustness of amplicon deep sequencing underlines its utility in clinical applications. J Mol Diagn 2013; 15: 473–484.

    CAS  Article  PubMed  Google Scholar 

  28. 28

    Dicker F, Schnittger S, Haferlach T, Kern W, Schoch C . Immunostimulatory oligonucleotide-induced metaphase cytogenetics detect chromosomal aberrations in 80% of CLL patients: a study of 132 CLL cases with correlation to FISH, IgVH status, and CD38 expression. Blood 2006; 108: 3152–3160.

    CAS  Article  PubMed  Google Scholar 

  29. 29

    Haferlach C, Dicker F, Schnittger S, Kern W, Haferlach T . Comprehensive genetic characterization of CLL: a study on 506 cases analysed with chromosome banding analysis, interphase FISH, IgV(H) status and immunophenotyping. Leukemia 2007; 21: 2442–2451.

    CAS  Article  PubMed  Google Scholar 

  30. 30

    ISCN (1995)Guidelines for cancer cytogenetics, Supplement to: An International System for Human Cytogenetic Nomenclature. Mitelman F, Karger S (eds), 1995.

  31. 31

    Dohner H, Stilgenbauer S, Benner A, Leupolt E, Krober A, Bullinger L et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med 2000; 343: 1910–1916.

    CAS  Article  PubMed  Google Scholar 

  32. 32

    Tobin G, Thunberg U, Johnson A, Thorn I, Soderberg O, Hultdin M et al. Somatically mutated Ig V(H)3-21 genes characterize a new subset of chronic lymphocytic leukemia. Blood 2002; 99: 2262–2264.

    CAS  Article  PubMed  Google Scholar 

  33. 33

    Thorselius M, Krober A, Murray F, Thunberg U, Tobin G, Buhler A et al. Strikingly homologous immunoglobulin gene rearrangements and poor outcome in VH3-21-using chronic lymphocytic leukemia patients independent of geographic origin and mutational status. Blood 2006; 107: 2889–2894.

    CAS  Article  Google Scholar 

  34. 34

    Papaemmanuil E, Cazzola M, Boultwood J, Malcovati L, Vyas P, Bowen D et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N Engl J Med 2011; 365: 1384–1395.

    CAS  Article  PubMed  Google Scholar 

  35. 35

    Weng AP, Ferrando AA, Lee W, Morris JP, Silverman LB, Sanchez-Irizarry C et al. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 2004; 306: 269–271.

    CAS  Article  PubMed  Google Scholar 

  36. 36

    Rosati E, Sabatini R, Rampino G, Tabilio A, Di IM, Fettucciari K et al. Constitutively activated Notch signaling is involved in survival and apoptosis resistance of B-CLL cells. Blood 2009; 113: 856–865.

    CAS  Article  Google Scholar 

  37. 37

    Orlicky S, Tang X, Willems A, Tyers M, Sicheri F . Structural basis for phosphodependent substrate selection and orientation by the SCFCdc4 ubiquitin ligase. Cell 2003; 112: 243–256.

    CAS  Article  PubMed  Google Scholar 

  38. 38

    Oberg C, Li J, Pauley A, Wolf E, Gurney M, Lendahl U . The Notch intracellular domain is ubiquitinated and negatively regulated by the mammalian Sel-10 homolog. J Biol Chem 2001; 276: 35847–35853.

    CAS  Article  Google Scholar 

  39. 39

    Wu G, Lyapina S, Das I, Li J, Gurney M, Pauley A et al. SEL-10 is an inhibitor of notch signaling that targets notch for ubiquitin-mediated protein degradation. Mol Cell Biol 2001; 21: 7403–7415.

    CAS  Article  PubMed  Google Scholar 

  40. 40

    Thompson BJ, Buonamici S, Sulis ML, Palomero T, Vilimas T, Basso G et al. The SCFFBW7 ubiquitin ligase complex as a tumor suppressor in T cell leukemia. J Exp Med 2007; 204: 1825–1835.

    CAS  Article  PubMed  Google Scholar 

  41. 41

    Treon SP, Xu L, Yang G, Zhou Y, Liu X, Cao Y et al. MYD88 L265P somatic mutation in Waldenstrom's macroglobulinemia. N Engl J Med 2012; 367: 826–833.

    CAS  Article  PubMed  Google Scholar 

  42. 42

    Ngo VN, Young RM, Schmitz R, Jhavar S, Xiao W, Lim KH et al. Oncogenically active MYD88 mutations in human lymphoma. Nature 2011; 470: 115–119.

    CAS  Article  PubMed  Google Scholar 

  43. 43

    Schwaederle M, Ghia E, Rassenti LZ, Obara M, l' Aquila ML, Fecteau JF et al. Subclonal evolution involving SF3B1 mutations in chronic lymphocytic leukemia. Leukemia 2013; 27: 1214–1217.

    CAS  Article  PubMed  Google Scholar 

  44. 44

    Strefford JC, Sutton LA, Baliakas P, Agathangelidis A, Malcikova J, Plevova K et al. Distinct patterns of novel gene mutations in poor-prognostic stereotyped subsets of chronic lymphocytic leukemia: the case of SF3B1 and subset #2. Leukemia 2013.

  45. 45

    Rossi D, Spina V, Bomben R, Rasi S, Dal-Bo M, Bruscaggin A et al. Association between molecular lesions and specific B-cell receptor subsets in chronic lymphocytic leukemia. Blood 2013; 121: 4902–4905.

    CAS  Article  Google Scholar 

  46. 46

    Dreger P, Schnaiter A, Zenz T, Bottcher S, Rossi M, Paschka P et al. TP53, SF3B1, and NOTCH1 mutations and outcome of allotransplantation for chronic lymphocytic leukemia: six-year follow-up of the GCLLSG CLL3X trial. Blood 2013; 121: 3284–3288.

    CAS  Article  PubMed  Google Scholar 

  47. 47

    Shedden K, Li Y, Ouillette P, Malek SN . Characteristics of chronic lymphocytic leukemia with somatically acquired mutations in NOTCH1 exon 34. Leukemia 2012; 26: 1108–1110.

    CAS  Article  PubMed  Google Scholar 

  48. 48

    Villamor N, Conde L, Martinez-Trillos A, Cazorla M, Navarro A, Bea S et al. NOTCH1 mutations identify a genetic subgroup of chronic lymphocytic leukemia patients with high risk of transformation and poor outcome. Leukemia 2013; 27: 1100–1106.

    CAS  Article  PubMed  Google Scholar 

  49. 49

    Pospisilova S, Gonzalez D, Malcikova J, Trbusek M, Rossi D, Kater AP et al. ERIC recommendations on TP53 mutation analysis in chronic lymphocytic leukemia. Leukemia 2012; 26: 1458–1461.

    CAS  Article  Google Scholar 

  50. 50

    Landau DA, Carter SL, Stojanov P, McKenna A, Stevenson K, Lawrence MS et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 2013; 152: 714–726.

    CAS  Article  PubMed  Google Scholar 

  51. 51

    Foa R, Del G I, Guarini A, Rossi D, Gaidano G . Clinical implications of the molecular genetics of chronic lymphocytic leukemia. Haematologica 2013; 98: 675–685.

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all co-workers in our laboratory for their excellent technical assistance and all patients and clinicians for their participation in this study. Next-generation deep-sequencing studies were supported in part by the IRON-II study framework (Roche Diagnostics, Penzberg, Germany).

Author information

Affiliations

Authors

Corresponding author

Correspondence to S Schnittger.

Ethics declarations

Competing interests

SS, WK, CH, and TH are part owners of the MLL Munich Leukemia Laboratory. SJ, SW, VG, KB, FD, TA, AR and AK are employed by the MLL Munich Leukemia Laboratory.

Additional information

SJ and SS designed the study, interpreted the data and wrote the manuscript. SJ, SW, VG, KB, FD and AK did molecular analyses. CH was responsible for chromosome banding and FISH analyses, WK for immunophenotyping and TH for cytomorphologic analyses. AR and TA collected and analyzed clinical data. All authors read and contributed to the final version of the manuscript.

Supplementary Information accompanies this paper on the Leukemia website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Jeromin, S., Weissmann, S., Haferlach, C. et al. SF3B1 mutations correlated to cytogenetics and mutations in NOTCH1, FBXW7, MYD88, XPO1 and TP53 in 1160 untreated CLL patients. Leukemia 28, 108–117 (2014). https://doi.org/10.1038/leu.2013.263

Download citation

Keywords

  • CLL
  • mutation
  • cytogenetics
  • prognosis

Further reading

Search

Quick links