Leukemia www.nature.com/leu ARTICLE OPEN CHRONIC LYMPHOCYTIC LEUKEMIA Different prognostic impact of recurrent gene mutations in chronic lymphocytic leukemia depending on IGHV gene somatic hypermutation status: a study by ERIC in HARMONY Larry Mansouri1,42, Birna Thorvaldsdottir 1,42, Lesley-Ann Sutton1,42, Georgios Karakatsoulis2,3, Manja Meggendorfer4, Helen Parker5, Ferran Nadeu 6,7, Christian Brieghel 8, Stamatia Laidou 2, Riccardo Moia 9, Davide Rossi10,11, Mark Catherwood12, Jana Kotaskova 13,14,15, Julio Delgado6,7,16,17, Ana E. Rodríguez-Vicente 18,19,20, Rocío Benito 18,19,20, Gian Matteo Rigolin21, Silvia Bonfiglio22, Lydia Scarfo22, Mattias Mattsson23, Zadie Davis 24, Ajay Gogia25, Lata Rani25, Panagiotis Baliakas 23, Hassan Foroughi-Asl 1, Cecilia Jylhä1, Aron Skaftason1, Inmaculada Rapado26,27, Fatima Miras26, Joaquín Martinez-Lopez26,27, Javier de la Serna 26,27, Jesús María Hernández Rivas18,19,20, Patrick Thornton28, María José Larráyoz29,30, María José Calasanz29,30, Viktória Fésüs31, Zoltán Mátrai32, Csaba Bödör 31, Karin E. Smedby33, Blanca Espinet 34, Anna Puiggros34, Ritu Gupta 25, Lars Bullinger35, Francesc Bosch36, Bárbara Tazón-Vega36, Fanny Baran-Marszak 37, David Oscier24, Florence Nguyen-Khac38, Thorsten Zenz 39, Maria Jose Terol40, Antonio Cuneo21, María Hernández-Sánchez 18,19,20, Sarka Pospisilova13,14,15, Ken Mills 12, Gianluca Gaidano9, Carsten U. Niemann 8, Elias Campo 6,7,16,17, Jonathan C. Strefford 5, Paolo Ghia 22,43, 1234567890();,: ✉ Kostas Stamatopoulos 1,2,43 and Richard Rosenquist 1,41,43 © The Author(s) 2022, corrected publication 2023 Recent evidence suggests that the prognostic impact of gene mutations in patients with chronic lymphocytic leukemia (CLL) may differ depending on the immunoglobulin heavy variable (IGHV) gene somatic hypermutation (SHM) status. In this study, we assessed the impact of nine recurrently mutated genes (BIRC3, EGR2, MYD88, NFKBIE, NOTCH1, POT1, SF3B1, TP53, and XPO1) in pre- treatment samples from 4580 patients with CLL, using time-to-first-treatment (TTFT) as the primary end-point in relation to IGHV gene SHM status. Mutations were detected in 1588 (34.7%) patients at frequencies ranging from 2.3–9.8% with mutations in NOTCH1 being the most frequent. In both univariate and multivariate analyses, mutations in all genes except MYD88 were associated with a significantly shorter TTFT. In multivariate analysis of Binet stage A patients, performed separately for IGHV- mutated (M-CLL) and unmutated CLL (U-CLL), a different spectrum of gene alterations independently predicted short TTFT within the two subgroups. While SF3B1 and XPO1 mutations were independent prognostic variables in both U-CLL and M-CLL, TP53, BIRC3 and EGR2 aberrations were significant predictors only in U-CLL, and NOTCH1 and NFKBIE only in M-CLL. Our findings underscore the need for a compartmentalized approach to identify high-risk patients, particularly among M-CLL patients, with potential implications for stratified management. Leukemia (2023) 37:339–347; https://doi.org/10.1038/s41375-022-01802-y INTRODUCTION [4, 5]. These include genomic aberrations detected by fluores- Chronic lymphocytic leukemia (CLL) is clinically heterogenous, cence in situ hybridization (FISH), such as del(17p), del(11q), ranging from an indolent condition without urgent need for trisomy 12 and del(13q), and the immunoglobulin heavy variable treatment, to an aggressive disease characterized by rapid (IGHV) gene somatic hypermutation (SHM) status [6–9]. In progression, resistance to therapy, and poor overall survival. The addition, TP53 sequence analysis is essential, as patients with Rai and Binet staging systems are used in clinical practice to assess TP53 mutations, irrespective of the presence of del(17p), prognosis in patients with CLL [1, 2]. However, in early-stage experience a more adverse outcome, even with targeted disease, which constitute the great majority of new diagnoses, therapies, particularly in the relapsed/refractory setting [5, 10–12]. these systems are unable to predict which cases will progress to a Over the past decade, next-generation sequencing (NGS) more aggressive disease [3]. That said, there are a number of studies have led to the discovery of recurrently mutated genes molecular markers with prognostic and/or predictive impact that in CLL, such as NOTCH1, SF3B1, BIRC3, XPO1, POT1, NFKBIE and should be assessed in all patients prior to treatment initiation EGR2, that are associated with poor clinical outcome [13–21]. On A full list of author affiliations appears at the end of the paper. Received: 11 October 2022 Revised: 8 December 2022 Accepted: 15 December 2022 Published online: 24 December 2022 L. Mansouri et al. 340 Table 1. Clinicobiological characteristics of the studied cohort. All cases Binet A Binet A M-CLL Binet A U-CLL Gender Male 2890 (63%) 1992 (59%) 1183 (58%) 708 (61%) Female 1690 (37%) 1370 (41%) 862 (42%) 445 (39%) Median age at diagnosis (years) 64.6 64.9 64.6 64.9 IGHV gene SHM status M-CLL 2454 (57%) 2045 (64%) U-CLL 1878 (43%) 1153 (36%) Unknown 239 164 Recurrent aberrationsa del(13q) 1856 (42%) 1465 (45%) 775 (57%) 302 (27%) trisomy12 566 (13%) 380 (12%) 72 (5%) 212 (19%) del(11q) 495 (11%) 277 (8%) 12 (1%) 225 (20%) del(17p) 236 (5%) 148 (5%) 42 (3%) 78 (7%) No recurrent abnormalities 1306 (29%) 1005 (31%) 469 (34%) 319 (28%) Unknown 121 87 35 17 Binet stage A 3362 (74%) B 825 (18%) C 387 (8%) Unknown 6 Treatment statusb Treated 2680 (59%) 1570 (47%) 640 (32%) 875 (76%) Untreated 1900 (41%) 1792 (53%) 1405 (68%) 278 (24%) SHM somatic hypermutation, M-CLL CLL with mutated IGHV genes, U-CLL CLL with unmutated IGHV genes. a According to the Döhner classification [6]. b At follow-up. the other hand, recurrent mutations in MYD88 have been MATERIALS AND METHODS correlated with a favorable outcome, though not conclusively Patients [22, 23]. Based on these findings, several studies have devised Our cohort included 4580 patients with CLL from 26 European centers prognostic models that incorporate clinicobiological factors as (Supplementary Table S1). Only pre-treatment samples were analyzed; the well as various combinations of recurrent gene mutations, aimed median time from diagnosis to sample collection was 3 months (data available for 3991 samples). The majority of cases were non-trial patients at improving risk stratification in CLL [24–26]. However, several from referral centers, while the cohort also included patients from the UK questions remain unanswered. While NOTCH1, SF3B1 and BIRC3 LRF CLL4 clinical trial [33] (n = 499). The median age at diagnosis was 64.6 mutations have been investigated in larger patient cohorts years (interquartile range, 56.7–71.7 years). Clinicobiological characteristics [17, 25, 27, 28], the prognostic role of the remaining gene for the cohort are shown in Table 1. All cases were diagnosed according to mutations is less well studied. Furthermore, the frequencies and the iwCLL guidelines [4]. Informed consent was obtained according to the clinical impact of gene mutations differ between the poor- Helsinki declaration and the study was approved by the local Ethics Review prognostic IGHV-unmutated (U-CLL) subgroup and the more Committees. favorable-prognostic IGHV-mutated CLL (M-CLL) subgroup [29–32]. In fact, in recent studies, a number of genetic markers were demonstrated to affect outcome in U-CLL and M-CLL Mutational analysis differently, indicating that a more compartmentalized approach All cases were assessed for mutations within the coding sequences of the BIRC3, EGR2, MYD88, NFKBIE, NOTCH1, POT1, SF3B1, TP53, and XPO1 genes. based on the IGHV gene SHM status might be necessary when The ATM gene was initially included, however, mutation data was only developing prognostic models [31, 32]. available for 3611 cases and considering the difficulties to interpret ATM In this study, we investigated the prognostic roles of nine genes mutation without germline DNA, it was excluded from further analysis. recurrently mutated in CLL, as well as their relative impact in Mutational screening was performed by NGS for the majority of the cohort U-CLL and M-CLL, with a particular focus on early-stage patients (80%), with targeted gene panels, covering all or hotspot exons of each and time-to-first-treatment (TTFT) as the primary clinical endpoint. gene, as the most frequent technique employed. Sanger sequencing was This analysis was performed in a well-annotated series of pre- performed in 18% of cases and the remaining methods combined, mostly treatment samples from 4580 patients with CLL, to our knowledge targeting hotspot mutations, accounted for 2% of analyses (Supplementary the largest series analyzed thus far, consolidated in the context of Table S1). For mutational data generated using NGS analysis, sequence a European multicenter effort coordinated by the European alignment, annotation and variant calling were performed at each center using a ≥5% variant allele frequency (VAF) threshold to classify mutated Research Initiative on CLL (ERIC) as partner of the HARMONY cases. Variant filtering was performed by including nonsynonymous Alliance. Our novel data highlight that different spectra of genetic variants and small insertions/deletions within the coding sequences and mutations predict clinical outcome in U-CLL and M-CLL, respec- by excluding variants with a population frequency >0.0001 in the gnomAD tively, with SF3B1 and XPO1 mutations being the most significant database unless variant was included as a somatic variant in the COSMIC prognostic markers irrespective of subgroup assignment. database. Leukemia (2023) 37:339 – 347 L. Mansouri et al. 341 A NOTCH1 SF3B1 TP53 Gene mutations XPO1 Mutant NFKBIE Wildtype POT1 BIRC3 IGHV mutational status MYD88 Mutated Unmutated EGR2 Unknown IGHV status Copy number abberation Positive del(13q) Negative trisomy(12) Unknown del(11q) del(17p) 500 1000 1500 1588 B C >3 Exclusive n.s. >3 Co−occurance *** M−CLL (%) U−CLL (%) NOTCH1 SF3B1 −log10(adj. p−value) TP53 15 *** XPO1 NFKBIE POT1 % mutated samples *** BIRC3 10 *** MYD88 *** *** EGR2 *** M-CLL 5 ** *** del(13q) trisomy(12) del(11q) del(17p) 0 tr i del( LL d e y(1 ) de (11 ) l(1 q) ) X 53 YD 3 M R2 EG 88 SF H1 TP 1 FK 1 BI T1 PO IE m q l 2 7p M RC 3B N PO so 13 -C 53 M 3 2 88 SF 1 1 1 T1 B E TC C R H 3B O BI TP YD PO R EG TC XP O FK BI N O N N Fig. 1 Overview of 1588 CLL cases carrying mutations in recurrently mutated genes. A Oncoplot of detected gene mutations (in order of frequency), IGHV gene somatic hypermutation (SHM) status and chromosomal aberrations. B Relative distribution of recurrent gene mutations stratified by IGHV gene SHM status, ** denotes a p value <0.01, while *** represents a p value <0.001. C Co-occurence of recurrently mutated genes and chromosomal aberrations. The mutation frequencies were compared in samples analyzed by NGS comparisons using the Cox–Mantel log-rank test determined differences and Sanger sequencing, examining both hotspot mutations (exemplified between subgroups and p values were adjusted using the by SF3B1 p.K700E, NOTCH1 p.P2514fs and EGR2 p.H383N mutations) or Benjamini–Hochberg method. The Cox proportional hazards model was entire genes without hotspot positions (exemplified by TP53 and XPO1), employed to assess the prognostic strength of each marker in multi- without significant differences in mutation rates when comparing the variable analysis. Stepwise variable selection using Akaike’s information methods (Supplementary Table 2). criterion was applied in the Cox regression model to examine the IGHV gene SHM status, available for 4332/4580 (94.6%) patients, was significant risk factors associated with TTFT, and the proportion of the determined using PCR amplification and sequence analysis of IGHV-IGHD- explainable log-likelihood was used to examine their relative importance. IGHJ gene rearrangements and employing a 98% identity cut-off to All statistical analyses were performed using R (version 4.1.2) and R Studio germline to define M-CLL (<98% identity) and U-CLL (≥98% identity) [9]. software. Plots were created using ggplot2 v3.3.5, ComplexHeatmap Chromosomal aberrations [data available for 4459/4580 (97.4%) cases] v2.11.1 and G3viz packages [34]. were detected using FISH with targeted probes for chromosomes 13q, 11q, 17p and 12 or Affymetrix 250K SNP-arrays and classified according to the Döhner hierarchical model [6]. RESULTS Frequency of recurrent gene mutations Statistical analysis For the nine genes analyzed, we detected at least one mutation in The chi-square test was employed for comparing the clinicobiological 1588/4580 (34.7%) patients (Fig. 1A and Supplementary Fig. S1A, variables and p values were Yates corrected. Co-occurrence and exclusivity B). Of these, mutations were found in a single gene in 1221/1588 matrices were constructed using two-sided Fisher’s exact tests and p values were corrected using Benjamini–Hochberg for multiple testing. TTFT (76.9%) patients, while 321 cases carried mutations in 2 genes, and was available for 4543/4580 (99.2%) cases and calculated from the 46 cases had mutations in 3–4 genes. Except for MYD88, mutations diagnostic date until date of first treatment; median follow-up was 3.6 were significantly more frequent in U-CLL (Fig. 1B), advanced- years for the entire cohort and 6.5 years for untreated patients. Survival stage patients (except for NFKBIE mutations) and patients curves were constructed using the Kaplan–Meier method, pairwise requiring treatment (Supplementary Table S2). In contrast, Leukemia (2023) 37:339 – 347 L. Mansouri et al. 342 MYD88 mutations were enriched in M-CLL, while no difference Most POT1-mutated patients showed favorable cytogenetics was seen when comparing early-stage versus late-stage patients [isolated del(13q) or no recurrent copy-number aberration or patients remaining untreated versus those requiring treatment (71.9% of all cases); Fig. 1A and Supplementary Fig. S3]. (Fig. 1A and Supplementary Table S3). BIRC3 mutations were observed in 122/4580 (2.7%) cases with 25/122 (20%) carrying the p.Q547fs mutation. The only other Clinicobiological profiles associated with recurrent gene mutation found in >5 cases was p.E433fs (n = 6). BIRC3 mutated mutations cases had the highest co-occurrence of del(11q) (35.6% of cases). NOTCH1 mutations were the most common, detected in 448/4580 In fact, a large proportion of patients (70.3%) carrying BIRC3 (9.8%) patients, with the highest frequency among U-CLL (17.6%) mutations harbored unfavorable cytogenetics [trisomy 12, of all genes studied (Fig. 1A, B). NOTCH1-mutated patients had a del(11q) or del(17p)] (Fig. 1A and Supplementary Fig. S3). relatively high proportion of co-occurring mutations in other MYD88 mutations were detected in 114/4580 (2.5%) patients, genes [181/448 (40.4%) cases], most often in TP53 (n = 45) or XPO1 with the p.L265P and p.V217F mutations identified in 78/114 (n = 41). As most centers performed targeted analysis of exon 34 (68.4%) and 14/114 (12.3%) cases, respectively. MYD88 mutations (86%), the previously reported 2 base-pair frameshift deletion were strongly associated with young age at diagnosis (median (p.2514fs) [13, 14] was detected in 329/448 (73.4%) mutated 59.9 years, p < 0.001) and favorable cytogenetics, with 62.5% of samples, while no other hotspot was observed within exon 34 (the cases carrying only del(13q) (Supplementary Fig. S3). Additionally, locations of all coding mutations identified in all genes are MYD88-mutated cases rarely [16/114 (14.0%) cases] carried a illustrated in Supplementary Fig. S2, while hotspot mutations for mutation in a second gene (Fig. 1C). all genes are summarized in Supplementary Table S4). As Finally, EGR2 mutations were the least common, observed in expected, NOTCH1 mutations were significantly associated with only 106/4580 (2.3%) cases. Two hotspot mutations were trisomy 12 (Fig. 1C and Supplementary Fig. S3). detected; p.H384N found in 56/106 (52.8%) cases, followed by SF3B1 mutations were detected in 428/4580 (9.3%) cases and the p.E356K mutation detected in 30/106 (28.3%) cases. EGR2 were the most frequent mutation in M-CLL (148/2454, 6.0%), mutations were associated with unfavorable cytogenetics (59% of advanced-stage patients at diagnosis (206/1212, 17.0%) and cases) (Fig. 1C and Supplementary Fig. S3 and Supplementary patients requiring treatment (368/2680, 13.7%). As centers Table S3). predominantly sequenced exons 14–16 (80%), the majority of mutations (>98%) were localized to these exons, with p.K700E Recurrent gene mutations and clinical outcome (191/423, 45.2%), p.G742D (54/423, 12.8%) and p.K666E/M/N/Q/R/ We assessed the clinical impact of each gene mutation T (39/423, 9.2%) being the most common hotspot mutations individually using TTFT as the primary endpoint. Patients carrying [16, 17, 25, 35–37]. Mutations in other genes were observed in del(17p) and/or mutations in TP53 were grouped as TP53 aberrant 151/423 (35.7%) SF3B1-mutated cases with TP53 mutations, the (TP53ab) as they exhibit a similarly poor outcome [10]: 421 most common co-occurring event (50/423, 11.8%). Interestingly, patients were classified as TP53ab of whom 44% carried TP53 BIRC3 and SF3B1 mutations appeared almost mutually exclusive, mutations, 24% harbored del(17p), while 33% had biallelic TP53 co-occurring in only 3/423 (0.7%) patients (Fig. 1C). Furthermore, aberrations. SF3B1 mutations were negatively associated with trisomy 12, only Except for MYD88, all mutated genes were associated with found in 3.9% of trisomy 12 cases (Fig. 1C and Supplementary significantly worse outcome (p < 0.001); MYD88-mutated patients Fig. S3 and Supplementary Table S3). displayed no significant difference in TTFT versus MYD88 wildtype TP53 mutations were identified in 322/4580 (7.0%) patients with patients (p = 0.092, Supplementary Fig. S4). Multiple TP53 muta- 35/322 (10.9%) cases carrying multiple TP53 mutations [31/35 tions (excluding del(17p)) had no added effect on TTFT, nor was (89%) cases carried two mutations, the remaining four cases had there a significant difference when comparing patients carrying a three mutations]. Mutations frequently coincided with del(17p) TP53 mutation with or without del(17p). We also investigated if [137/311, 44.1% (no data on genomic aberrations available for 11 carrying mutations in multiple genes affected clinical outcome. In cases)] and were the second most frequent mutation in M-CLL this analysis, cases harboring MYD88-mutations were grouped [122/2454 (5.0%)] (Fig. 1B, C and Supplementary Fig. S3). TP53 with cases wildtype for all other genes. Regardless of the gene mutations were detected along the entire coding sequence with mutated, patients with ≥2 mutations had a significantly worse p.R248Q/W (n = 16) being the most common mutation. TTFT compared to those with a single mutation (p < 0.001); while XPO1 mutations were identified in 176/4580 (3.8%) cases with no differences were identified when comparing patients with 2 most mutations detected in U-CLL (88%), patients requiring gene mutations versus those with ≥3 (Supplementary Fig. S5). treatment (90%) and patients carrying mutations in a second gene When stratifying patients based on IGHV gene SHM status, no (97/176, 55.1%). Additional mutations were most commonly found added effect was found in U-CLL when comparing cases with a in NOTCH1 (n = 41) or SF3B1 (n = 36) while, on the contrary, only single mutation to those with ≥2 mutations. In contrast, multiple 14 patients carried a second mutation in TP53. Patients with XPO1 mutations were associated with a worse outcome in M-CLL, mutations often carried del(11q) (20.8% of cases) (Fig. 1A, C and however, only 65/2454 (2.6%) cases carried mutations in more Supplementary Fig. S3). Hotspot mutations at p.E571A/G/K/Q/V than 1 gene. were detected in 154/175 (88%, mutation data missing for 1 As advanced-stage disease at diagnosis is an indication for patient) cases followed by p.D624G detected in 7% of patients. treatment, we performed the analysis in Binet stage A patients NFKBIE mutations were detected in 150/4580 (3.3%) patients. and obtained similar results, whereby mutations in all genes, Among these, 125/150 (83.3%) carried the previously reported except MYD88, resulted in significantly shorter TTFT (p < 0.001 for frameshift 4-bp deletion (p.Y254fs) [18] while an additional three all, Supplementary Fig. S6). We repeated the analysis in the Binet cases displayed a p.Y254* mutation. Besides MYD88, NFKIBE was stage A patient cohort, this time stratifying the patients based on the only other recurrently mutated gene with a similar distribution IGHV gene SHM status. Notably, only mutations in SF3B1 and XPO1 of mutated cases in early versus late-stage patients, while a could further discriminate patients with a statistically significant significant difference was noted when comparing NFKBIE muta- worse outcome in both U-CLL and M-CLL (Fig. 2). TP53 and BIRC3 tions stratified for IGHV gene SHM status or need for treatment aberrations were significantly correlated with TTFT in U-CLL only, (p < 0.001 for both comparisons) (Supplementary Table S3). while, NOTCH1 and NFKBIE mutations were significantly associated POT1 mutations were identified in 142/4580 (3.1%) cases; no with shorter TTFT only in M-CLL (Fig. 2). Finally, mutations in EGR2, mutational hotspot was evidenced with the most common POT1 and MYD88 conferred no additional prognostic information mutations, p.S38C/G/N/R, observed in only 9/142 (6.3%) cases. to patients stratified by IGHV gene SHM status (Fig. 2). Leukemia (2023) 37:339 – 347 L. Mansouri et al. 343 SF3B1 TP53 NOTCH1 + SF3B1wt/M−CLL (n=1950) +++++ + TP53wt/M−CLL (n=1918) + NOTCH1wt/M−CLL (n=1967) 1.00 ++++++ 1.00 ++++++++++ 1.00 + + +++ ++++++++++ + + SF3B1mut/M−CLL (n=84) ++ + TP53ab/M−CLL (n=116) +++++++++++++++ + + NOTCH1mut/M−CLL (n=67) ++++ +++++++++++++ +++ +++ ++++++++ + TP53wt/U−CLL (n=1001) +++ ++ ++++++++ ++++++ + SF3B1wt/U−CLL (n=1010) + +++++ + + + ++++ + NOTCH1wt/U−CLL (n=946) + ++ ++++++ + +++ +++++ +++ +++ ++++++ + TP53ab/U−CLL (n=138) + ++ ++++++++++++++ ++++ + +++ ++ + ++ + SF3B1mut/U−CLL (n=129) + + + + + ++ ++ ++ ++ ++++++ + NOTCH1mut/U−CLL (n=193) 0.75 ++++++ +++++++++ +++++++++ 0.75 +++ ++ ++++++++++++ +++++++++ 0.75 ++++ +++++++++ ++ +++ +++++++ +++++++ ++ +++++++++ ++ +++++++++++ + + +++++ +++ +++++++++ + +++ ++++++++ +++ + ++++++++++++ ++++ ++ + + + ++++ + ++ +++ +++++++++ + +++++++++++++ ++++++++++++ + +++ ++++ ++++++++++++++++ ++ + +++ ++++ +++++++++ ++ ++ ++ +++++++ +++ ++ +++ ++ +++ +++ ++++++++++++ ++++++++ +++ + ++++++++++++++++++++++++++ ++++++++++++++++++++++++ ++ +++++ ++ ++++++ + + ++ +++++++++++++++++++++++++ 0.50 ++ ++++ + ++++++++++++ ++ 0.50 ++++++ +++++++++++++ 0.50 +++ +++ ++ p < 0.01 +++++ ++++++++ ++ +++ +++ ++++++ ++++++++++++ + ++ + ++++ ++++ +++++++++++++ + + +++++ + + ++++++ +++ + +++++ +++++ +++ ++ + + +++++++++++++ + ++++ +++++++ + ++++ + p < 0.001 +++++++ p = 0.46 ++++ ++++ + ++ ++++ + ++++++ ++++ 0.25 ++ + 0.25 0.25 ++++ +++ ++++ + +++ ++ ++++++ ++++ ++ ++++++++ + + + ++++++ + ++++++ +++ ++ +++++++++++++ + + ++ p < 0.01 ++ + ++++++++++++++++++ +++ ++++ +++++++++++ + + + + + + + ++ + + + + + 0.00 p < 0.001 0.00 0.00 0 10 20 30 0 10 20 30 0 10 20 30 BIRC3 EGR2 NFKBIE + BIRC3wt/M−CLL (n=2001) + EGR2wt/M−CLL (n=2016) +++++ + NFKBIEwt/M−CLL (n=2005) 1.00 +++++ 1.00 +++++ 1.00 +++++++++++++++ + + BIRC3mut/M−CLL (n=33) +++++++++++++++ + + EGR2mut/M−CLL (n=18) +++++++++++++++ + + NFKBIEmut/M−CLL (n=29) + +++++++++ + ++++++++++ + +++++++++ + NFKBIEwt/U−CLL (n=1072) Proportion untreated + +++ + BIRC3wt/U−CLL (n=1101) ++ +++++++ + EGR2wt/U−CLL (n=1098) + + ++++++++ ++ +++++++++++++++ ++ +++++ ++ ++++++ + NFKBIEmut/U−CLL (n=67) ++ ++ ++++++++ +++++++++ + BIRC3mut/U−CLL (n=38) ++ + +++++++++++++++ + EGR2mut/U−CLL (n=41) ++ ++++ ++ ++++++++ +++++++++ 0.75 ++ +++ ++++++++++ 0.75 ++ + + ++++++++++ 0.75 +++++++++ + + + ++ + + + ++ +++ ++ +++++++ ++ ++++++++ +++ ++++++++ ++ ++++++++++ + + ++++++++++ +++ ++++++++++ +++ ++++++++++++ + ++++ ++++++++++++++++ + +++ ++++++++++++++++ +++ ++ +++++++++++++ ++ ++ + +++++++++++ + ++++++++ p = 0.07 +++++++++ ++ +++++++++++++++++++++++++++ ++ +++++++++++++ ++ + +++ +++++++++++++++++++++++++++ 0.50 ++ ++ 0.50 ++ +++ +++ +++++++++++++++++++ 0.50 + +++ +++ + + +++++++++++ +++ ++ ++++++++++++ + ++++ ++++++++++++ ++ ++++ + ++ +++ ++ +++ ++++ p = 0.2 ++++++ +++++++++++++ + ++ ++++ +++++ +++++++++++++ + ++++ +++++ +++++++++++++ + +++++ + +++++ ++++ + + ++ ++++++++++ ++++++++ ++++ ++++ ++++ p < 0.01 0.25 ++++ 0.25 ++++ 0.25 ++++ ++ +++ +++++++ + +++++++++ +++++ + ++++ + +++++++ ++++++ p = 0.032 +++++++++++++ + + ++ + + +++++++++++++ + + ++ + + p = 0.96 +++++++++++++++ ++ ++ + + 0.00 0.00 p = 0.06 0.00 0 10 20 30 0 10 20 30 0 10 20 30 XPO1 POT1 MYD88 1.00 +++++ + XPO1wt/M−CLL (n=2022) 1.00 ++ +++ + POT1wt/M−CLL (n=2020) 1.00 +++++ + + MYD88wt/M−CLL (n=1965) ++++++++++++++ + + ++++++++ +++++ + ++++++++ + XPO1mut/M−CLL (n=12) +++ +++ + POT1mut/M−CLL (n=14) + + MYD88mut/M−CLL (n=69) + +++++++++++ ++ ++++++++++++ + +++++++++++++ + +++++ + XPO1wt/U−CLL (n=1042) ++ +++++++++ + POT1wt/U−CLL (n=1078) + ++ + MYD88wt/U−CLL (n=1133) ++ +++++ +++++++ ++ +++++++++++++ ++ ++++++++ + +++++++++ + XPO1mut/U−CLL (n=97) +++ ++++++ + POT1mut/U−CLL (n=61) ++ ++++++++++ + MYD88mut/U−CLL (n=6) 0.75 ++ + ++++++++ 0.75 +++ +++++++++ 0.75 ++ ++++++++++++ ++ +++++++ +++ ++++++++ ++ +++++ ++ +++++++ ++ ++ + ++++ + + + + + +++++++ + + +++++++++++ +++++++++ +++ ++ ++++++++++ ++ +++++ ++ ++++++++++++ ++++++++++++++ +++ + ++ ++++++++++++++ ++ + ++++++++++++++ +++++++++++++++ ++ +++++++++ ++ ++++++++++++ +++ +++++++++ + +++++++++++++++++++++++ ++ ++++++++ ++ 0.50 ++++++ ++++++ 0.50 ++ +++ +++++++++++++++++++++++++ +++++++++++ p = 0.27 0.50 ++ ++ + ++++++++++++++++++++++++++ ++ ++++ ++++++++++++ +++ ++ +++ +++ ++++++++++++ + ++ +++ ++ + + + +++ ++ +++++++ +++++ +++++++++++++ + ++++++ +++ +++++++ +++++ +++++ ++++ p < 0.001 ++++ + +++++ + +++++++++++++ + +++ +++++ p = 0.32 +++++++++++++ + ++++ +++ ++++ + +++++++ ++++ ++++ 0.25 +++ 0.25 ++++ 0.25 ++++ +++ + ++++ ++++++ ++ ++++++++ +++++ ++++ ++++ p = 0.20 ++++++ + ++++++++++++++ +++++++++++++ + ++++++++++ + ++ + + ++ +++ + + ++ + ++ + + + + + + 0.00 p < 0.01 0.00 p = 0.39 0.00 0 10 20 30 0 10 20 30 0 10 20 30 Time to first treatment (yrs) Fig. 2 Time-to-first-treatment (TTFT) in Binet A CLL patients carrying recurrent gene mutations stratified based on IGHV gene SHM status. Pairwise comparisons were performed using the Cox–Mantel log-rank test. U-CLL, CLL with unmutated IGHV genes, M-CLL, CLL with mutated IGHV genes. We next investigated how hotspot versus non-hotspot muta- multivariate model focusing on the nine investigated genes, age tions within the same gene affected TTFT. As there were no at diagnosis as well as sex in the entire cohort. As before, TP53ab hotspot mutations detected in TP53 or POT1, these genes were rather than TP53 mutations were included as a variable and omitted from the analysis. Although all non-hotspot NOTCH1 NFKBIE-mutated cases were defined as those carrying only the mutations combined resulted in a worse outcome when hotspot p.Y254fs/p.Y254* mutation based on the association with compared to NOTCH1 wildtype patients (p < 0.001), cases carrying poor outcome as described above. All variables other than age at the hotspot p.2514fs mutation had a particularly poor outcome diagnosis and MYD88 mutations, were found to be significant when compared to non-hotspot mutations (p < 0.05, Fig. 3). A (Supplementary Table S5). In a second model, which also included similar trend was observed for SF3B1 mutations whereby patients IGHV gene SHM status, del(11q), trisomy 12 and Binet stage, SF3B1, with hotspot mutations (p.K700E, p.G742D, p.K666E/M/N/Q/R/T, TP53, EGR2, NFKBIE and XPO1 mutations remained as significant p.H662D/Q and p.I704F/N/S) appeared to have the worst outcome, variables, while NOTCH1, POT1 and BIRC3 mutations were no although the numbers for some hotspot mutations were low longer significant (Supplementary Table S6). (Fig. 3). Among patients with EGR2 mutations, those carrying the Next, we studied Binet A cases only using the same p.E356K mutation showed a tendency toward worse outcome multivariate models and obtained very similar results (Supple- compared to all other EGR2-mutated cases, although not mentary Tables S5 and S6). We repeated the analysis focusing on statistically significant, while for NFKBIE-mutated patients, only the 9 genes (including also age at diagnosis and sex) separately the hotspot p.Y254fs mutation significantly affected clinical for U-CLL and M-CLL. Notably, SF3B1 and XPO1 mutations were outcome (Fig. 3). No differences were detected when comparing the only independent variables in the multivariate analysis of variants in XPO1 or BIRC3, while for MYD88 mutated patients, both U-CLL and M-CLL with SF3B1 showing the highest hazard neither hotspot nor non-hotspot mutations differed significantly ratio (Fig. 4). Additionally, male sex, TP53, BIRC3 and EGR2 from wildtype patients (Fig. 3). aberrations were significant factors for U-CLL, while mutations in NOTCH1 and NFKBIE were significant markers in M-CLL. Using the Multivariate analysis reveals different impact of molecular extended model, including del(11q) and trisomy 12, revealed markers in U-CLL and M-CLL that SF3B1 and XPO1 were again the only independent genes in To assess the relationships between the different gene aberrations the multivariate analysis of both U-CLL and M-CLL (Supplemen- and their relevance for TTFT in CLL, we first performed a tary Table S7). Leukemia (2023) 37:339 – 347 L. Mansouri et al. 344 SF3B1 NOTCH1 BIRC3 1.00 + + wt (n=4108) 1.00 + + wt (n=4093) 1.00 + + + wt (n=4414) + + + + + + + + + other mutations (n=124) + + + other mutations (n=117) + + other mutations (n=95) + + + + + + +++ + p.K700E (n=191) +++ + p.P2514fs (n=324) +++ + p.Q547fs (n=25) ++++ ++++ + +++ +++ + +++ 0.75 + ++++ + p.G742D (n=54) 0.75 ++++++ 0.75 +++ + ++++++ + p.K666E/M/N/Q/R/T (n=38) + +++ p.P2514fs vs. other mut: p<0.05 +++ ++++ +++ ++ +++++++ ++++ + +++++++ + p.H662D/Q (n=19) + + ++++ + ++++ ++++++ ++ +++++++ +++ +++++++++ + + +++++++ + ++++++ p.K700E vs. other mut: p<0.001 + +++++++++++ + +++ 0.50 + +++++++ + ++ + + 0.50 ++ ++ +++++++ + +++ 0.50 + ++ ++++++++++++ + ++ + + + +++++++ p.K700E vs. H662D/Q: p<0.05 ++++++++ ++ ++++ ++++++++++++++ ++++++++ +++++++ + +++ +++++++++++ ++ ++ ++++++++++ ++++++++++ +++++++++++++++++++++ ++++++++++++ ++ + ++++++++++ + +++ +++++++++++++++++++ ++ ++++++++++++++++++++ ++++++++++++++++++++++++ + ++ ++++++++++++++++++++++ +++ +++++++++++++++++++++ ++ ++++++++++++++ ++++ ++++++++++++++++++++++ 0.25 +++++++++++++ 0.25 ++++ ++ ++++++++++ 0.25 + ++++++++++++++++++++ + + +++ +++++++ ++++ +++ ++++ +++++ ++ ++++++++++ + + ++ +++++++++ + + +++ + ++ ++ + + +++++++++ + ++ + ++++++ +++++++++++++ + + ++ ++ + + ++ + ++ + + + + ++ +++++++ + ++ + + + + ++ + 0.00 0.00 0.00 0 10 20 30 0 10 20 30 0 10 20 30 EGR2 NFKBIE 1.00 + + + wt (n=4430) 1.00 + + + + + + + + other mutations (n=20) + + + + + + + wt (n=4386) +++ ++ Proportion untreated +++ + p.H384N (n=56) +++ +++ +++ + other mutations (n=21) +++ + p.E356K (n=28) +++ + 0.75 +++ 0.75 +++ + p.Y254fs/p.Y254* (n=127) +++ +++ ++++ ++++ + + ++++ ++++ p.E356K vs. p.H384N: p<0.05 + +++++++ + +++++ +++++ +++++ ++++++ + 0.50 + +++++++ 0.50 +++++++ + +++++++ ++ ++++++ +++ ++++++++ + ++++++++ ++++++++ +++++++ + +++++++ ++++++++++ ++ ++++++++++++ +++++++++++++++++++++ ++++++++++++++++++++++++++++++++ + + + +++++++++++++++++++++++++++++ ++++++++++++++ ++++++++++++++++ ++++++++++++++++++++++++++++ 0.25 ++ + ++++++++++ ++++ 0.25 + ++++++++++++++ +++++++++ + +++ +++++ +++++++++++++ + + + +++ + ++ + + ++ + 0.00 0.00 0 10 20 30 0 10 20 30 XPO1 MYD88 + wt (n=4358) + wt (n=4422) 1.00 + + 1.00 + + + + + + other mutations (n=9) + + + other mutations (n=22) + + + + +++ + p.E571A/G/K/Q/V (n=154) +++ + + p.L265P (n=76) +++ +++ +++ + p.D624G (n=13) +++ ++ + p.V217F (n=14) 0.75 +++ 0.75 +++ +++ + + + + +++ + + p.V217F vs. p.L265P: p<0.05 + ++++++ ++++ + + ++++ +++ ++ ++++ p.V217F vs. wt: p=0.36 ++++ ++++ ++ ++++ ++++ ++++++++++ + +++++++++++ +++++++ + + +++ +++++++ 0.50 ++++++ 0.50 ++++++ ++ + +++++++++++++++ ++++ ++ ++++++++ ++++++++++++++++ +++++++++++ ++++++++++++++++++ ++++++++++ + ++ +++++++++++++++++ +++++++++++++++ + +++++++++++++++++++++++++++ + +++++++++++ ++++++ ++++++++ ++++++++++++++++++++++++++++ 0.25 ++ ++++++++++++++ 0.25 ++++++++++++++ +++++++ ++++ ++++++++ ++++ + +++++++++ + +++++++++ + ++ + + 0.00 0.00 0 10 20 30 0 10 20 30 Time to first treatment (yrs) Fig. 3 Clinical impact of hotspot versus non-hotspot mutations in recurrently mutated genes assessed using time-to-first-treatment (TTFT) as clinical endpoint. Pairwise comparisons were performed using the Cox–Mantel log-rank test. DISCUSSION U-CLL, n=1139 M-CLL, n=2035 Over the last 10 years, whole-exome and/or whole-genome Risk HR (CI95) HR (CI95) sequencing studies have dissected the genomic landscape of CLL, Male sex 1.19 (1.04-1.37)* 1.05 (0.89-1.23) demonstrating that only a few recurrent gene mutations are Age >64.5 yrs 0.99 (0.86-1.13) 0.96 (0.82-1.13) present in more than 5–10% of cases (ATM, NOTCH1, SF3B1, TP53), SF3B1 1.50 (1.22-1.84)*** 2.64 (1.98-3.52)*** while the majority are seen in a minor proportion of cases (<1–5%) TP53ab 1.27 (1.04-1.55)* 1.09 (0.78-1.50) [20, 21, 32]. To date, recurrent mutations in more than 25 genes NOTCH1 1.01 (0.85-1.20) 1.68 (1.17-2.41)** have been associated with clinical outcome, most of which affect BIRC3 1.54 (1.09-2.19)* 1.52 (0.87-2.63) key signaling pathways and cellular processes [16, 20, 21, 25, 26, 38]. EGR2 1.43 (1.01-2.03)* 1.83 (0.95-3.54) Most of these gene mutations have been linked to a clinically more NFKBIE hotspot 1.10 (0.82-1.48) 2.13 (1.23-3.71)* aggressive disease with shorter TTFT and overall survival. However, XPO1 1.32 (1.05-1.66)* 2.41 (1.23-4.74)* while some of these gene mutations have been more comprehen- POT1 0.92 (0.67-1.26) 1.59 (0.71-3.56) sively studied in relatively large cohorts, others are less well MYD88 0.54 (0.20-1.45) 1.27 (0.85-1.88) explored. Considering the relatively low frequency of mutations in 0.5 1.0 2.0 5.0 0.5 1.0 2.0 5.0 many CLL-related genes, large-scale studies are necessary to offer Hazard ratio Hazard ratio robust insights into the frequency of these mutations and their # events: 862 # events: 634 global p-value: 5.64e-06 global p-value: 3.19e-09 impact on prognosis [39]. Another relevant aspect concerns the asymmetric distribution Fig. 4 Multivariate analysis in Binet stage A CLL patients with of genomic aberrations between U-CLL and M-CLL, with an unmutated IGHV genes (U-CLL) and mutated IGHV genes (M-CLL). CI95, 95% confidence interval; * indicates a p value <0.05, ** denotes enrichment of poor-prognostic alterations in the former a p value <0.01, while *** represents a p value <0.001. [31, 32, 40]. Moreover, the prognostic impact of gene mutations may differ depending on IGHV gene SHM status [29, 31, 32], prompting the suggestion that separate prognostic indices are Finally, by investigating the relative impact of the risk factors warranted for U-CLL and M-CLL. associated with TTFT, SF3B1 mutations was the most significant In this study, we analyzed nine CLL-related genes in a cohort of marker followed by XPO1 mutations in Binet stage A M-CLL more than 4500 CLL cases to gain comprehensive insight into the patients, while for Binet stage A U-CLL cases, SF3B1 mutations was impact of different gene mutations on TTFT, particularly in relation the most significant factor by far (Supplementary Fig. S7). to IGHV gene SHM status. The median age at diagnosis was 64.5 Leukemia (2023) 37:339 – 347 L. Mansouri et al. 345 years, which is lower than expected and possibly explained by the targeted hotspot mutations [e.g., NFKBIE (8% of cases) and fact that most contributing institutes are referral centers where MYD88 (4% of cases)], or hotspot exons (e.g., SF3B1 and NOTCH1) patients tend to be younger at admission. As anticipated, a higher could potentially underestimate the true frequency of muta- prevalence of NOTCH1, SF3B1 and TP53 aberrations was confirmed, tions. Here, we found no significant differences in mutation rates while mutations in the remaining genes were less frequent when comparing NGS and Sanger sequencing for either hotspot (between 2–4% of patients). The vast majority of mutated patients or non-hotspot genes. Additionally, in the case of NFKBIE, only carried mutations in a single gene, whereas a minor proportion the hotspot mutations were clinically relevant, whereas for the harbored ≥2 mutations. In line with previous studies, several remaining genes the significance, if any, of non-hotspot genes showed prominent mutation hotspots (e.g., EGR2, MYD88, mutations remains unknown. However, we cannot fully exclude NFKBIE, BIRC3, NOTCH1, SF3B1, XPO1), however this was not that mutations with low frequencies (VAF 5–15%) were missed observed in other genes (e.g., POT1, TP53) [18, 19, 41–44]. As using Sanger sequencing. In addition, the majority of samples previously reported, NOTCH1 mutations were associated with (>80%) derived from non-purified PBMC samples (<20% of trisomy 12, BIRC3 mutations with del(11q) and MYD88 mutations samples were purified), which could potentially have affected with sole del(13q) [22, 25, 45, 46]. Notably, certain lesions the ability to detect minor subclones, although the vast majority appeared almost mutually exclusive, such as BIRC3 and SF3B1 of unpurified CLL samples have a high tumor percentage mutations, and TP53 aberrations and XPO1 mutations, implying (>80–90%). Another caveat concerns the differences in treat- that acquisition of either of these events is sufficient to develop a ment given at the different centers, precluding a meaningful more aggressive disease course [47]. For all genes investigated, analysis of the impact of gene mutations on overall survival. except for MYD88, there was a striking enrichment of mutations in Moreover, most patients have received chemo(immuno)therapy, U-CLL compared to M-CLL, particularly for NOTCH1, XPO1, NFKBIE hence, in the coming years it will be important to conduct and EGR2. Nevertheless, for all genes investigated, mutations were similar large-scale, real-world analysis of the impact of treatment also detected in a minor proportion of M-CLL patients (1–6% of in patients using targeted treatments. cases). In conclusion, we reinforce SF3B1 mutations as a key In univariate analysis of TTFT, all gene mutations, except for biomarker with a very strong negative impact on TTFT in both MYD88, were associated with a significantly worse outcome, both M-CLL and U-CLL and also highlight XPO1 as an additional highly in the entire study cohort and when restricting the analysis to relevant gene in both subgroups. We also demonstrate that TP53 Binet stage A patients. In multivariate analysis in Binet stage A aberrations are clinically relevant in U-CLL, yet they appear to patients, all recurrently mutated genes, again excepting MYD88, have no or a limited effect on TTFT in M-CLL. From a clinical were independently associated with shorter TTFT. However, when perspective, our results may assist in identifying high-risk performing the same analysis in U-CLL and M-CLL separately, only patients within the heterogeneous M-CLL subgroup with SF3B1 and XPO1 mutations retained significance in both potential implications for stratified management and treatment subgroups. More specifically, NOTCH1 and NFKBIE mutations were decisions, also in prospective clinical trials. Since most clinical significant only in M-CLL, while TP53, BIRC3 and EGR2 aberrations laboratories are currently applying NGS-based gene panel were significant only in U-CLL. To evaluate the relative importance sequencing to detect TP53 mutations, often including other of the different gene mutations, we also performed stepwise CLL-related genes (that are usually not reported), it would be variable selection in M-CLL and U-CLL separately. This analysis informative to extend the analysis to other genes to identify revealed that SF3B1 and XPO1 mutations had the highest relative high-risk patients in routine diagnostics. Finally, future efforts to impact on TTFT in M-CLL and SF3B1 and TP53 aberrations in U-CLL develop prognostic schemes including gene mutations and (Supplementary Fig. 7). other established prognostic factors should apply a more Most previous studies have analyzed the impact of gene compartmentalized approach, hopefully paving the way for mutations in relation to different clinical endpoints (e.g., TTFT, personalized medicine approaches for patients belonging to the progression-free survival and overall survival) [25, 44, 48]. Inter- M-CLL and U-CLL subgroups. national prognostic schemes have also been developed to identify patients with high-risk disease (CLL-IPI) or high risk of progression (IPS-E) using similar endpoints [49, 50]. 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This in the mutational landscape between IgHV(mut) and IgHV(unmut) subgroups. work was in part supported by; Associazione Italiana per la Ricerca sul Cancro—AIRC, Leukemia. 2018;32:573. Milano, Italy (Investigator Grant #20246 and Special Program on Metastatic Disease— 31. Baliakas P, Moysiadis T, Hadzidimitriou A, Xochelli A, Jeromin S, Agathangelidis A, 5 per mille #21198); ERA NET TRANSCAN-2 Joint Transnational Call for Proposals: JTC et al. Tailored approaches grounded on immunogenetic features for refined 2014 (project #143 GCH-CLL) and JTC 2016 (project #179 NOVEL), project code (MIS) prognostication in chronic lymphocytic leukemia. Haematologica. 2019;104:360–9. 5041673; Bando della Ricerca Finalizzata 2018, Ministero della Salute, Roma, Italy 32. Knisbacher BA, Lin Z, Hahn CK, Nadeu F, Duran-Ferrer M, Stevenson KE, et al. (progetto RF-2018–12368231); “la Caixa” Foundation (Health Research 2017 Program Molecular map of chronic lymphocytic leukemia and its impact on outcome. Nat HR17-00221); the American Association for Cancer Research (2021 AACR-Amgen Genet. 2022;54:1664–74. Fellowship in Clinical/Translational Cancer Research, 21-40-11-NADE), the European Leukemia (2023) 37:339 – 347 L. Mansouri et al. 347 Hematology Association (EHA Junior Research Grant 2021, RG-202012-00245), and COMPETING INTERESTS the Lady Tata Memorial Trust (International Award for Research in Leukaemia 2021- PG: Honoraria/advisory board: AbbVie, Acerta/AstraZeneca, Adaptive, ArQule/MSD, 2022, LADY_TATA_21_3223); the Hellenic Precision Medicine Network in Oncology; BeiGene, CelGene/Juno, Gilead, Janssen, Loxo/Lilly, Sunesis. Research funding: project ODYSSEAS (Intelligent and Automated Systems for enabling the Design, AbbVie, Gilead, Janssen, Novartis, Sunesis; LS: advisory board AbbVie, AstraZeneca, Simulation and Development of Integrated Processes and Products) implemented Janssen; RR: honoraria/advisory board: Abbvie, AstraZeneca, Janssen, Illumina and under the “Action for the Strategic Development on the Research and Technological Roche; KS: honoraria/advisory board: AbbVie, Acerta/AstraZeneca, Gilead, Janssen. Sector”, funded by the Operational Programme “Competitiveness, Entrepreneurship Research funding: AbbVie, Gilead, Janssen; PB: honoraria from Abbvie, Gilead and and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Janssen. Research funding from Gilead; GG: Advisory Board/Speaker’s bureau: Union, with grant agreement no: MIS 5002462”; MH CZ—DRO (FNBr, 65269705), Janssen, Abbvie, AstraZeneca, Beigene; LB: honoraria/advisory board: Abbvie, Amgen, NV19-03-00091 and the project National Institute for Cancer Research (Programme Astellas, BMS/Celgene, Daiichi Sankyo, Gilead, Hexal, Janssen, Jazz Pharmaceuticals, EXCELES, ID Project No. LX22NPO5102)—Funded by the European Union—Next- Menarini, Novartis, Pfizer, Sanofi, Seattle Genetics. Research funding: Bayer, Jazz Generation EU; Instituto de Salud Carlos III (ISCIII), “PI21/00983”, co-funded by the Pharmaceuticals; GMR: honoraria from Abbvie, AstraZeneca, Gilead and Janssen. European Union; the EU’s Horizon 2020 research and innovation program under Research funding from Gilead: CB: Honoraria/advisory board: AbbVie, Janssen. The grant agreement No. 739593, by the Ministry of Innovation and Technology of other authors declare no competing financial interests. Hungary from the National Research, Development and Innovation Fund, financed under the K21_137948, FK20_134253, TKP2021-EGA-24 and TKP2021-NVA-15 funding schemes, and Elixir Hungary; Instituto de Salud Carlos III (ISCIII), “PI21/00983”, co- ADDITIONAL INFORMATION funded by the European Union; Fondo di Ateneo per la Ricerca (FAR) 2019, 2020 and Supplementary information The online version contains supplementary material 2021 of the University of Ferrara (GMR; AC), Associazione Italiana contro le Leucemie- available at https://doi.org/10.1038/s41375-022-01802-y. Linfomi e Mieloma ONLUS Ferrara (AC; GMR), BEAT Leukemia Foundation Milan Italy (AC); the Danish Cancer Society and the CLL-CLUE project under the frame of ERA Correspondence and requests for materials should be addressed to Richard Rosenquist. PerMed; Cancer Research UK (ECRIN-M3 accelerator award C42023/A29370, South- ampton Experimental Cancer Medicine Centre grant C24563/A15581, Cancer Reprints and permission information is available at http://www.nature.com/reprints Research UK Southampton Centre grant C34999/A18087, and programme C2750/ A23669); the Swedish Cancer Society (19 0425 Pj 01 H), the Swedish Research Council Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in (2020-01750), the Knut and Alice Wallenberg Foundation (2016.0373), Region published maps and institutional affiliations. Stockholm (ALF/FoUI-962423), and Radiumhemmets Forskningsfonder (194133), Stockholm; and Lion’s Cancer Research Foundation, Uppsala. Open Access This article is licensed under a Creative Commons AUTHOR CONTRIBUTIONS Attribution 4.0 International License, which permits use, sharing, LM, BT and LS summarized, analyzed and interpreted data and wrote the paper; BT, adaptation, distribution and reproduction in any medium or format, as long as you give GK, HF-A and AS performed bioinformatics/statistical analysis; MM, HP, FN, CB, SL, RM, appropriate credit to the original author(s) and the source, provide a link to the Creative DR, MC, JK, JD, AER-V, RB, GMR, SB, LC, MaMa, ZD, AG, LR, PB, CJ, IR, FM, JM-L, JdlS, Commons license, and indicate if changes were made. The images or other third party JMHR, PT, MJL, MJC, VF, ZM, CsBö, KES, BE, AP, RG, LB, FB, BT-V, FB-M, DO, FN-K, TZ, material in this article are included in the article’s Creative Commons license, unless MJT, AC, MH-S, SP, KM, GG, CUN, EC and JCS provided samples and clinical data; PG, indicated otherwise in a credit line to the material. If material is not included in the KS and RR designed the study, interpreted data and wrote the paper; all authors article’s Creative Commons license and your intended use is not permitted by statutory edited and approved the paper for submission. regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http:// creativecommons.org/licenses/by/4.0/. FUNDING Open access funding provided by Karolinska Institute. © The Author(s) 2022, corrected publication 2023 1 Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden. 2Centre for Research and Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece. 3Department of Mathematics, University of Ioannina, Ioannina, Greece. 4MLL Munich Leukemia Laboratory, Munich, Germany. 5Cancer Genomics, School for Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK. 6Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. 7Centro de Investigación Biomédica en Red de Can ́ cer (CIBERONC), Madrid, Spain. 8Department of Hematology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark. 9Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy. 10Division of Hematology, Oncology Institute of Southern Switzerland, Bellinzona, Switzerland. 11Laboratory of Experimental Hematology, Institute of Oncology Research, Bellinzona, Switzerland. 12 Patrick G Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast, UK. 13Department of Internal Medicine—Hematology and Oncology, University Hospital Brno, Brno, Czech Republic. 14Faculty of Medicine, Masaryk University, Brno, Czech Republic. 15Central European Institute of Technology, Masaryk University, Brno, Czech ́ of Barcelona, Barcelona, Spain. 17Universitat de Barcelona, Barcelona, Spain. 18Cancer Research Center (IBMCC) CSIC—University of Salamanca, Republic. 16Hospital Clinic Salamanca, Spain. 19Instituto de Investigación Biomédica (IBSAL), Salamanca, Spain. 20Department of Hematology, University Hospital of Salamanca, Salamanca, Spain. 21 Hematology—Department of Medical Sciences, University of Ferrara, Ferrara, Italy. 22Università Vita Salute San Raffaele and IRCCS Ospedale San Raffaele, Milano, Italy. 23 Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden. 24Molecular Pathology Department, University Hospitals Dorset, Bournemouth, UK. 25All India Institute of Medical Sciences (AIIMS), New Delhi, India. 26Hospital Universitario 12 Octubre, Madrid, Spain. 27Spanish National Cancer Research (CNIO), Madrid, Spain. 28Haematology Department, Beaumont Hospital, Dublin, Ireland. 29Hematological Diseases Laboratory, CIMA LAB Diagnostics, University of Navarra, Pamplona, Spain. 30 IdiSNA, Navarra Institute for Health Research, Pamplona, Spain. 31HCEMM-SE Molecular Oncohematology Research Group, Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary. 32Central Hospital of Southern Pest—National Institute of Hematology and Infectious Diseases, Budapest, Hungary. 33Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden. 34Molecular Cytogenetics Laboratory, Pathology Department, Hospital del Mar and Translational Research on Hematological Neoplasms Group, Hospital del Mar Research Institute (IMIM), Barcelona, Spain. 35Department of Hematology, Oncology and Cancer Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany. 36Department of Hematology, Hospital Universitari Vall d’Hebron (HUVH), Experimental Hematology, Vall d’Hebron Institute of Oncology (VHIO), Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain. 37Service d’hématologie Biologique Hôpital Avicenne Assistance Publique des Hôpitaux de Paris, Bobigny, France. 38Sorbonne Université, Service d’Hématologie Clinique, Hôpital Pitié-Salpêtrière, APHP, Paris, France. 39Department of Oncology and Haematology, University Hospital and University of Zurich, Zurich, Switzerland. 40Department of Hematology, INCLIVA Research Insitute, University of Valencia, Valencia, Spain. 41Clinical Genetics, Karolinska University Hospital, Solna, Sweden. 42These authors contributed equally: Larry Mansouri, Birna Thorvaldsdottir, Lesley-Ann Sutton. 43These authors jointly supervised this work: Paolo Ghia, Kostas Stamatopoulos, Richard Rosenquist. ✉email: richard.rosenquist@ki.se Leukemia (2023) 37:339 – 347