The impact of genetic alterations in CLL was first shown using cytogenetics and chromosome banding analysis in the early 1980’s, where two notable discoveries were the deletion of chromosome 13q [del(13q)] and trisomy 12 (9, 10). Due to the inherent difficulties in culturing CLL cells, FISH analysis was introduced at the turn of the millennium to detect a panel of clinically relevant cytogenetic aberrations i.e., del(11q), del(13q), del(17p), and trisomy 12 ( Table 1 ) (14).

The del(13q) aberration occurs in more than 50% of cases and in 35 to 40% as the lone genetic aberration. It usually indicates a more favorable prognosis, stable disease, and is associated with the longest time-to-first treatment (TTFT) and OS (14). The minimally deleted region covers two microRNAs, MIR15A and MIR16, which negatively regulate BCL2 expression levels (15). del(17p), involving the TP53 gene, is seen in 5–10% of cases and associated with a rapidly progressing disease, resistance to chemoimmunotherapy, and a very poor clinical outcome ( Table 1 ). In roughly 40-60% of cases with TP53 aberrations, del(17p) is detected together with a TP53 mutation, while another 20-30% of cases carry one or two TP53 mutations without del(17p) ( Figure 1 ) (11, 16–19). del(11q), involving the ATM gene, a tumor suppressor involved in recognizing DNA damage, is found deleted in 10–20% of cases (where a minor proportion (20-25%) of these also carry a second ATM mutation) (20) while trisomy 12 is detected in 10-15% of cases. Both of these latter abnormalities are associated with an intermediate prognosis.

In more recent years and owing to improved culturing protocols for cytogenetic analysis, a renewed interest in detecting karyotypic complexity in CLL, defined as five or more chromosomal aberrations, has emerged ( Table 1 ) (13). These patients have a particularly poor response to chemoimmunotherapy but also to targeted therapy (13, 21), at least in the relapse/refractory setting. However, analysis of complex karyotype is not generally recommended outside of clinical trials (5).

In addition to cytogenetic alterations, a notable and important development was the discovery of the IGHV gene mutational status in 1999; this finding enabled the classification of CLL using the clonotypic SHM status into favorable-prognostic IGHV-mutated (M-CLL; <98% identity to germline) in 60-70% of patients or poor-prognostic IGHV-unmutated (U-CLL; ≥98% identity to germline) in 30-40% of patients ( Table 1 ; Figure 2 ) (22, 23). While the IGHV gene mutational status is one of the strongest prognostic markers in CLL, it has also become a predictive marker relevant for therapy selection (discussed further below) (24). Furthermore, it was shown that patients with TP53 mutations, irrespective of del(17p), have a similarly poor outcome comparable to those with del(17p) (11). Therefore, TP53 sequencing analysis was added to FISH analysis to detect both types of aberrations ( Table 1 ). For both of these molecular tests (IGHV SHM status and TP53 sequencing), PCR and Sanger sequencing were applied in routine diagnostics to assess their mutational status (11). Sanger sequencing is however a low-throughput and a time-consuming technique, in particular for large genes without hotspot mutations (e.g., TP53 and ATM); these issues were resolved by the introduction of high-throughput NGS-based techniques in the last decade (20, 25). Further underscoring the relevance of TP53 aberrations and the IGHV gene SHM status, these markers were included in the CLL international prognostic index (CLL-IPI) (26), along with age, β2M levels and stage, which risk-stratifies patients into four categories (low, intermediate, high and very high risk).

NGS technologies utilizing sequencing by synthesis chemistry, where fluorescently labelled dNTPs are sequenced in a massive parallel manner, have enabled the comprehensive characterization of the genomic landscape of CLL. In 2011, by applying whole-exome sequencing (WES) or whole-genome sequencing (WGS), large-scale efforts provided a first detailed molecular map of CLL (27–30). The somatic mutation rate in CLL was estimated to be approximately 0.7 per megabase (this estimate has increased to 1.1 in later studies including larger cohorts), which is comparable to other hematological malignancies, but markedly lower than what can be observed in solid epithelial tumors where the mutation burden is generally observed to be 5–20 times higher than in CLL (28, 31). Important new driver genes included MYD88, NOTCH1, SF3B1, POT1, and XPO1, and mutations in these genes were associated with clinical outcome (27–30).

Following these first descriptions, two seminal papers were published in 2015 which further improved the portrayal of the genomic landscape of CLL, proposing 44 and 59 driver mutations, respectively (32, 33). However, only a few recurrent genomic aberrations (i.e., alterations in ATM, NOTCH1, SF3B1, TP53) were observed in more than 10% of cases, followed by a very long tail of hundreds of low-frequency mutated genes present in less than 1-5% of cases. Despite this large heterogeneity, the genomic aberrations could be grouped into more commonly affected signaling pathways and cellular processes, such as B cell receptor (BcR)/NF-κB signaling (BIRC3, NFKBIE), NOTCH signaling (NOTCH1, FBXW7), DNA repair (ATM, TP53), and RNA and ribosome processing (RPS15, SF3B1, XPO1) ( Figure 3 ). The clinical impact of this functional categorization of drivers into cellular pathways was further supported by a study published in 2020 by Brieghel et al, where the number of affected pathways was more important than the number of driver mutations in predicting clinical outcome (38).

The SF3B1 gene is one of the most frequently mutated genes in CLL, reported to be mutated in 5-15% of early-stage patients with an increasing frequency in relapsed and refractory patients (16-28%) (18, 29, 30, 34, 39–42). Notably, SF3B1 mutations are particularly enriched in patients belonging to stereotyped subset #2 (up to 45%) (43, 44). SF3B is a complex that forms a functional unit with the U2 small nuclear ribonucleoprotein (snRNP) within the catalytic center of the spliceosome, which is responsible for pre-mRNA processing into mRNA through the excision of introns. The clustering of mutations in hotspot exons (exons 14-16) of the SF3B1 gene suggests that these mutations have been subject to positive selection and serve as driving events in CLL pathology by giving rise to alternative splicing that affects cellular processes such as DNA damage response, telomere maintenance, and NOTCH signaling (29, 45–48).

Another important discovery was that of NOTCH1 mutations, which were identified in 8-12% of early-stage patients (18, 27, 28, 34); a number that increases to 13-21% in chemoimmunotherapy refractory patients and above 30% at Richter transformation (28, 41), i.e., progression to a diffuse large B-cell lymphoma with a clinically significantly worse outcome. NOTCH1 mutations are also enriched in patients with trisomy 12 and in poor-prognostic stereotyped subsets #1 and #8 (44, 49, 50). NOTCH1 is a transmembrane receptor protein ( Figure 3 ) that after activation contributes to the formation of a transcription factor complex consisting of the NOTCH intracellular domain (NCID) and recombinant signal binding protein for immunoglobulin kappa J (RBPJ). A majority of NOTCH1 mutations (>70%) constitute a 2-base pair frameshift deletion in the PEST domain of the terminal exon, resulting in a premature stop codon and loss of the genetic motive needed for degradation recognition (28, 34, 49). The activity of this transcription factor complex results in the transactivation of downstream target genes, such as HES1 and MYC (49, 51, 52), hence leading to constitutive NF-κB activation (53). Further analysis of the non-coding part of the genome by Puente et al. revealed mutations also in the 3’UTR of NOTCH1 (32). These non-coding mutations gave an alternative splicing of the PEST domain leading to an increased stability of the protein similar to coding NOTCH1 mutations.

A further recurrent event resulting in the constitutive activation of the NF-κB pathway in CLL is mutations in NFKBIE, which encodes IκBϵ and constitutes one of the negative regulators of the NF-κB pathway (54). NFKBIE mutations occur at a frequency of 3-7% in early-stage patients, which rises to 15% in poor-prognostic stereotyped subsets #1, #5 and #6, and are most commonly observed as a 4-bp frameshift deletion leading to a truncated protein and reduced p65 inhibition (34, 35, 54).

BIRC3 mutations are another recurrent event in CLL with a relatively low frequency at diagnosis (3-4%) (18, 34, 55), but with a higher frequency in fludarabine-refractory patients (up to 24%) (55). BIRC3 contributes to a protein complex that negatively regulates MAP3K14, a central regulatory element in the non-canonical NF-κB pathway ( Figure 3 ). Most of the genetic lesions affecting BIRC3 have been identified as insertions/deletions (indels) resulting in frameshift mutations or premature stop codons located in two hotspot regions between amino acids 367-438 and 537-564 (55, 56).

Additionally, EGR2, a transcription factor activated by ERK phosphorylation during BcR signaling, has been found mutated in 2-4% of CLL patients and up to 7-8% in those with advanced-stage disease or Richter transformation (34, 36, 54). EGR2 is most commonly affected by missense mutations in exon 2. Notably, patients with EGR2 mutations have a particularly poor outcome, comparable to patients carrying TP53 aberrations (36).

Finally, a more recently characterized genetic lesion in the pathobiology of CLL is mutations in RPS15 (ribosomal protein S15), which constitutes a component of the 40S ribsosomal subunit ( Figure 3 ). These abnormalities occur at a frequency of 4% in early-stage patients (33, 57) and increase to 20% in patients relapsing following FCR treatment (37). In addition to its role in altering ribosomal fidelity (58, 59), RPS15 may also function as a negative regulator of MDM2-mediated degradation of p53 (37). RPS15 mutations predominately occur as somatic missense mutations in a 7 amino-acid region of exon 4 and are considered an early clonal event in CLL (37).

In a recent study by Knisbacher et al, where WES and WGS data from two previous large efforts were merged and re-analyzed (totaling 1074 CLL cases), the authors could identify 202 candidate genetic drivers in CLL. This included 109 new drivers involving both single nucleotide variants (SNVs), indels and copy-number variants (CNVs) (60). The great majority of patients (>96%) were shown to carry at least one driver lesion, leading to a more complete picture of common pathways hit by genetic lesions, including those involved in genomic stability, chromatin remodeling and ribosomal functioning and biogenesis ( Figure 3 ). Examples of novel findings include mutations in ZFP36L1, which functions as a tumor suppressor by negatively regulating NOTCH1 activity, and genetic aberrations in INO80, which is involved in genomic stability by coding for the catalytic subunit of a chromatin remodeling complex (60). Additionally, the investigators could also detect genetic lesions shared with other hematological neoplasias, e.g., RFX7 in diffuse large B-cell lymphoma and Burkitt’s lymphoma as well as the 5q-deletion observed in myelodysplastic syndrome. Finally, the authors noted loss of mitochondrial uncoupling proteins UCP2 and UCP3, caused by del(11)(q13.4), and found in 3% of patients and suggested to act as tumor suppressors by regulating the mitochondrial membrane potential and the efficiency of oxidative phosphorylation (OXPHOS). Importantly, the authors provided further evidence that U-CLL and M-CLL display different genomic landscapes, including significant enrichment of driver mutations in U-CLL compared to M-CLL, with a ratio of approximately 2.8:1 in untreated patients (60). Two examples of genes that were detected as part of this subgroup analysis in U-CLL included NFKB1, involved in NF-κB signaling, and RRM1, involved in DNA replication and repair and a target of nucleoside analogues such as fludarabine.

In another recent effort, using WGS to characterize 485 CLL patients from the 100 000 Genomes Project in the UK, Robbe and colleagues identified 56 genomic alterations linked to disease outcome, of which 33 were also affected by CNVs and non-coding mutations in regulatory elements. Notably, they detected genetic alterations in 72 promoters, including those associated with known driver genes in CLL, e.g., BIRC3, IKZF3 and TP53, as well as the PAX5 super enhancer region. An example of a novel driver includes del(1)(q42.3), harboring the IRF2BP2 gene, which could also be affected by SNVs. IRF2BP2 has previously been implicated in the differentiation of immature B-cells (61). Other examples concern the detection of translocations, where WGS provides a considerable advantage, including t(14;22) with breakpoints in WDHD1, indicated to affect translation efficiency in other forms of cancers (62), and the recurrent t(5;6) encompassing CTNND2 (encodes δ-catenin involved in Wnt signaling) and ARHGAP18 (involved in cell polarization and migration) in 2-3% of patients (63, 64).

Today, more than 50 genetic lesions have been linked to disease outcome, including BIRC3, EGR2, MYD88, NOTCH1, NFKBIE, POT1, RPS15, SETD2, SF3B1, TP53, and XPO1 mutations, amongst others (12, 18, 30, 34–37, 49, 55, 65–68). Several retrospective large-scale studies have confirmed the clinical impact of these gene mutations as important prognostic risk factors for both TTFT and OS and proposed different prognostic indices (18, 42, 69). For instance, using recursive partitioning based on OS, Rossi et al. integrated cytogenetic and genetic alterations and proposed four risk groups: i) high-risk harboring TP53 and/or BIRC3 aberrations, ii) intermediate-risk with NOTCH1, SF3B1 mutations and/or del(11q), iii) low-risk including trisomy 12 or patients without aberrations, and iv) very low-risk with patients carrying del(13q) (69). In another large-scale study by Baliakas et al. including 3490 patients, sequencing analysis of five genes (BIRC3, MYD88, NOTCH1, TP53, and SF3B1) showed that TP53 and SF3B1 mutations were the strongest factors in multivariate analysis of TTFT (18).

As mentioned, recent studies have also revealed diverse genomic landscapes in M-CLL and U-CLL and indicated that genetic aberrations may affect outcome differently in U-CLL and M-CLL patients (60, 70). To address this issue, Mansouri et al. recently assessed the mutation status of 9 genes (BIRC3, EGR2, MYD88, NFKBIE, NOTCH1, POT1, SF3B1, TP53, and XPO1) in 4580 patients in relation to the IGHV gene SHM status (34). While SF3B1 and XPO1 mutations were strong independent prognostic factors in both U-CLL and M-CLL, TP53, BIRC3 and EGR2 alterations predicted outcome only in U-CLL patients and NOTCH1 and NFKBIE in only M-CLL patients (34). These latter findings highlight that all mutations do not confer the same negative impact and that we need a more compartmentalized approach to identify high-risk patients, where genetic aberrations are considered in the context of the IGHV gene SHM status.

Admittedly, most of these retrospective studies included patients treated with chemoimmunotherapy and few prospective studies have been carried out, in particular for patients treated with targeted therapy.

By combining WES with array-based copy-number analysis, Landau et al. performed in 2013 a detailed investigation of the cancer cell fraction of different CLL-related genomic aberrations. Based on their findings, only a few aberrations were demonstrated to be clonal, i.e., present in the entire cell population (i.e., MYD88 mutations, del(13q), and trisomy 12), while the vast majority of genomic lesions were present only in a proportion of the cells at subclonal levels (71). Moreover, the clonal dynamics over time was assessed by analyzing samples taken before and after therapy, revealing major clonal shifts in patients relapsing after chemoimmunotherapy. These data have since been confirmed in follow-up studies in larger cohorts treated with chemoimmunotherapy as well as in patients treated with targeted therapy (33, 60, 72).

One such subclonal aberration with potential clinical impact is TP53 mutations. In a series of papers using deep-sequencing, investigators have detected a significant proportion (2.5-9%) of patients carrying minor subclones with TP53 mutations (variant allele frequency (VAF) <10%) that were wildtype for TP53 using Sanger sequencing (73–75). Importantly, patients carrying minor TP53 mutated subclones appear to have similarly poor outcome as patients with TP53 mutations detected by Sanger sequencing, at least when treated with chemoimmunotherapy. That said, recent studies based on targeted therapies did not appear to favor selection of TP53-aberrant clones in a similar way as chemotherapy (76).

With the introduction of targeted therapies, resistance mutations were detected for patients treated with BTK (e.g., ibrutinib, acalabrutinib) and BCL2 inhibitors (e.g., venetoclax). In patients progressing during treatment with ibrutinib, between 65-90% of patients display BTK and/or PLCG2 mutations (77–79). BTK mutations are predominantly seen at the binding site of ibrutinib (amino acid position C481), while mutations of the downstream signaling molecule PLCG2 usually result in a gain-of-function promoting sustained BcR signaling ( Figure 3 ). Notably, up to 40% of patients with BTK/PLCG2 mutations carry minor subclones with VAFs below 10%, or even below 1%, which raises the question as to how these mutations are involved in causing a clinical relapse (77–79). In a proportion of patients relapsing on ibrutinib, other mechanisms have been reported, such as del(8p) (causing loss of TRAIL-R expression) or BIRC3/NFKBIE mutations (80). For patients relapsing on venetoclax, BCL2 mutations have been linked to development of resistance ( Figure 3 ) (81) but other mechanisms have also been described, such as upregulation of MCL1 and NOTCH2 (82).