Abstract
To identify genomic alterations contributing to the pathogenesis of high-risk chronic lymphocytic leukemia (CLL) beyond the well-established role of TP53 aberrations, we comprehensively analyzed 75 relapsed/refractory and 71 treatment-naïve high-risk cases from prospective clinical trials by single nucleotide polymorphism arrays and targeted next-generation sequencing. Increased genomic complexity was a hallmark of relapsed/refractory and treatment-naïve high-risk CLL. In relapsed/refractory cases previously exposed to the selective pressure of chemo(immuno)therapy, gain(8)(q24.21) and del(9)(p21.3) were particularly enriched. Both alterations affect key regulators of cell-cycle progression, namely MYC and CDKN2A/B. While homozygous CDKN2A/B loss has been directly associated with Richter transformation, we did not find this association for heterozygous loss of CDKN2A/B. Gains in 8q24.21 were either focal gains in a MYC enhancer region or large gains affecting the MYC locus, but only the latter type was highly enriched in relapsed/refractory CLL (17%). In addition to a high frequency of NOTCH1 mutations (23%), we found recurrent genetic alterations in SPEN (4% mutated), RBPJ (8% deleted) and SNW1 (8% deleted), all affecting a protein complex that represses transcription of NOTCH1 target genes. We investigated the functional impact of these alterations on HES1, DTX1 and MYC gene transcription and found derepression of these NOTCH1 target genes particularly with SPEN mutations. In summary, we provide new insights into the genomic architecture of high-risk CLL, define novel recurrent DNA copy number alterations and refine knowledge on del(9p), gain(8q) and alterations affecting NOTCH1 signaling. This study was registered at ClinicalTrials.gov with number NCT01392079.Introduction
Advanced understanding of the pathophysiology of chronic lymphocytic leukemia (CLL) has led to targeted therapy approaches such as inhibition of B-cell receptor signaling by BTK inhibitors or PI3K inhibitors and antagonism of BCL-2.21 These treatment strategies clearly improved the clinical outcome of high-risk CLL,21 although inadequate responses have been observed in as yet insufficiently characterized subgroups of patients.53 In the era of chemo(immuno)therapy, high-risk CLL was defined by TP53 deletion/mutation or refractoriness to purine analog-based treatment (no response or progression-free survival <6 months).6 For chemotherapy-free regimens, the prognostic value of TP53 alterations is less clear, but the presence of a complex karyotype, which often occurs together with TP53 deletion/mutation,7 has been identified as an independent risk factor for early progression during venetoclax or ibrutinib treatment.98 However, a more recent study has shown that CLL with a complex karyotype is a heterogeneous group with variable clinical behaviors.10
To better understand treatment failure in CLL, a comprehensive characterization of the genomic architecture in high-risk CLL is vital. With 5-10% high-risk cases included in large-scale studies on DNA copy number changes and gene mutations, these cases were underrepresented for systematic analyses restricted to this subgroup.1411
Available results from single nucleotide polymorphism (SNP)-array profiling of high risk CLL support the notion of increased genomic complexity in the majority of these cases.1311 However, it should be noted that TP53 dysfunction and defects in other DNA damage response systems such as ATM cause chromosomal instability with random secondary events not necessarily associated with adverse prognosis.15 This constitutes a challenge, to identify those alterations contributing to a high-risk form of disease.
In order to get a more thorough understanding of the pivotal genomic alterations contributing to high-risk CLL biology, we performed high-resolution SNP-array profiling and targeted sequencing on 75 relapsed/refractory CLL cases including 18 cases without TP53 alterations. We extended our cohort by including 71 treatment-naïve, TP53-deficient, primary high-risk cases. All patients’ samples were derived from prospective clinical trials of the French/German CLL study groups (FCLLSG/GCLLSG).
To identify DNA copy number alterations (CNA) occurring more often than would be expected by chance, we applied the Genomic Identification of Significant Targets in Cancer algorithm 2.0 (GISTIC2.0).16 In relapsed/refractory CLL, in which tumor cell clones underwent selective pressure imposed by therapy, CNA with significance assigned by GISTIC2.0 harbored genes with key roles in cell-cycle control. Furthermore, we identified NOTCH1 as a central pathway frequently affected by genomic alterations enhancing its signaling strength.
Methods
Patients and samples
The study included peripheral blood mononuclear cells (PBMC) from 146 high-risk cases (TP53 aberration or refractoriness to purine analogs) enrolled on prospective trials of the GCLLSG/FCLLSG (CLL2O trial, clinicaltrials.gov identifier: NCT01392079; CLL8 trial, NCT00281918; CLL11 trial, NCT01010061). Written informed consent from all patients and ethics committee approval were obtained in accordance with the Declaration of Helsinki.
Selection of cases was guided by sample availability and included 110 of 135 cases from the CLL2O trial,17 27 of 51 cases with 17p deletion from the CLL8 trial18 and nine of 52 cases with 17p deletion from the CLL11 trial.19 All samples were taken at trial enrollment and tumor cells were enriched via CD19 immunomagnetic beads (MACS, Miltenyi Biotec, Bergisch Gladbach, Germany). CD19 negative PBMC fractions with a tumor cell load <5% were available for paired analysis in 91 cases. Cases lacking matched normal material were analyzed against a pool of ten gender-matched reference samples.
IGHV mutational analysis, fluorescence in situ hybridization (FISH) studies for 11q22.3, 13q14, 12p11.1-q11, 17p13.1, t(11;14)(q13;q23) and TP53 mutational analysis were performed at trial enrollment. Cases positive for t(11;14)(q13;q23) were excluded from the study. Telomere length was determined as described previously.20
Single nucleotide polymorphism array and gene enrichment analysis
Analysis for CNA, including copy neutral losses of heterozygosity, was done using 6.0 SNP arrays (Affymetrix, Santa Clara, CA, USA). CNA positions and gene locations were determined with the UCSC Genome Browser, assembly March 2006, NCBI36/hg18. CNA frequencies were compared to those observed in treatment-naïve, standard-risk cases (n=304, no TP53 deletion/mutation).13 Microarray raw data were made publicly available at Gene Expression Omnibus (GEO accession number: GSE131114).
GISTIC2.0 was applied on manually curated DNA copy number data.16 According to default settings, CNA with a q value <0.25 were defined as significant. CNA that reached high confidence levels for being significantly enriched (q value <0.01) were manually curated for minimally affected regions. Genes located within these minimally affected regions were assigned to WikiPathways2221 and analyzed for pathway enrichments using PathVisio, version 3.2.3.2423
Next-generation sequencing
Amplicon-based, targeted next-generation sequencing (tNGS) was performed on TP53 exons 2-11, NOTCH1 exon 34, and SF3B1 exons 13-16. In 17 cases TP53, NOTCH1 and SF3B1 mutational status was determined as previously described.25 All coding regions of MGA, SPEN, RBPJ, and SNW1 (in 108 cases each), and CDKN2A and MYC (in 93 cases each) were screened by tNGS.
Quantitative gene expression analysis
Gene expression of CCAT1, MGA, RBPJ, SNW1, HES1, DTX1, MYC, CDKN2A, p14ARF, and p15INK4b was analyzed by quantitative reverse transcription (qRT) polymerase chain reaction (PCR) (TaqMan Gene Expression Assays; Applied Biosystems, Foster City, CA, USA). Sample selection was based on highly clonal presence of respective CNA/gene mutations (log2 ratio <−0.8 for deletions and >0.75 for gains; variant allele frequency >0.3 for mutations). Promoter DNA methylation of CDKN2A/B transcripts was assessed by bisulfite PCR followed by Sanger sequencing.
Statistical analysis
Associations between genomic alterations were tested by Fisher exact tests; differences between datasets by Mann Whitney tests. All statistical tests were two-sided and conducted in GraphPad Prism version 7.0.
Further details are available in the Online Supplementary Methods.
Results
Cohort characteristics
High-resolution genome-wide copy number analysis was performed on CD19-enriched PBMC from 146 CLL patients with high-risk disease. Cases belonged to the following subgroups. (i) Patients refractory to purine analogs from the CLL2O trial (n=49), who had received a median of three previous lines of therapy (range, 1 to 7 lines of therapy). This cohort encompassed patients with TP53 loss and/or mutation (n=31; refractory) and patients without TP53 aberration (n=18; refractory). (ii) Relapsed patients with TP53 loss from the CLL2O trial (n=26; relapsed), who had received a median of two previous lines of therapy (range, 1 to 4 lines of therapy). (iii) Primary high-risk treatment-naïve patients with TP53 loss (n=71; treatment-naïve) from the CLL2O (n=35), CLL8 (n=27) and CLL11 trials (n=9). Standard-risk cases used as the reference cohort were derived from the CLL8 trial and included all available cases without a TP53 alteration (n=304; treatment naïve) (Figure 1; characteristics of the patients and samples are provided in Online Supplementary Table S1).
Landscape of genomic copy number alterations in high-risk chronic lymphocytic leukemia
First, we assessed genomic complexity in cases grouped by clinical and genetic characteristics. For this, we used paired cases only to focus on somatically acquired CNA. No difference in median CNA numbers was observed between 39 treatment-naïve primary high-risk and 38 relapsed/refractory high-risk tumors (5.6 versus 5.8 CNA per case mean) so that genomic complexity with TP53 deficiency was independent of previous therapy. In the absence of TP53 abnormalities, tumors with ATM loss (n=7) had 5.4 CNA per case mean. Refractory high-risk cases lacking TP53 and ATM abnormalities had two or fewer CNA in the majority of cases (5 of 7 cases) (Figure 2).
More than half of the high-risk cases showed complex CNA with two or more switches between two copy number states on at least one chromosome (87/146 cases, 60%). Thirteen CNA in 12/146 cases (8%) fulfilled the formal criteria of chromothripsis, which is defined by ten or more switches between two or more copy number states on an individual chromosome.26 Reducing the required number of copy number switches to eight and six increased numbers to 23 CNA in 16 cases (11%) and 50 CNA in 32 cases (22%), respectively.
Overall, the cohort of high-risk CLL cases comprised more than 1,500 individual copy number changes (Online Supplementary Table S2). To identify CNA that were significantly enriched, we conducted GISTIC2.0 analysis. While therapy-naïve patients only had 11 significantly enriched CNA, GISTIC2.0 assigned significance to 28 CNA within the treatment-naïve cohort and to 20 CNA within the relapsed/refractory cohort when using the default q value of 0.25 as the cut-off for significance (Figure 3).
To further reduce complexity, we focused our analysis on CNA that reached high confidence levels for being significantly enriched. High confidence was defined by a GISTIC q value <0.01 in at least one of three GISTIC2.0 analyses: (i) the entire high-risk cohort, (ii) the treatment naïve cohort, and (iii) the relapsed/refractory cohort (for minimally affected regions and confidence levels see Table 1). We compared frequencies observed in high-risk cases with those observed in treatment-naïve standard-risk cases. Apart from +(2)(p16.1 p15) and del(6)(q21), all CNA listed in Table 1 occurred at least two times more often in the cohort of high-risk cases: +(8)(q24.21) in 16.4% versus 3.6%, del(8)(p23.1) in 15.8% versus 0.3%, del(18)(p11.31) in 13.7% versus 2.0%, del(10)(q24.32) in 11.0% versus 1.3%, del(15)(q15.1) in 10.3% versus 3.6%, del(3)(p21.31) in 10.3% versus 0.3%, and del(14q) in 7.5% versus 0.7%. Losses in 14q were heterogeneous and a continuous minimally deleted region could not be identified. The two minimally deleted regions in 14q24.3 and 14q31.3 were defined by one case with a discontinuous deletion.
GISTIC2.0 also assigned q values <0.01 to novel CNA: +(17)(p11.2) found in 8.2%, +(17)(q23.2) in 7.5%, del(3)(p25.3) in 9.6%, del(3)(p24.1) in 8.9%, del(4)(p15.2) in 8.2%, and del(9)(p21.3) in 8.9% of high-risk cases. Boundaries of minimally affected chromosomal regions in 3p25.3, 3p24.1, and 17q23.2 derived from complex discontinuous CNA.
GISTIC2.0 results for the treatment naïve and relapsed/refractory cohort shared similarities, but the latter group had fewer significant CNA. This suggested a selection of clones with CNA contributing to CLL high-risk biology and failure of previous chemo(immuno)therapy. In the relapsed/refractory cohort, only gain(8)(q24.21) and del(9)(p21.3) retained GISTIC q values <0.01 beyond CNA routinely assessed by FISH (Table 1). Of note, the minimally affected regions in 8q24.21 and 9p21.3 both contained key regulators of cell cycle progression, namely MYC and CDKN2A/B.
In contrast, del(18p), del(15)(q15.1), del(4)(p15), del(14q) and gain(2p) failed to reach significant GISTIC q values in the relapsed/refractory cohort, so that these CNA less likely conferred refractoriness.
We next analyzed the 146 high-risk cases for associations between genomic lesions. The distribution of genomic lesions across samples is depicted in Figure 4 for relapsed/refractory cases and in Online Supplementary Figure S1 for treatment naïve cases. Comparing cases with and without TP53 alteration, only del(11)(q22.3) significantly associated with refractory cases (P<0.01). Testing for dependence of a genomic lesion on the presence of a TP53 alteration was hampered by the low number of refractory cases included in the study. Loss of CDKN2A/B was associated with gain of the MYC gene locus, with MGA mutation, and with loss of MGA by del(15)(q15.1) (P=0.028, P=0.044, and P=0.03, respectively). Interestingly, cases with co-occurring CDKN2A/B loss and MYC gain were among those with the shortest telomeres as a potential sign of higher proliferation rates in these tumors. Co-occurrence of coding NOTCH1 and SF3B1 mutations was lower than would have been expected to occur by chance (3/145 cases, P=0.034), although both mutations were frequent. NOTCH1 mutations occurred in 24% relapsed/refractory and 21% treatment-naïve cases and SF3B1 mutations in 24% relapsed/refractory and 23% treatment naïve cases.
Lastly, to identify distinct molecular pathways affected by CNA with significant GISTIC q values, we analyzed genes located in minimally involved regions as shown in Table 1 by PathVisio. PathVisio assigned significance to 29 pathways. Due to TP53, ATM, and CDKN2A/B loss and MYC gain, 12 of 29 pathways were related to DNA damage response, apoptosis or cell cycle control. Of note, “NOTCH1 signaling” was identified as one significant pathway based on loss of two genes associated with NOTCH1 target gene repression (RPBJ and SNW1) and gain of the NOTCH1 target gene MYC. Toll like receptor signaling was the pathway with the most significant P value, but this was due to loss of an interferon cluster located in 9p21.3 (Figure 5).
Since our results drew attention to CDKN2A/B, MYC and NOTCH1 signaling, we next shed more light on genetic lesions relating to them.
Characterization of del(9)(p21.3) as a novel recurrent copy number alteration in high-risk chronic lymphocytic leukemia
Deletions in 9p21.3 were found in 13 patients (7% treatment-naïve; 11% relapsed/refractory cases). This high frequency was surprising, since del(9)(p21.3) has not been observed in standard-risk cases at treatment initiation.13 The minimally deleted region encompassed CDKN2A, CDKN2B, MTAP, DMRTA1 and an interferon gene cluster (Figure 6A). Two cases had focal homozygous deletions within larger monoallelic deletions, but the CDKN2A/B loci were covered by the homozygous deletion in only one case. The minimally deleted homozygous region contained only DMRTA1 coding for a transcription factor with unknown target genes (Figure 6B). Somatic CDKN2A mutations were not found in the high-risk cases screened by tNGS.
Consistent with a role of CDKN2A/B in proliferation control, loss of their gene loci has been directly associated with Richter transformation (RT), since del(9)(p21) had so far been only observed in lymph node biopsies with histologically confirmed RT and not in corresponding peripheral blood samples acquired during the CLL phase.2827 We, therefore, next investigated associations between del(9)(p21.3) and the development of RT, the presence of which had been an exclusion criterion at enrollment on each clinical trial. During the observation period, only one patient with del(9)(p21.3) developed histologically confirmed RT. This patient was under alemtuzumab maintenance therapy and was diagnosed with RT 557 days after trial enrollment. The observation periods for the other 11 patients ranged from 15 to 1387 days. Short periods of less than 4 months, observed in five of the 11 patients, were related to progressive disease (n=2; 38 and 75 days after trial enrollment), fatal infections (n=3), or early patient dropout (n=1). The first patient with progressive disease had marked lymphadenopathy (abdominal lymph nodes >10 cm) but a normal lactate dehydrogenase level of 227 U/L. The second patient had a homozygous CDKN2A deletion with a concurrent MYC gain, rapidly progressive lymphadenopathy, and a vastly increased lactate dehydrogenase level of 1026 U/L. Lymph node biopsies were not taken in either case.
We also searched for patients who developed RT during their observation period and identified 14 patients with histologically confirmed RT all of whom belonged to the relapsed/refractory cohort. The diagnosis of RT was made at a median of 407 days after trial enrollment (range, 21-568 days). In general, genomic profiles obtained from tumor cell-enriched PBMC taken at trial enrollment did not differ between patients who developed RT during follow up and the other relapsed/refractory cases (Online Supplementary Figure S2). Three tumors transformed during the first 6 weeks after trial enrollment (#2O_CLLL033 at day 21; #2O_CLL056 at day 36; and #2O_CLL037 at day 41). Interestingly, these three tumors had alterations affecting NOTCH1 as well as MYC signaling alongside TP53 dysfunction (Online Supplementary Tables S1 and S2).
Overall, del(9)(p21.3) was less frequently associated with RT than previous publications had suggested. Our next aim was, therefore, to understand whether CDKN2A/B expression was compensated by the second allele in cases with heterozygous loss. In 9p disome cases, gene expression levels of CDKN2A and CDKN2B were variable. Although the median expression levels of CDKN2A transcripts were significantly lower in cases with del(9)(p21.3), the respective values in cases with heterozygous loss did not fall below the range of values observed in 9p disome cases (Figure 6C, details in Online Supplementary Table S3). Promoter methylation, serving as an explanation of abnormally reduced CDKN2A/B expression in 9p-disome cases, was not found for p14ARF, p16INK4A and p15INK4B transcripts (based on 8 selected 9p disome cases including 2 cases with noticeably low expression levels of CDKN2A transcripts and p14ARF; data not shown).
Characterization of genomic lesions relating to MYC
Gains on 8q were found in 16% of high-risk cases, with a comparable distribution between treatment-naïve and relapsed/refractory cases (15% and 17%, respectively). Two types of gains were identified: (i) broad gains covering the MYC locus; (ii) focal gains with a size <500 kb in 8q24.21 affecting a super enhancer region proximal of the MYC locus. The broad type of gains was far more frequent in high-risk cases than in standard-risk ones (14.4% versus 2.2%). The frequency of focal gains was comparable in both risk groups (1.4% and 1.3%) and no relapsed/refractory case was affected. Only one COSMIC-listed MYC mutation was found in 93 high-risk cases screened.
Despite their low frequency, the focal gains in 8q24.21 raised our interest, since they were the only recurrent gains in non-coding DNA regions found throughout the entire CLL genome. Their minimally gained region encompassed three long non-coding RNA, namely CASC19, CCAT1, and CASC21 (Online Supplementary Figure S3). Of these, Colon Cancer Associated 1 (CCAT1) has been associated with adverse risk in several solid cancers,3029 since its transcript has been described to stabilize a chromosome loop between MYC and the enhancer region.31 However, CCAT1 expression in CLL cases with 8q gain did not exceed normal expression levels (Online Supplementary Figure S4).
Besides chromosomal gains on 8q, we and others have previously identified MGA deletion/mutation as a potential alternative mechanism for increased MYC activity in CLL.3213 MGA is a protein that forms a heterodimer with MAX and this heterodimer antagonizes MYC induced transcriptional changes.33 Against the background of frequent MYC gain in relapsed/refractory cases, loss of a significant GISTIC q value for del(15)(q15.1) in the relapsed/refractory cohort was surprising, since its minimally deleted region encompasses the MGA gene locus. We performed qRT PCR to determine MGA gene expression in cases with heterozygous 15q deletion relative to 15q disome cases and found that MGA transcription in cases with heterozygous MGA loss was fully compensated by the remaining allele (Online Supplementary Figure S5). Nonetheless, we found an increased frequency of truncating MGA mutations with five mutations in 4/108 high-risk cases (3.7%). As all MGA mutated cases belonged to the relapsed/refractory cohort, they were clearly enriched in this cohort (4/74 cases; 5.4%).
Characterization of genomic lesions related to NOTCH1 signaling
Based on our PathVisio results, we also investigated genomic alterations associated with NOTCH1 signaling. As a novel finding, three components of a protein complex repressing NOTCH1 target genes were recurrently affected: RBPJ in 4p15.2 was deleted in 8.2%; SPEN was mutated in 3.7% (4/108 cases); and SNW1 in 14q24.3 coding for an unconfirmed component of the repressor complex was deleted in 7.5% of cases (Figure 7A, C, E).
RBPJ is essential for DNA binding of the NOTCH1 intracellular domain (NICD1), which is released as a transcription factor upon activation of the NOTCH1 cell surface receptor. In the absence of NICD1 in the nucleus, RBPJ forms a protein complex with SHARP encoded by SPEN and other proteins that recruit histone deacetylases to condense the chromatin around NOTCH1 target genes.3634 Disruption of this repressor complex has been associated with tumorigenesis.37 SKIP encoded by SNW1 is an unconfirmed complex component that has been associated with recruitment of histone deacetylases to NOTCH1-regulated genes.34
Median gene expression levels of RBPJ and SNW1 were lower in cases with deletion, supporting a potential functional relevance of RBPJ and SNW1 loss (Figure 7B, D). This led us to hypothesize that disruption of the NOTCH1 repressor complex might lead to de-repression of NOTCH1 target genes. We therefore measured expression levels of HES1, DTX1 and MYC as genes known to be NOTCH1-regulated in primary CLL samples.3938 Non-CD19-enriched PBMC samples were used for this analysis of NOTCH1 target gene expression, since EDTA-containing sorting buffer used for cell enrichment can activate NOTCH1 signaling ex vivo (Online Supplementary Figure S6).40 Sample suitability was determined by the following selection criteria: (i) tumor cell count ≥70%; (ii) variant allele frequency >0.3 for SPEN and RBPJ mutations; and (iii) log2 ratio <−0.8 for del(4)(p15.2) or del(14)(q24.3)
Six of nine available SPEN mutant samples and one available RBPJ mutant sample fulfilled these requirements. Target gene expression did not correlate with the number of non-B cells per sample (Online Supplementary Figure S7). In line with our hypothesis, SPEN mutant tumors had significantly higher median expression levels of HES1 and DTX1 and four SPEN mutant cases had remarkably high expression levels of MYC.
Of 14 RBPJ-deleted samples with available RNA, seven fulfilled our requirements. In this highly selected group of cases, RBPJ deletion occurred together with an activating NOTCH1 mutation in three out of the seven cases. HES1 expression was very variable, but in cases with combined RBPJ and NOTCH1 disruption it was highly increased. DTX1 expression was more independent of the NOTCH1 mutation status and its median was significantly higher in cases with RBPJ loss. However, expression of MYC was not altered in 4p deleted cases.
Of 12 SNW1-deleted cases with available RNA, only two cases fulfilled our requirements (Figure 7F; details in Online Supplementary Table S4).
Discussion
Genome-wide screening for CNA in high-risk CLL revealed complex genomic aberrations in the majority of cases, which was in contrast to the high genomic stability observed in treatment-naïve, standard-risk CLL without TP53 or ATM alterations.13 Our cohort consisted mainly of TP53-deficient cases and we found genomic instability to the same degree in patients who had or had not been previously treated.
Our focus was to identify CNA within complex genomic rearrangements relevant to the development of high-risk CLL. Using GISTIC2.0 as a systematic approach to distinguish meaningful chromosomal aberrations from random background alterations,16 we identified CDKN2A/B loss, MYC gain and abnormally strong NOTCH1 signaling as significantly enriched alterations. The genomic profiles observed in peripheral blood CLL cells taken from high-risk patients shared extensive similarities with the profiles found in RT, in which CDKN2A/B loss, MYC gain and activating NOTCH1 mutations have also been identified as hallmark genomic lesions alongside TP53 alterations.2827 However, none of the respective alterations could be directly associated with transformation so that extrinsic stimuli from the microenvironment and/or additional intrinsic stimuli are required to induce transformation.
Loss of CDKN2A/B is a sparsely described CNA in CLL,41 although homozygous deletion has recently been associated with resistance to venetoclax treatment.5 The role in CLL progression of the signaling network around MYC, which comprises transcriptional inhibitors such as MGA,42 is poorly understood. The CDKN2A/B gene loci encode cell-cycle regulators decelerating cell proliferation at multiple levels.43 The CDKN2A gene product p14 tightly controls pro-proliferative activities of MYC so that reduced CDKN2A expression in combination with increased MYC expression can result in accelerated proliferation.44 Enrichment of CDKN2A/B loss and MYC gain in the relapsed/refractory cohort and frequent co-occurrence of both aberrations therefore hints at a key role for deficient cell-cycle control in the development of high-risk CLL.
The small focal gains within the 8q24.21 super-enhancer region are relevant with regard to them harboring binding sites for the NICD1 transcription factor.38 MYC is a well established target gene of NOTCH1,39 and tight bonds between aberrantly strong NOTCH1 signaling and increased MYC activity have been observed in T-cell acute lymphoblastic leukemia and were also shown to exist in CLL.3938 Hence, genomic aberrations with an activating effect on NOTCH1 signaling strength can indirectly increase the number of cases with enhanced MYC activity. Besides coding activating NOTCH1 mutations, which prolong NICD1 transcription factor activity,45 various other genomic alterations were identified to interfere with NOTCH1 signaling. These alterations include non-coding NOTCH1 mutations in the 3’ untranslated region,46 FBXW7 mutations,47 MED12 mutations,48 as well as SPEN mutations and probably also RBPJ alterations.
The fact that NOTCH1 and SF3B1 mutations are often mutually exclusive raises the question as to what extent SF3B1 mutation can disturb the physiological balance between NOTCH1 signaling and MYC transcription. This question is based on the observation that the strength of NOTCH1 signaling depends on DVL2, which inhibits transcriptional activation by NOTCH1.49 Mutations in SF3B1 lead to alternative splicing of DVL2 and the resulting splice variant has been shown to lack its ability to modulate NOTCH1 signaling.50 SF3B1 mutations may, therefore, constitute another frequent mechanism to strengthen the NOTCH1-MYC signaling axis.
Taken together, our results raise the hypothesis that multiple genetic lesions in high risk CLL converge in upregulated MYC activity. Testing this hypothesis requires an integrative analysis of the genome, transcriptome and proteome in samples strictly processed at 4°C and in the absence of calcium chelators. For translation of our results into clinical practice, a systematic record of genomic alterations identified as meaningful in our study needs to be obtained in more recent, prospective clinical trials including treatment arms based on BTK or Pi3K inhibition and/or antagonism of BCL-2. Novel potential markers must be tested for relevance in each treatment arm and markers proving to be relevant must be utilized for the assembly of a genomic clinical database. In the long-term, such an approach will allow an estimation of the likelihood of benefit or disadvantage from a given treatment regimen, hence paving the way towards more personalized treatment choices in the future management of CLL patients.
Acknowledgments
The authors would like to thank the patients who participated in the CLL2O, CLL8, and CLL11 trials; the investigators who treated patients and submitted samples; and Christina Galler, Sabrina Rau, and Jacqueline Fiegel for excellent assistance and support.
Footnotes
- Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/105/5/1379
- FundingThis study was supported by the German Research Foundation (DFG: ED 256/1 1; SFB 1074/B2 and Z1), by grants from the German Federal Ministry of Education and Research (BMBF, PRECiSe) and by the Barts Charity Fund. Central genetic diagnostics were partly funded by F. Hoffmann-La Roche.
- Received February 5, 2019.
- Accepted August 23, 2019.
References
- Hallek M, Shanafelt TD, Eichhorst B. Chronic lymphocytic leukaemia. Lancet. 2018; 391(10129):1524-1537. Google Scholar
- Parikh SA. Chronic lymphocytic leukemia treatment algorithm 2018. Blood Cancer J. 2018; 8(10):93. Google Scholar
- Woyach JA, Johnson AJ. Targeted therapies in CLL: mechanisms of resistance and strategies for management. Blood. 2015; 126(4):471-477. PubMedhttps://doi.org/10.1182/blood-2015-03-585075Google Scholar
- Burger JA, Landau DA, Taylor-Weiner A. Clonal evolution in patients with chronic lymphocytic leukaemia developing resistance to BTK inhibition. Nat Commun. 2016; 7:11589. PubMedhttps://doi.org/10.1038/ncomms11589Google Scholar
- Herling CD, Abedpour N, Weiss J. Clonal dynamics towards the development of venetoclax resistance in chronic lymphocytic leukemia. Nat Commun. 2018; 9(1):727. Google Scholar
- Stilgenbauer S, Zenz T. Understanding and managing ultra high-risk chronic lymphocytic leukemia. Hematology Am Soc Hematol Educ Program. 2010; 2010:481-488. PubMedhttps://doi.org/10.1182/asheducation-2010.1.481Google Scholar
- 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(12):2442-2451. PubMedhttps://doi.org/10.1038/sj.leu.2404935Google Scholar
- Anderson MA, Tam C, Lew TE. Clinicopathological features and outcomes of progression of CLL on the BCL2 inhibitor venetoclax. Blood. 2017; 129(25):3362-3370. PubMedhttps://doi.org/10.1182/blood-2017-01-763003Google Scholar
- Thompson PA, O’Brien SM, Wierda WG. Complex karyotype is a stronger predictor than del(17p) for an inferior outcome in relapsed or refractory chronic lymphocytic leukemia patients treated with ibrutinib-based regimens. Cancer. 2015; 121(20):3612-3621. PubMedhttps://doi.org/10.1002/cncr.29566Google Scholar
- Baliakas P, Jeromin S, Iskas M. Cytogenetic complexity in chronic lymphocytic leukemia: definitions, associations, and clinical impact. Blood. 2019; 133(11):1205-1216. PubMedhttps://doi.org/10.1182/blood-2018-09-873083Google Scholar
- Gunnarsson R, Isaksson A, Mansouri M. Large but not small copy-number alterations correlate to high-risk genomic aberrations and survival in chronic lymphocytic leukemia: a high-resolution genomic screening of newly diagnosed patients. Leukemia. 2010; 24(1):211-215. PubMedhttps://doi.org/10.1038/leu.2009.187Google Scholar
- Ouillette P, Collins R, Shakhan S. Acquired genomic copy number aberrations and survival in chronic lymphocytic leukemia. Blood. 2011; 118(11):3051-3061. PubMedhttps://doi.org/10.1182/blood-2010-12-327858Google Scholar
- Edelmann J, Holzmann K, Miller F. High-resolution genomic profiling of chronic lymphocytic leukemia reveals new recurrent genomic alterations. Blood. 2012; 120(24):4783-4794. PubMedhttps://doi.org/10.1182/blood-2012-04-423517Google Scholar
- Landau DA, Tausch E, Taylor-Weiner AN. Mutations driving CLL and their evolution in progression and relapse. Nature. 2015; 526(7574):525-530. PubMedhttps://doi.org/10.1038/nature15395Google Scholar
- Negrini S, Gorgoulis VG, Halazonetis TD. Genomic instability–an evolving hallmark of cancer. Nat Rev Mol Cell Biol. 2010; 11(3):220-228. PubMedhttps://doi.org/10.1038/nrm2858Google Scholar
- Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GIS-TIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011; 12(4):R41. PubMedhttps://doi.org/10.1186/gb-2011-12-4-r41Google Scholar
- Stilgenbauer SCF, Leblond V, Delmer A. Alemtuzumab combined with dexamethasone, followed by alemtuzumab maintenance or allo-SCT in “ultra high-risk” CLL: final results from the CLL2O phase II study. Blood. 2014; 124(21):1991. Google Scholar
- Hallek M, Fischer K, Fingerle-Rowson G. Addition of rituximab to fludarabine and cyclophosphamide in patients with chronic lymphocytic leukaemia: a randomised, open-label, phase 3 trial. Lancet. 2010; 376(9747):1164-1174. PubMedhttps://doi.org/10.1016/S0140-6736(10)61381-5Google Scholar
- Goede V, Fischer K, Busch R. Obinutuzumab plus chlorambucil in patients with CLL and coexisting conditions. N Engl J Med. 2014; 370(12):1101-1110. PubMedhttps://doi.org/10.1056/NEJMoa1313984Google Scholar
- Steinbrecher D, Jebaraj BMC, Schneider C. Telomere length in poor-risk chronic lymphocytic leukemia: associations with disease characteristics and outcome. Leuk Lymphoma. 2018; 59(7):1614-1623. Google Scholar
- Kelder T, van Iersel MP, Hanspers K. WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 2012; 40(Database issue):D1301-1307. PubMedhttps://doi.org/10.1093/nar/gkr1074Google Scholar
- Kutmon M, Riutta A, Nunes N. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 2016; 44(D1):D488-494. PubMedhttps://doi.org/10.1093/nar/gkv1024Google Scholar
- van Iersel MP, Kelder T, Pico AR. Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics. 2008; 9:399. PubMedhttps://doi.org/10.1186/1471-2105-9-399Google Scholar
- Kutmon M, van Iersel MP, Bohler A. PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol. 2015; 11(2):e1004085. PubMedhttps://doi.org/10.1371/journal.pcbi.1004085Google Scholar
- Stilgenbauer S, Schnaiter A, Paschka P. Gene mutations and treatment outcome in chronic lymphocytic leukemia: results from the CLL8 trial. Blood. 2014; 123(21):3247-3254. PubMedhttps://doi.org/10.1182/blood-2014-01-546150Google Scholar
- Rausch T, Jones DT, Zapatka M. Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell. 2012; 148(1-2):59-71. PubMedhttps://doi.org/10.1016/j.cell.2011.12.013Google Scholar
- Chigrinova E, Rinaldi A, Kwee I. Two main genetic pathways lead to the transformation of chronic lymphocytic leukemia to Richter syndrome. Blood. 2013; 122(15):2673-2682. PubMedhttps://doi.org/10.1182/blood-2013-03-489518Google Scholar
- Fabbri G, Khiabanian H, Holmes AB. Genetic lesions associated with chronic lymphocytic leukemia transformation to Richter syndrome. J Exp Med. 2013; 210(11):2273-2288. PubMedhttps://doi.org/10.1084/jem.20131448Google Scholar
- Xin Y, Li Z, Shen J, Chan MT, Wu WK. CCAT1: a pivotal oncogenic long non-coding RNA in human cancers. Cell Prolif. 2016; 49(3):255-260. Google Scholar
- Chen Y, Xie H, Gao Q, Zhan H, Xiao H, Zou Y. Colon cancer associated transcripts in human cancers. Biomed Pharmacother. 2017; 94:531-540. https://doi.org/10.1016/j.biopha.2017.07.073Google Scholar
- Kim T, Cui R, Jeon YJ. Long-range interaction and correlation between MYC enhancer and oncogenic long noncoding RNA CARLo-5. Proc Natl Acad Sci U S A. 2014; 111(11):4173-4178. PubMedhttps://doi.org/10.1073/pnas.1400350111Google Scholar
- De Paoli L, Cerri M, Monti S. MGA, a suppressor of MYC, is recurrently inactivated in high risk chronic lymphocytic leukemia. Leuk Lymphoma. 2013; 54(5):1087-1090. PubMedhttps://doi.org/10.3109/10428194.2012.723706Google Scholar
- Hurlin PJ, Steingrimsson E, Copeland NG, Jenkins NA, Eisenman RN. Mga, a dual-specificity transcription factor that interacts with Max and contains a T-domain DNA-binding motif. EMBO J. 1999; 18(24):7019-7028. PubMedhttps://doi.org/10.1093/emboj/18.24.7019Google Scholar
- Kao HY, Ordentlich P, Koyano-Nakagawa N. A histone deacetylase corepressor complex regulates the Notch signal transduction pathway. Genes Dev. 1998; 12(15):2269-2277. PubMedhttps://doi.org/10.1101/gad.12.15.2269Google Scholar
- Oswald F, Kostezka U, Astrahantseff K. SHARP is a novel component of the Notch/RBP-Jkappa signalling pathway. EMBO J. 2002; 21(20):5417-5426. PubMedhttps://doi.org/10.1093/emboj/cdf549Google Scholar
- Borggrefe T, Oswald F. The Notch signaling pathway: transcriptional regulation at Notch target genes. Cell Mol Life Sci. 2009; 66(10):1631-1646. PubMedhttps://doi.org/10.1007/s00018-009-8668-7Google Scholar
- Kulic I, Robertson G, Chang L. Loss of the Notch effector RBPJ promotes tumorigenesis. J Exp Med. 2015; 212(1):37-52. PubMedhttps://doi.org/10.1084/jem.20121192Google Scholar
- Fabbri G, Holmes AB, Viganotti M. Common nonmutational NOTCH1 activation in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A. 2017; 114(14):E2911-E2919. PubMedhttps://doi.org/10.1073/pnas.1702564114Google Scholar
- Palomero T, Lim WK, Odom DT. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc Natl Acad Sci U S A. 2006; 103(48):18261-18266. PubMedhttps://doi.org/10.1073/pnas.0606108103Google Scholar
- Rand MD, Grimm LM, Artavanis-Tsakonas S. Calcium depletion dissociates and activates heterodimeric notch receptors. Mol Cell Biol. 2000; 20(5):1825-1835. PubMedhttps://doi.org/10.1128/MCB.20.5.1825-1835.2000Google Scholar
- Martel V, Guerci A, Humbert JC. De novo methylation of tumour suppressor genes CDKN2A and CDKN2B is a rare finding in B-cell chronic lymphocytic leukaemia. Br J Haematol. 1997; 99(2):320-324. PubMedhttps://doi.org/10.1046/j.1365-2141.1997.3953209.xGoogle Scholar
- Grandori C, Cowley SM, James LP, Eisenman RN. The Myc/Max/Mad network and the transcriptional control of cell behavior. Ann Rev Cell Dev Biol. 2000; 16:653-699. PubMedhttps://doi.org/10.1146/annurev.cellbio.16.1.653Google Scholar
- Hannon GJ, Beach D. p15INK4B is a potential effector of TGF-beta-induced cell cycle arrest. Nature. 1994; 371(6494):257-261. PubMedhttps://doi.org/10.1038/371257a0Google Scholar
- Cleveland JL, Sherr CJ. Antagonism of Myc functions by Arf. Cancer Cell. 2004; 6(4):309-311. PubMedhttps://doi.org/10.1016/j.ccr.2004.09.020Google Scholar
- Weng AP, Ferrando AA, Lee W. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science. 2004; 306(5694):269-271. PubMedhttps://doi.org/10.1126/science.1102160Google Scholar
- Puente XS, Bea S, Valdes-Mas R. Non-coding recurrent mutations in chronic lymphocytic leukaemia. Nature. 2015; 526(7574):519-524. PubMedhttps://doi.org/10.1038/nature14666Google Scholar
- Close V, Close W, Kugler SJ. FBXW7 mutations reduce binding of NOTCH1, leading to cleaved NOTCH1 accumulation and target gene activation in CLL. Blood. 2019; 133(8):830-839. PubMedhttps://doi.org/10.1182/blood-2018-09-874529Google Scholar
- Wu B, Slabicki M, Sellner L. MED12 mutations and NOTCH signalling in chronic lymphocytic leukaemia. Br J Haematol. 2017; 179(3):421-429. https://doi.org/10.1111/bjh.14869Google Scholar
- Collu GM, Hidalgo-Sastre A, Acar A. Dishevelled limits Notch signalling through inhibition of CSL. Development. 2012; 139(23):4405-4415. PubMedhttps://doi.org/10.1242/dev.081885Google Scholar
- Wang L, Brooks AN, Fan J. Transcriptomic characterization of SF3B1 mutation reveals its pleiotropic effects in chronic lymphocytic leukemia. Cancer Cell. 2016; 30(5):750-763. PubMedhttps://doi.org/10.1016/j.ccell.2016.10.005Google Scholar