As the most common leukemia in the Western world, chronic lymphocytic leukemia (CLL) represents a prime example to demonstrate how modern genomic technologies have rapidly contributed to an improved understanding of cancer in general and how genomics have already started to translate into clinical practice.
In all cancers, characterizing the impact of the genetic aberrations on the phenotype of the disease has led to novel insights into the pathology underlying the disease. CLL is a neoplasm of mature B cells, a tissue in which the immunoglobulin genes are genetically modified to produce a functional B-cell receptor. The specific configuration of the immunoglobulin genes therefore gave further insight into the differentiation process of the malignant clone.1 While non-malignant B cells use the full repertoire of immunoglobulin gene segments to produce the largest possible number of different B-cell receptors, in CLL cells the usage of immunoglobulin heavy chain variable gene segments (IGHV) is drastically restricted and biased towards a limited number of genes.2 In addition, the process of somatic hypermutation of the variable region, which in non-malignant B cells leads to a change in binding affinity, allows CLL patients to be stratified into two biological and prognostic subgroups according to whether they harbor mutated or non-mutated IGHV genes.1 These genetic findings led to the current model of the pathomechanism of CLL, according to which stimulation by (self-) antigens provides a pro-survival and possibly pro-proliferative advantage for CLL (precursor) cells, most likely leading initially to oligoclonal and subsequently monoclonal selection of malignant cells (Figure 1B). This model was recently improved significantly by findings from a murine xenotransplant model. Here, hematopoietic stem cells purified from CLL patients and transplanted into immunosuppressed mice matured into mono- or oligo-clonal B cells and produced a CLL-like phenotype.3 This indicates that the hematopoietic stem cells of CLL patients already have the inherent ability to initiate a CLL precursor state.
The detection of genomic aberrations has been of key importance towards understanding the mechanism of chemotherapy of CLL cells and has enabled the construction of a hierarchical model of genetic lesions that correlate with outcome.4 Specifically, deletion of the ATM and TP53 tumor suppressor genes, localized at 11q22-q23 and 17p13 respectively, was shown to be associated with poor outcome. Loss of function of these genes likely provides an advantage to the malignant B cells under the selective pressure of chemotherapy, in particular in the case of TP53 deletion and/or mutation (Figure 1).5 Over the last years, the prognostic impact of these cytogenetic aberrations has been translated into clinical practice, and while early clinical prognostic parameters such as age, stage and serum markers are still in use,6 they are nowadays complemented by the information from fluorescence in-situ hybridization (FISH) and mutation analysis of the IGHV genes.1
While interphase FISH has proven valuable, the advent of microarrays that allow genome-wide genomic profiling has further advanced the field. This novel technology increases the resolution of the detection of genomic gains and losses enormously. Resolution is basically limited only by the number and the genomic distribution of arrayed elements on the respective platforms. While the first microarrays used for comparative genomic hybridization had a resolution in the range of several hundred kilo-base pairs (kb), later array platforms had a greatly improved resolution of less than 100 kb in selected areas and already allowed the detection of novel CLL-associated genomic aberrations.7 Recently, novel single nucleotide polymorphism-based microarrays that allow even more detailed insights have been developed. Using these arrays, in a critical region on chromosome 13q14, the most common genetic aberration in CLL, precise mapping has identified the prognostic impact of the size of the deletion, pointing towards a multigeneic tumor suppressor mechanism in that region.8 Recent studies with high-resolution single nucleotide polymorphism microarrays were also able to identify new risk variants for CLL via genome-wide association studies i.e. linkage analyses. A first study tagging 299,983 single nucleotide polymorphisms in a total of 2,503 CLL cases and 5,789 controls identified four new CLL risk loci at 2q37.3 (rs757978, FARP2), 8q24.21 (rs2456449), 15q21.3 (rs7169431), and 16q24.1 (rs305061), and suggested that there were additional risk loci at 15q25.2 (rs783540, CPEB1) and 18q21.1 (rs1036935).9 While these data provide further evidence for genetic susceptibility to CLL (Figure 1), in this issue of Haematologica Wade et al. present a linkage analysis based on 346,831 single nucleotide polymorphisms in 356 CLL patients from a clinical phase III trial in order to identify genomic single nucleotide polymorphism variants. They correlated the single nucleotide polymorphism variants with the response to first-line chemotherapy with fludarabine, chlorambucil or a combination of fludarabine plus cyclophosphamide.10 Interestingly, one of the single nucleotide polymorphisms (rs2255235) that could be associated with progression-free survival is localized at the 5′ end of β2 microglobulin (B2M), a part of the major histo-compatibility complex. The serum levels of β2 microglobulin protein correlate with the tumor burden and β2 microglobulin is, therefore, a prognostic marker in CLL, with high levels of β2 microglobulin correlating with a worse outcome.6 In the study by Wade et al., the variation at the single nucleotide polymorphism rs2255235 in the B2M gene did not, however, correlate with the levels of serum β2 microglobulin.10 The significance of this single nucleotide polymorphism does, therefore, remain to be investigated further, especially with regard to a potential prognostic benefit of β2 microglobulin serum levels. In addition, the correlation of a single nucleotide polymorphism (rs11158493) in the PPP2R5E gene is of interest.10 Recently, a PPP2R5E interacting partner and a member of the same gene family, PPP2R1B, were reported to be localized in 11q22-q23, a critical recurrently deleted region in CLL. A second tumor suppressor in this region, besides ATM, has been postulated.11 While no genetic aberrations have been found in PPP2R1B at 11q22-q23, its gene product seems to be differently spliced in CLL cells.11 These findings suggest that single nucleotide polymorphisms might play a significant role in the pathogenicity of this gene family. However, although the findings of Wade et al. are very interesting, it must be noted that none of the associations was statistically significant in an independent series of 380 cases of CLL.10 Thus, all prognostic and predictive effects associated with single nucleotide polymorphisms identified based on linkage analyses should be cautiously interpreted and warrant further validation in independent data sets prior to clinical use.
As for microarray studies of genomic aberrations, microarray-based gene expression profiles have started to be developed into clinical diagnostic tools in lymphoma and leukemia. However, in the future, recent advances in sequencing technologies are very likely to initiate the next diagnostic revolution. Here again, CLL can be viewed as a model disease entity since next-generation sequencing was successfully applied for whole-genome sequencing of four cases of CLL.12 Forty-six somatic mutations that potentially affect gene function were identified. The most frequently recurrent aberrations were gain-of-function mutations of the outer-membrane receptor and transcriptional activator NOTCH1. Found in 12% of CLL patients, most NOTCH1 mutated cases seem to carry unmutated IGHV genes. In addition, the authors demonstrated that the mutations mostly affected the functional PEST domain of NOTCH1, thereby leading to enhanced stability of the protein and to differential gene expression patterns of target genes in CLL patients.12 Interestingly, an elevated rate of NOTCH1 mutation was also recently observed during disease progression towards Richter’s transformation as well as in chemotherapy-refractory CLL,13 underscoring the importance of this gene in the pathomechanism of the disease. Furthermore, recent developments of sequencing technology, including so-called paired-end next-generation sequencing, offer improved detection of chromosomal rearrangements. The methodology led to the discovery of cataclysmic shattering of a single chromosome in cancer.14 Initially observed in CLL, this unprecedented genomic aberration, termed chromothripsis, may be present in 2–3% of all cancers.14
In addition to genetic aberrations, epigenetic modifications seem to play a major role in the pathomechanism of CLL. First genome-wide analyses revealed that up to 8% of all CpG units are aberrantly methylated in CLL cells.15 An even more striking finding in CLL was the aberrant epigenetic silencing of the death-associated protein kinase 1 (DAPK1) in virtually all CLL samples analyzed (67/69).16 In addition, epigenetic lesions seemed to precede cytogenetic aberrations in a CLL mouse model.17 Similarly, in 13q14, the region that is most commonly genetically inactivated, we have identified a complex epigenetic tumor suppressor mechanism.18 It is, therefore, possible that a similar sequence of events at 13q14 leads to the development of CLL: first a gradual epigenetic silencing of a tumor suppressor mechanism which is then superseded by full genetic inactivation by deletion (Figure 1).19
An initial epigenetic aberration predisposing to CLL could also explain the surprising recent finding that hematopoietic stem cells isolated from CLL patients are capable of forming a transplantable CLL precursor-like disease in mice, suggesting a common aberration already present in the hematopoietic stem cells (Figure 1A).3 While this hematopoietic stem cell defect could also be a genetic lesion, the cytogenetic aberration(s) present in the mature CLL cells were not detected in the hematopoietic stem cells from CLL patients, and no common genetic mutation was found in the coding regions of four CLL whole genomes sequenced which could thus be causative.12 It remains to be seen whether mutations in non-coding RNA genes or sequence variants in intergenic regions could be the underlying cause of the development of CLL: for example, in the most commonly deleted region in 13q14, a rare sequence variation was found in the non-coding RNA genes miR15a and miR16-1 leading to a defect in the maturation process of these non-coding genes.20 Thus, genomics will lead the way to improved characterization of the pathomechanisms underlying CLL and further advance prognostication and risk-adapted management for patients.
We thank all the members of the German CLL Study Group (GCLLSG) for their continuous support of the study group and its treatment protocols.
- Daniel Mertens studied biochemistry at the University of Tübingen and took his PhD under Prof. Lichter at the German Cancer Research Center (DKFZ) in Heidelberg. He received a Junior Group at the DKFZ and at the same time a Max-Eder Research Group at the Department of Internal Medicine III of the University of Ulm (Prof. Döhner). These groups are combined in the Cooperation Unit “Mechanisms of Leukemogenesis” which allows the translational application of basic research at the DKFZ to preclinical research at the University of Ulm. The focus of his research is on the role of epigenetics and the microenvironment in leukemogenesis.
- Lars Bullinger received his MD at the University of Heidelberg and was trained in internal medicine and hematology/oncology at the Universities of Heidelberg and Ulm. He spent a postdoctoral fellowship at Stanford University (Prof. Pollack) and recently a sabbatical at the Dana Farber Cancer Institute (Prof. Armstrong). He is currently working as a physician at the Department of Internal Medicine III of Ulm University; his research interests are centred on the dissection of the molecular pathogenesis of leukemias using “-omic” approaches.
- Stephan Stilgenbauer received his MD at the University of Heidelberg and was trained in internal medicine and hematology/oncology at the Universities of Heidelberg and Ulm. He spent a postdoctoral fellowship at the DKFZ in Heidelberg (Prof. P. Lichter) and is currently deputy chairman and associate professor at the Department of Internal Medicine III, Ulm University. His research focus is on molecular pathogenesis and translation into novel treatment strategies in hematologic malignancies.
- Related Original Article on page 1496
- Funding: this work was supported in part by the Deutsche Krebshilfe (109674), DFG (STI 296/2-1), Deutsche José Carreras Leukämie-Stiftung e. V. (R R08/26f), and the CLL Global Research Foundation. LB was supported in part by the Deutsche Forschungsgemeinschaft (Heisenberg-Stipendium BU 1339/3-1).
- Financial and other disclosures provided by the author using the ICMJE (www.icmje.org) Uniform Format for Disclosure of Competing Interests are available with the full text of this paper at www.haematologica.org.
- Zenz T, Mertens D, Kuppers R, Dohner H, Stilgenbauer S. From pathogenesis to treatment of chronic lymphocytic leukaemia. Nat Rev Cancer. 2010; 10(1):37-50. PubMedGoogle Scholar
- Ghia P, Chiorazzi N, Stamatopoulos K. Microenvironmental influences in chronic lymphocytic leukaemia: the role of antigen stimulation. J Intern Med. 2008; 264(6):549-62. PubMedhttps://doi.org/10.1111/j.1365-2796.2008.02030.xGoogle Scholar
- Kikushige Y, Ishikawa F, Miyamoto T, Shima T, Urata S, Yoshimoto G. Self-renewing hematopoietic stem cell is the primary target in pathogenesis of human chronic lymphocytic leukemia. Cancer Cell. 2011; 20(2):246-59. PubMedhttps://doi.org/10.1016/j.ccr.2011.06.029Google Scholar
- Dohner H, Stilgenbauer S, Benner A, Leupolt E, Krober A, Bullinger L. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med. 2000; 343(26):1910-6. PubMedhttps://doi.org/10.1056/NEJM200012283432602Google Scholar
- Stilgenbauer S, Sander S, Bullinger L, Benner A, Leupolt E, Winkler D. Clonal evolution in chronic lymphocytic leukemia: acquisition of high-risk genomic aberrations associated with unmutated VH, resistance to therapy, and short survival. Haematologica. 2007; 92(9):1242-5. PubMedhttps://doi.org/10.3324/haematol.10720Google Scholar
- Hallek M, Cheson BD, Catovsky D, Caligaris-Cappio F, Dighiero G, Dohner H. Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International Workshop on Chronic Lymphocytic Leukemia (IWCLL) updating the National Cancer Institute-Working Group (NCI-WG) 1996 guidelines. Blood. 2008; 111(12):5446-56. PubMedhttps://doi.org/10.1182/blood-2007-06-093906Google Scholar
- Schwaenen C, Nessling M, Wessendorf S, Salvi T, Wrobel G, Radlwimmer B. Automated array-based genomic profiling in chronic lymphocytic leukemia: development of a clinical tool and discovery of recurrent genomic alterations. Proc Natl Acad Sci USA. 2004; 101(4):1039-44. PubMedhttps://doi.org/10.1073/pnas.0304717101Google Scholar
- Ouillette P, Erba H, Kujawski L, Kaminski M, Shedden K, Malek SN. Integrated genomic profiling of chronic lymphocytic leukemia identifies subtypes of deletion 13q14. Cancer Res. 2008; 68(4):1012-21. PubMedhttps://doi.org/10.1158/0008-5472.CAN-07-3105Google Scholar
- Crowther-Swanepoel D, Broderick P, Di Bernardo MC, Dobbins SE, Torres M, Mansouri M. Common variants at 2q37.3, 8q24.21, 15q21.3 and 16q24.1 influence chronic lymphocytic leukemia risk. Nat Genet. 2010; 42(2):132-6. PubMedhttps://doi.org/10.1038/ng.510Google Scholar
- Wade R, Di Bernardo MC, Richards S, Rossi D, Crowther-Swanepoel D, Gaidano G. Association between single nucleotide polymorphism-genotype and outcome of patients with chronic lymphocytic leukemia in a randomized chemotherapy trial. Haematologica. 2011; 96(10):1496-503. PubMedhttps://doi.org/10.3324/haematol.2011.043471Google Scholar
- Kalla C, Scheuermann MO, Kube I, Schlotter M, Mertens D, Dohner H. Analysis of 11q22-q23 deletion target genes in B-cell chronic lymphocytic leukaemia: evidence for a pathogenic role of NPAT, CUL5, and PPP2R1B. Eur J Cancer. 2007; 43(8):1328-35. PubMedhttps://doi.org/10.1016/j.ejca.2007.02.005Google Scholar
- Puente XS, Pinyol M, Quesada V, Conde L, Ordonez GR, Villamor N. Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. Nature. 2011; 475(7354):101-5. PubMedhttps://doi.org/10.1038/nature10113Google Scholar
- Fabbri G, Rasi S, Rossi D, Trifonov V, Khiabanian H, Ma J. Analysis of the chronic lymphocytic leukemia coding genome: role of NOTCH1 mutational activation. J Exp Med. 2011; 208(7):1389-401. PubMedhttps://doi.org/10.1084/jem.20110921Google Scholar
- Stephens PJ, Greenman CD, Fu B, Yang F, Bignell GR, Mudie LJ. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell. 2011; 144(1):27-40. PubMedhttps://doi.org/10.1016/j.cell.2010.11.055Google Scholar
- Rush LJ, Raval A, Funchain P, Johnson AJ, Smith L, Lucas DM. Epigenetic profiling in chronic lymphocytic leukemia reveals novel methylation targets. Cancer Res. 2004; 64(7):2424-33. PubMedhttps://doi.org/10.1158/0008-5472.CAN-03-2870Google Scholar
- Raval A, Tanner SM, Byrd JC, Angerman EB, Perko JD, Chen SS. Downregulation of death-associated protein kinase 1 (DAPK1) in chronic lymphocytic leukemia. Cell. 2007; 129(5):879-90. PubMedhttps://doi.org/10.1016/j.cell.2007.03.043Google Scholar
- Chen SS, Raval A, Johnson AJ, Hertlein E, Liu TH, Jin VX. Epigenetic changes during disease progression in a murine model of human chronic lymphocytic leukemia. Proc Natl Acad Sci USA. 2009; 106(32):13433-8. PubMedhttps://doi.org/10.1073/pnas.0906455106Google Scholar
- Mertens D, Wolf S, Tschuch C, Mund C, Kienle D, Ohl S. Allelic silencing at the tumor-suppressor locus 13q14.3 suggests an epigenetic tumor-suppressor mechanism. Proc Natl Acad Sci USA. 2006; 103(20):7741-6. PubMedhttps://doi.org/10.1073/pnas.0600494103Google Scholar
- Mertens D, Philippen A, Ruppel M, Allegra D, Bhattacharya N, Tschuch C. Chronic lymphocytic leukemia and 13q14: miRs and more. Leuk Lymphoma. 2009; 50(3):502-5. PubMedhttps://doi.org/10.1080/10428190902763509Google Scholar
- Allegra D, Mertens D. In-vivo quantification of primary microRNA processing by Drosha with a luciferase based system. Biochem Biophys Res Commun. 2011; 406(4):501-5. PubMedhttps://doi.org/10.1016/j.bbrc.2011.02.055Google Scholar