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Mutations driving CLL and their evolution in progression and relapse


Which genetic alterations drive tumorigenesis and how they evolve over the course of disease and therapy are central questions in cancer biology. Here we identify 44 recurrently mutated genes and 11 recurrent somatic copy number variations through whole-exome sequencing of 538 chronic lymphocytic leukaemia (CLL) and matched germline DNA samples, 278 of which were collected in a prospective clinical trial. These include previously unrecognized putative cancer drivers (RPS15, IKZF3), and collectively identify RNA processing and export, MYC activity, and MAPK signalling as central pathways involved in CLL. Clonality analysis of this large data set further enabled reconstruction of temporal relationships between driver events. Direct comparison between matched pre-treatment and relapse samples from 59 patients demonstrated highly frequent clonal evolution. Thus, large sequencing data sets of clinically informative samples enable the discovery of novel genes associated with cancer, the network of relationships between the driver events, and their impact on disease relapse and clinical outcome.

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Figure 1: The landscape of putative driver gene mutations and recurrent somatic copy number variations in CLL.
Figure 2: Selected novel, putative driver gene maps.
Figure 3: Inferred evolutionary history of CLL.
Figure 4: Associations of CLL drivers with clinical outcome.
Figure 5: Matched pre-treatment and relapse samples reveal patterns of clonal evolution in relation to therapy.

Accession codes

Data deposits

CLL8 WES data is deposited in dbGaP under accession code phs000922.v1.p1.


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We thank all members of the Broad Institute’s Biological Samples, Genetic Analysis and Genome Sequencing Platforms, who made this work possible (NHGRI-U54HG003067). We further thank all patients and their physicians for CLL8 trial participation and donation of samples; M. Mendila, N. Valente, S. Zurfluh, M. Wenger and J. Wingate for their support in conception and conduct of the CLL8 trial. D.A.L. is supported by an ACS Postdoctoral Fellowship, ASH Scholar Award, and the Burroughs Wellcome Fund Career Award for Medical Scientists and by the NIH Big Data to Knowledge initiative (BD2K, 1K01ES025431-01). J.G.R. was supported by the European Research Council (ERC) start grant 279307: Graph Games, Austrian Science Fund (FWF) grant no. P23499-N23, and FWF NFN grant no S11407-N23 RiSE. S.B. is supported by the German Jose Carreras Leukemia Foundation (project R 06/03v). M.H. is supported by the Deutsche Forschungsgemeinschaft (KFO 286, Project 6). S.S. is supported by the Else Kröner-Fresenius-Stiftung (2010_Kolleg24, 2012_A146), Virtual Helmholtz Institute (VH-VI-404), CLL Global Research Foundation (Alliance), and Deutsche Forschungsgemeinschaft (SFB 1074 projects B1, B2). C.J.W. acknowledges support from the Blavatnik Family Foundation, AACR (SU2C Innovative Research Grant), and NIH/NCI (1R01CA182461-02, 1R01CA184922-01, 1U10CA180861-01).

Author information

Authors and Affiliations



All authors contributed extensively to the work presented in this paper. D.A.L., D.N., G.G., E.T., S.S. and C.J.W. contributed to study conception and design. E.T., S.B., J.E., S.K., M.K., M.R., A.F., K.F., H.D., M.H. and S.S. performed patient selection, provided the DNA samples, and prior matched clinical and genetic data sets. C.L.S., S.G. and E.S.L. enabled sample sequencing. D.A.L., A.N.T., C.S., M.L., K.C., M.R., J.M.H., S.L.C. and G.G. contributed to the computational genomics analysis. D.A.L., E.T., J.G.R., J.B., S.K., I.B., D.M., M.A.N., D.N., G.G., S.S. and C.J.W. contributed to additional data analysis as well as manuscript preparation. All authors contributed to the writing of the manuscript.

Corresponding authors

Correspondence to Gad Getz, Stephan Stilgenbauer or Catherine J. Wu.

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Competing interests

S.B., S.S. and M.H. have received research support and honoraria from F. Hoffmann-La Roche. S.B. is a recipient of honoraria and research support from AbbVie and of research support from Celgene. All other authors have no conflicts of interest.

Extended data figures and tables

Extended Data Figure 1 Candidate CLL cancer genes discovered in the combined cohort of 538 primary CLL samples.

Significantly mutated genes identified in 538 primary CLL. Top panel: the rate of coding mutations (mutations per megabase) per sample. Centre panel: detection of individual genes found to be mutated (sSNVs or sIndels) in each of the 538 patient samples (columns), colour-coded by type of mutation. Only one mutation per gene is shown if multiple mutations from the same gene were found in a sample. Right panel: Q-values (red, Q < 0.1; purple dashed, Q < 0.25) and Hugo symbol gene identification. New candidate CLL genes are marked with asterisks. Left panel: the percentages of samples affected with mutations (sSNVs and sIndels) in each gene. Bottom panel: plots showing allelic fractions and the spectrum of mutations (sSNVs and sIndels) for each sample.

Extended Data Figure 2 Cellular networks and processes affected by putative CLL drivers.

Putative CLL cancer genes cluster in pathways that are central to CLL biology such as Notch signalling, inflammatory response and B-cell receptor signalling. In addition, proteins that participate in central cellular processes such as DNA damage repair, chromatin modification and mRNA processing, export and translation are also recurrently affected. New CLL subpathways highlighted by the current driver discovery effort are shown in yellow boxes. Red circles indicate putative driver genes previously identified3; purple circles indicate genes newly identified in the current study.

Extended Data Figure 3 RNA-seq expression data for candidate CLL genes and targeted candidate driver validation.

a, Matched RNA-seq and WES data were available for 156 CLLs (103 CLLs previously reported3 and 53 CLLs from the ICGC studies1). From the WES of these 156 cases, we identified 318 driver mutations (sSNVs and sIndels). For each site, we quantified the number of alternative reads corresponding to the somatic mutation in matched RNA-seq data. We subsequently counted the number of instances in which a mutation was detected (‘detected’) and compared it to the number of instances in which mutation detection had >90% power based on the allelic fraction in the WES and the read depth in the RNA-seq data (‘powered’). Overall, we detected 78.1% of putative CLL gene mutations at sites that had >90% power for detection in RNA-seq data. b, Targeted orthogonal validation (Access Array System, Fluidigm) was performed for 71 mutations (sSNVs and sIndels) in putative CLL genes, affecting 47 CLLs from the CLL8 cohort (selected on the basis of sample availability). With a mean depth of coverage of 7,472×, 65 of the 71 mutations (91.55%) validated, with a higher variant allele fraction compared with normal sample DNA (binomial P < 0.01).

Extended Data Figure 4 Gene mutation maps for candidate CLL genes.

av, Individual gene mutation maps are shown for all newly identified candidate CLL cancer genes not included in Fig. 2. The plots show mutation subtype (for example, missense, nonsense) and position along the gene.

Extended Data Figure 5 CLL copy number profiles.

Copy number profile across 538 CLLs detected from WES data from primary samples (see Supplementary Methods).

Extended Data Figure 6 Annotation of drivers based on clinical characteristics and co-occurrence patterns.

a, Putative drivers affecting greater than 10 patients were assessed for enrichment in IGHV mutated versus unmutated CLL subtype (Fisher’s exact test, magenta line denotes P = 0.05). b, Putative drivers affecting greater than 10 patients were assessed for enrichment in samples that received therapy before sampling (Fisher’s exact test). Putative drivers affecting greater than 10 patients were tested for co-occurrence. c, d, Significantly high (c) or low (d) co-occurrences are shown (Q < 0.1, Fisher’s exact test with Benjamini Hochberg, false discovery rate, after accounting for prior therapy and IGHV mutation status, see Supplementary Methods).

Extended Data Figure 7 Mutation spectrum analysis, clonal versus subclonal sSNVs.

The spectrum of mutation is shown for the clonal and subclonal subsets of coding somatic sSNVs across WES of 538 samples. The rate is calculated by dividing the number of trinucleotides with the specified sSNVs by the covered territory containing the specified trinucleotide. Both clonal and subclonal sSNVs were similarly dominated by C > T transitions at CpG sites. Thus, this mutational process that was previously associated with ageing39 not only predates oncogenic transformation (since clonal mutations will be highly enriched in mutations that precede the malignant transformation40) but also is the dominant mechanism of malignant diversification after transformation in CLL.

Extended Data Figure 8 The CLL driver landscape in the CLL8 cohort.

Somatic mutation information shown across the 55 candidate CLL cancer genes and recurrent somatic CNAs (rows) for 278 CLL samples collected from patients enrolled on the CLL8 clinical trial primary that underwent WES (columns). Recurrent somatic CNA labels are listed in blue, candidate CLL cancer genes are listed in bold if previously identified in Landau et al.3, and with an asterisk if newly identified in the current study.

Extended Data Figure 9 CLL8 patient cohort clinical outcome (from 278 patients) information by CLL cancer gene.

Kaplan–Meier analysis (with logrank P values) for putative drivers not associated with significant impact on progression-free survival (PFS) or overall survival (OS) in the cohort of 278 patients that were treated as part of the CLL8 trial. For candidate CLL genes tested here for the first time regarding impact on outcome, a Bonferroni P value is also shown.

Extended Data Figure 10 Comparison of pre-treatment and relapse cancer cell fraction (CCF) for non-silent mutations in candidate CLL genes across 59 CLLs.

For each CLL gene mutated across the 59 CLLs that were sampled longitudinally, the modal CCF is compared between the pre-treatment and relapse samples. CCF increases (red), decreases (blue) or stable CCF (grey) over time are shown (in addition to CLL genes shown in Fig. 5). A significant change in CCF over time (red or blue) was determined if the 95% CI of the CCF in the pre-treatment and relapse samples did not overlap.

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1, 5, 7, 8 and 9, Supplementary Methods and a Supplementary Bibliography. (PDF 386 kb)

Supplementary Data

This file contains Supplementary Table 2. (XLSX 328 kb)

Supplementary Data

This file contains Supplementary Table 3. (XLSX 2452 kb)

Supplementary data

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Supplementary Data

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Supplementary Data

This file contains Supplementary Table 10. (XLSX 246 kb)

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Landau, D., Tausch, E., Taylor-Weiner, A. et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).

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