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M. Zapatka et al.
relapse, refractory) at the second time point. Differences in the distribution of the allele frequency changes between the three patient groups were identified using a bootstrapped Kolmogorov-Smirnov test with n=10,000.
Copy number variations calling and calculation of absolute copy numbers
Estimation of the copy number state based on the exome sequencing data was achieved using Varscan 2 on the target regions.30 Absolute copy numbers were calculated as previously described.31
Calculation of cancer cell fraction
Cancer cell fractions (CCF) integrating sample purity (estimated by fluorescence-activated cell sorting [FACS]), ploidy inferred from fluorescence in situ hybridization (FISH), copy number states calculated from WES and allelic fraction and coverage of somatic variants were calculated for the patients with available germline samples following the approach previously outlined in20.
Estimation of clonal composition by TrAP
Changes in clonal tumor composition were calculated integrat- ing the CCF at the respective time points using TrAP (tree approach to clonality).32
Quantification of DNA methylation and estimation of correlation between time points
DNA methylation from the first and second time point of ten patient phases (three long-term untreated, two relapsed and five refractory) was assessed by Illumina Infinium HumanMethylation450 BeadChips according to the manufac- turer’s protocol.
Details on the individual approaches are further described in the Online Supplementary Appendix.
Results
The clinical course of patients grouped into distinct phases
The clonal evolution in malignant B-cell populations of CLL patients was studied by longitudinal analyses in a total of 25 patients and 54 tumor samples. For 21 patients, the baseline sample was obtained prior to any CLL thera- py, whereas four additional patients were pretreated before enrollment in our study. A common case history in CLL can consist of different phases including an untreated phase with a watch and wait strategy in the beginning fol- lowed by one or more therapies with either durable or very short responses or even refractoriness to the ongoing treatment. We observed such clinical phases in our patients throughout their individual medical history. For example, some of the long-term untreated patients required therapy at a later stage (e.g., HU-1-06) and some patients with initially long-lasting response became refrac- tory after a subsequent treatment (e.g., HU-1-11). Therefore, we divided the individual patient histories into different clinical phases rather than using a rigid division of patients into categories. Individuals can go through sev- eral of these phases with sampling at the beginning and at the end of each phase. Three clinical disease patterns were distinguished and in total we identified 29 phases: six phases were evaluated as long-term untreated, five as relapsed after initially durable response to therapy, and 18 as treatment refractory. Details of the clinical course of
patients and patient phases including treatment, treatment response and sampling, as well as cytogenetic grouping and the immunoglobulin heavy-chain variable region gene (IGHV) mutation status are presented in the Online Supplementary Tables S1 to S3 and in the Online Supplementary Figure S1.
Increased mutation rate is associated with refractory disease
Identification of mutations was performed by compara- tive WES of CD19+ enriched PBMC and, as non-malig- nant control, the sorted CD19-negative fraction of PBMC from the same patient. Over the course of this longitudi- nal study, no IGHV status switch was identified. In IGHV- mutated cases, the major IGHV clone did not change, and IGHV mutations and SNP fingerprinting were used to con- firm sample identity.
Based on limited material for sorting of non-neoplastic cells, for 19 of 25 patients a non-tumor control was avail- able for mutation detection. Applying established algo- rithms33 for the calling of SNV and small insertions and deletion (Indels), we observed an average of 15.1 muta- tions per sample (range, 2-36) (Online Supplementary Tables S4 to S6). A prediction of the response to therapy was not possible based on mutation numbers, as samples taken before long lasting response to therapy and before refrac- tory disease had similar numbers of mutations (11.3 [range, 1-30] and 15.8 [range, 2-34] respectively P-value Mann-Whitney test P=0.36; Figure 1A and B). Samples obtained before any therapy as well as post-therapeutic samples from relapsed patients had the lowest number with 13.5 (range, 2-30) and 13.0 (range, 6-25) mutations in contrast to refractory patients with 17.9 (range, 4-36) mutations, respectively (Figure 1B) (Kruskal Wallis test P=0.30). We identified 1.5 known driver events per sample with the largest variation and highest number of SNV/Indels in refractory CLL samples. All cases except HU-1-08, HU-1-11, and HU-1-21 harbored SNV/Indels in known or candidate CLL driver genes.4,34 Indeed, candi- dates previously associated with adverse outcome like BIRC3, EGR2 and SAMHD were identified predominantly in refractory cases, but preceded good response to (chemo)therapy and therefore did not determine outcome (e.g., patients HU-1-19 or HU-1-15). In addition, this study revealed genes that had so far not been associated with CLL but were mutated in more than one of the analyzed patients: MC5R, MYH2, RFX7, ROBO2 and SLITRK5.
Clonal evolution of leukemic cells is dominant in patients with refractory disease
Clonal evolution was modeled on the basis of single nucleotide variants that were assessed in longitudinal sample collections. FISH analysis with a panel of diagnos- tic probes10 in a subset of samples revealed near diploidy of the neoplastic cells. Interestingly, no changes in cytoge- netic aberrations in long-term untreated phases could be identified based on FISH data (Online Supplementary Table S1). Most patients retained their karyotype after treat- ment, but HU-1-19 acquired a deletion in chromosome 17p. Since neoplastic B-cell content was generally higher than 80%, AF were used as basis for modeling evolution over time. To this aim, SNV were identified that displayed variable AF between the time points of molecular analysis. During long-term untreated phases, AF remained stable, which is in accordance with an unchanged clonal compo-
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