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Clonal evolution in relapsed/refractory CLL cases
Early cytogenetic and molecular cytogenetic studies reported unequivocal evidence for occurrence of clonal evolution in CLL, albeit rare.14-17 More recently, high-resolu- tion microarray and next-generation sequencing (NGS) based approaches were applied to track subclonal hetero- geneity and clonal evolution in CLL. Based on a single nucleotide polymorphism (SNP) micro-array analysis of pretreatment and relapsed samples from 42 patients, DNA copy number variations (CNV) were reported that expand or newly occur at relapse.18 The respective genomic regions contain candidate driver genes of relapse and/or chemotherapy resistance. Somatic mutation profiling of CLL by NGS revealed recurrent gene alterations19 and con- firmed molecular heterogeneity.20 The comprehensive analysis of 149 CLL cases allowed to distinguish clonal (MYD88, trisomy 12, and del(13q)) and subclonal (SF3B1 and TP53) driver mutations20 and this order was validated by the same group in a huge clinical study.4 While a consid- erable number of driver genes and recurrent genomic alter- ations were identified via whole-exome sequencing (WES) analysis of a cumulative number of more than 1,000 CLL patients, there are only few studies that decipher changes of drivers over the course of disease. Mutation profiling of three CLL patients over time indicated heterogeneous clonal evolution patterns.21 By a similar approach, ten of 12 CLL cases treated with chemotherapy were shown to undergo evolution of sub-clones with respective driver mutations (SF3B1 and TP53), while this was detected in only one of six cases that were not treated.22 While one study reported that clonal composition remained stable at disease progression and relapse23 another study referred that 13 of 28 sequentially sampled cases underwent genetic change of >20% with nine of them (but none of the non- evolving cases) also displaying epigenetic evolution.24
A number of deep sequencing studies focused on a tar- geted panel for candidate genes in CLL and provided evi- dence of clonal outgrowth over time i.e., of TP53 after treatment.25-27 Despite that, their major focus was on untreated patient samples and the response to therapy was not considered as a predictor of evolution. In addi- tion, targeted analysis of a restricted number of drivers can give an idea of clonal rigidity, but fail to show emergence and outgrowth of new subclones characterized by vari- ants not covered with the panel. A similar approach con- sidering aberrations in addition to known driver muta- tions deciphered the history of these alterations by inte- grating longitudinal and cross-sectional data in 70 patients.28 While the distinction of evolutionary early and late events showed a similar pattern to Landau et al., again the association with patient outcome was not addressed. The biggest WES cohort with sequential sampling in CLL included 59 patients from CLL8 with samples before and after relapse to FC/FCR (fludarabine, cyclophosphamide, rituximab) showing changes of cell fractions characterized by specific drivers as well as linear versus branched evolu- tion patterns in 57 of 59 cases.4 However, this group con- sisted only of relapsed cases with a missing control of refractory and long-term untreated patients. Due to the fact, that again type and duration of response were not considered as parameters, a link between treatment, out- come and dynamic genomic changes in CLL is barely explored. Although a connection of response to therapy and dynamic genomic changes is plausible, it remains unclear how clonal evolution is linked to long-term stable, to relapsed or to refractory disease.
In order to elucidate the clonal evolution of CLL cell populations in the presence or absence of therapy, we per- formed a long-term longitudinal mutation profiling study of a multifarious cohort of CLL patients with a well anno- tated patient history.
Aberrant TP53 dictates the clinical course of the disease, it is a key driver of acquired resistance and potentially supersedes other parameters. Therefore, we excluded patients with del17p or mutated TP53 status at baseline as we presumed that these patients had acquired the most relevant evolution marker already. Samples were obtained at different time points before and after treatment in three different clinical groups: i) long-term untreated cases with stable disease and no need for treatment over at least 4 years, ii) relapsed cases with durable response to therapy of at least 2 years, and iii) refractory cases without response to treatment (stable disease [SD], progressive dis- ease [PD]) or cases that progressed with requirement of a subsequent therapy within 1 year. WES was performed and data were subsequently partially validated by targeted resequencing of identified mutations.
Methods
Sample collection
We compiled an inventory of CLL patient samples before and after treatment and sequenced tumor and non-tumor control DNA (25 patients and 54 tumor samples including 21 patients with baseline samples prior to any therapy). Our inclusion crite- ria were: (i) no del17p or mutated TP53 status at baseline, (ii) patients fitting to any of the three groups (a) long-term untreated cases with stable disease and no need for treatment over at least 4 years, (b) relapsed cases with durable response to therapy of at least 2 years, and (c) refractory cases without response to treat- ment (SD, PD) or cases that progressed with requirement of a subsequent therapy within 1 year.
All patients gave informed consent according to the Helsinki Declaration. Sample acquisition for sequencing purposes was approved by a local Ethics Review Committee (Ethikkommision Ulm University, ethik-kommission@uni-ulm.de, 17.06.2008, 96/08-UBB/se).
Peripheral blood mononuclear cell (PBMC) samples were enriched for tumor (CD19+) and normal CD19-cells using MACS microbead cell separation (Miltenyi Biotec, Bergisch Gladbach, Germany). Genomic DNA was isolated from unsort- ed and sorted CLL cells using All Prep Kit (Qiagen, Hilden, Germany). Quality and quantity of the purified DNA were assessed with the Qubit dsDNA BR Assay Kit (Lifetech tech- nologies, Carlsbad, CA).
Sequencing
WES was performed on Illumina HiSeq 2000 machines. Exome libraries were created using the TruSeq Exome Library Prep Kit or Agilent SureSelect enrichment Human Exome V4 Kit according to the manufacturer's protocols. Alignment and variant calling were performed as previously described in29.
Allele frequency changes in patient groups
Per patient single nucleotide variants (SNV) with genotype change were identified and differences in alternative allele fre- quency (aAF) calculated between consecutive time points. aAF were clustered per patient into six clusters to give each time point equal weight regardless of the number of SNV detected. Each change in aAF was grouped according to the status (untreated,
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