Gohil, Satyen

Abstract BACKGROUND Whilst the broad clonal architecture of naturally progressing chronic lymphocytic leukemia (CLL) has been described, a comprehensive picture of how chemotherapy and targeted agents reshape that landscape is lacking. Here we integrate clone-specific growth kinetics with mutational profiles, transcriptomic subtypes, and CpG-methylation-based epigenetic classes to capture the multidimensional evolutionary responses of CLL under native conditions and during treatment. METHODS We previously reported a systematic whole-exome sequencing (WES) analysis of 417 leukemia–germline pairs from 169 treatment-naïve patients ≥ 65 y (Karisani et al., Blood 2024). The treatment arm comprised 83 patients who received ibrutinib and 39 who received chlorambucil in the RESONATE-2 trial (NCT01722487). Forty-seven age-matched “watch-and-wait” patients from the CLL Research Consortium served as untreated controls. Peripheral-blood samples were collected at baseline and again ~300 d and ~600 d after therapy initiation (or diagnosis in the watch-and-wait cohort), enabling longitudinal clonal tracking. WES data were processed with PhylogicNDT to reconstruct clonal architectures, define phylogenies, and identify significant clonal shifts. MutSig tools identified mutations enriched in expanding or contracting clones. Pretreatment RNA-sequencing profiles were grouped into transcriptional subtypes by consensus clustering based on the Louvain algorithm. Differentially methylated regions plus epitypes were called from reduced representation bisulfite sequencing data. RESULTS Across the 169 patients with WES, we resolved 579 subclonal clusters. Significant clonal shifts (defined as a distribution shift of cancer cell fraction in a subclone of >95% between two timepoints) were uncommon during watch-and-wait (36%) but markedly higher after therapy — chlorambucil 67% (q = 0.01), and ibrutinib 77% (q < 10-4). In ibrutinib-treated CLL, regressing subclones were enriched for KRAS (q<10-3) and SF3B1 mutations (q<10-3), whereas expanding subclones preferentially harboured mutations in BIRC3 (q<10-4), NOTCH1 (q <10-3), TP53 (q=0.02), and POT1 (q=0.098). In chlorambucil-treated patients, no specific mutations were found to be enriched in expanding or regressing subclones. Using the recently defined CLL expression clusters (ECs) in patients treated with chemoimmunotherapy as defined by Knisbacher et al. (Nat Genet 2022), we were unable to identify any relationship between ECs and outcomes. We therefore sought to identify de novo ECs with prognostic relevance in the context of BTK-inhibition (BTKi-ECs). We identified a transcriptionally defined patient subgroup (C4, n=20) associated with inferior progression-free survival (median PFS = 63 months, log-rank q=0.05), independent of known prognostic clinical and molecular features, including epitypes, in multivariate analysis (Cox HR=3.7, 95% CI: 1.6-8.6, q=0.02). Gene-set enrichment in this cluster highlighted TNF-ɑ signalling via NF-κB (REL, TNFAIP3, NFKB2) and G2M checkpoint pathways (SMC4, EZH2). We also identified a distinct transcriptionally defined patient subgroup (C3, n=18) that was enriched for patients harboring at least one contracting subclone and no expanding subclones (q=0.005). This cluster showed a relative depletion of mutations within the chromatin modification pathway (6% vs 50%, q<0.01). Gene-set enrichment in this cluster highlighted a downregulation of genes in the TNF-ɑ signalling via NF-κB pathway (SOCS3, ICOSLG, NFKB2). CONCLUSION BTK inhibition and chemotherapy drive distinct evolutionary trajectories in CLL. Multi-omics profiling reveals mutation-defined subclones with divergent sensitivity to targeted therapy and delineates two informative expression states: a high-risk NF-κB/inflammatory cluster (C4; poor PFS) and a cluster enriched with contracting subclones with depleted TNF-ɑ signalling via NF-κB (C3). Integrating clonal growth kinetics with genomic, transcriptomic, and methylation data can potentially identify patients likely to progress on BTK inhibitors and identify actionable pathways for adaptive or combination therapy.

BACKGROUND Our knowledge of how the genomic landscape of chronic lymphocytic leukemia (CLL) is impacted by the selective pressure imposed by chemotherapy and targeted agents remains incomplete. We hypothesize that the extent of clonal evolution, baseline transcriptional state, and the overall and clone-specific growth kinetics of CLL will vary under native conditions or following exposure to chemotherapy and targeted therapy. METHODS We report the systematic genomic analysis of leukemia-normal whole-exome sequencing (WES) of 420 samples collected from 170 patients who were >65 years of age with treatment-naïve CLL. The treatment-cohort consisted of 83 patients who received ibrutinib (420mg daily) and 40 patients who received chlorambucil (0.5 mg/kg up to maximum 0.8 mg/kg, on days 1 and 15 of a 28-day cycle, up to 12 cycles) as part of the RESONATE-2 trial (NCT01722487). The ‘watch and wait’ (WW) cohort comprised 47 age-matched patients whose samples originated from the CLL Research Consortium and were untreated for their CLL during sampling. Sampling occurred at baseline and at day 300, and/or day 600, following initiation of therapy or following a diagnosis of CLL for those in the WW cohort. The WW cohort had more cases with a mutated IGHV (72% vs 40%), del17p (14% vs 0%), and del13q (72% vs 45%) when compared to the treatment cohort. Baseline CLL RNA-seq data were available for 150 patients. We used PhylogicNDT to reconstruct the clonal architecture from WES. By determining the fraction of cancer cells with each mutation (cancer cell fraction [CCF]) and their multiplicities (number of DNA molecules carrying a mutation per cancer cell), subclones were identified through clustering of CCFs of mutational events across samples from each patient. This allowed us to estimate the phylogenetic relationships and significant clonal shifts (defined as a distribution shift of CCF in a subclone of >95% between two timepoints, corresponding to a median ΔCCF of 18%) for each patient's CLL and to relate these shifts to somatic and clinical features. RESULTS In total, we identified 791 subclonal clusters across the three patient cohorts (ibrutinib = 406, chlorambucil = 189, WW = 196). We observed higher rates of significant clonal shifts between any two time points in patients exposed to chlorambucil (68%, q=0.008) and ibrutinib (77%, q<0.0001) when compared to patients on WW (36%). There was no significant difference in the number of patients exhibiting a clonal shift between those receiving ibrutinib and chlorambucil (q=0.3). More patients treated with ibrutinib (54%, q=0.0002) and chlorambucil (50%, q=0.004) harbored a clone that reduced in size than those on WW (17%). More patients treated with ibrutinib (71%, q<0.0002) and chlorambucil (55%, q=0.12) possessed at least one clone that increased in size during therapy than patients receiving WW (34%). In ibrutinib-treated patients, SF3B1 mutations (q=0.13, 14% vs 5%) and XPO1 mutations (q=0.13, 5% vs 0.3%) were enriched in subclones that significantly reduced over the complete time period. We also observed more BIRC3 mutations in subclones that had a significant shift in CCF when compared to subclones that remained stable over time (q=0.2, 5% vs 2%). Consistent with the overall study, with 8 years of follow-up in this patient subset, long-term progression-free survival (PFS) was significantly higher in the patients treated with ibrutinib when compared to chlorambucil (P<0.0001). This PFS benefit was also seen regardless of subclonal growth patterns. Using baseline RNA-seq data, we identified 7 of the 8 recently defined CLL expression clusters (EC), that were identified in the context of chemoimmunotherapy for CLL (Knisbacher et al. Nat Gen 2022), and could recapitulate the relationship to outcomes in individuals treated with chlorambucil. However, despite the larger cohort, no significant relationship between EC clusters and PFS were observed in ibrutinib-treated patients, suggesting that a distinct predictive baseline transcriptional signature may be present in ibrutinib-treated patients. CONCLUSION Our analyses highlight the need to interrogate the molecular features of CLL in relation to the impact of therapeutic selective pressure imposed by BTK inhibition, which appear distinct from exposure to chemotherapy. Ongoing analyses aim to identify relationships between patterns of growth and subclonal growth rates molecular and clinical features.