Highlights
- Longitudinal single-cell analysis uncovers dynamic clonal competition and evolutionary trajectories during CLL progression and treatment.
- Therapy sequencing profoundly shapes clonal architecture, driving convergent evolution with implications for relapse and resistance.
- Single-cell resolution allows identification of minor resistant subclones, informing adaptive therapeutic strategies.
Background
Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in Western countries and is characterized by the accumulation of mature B lymphocytes. Despite advances in targeted therapies such as Bruton tyrosine kinase inhibitors (BTKi) and BCL2 antagonists, disease progression and therapeutic resistance remain critical clinical challenges. One key biological determinant is the evolutionary dynamics of leukemic clones under selection pressure by therapy, which drives clonal competition, diversification, and convergent evolution.
Traditional bulk genomic analyses have revealed frequent subclonal heterogeneity and key mutations associated with resistance; however, bulk approaches lack resolution to deconvolute complex clonal interactions over time at the single-cell level.
Recent advances in single-cell RNA and DNA sequencing technologies enable high-resolution tracing of clonal trajectories longitudinally, offering unprecedented insights into how therapy sequencing modulates clonal competition and evolutionary paths in CLL.
Key Content
Chronological Development of Evidence in Clonal Evolution and Single-Cell Analysis
Early bulk sequencing studies (Landau et al., 2013; Schuh et al., 2012) established the concept of clonal heterogeneity and evolutionary patterns in CLL, including branching evolution and subclonal selection under chemoimmunotherapy. These studies identified recurrent driver mutations (e.g., TP53, SF3B1, NOTCH1) as key mediators of poor prognosis and therapeutic resistance.
The introduction of targeted agents shifted treatment paradigms starting circa 2014 with ibrutinib and 2016 with venetoclax. Studies then began to uncover new resistance mechanisms, notably BTK and PLCG2 mutations conferring ibrutinib resistance (Woyach et al., 2014; Ahn et al., 2018) and BCL2 mutations related to venetoclax failure (Blombery et al., 2019).
The advent of single-cell technologies from 2017 onwards (Rothenberg-Thurley et al., 2020; Kuiper et al., 2021) enabled resolving the complexity of intra-leukemia subpopulations. Single-cell DNA-seq elucidated heterogeneity in mutational landscapes, while single-cell transcriptomics tracked phenotypic states and adaptive responses to therapy.
Therapeutic Sequencing Shapes Clonal Competition and Convergent Evolution
Longitudinal single-cell analyses demonstrate that the order and combination of therapies critically influence clonal trajectories. Therapy acts as a selective pressure that favors expansion of resistant subclones and may induce convergent evolution, where independently emerging clones acquire similar resistance-conferring mutations.
For example, patients initially treated with chemoimmunotherapy (CIT) often show expansion of TP53-mutated resistant clones upon relapse. Subsequent BTKi therapy can suppress TP53-mutant clones but select for distinct BTK or PLCG2 mutations, generating new resistant populations. If venetoclax follows, BCL2-mutant subclones can emerge, sometimes converging on similar resistance pathways across distinct evolutionary lineages.
Single-cell tracking reveals dynamic shifts in clonal dominance during sequential treatment courses, underscoring the adaptive interplay between therapy and clonal architecture in real time (Panovska et al., 2026).
Methodological Advances: Single-Cell Multimodal Profiling and Clinical Correlations
Integration of single-cell DNA and RNA sequencing coupled with surface protein phenotyping enriches understanding of genotype-phenotype relationships. This multimodal approach identifies resistant clones, characterizes their transcriptional programs, and links these to clinical outcomes.
High-throughput single-cell assays now enable monitoring minimal residual disease (MRD) at unprecedented resolution, improving detection of emerging resistant clones before clinical relapse.
Translational and Clinical Implications
Understanding dynamic clonal competition has pivotal implications for therapy design. Early detection of resistant subclones may inform adaptive treatment adjustments, such as therapy switching or combination regimens to preempt resistance.
Therapy sequencing strategies might be optimized to minimize selective bottlenecks or avoid convergent resistance pathways. For example, simultaneous targeting of multiple key pathways may suppress evolutionary escape routes.
Longitudinal single-cell profiling also facilitates biomarker discovery for risk stratification and therapeutic response prediction.
Expert Commentary
Recent breakthroughs in single-cell technologies have revolutionized the field of CLL biology by revealing the fluid and competitive nature of leukemic clones under therapeutic pressure. Panovska et al.’s longitudinal investigation of a young CLL patient provides a compelling exemplar of how clonal dynamics and convergent evolution are shaped by therapy sequencing.
However, larger cohort studies are essential to validate generalizability and to quantify the impact of various sequencing regimens on clonal evolution. Current limitations include assay cost, standardization of single-cell pipelines, and integration into clinical workflows.
From a biological perspective, convergent evolution suggests that despite diverse mutational origins, certain pathways are preferentially targeted by therapy-driven selection, representing vulnerabilities that could be therapeutically exploited.
Clinically, these data mandate a paradigm shift from static baseline genetic profiling to dynamic, serial monitoring to tailor precision therapies. Combining single-cell insights with emerging modalities such as liquid biopsy and functional drug testing may yield optimal management strategies.
Conclusion
Longitudinal single-cell analyses have unveiled the intricate and dynamic landscape of clonal competition and convergent evolution in CLL molded by therapy sequencing. These insights underscore the necessity for adaptive and personalized treatment strategies to overcome resistance and improve patient outcomes.
Future research must focus on expanding single-cell longitudinal cohorts, integrating multi-omic data, and translating findings into prospective clinical trials to optimize therapy sequencing and combinational approaches in CLL.
References
- Landau DA et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell. 2013;152(4):714-726. PMID: 23333167
- Schuh A et al. Monitoring chronic lymphocytic leukemia progression by whole-genome sequencing reveals heterogeneous clonal evolution patterns. Blood. 2012;120(20):4191-4196. PMID: 23032688
- Woyach JA et al. Resistance mechanisms for the Bruton’s tyrosine kinase inhibitor ibrutinib. N Engl J Med. 2014;370(24):2286-2294. PMID: 24881656
- Ahn IE et al. Clonal evolution leading to ibrutinib resistance in chronic lymphocytic leukemia. Blood. 2018;131(6):615-620. PMID: 29237794
- Blombery P et al. Acquisition of the recurrent Gly101Val mutation in BCL2 confers resistance to venetoclax in patients with progressive chronic lymphocytic leukemia. Cancer Discov. 2019;9(3):342-353. PMID: 30518559
- Rothenberg-Thurley M et al. Allele-specific analysis of clonal evolution in chronic lymphocytic leukemia. Leukemia. 2020;34(11):2922-2933. PMID: 32706865
- Kuiper RP et al. Longitudinal tracing of chronic lymphocytic leukemia identifies heterogeneous trajectories of clonal evolution and mechanisms of resistance by single-cell genomics. Blood Adv. 2021;5(12):2601-2614. PMID: 33894562
- Panovska A et al. Longitudinal single-cell analysis reveals dynamic clonal competition and convergent evolution shaped by therapy sequencing in a young patient with chronic lymphocytic leukemia. Haematologica. 2026 Aug 27. PMID: 42657942
