Decoding Clonal Evolution and Immunophenotype in NPM1-Mutated AML through Single-Cell Multiomics

Highlight

• Single-cell multiomic profiling elucidates clonal architecture in NPM1-mutated AML.
• Distinct genotype-immunophenotype relationships persist across diagnosis and relapse, suggesting mutation-driven lineage hardwiring.
• Signaling mutations increase clonal complexity at relapse, correlating with overall survival outcomes.
• Longitudinal sampling reveals dynamic clonal and immunophenotypic changes during frontline therapy.

Study Background

Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy characterized by clonal proliferation of myeloid precursor cells. The subtype with NPM1 (nucleophosmin 1) mutations represents a substantial subset of AML cases, often demonstrating unique molecular and clinical features. Despite advances in therapy, relapse remains a major challenge with complex clonal architectures driving disease evolution. Conventional bulk sequencing methods provide aggregate mutational profiles but lack resolution to dissect intra-patient clonal heterogeneity and the genotype-immunophenotype interplay underpinning leukemic progression.

The ability to correlate specific genetic mutations to immunophenotypic features at single-cell resolution can shed light on the leukemia cell lineage trajectories and resistance mechanisms. Understanding how clonal complexity evolves between diagnosis and relapse and during treatment is essential for identifying prognostically relevant clones and potential therapeutic vulnerabilities.

Study Design

This study employed simultaneous single-cell molecular profiling and immunophenotyping on 43 bone marrow or blood samples derived from 32 patients with NPM1-mutated AML. Samples were collected at various disease stages, including diagnosis and relapse, to capture the clonal dynamics throughout disease progression and therapy response. The approach integrated targeted mutational analysis with cell surface marker profiling to define genotype-immunophenotype relationships within individual leukemic clones.

The longitudinal nature of sampling from patients undergoing frontline AML therapy allowed for investigation of temporal changes in clonal composition and phenotypic states. Correlations between signaling pathway mutations and clonal complexity were analyzed vis-à-vis clinical outcomes, particularly overall survival.

Key Findings

Clonal Architecture at Diagnosis and Relapse: The study revealed that AML samples at diagnosis and relapse largely shared overall clonal architecture patterns but with notable evolution. In particular, signaling mutations (such as FLT3 or RAS pathway alterations) emerged as key drivers of increasingly complex clonal structures at relapse. These mutations contributed to a diversification of clones that was significantly associated with inferior overall survival.

Genotype-Immunophenotype Relationships: Each clone’s mutational profile was linked with distinct immunophenotypic features, including differential expression of surface markers defining leukemic lineage states. Fascinatingly, these genotype-immunophenotype relationships were maintained regardless of disease state (diagnosis or relapse), affirming the hypothesis that leukemic lineage trajectories are shaped – or “hard-wired” – by the mutations acquired during clonal evolution.

Longitudinal Dynamics During Therapy: Serial sampling uncovered dynamic shifts in both the genetic makeup and immunophenotypic landscape of leukemic cells as patients underwent frontline AML treatment. The data demonstrated that clonal expansions, contractions, and phenotypic shifts aligned with the previously defined genotype-immunophenotype signatures, highlighting plasticity and selection pressures imposed by therapy.

These insights were supported by integration of multiomic single-cell data, providing a granular map that pinpoints how mutant clones evolve and adapt over the clinical course.

Expert Commentary

This study exemplifies the power of single-cell multiomics in revealing the complex interplay between genotype and phenotype in AML clonal progression. The persistence of distinct genotype-immunophenotype relationships suggests that mutation-driven lineage commitment may constrain the evolutionary pathways of leukemic clones, potentially influencing treatment resistance and relapse patterns.

Significantly, the correlation between heightened clonal complexity at relapse and poorer survival underscores the clinical relevance of monitoring clonal evolution at high resolution. While conventional bulk sequencing remains valuable, single-cell approaches offer an indispensable tool for precise risk stratification and tailored therapy design.

Limitations include the relatively modest sample size and focus on NPM1-mutated AML, which may limit generalizability to other AML subtypes. However, the methodology sets a precedent for future investigations incorporating broader patient cohorts and integration with functional assays to elucidate underlying mechanisms of clonal fitness and drug resistance.

Conclusion

The multiomic single-cell analysis of NPM1-mutated AML provides critical insights into how mutations shape immunophenotypic lineage trajectories and drive clonal evolution during disease progression and treatment. These findings highlight the potential of linking genotype to phenotype for prognostic assessment and therapeutic targeting in AML.

Future research focusing on integrating these molecular insights with clinical trials could optimize patient-specific strategies aimed at eradicating resistant clones and improving long-term outcomes in AML.

Funding and ClinicalTrials.gov

The study was supported by grants from various leukemia research foundations and governmental scientific bodies (specific funding sources cited within the original publication). No clinical trial registration was explicitly reported in the abstract.

References

1. Drucker M, Lee D, Bowman M, et al. Genotype-immunophenotype relationships in NPM1-mutated AML clonal evolution uncovered by single-cell multiomic analysis. Blood. 2026;147(26):3209-3216. PMID: 41950000.

2. Papaemmanuil E, et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med. 2016;374(23):2209-2221.

3. Miles LA, Bowman RL, Merlinsky TR, et al. Single-cell mutation analysis of clonal evolution in myeloid malignancies. Nat Commun. 2020;11(1):1928.