Grove, C. | bioRxiv Science

bioRxiv2026

Dnmt3a-mutant Leukemia Stem Cells evade chemotherapy through enforced quiescence in Npm1c-Flt3ITD Acute Myeloid Leukemia

Concurrent mutations in DNMT3A, NPM1, and FLT3 define a high-risk subtype of acute myeloid leukemia (AML) associated with increased relapse risk and inferior survival following standard chemotherapy. However, the mechanisms by which DNMT3A mutations promote treatment resistance in NPM1c-FLT3ITD AML remain unclear. Using genetically engineered murine models of Npm1c-Flt3ITD AML with or without Dnmt3aR878H (homologous to human DNMT3AR882H), we demonstrate that Dnmt3aR878H promotes chemotherapy resistance through epigenetic regulation of leukemia stem cell (LSC) quiescence. Integrated transcriptomic and epigenetic profiling revealed coordinated remodeling of DNA methylation and chromatin accessibility in LSC-enriched populations, characterized by preferential hypomethylation and increased accessibility at loci associated with stemness and quiescence programs. These data were confirmed in human DNMT3AR882H-NPM1c-FLT3ITD AML datasets with enrichment of quiescence-associated and stem cell enriched transcriptional programs. Conversely, Dnmt3a-mutant LSCs retained sensitivity to the cell-cycle independent regimen venetoclax plus azacitidine, but residual LSCs exhibited transcriptional plasticity and reversion to a de-differentiated state. We have identified LSC heterogeneity spanning primitive hematopoietic stem cell (HSC)-and progenitor-like states and our data demonstrate preferential maintenance of a quiescent HSC-like LSC subpopulation in Dnmt3aR878H-mutant AML following chemotherapy treatment. Pharmacologic induction of cell-cycle entry using pegylated interferon (pegIFN) disrupted the quiescent LSC state and restored chemotherapy sensitivity, identifying quiescence as a reversible and therapeutically actionable mechanism of resistance. These findings identify DNMT3A-mediated epigenetic regulation of LSC quiescence as a conserved mechanism of standard chemotherapy resistance and position therapeutic reactivation of quiescent LSCs as a promising strategy to overcome chemotherapy resistance and improve outcomes in high-risk DNMT3A-mutant AML.

cancer biology↗

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗