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Deininger, M.

Publications and source records attributed to Deininger, M..

3 recordsLinked to original sources

Single-cell RNA editing defines clinically relevant cellular states in chronic myelomonocytic leukemia

BackgroundChronic myelomonocytic leukemia (CMML) is a clinically heterogeneous myeloid malignancy with limited therapeutic options and suboptimal risk stratification. Although single-cell RNA sequencing has refined disease classification through gene expression profiling, post-transcriptional mechanisms--particularly adenosine-to-inosine (A-to-I) RNA editing--remain unexplored at single-cell resolution. We hypothesized that cell-specific RNA editing programs contribute to CMML heterogeneity and define distinct, clinically actionable cellular states in CMML. MethodsWe developed a single-cell-aware computational framework for high-confidence identification and quantification of RNA editing events. Candidate sites were detected at pseudo-bulk depth using stringent filters and subsequently quantified at single-cell resolution. The pipeline incorporated dual alignment, barcode correction, artifact removal, and exclusion of genomic variants to ensure specificity. We applied this framework to discovery and independent validation CMML cohorts. Editing-defined cellular states were identified by unsupervised clustering of single-cell editing profiles and evaluated for associations with clinical stage, TET2 status, survival, and response to hypomethylating agent (HMA) therapy. Regulatory mechanisms were assessed by analyzing ADAR1/ADAR2 expression and relationships between editing levels and target gene expression. ResultsWe identified 3,326 high-confidence A-to-I RNA editing sites and delineated reproducible editing-defined cellular states. A granulocyte-monocyte progenitor-like editing state (edClu1_sub0) aligned with an inflammatory, monocytic-biased transcriptional program and was significantly associated with adverse survival, advanced-stage disease and TET2-mutant CMML, supporting it as a high-risk biomarker-defined subpopulation. In contrast, states such as edClu3 and edClu6 were enriched in earlier-stage, TET2-wild-type CMML and correlated with improved outcomes. Editing-defined states demonstrated systematic remodeling following HMA therapy, indicating treatment-responsive post-transcriptional programs. The high-risk state exhibited elevated ADAR1 and reduced ADAR2 expression, suggesting enzyme-specific regulatory imbalance as a potential therapeutic vulnerability. Integrative analyses further nominated immune-related genes--including LAPTM5, CTSS, and CD83--as CMML-specific oncogenic RNA editing targets, with coordinated increases in editing and expression within the aggressive state. ConclusionsRNA editing represents a clinically informative and mechanistically relevant layer that refines CMML stratification at single-cell resolution, independent of gene expression. These findings provide a framework for integrating post-transcriptional regulation into precision oncology and highlight RNA editing signatures as biomarkers for risk assessment, treatment monitoring, and therapeutic targeting in hematologic malignancies.

cancer biology↗

Cross-Species Morphology Learning Enables Nucleic Acid-Independent Detection of Live Mutant Blood Cells

In both neonates and adults, the presence of malignancy-associated mutations in peripheral blood (PB) correlates with an elevated risk of future neoplastic transformation, with certain mutations, such as KMT2A rearrangements, exhibiting near-complete penetrance. If feasible, pre-malignant screening could enable early intervention and even disease prevention. However, nucleic acid sequencing- and hybridization-based mutation detection have limited cost-efficiency, constraining their use in screening. Here, we introduce a computer vision platform that can identify mutant cells in fresh PB samples that carry KMT2A-MLLT3 (a frequent mutation in pediatric and adult leukemias and detectable in newborn blood samples) or JAK2V617F (a frequent mutation in myeloproliferative neoplasms and clonal hematopoiesis). This is achieved by high-throughput single-cell imaging and mutation detection by machine learning (ML)-powered morphology recognition. The ML models were developed by cross-species learning of conserved features between mutant cells from mouse genetic models and from human samples, enabling a cost-effective approach for detecting mutations in live blood cells. This platform holds promise for pre-malignant screening in asymptomatic neonates and adults with KMT2A-MLLT3 or JAK2V617Fmutation and is potentially generalizable to the detection other malignancy-associate mutations. Our platform provides a novel single-cell morphological data modality that complements existing single-cell genomics.

pathology↗

The 14q32.31 DLK1-DIO3 MIR300 tumor suppressor promotes leukemogenesis by inducing cancer stem cell quiescence and inhibiting NK cell anti-cancer immunity.

Drug-resistance of tumor-initiating cells, impaired NK cell immune-response, PP2A loss-of-function and aberrant miRNA expression are cancer features resulting from microenvironmental- and tumor-specific signals. Here we report that genomic-imprinted MIR300 is a cell context-independent dual function tumor suppressor which is upregulated in quiescent leukemic stem (LSC) and NK cells by microenvironmental signals to induce quiescence and impair immune-response, respectively, but inhibited in CML and AML proliferating blasts to prevent PP2A-induced apoptosis. MIR300 anti-proliferative and PP2A-activating functions are differentially activated through dose-dependent CCND2/CDK6 and SET inhibition, respectively. LSCs escape PP2A-mediated apoptosis through TUG1 lncRNA that uncouples and limits MIR300 functions to cytostasis by regulating unbound-MIR300 levels. Halting MIR300 homeostasis restores NK cell activity and suppresses leukemic but not normal hematopoiesis by eradicating nearly all LSCs. Thus, MIR300 tumor suppressor activity is essential and therapeutically important for LSC-driven leukemias.

cancer biology↗