Targeting LMO2-induced autocrine FLT3 signaling to overcome chemoresistance in early T-cell precursor acute lymphoblastic leukemia
Bibliographic record
Abstract
Early T-cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL) is an immature subtype of T-cell acute lymphoblastic leukemia (T-ALL) commonly show deregulation of the LMO2-LYL1 stem cell transcription factors, activating mutations of cytokine receptor signaling, and poor early response to intensive chemotherapy. Previously, studies of the Lmo2 transgenic mouse model of ETP-ALL identified a population of stem-like T-cell progenitors with long-term self-renewal capacity and intrinsic chemotherapy resistance linked to cellular quiescence. Here, analyses of Lmo2 transgenic mice, patient-derived xenografts, and single-cell RNA-sequencing data from primary ETP-ALL identified a rare subpopulation of leukemic stem cells expressing high levels of the cytokine receptor FLT3. Despite a highly proliferative state, these FLT3-overexpressing cells had long-term self-renewal capacity and almost complete resistance to chemotherapy. Chromatin immunoprecipitation and assay for transposase-accessible chromatin sequencing demonstrated FLT3 and its ligand may be direct targets of the LMO2 stem-cell complex. Media conditioned by Lmo2 transgenic thymocytes revealed an autocrine FLT3-dependent signaling loop that could be targeted by the FLT3 inhibitor gilteritinib. Consequently, gilteritinib impaired in vivo growth of ETP-ALL and improved the sensitivity to chemotherapy. Furthermore, gilteritinib enhanced response to the BCL2 inhibitor venetoclax, which may enable "chemo-free" treatment of ETP-ALL. Together, these data provide a cellular and molecular explanation for enhanced cytokine signaling in LMO2-driven ETP-ALL beyond activating mutations and a rationale for clinical trials of FLT3 inhibitors in ETP-ALL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".