<i>In Vivo</i> Screening Unveils Pervasive RNA-Binding Protein Dependencies in Leukemic Stem Cells and Identifies ELAVL1 as a Therapeutic Target
Why this work is in the frame
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Bibliographic record
Abstract
Acute myeloid leukemia (AML) is fueled by leukemic stem cells (LSC) whose determinants are challenging to discern from hematopoietic stem cells (HSC) or uncover by approaches focused on general cell properties. We have identified a set of RNA-binding proteins (RBP) selectively enriched in human AML LSCs. Using an in vivo two-step CRISPR-Cas9 screen to assay stem cell functionality, we found 32 RBPs essential for LSCs in MLL-AF9;NrasG12D AML. Loss-of-function approaches targeting key hit RBP ELAVL1 compromised LSC-driven in vivo leukemic reconstitution, and selectively depleted primitive malignant versus healthy cells. Integrative multiomics revealed differentiation, splicing, and mitochondrial metabolism as key features defining the leukemic ELAVL1-mRNA interactome with mitochondrial import protein, TOMM34, being a direct ELAVL1-stabilized target whose repression impairs AML propagation. Altogether, using a stem cell-adapted in vivo CRISPR screen, this work demonstrates pervasive reliance on RBPs as regulators of LSCs and highlights their potential as therapeutic targets in AML. SIGNIFICANCE: LSC-targeted therapies remain a significant unmet need in AML. We developed a stem-cell-adapted in vivo CRISPR screen to identify key LSC drivers. We uncover widespread RNA-binding protein dependencies in LSCs, including ELAVL1, which we identify as a novel therapeutic vulnerability through its regulation of mitochondrial metabolism. This article is highlighted in the In This Issue feature, p. 171.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it