PI3K p110δ uniquely promotes gain-of-function Shp2-induced GM-CSF hypersensitivity in a model of JMML
Bibliographic record
Abstract
Although hyperactivation of the Ras-Erk signaling pathway is known to underlie the pathogenesis of juvenile myelomonocytic leukemia (JMML), a fatal childhood disease, the PI3K-Akt signaling pathway is also dysregulated in this disease. Using genetic models, we demonstrate that inactivation of phosphatidylinositol-3-kinase (PI3K) catalytic subunit p110δ, but not PI3K p110α, corrects gain-of-function (GOF) Shp2-induced granulocyte macrophage-colony-stimulating factor (GM-CSF) hypersensitivity, Akt and Erk hyperactivation, and skewed hematopoietic progenitor distribution. Likewise, potent p110δ-specific inhibitors curtail the proliferation of GOF Shp2-expressing hematopoietic cells and cooperate with mitogen-activated or extracellular signal-regulated protein kinase kinase (MEK) inhibition to reduce proliferation further and maximally block Erk and Akt activation. Furthermore, the PI3K p110δ-specific inhibitor, idelalisib, also demonstrates activity against primary leukemia cells from individuals with JMML. These findings suggest that selective inhibition of the PI3K catalytic subunit p110δ could provide an innovative approach for treatment of JMML, with the potential for limiting toxicity resulting from the hematopoietic-restricted expression of p110δ.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".