Discovery of a signaling feedback circuit that defines interferon responses in myeloproliferative neoplasms
Bibliographic record
Abstract
Interferons (IFNs) are key initiators and effectors of the immune response against malignant cells and also directly inhibit tumor growth. IFNα is highly effective in the treatment of myeloproliferative neoplasms (MPNs), but the mechanisms of action are unclear and it remains unknown why some patients respond to IFNα and others do not. Here, we identify and characterize a pathway involving PKCδ-dependent phosphorylation of ULK1 on serine residues 341 and 495, required for subsequent activation of p38 MAPK. We show that this pathway is essential for IFN-suppressive effects on primary malignant erythroid precursors from MPN patients, and that increased levels of ULK1 and p38 MAPK correlate with clinical response to IFNα therapy in these patients. We also demonstrate that IFNα treatment induces cleavage/activation of the ULK1-interacting ROCK1/2 proteins in vitro and in vivo, triggering a negative feedback loop that suppresses IFN responses. Overexpression of ROCK1/2 is seen in MPN patients and their genetic or pharmacological inhibition enhances IFN-anti-neoplastic responses in malignant erythroid precursors from MPN patients. These findings suggest the clinical potential of pharmacological inhibition of ROCK1/2 in combination with IFN-therapy for the treatment of MPNs.
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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 | 0.000 |
| 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".