RSK1 is an exploitable dependency in myeloproliferative neoplasms and secondary acute myeloid leukemia
Bibliographic record
Abstract
Myeloid malignancies are heterogenous disorders characterized by distinct molecular drivers but share convergence of oncogenic signaling pathways and propagation by ripe pro-inflammatory niches. Here, we establish a comprehensive transcriptional atlas across the spectrum of myeloproliferative neoplasms (MPN) and secondary acute myeloid leukemia (sAML) through RNA-sequencing of 158 primary samples encompassing CD34+ hematopoietic stem/progenitor cells and CD14+ monocytes. Supported by mass cytometry (CyTOF) profiling, we reveal aberrant networks of PI3K/AKT/mTOR signalling and NFκB-mediated hyper-inflammation. Combining ATAC-Seq, CUT&Tag, RNA-seq, and CyTOF, we demonstrate that targeting of ribosomal protein S6 kinase A1 (RSK1) suppresses NFκB activation and diminishes pro-inflammatory mediators including tumor necrosis factor (TNF) associated with MPN disease severity and transformation. We further evaluate a therapeutic approach utilizing a first-in-class RSK inhibitor, PMD-026, currently in Phase 2 development for breast cancer, for use in myeloid malignancies. Treatment with PMD-026 suppressed disease burden across seven syngeneic and patient-derived xenograft leukemia mouse models spanning the spectrum of driver and disease-modifying mutations. These findings uncover a therapeutic avenue for a conserved dependency across MPN and sAML. Secondary acute myeloid leukemias (sAMLs) evolving from myeloproliferative neoplasms (MPNs) associate with poor prognosis. Here authors identify RSK1 as a vulnerability for MPN and sAML and show the efficacy of a first-in-class RSK inhibitor, PMD-026, against these types of myeloid malignancies.
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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) | 0.002 | 0.001 |
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".