Ratio of stemness to interferon signalling as a biomarker and therapeutic target of myeloproliferative neoplasm progression to acute myeloid leukaemia
Bibliographic record
Abstract
Progression to aggressive secondary acute myeloid leukaemia (sAML) poses a significant challenge in the management of myeloproliferative neoplasms (MPNs). Since the physiopathology of MPN is closely linked to the activation of interferon (IFN) signalling and that AML initiation and aggressiveness is driven by leukaemia stem cells (LSCs), we investigated these pathways in MPN to sAML progression. We found that high IFN signalling correlated with low LSC signalling in MPN and AML samples, while MPN progression and AML transformation were characterized by decreased IFN signalling and increased LSC signature. A high LSC to IFN expression ratio in MPN patients was associated with adverse clinical prognosis and higher colony forming potential. Moreover, treatment with hypomethylating agents (HMAs) activates the IFN signalling pathway in MPN cells by inducing a viral mimicry response. This response is characterized by double-stranded RNA (dsRNA) formation and MDA5/RIG-I activation. The HMA-induced IFN response leads to a reduction in LSC signature, resulting in decreased stemness. These findings reveal the frequent evasion of viral mimicry during MPN-to-sAML progression, establish the LSC-to-IFN expression ratio as a progression biomarker, and suggests that HMAs treatment can lead to haematological response in murine models by re-activating dsRNA-associated IFN signalling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".