Success in bone marrow failure? Novel therapeutic directions based on the immune environment of myelodysplastic syndromes
Bibliographic record
Abstract
Abstract Myelodysplastic syndromes (MDS) are clonal neoplasms of aging that are associated with BM failure, related cytopenias, fatigue, susceptibility to infections, bruising, bleeding, a shortened lifespan, and a propensity for leukemic transformation. Most frail, elderly patients are not candidates for curative allogeneic BM transplantations and instead receive expectant management, supportive blood transfusions, or empirical, nontargeted therapy. It has been known for some time that MDS arises in an abnormal BM immune environment; however, connections have only recently been established with recurring MDS-associated mutations. Understanding how mutant clones alter and thrive in the immune environment of marrow failure at the expense of normal hematopoiesis opens the door to novel therapeutic strategies that are aimed at restoring immune and hematopoietic balance. Several examples are highlighted in this review. Haploinsufficiency of microRNAs 145 and 146a in MDS with chromosome 5q deletions leads to derepression of TLR4 signaling, dysplasia, and suppression of normal hematopoiesis. Moreover, mutations of TET2 or DNMT3A—regulators of cytosine methylation—are among the earliest in myeloid cancers and are even found in healthy adults with cryptic clonal hematopoiesis. In innate immune cells, TET2 and DNMT3A mutations impair the resolution of inflammation and production of type I IFNs, respectively. Finally, a common result of MDS-associated mutations is the inappropriate activation of the NLRP3 inflammasome, with resultant pyroptotic cell death, which favors mutant clone expansion. In summary, MDS-associated mutations alter the BM immune environment, which provides a milieu that is conducive to clonal expansion and leukemic progression. Restoring this balance may offer new therapeutic avenues for patients with MDS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".