De Novo Acute Myeloid Leukemia in Adults
Bibliographic record
Abstract
MicroRNA (MIR) signatures are critical to pathobiology and prognosis of acute myeloid leukemia (AML). MIR223 is expressed at low levels in progenitor cells, whereas high expression is induced by granulocytic differentiation. Novel-targeted therapies through epigenetic manipulation of MIR223 regulators are being explored in AML but correlative data between established clinical prognostic markers and MIR223 expression in AML is lacking. MIR223 has inverse relationship with LMO2 protein expression and our group has recently reported a close association between LMO2 protein expression and chromosomal findings in AML patients. In this study, we examined the expression of MIR223 in a large cohort of AML patients and correlated it with LMO2 protein expression, cytogenetic data, degree of differentiation [French-American and British (FAB)/World Health Organization classifications], and overall survival. MIR223 expression was upregulated in only a subset of patients (37%). Suppression of MIR223 was more frequent among patients with aneuploid karyotype compared with diploid karyotype (P=0.005). In AML, not otherwise specified category, AML with maturation (FAB-M2) showed higher levels of MIR223 when compared with either AML without maturation (FAB M0/M1) (P=0.001); AML with monoblastic differentiation (FAB M4/M5) (P=0.004) or AML with myelodysplasia-related changes (P=0.011). Among cytogenetic risk groups, suppression of MIR223 was universal (>95%) in high-risk group when compared with intermediate-risk group (P=0.004). No correlation between MIR223 and LMO2 protein expression was identified. In conclusion, we have shown that suppression of MIR223 expression, as compared with controls, is associated with lack of differentiation and adverse cytogenetic profile, but unrelated with LMO2 protein expression or overall survival.
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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) | 0.004 | 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".