High expression of HMGA2 independently predicts poor clinical outcomes in acute myeloid leukemia
Bibliographic record
Abstract
In acute myeloid leukemia (AML), risk stratification based on cytogenetics and mutation profiling is essential but remains insufficient to select the optimal therapy. Accurate biomarkers are needed to improve prognostic assessment. We analyzed RNA sequencing and survival data of 430 AML patients and identified HMGA2 as a novel prognostic marker. We validated a quantitative PCR test to study the association of HMGA2 expression with clinical outcomes in 358 AML samples. In this training cohort, HMGA2 was highly expressed in 22.3% of AML, mostly in patients with intermediate or adverse cytogenetics. High expression levels of HMGA2 (H + ) were associated with a lower frequency of complete remission (58.8% vs 83.4%, P < 0.001), worse 3-year overall survival (OS, 13.2% vs 43.5%, P < 0.001) and relapse-free survival (RFS, 10.8% vs 44.2%, P < 0.001). A positive HMGA2 test also identified a subgroup of patients unresponsive to standard treatments. Multivariable analyses showed that H + was independently associated with significantly worse OS and RFS, including in the intermediate cytogenetic risk category. These associations were confirmed in a validation cohort of 260 patient samples from the UK NCRI AML17 trial. The HMGA2 test could be implemented in clinical trials developing novel therapeutic strategies for high-risk AML.
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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 | 0.002 |
| 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".