RNA sequencing as an alternative tool for detecting measurable residual disease in core-binding factor acute myeloid leukemia
Bibliographic record
Abstract
Abstract DNA sequencing-based measurable residual disease (MRD) detection has shown to be clinically relevant in AML. However, the same methodology cannot be applied to fusion gene-driven subtypes of AML such as core-binding factor AML (CBF-AML). Here in this study, we evaluated the effectiveness of using DNA and RNA sequencing in MRD detection and in tracking clonal dynamics in CBF-AML. Using RNA-seq, we were able to quantify expression levels of RUNX1-RUNX1T1 and CBFB-MYH11 at diagnosis and their levels of reduction during remission (P < 6.3e−05 and P < 2.2e−13). The level of reduction of RUNX1-RUNX1T1 as measured by RNA-seq and qPCR were highly correlated (R2 = 0.74, P < 5.4e−05). A decision tree analysis, based on 3-log reduction of RUNX1-RUNX1T1 and cKIT-D816mut at diagnosis, stratified RUNX1-RUNX1T1 AML patients into three subgroups. These three subgroups had 2-year overall survival rates at 87%, 74%, and 33% (P < 0.08) and 2-year relapse incidence rates at 13%, 42%, and 67% (P < 0.05). On the other hand, although low residual allelic burden was common, it was not associated with long-term outcome, indicating that mutation clearance alone cannot be interpreted as MRD-negative. Overall, our study demonstrates that the clinical utility of RNA sequencing as a potential tool for MRD monitoring in fusion gene-driven AML such as RUNX1-RUNX1T1 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".