Differences in outcomes with intensive chemotherapy in AML patients according to mutational status – recent real-world results
Bibliographic record
Abstract
Abstract Background Most studies have found that European LeukemiaNet (ELN) Intermediate risk AML patients treated with intensive chemotherapy (IC) have an inferior prognosis compared with ELN favorable risk patients, but this has generally been based on older data. Within the ELN Adverse risk group it is unclear how outcomes compare among the different mutations. Methods We retrospectively evaluated real world outcomes of all newly diagnosed AML patients treated with IC at a single center between Jan. 2018 – Apr. 2025. All pts underwent cytogenetic analysis and molecular profiling by NGS and classified according to ELN2022. ELN favorable (fav) and intermediate (int) risk patients received induction with 7+3 (+ midostaurin for FLT3-mutated pts, and gemtuzumab ozogamicin for some favorable risk pts). ELN adverse (adv) risk pts received either 7+3, CPX-351 or FLAG-Ida at physician discretion. High dose cytarabine was used as consolidation therapy. ELN int and adv risk patients were referred for allogeneic stem cell transplant (HSCT) in CR1, while fav risk patients were only referred if high risk features were present (e.g. inadequate MRD response after 2 cycles of IC or CBF with KIT mutation). Results were correlated with ELN2022 risk group, age and de novo vs. secondary AML. Results A total of 229 patients received IC; the breakdown by ELN2022 risk groups was fav 69, int 67, adv 88 (of which 24 were TP53 mutated), and 5 unknown. The CR/CRi rates with 1-2 inductions for fav, int, adv non-TP53 mutated and TP53mut pts were 88%, 82%, 64% and 46%, respectively (p<0.001 comparing all 4, p=NS comparing fav vs. int). HSCT rates for patients achieving CR/CRi/MLFS were 17% fav, 72% int, 85% adv non-TP53 and 64% TP53mut, respectively. The estimated 5-year OS for fav, int, adv non-TP53 mutated and TP53mut was 80%, 74%, 33% and 0%, respectively (p<0.001). The 5-year RFS was 71%, 66%, 52% and 0%, respectively (p=0.003). There was no significant difference in OS or RFS between ELN fav vs. int risk patients. For patients who underwent HSCT in CR1 the 5-year OS for ELN fav, int, adv nonTP53 and TP53mut was 90%, 90%, 48% and 0%, respectively (p<0.001). Within the ELN int group with FLT3-ITD mutations the 5-year OS was 77%; for patient with NPM1 mutations the 5-year OS and RFS were 80% and 63%, respectively. Other predictors of OS on univariate analysis included age < 60 vs. 60+ (p<0.001) and secondary vs. de novo AML (p<0.001). On multivariate Cox regression analysis, ELN2022 risk group was independently predictive of OS (HR 2.184, 95% CI 1.687-2.828, p<0.001), while age group was only weakly predictive (HR 1.663, p=0.028) and secondary vs. de novo was not predictive (HR 0.638, p=0.095). Among the adv risk non-TP53 mutated group, the OS was higher in the RUNX1 mutated group, with an est. 5-year OS of 54%, compared with 16% for those with other myelodysplasia-related (MR) gene mutations (p=0.017). For those undergoing HSCT in CR1, the est. 5-year OS for RUNX1 mutated patients was 80% vs. 24% for those with other MR mutations (p=0.055). Conclusions The OS and RFS for ELN2022 int risk patients treated with IC has improved in recent years and is now approaching that of fav risk pts, likely related to the high HSCT rate achievable in int risk patients. ELN adv risk patients continue to have inferior outcomes, even with transplant; however, within this group, RUNX1 mutated patients have a better outcome with IC followed by HSCT than those with other MR gene mutations. Revised prognostic scoring systems should take into account this heterogeneity within the adv risk group, as well as more recent outcome data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".