Prognostic impact of molecular genetic and cytogenetic alterations in newly diagnosed acute myeloid leukemia treated with azacitidine and venetoclax: A real-world cohort study
Bibliographic record
Abstract
Abstract Introduction:Azacitidine and venetoclax (Aza-Ven) is the standard of care for older or unfit patients (pts) with newly diagnosed AML. However, outcomes remain heterogeneous, and the prognostic relevance of genetic abnormalities is not yet fully established in this setting. Risk stratification models have been developed for pts treated with Aza-Ven, such as the European LeukemiaNetwork (ELN) 2024 classification, but validation in independent cohorts remains limited. Methods:We retrospectively analyzed pts treated with Aza-Ven at the Princess Margaret Cancer Centre between 2017 and 2024. Clinical, cytogenetic, and molecular data were collected, andpts were risk stratified using ELN 2024 classification. Overall survival (OS)and response rates were evaluated. Composite complete remission (CRc) rate was defined as a combination of complete remission (CR) and CR with incomplete or hematological recovery (CRi/CRh).Overall response rate (ORR) included CRc, morphological leukemia-free state (MLFS) and partial remission (PR). Kaplan–Meier methodand Cox regression models were used for survival analyses. Results:A total of 132 pts treated with Aza-Ven were included. Median age was 73 years (range, 34–90); 62% were male; 67% had an ECOG of 0-1. Prior MDSor MDS/MPN overlap was noted in 26 (20%) pts and 18 (14%) had prior exposure to cytotoxic therapy.Complex karyotype (CK) was identifiedin 31 (23%) pts. The most common mutations (mut) wereASXL1 (n=34, 26%), SRSF2 (n=33, 25%), TET2 (n=28, 21%), DNMT3A (n=24, 18%), and NPM1 (n=24, 18%). Additionally, 22 (17%) pts had TP53 mut, 21 (16%) pts had FLT3-ITD mut,21 (16%) had IDH2 mut, 15 (11%) hadNRAS/KRASmut, 9 (7%) had IDH1 mut, and 7 (5%) had DDX41 mut. Eighty-one (61%) pts had ≥1 myelodysplasia-related gene mutation (MRGM) as defined by ELN. Among 132 pts, the CRc rate was 61%, includingCR rate of 38%, CRi rate of 22% and CRh rate of 2%. The ORR was 76%, with MLFS in 12%and PR in 2%. With a median follow-up of 21.4 months, the median OS was 13.2 months (95% CI, 9.9–15.8); 1- and 2-year OS rates were 56% (95% CI, 48 - 66) and 35% (95% CI, 27 – 47), respectively. Among VIALE-A–eligible pts (n=83), median OS was 14.5 months (95% CI, 10.4 – 26.3); 2-year OS was 40% (95% CI, 29 – 55). In pts with IDH1and IDH2 mut, the median OS was of 26 months (95% CI, 11 – NR) and 41 months (95% CI, 16 –NR), respectively, and the 2-year OS rate was64% (95% CI, 37 – 100) and 68% (95% CI, 48-97), respectively. (HR 0.37, p<0.01IDH1/2 mut vs others). Pts with NRAS/KRASmut had a median OS of 8.6 months (95% CI, 3.7 –NR) and 1-year OS rate of 35% (95% CI, 16 – 76)(HR 2.16, p=0.02, NRAS/KRAS mut vs others).Pts withTP53 had a median OS and 1-year OS rate of 12.6 months (95% CI, 6.5 – NA) and 55% (95% CI, 36 – 82), respectively(HR 1.61, p=0.10). Pts with FLT3-ITD mutation had a median OS of 11.1 months (95% CI, 6.0 –NA) and 2-year OS rate of 31% (95% CI, 15 – 64) (HR 1.24, p=0.52). Pts with DDX41 mutation had 1-year OS rate of 83% (95% CI, 58 – 100), with median not reached (HR 0.42, p=0.22).Pts with TET2 mutations had worse OS (HR 1.85, p=0.02). BCOR (HR 1.67, p=0.10) and CBL (HR 2.48, p=0.09) mut were marginally associated with worse OS. ASXL1, SRSF2, DNMT3A, NPM1 and RUNX1 were not associated with OS.Presence of ≥ 1 MRGM was not associated with OS (HR 1.18, p=0.50). However, pts with high mutation burden (≥4 mut)had a median OS of 8.6 months (95% CI, 7–16) versus 14 months (95% CI, 12–26) in those with low mutation burden (p=0.05). Patients with CK had a significantly worse OS with median of 9.7 months (95% CI, 8.1 – 14.4) and 1-year rate of 43% (95% CI 27-67) (HR 1.77, p=0.03). According to ELN 2024, median OS was26 months (95% CI, 12–NR), 11 months (95% CI, 8–NR), and 13 months (95% CI, 7–NR) for favorable, intermediate, and adverse groups, respectively (p=0.08). Compared to favorable group, adverse group had significantly worse OS (HR 1.93, p=0.04), with a trend for the intermediate group (HR 1.58, p=0.11). Conclusions:In this real-world cohort of AML pts treated withAza-Ven, we validate the favorable prognosis of pts with IDH1/2 and DDX41 mut, and adverse prognosis of pts with NRAS/KRAS and TP53mut. We identify TET2mut, CK and high mutation burden as potential factors associated with worse OS. Larger multicenter real-world studies are needed to refine and validate risk stratification models in ptswith AML treated withAza-Ven.
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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) | 0.000 | 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 | 0.001 |
| 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".