Influence of Pre-Treatment Features and Therapy Choice By Physicians on Overall Survival in Older Adults with Acute Myeloid Leukemia: A Report from the Beat AML Master Trial
Bibliographic record
Abstract
Background: Acute myeloid leukemia (AML) therapy in older patients (age ≥ 60) has undergone a transformation with the introduction of multiple targeted therapies, which has allowed more patients to receive therapy than before. The most successful has been the combination of venetoclax + azacitidine (VA) which generated higher overall complete response rates in phase 2 studies than would be expected with azacitidine alone, leading to accelerated FDA approval on 21Nov2018. The VIALE-A phase 3 study confirmed the improved overall survival (OS, median 14.7 versus 9.6 months) of VA, leading to full FDA approval for marketing. This retrospective analysis provides real world data on the impact of baseline clinical and genomic markers of individuals receiving intensive chemotherapy versus VA. Methods: The precision medicine Beat AML Master Trial (NCT03013998) assigns patients to biomarker specific sub-study treatments based on targeted DNA sequencing and cytogenetics. However, a large subset of patients did not enroll on a sub-study, but were followed for off-study treatment and OS. From this subset, patients enrolled onward from 21Nov2018, when VA received accelerated approval, were examined for differences in clinical/genetic characteristics and OS in those receiving venetoclax + hypomethylating agent (V/HMA), any form of intensive chemotherapy (IC), alternative non-intensive therapy (NIT), or no therapy. The genetic mutations identified in this study are characterized by a variant allele frequency of 20%+, as defined in the trial for determining the dominant clone. The method of Kaplan-Meier was used to estimate OS, and the Cox model was fit to associate patient characteristics with OS. Results: From 21Nov2018, a total of 468 AML patients consented to the Beat AML trial and did not enroll to a sub-study. Treatment was chosen by the investigator based upon available clinical and genomic data. Of these 468 patients, 62 did not receive treatment, 2 had treatment data missing, 226 were treated with V/HMA, 112 with IC, and 66 with NIT. Demographics for all patients include median age 71 (60-90), 40% female, performance status (PS) (0-20%; 1-55%; 2-22%; 3-3%), median white blood cell (WBC) 4.1 (range 0.3-298.6), complex karyotype (CK) 20.6%, core binding factor (CBF) 7.8%, KMT2A-rearranged 3.6%, NPM1-mutated (m) 15.6%, IDH2m 14.6%, TP53m 13.2%, FLT3-ITD/TKD 12.9%, and NRASm/ PTPN11m/ KRASm/ NF1m/ CBLm 23.6%. Demographics that were significantly different (p<0.01) among the three groups (V/HMA, IC, and NIT) include age (younger in IC), PS (worse in NIT), AST/ALT (worse in NIT), CBF (more in IC), CK and TP53m (less in IC). Of the 404 patients who underwent treatment with V/HMA, IC, or NIT, 208 have died with those surviving having a median follow-up of 22.3 months. Figure 1 summarizes the OS of each treatment group. The median OS (95% CI) from time of initiating therapy is 13.6 (10.9-16.8) for V/HMA, 33.8 (20.7-not reached) for IC, and 11.6 (5.2-21.3) months for NIT. Univariable analysis for OS was significant at p<0.05 for increased age, WBC, PS, hemoglobin, CBF, CK, NPM1m, TP53m, TET2m and IC versus V/HMA. Multivariable analysis was significant at p<0.05 (hazard ratio) for increased age (1.14), PS (1.83), WBC (1.08), hemoglobin (0.91), and select genomic aberrations including CBF (0.37), NPM1m (0.35), and TP53m (2.1). Conclusions: These results from a large cohort of older AML patients treated with V/HMA, IC, or NIT show that their outcome is best defined by pre-treatment clinical features previously identified including age, performance status, WBC and hemoglobin along with limited genomic characteristics including CBF, NPM1m, and TP53m. While univariate analysis of OS favored IC over V/HMA and NIT, multivariable analysis supports that this advantage was most likely due to the favorable clinical and genomic features. The OS of V/HMA patients in this cohort is similar to that in the VIALE-A registration study of VA, providing further justification for this treatment for older AML patients deemed ineligible for intensive chemotherapy. Additionally, this data supports use of the patient cohort in this study for ongoing work in understanding and/or validating biomarkers associated with survival with V/HMA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".