PS1027 ASSESSMENT OF MOLECULAR MRD KINETICS BY ERROR‐CORRECTED NEXT‐GENERATION SEQUENCING PROVIDES INDEPENDENT PROGNOSTIC INFORMATION IN ADULT AML PATIENTS
Bibliographic record
Abstract
Background: Identification of AML patients at high risk of relapse after achieving a complete remission (CR) with induction chemotherapy enables the use of personalized post‐remission treatment strategies to prevent relapse. Detection of molecular measurable residual disease (mMRD) by conventional next‐generation sequencing (NGS) at a single time point post‐induction has previously been associated with a higher incidence of relapse and inferior overall survival (OS). Aims: In this study, we used error‐corrected NGS (EC‐NGS) to evaluate whether changes in mMRD levels between two time points during remission provide additional prognostic information over single time point assessments. Methods: 88 AML patients who received standard induction chemotherapy and achieved a CR were evaluated. Targeted NGS of 54 genes associated with myeloid malignancies was performed at diagnosis. We collected peripheral blood (PB) samples upon count recovery following induction chemotherapy and each cycle of consolidation chemotherapy. To detect mMRD, we used a custom 37‐gene hybrid‐capture panel and EC‐NGS based on the Duplex Sequencing approach. PB samples collected at two different time points during remission were analyzed for each patient. The Cox proportional hazards model was used to relate predictor variables to time to relapse and OS. P‐values <0.05 were considered significant. Results: Sequencing analysis of diagnostic samples identified at least one putative oncogenic mutation in 82 of the 88 patients (93%). EC‐NGS of samples collected at the first remission time point (T1) identified at least one persistent mutation in 63 of the 82 patients (77%); 40% of the persistent mutations were in DNMT3A , TET2 , and ASXL1 (DTA). The persistence of DTA or non‐DTA mutations, when considered separately, did not correlate with risk of relapse or OS. The co‐persistence of at least one DTA and one non‐DTA mutation was associated with a higher risk of relapse (HR: 2.61; 95% CI, 1.16 to 5.83; P = 0.02) but not OS (P = 0.56). To evaluate whether changes in mMRD level correlate with clinical outcomes, we analyzed the subset of mutations that were detected at diagnosis and persisted in both T1 and T2. We calculated the fold change (FC) in variant allele frequency (VAF) between T1 and T2 for each mutation and used the maximum FC among all mutations of each patient (maxFC) as a continuous variable for regression analysis. High maxFC values restricted to mutations found at diagnosis (maxFC D ) were significantly correlated with a higher risk of relapse (P = 0.002) and inferior OS (P = 0.003). We extended the analysis to all non‐synonymous mutations that were detected in T1 and T2 including ones that were not identified at diagnosis. The mutations were predominantly in DNMT3A , TET2 , ASXL1 , and TP53 . Higher maxFC values among mutations found in remission samples (maxFC R ) was strongly associated with an increased risk of relapse (P < 0.0001; Fig 1A) and inferior OS (P < 0.0001; Fig 1B). In multivariable regression analysis, maxFC R remained an independent risk factor for relapse after adjustment for age, WBC, ELN 2017 risk group, maxFC D , MRD by flow cytometry, and persistence of mutations found at diagnosis (Table 1). Summary/Conclusion: Our analysis demonstrates that assessment of mMRD kinetics using EC‐NGS provides additional prognostic information over mMRD monitoring at a single time point. We developed an analytical framework to analyze mMRD results in remission and identified a novel parameter (maxFC R ) that is independently associated with risk of relapse and OS. image
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".