Post-transplant monitoring of multiparameter flow-cytometry (MFC)-based measurable residual disease (MRD) assessment combined with T-cell chimerism following allogeneic stem cell transplantation (HCT) in Acute Myeloid Leukemia (AML) and myelodysplastic syndrome (MDS)
Bibliographic record
Abstract
Abstract Introduction Post-transplant surveillance using measurable residual disease (MRD) and donor chimerism provides critical information to assess relapse risk and graft stability in acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS) patients (pts) after allogeneic hematopoietic stem cell transplantation (HCT). Multiparameter flow cytometry (MFC)-based MRD assessment has become routinely test after HCT; emerging evidence suggests that the combined use of MFC-based MRD and chimerism analysis may offer improved prognostic stratification for pts underwent HCT. The present study evaluated MFC-MRD assays in both pre-HCT and day 60 post-HCT as well as T-cell chimerism (TCC) and myeloid-cell (MCC) assessments at days 30 and 60, and its prognostic impact on relapse risk and other outcomes following allogeneic HCT in 196 AML or MDS pts. Patients and methods: We retrospectively analyzed 196 pts (AML, n=148; MDS, n=48) underwent allogenic HCT between 2021 and 2024. MFC-based MRD was assessed as pre-transplant evaluation and at D60 after HCT using marrow specimen while TCC and MCC assays were assessed at D30 and/or D60 using peripheral blood samples. MFC-MRD, TCC and MCC at each time points were analyzed longitudinally, recursive partitioning (rpart) method, which can define an optimal threshold providing the best stratification power, was applied to determine the key risk features strongly associated with relapse free survival (RFS). We have incorporated pre- and D60 MFC-MRD values (%) as well as D30/D60 TCC values into it and identified two key nodes determining RFS: 1) D60 MFC-MRD with 0.939% cutoff as the first node and 2) D30 TCC with 83.25% as the second node in the final model. Prognostic impact was analyzed with respect to overall survival (OS), RFS, cumulative relapse incidence (CIR), non-relapse mortality (NRM), and GvHD incidence according to the 2 nodes. Results Myeloablative conditioning (MAC) in 36% (n=71), while reduced-intensity conditioning (RIC) in 64% (n=125). Majorities (93%) received PTCy-based GvHD prophylaxis, including 141 who received dual T-cell depleted with PTCy and ATG. Primary graft failure occurred in 6 pts (2.9%). Pre-HCT MFC-MRD level has further declined at D60 (p= 0.0017 by pairwise t-test). With 0.1% as a definition of MFC-MRD positivity, the proportion of the pts with MFC-MRD positivity was reduced from 52% (n=102/196) at pre-HCT to 38.2% (n=75/196) at D60. TCC level has gone up from 89.7±16.3 at D30 to 92.3±14.9% at D60 (mean±SD, p=0.0064). With the definition of ≥ 95% for full donor chimerism (FDC), the proportion of the pts achieved FDC was noted in 59.0% (n=115) at D30 and 74.5% (n=146) at D60. For MCC, with a definition of 95% for FDC, 98% and 99% of the pts showed FDC at day 30/60. With a median follow-up of 22 months among survivors, 2-years’ OS and RFS rates were 74.2% and 67.5%, while NRM and CIR was 11.2% and 20.9%. Pre-HCT MFC-MRD level was not associated with RFS (p= 0.13), but D60 MFC-MRD level provides strong association with RFS (p= 0.01), identified as the first node in rpart analysis. The pts having MFC-MRD level≥0.939% (i.e. higher disease burden) showed 2 years’ RFS rate of 53.0% (i.e. adverse risk group), while those having undetectable or <0.939% MFC-MRD showed 69.8% of RFS rate at 2 years (p=0.044). The next node was D30 TCC with 83.25%, which stratified the non-adverse-risk group (i.e. D60 MFC-MRD level ≤0.93%) into intermediate (61.2% of 2 yrs’ RFS, ≥83.25% TCC D30) and favorable risk group (83.6% 2 yrs’ of RFS, <83.25% TCC D30). The 3 groups, divided by two nodes (i.e. D60 MFC-MRD 0.93% and D30 TCC with 83.25%), showed excellent risk stratification for RFS (p=0.0064) and for relapse risk (p=0.0061). The CIR was 5.1%, 17.2% and 33.6% at 1 year, respectively. This 3-group system could stratify AML pts for RFS (91.0% vs 61.7% vs 30.5%; p<0.0001) but not for MDS pts (88.9% vs 61.4% vs 59.8%; p=0.397). CIR was significantly different for AML 2.3%, 17.2% and 54.3%; (p<0.0001) at 1 year, respectively, but not for MDS (13.8% vs 17.6% vs 0%; p= 0.257). No significant differences in OS or RFS were observed for T-cell chimerism at D60 or for myeloid chimerism at D30/D60. Conclusion The combination of MFC-MRD at day 60 post HCT and TCC at day 30 could stratify the pts according to the relapse risk following HCT, particularly in AML pts. Novel GvHD prophylaxis including PTCy would require further investigation on its impact on dynamics of post-transplant TCC level and relapse risk.
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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 | 0.000 | 0.001 |
| 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 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".