Younger Matched Unrelated Donors Confer a Decreased Relapse Risk As Compared to Older Sibling Donors for Adult B-Cell ALL Patients Undergoing Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Background: The incidence of acute lymphoblastic leukemia (ALL) has increased due to aging population and the median age at diagnosis is 56 years among adults. Allogeneic hematopoietic cell transplant (alloHCT) offers cure for ALL patients, however, disease relapse remains a major issue. Human leukocyte antigen (HLA)-matched sibling donors (MSD) are considered the ideal donor type, mainly due to lower incidence and severity of graft-versus-host-disease (GVHD) and improved disease-free survival (DFS) in ALL. However, siblings are also likely to be older, often with higher comorbidities, and adult ALL patients typically undergo reduced-intensity conditioning and need novel strategies to reduce the post-alloHCT relapse risk. While recent data have suggested relapse reduction and survival benefit with younger matched unrelated donors (MUD) in AML and MDS, the impact of donor age on alloHCT outcomes in adult ALL patients remain unknown. Methods: This retrospective cohort study from the Center for International Blood and Marrow Transplant Research database (CIBMTR) data in B-cell ALL patients 50 years or older, undergoing alloHCT from older (>50) MSDs or younger (<35) MUDs between 2011 and 2018, included common allograft types (PB vs BM), conditioning regimens (RIC/NMA vs MAC), and graft-versus-host-disease (GVHD) prophylaxis strategies (FK-based vs CSA-based vs. others). Other exclusion criteria were recipients of ex vivo T-cell depleted grafts, recipients of mismatched unrelated donors, cord blood or identical twin transplants. The primary outcome, compared between the 2 donor age groups, was relapse risk whereas secondary outcomes included non-relapse mortality (NRM), DFS, and overall survival (OS). Cumulative incidence estimates were calculated for competing risks outcomes, including NRM and relapse. The Kaplan-Meier method was used to estimate the probabilities for survival. To evaluate potential risk factors, multivariable cox regression was used, and relevant transplant-related covariates were considered. Interactions between the main effect (donor age group) and significant risk factors were tested. Fine and Gray model was used for NRM and relapse. Given the multicenter nature of the database, center effect was tested for OS by fitting a gamma frailty model. Results: Among 925 eligible patients in the study cohort, 386 underwent alloHCT with an older MSD (median age, 59 [range: 50-75]) whereas 539 received transplant from a younger MUD (median age, 60 [range: 50-77]). In multivariable analysis, younger MUDs conferred a significantly decreased risk of relapse (HR 0.68; 95% CI, 0.53-0.87; p=.002) versus older MSDs (Table 1; Figure 1). The adjusted cumulative incidence of relapse at 5 years was significantly lower with younger MUDs compared to older MSDs (26% vs 37%; p=.001) (Table 2). Younger MUDs conferred an increased NRM compared to older MSDs (HR 1.38; 95% CI, 1.05-1.82; p=.02) (Table1; Figure 1). The adjusted cumulative incidence of NRM at 5 years was also significantly higher in B-ALL patients who underwent alloHCT from younger MUDs compared to older MSDs (31% vs 22%; p=.006) (Table2). There were no differences in OS or DFS rates of alloHCT with younger MUDs vs older MSDs (OS: HR 1.09; 95% CI, 0.90-1.32; p=.37; DFS: HR 0.95; 95% CI, 0.79-1.14; p=.57) (Table 1; Figure 1). The adjusted 5-year OS probability was 48% in younger MUDs (95% CI, 43%-52%) and 51% among older MSDs (95% CI, 45%-56%; p=0.37). The adjusted 5-year DFS probability was 44% with younger MUDs (95% CI, 40%-49%) compared to 42% with older MSDs (95% CI, 37%-47%; p=.59) (Table2). Conclusions: This is the largest study to date performed in a mature, prospectively collected registry cohort that aids in donor selection and found significant relapse reduction in adult B-ALL patients who underwent alloHCT with younger MUDs as compared to older MSDs. The results highlight that younger MUD donor type exerts a stronger GVL effect and should be preferred in patients at a higher risk for post-alloHCT relapse. The higher NRM associated with younger MUDs will need enhanced GVHD prophylaxis, infection preemption and preventative strategies, and supportive cares measures to consolidate the leukemia-free survival. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".