Donor-source-specific competing risks of relapse and non-relapse mortality in adult B-ALL allogeneic hematopoietic stem cell transplantation

Donor-source-specific competing risks of relapse and non-relapse mortality in adult B-ALL allogeneic hematopoietic stem cell transplantation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Donor-source-specific competing risks of relapse and non-relapse mortality in adult B-ALL allogeneic hematopoietic stem cell transplantation Dae-Ho Choi, Chul Won Jung, Jun Ho Jang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9480094/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains a cornerstone of therapy for adult B-cell acute lymphoblastic leukemia (B-ALL), yet simultaneous comparisons across all four major donor categories within a uniform transplant platform are scarce. Methods We retrospectively analyzed 190 consecutive adults with B-ALL who underwent first allo-HSCT between 2008 and 2025 at a single center using an rabbit anti-thymocyte globulin (rATG)–based, cyclophosphamide plus fractionated total body irradiation (CyTBI, 999 cGy) platform: matched sibling donor (MSD, n = 72), matched unrelated donor (MUD 8/8, n = 46), mismatched unrelated donor (MMUD 7/8 or 6/8, n = 40), and haploidentical donor (Haplo, n = 32). Cumulative incidence of relapse (CIR) and non-relapse mortality (NRM) were estimated in a competing-risk framework; the Fine–Gray subdistribution hazard regression model was used for multivariable analysis. Results Five-year overall survival did not differ significantly across donor groups (MSD 51.9%, MUD 52.8%, MMUD 59.2%, Haplo 31.6%; P = 0.14). Competing risk analysis revealed a clear gradient: five-year CIR decreased from MSD (38.5%) through MUD (35.4%) and MMUD (27.5%) to Haplo (23.8%; P = 0.40), whereas NRM progressively increased (MSD 13.3%, MUD 19.9%, MMUD 22.5%, Haplo 43.5%; P = 0.01). Despite this marked separation, relapse-free survival did not differ significantly (P = 0.43). On Fine–Gray regression, haploidentical transplantation was independently associated with a 4.4-fold higher NRM (sHR 4.44; 95% CI 1.87–10.57; P < 0.001) compared with MSD. Conclusions Within a standardized rATG-based transplant platform, donor source produced opposing gradients of relapse and NRM that offset each other within composite survival endpoints. These findings support the rationale for transitioning to post-transplant cyclophosphamide (PTCy)–based approaches in the haploidentical setting and for risk-adapted platform selection in adult B-ALL. acute lymphoblastic leukemia allogeneic hematopoietic stem cell transplantation haploidentical donor non-relapse mortality competing risk analysis graft-versus-leukemia effect Figures Figure 1 Figure 2 Figure 3 Introduction Allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains a cornerstone of post-remission therapy for adults with B-cell acute lymphoblastic leukemia (B-ALL).[ 1 – 3 ] While a matched sibling donor (MSD) has traditionally been the preferred graft source, the expanding use of matched unrelated donors (MUD), mismatched unrelated donors (MMUD), and haploidentical donors (Haplo) has broadened transplant access for patients lacking an HLA-identical sibling.[ 4 – 6 ] More recently, blinatumomab—both as consolidation therapy in patients with remission and for patients with relapsed/refractory disease —has expanded the therapeutic options for B-ALL.[ 7 , 8 ] In addition, chimeric antigen receptor T-cell (CAR-T) cell therapy has further broadened the treatment landscape for relapsed/refractory B-ALL.[ 9 ] Nevertheless, allo-HSCT remains a critical component of curative strategy, and the choice of optimal donor source continues to carry major clinical implications. Theoretical considerations suggest that increasing HLA disparity may enhance the graft-versus-leukemia (GVL) effect through greater alloreactivity but simultaneously raising non-relapse mortality (NRM) through graft-versus-host disease (GVHD) and immune-mediated organ damage.[ 10 , 11 ] Whether this trade-off follows a gradient across donor sources and how it influences overall survival in B-ALL remain incompletely characterized. Moreover, the optimal donor source may differ according to measurable residual disease (MRD) status, yet MRD-based risk stratification has not been integrated into the evaluation of donor-source-specific outcomes.[ 12 , 13 ] Large registry studies from the European Society for Blood and Marrow Transplantation (EBMT) have compared selected pairs of donor types in ALL and reported largely comparable survival outcomes between haploidentical and MSD or MUD transplants.[ 14 , 15 ] A prospective Chinese multicenter study similarly demonstrated comparable outcomes between haploidentical and MSD transplants in Philadelphia chromosome–negative high-risk ALL in first complete remission.[ 16 ] However, these registry studies included heterogeneous conditioning regimens, graft-versus-host disease (GVHD) prophylaxis strategies, and graft sources across contributing centers, and the Beijing Protocol utilizes a distinctive graft platform that limits direct comparability with Western approaches.[ 17 ] Furthermore, simultaneous comparisons of all four major donor categories—MSD, MUD, MMUD, and Haplo—in B-ALL within a single transplant platform have rarely been performed. At our institution, adult B-ALL patients undergoing allo-HSCT have been managed on a quasi-standardized rATG-based platform featuring CyTBI myeloablative conditioning (TBI 999 cGy), peripheral blood stem cell grafts, and donor-group-specific but internally uniform GVHD prophylaxis (cyclosporine A plus methotrexate for MSD and haploidentical donors; tacrolimus plus methotrexate for MUD/MMUD). This homogeneous practice provides a unique opportunity to evaluate the independent effect of donor source on transplant outcomes while minimizing platform-related confounders. We therefore conducted a competing risk analysis of relapse and NRM across the four donor categories to delineate the GVL–NRM balance in adult B-ALL. Methods Patients and study design We retrospectively reviewed all consecutive adults (≥ 18 years) with B-ALL who underwent their first allo-HSCT at Samsung Medical Center between January 2008 and November 2025. Patients who received post-transplant cyclophosphamide (PTCy)-based GVHD prophylaxis (n = 10), those who received transplants from a one-locus mismatched related (non-haploidentical) donor (n = 3), and one patient with chronic myeloid leukemia in lymphoid blast crisis presenting as B-ALL were excluded, yielding a final cohort of 190 patients. The study was approved by the Institutional Review Board of Samsung Medical Center (IRB No. 2026-03-102-001). Transplant procedures Myeloablative conditioning consisted of cyclophosphamide (total dose 120 mg/kg) and fractionated total body irradiation (TBI, 999 cGy) in most patients (91.6%), with the remainder receiving fludarabine/busulfan-based regimens with or without low-dose TBI (400 cGy). Anti-thymocyte globulin (rATG, thymoglobulin) was administered in 97.4% of patients. The graft source was peripheral blood stem cells in all but one patient who received cord blood. GVHD prophylaxis was donor-group-specific: cyclosporine A plus short-course methotrexate (4 doses) for MSD and haploidentical transplants, and tacrolimus plus short-course methotrexate for MUD and MMUD transplants. Definitions and endpoints The primary endpoint was overall survival (OS). Secondary endpoints included relapse-free survival (RFS), cumulative incidence of relapse (CIR), NRM, and GVHD-free relapse-free survival (GRFS). Donor groups were classified as MSD (HLA 8/8 matched sibling), MUD (HLA 8/8 matched unrelated), MMUD (HLA 7/8 or 6/8 unrelated), and Haplo (haploidentical, ≥ 2 HLA loci mismatched). OS was measured from transplant to death from any cause. RFS was measured from transplant to relapse, death, or last follow-up. NRM was defined as death without prior relapse. GRFS events included grade III–IV acute GVHD, moderate/severe chronic GVHD, relapse, or death, whichever occurred first.[ 18 ] Statistical analysis OS, RFS, and GRFS were estimated using the Kaplan–Meier method and compared using the log-rank test. CIR and NRM were estimated using cumulative incidence functions in a competing risk framework (relapse and NRM as competing events) and compared using Gray’s test.[ 19 ] Multivariable Cox proportional hazards models for OS and RFS included donor group, age at transplant, sex, Philadelphia chromosome (Ph) status, disease status at HSCT (CR1 vs. non-CR1), and transplant era (2008–2015 vs. 2016–2025). Fine–Gray subdistribution hazard regression was used for multivariable competing risk analysis of CIR and NRM.[ 20 ] A landmark analysis at day + 100 was performed to evaluate outcomes after the period of highest early NRM. As a sensitivity analysis, MSD, MUD, and MMUD were pooled as “conventional donors” and compared with haploidentical donors. The median follow-up was estimated by the reverse Kaplan–Meier method. All tests were two-sided with a significance level of 0.05. Statistical analyses were performed using R version 4.3.3. Results Patient characteristics A total of 190 patients were included: MSD (n = 72), MUD (n = 46), MMUD (n = 40), and Haplo (n = 32). The median age at transplant was 42.7 years (range, 17.6–69.2); the haploidentical group was older (median 52.8 years, P = 0.001). Most patients (91.1%) were transplanted in first complete remission (CR1). Philadelphia chromosome-positive (Ph+) B-ALL accounted for 41.1%, distributed evenly across groups (P = 0.79). The haploidentical group was predominantly transplanted in the later era (90.6% in 2016–2025 vs. 47.5–62.5% in other groups; P < 0.001). TBI-based conditioning was used less frequently in the haploidentical group (81.2% vs. 91.7–97.5%; P < 0.01). Neutrophil and platelet engraftment were significantly delayed in the haploidentical group (median 17.5 and 20.5 days, respectively; P = 0.02 and P < 0.01). (Table 1 ) Table 1 Baseline characteristics of 190 adult patients with B-ALL who underwent allogeneic HSCT, stratified by donor source Variable Overall (N = 190) MSD (n = 72) MUD (n = 46) MMUD (n = 40) Haplo (n = 32) P value Patient characteristics Age at HSCT, median (range) 42.7 (17.6–69.2) 44.8 (19.9–69.2) 37.2 (17.6–68.2) 41.4 (30.3–63.9) 52.8 (21.6–67.9) 0.001 Male sex, n (%) 88 (46.3) 34 (47.2) 22 (47.8) 18 (45.0) 14 (43.8) 0.981 Disease characteristics Ph(+), n (%) 78 (41.1) 27 (37.5) 19 (41.3) 19 (47.5) 13 (40.6) 0.785 CDKN2A deletion, n (%) 41 (21.6) 13 (18.1) 9 (19.6) 12 (30.0) 7 (21.9) 0.509 KMT2A rearrangement, n (%) 20 (10.5) 7 (9.7) 7 (15.2) 1 (2.5) 5 (15.6) 0.192 Disease status at HSCT CR1 173 (91.1) 67 (93.1) 44 (95.7) 33 (82.5) 29 (90.6) 0.161 Non-CR1 17 (8.9) 5 (6.9) 2 (4.3) 7 (17.5) 3 (9.4) Transplant characteristics Transplant era 2008–2015 74 (38.9) 27 (37.5) 23 (50.0) 21 (52.5) 3 (9.4) < 0.001 2016–2025 116 (61.1) 45 (62.5) 23 (50.0) 19 (47.5) 29 (90.6) TBI-based conditioning, n (%) 174 (91.6) 66 (91.7) 43 (93.5) 39 (97.5) 26 (81.2) 0.002 GVHD prophylaxis CsA + MTX 102 (53.7) 71 (98.6) 0 (0.0) 0 (0.0) 31 (96.9) Tacro + MTX 85 (44.7) 0 (0.0) 45 (97.8) 40 (100.0) 0 (0.0) ATG used, n (%) 185 (97.4) 70 (97.2) 45 (97.8) 39 (97.5) 31 (96.9) 1.000 Donor characteristics Donor age, median (range) 32.0 (0.0–66.0) 42.5 (17.0–66.0) 25.0 (18.0–44.0) 31.0 (0.0–43.0) 32.0 (14.0–65.0) < 0.001 Female donor to male recipient, n (%) 25 (13.2) 12 (16.7) 4 (8.7) 3 (7.5) 6 (18.8) 0.311 ABO mismatch, n (%) 106 (55.8) 30 (41.7) 32 (69.6) 30 (75.0) 14 (43.8) 0.001 CD34 + dose (×10⁶/kg), median (IQR) 6.0 (4.1–9.4) 5.7 (4.2–6.0) 6.5 (4.4–11.6) 5.8 (3.8–8.9) 6.6 (3.8–9.0) 0.717 Engraftment Neutrophil (days), median (range) 15.0 (10.0–32.0) 15.0 (10.0–32.0) 15.0 (10.0–32.0) 16.0 (10.0–27.0) 17.5 (11.0–28.0) 0.024 Platelet > 20K (days), median (range) 17.5 (8.0–244.0) 13.0 (8.0–203.0) 17.0 (9.0–46.0) 20.0 (11.0–244.0) 20.5 (9.0–89.0) 0.001 Post-transplant complications Acute GVHD (any), n (%) 69 (36.3) 20 (27.8) 20 (43.5) 16 (40.0) 13 (40.6) 0.286 Acute GVHD grade III–IV, n (%) 12 (6.3) 4 (5.6) 3 (6.5) 3 (7.5) 2 (6.3) 0.982 Chronic GVHD (any), n (%) 94 (49.5) 43 (59.7) 17 (37.0) 23 (57.5) 11 (34.4) 0.020 Chronic GVHD moderate/severe, n (%) 46 (24.2) 22 (30.6) 5 (10.9) 11 (27.5) 8 (25.0) 0.098 Data are presented as n (%) or median (range) unless otherwise specified. P values were calculated using the Kruskal-Wallis test for continuous variables and the chi-squared or Fisher exact test for categorical variables. Three patients received alternative GVHD prophylaxis: cyclosporine A alone (n = 1, MSD), tacrolimus alone (n = 1, MUD), and missing data (n = 1, Haplo). One MMUD patient received cord blood as the graft source. Overall survival and relapse-free survival After a median follow-up of 75.8 months (MSD 73.8, MUD 93.3, MMUD 80.0, Haplo 39.2 months), the five-year OS rates were 51.9%, 52.8%, 59.2%, and 31.6% for MSD, MUD, MMUD, and Haplo, respectively (log-rank P = 0.14; Fig. 1 A). On pairwise comparison, the only statistically significant difference was MSD versus Haplo (P = 0.03), with a borderline trend for MUD versus Haplo (P = 0.06). Five-year RFS followed a similar pattern (P = 0.43; Fig. 1 B). On multivariable Cox regression, haploidentical donor was associated with a non-significant trend toward worse OS (adjusted hazard ratio [aHR] 1.75; 95% CI 0.93–3.27; P = 0.08). Independent adverse prognostic factors for OS included older age (aHR 1.03 per year; P < 0.01), non-CR1 status (aHR 2.71; P < 0.01), and earlier transplant era (2016–2025: aHR 0.59; P = 0.03). (Supplementary Table S1 ) Competing risk analysis: relapse and NRM Competing risk analysis revealed a striking gradient. The five-year CIR decreased from MSD (38.5%) through MUD (35.4%) and MMUD (27.5%) to Haplo (23.8%), though this trend did not reach statistical significance (Gray’s P = 0.40). Conversely, the five-year NRM progressively increased: MSD 13.3%, MUD 19.9%, MMUD 22.5%, and Haplo 43.5% (Gray’s P = 0.01). (Fig. 2 ) On multivariable Fine–Gray regression (Table 2 ), haploidentical transplant was independently associated with a 4.4-fold increased NRM (subdistribution hazard ratio [sHR] 4.44; 95% CI 1.87–10.57; P < 0.001) and a non-significant trend toward lower relapse (sHR 0.45; 95% CI 0.18–1.10; P = 0.08) compared with MSD. Other independent predictors of NRM included older age (sHR 1.05 per year; P < 0.01), Ph-positive status (sHR 2.32; P < 0.01), and later transplant era (sHR 0.39; P < 0.01). Independent predictors of relapse included male sex (sHR 2.10; P < 0.01) and Ph-negative status (as shown by the protective effect of Ph positivity; sHR 0.45; P = 0.01 for Ph+). Table 2 Fine–Gray competing risk regression for NRM and cumulative incidence of relapse A. Non-relapse mortality Variable sHR 95% CI P value Donor source (ref: MSD) MUD 1.81 0.73–4.49 0.20 MMUD 1.59 0.62–4.06 0.34 Haplo 4.44 1.87–10.57 < 0.001 Age (per year) 1.05 1.02–1.07 < 0.01 Male sex 1.16 0.63–2.16 0.63 Ph(+) 2.32 1.23–4.37 < 0.01 Non-CR1 1.77 0.69–4.58 0.24 Era 2016–2025 0.39 0.20–0.78 < 0.01 B. Cumulative incidence of relapse Variable sHR 95% CI P value Donor source (ref: MSD) MUD 0.81 0.44–1.49 0.50 MMUD 0.62 0.28–1.35 0.23 Haplo 0.45 0.18–1.10 0.08 Age (per year) 1.00 0.98–1.02 0.82 Male sex 2.10 1.25–3.54 < 0.01 Ph(+) 0.45 0.25–0.82 0.01 Non-CR1 2.49 1.22–5.07 0.01 Era 2016–2025 1.01 0.56–1.82 0.97 sHR, subdistribution hazard ratio. Multivariable Fine–Gray regression with relapse and NRM as competing events. Bold P values indicate statistical significance (P < 0.05). Landmark analysis Twenty-seven patients were excluded from the landmark analysis (23 deaths [22 NRM, 1 relapse] and 4 censored before day + 100), with haploidentical recipients contributing disproportionately to early mortality (8/32 [25.0%] vs. 5/72 [6.9%] in MSD). Among the 163 day + 100 survivors, the five-year OS no longer differed significantly across groups (MSD 55.0%, MUD 60.8%, MMUD 71.7%, Haplo 39.2%; P = 0.22; Fig. 3 ). However, the five-year NRM after day + 100 remained significantly higher in the haploidentical group (31.3% vs. 8.1–10.3% in others; Gray’s P = 0.04), indicating ongoing late NRM beyond the peri-transplant period. (Supplementary Table S2) Subgroup analysis by Ph status Among Ph-positive patients (n = 78), OS did not differ by donor source (P = 0.32; Supplementary Figure S1 A), and the NRM gradient did not reach significance (Gray's P = 0.23; Supplementary Figure S1 B). Notably, in the full-cohort Fine–Gray model (Table 2 ), Ph-positive status was independently associated with higher NRM (sHR 2.32; P < 0.01) but lower relapse (sHR 0.45; P = 0.01), suggesting a distinct competing risk profile. Among Ph-negative patients (n = 112), the NRM gradient persisted with statistical significance (Gray’s P = 0.04; Supplementary Figure S1 D), whereas OS did not differ significantly (P = 0.56; Supplementary Figure S1 C). GRFS and sensitivity analysis Two-year GRFS rates were 50.8%, 50.0%, 42.9%, and 32.7% for MSD, MUD, MMUD, and Haplo, respectively (P = 0.16). In a sensitivity analysis pooling conventional donors (MSD + MUD+MMUD, n = 158) versus Haplo (n = 32), OS was significantly better in the conventional group (five-year OS 53.9% vs. 31.6%; P = 0.02), with significantly lower NRM (17.5% vs. 43.5%; P < 0.01) and comparable relapse (34.6% vs. 23.8%; P = 0.28; Supplementary Figure S2). Discussion In this single-center study of 190 adults with B-ALL transplanted on a standardized rATG-based platform, we demonstrate a clear and opposing gradient of relapse and NRM across four donor sources. Haploidentical transplantation was associated with the lowest cumulative incidence of relapse but the highest NRM, offsetting the GVL advantage. As a result, relapse-free survival did not differ significantly across the four donor groups (P = 0.43), although OS was significantly inferior in haploidentical recipients compared with pooled conventional donors (P = 0.02). MSD, MUD, and MMUD donors yielded comparable survival outcomes. Our findings are consistent with large registry studies that have suggested a GVL–NRM trade-off across donor types. An EBMT registry analysis reported that haploidentical HSCT for ALL was associated with higher NRM but lower relapse incidence compared with MSD, with comparable LFS and OS.[ 14 ] In a parallel EBMT analysis of PTCy-based transplants in ALL CR1, haploidentical recipients similarly showed higher NRM but comparable leukemia-free survival relative to MSD and MUD.[ 15 ] Consistent with this pattern, PTCy-based haploidentical transplantation in AML also demonstrated increased NRM (HR 2.6) offset by reduced relapse (HR 0.7) compared with MSD and MUD.[ 21 ] A large CIBMTR analysis similarly reported comparable leukemia-free survival across haploidentical, sibling, unrelated, and cord blood donor sources in ALL.[ 22 ] A key distinction of the present study is that our cohort was managed within a quasi-standardized transplant platform, where conditioning (CyTBI 999 cGy), graft source (PBSC), and GVHD prophylaxis were uniform within each donor group. This minimized platform-related confounding that inevitably affects multicenter registry analyses, allowing the GVL–NRM gradient to emerge with greater clarity. Indeed, the subdistribution hazard ratio for NRM associated with haploidentical transplantation in our study (sHR 4.44) was numerically higher than those reported in these EBMT analyses,[ 15 , 21 ] likely reflecting the broader immunological impact of rATG-based prophylaxis, which depletes both recipient and donor T cells while preserving donor alloreactivity after engraftment.[ 23 , 24 ] Notably, an EBMT registry comparison of ATG versus PTCy in haploidentical ALL reported lower relapse and superior survival with PTCy,[ 25 ] underscoring that the GVL–NRM balance differs fundamentally between these platforms. Despite this marked separation in competing risks, RFS did not differ significantly across donor groups (P = 0.43; haploidentical aHR 1.44, P = 0.24), illustrating how composite endpoints can obscure clinically meaningful differences in the mode of treatment failure. The immunological basis of this observation merits discussion. PTCy selectively eliminates rapidly proliferating alloreactive T cells on days + 3 and + 4, thereby reducing both GVHD and—to a lesser extent—GVL.[ 26 , 27 ] In contrast, rATG administered pre-transplant primarily depletes recipient T cells, allowing donor T-cell alloreactivity to be relatively preserved after engraftment.[ 23 , 24 ] Our data suggest that, within the rATG-based framework, haploidentical transplantation retains a potent GVL effect but at the cost of substantially higher NRM, predominantly driven by infection-related deaths. This trade-off supports the hypothesis that platform selection—rATG-based versus PTCy-based—may need to be tailored according to the predominant risk in individual patients, although prospective validation is required. The landmark analysis at day + 100 provided further insight into the temporal pattern of NRM. By day + 100, 23 of 27 excluded patients had died (22 NRM, 1 relapse; 4 censored), with haploidentical recipients bearing a disproportionate burden (25.0% vs. 6.9% for MSD). Among the 163 patients surviving beyond day + 100, overall survival no longer differed significantly between donor groups (P = 0.22); however, late NRM beyond day + 100 remained significantly higher in the haploidentical cohort (31.3% vs. 8.1–10.3% for other donor types; P = 0.04). While elevated early NRM in haploidentical transplantation is anticipated given the risks of engraftment failure and early infectious complications, the persistence of excess late NRM suggests ongoing immune deficiency extending well beyond the engraftment period. This pattern may distinguish rATG-based haploidentical platforms from PTCy-based approaches, in which late NRM has been reported to normalize relative to matched donor transplantation.[ 28 ] Notably, rates of severe acute GVHD (grade III–IV) were comparable across all donor groups (5.6–7.5%), and the predominant causes of NRM in the haploidentical group were infection-related—pneumonia, sepsis, and septic shock—rather than GVHD-related, consistent with protracted T-cell immunodeficiency in the rATG-based setting.[ 29 ] These findings suggest that strategies to accelerate immune reconstitution and enhance infection prophylaxis may be central to reducing NRM in rATG-based haploidentical HSCT. An unexpected finding was the bidirectional competing risk profile of Ph-positive B-ALL, which was independently associated with both higher NRM (sHR 2.32) and lower relapse (sHR 0.45). While post-transplant TKI maintenance was not routinely administered in our cohort,[ 30 ] the reduced relapse in Ph+ patients may reflect effective pre-transplant disease control with TKI-containing induction and/or the favorable effect of deep molecular responses achieved before transplant. The higher NRM in Ph+ patients may relate to cumulative toxicity from prolonged TKI exposure and intensive pre-transplant therapy, although this warrants further investigation. However, these results should be interpreted with caution, as Ph-positive patients in our cohort were older (median 45.5 vs. 41.1 years) and more frequently transplanted in CR1 (96.2% vs. 87.5%) compared with Ph-negative patients, and these baseline differences may have contributed to the observed competing risk profile despite multivariable adjustment. The improved NRM in the later transplant era (sHR 0.39; P < 0.01) is encouraging and likely reflects advances in supportive care, infection prophylaxis, and transplant experience. Specifically, the widespread adoption of mold-active antifungal prophylaxis,[ 31 ] the availability of letermovir for cytomegalovirus prophylaxis,[ 32 ] improved molecular surveillance for viral reactivation, and growing institutional familiarity with alternative donor management have collectively contributed to a more favorable NRM landscape. Nevertheless, haploidentical NRM remained the highest among all donor groups even in the later era, underscoring that incremental improvements in supportive care alone are insufficient to close the gap and reinforcing the rationale for a fundamental platform shift. Of note, our institution began transitioning to PTCy-based GVHD prophylaxis for unrelated and haploidentical donors in 2025, a practice change informed in part by the high NRM observed in the rATG-based haploidentical cohort presented here. This study has several limitations. First, the retrospective single-center design and relatively small sample size, particularly in the haploidentical group (n = 32), limit statistical power and generalizability. Second, the haploidentical group had shorter follow-up and was predominantly transplanted in the later era, introducing potential confounding despite adjustment for era in multivariable models. Although the haploidentical group was significantly older, the per-year effect of age on NRM (sHR 1.05) was modest relative to the donor-source effect (sHR 4.44), and the predominance of later-era transplants in the haploidentical group (90.6%) would have biased NRM estimates in favor of, rather than against, this group. Third, MRD status at transplant was not available, which is an increasingly recognized prognostic factor.[ 12 , 13 ] Fourth, GVHD prophylaxis was partially confounded with donor type by design (CsA/MTX for MSD and Haplo; tacrolimus/MTX for MUD and MMUD), precluding a clean separation of their independent effects. Finally, the transition to PTCy-based prophylaxis in 2025 means that the NRM rates reported here may not reflect current practice. In conclusion, within a standardized rATG-based transplant platform for adult B-ALL, we demonstrate a gradient of decreasing relapse and increasing NRM from MSD through MUD and MMUD to haploidentical donors, with these opposing risks offsetting each other in composite survival endpoints. While MSD, MUD, and MMUD provided comparable outcomes, haploidentical transplantation was associated with a 4.44-fold higher NRM that negated the GVL advantage. These findings highlight the critical importance of NRM reduction strategies in haploidentical HSCT and support the rationale for risk-adapted platform selection based on individual relapse risk.[ 33 ] Abbreviations HSCT hematopoietic stem cell transplantation MSD matched sibling donor MUD matched unrelated donor (8/8) MMUD mismatched unrelated donor (7/8 or 6/8) Haplo haploidentical donor Ph Philadelphia chromosome CDKN2A cyclin-dependent kinase inhibitor 2A KMT2A lysine methyltransferase 2A TBI total body irradiation CsA cyclosporine A Tacro tacrolimus MTX methotrexate ATG anti-thymocyte globulin GVHD graft-versus-host disease IQR interquartile range. Declarations Ethics approval and consent to participate This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (as revised in 2013). Approval was obtained from the Institutional Review Board of Samsung Medical Center (IRB No. 2026-03-102-001). The requirement for individual informed consent was waived by the IRB given the retrospective design. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Authors’ contributions D.H.C. designed the study, collected and analyzed data, performed statistical analysis, and wrote the manuscript. C.W.J. contributed to data interpretation and critically reviewed the manuscript. J.H.J. conceived and supervised the study, contributed to data interpretation, and critically reviewed the manuscript. All authors read and approved the final manuscript. Funding This work received no external funding. Author Contribution D.H.C. designed the study, collected and analyzed data, performed statistical analysis, and wrote the manuscript. C.W.J. contributed to data interpretation and critically reviewed the manuscript. J.H.J. conceived and supervised the study, contributed to data interpretation, and critically reviewed the manuscript. All authors read and approved the final manuscript. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request, subject to institutional and ethical regulations. References Bassan R, Hoelzer D (2011) Modern therapy of acute lymphoblastic leukemia. J Clin Oncol 29:532–543. https://doi.org/10.1200/JCO.2010.30.1382 Fielding AK, Richards SM, Chopra R, Lazarus HM, Litzow MR, Buck G, Durrant IJ, Luger SM, Marks DI, Franklin IM, McMillan AK, Tallman MS, Rowe JM, Goldstone AH, Medical Research Council of the United Kingdom Adult ALLWP, Eastern Cooperative Oncology G (2007) Outcome of 609 adults after relapse of acute lymphoblastic leukemia (ALL); an MRC UKALL12/ECOG 2993 study. Blood 109:944–950. https://doi.org/10.1182/blood-2006-05-018192 Malard F, Mohty M (2020) Acute lymphoblastic leukaemia. 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J Hematol Oncol 14:53. https://doi.org/10.1186/s13045-021-01065-7 Sanz J, Galimard JE, Labopin M, Afanasyev B, Sergeevich MI, Angelucci E, Kroger N, Koc Y, Ciceri F, Diez-Martin JL, Arat M, Sica S, Rovira M, Aljurf M, Tischer J, Savani B, Ruggeri A, Nagler A, Mohty M (2021) Post-transplant cyclophosphamide containing regimens after matched sibling, matched unrelated and haploidentical donor transplants in patients with acute lymphoblastic leukemia in first complete remission, a comparative study of the ALWP of the EBMT. J Hematol Oncol 14:84. https://doi.org/10.1186/s13045-021-01094-2 Wang Y, Liu QF, Xu LP, Liu KY, Zhang XH, Ma X, Wu MQ, Wu DP, Huang XJ (2016) Haploidentical versus Matched-Sibling Transplant in Adults with Philadelphia-Negative High-Risk Acute Lymphoblastic Leukemia: A Biologically Phase III Randomized Study. Clin Cancer Res 22:3467–3476. https://doi.org/10.1158/1078-0432.CCR-15-2335 Lv M, Chang YJ, Huang XJ (2019) Update of the Beijing Protocol haplo-identical hematopoietic stem cell transplantation. Bone Marrow Transpl 54:703–707. https://doi.org/10.1038/s41409-019-0605-2 Holtan SG, DeFor TE, Lazaryan A, Bejanyan N, Arora M, Brunstein CG, Blazar BR, MacMillan ML, Weisdorf DJ (2015) Composite end point of graft-versus-host disease-free, relapse-free survival after allogeneic hematopoietic cell transplantation. Blood 125:1333–1338. https://doi.org/10.1182/blood-2014-10-609032 Gray RJ (1988) A Class of K-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk. Annals Stat 16:1141–1154 Fine JP, Gray RJ (1999) A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc 94:496–509. https://doi.org/10.1080/01621459.1999.10474144 Sanz J, Galimard JE, Labopin M, Afanasyev B, Angelucci E, Ciceri F, Blaise D, Cornelissen JJ, Meijer E, Diez-Martin JL, Koc Y, Rovira M, Castagna L, Savani B, Ruggeri A, Nagler A, Mohty M, Acute Leukemia Working Party of the European Society for B, Marrow T (2020) Post-transplant cyclophosphamide after matched sibling, unrelated and haploidentical donor transplants in patients with acute myeloid leukemia: a comparative study of the ALWP EBMT. J Hematol Oncol 13:46. https://doi.org/10.1186/s13045-020-00882-6 Wieduwilt MJ, Metheny L, Zhang MJ, Wang HL, Estrada-Merly N, Marks DI, Al-Homsi AS, Muffly L, Chao N, Rizzieri D, Gale RP, Gadalla SM, Cairo M, Mussetti A, Gore S, Bhatt VR, Patel SS, Michelis FV, Inamoto Y, Badawy SM, Copelan E, Palmisiano N, Kharfan-Dabaja MA, Lazarus HM, Ganguly S, Bredeson C, Diaz Perez MA, Cassaday R, Savani BN, Ballen K, Martino R, Wirk B, Bacher U, Aljurf M, Bashey A, Murthy HS, Yared JA, Aldoss I, Farhadfar N, Liu H, Abdel-Azim H, Waller EK, Solh M, Seftel MD, van der Poel M, Grunwald MR, Liesveld JL, Kamble RT, McGuirk J, Munker R, Cahn JY, Lee JW, Freytes CO, Krem MM, Winestone LE, Gergis U, Nathan S, Olsson RF, Verdonck LF, Sharma A, Ringden O, Friend BD, Cerny J, Choe H, Chhabra S, Nishihori T, Seo S, George B, Baxter-Lowe LA, Hildebrandt GC, de Lima M, Litzow M, Kebriaei P, Hourigan CS, Abid MB, Weisdorf DJ, Saber W (2022) Haploidentical vs sibling, unrelated, or cord blood hematopoietic cell transplantation for acute lymphoblastic leukemia. Blood Adv 6:339–357. https://doi.org/10.1182/bloodadvances.2021004916 Mohty M (2007) Mechanisms of action of antithymocyte globulin: T-cell depletion and beyond. Leukemia 21:1387–1394. https://doi.org/10.1038/sj.leu.2404683 Bacigalupo A, Lamparelli T, Barisione G, Bruzzi P, Guidi S, Alessandrino PE, di Bartolomeo P, Oneto R, Bruno B, Sacchi N, van Lint MT, Bosi A, Gruppo Italiano Trapianti Midollo O (2006) Thymoglobulin prevents chronic graft-versus-host disease, chronic lung dysfunction, and late transplant-related mortality: long-term follow-up of a randomized trial in patients undergoing unrelated donor transplantation. Biol Blood Marrow Transpl 12:560–565. https://doi.org/10.1016/j.bbmt.2005.12.034 Nagler A, Kanate AS, Labopin M, Ciceri F, Angelucci E, Koc Y, Gulbas Z, Arcese W, Tischer J, Pioltelli P, Ozdogu H, Afanasyev B, Wu D, Arat M, Peric Z, Giebel S, Savani B, Mohty M (2021) Post-transplant cyclophosphamide versus anti-thymocyte globulin for graft-versus-host disease prevention in haploidentical transplantation for adult acute lymphoblastic leukemia. Haematologica 106:1591–1598. https://doi.org/10.3324/haematol.2020.247296 Luznik L, Bolanos-Meade J, Zahurak M, Chen AR, Smith BD, Brodsky R, Huff CA, Borrello I, Matsui W, Powell JD, Kasamon Y, Goodman SN, Hess A, Levitsky HI, Ambinder RF, Jones RJ, Fuchs EJ (2010) High-dose cyclophosphamide as single-agent, short-course prophylaxis of graft-versus-host disease. Blood 115:3224–3230. https://doi.org/10.1182/blood-2009-11-251595 Wachsmuth LP, Patterson MT, Eckhaus MA, Venzon DJ, Gress RE, Kanakry CG (2019) Post-transplantation cyclophosphamide prevents graft-versus-host disease by inducing alloreactive T cell dysfunction and suppression. J Clin Invest 129:2357–2373. https://doi.org/10.1172/JCI124218 McCurdy SR, Kanakry JA, Showel MM, Tsai HL, Bolanos-Meade J, Rosner GL, Kanakry CG, Perica K, Symons HJ, Brodsky RA, Gladstone DE, Huff CA, Pratz KW, Prince GT, Dezern AE, Gojo I, Matsui WH, Borrello I, McDevitt MA, Swinnen LJ, Smith BD, Levis MJ, Ambinder RF, Luznik L, Jones RJ, Fuchs EJ, Kasamon YL (2015) Risk-stratified outcomes of nonmyeloablative HLA-haploidentical BMT with high-dose posttransplantation cyclophosphamide. Blood 125:3024–3031. https://doi.org/10.1182/blood-2015-01-623991 Bosch M, Dhadda M, Hoegh-Petersen M, Liu Y, Hagel LM, Podgorny P, Ugarte-Torres A, Khan FM, Luider J, Auer-Grzesiak I, Mansoor A, Russell JA, Daly A, Stewart DA, Maloney D, Boeckh M, Storek J (2012) Immune reconstitution after anti-thymocyte globulin-conditioned hematopoietic cell transplantation. Cytotherapy 14:1258–1275. https://doi.org/10.3109/14653249.2012.715243 Giebel S, Czyz A, Ottmann O, Baron F, Brissot E, Ciceri F, Cornelissen JJ, Esteve J, Gorin NC, Savani B, Schmid C, Mohty M, Nagler A (2016) Use of tyrosine kinase inhibitors to prevent relapse after allogeneic hematopoietic stem cell transplantation for patients with Philadelphia chromosome-positive acute lymphoblastic leukemia: A position statement of the Acute Leukemia Working Party of the European Society for Blood and Marrow Transplantation. Cancer 122:2941–2951. https://doi.org/10.1002/cncr.30130 Cornely OA, Maertens J, Winston DJ, Perfect J, Ullmann AJ, Walsh TJ, Helfgott D, Holowiecki J, Stockelberg D, Goh YT, Petrini M, Hardalo C, Suresh R, Angulo-Gonzalez D (2007) Posaconazole vs. fluconazole or itraconazole prophylaxis in patients with neutropenia. N Engl J Med 356:348–359. https://doi.org/10.1056/NEJMoa061094 Marty FM, Ljungman P, Chemaly RF, Maertens J, Dadwal SS, Duarte RF, Haider S, Ullmann AJ, Katayama Y, Brown J, Mullane KM, Boeckh M, Blumberg EA, Einsele H, Snydman DR, Kanda Y, DiNubile MJ, Teal VL, Wan H, Murata Y, Kartsonis NA, Leavitt RY, Badshah C (2017) Letermovir Prophylaxis for Cytomegalovirus in Hematopoietic-Cell Transplantation. N Engl J Med 377:2433–2444. https://doi.org/10.1056/NEJMoa1706640 Greco R, Ruggeri A, McLornan DP, Snowden JA, Alexander T, Angelucci E, Averbuch D, Bazarbachi A, Hazenberg MD, Kalwak K, Kenyon M, Mekelenkamp H, Neven B, Pedrazzoli P, Peric Z, Risitano AM, Sanchez-Ortega I, Ciceri F, Sureda A (2025) Indications for haematopoietic cell transplantation and CAR-T for haematological diseases, solid tumours and immune disorders: 2025 EBMT practice recommendations. Bone Marrow Transpl 60:1499–1525. https://doi.org/10.1038/s41409-025-02701-3 Additional Declarations No competing interests reported. Supplementary Files Table1Baselinecharacteristics.docx Table2FineGray.docx Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 24 Apr, 2026 Submission checks completed at journal 24 Apr, 2026 First submitted to journal 21 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9480094","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634241088,"identity":"a93f35ac-90cb-40d1-bc31-38ed757fd751","order_by":0,"name":"Dae-Ho Choi","email":"","orcid":"","institution":"Samsung Medical Center, Sungkyunkwan University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dae-Ho","middleName":"","lastName":"Choi","suffix":""},{"id":634241089,"identity":"f7ca6e3a-7e8f-478d-ac88-92a53a9cd8bf","order_by":1,"name":"Chul Won Jung","email":"","orcid":"","institution":"Samsung Medical Center, Sungkyunkwan University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chul","middleName":"Won","lastName":"Jung","suffix":""},{"id":634241090,"identity":"2b91056d-04b2-4d9b-af1e-0280bdd37de7","order_by":2,"name":"Jun Ho Jang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACxhnMBw4w8ABZ7A1Ea2FLhGjhOUCsNRI8xhC1EglE6mCe3WBwgEHGLk8+8vnDTzf+HM6Td2B++OgGPofNOZAAdFhyseHtHGPp3LbDxYYH2IyNc/BpmZEA8j5z4sbZOQzSuQ2HEzc28LBJ49eS2ADUUp+4cebxx79z/hClJZkBqOVw4nwJBjPpHDYgg4GgljSGAwk8xxM38OSYWee2pSduYCbgF8MZ+Z8/fOypTpzffvzx7Zw/1kBG88PHeLU0AInEHgYGYFBDgMFhPMpBQB5M/gAyGmAiDbjUjoJRMApGwUgFAAF6U8ASPEFOAAAAAElFTkSuQmCC","orcid":"","institution":"Samsung Medical Center, Sungkyunkwan University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"Ho","lastName":"Jang","suffix":""}],"badges":[],"createdAt":"2026-04-21 07:09:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9480094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9480094/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108803964,"identity":"279ad7f6-667d-4aae-a867-8238169924a5","added_by":"auto","created_at":"2026-05-08 15:12:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2485151,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival (A) and relapse-free survival (B) by donor source. Kaplan–Meier estimates with number at risk. P values by log-rank test.\u003c/p\u003e","description":"","filename":"Fig1OSRFS.png","url":"https://assets-eu.researchsquare.com/files/rs-9480094/v1/ed7fdaa1b71dcf48f7479a35.png"},{"id":108803984,"identity":"65c898ba-9d12-4ddd-8a19-0cb45565448a","added_by":"auto","created_at":"2026-05-08 15:13:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1869694,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of relapse (A) and non-relapse mortality (B) by donor source in a competing risk framework. Shaded areas represent 95% confidence intervals. P values by Gray’s test.\u003c/p\u003e","description":"","filename":"Fig2CIRNRM.png","url":"https://assets-eu.researchsquare.com/files/rs-9480094/v1/cfc3b02352ab937923827852.png"},{"id":108804763,"identity":"946b8a55-3666-400d-b58b-b4c495c7fd47","added_by":"auto","created_at":"2026-05-08 15:23:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1949848,"visible":true,"origin":"","legend":"\u003cp\u003eLandmark analysis at day +100. Overall survival (A) and relapse-free survival (B) among patients surviving beyond day +100 (n=163).\u003c/p\u003e","description":"","filename":"Fig3Landmark.png","url":"https://assets-eu.researchsquare.com/files/rs-9480094/v1/09807b33f1c22845e4c6e81d.png"},{"id":108809520,"identity":"49c6a3a7-8b08-4bda-854d-9aab2e81a281","added_by":"auto","created_at":"2026-05-08 15:53:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4889510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9480094/v1/e05b57c1-8296-4feb-8f0d-d70bcc829acf.pdf"},{"id":108804770,"identity":"acd3502b-e4d7-444f-9ce4-a63be66aece0","added_by":"auto","created_at":"2026-05-08 15:23:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21535,"visible":true,"origin":"","legend":"","description":"","filename":"Table1Baselinecharacteristics.docx","url":"https://assets-eu.researchsquare.com/files/rs-9480094/v1/81750fa5ee802dc977494bc3.docx"},{"id":108508645,"identity":"88d4b3d4-7f1d-43f4-bb1d-82f3ae70e65e","added_by":"auto","created_at":"2026-05-05 12:16:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16219,"visible":true,"origin":"","legend":"","description":"","filename":"Table2FineGray.docx","url":"https://assets-eu.researchsquare.com/files/rs-9480094/v1/472e797a03875f37ecc15728.docx"},{"id":108508646,"identity":"83f2ea12-8baf-40b2-acc3-05e300210733","added_by":"auto","created_at":"2026-05-05 12:16:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":614117,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9480094/v1/3337d97ec5bc8987dd3651f7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Donor-source-specific competing risks of relapse and non-relapse mortality in adult B-ALL allogeneic hematopoietic stem cell transplantation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAllogeneic hematopoietic stem cell transplantation (allo-HSCT) remains a cornerstone of post-remission therapy for adults with B-cell acute lymphoblastic leukemia (B-ALL).[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] While a matched sibling donor (MSD) has traditionally been the preferred graft source, the expanding use of matched unrelated donors (MUD), mismatched unrelated donors (MMUD), and haploidentical donors (Haplo) has broadened transplant access for patients lacking an HLA-identical sibling.[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] More recently, blinatumomab\u0026mdash;both as consolidation therapy in patients with remission and for patients with relapsed/refractory disease \u0026mdash;has expanded the therapeutic options for B-ALL.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] In addition, chimeric antigen receptor T-cell (CAR-T) cell therapy has further broadened the treatment landscape for relapsed/refractory B-ALL.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Nevertheless, allo-HSCT remains a critical component of curative strategy, and the choice of optimal donor source continues to carry major clinical implications.\u003c/p\u003e \u003cp\u003eTheoretical considerations suggest that increasing HLA disparity may enhance the graft-versus-leukemia (GVL) effect through greater alloreactivity but simultaneously raising non-relapse mortality (NRM) through graft-versus-host disease (GVHD) and immune-mediated organ damage.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Whether this trade-off follows a gradient across donor sources and how it influences overall survival in B-ALL remain incompletely characterized. Moreover, the optimal donor source may differ according to measurable residual disease (MRD) status, yet MRD-based risk stratification has not been integrated into the evaluation of donor-source-specific outcomes.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eLarge registry studies from the European Society for Blood and Marrow Transplantation (EBMT) have compared selected pairs of donor types in ALL and reported largely comparable survival outcomes between haploidentical and MSD or MUD transplants.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] A prospective Chinese multicenter study similarly demonstrated comparable outcomes between haploidentical and MSD transplants in Philadelphia chromosome\u0026ndash;negative high-risk ALL in first complete remission.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] However, these registry studies included heterogeneous conditioning regimens, graft-versus-host disease (GVHD) prophylaxis strategies, and graft sources across contributing centers, and the Beijing Protocol utilizes a distinctive graft platform that limits direct comparability with Western approaches.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Furthermore, simultaneous comparisons of all four major donor categories\u0026mdash;MSD, MUD, MMUD, and Haplo\u0026mdash;in B-ALL within a single transplant platform have rarely been performed.\u003c/p\u003e \u003cp\u003eAt our institution, adult B-ALL patients undergoing allo-HSCT have been managed on a quasi-standardized rATG-based platform featuring CyTBI myeloablative conditioning (TBI 999 cGy), peripheral blood stem cell grafts, and donor-group-specific but internally uniform GVHD prophylaxis (cyclosporine A plus methotrexate for MSD and haploidentical donors; tacrolimus plus methotrexate for MUD/MMUD). This homogeneous practice provides a unique opportunity to evaluate the independent effect of donor source on transplant outcomes while minimizing platform-related confounders. We therefore conducted a competing risk analysis of relapse and NRM across the four donor categories to delineate the GVL\u0026ndash;NRM balance in adult B-ALL.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and study design\u003c/h2\u003e \u003cp\u003eWe retrospectively reviewed all consecutive adults (\u0026ge;\u0026thinsp;18 years) with B-ALL who underwent their first allo-HSCT at Samsung Medical Center between January 2008 and November 2025. Patients who received post-transplant cyclophosphamide (PTCy)-based GVHD prophylaxis (n\u0026thinsp;=\u0026thinsp;10), those who received transplants from a one-locus mismatched related (non-haploidentical) donor (n\u0026thinsp;=\u0026thinsp;3), and one patient with chronic myeloid leukemia in lymphoid blast crisis presenting as B-ALL were excluded, yielding a final cohort of 190 patients. The study was approved by the Institutional Review Board of Samsung Medical Center (IRB No. 2026-03-102-001).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTransplant procedures\u003c/h3\u003e\n\u003cp\u003eMyeloablative conditioning consisted of cyclophosphamide (total dose 120 mg/kg) and fractionated total body irradiation (TBI, 999 cGy) in most patients (91.6%), with the remainder receiving fludarabine/busulfan-based regimens with or without low-dose TBI (400 cGy). Anti-thymocyte globulin (rATG, thymoglobulin) was administered in 97.4% of patients. The graft source was peripheral blood stem cells in all but one patient who received cord blood. GVHD prophylaxis was donor-group-specific: cyclosporine A plus short-course methotrexate (4 doses) for MSD and haploidentical transplants, and tacrolimus plus short-course methotrexate for MUD and MMUD transplants.\u003c/p\u003e\n\u003ch3\u003eDefinitions and endpoints\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was overall survival (OS). Secondary endpoints included relapse-free survival (RFS), cumulative incidence of relapse (CIR), NRM, and GVHD-free relapse-free survival (GRFS). Donor groups were classified as MSD (HLA 8/8 matched sibling), MUD (HLA 8/8 matched unrelated), MMUD (HLA 7/8 or 6/8 unrelated), and Haplo (haploidentical, \u0026ge;\u0026thinsp;2 HLA loci mismatched). OS was measured from transplant to death from any cause. RFS was measured from transplant to relapse, death, or last follow-up. NRM was defined as death without prior relapse. GRFS events included grade III\u0026ndash;IV acute GVHD, moderate/severe chronic GVHD, relapse, or death, whichever occurred first.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eOS, RFS, and GRFS were estimated using the Kaplan\u0026ndash;Meier method and compared using the log-rank test. CIR and NRM were estimated using cumulative incidence functions in a competing risk framework (relapse and NRM as competing events) and compared using Gray\u0026rsquo;s test.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Multivariable Cox proportional hazards models for OS and RFS included donor group, age at transplant, sex, Philadelphia chromosome (Ph) status, disease status at HSCT (CR1 vs. non-CR1), and transplant era (2008\u0026ndash;2015 vs. 2016\u0026ndash;2025). Fine\u0026ndash;Gray subdistribution hazard regression was used for multivariable competing risk analysis of CIR and NRM.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] A landmark analysis at day\u0026thinsp;+\u0026thinsp;100 was performed to evaluate outcomes after the period of highest early NRM. As a sensitivity analysis, MSD, MUD, and MMUD were pooled as \u0026ldquo;conventional donors\u0026rdquo; and compared with haploidentical donors. The median follow-up was estimated by the reverse Kaplan\u0026ndash;Meier method. All tests were two-sided with a significance level of 0.05. Statistical analyses were performed using R version 4.3.3.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 190 patients were included: MSD (n\u0026thinsp;=\u0026thinsp;72), MUD (n\u0026thinsp;=\u0026thinsp;46), MMUD (n\u0026thinsp;=\u0026thinsp;40), and Haplo (n\u0026thinsp;=\u0026thinsp;32). The median age at transplant was 42.7 years (range, 17.6\u0026ndash;69.2); the haploidentical group was older (median 52.8 years, P\u0026thinsp;=\u0026thinsp;0.001). Most patients (91.1%) were transplanted in first complete remission (CR1). Philadelphia chromosome-positive (Ph+) B-ALL accounted for 41.1%, distributed evenly across groups (P\u0026thinsp;=\u0026thinsp;0.79). The haploidentical group was predominantly transplanted in the later era (90.6% in 2016\u0026ndash;2025 vs. 47.5\u0026ndash;62.5% in other groups; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). TBI-based conditioning was used less frequently in the haploidentical group (81.2% vs. 91.7\u0026ndash;97.5%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Neutrophil and platelet engraftment were significantly delayed in the haploidentical group (median 17.5 and 20.5 days, respectively; P\u0026thinsp;=\u0026thinsp;0.02 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of 190 adult patients with B-ALL who underwent allogeneic HSCT, stratified by donor source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;190)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMSD (n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUD (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMMUD (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHaplo (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at HSCT, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.7 (17.6\u0026ndash;69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.8 (19.9\u0026ndash;69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.2 (17.6\u0026ndash;68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.4 (30.3\u0026ndash;63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.8 (21.6\u0026ndash;67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePh(+), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDKN2A deletion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMT2A rearrangement, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease status at HSCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173 (91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-CR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransplant characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransplant era\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBI-based conditioning, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174 (91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (91.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39 (97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGVHD prophylaxis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCsA\u0026thinsp;+\u0026thinsp;MTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31 (96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTacro\u0026thinsp;+\u0026thinsp;MTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATG used, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e185 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39 (97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31 (96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonor age, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.0 (0.0\u0026ndash;66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.5 (17.0\u0026ndash;66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.0 (18.0\u0026ndash;44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.0 (0.0\u0026ndash;43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.0 (14.0\u0026ndash;65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale donor to male recipient, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABO mismatch, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106 (55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD34\u0026thinsp;+\u0026thinsp;dose (\u0026times;10⁶/kg), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.0 (4.1\u0026ndash;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7 (4.2\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.5 (4.4\u0026ndash;11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8 (3.8\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.6 (3.8\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEngraftment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (days), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0 (10.0\u0026ndash;32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0 (10.0\u0026ndash;32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.0 (10.0\u0026ndash;32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (10.0\u0026ndash;27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.5 (11.0\u0026ndash;28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet \u0026gt;\u0026thinsp;20K (days), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.5 (8.0\u0026ndash;244.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0 (8.0\u0026ndash;203.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.0 (9.0\u0026ndash;46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.0 (11.0\u0026ndash;244.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.5 (9.0\u0026ndash;89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-transplant complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute GVHD (any), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute GVHD grade III\u0026ndash;IV, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic GVHD (any), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic GVHD moderate/severe, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eData are presented as n (%) or median (range) unless otherwise specified. P values were calculated using the Kruskal-Wallis test for continuous variables and the chi-squared or Fisher exact test for categorical variables. Three patients received alternative GVHD prophylaxis: cyclosporine A alone (n\u0026thinsp;=\u0026thinsp;1, MSD), tacrolimus alone (n\u0026thinsp;=\u0026thinsp;1, MUD), and missing data (n\u0026thinsp;=\u0026thinsp;1, Haplo). One MMUD patient received cord blood as the graft source.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOverall survival and relapse-free survival\u003c/h3\u003e\n\u003cp\u003eAfter a median follow-up of 75.8 months (MSD 73.8, MUD 93.3, MMUD 80.0, Haplo 39.2 months), the five-year OS rates were 51.9%, 52.8%, 59.2%, and 31.6% for MSD, MUD, MMUD, and Haplo, respectively (log-rank P\u0026thinsp;=\u0026thinsp;0.14; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). On pairwise comparison, the only statistically significant difference was MSD versus Haplo (P\u0026thinsp;=\u0026thinsp;0.03), with a borderline trend for MUD versus Haplo (P\u0026thinsp;=\u0026thinsp;0.06). Five-year RFS followed a similar pattern (P\u0026thinsp;=\u0026thinsp;0.43; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). On multivariable Cox regression, haploidentical donor was associated with a non-significant trend toward worse OS (adjusted hazard ratio [aHR] 1.75; 95% CI 0.93\u0026ndash;3.27; P\u0026thinsp;=\u0026thinsp;0.08). Independent adverse prognostic factors for OS included older age (aHR 1.03 per year; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), non-CR1 status (aHR 2.71; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and earlier transplant era (2016\u0026ndash;2025: aHR 0.59; P\u0026thinsp;=\u0026thinsp;0.03). (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCompeting risk analysis: relapse and NRM\u003c/h3\u003e\n\u003cp\u003eCompeting risk analysis revealed a striking gradient. The five-year CIR decreased from MSD (38.5%) through MUD (35.4%) and MMUD (27.5%) to Haplo (23.8%), though this trend did not reach statistical significance (Gray\u0026rsquo;s P\u0026thinsp;=\u0026thinsp;0.40). Conversely, the five-year NRM progressively increased: MSD 13.3%, MUD 19.9%, MMUD 22.5%, and Haplo 43.5% (Gray\u0026rsquo;s P\u0026thinsp;=\u0026thinsp;0.01). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) On multivariable Fine\u0026ndash;Gray regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), haploidentical transplant was independently associated with a 4.4-fold increased NRM (subdistribution hazard ratio [sHR] 4.44; 95% CI 1.87\u0026ndash;10.57; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a non-significant trend toward lower relapse (sHR 0.45; 95% CI 0.18\u0026ndash;1.10; P\u0026thinsp;=\u0026thinsp;0.08) compared with MSD. Other independent predictors of NRM included older age (sHR 1.05 per year; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Ph-positive status (sHR 2.32; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and later transplant era (sHR 0.39; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Independent predictors of relapse included male sex (sHR 2.10; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Ph-negative status (as shown by the protective effect of Ph positivity; sHR 0.45; P\u0026thinsp;=\u0026thinsp;0.01 for Ph+).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFine\u0026ndash;Gray competing risk regression for NRM and cumulative incidence of relapse\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA. Non-relapse mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonor source (ref: MSD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026ndash;4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaplo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u0026ndash;10.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026ndash;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePh(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026ndash;4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-CR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEra 2016\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB. Cumulative incidence of relapse\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003esHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor source (ref: MSD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026ndash;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaplo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026ndash;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePh(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026ndash;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-CR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u0026ndash;5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEra 2016\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026ndash;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003esHR, subdistribution hazard ratio. Multivariable Fine\u0026ndash;Gray regression with relapse and NRM as competing events. Bold P values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLandmark analysis\u003c/h2\u003e \u003cp\u003eTwenty-seven patients were excluded from the landmark analysis (23 deaths [22 NRM, 1 relapse] and 4 censored before day\u0026thinsp;+\u0026thinsp;100), with haploidentical recipients contributing disproportionately to early mortality (8/32 [25.0%] vs. 5/72 [6.9%] in MSD). Among the 163 day\u0026thinsp;+\u0026thinsp;100 survivors, the five-year OS no longer differed significantly across groups (MSD 55.0%, MUD 60.8%, MMUD 71.7%, Haplo 39.2%; P\u0026thinsp;=\u0026thinsp;0.22; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the five-year NRM after day\u0026thinsp;+\u0026thinsp;100 remained significantly higher in the haploidentical group (31.3% vs. 8.1\u0026ndash;10.3% in others; Gray\u0026rsquo;s P\u0026thinsp;=\u0026thinsp;0.04), indicating ongoing late NRM beyond the peri-transplant period. (Supplementary Table S2)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis by Ph status\u003c/h2\u003e \u003cp\u003eAmong Ph-positive patients (n\u0026thinsp;=\u0026thinsp;78), OS did not differ by donor source (P\u0026thinsp;=\u0026thinsp;0.32; Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA), and the NRM gradient did not reach significance (Gray's P\u0026thinsp;=\u0026thinsp;0.23; Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Notably, in the full-cohort Fine\u0026ndash;Gray model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), Ph-positive status was independently associated with higher NRM (sHR 2.32; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but lower relapse (sHR 0.45; P\u0026thinsp;=\u0026thinsp;0.01), suggesting a distinct competing risk profile. Among Ph-negative patients (n\u0026thinsp;=\u0026thinsp;112), the NRM gradient persisted with statistical significance (Gray\u0026rsquo;s P\u0026thinsp;=\u0026thinsp;0.04; Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD), whereas OS did not differ significantly (P\u0026thinsp;=\u0026thinsp;0.56; Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGRFS and sensitivity analysis\u003c/h2\u003e \u003cp\u003eTwo-year GRFS rates were 50.8%, 50.0%, 42.9%, and 32.7% for MSD, MUD, MMUD, and Haplo, respectively (P\u0026thinsp;=\u0026thinsp;0.16). In a sensitivity analysis pooling conventional donors (MSD\u0026thinsp;+\u0026thinsp;MUD+MMUD, n\u0026thinsp;=\u0026thinsp;158) versus Haplo (n\u0026thinsp;=\u0026thinsp;32), OS was significantly better in the conventional group (five-year OS 53.9% vs. 31.6%; P\u0026thinsp;=\u0026thinsp;0.02), with significantly lower NRM (17.5% vs. 43.5%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and comparable relapse (34.6% vs. 23.8%; P\u0026thinsp;=\u0026thinsp;0.28; Supplementary Figure S2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-center study of 190 adults with B-ALL transplanted on a standardized rATG-based platform, we demonstrate a clear and opposing gradient of relapse and NRM across four donor sources. Haploidentical transplantation was associated with the lowest cumulative incidence of relapse but the highest NRM, offsetting the GVL advantage. As a result, relapse-free survival did not differ significantly across the four donor groups (P\u0026thinsp;=\u0026thinsp;0.43), although OS was significantly inferior in haploidentical recipients compared with pooled conventional donors (P\u0026thinsp;=\u0026thinsp;0.02). MSD, MUD, and MMUD donors yielded comparable survival outcomes.\u003c/p\u003e \u003cp\u003eOur findings are consistent with large registry studies that have suggested a GVL\u0026ndash;NRM trade-off across donor types. An EBMT registry analysis reported that haploidentical HSCT for ALL was associated with higher NRM but lower relapse incidence compared with MSD, with comparable LFS and OS.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] In a parallel EBMT analysis of PTCy-based transplants in ALL CR1, haploidentical recipients similarly showed higher NRM but comparable leukemia-free survival relative to MSD and MUD.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Consistent with this pattern, PTCy-based haploidentical transplantation in AML also demonstrated increased NRM (HR 2.6) offset by reduced relapse (HR 0.7) compared with MSD and MUD.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] A large CIBMTR analysis similarly reported comparable leukemia-free survival across haploidentical, sibling, unrelated, and cord blood donor sources in ALL.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] A key distinction of the present study is that our cohort was managed within a quasi-standardized transplant platform, where conditioning (CyTBI 999 cGy), graft source (PBSC), and GVHD prophylaxis were uniform within each donor group. This minimized platform-related confounding that inevitably affects multicenter registry analyses, allowing the GVL\u0026ndash;NRM gradient to emerge with greater clarity. Indeed, the subdistribution hazard ratio for NRM associated with haploidentical transplantation in our study (sHR 4.44) was numerically higher than those reported in these EBMT analyses,[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] likely reflecting the broader immunological impact of rATG-based prophylaxis, which depletes both recipient and donor T cells while preserving donor alloreactivity after engraftment.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Notably, an EBMT registry comparison of ATG versus PTCy in haploidentical ALL reported lower relapse and superior survival with PTCy,[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] underscoring that the GVL\u0026ndash;NRM balance differs fundamentally between these platforms. Despite this marked separation in competing risks, RFS did not differ significantly across donor groups (P\u0026thinsp;=\u0026thinsp;0.43; haploidentical aHR 1.44, P\u0026thinsp;=\u0026thinsp;0.24), illustrating how composite endpoints can obscure clinically meaningful differences in the mode of treatment failure.\u003c/p\u003e \u003cp\u003eThe immunological basis of this observation merits discussion. PTCy selectively eliminates rapidly proliferating alloreactive T cells on days\u0026thinsp;+\u0026thinsp;3 and +\u0026thinsp;4, thereby reducing both GVHD and\u0026mdash;to a lesser extent\u0026mdash;GVL.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] In contrast, rATG administered pre-transplant primarily depletes recipient T cells, allowing donor T-cell alloreactivity to be relatively preserved after engraftment.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Our data suggest that, within the rATG-based framework, haploidentical transplantation retains a potent GVL effect but at the cost of substantially higher NRM, predominantly driven by infection-related deaths. This trade-off supports the hypothesis that platform selection\u0026mdash;rATG-based versus PTCy-based\u0026mdash;may need to be tailored according to the predominant risk in individual patients, although prospective validation is required.\u003c/p\u003e \u003cp\u003eThe landmark analysis at day\u0026thinsp;+\u0026thinsp;100 provided further insight into the temporal pattern of NRM. By day\u0026thinsp;+\u0026thinsp;100, 23 of 27 excluded patients had died (22 NRM, 1 relapse; 4 censored), with haploidentical recipients bearing a disproportionate burden (25.0% vs. 6.9% for MSD). Among the 163 patients surviving beyond day\u0026thinsp;+\u0026thinsp;100, overall survival no longer differed significantly between donor groups (P\u0026thinsp;=\u0026thinsp;0.22); however, late NRM beyond day\u0026thinsp;+\u0026thinsp;100 remained significantly higher in the haploidentical cohort (31.3% vs. 8.1\u0026ndash;10.3% for other donor types; P\u0026thinsp;=\u0026thinsp;0.04). While elevated early NRM in haploidentical transplantation is anticipated given the risks of engraftment failure and early infectious complications, the persistence of excess late NRM suggests ongoing immune deficiency extending well beyond the engraftment period. This pattern may distinguish rATG-based haploidentical platforms from PTCy-based approaches, in which late NRM has been reported to normalize relative to matched donor transplantation.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Notably, rates of severe acute GVHD (grade III\u0026ndash;IV) were comparable across all donor groups (5.6\u0026ndash;7.5%), and the predominant causes of NRM in the haploidentical group were infection-related\u0026mdash;pneumonia, sepsis, and septic shock\u0026mdash;rather than GVHD-related, consistent with protracted T-cell immunodeficiency in the rATG-based setting.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] These findings suggest that strategies to accelerate immune reconstitution and enhance infection prophylaxis may be central to reducing NRM in rATG-based haploidentical HSCT.\u003c/p\u003e \u003cp\u003eAn unexpected finding was the bidirectional competing risk profile of Ph-positive B-ALL, which was independently associated with both higher NRM (sHR 2.32) and lower relapse (sHR 0.45). While post-transplant TKI maintenance was not routinely administered in our cohort,[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] the reduced relapse in Ph+ patients may reflect effective pre-transplant disease control with TKI-containing induction and/or the favorable effect of deep molecular responses achieved before transplant. The higher NRM in Ph+ patients may relate to cumulative toxicity from prolonged TKI exposure and intensive pre-transplant therapy, although this warrants further investigation. However, these results should be interpreted with caution, as Ph-positive patients in our cohort were older (median 45.5 vs. 41.1 years) and more frequently transplanted in CR1 (96.2% vs. 87.5%) compared with Ph-negative patients, and these baseline differences may have contributed to the observed competing risk profile despite multivariable adjustment.\u003c/p\u003e \u003cp\u003eThe improved NRM in the later transplant era (sHR 0.39; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) is encouraging and likely reflects advances in supportive care, infection prophylaxis, and transplant experience. Specifically, the widespread adoption of mold-active antifungal prophylaxis,[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] the availability of letermovir for cytomegalovirus prophylaxis,[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] improved molecular surveillance for viral reactivation, and growing institutional familiarity with alternative donor management have collectively contributed to a more favorable NRM landscape. Nevertheless, haploidentical NRM remained the highest among all donor groups even in the later era, underscoring that incremental improvements in supportive care alone are insufficient to close the gap and reinforcing the rationale for a fundamental platform shift. Of note, our institution began transitioning to PTCy-based GVHD prophylaxis for unrelated and haploidentical donors in 2025, a practice change informed in part by the high NRM observed in the rATG-based haploidentical cohort presented here.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the retrospective single-center design and relatively small sample size, particularly in the haploidentical group (n\u0026thinsp;=\u0026thinsp;32), limit statistical power and generalizability. Second, the haploidentical group had shorter follow-up and was predominantly transplanted in the later era, introducing potential confounding despite adjustment for era in multivariable models. Although the haploidentical group was significantly older, the per-year effect of age on NRM (sHR 1.05) was modest relative to the donor-source effect (sHR 4.44), and the predominance of later-era transplants in the haploidentical group (90.6%) would have biased NRM estimates in favor of, rather than against, this group. Third, MRD status at transplant was not available, which is an increasingly recognized prognostic factor.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Fourth, GVHD prophylaxis was partially confounded with donor type by design (CsA/MTX for MSD and Haplo; tacrolimus/MTX for MUD and MMUD), precluding a clean separation of their independent effects. Finally, the transition to PTCy-based prophylaxis in 2025 means that the NRM rates reported here may not reflect current practice.\u003c/p\u003e \u003cp\u003eIn conclusion, within a standardized rATG-based transplant platform for adult B-ALL, we demonstrate a gradient of decreasing relapse and increasing NRM from MSD through MUD and MMUD to haploidentical donors, with these opposing risks offsetting each other in composite survival endpoints. While MSD, MUD, and MMUD provided comparable outcomes, haploidentical transplantation was associated with a 4.44-fold higher NRM that negated the GVL advantage. These findings highlight the critical importance of NRM reduction strategies in haploidentical HSCT and support the rationale for risk-adapted platform selection based on individual relapse risk.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eHSCT\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ehematopoietic stem cell transplantation\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMSD\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ematched sibling donor\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMUD\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ematched unrelated donor (8/8)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMMUD\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003emismatched unrelated donor (7/8 or 6/8)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eHaplo\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ehaploidentical donor\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePh\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ePhiladelphia chromosome\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCDKN2A\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ecyclin-dependent kinase inhibitor 2A\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eKMT2A\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003elysine methyltransferase 2A\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eTBI\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003etotal body irradiation\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCsA\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ecyclosporine A\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eTacro\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003etacrolimus\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMTX\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003emethotrexate\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eATG\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eanti-thymocyte globulin\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eGVHD\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003egraft-versus-host disease\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eIQR\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003einterquartile range.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (as revised in 2013). Approval was obtained from the Institutional Review Board of Samsung Medical Center (IRB No. 2026-03-102-001). The requirement for individual informed consent was waived by the IRB given the retrospective design.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eD.H.C. designed the study, collected and analyzed data, performed statistical analysis, and wrote the manuscript. C.W.J. contributed to data interpretation and critically reviewed the manuscript. J.H.J. conceived and supervised the study, contributed to data interpretation, and critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.H.C. designed the study, collected and analyzed data, performed statistical analysis, and wrote the manuscript. C.W.J. contributed to data interpretation and critically reviewed the manuscript. J.H.J. conceived and supervised the study, contributed to data interpretation, and critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request, subject to institutional and ethical regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBassan R, Hoelzer D (2011) Modern therapy of acute lymphoblastic leukemia. 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Bone Marrow Transpl 60:1499\u0026ndash;1525. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41409-025-02701-3\u003c/span\u003e\u003cspan address=\"10.1038/s41409-025-02701-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"acute lymphoblastic leukemia, allogeneic hematopoietic stem cell transplantation, haploidentical donor, non-relapse mortality, competing risk analysis, graft-versus-leukemia effect","lastPublishedDoi":"10.21203/rs.3.rs-9480094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9480094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAllogeneic hematopoietic stem cell transplantation (allo-HSCT) remains a cornerstone of therapy for adult B-cell acute lymphoblastic leukemia (B-ALL), yet simultaneous comparisons across all four major donor categories within a uniform transplant platform are scarce.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 190 consecutive adults with B-ALL who underwent first allo-HSCT between 2008 and 2025 at a single center using an rabbit anti-thymocyte globulin (rATG)\u0026ndash;based, cyclophosphamide plus fractionated total body irradiation (CyTBI, 999 cGy) platform: matched sibling donor (MSD, n\u0026thinsp;=\u0026thinsp;72), matched unrelated donor (MUD 8/8, n\u0026thinsp;=\u0026thinsp;46), mismatched unrelated donor (MMUD 7/8 or 6/8, n\u0026thinsp;=\u0026thinsp;40), and haploidentical donor (Haplo, n\u0026thinsp;=\u0026thinsp;32). Cumulative incidence of relapse (CIR) and non-relapse mortality (NRM) were estimated in a competing-risk framework; the Fine\u0026ndash;Gray subdistribution hazard regression model was used for multivariable analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFive-year overall survival did not differ significantly across donor groups (MSD 51.9%, MUD 52.8%, MMUD 59.2%, Haplo 31.6%; P\u0026thinsp;=\u0026thinsp;0.14). Competing risk analysis revealed a clear gradient: five-year CIR decreased from MSD (38.5%) through MUD (35.4%) and MMUD (27.5%) to Haplo (23.8%; P\u0026thinsp;=\u0026thinsp;0.40), whereas NRM progressively increased (MSD 13.3%, MUD 19.9%, MMUD 22.5%, Haplo 43.5%; P\u0026thinsp;=\u0026thinsp;0.01). Despite this marked separation, relapse-free survival did not differ significantly (P\u0026thinsp;=\u0026thinsp;0.43). On Fine\u0026ndash;Gray regression, haploidentical transplantation was independently associated with a 4.4-fold higher NRM (sHR 4.44; 95% CI 1.87\u0026ndash;10.57; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with MSD.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWithin a standardized rATG-based transplant platform, donor source produced opposing gradients of relapse and NRM that offset each other within composite survival endpoints. These findings support the rationale for transitioning to post-transplant cyclophosphamide (PTCy)\u0026ndash;based approaches in the haploidentical setting and for risk-adapted platform selection in adult B-ALL.\u003c/p\u003e","manuscriptTitle":"Donor-source-specific competing risks of relapse and non-relapse mortality in adult B-ALL allogeneic hematopoietic stem cell transplantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 12:16:03","doi":"10.21203/rs.3.rs-9480094/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T00:04:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140075893297862190435325130665870917866","date":"2026-04-29T15:01:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102852203619666378598214833639554810006","date":"2026-04-25T01:06:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T14:32:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T12:27:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-24T12:26:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2026-04-21T06:50:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2e7eb57b-4fb7-43e5-a1c1-c84af1400683","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T00:04:36+00:00","index":21,"fulltext":""},{"type":"reviewerAgreed","content":"140075893297862190435325130665870917866","date":"2026-04-29T15:01:42+00:00","index":19,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T12:16:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 12:16:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9480094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9480094","identity":"rs-9480094","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}