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Haploidentical-related donor transplantation using posttransplant cyclophosphamide (PTCy-haplo) and cord blood transplantation (CBT) are valid alternatives for patients with hematological malignancies when HLA-matched donor transplantation (MDT) is unavailable. However, the effects of graft-versus-host disease (GVHD) on outcomes after these transplants have not been fully elucidated. Therefore, we evaluated the effects of acute and chronic GVHD on transplant outcomes after PTCy-haplo transplants and compared them with CBT and MDT. We included a total of 914 adult patients with hematological malignancies in the Kyoto Stem Cell Transplantation Group registry who received PTCy-haplo (N = 120), CBT (N = 402), and MDT (N = 392), and achieved neutrophil engraftment. A multivariate analysis revealed that grade I-II acute GVHD improved of overall survival (OS) after PTCy-haplo [hazard ratio (HR) = 0.39, P = 0.018] and CBT (HR = 0.48, P < 0.001), but not after MDT (HR = 0.80, P = 0.267) compared with patients without acute GVHD. Grade I-II acute GVHD had a trend toward reducing the risk of nonrelapse mortality (NRM) after PTCy-haplo (HR = 0.13, P = 0.060) and this positive effect was significant after CBT (HR = 0.39, P = 0.003). A negative impact of grade III-IV acute GVHD on NRM was observed after CBT and MDT, but not after PTCy-haplo. Limited chronic GVHD had a positive impact on OS after CBT and MDT, but not after PTCy-haplo. In conclusion, mild acute GVHD improved outcomes after PTCy-haplo and CBT, and limited chronic GVHD improved outcomes after CBT and MDT. These data indicated that the effects of GVHD on transplant outcomes depended on transplant platforms.

[Objectives:] We conducted a phase II trial to prospectively evaluate the efficacy and safety of bortezomib-cyclophosphamide-dexamethasone (VCD) induction, autologous stem cell transplantation (ASCT), VCD consolidation, and bortezomib maintenance in transplant-eligible newly diagnosed multiple myeloma (NDMM) patients in Japan (UMIN000010542). [Methods:] From 2013 to 2016, 42 patients with a median age of 58 (range 42–65) years with NDMM were enrolled in 15 centers. The primary endpoint was the complete response (CR) /stringent CR (sCR) rate after transplantation, and overall/progression-free survival rates were also evaluated. [Results:] Following induction therapy, the overall response rate was obtained in 71% of patients, including a CR/sCR of 10% and a very good partial response (VGPR) of 26%. Twenty-six of the 42 patients completed ASCT following the protocol and CR/sCR and VGPR rate 100 days after ASCT was 26% and 17%, respectively. During consolidation therapy, 3 of the 24 patients achieved deeper responses. Eight of the 18 patients completed 2-year bortezomib maintenance without disease progression and grade 3/4 toxicities. Five patients were VGPR or partial response after ASCT but maintained response with 2-year bortezomib maintenance. Two-year overall and progression-free survival rates were 92.5% (95% confidence interval [CI]: 78.5%−97.5%) and 62.6% (95% CI: 45.8%−75.5%), respectively. Grade 3/4 toxicities (≥ 10%) included neutropenia (19%) and anemia (17%) in induction, and thrombocytopenia (29%) in consolidation. [Conclusion:] VCD induction/consolidation and bortezomib maintenance with ASCT for NDMM resulted in a high CR/sCR rate and provided good overall/progression-free survival in Japan.

[Background.] Unrelated cord blood (UCB) and haploidentical related donor transplantation using posttransplant cyclophosphamide (PTCy-haplo) have become alternative options to treat patients with hematological malignancies without a HLA-matched donor. [Methods.] We conducted a retrospective study using registry data from the Kyoto Stem Cell Transplantation Group for patients with hematological malignancies who received their first allogeneic hematopoietic cell transplantation using a single UCB unit (n = 460) or PTCy-haplo (N = 57) between 2013 and 2019. [Results.] We found that overall survival in the UCB group was comparable to that in the PTCy-haplo group (hazard ratio, 1.00; 95% confidence interval, 0.66-1.52), although neutrophil and platelet engraftment were significantly delayed. Nonrelapse mortality risk and the incidence of graft-versus-host disease in the UCB group were also comparable to those in the PTCy-haplo group. Although the relapse risk was similar between the UCB group and the PTCy-haplo group regardless of the disease risk, acute myeloid leukemia patients benefit from UCB transplant with a significantly lower relapse rate (hazard ratio, 0.38; 95% confidence interval, 0.18-0.76). [Conclusions.] UCB transplant gives outcomes comparable to PTCy-haplo transplant, and both UCB and PTCy-haplo units are suitable as alternative donor sources for patients without an HLA-matched sibling or unrelated donor.

Graft-versus-host-disease-free, relapse-free survival (GRFS) is a useful composite endpoint that measures survival without relapse or significant morbidity after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We aimed to develop a novel analytical method that appropriately handles right-censored data and competing risks to understand the risk for GRFS and each component of GRFS. This study was a retrospective data-mining study on a cohort of 2207 adult patients who underwent their first allo-HSCT at the Kyoto Stem Cell Transplantation Group (KSCTG), a multi-institutional joint research group of 17 transplantation centers in Japan. The primary endpoint was GRFS. A stacked ensemble of Cox proportional hazard regression and seven machine learning algorithms was applied to develop a prediction model. The median age of patients was 48 years. For GRFS, the stacked ensemble model achieved better predictive accuracy evaluated by C-index than other top-of-the-art competing risk models (ensemble model: 0.670, Cox-PH: 0.668, Random Survival Forest: 0.660, Dynamic DeepHit: 0.646). The probability of GRFS after 2 years was 30.54% for the high-risk and 40.69% for the low-risk group, respectively (hazard ratio [HR] compared to the low-risk group: 2.127; 95% CI: 1.19-3.80). We developed a novel predictive model for survival analysis that showed superior risk stratification to existing methods using a stacked ensemble of multiple machine learning algorithms.