Bortezomib-cyclophosphamide-dexamethasone induction/consolidation and bortezomib maintenance for transplant-eligible newly diagnosed multiple myeloma: phase 2 multicenter trial

[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

Resistance of KIR ligand-missing leukocytes to natural killer cells in vivo in patients with acquired aplastic anemia

金沢大学医薬保健研究域医学系　Graduate School of Medical SciencesThe loss of killer cell immunoglobulin-like receptor-ligands (KIR-Ls) due to the copy number neutral loss of heterozygosity of chromosome 6p (6pLOH) in leukocytes of patients with acquired aplastic anemia (AA) may alter the susceptibility of the affected leukocytes to NK cell killing in vivo. We studied 408 AA patients, including 261 who were heterozygous for KIR-Ls, namely C1/C2 or Bw6/Bw4, for the presence of KIR-L-missing (KIR-L[-]) leukocytes. KIR-L(-) leukocytes were found in 14 (5.4%, C1, n= 4, C2, n=3, and Bw4, n= 7) of the 261 patients, in whom corresponding KIR(+) licensed NK cells were detected. The incidence of 6pLOH in the 261 patients (18.0%) was comparable to that in 147 patients (13.6%) who were homozygous for KIR-L genes. The percentages of HLA-lacking granulocytes (0.8-50.3%, median 15.2%) in the total granulocytes of the patients with KIR-L(-) cells were significantly lower than those (1.2-99.4%, median 55.4%) in patients without KIR-L(-) cells. KIR2DS1 and KIR3DS1 were only possessed by three of the 14 patients, two of whom had C2/C2 leukocytes after losing C1 alleles. The expression of the KIR3DS1 ligand HLA-F was selectively lost on KIR-L(-) primitive hematopoietic stem cells (HSCs) derived from 6pLOH(+) iPS cells in one of the KIR3DS1(+) patients. These findings suggest that human NK cells are able to suppress the expansion of KIR-L(-) leukocytes but are unable to eliminate them partly due to the lack of activating KIRs on NK cells and the low HLA-F expression level on HSCs in AA patients.Embargo Period 6 month

高親和性インターロイキン2受容体の機能および構造に関する研究

本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである京都大学0048新制・課程博士博士(医学)甲第5342号医博第1461号新制||医||558(附属図書館)UT51-93-F99京都大学大学院医学研究科内科系専攻(主査)教授 湊 長博, 教授 淀井 淳司, 教授 大熊 稔学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA

Folate-appended cyclodextrin improves the intratumoral accumulation of existing boron compounds.

In this study, the tumor accumulation and antitumor effect of folate-modified cyclodextrin (ND201) purified with folate receptor (FR) connotated with BSH were examined. ND201 and BSH were stably bound in blood, and the mixing ratio 1:1 was most efficient. ND-BSH showed higher boron concentration (38.5 ppm) than BSH alone (11.25 ppm). The maximum ND-BSH tumor/blood ratio was also markedly higher (6.58) than that of BSH alone (1.04). ND-BSH showed a significant antitumor effect compared with BSH after neutron irradiation

Establishment of a Predictive Model for GvHD-free, Relapse-free Survival after Allogeneic HSCT using Ensemble Learning

アンサンブル学習を用いた造血幹細胞移植予後予測モデルの開発 --機械学習を用いた新規生存時間解析手法の実装--. 京都大学プレスリリース. 2021-12-28.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