Post-Transplant Cyclophosphamide Versus Tacrolimus and Methotrexate to Prevent Graft-Versus-Host Disease in Recipients of Matched Donor Transplantation: Comparison of Sequential Cohorts in a Prospective Trial
Bibliographic record
Abstract
Abstract Background: Myeloablative conditioning can be given safely to older patients by simply administering busulfan over a longer period (fractionated busulfan regimen) than our "standard" four-day fludarabine-busulfan (Flu-Bu) regimen. (Popat et al Lancet Haematology 2018). To further improve outcomes of this fractionated (f-Bu) regimen in patients undergoing matched donor hematopoietic cell transplantation (HCT), we added cladribine (Clad) to f-Bu-Flu regimen in a prospective clinical trial (NCT02250937). GVHD prophylaxis was tacrolimus and methotrexate (Tac/MTX). After enrolling the first 29 patients, the study was amended and GVHD prophylaxis was changed to post-transplant cyclophosphamide (PTCy) and tacrolimus for the next 53 patients based on very promising data observed in patients undergoing haploidentical transplantation. We hypothesized that PTCy will reduce GVHD and non-relapse mortality (NRM), thus improving survival in patients undergoing HCT from a matched donor. In this study, we compared these two sequential cohorts with reference to GVHD prophylaxis. Methods: Between 2/2015 and 12/2018, 82 patients with AML or MDS, 18-70 years of age, with adequate organ function and 8/8-HLA matched related (n=38%) or unrelated (62%) donors were enrolled. The conditioning regimen was f-Bu to target an area under the concentration vs time curve (AUC) of 20,000 ± 12% μmol.min given over a period of 2-3 weeks. The first two doses of busulfan (80 mg/m2 IV each) were administered either consecutively (days -13 and -12) or with further fractionation, one week apart (days -20 and -13) on outpatient basis. Then, inpatient fludarabine 10 mg/m 2, and cladribine 10 mg/m 2 were given followed by Bu on days -6 to -3. GVHD prophylaxis was Tac/Mtx (n=29) or PTCy 50mg/kg on days 3 and 4 followed by tacrolimus from day 5 (n=53). Results: Baseline characteristics were similar between the PTCy and Tac/Mtx cohorts. The median age was 59 (range, 18-70) and 61 (range, 24-70) years (P=0.20); 49% and 59% had primary induction failure at HCT (P=0.58); High or very-high disease risk index was present in 40% and 41%, (P=0.40); Comorbidity index score >3 was present in 42% and 40% (P=0.24); Donor was a sibling in 34% and 45% (P=0.35), and peripheral blood graft was used in 81% and 76% (P=0.58), respectively in PTCy and Tac/Mtx cohorts. Median follow up was 42.7 months. In the PTCy and Tac/Mtx cohorts, at 3-years overall survival was 69% vs 38% (P=0.0004), NRM was 8% vs 28% (P=0.009) [Table 1, Figure 1], incidence of grade 3-4 acute GVHD at day 100 was 4% vs 17% (P=0.04), and chronic GVHD was 21% vs 28% at 3 years (P=0.53). Median time to neutrophil engraftment was prolonged by 3 days with PTCy (15 vs 12 days; P<0.0001). Full donor chimerism at day 30 was noted in 81% vs 28%, in the PTCy and Tac/Mtx cohorts respectively, (P=0.005). The toxicity profile was similar except neutropenic fever (likely cytokine-related) was higher in PTCy group (60% v/s 24%, P=0.002). Likewise, the incidence of hemorrhagic cystitis was higher in the PTCy group (36% vs 14%, p-0.04). Most of the later events were grade 1 or 2. Conclusion: Compared with Tac/Mtx, PTCy reduced severe acute GVHD and NRM, and improved survival in AML/MDS patients up to the age of 70 years who received myeloablative fractionated busulfan conditioning and transplant from a matched donor. Figure 1 Figure 1. Disclosures Popat: Bayer: Research Funding; Abbvie: Research Funding; Novartis: Research Funding; Incyte: Research Funding. Mehta: CSLBehring: Research Funding; Kadmon: Research Funding; Syndax: Research Funding; Incyte: Research Funding. Hosing: Nkarta Therapeutics: Membership on an entity's Board of Directors or advisory committees. Rezvani: Navan Technologies: Other: Scientific Advisory Board; Caribou: Other: Scientific Advisory Board; GemoAb: Other: Scientific Advisory Board ; GSK: Other: Scientific Advisory Board ; Pharmacyclics: Other: Educational grant, Research Funding; Bayer: Other: Scientific Advisory Board ; Takeda: Other: License agreement and research agreement, Patents & Royalties; Affimed: Other: License agreement and research agreement; education grant, Patents & Royalties, Research Funding; Virogin: Other: Scientific Advisory Board ; AvengeBio: Other: Scientific Advisory Board . Qazilbash: Janssen: Research Funding; Biolline: Research Funding; Oncopeptides: Other: Advisory Board; NexImmune: Research Funding; Angiocrine: Research Funding; Amgen: Research Funding; Bristol-Myers Squibb: Other: Advisory Board. Kadia: Liberum: Consultancy; AstraZeneca: Other; Sanofi-Aventis: Consultancy; Genfleet: Other; Pulmotech: Other; Dalichi Sankyo: Consultancy; Genentech: Consultancy, Other: Grant/research support; Amgen: Other: Grant/research support; Astellas: Other; Jazz: Consultancy; Aglos: Consultancy; Ascentage: Other; Cellonkos: Other; Novartis: Consultancy; Pfizer: Consultancy, Other; AbbVie: Consultancy, Other: Grant/research support; BMS: Other: Grant/research support; Cure: Speakers Bureau. Kantarjian: BMS: Research Funding; Daiichi-Sankyo: Research Funding; Pfizer: Honoraria, Research Funding; KAHR Medical Ltd: Honoraria; Novartis: Honoraria, Research Funding; Jazz: Research Funding; Precision Biosciences: Honoraria; Amgen: Honoraria, Research Funding; Aptitude Health: Honoraria; Immunogen: Research Funding; Ascentage: Research Funding; AbbVie: Honoraria, Research Funding; NOVA Research: Honoraria; Ipsen Pharmaceuticals: Honoraria; Astellas Health: Honoraria; Astra Zeneca: Honoraria; Taiho Pharmaceutical Canada: Honoraria. Shpall: Adaptimmune: Consultancy; Magenta: Consultancy; Novartis: Honoraria; Affimed: Patents & Royalties; Navan: Consultancy; Magenta: Honoraria; Novartis: Consultancy; Takeda: Patents & Royalties; Bayer HealthCare Pharmaceuticals: Honoraria; Axio: Consultancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".