High Pre-HCT Exposure to Thymoglobulin Is Associated with a Low Incidence of Relapse
Bibliographic record
Abstract
Abstract Introduction The most frequent serious complications of allogeneic hematopoietic cell transplant (HCT) are leukemia relapse and graft-vs-host disease (GVHD). Rabbit anti-T cell globulin (ATG, Thymoglobulin) reduces GVHD without increasing relapse, as shown in 3 randomized studies (Chang et al. Cancer 2017, Walker et al. Lancet Oncol 2016, Bacigalupo et al. BBMT 2006). ATG has a direct anti-leukemic effect in vitro (Dabas et al. BBMT 2016, Dabas et al. BMT 2018 in press). In spite of that, in the randomized studies ATG did not reduce relapse. We hypothesized that this could be due to differential effects of ATG pre- vs post-HCT on relapse. Specifically, post-HCT (but not pre-HCT), the anti-leukemic effect of ATG could be countered by ATG impairing graft-vs-leukemia effect (GVL). Here we set out to determine whether pre- vs post-HCT ATG area-under-the-curve (AUC) is associated with relapse and GVHD, the latter possibly being a surrogate for GVL. Methods A total of 153 patients were studied. All received first allogeneic HCT using conditioning with fludarabine (250 mg/m2), busulfan (~12.8 mg/kg) + TBI (4 Gy) and GVHD prophylaxis with rabbit ATG (0.5 mg/kg on day -2, 2 mg/kg on day -1 and 2 mg/kg on day 0 before graft infusion), methotrexate and cyclosporine. Serum was collected at the end of the last ATG infusion, immediately pre-graft infusion, 30 minutes after the end of graft infusion and days 7 and 28. Serum levels of ATG capable of binding to total mononuclear cells (MNCs) were determined using a flow cytometry-based assay (similar to Podgorny et al. BBMT 2010). Pre-HCT ATG AUC was estimated from ATG levels at the end of the last ATG infusion and pre-graft infusion. Post-HCT ATG AUC was estimated from the ATG levels 30 minutes post graft infusion and on days 7 and 28. AUCs were split into above and below median for analysis. Multivariate competing risks regression (according to Fine and Gray) was used to determine whether high ATG AUC was associated with the cumulative incidence of relapse. Multivariate Cox regression was used to determine if high ATG AUC was associated with relapse-free survival (RFS). Results High (above median) pre-HCT AUC was associated with a lower incidence of relapse (SHR=0.430, P=0.029) (Fig. 1, top). It was also associated with improved RFS (HR=0.534, P=0.040). For post-HCT AUC, there appeared to be a trend toward an opposite association - high (above median) post-HCT AUC associated with a higher incidence of relapse (SHR=1.470, P=0.302) (Fig. 1, bottom). There was no association between high post-HCT AUC and RFS. Regarding GVHD, there appeared to be a trend toward an association between high pre-HCT AUC and a lower incidence of significant GVHD (grade 2-4 aGVHD or moderate-severe cGVHD) (SHR=0.667, P=0.128) (Fig. 2, top). A strong and significant association was observed between high post-HCT AUC and a lower incidence of significant GVHD (SHR=0.462, P=0.005) (Fig. 2, bottom). Conclusion High pre-HCT ATG AUC appears to result in less relapse and improved RFS. This may be the in vivo reflection of the known anti-leukemic activity of ATG in vitro. In contrast, high post-HCT AUC appears to have a neutral or deleterious impact on relapse. As high post-HCT AUC is strongly associated with a low incidence of GVHD, which may be a surrogate for weak GVL, it is possible that after HCT, the anti-leukemic effect of ATG is overshadowed by its anti-GVL effect. Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".