Immune Reconstitution After Antithymocyte Globulin (ATG)-Conditioned Hematopoietic Cell Transplantation (HCT)
Bibliographic record
Abstract
Abstract Abstract 1981 Introduction: Immune reconstitution after HCT is important for curbing infections and malignancy. ATG has been increasingly used to prevent graft-vs-host disease (GVHD), however, its impact on immune reconstitution has not been well studied. Here we studied (1) immune reconstitution after ATG-conditioned HCT, (2) compared it to non-ATG-conditioned HCT, and (3) determined factors influencing the immune reconstitution. Patients and Methods: Immune subset cell counts were determined on day 28, 56, 84, 180, 365 and 730 post transplant in 125 recipients of allogeneic filgrastim-mobilized blood stem cells who received ATG (Thymoglobulin, 4.5 mg/kg) during conditioning. The subset counts were also determined in 47 non-ATG-conditioned patients (otherwise similarly treated). Subset counts (in blood) and ATG levels (in serum) were quantified by flow cytometry. Mann-Whitney rank sum test was used to compare subset counts (1) in ATG-conditioned patients vs donors, (2) in ATG-conditioned patients vs non-ATG-conditioned patients, and (3) between subgroups of ATG-conditioned patients; Spearman rank correlation test was used to determine associations between subset counts and ordinal variables like ATG levels. Results: (1) After ATG-conditioned HCT, the counts of the following subsets normalized (became not significantly lower than in donors) by day 28: NK cells, monocytes, myeloid dendritic cells (MDCs), and plasmacytoid dendritic cells (PDCs). The counts of the following subsets normalized by day 84: memory/effector CD8 T cells, and CD4−CD8− T cells. The counts of naïve B cells normalized by day 180. The counts of the following subsets have not normalized by day 365 or 730: memory B cells (both isotype switched and unswitched), both naïve and memory/effector CD4 T cells, naïve CD8 T cells, CD4+CD8+ T cells, and invariant NKT (iNKT) cells. (2) Compared to non-ATG-conditioned HCT, counts of B cells, CD4 T cells and CD8 T cells were significantly lower after ATG-conditioned HCT on day 28. Thereafter, recovery of both naïve and memory B cells and memory/effector CD8 T cells was significantly faster in ATG-conditioned patients, leading to higher total B and higher total CD8 T cell counts on day 84 (Figure). On the contrary, recovery of naïve CD8 T cells and both naïve and memory/effector CD4 T cells was significantly slower, the latter leading to low total CD4 T cell counts throughout the first year (Figure). (3) Reconstitution after ATG-conditioned HCT was influenced by (a) the number of cells of the same subset transferred with the graft in case of increased memory B cells, naïve CD4 T cells, naïve CD8 T cells, iNKT cells and MDCs, (b) age of recipient in case of decreased naïve CD4 T cells and naïve CD8 T cells, (c) cytomegalovirus (CMV) serostatus of recipient in case of increased memory/effector T cells, (d) GVHD in case of increased naïve B cells, and (e) day 7 or 28 ATG levels in case of decreased T cell subsets. Conclusion: (1) Reconstitution after ATG conditioned HCT is very fast for NK cells, monocytes, MDCs and PDCs, fast for memory/effector CD8 T cells and CD4−CD8− T cells, slow for naïve B cells, and very slow for memory B cells, both naïve and memory/effector CD4 T cells, naïve CD8 T cells, CD4+CD8+ T cells and iNKT cells. (2) Compared to no ATG, the patients conditioned with ATG have lower counts of B and T cells on day 28. Thereafter, the ATG-conditioned patients have faster recovery of both naïve and memory B cells and memory/effector CD8 T cells, and slower recovery of both naïve and memory/effector CD4 T cells and naïve CD8 T cells. (3) Similar to what has been described for non-ATG-conditioned HCT, reconstitution after ATG-conditioned HCT is influenced by the number of the immune cells transferred with the graft, recipient age, recipient CMV serostatus and GVHD. Moreover, the reconstitution after ATG-conditioned HCT is influenced by ATG clearance. 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".