FOXP3 and Helios Expressing CD4+ T Conventional Cells Correlate with T Cell Activation after Orca-T Allogeneic T Cell Immunotherapy
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only curative therapy for many hematologic malignancies . The primary non-relapse complication preventing the widespread use of allo-HSCT is graft-versus-host disease (GVHD). The use of T regulatory cells (Tregs) to prevent GVHD has emerged as a promising allogeneic T cell immunotherapy in the form of Orca-T. Orca-T consists of the sequential infusion of CD34+ hematopoietic stem cells and high-purity Tregs followed by conventional T cells. However, the precise differences in immune states which may influence clinical outcomes after Orca-T compared with unmanipulated peripheral blood stem cell (PBSC) grafts remains unexplored. Using peripheral blood specimens longitudinally collected between 3 weeks and 1 year after leukemia treatment, we report single-cell mRNA sequencing (scRNA-seq) and flow cytometric analysis of 51 HLA-matched patients receiving either Orca-T or unmanipulated PBSC grafts. Orca-T recipients exhibited increased frequencies of effector memory CD4+ T cells 3 weeks after treatment (47.0% Orca-T vs. 34.9% PBSC, p=0.003) and this difference persisted through 6 months after treatment despite the 50 to 100-fold reduced number of T cells infused with Orca-T. To identify potentially important T cell populations that drive clinical differences between Orca-T and unmanipulated PBSC grafts, sorted T cells subsets or whole PBSC from 16 total patients were captured for scRNA-seq analysis 3 weeks post-treatment ( Fig. 1A ). Transcriptomic analysis identified increased expression of FOXP3 and Helios (cluster 6) amongst CD4+CD25- T conventional cells (Tcons) in Orca-T treated patients ( Fig. 1B-D , 7.8% Orca-T vs. 3.0% PBSC, p=0.035). Using flow cytometry, we then confirmed the increased frequency of CD4+CD25-FOXP3+Helios+ Tcons - but not Tregs – 3 weeks post-treatment in patients receiving Orca-T (11.6% Orca-T vs. 4.4% PBSC, p=0.018). Further, we discovered that this T cell subset correlated significantly with the frequencies of multiple activated CD4+ and CD8+ T cell populations 3 months post-treatment, regardless of which therapy patients received ( Fig. 2A-B ). Overall, this study identifies an increase in T cell activation early after Orca-T immunotherapy , and we propose that the sequential addition of high-purity Tregs directs the immune reconstitution of CD4+ T cells towards a potentially immunomodulatory, FOXP3 and Helios-expressing phenotype. Further, we provide an in-depth examination of various immune activation states very early after cellular therapy for leukemia and we identify a novel T cell subset which may be predictive of long-term immune activation after T cell infusion.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".