HIV-Specific T Cells Generated from Naive T Cells Suppress HIV In Vitro and Recognize Wide Epitope Breadths
Bibliographic record
Abstract
The Berlin Patient represents the first and only functional HIV cure achieved by hematopoietic stem cell transplant (HSCT). In subsequent efforts to replicate this result, HIV rebounded post-HSCT after withdrawal of antiretroviral therapy. Providing HIV-specific immunity through adoptive T cell therapy may prevent HIV rebound post-HSCT by eliminating newly infected cells before they can seed systemic infection. Adoptive T cell therapy has demonstrated success in boosting Epstein-Barr virus and cytomegalovirus-specific immunity post-HSCT, controlling viral reactivation. However, T cell immunotherapies to boost HIV-specific immunity have been limited by single-epitope specificity and minimal persistence or efficacy in vivo. To improve this strategy, we sought to generate allogeneic HIV-specific T cells from human leukocyte antigen (HLA)-A02+ HIV-negative adult or cord blood donors. We focused on HLA-A02+ donors due to well-characterized epitope restrictions observed in HIV+ populations. We show that multi-antigen HIV-specific T cells can be generated from naive T cells of both cord blood and adults using a reproducible good manufacturing practice (GMP)-grade protocol. This product lysed antigen-pulsed targets and suppressed active HIV in vitro. Interestingly, these cells displayed broad epitope recognition despite lacking recognition of the common HLA-A02-restricted HIV epitope Gag SL9. This first demonstration of functional multi-antigen HIV-specific T cells has implications for improving treatment of HIV through allogeneic HSCT. Patel et al. demonstrate the ability to generate HIV-specific T cells from HIV-seronegative adults and cord blood with a good-manufacturing-practice-compliant strategy. These immunotherapies are multi-antigen specific, display cytotoxicity, and suppress HIV in vitro, providing a promising platform for adoptive T cell therapy in a post-transplant setting.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".