Abstract A041: Rejuvenation: Innovative technology to improve T-cell antitumor properties through partial reprogramming
Bibliographic record
Abstract
Abstract The T-cell identity and age determine function and fitness over a T-cell’s lifespan. This is particularly relevant when T cells are derived from patients with chronic viral infection or cancer. It is well known that increased T-cell age and differentiation and increased effector and exhausted phenotypes are associated with reduced anti-tumor efficacy and the need for higher infusion T-cell numbers for the treatment of hematological or solid tumors during adoptive cell therapies (ACT) (Kishton 2022). In an effort to overcome these barriers, methods to de-differentiate T cells into induced pluripotent stem cells (iPSCs) that return to embryonic immaturity, but lose their functional identity, have been extensively explored in the past years. Early work revealed several challenges to re-differentiate iPSCs into T cells with the desired functional phenotype, requiring a complex and time-consuming process. Here, we bring our novel strategy that counters the impact of aging on T-cell function through cellular rejuvenation without de-differentiating to iPSCs. We achieved T-cell rejuvenation via partial reprogramming of aged T cells by transiently expressing transcription factors associated with iPSC reprogramming. This proprietary partial reprogramming methodology reduces epigenetic age and rejuvenates T cells while maintaining the phenotype and function of conventional T cells. We were the first to illustrate the ability to reduce the epigenetic age of T cells without fully de-differentiating to iPSCs. Our initial studies with PBMCs showed a significant reduction in epigenetic age. On subsequent RNAseq analyses, we observed that rejuvenated and conventional T cells have equivalent transcriptomes, suggesting the maintenance of identity. Functionally, the TRJ cells are characterized by greatly improved cell-expansion capacity, together with increased expression of markers associated with T-cell stemness, including CCR7 and CD62L. In vitro studies of NY-ESO-1-targeted T-cell receptor (TCR) or a CD19-targeted chimeric antigen receptor (CAR) TRJ cells exhibited improved antitumor properties compared with non-rejuvenated T-cell control (TCT) cells in sequential cell-killing assays. We also confirmed the enhanced in vivo antitumor efficiency of NY-ESO-1 TCR TRJ cells in a murine xenograft tumor model. When tumor-infiltrating lymphocytes (TILs) are rejuvenated with the same rejuvenation technology the TILs showed enhanced cell-expansion capacity, and improvements in T-cell stemness phenotype. These results suggest the potential application of T-cell rejuvenation across multiple adoptive T-cell therapeutic modalities and may potentially improve outcomes for patients with solid tumors. Citation Format: Jessica Fioravanti, Yasuhiro Yamazaki, Takuya Maeda, Yin Huang, Naritaka Tamaoki, Kriti Bahl, Burak Kutlu, Shobha Potluri, Gary Lee, Nicholas P Restifo, Raul Vizcardo. Rejuvenation: Innovative technology to improve T-cell antitumor properties through partial reprogramming [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A041.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".