CRISPR-mediated deletion of the Protein tyrosine phosphatase, non-receptor type 22 (PTPN22) improves human T cell function for adoptive T cell therapy
Bibliographic record
Abstract
Abstract Adoptive T cell transfer has improved the treatment of cancer patients. However, treatment of solid tumors is still challenging and new strategies that optimize T cell function and response duration in the tumor could be beneficial additions to cancer therapy. In this study, we deleted the intracellular phosphatase PTPN22 and the endogenous TCR α chain from human PBMC-derived T cells using CRISPR/Cas9 and transduced them with TCRs specific for a defined antigen. Deletion of PTPN22 in human T cells increased the secretion of IFNγ and GM-CSF in multiple donors. The cells retained a polyfunctional cytokine expression after re-stimulation and greater numbers of PTPN22 KO T cells expressed inflammatory cytokines compared to unmutated control cells. PTPN22 KO T cells seemed to be more polyfunctional at low antigen concentrations. Additionally, we were able to show that that PTPN22 KO T cells were more effective in controlling tumor cell growth. This suggests that they might be more functional within the suppressive tumor microenvironment thereby overcoming the limitations of immunotherapy for solid tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".