Immunotherapeutic implications of negative regulation by protein tyrosine phosphatases in T cells: the emerging cases of PTP1B and TCPTP
Bibliographic record
Abstract
In the context of inflammation, T cell activation occurs by the concerted signals of the T cell receptor (TCR), co-stimulatory receptors ligation, and a pro-inflammatory cytokine microenvironment. Fine-tuning these signals is crucial to maintain T cell homeostasis and prevent self-reactivity while offering protection against infectious diseases and cancer. Recent developments in understanding the complex crosstalk between the molecular events controlling T cell activation and the balancing regulatory cues offer novel approaches for the development of T cell-based immunotherapies. Among the complex regulatory processes, the balance between protein tyrosine kinases (PTK) and the protein tyrosine phosphatases (PTPs) controls the transcriptional and metabolic programs that determine T cell function, fate decision, and activation. In those, PTPs are de facto regulators of signaling in T cells acting for the most part as negative regulators of the canonical TCR pathway, costimulatory molecules such as CD28, and cytokine signaling. In this review, we examine the function of two close PTP homologs, PTP1B (PTPN1) and T-cell PTP (TCPTP; PTPN2), which have been recently identified as promising candidates for novel T-cell immunotherapeutic approaches. Herein, we focus on recent studies that examine the known contributions of these PTPs to T-cell development, homeostasis, and T-cell-mediated immunity. Additionally, we describe the signaling networks that underscored the ability of TCPTP and PTP1B, either individually and notably in combination, to attenuate TCR and JAK/STAT signals affecting T cell responses. Thus, we anticipate that uncovering the role of these two PTPs in T-cell biology may lead to new treatment strategies in the field of cancer immunotherapy. This review concludes by exploring the impacts and risks that pharmacological inhibition of these PTP enzymes offers as a therapeutic approach in T-cell-based immunotherapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".