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Record W4415648825 · doi:10.1016/j.isci.2025.113610

Uncovering the individual immunotherapeutic roles of PTPN1 and PTPN2 in T cells during dual inhibition

2025· article· en· W4415648825 on OpenAlexafffund
Luis‐Alberto Pérez‐Quintero, Alexandre Poirier, Chu-Han Feng, Zuzet Martínez-Córdova, Isabelle Aubry, Cédric Carli, Samaneh Kamyabiazar, Alain Pacis, Yevgen Zolotarov, Kelly-Anne Pike, Jean‐Sébastien Delisle, Michel L. Tremblay

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsMcGill UniversityHôpital Maisonneuve-RosemontMcGill University Health Centre
FundersCanadian Institutes of Health ResearchCole FoundationMitacsMcGill University
KeywordsProtein tyrosine phosphataseCancer immunotherapyImmunotherapyPhosphataseCancer cellDual-specificity phosphataseCancerBlockadeTranscriptome

Abstract

fetched live from OpenAlex

Cancer immunotherapy benefits only a subset of tumor types, underscoring the need for new targets. The protein tyrosine phosphatases PTPN1 and PTPN2 have emerged as promising candidates and stand out for being amenable to small-molecule inhibition. However, competitive inhibitors inhibit both phosphatases equally, leaving a gap in our understanding of their individual contributions. Herein, we dissected the impact of single and double PTPN1/2 deletion on T cell cytotoxicity. Our results show that while PTPN2 functionally dominates over PTPN1, additional deletion of PTPN1 is required for maximal effect. Transcriptomic profiling revealed that dual deficiency synergistically enhanced the IL-10/STAT3 axis, providing mechanistic insight into their cooperative inhibitory roles. Furthermore, we demonstrate that phosphotyrosine mimetic inhibitors of PTPN1, which were already found to be safe in humans, can be repurposed for cancer immunotherapy. This strategy can be used to increase the efficacy of PD-1 blockade and broaden its use to other cancer types.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.235
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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