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Record W4414465197 · doi:10.1158/2326-6074.cimm25-a018

Abstract A018: Targeting PTPN1/PTPN2 with KQ-791 enhances NK cell cytotoxicity during tumor rechallenge

2025· article· en· W4414465197 on OpenAlexaffabout
Sonali Uttam, Chu-Han Feng, Isabelle Aubry, David Langlais, Pierre Laneuville, Michel L. Tremblay

Bibliographic record

VenueCancer Immunology Research · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsMcGill University
Fundersnot available
KeywordsCytotoxic T cellCytotoxicityLymphokine-activated killer cellCytolysisNK-92Immune systemCancer immunotherapyImmunotherapyInterleukin 12

Abstract

fetched live from OpenAlex

Abstract Introduction and Hypothesis: Natural killer (NK) cells are cytolytic innate lymphocytes that play a key role in immune surveillance and elimination of malignant cells, particularly those that evade T cell responses and contribute to metastasis. NK cells eliminate tumor cells through multiple cytotoxic mechanisms, including direct release of cytotoxic granules to induce tumor cells apoptosis. Their ability to recognize and kill tumor cells without prior antigen presentation makes them attractive candidates for allogeneic cell-based cancer immunotherapy. However, the lack of persistent cytotoxic activity in cancer patients remains challenging to improve NK cell-based immunotherapy. Protein tyrosine phosphatases PTPN1 (PTP1B) and PTPN2 (TC-PTP) are negative regulators of cytokine signaling and immune cell activation. In our previous studies, we showed that pharmacological dual-inhibition of PTPN1/PTPN2 enhanced expression of cytokine-mediated cytotoxic granules in NK cells. Since NK cell mediated serial killing involves persistent release of cytolytic granules, we hypothesize that dual inhibition of PTPN1 and PTPN2 will enhance NK cell-mediated persistent cytotoxicity during tumor rechallenges. Experimental Methods: We used a competitive small molecule inhibitor, KQ-791, targeting PTPN1/PTPN2 in NK cells. To assess the effect of KQ-791 on NK cell cytotoxicity over repeated tumor encounters, we used the Incucyte Live-Cell Analysis System. NK-92 cells were pre-treated for with KQ-791 or PBS, prior to co-culture. GFP expressing U87 tumor cells and NK-92 NK cells were then assayed in co-culture at various effector: target cell ratios. To model tumor rechallenge, fresh U87-GFP cells were added every 2–3 days for five total challenges. IL-2 was replenished at each rechallenge. In one experimental arm, KQ-791 (or PBS) was added again at alternate rechallenges. Tumor cell burden was assessed by measuring integrated fluorescence intensity of GFP+ U87 cells. Images were acquired periodically and analyzed to quantify tumor cell killing. Results: NK cells pre-treated with KQ-791 showed enhanced tumor clearance across all the effector: target ratios at the initial tumor challenge as well as for four subsequent tumor rechallenges. The difference in tumor killing was most evident for the 1:1 effector: target cell ratio. Retreatment with KQ-791 enhanced killing at tumor rechallenge. Conclusion: These findings demonstrate that pharmacological inhibition of PTPN1 and PTPN2 using KQ-791 enhances NK-92–mediated persistent tumor cell killing during repeated tumor challenges. The observed effect was particularly pronounced at lower effector-to-target ratios, highlighting the potential of KQ-791 to improve NK cell persistence and efficacy under limiting conditions. This study provides evidence supporting the use of PTPN1/2 inhibitors to potentiate NK cell–based immunotherapy, warranting further investigation in primary NK cells and in vivo models. Citation Format: Sonali Uttam, Chu-Han Feng, Isabelle Aubry, David Langlais, Pierre Laneuville, Michel L. Tremblay. Targeting PTPN1/PTPN2 with KQ-791 enhances NK cell cytotoxicity during tumor rechallenge [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A018.

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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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.337
Teacher spread0.304 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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