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1303 Phase I/Ib study of next generation JAK-STAT CAR-T therapy for patients with relapsed or refractory CD19 <sup>+</sup> B-cell lymphoma, chronic lymphocytic leukemia (CLL), small lymphocytic lymphoma (SLL)

2025· article· W4415871413 on OpenAlexaff
Christine Chen, John Kuruvilla, Valentin Sotov, Chesa Bouhs, Celia-Marie Cassiani, Sawako Elston, Kathryn Fisher, Michael Fyrsta, О. А. Левина, Kiichi Murakami, Jessica Nie, Disha Prajapati, Michelle Restrepo, Elizabeth Scheid, Sylvia Tran, Minge Xu, Trina Wang, Maki Tanaka, Shinya Tanaka, Linh Nhat Nguyen, Marcus O. Butler

Bibliographic record

Venuenot available
Typearticle
Language
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsChronic lymphocytic leukemiaRefractory (planetary science)LymphomaCD20Phases of clinical researchCD19IbrutinibLeukemia

Abstract

fetched live from OpenAlex

Background Chimeric antigen receptors (CAR) are synthetic receptors that combine an antigen-specific extracellular domain with key functional intracellular domains of a T cell receptor (TCR). While regulatory approval for CD19 targeting CAR-T cell therapy has been achieved for acute lymphoblastic leukemia (ALL), diffuse large B cell lymphoma (DLBCL), and primary mediastinal large B cell lymphoma (PMBCL), this therapy in other cancers which also express CD19 requires further optimization. Optimal T cell activation and proliferation requires multiple signals, including T cell receptor engagement (signal 1), co-stimulation (signal 2), and cytokine engagement (signal 3). Current clinical CAR-T cell therapies provide signals 1 and 2, but not signal 3. The forced expression of cytokine genes in CAR-T cells improves their persistence and antitumor effects in vivo, highlighting the importance of signal 3 for CAR-T cell functions.Methods To investigate the safety of CAR-T cells incorporating signal 3, we embarked on a clinical trial ( NCT05963217) with the novel ‘JAK/STAT’ CAR.1 We developed a CD19 CAR construct capable of inducing cytokine signaling upon antigen stimulation with a JAK/STAT CAR that encodes a truncated cytoplasmic domain of IL-2Rβ and a STAT3-binding YXXQ motif together with CD3ζ and CD28 domains (28-ΔIL2RB-z (YXXQ)). A retrovirus vector encoding the novel JAK/STAT CAR was used. We embarked on a single institution, investigator initiated clinical trial with ‘in house’ manufactured CD19 JAK-STAT CAR T-cells targeting CD19+ lymphoma or chronic lymphocytic leukemia (CLL).Results To date, 4 subjects have received CART T-cell infusions. In Cohort 1, 3x10 5/kg JAK/STAT CAR-T cells were infused into 3 subjects: 2 follicular lymphoma (FL) and 1 CLL. One subject with CLL was treated in Cohort 2 (1x106/kg). In all patients, no severe adverse events were observed other than expected hematological complications. One FL patient treated with the lowest dose experienced a complete metabolic response lasting 9 months. The CLL patient treated in Cohort 2 achieved minimal residual disease (MRD) negative status which continues 9+ months. Both of these responding patients demonstrated engraftment of CAR-T cells and no dose limiting toxicities.Conclusions Next generation CAR-T cell therapy incorporating JAK/STAT signalling is safe at initial doses studied and demonstrates promising clinical activity.Trial Registration clinicaltrials.gov registration number is NCT05963217.Reference Kagoya, Y. et al. A novel chimeric antigen receptor containing a JAK-STAT signaling domain mediates superior antitumor effects. Nature Medicine 2018; 24: 352–359.Ethics Approval This abstract presents results from a clinical study that has obtained Research Ethics Board approval (UHN CAPCR# 22-2039) and is registered at clinicaltrials.gov (NCT05963217).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.045
GPT teacher head0.310
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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".

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Citations0
Published2025
Admission routes1
Has abstractno

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