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A phase I/II trial investigating the safety and efficacy of autologous TAC T cells targeting HER2 in relapsed or refractory solid tumors.

2023· article· en· W4317891402 on OpenAlexaff
Benjamin L. Schlechter, Daniel J. Olson, Samuel D. Saibil, Mridula George, Kelly Gruber, Riemke Bouvier, Brooke Pieke, Jill Geisburger, Kara M. Moss, Nathan Ternus, Deyaa R Adib, Ecaterina E. Dumbrava

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCytokine release syndromeChimeric antigen receptorT cellTolerabilityCancerImmunotherapyClinical trialCancer researchOncologyInternal medicineImmune systemImmunologyAdverse effect

Abstract

fetched live from OpenAlex

TPS816 Background: HER2 overexpression is well established as a therapeutic target in breast cancer and can be seen in a variety of gastrointestinal malignancies, notably in gastroesophageal cancer where positivity ranges from 4.4% to 53.4%. The recent approval of HER2-targeted therapies to treat gastroesophageal adenocarcinomas has validated HER2 as an actionable target in these diseases. Recent data also support the emerging role of HER2 directed therapy in colorectal cancer, among other solid tumors. Although patient outcomes have improved, this remains an area of significant unmet medical need. The T cell antigen coupler (TAC) technology is a novel approach to modifying a patient’s own T cells, allowing them to recognize and treat HER2+ solid tumors. The TAC receptor is composed of a HER2-binding domain, similar to a traditional chimeric antigen receptor (CAR) T cell, however unlike CAR T cells, it uses the signaling pathway of the natural T cell receptor (TCR) to avoid off target toxicity. This novel “TAC receptor” is hypothesized to deliver a targeted anti-cancer T cell response with a much-improved safety profile as compared with traditional CAR T cells such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome events (ICANS). In this ongoing clinical trial (NCT04727151), subjects undergo leukapheresis, bridging therapy (if needed) while TAC T cells are engineered, lymphodepletion chemotherapy (LDC), and finally TAC01-HER2 infusion. Methods: The Phase 1 dose escalation study is underway to investigate the safety and tolerability of TAC01-HER2 in adult subjects with HER2+ solid tumors (1+, 2+ or 3+) who have progressed after ≥2 lines of therapy at dose levels 0.3, 0.8, 3, and 8 x 106 cells/kg. Dose limiting toxicities (DLTs) are assessed up to day 28. A Phase 2 will further evaluate the safety, efficacy, and pharmacokinetics of the optimal TAC01-HER2 dose in various HER2+ tumors. As of 19 August 2022, 8 subjects have been treated at Dose Levels (DL) 1 and 2, with no observed DLTs, CRS, or ICANS. Five subjects had 11 serious adverse events, all unrelated to TAC01-HER2 treatment. A majority of adverse events were related to LDC and/or the underlying disease. At DL 2, a partial response was observed in a subject with refractory metastatic gastric adenocarcinoma (3+ HER2) on day 29, with a 36.5% reduction in measurable disease. Two additional subjects at DL 2 had stable disease, one with refractory gall bladder cancer (3+ HER2) and one with refractory colorectal cancer (2+ HER2) with no change in tumor measurements compared to baseline. Dose escalation of TAC01-HER2 is ongoing, with the first subject being treated at DL 3. These results in a heavily pre-treated gastrointestinal cancer population show manageable safety and promising efficacy with a novel T cell therapy that may have broad clinical applicability in HER+ cancers. Clinical trial information: NCT04727151 .

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.169
GPT teacher head0.510
Teacher spread0.341 · 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 designRandomized 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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