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261 Evidence for durable anti-tumor responses by TAC-T cells in preclinical models of solid tumors

2022· article· en· W4308398717 on OpenAlexaff
Heather MacGregor, Duane Moogk, Stacey X. Xu, Joanne A. Hammill, Philbert Ip, Ling Wang, Swati Shetty, Kyle MacDonald, Laura Shaver, Sailaja Pirati, Prabha Lal, Christopher W. Helsen, Jonathan L. Bramson, Sadhak Sengupta, Andreas G. Bader

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2022
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCancer researchFlow cytometryT cellAntigenBone marrowImmune systemCytotoxic T cellBiologyMolecular biologyImmunologyIn vitro

Abstract

fetched live from OpenAlex

<h3>Background</h3> T cell antigen coupler (TAC) is a chimeric receptor that redirects T cells (TAC-T) towards surface-expressed tumor antigens to create safe and durable anti-cancer immune responses. The TAC activates T cells by co-opting the endogenous T cell receptor machinery via a CD3ε-specific binding motif and a cytoplasmic co-receptor tail. TAC01-HER2, a first-in-class TAC-T product targeting HER2 (ERBB2), has entered a phase I/II clinical trial. Here, we have characterized the fate of TAC-T cells during anti-tumor responses <i>in vitro</i> and <i>in vivo</i>. <h3>Methods</h3> <i>In vitro</i>, HER2-specific TAC-T products were challenged with HER2-expressing and HER2-negative tumors. Kinetics of proliferation, degranulation, activation, differentiation, and memory generation was assessed by flow cytometry. TAC-T products were subjected to multiple rounds of tumor cell exposure <i>in vitro</i> to test the durability of the T-cell-mediated immune response. Bioinformatic clustering analysis of flow cytometry data was performed to identify T cell populations and track them over time. T cell expansion in blood, tumor, bone marrow, and spleen were evaluated <i>in vivo</i> after primary xenograft tumor treatment and secondary tumor rechallenge. Tumor- and spleen-infiltrating or circulating T cells were phenotyped by flow cytometry after treatment with TAC-T cells. <h3>Results</h3> Co-culture studies revealed that TAC-T products become rapidly activated and degranulate upon contact with HER2-expressing, but not HER2-negative, cell lines. Activation coincided with rapid downregulation of the TAC receptor. A large proportion of the T cells expressed activation markers, and a majority of these also expressed degranulation markers, indicating ongoing cytotoxicity. <i>In vitro</i> and <i>in vivo</i> studies demonstrated a CD8-biased response characterized by a considerable expansion in the activated CD8 population enriched at the tumor site. Later, activation and differentiation markers returned to baseline concurrently with the re-emergence of surface TAC expression, initiating T cell proliferation. Importantly, central memory T cells were expanded, and stem-like cells were maintained, suggesting strong self-renewal potential. <i>In vitro</i> serial cytotoxicity assays showed that TAC-T products could repeatedly kill tumor cells up to several rounds. In tumor rechallenge experiments, a single dose of TAC-T cells expanded to clear solid tumor xenografts and protected mice from a second tumor challenge 30 days post initial tumor clearance, indicating long-lasting T cell persistence. <h3>Conclusions</h3> The TAC-T product mounts an effective anti-tumor response in multiple preclinical models, comprising activated TAC-T cells that do not become terminally exhausted but are dominated by an activated CD8 response and supported by the expansion of a memory population, indicating robust self-renewal capacity. <h3>Ethics Approval</h3> Animal studies were approved by McMaster University’s Animal Research Ethics Board under AUP 20-10-37.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.032
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.096
GPT teacher head0.366
Teacher spread0.270 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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