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Record W3083401631 · doi:10.1158/1538-7445.am2020-3233

Abstract 3233: Reduced inflammatory cytokine production by CAR-modified Th1/Tc1-polarized T-Rapa cells is not sufficient to reduce monocyte-driven production of IL-6

2020· article· en· W3083401631 on OpenAlexaff
Robyn A. A. Oldham, Tania C. Felizardo, Nathaniel Zhu, Daniel H. Fowler, Jeffrey A. Medin

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCytokineImmunologyBiologyChimeric antigen receptorT cellInterleukin 12Interleukin 21Cancer researchCytotoxic T cellImmune systemIn vitro

Abstract

fetched live from OpenAlex

Abstract Introduction: Chimeric antigen receptor (CAR) engineered T cells have delivered remarkable anti-tumor effects. However, the potential for severe and life-threatening cytokine release syndrome (CRS) represents a therapeutic obstacle. CRS is in large part IL-6 mediated, as indicated by the recent FDA approval of tocilizumab after CAR-T therapy. However, because CRS remains a significant clinical toxicity, additional approaches are necessary. Previous experimental data has shown that T-Rapa cells (T cells grown ex vivo in rapamycin) secrete reduced levels of some cytokines, and express a central memory phenotype that is associated with increased in vivo persistence. In the clinical context, allogeneic T-Rapa cells of a Th2 phenotype were associated with a low rate of GVHD, which is, in part, a cytokine-mediated syndrome. As such, T-Rapa cells represent a novel CAR effector cell type (CAR-T-Rapa). We hypothesized that Th1/Tc1 polarized CAR-T-Rapa cells would mediate efficient cytotoxicity, secrete reduced inflammatory T cell cytokines, and thereby result in a reduced propensity for IL-6 secretion, which is predominantly a monocyte-derived cytokine. Methods: Human CD3+ cells were treated with rapamycin in the presence of IFN-α and IL-2 to produce T-Rapa cells with a Th1/Tc1 phenotype, and were transduced with a CD19-41BB-CD3ζ CAR. T cells were expanded and used in assays including FACS assessment of T cell phenotype, co-culture assays, and 51Cr release assays, in comparison with non-rapamycin treated CAR T cells and non-transduced controls. ELISA and Luminex were used for assessment of cytokine levels in cell culture supernatants. Results: CAR-T and CAR-T-Rapa cells exhibited equal levels of cytotoxicity against CD19+ tumor cell lines. Both CAR-T and CAR-T-Rapa cells produced comparable amounts of IL-2 and undetectable IL-6 following co-culture with CD19+ tumor cell lines. However, CAR-T-Rapa cells produced significantly less IFN- γ, IL-4, TNF-α, and GM-CSF than CAR-T cells. When T cells were co-cultured with both tumor cells and monocytes, a high level of IL-6 secretion was detected in both CAR-T and CAR-T-Rapa conditions. Conclusions: CAR-T-Rapa cells are effective killers, while producing considerably less inflammatory cytokines in comparison to CAR-T cells. However, in the monocyte-replete condition, CAR-T-Rapa cells did not result in reduced IL-6 secretion, thereby indicating that controlling T cell inflammatory cytokine levels may not be sufficient to mitigate CRS. Citation Format: Robyn A. Oldham, Tania Felizardo, Nathaniel Zhu, Daniel H. Fowler, Jeffrey A. Medin. Reduced inflammatory cytokine production by CAR-modified Th1/Tc1-polarized T-Rapa cells is not sufficient to reduce monocyte-driven production of IL-6 [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3233.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.103
GPT teacher head0.390
Teacher spread0.287 · 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 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
Published2020
Admission routes1
Has abstractyes

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