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Record W4417412714 · doi:10.33540/3350

Empowering CAR T Cell Therapy

2025· dissertation· W4417412714 on OpenAlexaff
Thomas Kimman

Bibliographic record

Venuenot available
Typedissertation
Language
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsGranzyme BCytotoxic T cellT cellChimeric antigen receptorGranzymeImmune systemImmunotherapyEffector

Abstract

fetched live from OpenAlex

Chimeric antigen receptors (CAR) T cell therapies have already demonstrated favourable clinical outcomes, however tumor resistance mechanisms remain a major obstacle. This thesis focuses on the primary function of CAR T cells in cancer therapy: the mechanisms by which they kill tumor cells. By gaining a fundamental understanding of how CAR T cells induce tumor cell death and how tumors resist this cytotoxicity, we can design new strategies to overcome resistance while avoiding systemic side effects. The main mechanism by which CAR T cells eliminate tumor cells is through the release of cytotoxic granule contents into the target cell. The key effector protein that induces regulated cell death (apoptosis) is granzyme B. In human biology, Serpin B9 is known as a specific inhibitor of granzyme B. Chapter 2 identifies Serpin B9 as a novel intrinsic resistance mechanism to CD19- and CD20-directed CAR T cell therapy. Previous studies have shown that preclinical inhibition of MCL-1 can effectively eliminate multiple myeloma (MM) cells. However, clinical trials have revealed severe systemic toxicity associated with MCL-1 inhibition, halting further clinical development. To make this potentially effective strategy feasible, we investigated synergistic combinations of clinically available agents. Chapter 3 demonstrates that inhibition of P70S6K1 and MCL-1 has a synergistic effect, thereby allowing treatment with lower doses of MCL-1 inhibitors and minimizing adverse effects. In Chapter 4, we explore innovative methods to specifically target MCL-1 in MM. To this end, we developed a strategy to express the MCL-1 antagonist NOXA within the cytotoxic granules of BCMA-targeted CAR T cells. We show that NOXA can be directed to the granules by fusing it to granzyme B. Upon recognition of an MM cell, NOXA is released into the tumor cell. This arming of BCMA CAR T cells with a pro-apoptotic NOXA, enhances their antitumor activity. Besides we also reveal MCL-1 expression by MM cells as a CAR T resistance mechanism. Chapter 5 describes an international patent filing related to fusion proteins based on the NOXA scaffold. In this approach, the effector BH3 domain of NOXA can be exchanged with BH3 domains from other pro-apoptotic BCL-2 family proteins. This design could support personalized therapies tailored to tumor-specific dependencies on survival proteins. Cytotoxic T cells mainly induce apoptosis. This clean form of cell death does not release inflammatory mediators such as damage-associated molecular patterns (DAMPs). In Chapter 6, we take the first steps toward using our CARgo delivery method to induce immunogenic cell death in vitro. We anticipate that inducing an immunogenic form of tumor cell death could shift the balance in favor of tumor-clearing CAR T cells, especially in solid tumor settings where T cell infiltration is limited. Collectively, this thesis provides new insights into intrinsic tumor resistance mechanisms involving Serpin B9 and MCL-1, and presents innovative strategies using CAR T cells to deliver pro-apoptotic or immunogenic cell death–inducing proteins to tumor cells. Together, these studies mark a step forward in overcoming resistance and advancing the next generation of CAR T cell therapies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.031
GPT teacher head0.380
Teacher spread0.349 · 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 designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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