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Record W4362541661 · doi:10.1158/1538-7445.am2023-1785

Abstract 1785: Multi-step engineering of gene-edited CAR T cells using RNA lipid nanoparticles

2023· article· en· W4362541661 on OpenAlexaff
Samuel Clarke, R Geczy, A Balgi, Sejun Park, R Zhao, M Swaminathan, R. Tieu, Nguyền Vũ Hoàng, C Webb, E Watt, M Wong, Mami Fujisawa, Nikita Jain, Angela Zhang, A.M. Thomas

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsPrecision Nanosystems (Canada)
Fundersnot available
KeywordsCD19Chimeric antigen receptorMolecular biologyTransfectionGene expressionBiologyCell biologyChemistryFlow cytometryT cellGeneImmune systemImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Autologous chimeric antigen receptor (CAR) T therapies utilize patient cells and can be limited by cell quality, and the high manufacturing burden of viral vectors. As such, there is a need for allogeneic, “off-the-shelf” CAR T cells to make these transformative treatments widely available. However, allogeneic therapies require multiple genetic engineering steps to express CAR and to delete proteins responsible for graft-versus-host disease. Messenger RNA (mRNA) is a promising approach for expression of therapeutic proteins and gene editing nucleases. In this work, we demonstrate a new method for multi-step engineering of gene-edited CAR T cells using RNA lipid nanoparticles (LNPs). LNPs encapsulating Spy-Cas9 mRNA, TCR and CD52 guide RNA (sgRNA), and CAR mRNA were produced using microfluidics. The CAR construct contained an anti-CD19 scFv binding domain and CD3ζ/4-1BB co-stimulatory domains. Microgram quantities of RNA LNPs were produced to optimize LNP packaging, cargo ratios, and sgRNA combinations. Lead candidates were scaled to milligrams. Purified human primary T cells were cultured, activated, and expanded in serum-free media in plates, flasks and bioreactors. CAR+, TCR− or CD52− cells were generated by addition of the corresponding LNP to activated cells. Cytotoxic killing was determined by co-culture assays with leukemia cells. Gene knockout, CAR expression, viability and cell killing were measured using flow-cytometry. CD19 CAR was selected as a relevant protein for expression, with TCR and CD52 proteins as gene knockout targets. Single-step addition of CAR LNPs to T cells resulted in transfection efficiencies of 95.0 ± 2.1% and high protein expression. Upon TCR or CD52 LNP addition to T cells, the onset of gene editing was within 48 hours, reaching single target knockout efficiencies of 92.3 ± 3.0% (TCR−), and double knockouts (TCR−/CD52−) of 74.5 ± 6.1%. Similar results were obtained when comparing different LNP batch sizes (microgram to milligram RNA) and cell culture vessels (125,000 to 45 million cells), demonstrating scalability of both the LNP production and cell treatment. Cell viabilities above 90% were maintained at all steps and for all RNA LNPs. Finally, as proof-of-concept for multi-step engineering, sequential addition of TCR LNPs and CAR LNPs resulted in simultaneous CAR expression and TCR gene knockout. These “off-the-shelf” gene-edited CAR T cells were functionally equivalent to non-edited cells in a B cell killing assay, efficiently clearing over 80% of leukemia target cells at a 1:1 ratio. Our findings demonstrate the advantages of LNPs for RNA delivery to T cells. The simple and gentle nature of LNP cell treatment allows for multiple genetic engineering steps for simultaneous expression and deletion of proteins. Furthermore, LNPs can be easily manufactured using microfluidics, enabling small-scale screening of RNA libraries and rapid scale-up of lead candidates for clinical translation. Citation Format: Samuel Clarke, R Geczy, A Balgi, S Park, R Zhao, M Swaminathan, R Tieu, N Hoang, C Webb, E Watt, M Wong, M Fujisawa, N Jain, Angela Zhang, Anitha Thomas. Multi-step engineering of gene-edited CAR T cells using RNA lipid nanoparticles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1785.

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 score0.001
Threshold uncertainty score0.004

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.171
GPT teacher head0.433
Teacher spread0.262 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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