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Record W4368373774 · doi:10.1016/j.ymthe.2023.04.021

A chemically inducible IL-2 receptor signaling complex allows for effective in vitro and in vivo selection of engineered CD4+ T cells

2023· article· en· W4368373774 on OpenAlexfundno aff
Peter J. Cook, Su Jung Yang, Gene Uenishi, Annaiz Grimm, Samuel E. West, Lijie Wang, Chester Jacobs, Andrea Repele, Travis Drow, Ahmad Boukhris, Noelle Dahl, Karen Sommer, Andrew M. Scharenberg, David J. Rawlings

Bibliographic record

VenueMolecular Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
FundersAkebia TherapeuticsCanadian Institute of Steel ConstructionChildren’s Hospital of Wisconsin Research InstituteSeattle Children's Research InstituteLeona M. and Harry B. Helmsley Charitable Trust
KeywordsIn vivoCell biologyBiologyT-cell receptorFOXP3CD19Chimeric antigen receptorIn vitroT cellCancer researchImmunologyAntigenImmune systemGenetics

Abstract

fetched live from OpenAlex

Engineered T cells represent an emerging therapeutic modality. However, complex engineering strategies can present a challenge for enriching and expanding therapeutic cells at clinical scale. In addition, lack of in vivo cytokine support can lead to poor engraftment of transferred T cells, including regulatory T cells (Treg). Here, we establish a cell-intrinsic selection system that leverages the dependency of primary T cells on IL-2 signaling. FRB-IL2RB and FKBP-IL2RG fusion proteins were identified permitting selective expansion of primary CD4+ T cells in rapamycin supplemented medium. This chemically inducible signaling complex (CISC) was subsequently incorporated into HDR donor templates designed to drive expression of the Treg master regulator FOXP3. Following editing of CD4+ T cells, CISC+ engineered Treg (CISC EngTreg) were selectively expanded using rapamycin and maintained Treg activity. Following transfer into immunodeficient mice treated with rapamycin, CISC EngTreg exhibited sustained engraftment in the absence of IL-2. Furthermore, in vivo CISC engagement increased the therapeutic activity of CISC EngTreg. Finally, an editing strategy targeting the TRAC locus permitted generation and selective enrichment of CISC+ functional CD19-CAR-T cells. Together, CISC provides a robust platform to achieve both in vitro enrichment and in vivo engraftment and activation, features likely beneficial across multiple gene-edited T cell applications. Engineered T cells represent an emerging therapeutic modality. However, complex engineering strategies can present a challenge for enriching and expanding therapeutic cells at clinical scale. In addition, lack of in vivo cytokine support can lead to poor engraftment of transferred T cells, including regulatory T cells (Treg). Here, we establish a cell-intrinsic selection system that leverages the dependency of primary T cells on IL-2 signaling. FRB-IL2RB and FKBP-IL2RG fusion proteins were identified permitting selective expansion of primary CD4+ T cells in rapamycin supplemented medium. This chemically inducible signaling complex (CISC) was subsequently incorporated into HDR donor templates designed to drive expression of the Treg master regulator FOXP3. Following editing of CD4+ T cells, CISC+ engineered Treg (CISC EngTreg) were selectively expanded using rapamycin and maintained Treg activity. Following transfer into immunodeficient mice treated with rapamycin, CISC EngTreg exhibited sustained engraftment in the absence of IL-2. Furthermore, in vivo CISC engagement increased the therapeutic activity of CISC EngTreg. Finally, an editing strategy targeting the TRAC locus permitted generation and selective enrichment of CISC+ functional CD19-CAR-T cells. Together, CISC provides a robust platform to achieve both in vitro enrichment and in vivo engraftment and activation, features likely beneficial across multiple gene-edited T cell applications.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.300
Teacher spread0.276 · 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

Citations25
Published2023
Admission routes1
Has abstractno

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