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464 Anti-CTLA-4 therapy depletes Tregs and expands ICOS<sup>+</sup> T-cells in neuroblastoma tumors with induced DNA mismatch repair deficiency

2022· article· en· W4308396215 on OpenAlexaff
Megan Hong, René Figueredo, Saman Maleki

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2022
Typearticle
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsCancer researchCytotoxic T cellCTLA-4EffectorT cellImmunologyBiologyNeuroblastomaImmune systemCell cultureGenetics

Abstract

fetched live from OpenAlex

<h3>Background</h3> Cytotoxic T lymphocyte-associated protein 4 (CTLA-4) highly expressed on regulatory T-cells (Tregs) inhibit the activation of pro-inflammatory T-cells responsible for eliminating cancer cells. Anti-CTLA-4 can enhance T-cell activation by increasing CD28 co-stimulatory signaling through CTLA-4 blockade or depletion of Tregs by Fc-dependent effector mechanisms. Strategies to improve its therapeutic efficacy are needed as patient response rates to anti-CTLA-4 are low. Response to anti-CTLA-4 has been positively correlated with tumor mutation burden (TMB). Defects in the DNA mismatch repair (MMR) pathway can increase TMB and the production of neoantigens that promote anti-tumor immune responses. Here we investigate the underlying mechanism(s) to which induced MMR deficiency in an immunologically-cold and low TMB tumor model can enhance the therapeutic effect of anti-CTLA-4. We hypothesize that induced MMR deficiency in tumors enhances anti-CTLA-4-mediated Treg depletion and increases the infiltration and activation of effector T-cells. <h3>Methods</h3> MMR deficiency was induced in a syngeneic murine neuro-2a neuroblastoma cell line by knocking-out <i>MLH1</i> expression using CRISPR-Cas9. Wildtype MMR-proficient (pMMR) or induced MMR-deficient (idMMR) neuro-2a cells were inoculated into immunocompetent A/J mice and treated with anti-CTLA-4. Tumors were immunophenotyped by flow cytometry and mixed-lymphocyte reaction assays were used to examine the effects of MMR deficiency and anti-CTLA-4 on T-cell activation and proliferation. <h3>Results</h3> Induced MMR deficiency in neuroblastoma tumors enhances the anti-tumor immune response induced by anti-CTLA-4. MMR deficiency in neuroblastoma tumors promoted anti-CTLA-4-mediated Treg depletion and increased intratumoral CD3<sup>+</sup> T-cells. idMMR neuroblastoma tumors had an increase of ICOS<sup>+</sup> T-cells compared to pMMR tumors. In addition, ICOS<sup>+</sup> T-cells were increased further with anti-CTLA-4 treatment. <h3>Conclusions</h3> Our data show that inducing MMR deficiency in low TMB and immune-cold neuroblastoma tumors can enhance the anti-tumor effect of anti-CTLA-4 by increasing T-cell activation and depletion of Tregs. By understanding the underlying mechanism(s) of anti-CTLA-4 in idMMR tumors, it may justify targeting the MMR pathway to improve the response to immune checkpoint inhibitors in patients with immunologically-cold and/or low TMB tumors that are refractory to immunotherapy. Future studies will assess how inducing MMR deficiency alters the tumor microenvironment to enable anti-CTLA-4-mediated Treg depletion and the significance of ICOS<sup>+</sup> T-cells in the efficacy of anti-CTLA-4 therapy in this setting.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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.

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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