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Abstract B025: Neutrophil depletion enhances the anti-tumor activity of CAR-T cells in an autochthonous model of non-small cell lung cancer

2023· article· en· W4389227694 on OpenAlexaboutno aff
Sergio Ortiz‐Espinosa, Shivani Srivastava

Bibliographic record

VenueCancer Immunology Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsCancer researchT cellCD8ImmunotherapyMedicineInfiltration (HVAC)CellLung cancerCancer immunotherapyImmune systemImmunologyPathologyBiology

Abstract

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Abstract CAR-T cell therapy has produced durable clinical responses in hematological malignancies, but efficacy in more common solid tumors is limited in part by poor trafficking of CAR-T cells to tumors. Identifying strategies to improve CAR-T cell trafficking to and persistence within tumors, thus, are needed to improve efficacy against solid tumors. We previously adapted the Kras/p53 (KP) autochthonous model of non-small cell lung cancer (NSCLC) to express the CAR target ROR1 to study the mechanisms limiting the activity of ROR1 CAR-T cells. Similar to what we observed in a phase 1 trial, ROR1 CAR-T cells infiltrated KPROR1 tumors poorly and induced limited tumor control. We showed that using immunogenic chemotherapy to induce the production of T cell-recruiting chemokines significantly improved CAR-T cell infiltration into lung tumors. However, despite improved survival, many tumor nodules remained devoid of CAR-T cells, suggesting that additional mechanisms likely limit CAR-T cell access to tumors. Neutrophils have been shown to inhibit T cell infiltration and function within tumors, and high neutrophil infiltrates in NSCLC patients are associated with poor T cell infiltration and response to immunotherapy. To characterize how neutrophils may impact CAR-T cell localization, we treated KPROR1 mice with ROR1 CAR-T cells or left untreated and analyzed the co-localization of CD8+ T cells and Ly6G+ neutrophils within tumors by multiplex IHC. In untreated mice, neutrophils were primarily found around the perimeters of large tumors (>105 mm2) but were absent from small tumors (<105 mm2). Upon treatment with CAR-Ts, CD8+ T cell frequency increased across all tumors, but intratumoral T cell infiltration was significantly higher in small neutrophil-low tumors than in large neutrophil-high tumors, suggesting that neutrophil-high tumors may be less accessible to CAR-T cells. Interestingly, CAR-T treatment induced a significant increase in neutrophils, with CD8+ T cells in closer proximity to neutrophils, suggesting that CAR-Ts may promote neutrophil recruitment. We hypothesized that this increase in neutrophils might further suppress CAR-T activity. To test this, we pre-treated tumor-bearing KPROR1 mice with anti-Gr1 or a C5aR1 inhibitor, both of which have been shown to reduce neutrophil recruitment to tumors, and injected control or ROR1 CAR-T cells 2 days later. Anti-Gr1 and C5aR1 inhibitor treatments continued for 17 days after CAR-T cell injection. Whereas treatment with CAR-T cells alone had minimal effects, co-treatment with either anti-Gr-1 or C5aR1 inhibitor significantly improved survival. Our findings suggest that neutrophils may limit CAR-T infiltration and/or persistence within tumors and that neutrophil depletion can enhance the anti-tumor activity of CAR-T cells. Future work is aimed at understanding the mechanisms by which neutrophils may limit CAR-T activity. Overall, our results suggest that targeting neutrophils may be a promising strategy to overcome the major challenges in CAR-T therapy for solid tumors. Citation Format: Sergio Ortiz-Espinosa, Shivani Srivastava. Neutrophil depletion enhances the anti-tumor activity of CAR-T cells in an autochthonous model of non-small cell lung cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B025.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.177
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.402
Teacher spread0.322 · 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.

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
Published2023
Admission routes1
Has abstractyes

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