{"doi":"10.1136/jitc-2022-sitc2022.0975","title":"975 Characterization of signaling and metabolic differences between γδ and αβ CAR T cells","abstract":"<h3>Background</h3> γδ T cell-based immunotherapies have emerged as an alternative to the traditional αβ T cell-based products. For example, our group has shown that γδ CAR T cells can reduce tumor burden and mitigate tumor-induced bone deterioration in a preclinical model of bone metastatic prostate cancer. In the present study, we investigated the signaling events triggered by a second-generation CAR (originally designed for αβ T cells) in γδ T cells, to test the hypothesis that CAR-induced signaling varies depending on the T cell subset. The ultimate goal of our study is to gain mechanistic insight into the biology of γδ T cells and to inform future CAR design for improved γδ-based adoptive cell therapies. <h3>Methods</h3> We designed our analysis as a side-by-side comparison of phosphorylation events, resulting from CAR activation, between γδ and αβ T cells. These were expanded in parallel from a healthy donor and transduced with a retroviral vector (MSGV1) to express a second-generation anti-PSCA CAR (PSCA-8t28z). CAR-T cells were then cocultured independently with metabolically labelled C4-2B-PSCA tumor cells, for 1 hour, and protein phosphorylation was quantified using <b>L</b>iquid <b>C</b>hromatography–<b>T</b>andem <b>M</b>ass <b>S</b>pectrometry (LC/MS-MS). Statistically significant differences in phosphorylation were defined using a Welch’s <i>t</i>-test (fold-change ≥ 1.5 and <i>p</i>-value &lt; 0.05). Using Qiagen’s Ingenuity Pathway Analysis software, we identified canonical pathways that were significantly overrepresented in the population of proteins that displayed differential phosphorylation in γδ CAR T cells relative to αβ. We used the SeaHorse XF kits for Glycolytic Rate Assay and T Cell Metabolic Profiling for functional characterization of the metabolic properties of CAR-T cells. Finally, flow cytometry was used to analyze Glut-1 expression (anti-Glut1, clone 202915), glucose intake (2NBDG), mitochondrial mass (MitoTracker Green), and mitochondrial membrane polarization (TMRE). <h3>Results</h3> We identified 323 phosphorylation events that were differentially abundant between T cell subsets. Within this group, glycolysis and gluconeogenesis were within the top overrepresented canonical pathways. Stimulated γδ T cells showed significantly lower glycolytic rate compared to αβ. CAR expression was accompanied by higher glycolytic rate and expression of Glut-1 receptor in both T-cell types. Finally, oxidative phosphorylation (OXPHOS) was lower in γδ CAR T cells, potentially related to their also lower mitochondrial mass. <h3>Conclusions</h3> CAR-induced signaling varies among T cell subsets, and γδ T cells display lower glycolytic and OXPHOS rates upon activation. Ongoing efforts are focused on delineating molecular causes and functional consequences of the metabolic differences between αβ and γδ T cells. <h3>Acknowledgements</h3> This work has been supported in part by the Bioinformatics, the Proteomics, and the Flow Cytometry Core Facilities at the Moffitt Cancer Center, an NCI designated Comprehensive Cancer Center (P30-CA076292). We would also like to thank Dr. Paulo Rodriguez, and Dr. Gina DeNicola for their input in the analysis of the results. <h3>Ethics Approval</h3> All animal experiments were performed under University of South Florida IACUC approval (R1762; R7429) and in accordance with the Guidelines for the Care and Use of Laboratory Animals manual published by the National Institutes of Health.","journal":"Regular and Young Investigator Award Abstracts","year":2022,"id":312914,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9514,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":255129,"name":"Daniel Abate‐Daga","orcid":"0000-0002-2571-0215","position":1,"is_corresponding":false},{"id":526635,"name":"Leticia Tordesillas","orcid":"0000-0002-4643-7553","position":2,"is_corresponding":false},{"id":1011202,"name":"Xiomar Bustos","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T00:33:40.460845Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}