{"doi":"10.1136/jitc-2022-sitc2022.0952","title":"952 The temporal contribution of interferon-γ in driving T-cell exhaustion and response to immune checkpoint blockade","abstract":"<h3>Background</h3> Immune checkpoint blockade (ICB) therapies have revolutionized treatment for cancer patients, yet minority do not respond, highlighting the importance of understanding therapeutic resistance. Interferon-gamma (IFNγ) drives protective T cell responses and augments anti-tumor immunity yet also promotes T cell exhaustion during tumor progression. Such dichotomy exhibited by IFNγ impacts immunotherapeutic responses. However, whether IFNγ continues to modulate immune responses on exhausted T (T<sub>EX</sub>) cells remains unanswered. Our lab has been extensively studying lineage plasticity of T<sub>EX</sub> cells by lineage-tracing lymphocyte activation gene-3 (LAG3)-expressing cells in the context of anti-tumor responses. We hypothesize that understanding the temporal expression profile of IFNγ on T<sub>EX</sub> cells will help address mechanistic insights into diverse responses to ICB. <h3>Methods</h3> To evaluate the pleiotropic role of IFNγ, we established two unique murine models, one assessing temporal tamoxifen induced global transcriptional expression of <i>Ifnγ</i> on <i>Lag3</i>-expressing cells (<i>Lag3</i><sup>iCreERT2</sup><i>Ifnγ</i><sup>YFP</sup><i>Rosa26</i><sup>LSL-tdTomato</sup>). This allows the immune cell-specific contribution of IFNγ on progenitor T<sub>EX</sub> and terminal T<sub>EX</sub> cells to be assessed. The second model temporally induces genetic deletion of <i>Ifnγ</i> on<i> Lag3-</i>expressing cells<i> (Lag3</i><sup>iCreERT2</sup><i>Ifnγ</i><sup>L/<i>L</i></sup><i>Rosa26</i><sup>LSL-tdTomato</sup>). Melanoma (B16F10) and adenocarcinoma (MC38) models were used to evaluate tumor growth and survival kinetics, T<sub>EX</sub> cell profile and response to ICB therapy. <h3>Results</h3> Assessing the transcriptional profile of <i>Ifnγ</i> shows that IFNγ is expressed by LAG3-expressing cells as early as D7, a progenitor T<sub>EX</sub> state, with NK and NKT cells as major contributors. Expression of IFNγ is maintained through a terminally exhausted state (D23), with CD8<sup>+</sup> T cells as the major IFNγ<i>-</i>producing cells. Our <i>Lag3</i><sup>iCreERT2</sup><i>Ifnγ</i><sup>L/<i>L</i></sup><i>Rosa26</i><sup>LSL-tdTomato</sup> model system, allows to examine the pleiotropic effects of IFNγ in early immune responses as well as T cell exhaustion (early vs terminal). Early temporal deletion of IFNγ (D5~D7) potentiates tumor growth in MC38 model with no survival advantage with anti-PD1. This suggests that during initial antigen exposure, IFNγ is necessary for reinvigoration of anti-tumor response and deletion of <i>Ifnγ</i> augments T cell exhaustion. However, with later time-point deletion (D11~D13), we observed 50% survival advantage with anti-PD1 ICB, which suggests modulating tumor microenvironment in a time-dependent manner is the key to augmenting ICB response. <h3>Conclusions</h3> This study highlights the distinct temporal response patterns and exhaustion profile with deletion of <i>Ifnγ</i> from pre-exhausted to terminally exhausted <i>Lag3+</i> cells. IFNγ production at early time points (D5~D7) is a key mediator of anti-tumor immunity while the deletion of <i>Ifnγ</i> at terminal points (D11~D13) highlights better ICB response. <h3>Acknowledgements</h3> This study was supported by the NIH – P01 AI108545 to D.A.A.V., A.H.S., and E.J.W.","journal":"Regular and Young Investigator Award Abstracts","year":2022,"id":312904,"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":0.9507,"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":292549,"name":"Chang Liu","orcid":"0000-0001-8924-1772","position":1,"is_corresponding":false},{"id":292547,"name":"Lawrence P. Andrews","orcid":"0000-0002-4923-142X","position":2,"is_corresponding":false},{"id":744748,"name":"Madhu Malinee","orcid":"0000-0003-2872-5081","position":3,"is_corresponding":false},{"id":629747,"name":"Carly Cardello","orcid":"0000-0002-2810-7681","position":4,"is_corresponding":false},{"id":255411,"name":"Creg J. Workman","orcid":"0000-0002-9964-9509","position":5,"is_corresponding":false},{"id":109278,"name":"Dario A.A. Vignali","orcid":"0000-0002-2771-5992","position":6,"is_corresponding":false},{"id":471837,"name":"Vaishali Aggarwal","orcid":"0000-0001-7964-3479","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":[]}