{"doi":"10.17615/8bd1-2393","title":"Tumor neoantigen heterogeneity impacts bystander immune inhibition of pancreatic cancer growth","abstract":"The immunogenic clonal-fraction threshold in heterogeneous solid-tumor required to induce effective bystander-killing of non-immunogenic subclones is unknown. Pancreatic cancer poses crucial challenges for immune therapeutic interventions due to low mutational-burden and consequent lack of neoantigens. Here, we designed a model to incorporate artificial-neoantigens into genes-of -interest in cancer-cells and to test their potential to actuate bystander-killing. By precisely controlling a neoantigen's abundance in the tumor, we studied the impact of neoantigen frequency on immune-response and immune-escape. Our results showed single, strong, widely-expressed neoantigen could lead to robust antitumor response when over 80% of cancer cells express the neoantigen. Further, immunological assays demonstrated T-cell responses against non-target self-antigen on KRAS-oncoprotein, when we inoculated animals with a high frequency of tumor-cells expressing test-neoantigen. Using nanoparticle-based gene-therapy, we successfully altered tumor-microenvironment by perturbing interleukin-12 and interleukin-10 gene-expression. The subsequent microenvironment-remodeling reduced the neoantigen frequency threshold at which bioluminescent signal intensity for tumor-burden decreased 1.5-log-fold, marking robust tumor-growth inhibition, from 83% to 29%. Our results thus suggest bystander killing is inefficient in immunologically-cold tumors like pancreatic-cancer and requires high neoantigen abundance. However, bystander killing mediated antitumor response can be rescued by adjuvant-immune therapy.","journal":"UNC Libraries","year":2024,"id":499620,"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":0.9569,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":295364,"name":"Xin Zhou","orcid":"0000-0002-5285-947X","position":1,"is_corresponding":false},{"id":254423,"name":"Leaf Huang","orcid":"0000-0002-9421-8283","position":2,"is_corresponding":false},{"id":69694,"name":"Benjamin G. Vincent","orcid":"0000-0002-1762-2282","position":3,"is_corresponding":false},{"id":1348320,"name":"Manash C. Das","orcid":null,"position":4,"is_corresponding":false},{"id":1347546,"name":"Ruilin Liu","orcid":"0009-0004-3687-3927","position":5,"is_corresponding":false},{"id":1347547,"name":"Ye Liu","orcid":"0000-0003-1716-7037","position":6,"is_corresponding":false},{"id":380759,"name":"Anirban Das","orcid":"0000-0002-1417-5173","position":7,"is_corresponding":false},{"id":1347545,"name":"Jingao Li","orcid":"0000-0002-0389-7576","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:09:53.188165Z","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":[]}