{"doi":"10.3389/fonc.2021.664236","title":"High-Affinity Chimeric Antigen Receptor With Cross-Reactive scFv to Clinically Relevant EGFR Oncogenic Isoforms","abstract":"Tumor heterogeneity is a key reason for therapeutic failure and tumor recurrence in glioblastoma (GBM). Our chimeric antigen receptor (CAR) T cell (2173 CAR T cells) clinical trial (NCT02209376) against epidermal growth factor receptor (EGFR) variant III (EGFRvIII) demonstrated successful trafficking of T cells across the blood–brain barrier into GBM active tumor sites. However, CAR T cell infiltration was associated only with a selective loss of EGFRvIII+ tumor, demonstrating little to no effect on EGFRvIII - tumor cells. Post-CAR T-treated tumor specimens showed continued presence of EGFR amplification and oncogenic EGFR extracellular domain (ECD) missense mutations, despite loss of EGFRvIII. To address tumor escape, we generated an EGFR-specific CAR by fusing monoclonal antibody (mAb) 806 to a 4-1BB co-stimulatory domain. The resulting construct was compared to 2173 CAR T cells in GBM, using in vitro and in vivo models. 806 CAR T cells specifically lysed tumor cells and secreted cytokines in response to amplified EGFR, EGFRvIII, and EGFR-ECD mutations in U87MG cells, GBM neurosphere-derived cell lines, and patient-derived GBM organoids. 806 CAR T cells did not lyse fetal brain astrocytes or primary keratinocytes to a significant degree. They also exhibited superior antitumor activity in vivo when compared to 2173 CAR T cells. The broad specificity of 806 CAR T cells to EGFR alterations gives us the potential to target multiple clones within a tumor and reduce opportunities for tumor escape via antigen loss.","journal":"Frontiers in Oncology","year":2021,"id":165073,"datarank":0.5456379239589579,"base_score":3.6375861597263857,"endowment":3.6375861597263857,"self_citation_contribution":0.5456379239589579,"citation_network_contribution":0.0,"self_endowment_contribution":0.5456379239589579,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":37,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.956,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":263102,"name":"Zev A. Binder","orcid":"0000-0003-1158-231X","position":1,"is_corresponding":false},{"id":687889,"name":"Yibo Yin","orcid":"0000-0001-8003-3547","position":2,"is_corresponding":false},{"id":688615,"name":"Logan Zhang","orcid":null,"position":3,"is_corresponding":false},{"id":687890,"name":"Jiasi Vicky Zhang","orcid":"0000-0002-4318-3754","position":4,"is_corresponding":false},{"id":227991,"name":"Daniel Y. Zhang","orcid":"0000-0001-9962-9295","position":5,"is_corresponding":false},{"id":242813,"name":"Michael C. Milone","orcid":"0000-0002-1580-9844","position":6,"is_corresponding":false},{"id":21533,"name":"Guo‐li Ming","orcid":"0000-0002-2517-6075","position":7,"is_corresponding":false},{"id":123278,"name":"Hongjun Song","orcid":"0000-0002-8720-5310","position":8,"is_corresponding":false},{"id":90587,"name":"Donald M. O’Rourke","orcid":"0000-0002-8479-7314","position":9,"is_corresponding":false},{"id":674387,"name":"Radhika Thokala","orcid":"0000-0002-4279-4115","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:45:36.425208Z","pmid":"34568006","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":[]}