Therapeutically targeting endometrial cancer in preclinical models by ICAM1 antibody-drug conjugates

Objective. The incidence of mortality and morbidity from endometrial cancer (EC) is increasing annually, and there is a paucity of effective targeted therapies for the condition. Antibody-drug conjugates (ADCs) represent a promising approach to tumor-targeted therapy. In this study, we aim to identify a novel molecular target for the preclinical development of EC-targeted ADCs. Methods. Through quantitative and unbiased bioinformatics analyses intercellular adhesion molecule-1 (ICAM1) was identified as a potential cell membrane target. Two ADCs, ICAM1-MMAE and ICAM1-DXd, were subsequently developed by conjugating ICAM1 monoclonal antibodies with microtubule inhibitors and DNA topoisomerase inhibitors, respectively. The preclinical efficacy and biosafety of these ICAM1 ADCs were validated in both in vitro and in vivo models. Furthermore, transcriptomic analysis was conducted to elucidate the therapeutic effects of the ICAM1 ADCs. Results. Quantitative flow screening and bioinformatics analyses revealed significant overexpression of ICAM1 in EC. ICAM1-MMAE and ICAM1-DXd were developed using clinically effective linkers and payloads. In preclinical models, ICAM1 ADCs showed superior antitumor efficacy compared to standard chemotherapy, achieving sustained tumor regression with an excellent safety profile in both subcutaneous and orthotopic xenograft models. Transcriptomic analysis further revealed that ICAM1-DXd potently activated tumor immunity. Conclusions. ICAM1 was identified as a promising cell membrane protein target for ADC development in EC. As-synthesized ICAM1 ADCs demonstrated potent antitumor activity, favorable biosafety profiles in vitro and in vivo, and the ability to activate tumor immunity. These findings support the potential of ICAM1 ADCs as a therapeutic strategy and warrant further investigation in clinical studies. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar tech-nologies.