作者
Cody Scandore,Keith Johnson,Chris May,Rebecca Howell,Judy Guernsey,Tali Vittum,Noah Dephoure,Hengyu Xu,Heath E. Klock,Ertan Eryilmaz,Clare F. Malone,Francesca Nardi,Michael C. Warren,Danielle Elise Zimmerman,Pamela M. Holland,Scott A. Lesley,Niyi Fadeyi,Rob Oslund
摘要
Abstract Despite advances in cancer therapeutics, there remains a critical need to identify new protein targets and targeting approaches for drug development, particularly those with improved efficacy and safety profiles. To address these challenges, InduPro leverages a high-resolution proximity proteomics technology that uses photocatalyst-generated reactive probes to label cell surface protein microenvironments (1, 2). Combining this technology with quantitative mass spectrometry, we achieve high-throughput characterization of the plasma membrane protein interactome at an unprecedented detail. The integration of this specialized proximity proteomics data with clinical protein expression profiles through computational graphs and graph convolutional neural networks (GCNNs) provides powerful opportunities for surface protein target analysis and unique biological insight. This approach enables the identification of surface antigens in close proximity to tumor associated antigens (TAAs) within tumor microenvironments. Here, we refer to these proximal antigens as tumor associated proximity antigens (TAPAs). The co-localization and interrelated biological functions of these antigens facilitate precise dual-targeting of malignant cells, while sparing healthy tissues. This approach addresses existing limitations in cancer therapeutics, particularly the off-target effects associated with TAAs expressed on normal cells, and the poor efficacy of some tumor-specific antigens as standalone targets. Through leveraging these TAPAs, we can explore a diverse range of bispecific therapeutic modalities, including but not limited to antibody-drug conjugates (ADCs), T-cell engagers (TCEs), and radioligand therapies (RLTs). By applying our MInt platform to cancer cell lines, patient-derived tumor samples, and animal model xenografts, we have generated a database of protein interaction networks for many TAAs comprising millions of data points. This has resulted in the identification of novel TAA x TAPA bispecific pairings across multiple indications. This platform has led to multiple preclinical programs, including IDP-001, a bispecific ADC targeting EGFR and CDCP1 in solid tumors. Here, we showcase our discovery engine for novel target identification as a powerful method for uncovering druggable protein targets with improved therapeutic windows. This approach has the potential to expand the repertoire of cancer targets and enable the next generation of bispecific therapeutics. Ref: (1) Oslund, RC et al., Nature Chem. Bio. 18 (2022) 850-858 (2) Geri, JB et al., Science. 367 (2020) 1091-1097 Citation Format: Cody Scandore, Kendall Johnson, Chris May, Rebecca Howell, Jeffrey Guernsey, Tali Vittum, Noah Dephoure, Hengyu Xu, Heath Klock, Ertan Eryilmaz, Clare Malone, Francesca Nardi, Michael Warren, Danielle Zimmerman, Pamela Holland, Scott Lesley, Niyi Fadeyi, Rob Oslund. Application of a membrane interactomics (MInt) platform for novel surface target discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4244.