A Multi-Layered Proteogenomic Framework for the Prioritization of Cell Surface Therapeutic Targets: Proof-of-Concept for Metastatic Colorectal Cancer.

BACKGROUND: Identification of tumor-specific cell surface targets is a critical step in the development of precision oncology therapeutics, including radioligand- and antibody-based approaches. However, existing strategies often rely on single-layer analyses and lack systematic integration of proteomic, genomic, and clinical metadata. METHODS: We developed a multi-layered proteogenomic filtering framework integrating quantitative proteomics from colorectal cancer (CRC) cohorts with curated metadata on protein localization, normal tissue expression, and druggability. Eleven complementary filtering strategies were applied, followed by manual curation for extracellular accessibility and composite scoring based on protein rank, localization, and clinical relevance. RESULTS: Application of the pipeline to metastatic CRC (mCRC) identified multiple high-confidence candidate targets, including GPRC5A, SLC2A1, CD47, DPEP1 and IFITM1. The average pairwise overlap between filtering strategies was low (0.11), indicating limited redundancy and complementary target identification across approaches. Importantly, candidates detected by multiple strategies were significantly enriched for established biomarkers (FAP, CEACAM5, ITGAV, ITGB4), which were exclusively found among multi-strategy candidates (10.3% vs. 0%; Fisher's exact test, p = 0.0064), supporting overlap-based prioritization as a marker of biological and translational relevance. Composite scoring further prioritized GPRC5A as a leading candidate. Additional validation layers supported tumor-enriched expression, plasma membrane localization, and relevance across multiple cancer indications. CONCLUSIONS: This study presents a scalable framework for prioritization of cell surface therapeutic targets, using mCRC as proof-of-concept indication. By integrating multiple data layers and incorporating translational criteria early in the discovery process, this approach may facilitate more efficient identification of targets for downstream development, including antibody- and radioligand-based therapies.