Abstract 3773: ROR1 and PMEPA1 as combined predictive biomarkers for metastasis in triple-negative breast cancer: Multi-omics and clinical validation

Abstract Background: Triple-negative breast cancer (TNBC) remains the most aggressive subtype of breast cancer, characterized by high heterogeneity, early metastasis, and lack of effective targeted therapies. Early identification of high-risk patients is crucial for improving outcomes. Previously, we identified receptor tyrosine kinase-like orphan receptor 1 (ROR1) as a TNBC-specific extracellular vesicle (EV) marker capable of capturing tumor-derived EVs (2025 AACR #Abstract 2005). Building on this foundation, we conducted an integrated multi-omics and clinical validation study to uncover novel ROR1-associated molecules that could enhance the prediction of metastatic potential in TNBC. Methods: EV proteomics were used to confirm TNBC-specific markers. scRNA-seq (GSE176078) and TCGA-BRCA bulk RNA-seq data were analyzed. hdWGCNA identified hub genes in epithelial-mesenchymal transition (EMT)-high invasive TNBC cells. LASSO, support vector machine (SVM), and random forest algorithms were applied to screen EMT-related metastasis genes. TCGA data were split (7:3) into training and validation sets, and AutoGluon machine learning with undersampling constructed the metastasis prediction model. For clinical validation, TNBC patients were categorized by metastatic status, immunofluorescence staining of PMEPA1 and ROR1 was performed on TNBC tumor tissues, with CKpan distinguishing tumor parenchyma from stroma. Results: Proteomic and computational analyses identified PMEPA1 as a key metastasis-associated gene, showing the highest predictive performance (AUC) in the WeightedEnsemble-L2 model. In clinical samples, patients who later developed distant metastases exhibited significantly higher PMEPA1 and ROR1 expression than those who remained metastasis-free. Co-localization analysis showed both proteins enriched in CKpan+ tumor regions. The combination of ROR1 and PMEPA1 improved predictive performance for TNBC metastasis compared to either marker alone, consistent with bioinformatics predictions. These findings suggest cooperative involvement of ROR1 and PMEPA1 in TNBC progression. Conclusions: PMEPA1 is identified as a novel ROR1-associated molecule involved in TNBC metastasis. Co-expression of PMEPA1 and ROR1 serves as a robust predictor for metastatic progression. Integrating machine learning-based transcriptomic modeling with clinical validation provides a promising framework for early metastasis risk stratification in TNBC. Ongoing studies aim to elucidate the ROR1-PMEPA1 signaling axis as a potential therapeutic target. Research Sponsor: National Natural Science Foundation of China (No. 82202603). Citation Format: Hao Wang, Linze Xu, Song An, Yang Liu. ROR1 and PMEPA1 as combined predictive biomarkers for metastasis in triple-negative breast cancer: Multi-omics and clinical validation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3773.