Discovery of novel biomarkers for glioblastoma using VHH antibodies.

e14068 Background: Although comprehensive genomic profiling has revealed the molecular features of glioblastoma (GBM), most identified genomic alterations fail to translate into effective therapeutic targets to date, underscoring a critical gap between molecular characterization and druggable GBM biology. Tumor cell surface antigens play a crucial role in novel cancer therapies such as CAR-T and antibody-drug conjugates. The lack of clinically actionable, tumor-selective cell-surface biomarkers remains a fundamental obstacle to such novel approaches for GBM. Alpaca-derived variable domain of heavy chain of heavy chain (VHH) antibodies possess a simple structure, are easily digitized through sequencing, and enable AI-driven biomarker discovery. Methods: We established Inverse BioMarker Exploration Technology (IBMET) using VHH, a fundamentally different biomarker discovery approach that treats antibodies as structural sensors rather than affinity reagents. A large-scale alpaca-derived VHH repertoire was generated through immunization using multiple GBM cell lines, followed by phage display and deep sequencing. Instead of selecting antibodies against predefined targets, IBMET applies enrichment-based statistical analysis across millions of VHHs to identify convergent binding patterns indicative of shared tumor-specific structural epitopes. Candidate VHHs were validated by immunohistochemistry and immunofluorescence using extensive panels of GBM and normal human tissues. Antigen identity was resolved by cross-linking immunoprecipitation and LC–MS/MS. Clinical relevance was assessed in an independent GBM cohort (n = 20). Results: IBMET uncovered multiple previously inaccessible structural biomarker candidates in GBM. The lead clone, VHH19, selectively recognized a novel antigen protein not previously implicated in GBM biology. VHH19 demonstrated robust and highly tumor-specific staining in GBM tissues, with negligible reactivity across normal organs. Notably, VHH19 positivity was observed in over 20% of GBM cases, defining a clinically relevant tumor subset unified by a shared structural epitope rather than genomic alteration or expression level. These findings reveal a previously unrecognized layer of tumor stratification based on conformational antigen states. Conclusions: IBMET represents a paradigm shift in biomarker discovery by enabling direct, high-throughput identification of structurally defined tumor-selective targets independent of prior molecular assumptions. By bridging antibody-level structural recognition with translational validation, this approach opens a new route to actionable targets in GBM, with immediate implications for antibody–drug conjugates, radioligand therapies, and companion diagnostics. Four GBM-selective VHH antibodies identified through IBMET will be released for research use in June 2026, facilitating rapid downstream translation.