Expert Perspectives on TROP2 NMR: Preclinical Evidence and Rationale

Preclinical research recently presented at the 2025 American Association for Cancer Research Annual Meeting highlighted the potential of the TROP2 NMR as a predictive biomarker for TROP2-directed therapies, particularly in the context of datopotamab deruxtecan . Unlike traditional biomarker assessments that rely primarily on visual interpretation of protein expression, this approach integrates deep learning and computational pathology to evaluate the extent of antibody-antigen interaction at the membrane level, which is critical for internalization and payload delivery. Studies using lung cancer cell lines demonstrated that simple presence of TROP2 on the cell surface was not sufficient; rather, internalization, as quantified by NMR, correlated strongly with cytotoxic response.

The data showed that lower NMR scores were associated with more effective internalization of the therapeutic payload, indicating higher likelihood of treatment efficacy. This inverse relationship, where lower values predict better outcomes, is atypical compared with many other biomarkers. The preclinical findings suggest that NMR scoring may help distinguish between tumors likely to benefit from antibody-drug conjugates and those that will not, refining patient selection well beyond what conventional IHC can offer. These insights support the growing importance of functional biomarkers—those that consider not only target presence but also biological activity relevant to therapeutic action.

Looking ahead, the use of TROP2 NMR in clinical trials is expanding. Current and future studies, including those embedding NMR scoring into statistical plans, aim to validate this biomarker’s predictive power in prospective patient cohorts. If confirmed, this could fundamentally reshape diagnostic strategies, requiring broader adoption of digital pathology and computational tools in routine oncology practice. Beyond lung cancer, the approach has broader implications, with potential to enhance precision diagnostics across multiple tumor types through integration of AI-based image analysis into biomarker development and companion diagnostics.