Advancing Biomarker-Driven Strategies in NSCLC: Exploring the Emerging Role of QCS and TROP2 NMR - Episode 8
Looking Toward Potential Future Integration of TROP2 NMR and QCS: Changing Workflows and Anticipated Challenges
Panelists discuss the significant changes needed to integrate quantitative continuous scoring (QCS) and TROP2 normalized membrane ratio (NMR) assays into routine clinical practice, emphasizing the current limited adoption of digital pathology, the likely use of artificial intelligence (AI)-based assays as send-out tests initially, and the importance of expanding digital infrastructure and collaboration to enable precise, automated biomarker evaluation that can guide personalized oncology treatment.
Looking ahead to integrating QCS and TROP2 NMR assays into routine clinical practice, significant changes will be required in pathology workflows. Currently, one of the biggest hurdles is that digital pathology, which is essential for AI-driven biomarker analysis, is still not widely adopted. Only approximately 10% to 15% of major academic centers use digital platforms for primary diagnosis, and many smaller or community labs remain fully analog. Because traditional immunohistochemistry scoring is manual and subjective, transitioning to digital methods will be key to enable consistent, automated, and quantitative biomarker evaluation.
In the near term, it is likely that these AI-based assays will function as send-out tests, relying on specialized bioinformatics pipelines and cloud computing infrastructure that many pathology labs do not yet possess. Fortunately, most labs are already equipped to perform antibody testing, so samples can be prepared locally and then digitized externally for analysis. This hybrid approach allows broader access while the necessary digital infrastructure expands. The rise of digital pathology not only facilitates more precise quantification of biomarkers but also opens new possibilities to explore beyond conventional scoring methods, enabling richer, data-driven insights into tumor biology.
Despite these exciting advancements, challenges remain. Widespread adoption depends on increasing digital pathology integration, developing streamlined workflows, and fostering collaboration between pathologists and oncologists. As more predictive companion diagnostics and AI-driven biomarkers enter clinical pipelines, pathology labs will need to adapt to deliver timely and accurate results that guide personalized treatments. Though biomarker testing has long been a critical goal in oncology, progress is accelerating, and these technologies hold promise to transform diagnostics and improve patient care in the near future.
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