Defining and Understanding TROP2 Normalized Membrane Ratio (NMR)

Conventional IHC methods for evaluating biomarkers like TROP2 in non–small cell lung cancer have inherent limitations that can hinder effective patient stratification for targeted therapies. Traditional scoring systems are typically manual, subjective, and focused on visual interpretations of staining intensity within specific compartments such as the nucleus, cytoplasm, or membrane. Although these assessments can indicate presence or absence of the target, they often fail to capture the dynamic aspects of ADC function—most notably, the internalization of the therapeutic payload into cancer cells, which is critical for efficacy.

To address this gap, advanced methodologies like QCS and the NMR are being developed. These computational approaches use deep learning algorithms and digital pathology to evaluate protein expression at the cellular level with much greater precision. QCS quantifies staining by segmenting each cell and its subcompartments, measuring optical density, and calculating ratios that reflect internalization potential. This creates a more reliable, reproducible, and objective assessment compared with traditional IHC. Importantly, it also allows for development of predictive thresholds based on clinical outcomes, offering a more personalized approach to treatment selection.

The workflow for applying these technologies involves digitizing stained tissue slides through whole slide imaging, transforming them into detailed pixel-based data sets. These digital images are then analyzed using cloud-based computational platforms that generate quantitative scores based on membrane and intracellular binding. A predefined cutoff is applied to determine positivity, similar to molecular assays. This approach merges pathology, computational science, and bioinformatics, paving the way for more refined biomarker assessments. As the field of oncology increasingly embraces ADCs and other precision therapies, these innovations in digital pathology will likely play a key role in optimizing treatment strategies and improving patient outcomes.