Emerging Biomarkers in NSCLC: TROP2 Scoring and MTAP Loss

In this segment, Dr. Jani asks how emerging biomarkers beyond classic driver mutations—specifically TROP2 and MTAP—are influencing later-line sequencing and clinical trial referrals. Dr. Singhi explains that unlike classic driver mutations such as EGFR and ALK, where the biomarker directly selects for a targeted therapy, TROP2 functions more as a therapeutic target than a patient-selection biomarker. He notes that TROP2 expression is found across the majority of non-small cell lung cancer (NSCLC), which is precisely why the field is working to refine predictive approaches beyond simple expression status.

Dr. Singhi highlights two AI-driven digital pathology methods: Normalized Membrane Ratio (NMR) and Quantitative Continuous Scoring (QCS). These approaches use AI-based algorithms and cell segmentation to quantify the balance of membrane versus cytoplasmic TROP2, moving beyond the somewhat subjective visual scoring of traditional immunohistochemistry (IHC). This distinction matters because the membrane-to-cytoplasmic ratio may reflect internalization potential, which is critical to how antibody-drug conjugates (ADCs) function. He notes that clinical trials are actively evaluating whether differences in NMR scores correlate with improved responses to TROP2-directed ADCs.

Turning to MTAP loss, Dr. Singhi describes this as an emerging biomarker—not yet a routine standard-of-care selector—but one he is actively checking for in his patients' NGS reports and considering IHC testing to identify candidates for PRMT5-directed clinical trials. Dr. Jani closes by emphasizing that artificial intelligence is already here in oncology, actively shaping precision medicine approaches beyond documentation into therapy selection. Dr. Singhi agrees, stressing the importance of embracing AI to improve patient care.

In the next episode, "MET Alterations in NSCLC: Amplification, Mutations, and Therapeutic Strategies," the experts break down the distinct facets of MET biology—amplification, exon 14 skipping mutations, and protein overexpression—and discuss how to translate these findings into practical sequencing decisions.