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| Home > Publications database > ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes. |
| Home > Publications database > ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes. |
| Journal Article | DKFZ-2026-01642 |
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2026
AACR
Philadelphia, Pa. [u.a.]
Abstract: Antibody-drug conjugates (ADC) targeting trophoblast cell surface antigen 2 (TROP-2) and cMET are entering clinical trials in non-small cell lung cancer (NSCLC). Their translation depends on reliable biomarker assessment, a task still dominated by subjective visual scoring and inconsistent reproducibility.We built a modular Artificial Intelligence (AI) pipeline that detects cells, classifies carcinoma cells, and quantifies membranous and cytoplasmic expression. A membranous scorer trained on TROP-2 was applied zero-shot to cMET, human epidermal growth factor receptor 2 (HER2), and PD-L1. For TROP-2, cMET, and HER2, expression was quantified using the H-score, defined as (1× % weakly positive cells) + (2× % moderately positive cells) + (3× % strongly positive cells), with negative cells excluded (range, 0-300), and categorized as negative (0-50), weakly positive (50-100), moderately positive (100-200), or strongly positive (200-300). PD-L1 expression was assessed using both the H-score and the tumor proportion score (TPS), defined as the percentage of viable tumor cells showing membranous PD-L1 staining relative to all viable tumor cells, multiplied by 100. The analysis covered 1,142 resected NSCLCs, integrating expression maps with clinicopathologic, molecular, and tumor microenvironment (TME) features.The AI scorer recapitulated pathologist annotations with near-perfect correlation [Pearsons's correlation (r) = 0.98-0.99, Spearman's rho (ρ) = 97-98, Kendall's tau (τ) = 0.88-0.89, Lin's concordance correlation coefficient (CCC) = 0.97-0.98] for TROP-2 and generalized to other markers [cMET r = 0.99, ρ = 0.96, τ = 0.91, CCC = 0.96; HER2 r = 0.93, ρ = 0.72, τ = 0.60, CCC = 0.92; and PD-L1 (TPS) r = 0.85, ρ = 0.84, τ = 0.68, CCC = 0.82/(H-score) r = 0.87, ρ = 0.86, τ = 0.70, CCC = 0.85]. Its agreement with six pathologists matched interobserver variability (0.86-0.96). Expression maps revealed contrasting spatial and cellular patterns: TROP-2 dominated lung squamous cell carcinoma [LUSC; mean H-Scores 141.3/103.2 vs. 74.5/45.7 in lung adenocarcinoma (LUAD) for membrane/cytoplasm] and marked immune-deserted tumors. cMET prevailed in LUAD (mean 50.4 vs. 20.6 in LUSC), colocalized with fibroblast-rich, immune-active TME, and KRAS mutations.Foundation model-based scoring produces expert-level, scalable biomarker quantification. The resulting TME phenotypes-TROP-2-high immune-deserted versus cMET-high, immune-active-reveal therapeutic implications for combining ADCs with immunotherapies or kinase inhibitors.
Keyword(s): Humans (MeSH) ; Carcinoma, Non-Small-Cell Lung: drug therapy (MeSH) ; Carcinoma, Non-Small-Cell Lung: pathology (MeSH) ; Carcinoma, Non-Small-Cell Lung: metabolism (MeSH) ; Carcinoma, Non-Small-Cell Lung: genetics (MeSH) ; Antigens, Neoplasm: metabolism (MeSH) ; Antigens, Neoplasm: genetics (MeSH) ; Lung Neoplasms: drug therapy (MeSH) ; Lung Neoplasms: pathology (MeSH) ; Lung Neoplasms: metabolism (MeSH) ; Biomarkers, Tumor: metabolism (MeSH) ; Cell Adhesion Molecules: metabolism (MeSH) ; Cell Adhesion Molecules: antagonists & inhibitors (MeSH) ; Cell Adhesion Molecules: genetics (MeSH) ; Immunoconjugates: therapeutic use (MeSH) ; Immunoconjugates: pharmacology (MeSH) ; Artificial Intelligence (MeSH) ; Phenotype (MeSH) ; Erb-b2 Receptor Tyrosine Kinases: metabolism (MeSH) ; B7-H1 Antigen (MeSH) ; Female (MeSH) ; TACSTD2 protein, human ; Antigens, Neoplasm ; Biomarkers, Tumor ; Cell Adhesion Molecules ; Immunoconjugates ; Erb-b2 Receptor Tyrosine Kinases ; B7-H1 Antigen ; ERBB2 protein, human
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