ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes

Read full text
Authors: Philipp Anders, Marvin Sextro, Katja Lingelbach, Kai Standvoss, Suhas Pandhe, Sandip Ghosh, Cornelius Böhm, Stephan Tietz, Rosemarie Krupar, Lars Tharun, Marie‐Lisa Eich, Julika Ribbat-Idel
+20 more Evelyn Ramberger, Xizi Liang, Verena Aumiller, S. Merkelbach-Bruse, Alexander Quaas, N. Frost, Georg Schlachtenberger, Matthias B. Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens‐Carsten Rückert, Reinhard Büttner, Christian Grohé, David Horst, Maximilian Alber, Lukas Ruff, Frederick Klauschen, Gabriel Dernbach, Philipp Seegerer, Simon Schallenberg
Cited by: 1
References: 58

Abstract

PURPOSE: 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. EXPERIMENTAL DESIGN: 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. RESULTS: 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. CONCLUSIONS: 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.

Fields of Study & Subjects

HER2/EGFR in Cancer Research Radiomics and Machine Learning in Medical Imaging Monoclonal and Polyclonal Antibodies Research

Publication details

Journal / source
Clinical Cancer Research
Publisher
American Association for Cancer Research (AACR)
Volume
32
Issue
13
Pages
2765–2786
Publication date
7 April 2026
Language
EN