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

Philipp Anders1,2, Marvin Sextro3,4,5, Katja Lingelbach3

  • 1Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.

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An AI pipeline accurately quantifies biomarkers for non-small cell lung cancer (NSCLC) treatments. This approach overcomes subjective scoring, enabling precise assessment for antibody drug conjugates (ADCs) and immunotherapies.

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Area of Science:

  • Computational pathology
  • Biomarker discovery
  • Artificial intelligence in oncology

Background:

  • Antibody drug conjugates (ADCs) targeting TROP-2 and cMET are emerging therapies for non-small cell lung cancer (NSCLC).
  • Accurate biomarker assessment is crucial for ADC efficacy but is currently limited by subjective visual scoring and poor reproducibility.

Purpose of the Study:

  • To develop and validate a modular AI pipeline for objective and reproducible quantification of cancer biomarkers in NSCLC.
  • To assess the generalizability of the AI scorer across different biomarkers, including TROP-2, cMET, HER2, and PD-L1.
  • To integrate AI-driven expression maps with clinicopathologic, molecular, and tumor microenvironment (TME) features to identify novel therapeutic strategies.

Main Methods:

  • A modular AI pipeline was developed to detect cells, classify carcinoma cells, and quantify membranous and cytoplasmic protein expression.
  • A scorer trained on TROP-2 was applied zero-shot to cMET, HER2, and PD-L1.
  • The pipeline analyzed 1,142 resected NSCLC samples, integrating expression data with comprehensive clinical, molecular, and TME features.

Main Results:

  • The AI scorer demonstrated high concordance with pathologist annotations for TROP-2 (r=0.98-0.99) and generalized effectively to cMET, HER2, and PD-L1.
  • AI performance closely matched interobserver variability among six pathologists.
  • Expression mapping revealed distinct TROP-2 (high in LUSC, associated with immune-deserted TME) and cMET (high in LUAD, associated with immune-active TME and KRAS mutations) phenotypes.

Conclusions:

  • Foundation model-based AI scoring provides expert-level, scalable biomarker quantification for NSCLC.
  • Distinct TME phenotypes associated with TROP-2 and cMET expression suggest potential therapeutic strategies, such as combining ADCs with immunotherapies or kinase inhibitors.