ADC target profiling in NSCLC: Generalizable AI separates TROP-2 and cMET phenotypes

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; Evelyn Ramberger; Xizi Liang; Verena Aumiller; S. Merkelbach-Bruse; Alexander Quaas; N. Frost; Georg Schlachtenberger; Matthias Heldwein; Ulrich Keilholz; K. Hekmat; Jens‐Carsten Rückert; Reinhard Büttner; Christian Grohé; David Horst; Maximilian Alber; Lukas Ruff; Frederick Klauschen; Gabriel Dernbach; Philipp Seegerer; Simon Schallenberg

doi:10.1158/1078-0432.ccr-25-4513

Key Points

This research aims to evaluate how generalizable AI can distinguish between TROP-2 and cMET phenotypes in non-small cell lung cancer (NSCLC).
Utilized foundation model-based scoring for biomarker quantification.
Analyzed TME phenotypes to categorize as TROP-2-high immune-deserted and cMET-high immune-active.
Evaluated therapeutic implications of identified phenotypes.
AI effectively distinguished TROP-2 and cMET phenotypes in NSCLC.
Identified therapeutic combinations involving ADCs and immunotherapies or kinase inhibitors.
Results demonstrate significant variability in immune activity between the two phenotypes.

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Implication

Generalizable AI separates TROP-2 and cMET phenotypes in non-small cell lung cancer, revealing therapeutic implications.

Abstract

Foundation model-based scoring produces expert-level, scalable biomarker quantification. The resulting TME phenotypes - TROP-2-high immune-deserted vs cMET-high, immune-active - reveal therapeutic implications for combining ADCs with immunotherapies or kinase inhibitors.

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Cite This Study

Anders et al. (2026) studied this question.

synapsesocial.com/papers/69d893a86c1944d70ce04a8a https://doi.org/10.1158/1078-0432.ccr-25-4513

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ADC target profiling in NSCLC: Generalizable AI separates TROP-2 and cMET phenotypes

Key Points

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Cite This Study

ADC target profiling in NSCLC: Generalizable AI separates TROP-2 and cMET phenotypes

Key Points

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Implication

Abstract

Discussion

Cite This Study

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