What question did this study set out to answer?

This research aims to evaluate how generalizable AI can distinguish between TROP-2 and cMET phenotypes in non-small cell lung cancer (NSCLC).

April 10, 2026Open Access

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

Puntos clave

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.

Resumen

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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Authors

Philipp Anders

Marvin Sextro

Katja Lingelbach

Journals

Clinical Cancer Research

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Institutions

Ludwig-Maximilians-Universität München

Charité - Universitätsmedizin Berlin

University of Cologne

References and Citations

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

Puntos clave

Resumen

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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