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

Zenodo (CERN European Organization for Nuclear Research) 2026 Dataset Open access

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Authors: Anders, Philipp, 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, Sabine Merkelbach‐Bruse, Alexander Quaas, N. Frost, Georg Schlachtenberger, Matthias B. Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens-Carsten Rückert, Reinhard Büttner, Christian Grohe, David Horst, Maximilian Alber, Lukas Ruff, Frederick Klauschen, Gabriel Dernbach, Philipp Seegerer, Simon Schallenberg
DOI: 10.5281/zenodo.18494664
Cited by: 0

Abstract

This dataset accompanies the paper "ADC target profiling in NSCLC: Generalizable AI separates TROP-2 and cMET phenotypes" and provides comprehensive clinicopathological, molecular, and expression data for a cohort of resected non-small cell lung cancer (NSCLC) patients. Contents: Patient Data Table (cohort_data.csv) QC flags (tumor content, rejection criteria for cMET/TROP-2) Clinical characteristics (age, sex, smoking status, ECOG) Tumor characteristics (histological subtype, grade, diameter) Pathological staging (pTNM, UICC 8th edition) Molecular pathology (mutation status for EGFR, KRAS, ALK, ROS1, TP53, and others) Survival outcomes (OS) AI-derived H-scores for: TROP-2 (membranous) TROP-2 (cytoplasmic) cMET (membranous) TMA Spot Images Immunohistochemistry-stained tissue microarray (TMA) spots for TROP-2 and cMET Linked via case_uuid to the patient data table

Fields of Study & Subjects

Lung Cancer Treatments and Mutations Lung Cancer Research Studies Cancer Genomics and Diagnostics

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
5 February 2026
Document type
Dataset