Distinct response profiles of TROP2-targeting ADCs Dato-DXd and sac-TMT across a large panel of cancer cell lines

Antibody-drug conjugates (ADCs) are targeted cancer therapies that deliver cytotoxic drugs directly to tumors by binding to receptors overexpressed on cancer cells. Following receptor binding, the receptor-bound ADC is internalized and the drug is released, triggering cell death. Trophoblast cell surface antigen 2 (TROP2) is a cell surface receptor highly expressed in epithelial tumors, making it an attractive ADC target. Sacituzumab govitecan gained FDA approval in 2020 for triple-negative breast cancer (TNBC). However, its short plasma half-life motivated the development of more stable alternatives. Two newer ADCs, sacituzumab tirumotecan (sac-TMT) and datopotamab deruxtecan (Dato-DXd), have since been approved for clinical use. Both carry a topoisomerase-1 inhibitor payload but differ in antibody and linker design. Sac-TMT is approved for TNBC in China and has received FDA breakthrough designation for EGFR -mutated non-small cell lung cancer (NSCLC). Dato-DXd received FDA approval in 2025 for HR + /HER2 - breast cancer and EGFR -mutated NSCLC. Despite observed correlations between TROP2 expression and drug sensitivity, these ADCs are approved regardless of tumor TROP2 levels. This suggests that TROP2 expression alone may not predict therapeutic benefit, and alternative biomarkers may better explain clinical efficacy. To explore this further, Dato-DXd and sac-TMT were profiled on a panel of approximately 270 cancer cell lines, representing diverse tumor types, including eight TNBC, three HR + /HER2 - , and six EGFR-mutated NSCLC models. The TROP2 ( TACSTD2 ) gene was widely expressed with over 2500-fold difference in basal expression levels across the panel. Surface TROP2 expression levels were quantified by flow cytometry in a subset of cell lines. Cells were exposed to a 9-point dose range of each ADC, and viability was assessed by intracellular ATP measurement. Half-maximum inhibitory concentrations (IC 50 ) were derived and integrated with genomic, transcriptomic and proteomic datasets to identify molecular predictors of drug response. Dato-DXd exhibited a more selective inhibition profile than sac-TMT. When the IC 50 “fingerprints” of the two ADCs were compared to a reference dataset of 248 anticancer agents, Dato-DXd clustered with EGFR inhibitors, whereas sac-TMT showed highest similarity to topoisomerase I inhibitors, indicating mechanistically distinct selectivity patterns. Moreover, correlation between drug sensitivity and TROP2 gene expression was stronger for Dato-DXd than for sac-TMT. This large-scale cell panel profiling study underscores the power of integrative bioinformatic analyses to uncover biomarkers of ADC response, and identifies cell models suitable for investigating mechanisms of action, such as receptor internalization, payload processing, and bystander effects.