Label-free morphometric profiling reveals early drug responses in 3D tumor spheroids

Quantitative and scalable analysis of drug responses in three-dimensional (3D) tumor models remains a major challenge for phenotypic drug discovery. Here, we present m3DinAI Drug Quest, a label-free high-content imaging (HCI) framework for longitudinal morphometric profiling of tumor spheroids. Using brightfield imaging combined with multiparametric feature extraction, we generate time-resolved phenotypic signatures of triple-negative breast cancer (TNBC) spheroids treated with chemotherapeutic agents spanning distinct mechanisms of action. To quantify early drug-induced effects, we introduce the morphological disruption concentration (MDC), defined as the minimal dose that induces reproducible structural alterations in 3D spheroids. MDC reveals drug responses not captured by conventional cell viability assays. Unsupervised dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP) further identifies treatment-specific morphological signatures that distinguish drug classes based on label-free imaging data. Together, these results establish label-free morphometric profiling as a scalable approach for characterizing drug responses in 3D tumor models and position MDC as a complementary metric to conventional potency measurements. This framework is readily applicable to diverse 3D culture systems.