Deciphering Anti-Cancer Drug Efficacy Through Nanomechanical Vibrations in Living Gastric Cancer Organoids

Cancer poses a severe threat to human health. The limited sensitivity and efficiency of conventional drug screening methods create a pressing need for novel anti-cancer drug screening platforms. This study developed a drug efficacy assessment platform that integrates patient-derived gastric cancer organoids, atomic force microscopy (AFM)-based nanomechanical vibration detection, deep learning analysis and an organoid mechanical model. The platform enables non-destructive detection of intrinsic nano-vibrations of organoids, revealing that their amplitude and spectral characteristics are sensitive to the cytoskeleton, cell-cell junctions, and cellular metabolism. Through the optimization of a deep learning model, we achieved high-precision classification of organoids under pharmacological perturbation. The classification requires only 0.1 s of vibration data and achieves an overall accuracy of 97%. Applied to gastric cancer organoids, our platform detected the effects of picomolar (pM) concentrations of paclitaxel, notably preceding any observable morphological changes or apoptosis signals. Furthermore, the organoid mechanical model accurately predicted drug-induced alterations in both the nanomechanical vibration amplitude and spectrum. These findings validate the platform's advantage for the highly sensitive, non-destructive, and efficient screening of anti-cancer drugs. Collectively, this work establishes a paradigm for organoid-based drug evaluation through dynamic biophysical profiling and holds promise for advancing personalized cancer treatment strategies.