A multidimensional biosensor system to guide LUAD individualized treatment

Lung cancer, mainly non-small cell lung cancer (NSCLC), has been a global health problem, leading to maximum cancer death. Across adenocarcinoma patients, significant genetic and phenotypic heterogeneity was identified as responsible for individual cancer drug resistance, driving an urgent need for individualized treatment. High expectation has been set on individualized treatment for better responses and extended survival. There are pressing needs for and significant advantages of testing dosages and drugs directly on patient-specific cancer cells for preclinical drug testing and personalized drug selection. Monitoring the drug response based on patient-derived cells (PDCs) is a step toward effective drug development and individualized treatment. Despite the dependence on optical labels, optical equipment, and other complex manual operation, we here report a multidimensional biosensor system to guide adenocarcinoma individualized treatment by integrating 2D and 3D PDC models and cellular impedance biosensors. The cellular impedance biosensors were applied to quantitate drug response in 2D and 3D environments. Compared with 2D plate culture, 3D cultured cells were found to show higher resistance to anti-cancer drugs. Cell-cell, cell-ECM, and mechanical interactions in the 3D environment led to stronger drug resistance. The in vivo results demonstrated the reliability of the multidimensional biosensor system. Cellular impedance biosensors allow a fast, non-invasive, and quantitative manner for preselected drug screening in individualized treatment. Considering the potential for good distinguishment of different anti-cancer drugs, our newly developed strategy may contribute to drug response prediction in individualized treatment and new drug development.

Automated summary

Individualized treatment for lung cancer, especially in adenocarcinoma patients, is essential due to significant genetic heterogeneity. The use of cellular impedance biosensors with 2D and 3D cell models can accurately predict drug responses and guide personalized drug selection.