Jenna McCormack

Yuan, Y.-C., Xu, B., McCormack, J., Huang, X., Ma, J., Marshall, T., Sun, Y., Singh, H., Fanelli, A., Kulkarni, G., Seo, J.H., Gilbride, P., Wei, B., Wang, B., Liu, Y., Ma, F., Zhou, L., Wang, S., Qian, X., Xie, Z., Golland, P., Si, L., Zhang, Y.S., Xie, X. and Bai, H. (2026), Deep Learning-Powered Scalable Cancer Organ Chip for Cancer Precision Medicine (Adv. Sci. 26/2026). Adv. Sci., 13: e75309. https://doi.org/10.1002/advs.75309

1 Introduction The passage of the United States Food and Drug Administration (FDA) Modernization Act 2.0 [1] and the introduction of Act 3.0 [2] have catalyzed the integration of advanced in vitro models during drug development, notably in the challenging field of oncology, with a less than 5% success rate [3].

Abstract Functional precision oncology represents an emerging approach to genomic approaches by testing treatment options directly on patient-derived models. Current assays, including the use of patient-derived xenograft (PDX) and patient-derived organoid (PDOs), faces major barriers in clinical use due to technical challenges, such as standardization, cost, assay time, scalability, and faithful mimicry of patient tumor microenvironment (TME).