Search

Yanagisawa, K.; Konno, M.; Liu, H.; Irie, S.; Mizushima, T.; Mori, M.; Doki, Y.; Eguchi, H.; Matsusaki, M.; Ishii, H. A Four-Dimensional Organoid System to Visualize Cancer Cell Vascular Invasion. Biology 2020, 9, 361., Vascular invasion of cancer is a critical step in cancer progression, but no drug has been developed to inhibit vascular invasion. To achieve the eradication of cancer metastasis, elucidation of the mechanism for vascular invasion and the development of innovative treatment methods are required. Here, a simple and reproducible vascular invasion model is established using a vascular organoid culture in a fibrin gel with collagen microfibers. Using this model, it was possible to observe and evaluate the cell dynamics and histological positional relationship of invasive cancer cells in four dimensions. Cancer-derived exosomes promoted the vascular invasion of cancer cells and loosened tight junctions in the vascular endothelium. As a new evaluation method, research using this vascular invasion mimic model will be advanced, and applications to the evaluation of the vascular invasion suppression e_ect of a drug are expected.

It is known that single or isolated tumor cells enter cancer patients&rsquo, circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system.