It is known that single or isolated tumor cells enter cancer patients’ 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.
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 effect of a drug are expected.
In recent years, innovative technologies that extract feature descriptions from the large volume of data on speech recognition, visual object recognition and detection as well as many other domains, such as drug discovery and DNA sequence annotations by deep learning techniques and applying them to automatic recognition etc. are drawing attention. As cancer research aiming at applying deep learning techniques to cases that are resistant to surgical therapy and drug therapy in metastatic colorectal cancer, we developed a fundamental technology that can predict the resistance of free cancer cells to fluorinated pyrimidine anticancer drugs by deep learning from the morphological image data taken from images. An experimental model was used in our investigation in order to clarify whether or not its image recognition ability can be applied to the determination of drug resistance of free cancer cells circulating in the peripheral blood. That is, a cell line established by inducing a resistance to FTD or 5 FU added to the cell culture solution was prepared over several months and the ability to recognize the tolerance of the drug was examined from a large volume of image data, and it was shown that it can be distinguished dominantly in a short-term culture system. Further, as a result of examination after separation at the single cell level, it was possible to distinguish fluorescent-labeled resistant strains dominantly. In addition, we were able to recognize the drug resistance character well by injecting resistant strains intravenously into the mice to prepare a model of free cancer cells and collecting circulating free cancer cells. Moreover, as a pre-clinical model, resistant strains were mixed with susceptible strains at various ratios and transplanted into mice and experimented. As a result, the nature of the resistance to treatment was predicted by image recognition, and death of the mice due to cancer was well correlated with the malignant trait of drug-resistant cancer cells. Then, by linking the feature expression obtained from the image and the Omics data, a detailed stratification of treatment resistance was possible. From the above, a technique in the mouse that can distinguish free cancer cells collected from the peripheral blood by deep learning of images was constructed, and a foundation to be applied to medical treatment and precision medical care in the future was established. Citation Format: Kiminori Yanagisawa, Masamitsu Konno, Masayasyu Toratani, Hirohiko Niioka, Ayumu Asai, Jun Koseki, Kenta Tsunekuni, Taroh Satoh, Kazuhiko Ogawa, Jun Miyake, Yuichiro Doki, Masaki Mori, Hideshi Ishii. Deep learning recognizes FTD-resistant isolated cancer cells of colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2859.
A 65-year-old woman presented with a chief complaint of swelling of the left inguinal region. Abdominal CT showed protrusion of fatty tissue at the left lateral margin of the rectus abdominis muscle. A left Spigelian hernia was diagnosed, and laparoscopic hernia repair was performed by a totally extraperitoneal approach (TEP). A left direct inguinal hernia was also noticed during surgery, and both hernias were repaired at the same time using mesh. Spigelian hernias with fragility of the abdominal wall may occur with other hernias. Laparoscopic surgery can be useful for simultaneous diagnosis and repair.
Although the stiffness of tissues likely is involved in the malignant behavior of tumors, it remains to be clarified which molecules control the nature, how it is involved in the invasiveness of tumors, or whether any marker is available for the prediction of cancer patient prognosis. In the present study, we studied the role of Myl9, a non-muscle-type, myosin light chain by the experiment in vitro, and assessed the usefulness in the stratification of patients in vivo as the precision medicine. Given that Myl9 is involved in the contraction of cell skeleton, cell hardness, and alterations of cell morphology in various tissues, we first examined whether the expression of the Myl9 is associated with the clinical status of tumors by immunohistochemistry. The results of 45 cases with colon cancer and pancreatic cancer indicated that the increased expression of Myl9 is associated significantly with a reduced provability of overall survival and disease-free survival of those cancers. Moreover, we noted the differential expression of Myl9 in epithelial cancer cells and mesenchymal fibroblasts, i.e., the accumulation of Myl9 staining in cell nuclei of fibroblasts. The experiment of 3-dimensional culture with cancer cells and fibroblasts confirmed the results. Furthermore, we investigated whether Myl9 overexpression is involved in the biologic behavior of gastrointestinal cancer cells. The results showed that the rentiviral-mediated overexpression of Myl9 resulted in an increase of cell proliferation and invasion as well as tumorigenicity in mice. The present study indicates that Myl9 protein can play a fundamental role in the malignant behaviors of gastrointestinal cancer cells. Citation Format: Masamitsu Konno, Kiminori Yanagisawa, Katsunori Matsushita, Ayumu Asai, Jun Koseki, Michiya Matsusaki, Shinji Deguchi, Yuichiro Doki, Masaki Mori, Hideshi Ishii. Mechanosensor MYL9 regulates cancer cell malignancy in gastrointestinal tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5164.
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