A four-dimensional organoid system to visualize cancer cell vascular invasion

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

Convolutional neural network can recognize drug resistance of single cancer cells

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

Expression of asporin reprograms cancer cells to acquire resistance to oxidative stress

Asporin (ASPN), a small leucine-rich proteoglycan expressed predominantly by cancer associated fibroblasts (CAFs), plays a pivotal role in tumor progression. ASPN is also expressed by some cancer cells, but its biological significance is unclear. Here, we investigated the effects of ASPN expression in gastric cancer cells. Overexpression of ASPN in 2 gastric cancer cell lines, HSC-43 and 44As3, led to increased migration and invasion capacity, accompanied by induction of CD44 expression and activation of Rac1 and MMP9. ASPN expression increased resistance of HSC-43 cells to oxidative stress by reducing the amount of mitochondrial reactive oxygen species. ASPN induced expression of the transcription factor HIF1 alpha and upregulated lactate dehydrogenase A (LDHA) and PDH-E1 alpha, suggesting that ASPN reprograms HSC-43 cells to undergo anaerobic glycolysis and suppresses ROS generation in mitochondria, which has been observed in another cell line HSC-44PE. By contrast, 44As3 cells expressed high levels of HIF1 alpha in response to oxidant stress and escaped apoptosis regardless of ASPN expression. Examination of xenografts in the gastric wall of ASPN(-/-) mice revealed that growth of HSC-43 tumors with increased micro blood vessel density was significantly accelerated by ASPN; however, ASPN increased the invasion depth of both HSC-43 and 44As3 tumors. These results suggest that ASPN has 2 distinct effects on cancer cells: HIF1 alpha-mediated resistance to oxidative stress via reprogramming of glucose metabolism, and activation of CD44-Rac1 and MMP9 to promote cell migration and invasion. Therefore, ASPN may be a new therapeutic target in tumor fibroblasts and cancer cells in some gastric carcinomas

The low expression of miR-451 predicts a worse prognosis in non-small cell lung cancer cases

Purpose miR-451 is a tumor suppressive microRNA with several target genes, including Macrophage migration inhibitory factor (MIF). As little is known about the expression and clinicopathological significance of mir-451 in NSCLC, we performed a clinicopathological study of 370 NSCLC cases to clarify them. Cell biological experiments were also performed on NSCLC cell lines to confirm the tumor-suppressive role of miR-451 and whether or not MIF is targeted by miR-451. Methods We analyzed 370 NSCLC cases for the miR-451 expression by quantitative real-time polymerase chain reaction and the MIF expression by immunohistochemistry. Eighty-four background lung tissue samples were also evaluated for the miR-451 expression. The clinicopathological and genetic factors surveyed were the disease-free survival, smoking status, histological type, disease stage, EGFR gene mutations and ALK rearrangements. In 286 adenocarcinoma cases, the invasive status (adenocarcinoma in situ, minimally invasive adenocarcinoma and invasive adenocarcinoma) was also evaluated. Five NSCLC cell lines (H23, H441, H522, H1703, and H1975) were cultured and evaluated for their miR-451 and MIF expression. The cell lines with lower miR-451 and higher MIF expressions were then selected and transfected with miR-451-mimic to observe its effects on MIF expression, Akt and Erk status, cell proliferation, and cell migration. Results The miR-451 expression was down-regulated in cancer tissues compared with background lung tissues (P<0.0001). Factors such as advanced disease stage, positive pleural invasion and nodal status and being a smoker were significantly correlated with a lower expression of miR-451 (P<0.05 each), while EGFR gene mutations and ALK rearrangements were not. In adenocarcinoma, invasive and minimally invasive adenocarcinoma showed lower expression of miR-451 than adenocarcinoma in situ (P<0.0005, respectively). A survival analysis showed that a lower expression of miR-451 was an independent predictor of a poor prognosis for NSCLC (P<0.05). The MIF expression was inversely correlated with the miR-451 expression. Out of 5 NSCLC cell lines examined, H441 and H1975 showed higher MIF and lower miR-451 expressions. After the transfection of miR-451-mimic, the MIF expression and phosphorylated Akt expression of these cell lines was suppressed, as were cell proliferation and cell migration. Conclusion This clinicopathological study of 370 NSCLC cases and the cell biological studies of NSCLC cell lines clarified the tumor-suppressive role of miR-451 and its prognostic value. We also validated MIF as a target of miR-451 in NSCLC

Computational analyses for cancer biology based on exhaustive experimental backgrounds

Antitumor drug therapy plays a very important role in cancer treatment. However, resistance to chemotherapy is a serious issue. Many studies have been conducted to understand and verify the cause of chemoresistance from multiple points of view such as oncogenes, tumor suppressor genes, DNA mutations and repairs, autophagy, cancer stemness, and mitochondrial metabolism and alteration. Nowadays, not only medical data from hospitals but also public big data exist on internet websites. Consequently, the importance of computational science has vastly increased in biological and medical sciences. Using statistical or mathematical analyses of these medical data with conventional experiments, many researchers have recently shown that there is a strong relationship between the biological metabolism and chemoresistance for cancer therapy. For example, folate metabolism that mediates one-carbon metabolism and polyamine metabolism have garnered attention regarding their association with cancer. It has been suggested that these metabolisms may be involved in causing resistance to chemotherapy

Theoretical Computational Analysis Predicts Interaction Changes Due to Differences of a Single Molecule in DNA

Theoretical methods, such as molecular mechanics and molecular dynamics, are very useful in understanding differences in interactions at the single molecule level. In the life sciences, small conformational changes, including substituent modifications, often have a significant impact on function in vivo. Changes in binding interactions between nucleic acid molecules and binding proteins are a prime example. In this study, we propose a strategy to predict the complex structure of DNA-binding proteins with arbitrary DNA and analyze the differences in their interactions. We tested the utility of our strategy using the anticancer drug trifluoro-thymidine (FTD), which exerts its pharmacological effect by incorporation into DNA, and confirmed that the binding affinity of the BCL-2-associated X sequence to the p53 tetramer is increased by FTD incorporation. On the contrary, in p53-binding sequences extracted from FTD-resistant cells, the binding affinity of DNA containing FTD was found to be greatly reduced compared to normal DNA. This suggests that thymidine randomly substituted for FTD in resistant cells may acquire resistance by entering a position that inhibits binding to DNA-binding proteins. We believe that this is a versatile procedure that can also take energetics into account and will increase the importance of computational science in the life sciences

Theoretical Computational Analysis Predicts Interaction Changes Due to Differences of a Single Molecule in DNA

Theoretical methods, such as molecular mechanics and molecular dynamics, are very useful in understanding differences in interactions at the single molecule level. In the life sciences, small conformational changes, including substituent modifications, often have a significant impact on function in vivo. Changes in binding interactions between nucleic acid molecules and binding proteins are a prime example. In this study, we propose a strategy to predict the complex structure of DNA-binding proteins with arbitrary DNA and analyze the differences in their interactions. We tested the utility of our strategy using the anticancer drug trifluoro-thymidine (FTD), which exerts its pharmacological effect by incorporation into DNA, and confirmed that the binding affinity of the BCL-2-associated X sequence to the p53 tetramer is increased by FTD incorporation. On the contrary, in p53-binding sequences extracted from FTD-resistant cells, the binding affinity of DNA containing FTD was found to be greatly reduced compared to normal DNA. This suggests that thymidine randomly substituted for FTD in resistant cells may acquire resistance by entering a position that inhibits binding to DNA-binding proteins. We believe that this is a versatile procedure that can also take energetics into account and will increase the importance of computational science in the life sciences

Computational healthcare: present and future perspectives (Review)

Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare