The Human Genome Project, completed in 2003 by an international consortium, is considered one of the most important achievements for mankind in the 21st century ....
Several protein lysine methyltransferases and demethylases have been identified to have critical roles in histone modification. A large body of evidence has indicated that their dysregulation is ...involved in the development and progression of various diseases, including cancer, and these enzymes are now considered to be potential therapeutic targets. Although most studies have focused on histone methylation, many reports have revealed that these enzymes also regulate the methylation dynamics of non-histone proteins such as p53, RB1 and STAT3 (signal transducer and activator of transcription 3), which have important roles in human tumorigenesis. In this Review, we summarize the molecular functions of protein lysine methylation and its involvement in human cancer, with a particular focus on lysine methylation of non-histone proteins.
Protein methylation is one of the important post‐translational modifications. Although its biological and physiological functions were unknown for a long time, we and others have characterized a ...number of protein methyltransferases, which have unveiled the critical functions of protein methylation in various cellular processes, in particular, in epigenetic regulation. In addition, it had been believed that protein methylation is an irreversible phenomenon, but through identification of a variety of protein demethylases, protein methylation is now considered to be dynamically regulated similar to protein phosphorylation. A large amount of evidence indicated that protein methylation has a pivotal role in post‐translational modification of histone proteins as well as non‐histone proteins and is involved in various processes of cancer development and progression. As dysregulation of this modification has been observed frequently in various types of cancer, small‐molecule inhibitors targeting protein methyltransferases and demethylases have been actively developed as anticancer drugs; clinical trials for some of these drugs have already begun. In this review, we discuss the biological and physiological importance of protein methylation in human cancer, especially focusing on the significance of protein methyltransferases as emerging targets for anticancer therapy.
Although protein methylation was found around a half‐century ago, biological and physiological functions of protein methylation remained unknown for a long time. In the 21st century, we and other researchers characterized a number of protein methyltransferases and elucidated their functions, in particular focusing on their epigenetic regulation through histone methylation. Now, protein methyltransferases are attracting considerable attention as new targets for development of anticancer therapy.
In recent years, artificial intelligence (AI) technology has attracted attention owing to new developments, such as machine learning technology with deep learning at its core and the increasing ...availability of large amounts of data (big data). The U.S. FDA has approved more than 100 AI-based medical devices. In Japan, several AI-based medical devices have also been approved and are in use in clinical practice. This review describes the current status of medical AI research and development in Japan and the associated challenges. It also discusses the future direction of medical AI research and development.
Deep learning is a promising method for medical image analysis because it can automatically acquire meaningful representations from raw data. However, a technical challenge lies in the difficulty of ...determining which types of internal representation are associated with a specific task, because feature vectors can vary dynamically according to individual inputs. Here, based on the magnetic resonance imaging (MRI) of gliomas, we propose a novel method to extract a shareable set of feature vectors that encode various parts in tumor imaging phenotypes. By applying vector quantization to latent representations, features extracted by an encoder are replaced with a fixed set of feature vectors. Hence, the set of feature vectors can be used in downstream tasks as imaging markers, which we call deep radiomics. Using deep radiomics, a classifier is established using logistic regression to predict the glioma grade with 90% accuracy. We also devise an algorithm to visualize the image region encoded by each feature vector, and demonstrate that the classification model preferentially relies on feature vectors associated with the presence or absence of contrast enhancement in tumor regions. Our proposal provides a data-driven approach to enhance the understanding of the imaging appearance of gliomas.
Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system ...for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of colorectal cancer during colonoscopy; the AI system shows the sensitivity and specificity are 97.3% (95% confidence interval CI = 95.9%-98.4%) and 99.0% (95% CI = 98.6%-99.2%), respectively, and the area under the curve is 0.975 (95% CI = 0.964-0.986) in the validation set. Moreover, the sensitivities are 98.0% (95% CI = 96.6%-98.8%) in the polypoid subgroup and 93.7% (95% CI = 87.6%-96.9%) in the non-polypoid subgroup; To accelerate the detection, tensor metrics in the trained model was decomposed, and the system can predict cancerous regions 21.9 ms/image on average. These findings suggest that the system is sufficient to support endoscopists in the high detection against non-polypoid lesions, which are frequently missed by optical colonoscopy. This AI system can alert endoscopists in real-time to avoid missing abnormalities such as non-polypoid polyps during colonoscopy, improving the early detection of this disease.
Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the ...performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (
= 0.0081) and 75.1% (
< 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.
To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. ...Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.
PDF
