Epithelial–mesenchymal transition (EMT) encompasses dynamic changes in cellular organization from epithelial to mesenchymal phenotypes, which leads to functional changes in cell migration and invasion. EMT occurs in a diverse range of physiological and pathological conditions and is driven by a conserved set of inducing signals, transcriptional regulators and downstream effectors. With over 5,700 publications indexed by Web of Science in 2019 alone, research on EMT is expanding rapidly. This growing interest warrants the need for a consensus among researchers when referring to and undertaking research on EMT. This Consensus Statement, mediated by ‘the EMT International Association’ (TEMTIA), is the outcome of a 2-year-long discussion among EMT researchers and aims to both clarify the nomenclature and provide definitions and guidelines for EMT research in future publications. We trust that these guidelines will help to reduce misunderstanding and misinterpretation of research data generated in various experimental models and to promote cross-disciplinary collaboration to identify and address key open questions in this research field. While recognizing the importance of maintaining diversity in experimental approaches and conceptual frameworks, we emphasize that lasting contributions of EMT research to increasing our understanding of developmental processes and combatting cancer and other diseases depend on the adoption of a unified terminology to describe EMT.

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

Like a set of bookends, cellular, molecular, and genetic changes of the beginnings of life mirror those of one of the most common cause of death—metastatic cancer. Epithelial to mesenchymal transition (EMT) is an important change in cell phenotype which allows the escape of epithelial cells from the structural constraints imposed by tissue architecture, and was first recognized by Elizabeth Hay in the early to mid 1980's to be a central process in early embryonic morphogenesis. Reversals of these changes, termed mesenchymal to epithelial transitions (METs), also occur and are important in tissue construction in normal development. Over the last decade, evidence has mounted for EMT as the means through which solid tissue epithelial cancers invade and metastasize. However, demonstrating this potentially rapid and transient process in vivo has proven difficult and data connecting the relevance of this process to tumor progression is still somewhat limited and controversial. Evidence for an important role of MET in the development of clinically overt metastases is starting to accumulate, and model systems have been developed. This review details recent advances in the knowledge of EMT as it occurs in breast development and carcinoma and prostate cancer progression, and highlights the role that MET plays in cancer metastasis. Finally, perspectives from a clinical and translational viewpoint are discussed. J. Cell. Physiol. 213: 374–383, 2007.

Epithelial-to-mesenchymal transition (EMT) increases cell migration and invasion, and facilitates metastasis in multiple carcinoma types, but belies epithelial similarities between primary and secondary tumors. This study addresses the importance of mesenchymal-to-epithelial transition (MET) in the formation of clinically significant metastasis. The previously described bladder carcinoma TSU-Pr1 (T24) progression series of cell lines selected in vivo for increasing metastatic ability following systemic seeding was used in this study. It was found that the more metastatic sublines had acquired epithelial characteristics. Epithelial and mesenchymal phenotypes were confirmed in the TSU-Pr1 series by cytoskeletal and morphologic analysis, and by performance in a panel of in vitro assays. Metastatic ability was examined following inoculation at various sites. Epithelial characteristics associated with dramatically increased bone and soft tissue colonization after intracardiac or intratibial injection. In contrast, the more epithelial sublines showed decreased lung metastases following orthotopic inoculation, supporting the concept that EMT is important for the escape of tumor cells from the primary tumor. We confirmed the overexpression of the IIIc subtype of multiple fibroblast growth factor receptors (FGFR) through the TSU-Pr1 series, and targeted abrogation of FGFR2IIIc reversed the MET and associated functionality in this system and increased survival following in vivo inoculation in severe combined immunodeficient mice. This model is the first to specifically model steps of the latter part of the metastatic cascade in isogenic cell lines, and confirms the suspected role of MET in secondary tumor growth. (Cancer Res 2006; 66(23): 11271-8)