{"title":"Emerging strategies to investigate the biology of early cancer","authors":"Ran Zhou, Xiwen Tang, Yuan Wang","doi":"10.1038/s41568-024-00754-y","DOIUrl":null,"url":null,"abstract":"Early detection and intervention of cancer or precancerous lesions hold great promise to improve patient survival. However, the processes of cancer initiation and the normal–precancer–cancer progression within a non-cancerous tissue context remain poorly understood. This is, in part, due to the scarcity of early-stage clinical samples or suitable models to study early cancer. In this Review, we introduce clinical samples and model systems, such as autochthonous mice and organoid-derived or stem cell-derived models that allow longitudinal analysis of early cancer development. We also present the emerging techniques and computational tools that enhance our understanding of cancer initiation and early progression, including direct imaging, lineage tracing, single-cell and spatial multi-omics, and artificial intelligence models. Together, these models and techniques facilitate a more comprehensive understanding of the poorly characterized early malignant transformation cascade, holding great potential to unveil key drivers and early biomarkers for cancer development. Finally, we discuss how these new insights can potentially be translated into mechanism-based strategies for early cancer detection and prevention. Understanding the early steps of cancer development is crucial for cancer prevention. In this Review, the authors summarize the advantages and limitations of clinical samples, autochthonous mouse models and organoid models, alongside advanced techniques such as direct imaging, lineage tracing and AI, to enhance understanding of early cancer progression.","PeriodicalId":19055,"journal":{"name":"Nature Reviews Cancer","volume":"24 12","pages":"850-866"},"PeriodicalIF":72.5000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Cancer","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s41568-024-00754-y","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Early detection and intervention of cancer or precancerous lesions hold great promise to improve patient survival. However, the processes of cancer initiation and the normal–precancer–cancer progression within a non-cancerous tissue context remain poorly understood. This is, in part, due to the scarcity of early-stage clinical samples or suitable models to study early cancer. In this Review, we introduce clinical samples and model systems, such as autochthonous mice and organoid-derived or stem cell-derived models that allow longitudinal analysis of early cancer development. We also present the emerging techniques and computational tools that enhance our understanding of cancer initiation and early progression, including direct imaging, lineage tracing, single-cell and spatial multi-omics, and artificial intelligence models. Together, these models and techniques facilitate a more comprehensive understanding of the poorly characterized early malignant transformation cascade, holding great potential to unveil key drivers and early biomarkers for cancer development. Finally, we discuss how these new insights can potentially be translated into mechanism-based strategies for early cancer detection and prevention. Understanding the early steps of cancer development is crucial for cancer prevention. In this Review, the authors summarize the advantages and limitations of clinical samples, autochthonous mouse models and organoid models, alongside advanced techniques such as direct imaging, lineage tracing and AI, to enhance understanding of early cancer progression.
期刊介绍:
Nature Reviews Cancer, a part of the Nature Reviews portfolio of journals, aims to be the premier source of reviews and commentaries for the scientific communities it serves. The correct abbreviation for abstracting and indexing purposes is Nat. Rev. Cancer. The international standard serial numbers (ISSN) for Nature Reviews Cancer are 1474-175X (print) and 1474-1768 (online). Unlike other journals, Nature Reviews Cancer does not have an external editorial board. Instead, all editorial decisions are made by a team of full-time professional editors who are PhD-level scientists. The journal publishes Research Highlights, Comments, Reviews, and Perspectives relevant to cancer researchers, ensuring that the articles reach the widest possible audience due to their broad scope.