早期癌症生物学研究的新策略
Emerging strategies to investigate the biology of early cancer
DOI 原文链接
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影响因子:66.8
分区:医学1区 Top / 肿瘤学1区
发表日期:2024 Dec
作者:
Ran Zhou, Xiwen Tang, Yuan Wang
DOI:
10.1038/s41568-024-00754-y
摘要
癌症或癌前病变的早期检测和干预有望显著改善患者生存率。然而，癌症起始过程以及在非癌组织背景下的正常-癌前-癌症的进展仍然理解有限。这在一定程度上由于缺乏早期临床样本或合适的模型来研究早期癌症。在本文中，我们介绍了临床样本与模型系统，如原生鼠模型、类器官衍生模型或干细胞衍生模型，允许对早期癌症发生进行纵向分析。我们还介绍了增强我们对癌症起始和早期发展理解的新技术和计算工具，包括直接成像、谱系追踪、单细胞和空间多组学，以及人工智能模型。这些模型和技术共同促进了对早期恶性转化级联反应的全面理解，具有揭示关键驱动因素和早期生物标志物的巨大潜力。最后，我们讨论了如何将这些新见解转化为机制基础的早期癌症检测和预防策略。
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.