癌症或癌前病变的早期检测和干预有望提高患者的生存率。然而，人们对非癌组织背景下癌症发生和正常癌前癌进展的过程仍知之甚少。这在一定程度上是由于早期临床样本或研究早期癌症的合适模型的稀缺。在这篇综述中，我们介绍了临床样本和模型系统，例如本地小鼠和类器官衍生或干细胞衍生模型，可以对早期癌症发展进行纵向分析。我们还介绍了新兴技术和计算工具，以增强我们对癌症发生和早期进展的理解，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。总之，这些模型和技术有助于更全面地了解特征不明的早期恶性转化级联，具有揭示癌症发展的关键驱动因素和早期生物标志物的巨大潜力。最后，我们讨论如何将这些新见解转化为基于机制的早期癌症检测和预防策略。© 2024。Springer Nature Limited。

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.© 2024. Springer Nature Limited.