Early Cancer Biology: Emerging Models and Multi-Omics Tools for Detecting Precancerous Transformation
This Nature Reviews Cancer review introduces clinical samples, autochthonous mouse models, and organoid-derived systems for studying cancer initiation, alongside emerging techniques including single-cell and spatial multi-omics, lineage tracing, and AI-based analysis. The authors discuss how these approaches can unveil early biomarkers and key molecular drivers of malignant transformation, with direct relevance to the development of liquid biopsy and tissue-based early detection diagnostics.
The original study
Emerging strategies to investigate the biology of early cancer.
- Authors
- Zhou R, Tang X, Wang Y
- Journal
- Nature reviews. Cancer
- Type
- Journal Article, Review
- PMID
- 39433978
Original 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.