Harnessing multi-omics approaches to decipher tumor evolution and improve diagnosis and therapy in lung cancer

With the advancement of novel technologies such as whole-genome sequencing, single-cell sequencing, and spatial transcriptomics, single-omics analyses have already promoted the research of tumorigenesis as well as development and have partly elucidated the evolutionary processes of lung cancer. However, it is still difficult to distinguish these confounding features via single dimensional approaches due to the complexity, heterogeneity and cell-cell interactions with the immune microenvironment in lung cancer. Multi-omics approaches provide a holistic framework for constructing detailed tumor ecosystem landscapes, thereby facilitating the development of a more robust classification system for precision diagnosis and treatment, and aiding in the discovery of novel cancer biomarkers. In this review, we summarize the potential and applications of multi-omics approaches in characterizing intratumor heterogeneity and the tumor microenvironment throughout the course of lung cancer development. By further discussing the discovery and application of diagnostic and therapeutic biomarkers across precancerous lesions, early-stage lung cancer, tumor progression, metastasis, and therapy resistance, we outline the current challenges and future prospects of using multi-omics to identify reliable biomarkers. Moreover, we emphasize that integrative multi-omics models hold great promise for elucidating the complex interactions within the lung cancer ecosystem, thereby contributing to improved diagnostic accuracy, optimized therapeutic strategies, and better patient outcomes.