Unraveling Tumor Heterogeneity in Lung Cancer: Insights from Metabolomic Profiling

Lung cancer research has entered an era characterized by extraordinary advances in profiling tumor complexity. Despite the growing arsenal of targeted therapies, lung cancer remains one of the most lethal cancers worldwide, largely due to its remarkable intratumoral heterogeneity. Cancer cells within a single tumor can vary substantially in transcriptional programs, metabolic states, lineage identities, and their capacity to resist therapies. Current studies, leveraging technologies such as metabolomic profiling, have begun to reveal the layered diversity of lung cancer cell populations. Landmark work demonstrates variable cell state plasticity, lineage switching, and the emergence of resistant cellular subclones under therapeutic pressure. However, questions persist regarding the interplay between transcriptional heterogeneity, metabolic reprogramming, and their joint impact on disease progression.This Research Topic aims to elucidate the cancer cell-intrinsic components of tumor heterogeneity in lung cancer through the integration of metabolomic profiling and multi-omics data. By resolving cellular diversity at unprecedented resolution, this initiative seeks to connect molecular states with functional outcomes—such as therapy resistance, clonal evolution, and metastatic potential—across non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), and other thoracic malignancies. Core goals include deciphering the metabolic adaptations of distinct cancer cell populations, understanding lineage plasticity, and developing integrative analytical approaches that bridge metabolomic data.To gather further insights in this rapidly evolving area, the scope of this Research Topic is focused on lung cancer cellular heterogeneity as revealed by metabolomics, with an emphasis on cell-intrinsic mechanisms. Submissions are encouraged that address cancer cell state diversity, metabolic phenotypes, and the analytical integration of multi-omics datasets; studies exploring tumor microenvironmental interactions are beyond the immediate focus, except where directly linked to intrinsic cancer cell programs.We welcome research addressing, but not limited to, the following themes:- Profiling metabolic and lipidomic phenotypes among lung cancer cell populations- Integrating transcriptomic and metabolomic data to connect gene expression with metabolic function- Investigating lineage plasticity, cell state transitions, and clonal evolution- Elucidating mechanisms by which intrinsic transcriptional or metabolic diversity drives therapy resistance and progression- Developing computational or bioinformatic methods for multi-omics data integration in lung cancer researchPlease note manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.The Topic Editors did not declare any potential competing interests in relation to the Research Topic theme. Topic editors: Bhavik Dalal, Manash K. Paul Source: https://www.frontiersin.org/research-topics/84350/unraveling-tumor-heterogeneity-in-lung-cancer-insights-from-metabolomic-profiling