Single-cell and multi-omics integration delineates the landscape of gene-wise intratumor heterogeneity and identifies prognostic biomarkers for immunotherapy in non-small cell lung cancer.

Abstract

Background: Gene-wise intratumor heterogeneity (ITH), defined as spatial variability in the expression of individual genes across tumor regions, remains incompletely characterized in non-small cell lung cancer (NSCLC). Identifying low-ITH genes as predictive biomarkers offers a promising strategy to enhance the reliability of immunotherapy outcome prediction.

Methods: We profiled gene-wise ITH using multi-region scRNA-seq data and a computational framework combining variance and clustering metrics. Prognostic low-ITH genes were screened via immunotherapy RNA-seq datasets and six machine learning algorithms to construct the LITHrisk score. Transcriptomic profiling and in vitro experiments elucidated the biological and functional relevance of key score components.

Results: The gene-wise ITH landscape in NSCLC was delineated, with several critical immune checkpoint genes, including PD-L1, LAG3, and TIM-3, being identified as low-ITH genes. A low-ITH-derived signature, the LITHrisk score (PD-L1, CDK11B, CXCL13, RPS4Y1, AKR1B10, CRLF1), effectively predicted immunotherapy prognosis and response in NSCLC and other cancers. Tumors with a low LITHrisk score were characterized by enrichment of immune response-related pathways and heightened infiltration of immune cells, such as T cells, B cells, and myeloid cells. Furthermore, we found that component genes of the score were linked to key tumor characteristics: AKR1B10 was associated with tumor immune interactions, functioning via SPP1 regulation to suppress anti-tumor immunity, while CRLF1 was implicated in promoting a fibroblast-enriched microenvironment and modulating tumor cell motility.

Conclusion: Our study provides a practical transcriptome-based tool for advancing precision immunotherapy and nominates potential therapeutic targets for enhancing treatment efficacy.