Gene signatures characterizing driver mutations in lung squamous carcinoma are predictive of the progression of pre-cancer lesions.

UNLABELLED

Patients with lung squamous cell carcinoma (LUSC) are often diagnosed at advanced stages, limiting opportunities for early intervention. LUSC develops through a multistep progression from low-grade lesions to high-grade lesions, including carcinoma in situ (CIS), of which approximately half progress to invasive cancer while the other half regress. Although frequent mutations and copy number alterations have been documented in LUSC and observed in precursor lesions, their prognostic significance in precancerous stages remains largely unexplored. In this study, we leveraged gene expression data from LUSC tumors in The Cancer Genome Atlas (TCGA) to derive transcriptional signatures corresponding to 34 key driver genomic aberrations, including mutations, amplifications, and deletions. These tumor-derived gene signatures were then applied to precancerous lesion datasets to assess their ability to characterize developmental stages and predict progression risk. We found that many of these signatures increased progressively across lesion stages, reflecting their roles in early tumorigenesis. In particular, several signatures accurately predicted which CIS lesions would progress to invasive cancer. Furthermore, these signature scores were more strongly associated with patient prognosis in LUSC than the presence of genomic aberrations alone. We also examined the relationship between driver-associated signatures and the tumor immune microenvironment. Signature scores were significantly correlated with immune features such as immune cell infiltration and immune checkpoint gene expression, including CD274 (PD-L1). Interestingly, these associations varied across lesion stages, indicating dynamic immune interactions during cancer evolution. Together, our findings demonstrate that tumor-derived driver gene expression signatures provide valuable insight into the biology and progression risk of precancerous lesions, offering potential utility for early detection and intervention strategies in LUSC.

NOVELTY AND IMPACT

In this study, we developed gene expression signatures using lung squamous cell carcinoma (LUSC) transcriptomic and genomic data and apply them to characterize precancerous lesions. These signatures showed high performance in predicting the progression risk of carcinoma in situ (CIS), offering a valuable tool for personalized prevention and early intervention in LUSC. These findings have the potential to improve early lung cancer detection and provide new insights into the tumor microenvironment in the context of precancer evolution of LUSC.