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Archetype analysis of lung adenocarcinoma premalignancy links heterogeneity in premalignant lesions to diverging features of invasive disease

Anderson, K. E.; Tran, L. M.; Krysan, K.; Kefella, Y.; Yu, L.; Fishbein, G. A.; Rodriguez, E. M.; Shabihkhani, M.; Stefanko, D. P.; Green, E.; Liu, G.; Liu, H.; Zhang, S.; Kane, E. E.; Mehrad, M.; Spira, A. E.; Dubinett, S. M.; Burks, E. J.; Mazzilli, S. A.; Lenburg, M. E.; Beane, J. E.

2025-10-06 cancer biology
10.1101/2025.10.04.680402 bioRxiv
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ABSTRACTLung adenocarcinoma (LUAD), the most common form of lung cancer, is a heterogeneous disease with highly variable histopathologic features and clinical outcomes. LUAD premalignant lesions (PMLs) are localized proliferative lesions of atypical pneumocytes that proliferate along the alveolar walls. In this study, we characterized the molecular heterogeneity across PMLs, regardless of histologic designation, and identified four unique groups or "archetypes" of PMLs using bulk RNA and exome sequencing data from laser captured microdissected PMLs, normal, and tumor tissue from LUAD resection cases. The archetypes were defined based on expression of recurrent gene co-expression modules discovered using the LUAD PMLs we profiled and three publicly available datasets. One PML archetype, termed "proliferation", was associated with increased expression of genes involved in cell proliferation, a pro-tumor immune environment, and enrichment for EGFR driver mutations. Another archetype, termed "normal-like", was associated with similar features to normal tissue, an anti-tumor immune response, and lacked enrichment for driver mutations compared to other archetypes. We projected the PML archetypes into independent gene expression datasets profiling LUAD and found that tumors closest to the proliferation archetype were enriched for multiple features of aggressive LUAD, including high tumor grade and lymph node invasion, and had significantly lower disease-free survival. Proliferation archetype PMLs may represent a subset of lesions with more aggressive features that could improve patient stratification and suggest novel targets for lung cancer interception.

Published in Molecular Cancer Research (predicted rank #7) · training set

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