Abstract 3923: Lung cancer autochthonous model gene expression database

Abstract Lung cancer, the leading cause of cancer mortality, exhibits diverse histological subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models remain disparate, siloed, and difficult to compare centrally. To address this, we established the Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB), an extensive repository encompassing 1, 354 samples from 77 transcriptomic datasets, including 974 from genetically engineered mouse models (GEMMs), 368 from carcinogen-induced models, and 12 from a spontaneous model. Importantly, 85% of the data were reprocessed through standardized pipelines tailored to each platform, including RNA-seq and five microarray platforms, enhancing comparability across studies. LCAMGDB aligns 859 GEMM tumors with human lung cancer mutations, revealing the need to expand the diversity of genetic aberrations modeled. Our analysis highlights underrepresentation of key alterations such as ROS1, RET, and MET, which are pivotal in human cancers. Furthermore, we are actively updating LCAMGDB to include 989 additional samples from 44 studies, augmenting the current collection of RNA-seq data by nearly 80%. Complementing the database, we developed a web application at https://lccl.shinyapps.io/LCAMGDB/ offering researchers data review and download, as well as intuitive tools for dynamic gene expression analysis and cross-study comparisons. Future iterations aim to integrate pathology data through engagement with the lung cancer mouse model community, potentially complementing transcriptomic insights. LCAMGDB bridges gaps between mouse models and human lung cancer, laying the groundwork for translational research and collaborative advancements. By standardizing and expanding this resource, we aim to improve the fidelity and relevance of preclinical studies for understanding lung cancer biology and developing therapies. Citation Format: Ling Cai. Lung cancer autochthonous model gene expression database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3923.