The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

Lung cancer, the leading cause of cancer mortality, exhibits diverse histologic subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. In this study, we established the Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMM), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCAMGDB aligned 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in the GEMMs. To accompany this resource, a web application was developed that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCAMGDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance. Significance: The Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB) provides a comprehensive and accessible resource for the research community to investigate lung cancer biology in mouse models.

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Journal
Cancer Research
Published
2025-04-29
DOI
https://doi.org/10.1158/0008-5472.can-24-1607
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
0.00

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article

The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

Fangjiang Wu, J. Edward van Veen, Arun R. Pandiri, Min Gyu Lee et al.
Cancer Research
Cancer-related molecular mechanisms research
article

The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

Fangjiang Wu, J. Edward van Veen, Arun R. Pandiri, Min Gyu Lee, Kekoa Taparra, Qinbo Zhou, Guanghua Xiao, Ralph J. DeBerardinis, Charles A. Powell, George K. Acquaah-Mensah, Eric L. Snyder, Yonathan Lissanu, Bo Yao, Barbara M. Grüner, Ling Cai, Corina Lorz, Sarah A. Mazzilli, Sheng Rong Ng, Kang‐Yi Su, Per Lindahl, John D. Minna, Rocı́o Sotillo, Charles M. Rudin, Trudy G. Oliver, Giorgio Ramadori, Matthew G. Oser, Vassilis Aidinis, Jennifer Beane, Monte M. Winslow, Roger A. Moorehead, Tetsuro Taki, Edward E. Morrisey, Jonathan M. Kurie, Shinji Mii, Herwig P. Moll, Deshui Jia, Phuoc T. Tran, Ying Gao, Paweł K. Mazur, David MacPherson, Rosanna Martinelli, Carla P. Concepcion-Crisol, Ting Chen, Yang Xie, Robert A. Jones, Yifeng Xia, Mirentxu Santos, Sandip Biswal
article en

Abstract

Lung cancer, the leading cause of cancer mortality, exhibits diverse histologic subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. In this study, we established the Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMM), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCAMGDB aligned 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in the GEMMs. To accompany this resource, a web application was developed that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCAMGDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance. Significance: The Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB) provides a comprehensive and accessible resource for the research community to investigate lung cancer biology in mouse models.

Cancer ResearchVol. 85(10)
Boston University (US), University of Geneva (CH), University of Maryland, Baltimore (US), University of Vienna (AT), Bloomberg (United States) (US), Salk Institute for Biological Studies (US), University of Salerno (IT), The University of Texas MD Anderson Cancer Center (US), Howard Hughes Medical Institute (US), Johns Hopkins University (US), University of California, Los Angeles (US), Massachusetts College of Pharmacy and Health Sciences (US), National Taiwan University (TW), Duke University (US), German Cancer Research Center (DE), Heidelberg University (DE), Johns Hopkins Medicine (US), CUNY School of Law (US), Huntsman Cancer Institute (US), University of Massachusetts Boston (US), Kettering University (US), Fred Hutch Cancer Center (US), National Institute of Environmental Health Sciences (US), Southwestern Medical Center (US), Stanford Health Care (US), Dana-Farber Cancer Institute (US), Essen University Hospital (DE), Shanghai East Hospital (CN), Science History Institute (US), West German Heart and Vascular Center Essen (DE), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), Taipei Medical University (TW), New York University (US), Nagoya University (JP), Alexander Fleming Biomedical Sciences Research Center (GR), Institute of Molecular and Cell Biology (SG), Medical University of Vienna (AT), University of Guelph (CA), The University of Texas Southwestern Medical Center (US), University of Gothenburg (SE), Stanford University (US), Icahn School of Medicine at Mount Sinai (US)
Howard Hughes Medical Institute, American Cancer Society, Deutsche Forschungsgemeinschaft, Instituto de Salud Carlos III, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Dental and Craniofacial Research
Good health and well-being
Openalex Percentile: Top 88%
Cancer-related molecular mechanisms research
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