Journal Article

DKFZ-2025-00903

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models.

Cai, L. ; Wu, F. ; Zhou, Q. ; Gao, Y. ; Yao, B. ; DeBerardinis, R. J. ; Acquaah-Mensah, G. K. ; Aidinis, V. ; Beane, J. E. ; Biswal, S. ; Chen, T. ; Concepcion-Crisol, C. P. ; Grüner, B. M. ; Jia, D. ; Jones, R. A. ; Kurie, J. M. ; Lee, M. G. ; Lindahl, P. ; Lissanu, Y. ; Lorz, C. ; MacPherson, D. ; Martinelli, R. ; Mazur, P. K. ; Mazzilli, S. A. ; Mii, S. ; Moll, H. P. ; Moorehead, R. A. ; Morrisey, E. E. ; Ng, S. R. ; Oser, M. G. ; Pandiri, A. R. ; Powell, C. A. ; Ramadori, G. ; Santos, M. ; Snyder, E. L. ; Sotillo, R.DKFZ* ; Su, K.-Y. ; Taki, T. ; Taparra, K. ; Tran, P. T. ; Xia, Y. ; van Veen, J. E. ; Winslow, M. M. ; Xiao, G. ; Rudin, C. M. ; Oliver, T. G. ; Xie, Y. ; Minna, J. D.

2025
AACR Philadelphia, Pa.

This record in other databases:  

Please use a persistent id in citations: doi:10.1158/0008-5472.CAN-24-1607

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.

Note: 2025 May 15;85(10):1769-1783

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Contributing Institute(s):

1. B220 Molekulare Grundlagen thorakaler Tumoren (B220)

Research Program(s):

1. 312 - Funktionelle und strukturelle Genomforschung (POF4-312) (POF4-312)

Appears in the scientific report 2025

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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