Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2025 May 15;85(10):1769-1783.
doi: 10.1158/0008-5472.CAN-24-1607.

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

Affiliations
Comparative Study

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

Ling Cai et al. Cancer Res. .

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare no potential conflicts of interest.

References

    1. Al Bakir M, Huebner A, Martinez-Ruiz C, Grigoriadis K, Watkins TBK, Pich O, et al., The evolution of non-small cell lung cancer metastases in TRACERx. Nature, 2023. 616(7957): p. 534–542. - PMC - PubMed
    1. Campbell JD, Alexandrov A, Kim J, Wala J, Berger AH, Pedamallu CS, et al., Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat Genet, 2016. 48(6): p. 607–16. - PMC - PubMed
    1. Cancer Genome Atlas Research, N., Comprehensive molecular profiling of lung adenocarcinoma. Nature, 2014. 511(7511): p. 543–50. - PMC - PubMed
    1. Frankell AM, Dietzen M, Al Bakir M, Lim EL, Karasaki T, Ward S, et al., The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature, 2023. 616(7957): p. 525–533. - PMC - PubMed
    1. George J, Lim JS, Jang SJ, Cun Y, Ozretic L, Kong G, et al., Comprehensive genomic profiles of small cell lung cancer. Nature, 2015. 524(7563): p. 47–53. - PMC - PubMed

Publication types

Grants and funding

LinkOut - more resources