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0This study introduces CTDPathSim2.0, a computational framework designed to identify which laboratory cell lines most accurately represent individual patient tumors. Researchers developed this tool because cell lines often undergo genetic changes in vitro, causing them to deviate from the biology of actual human cancers. By integrating multi-omics data—including gene expression, DNA methylation, and copy number aberrations—the pipeline calculates a similarity score that accounts for immune cell presence and biological pathway activities. Results demonstrate that this method effectively predicts drug responses for FDA-approved treatments across 22 cancer types, outperforming existing transcriptome-based models. Ultimately, this technology provides a more reliable foundation for preclinical drug testing and the advancement of personalized medicine.
References:
Bose, B., Bozdag, S. Identifying cell lines across pan-cancer to be used in preclinical research as a proxy for patient tumor samples. Commun Biol 7, 1101 (2024). doi.org
