Outcomes of anticancer therapy vary dramatically among patients due to diverse genetic and molecular backgrounds, highlighting extensive intertumoral heterogeneity. The fundamental tenet of precision oncology defines molecular characterization of tumors to guide optimal patient-tailored therapy. Towards this goal, we have established a compilation of pharmacological landscapes of 462 patient-derived tumor cells (PDCs) across 14 cancer types, together with genomic and transcriptomic profiling in 385 of these tumors. Compared with the traditional long-term cultured cancer cell line models, PDCs recapitulate the molecular properties and biology of the diseases more precisely. Here, we provide insights into dynamic pharmacogenomic associations, including molecular determinants that elicit therapeutic resistance to EGFR inhibitors, and the potential repurposing of ibrutinib (currently used in hematological malignancies) for EGFR-specific therapy in gliomas. Lastly, we present a potential implementation of PDC-derived drug sensitivities for the prediction of clinical response to targeted therapeutics using retrospective clinical studies.
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All sequenced data have been deposited in the European Genome-phenome Archive (EGA) under accession EGAS00001002515. Processed data and basic association analysis are publicly available through an interactive web portal (the Cancer-Drug eXplorer (cDx); see URLs).
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This research was supported by a grant of the Korea Health Technology Research and Development project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (HI14C3418). This work has been funded by NIH grants (R01 CA185486, R01 CA179044, U54 CA193313 and U54 209997) and NSF/SU2C/V Foundation Ideas Lab Multidisciplinary Team (PHY-1545805) and Hong Kong RGC grants (N_HKUST601/17 and C6002-17G). The biospecimens for this study were provided by the Samsung Medical Center BioBank.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figures 1–15
Clinical information of the pan-cancer patients included in this study
CancerSCAN (targeted exome sequencing) gene list
GliomaSCAN (targeted exome sequencing) gene list
List of detected genomic alterations (mutation, fusion, copy number variation)
List of the 60-drug panel
Sixty-drug library quality control
Area under the curve (AUC) for the dose–response curve (DRC)
Half-maximal inhibitory concentration of drug sensitivity
Cancer-type-specific drug associations
Topolgoical data analysis of cancer-type-specific drug associations
Single genomic alteration–drug associations
Genetic features associated with panobinostat response using dNetFS
Genetic features associated with EGFR inhibitor response using dNetFS
Clinical responses in retrospective cases
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Lee, J., Liu, Z., Sa, J.K. et al. Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy. Nat Genet 50, 1399–1411 (2018). https://doi.org/10.1038/s41588-018-0209-6
Journal of Hepatology (2020)
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