Abstract
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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Data availability
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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Acknowledgements
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.
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J.-K.L., Z.L., J.K.S., S.S. and J.W. are co-first authors. J.-K.L., Z.L., J.K.S., S.S. and J.W. performed the majority of the experiments and analyses. Z.L. and M.B. analyzed the therapeutic landscape of PDCs and pharmacogenomic interactions. D.I.S.R., O.E. and T.C. designed and constructed the cDx interactive webportal. S.W.C., D.-S.K., D.-H.N., S.T.K. and J.L. interpreted the clinical data. J.-K.L., S.S., J.-W.O., M.S., H.J.K., S.H.K., G.H.R. and Y.-J.K. organized and analyzed the drug-screening experiments. Y.J.Shin, H.J.K., Y.J.Seo, M.L., S.Y.K., M.-H.S., J.K., T.L., S.-Y.S., K.-M.K., M.K., J.O.P. and Y.Y. organized and processed the specimens for patient-derived cultures and genome analysis. D.K. and M.L. conducted the animal experiments. J.K.S., H.J.C., I.-H.L., H.S., N.K.D.K., J.S.B. and W.-Y.P. analyzed the genomic profiling. D.-S.K., J.W.C., H.J.S., J.-I.L., J.-W.L., H.-C.K., J.E.L., M.G.C., S.W.S., Y.M.S., J.I.Z. and B.C.J. provided surgical specimens. J.-K.L., Z.L., J.K.S., S.S. and J.W. wrote the manuscript with the feedback from J.L., R.G.W.V., A.I., J.L., R.R. and D.-H.N. J.L., R.R. and D.-H.N. designed and supervised the entire project.
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Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–15
Supplementary Table 1
Clinical information of the pan-cancer patients included in this study
Supplementary Table 2
CancerSCAN (targeted exome sequencing) gene list
Supplementary Table 3
GliomaSCAN (targeted exome sequencing) gene list
Supplementary Table 4
List of detected genomic alterations (mutation, fusion, copy number variation)
Supplementary Table 5
List of the 60-drug panel
Supplementary Table 6
Sixty-drug library quality control
Supplementary Table 7
Area under the curve (AUC) for the dose–response curve (DRC)
Supplementary Table 8
Half-maximal inhibitory concentration of drug sensitivity
Supplementary Table 9
Cancer-type-specific drug associations
Supplementary Table 10
Topolgoical data analysis of cancer-type-specific drug associations
Supplementary Table 11
Single genomic alteration–drug associations
Supplementary Table 12
Genetic features associated with panobinostat response using dNetFS
Supplementary Table 13
Genetic features associated with EGFR inhibitor response using dNetFS
Supplementary Table 14
Clinical responses in retrospective cases
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Lee, JK., 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
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DOI: https://doi.org/10.1038/s41588-018-0209-6
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