Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy

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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Fig. 1: Patient tumor and derived cell resources for pharmacogenomics analysis.
Fig. 2: Therapeutic landscape of PDCs and lineage-specific responses
Fig. 3: Pharmacogenomic interactions in PDCs.
Fig. 4: Genomic and transcriptomic correlates of panobinostat sensitivity
Fig. 5: Predictive biomarkers for response to EGFR inhibitors in EGFR-altered GBM PDCs.
Fig. 6: Clinical feasibility of PDC drug-screening-guided precision oncology.
Fig. 7: Schematic illustration of the major lineage-specific and genomic associated drug interactions.

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.

Corresponding authors

Correspondence to Jeeyun Lee or Raul Rabadan or Do-Hyun Nam.

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The authors declare no competing interests.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15

Reporting Summary

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, 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

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