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

Nature Genetics volume 50, pages 1399–1411 (2018)Cite this article

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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.

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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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Author notes

  1. These authors contributed equally: Jin-Ku Lee, Zhaoqi Liu, Jason K. Sa, Sang Shin, Jiguang Wang.

Authors and Affiliations

  1. Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea

    Jin-Ku Lee, Jason K. Sa, Sang Shin, Hee Jin Cho, Seung Won Choi, In-Hee Lee, Yong Jae Shin, Hyun Ju Kang, Donggeon Kim, Yun Jee Seo, Hyemi Shin, Mijeong Lee, Sung Heon Kim, Yong-Jun Kwon, Jeong-Woo Oh, Minsuk Song, Misuk Kim & Do-Hyun Nam

  2. Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Jin-Ku Lee, Yong Jae Shin, Sung Heon Kim, Doo-Sik Kong, Jung Won Choi, Ho Jun Seol, Jung-Il Lee & Do-Hyun Nam

  3. Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea

    Jin-Ku Lee, Jason K. Sa, Hee Jin Cho, In-Hee Lee, Yong Jae Shin, Hyun Ju Kang, Donggeon Kim, Yun Jee Seo, Misuk Kim, Yeup Yoon, Gyu Ha Ryu, Nayoung K. D. Kim, Joon Seol Bae & Woong-Yang Park

  4. Department of Systems Biology, Columbia University, New York, NY, USA

    Zhaoqi Liu, Mykola Bordyuh, Oliver Elliott, Timothy Chu, Daniel I. S. Rosenbloom & Raul Rabadan

  5. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Zhaoqi Liu, Mykola Bordyuh, Oliver Elliott, Timothy Chu, Daniel I. S. Rosenbloom & Raul Rabadan

  6. Department of Health Sciences and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea

    Sang Shin, Seung Won Choi, Hyemi Shin, Mijeong Lee, Jeong-Woo Oh, Joon Oh Park, Yeup Yoon, Woong-Yang Park, Jeeyun Lee & Do-Hyun Nam

  7. Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China

    Jiguang Wang

  8. Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Jiguang Wang

  9. Center of Systems Biology and Human Health, Hong Kong University of Science and Technology, Hong Kong, China

    Jiguang Wang

  10. Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Sun Young Kim, Moon-Hee Sim, Jusun Kim, Taehyang Lee, Seung Tae Kim, Joon Oh Park & Jeeyun Lee

  11. Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Kyoung-Mee Kim & Sang-Yong Song

  12. Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Jeong-Won Lee

  13. Deparment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Hee-Cheol Kim, Jeong Eon Lee & Min Gew Choi

  14. Department of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Sung Wook Seo

  15. Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Young Mog Shim & Jae Ill Zo

  16. Deparment of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Byong Chang Jeong

  17. Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea

    Nayoung K. D. Kim, Joon Seol Bae & Woong-Yang Park

  18. Department of Stem Cell Biology and Regenerative Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Jeongwu Lee

  19. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    Roel G. W. Verhaak

  20. Institute for Cancer Genetics, Columbia University, New York, NY, USA

    Antonio Iavarone

  21. Department of Neurology, Columbia University, New York, NY, USA

    Antonio Iavarone

  22. Department of Pathology, Columbia University, New York, NY, USA

    Antonio Iavarone

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  1. Jin-Ku Lee
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Contributions

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, 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, 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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