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

Jin Ku Lee, Zhaoqi Liu, Jason K. Sa, Sang Shin, Jiguang Wang, Mykola Bordyuh, Hee Jin Cho, Oliver Elliott, Timothy Chu, Seung Won Choi, Daniel I.S. Rosenbloom, In Hee Lee, Yong Jae Shin, Hyun Ju Kang, Donggeon Kim, Sun Young Kim, Moon Hee Sim, Jusun Kim, Taehyang Lee, Yun Jee SeoHyemi Shin, Mijeong Lee, Sung Heon Kim, Yong Jun Kwon, Jeong Woo Oh, Minsuk Song, Misuk Kim, Doo Sik Kong, Jung Won Choi, Ho Jun Seol, Jung Il Lee, Seung Tae Kim, Joon Oh Park, Kyoung Mee Kim, Sang Yong Song, Jeong Won Lee, Hee Cheol Kim, Jeong Eon Lee, Min Gew Choi, Sung Wook Seo, Young Mog Shim, Jae Ill Zo, Byong Chang Jeong, Yeup Yoon, Gyu Ha Ryu, Nayoung K.D. Kim, Joon Seol Bae, Woong Yang Park, Jeongwu Lee, Roel G.W. Verhaak, Antonio Iavarone, Jeeyun Lee, Raul Rabadan, Do Hyun Nam*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

127 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1399-1411
Number of pages13
JournalNature Genetics
Issue number10
Publication statusPublished - 1 Oct 2018
Externally publishedYes


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