Pharmacogenomic Landscape of Patient-derived Tumor Cells Informs Precision Oncology Therapy

Overview

Journal Nat Genet

Specialty Genetics

Date 2018 Sep 29

PMID 30262818

Citations 84

Authors

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 Seo

Hyemi 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

Affiliations

Soon will be listed here.

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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