KIM Sungjoon and CHO Helen visiting KAIST (4.11)

Post date: Mar 10, 2016 1:26:22 AM

Dr. KIM Sungjoon at Novatis and Dr. CHO Helen at Pfizer are visiting Korea along with their daughter in order to participate in a celebration party for the 80th birthday of Sungjoon's father. On a visit to KAIST on April 11, Sungjoon offered a lunch to the lab members who were working on the election day at the Manna Yuseong restaurant and gave a briefing to the professors of Basic Research Lab of Gastric Cancer on his recent Nature paper of Broad-Novartis Cancer Cell Line Encyclopedia (CCLE) that was published online on March 29.

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A. Margolin, Sungjoon Kim, et al.

Nature 483,603–307(29 March 2012)doi:10.1038/nature11003Received 25 July 2011 Accepted 01 March 2012 Published online 28 March 2012

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens.