| Literature DB >> 28263318 |
Jin-Ku Lee1,2, Jiguang Wang3,4,5, Jason K Sa1,2,6, Erik Ladewig3,4, Hae-Ock Lee7, In-Hee Lee1,2, Hyun Ju Kang1,2, Daniel S Rosenbloom3,4, Pablo G Camara3,4, Zhaoqi Liu3,4, Patrick van Nieuwenhuizen3,4, Sang Won Jung1,2,6, Seung Won Choi1,2,6, Junhyung Kim1,2, Andrew Chen3,4, Kyu-Tae Kim7, Sang Shin1,2,6, Yun Jee Seo1,2, Jin-Mi Oh1,2, Yong Jae Shin1,2,7, Chul-Kee Park8, Doo-Sik Kong2, Ho Jun Seol2, Andrew Blumberg9, Jung-Il Lee2, Antonio Iavarone10,11,12, Woong-Yang Park6,7, Raul Rabadan3,4, Do-Hyun Nam1,2,6.
Abstract
Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies. However, this proposition is complicated by spatial and temporal heterogeneity. Here we study genomic and expression profiles across 127 multisector or longitudinal specimens from 52 individuals with glioblastoma (GBM). Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, whereas geographically separated, multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells (PDCs) shows that therapeutic response is associated with genetic similarity, and multifocal tumors that are enriched with PIK3CA mutations have a heterogeneous drug-response pattern. We show that targeting truncal events is more efficacious than targeting private events in reducing the tumor burden. In summary, this work demonstrates that evolutionary inference from integrated genomic analysis in multisector biopsies can inform targeted therapeutic interventions for patients with GBM.Entities:
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Year: 2017 PMID： 28263318 PMCID： PMC5627771 DOI： 10.1038/ng.3806
Source DB: PubMed Journal: Nat Genet ISSN： 1061-4036 Impact factor: 38.330