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Spatiotemporal Genomic Architecture Informs Precision Oncology in Glioblastoma

Authors Lee, Jin-Ku
Wang, Jiguang View this author's profile
Sa, Jason K.
Ladewig, Erik
Lee, Hae-Ock
Lee, In-Hee
Kang, Hyun Ju
Rosenbloom, Daniel S.
Camara, Pablo G.
Liu, Zhaoqi
Van Nieuwenhuizen, Patrick
Jung, Sang Won
Choi, Seung Won
Kim, Junhyung
Chen, Andrew
Kim, Kyu-Tae
Shin, Sang
Seo, Yun Jee
Oh, Jin-Mi
Shin, Yong Jae
Park, Chul-Kee
Kong, Doo-Sik
Seol, Ho Jun
Blumberg, Andrew
Lee, Jung-Il
Iavarone, Antonio
Park, Woong-Yang
Rabadan, Raul
Nam, Do-Hyun
Issue Date 2017
Source Nature Genetics , v. 49, (4), March 2017, p. 594-599
Summary 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.
ISSN 1061-4036
Language English
Format Article
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