Dataset Information

Spatiotemporal genomic architecture informs precision oncology in glioblastoma.

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.

SUBMITTER: Lee JK

PROVIDER: S-EPMC5627771 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Spatiotemporal genomic architecture informs precision oncology in glioblastoma.

Lee Jin-Ku JK Wang Jiguang J Sa Jason K JK Ladewig Erik E Lee Hae-Ock HO Lee In-Hee IH Kang Hyun Ju HJ Rosenbloom Daniel S DS Camara Pablo G PG Liu Zhaoqi Z van Nieuwenhuizen Patrick P Jung Sang Won SW Choi Seung Won SW Kim Junhyung J Chen Andrew A Kim Kyu-Tae KT Shin Sang S Seo Yun Jee YJ Oh Jin-Mi JM Shin Yong Jae YJ Park Chul-Kee CK Kong Doo-Sik DS Seol Ho Jun HJ Blumberg Andrew A Lee Jung-Il JI Iavarone Antonio A Park Woong-Yang WY Rabadan Raul R Nam Do-Hyun DH

Nature genetics 20170306 4

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, mult ...[more]

PMID: 28263318

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Project description:BackgroundGenetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL.ResultsWe find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models.ConclusionsIn a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs.

Dataset Information

Spatiotemporal genomic architecture informs precision oncology in glioblastoma.

Publications

Spatiotemporal genomic architecture informs precision oncology in glioblastoma.

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