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Patterns of structural variation define prostate cancer across disease states
Meng Zhou, … , Srinivas R. Viswanathan, Gavin Ha
Meng Zhou, … , Srinivas R. Viswanathan, Gavin Ha
Published August 9, 2022
Citation Information: JCI Insight. 2022;7(17):e161370. https://doi.org/10.1172/jci.insight.161370.
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Research Article Genetics Oncology

Patterns of structural variation define prostate cancer across disease states

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Abstract

The complex genomic landscape of prostate cancer evolves across disease states under therapeutic pressure directed toward inhibiting androgen receptor (AR) signaling. While significantly altered genes in prostate cancer have been extensively defined, there have been fewer systematic analyses of how structural variation shapes the genomic landscape of this disease across disease states. We uniformly characterized structural alterations across 531 localized and 143 metastatic prostate cancers profiled by whole genome sequencing, 125 metastatic samples of which were also profiled via whole transcriptome sequencing. We observed distinct significantly recurrent breakpoints in localized and metastatic castration-resistant prostate cancers (mCRPC), with pervasive alterations in noncoding regions flanking the AR, MYC, FOXA1, and LSAMP genes enriched in mCRPC and TMPRSS2-ERG rearrangements enriched in localized prostate cancer. We defined 9 subclasses of mCRPC based on signatures of structural variation, each associated with distinct genetic features and clinical outcomes. Our results comprehensively define patterns of structural variation in prostate cancer and identify clinically actionable subgroups based on whole genome profiling.

Authors

Meng Zhou, Minjeong Ko, Anna C.H. Hoge, Kelsey Luu, Yuzhen Liu, Magdalena L. Russell, William W. Hannon, Zhenwei Zhang, Jian Carrot-Zhang, Rameen Beroukhim, Eliezer M. Van Allen, Atish D. Choudhury, Peter S. Nelson, Matthew L. Freedman, Mary-Ellen Taplin, Matthew Meyerson, Srinivas R. Viswanathan, Gavin Ha

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Figure 4

Clustering of mCRPC SV signatures.

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Clustering of mCRPC SV signatures.
SV signature analysis and hierarchica...
SV signature analysis and hierarchical clustering identify 9 distinct molecular groups in the mCRPC cohort of 101 samples sequenced with standard short reads. Top: Dendrogram of the clustering of SV signature exposure. The prevalence of each signature was computed based on having ≥0.05 exposure (proportion of SVs). Middle: Enrichment of altered prostate cancer drivers. Enriched alterations in clusters 1, 3, 5, 6, and 7 are shown based on statistical significance by χ2 test. Bottom: Composition of SV types and sizes for each SV cluster, separated by non-clustered (nc) and clustered (c) SV events. The number of samples per cluster is indicated in the corresponding cluster label.

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