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

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 2

Genome-wide analysis of genomic rearrangements in mCRPC and comparisons with localized prostate cancer.

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Genome-wide analysis of genomic rearrangements in mCRPC and comparisons ...
(A) Analysis of SRBs identified regions of rearrangement hotspots, genome-wide, using a gamma-Poisson regression model. Each dot corresponds to a 100 kb bin (n = 26,663 total bins). Statistically significant SRB bins with FDR (Benjamini-Hochberg) q value ≤ 0.1 (n = 55) are colored based on the distance to the nearest known prostate cancer driver gene, within 1 Mb. (B) Comparison of SV alteration frequency in mCRPC (n = 143) versus primary localized prostate cancer (n = 278). The union set of genes (n = 14) within 1 Mb of SRB hotspot regions in mCRPC and localized prostate cancer cohorts was included in the comparison. (C) Patterns of rearrangements at the loci of driver genes identified at SRB regions in mCRPC cohort of 143 tumors. Cumulative counts of intrachromosomal SV events (tandem duplications [TandemDup], deletions, and inversions) were computed based on the breakpoints and span of the events. Interchromosomal translocations are not shown. Genome coordinates based on hg38 build. (D) Overlap of ARBS within SRB hotspots of mCRPC (55 regions) and primary localized prostate (47 regions) cohorts. χ2 test of independence P values is shown. (E) Fusion status and expression of selected genes in the ETS transcription factor gene family in the mCRPC cohort with WGS and RNA-Seq data. Fusion type was defined as the data evidence that supported the event: DNA only, corresponds to WGS; RNA only, corresponds to RNA-Seq; DNA+RNA, corresponds to support from both WGS and RNA-Seq. Each dot represents a tumor sample and is colored based on fusion type of each sample; gray indicates no evidence of fusion event. (F) Fusion profile of ETV1. DNA rearrangement breakpoints supporting the fusion (purple bars) are indicated with the corresponding fusion partners. (G) Summary of fusion partners for selected genes in ETS transcription factor gene family in mCRPC cohort. Fusion events and partners are indicated by flow connections. Total counts of individual fusion events and partners across the cohort are shown.