The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact
van Dessel, Lisanne F.; van Riet, Job; Smits, Minke; Zhu, Yanyun; Hamberg, Paul; van der Heijden, Michiel S.; Bergman, Andries M.; van Oort, Inge M.; de Wit, Ronald; Voest, Emile E.; Steeghs, Neeltje; Yamaguchi, Takafumi N.; Livingstone, Julie; Boutros, Paul C.; Martens, John W. M.; Sleijfer, Stefan; Cuppen, Edwin; Zwart, Wilbert; van de Werken, Harmen J. G.; Mehra, Niven; Lolkema, Martijn P.
(2019) Nature Communications, volume 10, issue 1
(Article)
Abstract
Metastatic castration-resistant prostate cancer (mCRPC) has a highly complex genomic landscape. With the recent development of novel treatments, accurate stratification strategies are needed. Here we present the whole-genome sequencing (WGS) analysis of fresh-frozen metastatic biopsies from 197 mCRPC patients. Using unsupervised clustering based on genomic features, we define eight distinct
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genomic clusters. We observe potentially clinically relevant genotypes, including microsatellite instability (MSI), homologous recombination deficiency (HRD) enriched with genomic deletions and BRCA2 aberrations, a tandem duplication genotype associated with CDK12 −/− and a chromothripsis-enriched subgroup. Our data suggests that stratification on WGS characteristics may improve identification of MSI, CDK12 −/− and HRD patients. From WGS and ChIP-seq data, we show the potential relevance of recurrent alterations in non-coding regions identified with WGS and highlight the central role of AR signaling in tumor progression. These data underline the potential value of using WGS to accurately stratify mCRPC patients into clinically actionable subgroups.
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Keywords: General Physics and Astronomy, General Chemistry, General Biochemistry,Genetics and Molecular Biology, Journal Article
ISSN: 2041-1723
Publisher: Nature Publishing Group
Note: Funding Information: This publication and the underlying study have been made possible in parts by the data that the Hartwig Medical Foundation and the Center of Personalized Cancer Treatment (CPCT) have made available to the study. We would like to thank the local principal investigators of all contributing centers for their help with patient enrollment (listed in Supplementary Table 1). We would also like to thank Tesa M. Severson for her help with the computational analyses of the ChIP-seq data, Suzan Stelloo for providing ChIP-seq results on cell lines and Arne van Hoeck for providing the CHORD (HR-deficiency) prediction scores. We thank Dr. Joost van Rosmalen for his advises on the statistical analyses. In addition, we would like to thank the Barcode for Life foundation for making this research possible. Figure 1 was created with BioRender.com. This work was supported in parts by a KWF-Alpe d’HuZes project [NKI 2014-7080], a grant from Astellas Pharma [Lolkema/NL-72-RG-11] and a Johnson & Johnson grant [212082PCR3014]. This work was supported by the NIH/NCI under award number P30CA016042. H.J.G.v.D.W., J.v.R. and the Erasmus MC Cancer Computational Biology Center (CCBC) were financed through a grant from the Daniel den Hoed foundation. Publisher Copyright: © 2019, The Author(s). Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
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