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.

doi:https://doi.org/10.1038/s41467-019-13084-7

The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact

DSpace/Manakin Repository

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 ... read more

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Keywords: General Physics and Astronomy, General Chemistry, General Biochemistry,Genetics and Molecular Biology, Journal Article

DOI: https://doi.org/10.1038/s41467-019-13084-7

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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