Letter abstract


Nature Medicine 15, 559 - 565 (2009)
Published online: 12 April 2009 | Corrected online: 7 July 2009 | doi:10.1038/nm.1944



There is an Erratum (July 2009) associated with this Letter.

Copy number analysis indicates monoclonal origin of lethal metastatic prostate cancer

Wennuan Liu1,9, Sari Laitinen2,9, Sofia Khan3, Mauno Vihinen3, Jeanne Kowalski4, Guoqiang Yu5, Li Chen5, Charles M Ewing6, Mario A Eisenberger7, Michael A Carducci7, William G Nelson7, Srinivasan Yegnasubramanian7, Jun Luo6,7, Yue Wang5, Jianfeng Xu1, William B Isaacs6,7, Tapio Visakorpi2 & G Steven Bova6,7,8

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Many studies have shown that primary prostate cancers are multifocal1, 2, 3 and are composed of multiple genetically distinct cancer cell clones4, 5, 6. Whether or not multiclonal primary prostate cancers typically give rise to multiclonal or monoclonal prostate cancer metastases is largely unknown, although studies at single chromosomal loci are consistent with the latter case. Here we show through a high-resolution genome-wide single nucleotide polymorphism and copy number survey that most, if not all, metastatic prostate cancers have monoclonal origins and maintain a unique signature copy number pattern of the parent cancer cell while also accumulating a variable number of separate subclonally sustained changes. We find no relationship between anatomic site of metastasis and genomic copy number change pattern. Taken together with past animal and cytogenetic studies of metastasis7 and recent single-locus genetic data in prostate and other metastatic cancers8, 9, 10, these data indicate that despite common genomic heterogeneity in primary cancers, most metastatic cancers arise from a single precursor cancer cell. This study establishes that genomic archeology of multiple anatomically separate metastatic cancers in individuals can be used to define the salient genomic features of a parent cancer clone of proven lethal metastatic phenotype.

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  1. Center for Cancer Genomics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
  2. Laboratory of Cancer Genetics Institute of Medical Technology, University of Tampere and Tampere University Hospital, Tampere, Finland.
  3. Laboratory of Bioinformatics, Institute of Medical Technology, University of Tampere and Tampere University Hospital, Tampere, Finland.
  4. Department of Oncology Biostatistics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  5. Computational Bioinformatics and Bio-imaging Laboratory, Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia, USA.
  6. Department of Urology Genetic Medicine and Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  7. Department of Oncology, Genetic Medicine and Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  8. Project to Eliminate Lethal Prostate Cancer Laboratory, Departments of Pathology, Genetic Medicine and Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  9. These authors contributed equally to this work.

Correspondence to: G Steven Bova6,7,8 e-mail: gbova2@jhmi.edu

* In the version of this article initially published, some of the sample identifiers were missing from Figure 1e. The error has been corrected in the HTML and PDF versions of the article.


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