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Clin Cancer Res. 2017 Feb 1;23(3):630-635. doi: 10.1158/1078-0432.CCR-16-0234. Epub 2016 Nov 18.

How Subclonal Modeling Is Changing the Metastatic Paradigm.

Author information

1
Cancer Research UK Cambridge Institute, University of Cambridge, United Kingdom.
2
The Francis Crick Institute, London, United Kingdom.
3
Department of Human Genetics, University of Leuven, Leuven, Belgium.
4
Department of Surgery, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia.
5
Australian Prostate Cancer Research Centre at Epworth Hospital, Australia.
6
Oxford Big Data Institute, Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom.
7
Cancer Research UK Cambridge Institute, University of Cambridge, United Kingdom. chovens@unimelb.edu.au florian.markowetz@cruk.cam.ac.uk.
8
Department of Surgery, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia. chovens@unimelb.edu.au florian.markowetz@cruk.cam.ac.uk.

Abstract

A concerted effort to sequence matched primary and metastatic tumors is vastly improving our ability to understand metastasis in humans. Compelling evidence has emerged that supports the existence of diverse and surprising metastatic patterns. Enhancing these efforts is a new class of algorithms that facilitate high-resolution subclonal modeling of metastatic spread. Here we summarize how subclonal models of metastasis are influencing the metastatic paradigm. Clin Cancer Res; 23(3); 630-5.

PMID:
27864419
DOI:
10.1158/1078-0432.CCR-16-0234
[Indexed for MEDLINE]
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