Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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. ©2016 AACR.

Original publication

DOI

10.1158/1078-0432.CCR-16-0234

Type

Journal article

Journal

Clin Cancer Res

Publication Date

01/02/2017

Volume

23

Pages

630 - 635

Keywords

Algorithms, Animals, Cell Communication, Cell Lineage, Clone Cells, DNA Mutational Analysis, DNA, Neoplasm, Disease Progression, Humans, Mice, Models, Biological, Mutation, Neoplasm Metastasis, Neoplastic Cells, Circulating, Neoplastic Stem Cells, Sequence Analysis, DNA, Time Factors, Whole Genome Sequencing