bioRxivCancer BiologyPreprint
Positively Selected Enhancer Elements Endow Tumor Cells with MetastaticCompetence
James J. Morrow, Ian Bayles, Alister PW Funnell, Tyler E. MilleriD, Alina Saiakhova, Michael M. Lizardo, Cynthia F. BartelsiD, Maaike Y. Kapteijn, Stevephen HungiD, Arnulfo Mendoza, Daniel R. Chee, Jay T. Myers, Frederick Allen, Marco Gambarotti, Alberto Righi, Analisa DiFeo, Brian P. Rubin, Alex Y. Huang, Paul S. Meltzer, Lee J. Helman, Piero Picci, Henri Versteeg, John Stamatoyannopoulos, Chand Khanna, Peter C. ScacheriiD
Abstract
Metastasis results from a complex set of traits acquired by tumor cells, distinct from those necessary for tumorigenesis. Here, we investigate the contribution of enhancer elements to the metastatic phenotype of osteosarcoma. Through epigenomic profiling, we identify substantial differences in enhancer activity between primary and metastatic tumors in human patients as well as nearisogenic pairs of high and low lung-metastatic osteosarcoma cells. We term these regions Metastatic Variant Enhancer Loci (Met-VELs). We demonstrate that these Met-VELs drive coordinated waves of gene expression during metastatic colonization of the lung. Met-VELs cluster non-randomly, indicating that activity of these enhancers and their associated gene targets are positively selected. As evidence of this causal association, osteosarcoma lung metastasis is inhibited by global interruptions of Met-VEL-associated gene expression via pharmacologic BET inhibition, by knockdown of AP-1 transcription factors that occupy Met-VELs, and by knockdown or functional inhibition of individual genes activated by Met-VELs, such as F3. We further show that genetic deletion of a single Met-VEL at the F3 locus blocks metastatic cell outgrowth in the lung. These findings indicate that Met-VELs and the genes they regulate play a functional role in metastasis and may be suitable targets for anti-metastatic therapies.
- organism: human
- assay: ChIP-seq
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
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