Chromatin accessibility associates with protein-RNA correlation in human cancer

Akshay Sanghi, Joshua J. Gruber, Ahmed Metwally, Lihua Jiang, Warren Reynolds, John Sunwoo, Lisa Orloff, Howard Y. Chang, Maya Kasowski and Michael P. Snyder ()
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Akshay Sanghi: Stanford University
Joshua J. Gruber: Stanford University
Ahmed Metwally: Stanford University
Lihua Jiang: Stanford University
Warren Reynolds: Stanford University
John Sunwoo: Stanford University School of Medicine
Lisa Orloff: Stanford University School of Medicine
Howard Y. Chang: Stanford University
Maya Kasowski: Stanford University
Michael P. Snyder: Stanford University

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract Although alterations in chromatin structure are known to exist in tumors, how these alterations relate to molecular phenotypes in cancer remains to be demonstrated. Multi-omics profiling of human tumors can provide insight into how alterations in chromatin structure are propagated through the pathway of gene expression to result in malignant protein expression. We applied multi-omics profiling of chromatin accessibility, RNA abundance, and protein abundance to 36 human thyroid cancer primary tumors, metastases, and patient-match normal tissue. Through quantification of chromatin accessibility associated with active transcription units and global protein expression, we identify a local chromatin structure that is highly correlated with coordinated RNA and protein expression. In particular, we identify enhancers located within gene-bodies as predictive of correlated RNA and protein expression, that is independent of overall transcriptional activity. To demonstrate the generalizability of these findings we also identify similar results in an independent cohort of human breast cancers. Taken together, these analyses suggest that local enhancers, rather than distal enhancers, are likely most predictive of cancer gene expression phenotypes. This allows for identification of potential targets for cancer therapeutic approaches and reinforces the utility of multi-omics profiling as a methodology to understand human disease.

Date: 2021
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DOI: 10.1038/s41467-021-25872-1

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