Nature GeneticsGenomicsPeer-reviewed journal article
Comprehensive Molecular Characterization of Mitochondrial Genomes in Human Cancers
Yuan YuaniD, Young Seok JuiD, Young-Wook KimiD, Jun LiiD, Yumeng WangiD, Sung-Soo YooniD, Yang YangiD, Iñigo MartincorenaiD, Chad J. CreightoniD, John N. WeinsteiniD, Yanxun XuiD, Leng HaniD, Hyung‐Lae KimiD, Hidewaki NakagawaiD, Keunchil ParkiD, Peter J. CampbelliD, Han LiangiD, Lauri A. AaltoneniD, Federico AbascaliD, Adam Abeshouse, Hiroyuki AburataniiD, David J. AdamsiD, Nishant AgrawaliD, Keun Soo AhniD, Sung-Min Ahn, , Mario BerríosiD
Abstract
Mitochondria are essential cellular organelles that play critical roles in cancer development. Through International Cancer Genome Consortium, we performed a multidimensional characterization of mitochondrial genomes using the whole-genome sequencing data of ~2,700 patients across 37 cancer types and related RNA-sequencing data. Our analysis presents the most definitive mutational landscape of mitochondrial genomes including a novel hypermutated case. We observe similar mutational signatures across cancer types, suggesting powerful endogenous mutational processes in mitochondria. Truncating mutations are remarkably enriched in kidney, colorectal and thyroid cancers and associated with the activation of critical signaling pathways. We find frequent somatic nuclear transfers of mitochondrial DNA (especially in skin and lung cancers), some of which disrupt therapeutic target genes (e.g., ERBB2). The mitochondrial DNA copy number shows great variations within and across cancers and correlates with clinical variables. Co-expression analysis highlights the function of mitochondrial genes in oxidative phosphorylation, DNA repair, and cell cycle; and reveals their connections with clinically actionable genes. Our study, including an open-access data portal, lays a foundation for understanding the interplays between the cancer mitochondrial and nuclear genomes and translating mitochondrial biology into clinical applications.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: differential expression
- approach: statistical model
- Citations
- 468
- FWCI
- 20.43
- Field percentile
- top 1%
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