A systematic cataloging of genes affected by genomic rearrangement, using multiple patient cohorts and cancer types, can provide insight into cancer-relevant alterations outside of exomes. By integrative analysis of whole-genome sequencing (predominantly low pass) and gene expression data from 1,448 cancers involving 18 histopathological types in The Cancer Genome Atlas, we identified hundreds of genes for which the nearby presence (within 100 kb) of a somatic structural variant (SV) breakpoint is associated with altered expression. While genomic rearrangements are associated with widespread copy-number alteration (CNA) patterns, approximately 1,100 genes—including overexpressed cancer driver genes (e.g., TERT, ERBB2, CDK12, CDK4)...
Both proteome and transcriptome data can help assess the relevance of non-coding somatic alterations...
A key mutational process in cancer is structural variation, in which rearrangements delete, amplify ...
Although exome sequencing data are generated primarily to detect single-nucleotide variants and inde...
The impact of somatic structural variants (SVs) on gene expression in cancer is largely unknown. Her...
The impact of somatic structural variants (SVs) on gene expression in cancer is largely unknown. Her...
Determining how somatic copy-number alterations (SCNAs) promote cancer is an important goal. We char...
Identification of somatic rearrangements in cancer genomes has accelerated through analysis of high-...
Comprehensive identification of somatic structural variations (SVs) and understanding their mutation...
SummaryIdentification of somatic rearrangements in cancer genomes has accelerated through analysis o...
Identification of somatic rearrangements in cancer genomes has accelerated through analysis of high-...
Chromatin is folded into successive layers to organize linear DNA. Genes within the same topological...
Chromatin is folded into successive layers to organize linear DNA. Genes within the same topological...
We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver muta...
Both proteome and transcriptome data can help assess the relevance of non-coding somatic alterations...
A key mutational process in cancer is structural variation, in which rearrangements delete, amplify ...
Although exome sequencing data are generated primarily to detect single-nucleotide variants and inde...
The impact of somatic structural variants (SVs) on gene expression in cancer is largely unknown. Her...
The impact of somatic structural variants (SVs) on gene expression in cancer is largely unknown. Her...
Determining how somatic copy-number alterations (SCNAs) promote cancer is an important goal. We char...
Identification of somatic rearrangements in cancer genomes has accelerated through analysis of high-...
Comprehensive identification of somatic structural variations (SVs) and understanding their mutation...
SummaryIdentification of somatic rearrangements in cancer genomes has accelerated through analysis o...
Identification of somatic rearrangements in cancer genomes has accelerated through analysis of high-...
Chromatin is folded into successive layers to organize linear DNA. Genes within the same topological...
Chromatin is folded into successive layers to organize linear DNA. Genes within the same topological...
We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver muta...
Both proteome and transcriptome data can help assess the relevance of non-coding somatic alterations...
A key mutational process in cancer is structural variation, in which rearrangements delete, amplify ...
Although exome sequencing data are generated primarily to detect single-nucleotide variants and inde...