Genomic basis for RNA alterations in cancer

Abstract

PCAWG working groups focused on unified analyses of copynumber variation 6 , structural variants 7,8 , germline variants 5 , mutational signatures 9 and identification of driver genes 8 , among others 5. Here, we report the joint analysis of available matched transcriptome and genome profiling for 1,188 samples from 27 tumour types by the PCAWG Transcriptome Working Group 5 , providing the largest, to our knowledge, resource of RNA phenotypes and their underlying genetic changes in cancer so far (Extended Data Fig. 1, Methods, Supplementary Results, Supplementary Table 23). We demonstrate the importance of transcriptomics data in understanding how different dimensions of specific DNA alterations contribute to carcinogenesis and map out the landscape of cancer-related RNA alterations.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

“…Gene-expression data were provided by the PCAWG Transcriptome Core Group 66 , and also generated using the same approach for an extended set of non-coding transcripts (Supplementary Methods).…”

High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations

“…While SVs can result in two distant genes being brought together to form fusion gene rearrangements (e.g., BCR-ABL1 or TMPRSS2-ERG) 12 , this present study focuses on SVs impacting gene regulation in the absence of fusion events or copy number alterations, e.g., SVs with breakpoints occurring upstream or downstream of the gene and involving rearrangement of cisregulatory elements. In a recent study involving integration of gene expression with low-pass whole-genome sequencing for more than 1000 cancer cases 13 , evidence for a widespread impact of somatic SVs on gene expression patterns was observed, though a noted limitation with that study involved the level of coverage (~6-8×) of low-pass sequencing.…”

Pathogenic impact of transcript isoform switching in 1209 cancer samples covering 27 cancer types using an isoform-specific interaction network

“…Also, most mutations were unique and found at various locations within a gene's structure. To counteract the data sparsity we combined different mutations which however further reduced the power of our correlation analysis (PCAWG Transcriptome Core Group et al, 2020). Secondly, the causative mutations could lie outside the cMDT genes like in splicing factors.…”

“…Transcript isoform-specific expression levels for 1393 Pan-Cancer Analysis of Whole Genomes (PCAWG) samples (syn7536587) and 3249 Genotype-Tissue Expression (GTEx, V4) (Lonsdale et al, 2013) samples (syn7596599) were provided by the PCAWG project for download from a dedicated Synapse database (https://www.synapse.org). Expression levels were given in Transcript Per Million (TPM) counts computed for all known transcripts in Ensembl version 75 using Kallisto (v.0.42.1) (Bray et al, 2016) with default parameters (see Method section in PCAWG Transcriptome Core Group paper (PCAWG Transcriptome Core Group et al, 2020), for more details). 1209 PCAWG samples remained after selecting those labeled as whitelisted and as a tumor in the RNAseq metadata file (syn7416381).…”

“…Interestingly, 395 GWAS associated SNPs overlapped with cis-sQTLs whose genes were not differentially expressed, giving additional insights into the functional mechanism of GWAS results. An independent Pan-Cancer Analysis of Whole Genomes (PCAWG) analysis group that focused on cancer transcriptomes found over 1800 splicing alterations, which correlated with nearby mutations in intronic regions (PCAWG Transcriptome Core Group et al, 2020). They identified over 5,200 mutations mostly located in or near splice-sites which had a major impact on the expression of cassette exons.…”