We propose to definitively characterise the somatic genetics of matched pair breast cancer cell lines through generation of comprehensive... Show More
We propose to definitively characterise the somatic genetics of matched pair breast cancer cell lines through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.
Alternative Stable ID
This study includes 3 datasets:
Click on a Dataset Accession in the table below to learn more, and to find out who to contact about access to these data
The disordered transcriptomes of cancerÂ encompass direct effects of somatic mutation on transcription; co-ordinatedÂ secondary alterations in transcriptional pathways; and increasedÂ transcriptional noise. To catalogue the rules governing how somatic mutationÂ Overall, 59% of 6980 exonicÂ substitutions wereÂ expressed. Compared to other classes, nonsense mutations showed lowerÂ expression levels than expected withÂ patterns characteristic ofÂ nonsense-mediated decay. 14% of 4234 genomic rearrangements causedÂ transcriptional abnormalities,Â including exon skips, exon reusage, fusionÂ transcripts and premature poly-adenylation. We found productive, stableÂ transcriptionÂ from sense-to-antisense gene fusions and gene-to-intergenicÂ rearrangements, suggesting that these mutation classes may drive moreÂ transcriptional disruption than previously suspected. Systematic integration ofÂ transcriptome with genome data therefore reveals theÂ rules by whichÂ transcriptional machinery interprets somatic mutation.
CRISPR-Cas9 genome editing is widely used to study gene function, from basic biology to biomedical research. Structural rearrangements are a ubiquitous feature of cancer cells and their impact on the functional consequences of CRISPR-Cas9 gene-editing has not yet been assessed. Utilizing CRISPR-Cas9 knockout screens for 250 cancer cell lines, we demonstrate that targeting structurally rearranged regions, in particular tandem or interspersed amplifications, is highly detrimental to cellular fitness in a gene independent manner. In contrast, amplifications caused by whole chromosomal duplications have little to no impact on fitness. This effect is cell line specific and dependent on the ploidy status. We devise a copy-number ratio metric that substantially improves the detection of gene-independent cell fitness effects in CRISPR-Cas9 screens. Furthermore, we develop a computational tool, called Crispy, to account for these effects on a single sample basis and provide corrected gene fitness effects. Our analysis demonstrates the importance of structural rearrangements in mediating the effect of CRISPR-Cas9-induced DNA damage, with implications for the use of CRISPR-Cas9 gene-editing in cancer cells.