Contribution of allelic imbalance to colorectal cancer.
Kimmo PalinEsa PitkänenMikko TurunenBiswajyoti SahuPäivi PihlajamaaTeemu KiviojaEevi KaasinenNiko VälimäkiUlrika A HänninenTatiana CajusoMervi AavikkoSari TuupanenOuti KilpivaaraLinda van den BergJohanna KondelinTomas TanskanenRiku KatainenMarta GrauHeli RauanheimoRoosa-Maria PlakettiAurora TairaPäivi SuloTuomo HartonenKashyap DaveBernhard SchmiererSandeep BotlaMaria SokolovaAnna VähärautioKornelia GladyszHalit OngenEmmanouil T DermitzakisJesper Bertram BramsenTorben Falck ØrntoftClaus Lindbjerg AndersenAri RistimäkiAnna LepistöLaura Renkonen-SinisaloJukka-Pekka MecklinJussi TaipaleLauri A AaltonenPublished in: Nature communications (2018)
Point mutations in cancer have been extensively studied but chromosomal gains and losses have been more challenging to interpret due to their unspecific nature. Here we examine high-resolution allelic imbalance (AI) landscape in 1699 colorectal cancers, 256 of which have been whole-genome sequenced (WGSed). The imbalances pinpoint 38 genes as plausible AI targets based on previous knowledge. Unbiased CRISPR-Cas9 knockout and activation screens identified in total 79 genes within AI peaks regulating cell growth. Genetic and functional data implicate loss of TP53 as a sufficient driver of AI. The WGS highlights an influence of copy number aberrations on the rate of detected somatic point mutations. Importantly, the data reveal several associations between AI target genes, suggesting a role for a network of lineage-determining transcription factors in colorectal tumorigenesis. Overall, the results unravel the contribution of AI in colorectal cancer and provide a plausible explanation why so few genes are commonly affected by point mutations in cancers.
Keyphrases
- genome wide
- copy number
- artificial intelligence
- mitochondrial dna
- dna methylation
- big data
- crispr cas
- high resolution
- genome wide identification
- machine learning
- transcription factor
- deep learning
- healthcare
- bioinformatics analysis
- electronic health record
- squamous cell carcinoma
- single cell
- genome editing
- papillary thyroid
- dna binding
- high speed
- young adults