RESUMO
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.
Assuntos
Desequilíbrio Alélico , Neoplasias Colorretais/genética , Predisposição Genética para Doença , Sistemas CRISPR-Cas , Aberrações Cromossômicas , Cromossomos Humanos Par 8 , Neoplasias Colorretais/patologia , Variações do Número de Cópias de DNA , Dinamarca , Perfilação da Expressão Gênica , Genômica , Genótipo , Humanos , Perda de Heterozigosidade , Repetições de Microssatélites , Fenótipo , Mutação Puntual , Proteínas Proto-Oncogênicas p21(ras)/genética , RNA Interferente Pequeno/genética , Fatores de Transcrição/genética , Proteína Supressora de Tumor p53/genética , Sequenciamento Completo do GenomaRESUMO
Microsatellite instability (MSI) leads to accumulation of an excessive number of mutations in the genome, mostly small insertions and deletions. MSI colorectal cancers (CRCs), however, also contain more point mutations than microsatellite-stable (MSS) tumors, yet they have not been as comprehensively studied. To identify candidate driver genes affected by point mutations in MSI CRC, we ranked genes based on mutation significance while correcting for replication timing and gene expression utilizing an algorithm, MutSigCV Somatic point mutation data from the exome kit-targeted area from 24 exome-sequenced sporadic MSI CRCs and respective normals, and 12 whole-genome-sequenced sporadic MSI CRCs and respective normals were utilized. The top 73 genes were validated in 93 additional MSI CRCs. The MutSigCV ranking identified several well-established MSI CRC driver genes and provided additional evidence for previously proposed CRC candidate genes as well as shortlisted genes that have to our knowledge not been linked to CRC before. Two genes, SMARCB1 and STK38L, were also functionally scrutinized, providing evidence of a tumorigenic role, for SMARCB1 mutations in particular.