Adaptation to spindle assembly checkpoint inhibition through the selection of specific aneuploidies.
Manuel Alonso Y AdellTamara C KlocknerRudolf HöflerLea WallnerJulia SchmidAna MarkovicAnastasiia MartyniakChristopher S CampbellPublished in: Genes & development (2023)
Both the presence of an abnormal complement of chromosomes (aneuploidy) and an increased frequency of chromosome missegregation (chromosomal instability) are hallmarks of cancer. Analyses of cancer genome data have identified certain aneuploidy patterns in tumors; however, the bases behind their selection are largely unexplored. By establishing time-resolved long-term adaptation protocols, we found that human cells adapt to persistent spindle assembly checkpoint (SAC) inhibition by acquiring specific chromosome arm gains and losses. Independently adapted populations converge on complex karyotypes, which over time are refined to contain ever smaller chromosomal changes. Of note, the frequencies of chromosome arm gains in adapted cells correlate with those detected in cancers, suggesting that our cellular adaptation approach recapitulates selective traits that dictate the selection of aneuploidies frequently observed across many cancer types. We further engineered specific aneuploidies to determine the genetic basis behind the observed karyotype patterns. These experiments demonstrated that the adapted and engineered aneuploid cell lines limit CIN by extending mitotic duration. Heterozygous deletions of key SAC and APC/C genes recapitulated the rescue phenotypes of the monosomic chromosomes. We conclude that aneuploidy-induced gene dosage imbalances of individual mitotic regulators are sufficient for altering mitotic timing to reduce CIN.
Keyphrases
- copy number
- genome wide
- papillary thyroid
- cell cycle
- squamous cell
- dna damage
- dna methylation
- induced apoptosis
- early onset
- cell proliferation
- squamous cell carcinoma
- young adults
- gene expression
- transcription factor
- genome wide identification
- big data
- cell cycle arrest
- high glucose
- artificial intelligence
- genetic diversity
- stress induced