In a recent study published in Nature,Cosenza et al.1 introduced MAGIC(machine-learning-assisted genomics and imaging convergence),a versatile platform that photolabels cells with micronuclei and other nuclear atypia,...In a recent study published in Nature,Cosenza et al.1 introduced MAGIC(machine-learning-assisted genomics and imaging convergence),a versatile platform that photolabels cells with micronuclei and other nuclear atypia,enabling direct quantification of de novo chromosomal abnormality(CA)formation and ongoing chromosomal instability(CIN)at single-cell resolution.Using this framework,the authors show how spontaneous and induced lesions from pre-mitotic DNA damage and mitotic errors drive distinct CIN trajectories,with TP53 inactivation markedly amplifying CA burden and complexity,highlighting how CIN emergence and propagation prior to clonal expansion may inform tumour initiation,progression and the identification of actionable biomarkers and therapeutic vulnerabilities.展开更多
基金funded in part by the CRIS Cancer Foundation(excellence202344)to C.S.
文摘In a recent study published in Nature,Cosenza et al.1 introduced MAGIC(machine-learning-assisted genomics and imaging convergence),a versatile platform that photolabels cells with micronuclei and other nuclear atypia,enabling direct quantification of de novo chromosomal abnormality(CA)formation and ongoing chromosomal instability(CIN)at single-cell resolution.Using this framework,the authors show how spontaneous and induced lesions from pre-mitotic DNA damage and mitotic errors drive distinct CIN trajectories,with TP53 inactivation markedly amplifying CA burden and complexity,highlighting how CIN emergence and propagation prior to clonal expansion may inform tumour initiation,progression and the identification of actionable biomarkers and therapeutic vulnerabilities.
