生物
计算生物学
上位性
克拉斯
癌症
疾病
表型
重编程
进化生物学
遗传学
表观遗传学
机制(生物学)
突变
基因组
癌症的体细胞进化
进化动力学
基因
基因组学
等位基因
癌细胞
系统生物学
生物信息学
过程(计算)
物候学
资源(消歧)
后生
人类遗传学
适应(眼睛)
鉴定(生物学)
模式生物
计算机科学
遗传模型
作者
Sebastian Mueller,Niklas de Andrade Krätzig,Markus Tschurtschenthaler,Miguel G. Silva,Chiara Thordsen,Riccardo Trozzo,Perrine SIMON,Frederic Saab,Thorsten Kaltenbacher,Magdalena Żukowska,Daniele Lucarelli,Rupert Öllinger,Joscha Griger,Nina Groß,Tanja Groll,J. Löprich,Antonio Enrico Zaurito,Linus Schömig,Jeroen M. Bugter,Stefanie Bärthel
出处
期刊:Nature
[Nature Portfolio]
日期:2026-02-25
标识
DOI:10.1038/s41586-026-10187-2
摘要
Oncogenes such as KRAS display marked tissue specificity in their oncogenic potential, genetic interactions and phenotypic effects, but the underlying determinants remain largely unresolved1-5. Here, to address these questions, we developed the Mouse Cancer Cell line Atlas, a broad-utility resource of 590 comprehensively characterized models across a wide range of entities ( www.mcca.tum.de ). Comparative and functional studies using this platform, human cohorts and mice identified core principles underlying tissue-specific evolution of KRAS-initiated cancers. First, we show that mutant KRAS dosage gain through allelic imbalance exerts cell-type-specific effects, defining its timing across entities, as exemplified by dosage-sensitive developmental reprogramming during pancreatic cancer initiation. Second, we highlight how tissue- and stage-specific evolutionary requirements, such as block of differentiation in the intestine, select for KRAS-collaborating alterations. Third, we identified context-dependent epistatic KRAS-tumour suppressor interactions and show that reciprocal dosage sensitivities dictate the entity-specific patterns of cancer gene alterations, explaining their frequency, zygosity and acquisition chronology. These findings highlight how intrinsic and acquired determinants instruct cancer evolution in different tissues, with predictable molecular patterns, temporal dynamics and phenotypic outcomes. Our study provides major advances towards a mechanistic understanding of cancer genomes.
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