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Modeling Genomic Instability and Selection Pressure in a Mouse Model of Melanoma.

Lawrence N Kwong | Lihua Zou | Sharmeen Chagani | Chandra Sekhar Pedamallu | Mingguang Liu | Shan Jiang | Alexei Protopopov | Jianhua Zhang | Gad Getz | Lynda Chin
Cell reports | 2017

Tumor evolution is an iterative process of selection for pro-oncogenic aberrations. This process can be accelerated by genomic instability, but how it interacts with different selection bottlenecks to shape the evolving genomic landscape remains understudied. Here, we assessed tumor initiation and therapy resistance bottlenecks in mouse models of melanoma, with or without genomic instability. At the initiation bottleneck, whole-exome sequencing revealed that drug-naive tumors were genomically silent, and this was surprisingly unaffected when genomic instability was introduced via telomerase inactivation. We hypothesize that the strong engineered alleles created low selection pressure. At the therapy resistance bottleneck, strong selective pressure was applied using a BRAF inhibitor. In the absence of genomic instability, tumors acquired a non-genomic drug resistance mechanism. By contrast, telomerase-deficient, drug-resistant melanomas acquired highly recurrent copy number gains. These proof-of-principle experiments demonstrate how different selection pressures can interact with genomic instability to impact tumor evolution.

Pubmed ID: 28514651

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Associated grants

  • Agency: NCI NIH HHS, United States
    Id: P01 CA163222
  • Agency: NCI NIH HHS, United States
    Id: U54 CA163125

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GISTIC (tool)

RRID:SCR_000151

Software to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, they improve the estimation of background rates for each category.

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GraphPad Prism (tool)

RRID:SCR_002798

Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.

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