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On measuring selection in cancer from subclonal mutation frequencies

机译:从亚克隆突变频率测量癌症的选择

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摘要

Recently available cancer sequencing data have revealed a complex view of the cancer genome containing a multitude of mutations, including drivers responsible for cancer progression and neutral passengers. Measuring selection in cancer and distinguishing drivers from passengers have important implications for development of novel treatment strategies. It has recently been argued that a third of cancers are evolving neutrally, as their mutational frequency spectrum follows a 1/f power law expected from neutral evolution in a particular intermediate frequency range. We study a stochastic model of cancer evolution and derive a formula for the probability distribution of the cancer cell frequency of a subclonal driver, demonstrating that driver frequency is biased towards 0 and 1. We show that it is difficult to capture a driver mutation at an intermediate frequency, and thus the calling of neutrality due to a lack of such driver will significantly overestimate the number of neutrally evolving tumors. Our approach provides quantification of the validity of the 1/f statistic across the entire range of relevant parameter values. We also show that our conclusions remain valid for non-exponential models: spatial 3d model and sigmoidal growth, relevant for early- and late stages of cancer growth.
机译:最近可获得的癌症测序数据已揭示了包含许多突变的癌症基因组的复杂视图,其中包括导致癌症进展的驾驶员和中性乘客。测量癌症中的选择并将驾驶员与乘客区分开来对开发新的治疗策略具有重要意义。最近有人争论说,三分之一的癌症正在中性进化,因为它们的突变频谱遵循在特定的中频范围内中性进化预期的1 / f幂定律。我们研究了癌症进化的随机模型,并得出了亚克隆驱动器癌细胞频率概率分布的公式,证明了驱动器频率偏向0和1。我们证明了很难捕获到一个驱动子突变。中频,因此由于缺乏这种驱动因素而导致的中立性将大大高估中性进化肿瘤的数量。我们的方法提供了在相关参数值的整个范围内1 / f统计量有效性的量化。我们还表明,我们的结论对于非指数模型仍然有效:空间3d模型和S形增长,与癌症生长的早期和晚期有关。

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