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On Statistical Modeling of Sequencing Noise in High Depth Data to Assess Tumor Evolution
Journal of Statistical Physics ( IF 1.6 ) Pub Date : 2017-12-21 , DOI: 10.1007/s10955-017-1945-1
Raul Rabadan 1 , Gyan Bhanot 2 , Sonia Marsilio 3 , Nicholas Chiorazzi 3 , Laura Pasqualucci 4 , Hossein Khiabanian 5
Affiliation  

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerge from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

中文翻译:

高深度数据中测序噪声的统计建模以评估肿瘤进化

癌症死亡的原因之一是肿瘤进化为耐药性疾病。一线治疗通常针对优势克隆,通过治疗诱导的自然选择获得适应性的先前存在的克隆可能会出现耐药性。可以使用靶向测序分析通过分析高深度数据中的噪声来鉴定此类突变。在这里,我们为测序错误背景开发了一个全面的、无偏见的模型。我们发现足够深的 DNA 测序数据中的噪声可以通过聚合负二项式分布来近似。频率高于噪声的突变可能具有预后价值。我们使用模拟的指数扩展群体以及来自细胞系和患者样本稀释实验的数据来评估我们的模型,证明其在预测肿瘤进展方面的效用。我们的结果可能有可能识别出可能导致复发的重要突变。这些结果与治疗前临床环境相关,以确定适当的治疗方法并为潜在的复发预处理做准备。
更新日期:2017-12-21
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