Finding driver mutations in cancer: Elucidating the role of background mutational processes
Finding driver mutations in cancer: Elucidating the role of background mutational processes
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寻找癌症中的驱动突变：阐明背景突变过程的作用
DOI：
10.1371/journal.pcbi.1006981
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发表时间：
2019-04-01
影响因子：
4.3
通讯作者：
Panchenko, Anna R.
中科院分区：
文献类型：
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作者：
Brown, Anna-Leigh;Li, Minghui;Panchenko, Anna R.
众所周知，鉴定癌症中的驾驶员突变是困难的。迄今为止，患者突变的复发仍然是突变驱动器状态最可靠的标记之一。但是，由于DNA复制和修复机械，内源性和外源性诱变剂的各种形式的不忠性引起的背景突变率的差异，某些突变比其他突变更可能发生。我们计算了核苷酸和密码子突变性，以研究背景过程在塑造癌症中观察到的突变光谱方面的贡献。我们开发并测试了概率泛滥和癌症特异性模型，这些模型通过背景突变性调整患者中突变复发的数量，以便找到可能在癌症中选择的突变。我们表明，较高的突变性值的突变具有较高的观察到的复发频率，尤其是在肿瘤抑制基因中。这种趋势对于具有中性功能影响的胡说八道和沉默突变或突变是突出的。然而，在致癌基因中，高度重复的突变的特征是相对较低的突变性，导致U形趋势反转。在任何肿瘤中尚未观察到的突变具有相对较低的突变性值，这表明背景突变性可能会限制突变的发生。我们从58个基因中编辑了一个错义突变的数据集，并具有经过实验验证的功能和各种研究的影响。我们发现，驾驶员突变的突变性低于乘客的突变性，因此通过突变性可以显着改善突变和驱动突变预测的排名来调整突变复发频率。即使没有涉及现有数据的培训，我们的方法与最先进的方法相似或更好地执行。然而，只有一小部分患者中发现的突变是导致细胞转化导致癌症的原因。这些所谓的驱动因素是肿瘤分子谱的特征，可能有助于预测患者的临床结果。癌症研究的主要问题之一是优先考虑突变。患者突变的复发仍然是其驾驶员状态中最可靠的标记之一。但是，DNA损伤和修复过程不会统一影响基因组，并且某些突变比其他突变更有可能发生。此外，突变概率（突变性）随癌症类型而变化。我们开发了通过癌症特异性背景突变性来调整患者突变复发的数量，以便优先考虑癌症突变。使用全面的实验数据集，我们发现驾驶员突变的突变性低于乘客的突变性，因此通过突变性可显着改善突变和驱动突变预测的突变性能来调整突变复发频率。
Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status. However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens. We calculated nucleotide and codon mutability to study the contribution of background processes in shaping the observed mutational spectrum in cancer. We developed and tested probabilistic pan-cancer and cancer-specific models that adjust the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer. We showed that mutations with higher mutability values had higher observed recurrence frequency, especially in tumor suppressor genes. This trend was prominent for nonsense and silent mutations or mutations with neutral functional impact. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in an inversed U-shaped trend. Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit mutation occurrence. We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from various studies. We found that mutability of driver mutations was lower than that of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction. Even though no training on existing data was involved, our approach performed similarly or better to the state-of-the-art methods. Availability https://www.ncbi.nlm.nih.gov/research/mutagene/gene Author Summary Cancer development and progression is associated with accumulation of mutations. However, only a small fraction of mutations identified in a patient is responsible for cellular transformations leading to cancer. These so-called drivers characterize molecular profiles of tumors and could be helpful in predicting clinical outcomes for the patients. One of the major problems in cancer research is prioritizing mutations. Recurrence of a mutation in patients remains one of the most reliable markers of its driver status. However, DNA damage and repair processes do not affect the genome uniformly, and some mutations are more likely to occur than others. Moreover, mutational probability (mutability) varies with the cancer type. We developed models that adjust the number of mutation recurrences in patients by cancer-type specific background mutability in order to prioritize cancer mutations. Using a comprehensive experimental dataset, we found that mutability of driver mutations was lower than that of passengers, and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction.