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arXiv 1607.02447q-bio.QMq-bio.GN
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肿瘤互斥突变的加权精确检验

A Weighted Exact Test for Mutually Exclusive Mutations in Cancer

Mark D. M. Leiserson, Matthew A. Reyna, Benjamin J. Raphael

摘要:

驱动癌症发展的途径中的体细胞突变往往在肿瘤之间是相互排斥的,这为区分驱动突变与大量随机乘客突变提供了信号。这种互斥信号可能被一组样品中的高和高度可变的突变率混淆。目前的排他性统计测试,包括每个基因和每个样本的突变频率是计算昂贵的,并具有有限的精度。我们制定了一个加权的精确检验,用于评估突变排他性的意义,在任意数量的突变事件。我们的测试条件是关于具有突变的样本数量以及每个事件、每个样本的突变概率。我们提供了一个递归公式来计算$p$ -值的加权测试,以及一个高度准确和有效的鞍点近似的测试。我们使用我们的测试来近似一个常用的置换测试的排他性条件,每个事件,每个样本的突变频率。然而,我们的测试是更有效的,它恢复更显着的结果比排列测试。我们使用我们的加权排他性测试(WExT)软件来分析来自癌症基因组图谱的数百个结直肠和子宫内膜样本,这是两种通常具有极高突变率的癌症类型。在这两种癌症类型中,加权测试识别出癌症基因中相互排斥的突变集,比早期方法具有更少的假阳性。

英文摘要:

The somatic mutations in the pathways that drive cancer development tend to be mutually exclusive across tumors, providing a signal for distinguishing driver mutations from a larger number of random passenger mutations. This mutual exclusivity signal can be confounded by high and highly variable mutation rates across a cohort of samples. Current statistical tests for exclusivity that incorporate both per-gene and per-sample mutational frequencies are computationally expensive and have limited precision. We formulate a weighted exact test for assessing the significance of mutational exclusivity in an arbitrary number of mutational events. Our test conditions on the number of samples with a mutation as well as per-event, per-sample mutation probabilities. We provide a recursive formula to compute $p$-values for the weighted test exactly as well as a highly accurate and efficient saddlepoint approximation of the test. We use our test to approximate a commonly used permutation test for exclusivity that conditions on per-event, per-sample mutation frequencies. However, our test is more efficient and it recovers more significant results than the permutation test. We use our Weighted Exclusivity Test (WExT) software to analyze hundreds of colorectal and endometrial samples from The Cancer Genome Atlas, which are two cancer types that often have extremely high mutation rates. On both cancer types, the weighted test identifies sets of mutually exclusive mutations in cancer genes with fewer false positives than earlier approaches.

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