Single nucleotide variant (SNV) plays an important role in cellular proliferation and tumorigenesis in various types of human cancer. Next-generation sequencing (NGS) has provided high-throughput data at an unprecedented resolution to predict SNVs. Currently, there exist many computational methods for either germline or somatic SNV discovery from NGS data, but very few of them are versatile enough to adapt to any situations. In the absence of matched normal samples, the prediction of somatic SNVs from single-tumor samples becomes considerably challenging, especially when the tumor purity is unknown. Here, we propose a new approach, STIC, to predict somatic SNVs and estimate tumor purity from NGS data without matched normal samples. The main features of STIC include: (1) extracting a set of SNV-relevant features on each site and training the BP neural network algorithm on the features to predict SNVs; (2) creating an iterative process to distinguish somatic SNVs from germline ones by disturbing allele frequency; and (3) establishing a reasonable relationship between tumor purity and allele frequencies of somatic SNVs to accurately estimate the purity. We quantitatively evaluate the performance of STIC on both simulation and real sequencing datasets, the results of which indicate that STIC outperforms competing methods.

中文翻译：

---

STIC：预测癌症基因组中的单核苷酸变异和肿瘤纯度

单核苷酸变体 (SNV) 在各种人类癌症的细胞增殖和肿瘤发生中起重要作用。下一代测序 (NGS) 以前所未有的分辨率提供了高通量数据来预测 SNV。目前，存在许多用于从 NGS 数据中发现种系或体细胞 SNV 的计算方法，但其中很少有足够通用的方法来适应任何情况。在没有匹配的正常样本的情况下，从单个肿瘤样本中预测体细胞 SNV 变得相当具有挑战性，尤其是在肿瘤纯度未知的情况下。在这里，我们提出了一种新方法 STIC，用于预测体细胞 SNV 并在没有匹配正常样本的情况下从 NGS 数据估计肿瘤纯度。STIC的主要特点包括：(1) 在每个站点上提取一组与SNV相关的特征，并在这些特征上训练BP神经网络算法来预测SNV；(2) 创建一个迭代过程，通过干扰等位基因频率来区分体细胞 SNV 和种系 SNV；(3)在肿瘤纯度和体细胞SNV的等位基因频率之间建立合理的关系，以准确估计纯度。我们定量评估 STIC 在模拟和真实测序数据集上的性能，结果表明 STIC 优于竞争方法。(3)在肿瘤纯度和体细胞SNV的等位基因频率之间建立合理的关系，以准确估计纯度。我们定量评估 STIC 在模拟和真实测序数据集上的性能，结果表明 STIC 优于竞争方法。(3)在肿瘤纯度和体细胞SNV的等位基因频率之间建立合理的关系，以准确估计纯度。我们定量评估 STIC 在模拟和真实测序数据集上的性能，结果表明 STIC 优于竞争方法。