体细胞突变的相关混合隶属度建模
Correlated Mixed Membership Modeling of Somatic Mutations
摘要：
癌症体细胞突变谱的最新研究寻求识别用于个性化医学中的靶向治疗的突变。然而，谱的分析并不是微不足道的，因为每个谱是异质的，并且存在影响癌症基因之间的因果关系的多种混杂因素，例如癌症（子）类型、生物学过程、突变总数和非线性突变相互作用。此外，癌症在生物学上是多余的，即，不同的突变可导致相似生物过程的改变，因此鉴定所有可能的突变组合集对于有效的患者治疗是重要的。为了模拟这种现象，我们提出了相关零膨胀负二项过程来推断体细胞突变谱的内在结构，通过潜在的表示。该随机过程考虑了不同但相关的共现突变，其使用与相关β-伯努利过程和概率参数混合的谱特异性负二项分散参数来对谱异质性进行建模。这些模型参数通过使用来自癌症基因组档案（TCGA）的泛癌症数据集经由摊销和随机变分推断的迭代优化来推断。通过检查潜在空间，我们确定了体细胞突变之间的生物学相关性。
英文摘要：
Recent studies of cancer somatic mutation profiles seek to identify mutations for targeted therapy in personalized medicine. Analysis of profiles, however, is not trivial, as each profile is heterogeneous and there are multiple confounding factors that influence the cause-and-effect relationships between cancer genes such as cancer (sub)type, biological processes, total number of mutations, and non-linear mutation interactions. Moreover, cancer is biologically redundant, i.e., distinct mutations can result in the alteration of similar biological processes, so it is important to identify all possible combinatorial sets of mutations for effective patient treatment. To model this phenomena, we propose the correlated zero-inflated negative binomial process to infer the inherent structure of somatic mutation profiles through latent representations. This stochastic process takes into account different, yet correlated, co-occurring mutations using profile-specific negative binomial dispersion parameters that are mixed with a correlated beta-Bernoulli process and a probability parameter to model profile heterogeneity. These model parameters are inferred by iterative optimization via amortized and stochastic variational inference using the Pan Cancer dataset from The Cancer Genomic Archive (TCGA). By examining the the latent space, we identify biologically relevant correlations between somatic mutations.
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