Discovering Combinations of Genomic Aberrations Associated With Cancer
发现与癌症相关的基因组异常组合AI 解读 ↓
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发现与癌症相关的基因组异常组合
- 研究问题
- 如何从癌症基因组拷贝数变异中识别相关异常组合，并通过概率模型表达肿瘤异质性以及异常之间的正负依赖？
- 方法与思路
- 作者结合随机基因组不稳定、异常组合触发的肿瘤选择和组合网络，使用双重 Pólya 先验及 MCMC 进行后验计算。
- 材料陈述的贡献
- 摘要称组合网络是可识别参数；在一百一十六例肾细胞癌重分析中，方法输出异常相关概率、组合关系及异常与肿瘤聚类。
- 阅读关注点
- 应核对选择条件如何产生异常依赖、组合网络的识别条件、先验与后验计算，以及聚类和癌症相关性解释的边界。
- 阅读边界
- 仅据稿件版本未注明的 OpenAlex 摘要；具体识别条件与后验结果未说明，模型给出的相关性不能直接视为致癌因果证据。
原文摘要
This article introduces a model-based statistical methodology for the analysis of copy-number variations in cancer genomes measured by comparative genomic hybridization. The methodology allows one to infer combinations of genomic aberrations associated with the cancer phenotype. The stochastic model conjoins two features of cancer biology to infuse some context into an otherwise unsupervised learning problem. It asserts random genomic instability in a potential progenitor cell, followed by selection into a tumor of the descending cell lineage if the lineage experiences certain ensembles of genomic aberration. Disease heterogeneity is reflected in the possibility of a network containing multiple ensembles. The network of ensembles is an identifiable parameter. By forming the sampling model conditionally on selection, statistical dependencies (both positive and negative) can be induced between aberrations, and the model entails heterogeneity in the marginal rate of occurrence of aberrations. A double-Pólya distribution is introduced as a prior over the network of ensembles, and Markov chain Monte Carlo is developed to enable posterior computation. As an example, the methodology is used to reanalyze genomic aberrations from 116 renal cell carcinomas. It produces posterior probabilities that any given aberration is relevant to oncogenesis, posterior probabilities that pairs of aberrations reside in a common ensemble, and a point estimate of the network of ensembles. The methodology provides a model-based clustering of all measured aberrations according to these estimated ensembles and a model-based clustering of tumors according to the probable ensembles of genomic aberration that they have experienced. Although it is formulated here to analyze aberrations in cancer genomes, the instability-selection-network model may provide an approach to modeling dependence in correlated binary data on various biological systems. Limitations and possible extensions of the methodology are discussed.