Robust, scalable, and accurate discovery of mutational signatures

从肿瘤基因组序列推断的突变特征有可能提供环境暴露的记录，并可以提供致癌病因学的线索。然而，对于具有生物学意义的推断特征，每个特征必须准确地表示每个诱变过程中不同突变类型的贡献。使用非负矩阵分解（NMF）的启发式算法主要用于发现突变签名。但这些方法是不灵活的，非鲁棒性的，并且需要大量的计算。该项目的目标是开发计算效率高的算法，尽管不完美的建模假设，可以发现生物学上有意义的签名。目标1支持这一目标，通过开发一个新的框架，可扩展的，易于使用的，准确的变分推理-一种广泛使用的方法来近似贝叶斯推理-这是适用于突变发现模型。目标2开发统计方法，从使用建议的变分推理框架获得的推理中提取生物学上有意义的签名。目标1和目标2中提出的方法的准确性和统计有效性通过对合成数据和真实的数据的理论分析和数值试验得到保证。最后，目标3通过（1）将目标1和2中开发的方法应用于大型泛癌症数据集和（2）开发一种新的模型来改进目前对突变过程的理解，该模型允许在每个签名中结构化地掺入单碱基和双碱基取代以及插入和缺失。拟议的工作很好地取代了用于发现有意义的数据表示的分析学，因此对如何分析其他基因组数据类型（如单细胞RNA-seq）产生了长期影响。这项工作也与NIGMS直接相关，因为它福尔斯“DNA和RNA代谢（修复）”，因为许多突变过程与异常DNA修复或与衰老相关的“时钟样”分子机制有关，这可以在组织学正常的组织中观察到

The mutational signatures inferred from tumor genome sequences have the potential to provide a record of environmental exposure and can give clues about the etiology of carcinogenesis. However, for inferred signatures to be biologically meaningful, each signature must accurately represent the contribution of different mutation types in each mutagenic process. Heuristic algorithms using non-negative matrix factorization (NMF) have primarily been used to discover mutational signatures. But these approaches are inflexible, non-robust, and require massive amounts of computation. The objective of the proposed project is to develop computationally efficient algorithms that, despite imperfect modeling assumptions, can discover biologically meaningful signatures. Aim 1 supports this objective by developing a new framework for scalable, easy-to-use, and accurate variational inference – a widely used approach to approximate Bayesian inference – that is applicable to mutational discovery models. Aim 2 develops statistical methods to extract biologically meaningful signatures from the inferences obtained using the proposed variational inference framework. The accuracy and statistical validity of the methods developed in Aims 1 and 2 is ensured through theoretical analysis and numerical experiments on synthetic and real data. Finally, Aim 3 improves upon the current understanding of mutational processes by (1) applying the methods developed in Aims 1 and 2 to a large Pan-Cancer dataset and (2) by developing a novel model that allows for the structured incorporation of single-base and double-base substitutions, and insertions and deletions in each signature. The proposed work is well-positioned to replace heuristics used for discovering meaningful representations of data, and so have long-term impact on how other genomic data types such as single-cell RNA-seq are analyzed. This work is also directly relevant to the NIGMS as it falls under “DNA and RNA metabolisms (repair)” since many mutational processes are related to aberrant DNA repair or “clock-like” molecular mechanisms that are associated with aging, which can be observed in histologically normal appearing tissue