Mutational signatures are powerful summaries of the mutational processes altering the DNA of cancer cells. The usual approach to mutational signature analysis consists of decomposing the matrix of mutation counts from a sample of patients using non-negative matrix factorization (NMF). However, this ignores the heterogeneous patterns of mutation rates along the genome. In this paper, we introduce Poisson process factorization (PPF), which addresses this limitation by employing an inhomogeneous Poisson point process model to infer mutational signatures and their activities as they vary across the genome. PPF generalizes the baseline NMF model by representing a patient's exposure to each signature as a locus-specific function that depends on genomic covariates and patient-specific copy numbers via a log-linear model. This quantifies the relationships between genomic features and mutational signatures, and enables attribution of individual mutations to signatures. We develop tractable algorithms for maximum a posteriori estimation and posterior inference via Markov chain Monte Carlo. We demonstrate the method on simulated data and real data from breast cancer, using genomic covariates representing histone modifications, cell replication timing, nucleosome positioning, and DNA methylation.

翻译：突变特征是描述癌症细胞DNA突变过程的强有力概括。传统的突变特征分析方法通常采用非负矩阵分解（NMF）对患者样本的突变计数矩阵进行分解。然而，这种方法忽略了基因组中突变率的异质性分布模式。本文提出泊松过程分解（PPF）方法，通过采用非齐次泊松点过程模型来推断突变特征及其在基因组不同区域的活动变化，从而克服了这一局限性。PPF通过将对数线性模型与患者特异性拷贝数相结合，将患者对每个特征的暴露量表示为依赖于基因组协变量的位点特异性函数，从而推广了基准NMF模型。该方法量化了基因组特征与突变特征之间的关系，并能够将单个突变归因于特定特征。我们开发了基于最大后验估计和马尔可夫链蒙特卡洛后验推断的可处理算法。通过在模拟数据和乳腺癌真实数据上的实验验证了该方法的有效性，所使用的基因组协变量包括组蛋白修饰、细胞复制时序、核小体定位和DNA甲基化。