Statistical methods for cancer genomics and cell-free DNA analysis

项目摘要/摘要 如果及早发现，许多癌症都可以成功治疗，从而导致高存活率。不幸的是， 癌症通常只在晚期才被发现，因为目前的筛查技术对fi的敏感性不高。 城市和特殊fi市的肿瘤比例较低。此外，筛查本身往往是侵入性的，甚至是有害的，导致 卫生政策专家建议推迟或避免筛查，因为这样做的坏处可能会超过 Benefit.无细胞dna(Cfdna)测序展示了一种令人兴奋的新的可能性，即高精度、非 侵袭性癌症筛查。当细胞死亡时，它们通常会将小片段的DNA释放到体内， 这些无细胞的DNA片段暂时在血液中循环。因此，当癌症出现时， 从常规抽血中获得的血浆中含有来自癌细胞的DNA片段。通过执行基因组 对这种血浆cfDNA进行测序，就有可能非侵入性地检测和分析癌症。然而，广告- 为了从噪声中提取信号，需要先进的统计方法。肿瘤来源的部分 CfDNA片段非常小，对于早期癌症来说，大约是1/1000或更少。主要目标 拟议项目的一部分是开发和测试一套fl可执行的统计方法，用于癌症检测和 使用低肿瘤部位的cfDNA测序数据进行分析。我们的中心假设是结构化的问题- CfDNA数据中癌症基因组信号的可靠性模型，以及对错误和偏差的仔细处理， 将使癌症检测和Classifi阳离子具有高灵敏度和特异性fi城市。(目标1)发展强大的非 参数泊松回归框架，应用于突变签名。突变的过程 导致癌症表现出典型的全基因组签名，这些签名是自然地使用非负值建模的 矩阵分解(NMF)。我们将Poisson NMF模型推广到非参数多层贝叶斯模型 具有潜在癌症类型/亚型、协变量、已知生物学结构、 以及癌症基因组的大型数据库。(目标2)为复杂的模型开发基于语法的方法 顺序数据，应用于SCNA。精确的全基因组SCNA建模需要连续的和分散的 克里特潜伏态、异步发射、非均匀转移核和基于以下条件的先验信息 先前观察到的癌症/正常基因组。我们开发了一种复序列的文法和算法 具有这些功能的型号。(Aim3)开发综合贝叶斯框架，用于稳健的癌症检测 CfDNA测序。我们将在癌症的分层模型中结合目标1和目标2中的方法 类型/亚型作为潜在变量。(目标4)开发软件、提供文档和传播成果 以便于重现性。我们将提供用户友好的开源软件、经过预处理的公共数据，以及 完整的文档，以实现重复性并最大限度地提高易用性。

PROJECT SUMMARY/ABSTRACT If detected early, many cancers can be successfully treated, leading to a high rate of survival. Unfortunately, cancer is often detected only at late stages since current screening technologies have insufficient sensitiv- ity and specificity at low tumor fractions. Further, screening itself is often invasive or even harmful, leading health policy experts to recommend delaying or avoiding screening since the disadvantages may outweigh the benefit. Cell-free DNA (cfDNA) sequencing presents an exciting recent possibility for highly accurate, non- invasive cancer screening. When cells die, they often release small fragments of their DNA into the body, and these cell-free DNA fragments temporarily circulate in the bloodstream. Thus, when cancer is present, plasma obtained from routine blood draws contains DNA fragments from cancer cells. By performing genome sequencing on this plasma cfDNA, it is possible to non-invasively detect and analyze cancers. However, ad- vanced statistical methods are needed to extract the signal from the noise. The fraction of tumor-derived cfDNA fragments is very small, on the order of 1/1000 or less for early stage cancers. The main objective of the proposed project is to develop and test a flexible suite of statistical methods for cancer detection and analysis using cfDNA sequencing data at low tumor fractions. Our central hypothesis is that structured prob- abilistic models of genomic signals of cancer in cfDNA data, along with careful handling of errors and biases, will enable cancer detection and classification with high sensitivity and specificity. (Aim 1) Develop robust non- parametric Poisson regression framework, applied to mutational signatures. The mutational processes that lead to cancer exhibit characteristic genome-wide signatures that are naturally modeled using nonnegative matrix factorization (NMF). We generalize the Poisson NMF model to a nonparametric hierarchical Bayesian regression model with priors informed by latent cancer type/subtype, covariates, known biological structure, and large databases of cancer genomes. (Aim 2) Develop grammar-based methods for complex models of sequential data, applied to SCNAs. Accurate genome-wide SCNA modeling requires continuous and dis- crete latent states, asynchronous emissions, inhomogeneous transition kernels, and informed priors based on previously observed cancer/normal genomes. We develop a grammar and algorithms for complex sequence models with these features. (Aim3) Develop integrated Bayesian framework for robust cancer detection from cfDNA sequencing. We will combine the methods from Aims 1 and 2 in a hierarchical model with cancer type/subtype as a latent variable. (Aim 4) Develop software, provide documentation, and disseminate results to facilitate reproducibility. We will provide user-friendly open-source software, preprocessed public data, and thorough documentation to enable reproducibility and maximize ease-of-use.