Statistical methods for cancer genomics and cell-free DNA analysis

项目总结/摘要 如果早期发现，许多癌症可以成功治疗，从而提高生存率。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灵敏度不足， 低肿瘤分数下的特异性和特异性。此外，筛查本身往往是侵入性的，甚至是有害的， 卫生政策专家建议推迟或避免筛查，因为其弊大于利。 贝内.无细胞DNA（cfDNA）测序为高度准确、非限制性的DNA测序提供了令人兴奋的最新可能性。 侵袭性癌症筛查当细胞死亡时，它们通常会释放小片段的DNA到体内， 这些游离的DNA片段暂时在血液中循环。因此，当癌症存在时， 从常规抽血中获得的血浆含有来自癌细胞的DNA片段。通过执行基因组 通过对该血浆cfDNA进行测序，可以非侵入性地检测和分析癌症。然而，AD- 需要先进的统计方法从噪声中提取信号。肿瘤衍生的部分 cfDNA片段非常小，对于早期癌症为约1/1000或更小。主要目标 拟议项目的一个主要目标是开发和测试一套灵活的癌症检测统计方法， 在低肿瘤分数下使用cfDNA测序数据进行分析。我们的中心假设是结构化的概率- 在cfDNA数据中建立癌症基因组信号的模型，沿着小心处理错误和偏差， 将使癌症检测和分类具有高灵敏度和特异性。(Aim（1）建立健全的非 参数泊松回归框架，适用于突变签名。突变过程 导致癌症表现出特征性全基因组特征， 矩阵分解（NMF）。我们将Poisson NMF模型推广为非参数的多层贝叶斯模型 具有由潜在癌症类型/亚型、协变量、已知生物结构 和癌症基因组的大型数据库。(Aim 2）为复杂的模型开发基于语法的方法， 顺序数据，应用于SCNA。准确的全基因组SCNA建模需要连续和离散的数据， 具体的潜在状态，异步排放，非均匀过渡内核，并根据知情先验 以前观察到的癌症/正常基因组。我们开发了一个文法和算法的复杂序列 具有这些特征的模型。（目标3）制定综合贝叶斯框架，用于从 cfDNA测序。我们将联合收割机结合目标1和2的方法，建立一个癌症分层模型 类型/子类型作为潜在变量。(Aim 4）开发软件，提供文档，传播成果 以促进再现性。我们将提供用户友好的开源软件，预处理的公共数据， 全面的文档记录，以实现再现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.