Statistical methods for cancer genomics and cell-free DNA analysis

癌症基因组学和游离 DNA 分析的统计方法

基本信息

批准号：
10612900
负责人：
Jeffrey Wayne Miller
金额：
$ 33.95万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10612900
关键词：
Algorithms Bayesian Modeling Benign Bioinformatics Biological Blood Blood Circulation Cancer Biology Cancer Detection Cells Cessation of life Characteristics Classification Collection Complex Computer software DNA DNA analysis DNA sequencing Data Databases Detection Diploidy Disadvantaged Documentation Early Diagnosis Early treatment Exhibits GENIE Genome Genomics Goals Health Policy Learning Malignant Neoplasms Methodology Methods Modeling Mutation Mutation Detection Noise Non-Invasive Detection Performance Plasma Plasma Cells Positioning Attribute Process Psychological reinforcement Recommendation Reproducibility Sampling Screening for cancer Sensitivity and Specificity Signal Transduction Software Tools Specific qualifier value Statistical Methods Statistical Models Structure Survival Rate System Techniques Technology Testing The Cancer Genome Atlas Tumor-Derived Work cancer cell cancer classification cancer genome cancer genomics cancer type cell free DNA cell repository complex data driver mutation dynamic system experience flexibility genome sequencing genome-wide high standard novel open source screening signal processing software development sound tool transcriptome sequencing tumor tumor DNA user-friendly

项目摘要

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.

项目概要/摘要如果及早发现，许多癌症都可以成功治疗，从而提高生存率。由于目前的筛查技术灵敏度不够，癌症往往只能在晚期才被发现。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，因为弊大于利游离 DNA (cfDNA) 测序为高度准确、非测序提供了令人兴奋的可能性。侵入性癌症筛查当细胞死亡时，它们通常会将其 DNA 的小片段释放到体内，这些游离 DNA 片段在血液中暂时消失，因此，当癌症存在时，通过进行基因组分析，从常规抽血中获得的血浆含有来自癌细胞的 DNA 片段。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。需要先进的统计方法从噪声中提取信号。 cfDNA 片段非常小，对于早期癌症而言约为 1/1000 或更小。拟议项目的目的是开发和测试一套灵活的统计方法，用于癌症检测和我们的中心假设是使用低肿瘤分数的 cfDNA 测序数据进行分析。 cfDNA 数据中癌症基因组信号的能力模型，以及仔细处理错误和偏差，将使癌症检测和分类具有高灵敏度和特异性（目标 1）开发稳健的非参数泊松回归框架，应用于突变特征突变过程。导致癌症表现出特征性的全基因组特征，这些特征是使用非负自然建模的我们将泊松 NMF 模型推广为非参数分层贝叶斯模型。回归模型的先验信息由潜在癌症类型/亚型、协变量、已知生物结构、和癌症基因组的大型数据库（目标 2）为复杂模型开发基于语法的方法。应用于 SCNA 的连续数据需要连续且离散的全基因组 SCNA 建模。具体的潜在状态、异步发射、非齐次转换内核以及基于的知情先验我们为复杂序列开发了语法和算法。（目标 3）开发综合贝叶斯框架，用于稳健的癌症检测我们将在癌症的分层模型中结合目标 1 和 2 的方法。类型/子类型作为潜在变量（目标 4）开发软件、提供文档并传播结果。为了促进可重复性，我们将提供用户友好的开源软件、预处理的公共数据，以及完整的文档可实现可重复性并最大限度地提高易用性。