Statistical methods for cancer mutational signatures

癌症突变特征的统计方法

基本信息

批准号：
10278549
负责人：
Giovanni Luigi PARMIGIANI
金额：
$ 41.7万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10278549
关键词：
Address Bayesian Modeling Bioconductor Biology Carcinogens Clinical Clinical Trials Computer software DNA DNA Repair Data Data Analyses Data Analytics Development Disease Frequencies Genomics Goals Grant Growth Learning Malignant Neoplasms Methods Modeling Morphologic artifacts Multiple Myeloma Multivariate Analysis Mutation Mutation Analysis Oncogenic Pattern Pharmaceutical Preparations Probability Process Research Personnel Resort Role Scientist Software Tools Somatic Mutation Statistical Data Interpretation Statistical Methods Statistical Models Study models Techniques Testing Tissues Tobacco smoking behavior Tweens Ultraviolet Rays Uncertainty Visualization Work analytical tool base cancer cell cancer type carcinogenicity clinical application clinical translation exome experience genome sequencing interest novel open source pressure programs success tool tumor tumor DNA whole genome

项目摘要

Project Summary Carcinogens and evolutionary pressures generate unique patterns in the types of somatic mutations observed in the DNA of cancer cells. Mutational signature analysis investigates these patterns. Following promising initial successes, the scope and variety of questions scientists ask in mutational signature analysis far exceed the availability of robust data analytic tools to address them. The overarching goal of this project is to develop, test, and apply a class of statistical models able to compre- hensively support rigorous statistical inference on most of the important scientiﬁc questions arising in this novel ﬁeld. Speciﬁcally, we will generalize current approaches in fundamental ways to incorporate previously proposed signatures, and account for multiple studies/conditions, covariates, paired/longitudinal data and batch effects. We will develop a comprehensive free and open-source R package, conforming to Bioconductor standards, al- lowing users to implement our analyses and their visualizations. Methods will leverage the investigative groups' extensive experience in Bayesian modeling, multi-study modeling, multivariate analysis, and statistical genomics. Development will proceed hand in hand with discovery efforts within the Dana Farber multiple myeloma genomics program of which the PI is integral part. Given the fast growth in whole exome and whole genome sequencing of tumors, and the corresponding growth in the use of mutational signature analysis, we expect our tools to have a substantial impact, by enabling cancer researchers to a) carry out more accurate analysis and b) more reliably evaluate the accuracy of their results. Thus we expect this work to substantially accelerate the rate of discovery and clinical translation of the biology of mutational signatures in cancer.

项目概要致癌物和进化压力在观察到的体细胞突变类型中产生独特的模式癌细胞 DNA 中的突变特征分析研究了这些有前景的模式。尽管取得了成功，但科学家在突变特征分析中提出的问题的范围和多样性远远超过了可用强大的数据分析工具来解决这些问题。该项目的总体目标是开发、测试和应用一类能够综合强烈支持对这部小说中出现的大多数重要科学问题的严格统计推断具体来说，我们将以基本方式概括当前的方法，以纳入先前提出的方法签名，并考虑多项研究/条件、协变量、配对/纵向数据和批次效应。我们将开发一个全面的免费开源 R 包，符合 Bioconductor 标准，al- 让用户实施我们的分析和他们的可视化方法将利用调查小组的成果。在贝叶斯建模、多研究建模、多变量分析和统计基因组学方面拥有丰富的经验。开发工作将与 Dana Farber 多发性骨髓瘤基因组学的发现工作同步进行 PI 是其中不可分割的一部分的计划。鉴于肿瘤全外显子组和全基因组测序的快速增长，以及相应的增长在使用突变特征分析时，我们希望我们的工具能够产生重大影响，使癌症研究人员 a) 进行更准确的分析，b) 更可靠地评估其结果的准确性。因此，我们预计这项工作将大大加快生物学的发现和临床转化的速度癌症中的突变特征。