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

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

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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.

项目摘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在癌细胞的DNA中。突变签名分析研究了这些模式。遵循承诺初始成功，科学家在突变签名分析中提出的范围和各种问题远远超出了可用性的可靠数据分析工具来解决它们。该项目的总体目标是开发，测试和应用一类统计模型可以进行组合在这部小说中敏锐地支持严格的统计推断对大多数重要的科学问题领域，我们将以基本方式概括当前的方法，以纳入先前提议的签名，并说明多个研究/条件，协变量，配对/纵向数据和批处理效应。我们将开发一个全面的免费和开源R套件，符合生物导体标准，降低用户实施我们的分析及其可视化。方法将利用调查小组的在贝叶斯建模，多研究建模，多元分析和统计基因组学方面的丰富经验。开发将与Dana Farber多发性骨髓瘤基因组学内的发现工作息息相关 PI是不可或缺的一部分的程序。鉴于肿瘤的整个外显子组和整个基因组测序的快速生长，以及相应的生长在使用突变签名分析时，我们希望我们的工具能够通过实现癌症产生重大影响研究人员a）进行更准确的分析和b）更可靠地评估其结果的准确性。我们希望这项工作将大大加快发现的发现和临床翻译的速度癌症中的突变特征。