Statistical methods for cancer mutational signatures

癌症突变特征的统计方法

• 批准号: 10662461
• 负责人: Giovanni Luigi PARMIGIANI
• 金额: $ 23.11万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 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/10662461
• 关键词: Acceleration; Address; Assessment tool; Bayesian Modeling; Bioconductor; Biology; Carcinogens; Clinical Trials; Computer software; DNA; DNA Repair; Data; Data Analyses; Data Analytics; Development; Disease; Drug resistance; Frequencies; Genomics; Goals; Grant; Growth; Learning; Malignant Neoplasms; Maps; Methods; Modeling; Morphologic artifacts; Multiple Myeloma; Multivariate Analysis; Mutation; Mutation Analysis; Oncogenic; Pattern; 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; Ultraviolet Rays; Uncertainty; Visualization; Work; analytical tool; cancer cell; cancer type; carcinogenesis; carcinogenicity; clinical application; clinical translation; exome; experience; genome sequencing; interest; novel; open source; pressure; programs; success; tool; tumor; tumor DNA; whole genome

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 scienti?c questions arising in this novel ?eld. Speci?cally, 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。在有希望的初始内容之后 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，测试和应用一类能够组合的统计模型 在这部小说中，对大多数重要的科学问题的严格统计推断对严格的统计推断 ？特定的cally，我们将以基本方式概括当前的方法来纳入先前提议的 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， 降低用户以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。 因此，我们期望这项工作将大大加快发现的发现率和生物学的临床翻译率 癌症中的突变特征。