Statistical methods for cancer mutational signatures

项目摘要 致癌物质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。突变特征分析研究这些模式。继前景看好的首字母 成功，科学家在突变特征分析中提出的问题的范围和种类远远超过 提供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压缩的统计模型 强烈支持对这部小说中出现的大多数重要科学问题进行严格的统计推断 ？埃尔德。具体地说，我们将从根本上概括当前的方法，以纳入之前提出的 签名，并解释多项研究/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和开源的R包，符合BioConductor的标准，等- 降低用户实现我们的分析及其可视化的能力。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有丰富的经验。 开发将与Dana Farber多发性骨髓瘤基因组学中的发现工作携手进行 计划，PI是其不可分割的一部分。 鉴于肿瘤的整个外显子组和全基因组序列的快速增长，以及相应的生长 在突变特征分析的使用中，我们预计我们的工具将产生重大影响，使癌症成为可能 研究人员需要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翻译的速度 癌症中的突变特征。

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.