Statistical methods for cancer mutational signatures

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了独特的模式 在癌细胞的DNA中。突变特征分析研究这些模式。在充满希望的初期 成功，科学家在突变签名分析中提出的问题的范围和种类远远超过了 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，测试和应用一类统计模型，能够比较， 坚定地支持对大多数重要科学的严格统计推断？C.这部小说中出现的问题 ? eld.特殊？卡利，我们将概括目前的方法，在根本上纳入以前提出的 签名，并考虑多个研究/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标准， 让用户能够实现我们的分析和可视化。方法将利用调查小组的 在贝叶斯建模、多研究建模、多变量分析和统计基因组学方面拥有丰富的经验。 开发将与达纳法伯多发性骨髓瘤基因组学中的发现工作携手进行 PI是其中不可分割的一部分。 鉴于肿瘤的全外显子组和全基因组测序的快速增长，以及相应的增长， 在突变特征分析的使用中，我们希望我们的工具能够产生实质性的影响， 研究人员可以a）进行更准确的分析，以及B）更可靠地评估其结果的准确性。 因此，我们希望这项工作能大大加快发现和临床转化的生物学， 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.