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.

Project Narrative Mutational signature analysis is an increasingly important technique in cancer discovery, based on extracting patterns from the changes in tumor DNA, to reveal important aspects of the underlying carcinogenic process. The goal of this proposal is to develop, test, and pilot a general, robust, and comprehensive approach for the statistical analysis of the mutation data typically used to learn mutational signatures. The result will be open source and freely available software tools that will enable more rapid discovery, and also provide a more realistic assessment of the accuracy of results.