Abstract 2173: Robust estimation of mutation burden

Developing a more robust approach to measure mutational burden is of central importance to improving the characterization of the molecular profile of tumor and may improve our ability to predict tumor progression or response to therapy in patients. Mutational burden is typically calculated as a direct enumeration of called somatic mutations per megabase covered. However, there is a growing appreciation that tumor purity, variable sequencing coverage, and copy number alterations can substantially impact the accurate identification any specific somatic mutation. Furthermore, population genetic theory and empirical data indicate that in many cases the vast majority of somatic mutations appear in only a small subpopulation of tumor cells, a context in which there is a high likelihood that an individual subclonal mutation may not be identified by conventional analysis. This tendency to miss low frequency mutations is highly variable and dependent, in part, upon sample purity and results in a strong source of bias not addressed in existing methods to measure variant allele frequencies. We present a novel computational method that incorporates these sources of bias in a coherent probabilistic framework that enables maximum-likelihood inference of relevant population parameters such as mutation burden. We apply our method to simulated data as well as patient tumor samples diluted with varying known proportions of normal DNA. We show that our approach allows us to generate estimates of mutation burden that are robust to the substantial variations in purity and sequencing coverage that are frequently encountered in patient tumor analysis. Hence, our novel method may improve the accurate detection and quantification of variant alleles in patient tumors to better understand their genetic landscape and guide clinical management. Citation Format: Oscar Westesson, Rasmus Nielsen, John St John, Aleah Caulin, Nicholas Hahner, Stewart Stewart, Catherine Foo, Kimberly Lung, Jeff Catalano, Mandy Lee, Petros Giannikopoulos, Will Polkinghorn, Jonathan Wiessman, Aviv Regev, Trever Bivona. Robust estimation of mutation burden. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2173. doi:10.1158/1538-7445.AM2015-2173