Abstract 1663: Analytical methods to identify tumor heterogeneity and rare subclones in single cell DNA sequencing data from targeted panels
Authors: Manivannan Manimozhi , Sahu Sombeet , Wang Shu , Kim Dong , Vissa Niranjan , Wilheim Jordan , Sciambi Adam , Li Nianzhen , Durruthy-Durruthy Robert , Parikh Anup , Jones Keith 
Published: August 2019

Abstract
Current bulk sequencing paradigms are inadequate to characterize complicated biological systems where somatic variation is buried in the landscape of populations. Single-cell DNA sequencing can simultaneously identify zygosity, rare alleles and determine whether mutations co-occur within the same cell. While high-throughput single-cell DNA analysis has been a recent innovation, it is essential to develop new capabilities for assessing genetic variation present in rare cells and to better understand the role that these cells play in the evolution of tumor progression. To address these challenges and enable the characterization of genetic diversity in cancer cell populations, we developed analytical methods to identify mutation signatures which define subclones present in a tumor population. Here we present a workflow for subclone identification using data generated on the Tapestri single-cell DNA platform. The analytical pipeline steps include obtaining raw reads from the sequencer, removing adapters, aligning and mapping the reads, calling individual cells, error correction of reads assigned to cells and identifying genetic variants within each cell. After filtering for high quality variants, we then filter for data completeness to ensure high quality data is used in downstream processing. The variant-cell matrix is then subjected to clustering to identify subclones. Top variants that define the signature of each subclone are also identified. To validate our methodology, we used data from two different targeted panels on different model systems with known truth mutations. Our pipeline shows the distinct clusters correlating with titration and cell line ratios. Cluster associated signature mutations were also identified. The pipeline can be used for multi sample analysis with time-series data from diagnosis to relapse or from primary site to metastasis to understand clonal diversity. These data demonstrate the utility of the Tapestri platform, the analytical pipeline, and associated data visualization capability. Our approach addresses key issues of identifying rare subpopulations of cells down to 0.1%, and transforms the ability to accurately characterize clonal heterogeneity in tumor samples. This high throughput method advances research efforts to improve patient stratification and therapy selection for various cancer indications.
Citation Format: Manimozhi Manivannan, Sombeet Sahu, Shu Wang, Dong Kim, Niranjan Vissa, Jordan Wilheim, Adam Sciambi, Nianzhen Li, Robert Durruthy-Durruthy, Anup Parikh, Keith Jones. Analytical methods to identify tumor heterogeneity and rare subclones in single cell DNA sequencing data from targeted panels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1663.