Analytical methods to identify tumor heterogeneity and rare subclones in single-cell DNA sequencing data from targeted panels
Creators: name: Sombeet Sahu
name: Manimozhi Manivannan
name: Wang, Shu
name: Kim, Dong
name: Saurabh Gulati
name: Sciambi, Adam
name: Nianzhen Li
name: Beard, Nigel
Publisher: name: F1000 Research Limited
Year: 2019

description: Abstract<br>Background: With the advancements of single-cell sequencing technologies it is now possible to interrogate thousands of cells in a single experiment. Single-cell RNA-Seq has been available for several years but high-throughput single-cell DNA analysis is in its infancy. Therefore, 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 a novel approach to identify mutation signatures which define subclones present in a tumor population. <br><br>Methods: Here we present subclone identification method using data generated on the Mission Bio TapestriⓇ Platform and analyzed by Tapestri analytical workflow. The pipeline steps involve obtaining raw reads from the sequencer, removing adapters, aligning and mapping the reads, calling individual cells, and identifying genetic variants within each cell. After filtering for high quality variants, we then filter for data completeness to ensure only high quality data is used in downstream processing. The variant-cell matrix is then subjected to identification of subclones. Top variants that define the signature of each subclone are also identified. To validate our methodology, we used two different targeted sequencing panels on 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.<br><br>Results and Conclusions <br>With the analytical method demonstrated above, we show the distinct clusters correlating with titration and cell line ratio. We also identify the cluster associated signature mutations. These data demonstrate the utility of the Tapestri Platform, and the analytical pipeline, and associated data visualization capability. Our approach has the potential to address the key issues of identifying rare subpopulations of cells and transforms our ability to accurately characterize clonal heterogeneity in tumor samples. This high-throughput method of accurately characterizing clonal populations should lead to improved patient stratification and therapy selection for various cancer indications.