Abstract 40: scBayes: A computational method to study tumor subclone-specific gene expression and chromatin accessibility using single-cell RNA sequencing and single-cell ATAC sequencing in combination of bulk DNA sequencing

Although critical for understanding the underlying cancer biology, the study of tumor cell phenotype and epigenetic state from bulk gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) data is often challenging because tumor samples are typically mixtures of different cell populations due to normal tissue contamination, immune cell infiltration, and tumor subclonality. These cell populations may have vastly divergent transcriptomic and epigenetic characteristics, resulting in mixtures of signals that are often impossible to deconvolute. Here we present scBayes, a computational method that allows one to characterize the transcriptional behavior and/or epigenetic state corresponding to each tumor subclone present in a tumor sample or multiple serial or multisite samples from a given cancer patient. This tool starts with genomically defined subclones reconstructed from bulk DNA sequencing data, e.g., using our own published SubcloneSeeker algorithm. scBayes then layers single-cell gene expression (scRNA-seq) and/or chromatin accessibility (scATAC-seq) data on this genomic subclone “backbone” to assign individual cells to one of the tumor subclones or normal cell compartment, facilitating subclone-specific expression or epigenetic analysis. This allows (1) the identification of tumor expression or epigenetic signals without normal tissue interference, (2) the comparison between tumor and normal cells from the same sample, (3) the comparison of expression and epigenetic state across distinct tumor subclones, and (4) the study of transcriptional/epigenetic plasticity through the comparison of the cell states of the same genomically defined subclone across different time points or metastatic sites. scBayes achieves cell-to-subclone assignment by assessing the presence of somatic variants (CNVs or SNVs) that define cancer subclones in each individual cell from the single-cell sequencing reads. Because per-cell sequencing coverage is sparse, and therefore variant dropout frequent, scBayes utilizes a Bayesian probabilistic framework to calculate the likelihood of a particular cell originating from each subclone (including the normal clone, defined as having no somatic mutations) and makes a probabilistic assignment, maximizing the utilization of the sparse single-cell sequencing data for subclone assignment. We successfully applied this method to multiple longitudinal and metastatic cancer patient datasets, representing both bulk WES and WGS DNA sequencing, as well as single-cell datasets collected using the 10X Chromium and Fluidigm C1 platforms. Our algorithmic approach enables comparative gene expression and pathway analysis, and open chromatin accessibility assessment, across subclones. scBayes is open source and freely available at https://github.com/yiq/scBayes. Citation Format: Yi Qiao, Xiaomeng Huang, Gabor Marth. scBayes: A computational method to study tumor subclone-specific gene expression and chromatin accessibility using single-cell RNA sequencing and single-cell ATAC sequencing in combination of bulk DNA sequencing [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 40.