LAUSR

Several approaches are now available for subclonal reconstruction of heterogeneous tumor biopsies from somatic variant allele frequencies measured in bulk DNA sequencing datasets, including our own SubcloneSeeker program. However, to understand how chemoresistance, relapse, or metastasis evolves at a subclonal level, we also need to study the molecular phenotype corresponding to each subclone. Single-cell RNAseq technologies now allow one to study the transcriptomic characteristics and phenotypic behaviors of individual cells, but new algorithms are needed to link these cells to the genomically defined tumor subclones they represent. Here we present a computational approach to make such cell assignments, using a combination of bulk DNA sequencing data (WGS or WES), and single cell RNA sequencing (scRNAseq) collected from the same tissues. Subclones constructed from bulk DNA sequencing data are defined by specific combinations of somatic mutations (SNVs and CNVs). Our algorithm uses the scRNA-seq reads to assess which of these subclone-defining mutations are present in each individual cell, then utilizes a Bayesian probabilistic framework for making the cell-to-subclone assignment. This framework overcomes the challenges of sparse scRNA-seq read coverage, and maximizes the accuracy and efficiency of cell assignment. We have successfully applied this method to multiple longitudinal and metastatic cancer patient datasets, representing both WES and WGS bulk DNA sequencing, as well as datasets collected using the 10X Chromium and Fluidigm C1 technologies. Our algorithm uses both somatically acquired CNV and SNV events in the tumor for cell assignment. Using our approach, as many of 80% of tumor cells could be assigned to specific subclones, enabling comparative gene expression and pathway analysis across subclones. Citation Format: Yi Qiao, Xiaomeng Huang, Samuel Brady, Andrea Bild, David Bowtell, William Johnson, Gabor Marth. Subclone-specific evolution of tumor phenotypes – A framework to study subclone-specific gene expression from a combination of bulk DNA and single cell RNA sequencing data [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 4689.