Abstract
Although scRNA-seq is now ubiquitously adopted in studies of intratumor heterogeneity, detection of somatic mutations and inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that clusters single cells into genetically distinct subclones and reconstructs the phylogenetic tree relating the subclones. DENDRO utilizes transcribed point mutations and accounts for technical noise and expression stochasticity. We benchmark DENDRO and demonstrate its application on simulation data and real data from three cancer types. In particular, on a mouse melanoma model in response to immunotherapy, DENDRO delineates the role of neoantigens in treatment response.
Highlights
DNA alterations, especially single nucleotide alteration (SNA), and epigenetic modulation both contribute to intratumor heterogeneity [1], which mediates tumor initiation, progression, metastasis, and relapse [2, 3]
DENDRO generates a parsimony tree using the subclone-level mutation profiles to more accurately reflect the evolutionary relationship between the subclones
DENDRO starts with mutations detected directly from the scRNA-seq reads, which are very noisy due to a combination of factors: (1) errors are introduced in reverse-transcription, sequencing, and mapping; (2) low sequencing depth and low molecule conversion efficiency leading to technical dropouts; and (3) expression burstiness at the single-cell level leading to biological dropouts
Summary
DNA alterations, especially single nucleotide alteration (SNA), and epigenetic modulation both contribute to intratumor heterogeneity [1], which mediates tumor initiation, progression, metastasis, and relapse [2, 3]. Identifying subclonal DNA alterations and assessing their impact on intratumor transcriptional dynamics can elucidate the mechanisms of tumor evolution and, further, uncover potential targets for therapy. To characterize intratumor genetic heterogeneity, most prior studies have used bulk tumor DNA sequencing [5,6,7,8,9,10,11,12], but these approaches have limited resolution and power [13]. In breast cancer, single-cell DNA sequencing (scDNA-seq) was used to distinguish normal cells from malignant cells, the latter of which were further classified
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