Genetic Heterogeneity Profiling and Subclone Detection by Single Cell RNA Sequencing

Nancy Zhang (November 4, 2019)

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Abstract

Detection of genetically distinct subclones and profiling the transcriptomic differences between them is needed for studying the evolutionary dynamics of tumors, as well as for accurate prognosis and effective treatment of cancer in the clinic.  For the profiling of intra-tumor transcriptional heterogeneity, single cell RNA-sequencing (scRNA-seq) is now ubiquitously adopted in ongoing and planned cancer studies. Detection of somatic DNA mutations and inference of clonal membership from scRNA-seq, however, is currently unreliable. In this talk, I will describe DENDRO, a new method for subclone detection and DNA mutation profiling using single cell transcriptomic sequencing data.  DENDRO utilizes information from single nucleotide mutations in transcribed regions, and accounts for technical noise and expression stochasticity at the single cell level. I will show accuracy evaluations based on spike-in datasets and on scRNA-seq data with known subpopulation structure.  Then, I will describe several case studies:  We applied DENDRO to delineate subclonal expansion in a mouse melanoma model in response to immunotherapy, highlighting the role of neoantigens in treatment response. We also applied DENDRO to primary and lymph-node metastasis samples in breast cancer, where the new approach allowed us to better understand the relationship between genetic and transcriptomic intratumor variation.