RT Journal Article
SR Electronic
T1 Genetic Heterogeneity Profiling by Single Cell RNA Sequencing
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 457622
DO 10.1101/457622
A1 Zilu Zhou
A1 Bihui Xu
A1 Andy Minn
A1 Nancy R Zhang
YR 2019
UL http://biorxiv.org/content/early/2019/10/17/457622.abstract
AB Detection of genetically distinct subclones and profiling the transcriptomic differences between them is important 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. We propose DENDRO, an analysis method for scRNA-seq data that detects genetically distinct subclones, assigns each single cell to a subclone, and reconstructs the phylogenetic tree describing the tumor’s evolutionary history. DENDRO utilizes information from single nucleotide mutations in transcribed regions and accounts for technical noise and expression stochasticity at the single cell level. The accuracy of DENDRO was benchmarked on spike-in datasets and on scRNA-seq data with known subpopulation structure. 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.SNASingle nucleotide alterationscDNA-seqSingle-cell DNA sequencingscRNA-seqSingle-cell RNA sequencingPDXPatient-derived xenograftTPMTranscripts per kilobase millionICBImmune checkpoint blockadeTMBTumor mutational burden