RNDCLONE: TUMOR SUBCLONE RECONSTRUCTION BASED ON INTEGRATING DNA AND RNA SEQUENCE DATA

• 成果类型: Article
• 署名作者: Zhou, Tianjian; Sengupta, Subhajit; Muller, Peter; Ji, Yuan
• 署名单位: University of Chicago; NorthShore University Health System; University of Texas System; University of Texas Austin
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1368
• 发表日期: 2020
• 页码: 1856-1877
• 关键词: bayesian-inference; mutations; Heterogeneity; EVOLUTION; number; MODEL

摘要

Tumor cell population consists of genetically heterogeneous subpopulations, known as subclones. Bulk sequencing data using high-throughput sequencing technology provide total and variant DNA and RNA read counts for many nucleotide loci as a mixture of signals from different subclones. We present RNDClone as a tool to deconvolute the mixture and reconstruct the subclones with distinct DNA genotypes and RNA expression profiles. In particular, we infer the number and population frequencies of subclones as well as subclonal copy numbers, variant allele numbers and gene expression levels by jointly modeling DNA and RNA read counts from the same tumor samples based on generalized latent factor models. Incorporating data at the RNA level provides new insights into intra-tumor heterogeneity in addition to the existing DNA-based inference. Performance of RNDClone is assessed using simulated and real-world datasets, including an analysis of three samples from a lung cancer patient in The Cancer Genome Atlas (TCGA). A potential fatal subclone is identified from the primary tumor which could explain the rapid prognosis and sudden death of the patient despite a promising diagnosis by conventional standards. The R package RNDClone is available in the Supplementary Material (Zhou et al. (2020)) and online at https://github.com/tianjianzhou/RNDClone.