title: Measuring intratumor heterogeneity by network entropy using RNA-seq data.
abstract: Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea.
authors.forename: Youngjune
authors.initials: Y
authors.lastname: Park
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea.
authors.forename: Sangsoo
authors.initials: S
authors.lastname: Lim
authors.affiliation: Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 133-791, Korea.|Research Institute for Natural Sciences, Hanyang University, Seoul, 133-791, Korea.
authors.forename: Jin-Wu
authors.initials: JW
authors.lastname: Nam
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, 151-742, Korea.|Bioinformatics Institute, Seoul National University, Seoul, 151-742, Korea.
authors.forename: Sun
authors.initials: S
authors.lastname: Kim
mesh_terms: D000818:Animals; D002453:Cell Cycle / Q000235:genetics; D045744:Cell Line, Tumor; D019277:Entropy; D018740:Genetic Heterogeneity; D016678:Genome / Q000235:genetics; D006801:Humans; D051379:Mice; D009369:Neoplasms / Q000235:genetics*; D060066:Protein Interaction Maps / Q000235:genetics*; D012313:RNA / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods; D016019:Survival Analysis; D059467:Transcriptome / Q000235:genetics
chemical_list: D012313:RNA