Defining Multiple Layers of Intratumor Heterogeneity Based on Variations of Perturbations in Multi-omics Profiling

Abstract

Intratumor heterogeneity (ITH) is associated with tumor progression, relapse, immunoevasion, and drug resistance. Existing algorithms for measuring ITH are limited to at a single molecular level. We proposed a set of algorithms for measuring ITH at the genome (somatic copy number alterations (CNAs) and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. These algorithms were designed based on a common concept: information entropy. By analyzing 33 TCGA cancer types, we demonstrated that these ITH measures had the typical properties of ITH, namely their significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. Furthermore, we showed that the correlations between ITH measures at identical molecular levels were stronger than those at different molecular levels. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Finally, we integrated the seven ITH measures into an ITH measure, which displayed more prominent properties of ITH than the ITH measures at a single molecular level. This analysis of multi-level ITH provides novel insights into tumor biology and potential values in clinical practice for pan-cancer.