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.

An algorithm to quantify intratumor heterogeneity based on alterations of gene expression profiles

“…We explored the correlation between ITH scores defined by seven different methods (DEPTH, tITH 24 , sITH 26 , PhyloWGS 6 , EXPANDS 4 , 5 , ABSOLUTE 2 , and MATH 3 ) in TCGA pan-cancer and individual cancer types. As expected, 19 of the 21 pairwise correlations were significantly positive in pan-cancer ( p < 0.01, 0.09 ≤ ρ ≤ 0.55) (Fig.…”

“…Because alterations in genome and epigenome profiles often lead to heterogeneous gene expression profiles in tumors, defining ITH based on gene expression profiles is a viable approach 21 – 23 . Park et al proposed a method (tITH) for evaluating transcriptome-based ITH using RNA-Seq data 24 . This method has exhibited certain effectiveness in evaluating ITH, e.g., the positive correlation between its ITH and genetic ITH characterized by DNA-based methods, and the negative correlation between the ITH and survival prognosis in the tumor.…”

“…We further validated these features of ITH evaluated by DEPTH in more than 10,000 tumor samples apart from TCGA. We compared our method with six other ITH evaluation methods, including ABSOLUTE 2 , MATH 3 , EXPANDS 4 , 5 , PhyloWGS 6 , tITH 24 , and sITH 26 . Our data showed that DEPTH is an effective and robust method for evaluating ITH, and its performance is superior to or comparable to that of the other methods.…”

Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors

“…We measured dysregulation in key cancer-related pathways (Figure 2D, Supplementary Figure 14, Supplementary Table 8) using a network based Jensen-Shannon Divergence (nJSD) score (Park et al, 2016), with values ranging from 0 to 1, where high values represents increased perturbation compared to matched normal tissue. NFKB activation pathway, which is associated with cell survival, was found to be highly deregulated in certain regions of tumors P3-R1 and P3-R3, P4-R1 and P5-R3.…”

Histopathology of thymectomy specimens from the MGTX-trial: Entropy analysis as strategy to quantify spatial heterogeneity of lymphoid follicle and fat distribution

“…Beyond the analysis of heterogeneity of tissue images, entropy analysis may help to quantify the heterogeneity of RNA-seq data [ 31 ] and the heterogeneity of tumors in terms of subclone composition. [ 32 ] Finally, through correlation with clinical endpoints, entropy analysis might have the potential to detect new, quantitative types of predictive biomarkers on the morphological and molecular level in neoplastic as well as reactively changed tissue.…”