ArticleScientific reports2016
Measuring intratumor heterogeneity by network entropy using RNA-seq data.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 76 citations in OpenAlex.
- CDState Resolves Malignant Cell Heterogeneity from Bulk Tumor RNA Sequencing Data.Cancer research · 2026Article
- ITHindex: An integrated web-based platform for intratumor heterogeneity evaluation.PLOS digital health · 2026Article
- Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors.NPJ precision oncology · 2026Article
- QeITH: Quantifies Tumor Ecosystem Heterogeneity to Predict Cancer Progression and Treatment Benefit.Computational and structural biotechnology journal · 2026Article
- Diagnostic Entropy as an Administrative Measure of Cross-System Diagnostic Dispersion in Noncommunicable Disease Inpatients: A Methodological Validation Study.Journal of nursing management · 2026Article
- DEPTH2 score was associated with cell proliferation and immune cell infiltrations but not with systemic treatment response in breast cancer.Scientific reports · 2025Article
- Decoding breast cancer heterogeneity: a novel three-gene signature links intratumoral heterogeneity with tumor microenvironment and patient outcomes.Discover oncology · 2025Article
- Characterization of stem cell landscape and identification of stemness-relevant prognostic gene signature to aid immunotherapy in breast cancer.Discover oncology · 2025Article
- Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment.Frontiers in genetics · 2025Article
- Intratumor heterogeneity score reveals immune landscape and survival stratification in colorectal cancer.Frontiers in immunology · 2025Article
- Comparative network-based analysis of toll-like receptor agonist, L-pampo signaling pathways in immune and cancer cells.Scientific reports · 2024Article
- Molecular landscape and multi-omic measurements of heterogeneity in fetal adenocarcinoma of the lung.NPJ precision oncology · 2024Article
- Spatially resolved tissue imaging to analyze the tumor immune microenvironment: beyond cell-type densities.Journal for immunotherapy of cancer · 2024Review
- The evolution of lung adenocarcinoma precursors is associated with chromosomal instability and transition from innate to adaptive immune response/evasion.Research square · 2024Article
- Mutant-allele dispersion correlates with prognosis risk in patients with advanced non-small cell lung cancer.Journal of cancer research and clinical oncology · 2023Article
- Non-canonical integrin signaling activates EGFR and RAS-MAPK-ERK signaling in small cell lung cancer.Theranostics · 2023Article
- Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer.Genome medicine · 2022Article
- A compendium of co-regulated mitoribosomal proteins in pan-cancer uncovers collateral defective events in tumor malignancy.iScience · 2022Article
- Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types.Biology open · 2022Article
- DEPTH2: an mRNA-based algorithm to evaluate intratumor heterogeneity without reference to normal controls.Journal of translational medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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