ArticleJournal of translational medicine2022
DEPTH2: an mRNA-based algorithm to evaluate intratumor heterogeneity without reference to normal controls.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A machine learning-derived intratumoral heterogeneity-related signature predicts the prognosis for and therapeutic response in patients with skin cutaneous melanoma.Translational cancer research · 2026Article
- ITHindex: An integrated web-based platform for intratumor heterogeneity evaluation.PLOS digital health · 2026Article
- Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.Oncology letters · 2026Article
- Identification and validation of an intratumor heterogeneity-related prognostic signature in triple-negative breast cancer: a study based on integrative machine learning and single-cell sequencing.Breast cancer research : BCR · 2026Article
- QeITH: Quantifies Tumor Ecosystem Heterogeneity to Predict Cancer Progression and Treatment Benefit.Computational and structural biotechnology journal · 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
- PRMT5 Inhibitor Synergizes with Chemotherapy to Induce Resembling Mismatch Repair Deficiency and Enhance Anti-TIGIT Therapy in Microsatellite-Stable Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A comprehensive investigation of associations between cell death pathways and molecular and clinical features in pan-cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Intratumor heterogeneity related signature for clinical outcome and immunotherapy advantages in lung adenocarcinoma.Discover oncology · 2025Article
- Machine learning based intratumor heterogeneity related signature for prognosis and drug sensitivity in breast cancer.Scientific reports · 2025Article
- Intratumor heterogeneity score reveals immune landscape and survival stratification in colorectal cancer.Frontiers in immunology · 2025Article
- Breaking the heterogeneity barrier: a robust prognostic signature for survival stratification and immune profiling in triple-negative breast cancer.Frontiers in immunology · 2025Article
- Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.Briefings in bioinformatics · 2024Article
- Machine learning based intratumor heterogeneity signature for predicting prognosis and immunotherapy benefit in stomach adenocarcinoma.Scientific reports · 2024Article
- Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer.Scientific reports · 2024Article
- A platelet-related signature for predicting the prognosis and immunotherapy benefit in bladder cancer based on machine learning combinations.Translational andrology and urology · 2024Article
- Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.Translational andrology and urology · 2024Article
- Article
- Identification of breast cancer subgroups and immune characterization based on glutamine metabolism-related genes.BMC medical genomics · 2024Article
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Abstract
backgroundIntratumor heterogeneity (ITH) is associated with tumor progression, unfavorable prognosis, immunosuppression, genomic instability, and therapeutic resistance. Thus, evaluation of ITH levels is valuable in cancer diagnosis and treatment.
methodsWe proposed a new mRNA-based ITH evaluation algorithm (DEPTH2) without reference to normal controls. DEPTH2 evaluates ITH levels based on the standard deviations of absolute z-scored transcriptome levels in tumors, reflecting the asynchronous level of transcriptome alterations relative to the central tendency in a tumor.
resultsBy analyzing 33 TCGA cancer types, we demonstrated that DEPTH2 ITH was effective in measuring ITH for its significant associations with tumor progression, unfavorable prognosis, genomic instability, reduced antitumor immunity and immunotherapy response, and altered drug response in diverse cancers. Compared to other five ITH evaluation algorithms (MATH, PhyloWGS, ABSOLUTE, DEPTH, and tITH), DEPTH2 ITH showed a stronger association with unfavorable clinical outcomes, and in characterizing other properties of ITH, such as its associations with genomic instability and antitumor immunosuppression, DEPTH2 also displayed competitive performance.
conclusionsDEPTH2 is expected to have a wider spectrum of applications in evaluating ITH in comparison to other algorithms.
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