ArticleNPJ genomic medicine2021
Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors.
Article in NPJ genomic medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 28 citations in OpenAlex.
- Targeting Ovarian Neoplasms: Subtypes and Therapeutic Options.Medicina (Kaunas, Lithuania) · 2025Review
- Improved tumor-type informed compared to tumor-informed mutation tracking for ctDNA detection and microscopic residual disease assessment in epithelial ovarian cancer.Journal of experimental & clinical cancer research : CR · 2025Article
- ABCB1 confers resistance to carboplatin by accumulating stem-like cells in the G2/M phase of the cell cycle in p53Cell death discovery · 2025Article
- ORCO: Ollivier-Ricci Curvature-Omics-an unsupervised method for analyzing robustness in biological systems.Bioinformatics (Oxford, England) · 2025Article
- Insights into targeting LKB1 in tumorigenesis.Genes & diseases · 2025Review
- Establishment of Novel High-Grade Serous Ovarian Carcinoma Cell Line OVAR79.International journal of molecular sciences · 2024Article
- Comparative transcriptomic, epigenomic and immunological analyses identify drivers of disparity in high-grade serous ovarian cancer.NPJ genomic medicine · 2024Article
- Unraveling the potential biomarkers of immune checkpoint inhibitors in advanced ovarian cancer: a comprehensive review.Investigational new drugs · 2024Review
- ORCO: Ollivier-Ricci Curvature-Omics - an unsupervised method for analyzing robustness in biological systems.bioRxiv : the preprint server for biology · 2024Article
- Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer.Journal of ovarian research · 2024Article
- Charting cellular differentiation trajectories with Ricci flow.Nature communications · 2024Article
- Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival.Blood cancer journal · 2023Article
- Geometric graph neural networks on multi-omics data to predict cancer survival outcomes.Computers in biology and medicine · 2023Article
- Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology.JCO clinical cancer informatics · 2023Article
- Review
- Immune checkpoint blockade in glioblastoma: from tumor heterogeneity to personalized treatment.The Journal of clinical investigation · 2023Review
- Exploring the future of research in the Tp53 field.Cell death and differentiation · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Network analysis methods can potentially quantify cancer aberrations in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high-grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs) and to rank key genes associated with prognosis. Copy number alterations (CNAs) from targeted and whole-exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein-protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier-Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan-Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. The network curvature analysis stratified patients at high risk of mortality with p = 0.00047 in Kaplan-Meier analysis in HGSOC patients receiving ICI. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. Network curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.