ArticleNature communications2025
Pan-cancer copy number analysis identifies optimized size thresholds and co-occurrence models for individualized risk stratification.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Genomic characterization of ER-positive primary tumors and corresponding relapses identifies potentially targetable alterations.NPJ breast cancer · 2026Article
- A proteogenomic RNA processing mechanism drives sex differences in meningioma.Research square · 2026Article
- PScnv: personalized self-normalizing CNV detection with a hierarchical multi-phase framework.Bioinformatics (Oxford, England) · 2026Article
- CNAttention: an attention-based deep multiple-instance method for uncovering copy number aberration signatures across cancers.Briefings in bioinformatics · 2026Article
- Rational Radionuclide Therapy for Recurrent Meningioma.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Review
- Pan-cancer copy number analysis identifies optimized size thresholds and co-occurrence models for individualized risk stratification.Nature communications · 2025Article
- Emerging Roles of PTTG1/Securin in Breast Cancer.Journal of Cancer · 2025Review
- Artificial intelligence pipeline predicts the integrated molecular-morphologic risk score of meningiomas from routine preoperative MRI.Neuro-oncology advancesArticle
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Abstract
Chromosome instability leading to aneuploidy and accumulation of copy number gains or losses is a hallmark of cancer. Copy number alteration (CNA) signatures are increasingly used for cancer risk stratification, but size thresholds for defining CNAs across cancers are variable and the biological and clinical implications of CNA size heterogeneity and co-occurrence are incompletely understood. Here we analyze CNA and clinical data from 691 meningiomas and 10,383 tumors from The Cancer Genome Atlas to develop cancer- and chromosome-specific size-dependent CNA and CNA co-occurrence models to predict tumor control and overall survival. Our results shed light on technical considerations for biomarker development and reveal prognostic CNAs with optimized size thresholds and co-occurrence patterns that refine risk stratification across a diversity of cancer types. These data suggest that consideration of CNA size, focality, number, and co-occurrence can be used to identify biomarkers of aggressive tumor behavior that may be useful for individualized risk stratification.
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