ArticleNature methods2013
Network-based stratification of tumor mutations.
Article in Nature methods, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 433 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
433 citing papers in PubMed.
- Genetic landscape of an in vivo protein interactome.Nature genetics · 2026Article
- Identifying multigenic modules under selection in the tumor genome.Bioinformatics (Oxford, England) · 2026Article
- Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.Bioinformatics (Oxford, England) · 2026Article
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Social Network Clustering Analysis for Detection of Associated Genetic Co-Mutations in Patients with Actionable Driver Mutations in NSCLC.Life (Basel, Switzerland) · 2026Article
- Multi-omics data integration using time-to event endpoint and supervised Cox penalized regression: a comprehensive review.Clinical and experimental medicine · 2026Review
- Network resilience of the human interactome to pan-cancer mutations reveals conserved pathway vulnerabilities.iScience · 2026Article
- IRDRM: Exploring Biological Traits and Clinical Significance in Breast Cancer Through Gene Pairs and Somatic Mutation Profiles.Interdisciplinary sciences, computational life sciences · 2026Article
- Mini-Galaxy: Rethinking Complex Human Diseases Through the Lens of Systems Biology and Multilayered AI Network Perspectives.International journal of molecular sciences · 2026Review
- NetG2P: Network-based genotype-to-phenotype transformation identifies key signaling crosstalk for prognosis in pan-cancer study.BMC biology · 2026Article
- OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology.bioRxiv : the preprint server for biology · 2026Article
- Patient Similarity Networks for Irritable Bowel Syndrome: Revisiting Brain Morphometry and Cognitive Features.Diagnostics (Basel, Switzerland) · 2026Article
- UNITe: exploiting conserved links between oncogenesis and homeostasis to identify novel cancer drivers.Frontiers in oncology · 2026Article
- Uncovering the mechanisms of synergistic drug combinations in non-small cell lung cancer through metagene-based classification.PloS one · 2026Article
- A Roadmap for the Future of Systems Biology in Cancer Research.Cancer research · 2025Review
- Deep Clustering-Based Metabolic Stratification of Non-Small Cell Lung Cancer Patients Through Integration of Somatic Mutation Profile and Network Propagation Algorithm.Interdisciplinary sciences, computational life sciences · 2025Article
- Subtype Characterization of Ovarian Cancer Cell Lines Using Machine Learning and Network Analysis: A Pilot Study.Cancers · 2025Article
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- Developing a novel aging assessment model to uncover heterogeneity in organ aging and screening of aging-related drugs.Genome medicine · 2025Article
- DeepGraphMut: a graph-based deep learning method for cancer prognosis using somatic mutation profile.Briefings in bioinformatics · 2025Article
373 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
5 authors.
Funding
Abstract
Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.
Indexed as
Identifiers
What OpenQuestion holds
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.