ArticleNature communications2018
Typing tumors using pathways selected by somatic evolution.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
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- Oncogene addiction to GNAS in GNASOncogene · 2022Article
- Artificial intelligence, molecular subtyping, biomarkers, and precision oncology.Emerging topics in life sciences · 2021Review
- Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine.Genome medicine · 2021Article
- Leveraging the Cell Ontology to classify unseen cell types.Nature communications · 2021Article
- NECo: A node embedding algorithm for multiplex heterogeneous networks.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2020Article
- Gaussian Embedding for Large-scale Gene Set Analysis.Nature machine intelligence · 2020Article
- Spectrum: fast density-aware spectral clustering for single and multi-omic data.Bioinformatics (Oxford, England) · 2020Article
- A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Network Medicine in the Age of Biomedical Big Data.Frontiers in genetics · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient's tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data.
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What Socratic 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 Socratic graph.