ArticlePloS one2014
Inferring the temporal order of cancer gene mutations in individual tumor samples.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- A phylogenetic approach to inferring the order in which mutations arise during cancer progression.PLoS computational biology · 2022Article
- Parsimonious Clone Tree Integration in cancer.Algorithms for molecular biology : AMB · 2022Article
- Effects of ordered mutations on dynamics in signaling networks.BMC medical genomics · 2020Article
- Identification of neutral tumor evolution across cancer types.Nature genetics · 2016Article
- A computational model incorporating neural stem cell dynamics reproduces glioma incidence across the lifespan in the human population.PloS one · 2014Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The temporal order of cancer gene mutations in tumors is essential for understanding and treating the disease. Existing methods are unable to infer the order of mutations that are identified at the same time in individual tumor samples, leaving the heterogeneity of the order unknown. Here, we show that through a complex network-based approach, which is based on the newly defined statistic -carcinogenesis information conductivity (CIC), the temporal order in individual samples can be effectively inferred. The results suggest that tumor-suppressor genes might more frequently initiate the order of mutations than oncogenes, and every type of cancer might have its own unique order of mutations. The initial mutations appear to be dedicated to acquiring the function of evading apoptosis, and some order constraints might reflect potential regularities. Our approach is completely data-driven without any parameter settings and can be expected to become more effective as more data will become available.
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