ArticlePeerJ2021
The Erlang distribution approximates the age distribution of incidence of childhood and young adulthood cancers.
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Unique unbiased median solution for even sample sizes.bioRxiv : the preprint server for biology · 2025Article
- Estimation of discrete mixed Poisson-Erlang distribution with applications to medical data.PloS one · 2025Article
- A dynamic network model predicts the phenotypes of multicellular clusters from cellular properties.Current biology : CB · 2024Article
- Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts.PLoS genetics · 2022Article
- Article
- Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients.PeerJ · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is widely believed that cancers develop upon acquiring a particular number of (epi) mutations in driver genes, but the law governing the kinetics of this process is not known. We have previously shown that the age distribution of incidence for the 20 most prevalent cancers of old age is best approximated by the Erlang probability distribution. The Erlang distribution describes the probability of several successive random events occurring by the given time according to the Poisson process, which allows an estimate for the number of critical driver events.
methodsHere we employ a computational grid search method to find global parameter optima for five probability distributions on the CDC WONDER dataset of the age distribution of childhood and young adulthood cancer incidence.
resultsWe show that the Erlang distribution is the only classical probability distribution we found that can adequately model the age distribution of incidence for all studied childhood and young adulthood cancers, in addition to cancers of old age.
conclusionsThis suggests that the Poisson process governs driver accumulation at any age and that the Erlang distribution can be used to determine the number of driver events for any cancer type. The Poisson process implies the fundamentally random timing of driver events and their constant average rate. As waiting times for the occurrence of the required number of driver events are counted in decades, and most cells do not live this long, it suggests that driver mutations accumulate silently in the longest-living dividing cells in the body-the stem cells.
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