RT Journal Article
SR Electronic
T1 Erlang distribution predicts the number of driver events for childhood and young adulthood cancers
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 231027
DO 10.1101/231027
A1 Aleksey V. Belikov
YR 2017
UL http://biorxiv.org/content/early/2017/12/07/231027.abstract
AB It is assumed that cancers develop upon randomly acquiring (epi)mutations in driver genes, but their exact number for each cancer type is not known. I have recently shown that the age distribution of incidence for 20 most prevalent cancers of old age is closely approximated by the Erlang probability distribution. I then used it to predict the number of driver events for these cancer types, as it describes the probability of several successive random events occurring precisely by the given time. However, 4 other probability distributions out of 16 tested also provided acceptable fits to the incidence data, leaving some doubt about the validity of predictions. Here I show that the Erlang distribution is the only classical probability distribution that can adequately model the age distribution of incidence for all studied childhood/young adulthood cancers. This validates it as the universal law describing cancer development at any age and as a useful tool to predict the number of driver events for any cancer type.