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The probability to get cancer can be predicted for a person with a fairly high accuracy using information about how often have tumors in people of different ages, says biologist Alexey Belikov in an article in the journal Scientific Reports.

«Feature of my model is that it takes into account the random nature of occurrence of mutations and can predict the number of mutations needed for cancer development», — says Alexey Belikov, laboratory of development of innovative medicines and agrobiotechnology MIPT.

Cancer is considered today one of the leading causes of death in developed countries, and its main feature is the fact that the frequency of its development grows significantly in older years. Scientists assume that this is due to two things – the deterioration of the body’s ability to repair breaks in DNA at the onset of old age and the accumulation of the number of potentially dangerous but non-fatal mutations in the genome.

As the press service of MIPT, enough scientists have been trying to use both of these patterns for predicting the likelihood of developing cancer in one person or another, but so far such predictions or not at all, or have extremely low accuracy.

Belikov found a solution to this problem by collecting a huge database, which included 20 million cases of cancer in different countries. By analyzing these statistics, he noted that the probability to get cancer obey the rule that mathematicians call «the erlang distribution».

This mathematical pattern was discovered by engineer Agaram by Erlang in the early 20th century, when he tried to estimate how much the telephone lines necessary for handling the number of calls that could effect subscribers. They derived formulas allow us to estimate the probability of occurrence of one or more random events such as the occurrence of mutations in human DNA that is suited to the prediction of the chance of getting cancer.

As noted Belikov, his theory allows to predict the development of any subtype of cancer, which age available morbidity statistics. Interestingly, this idea is applicable both in clinical practice and basic science. So, it can help genetics specialist to learn the fate of his patient to analyze his DNA, and scientists to find mutations that play key role in development of different cancer subtypes.

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