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06
Mar
2014

New computer software could spot cancer gene mutations

A team from the Breakthrough Breast Cancer Research Centre at The Institute of Cancer Research, London, created the tool through comparing thousands of known cancer-causing mutations with other mutations which are found commonly in healthy people.

Details of how researchers developed the tool, called InCa – short for Index of Carcinogenicity – have been published in the journal PLOS ONE. By comparing the effect of a DNA change to a protein, for example its likely effect on its interaction with other proteins, or its overall stability, InCa assigns a score to DNA mutations to indicate the risk they carry of causing cancer.

The study was designed to demonstrate InCa’s accuracy in spotting known cancer genes. The most frequently mutated proteins the researchers found included p53, PTEN, EGFR, CDKN2A and PIK3CA – all well-known cancer causing genes. But in the future, the tool could be used to discover new mutations that cause cancer.

One of InCa’s benefits is its apparent ability to distinguish between the ‘driver’ mutations that cause cancer and the irrelevant ‘passenger’ mutations that do not.

Study co-leader Dr Konstantinos Mitsopoulos, Staff Scientist at the ICR, said: “Although many genes with a known effect on cancer risk have been found, and much-studied, we know that there are many still waiting to be discovered. But one challenge facing cancer researchers is to pick out the driver mutations that really cause cancer from the mutations which simply occur because of cancer’s general genetic instability.

“Our study showed that our new software, called InCa, can tell the difference between driver and passenger mutations. It does this by analysing a mutation’s potential effect on several aspects of protein structure and function. In the future, tools like InCa could play an important part in discovering important new mutations that drive the development of cancer.”

 

  • Octavio Espinosa, Konstantinos Mitsopoulos et al (2014). Deriving a Mutation Index of Carcinogenicity Using Protein Structure and Protein Interfaces. PLOS ONE. DOI: 10.1371/journal.pone.0084598.
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