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Computational Method to Detect Top Genetic Cancer Suspects Developed

by Rajashri on September 6, 2009 at 7:55 AM
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 Computational Method to Detect Top Genetic Cancer Suspects Developed

A new computational method that can help scientists to sift through hundreds of genetic mutations to highlight the DNA changes that are most likely to promote cancer has been developed by Johns Hopkins engineers.

The computational method is called CHASM, short for Cancer-specific High-throughput Annotation of Somatic Mutations.

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According to its inventors, the purpose behind making it is to provide critical help to researchers who are poring over numerous newly discovered gene mutations, many of which are harmless or have no connection to cancer.

They say that the new software will enable scientists to focus more of their attention on the mutations that are most likely to trigger tumours.
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They have even reported the results of a test of the method on brain cancer DNA in the journal Cancer Research.

Rachel Karchin, an assistant professor of biomedical engineering, and doctoral student Hannah Carter have revealed that the new process focuses on missense mutations, meaning protein sequences that each possess a single tiny variation from the normal pattern.

A small percentage of these genetic errors can reduce the activity of proteins that usually suppress tumours or hyperactivate proteins that make it easier for tumours to grow, thereby allowing cancer to develop and spread.

However, it can prove very difficult to find these genetic offenders.

"It's very expensive and time-consuming to test a huge number of gene mutations, trying to find the few that have a solid link to cancer. Our new screening system should dramatically speed up efforts to identify genetic cancer risk factors and help find new targets for cancer-fighting medications," said Karchin.

Karchin and Carter plan to post their system on the Web, and will allow researchers worldwide to use it freely to prioritise their studies.

They hope that it could easily be adapted to rank the mutations that might be linked to different forms of the disease, such as breast cancer or lung cancer.

Source: ANI
RAS
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