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New Computational Model Can Detect Mutations in Breast Cancer

The study included results from over 3,200 patients with breast cancer

Researchers have developed a computational model which is effective in detecting and identifying genetic mutations in breast tumours.

The study included results from over 3,200 patients with breast cancer.

The researchers used RNA sequencing, a sensitive, precise tool which has very gradually started to be applied clinically, although not yet for breast cancer.

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The study, published in the journal EMBO Molecular Medicine, used breast tumours for analysis from the unique Swedish SCAN-B project.

“We hope that SCAN-B RNA sequencing will be in clinical use as early as next year, mainly to help in the identification of which breast tumours are high-risk and which are low-risk,” said study researcher Lao Saal from Lund University in Sweden.

“The aim is for the patient to know, already a week after surgery to remove the tumour, which personalised treatment is best suited to the individual”,

Saal added.

When the Lund team analysed the genetic mutations in the breast tumours of the patients in the study, they found that almost 87 per cent had at least one mutation for which potential drugs already exist.

New analytical model detects mutations in breast cancer: Study
The Lund team found that almost 87% breast tumours had at least one mutation for which potential drugs already exist. Unsplash

Then they followed the patterns of mutations in the tumours and related them to patient outcomes.

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“We observed that 34 per cent of them had a mutation in a specific gene, PIK3CA, and that in general these patients had a good prognosis,”

the study researchers wrote.

“In 3 per cent of the patients we found mutations in another gene, ERBB2, which was associated with a worse prognosis,” they added.

The results of the study add another dimension to how RNA sequencing can be used as a potential future ‘clinical tool’. (IANS)

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