IIT team identifies proteins that can help predict metastatic breast cancer

Roorkee, Sep 29 (IANS): A team of researchers at the Indian Institute of Technology (IIT) Roorkee has identified and validated three proteins found in saliva that can predict metastatic Triple Negative Breast Cancer (TNBC).

The researchers said they developed a process by which biomarkers for TNBC can be identified in saliva, according to the study, published in the Journal of Proteomics.

"There have been many efforts in the past decades to identify biomarkers of metastatic TNBC, but there have not been any that have reached practical application," Kiran Ambatipudi, Associate Professor, Dept. of Biosciences and Bioengineering, IIT Roorkee, said in a statement.

The team's diagnosis method is based on salivary gland function, which is impaired in people with breast cancer. Their protein composition is also altered. Thus, an effective biomarker can be obtained if the difference can be identified and quantified.

Breast cancer is the most common cancer in women in India, with more than 1.6 lakh cases registered and more than eighty thousand deaths yearly due to breast cancer.

About 10 to 15 per cent of all breast cancers are metastatic TNBC, the most aggressive form that does not respond to the usual hormonal and HER2-protein targeting drugs.

For the study, the team collected saliva from healthy subjects and those diagnosed with TNBC. The proteins in these saliva samples were isolated and tested for change in abundance by targeted mass spectrometry.

The team discovered differences in the amounts of three salivary proteins -- lipocalin-1, SMR-3B, and plastin-2 -- between healthy subjects and cancer patients.

Further studies isolated five peptides (the building blocks of proteins) from these three proteins, which were starkly different between aggressive TNBC and healthy subjects.

These peptides could point to the presence of TNBC with 80 per cent sensitivity and 95 per cent specificity.



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Title: IIT team identifies proteins that can help predict metastatic breast cancer

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