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British researchers discover a protein that can control spread of breast cancer in body

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London: British researchers have identified a key protein that can control how breast cancer cells spread in the body.

The study sheds light on how cancer cells leave the blood vessels to travel to a new part of the body, said the researchers from the University of Manchester in Britain.

When tumor cells spread, they first enter the blood stream and grip onto the inner walls of blood vessels, the researchers elicited.

The cancer cells control a receptor protein called EPHA2 in order to push their way out of the vessels, they added.

When these cancer cells interact with the walls of the blood vessels, EPHA2 is activated and the tumor cells remain inside the blood vessels. When the EPHA2 is inactive, the tumor cells can push out and spread, revealed the study published in the journal Science Signaling.

The researchers used a technique that allowed them to map how cancer cells interact and exchange information with cells that make up the blood vessels.

“The next step is to figure out how to keep this receptor switched on, so that the tumor cells can’t leave the blood vessels – stopping breast cancer spreading and making the disease easier to treat successfully,” concluded the lead researcher Claus Jorgensen from the University of Manchester. (IANS)

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Novel AI Method Predicts Future Risk of Breast Cancer

The advantages held across different subgroups of women, said the study published in the journal Radiology

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Cancer
Cancer Ribbon. Pixabay

Researchers have developed a new tool with advanced artificial intelligence (AI) methods to predict a woman’s future risk of breast cancer.

For the study, the researchers used almost 90,000 full-resolution screening mammograms from about 40,000 women to train, validate and test the deep learning model.

“There’s much more information in a mammogram than just the four categories of breast density, “said study lead author Adam Yala from the Massachusetts Institute of Technology (MIT) in the US.

“By using the deep learning model, we learn subtle cues that are indicative of future cancer,” Yala added.

The research team recently compared three different risk assessment approaches. The first model relied on traditional risk factors, the second on deep learning that used the mammogram alone, and the third on a hybrid approach that incorporated both the mammogram and traditional risk factors into the deep learning model.

Cancer patient
Cancer patient.

The deep learning models yielded substantially improved risk discrimination over the Tyrer-Cuzick model, a current clinical standard that uses breast density in factoring risk.

When comparing the hybrid deep learning model against breast density, the researchers found that patients with non-dense breasts and model-assessed high risk had 3.9 times the cancer incidence of patients with dense breasts and model-assessed low risk.

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The advantages held across different subgroups of women, said the study published in the journal Radiology.

“Unlike traditional models, our deep learning model performs equally well across diverse races, ages and family histories,” said Regina Barzilay, Professor at MIT. (IANS)