Friday May 24, 2019

Researchers Identify Key Gene Behind Breast Cancer

If further tests confirmed that CBX2 was an "oncogene", it could be a potential therapeutic drug target for aggressive types of breast cancer

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Cancer
Cancer Ribbon. Pixabay

Australian researchers have tracked an elusive cancer-promoting gene that appears to be behind aggressive breast cancers, paving the way for crucial therapeutic drug treatment for the deadly disease.

Researchers from the University of Queensland, together with Albert Einstein College of Medicine in the US, developed a statistical approach “to reveal many previously hard-to-find genes that contribute to cancer”, Xinhua news agency reported.

“Even if a group of people all have the same type or even subtype of cancer, the molecular make-up of that cancer is different from person to person because the activity of genes varies between people,” said Jess Mar, Associate Professor at the varsity.

In the study, published in the British Journal of Cancer, the team used a method to “zoom in” on genetic information from cancer patients and identify genes with two distinct “bumps” of data — low activity in one group of patients but high activity in another.

Analysing breast cancer data from a major cancer genome patient database, the researchers identified five genes that were “over-active” in a subset of breast cancer patients and followed up on the most promising target, known as CBX2.

cancer
Key gene behind breast cancer identified. Pixabay

“Previous studies have shown that most healthy female tissue has low levels of CBX2 activity, while an aggressive subtype of breast cancer has been shown to have high levels of CBX2 activity,” Mar said.

“This suggested a possible link between CBX2 activity and breast cancer, but the nature of that link hadn’t been investigated,” she said.

“So we switched off the gene in a human breast cancer cell line and this slowed down the growth of those cancer cells, suggesting that CBX2 might promote tumour growth.”

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If further tests confirmed that CBX2 was an “oncogene”, it could be a potential therapeutic drug target for aggressive types of breast cancer, Mar said.

“Identifying ‘hidden’ oncogenes that are unique to smaller groups of cancer patients will open up new therapeutic avenues and move us closer to personalized medicine,” she said. (IANS)

Next Story

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)