Sunday September 23, 2018

Research: Two-headed Arrow Efficient For Killing The Ovarian Cancer

Further, the approach also avoids toxicity issues that have plagued previous antibody therapies

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Cancer
Two-headed arrow effective at killing ovarian cancer: Study. Pixabay
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Researchers, led by an Indian origin, have developed a two fisted, antibody approach that can effectively destroy ovarian cancer — the deadliest gynecological disease — and could also be utilised to kill breast, prostate and other solid tumours.

According to Jogender Tushir-Singh from the University of Virginia’s (UVA), a major problem with immune therapies for ovarian cancer is that the immune cells intended to kill the cancer cells could not infiltrate the solid tumour bed effectively.

So he engineered an antibody that he likens to a “two-headed arrow”.

One head of this dual pronged “arrow” strikes what is known as the death receptor on the cancer cells, forcing them to die, while the other head strikes a receptor known as FOLR1, a well established marker that suggests a poor prognosis.

“There are a lot of efforts in terms of cancer immune therapy, but the success of these are really limited in solid tumours,” Tushir-Singh said.

“I found that one of the problems is with the solid tumour microenvironment.

Cancer
Cancer Ribbon. Pixabay

“The microenvironment is highly hypoxic, anergic and, particularly in the case of ovarian cancer, some unusually large receptors form a protective fence around tumour cells, so even if the immune cells reach there, there are many obstacles,” he explained.

The newly engineered antibodies are over 100 times more effective at killing cancer cells than the antibodies that have made it to clinical trials.

Further, the approach also avoids toxicity issues that have plagued previous antibody therapies.

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“Liver toxicity has been the biggest problem for a lot of antibodies — they are taken out of the blood too fast and accumulate where not needed,” Tushir-Singh said.

“But by providing a good home for the antibodies in the tumour, we are keeping these antibodies away from the liver,” he noted. (IANS)

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Gut Microbiota Can Help Identify Liver Cancer: Researchers

Gut microbiota can help the body digest certain foods that the stomach and small intestine have not been able to digest.

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Liver Cancer
How gut microbiota can aid in early diagnosis of liver cancer.

Chinese researchers have identified gut microbiota as a new biomarker of liver cancer, that can help in early diagnosis as well as treatment of the condition.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer and the third leading cause of cancer-related death worldwide.

Due to the absence of specific symptoms in early stages and the lack of diagnostic markers, most patients with HCC are often diagnosed in an advanced stage.

Liver Cancer
AFP is a plasma protein that is produced in abundance by the liver cells. Pixabay

Researchers from China’s Zhejiang University, and Zhengzhou University, found that the microbial diversity in patients with cirrhosis was significantly lower than that in healthy people, but it increased when cirrhosis develops into cancer, the Xinhua reported.

Human gut microbiota has been considered the most important micro-ecosystem living with the body, containing tens of trillions of microorganisms, including at least 1,000 species of bacteria with more than 3 million genes.

Gut microbiota can help the body digest certain foods that the stomach and small intestine have not been able to digest.

Liver Cancer
A high-magnification image from a 2012 glioblastoma case is seen as an example in this College of American Pathologists image released from Northfield. VOA

For the study, appearing in the journal Gut, the team collected 486 fecal samples from across the country.

Also Read: Deaths Due to Cancer Increases to More Than 18 Mn Every Year: WHO

About 12 bacteria genera decreased and six increased in patients with early cancer compared with healthy people.

According to researchers, more data and further studies are needed to confirm the validity and reliability of the model. (IANS)