Friday December 13, 2019

New Therapy for Drug-Resistant Skin Cancer Suggested by Researchers

A team of researchers has managed to exploit a vulnerability in melanoma or skin cancer that develops resistance to a targeted therapy, providing a potential new therapeutic strategy to selectively kill the drug-resistant cancer cells.

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The reason for increased bleeding is not known. It may be because rivaroxaban is more 'potent', the paper published in the Journal of Clinical Oncology said. (IANS)
Representational image. Pixabay

A team of researchers has managed to exploit a vulnerability in melanoma or skin cancer that develops resistance to a targeted therapy, providing a potential new therapeutic strategy to selectively kill the drug-resistant cancer cells.

The study has shown that when cancer cells develop drug resistance, they also acquire a new vulnerability, the Xinhua reported.

The researchers, led by Rene Bernards of the Netherlands Cancer Institute and Oncode Institute in Denmark, exposed this new vulnerability in melanoma that has developed resistance to treatment with a BRAF inhibitor — a targeted therapy that blocks a signalling pathway in the cancer cell through which it gets the message to keep on dividing.

Since more than half of all melanoma patients have a mutation in this BRAF gene, the BRAF-inhibitor stops tumour growth in those patients.

But within a few months, the tumour cell adapts the original signalling pathway and becomes active again, and even hyperactive.

The researchers, however, found that the hyperactive resistant melanoma cells produced large amounts of reactive oxygen species, but cancer cells still sensitive to the drug did not do so.

Combining the new compound with vitamin D allowed certain protective genes to be expressed at much higher levels than they are in diseased cells.
Representational image, pixabay

The study, published in the journal Cell, found that the abundance of free radicals caused the resistant melanoma cells to stop dividing, but they did not die.

When tested on mice along with an existing drug, vorinostat, which is known to stimulate the production of free oxygen radicals, the researchers saw tumours shrink under the influence of the drug, the report said.

This laid the foundation for a new therapeutic strategy: Treating patients with BRAF-mutated melanoma, as usual, with signal pathway inhibitors.

When the tumour becomes resistant, stop giving those inhibitors and immediately treat the patients with vorinostat to kill the resistant cancer cells.

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“It is not a combination drug. It is very important that you first stop the signalling pathway inhibitors because they suppress the free radicals and thus eliminate the effects of vorinostat,” Bernards said. (IANS)

Next Story

Machine Learning Can Help Doctors to Improve End-Of-Life Conversation with Patients

A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families

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Machine Learning
A Research used Machine Learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients. Pixabay

Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication.

Some of the most important, and difficult, conversations in healthcare are the ones that happen amid serious and life-threatening illnesses.

Discussions of the treatment options and prognoses in these settings are a delicate balance for doctors and nurses who are dealing with people at their most vulnerable point and may not fully understand what the future holds.

“We want to understand this complex thing called a conversation. Our major goal is to scale up the measurement of conversations so we can re-engineer the healthcare system to communicate better,” said Robert Gramling, director of the Vermont Conversation Lab in the study published in the journal Patient Education and Counselling.

Gramling and his colleagues used machine learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients.

They broke each conversation into 10 parts with an equal number of words in each, and examined how the frequency and distribution of words referring to time, illness terminology, sentiment and words indicating possibility and desirability changed between each decile.

“We picked up some strong signals,” said Gramling.

Conversations tended to progress from talking about the past to talking about the future, and from sadder to happier sentiments. “There was quite a range, they went from pretty sad to pretty happy,” Gramling added.

Machine Learning
Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication. Pixabay

The consistent results across multiple conversations show just how much people make meaning out of stories in healthcare.

“What we found supports the importance of narrative in medicine,” he said.

That knowledge could eventually help healthcare practitioners understand what makes a “good” conversation about palliative care, and how different kinds of conversations might require different responses.
That could help create interventions that are matched to what the conversation indicates the patient needs the most.

ALSO READ: Light Alcohol Consumption Might Also Increase Cancer Risk: Study

A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families. (IANS)