Cancer test that can reveal best medicine for each patient could be a breakthrough – Daily Mail

Cancer test that can reveal best medicine for each patient could be a breakthrough in speeding up treatment, scientists say

  • Cancer patients have been given new hope with a test that analyses tumours 
  • It can predict the most effective drugs for individual patients with cancer  
  • The breakthrough will go a long way in the fight against cancer across the world 

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Cancer patients have been given new hope with a test that analyses tumours to predict the most effective drugs for individual patients.

The breakthrough produces a result in as little as 24 hours and is more accurate than current genetic approaches to personalising treatment.

Scientists from the Institute of Cancer Research in London say the technique, using artificial intelligence to analyse large amounts of data from tumour samples, allows doctors to quickly establish which drug combinations are most likely to work.

Researchers tested tumour cells from lung cancer patients, examining how seven drugs affected 52 proteins linked to the disease’s growth and spread.

Cancer patients have been given new hope with a test that analyses tumours to predict the most effective drugs for individual patients (File image)

Cancer patients have been given new hope with a test that analyses tumours to predict the most effective drugs for individual patients (File image)

Cancer patients have been given new hope with a test that analyses tumours to predict the most effective drugs for individual patients (File image)

Of 252 combinations of drugs tested, 128 showed some level of synergy, meaning their combined effect exceeded that of each individual drug added together.

Researchers now plan a larger follow-up study.

Institute chief Kristian Helin said: ‘One of the greatest challenges we face is the ability of cancer to evolve and become drug-resistant. 

‘We expect the future of cancer treatment will be in combining therapies to overcome resistance, but we need to get better at predicting which drug combinations will work best for individual patients.’

Study leader Professor Udai Banerji added: ‘Our test provides proof of concept for using AI to analyse changes in the way information flows within cancer cells and make predictions about how tumours are likely to respond to combinations of drugs.

‘With a rapid turnaround time of less than two days, the test has the potential to guide doctors in their judgements on which treatments are most likely to benefit individual cancer patients.

‘It is an important step to move forward from our current focus on using genetic mutations to predict response.

Study leader Professor Udai Banerji added: ¿Our test provides proof of concept for using AI to analyse changes in the way information flows within cancer cells and make predictions about how tumours are likely to respond to combinations of drugs (File image)

Study leader Professor Udai Banerji added: ¿Our test provides proof of concept for using AI to analyse changes in the way information flows within cancer cells and make predictions about how tumours are likely to respond to combinations of drugs (File image)

Study leader Professor Udai Banerji added: ‘Our test provides proof of concept for using AI to analyse changes in the way information flows within cancer cells and make predictions about how tumours are likely to respond to combinations of drugs (File image) 

‘Our findings show that our innovative approach is feasible, and makes more accurate predictions than genetic analysis for patients with non-small cell lung cancer.

‘Before this test can enter the clinic and guide personalised treatment, we will need to further validate our findings – for example, by carrying out a study where we run the test in patients already getting a treatment to check if the predictions are correct.’

Genetic analysis of tumours can reveal mutations that are fuelling cancer growth, allowing doctors to prescribe drugs that target these changes.

However genomics alone does not provide sufficiently accurate predictions of which is the best combination of drugs to use.