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Test predicts effective cancer drug combinations in 48 hours

Tuesday April 5th 2022

A prototype artificial intelligence test has been developed that can predict in 48 hours which drug combinations are likely to work for cancer patients, British scientists have announced.

Scientists at The Institute of Cancer Research, London used AI to analyse large-scale protein data from tumour samples, which predicted patients’ response to drugs more accurately than is currently possible.

They tested the new technique on individual cancer cells in the lab and tumour cells freshly isolated from lung fluid in people with non-small cell lung cancer.

Their study, published in the latest edition of Molecular Cancer Therapeutics, involved carrying out proteomic analyses in 52 important proteins before training machine learning algorithms to define the key protein changes that predict drug responses.

The algorithm was first used to predict how sensitive cells were to individual cancer drugs and it was found the technique could predict individual drug responses more accurately than genetic features, such as mutations in key genes EGFR, KRAS and PIK3CA, which are three genetic markers used in the clinic to predict drug responses in lung cancer.

The research team went on to predict sensitivity to drug combinations, using 21 different two-drug combinations in lung cancer cells with different gene faults, such as mutations in EGFR and KRAS.

Out of the 252 total drug combinations, 128 showed some level of synergy. Of these, the AI test correctly identified the top five ranked combinations 57% of the time and the top 10 ranked combinations 83% of the time.

The test successfully previously promising combinations, such as combining trametinib and capivasertib, or gefitinib and everolimus, in non-small cell lung cancer cell lines with EGFR mutations.

Possible new combinations were also identified, such as vemurafenib and capivasertib, which could potentially be effective for non-small cell lung cancer cell lines with no mutations in EGFR or KRAS.

The new study establishes proof of concept but the test will need further validation and researchers are already planning a larger follow-up study to test 15 drugs and to examine 12,000 proteins involved in signal transduction.

Study leader Professor Udai Banerji, professor of molecular cancer pharmacology at The Institute of Cancer Research, London, and consultant medical oncologist at The Royal Marsden NHS Foundation Trust, said the test has the potential to guide doctors in their judgments 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,” said Prof Banerji.

“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.”

The study was funded by the National Institute for Health Research (NIHR), Wellcome, Cancer Research UK and by The Institute of Cancer Research (ICR).

Professor Kristian Helin, chief executive of The ICR, said: “This new study is a great example of interdisciplinary collaboration, in integrating our understanding of cancer biology, AI and clinical medicine to provide proof of concept for a new test that can predict which combination treatments are most likely to work for patients.

“It demonstrates the potential power of AI and protein analysis to personalise treatment and could be an important step in helping us tackle drug resistance – hopefully helping us offer patients smarter, more personalised treatment options.”

Molecular Cancer Therapeutics 4 April 2022

Tags: Cancer | Pharmaceuticals | UK News

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