Scientists say a new computer tool that predicts how cancers may respond to a new drug before it has ever been given to patients will be “hugely beneficial” in designing new cancer drugs.
The sophisticated new software can forecast how cancer will resist drugs before they are administered.
Experts said the new program could transform the discovery of cancer drugs by predicting how tumours will become resistant to treatment long before it would first become apparent in clinical trials.
Based on the software’s predictions, researchers could start working on second-generation drugs to tackle treatment resistance before the first-generation drug is given to patients.
It could also lead to the development of tests to assess patients for resistance mutations before and during treatment – delivering precision medicine at the earliest stage.
Dr Teresa Kaserer, higher scientific officer at the Institute of Cancer Research (ICR), London, developed the prediction tool.
She said: “Our new approach can predict which mutations are likely to arise in response to drug treatment in different types of tumours.
“This will be hugely beneficial in designing new cancer drugs. Instead of reacting to what we see in the clinic – when it’s too late as patients have stopped responding to treatment – we can use our computational method to predict during the drug design stage how resistance will arise.
“It means we can begin designing second-generation treatments much earlier, as well as developing tests to select patients for treatment and monitor them while on the drug.
“This could be great news for patients, who could be switched to a second generation drug as soon as a resistance mutation appears.”
The prediction tool, which features in the journal Cell Chemical Biology, starts by analysing all the possible mutations that could occur around a drug target – generally between 350 and 1,200.
The researchers then apply the prediction software to prioritise the mutations down to just nine or 10 most likely to cause drug resistance – a more feasible number to investigate further in the laboratory.
The researchers tested their method on existing cancer drugs and drug targets – including 17 different drugs that target the important cancer-related proteins MAPK1, KIT, EGFR, Abl and ALK.
It was able to accurately predict many of the mutations that doctors see in the clinic, and for MAPK many that were generated in the lab.
The prediction tool is the first to include the evolutionary impact of a mutation on cancer cells. If a mutation meant the drug target could no longer perform its role in a cell, then that cell is unlikely to survive and go on to form drug-resistant tumours.
Lastly, the prediction tool identifies regions in the drug target where resistance “hotspots” – areas predicted to have multiple mutations – are likely to occur and prioritised mutations at these hotspots based on their likelihood of being formed in the cancer type under investigation.
For the cancer drug imatinib, the program accurately predicted a common mutation that causes resistance to the drug in some patients.
This approach also correctly predicted that the second-generation drug sunitinib would not be affected by this resistance mutation in the clinic.
Professor Julian Blagg, deputy director of the Cancer Research UK Cancer Therapeutics Unit at ICR, and study co-author said: “In recent years, targeted cancer therapies have brought significant benefits to patients, but the eventual emergence of drug resistance remains a major challenge.
“Predicting how a cancer drug target may mutate to kick out the therapeutic agents whilst maintaining its normal function can help us stay one step ahead of tumour evolution by creating new treatments that block a cancer’s escape routes.
“Our study has explored one of the ways tumours can become resistant to cancer drugs, but there are other escape routes cancer cells can take to avoid destruction.
“Our approach is an important first step, and we, along with other colleagues at the ICR, are looking to develop similar tools to identify, right at the start of cancer drug discovery, the alternative roads to drug resistance.”