New Software Aims to Predict Patients’ Resistance to Cancer Treatment
New computer software may be used to predict how cancers may respond to a new drug—before it has ever been given to patients. Researchers hope that this new tool could transform the discovery of cancer drugs by predicting how tumors become resistant to treatment before it first becomes apparent in clinical trials. More information on this software and its use was reported by Kaserer et al in Cell Chemical Biology.
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.
The new prediction tool was developed by scientists at The Institute of Cancer Research, London, with funding from Cancer Research UK.
How the Tool Works
The approach begins by analyzing 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 prioritize the mutations down to only 9 or 10 most likely to cause drug resistance 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. The software 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 results in the drug target no longer being able to perform its role in a cell, then that cell is unlikely to survive and go on to form drug-resistant tumors.
Mutations could be either single- or double-letter changes in genes that would lead to a change in the amino acids that make up a protein. Mutations also had to be in close vicinity to the site where the drug binds its target and had to affect the drug target in a way that means the drug binds less tightly.
Finally, the prediction tool identifies regions in the drug target where resistance hotspots—areas predicted to have multiple mutations—are likely to occur, and prioritizes mutations at these locations 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 (Sutent) wouldn’t be affected by this resistance mutation in the clinic.
Teresa Kaserer, PhD, Higher Scientific Officer at The Institute of Cancer Research, who developed the new prediction tool, said, “Our new approach can predict which mutations are likely to arise in response to drug treatment in different types of tumors. 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.”
She continued, “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 content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
