Researchers at University of California San Diego School of Medicine leveraged a machine learning (ML) algorithm to tackle one of the biggest challenges facing cancer researchers: predicting when cancer will resist chemotherapy.
All cells, including cancer cells, rely on complex molecular machinery to replicate DNA as part of the normal cell division process.
Most chemotherapies work by disrupting this DNA replication machinery in rapidly dividing tumour cells.
While scientists recognise that a tumour’s genetic composition heavily influences its specific drug response, the vast multitude of mutations found within tumours has made prediction of drug resistance a challenging prospect.
The new algorithm overcomes this barrier by exploring how numerous genetic mutations collectively influence a tumour’s reaction to drugs that impede DNA replication.
Specifically, they tested their model on cervical cancer tumours, successfully forecasting responses to cisplatin, one of the most common chemotherapy drugs.
The model was able to identify tumours at most risk for treatment resistance and was also able to identify much of the underlying molecular machinery driving treatment resistance.
Trey Ideker, PhD is a professor in Department of Medicine at UC San Diego of Medicine.
He said: “Clinicians were previously aware of a few individual mutations that are associated with treatment resistance, but these isolated mutations tended to lack significant predictive value.
“The reason is that a much larger number of mutations can shape a tumour’s treatment response than previously appreciated.
“Artificial intelligence bridges that gap in our understanding, enabling us to analyse a complex array of thousands of mutations at once.”
One of the challenges in understanding how tumours respond to drugs is the inherent complexity of DNA replication — a mechanism targeted by numerous cancer drugs.
“Hundreds of proteins work together in complex arrangements to replicate DNA,” Ideker said.
“Mutations in any one part of this system can change how the entire tumour responds to chemotherapy.”
The researchers focused on the standard set of 718 genes commonly used in clinical genetic testing for cancer classification, using mutations within these genes as the initial input for their ML model.
After training the model with publicly accessible drug response data, the model pinpointed 41 molecular assemblies — groups of collaborating proteins — where genetic alterations influence drug efficacy.
Ideker said: “Cancer is a network-based disease driven by many interconnected components, but previous machine learning models for predicting treatment resistance don’t always reflect this.
“Rather than focusing on a single gene or protein, our model evaluates the broader biochemical networks vital for cancer survival.”
After training their model, the researchers put it to the test in cervical cancer, in which roughly 35 per cent of tumours persist after treatment.
The model was able to accurately identify tumours that were susceptible to therapy, which were associated with improved patient outcomes.
The model also effectively pinpointed tumours likely to resist treatment.
And beyond forecasting treatment responses, the model helped shed light on its decision-making process by identifying the protein assemblies driving treatment resistance in cervical cancer.
The researchers emphasise that this aspect of the model — the ability to interpret its reasoning — is key to the model’s success and also for building trustworthy AI systems.
Ideker said: “Unravelling an AI model’s decision-making process is crucial, sometimes as important as the prediction itself.
“Our model’s transparency is one of its strengths, first because it builds trust in the model, and second because each of these molecular assemblies we’ve identified becomes a potential new target for chemotherapy.
“We’re optimistic that our model will have broad applications in not only enhancing current cancer treatment, but also in pioneering new ones.”
