A patient has cancer. It’s decision time.
Clinician and patient alike face, really, the ultimate challenge when making those decisions. They have to consider the patient’s individual circumstances, available treatment options, potential side effects, relevant clinical data such as the patient’s genetic profile and cancer specifics, and more.
“That’s a lot of information to hold,” said Uzma Asghar, PhD, MRCP, a British consultant medical oncologist at The Royal Marsden Hospital and a chief scientific officer at Concr LTD.
What if there were a way to test — quickly and accurately — all the potential paths forward?
That’s the goal of digital twins.
“What the [digital twin] model can do for the clinician is to hold all that information and process it really quickly, within a couple of minutes,” Asghar noted.
A digital twin is more than just a computer model or simulation because it copies a real-world person and relies on real-world data. Some digital twin programs also integrate new information as it becomes available. This technology holds promise for personalized medicine, drug discovery, developing screening strategies, and better understanding diseases.
How to Deliver a Twin
To create a digital twin, experts develop a computer model with data to hone its expertise in an area of medicine, such as cancer types and treatments. Then “you train the model on information it’s seen, and then introduce a patient and patient’s information,” said Asghar.
Asghar is currently working with colleagues to develop digital twins that could eventually help solve the aforementioned cancer scenario — a doctor and patient decide the best course of cancer treatment. But their applications are manifold, particularly in clinical research.
Digital twins often include a machine learning component, which would fall under the umbrella term of AI, said Asghar, but it’s not like ChatGPT or other generative AI modules many people are now familiar with.
“The difference here is the model is not there to replace the clinician or to replace clinical trials,” Asghar noted. Instead, digital twins help make decisions faster in a way that can be more affordable.
Digital Twins to Predict Cancer Outcomes
Asghar is currently involved in UK clinical trials enrolling patients with cancer to test the accuracy of digital twin programs.
At this point, these studies do not yet use digital twins to guide the course of treatment, which is something they hope to do eventually. For now, they are still at the validation phase — the digital twin program makes predictions about the treatments and then the researchers later evaluate how accurate the predictions turned out to be based on real information from the enrolled patients.
Their current model gives predictions for RECIST (response evaluation criteria in solid tumor), treatment response, and survival. In addition to collecting data from ongoing clinical trials, they’ve used retrospective data, such as from the Cancer Tumor Atlas, to test the model.
“We’ve clinically validated it now in over 9000 patients,” said Asghar, who noted that they are constantly testing it on new patients. Their data include 30 chemotherapies and 23 cancer types, but they are focusing on four: Triple-negative breast cancer, cancer of unknown primary, pancreatic cancer, and colorectal cancer.
“The reason for choosing those four cancer types is that they are aggressive, their response to chemotherapy isn’t as great, and the outcome for those patient populations, there’s significant room for improvement,” Asghar explained.
Currently, Asghar said, the model is around 80%-90% correct in predicting what the actual clinical outcomes turn out to be.
The final stage of their work, before it becomes widely available to clinicians, will be to integrate it into a clinical trial in which some clinicians use the model to make decisions about treatment vs some who don’t use the model. By studying patient outcomes in both groups, they will be able to determine the value of the digital twin program they created.