An artificial intelligence technique for detecting DNA fragments shed by tumours and circulating in a patient’s blood could help clinicians more quickly identify and determine if pancreatic cancer therapies are working.
After testing the method, called ARTEMIS-DELFI, in blood samples from patients participating in two large clinical trials of pancreatic cancer treatments, researchers found that it could be used to identify therapeutic responses.
ARTEMIS-DELFI and another method developed by investigators, called WGMAF, to study mutations were found to be better predictors of outcome than imaging or other existing clinical and molecular markers two months after treatment initiation.
However, ARTEMIS-DELFI was determined to be the superior test as it was simpler and potentially more broadly applicable.
Victor E. Velculescu, M.D., Ph.D. is co-director of the cancer genetics and epigenetics programme at the cancer centre.
Many patients with pancreatic cancer receive a diagnosis at a late stage, when cancer may progress rapidly.
Velculescu said: “Providing patients with more potential treatment options is especially vital as a growing number of experimental therapies for pancreatic cancer have become available.
“We want to know as quickly as we can if the therapy is helping the patient or not.
“If it is not working, we want to be able to switch to another therapy.”
Currently, clinicians use imaging tools to monitor cancer treatment response and tumour progression.
However, these tools produce results that may not be timely and are less accurate for patients receiving immunotherapies, which can make the results more complicated to interpret.
In the study, Velculescu and his colleagues tested two alternate approaches to monitoring treatment response in patients participating in the phase 2 CheckPAC trial of immunotherapy for pancreatic cancer.
One approach, called WGMAF (tumour-informed plasma whole-genome sequencing), analysed DNA from tumour biopsies as well as cell-free DNA in blood samples to detect a treatment response.
The other, called ARTEMIS-DELFI (tumour-independent genome-wide cfDNA fragmentation profiles and repeat landscapes), used machine learning, a form of artificial intelligence, to scan millions of cell-free DNA fragments only in the patient’s blood samples.
Both approaches were able to detect which patients were benefiting from the therapies.
However, not all patients had tumour samples, and many patients’ tumour samples had only a small fraction of cancer cells compared to the overall tissue, which also contained normal pancreatic and other cells, thereby confounding the WGMAF test.
The ARTEMIS-DELFI approach worked with more patients and was simpler logistically, Velculescu said.
The team then validated that ARTEMIS-DELFI was an effective treatment response monitoring tool in a second clinical trial called the PACTO trial.
The study confirmed that ARTEMIS-DELFI could identify which patients were responding as soon as four weeks after therapy started.
Lead study author Carolyn Hruban was a graduate student at Johns Hopkins during the study and is now a postdoctoral researcher at the Dana-Farber Cancer Institute
She said: “The ‘fast-fail’ ARTEMIS-DELFI approach may be particularly useful in pancreatic cancer where changing therapies quickly could be helpful in patients who do not respond to the initial therapy.
“It’s simpler, likely less expensive, and more broadly applicable than using tumour samples.”
The next step for the team will be prospective studies that test whether the information provided by ARTEMIS-DELFI helps clinicians more efficiently find an effective therapy and improve patient outcomes.
