In a bid to help cancer care become more personalized, researchers are developing computer simulations of tumors to predict how an individual patient’s cancer is likely to react to particular drugs.
The computer simulations – or “virtual tumors” – are the result of a collaboration between researchers at the University of Iowa College of Dentistry and a private company Cellworks Group Inc., and were presented at the 57th American Hematological Society Annual Meeting and Exposition in Orlando, FA, over the weekend.
Kim Alan Brogden, a professor in periodontics, says that with the help of the virtual tumors, they should be able to test the ability of individual drugs to overcome the immune system suppression that cancer can trigger in the patient, and:
“Thus, we are better able to zero in on what type of treatment would work best for that individual’s cancer.”
Many cancers are able to avoid attack from a patient’s immune system by overriding their “immune checkpoints” – molecules on immune cells that need to be activated or silenced to start an immune response.
Immunotherapy drugs that target these checkpoints hold a lot of promise as cancer treatments. They are often made of antibodies that unleash an attack on the cancer cells.
The researchers believe they can increase the effectiveness of the drugs by making them specific to the genetic makeup of the individual tumor’s cells.
To develop the approach, they first take the genetic information from a patient’s cancer cell and load it into the simulation to predict the responses of certain immune checkpoints to particular drugs.
They then grow live cells in the lab with the same genetic makeup and see if the drug produces the same reaction in the cells’ immune checkpoints.
If the responses from the virtual tumor and the real live cells match, then the team has identified a treatment that will work for that individual patient.
If the results are different, then more work needs to be done to align the model to the live cells.
Prof. Brogden says their current studies are producing around 85-86% correlation of matches. He concludes:
“Our goal is to develop a very patient-specific workflow that could be used early after cancer diagnosis to aid in the identification of effective cancer treatments.”
Successful therapy is about using precision medicine to find the right treatment for the right patient within a reasonable time, he adds.
He and his colleagues suggest the virtual and live models could also be used to screen combination treatments – for instance, comprising either more than one immunotherapy drug, or where an immunotherapy drug is combined with a chemotherapy drug.
Their goal is to produce a personalized cancer therapy that cuts treatment time and cost as well as help improve patients’ long-term prospects.
Meanwhile, Medical News Today recently learned of another significant step toward personalized medicine where researchers at the University of Toronto in Canada mapped over 1,500 genes essential for cancer survival. The team says the work is leading toward a functional map of cancer where drug targets are linked to DNA sequence variations.