Personalizing cancer treatment with help from 'virtual tumors'
摘要： “Virtual tumors” are computer simulations of individual patients’ cancer cells, built using their genetic data, to predict how specific immunotherapies or drug combinations will affect immune checkpoint responses and guide personalized treatment decisions.
- Virtual tumors use a patient’s tumor genetic profile to simulate and predict responses to immunotherapy drugs targeting immune checkpoints.
- The approach combines computational modeling with lab-grown live cancer cells to validate predicted drug responses, achieving ~85% correlation in early studies.
- Personalized virtual tumor models aim to improve treatment accuracy, reduce ineffective therapies, and lower overall treatment time and cost.
- The method supports testing of combination therapies, including multiple immunotherapies or immunotherapy with chemotherapy.
- This strategy aligns with precision medicine goals: matching the right drug to the right patient early in diagnosis.
*Written by Catharine Paddock PhD
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Published: Monday 7 December 2015 at 3am PST
Abstract
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.
However, Prof. Brogden - whose research expertise encompasses microbiology, inflammation and oral cancer - explains that some of these drugs only have a response rate of under 20.5% in patients.
The right treatment for the right patient
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.
常见问题
What are virtual tumors?
Virtual tumors are computer-based simulations of a patient’s cancer created using the tumor’s genetic information to predict how it will respond to specific drugs, especially immunotherapies.
How accurate are virtual tumor predictions?
Early research shows about 85–86% correlation between virtual tumor predictions and actual responses observed in lab-grown cancer cells with matching genetics.
Why is personalizing cancer treatment important?
Because many immunotherapies have low response rates (e.g., under 20.5%), personalization helps identify which patients will benefit, avoiding ineffective treatments and side effects.
Can virtual tumors test drug combinations?
Yes, researchers plan to use both virtual and live tumor models to evaluate combinations of immunotherapy drugs or immunotherapy with chemotherapy.
参考资料
Presentation at the 57th American Society of Hematology Annual Meeting
Described the development of virtual tumor models by University of Iowa and Cellworks Group Inc. in December 2015.
University of Toronto study on cancer-essential genes
Mentioned as related progress in personalized cancer medicine; mapped over 1,500 genes critical for cancer survival to link drug targets with DNA variations.