ASH 2015: Test Creates Simulations That May Help Predict Drug Responses, Drive Personalized Treatment
Researchers at the University of Iowa College of Dentistry are partnering with a private company to develop computer simulations that can help personalize cancer care by predicting how a patient will respond to a drug treatment. Their findings were presented by Brogden et al at the 57th American Society of Hematology (ASH) Annual Meeting (Abstract 1803).
The key is the creation of “virtual tumors” which are based on a patient's cancer cells and specific cancer genes.
“Virtual tumors can be used to test the ability of drug treatments to treat cancer cell-induced immunosuppression on the host,” said Kim Alan Brogden, PhD, Director of the Dows Institute for Dental Research at the UI College of Dentistry. “Thus, we are better able to zero in on what type of treatment would work best for that individual's cancer.”
Dr. Brogden then tries to replicate the process in lab by growing live cancer cells with the same genetic makeup and testing their response to the identical immunotherapy. If the response is the same, then they have identified a treatment that will work for that individual cancer patient.
“In our current studies,” Dr. Brogden said, “we are seeing a 85% to 86% correlation of matches.”
Test Details
In this test, researchers first take the genetic information from a cancer cell, import it to a computer simulation, and predict the response that cell would have to a particular treatment.
Next, they take live cancer cells, grow them in the laboratory, and determine the actual response that cell would have to the identical treatment.
If researchers get the same results from both experiments, they have a match. The cells growing in the laboratory have verified that the computer model works. If they give different results, then researchers have a mismatch, meaning the simulated model and lab tests are not in agreement and need to be aligned.
“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,” said Dr. Brogden.
Researchers said the technology is timely, particularly to pharmaceutical partners who want to test their cancer drugs using these simulated models.
Dr. Brogden explained that many cancers protect themselves from the immune system by overriding a patient's immune checkpoints. These checkpoints have become important targets for treating cancers through the use of checkpoint inhibitors—but some of these drugs only have a response rate of less than 20.5% in patients.
“Therefore, the success of current therapy depends upon a precision medicine approach: finding the right treatment for the right patient within a reasonable time,” Dr. Brogden said.
The simulation and laboratory models also allow for the screening of combination treatments, which could involve more than one immunotherapeutic agent or a combination of immunotherapeutic and chemotherapeutic agents.
Ultimately, researchers say they hope their work leads to a personalized medicine approach that will save treatment time, cut costs, and improve long-term prognoses for cancer patients.
The University of Iowa is partnering with Cellworks Group, Inc, on this technology.
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
