Genetics may predict bladder cancer immunotherapy response
Investigators from Cedars-Sinai Cancer have identified genetic signatures that could predict whether tumors in patients with bladder and other cancers will respond to immunotherapy. Their results, published today in the peer-reviewed Journal of the National Cancer Institute, could one day help guide clinicians to the most effective treatments for cancer patients.
“Our work indicates that these genetic signatures may prove to be tremendously valuable in predicting immunotherapy response in patients with bladder cancer, but also other tumor types,” said Dan Theodorescu, MD, PhD, director of Cedars-Sinai Cancer, the PHASE ONE Foundation Distinguished Chair and senior author of the study. “We will continue investigating these biomarkers with the goal of bringing them into clinical use and improving patient outcomes.”
During the past five years, anti-PD-1/PD-L1 therapy — a type of cancer immunotherapy that paves the way for the body’s immune system to attack tumor cells — has proved effective against many cancer types, according to Keith Syson Chan, PhD, a translational scientist, professor of Pathology and co-author of the study.
“It has proven very effective against melanoma and revolutionized lung cancer treatment,” Chan said. “Bladder cancer is considered one of the more responsive tumor types, but still has just a 25% durable response rate, so improvement is still needed.”
When a tumor causes a host immune reaction, immune cells usually are able to infiltrate the core of the tumor, and scientists call it a “hot” tumor. Some tumors, on the other hand, block immune cells from infiltrating and are known as “cold” tumors.
A previous study by Theodorescu implicated a gene — called discoidin domain receptor tyrosine kinase 2 (DDR2) — in contributing to anti-PD-1 resistance in animal models in several tumor types. In a collaboration between cancer biologists and bioinformatics researchers, this new study further investigated the DDR gene family using human cancer data sets in various tumor types.
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