Checkpoint-inhibiting drugs like Genentech's Tecentriq and Bristol-Myers Squibb’s Yervoy have transformed the treatment of some cancers by removing barriers that in the past would prevent patients’ immune systems from attacking and killing tumor cells. But these drugs don’t work for some patients, and for others they cause dangerous autoimmune responses. That’s why researchers around the world have been searching for ways to improve checkpoint inhibition.
Two ideas for doing just that were proposed this week. The first came from tissue engineers at the University of Chicago, who have been experimenting with peptides that attach to checkpoint-inhibiting drugs. The peptides bind to tissues in and near tumors, allowing the drugs to be injected directly into the cancer—potentially limiting off-target effects.
When the researchers tried their technology in mice, they were surprised by the results. When they attached the peptides to two types of checkpoint inhibitors—an anti-PD-L1 drug and an anti-CTLA4 compound—the medicines tethered themselves to the tumor sites and very little of the medication entered the bloodstream, as expected. None of the mice that got the conjugate drugs developed autoimmune responses, whereas all of the animals that were given standard checkpoint blockers did.
But the modified drugs also seemed to be more effective. They slowed down tumor growth and extended survival in mice with melanoma and breast tumors, according to the researchers, who detailed their findings in the journal Science Translational Medicine.
"We think we have developed a straightforward way to modify the really important immunotherapy drugs that work remarkably well in some patients but don't work at all in others," said lead author Jeffrey Hubbell, Ph.D., a professor of tissue engineering at the University of Chicago, in a press release.
Predicting who is most likely to benefit from immunotherapy could also improve treatment regimens. Toward that end, researchers at the Icahn School of Medicine at Mount Sinai have developed a new mathematical model that oncologists may be able to use in patient selection.
The model takes into account aspects of how a tumor evolves and how it interacts with the immune system. The researchers developed it by studying data from patients with melanoma or lung cancer who were treated with checkpoint inhibitors. They describe the model in the journal Nature.
The key to developing the model was scrutinizing neoantigens, proteins that allow the immune system to recognize foreign invaders. The mathematical model tracks neoantigens that are specific to tumors that are growing and mutating, providing a framework for understanding the circumstances by which some patients develop autoimmune responses to checkpoint inhibitors and others become resistant to their therapeutic effects.
"This approach will hopefully lead to better mechanistic predictive modeling of response and future design of therapies that further take advantage of how the immune system recognizes tumors," said senior author Benjamin Greenbaum, Ph.D., of Icahn’s Tisch Cancer Center in a press release.