Algorithm personalizes which cancer mutations are best targets for immunotherapy

As tumor cells multiply, they often spawn tens of thousands of genetic mutations. Figuring out which ones are the most promising to target with immunotherapy is like finding a few needles in a haystack. Now a new model developed by researchers in the Abramson Cancer Center hand-picks those needles so they can be leveraged in more effective, customized cancer vaccines. Cell Systems published the data on the model’s development, and the algorithm is already available online as an open source technology to serve as a resource.

computer rendering of microscopic images of DNA helix on the left, a tumor on the right.

“There are mutations in tumors that can lead to powerful immune responses, but for every one mutation that generates a robust response, about 50 mutations don’t work at all, which means the signal-to-noise ratio is not great,” says the study’s lead author Lee P. Richman, an MD/Ph.D. candidate in cancer biology in the Perelman School of Medicine. “Our model works like a filter that highlights the signal and shows us which targets to focus on.”

Currently, sequencing a tumor and identifying possible immunotherapies is based on a measurement called tumor mutations burden, essentially a measure of the rate of mutations present in a given tumor. Tumors with a high rate of mutation are more likely to respond to immunotherapy targeting inhibitors like PD-1. The problem is that as cancer cells divide, they mutate at random, and since they divide exponentially, the potential mutations are almost infinite. This means that while a given immunotherapy can target some percentage of cancer cells, it may not be enough to be an effective treatment for any given patient.

Read more at Penn Medicine News.