"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," said the study's lead author Lee P. Richman, an MD/PhD candidate in Cancer Biology in the Perelman School of Medicine at the University of Pennsylvania. "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 (TMB), 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.
Penn - Team - Model - Sequences - Samples
The Penn team's model looks instead at protein sequences from samples of individual patients and evaluates how much of it looks similar to healthy cells and how much looks different enough that the immune system might...
Wake Up To Breaking News!