The promise of so-called immune checkpoint inhibitors and other targeted immunotherapies for cancer is that they can destroy some hard-to-treat tumors much more effectively than old-school chemotherapy.
But there’s a big problem with immunotherapies, too. These drugs, which train the immune system to find and kill cancer cells, work only in a small subset of patients, and it’s really hard to determine in advance who will benefit.
Right now, doctors often look at several biomarkers, like age, tumor type, and the number of mutations found in cancer cells, to predict who might benefit from immunotherapy. Looking at mutations on a few hundred genes is the main way to determine if patients might benefit from immune checkpoint inhibitors, which per the American Cancer Society target “checkpoint” proteins on immune cells that can switch on or off to battle foreign invaders like cancer.
For the new study, scientists wanted to see if they could use a process known as whole exome sequencing to examine proteins on 20,000 genes and get a better picture of who might benefit from immune checkpoint inhibitors. Per MedlinePlus, this process involves looking at pieces of DNA called exons that provide instructions for making proteins.
Researchers analyzed gene sequences from cancer patients to identify genes with more mutations than they would expect. Six genes were found to have unusually high numbers of mutations, according to the study results, published July 8 in Nature Communications.
Then scientists looked to see if the genes were enriched (meaning overly abundant) in people who responded or didn’t respond to immunotherapy.
Two of the genes — KRAS, a gene often mutated in lung cancer, and BRAF, the most commonly mutated gene in melanoma — were enriched in patients who responded to immunotherapy. Two other genes — TP53 and BCLAF1 — were enriched in those who did not respond to immunotherapy.
Using the same two-step approach on collections of genes called pathways, the researchers determined that certain pathways known as MAPK signaling, p53 associated, and immunomodulatory also predicted immune checkpoint inhibitor response.
Finally, scientists combined data on the four genes and three pathways to develop a tool to predict immunotherapy response. This tool was better at predicting who would benefit from immune checkpoint inhibitors than traditional methods based on mutations of only a few hundred genes.
“These results suggest that the use of broader diagnostics such as whole exome or even whole genome sequencing may significantly improve our ability to predict who will respond to immunotherapy — essentially, showing that more data does help to better predict treatment response,” said Marcin Imieliński, MD, PhD, a co-senior author of the study and a core faculty member at the New York Genome Center in New York City, in a statement.