Scientists studying immune checkpoint inhibitor (ICI) therapies in advanced lung cancer patients have found that a new class of blood-based biomarkers—anti-frameshift peptide antibodies, or anti-FSP antibodies—could be developed into improved or orthogonal tests for predicting tumor responses to treatment. Moreover, they found that these same antibodies could be the first test to predict immune-related adverse events (irAEs) in lung cancer patients. Led by scientists from Calviri—a biotech developer of new vaccines against cancer and aging-associated diseases—the study was published in the peer-reviewed Journal of Translational Medicine.
Globally, more than 2 million people are diagnosed with lung cancer each year, and nearly 2 million more die from the disease. It’s currently the leading cause of cancer deaths, and in the United States alone, it is responsible for more deaths than colon, prostate, and breast cancers combined. While immune checkpoint inhibitors are widely used in lung cancer patients, low patient response rates, high costs, and the risk of therapy-related side effects temper enthusiasm. That’s why Calviri researchers are looking for new answers, and this new class of biomarkers could be key.
“In previous studies, other biomarkers have shown some encouraging results for predicting ICI therapy tumor responses. However, extraction and testing are elaborate, often unreliable, and sometimes not possible, and there is no test for predicting adverse events,” says Kathryn Sykes, the vice president of research and product development at Calviri and one of the new paper’s authors. “Our study explores anti-FSP antibodies as novel biomarkers, which can be simply and accurately measured from a small amount of blood.”
“Simple tests for accurate prediction of therapy outcomes would enable physicians to recommend treatments to patients most likely to respond, including those with cancers not usually responsive to ICIs,” says Mehmet Altan, an assistant professor and oncologist at The University of Texas MD Anderson Cancer Center Division of Cancer Medicine and a co-author on the new paper. “The next challenge will be to predict which patients will require combination therapies for positive outcomes versus those that do not and to predict what type of irAE they may experience.”
Sykes adds that the implications for this new class of biomarkers lie far beyond the study’s focus on lung cancer. “For other cancers, such as brain cancer—in which ICIs are not prescribed due to historically low response rates—a test to screen for patients who would respond could be life-saving.”
The study analyzed serum samples that were drawn from 74 patients diagnosed with advanced lung cancer before they received anti-PD-L1 or anti-PD-1 immunotherapy, either in combination with or without chemotherapy. Serum-antibodies recognizing FSPs were detected on peptide microchips. These biomarkers were then used to predict post-treatment tumor responses and adverse events with 90%-100% accuracy in a single test.
Sykes believes this could lead to better patient outcomes. “An accurate predictive test would identify those patients that require close monitoring for any toxicities; when these might occur, therapy doses could be modified or paused as needed,” she says, adding that it could also impact the inclusion of chemo with primary ICI therapy for some patients. “Chemo does not always further help patients beyond what the ICI can do. Knowing whether it is needed could change regimen recommendations.”
Although small, the study represents the first major step in accurately predicting outcomes for immunotherapy, and the Calviri team is optimistic about where the current results will lead. “We expect these results will justify the approach and facilitate access to the required larger serum sample cohorts for developing and licensing predictive diagnostics based on this technology,” says Stephen Albert Johnston, CEO of Calviri. “Future efforts will focus on improving this approach for addressing specific needs in lung cancer treatment and for evaluating this ICI predictive test platform for other research.”