Posted: Thursday, April 11, 2024
What mechanisms could be responsible for the frequent development of resistance in melanoma to immune checkpoint blockade? In attempting to find out, a research team believes they have identified a novel, strongly predictive immune checkpoint blockade response biomarker signature. Eytan Ruppin, MD, PhD, of the National Cancer Institute Center for Cancer Research, Bethesda, Maryland, and colleagues presented their work during the American Association of Cancer Research (AACR) Annual Meeting 2024 (Abstract LB002/2).
The team developed a machine learning model they call IRIS, for immunotherapy resistance cell-cell interaction scanner. It is designed to identify candidate ligand-receptor interactions that are likely to mediate immune checkpoint blockade resistance in the tumor microenvironment.
Using deconvolved transcriptomics data of the five largest melanoma immune checkpoint blockade therapy cohorts, the team pinpointed “a set of specific ligand-receptor pairs that are deactivated as tumors develop resistance, which we refer to as resistance-deactivated interactions,” they wrote. “Quite strikingly, the activity of these [interactions] in pretreatment samples offers a markedly stronger predictive signal for immune checkpoint blockade therapy response compared [with] those that are activated as tumors develop resistance.”
Many of these resistance deactivated interactions are involved in chemokine signaling, Dr. Ruppin and co-investigators continued. They added that in their view, the predictive accuracy of these interactions may be better than that of current published transcriptomics biomarker signatures across an array of melanoma immune checkpoint blockade data sets. Further, “we validate[d] on an independent large melanoma patient cohort that the [resistance-deactivated interactions’] activity is associated with CD8-positive T-cell infiltration and [is] enriched in hot/brisk tumors.”
Disclosure: For full disclosures of the study authors, visit abstractsonline.com.