Summary
Inflammation is a hallmark of the tumor microenvironment and an area of active investigation. To better understand how tumor inflammation differs from general inflammation — such as in gum disease — Drs. Prlic, Mair, and Erikson employed novel methods, including high-parameter flow cytometry and Ozette’s foundational technology, full annotation using shape-constrained trees (FAUST). FAUST is a machine-learning algorithm that discovers and annotates statistically relevant cellular phenotypes in an unsupervised manner. Pairing high-parameter flow cytometry with Ozette’s next-generation analysis helped decipher the complex immune landscape of tumors and revealed tumor-specific cell populations that modulate immune responses.
T cells are known orchestrators of inflammation — driving it, and in the case of T-regulatory cells, damping it, to respond to dynamic environments. FAUST revealed hundreds of distinct cellular phenotypes. In combination with single-cell RNA sequencing data and follow-up functional assays, these findings led Prlic et al. to identify one type of T-regulatory cell — a unique population expressing IL-1R1 and ICOS — that localized to solid tumors and suppressed immune defenses.
The Fred Hutch researchers compared their findings in head-and-neck solid tumors to 21 different cancer types. Transcriptomic data demonstrated that this T-regulatory cell population was present in all 19 solid-tumor cancers, but not in the two blood cancers. The team hypothesizes that targeting this newly identified population may be an effective way to more specifically target solid tumors — especially intriguing because solid tumors have not responded to immunotherapies as well as blood cancers, in part due to a lack of tumor-specific targets. They are now pursuing a bispecific antibody targeting IL-1R1- and ICOS-expressing T-regulatory cells to determine the impact on solid tumors.
