TerraFlow, A New High Parameter Data Analysis Tool, Reveals Systemic T-cell Exhaustion and Dysfunctional Cytokine Production in Classical Hodgkin Lymphoma

Abstract

The incredible variety of immune-related proteins presents enormous challenges in immuno-monitoring. Combinatorial expression of these proteins defines cell types that may influence disease. Using high-parameter flow cytometry, and a new data analysis algorithm (TerraFlow), immunophenotypes can be comprehensively surveyed for disease associations. In classical Hodgkin lymphoma, where systemic T-cell immunity has not been investigated in detail, we reveal immune perturbations in newly-diagnosed patients (compared to healthy controls): 1) reduced levels of early (CD127+ CCR7+) memory T-cells, 2) elevated levels of activated (CD278+) memory T-cells primed for apoptosis (CD95+) and expressing inhibitory/exhaustion receptors (CD272+, PD1+, CD152+, CD366+), 3) increased suppressive (GITR+) cells, and 4) a shift away from TH1 and TH2 cells (IFNg+, IL4+) toward IL17-producing cells. Many of these perturbations remain after treatment. Our results provide mechanistic support for past reports of immune deficiency in Hodgkin lymphoma, detail new immunotherapy and biomarker research targets, and suggest strategies for combination therapies.

OMIP‐097: High‐parameter phenotyping of human platelets by spectral flow cytometry

“…Automatic gating is an attractive alternative and may find novel subsets. Several options are available, including dimension reduction and clustering, but combinatorics and/or other machine learning approaches (e.g., Astrolabe, FAUST, and terraFlow) are most beneficial as they perform automatic subset labeling, which accelerates data interpretation when prior biological knowledge is lacking (i.e., in the emerging field of high‐parameter platelet phenotyping) [37–39]. Figure 1 shows the discovery of platelet subsets by automatic gating.…”