Signaling and Immunophenotypic Diversity in Pediatric Acute Myeloid Leukemia As Defined by 31-Parameter Single-Cell Mass Cytometry
Authors: Simonds Erin F. , Bendall Sean C , Davis Kara L. , Fienberg Harris G , Finck Rachel L , Amir El-ad D. , Radtke Ina , Downing James R. , Pe'er Dana , Nolan Garry P. 
Published: October 2019

Abstract
Abstract 2565
Acute myeloid leukemia (AML) encompasses a broad spectrum of cellular morphologies. This spectrum exists both within each patient in the form of a malignant developmental hierarchy, and across patients in the form of distinct disease classes with distinguishing mutational or pathological hallmarks. Flow cytometry has long been an essential tool for classifying AML surface markers, and has enabled improved diagnosis, minimal residual disease monitoring, and cell sorting for functional studies. Previous work by our laboratory and others (Irish et al., Cell 2004; Kornblau et al., Clin. Cancer Res. 2010) has shown that clinically relevant heterogeneity also exists at the level of single-cell intracellular signaling capabilities, particularly in response to ex vivo perturbations. Due to the constraints of fluorescent flow cytometry, the limited number of simultaneous parameters measured per cell in these studies was limited (e.g., 3–6). We hypothesized that there may exist additional layers of heterogeneity in both surface marker phenotypes and signaling responses which were invisible to the low-parameter systems used in these studies.
To address this challenge, we used a next-generation 31-parameter ‘mass cytometry’ platform to survey the diversity of signaling responses and their relationship to subsets defined by surface marker patterns. We measured the simultaneous co-expression of 16 surface markers and 15 dynamic intracellular signaling epitopes (e.g. phosphorylated kinase substrates) in a cohort of pediatric AML diagnosis bone marrow samples (n=18) and healthy adult bone marrow controls (n=3). Signaling dynamics were measured under 18 stimulation conditions, including a battery of cytokines, chemical stimuli, and small molecule kinase inhibitors. The resulting single-cell data was overlaid and compared using a new, unsupervised flow cytometry analysis and visualization program–SPADE (Spanning-tree Progression Analysis of Density-Normalized Events)–which links rare, transitional, and abundant cell types alike along a branching tree organized by phenotypic progression. Thus, we distilled the diversity of cellular phenotypes, their relative frequencies, and the corresponding signaling dynamics of each cell population onto a single graph structure representing the distinct and overlapping expression patterns in the 16-dimensional “immunophenotypic space” explored by the 21 individuals.
Striking signaling and immunophenotypic diversity was observed between the AML samples, particularly in response to pervanadate stimulation. Pervanadate liberates intracellular kinase cascades by broadly inhibiting tyrosine phosphatases, and thus reveals the cell's potential for kinase activity and the interplay of signaling circuits in different cell populations. Interestingly, two transcription factors implicated in AML pathogenesis – CREB and c-Cbl–often exhibited mutually exclusive phosphorylation patterns in different subsets of primitive (CD34+) cells. Specifically, CREB was readily phosphorylated at Ser133 upon pervanadate exposure in the CD34+CD123- cells, while c-Cbl phosphorylation at Tyr700 was restricted to CD34+CD123+ cells. As CD123 (IL-3Ra) has been implicated as a marker of AML stem cells, the finding that these cells are poised for c-Cbl signaling may suggest a potential therapeutic avenue.
This work demonstrates the utility of 31-parameter mass cytometry for the simultaneous characterization of multiple cancer stem cell markers and comparison of their expression patterns to the normal tissue hierarchy. These results will inform future studies of AML signaling biology, prognostic biomarkers, and indicators of minimum residual disease.
EFS and SCB contributed equally to this work.

Disclosures:
No relevant conflicts of interest to declare.