Identification of functionally primitive and immunophenotypically distinct subpopulations in secondary acute myeloid leukemia by mass cytometry

Background: Background: Mass cytometry (CyTOF) is a powerful tool for analyzing cellular networks at the single cell level. Due to the high-dimensional nature of this approach, analysis algorithms have been developed to visualize and interpret mass cytometry data. In this study, we applied these approaches to a cohort of patients with secondary acute myeloid leukemia (sAML). Methods: We utilized mass cytometry to interrogate localization and intensity of thrombopoietin-mediated intracellular signaling in sAML. Extracellular and intracellular phenotypes were dissected using SPADE, viSNE, and PhenoGraph. Results: Healthy controls exhibited highly localized signaling responses largely restricted to the hematopoietic stem/progenitor cell (HSPC) compartment. In contrast, sAML samples contained subpopulations outside the HSPC compartment exhibiting thrombopoietin (TPO) sensitivity comparable to or greater than immunophenotypically defined HSPCs. We employed unsupervised clustering by PhenoGraph to elucidate distinct subpopulations within these heterogeneous samples. One metacluster composed almost exclusively of Lin ⁻ CD61 ⁺ CD34 ⁻ CD38 ⁻ CD45 ^low cells was identified. This subpopulation was not readily identified by established manual gating approaches, and generally exhibited greater STAT phosphorylation in response to TPO stimulation than did Lin ⁻ CD61 ⁻ CD34 ⁺ CD38 ⁻ cells. Lin ⁻ CD61 ⁺ CD34 ⁻ CD38 ⁻ CD45 ^low cells were identified in three additional sAML patients analyzed independently using a manual gating approach based upon PhenoGraph results. Each patient exhibited a similar TPO hypersensitivity to the PhenoGraph metacluster. Conclusions: The identification of this cellular subpopulation highlights the limitations of manual gating in sAML. Our study demonstrates the potential for mass cytometry to elucidate rare subpopulations in highly heterogeneous tumors by utilizing unsupervised high dimensional analysis.