While the AML cells clearly moved in a new immunophenotypic direction and adopted new features, they also maintained characteristics of the pre-treatment AML blasts

While the AML cells clearly moved in a new immunophenotypic direction and adopted new features, they also maintained characteristics of the pre-treatment AML blasts. time during treatment on AML blasts from patient F003. Biaxial plots summarize six clinical timepoints (rows) for 24 markers (sets) for the AML blast cells from patient F003, gated as shown in Fig 3. The indicated marker is usually plotted around the x-axis using the same arcsinh15 scale as in other figures (e.g. Fig 1B). Plot labels are omitted to save space. Sigma-1 receptor antagonist 3 The y-axis is usually mass cytometry event length, which is used here to spread the events out Sigma-1 receptor antagonist 3 in the y-axis to create a compressed band plot view that allows rare subsets to be observed (see e.g. CD235a) that would be obscured in a traditional 1D histogram view (Physique B). Changes in individual markers over time during treatment on non-leukemia cells from patient F003. As in Physique B in S1 File, biaxial plots summarize six clinical timepoints (rows) for 24 markers (sets) for the non-leukemia cells from patient F003, gated as everything not in the leukemia blast gate shown in Fig 3. The indicated marker is usually plotted around the x-axis using the same arcsinh15 scale as in other figures (e.g. Fig 1B). Plot labels are omitted to save space. The y-axis is usually mass cytometry event length, which is used here to spread the events out in the y-axis to create a compressed band plot view that allows rare subsets to be observed (see e.g. CD16) that would be obscured in a traditional 1D histogram view (Physique C).(DOCX) pone.0153207.s001.docx (6.4M) GUID:?F88FA590-F9D4-4BB3-B792-88A276373404 Data Availability StatementAll data are within the paper, its Supporting Information files and deposited in FlowRepository (http://flowrepository.org/id/FR-FCM-ZZMC). Abstract The plasticity of AML drives poor clinical outcomes and confounds its longitudinal detection. However, the immediate impact of treatment around the leukemic and non-leukemic cells of the bone marrow and blood remains relatively understudied. Here, we conducted a pilot study of high dimensional longitudinal monitoring of immunophenotype in AML. To characterize changes in cell phenotype before, during, and immediately after induction treatment, we developed a 27-antibody panel for mass cytometry focused on surface diagnostic markers and applied it to 46 samples of blood or bone marrow tissue collected over time from 5 AML patients. Central goals were to determine whether changes in AML phenotype would be captured effectively by cytomic tools and to implement methods for describing the evolving phenotypes of AML cell subsets. Mass cytometry data were analyzed using established computational techniques. Within this pilot study, longitudinal immune Sigma-1 receptor antagonist 3 monitoring with mass cytometry revealed fundamental changes in leukemia phenotypes that occurred over time during and after induction in the refractory disease setting. Persisting AML blasts became more phenotypically distinct from stem and progenitor cells due to expression of novel marker patterns that differed from pre-treatment AML cells and from all cell types observed in healthy bone Sigma-1 receptor antagonist 3 marrow. This pilot study of single cell Vwf immune monitoring in AML represents a powerful tool for precision characterization and targeting of resistant disease. Introduction Acute myeloid leukemia is one of the deadliest adult cancers. The five-year overall survival is usually 21.3% for all those ages and 4.6% for individuals 65 and older [1]. Current standard of care therapy has remained relatively unchanged over the last 30 years despite efforts to improve these poor Sigma-1 receptor antagonist 3 outcomes [2]. AML genetic heterogeneity has been well characterized as contributing to poor outcomes [3C5], and longitudinal genetic analyses have suggested multiple models of clonal evolution to explain disease aggressiveness [6, 7]. While it is usually clear that cell subsets within a pretreatment leukemia cell population have differential responses to therapy, it is not known to what extent genetic and non-genetic cellular features confer these differential responses. A single-cell understanding of AML therapy response over time during early treatment will characterize how different treatments reprogram AML cell phenotypes and impact clonal dynamics. Immediate post-treatment changes may have lasting impacts on long term outcomes, and a better understanding of how AML cells change following treatment may highlight key targets of opportunity.