Single cell immune profiling by mass cytometry of newly diagnosed chronic phase chronic myeloid leukemia treated with nilotinib

Abstract

Monitoring of single cell signal transduction in leukemic cellular subsets has been proposed to provide deeper understanding of disease biology and prognosis, but has so far not been tested in a clinical trial of targeted therapy. We developed a complete mass cytometry analysis pipeline for characterization of intracellular signal transduction patterns in the major leukocyte subsets of chronic phase chronic myeloid leukemia. Changes in phosphorylated Bcr-Abl1 and the signaling pathways involved were readily identifiable in peripheral blood single cells already within three hours of the patient receiving oral nilotinib. The signal transduction profiles of healthy donors were clearly distinct from those of the patients at diagnosis. Furthermore, using principal component analysis, we could show that phosphorylated transcription factors STAT3 (Y705) and CREB (S133) within seven days reflected BCR-ABL1^IS at three and six months. Analyses of peripheral blood cells longitudinally collected from patients in the ENEST1st clinical trial showed that single cell mass cytometry appears to be highly suitable for future investigations addressing tyrosine kinase inhibitor dosing and effect. (clinicaltrials.gov identifier: 01061177).

Trial registration: ClinicalTrials.gov NCT01061177.

Figure 1.

Nilotinib dosing altered signal transduction of the CD34⁺ cell population in chronic phase chronic myeloid leukemia (CML) patients analyzed by mass cytometry. Patient peripheral blood (PB) was collected at trial inclusion (t=0; before dosing), and after three hours (t=3h) and at day 7 (t=7d) of nilotinib (300 mg BID). (A) The CD34⁺ subset identified by the SPADE algorithm was further analyzed by the viSNE algorithm. The CD34⁺ cells from 2 representative patients at the three time points are shown. For each patient, the range of expression of each cell surface marker (CD34, CD38 and CD25) is color-coded from minimum to maximum expression of the three longitudinal samples. (B) The ratio of CD34⁺ cells and the total PB counts was calculated for all patients (n=10) in all three longitudinal samples. We found a statistically significant decrease of CD34⁺ cells in the PB between t=0 and day 7, and between after three hours and day 7 (Friedman non-parametric with Dunn’s multiple comparison; **P≤0.01). (C) For all patients, CD34⁺CD25⁺ cells were identified on the viSNE plots (see gating scheme). The ratio between CD34⁺CD25⁺ and all CD34⁺ cells was calculated showing a statistically significant decrease in CD34⁺CD25⁺ cells between diagnosis and day 7, and between after three hours and day 7 (Friedman non-parametric with Dunn’s multiple comparison; **P≤0.01). (D) Intracellular signaling transduction targets of Bcr-Abl1. The 85^th percentile metal intensities of pAbl Y245, pCRKL Y207, pSTAT3 Y705, pSTAT5 Y694 and pCREB S133 were calculated and data of the patient cohort (n=10) for the CD34⁺CD38^low and CD34+CD38^high populations box plotted as a function of time. We observed a statistically significant change of pSTAT3 Y705 in the CD34⁺CD38^high population (Friedman non-parametric with Dunn’s multiple comparison, *P≤0.05) between samples collected at diagnosis and day 7.

Figure 2.

High-resolution single cell immune profiles of healthy and patient leukocytes. Longitudinally collected samples (before, after 3 hours and day 7 on nilotinib) from 8 patients in the ENEST1st trial, together with 4 healthy peripheral blood (PB) and bone marrow (BM) samples, were barcoded using the 20-plex metal barcoding kit (Fluidigm). (A) Data from the longitudinal samples from each patient, and the 4 healthy PB and BM samples, were pooled and clustered using the SPADE algorithm and manually annotated to identify cellular subsets. The SPADE tree analysis of patient 4702_0004 is shown. The size of each node represents the number of cells clustered and the expression of pSTAT3 Y705 is color-coded. The red bubble highlights the mature neutrophil population. (B and C) The relative abundance of the major PB populations identified in samples collected at diagnosis and after seven days of tyrosine kinase inhibitor (TKI) therapy is shown for the patient cohort (n=8, error bars showing standard error of mean, SEM), with the appropriate subpopulation in the healthy samples shown in a lighter shade. CD34⁺ cells could only be identified in 7 out of 8 patients. Wilcoxon matched-pairs rank test was used to identify statistically significant changes from before and after seven days of TKI therapy, where P≤0.05 was considered statistically significant.

Figure 3.

Single cell signaling profiles correlate to BCR-ABL1^IS molecular response. The phosphorylation level (75^th percentile of metal intensity) of the intracellular Bcr-Abl1 signaling network (n=10) was measured in the longitudinal samples (before, after 3 hours and after 7 days of nilotinib) in the patient cohort (n=8), together with 4 healthy peripheral blood (PB) and bone marrow (BM) samples. (A) We observed distinctly different states of signal transduction in the patient samples compared to the healthy controls. (B) The Lin-CD34⁺CD38^low and Lin⁻CD34⁺CD38^high cell populations were manually gated from the CD34⁺ population identified by SPADE, and the intracellular signal transduction measured. CD34⁺ cells could only be identified in 7 out of 8 patients. The same strategy was followed for healthy CD34⁺ cells. For both (A and B) statistical significance was determined using Friedman non-parametric with Dunn’s multiple comparison test, where P≤0.05 was considered statistically significant (*P≤0.05, **P≤0.01). (C) The BCR-ABL1^IS of each patient during tyrosine kinase inhibitor (TKI) therapy. The patient cohort was divided into two groups based on the BCR-ABL1^IS at three and six months (red and green). (D) An unsupervised PCA analysis of the signal transduction arcsinh fold change compared samples before and after seven days of nilotinib treatment in the mature neutrophils (Neutro) and myelocytes (Myelo) was performed using Unscramble software (CAMO Software). The categorical color-coding from (C) was superimposed on the resulting PCA plot, and the dashed line was drawn manually.