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  • 1
    In: Cell Reports, Elsevier BV, Vol. 22, No. 7 ( 2018-02), p. 1875-1888
    Type of Medium: Online Resource
    ISSN: 2211-1247
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
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  • 2
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 488-488
    Abstract: Background: Flow cytometric immuno-phenotyping of lineage-associated antigens is used in the diagnosis of BCP ALL to distinguish neoplastic B-cells. However, the resultant immunophenotypic expression patterns are inadequate to inform prognosis or choice of therapy. Mass cytometry allows for multi-parametric analysis of single cells to distinguish phenotypic and functional features of subpopulations from leukemia samples. Mass cytometric analysis of pediatric Ph+ BCP ALL constructs a novel model of ALL organized along the normal B cell developmental trajectory (Davis et al., Cell 2014). Leukemic cells share phenotypic features with their normal early B cell counterparts. Critical survival and proliferation signaling is also linked to phenotypic state. Further, the developmental state of leukemic populations common among the patients impacts in vitroresponse to inhibition of BCR-ABL kinase signaling. Methods: Mononuclear cells from diagnostic bone marrow samples were obtained from pediatric patients with Philadelphia chromosome positive BCP-ALL under informed consent (n=21) or healthy controls (n=5). Mass cytometry analysis of 40 proteins was performed at baseline state and perturbed state (IL-7, TSLP, anti-m, dasatinib, tofacitinib, BEZ-235) as previously described (Bendall et al., Science 2011). Analysis was restricted to progenitor and blast populations. Healthy bone marrow samples were gated as previously described along the trajectory of developing B cells (Davis et al., Cell2014). These populations were used as the foundation for a classifier in which each leukemia cell was assigned to its nearest healthy population based on a distance metric (Mahalanobis in nine dimensions). Results: Compared to healthy bone marrow controls, and as expected, ALL samples displayed overexpression of early B cell immunophenotypic markers including CD10 (healthy mean counts 3.83 vs. leukemic 283.3, p=0.03), CD34 (6.26 vs. 80.7; p=0.03), and TdT (2.03 vs.18.9; p=0.002). Leukemic cells expressed lower levels of CD45 and IgM compared to healthy developing B cells. Extended phenotyping revealed conserved patterns of protein expression consistent with different developmental stages in B cell development. We have previously identified the precise developmental ordering of human B cell fractions based on the combined expression of CD34, CD38, CD24, TdT (Davis et al., Cell 2014). ALL samples showed increased numbers of cells occupying B cell progenitor compartments compared to healthy bone marrow controls. To formalize this observation, a single-cell classifier was constructed based on the developmental trajectory of healthy B cells. Each leukemic cell was assigned its most related healthy B cell population based on the expression of nine developmental proteins. Across all samples, the size of the pre-proB (CD34+CD38+TdT+) and proB (CD34+CD38+TdT+CD24+) compartments expanded (12% and 33% in ALL vs. 1% and 2% in healthy, respectively) at the expense of progenitor and preB cell compartments. Interestingly, within a given sample, cells may expand within more than one progenitor compartment such that each leukemia had a corrupted, but distinct B cell developmental trajectory. In two diagnosis-relapse pairs, the relapse sample occupied a more mature phenotype compared to its diagnostic partner. Within the developmental compartments, blast cells retained functional features of their healthy counterparts. Blasts within the ProB cell compartment displayed higher basal levels of pSTAT5, pS6, p4EBP1 and pCreb than blasts in other developmental compartments. These cells were more proliferative based on higher mean expression of Ki67. In healthy bone marrow, cells in this developmental state are characterized by ligand-independent STAT5 activation. Indeed the leukemia’s overall level of pSTAT5 correlated with the percentage of cells in the ProB state (R2=0.71). Similarly, in the leukemic samples, patients with a high percentage of cells in this state were less able to respond to inhibition of STAT5 with the tyrosine kinase inhibitor, Dasatinib than cells in other developmental compartments. Conclusions: Deep proteomic profiling of BCP ALL establishes a single-cell classification linking phenotype with functional attributes of leukemic cells. This data demonstrates that leukemic cells are more or less sensitive to therapeutic intervention based on their developmental state. Disclosures Bendall: Fluidigm: Consultancy. Simonds:Fluidigm: Consultancy, Equity Ownership. Nolan:Fluidigm, Inc: Consultancy, Equity Ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
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  • 3
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 1903-1903
    Abstract: Introduction: Flow cytometry is commonly used to characterize bone marrow (BM) cells of patients with myelodysplastic syndrome (MDS). However the diagnostic utility of this technique has been limited. To address this, we utilized 31-parameter single cell mass cytometry (MCM) to comprehensively analyze primary MDS BM samples. Methods: Expression levels of 31 surface markers, including most previously reported aberrant markers in MDS, were measured on 30 whole BM samples from 10 patients with higher-risk MDS (HR-MDS; IPSS = Int2/High/RAEB-T), 10 with lower-risk MDS (LR-MDS; IPSS = Low/Int1), and 3 patients with non-clonal cytopenias. In addition, 5 BM samples from normal donors were simultaneously analyzed as internal reference comparisons. All samples were barcoded, such that 20 samples (MDS and healthy) could be combined into a single tube for simultaneous antibody staining and analysis. Aberrant marker expression was defined as a median expression level falling outside 4 times the absolute variance of the normal samples in each gated population. Further analysis compared manual gating with unsupervised clustering (spanning tree progression analysis of density-normalized events [SPADE]). Results: MCM analysis generated 31-parameter single-cell data that defined 28 major immunophenotypic populations for each sample. This enabled detection of an aberrant expression of 25/31 markers in at least one population, encompassing essentially every previously reported surface marker aberrancy in MDS. Additionally, 3 previously unrecognized aberrant expression patterns were identified by both manual gating and SPADE: increased CD321 (64% of samples) and CD99 (36% of samples); and decreased CD47 (14% of samples). We focused further analyses on the stem and progenitor cell compartment (HSC, MPP, CMP), in which 20 of the 22 MDS samples exhibited at least one aberrancy (average 2.7) in one of these 3 populations (RAEB-T samples exhibited an average of 4). By contrast, no aberrancies were detected within these populations in the 3 samples from patients with non-clonal cytopenias. In addition to the identification of aberrant expression patterns within the subdivided stem and progenitor cell populations (HSPC) of individual samples, analysis of the HSPC population (CD34+CD38low) as a whole, revealed significant increases (~2-fold) in median expression of CD117 (p=0.003) and HLA-DR (p=0.028) for MDS samples compared to normal. Differences in CD117 and HLA-DR could also be appreciated as aberrant expression patterns (outside 4-fold the variance of normal) in 12/22 and 13/22 samples, respectively. Comparison of marker expression within the HSPCs between patients with HR-MDS and LR-MDS also revealed significant differences. HR-MDS HSPCs were characterized by a ~2-fold increase in CD99 compared to LR-MDS (p=0.0018) and a ~3-fold decrease in CD45 compared to LR-MDS (p=8.8x10-5). Differences in CD99 and CD45 could also be appreciated as aberrant expression patterns in 7/12 and 6/12 of the HR-MDS samples, respectively. Finally, the distribution of cell frequencies across the immunophenotypic populations (by SPADE analysis or manual gating) was used to perform a hierarchical clustering of all samples. This clustered patients into groups with different clinical risk. The most significant single distinguishing feature between clinical risk groups was the increased frequency ( 〉 40-fold) of HSPCs in HR-MDS compared to LR-MDS (p=9x10-7) or normal (p=6.3x10-6). Furthermore, this high-parameter analysis detected a 〉 12-fold increase in the HSPC frequency in 2 patients with IPSS Int-2 disease with blast frequencies of 〈 5% (following therapy). Conclusions: This first application of MCM for the analysis of MDS detected all major established aberrant expression patterns in MDS, as well as novel aberrant expression patterns of CD321, CD99, and CD47. Importantly, using high-parameter single-cell analysis and internal normal reference samples, we detected numerous deviations from the immunophenotypic boundaries of normal hematopoiesis in every analyzed MDS sample. Clustering of the cell frequency distribution across the immunophenotypic populations also defined groups of patients with differing clinical risk. These results demonstrate that high-parameter diagnosticcytometry methods can greatly enhance the diagnostic utility of immunophenotypic analysis in MDS. Figure 1 Figure 1. Disclosures Behbehani: Fluidigm: Consultancy. Finck:Fluidigm: Consultancy. Nolan:Fluidigm, Inc: Consultancy, Equity Ownership.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
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  • 4
    In: Nature Cell Biology, Springer Science and Business Media LLC, Vol. 20, No. 8 ( 2018-8), p. 990-990
    Type of Medium: Online Resource
    ISSN: 1465-7392 , 1476-4679
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
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  • 5
    In: Cytometry Part B: Clinical Cytometry, Wiley, Vol. 98, No. 2 ( 2020-03), p. 131-145
    Abstract: We sought to enhance the cytometric analysis of myelodysplastic syndromes (MDS) by performing a pilot study of a single cell mass cytometry (MCM) assay to more comprehensively analyze patterns of surface marker expression in patients with MDS. Methods Twenty‐three MDS and five healthy donor bone marrow samples were studied using a 34‐parameter mass cytometry panel utilizing barcoding and internal reference standards. The resulting data were analyzed by both traditional gating and high‐dimensional clustering. Results This high‐dimensional assay provided three major benefits relative to traditional cytometry approaches: First, MCM enabled detection of aberrant surface maker at high resolution, detecting aberrancies in 27/31 surface markers, encompassing almost every previously reported MDS surface marker aberrancy. Additionally, three previously unrecognized aberrancies in MDS were detected in multiple samples at least one developmental stage: increased CD321 and CD99; and decreased CD47. Second, analysis of the stem and progenitor cell compartment (HSPCs), demonstrated aberrant expression in 21 of the 23 MDS samples, which were not detected in three samples from patients with idiopathic cytopenia of undetermined significance. These immunophenotypically abnormal HSPCs were also the single most significant distinguishing feature between clinical risk groups. Third, unsupervised clustering of high‐parameter MCM data allowed identification of abnormal differentiation patterns associated with immunophenotypically aberrant myeloid cells similar to myeloid derived suppressor cells. Conclusions These results demonstrate that high‐parameter cytometry methods that enable simultaneous analysis of all bone marrow cell types could enhance the diagnostic utility of immunophenotypic analysis in MDS.
    Type of Medium: Online Resource
    ISSN: 1552-4949 , 1552-4957
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
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  • 6
    In: Experimental Hematology, Elsevier BV, Vol. 64 ( 2018-08), p. S33-S34
    Type of Medium: Online Resource
    ISSN: 0301-472X
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
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  • 7
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 76, No. 14_Supplement ( 2016-07-15), p. 2693-2693
    Abstract: B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common type of childhood cancer and is characterized by the malignant expansion of B-lymphocyte progenitors in the bone marrow (BM). Current therapy improves the relapse-free survival in children to over 80%. However, the ∼20% of patients who relapse have a poor prognosis and there are no reliable tests that predict relapse using diagnostic samples. We reasoned that aligning BCP-ALL cells according to a formalized context of normal B-lymphocyte development would reveal hidden cell states associated with relapse, and potentially expose targets to augment therapy for patients at risk. Until recently, our ability to pinpoint the identities of B-cell progenitors had been hindered by the vast cellular diversity within the BM and by the scarcity of the primary BM samples. We applied a single-cell proteomics platform termed mass cytometry by time-of-flight (CyTOF). In CyTOF, elemental mass reporter tagged antibodies probe proteins defining cellular identity and signaling within those cells. CyTOF simultaneously quantifies & gt; 40 proteins per cell in millions of individual cells. We defined a cell-state signature for 15 developmental populations of B lymphocytes within the normal human BM. Using this signature we assigned each leukemia cell from 52 primary diagnostic samples to its closest match in B lymphopoiesis using a classifier based on Mahalanobis distance. When applied to BM samples from 4 healthy donors our classifier correctly assigned cells to the true developmental population (accuracy = 0.92, F-measure = 0.92). Using this classifier it was determined that each BCP-ALL sample contains a mix of developmental populations - with 97% of samples enriched in populations that span the pre-pro-B to pre-B transition. We identified 20 predictors (using a machine learning approach) in diagnostic samples that perfectly separate patients who will relapse from those who will not (lasso; predictive AUC = 0.83). This is superior to the NCI risk that is currently employed at clinical diagnosis. These predictors are informative and suggest that high basal activation of IL-7 signaling nodes (pSTAT5, pAKT) in pre-pro-B to pro-BII cells and poor response following pre-B-cell receptor engagement in pre-BI cells portend relapse. As such, these pathways might eventually be targeted via drug repurposing to improve outcomes and to guide therapy in the high-risk childhood BCP-ALL patients identified with our predictor signature. Such an approach to cancer cell developmental classification could be generally applicable across various investigations on understanding and preventing relapse. Citation Format: Zinaida Good, Jolanda Sarno, Astraea Jager, Nikolay Samusik, Wendy Fantl, Nima Aghaeepour, Robert Tibshirani, Sean C. Bendall, Giuseppe Gaipa, Andrea Biondi, Garry P. Nolan, Kara L. Davis. Relapse in BCP-ALL predicted by activated signaling in pro-B cell subsets. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2693.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2016
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  • 8
    In: Nature Neuroscience, Springer Science and Business Media LLC, Vol. 21, No. 4 ( 2018-4), p. 541-551
    Type of Medium: Online Resource
    ISSN: 1097-6256 , 1546-1726
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
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    SSG: 12
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  • 9
    In: Nature Cell Biology, Springer Science and Business Media LLC, Vol. 19, No. 5 ( 2017-05), p. 558-567
    Type of Medium: Online Resource
    ISSN: 1465-7392 , 1476-4679
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
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    SSG: 12
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  • 10
    In: Nature Medicine, Springer Science and Business Media LLC, Vol. 24, No. 4 ( 2018-4), p. 474-483
    Type of Medium: Online Resource
    ISSN: 1078-8956 , 1546-170X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
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