Multiomic Single-Cell Sequencing Reveals Patterns of Disease Evolution and Acute Transformation in Chronic Myelomonocytic Leukaemia

Abstract Introduction: Transformation of chronic myelomonocytic leukaemia (CMML) to secondary acute myeloid leukaemia (sAML) is frequent and invariably fatal, but poorly characterized. Understanding the transcriptional programs driving progression could reveal therapeutic strategies to prevent/delay transformation. We integrated serial single-cell CITE-seq on CD34+ cells from CMML patients before and after sAML, to evaluate transcriptional and hierarchical dynamics in the hematopoietic stem and progenitor (HSPC) compartment. Methods: Four CMML patients with matched bone marrow (BM) from CMML and sAML (± interim remission after azacitidine) were included. Three age-matched healthy controls were sourced from elective hip arthroplasties. Total 13 samples were processed. BM mononuclear cells were thawed and double MACS/FACS-enriched for CD34+ HSPCs. Cells were incubated with 14 HSPC/lineage-defining TotalSeq-A oligonucleotide-conjugated antibodies before parallel analysis of transcriptome and surface protein expression by 10X (Fig 1A). Results: Total 68,244 HSPCs were analyzed. Controls displayed near-identical UMAP distributions so were pooled for analyses. Optimal UMAP embeddings were generated for each CMML series independently (Fig 1B). UMAP clearly separated populations unique to CMML and sAML samples. Cell identities were inferred by unsupervised annotation from public datasets. CMML samples displayed loss of lymphoid progenitors, but also near-total absence of transcriptionally normal apical HSCs. All patients displayed aberrant expansions of progenitor populations, variously dominated by MPPs, CMPs or GMPs, as validated by antibody-derived tag (ADT) expression patterns. Pseudotime resolved patient-specific transformation trajectories, with blast expansions emerging from GMP (n=2), CMP/LMPP (n=1) or HSC/MPP (n=1). However, blasts consistently upregulated HSC-like transcriptional programs, in some cases with clear stepwise reversion from committed to stem-like transcriptional states along the computed trajectory (Fig 1B). Accordingly, blasts expressed classical HSC marker genes (ID2, DUSP1, CD52) alongside surface expression of private lineage markers but lacking ADT profiles of apical HSCs (Fig 1C). This decoupling, together with the pseudotime profiles, suggested progressive restoration of an HSC-like signature in downstream progenitors as a consistent feature. Post azacitidine there was marked expansion of megakaryocyte-erythroid progenitors, but despite recovery of normal hematopoiesis no restoration of the depleted apical HSCs. Subsequent sAML represented expansion of pre-existing (treatment-resistant) blasts in 3 case, but emergence of a new, transcriptionally-distinct blast population in the other. To characterise populations emergent at sAML we evaluated differentially expressed genes (DEG) vs respective CMML counterparts, then intersected gene lists across series. We found 319 genes upregulated in sAML conserved across ≥3 series. Among the most consistently upregulated genes at sAML were DUSP2 and all subunits of ITM2 (notably ITM2A, recently linked to AML). Pathways enriched in sAML included mTOR/PI3K/AKT and VEGF signalling (Fig 1D-E). Also overexpressed were several surface proteins (CD63, CD74, CD82, CD99) also aberrantly upregulated at earlier CMML, suggesting potential as biomarkers to predict/track transformation. Of the sAML signature genes 68 are considered 'druggable', including PIK3R1, NFKBIA and CXCL8. To identify early drivers of transformation we performed unsupervised clustering for each pooled series, comparing DEGs for successive intermediate CMML-to-sAML clusters along the computed trajectories. Common upregulated pathways included VEGF, MAPK and IL18 signaling, with 4 genes, notably the AP1 component JUNB, progressively upregulated during early transformation in all series (Fig 1F-G). Conclusions: We characterize the repartitioning of HSPC subsets in CMML, including loss of transcriptionally-normal apical HSCs, not restored upon azacitidine response; this may partially explain the transient nature of responses. Cell-of-origin and paths to acute transformation were heterogeneous, but with consistent co-opting of HSC-like programs in lineage-committed blasts. PIK3R1 and JUNB, and the PI3K/AKT/mTOR and VEGF pathways, emerge as candidate mediators of CMML transformation. Figure 1 Figure 1. Disclosures Somervaille: Novartis: Consultancy, Honoraria. Wiseman: Novartis: Consultancy; StemLine: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy; Astex: Research Funding.

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Single-cell multi-omics analysis of the immune response in COVID-19

Nature Medicine ◽

10.1038/s41591-021-01329-2 ◽

2021 ◽

Author(s):

Emily Stephenson ◽

◽

Gary Reynolds ◽

Rachel A. Botting ◽

Fernando J. Calero-Nieto ◽

...

Keyword(s):

Immune Response ◽

T Cells ◽

Single Cell ◽

Mononuclear Cells ◽

Severe Disease ◽

Effector Memory ◽

Peripheral Blood Mononuclear ◽

Cross Sectional ◽

Hematopoietic Stem ◽

Symptomatic Disease

AbstractAnalysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

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Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

Bioinformatics ◽

10.1093/bioinformatics/btab257 ◽

2021 ◽

Author(s):

Yixuan Qiu ◽

Jiebiao Wang ◽

Jing Lei ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

Expression Patterns ◽

R Package ◽

Supplementary Information ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Correlation Pattern ◽

Tissue Samples ◽

Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

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Revealing immune responses in the Mycobacterium avium subsp. paratuberculosis-infected THP-1 cells using single cell RNA-sequencing

PLoS ONE ◽

10.1371/journal.pone.0254194 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254194

Author(s):

Hong-Tae Park ◽

Woo Bin Park ◽

Suji Kim ◽

Jong-Sung Lim ◽

Gyoungju Nah ◽

...

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Single Cell ◽

Mycobacterium Avium ◽

Expression Patterns ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Cytokines And Chemokines

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic and debilitating disease in ruminants. MAP is also considered to be a possible cause of Crohn’s disease in humans. However, few studies have focused on the interactions between MAP and human macrophages to elucidate the pathogenesis of Crohn’s disease. We sought to determine the initial responses of human THP-1 cells against MAP infection using single-cell RNA-seq analysis. Clustering analysis showed that THP-1 cells were divided into seven different clusters in response to phorbol-12-myristate-13-acetate (PMA) treatment. The characteristics of each cluster were investigated by identifying cluster-specific marker genes. From the results, we found that classically differentiated cells express CD14, CD36, and TLR2, and that this cell type showed the most active responses against MAP infection. The responses included the expression of proinflammatory cytokines and chemokines such as CCL4, CCL3, IL1B, IL8, and CCL20. In addition, the Mreg cell type, a novel cell type differentiated from THP-1 cells, was discovered. Thus, it is suggested that different cell types arise even when the same cell line is treated under the same conditions. Overall, analyzing gene expression patterns via scRNA-seq classification allows a more detailed observation of the response to infection by each cell type.

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3055 Reconstruction of Patient-specific Distal Airway Regeneration Patterns in COPD

Journal of Clinical and Translational Science ◽

10.1017/cts.2019.352 ◽

2019 ◽

Vol 3 (s1) ◽

pp. 154-154

Author(s):

Seyed Babak Mahjour ◽

Kazunori Gomi ◽

Samir Rustam ◽

Phurbu Dolma ◽

Jamuna Krishnan ◽

...

Keyword(s):

Single Cell ◽

Stromal Cells ◽

Airway Remodeling ◽

Expression Patterns ◽

Mucous Cell ◽

Patient Specific ◽

Mucin 1 ◽

Cell Hyperplasia ◽

Chronic Lung Diseases ◽

Distal Airway

OBJECTIVES/SPECIFIC AIMS: The objective of this study was to reconstruct patient-specific distal airway patterns at the tissue- and single-cell resolution and develop personalized distal airway models based on utilization of patient-derived DABCs and autologous region-specific stromal cells. METHODS/STUDY POPULATION: Patient-specific distal airway units, containing parental small bronchiole (<2 mm in diameter, >12th generation) and daughter airway branches, including pre-terminal/terminal bronchioles, leading to alveoli (3-7 units/lung), were dissected. Epithelial and stromal cells were isolated from these units and processed for ddSeq single-cell RNA-sequencing (n=6 samples). Autologous DABCs and stromal cells were isolated, propagated, biobanked, and used for establishment of patient-specific distal airway models (3D-organoids and air-liquid interface-based airway wall model; n=10 samples). Region-specific tissue patterns were evaluated using immunofluorescence and laser-capture microdissection (LCM; n=6 samples). RESULTS/ANTICIPATED RESULTS: Single-cell-based human distal airway transcriptome map (constructed based on the analysis of >6,500 distal airway cells obtained from 6 subjects) identified physiological and COPD-relevant distal airway differentiation patterns, including distal airway-specific secretory phenotype (DASP) characterized with high expression of secretoglobins 3A2 and 3A1, surfactant proteins SFTPB and SFTPA2, and mucin 1, unique signatures of DABCs, and stromal (fibroblasts, smooth muscle, endothelial cell subpopulations) and immune (macrophage, T cells, B cell, mast cells). Immunofluorescence analysis and LCM confirmed distribution of cell type-specific markers with differential expression patterns of DABC and DASP signatures. Patient-derived DABC-stromal co-culture models reproduced 3 regenerative patterns: 1) physiological (high DABC-clonogenic potency, establishment of polarized differentiated organoids and DASP-expressing epithelia); 2) hypo-regenerative (failure of DABCs to form clones, spheres and mechanically stable differentiated epithelial barrier); and 3) hyperplastic (generation of DABC hyperplasia accompanied in some COPD samples by mucous-cell hyperplasia mimicking in vivo remodeling patterns). DISCUSSION/SIGNIFICANCE OF IMPACT: Patient-specific maps and models of distal airway regeneration patterns have been established in this study, which can be used to identify candidate pathways that mediate disease-relevant airway remodeling and potentially utilized as pre-clinical platforms for developing personalized therapeutic approaches to suppress the progression of distal airway remodeling in chronic lung diseases, including COPD.

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Clonotype-Immunophenotype Relationships in TET2 and IDH-Mutant Myeloid Transformation

Blood ◽

10.1182/blood-2021-151819 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 373-373

Author(s):

Linde A. Miles ◽

Robert L. Bowman ◽

Nicole Delgaudio ◽

Troy Robinson ◽

Martin P. Carroll ◽

...

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Board Of Directors ◽

Research Funding ◽

Expression Patterns ◽

Clonal Expansion ◽

Mutant Mice ◽

Triple Mutant ◽

Advisory Committees ◽

Disease Evolution

Abstract Large scale molecular profiling studies in AML patients have suggested that stepwise acquisition of somatic mutations is crucial in driving leukemic development. High variant allele frequency (VAF) mutations in epigenetic modifier genes, such as TET2 and IDH1/2, are thought to occur early in AML pathogenesis while oncogenic mutations with typically lower VAF mutations, including FLT3 and NRAS, are suggested to occur late in disease evolution. While bulk DNA sequencing has catalogued co-mutations found in individual AMLs, it cannot unveil the heterogeneity and composition of clones that makes up the disease. Elucidating the architecture and clone-specific molecular profiles at the single cell resolution will be key to understanding how sequential and/or parallel mutation acquisition drives myeloid transformation. To assess the clonal architecture of AML, we previously performed single cell DNA sequencing (scDNA seq) in 146 patients with myeloid malignancies. We have further identified specific mutational combinations driving clonal expansion in TET2- or IDH1/2- mutant AML samples. These studies suggest TET2 and IDH1/2 can cooperate to promote clonal expansion with DNMT3A and NPM1 (Figure 1A). However, TET2 or IDH1/2 mutant clones that acquired KRAS mutations underwent minimal clonal expansion, suggesting mutant-pair specific fitness alterations (Figure 1B). To further identify how co-mutational pairing impacted clonal fitness and differentiation, we integrated the scDNA platform with immunophenotypic profiling of 45 cell surface markers and analyzed new TET2- and IDH1/2- mutant AML samples (Figure 1C). We identified clone-specific differences in lineage markers depending on co-mutational partners. NPM1 co-mutant clones were enriched for more primitive markers (CD33), whereas NRAS co-mutant clones possessed high expression of myeloid differentiation markers (CD14/CD11b), suggestive of clone-specific fitness landscapes across hematopoietic differentiation. We also identified divergent clonotype-immunophenotype patterns in TET2- and IDH2-mutant clones harboring NPM1/RAS mutations, suggesting that initiating mutations may prime mutant clones for very different evolutionary trajectories as they acquire similar mutations in leukemogenesis (Figure 1D). To deterministically delineate the relationship between clonal evolution and myeloid transformation, we generated Cre-inducible single (Tet2 -/-), double (Tet2 -/-/Nras G12Dand Tet2 -/-/Npm1 cA/wt), and triple (Tet2 -/-/Npm1 cA/wt/Nras G12D) mutant mice and evaluated differences in chimerism, immunophenotype, and survival. We observed a shortened survival for double and triple mutant mice, compared to Tet2 -/- only mice (Figure 1E). As previously reported, Tet2 -/-/Nras G12D mice developed a CMML-like phenotype. Critically, the addition of Npm1 resulted in a more rapid disease onset and transformation to AML (Figure 1F). Moreover, triple mutant WBM transplanted to form a fully penetrant disease into secondary recipients, while double mutant Tet2 -/-/Nras G12D WBM failed to form disease within 3 months of transplant, suggesting a difference in the cell population responsible for disease propagation. Immunophenotypic alterations were evident with Tet2 -/-/ Nras G12D displaying an increase in Mac1 +Gr1 + cells compared to Tet2 -/-/Npm1 cA/wt/Nras G12D mice which possessed increased Mac1 +Gr1 - cells and expansion of lineage negative cells (Figure 1G). These findings align with the clonotype specific expression patterns observed in clinical specimen and suggest that myeloid transformation and maturation biases are influenced by specific mutational combinations. Figure 1 Figure 1. Disclosures Miles: Mission Bio: Honoraria, Speakers Bureau. Bowman: Mission Bio: Honoraria, Speakers Bureau. Carroll: Janssen Pharmaceutical: Consultancy; Incyte Pharmaceuticals: Research Funding. Levine: Astellas: Consultancy; Janssen: Consultancy; Auron: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; QIAGEN: Membership on an entity's Board of Directors or advisory committees; Mission Bio: Membership on an entity's Board of Directors or advisory committees; Isoplexis: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Incyte: Consultancy; Imago: Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Research Funding; Prelude: Membership on an entity's Board of Directors or advisory committees; Ajax: Membership on an entity's Board of Directors or advisory committees; Zentalis: Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Lilly: Honoraria; Morphosys: Consultancy.

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Single cell transcriptomic analysis of bloodstream form Trypanosoma brucei reconstructs cell cycle progression and differentiation via quorum sensing

10.1101/2020.12.11.420976 ◽

2020 ◽

Author(s):

Emma Marie Briggs ◽

Richard McCulloch ◽

Keith Roland Matthews ◽

Thomas Dan Otto

Keyword(s):

Cell Cycle ◽

Single Cell ◽

Trypanosoma Brucei ◽

Expression Patterns ◽

Life Cycles ◽

Bloodstream Form ◽

Cycle Phase ◽

Mammalian Host ◽

Marker Genes ◽

African Trypanosomes

The life cycles of African trypanosomes are dependent on several differentiation steps, where parasites transition between replicative and non-replicative forms specialised for infectivity and survival in mammal and tsetse fly hosts. Here, we use single cell transcriptomics (scRNA-seq) to dissect the asynchronous differentiation of replicative slender to transmissible stumpy bloodstream form Trypanosoma brucei. Using oligopeptide-induced differentiation, we accurately modelled stumpy development in vitro and captured the transcriptomes of 9,344 slender and stumpy stage parasites, as well as parasites transitioning between these extremes. Using this framework, we detail the relative order of biological events during development, profile dynamic gene expression patterns and identify putative novel regulators. Using marker genes to deduce the cell cycle phase of each parasite, we additionally map the cell cycle of proliferating parasites and position stumpy cell cycle exit at early G1, with subsequent progression to a distinct G0 state. We also explored the role of one gene, ZC3H20, with transient elevated expression at the key slender to stumpy transition point. By scRNA-seq analysis of ZC3H20 null parasites exposed to oligopeptides and mapping the resulting transcriptome to our atlas of differentiation, we identified the point of action for this key regulator. Using a developmental transition relevant for both virulence in the mammalian host and disease transmission, our data provide a paradigm for the temporal mapping of differentiation events and regulators in the trypanosome life cycle.

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A network regularized linear model to infer spatial expression pattern for single cells

10.21203/rs.3.rs-310810/v1 ◽

2021 ◽

Author(s):

Chaohao Gu ◽

Zhandong Liu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spatial Information ◽

Expression Profiles ◽

Single Cells ◽

Expression Patterns ◽

Capture Rate ◽

Marker Genes ◽

Spatial Expression ◽

Cellular Expression

Abstract Spatial gene-expression is a crucial determinant of cell fate and behavior. Recent imaging and sequencing-technology advancements have enabled scientists to develop new tools that use spatial information to measure gene-expression at close to single-cell levels. Yet, while Fluorescence In-situ Hybridization (FISH) can quantify transcript numbers at single-cell resolution, it is limited to a small number of genes. Similarly, slide-seq was designed to measure spatial-expression profiles at the single-cell level but has a relatively low gene-capture rate. And although single-cell RNA-seq enables deep cellular gene-expression profiling, it loses spatial information during sample-collection. These major limitations have stymied these methods’ broader application in the field. To overcome spatio-omics technology’s limitations and better understand spatial patterns at single-cell resolution, we designed a computation algorithm that uses glmSMA to predict cell locations by integrating scRNA-seq data with a spatial-omics reference atlas. We treated cell-mapping as a convex optimization problem by minimizing the differences between cellular-expression profiles and location-expression profiles with an L1 regularization and graph Laplacian based L2 regularization to ensure a sparse and smooth mapping. We validated the mapping results by reconstructing spatial- expression patterns of well-known marker genes in complex tissues, like the mouse cerebellum and hippocampus. We used the biological literature to verify that the reconstructed patterns can recapitulate cell-type and anatomy structures. Our work thus far shows that, together, we can use glmSMA to accurately assign single cells to their original reference-atlas locations.

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Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

10.1101/2020.11.07.373043 ◽

2020 ◽

Author(s):

Yixuan Qiu ◽

Jiebiao Wang ◽

Jing Lei ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

Expression Patterns ◽

R Package ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Correlation Pattern ◽

Tissue Samples ◽

Bulk Data ◽

Tissue Marker

AbstractMotivationMarker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern.ResultsTo capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list.Availability and implementationWe implement this method as an R package markerpen, hosted on https://github.com/yixuan/[email protected]

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Spatial mapping of single cells in the Drosophila embryo from transcriptomic data based on topological consistency

F1000Research ◽

10.12688/f1000research.24163.1 ◽

2020 ◽

Vol 9 ◽

pp. 1014

Author(s):

Maryam Zand ◽

Jianhua Ruan

Keyword(s):

Single Cell ◽

Gold Standard ◽

Gene Selection ◽

Single Cells ◽

Expression Patterns ◽

Drosophila Embryo ◽

Weighted Averaging ◽

Marker Genes ◽

Spatial Mapping ◽

Successful Recovery

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

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Cell of Origin Influences Leukemia Stem Cell Phenotype.

Blood ◽

10.1182/blood.v114.22.3459.3459 ◽

2009 ◽

Vol 114 (22) ◽

pp. 3459-3459

Author(s):

Andrei V. Krivtsov ◽

Amit U. Sinha ◽

Matthew C. Stubbs ◽

Andrew Kung ◽

Scott Armstrong

Keyword(s):

Stem Cells ◽

Hematopoietic Stem Cells ◽

Single Cell ◽

Conflicts Of Interest ◽

Genetic Program ◽

Cell Phenotype ◽

Data Sets ◽

Cell Of Origin ◽

Median Latency ◽

Hematopoietic Stem

Abstract Abstract 3459 Poster Board III-347 MLL-fusion proteins can transform either hematopoietic stem cells (HSC) or granulocyte macrophage progenitors (GMP) into leukemia stem cells (LSC). However, the leukemogenic process in HSC may differ from that in GMP. We transduced HSC and GMP with MLL-AF9 or control retroviruses. Single-cell sorted MLL-AF9 expressing HSC or GMP could be serially replated for over 9 passages. Upon transplantation into syngeneic mice, 86.3% (n=22) of HSC:MLL-AF9 single cell derived clones (SCC) induced AML with a median latency of 61 days, while 33.3% of GMP:MLL-AF9 SCC induced AMLs with median latency of 100 days. Immunophenotype analysis of the resultant leukemias demonstrated that long-term repopulating HSC (LT-HSC) and GMP-derived leukemias were quite similar, with a GMP-like (LGMP) population enriched in LSC in both cases. Gene expression analysis demonstrated that globally the LGMP isolated from HSC derived AMLs (AML:HSC) and GMP derived AMLs (AML:GMP) were similar to each other but possessed specific genetic programs reminiscent of the cell of origin (HSC or GMP). For example Evi1, Jun, and Fos oncogenes were highly expressed in HSC and AML:HSC, but expressed at low level in GMP or AML:GMP. The genetic program that distinguished LGMP:HSC from LGMP:GMP was found to be enriched in hematopoietic stem cells compared to more differentiated myeloid progenitors and correlate with genetic programs in and human MLL-rearranged AML associated with a poor clinical outcome in two independent MLL-rearranged AML data sets. In order to directly assess differences in treatment response for leukemias derived from different cells of origin, we treated leukemic mice with a chemotherapeutic agent often used to treatment human AMLs. Treatment of leukemic mice with Etoposide reduced the spleen weights in mice transplanted with AML:HSC to a lesser extent (28%) than in mice transplanted with AML:GMP (88%). Altogether, these data indicate that cell of origin of AML can influence the genetic program of fully developed leukemia, and thus could account for some of the heterogeneity in human leukemias and perhaps outcome. Disclosures No relevant conflicts of interest to declare.

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