AbstractThe functional diversity of cells that compose myeloid malignancies, i.e., the respective roles of genetic and epigenetic heterogeneity in this diversity, remains poorly understood. This question is addressed in chronic myelomonocytic leukemia, a myeloid neoplasm in which clinical diversity contrasts with limited genetic heterogeneity. To generate induced pluripotent stem cell clones, we reprogrammed CD34+ cells collected from a patient with a chronic myelomonocytic leukemia in which whole exome sequencing of peripheral blood monocyte DNA had identified 12 gene mutations, including a mutation in KDM6A and two heterozygous mutations in TET2 in the founding clone and a secondary KRAS(G12D) mutation. CD34+ cells from an age-matched healthy donor were also reprogrammed. We captured a part of the genetic heterogeneity observed in the patient, i.e. we analyzed five clones with two genetic backgrounds, without and with the KRAS(G12D) mutation. Hematopoietic differentiation of these clones recapitulated the main features of the patient’s disease, including overproduction of granulomonocytes and dysmegakaryopoiesis. These analyses also disclosed significant discrepancies in the behavior of hematopoietic cells derived from induced pluripotent stem cell clones with similar genetic background, correlating with limited epigenetic changes. These analyses suggest that, beyond the coding mutations, several levels of intraclonal heterogeneity may participate in the yet unexplained clinical heterogeneity of the disease.
Intratumoral heterogeneity is a major tenet of cancer biology. A tumor clone emerges from a single cell that has acquired one or several somatic mutations. Additional driver events that occur in individual daughter cells generate tumor sub-clones, each being endowed with specific functional properties and fitness.1 This intraclonal genetic heterogeneity may not explain all the functional heterogeneity among individual cells within a tumor clone. Epigenetic variation also contributes to the heterogeneity of cells that form a tumor.32
Chronic myelomonocytic leukemia (CMML) is a neoplastic disease whose limited genetic heterogeneity contrasts with its clinical diversity.4 CMML is defined by a persistent clonal monocytosis, with or without dysplasia.5 The mutational landscape contains a small number of somatic mutations in DNA methylation, histone modifier, splicing factor and signaling genes.6 Mapping of CMML clonal architecture identified early clonal dominance, intratumor heterogeneity in the hematopoietic stem and progenitor cell compartment in which mutations accumulate mostly linearly, and growth advantage to the most mutated cells.7 Hypomethylating agents, which are commonly used in severe dysplastic forms of the disease, can restore a balanced hematopoiesis.8 The response to these drugs, which correlates with DNA demethylation, can occur in the absence of any decrease in mutation allele burden measured in circulating monocytes,6 arguing for a role of epigenetic alterations in disease expression and outcome.9
One of the main limitations in studying CMML pathophysiology is the lack of appropriate models, either patient-derived cell lines or genetically modified animals, which faithfully reproduce disease features. Currently, the best available CMML preclinical model is xenotransplantation of CMML cells in immunocompromised mice, especially those with transgenic expression of human cytokines including granulocyte-macrophage colony-stimulating factor.1110 The modeling of myeloid malignancies by generating patient-derived induced pluripotent stem cells (iPSC) recently appeared as another opportunity to model these diseases and, although challenging, capture their genetic heterogeneity.1512 In CMML, we previously demonstrated that intraclonal heterogeneity was rarely detected in mature cells of the clone as a consequence of a growth advantage to most mutated cells with differentiation but was preserved in stem and progenitor cells.76 Therefore, we sorted CD34 cells from a CMML patient and reprogrammed these cells to capture some intraclonal genetic heterogeneity and characterize hematopoiesis derived from genetically close but distinct iPSC.
Generation, characterization and maintenance of induced pluripotent stem cells
CD34 cells collected from a healthy donor and a CMML patient, with informed consent and approval of the Ethics Committee (DC-2014-209), were infected with non-integrated Sendai virus encoding Klf4, Oct4, Sox2 and c-Myc to generate iPSC. An additional iPSC (Co6) was kindly provided by Dr Weiss.16 iPSC were passaged once a week to yield a cell suspension of small colonies (3-10 cells). Intracellular and extracellular pluripotency markers were detected by flow cytometry and ter-atoma formation was evaluated by intramuscular injection of iPSC into NOD/SCID/IL2rγ mice. Karyotyping and comparative genomic hybridization were performed. The procedures are detailed in the Online Supplementary Material.
Hematopoietic cell differentiation
A two-dimensional monolayer system was used to differentiate iPSC into CD34CD43 hematopoietic progenitor cells (HPC). Clonogenic assays were performed by mixing HPC in serum-free medium with MethoCult H4434 classic (Stem Cell Technologies, Grenoble, France) before plating the cell suspension in 35-mm dishes. Colonies were scored after 14 days and analyzed on a BD LSRFortessa X-20. HPC mixed with serum-free fibrin clots were seeded for 10 days in the presence of thrombopoietin and stem cell factor before measuring colony-forming unit-megakaryocyte (CFU-Mk) colonies. HPC were also suspended in serum-free liquid medium with growth factors for 10 days before flow analysis of cell surface markers and May-Grünwald-Giemsa staining of cytospins. More details are provided in the Online Supplementary Material.
Flow cytometry and cell sorting
The antibodies used are listed in Online Supplementary Table S1. Cells were analyzed using a BD LSRFortessa™ X-20 and Kaluza analysis software. HPC, monocytes and megakaryocytes were sorted on a BD Influx™ Cell sorter. Details are provided in the Online Supplementary Material.
Whole exome sequencing
We collected genomic DNA from sorted monocytes and CD3 T cells and iPSC to perform whole exome sequencing. Raw reads were aligned to the reference human genome hg19 (Genome Reference Consortium GRCh37) using BWA 0.5.9 (Burrows–Wheeler Aligner) backtrack algorithm with default parameters. A mutation was reported as present if the variant allele frequency was ≥4%. More details are provided in the Online Supplementary Material.
Genome-wide DNA methylation detected by enhanced reduced representation bisulfite sequencing
High-molecular weight DNA was sequenced on a HiSeq3000 Illumina sequencer and 50 bp reads were aligned against a bisulfite-converted human genome (hg19). Differentially methylated regions (DMR) identified an absolute methylation difference ≥40% with a false discovery rate <5% and were annotated using the ChIPenrich R package,17 which was also used for gene ontology and pathway analysis. For correspondence analysis and hierarchical clustering, tiles with the highest standard deviation (SD >0.03) were used. More details are provided in the Online Supplementary Material.
Statistical analysis was performed with GraphPad Prism software, using an unpaired t test and Mann-Whitney test, depending on distribution, similarity of variance, and sample number. The Kruskal-Wallis test was used for multiple comparisons.
Accession numbers for enhanced reduced representation bisulfite sequencing, whole exome sequencing and RNA sequencing data are GSE114115, E-MTAB-7917 and E-MTAB-7850, respectively.
Reprogramming of CD34+ cells from a patient with chronic monomyelocytic leukemia captures a part of the disease’s genetic heterogeneity
We reprogrammed CD34 cells collected from a CMML patient whose monocyte DNA whole exome sequencing had identified 12 mutations, including two mutations in TET2 (S1691fs and R1516X) and heterozygous mutations in KRAS(G12D) and KDM6A(R61X). The clinical and biological features of the patient’s disease are depicted in the Online Supplementary Material. Reprogramming of CD34 cells collected from an age-matched healthy donor generated control clones (Figure 1A, B). We selected nine clones (5 from the patient; 4 from the healthy donor) demonstrating pluripotency features, including morphology (Online Supplementary Figure S1A), expression of markers (Online Supplementary Figure S1B) and formation of teratomas in immunodeficient mice (Online Supplementary Figure S1C), without reprogramming-induced cytogenetic abnormalities (Online Supplementary Figure S1D). We also used an additional, independently generated control clone (Figure 1A).16 Whole exome sequencing and Sanger sequencing of patient-derived iPSC indicated that we had captured a part of the genetic heterogeneity of her leukemic clone, i.e., three clones (A1, A2, A4) recapitulated the founding clone while the other two (A3, A5) were reprogrammed from a KRAS(G12D) subclone (Figure 1A, B and Online Supplementary Figure S1E). In contrast with other studies,18 we did not reprogram any wildtype CD34 cells, probably due to the early clonal dominance that characterizes CMML clonal architecture, with very few residual wild-type cells in the stem cell compartment.7
Hematopoietic cells derived from chronic myelomonocytic leukemia induced pluripotent stem cells recapitulate the disease features
iPSC were induced to differentiate into CD34CD43 hematopoietic progenitors (Online Supplementary Figure S2A), which were plated for 10 days in methylcellulose in the presence of stem cell factor, interleukin-3, erythropoietin, and granulocyte-macrophage colony-stimulating factor (Figure 2A). The total numbers of colonies generated by healthy donor- and CMML iPSC-derived hematopoietic progenitors were similar (Figure 2B and Online Supplementary Figure S3A). The fraction of clusters (colonies <50 cells) generated by KRAS wildtype CMML iPSC was significantly higher than that generated by KRAS(G12D)-mutated CMML iPSC and control clones (Figure 2C and Online Supplementary Figure S3B, C). KRAS(G12D) clones produced larger granulocyte-macrophage (CFU-GM) and macrophage (CFU-M) colonies (Figure 2D) as well as a higher proportion of CFU-M colonies (Figure 2E). Compared to control clones, CMML-derived clones generated fewer granulocytic and multipotent progenitor colonies (Figure 2E, F and Online Supplementary Figure S3D) and more granulocyte-macrophage colonies (Figure 2E) whereas the proportions of erythroid colonies were not significantly different (Figure 2F and Online Supplementary Figure S3D). Colonies derived from CMML iPSC also demonstrated an increased fraction of CD14 cells (Figure 2G) at the expense of CD33, CD123, CD235a or CD41 populations (Online Supplementary Figure S3E, summary in Figure 2H).
Cells that formed CFU-M generated by CMML iPSC did not show the typical, fibroblast-like shape of macrophages generated by healthy donor-derived iPSC (Figure 3A, B) and expressed less CD16 and CD163 than those from control iPSC-derived CFU-M (Figure 3C). A similar defect in the generation of CD14 cells expressing both CD16 and CD163 was detected when iPSC-derived hematopoietic cells were induced to differentiate in liquid culture (Figure 3C).
Since the patient demonstrated megakaryocytic hyperplasia and dysplasia, we performed coagulum assays in the presence of stem cell factor and thrombopoietin to analyze the generation of megakaryocytes and platelets (Figure 3D). All iPSC generated a similar number of colonies (Figure 3E) but those generated by CMML iPSC were much larger (Figure 3F-H). We observed a decrease in the fraction of CD42 cells among CD41 cells (Figure 3I) and the fraction of CMML iPSC-derived megakaryocytes that produced platelets was decreased (Figure 3J, K).
CD34CD43 cells generated from iPSC were also cultured in liquid medium in the presence of stem cell factor, interleukin-3, interleukin-6, erythropoietin, granulocyte-macrophage colony-stimulating factor, thrombopoietin, Fms-like tyrosine kinase 3 ligand, granulocyte colony-stimulating factor and monocyte colony-stimulating factor for 10 days (Figure 4A). The quantity of cells generated by each clone and the number of viable cells after 10 days were not significantly different, except for clone A1 that demonstrated more dead cells (Figure 4B, C). Multiparameter flow cytometry analysis was used to measure each cell population obtained in culture (Online Supplementary Figure S2B). CMML iPSC generated a majority (~40%) of CD14 cells (Figure 4D, E). Although they both had a KRAS(G12D) mutation, the A5 clone generated more CD14 cells (~80%) than the A3 clone. In fact, monocyte production by the A3 clone was not significantly different from that of KRAS wildtype clones (Figure 4E, insert). Compared to control clones, KRAS(G12D) CMML iPSC generated fewer CD33, CD14, CD41, CD235a-cells (Figure 4F) and KRAS wildtype CMML clones generated fewer CD123, CD14, CD41, CD235a cells (Figure 4G). The generation of CD235a erythroid cells was more heterogeneous and higher in KRAS wildtype compared to KRAS(G12D) CMML clones (Figure 4H). The generation of CD41 cells was not significantly different between control and patient-derived iPSC (Figure 4I). These liquid cultures also revealed the defective differentiation of monocytes into macrophages (Figure 3C) and the defective generation of platelets (Figure 3D).
As expected, patient-derived clones showed a bias in their differentiation towards monocyte production. However, hematopoietic differentiation of CMML iPSC also demonstrated significant intraclonal heterogeneity that could not be explained by the sole genetic alterations detected in coding regions. The A1, A2 and A4 clones, which have the same mutations in coding regions, showed heterogeneous differentiation into CD235a and CD14 cells whereas A3 and A5, which are KRAS-mutated clones, showed a marked difference in their monocytic differentiation in liquid culture. A summary of this clonal heterogeneity is shown in Figure 4J.
While the viability of cells generated by control iPSC was high in all but one experiment, the viability of CMML iPSC, especially KRAS wildtype CMML iPSC, was much more heterogeneous than that of control clones, suggesting a higher sensitivity to small variations in culture conditions (Figure 5A). Of note, the number of generated cells (indicated by the diameter of the circles in Figure 5A) could remain high in cultures in which the cell death rate was elevated. In contrast, a decrease in cell viability was associated with an increase in the fraction of CD235a cells and a decrease in the fraction of CD14 cells generated by KRAS wildtype CMML iPSC (Figure 5A). By eliminating this culture condition-related variability in cell production, using a cut-off of 90% viability, we observed a much more robust trend in the differentiation of A1, A2 and A4 clones (Online Supplementary Figure S4). In contrast, even with this cut-off on viability, the A5 clone consistently produced more CD14 cells than the A3 clone (Figure 5B).
Epigenetic heterogeneity among induced pluripotent stem cell clones from the patient with chronic myelomonocytic leukemia
Epigenetic intraclonal heterogeneity has been reported in hematologic malignancies192 as well as in solid tumors,20 and co-dependency between epigenetic and genetic evolution has been questioned, e.g., in large B-cell lymphoma21 and acute myeloid leukemia.3 We performed DNA methylation analysis on CD34CD43 cells generated from iPSC to investigate their epigenetic state. Unsupervised analysis by correspondence analysis separated control- from CMML-derived cells. The latter showed a much greater diversity (Figure 6A). We then used a β binomial model implemented in methylSig22 to perform a supervised analysis of differentially methylated regions (DMR). We identified 5,651 hypermethylated DMR in CMML-derived cells compared to controls. In contrast, only 874 DMR were identified as more methylated in control iPSC-derived cells (Figure 6B, C). Genomic annotation revealed that, in CMML samples, DMR were depleted at promoter regions and CpG islands while being enriched at gene body and intergenic enhancers (P<0.001 in all cases) (Figure 6D). DMR detected at CpG islands, gene body enhancers and intergenic enhancers were significantly more often hypermethylated regions (P<0.001) (Online Supplementary Figure S5A). In accordance with changes in DNA methylation affecting enhancers, we observed significant enrichment of DMR within enhancers23 compared to background (36.8% at total DMR vs. 15.2% background, P-value <2.2×10). Motif enrichment analysis suggested that the main sequences targeted by DMR were motifs recognized by transcription factors of the ETS family (Online Supplementary Figure S5B). Gene ontology analysis of biological processes of DMR hypermethylated in CMML-derived cells showed enrichment of differentiation and hematopoietic development categories (Figure 6E), suggesting that DNA methylation differences between CMML- and control-derived cells may capture an epigenetic memory related to the biology of the disease, still present after having been reprogrammed. Finally, focusing on CMML iPSC-derived hematopoietic cells, unsupervised hierarchical clustering based on their DNA methylation profiles revealed high concordance with their phylogenetic background (Figure 6F).
In order to correlate changes in DNA methylation with those seen at the expression level, we first looked at the expression status of genes closest to DMR using a nearest gene annotation approach. Using this approach, only 114 genes showed overlapping changes in expression and DNA methylation. However, since focusing on DMR-nearest gene correlations may not correctly capture the three-dimensional nature of gene regulation, we next explored the role of DMR within specific topologically associated domains (TAD) identified using publicly available coordinates. We thus localized each DMR into a given TAD. For every gene within a TAD, we correlated gene expression and methylation levels across the samples using Pearson correlation. With this method, of 196 DMR identified in 66 TAD, we detected changes in gene expression in 72 genes (Online Supplementary Table S2).
To further explore the epigenetic differences between subclones of the original disease and the potential contribution of specific mutations to the epigenetic programming, we also compared DNA methylation profiles in KRAS wildtype (A1, A2, A4) and KRAS(G12D) (A3, A5) clones (Figure 7). Globally, clones that had acquired KRAS(G12D) seemed to be relatively hypomethylated compared to the KRAS wildtype clones (Figure 7A, B). Acquisition of KRAS(G12D) correlated with a new repartition of DMR, including a significant decrease in methylation at intronic regions (P<0.001), CpG shores (P<0.001), gene body (P<2.2 10) and intergenic enhancers (P<0.001) and a significant increase in methylation at promoter regions (P<0.001) and CpG islands (P<0.001) (Figure 7C). Importantly, using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, KRAS(G12D) acquisition was shown to induce significant changes in Wnt and Hedgehog signaling pathways, which may indicate a differential role for these pathways in the malignant transformation of the different clones (Figure 7D).
The reprogramming of CD34 cells from a CMML patient generated iPSC whose hematopoietic differentiation recapitulated the main features of the disease while demonstrating functional heterogeneity between clones (see graphical abstract in Figure 8).
Discrepancies between the functional properties of clones sharing a similar genetic background have been reported in organoids derived from colorectal cancer cells.24 We cannot exclude an effect of recurrent somatic mutations in non-coding regions, as described in some solid tumors,25 but such events have not been identified yet in CMML cells.6 A role for cell reprogramming2726 in this heterogeneity is also unlikely as control iPSC established independently from two healthy donors had a similar and reproducible behavior, in contrast to the differences observed between genetically identical CMML iPSC (Figure 4J). In addition differences sometimes observed between individual clones derived from the same genotype were shown to pre-exist in the tissue of origin rather than being induced by reprogramming.2928 The distinct behavior of CMML-derived iPSC could also reflect intrinsic heterogeneity in CD34 cell-priming for differentiation.3130 If such an intrinsic heterogeneity of CD34 cell-priming is a general property of CD34 cells, discrepancies should also have been observed between control iPSC. An alternative explanation is that heterogeneous priming is a specific feature of CMML progenitor cells, which could be related to intraclonal epigenetic het erogeneity. Our epigenetic analyses indicate that, in patient-derived clones, the global pattern of DNA methylation correlates with genetic alterations. However, limited differences in their methylation profiles could possibly account for their functional heterogeneity.
iPSC allow the combined investigation of all levels of intratumoral heterogeneity, from genetic, to epigenetic, to phenotypic and functional properties associated with the disease, offering unique opportunities to study diseases in which functional heterogeneity exceeds genetic heterogeneity, such as CMML.4 These benefits do, however, come at a cost. As recently reviewed,15 the derivation of iPSC from patients with a myeloid malignancy to model their disease has to face the relative refractoriness of malignant progenitors to reprogramming, which is in part related to their genetic background and could preclude the capture of intraclonal heterogeneity as some subclones may be reprogrammed more easily than others. The dysplastic nature of these cells, which often correlates with an increased sensitivity to apoptosis, is another challenge to overcome. Lastly, in the patients, the heterogeneous behavior of individual cells may be further influenced by clonal interference and non-cell-autonomous factors,32 contributing to the diversity of CMML phenotypic traits, and accounting for the current failure of treatments aiming at eradicating the malignant clone.
The authors would like to thank Dr Weiss for kindly providing a control iPSC clone. This work was supported by grants from the Ligue Nationale Contre le Cancer (équipe labelisée), Institut National du Cancer (INCA_8073; PRT-K 16-047), Fondation ARC (to AB), Cancéropole Ile de France (emergence program to LL), Siric SOCRATE (INCa-DGOS-INSERM_12551), Molecular Medicine in Oncology program (Agence Nationale de la Recherche), Gustave Roussy Cancer Center (taxe d’apprentissage), the University of Miami Sylvester Comprehensive Cancer Center, the Sylvester Comprehensive Cancer Center Onco-Genomics Shared resource, and the John P. Hussman Institute for Human Genomics, Center for Genome Technology at the University of Miami Miller School of Medicine.
- ↵* AB and LL contributed equally to this study
- Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/105/1/112
- Received October 4, 2018.
- Accepted April 30, 2019.
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