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Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia

Molecular Cancer volume 21, Article number: 166 (2022) Cite this article

Abstract

Background

Acute myeloid leukemia (AML) is a heterogeneous and aggressive blood cancer that results from diverse genetic aberrations in the hematopoietic stem or progenitor cells (HSPCs) leading to the expansion of blasts in the hematopoietic system. The heterogeneity and evolution of cancer blasts can render therapeutic interventions ineffective in a yet poorly understood patient-specific manner. In this study, we investigated the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs at genetic and transcriptional levels, and unveiled the underlying pathways and genes contributing to recurrence.

Methods

Whole-exome sequencing was used to detect somatic mutations and large copy number variations (CNVs). Single cell RNA-seq was performed to investigate the clonal heterogeneity between Dx-Re pairs and amongst patients.

Results

scRNA-seq analysis revealed extensive expression differences between patients and Dx-Re pairs, even for those with the same -presumed- initiating events. Transcriptional differences between and within patients are associated with clonal composition and evolution, with the most striking differences in patients that gained large-scale copy number variations at relapse. These differences appear to have significant molecular implications, exemplified by a DNMT3A/FLT3-ITD patient where the leukemia switched from an AP-1 regulated clone at Dx to a mTOR signaling driven clone at Re. The two distinct AML1-ETO pairs share genes related to hematopoietic stem cell maintenance and cell migration suggesting that the Re leukemic stem cell-like (LSC-like) cells evolved from the Dx cells.

Conclusions

In summary, the single cell RNA data underpinned the tumor heterogeneity not only amongst patient blasts with similar initiating mutations but also between each Dx-Re pair. Our results suggest alternatively and currently unappreciated and unexplored mechanisms leading to therapeutic resistance and AML recurrence.

Background

Acute myeloid leukemia (AML) is a malignancy of hematopoietic stem cells or early progenitors resulting from the accumulation of genetic aberrations that disturb key biological processes. Mutations may occur in myeloid progenitor populations, which confer self-renewal capacity to the progenitors [1]. In the past decades, numerous AML associated gene alterations have been identified that can be broadly grouped into seven functional categories [2]. The most frequent category comprises mutations that activate signal transduction pathways and induce proliferation or survival of HSPCs, such as FLT3, NRAS/KRAS and KIT. A second category describes mutations or fusions in genes coding for transcription factors that are required for hematopoietic maturation, like AML1-ETO (RUNX1-RUNX1T1) and CEBPA. Mutations in epigenetic regulators like IDH1/2, TET2, DNMT3A and ASXL1 comprise the third category of somatic events that are often acquired at an early stage [3]. The four remaining categories include aberrations in the Nucleophosmin (NPM1) gene, tumor suppressors and members of the spliceosome- and cohesin complexes.

Despite that current chemotherapies efficiently induce complete remission, AML patients frequently suffer from relapse and have low overall 5-years survival rates [4, 5]. Recurrence can emerge for example as a result of the expansion of pre-existing chemo-resistant subpopulations or by acquiring novel chemo-resistant subpopulations due to genomic alterations [6]. The advent of single-cell RNA sequencing provides revolutionary opportunities to assess the heterogeneity of cancer populations at the single-cell level and explore the transcriptional features of individual cell types, such as subpopulations contributing to the relapse. Several single-cell RNA sequencing studies revealed this transcriptional heterogeneity between and within tumor samples [7,8,9].

The prime aim of our study was to define the transcriptional changes between the Dx and Re samples. We used whole-exome sequencing (WES) to study the somatic mutations and copy number variations (CNVs) at Dx and Re. Furthermore, we applied single-cell RNA sequencing to analyze gene expression changes in AML samples between diagnosis and relapse. We profiled 5,612 high-quality cells at diagnosis and relapse from 6 AML patients, two intermediate risk cases with t(8;21) (AML1-ETO), three DNMT3A and one NUP98/NSD1. The latter four presented FLT3-ITD at the time of diagnosis and three of these patients were treated with the FLT3 inhibitor Midostaurin. Our study provides novel insights into recurrence and unveils vulnerabilities that could serve as new entry points for targeting relapse AMLs.

Methods

This is a brief description of the methods. A detailed description of each method is provided in Additional file 1.

AML samples and cell preparation

We processed 6 paired Dx-Re bone marrow aspirates from adult AML patients, with AML1-ETO (n = 2 intermediate risk cases), DNMT3A (n = 3) and NUP98-NSD1 fusion gene (n = 1). The latter four pairs presented FLT3-ITD at diagnosis with variable allele frequencies, and three of four patients were treated with FLT3-ITD inhibitor Midostaurin. Although FLT3-ITD is not an AML initiating lesion, nor an acknowledged World Health Organization (WHO) 2016 AML category, the mutation landscape and treatment is distinct from the AML1-ETO patients. Hence, we labelled patient s220 and s914 as “AML1-ETO” patients and termed patient s232, s292, s2275 and s3432 as “FLT3-ITD” patients. Patient characteristics are summarized in Supplemental Table 1. CD33/CD34 + cells were sorted into 384-well plates and stored at -80℃.

Single cell SORT-seq

SORT-seq [10] is based on the integration of single cell FACS sorting (Fluorescence-Activated Cell Sorter) with the CEL-Seq2 protocol [11]. Single cell libraries were paired-end sequenced on an Illumina NextSeq500 at an average depth of ~ 30 M reads per library. After filtering out low quality cells (genes detected < 500 or UMI count > 12,000 or mitochondrial UMIs > 30% or ERCC reads > 20%), 5,612 high-quality cells were acquired and used for the following analyses (Supplementary Fig. 2A and Supplemental Table 1).

Fusion genes detection

To quantify the reads per gene and detect fusion genes from bulk RNA-Seq, sequencing libraries were aligned to Gencode v37 reference genome version hg38 using STAR-Fusion v1.10.0 [12] in 2-pass mode, with parameters –CPU 12 –FusionInspector validate –examine_coding_effect –denovo_reconstruct.

Whole-exome sequencing

WES libraries were generated as previously described [13]. Diagnosis and relapse samples were compared with samples collected at CR (Complete Remission). Somatic variant calling and CNV detection were performed with the Genome Analysis Toolkit (GATK).

Pseudo-time trajectory analysis

We used Monocle3 [14] for pseudo-time analysis with default parameters, to assess the trajectories within the pairs. We used the DEGs obtained from Seurat 3.0 [15] to plot the dynamic changes of gene expression along the trajectories.

Definition of leukemic stem cells and cycling genes

The 17-gene leukemic stem cell (LSC17) score was calculated based on the equation by Ng et al. [16]. Cell cycle phase scores were calculated using Seurat 3.0 function CellCycleScoring with default parameters.

Results

Whole exome- and gene fusion analyses reveal clonal aberrations

Clonal expansion and evolution is a major determinant of AML relapse [17]. To identify the genomic landscape at Dx and Re, we performed whole exome sequencing (WES) and gene fusion detection based on bulk RNA-sequencing. We detected between 4 and 26 somatic mutations per sample (Fig. 1A, Supplemental Table 2). This analysis confirmed the presence of an inframe insertion in the juxtamembrane domain (JMD) between amino acid 583 and 611 in all four FLT3-ITD patients as well as AML1-ETO fusion transcripts in the AML1-ETO patients (Fig. 1A, B, Supplemental Table 2). Other AML-associated somatic variants, such as NPM1, WT1, CEBPA, IDH1, NRAS and DNMT3A were detected for the FLT3-ITD patients, often in a patient-specific manner. For both AML1-ETO patients, the WES analysis revealed a KIT mutation that is associated with poorer prognosis and increased risk of relapse [18, 19].

Fig. 1
figure 1

Whole exome- and gene fusion analysis between Dx and Re. (A) Oncoplot from WES showing 14 selected somatic mutations across 6 patients (red: n = 2 AML1-ETO; blue: n = 4 FLT3-ITD). We termed patient s232, s292, s2275 and s3432 as “FLT3-ITD”, although it is not an AML initiating lesion, nor an acknowledged WHO2016 AML category, but treatment with the FLT3 inhibitor Midostaurin is distinct from the AML1-ETO patients. Mutations with at least 5 reads on the ALT allele and VAF ≥ 0.05 are depicted as squares and the ones below this threshold are indicated as triangle. Vertical bars depict the number of mutations detected per sample; horizontal bars depict the (relative) frequency of a particular mutation. (B) Gene fusions detected from bulk RNA-seq. (C) Mutations with a VAF ≥ 0.2 at Dx or Re for which the VAF changed significantly. For all bars, p < 0.05, Fisher’s exact test with Benjamini–Hochberg correction. Red: mutations more abundant at Dx. Blue: mutations more abundant at Re

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Next, to identify clonal rearrangements that may have led to disease relapse, we screened for somatic mutations with a significantly altered variant allele frequency (VAF) between Dx and Re (VAF ≥ 0.2 and p < 0.05, Fisher’s exact test; methods; Fig. 1C). In the FLT3-ITD group, patient s232, WES and PCR analysis revealed two distinct FLT3-ITD mutations in the Dx sample, one of which one was lost at Re (p = 1.0 × 10–3; Fisher’s exact test based on WES reads; Fig. 1C, Supplemental Table 2). WES also revealed a NPM1 mutation (type A19) at Dx, whose AF was decreased at Re (p = 8.2 × 10–3) and a low-abundant NRAS mutation at Dx (VAF = 0.087) that was undetected at Re (VAF = 0; p = 2.0 × 10–4; Fig. 1C). For patient s2275, WES showed considerably shorter ITDs at Re compared to Dx (p = 4.6 × 10–41), which were confirmed by PCR (Supplemental Table 3). This patient (s2275) had a NUP98-NSD1 fusion at Dx and Re, consistent with the absence of DNMT3A and NPM1 mutations [20, 21]. Finally, this patient displayed copy neutral loss of heterozygosity at the q-arm of chr13 (13q-LOH), which increases the allelic burden of the FLT3-ITD [22]. This aberration existed in a small fraction of Dx cells, but its abundance and that of the FLT3-ITD allele increased drastically at Re (Supplemental Fig. 1, Supplemental Tables 2 and 3). Patient s292 displayed NPM1 and DNMT3A mutations that remained unaltered between DX and Re. Patient s3432 showed a retention of the FLT3-ITD, both in the insertion location and allelic ratio (WES and PCR). Somatic mutations in FAT3 (VAF = 0.238, p = 4.3 × 10–8), ITGB7 (VAF = 0.165, p = 1.32 × 10–6), UBA2 (VAF = 0.117, p = 6.32 × 10–3) and SLC4A3 (VAF = 0.135, p = 6.6 × 10–3) were significantly gained in the Re sample (Fig. 1C and Supplemental Table 2), whereas the last 53 Mb of chr7 was lost on one allele (Supplemental Fig. 1).

One of the AML1-ETO samples (s914) showed two distinct KIT mutations (VAF = 0.325; VAF = 0.138) at Dx, both of which were significantly reduced at Re (p < 4.7 × 10–7). Patient s220 gained a mutation in the FAT3 gene as well as copy number aberrations at chr2 and chr15 at Re (Supplemental Fig. 1).

To summarize, we confirmed the presence of FLT3-ITDs and AML1-ETO in four and two patients respectively. Additional somatic aberrations in AML-associated genes were patient-specific. FLT3-ITDs were altered in two patients. In one patient, one of the two FLT3-ITDs was lost at Re. For patient s232, a NPM1 mutation was detected at Dx (VAF = 0.6), but was decreased at Re (VAF = 0.1, p = 0.008, Fisher’s exact test). We also observed a significant reduction in two distinct KIT mutations in patient s914 between Dx and Re as well as patient-specific copy number changes at Re.

Single cell transcriptomics reveals distinct AML-phenotypes at Dx and Re

Next, to better understand the transcriptional phenotypes, their differences and possible mechanisms that led to disease progression, we profiled bone marrow cells obtained at Dx and after Re using single cell transcriptomics. In brief, single CD33+ or CD34+ bone marrow cells were FACS-sorted into 384-well plates following the SORT-seq method [10], we acquired 5,612 single cell profiles, in which 4,129 unique transcripts from 1,678 genes were detected on average (Supplemental Fig. 2A, methods).

After normalization, cells were clustered and visualized using the uniform manifold approximation and projection [23] (UMAP). AML1-ETO vs FLT3-ITD samples are separated by UMAP1 and Dx-Re pairs cluster relatively close together (Fig. 2A, B). Nevertheless, considerable heterogeneity between and within pairs exists (Fig. 2B). Strikingly, Dx-Re cells of FLT3-ITD patient s232 cluster in close proximity suggesting minor phenotypic and molecular alterations, even though this patient lost NPM1 and NRAS mutation at Re. Similarly, the expression changes between Dx and Re cells of patient s914 were minor, despite the significant loss of two KIT mutations. In contrast, Dx cells of patient s3432 are completely separated from Re cells, although one mutation in the FAT3 gene was detected in Re (VAF = 0.238) (Supplemental Table 2). Likewise, the Dx and Re cells of AML1-ETO patient s220 constitute distinct clusters, but only gained mutations in genes that are not associated with AML (Fig. 1C). Both patients did however display large copy number changes between Dx and Re (Supplemental Figs. 1 and 6A). Inferred CNV analysis confirmed that these aberrations were undetected for cells at Dx, but present for almost all cells at Re (Fig. 2C). As expected, these CNVs caused differential gene expression at these loci (Fig. 2D), such as LOXL1 and FAM81A implicated in cancer progression [24].

Fig. 2
figure 2

Single cell transcriptomics reveals distinct AML-phenotypes. (A) UMAP of the six AML pairs, colored by primary mutation (red: AML1-ETO; blue: FLT3-ITD); (B) UMAP colored by sample; (C) Copy number variation data derived from WES (left) and scRNA-seq (right) data for patient s220. Left: Relapse-specific copy number gain and loss at chr2 and chr15, respectively. Right: cell normalized gene expression signals (iCNV signal) in tiles of 3 Mb show the copy loss and gain at chr2 and chr15, respectively. The plot indicates that virtually all Re cells are affected, compared to none of the Dx cells. Bottom: tumor allele frequency at heterozygous SNPs confirms copy loss at chr2 and gain at chr15. (D) Boxplot of DEGs at the lost and gained segments of chr2 (n = 17 DEGs) and chr15 (n = 24 DEGs), respectively. (E) Heatmap showing the top 20 marker genes per primary mutation

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Next, we looked for gene signatures that discriminated AML1-ETO or FLT3-ITD patients. These signatures include well-established AML1-ETO markers, like upregulation of the transcriptional co-repressor RUNX1T1 (aka ETO), the transcription factor POU4F1 [25] and the myeloid differentiation protein MPO [26] (Fig. 2E, top). FLT3-ITD samples on the other hand are characterized by induced expression of VIM, ANXA1, MSI2 and LAPTM5. Other genes tend to be overexpressed only in a subset of the samples: HLA genes are overexpressed in AML1-ETO patient s220, but not in s914. In the FLT3-ITD samples, HOXA5 and HOXB3 genes that are overexpressed in NPM1-mutated AML [27], appear overexpressed in a patient-specific manner (Fig. 2E, bottom). Closer inspection of these and other NPM1-marker genes showed that these genes are indeed significantly higher expressed in the FLT3-ITD samples with an additional NPM1 mutation (NPM1mut) compared to NPM1WT samples (FC > 1.5 and p < 6.0 × 10–15; Supplemental Fig. 2D). Notably, HOX-genes are also highly expressed in FLT3-ITD patient s2275. In these samples, we detected a NUP98-NSD1 fusion gene that is characterized by upregulation of HOXA and HOXB genes [28] (Supplemental Fig. 2D).

In summary, single cell transcriptomics showed distinct clustering of AML1-ETO vs FLT3-ITD patients. Differential analysis confirmed upregulation of well-established marker genes as well as elevated expression of HOX genes in NPM1mut and the NUP98-NSD1 positive FLT3-ITD samples. On a global level, the transcriptional changes between Dx and Re are poorly explained by SNPs and INDELs in AML-associated genes, but rather seem to be associated with large-scale CNVs. To gain a deeper understanding of the mechanisms underlying these changes, we subsequently performed an in-depth analysis of Dx-Re pairs per AML-subtype and in a patient-specific setting.

Dx-Re transcriptomic changes are patient specific

Given this high intra- and inter-patient heterogeneity, we focused on the Dx-Re differences per patient in the remainder of this study. For this, we separated the UMAPs of the FLT3-ITD and AML1-ETO patients (Fig. 3A, B) and computed the differentially expressed genes between the Dx-Re pairs per patient. This analysis reinforced the notion that the differences in transcription between Dx and Re are highly patient-specific (Supplemental Figs. 2B, C, 3C, D).

Fig. 3
figure 3

Single cell transcriptomics reveals heterogeneity amongst patients. (A) UMAP of the four sample pairs with a FLT3-ITD, colored by sample (red: Dx; blue: Re); (B) UMAP of the two AML1-ETO sample pairs, colored by sample; (C) Heatmap displaying the top 5 marker genes per sample (FLT3-ITD); (D) Heatmap displaying the top 10 marker genes per sample

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The FLT3-ITD patients show a modest separation between the Dx and Re samples of patient s232 (Fig. 3A). Cluster analysis revealed two clusters at diagnosis (cluster 1–2) and one at relapse (cluster 3, Supplemental Fig. 3A). Re cells lost expression of members of the AP-1 transcription factor, like FOS, FOSB and ATF3 that were highly expressed in Dx cluster 1 (Supplemental Fig. 3B). Gene ontology (GO) analysis confirmed significant loss of expression for these and other genes involved in AP-1/ATF-2 related transcription at Re (Supplemental Fig. 3C). Furthermore, we evaluated the expression level of genes involved in PI3K/AKT/mTORC pathway, in which mTORC1 controls ribosomal biogenesis and protein translation. We found the targets of mTORC1, like RPS6KB1 and EIF4E, were differentially expressed at Re (Supplemental Fig. 3D), suggesting a pathway shift from AP-1 to mTORC1. Besides, we observed the upregulation of the upstream K/NRAS genes in Re, which may be markers for diagnosis/prognosis and treatment targets.

The UMAP for patient s292 showed 3 distinct clusters (Supplemental Fig. 4A). DEG between Dx clusters 1 and 2 revealed IDH1, an enzyme in the TCA cycle, and RAB31 involved in membrane fusion and exocytosis in clusters 1, whereas MPO and PROM1, markers for GMP cells, are differentially expressed in cluster2 (Supplemental Fig. 4B, C). Cells in cluster 3 originate from the Re sample and overexpressed genes like DDIT4 [29], PIM3 [30] and CD74 [31] were previously associated with poor prognosis (Supplemental Fig. 4B). GO analysis indicated regulation of cell death and apoptotic process terms in cluster 3 (Supplemental Fig. 4C).

The copy-neutral LOH at chr13q could be detected from the alternate allele frequency in the WES and scRNA-seq data of patient s2275 (Supplemental Fig. 5A). However, no genes located on chr13q were differentially expressed at Re compared to Dx, with the exception of ELF1 (twofold downregulated at Re). Single cell expression analysis revealed 5 clusters. Cluster 1 mainly originated from Dx cells, whereas cluster 5 almost entirely consisted of Re cells. Clusters 2–4 however were a mixture between Dx and Re cells (Supplemental Fig. 5B, C). DEGs revealed few differences between cluster 1 and 5, such as RNU4ATAC and RYBP involved in RNA biosynthesis and metabolism that are differential expressed in cluster 1 (Dx) (Supplemental Fig. 5C, D), whereas ITM2A and CLEC12A for leukocyte activation and LDHA for ribonucleotide metabolism are differentially expressed in cluster 5 (Re) (Supplemental Fig. 5C, D). The minor differences between Dx and Re is consistent with the fact that AML-associated mutations, such as FLT3-ITD, WT1, CEBPA and NUP98-NSD1 were present at Dx and retained at Re (Fig. 1A, B, Supplemental Table 2).  

Fig. 4
figure 4

Pathway switch between AP-1 and RAS signaling in high risk FLT3-ITD (s3432). (A) UMAP of Dx and Re cells for FLT3-ITD patient s3432 colored by timepoint (top) or cell cluster (bottom). (B) Heatmap displaying the top 10 cluster marker genes. Color represents row normalized expression values. (C) Overrepresented GO terms (category: biological pathway) in cluster 1 (Dx) and 3 (Re). P-values: hypergeometric test (BH-corrected). (D) The expression of genes related to AP-1 transcription factor network and RAS signaling pathway in each timepoint. (E) Calculation of LSC17 score for each cluster, and p-value was calculated using Student’s t-test. * p < 0.05, ** p < 0.01, *** p < 0.001

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The Dx and Re cells of patient s3432 formed distinct clusters that are highly separated from each other and the other FLT3-ITD patients (Fig. 3A). Cluster analysis detected four groups of cells that largely separated Dx (cluster 1) from Re cells (cluster 2–4; Fig. 4A). Cluster 1 had a characteristic gene signature of transcription factors involved in proliferation and cell growth (e.g., JUN, FOS, FOSB, EGR1, SOX4 and KLF6) that were significantly downregulated in the Re clusters (Fig. 4B, C). The Re-specific clusters 3–4 upregulated genes involved in the RAS/mTORC pathway, such as ANKRD28 and PIK3R1, whereas cluster 2 is hallmarked by cell cycle related genes, such as TOP2A and MKI67. Pathway enrichment analysis confirmed the overrepresentation of AP-1/ATF2 transcription factors in cluster 1 (Dx) and additionally revealed upregulation of genes involved in mTOR signaling, like RICTOR, PIK3R1 and HIF1A in cluster 3 (Re; Fig. 4C, D). This suggests a pathway switch from AP-1 in the diagnosis cells towards mTOR in the relapse cells. We further observed that KRAS and NRAS, genes upstream of mTORC, were also overexpressed in the Re sample (Supplemental Fig. 6C). Interestingly, cluster 4 in relapse is characterized by elevated exocytosis (Supplemental Fig. 6D) and increased expression of genes related to Tim-3-galectin-9 Secretory Pathway (e.g. ADGRL1, HAVCR2 and LGALS9) that protect AML cells against from the host immune system in an mTOR dependent manner [32] (Supplemental Fig. 6E), in particular from NK- and T-cell action. Finally, the leukemia stem cell (LSC) score, a 17-gene signature (LSC17) that correlates with aggressiveness of the leukemia and a poor outcome [16] was significantly higher in the Re clusters 3 and 4 compared to the Dx cluster 1 (Fig. 4E). For the other FLT3-ITD patients, none of the clusters had an elevated LSC17 score (data not shown).

Fig. 5
figure 5

Putative LSCs detected in AML1-ETO pair (s914). (A) UMAP of Dx and Re cells for AML1-ETO patient s914, colored by timepoint (top) and cell cluster (bottom). Cells in cluster 6 express ambiguous marker genes, and may be doublets or contaminated by ambient RNA and were discarded (see also Supplemental Fig. 6). (B) Heatmap depicting the top 7 cluster markers. Color represents row normalized expression values. (C) Pseudo-time trajectory colored by timepoint (top) or cell cluster (bottom). (D) Heatmap showing representative genes per cluster. (E) LSC17 score per cluster. * p < 0.05, ** p < 0.01, *** p < 0.001, Student’s t-test. (F) Barplots depicting the relative cell abundance per cell cycle phase (inferred from marker gene expression) for each cell cluster. Arrow: cells in cluster 2 and 3 predominantly reside in the G1 phase

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Leukemic stem cell-like cells in AML1-ETO

Higher MPO, a marker for granulocyte/monocyte progenitors (GMPs) expression [26] within both the AML1-ETO patients (Fig. 2E) implies that most cells are arrested at a “GMP-like” stage. The Dx and Re cells of AML1-ETO patient s220 were more separated compared to those of patient s914 (Fig. 3B, Supplemental Fig. 2C). 41 of the DEGs are transcribed at the amplified or lost loci and their altered transcription may in turn deregulate other genes.

Analysis on Dx-Re showed that the number of DEGs shared between these two AML1-ETO patients is minimal as for the FLT3-ITDs (Supplemental Fig. 2C). Therefore, we performed an in-depth analysis on the transcriptional dynamics between Dx and Re separately for these two patients. Focusing on patient s914 first, the synergic oncogenes (PIM1 and MYC [33]) responsible for tumorigenesis were co-differentially expressed at Re compared to Dx. Cluster analysis revealed five groups of cells (Fig. 5A, B) and a small cluster of scattered cells that expressed signatures of progenitors (CD34), erythrocytes (HBB), monocytes (LYZ), B-cells (MSA41) and cell cycle related genes (TOP2A, MKI67) (Supplemental Fig. 7) likely resulting from ambient RNA or cell doublets and hence were discarded in subsequent analyses.

Cluster 1 mainly consist of Dx cells and differentially expressed genes for differentiation and resistance to apoptosis, like AREG [34]. Interestingly, cells in cluster 2 express CD34 as well as genes involved in cell migration (ANXA1 [35], ANXA2 [36], VIM [37] and EMP1 [38]) but lacked the expression of MPO (Fig. 5B, D). To investigate whether and from which Dx cluster these potential Re LSCs originate, we aligned cells in pseudo-time based on the gradient of transcriptional differences using Monocle3. This trajectory analysis suggested a continuous transition between the Dx and Re sample (Fig. 5C). Cells in cluster 2 and 3 differentially expressed genes for hematopoietic stem cell maintenance (GDF11 [39], GATA2 [40]) and differentiation (GAS7 [41], CAMK1D [42]) markers as well as CD34 (Fig. 5B, D), indicating cluster 2 and 3 are the putative starting points of this trajectory. Besides, cluster 2 and 3 overexpressed genes CXCR4 [43] and CXCL8 [44] for tumor microenvironment (Fig. 5B, D). In line with those findings, we calculated the LSC17- and cell cycle scores for all clusters. We observed that cells in Dx cluster 3 have the highest LSC17 score followed by Re cluster 2 (Fig. 5E). Moreover, cells from cluster 2 and 3 mainly reside in the G1 phase of the cell cycle (Fig. 5F). Interestingly, the trajectory suggest that these cells differentiate into a population of cells that display DUSP6 and AP-1 related genes like JUN and FOS in the Re-specific clusters 3 and 4 (Fig. 5D).

UMAP shows that s220 cells separate according to Dx and Re which partitioned into 9 clusters (Fig. 6A). Clusters 1–4 contained Dx cells that were enriched for CXCL8 and CXCR4, genes associated with the interaction between leukemia blasts and stromal cells [43, 44]. Clusters 5–9 exclusively contained Re cells and were marked by expression of LOXL1 and FAM81A (Fig. 6B). Cell cycle-related genes (MCM6, TOP2A, MKI67) were highly expressed in cluster 1 and 9. Cluster 4 (Dx) and 5 (Re) are in close proximity to each other and share marker genes, such as CAMK1D, GAS7, ANXA1/2, VIM and CD34 (Fig. 6B, Supplemental Fig. 8A) suggesting that they are LSCs.

Fig. 6
figure 6

Putative LSCs detected in AML1-ETO pair (s220). (A) UMAP of Dx and Re cells for AML1-ETO patient s220, colored by timepoint (top) and cell cluster (bottom). (B) Heatmap depicting the top 5 marker genes per cluster. Color represents row normalized expression values. (C) LSC17 scores per cluster. * p < 0.05, ** p < 0.01, *** p < 0.001, Student’s t-test. (D) top: Barplots depicting the relative cell abundance per cell cycle phase (inferred from marker gene expression) for each cell cluster. Arrow: cells in cluster 4 and 5 predominantly reside in the G1 phase. Bottom: UMAP colored by cell cycle phase. (E) Pseudo-time trajectory colored by cell cluster (F) Heatmap depicting representative marker genes per cluster/inferred timepoint

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Alternative “branching” from Re and Dx LSC-like cells in AML1-ETO

Given the high similarities between clusters 4 and 5 and their elevated CD34 expression, we hypothesized that these clusters might be enriched in LSCs. Analysis showed that these clusters indeed have the highest LSC17-score and contain cells that reside predominantly in the G1 cell cycle phase (Fig. 6C, D). To better understand the transcriptional dynamics of cell populations originating from these LSCs, we applied pseudo-time gene expression analysis (Fig. 6E). This analysis reveals a trajectory starting from the presumed LSCs cluster 4 and 5 towards more differentiated cells that predominantly reside in the S-phase of the cell cycle and exhibit elevated expression of genes like TOP2A and MKI67 (Fig. 6D-F). For the Dx branch, genes involved in self-renewal that impede differentiation (GAS7 and CAMK1D) or are associated with cell migration (TPPP3, VIM, ANXA1/2) had elevated expression in cluster 4. We hypothesized that all other clusters of cells originate from this presumed Dx LSC population. Indeed, we observed a downregulation of these markers when cells are traced along the trajectory from cluster 4 to cluster 1 which is consistent with their differentiation into more mature myeloid cells. Furthermore, DUSP1 and DUSP6, genes required for cell differentiation and proliferation were upregulated as cells ‘moving away’ from cluster 4 along the Dx branch (Fig. 6F). In the Re branch, TPPP3, VIM, ANXA1/2, GAS7 and CAMK1D were upregulated in cluster 5 to a similar extent as in cluster 4. Compared to the more gradual downregulation in the Dx branch, these markers were largely lost when cells “branched” from cluster 5 to cluster 6 (Fig. 6F). The Re trajectory (cluster 5 towards cluster 9) is hallmarked by upregulation of numerous genes required for differentiation, leukemia progression and chemo-resistance, including RACK1 [45], EREG [46] and LOXL1 [24] (Fig. 6F). Another striking difference between the Dx and Re is that genes associated with the tumor microenvironment, the interaction between stroma cells and leukemic blasts (CXCR4 and CXCL8) were lower expressed in cluster 5 (Re) compared to cluster 4 (Dx, Fig. 6B). Gene Ontology analysis further revealed up-regulated genes in Dx enriched with terms associated with immune- and inflammatory response, whereas translation and biosynthesis related processes were highly enriched in Re (Supplemental Fig. 8B).

In summary, our data reveals a heterogeneous mixture of cells in the AML1-ETO patients. Dx vs Re cells were more heterogeneous in patient s220 compared to s914. Part of this heterogeneity is caused by large-scale CNVs that affect transcription at the affected loci. Deregulation of these genes may affect other genes and thus increase the transcriptional differences between Dx and Re. Interestingly in both patients, we found cells with a significantly elevated LSC17-score that are predominantly in the G1-phase. These cells appear to be at the origin of other cell populations that develop/branch in a way that is sample and stage specific. The signature genes for LSCs might be potentially therapeutic targets to improve the efficiency of AML treatment.

Discussion

To gain insight into the heterogeneity between AML subtypes and within Dx-Re pairs, we profiled the exome, gene fusions and single cell transcriptome of four FLT3-ITD and two AML1-ETO Dx-Re sample pairs. Here, we focussed on CD33/CD34 + stem- and progenitor cells using plate-based single cell technology. Clustering and differential expression analysis of single cell transcriptomes showed extensive intra- and inter-blasts heterogeneity. The strongest transcriptional differences were associated with patient-specific large scale copy number variation. Another source of heterogeneity were somatic variants (SNPs and INDELs) that point to highly patient-specific abundance and dynamics of AML clones.

We were unable to identify a common molecular mechanism that caused AML relapse across all patients. Instead, our data showed highly heterogeneous genomes and transcriptomes that were patient- and even disease stage specific. The question to what extend relapse-inducing molecular mechanisms are truly patient-specfic, or can be grouped into certain classes, calls for a much larger AML patient cohort than we currently profiled. Despite this limitation, we found differences in underlying resistance mechanisms that are not exclusively caused by clonal rearrangements. One patient showed a pathway switch from AP-1 dependency at Dx to mTOR signaling at Re that appeared to be independent of altered somatic mutations, suggesting that clonal rearrangements are not causing the relapse in this patient [47]. In contrast, significantly altered mutations (e.g., decrease/loss of NPM1 and KIT) in other patients were accompanied by minor transcriptional differences. Furthermore, the presence of quiescent LSCs that escape conventional therapeutic interventions could explain recurrence in the absence of clonal rearrangements [8, 48, 49]. In agreement with this hypothesis, we detected transcriptionally similar LSC-like cells in the Dx and Re samples of the two otherwise distinct AML1-ETO samples. While the expression of these LSC populations is similar at Dx and Re, their differentiation trajectories are remarkably different.

Our results share and extend findings from other recent single cell AML studies. One of them used a more stringent sorting approach to profile the transcriptome of leukemia initiating cells and reported increased BCL2 and CXCR4 signaling in relapse [8]. These genes were also highly expressed in some, but not all, of our diagnosis and relapse samples and also not consistentlty induced at relapse. Other studies based on larger numbers of unsorted cells [26] or deconvolution of bulk RNA-sequencing [50] detected a hierarchy from primitive to differentiated AML tumor cells. They showed that the composition of tumor cells is patient-specific and associated with chemotherapy and drug sensitivity. Although our sorting strategy removed the more mature blasts, we still found strong sample-to-sample heterogeneity within the CD33/CD34 + stem- and progenitor cells. Furthermore, we found sample-specifc activation of pathways such as mTOR and RAS signaling.

Taken together, our study and others have provided a first step to unravel the highly complex nature of the AML bone marrow using single cell technology. It is evident that a much better understanding is needed to ultimately reduce relapse rates and improve long-term survival. Rapid advances in single cell technology now allow profiling of the genome, transcriptome and epigenome for thousands of cells in parallel. This multi-layered information is needed to identify and trace distinct clones, their phenotypes and their (in)sensitivity to therapeutic interventions.

Conclusions

In conclusion, this study indicates that samples with extensive copy number variations showed larger transcriptional differences between Dx and Re, compared with those with SNPs and INDELs only. Importantly, we found pathway switches (e.g., AP-1 to mTOR) with few differential somatic mutations and vice versa. In two pairs of AML1-ETO samples, we found leukemic stem cell-like cells that shared the expression of common characteristic genes.

Availability of data and materials

The high-throughput datasets have been deposited in the European Genome-phenome Archive. The accession numbers for single cell RNA-seq, bulk RNA-Seq and Whole exome sequencing datasets are EGAD00001008373, EGAD00001008374 and EGAD00001008375, respectively.

Abbreviations

AML:

Acute Myeloid Leukemia

HSPCs:

Hematopoietic stem or progenitor cells

Dx:

Diagnosis

Re:

Relapse

CR:

Complete remission

WES:

Whole-exome sequencing

CNVs:

Copy number variations

FACS:

Fluorescence-Activated Cell Sorter

WHO:

World Health Organization

GATK:

Genome Analysis Toolkit

LSC17:

17-Gene leukemic stem cell

JMD:

Juxtamembrane domain

ITD:

Internal tandem duplication

UMAP:

Uniform manifold approximation and projection

GMPs:

Granulocyte/Monocyte progenitors

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Acknowledgements

Thank the lab members for fruitful discussions and suggestions.

Funding

This study was supported by the Princess Maxima Center for Pediatric Oncology, Utrecht the Netherlands, grants from ZonMw/ Bundesministerium fur Bildung und Forschung (German) (BMBF; DRAMA 01KT1603); VALERE: Vanvitelli per la Ricerca; Campania Regional Government Technology Platform “Lotta alle Patologie Oncologiche”: iCURE; Campania Regional Government FASE2: IDEAL; MIUR, Proof of Concept POC01_00043; Campania Regional Government: POR Campania FSE 2014–2020 ASSE III. Y.Z is a PhD student in co-tutele from the Translational medicine PhD program at Vanvitelli University. W.M and N.D.G are supported by the Italian National Operational Program on Research 2014–2020 (PON AIM 1859703–2). This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative.

Author information

Author notes

  1. Lucia Altucci, Wout Megchelenbrink, Lars Bullinger and Hendrik G. Stunnenberg are senior authors.

Authors and Affiliations

  1. Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, Vico L. De Crecchio 7, 80138, Naples, Italy

    Yanan Zhai, Nunzio del Gaudio, Lucia Altucci & Wout Megchelenbrink

  2. Prinses Maxima Centrum, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands

    Yanan Zhai, Prashant Singh, Peter Brazda, Wout Megchelenbrink & Hendrik G. Stunnenberg

  3. Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands

    Yanan Zhai, Peter Brazda, Nader Atlasy, Joost Martens & Hendrik G. Stunnenberg

  4. Medical Department, Division of Hematology, Oncology, and Cancer Immunology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin Institute of Health, Berlin, Germany

    Anna Dolnik & Lars Bullinger

  5. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Anna Dolnik & Lars Bullinger

  6. Department of Internal Medicine III, University Hospital of Ulm, Ulm, Germany

    Konstanze Döhner & Hartmut Döhner

  7. Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, EO, Italy

    Saverio Minucci

  8. Institute of Molecular Biology and Genetics, BIOGEM, Ariano Irpino, AV, Italy

    Lucia Altucci

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Contributions

Y.Z performed experiments; A.D and L.B provided WES data; Y.Z, P.S, W.M and H.S analyzed and interpreted data; A.D, P.B, N.A, N.D.G, K.D, H.D. S.M, J.M, L.A and L.B helped with data interpretation; Y.Z and H.S designed the research; Y.Z, P.S, W.M and H.S wrote the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Hendrik G. Stunnenberg.

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Ethics approval and consent to participate

Informed consent for both treatment and biobanking of leukemia samples according to the Declaration of Helsinki was given by all patients. Approval was obtained from the ethical review board of the University of Ulm (Ethikkommission der Universität Ulm).

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Not applicable.

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The authors declare no competing interests.

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Supplementary Information

Additional file 1

: Supplemental methods. 

Additional file 2: Supplemental Table 1.

Clinical information and sequencing details of the patients. 

Additional file 3: Supplemental Table 2.

Dynamic changes of mutations between Dx and Re (WES) and detected fusion genes from bulk RNA-Seq. 

Additional file 4: Supplemental Table 3.

Characterization of FLT3-ITD at diagnosis and relapse. 

Additional file 5:

 Supplemental Figure 1. Copy number variation based on WES data.Denoised copy ratios and alternate allele frequencies for each sample at Dx (red) and Re (cyan).

Additional file 6: Supplemental Figure 2.

Quality Control of single cell data. (A)Violin plots depicting the detected number of genes (top) and unique transcripts (bottom) per cell. (B) Venn diagram showing the number of DEGs between the Dx and Re sample, for a pairwise comparison in the four FLT3-ITD patients. Very few DEGs are shared between patients.  (C) Same as (B), for the two AML1-ETO patients.  (D) Violin plots depicting gene expression at known NPM1 target genes in FLT3-ITD samples with- and without NPM1 mutation. 

Additional file 7: Supplemental Figure 3.

Single cell landscape of Single cell landscape of FLT3-ITD patient s232. (A) UMAP of Dx and Re cells for FLT3-ITD patient s232 colored by timepoint (top) or cell cluster (bottom). (B)Heatmap displaying the top 20 cluster marker genes. Color represents row normalized expression values. (C) Overrepresented GO terms (category:biological pathway) in cluster 1 (Dx) and 3 (Re). P-values: hypergeometric test(BH-corrected). (D) Gene expression of selected mTORC1 pathway members. 

Additional file 8: Supplemental Figure 4.

Single cell landscape of FLT3-ITD patient s292. (A) UMAP of Dx and Re cells for FLT3-ITD patient s292 colored by timepoint (top) or cell cluster (bottom). (B) Heatmap displaying thetop 20 cluster marker genes. Color represents row normalized expression values.(C) overrepresented GO terms (category: biological pathway) per cluster. 

Additional file 9: Supplemental Figure 5.

Single cell landscape of FLT3-ITD patient s2275. (A) Copy number variation data derived from  WES (left) and scRNA-seq (right) data for patient s2275. Left: Copy neutral loss of heterozygosity of q arm of chr13. Right: cell normalized gene expression signal (iCNV signal). (B) UMAP of Dx and Re cells for FLT3-ITD patient s2275 colored by timepoint (top) or cell cluster (bottom). (C) Heatmap displaying the top 20 cluster marker genes. Color represents row normalized expression values. (D) overrepresented GO terms (category: biological pathway) for clusters 1 (Dx) and 5 (Re). 

Additional file 10: Supplemental Figure 6.

Relapse cells of FLT3-ITD patient s3432 are associated with exocytosis. (A) Copy number variation data derived from WES (left) and scRNA-seq (right) data for patient s3432. Left: Relapse-specific copy number loss at chr7. Right: cell normalized gene expression signal (iCNV signal). (B) Boxplot of DEGs at the lost segments of chr7 (n=8 DEGs). (C) Gene expression of selected RAS-pathway members. (D) overrepresented GO terms (category: biological pathway) for clusters 4. (E) Selected genes associated with exocytosis. Color depicts relative expression; size depicts the relative number of cells for which at least one transcript was detected. ** p < 0.01, *** p < 0.001,Wilcox rank sum / Mann-Whitney U test. 

Additional file 11: Supplemental Figure 7.

Cells in cluster 6 (blue circle, s914 AML1-ETO) simultaneously express hematopoietic stem-/progenitor- (CD34), monocyte (LYZ), B-cell (MS4A1), erythrocyte (HBB) and cell cycle (TOP2A, MKI67) marker genes. This indicates that these cells are doublets or contaminated by ambient RNA and were discarded from further analysis. Color bar represents the expression level of corresponding genes. 

Additional file 12: Supplemental Figure 8.

Single cell landscape of AML1-ETO patient s220. (A) Heatmap displaying the top 20 cluster marker genes. Color represents row normalized expression values. Marker genes shared between cluster 4 (Dx) and 5 (Re) are highlighted inside a black rectangle. (B) overrepresented GO terms (category: biological pathway) at Dx and Re. 

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Zhai, Y., Singh, P., Dolnik, A. et al. Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia. Mol Cancer 21, 166 (2022). https://doi.org/10.1186/s12943-022-01635-4

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  • DOI: https://doi.org/10.1186/s12943-022-01635-4

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Molecular Cancer

ISSN: 1476-4598