Single-cell heterogeneity and dynamic evolution of Ph-like acute lymphoblastic leukemia patient with novel TPR-PDGFRB fusion gene

doi:10.21203/rs.3.rs-2372797/v1

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Single-cell heterogeneity and dynamic evolution of Ph-like acute lymphoblastic leukemia patient with novel TPR-PDGFRB fusion gene

https://doi.org/10.21203/rs.3.rs-2372797/v1

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Background

Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is a refractory and recurrent subtype of B-cell ALL enriched with kinase-activating rearrangements. Incomplete understanding of the heterogeneity within the tumor cells presents a major challenge for the diagnosis and therapy of Ph-like ALL.

Methods

Single-cell RNA sequencing (scRNA-seq) was performed on 10,273 bone marrow mononuclear cells obtained from one patient with Ph-like ALL at diagnosis and after relapse. Integrative single-cell analysis was performed on this Ph-like ALL patient and two Ph⁺ ALL patients at diagnosis and relapse from a previous study.

Results

scRNA-seq analysis exhibited a comprehensive cell atlas of one Ph-like ALL patient with a novel TPR-PDGFRB fusion gene at diagnosis and relapse. Twelve heterogeneous B-cell clusters, four with strong MKI67 expression indicating highly proliferating B cells, were identified. A relapse-enriched B-cell subset associated with poor prognosis was discovered, implicating the transcriptomic evolution during disease progression. Integrative single-cell analysis was performed on Ph-like ALL and Ph⁺ ALL patients, and revealed Ph-like specific B-cell subpopulations and common CD8⁺ T cells characterized by the expression of the inhibitory receptor KLRB1.

Conclusions

Collectively, scRNA-seq of Ph-like ALL with a novel TPR-PDGFRB fusion gene provides valuable insights into the underlying heterogeneity associated with disease progression and offers useful information for the development of immunotherapeutic techniques in the future.

Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL)

Fusion gene

Single-cell RNA sequencing (scRNA-seq)

Cellular heterogeneity

Immunotherapy

Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is an aggressive neoplasm of B lymphoblasts, with a gene expression profile similar to that of Ph⁺ ALL but lacking the BCR-ABL1 translocation [1, 2]. The prevalence of Ph-like ALL is 15% in childhood B-cell ALL (B-ALL) and increases up to 20%-30% in adolescents and adults [3, 4], which is much higher than that of Ph⁺ ALL. Patients with Ph-like ALL appear to be significantly refractory and present with characteristics such as a high nonresponse, short duration of remission, and high relapse incidence, even while undergoing intensive chemotherapy [5, 6]. Similar to Ph⁺ ALL, the 5-year overall survival of Ph-like ALL does not exceed 20% [4, 6]. Yet, the underlying reasons behind the poor outcomes are not well understood.

Ph-like ALLs are a group of heterogeneous B-cell lymphoblasts that harbor a diverse range of kinase-activating alterations, which can be classified into ABL-class gene fusions involving ABL1, ABL2, CSF1R, and PDGFRB and JAK/STAT activating rearrangements involving CRLF2, JAK2, and EPOR [6, 7]. PDGFRB belongs to the ABL class of genes, and patients who are PDGFRB fusion positive account for ~ 8% of the Ph-like cases [8]. Furthermore, at least 12 partner genes of PDGFRB fusion have been identified, among which, EBF1 is the most common [4, 6, 9–12]. These patients generally exhibit minimal residue disease (MRD) and early response to the tyrosine kinase inhibitor (TKI) imatinib [8]. However, the biological basis for the clinical features in patients with PDGFRB fusion-positive B-ALL remains unclear.

Large-scale international studies based on genomic and transcriptomic sequencing have depicted the genetic landscape and heterogeneity within the Ph-like ALL group [4, 6, 9]. Previous studies have reported that multiple models of clonal evolution, based on genetic heterogeneity, ultimately contribute to drug resistance and subsequent relapse [13, 14], which remains a leading cause of death among these patients [15]. B-ALL bone marrows (BMs) encompass distinct cell types, and heterogeneous B-ALL blasts represent the vast majority of the cells. Single-cell RNA sequencing (scRNA-seq) could overcome tumor heterogeneity by comprehensively decoding the cellular composition and assessing the role of different cell types in promoting and supporting the survival and progression of the disease. Single-cell analysis revealed the extensive remodeling of the immune microenvironment during the progression of B-ALL [16, 17], pre-existing CD19 negative subclones of relapsed B-ALL patient [18], and the presence of an exhausted T cell subset with remarkable heterogeneity for B-ALL patients [19]. These studies uncovered cell components associated with disease recurrence and patient survival and provided potential immune-based therapeutic approaches for high-risk B-ALL patients. However, there is no study on the heterogeneity and immunological state of malignant B cells in patients with Ph-like ALL during the development and progression of leukemia.

In this study, we employed scRNA-seq to profile the transcriptomes of one Ph-like ALL patient with a novel TPR-PDGFRB fusion gene at diagnosis and relapse. An unprecedented view of all the major cellular components has been presented, and a B-cell subset specific in leukemic relapse BM was discovered, which might contribute to disease progression and inferior clinical outcomes.

Ethics statement

The study was approved by the Research Ethics Board of the Second Hospital of Dalian Medical University and conducted according to the Declaration of Helsinki. Informed written consent was obtained from all the participants.

Patient samples

BM samples were collected from patients with B-ALL (n = 13) and healthy donors (n = 8) at the Second Hospital of Dalian Medical University, from February 1, 2019 to November 10, 2020. The median age of the patients was 37 years (range, 25–56 years), and the ratio of men to women was 1.17:1. The clinical data of all B-ALL patients are presented in Additional file 2: Table S1. For a TPR-PDGFRB positive patient (Case13), five BM samples and one cerebrospinal fluid (CSF) sample were sequentially collected corresponding to the disease status as follows: primary diagnosis (primary: 190823); complete remission (CR: 200326); relapse (1st relapse: 200201, 2nd relapse: 200414, and 3rd relapse: 200911); and central nervous system leukemia (CNS-L: 200501). The detailed treatment course and regimens are provided in Additional file 3: Table S2 and Additional file 4: Table S3.

Cytogenetics and fluorescence in-situ hybridization (FISH)

R-banding karyotype analysis was conducted in the patient who was TPR-PDGFRB positive at diagnosis, following the standard clinical protocol, and described according to the international system for Human Cytogenetic Nomenclature. The FISH technique was used to detect the fusions using the standard clinical protocol. Briefly, 200 cells were analyzed for disruptions in PDGFRB. Interphase nuclei were probed using the PDGFRB break-apart probe (LPH031, CytoCell, Cambridge, UK) comprising a green 154-kb probe and a red 107-kb probe, which were positioned on each side of the PDGFRB gene.

Sanger sequencing and MRD evaluation

RNA was extracted from BM mononucleated cells (BMMCs) and converted to cDNA using the Accuprime Taq DNA Polymerase System (#12339016, Thermo Fisher). Reverse transcription-polymerase chain reaction (RT-PCR) amplification was performed using the following primers: (1) primers for the TPR-PDGFRB fusion transcript, forward: 5′-AGTCTGTAGGACGTGGCCTT-3′, reverse: 5′-TGGGGTCCACGTAGATGTACTC-3′; and (2) primers for GADPH, forward: 5′-AGCCACATCGCTCAGACAC-3′, reverse: 5′-GCCCAATACGACCAAATCC-3′. The PCR products were purified using the Amicon 0.5 mL 30K Centrifugal filter (#UFC50306, Millipore), and sequencing was performed with the same primer.

Additionally, the MRD was monitored using TPR-PDGFRB fusion transcript copies via RT-PCR and droplet digital PCR (ddPCR). The ABI Prism 7900HT Sequence Detection System and QX200 ddPCR (Bio-Rad) were used to detect the frequency of the TPR-PDGFRB fusion transcripts. The primers and probes used for the TPR-PDGFRB fusion gene, and control ABL1 gene were as follows: 5′-CAAATACCAGTGGGAAT-3′ (TPR-PDGFRB forward); 5′-TCACCTTCCATCGGATCTCGTAA-3′ (TPR-PDGFRB reverse); 5′-FAM-TGCCAAAGCATGATGAGGATGATAAGGGAG-BHQ1-3′ (TPR-PDGFRB fusion); 5′-CTAAAGGTGAAAAGCTCCG-3′ (ABL1 forward); 5′-GACTGTTGACTGGCGTGAT-3′ (ABL1 reverse); and 5′-FAM-CCATTTTTGGTTTGGGCTTCACACCATT-TAMRA-3′ (ABL1-wt). The relative quantity of the qRT-PCR was calculated using the △△CT method. The ddPCR workflow was presented as described previously [20], and the QuantaSoft tool (Bio-Rad) was used to quantify the ddPCR reads.

Western blot

The cells were harvested using lysis buffer. Subsequently, the cell lysates were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), transferred to nitrocellulose membranes, and immunoblotted with the following antibodies: PDGFRB (Cell Signaling Technology (CST); #3169), TPR (Abcam; #ab70610). The immunoblots were analyzed using the Odyssey system (LI-COR Biosciences).

Bulk RNA library preparation and sequencing

RNAs of the cryopreserved BMMCs were extracted using the QIAamp AllPrep RNA Mini Kit (Cat# 80204, QIAGEN), according to the manufacturer's specifications. Libraries were constructed using the TruSeq RNA Sample Preparation Kits (Illumina), and the quality of the library was assessed using Bioanalyzer 2100 (Agilent Technologies). Massively parallel RNA sequencing (RNA-seq) was performed on the NovaSeq platform (read length, paired end 150 bp; Novogene, Beijing, China).

Bulk RNA-seq data processing

For the gene expression, the sequencing data were mapped to the reference genome (hg38) using STAR [21], and the transcript coordinates were defined on the basis of the gene annotation format file (GTF file) from GENCODE (Release 27, GRCh38). The gene abundances are presented as Reads Per Kilobase per Million mapped reads (RPKM) using the “cuffnorm” command from the Cufflinks package [22]. The expression values in all the patients and healthy donors are presented in Additional file 5: Table S4.

In order to investigate the similarities in the expression between Case 13 and the B-ALL subtypes, the expression profiles were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments ALL Phase II (TARGET-ALL-P2: https://ocg.cancer.gov/programs/target/) and ERG datasets [23]. The subtype information of the TARGET-ALL-P2 cohort was obtained from Gu's report [24]. Only subtypes with specific molecular alterations and sufficient samples (n > 5) were retained. Correlation was performed using the “cor” function in R (http://cran.r-project.org/). Hierarchical clustering analysis based on the Ph-like ALL signature consisting of 192 genes [2] (Additional file 6: Table S5) was performed using the “hclust” function in R.

STAR-Fusion (https://github.com/STAR-Fusion/STAR-Fusion/wiki) was used to detect the fusion transcripts. Case 13 was found to harbor a novel TPR-PDGFRB fusion transcript (Additional file 7: Table S6). Enrichment analysis of the differential expression between the TPR-PDGFRB-positive samples and normal samples was conducted using the Molecular Signatures Database (MSigDB) chemical and genetic perturbations hallmark gene sets via gene set enrichment analysis (GSEA) [25].

scRNA-seq and data processing

scRNA-seq was performed on the BMMCs from the primary and 1st relapse samples collected from a patient who was TPR-PDGFRB positive using the chromium system (10X Genomics). Approximately 16,000 cells from each sample were loaded onto the chip, and the library was prepared using the Chromium Single Cell 3 Reagent Kit (v3) according to the manufacturer's instructions. The library was sequenced at one full lane per sample using a HiSeq4000 platform (Illumina) with 150-bp paired-end reads. The reads were aligned to the hg38/GRCh38 reference genome, and the gene expression was quantified to generate the gene-barcode unique molecular identifier (UMI) matrices using the CellRanger software package (v3.0.1). Low-quality cells, multiple cells or doublets, and cells with > 10% of the transcripts obtained from mitochondrial genes were excluded from the subsequent analysis.

scRNA-seq integrated analysis and unsupervised clustering

Seurat anchor-based integration analysis was performed to eliminate the biological and technical batch effects of the samples and scRNA-seq libraries [26]. A total of 10,273 cells from the primary and relapse samples obtained from the patient with Ph-like B-ALL were processed. The count data were normalized by a scale factor (10,000) followed by a natural-log transformation. The 2,000 highly variable genes (HVGs) which were detected using the “FindVariableGenes” function were subsequently used to identify the integration anchors based on the first 30 dimensions by employing the “IntegrateData” function. Principal component analysis (PCA) was performed using the “RunPCA” function to project the cells in a two-dimensional space. After a graph-based Louvain clustering, 17 clusters were partitioned using the “FindClusters” function with their 30 nearest neighbors and a resolution of 0.9. Finally, the gene expression and clustering results were visualized via t-Distributed Stochastic Neighbor Embedding (tSNE) using the RunTSNE function.

Determination of the type and state of the cell

To define the cell type, the results from the cell type annotation tools and the differentially expressed genes (DEGs) of clusters were taken into consideration. First, we inferred the broad cell identity of each cluster using the SingleR (v1.0.1) [27] and scHCL packages (v0.1.1) [28]. Then, the DEGs were identified using the Wilcoxon rank-sum test implemented using the “FindAllMarkers” function in the Seurat R package (v3.2.4); genes with a fold change (FC) > 1.5 and p-value < 0.05 were included (Additional file 8: Table S7). Finally, the cell type-specific marker genes (detected in at least 5% of the cell-type cells, FC > 1.5, p-value < 0.05) were selected via pairwise comparison using the Wilcoxon method. The five genes with the highest FC were used for the heatmap exhibition utilizing the cell type average expression matrix.

A previously reported core gene set consisting of 43 G1/S genes and 54 G2/M genes [29], was used to evaluate the state of the cell cycle based on the scores obtained from the Seurat “CellCycleScoring” function.

To examine the cytotoxicity, natural killer cell (NK), and inhibitory signatures within single cells, the Seurat “AddModuleScore” function was applied to score the levels of the specific signatures. The average expression levels of well-known feature genes [30, 31] including 5 exhausted genes (PDCD1, TIGIT, LAG3, HAVCR2, and CTLA4), 9 cytotoxic genes (NKG7, GNLY, CST7, PRF1, GZMA, GZMB, GZMH, IFNG, and TNFSF10), and 10 NK signature genes (KLRD1, FGFBP2, S1PR5, KLRC1, KLRC2, KLRC3, KLRB1, KLRK1, KLRG1, and FCGR3A) were used to calculate the feature scores. The cytotoxicity, NK, and inhibitory signature scores were defined on the basis of the established marker genes. A cell was considered having a high signature score if it was > 0.5.

The trajectory inference for the developmental trajectory of B cells was performed using Monocle2 (v2.14.0) [32]. The “subset” function was first used to extract the normalized mRNA counts of the B cells, and an object was created according to the Monocle2 tutorial. For the pseudotime analysis, the top 1,000 HGVs were selected using the “FindVariableGenes” function to order the cells.

Pathway analysis

Pairwise comparison using the “wilcoxauc” function in the presto R package (v1.0.0) was performed to investigate the biological states or functional differences of the distinct cell types. The ranked gene lists ordered by using log2FC were used to investigate the MSigDB Hallmark gene sets by GSEA. The DEGs (FC > 1.5) between the primary/relapsed specific B-cell clusters and other clusters detected using the “FindMarkers” function were enriched with the Hallmark and KEGG gene sets by using GSEA. The DEGs (Additional file 9: Table S8) between the primary/relapsed specific B-cell clusters and other clusters detected via the “FindMarkers” function were enriched with the Hallmark and KEGG gene sets by using GSEA.

Survival analysis

Survival analysis was performed on the bulk RNA cohort TARGET-ALL-P2. The gene expression and survival data were downloaded using the R function “GDCdownload” in the TCGAbiolinks package (v2.18.0). The relapsed B-cell feature was defined as the mean log2 (RPKM + 1) normalized expression of the top 10 DEGs (ACSM3, HRK, IGLC3, DPEP1, NSMCE1, IGLC2, IGKC, CD9, DDX54, and BTG2) between the relapsed specific clusters and other clusters. Similarly, the average expression levels of the top 10 DEGs (NSMCE1, CD9, CD79B, DDX54, CD81, H1FX, ACSM3, JUND, HRK, and AES) between the PDGFRB-specific B-cell clusters and other B-cell clusters represented the PDGFRB-specific B-cell feature (Additional file 10: Table S9). The samples were divided into high and low expression groups based on their median values. A Kaplan-Meier survival curve with the events table showing differences in survival time was plotted using the survminer package (v0.4.9). The survival risk and statistical significance were determined according to the hazard ratio (HR) and log-rank p-values reported in the survival package in R (v3.2-11).

Comparison analysis of KLRB1 transcriptional programs

We defined the CD8⁺ KLRB1 programs consisting of 216 up-regulated genes (log2FC > 0.25) by making comparisons between the CD8⁺ cells with KLRB1 and other cells using the “FindAllMarkers” function in Seurat (Additional file 11: Table S10). The CD8⁺ KLRB1 programs for pan-cancer and the other six cancer cohorts were obtained from Nathan's dataset [31]. The significance of the overlap in genes in the CD8⁺ KLRB1 programs between our cohort and Nathan's dataset was determined using a hypergeometric test.

Integrated analysis of Matthew's dataset

In order to compare the cell identity between the patients with Ph-like and Ph⁺ B-ALL, Matthew's dataset GSE130116 was downloaded from the Gene Expression Omnibus and re-analyzed. Then, the scRNA-seq data of the BM samples from two patients with Ph⁺ B-ALL at diagnosis and relapse were included for subsequent analyses. The criteria for quality control were as follows: doublets or multiple cells, cells with < 200 genes, and cells with > 10% of reads mapped to mitochondrial genes. Genes that were detected in fewer than 60 cells were filtered out. For the integrated analysis, the Seurat3 “anchor” method was used to remove the batch effect. The integrated dataset was processed for dimension reduction and cell clustering at a resolution of 0.7. Finally, cell type annotation and cell state evaluations, including the cell cycle score, signature score, and developmental trajectory, were performed as described above.

The patient diagnosed as Ph-like ALL was identified with a novel TPR-PDGFRB fusion gene

A 55-year-old woman (patient ID: Case 13) was referred to our hospital due to fatigue in August 2019 (Fig. 1A and Additional file 1: Fig. S1A). Karyotype analysis showed a normal karyotype (Additional file 1: Fig. S1B), and the wright-stained smear presented with 74% blasts at diagnosis (Additional file 1: Fig. S1C). Immunophenotyping revealed the positive expression of the B-lymphoid markers cCD79α, CD19 and CD10 at diagnosis (Additional file 1:Fig. S1D). A multiplex RT-PCR analysis for 43 leukemia-related fusion genes showed a negative result (data not shown). After conventional chemotherapy, the patient reached CR (Fig. 1A and Additional file 3–4: Tables S2-S3) but developed a recurrence of the disease (1st relapse: 200201) while preparing for a hematopoietic stem cell transplantation (Fig. 1A). The leukemia cells were cleared after prolonged induction therapy; however, she developed a second relapse soon after (2nd relapse: 200414).

The rapid relapse combined with negative BCR-ABL1 fusion observed in this patient is, to a great extent, in line with the features of Ph-like ALL. RNA-seq was performed on 13 patients with B-ALL (including Case 13) and 8 healthy donors (Additional file 5: Table S4), and comparison of the expression profiles with healthy donors revealed the enrichment of the BCR-ABL1 fusion signature in the Case 13 (Additional file 1: Fig. S2A). Furthermore, the patient (Case 13) exhibited higher expression similarities with the Ph-like and Ph⁺ ALLs than with the other ALL subtypes in the correlation analysis (Additional file 1: Fig. S2B). Hierarchical clustering analysis based on the Ph-like ALL signature consisting of 192 genes [2] (Additional file 6: Table S5), showed that this patient (Case 13) blended into the Ph⁺ cluster in all three cohorts (Additional file 1: Fig. S2C). These results suggest that the expression profile of our patient was similar to that of the patients with Ph⁺ ALL.

In general, molecular genetic alterations that activate cytokine receptor and kinase signaling account for approximately 80%-90% of Ph-like ALL cases [6, 7]. In the current study, we identified a novel TPR-PDGFRB in-frame fusion gene, which consists of exons 1–46 of the TPR gene with exons 11–23 of the PDGFRB gene (Fig. 1B and Table S6). The PDGFRB rearrangement was confirmed using FISH (Fig. 1C), and the junction sequences of the TPR-PDGFRB fusion transcript was validated using RT-PCR and Sanger sequencing (Fig. 1D). The presumed TPR-PDGFRB fusion protein includes four coiled-coil domains from TPR, one transmembrane domain, and one tyrosine kinase domain from PDGFRB (Fig. 1E). Immunoblot analysis confirmed the presence of chimeric kinase protein TPR-PDGFRB as a TPR- and PDGFRB-immunoreactive band (Fig. 1F). Together, these results illustrate that the patient should be diagnosed with Ph-like ALL.

Previous studies have reported that PDGFRB fusion patients with Ph-like ALL were refractory to conventional therapy but amenable to TKI [8, 33]. Imatinib was given as monotherapy but failed to induce CR within 2 weeks (Fig. 1A). Moreover, the patient exhibited CNS-L with 96.5% blasts in the CSF (Fig. 1A). Subsequently, dasatinib combined chemotherapy was administered, and CR was achieved within 4 months (Fig. 1A). Unfortunately, the disease relapsed after dasatinib discontinuation due to the exorbitant medical costs (3rd relapse: 200911; Fig. 1A). Finally, the patient showed no response to further treatment with ponatinib and died on October 20, 2020 (Fig. 1A).

The single-cell expression atlas and cell types in the Ph-like ALL patient

The transcriptome landscape of Ph-like ALL at single-cell resolution remains unknown. In this study, scRNA-seq was used to profile the gene expression in cells taken from the BM specimens at diagnosis and first relapse (Fig. 2A). After quality control, an anchoring-based integration analysis was conducted using 10,273 cells to account for the technical and biological variances between individual samples [26, 34]. A reduction in the dimensionality was observed using the t-distributed stochastic neighbor embedding (tSNE) projection method implemented in the Seurat toolkit (Fig. 2B).

Unsupervised clustering analysis identified 17 transcriptionally distinct cell clusters (Additional file 1: Fig. S3A), and the heatmap showed the top five cluster specific marker genes (Additional file 1: Fig. S3B and Additional file 8: Table S7). By coupling the cell-type annotation by “scHCL” package and the well-known lineage-specific marker genes, 17 clusters were classified into six hematopoietic cell types including B cells, T cells, natural killer (NK) cells, megakaryocytes-erythroid progenitors (MEPs), classical monocytes and non-classical monocytes (Fig. 2C).

For the tSNE representation, 12 B-cell clusters were characterized by the high expression of CD19, CD79B, CD24, IGHM, and VPREB3 (Fig. 2C-E). Clusters 11 and 17 were assigned to classical and non-classical monocytes based on the high expression of CD14 and FCGR3A (CD16), respectively (Fig. 2C and 2E). Besides the well-known markers, we also observed the specific expression of S100A12 in classical monocytes, and CSF1R, CDKN1C, C1QA and C1QB in non-classical monocytes (Fig. 2D and Additional file 1: Fig. S3C). T cells (cluster 9) markedly expressed CD3D and consisted of both CD4⁺ and CD8⁺ T cells (Fig. 2D-E and Additional file 1: Fig. S3D). NK cells (cluster 16) were characterized by the expression of signature genes GNLY and KLRC1 and cytotoxicity-related genes PRF1 and FCER1G (Fig. 2D-E and Additional file 1: Fig. S3D). Cluster 13 was defined as MEPs based on the high expression of HBD, CA1, and APOC1, which harbored the potential for proliferation due to the expression of the cell cycle genes CENPE and TPX2 (Fig. 2D-E and Additional file 1: Fig. S3E). Gene set enrichment analysis (GSEA) across different cell types using the MSigDB hallmark gene sets demonstrated significantly up-regulated enrichment of the gene sets associated with cell cycle progression, ectopic KRAS and TP53 signaling pathway in the B cells (Fig. 2F). Taken together, scRNA-seq captured the panorama of the BM cells from the primary and relapsed Ph-like ALL case and depicted all major cell types and their gene expression states.

Heterogeneous transcriptional profiles of malignant B cells in the Ph-like ALL patient

B-ALL is a B-cell malignancy characterized by the abnormal proliferation and accumulation of B-lymphoid progenitor cells that invade both the peripheral blood and BM. At the single-cell level, the B cells were grouped into 12 clusters, suggesting the transcriptomic heterogeneity of the B cells in Ph-like ALL (Fig. 3A). By comparing the expression profile with normal B cell progenitors using “singleR” method, we classified the 12 B cell clusters into six subsets (Fig. 3A). Based on the expression of marker genes, cluster 1 was assigned to CD20 (MS4A1) high and IGLL1 low PreB cells; the subset of IGKC high and IGLL1 high PreB cells was consisted of clusters 2, 3, and 14 (Fig. 3A-B). Clusters 4 and 6 were defined as IGKC low and IGLL1 high PreB cells, whereas cluster 5 consisted of CD34⁺ ProB cells (Fig. 3A-B). Because of the specific expression of GPR183 and AIM2, cluster 15 was defined as memory B cells (Fig. 3C).

A previously reported core gene set, which was shown to denote the G0/G1, S, and G2/M phases [29], were used to infer the cell cycle states. Clusters 7, 8, 10, and 12 were characterized by the strong expression of the G2/M and S-phase genes and were grouped into cycling PreB cells, likely corresponding to the highly proliferating malignant B cells (Fig. 3D). Furthermore, cells in clusters 7 and 10 highly expressed the cell cycle progression genes (e.g., PCNA, MCM7, TYMS, GINS2, and PCLAF), demonstrating the enrichment of the S-phase cells (Fig. 3D-E). Representative proliferating genes (such as MIK67, TOP2A, ASPM, and CCNB1) were enriched in clusters 8 and 12, indicating the G2/M phase cells (Fig. 3D-E).

Monocle2 was usually used to order cells based on their expression patterns to represent the distinct cellular fates or biological processes [32]. Here, we performed the pseudotime inference on six B-cell subsets, and revealed the distinct bifurcated architecture of the cell trajectory among different cell subsets, implying a divergence in the transcriptional state (Fig. 3F). According to the pseudotime, the six B cell subsets appeared to start principally from the CD34⁺ ProB and cycling PreB cells and moved toward two IGLL1 high PreB cell subsets, following to CD20 high or GPR183 high cell subsets (Fig. 3F). These results suggest the remarkable heterogeneity within malignant B cells, which is reflected in their distinct proliferating and differentiation states.

A relapse-specific subpopulation associated with disease progression in the patient with Ph-like ALL

To decipher the relationship between the clusters and disease progression, the tSNE representation was divided on the basis of disease status (Fig. 4A). And the percentage of cells labeled under different disease status within each cluster was calculated (Additional file 1: Fig. S4A). Surprisingly, we found that all non-B cell clusters (cluster 9: T cells; cluster 11: classical monocytes; cluster 13: MEPs; cluster 16: NK cells; cluster 17: non-classical monocytes) were enriched in relapse sample (Additional file 1: Fig. S4A-B). Meanwhile, functional annotation analysis based on the DEGs from bulk RNA-seq showed the significant enrichment of HEMATOPOIETIC_CELL_LINEAGE (Additional file 1: Fig. S4C). The volcano plot showed that most of the well-known non-B cell lineage marker genes were up-regulated in relapse bulk RNA-seq data, and the B-cell marker gene CD20 was significantly down-regulated (Additional file 1: Fig. S4D). Further, we also validated the specific expression of these lineage marker genes in different cell types based on the scRNA-seq data (Additional file 1: Fig. S4E).

For the B cell clusters, we discovered that clusters 4, 6, and 15 were enriched with cells at the onset of the disease (> 80%), along with the low expression of CD19 and high expression of CD79A and CD10 (Fig. 4A-B). GSEA of primary specific B-cell clusters revealed a down-regulation in the MYC targets, oxidative phosphorylation, and the pathways associated with fatty acid metabolism (Fig. 4C and Additional file 1: Fig. S4F). Furthermore, relapse-specific B-cell clusters 2 and 14 (> 90%) characterized by the absence of CD20 expression were identified (Fig. 4A-B), consisting with the observation from bulk RNA-seq data (Additional file 1: Fig. S4D). By comparing the two clusters with the other B-cell clusters, the top 10 up-regulated genes (fold change > 2 & P < 0.01; including ACSM3, HRK, DPEP1, NSMCE1, and BTG2) were identified (Fig. 4D and Additional file 9: Table S8). GSEA analysis using all the up-regulated genes showed that B-cell receptor signaling, glycolysis, and the hypoxia pathways were significantly enriched (Fig. 4E). The relapse-specific B-cell feature was determined as the mean expression of the top 10 up-regulated genes, based on which, the 198 patients with B-ALL from the TARGET-ALL-P2 (Therapeutically Applicable Research to Generate Effective Treatments ALL Phase II) cohort [24] were categorized into a high group or a low group. The patients in the group with the high relapsed B-cell feature was found to be significantly associated with a poor clinical prognosis (Fig. 4F, P = 0.0077). Furthermore, the expression levels of the specific marker genes ACSM3 and HRK were significantly higher in relapsed patients with CNS leukemia, indicating their potential contribution to brain infiltration in the patient with Ph-like ALL (Fig. 4G and Additional file 1: Fig. S4G). Taken together, these findings uncover the single-cell transcriptome-related dynamic evolution during disease progression.

Integrative single-cell analysis revealed the presence of a Ph-like specific B-cell subset and a common feature

Considering the similarities between Ph-like and Ph⁺ ALL as determined by bulk RNA-seq, the expression relationship at the single-cell level was evaluated by integrating the scRNA-seq datasets from a previous study that involved two patients with Ph⁺ B-ALL [16]. After quality control, a total of 25,559 cells were used for the anchoring-based integration analysis and 23 clusters were identified and visualized as tSNE projection (Fig. 5A-B). According to the cell type annotation tool and the well-known lineage markers, 23 clusters were labeled as B cells (CD19, CD79B, and MS4A1), T cells (CD3D and CD8A), NK cells (GNLY and NKG7), hematopoietic stem and progenitor cells (HSPC: FAM30A and CD34), erythrocytic cells (HBD and AHSP), or myeloid cells (CD68, MNDA, and CD14) (Fig. 5C and Additional file 1: Fig. S5A). Cell cycle scoring showed the high expression of the genes related to proliferation (e.g., MKI67, TOP2A, PCNA, and PCLAF) in two B-cell clusters (clusters 3 and 19) and two erythrocytic cell clusters (clusters 11 and 20, Fig. 5D).

tSNE representation of clusters split by the disease subtype and the cell percentage within each cluster showed that most of the cells (> 90%) in clusters 1 and 8 originated from the Ph-like samples (Fig. 5B and Additional file 1: Fig. S5B). GSEA analysis showed that Ph-like specific B-cell clusters (Cluster 1 and 8) displayed a higher stemness feature comparing with other B cell clusters (Fig. 5E), which is consistent with the comparison between PDGFRB fusion positive B-ALL and Ph⁺ B-ALL according to bulk RNA-seq data (Additional file 1: Fig. S5C). Further, we identified the significantly up-regulated genes which included CD81, AES, H1FX, and JUND in clusters 1 and 8 (Fig. 5F and Additional file 10: Table S9). Survival analysis of the Ph-like specific B-cell feature, defined as the mean expression of the up-regulated genes (fold change > 2 & P < 0.01; n = 7), demonstrated that patients with high levels of Ph-like specific B-cell feature were significantly associated with an adverse prognosis (Fig. 5G, P = 3e-04).

Recently, some researchers have reported that a subset of CD8⁺ T cells with KLRB1 (CD161) overexpression displayed different cytotoxic states and NK cell signatures [30, 31]. The expression of classical cytotoxic genes (Fig. 5H, left panel) and NK cell genes (Fig. 5H, middle panel) were evaluated in clusters 4, 9, 10, and 16 (T cells) and clusters 15 and 23 (NK cells) and observed the expression of KLRB1 in cluster 9 (CD8 negative) and cluster 10 (CD8 positive) (Fig. 5C). Signature scoring showed that cluster 10 possessed both high cytotoxicity signature scores and high NK cell signatures (Fig. 5I-J) as described in glioma [31], cluster 4 and 9 showed the expression similarity of memory T cells characterized by the expression of CCR7 and GPR183 respectively, and cluster 16 showed the feature of stress T cells (Additional file 1: Fig. S5D-E). Additionally, cluster 10 exhibited a low exhausted state characterized by the low expression of well-known co-inhibitory receptors (Fig. 5H, right panel). Next, the presence of associations between KLRB1⁺ CD8 T cells in B-ALL and similar transcriptional programs in other human tumor types were evaluated. Extensive and significant overlap in the KLRB1 transcriptional signatures from each of the five cancer types and pan-cancer KLRB1 program was noted (Additional file 1: Fig. S5F and Additional file 11: Table S10).

KLRB1, encoding the CD161 protein, binds to CLEC2D to inhibit NK cell-mediated cytotoxicity and acts as the inhibitory immune checkpoint in NK cells [35]. Our scRNA-seq data revealed that CLEC2D mRNA was also expressed in malignant B cells, except the T and NK cells (Additional file 1: Fig. S5G), indicating the potential for immune evasion. Furthermore, the expression of CD24 and its ligand SIGLEC10 was detected in B cells and myeloid cells, respectively (Additional file 1: Fig. S5G). Despite the patients with high SIGLEC10 expression were significantly associated with a worse prognosis (Additional file 1: Fig. S5H, P = 0.013), the SIGLEC10 displayed low expression in Ph-like subtype and high expression in Ph⁺ subtype (Additional file 1: Fig. S5I). In contrary, the CLEC2D and KLRB1 exhibited high expression in both Ph-like and Ph⁺ subtypes (Additional file 1: Fig. S5I). Further, we also observed the expression of CLEC2D and KLRB1 in other cancer types from TCGA data (Additional file 1: Fig. S5J). These data imply that several paths might be simultaneously involved in tumor immune escape in patients with B-ALL patients.

The definition of Ph-like ALL is originally based on the similarities between its gene expression patterns and those of Ph⁺ ALL. Following the wide application of next-generation sequencing, researchers have discovered a myriad of genomic alterations that activate kinase and cytokine receptor signaling as molecular markers in Ph-like ALL. In the present study, we confirmed the diagnosis of Ph-like ALL for a patient who harbored a new TPR-PDGFRB fusion gene (Fig. 1 and Additional file 1: Fig. S1) and showed the similarity of gene expression profile with Ph⁺ ALL (Additional file 1: Fig. S2). TPR has been reported to form oncogenic fusions as the 5′ partner in solid tumors [36–38] and hematological malignancies [39, 40]. Some researchers have proposed that the coiled-coil domain of the TPR fusion protein could promote the dimerization and activation of fusion kinases [36]. The chimeric TPR-PDGFRB protein contains all the four coiled-coil domains from TPR, indicating the potential contribution to enhanced kinase activity and leukemogenesis. Fusion genes have been widely used as molecular markers to evaluate MRD in leukemia [33]. In this study, we demonstrated that TPR-PDGFRB fusion gene is a new molecular marker for MRD tracking in Case 13 (Additional file 1: Fig. S6).

Tumor cellular heterogeneity leads to challenges in cancer diagnosis and treatment. Currently, scRNA-seq has become a powerful technology that allows for the prospective identification of heterogeneous cell types or states. Until 2020, scRNA-seq analysis focusing on B-ALL revealed the remarkable heterogeneity involving the B-ALL cell subsets, monocytes, and T cells [16–19]. However, to the best of our knowledge, this is the first study to determine the heterogeneity in the cellular composition of Ph-like ALL and their dynamics during disease progression. Single-cell profiling was used to allow for an unprecedented panorama of BM-resident immune cells from a patient with Ph-like ALL at diagnosis and relapse (Fig. 2). Furthermore, this study revealed the transcriptional heterogeneity among different B-cell clusters in Ph-like ALL (Fig. 3).

Several comprehensive and in-depth studies have identified functional relapse-associated genetic lesions that might contribute to poor survival [41–43]. However, the cells or types of cells that harbor the relapse-specific expression signature in Ph-like ALL remain unknown. In the current study, dynamic shifts of B cells and non-B-cell immune populations were observed during disease progression (Fig. 4 and Additional file 1: Fig. S4). Especially, the expression of marker genes ACSM3 and HRK were higher in relapsed B-ALL patients with CNS leukemia (Fig. 4G and Additional file 1: Fig. S4G), suggesting their potential as a biomarker for brain infiltration in B-ALL. Previous studies have been extensively reviewed the impact of specific genetic lesions [44], CNS niche [45], chemokines and cytokines [46, 47], growth factors [48], metabolic reprogramming and cell adhesion [49] in driving leukemic cells to the CNS. Although ACSM3 involving in the fatty acid metabolism [50] and HRK playing a pro-apoptotic role by interacting with BCL2 [51] have been reported, the functions on CNS leukemia remains unknown and requires further study.

In this study, a distinct expression feature present in two B-cell clusters enriched in the Ph-like ALL samples was observed, which were characterized by a high stemness and a poor prognosis (Fig. 5). Additionally, a CD8⁺ T cell population with KLRB1 ectopic expression was identified as a common feature between Ph⁺ ALL and Ph-like ALL. This T-cell subset demonstrated the co-expression of cytotoxicity and NK cell genes. CLEC2D, the ligand of KLRB1, is reported to be expressed by both T cells and NK cells and convey the inhibition signaling to protect target cells against NK cell-mediated killing [35]. The high expression of CLEC2D has been reported in GC cells, early plasmablasts, and GC-derived lymphomas, but not in B-ALL cells [52]. In the present study, the expression of CLEC2D was observed in malignant B cells (Additional file 1: Fig. S5G), indicating the potential for immune evasion. One study demonstrated the usefulness of CLEC2D-CD161 as a novel immune checkpoint therapy target in human lymphoma using a mouse model [53], with implications for other B-cell malignancies such as B-ALL.

Collectively, scRNA-seq presents a comprehensive atlas and dynamic shifts of cellular composition in one Ph-like ALL patient with a novel TPR-PDGFRB fusion gene, which might aid in improving our understanding of the cell heterogeneity and its implications in the progression of Ph-like ALL. The integrative single-cell analysis of Ph-like and Ph⁺ ALL revealed a Ph-like specific transcriptional signature; in addition, a CD8⁺ T cell subpopulation with KLRB1 ectopic expression was identified as a common feature. This study indicates the potential role of CLEC2D-CD161 as a novel immune checkpoint therapy target in B-ALL.

Ph-like ALL: Philadelphia chromosome-like acute lymphoblastic leukemia; scRNA-seq: Single-cell RNA sequencing; B-ALL: B-cell ALL; MRD: Minimal residue disease; TKI: Tyrosine kinase inhibitor; BMs: Bone marrows; BMMCs: BM mononucleated cells; CR: Complete remission; CSF: Cerebrospinal fluid; CNS-L: Central nervous system leukemia; FISH: Fluorescence in-situ hybridization; RT-PCR: Reverse transcription-polymerase chain reaction; ddPCR: Droplet digital PCR; SDS-PAGE: Sodium dodecyl sulfate-polyacrylamide gel electrophoresis; RNA-seq: RNA sequencing; RPKM: Reads Per Kilobase per Million mapped reads; TARGET-ALL-P2: Therapeutically Applicable Research to Generate Effective Treatments ALL Phase II; MSigDB: Molecular Signatures Database; UMI: Unique molecular identifier; HVGs: Highly variable genes; PCA: Principal component analysis; tSNE: t-Distributed Stochastic Neighbor Embedding; DEGs: Differentially expressed genes; FC: Fold change; NK: natural killer; GSEA: Gene set enrichment analysis; HR: Hazard ratio; MEPs: megakaryocytes-erythroid progenitors.

Acknowledgments

The authors thank all physicians and laboratory researchers for their assistance. The authors also appreciate Professor Bilian Jin and Haixin Lei's comments about this study.

Author contributions

Xuehong Zhang: Data computational analysis, bioinformatics analysis, visualization, methodology, funding acquisition, and writing original draft. Zhijie Hou: Fusion gene validation, and editing draft. Beibei Gao: Clinical information collecting. Zhijie Kang: Clinical information collecting. Dan Huang: Clinical samples collecting. C. Zhang: Clinical information collecting. Yuan Gao: Karyotype analysis. Jiacheng Lou: Editing draft. Furong Wang: Fusion gene validation. Dong Zhou: Clinical information collecting. Haina Wang: Editing draft. Ying Lu: Conceptualization, and funding acquisition. Quentin Liu: Conceptualization, and project administration. Jinsong Yan: Conceptualization, supervision, resources, funding acquisition, project administration, and editing draft.

Funding

This work was supported by the National Natural Science Foundation (81970131, 81370652, and 81770146 to Y.L.; 81800168 to X.-H.Z; and 81570124 to J.-S.Y), Foundation for the author of National Excellent Doctoral Dissertation of China (201074 to Y.L.), Talent Development Project of Shanghai Human Resource and Social Security Bureau (201322 to Y.L.), Science and Technology Innovation Leading Talent Program of Liaoning Province (XLYC1902036 to J.-S.Y.), Basic Research on the Application of Dalian Innovation Fund (2019J12SN56 to J.-S.Y.), Key R & D projects in Liaoning Province (2019JH8/10300027 to J.-S.Y.), and Key Project of the Educational Department of Liaoning Province (LZ2020003 to J.-S.Y).

Availability of data and materials

The data and materials applied in supporting the findings in this study are available from the corresponding author upon request.

Ethics approval and consent to participate

The research design, along with written informed consent obtained from the participating subjects, of this project was reviewed and approved by the Research Ethics Board of the Second Hospital of Dalian Medical University.

Consent for publication

All authors have reviewed and approved the submission and publication of this article.

Competing interests

The authors have declared no conflict of interest.

Author details

¹Department of Hematology, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, Dalian Key Laboratory of Hematology, Liaoning Key Laboratory of Hematopoietic Stem Cell Transplantation and Translational Medicine, Diamond Bay Institute of Hematology, the Second Hospital of Dalian Medical University, Dalian, China. ²Institute of Cancer Stem Cell, Dalian Medical University, Dalian, China. ³Department of Neurosurgery, the Second Hospital of Dalian Medical University, Dalian, China. ⁴Institute of Dermatology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. ⁵State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China.

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Single-cell heterogeneity and dynamic evolution of Ph-like acute lymphoblastic leukemia patient with novel TPR-PDGFRB fusion gene

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