RNA Splicing Modulation Selectively Impairs Leukemia Stem Cell Maintenance in Secondary Human AML

Abstract

Age-related human hematopoietic stem cell (HSC) exhaustion and myeloid-lineage skewing promote oncogenic transformation of hematopoietic progenitor cells into therapy-resistant leukemia stem cells (LSCs) in secondary acute myeloid leukemia (AML). While acquisition of clonal DNA mutations has been linked to increased rates of secondary AML for individuals older than 60 years, the contribution of RNA processing alterations to human hematopoietic stem and progenitor aging and LSC generation remains unclear. Comprehensive RNA sequencing and splice-isoform-specific PCR uncovered characteristic RNA splice isoform expression patterns that distinguished normal young and aged human stem and progenitor cells (HSPCs) from malignant myelodysplastic syndrome (MDS) and AML progenitors. In splicing reporter assays and pre-clinical patient-derived AML models, treatment with a pharmacologic splicing modulator, 17S-FD-895, reversed pro-survival splice isoform switching and significantly impaired LSC maintenance. Therapeutic splicing modulation, together with monitoring splice isoform biomarkers of healthy HSPC aging versus LSC generation, may be employed safely and effectively to prevent relapse, the leading cause of leukemia-related mortality.

Figure 1. Splice Isoform Signatures of Human Hematopoietic Stem and Progenitor Cell Aging

Whole transcriptome sequencing was performed on RNA from FACS-purified HSC (CD34⁺CD38⁻ Lin⁻) and HPC (CD34⁺CD38⁺ Lin⁻) cells from normal young and aged samples (HSC: n=4 young, n=4 aged; HPC: n=6 per group plus a validation set of 2 additional samples per group). Gene and isoform expression data in FPKM were used to calculate average log 2 fold change (L2FC) and p values and FDR correction. (A) Schematic diagram of pre-mRNA splicing, adapted from the KEGG splicing pathway. (B) GSEA spliceosome enrichment plots for human aged versus young HSC and HPC. (C) Volcano plot analysis of all transcripts (FPKM>1) in aged versus young HSC (upper panel) or HPC (lower panel). L2FC was calculated for each transcript using FPKM+1 values. (D, E) Splice isoform heat maps were made using GENE-E and expression data (Ensembl GFCh37) for the top 75 differentially expressed isoforms (FPKM>1, FDR<5%, absolute L2FC>1) comparing samples in each discovery sample set, ranked by Volcano Vector Value (see Supplemental Materials). (F) Intersection of FDR-corrected differentially expressed isoforms in aging HSC and HPC. (G) All significantly differentially expressed genes (FPKM>1, p<0.05, L2FC>1) in discovery sets of normal aged versus young HSC and HPC were probed for human transcription factors, and commonly DE transcription factors were identified. (H) LncRNA signatures of human HSC (upper) and HPC (lower) aging (FPKM>1, p<0.05, L2FC>1). See also Figure S1 and Tables S2–S4.

Figure 2. Splicing Deregulation Distinguishes sAML, MDS and Normal Aged Progenitors

Whole transcriptome sequencing data (gene and isoform FPKMs) was analyzed for FACS-purified progenitors from 7 secondary (s)AML, 2 de novo AML, 5 MDS patients, and 6 normal age-matched control samples (aging HPC discovery sample set). (A) GSEA spliceosome enrichment plot showing significant disruption of splicing genes in sAML. (B) Waterfall plot showing average L2FC of all significantly differentially expressed (FDR<5%) KEGG spliceosome components comparing RNA-Seq data from sAML versus normal age-matched HPC. (C) Volcano plot analysis of all transcripts (FPKM>1) in sAML or normal age-matched progenitors. L2FC was calculated for each transcript using FPKM+1 values. (D) A heat map was made using GENE-E for the top 75 isoforms (sAML versus aged normal HPC) ranked by Volcano Vector Value (see Supplemental Materials) for transcripts with FPKM>1, FDR<5%, p<0.05, and absolute L2FC>1. Comparative expression profiles in MDS progenitors are shown for clustering analysis. (E) Cytoscape network analysis of gene interactions between the top differentially expressed (DE) isoforms (p<0.05) in sAML LSC versus aged normal HPC. (F) RNA-Seq-based quantification of CD44-012 expression levels (FDR<5%). (G, H) RNA-Seq-based (G, FDR<5%) and splice isoform-specific qRT-PCR (H) quantification of PTK2B-202 expression levels. **p<0.01 by unpaired, two-tailed Student’s t-test. (I) Overall survival (OS) of AML patients (n=156) separated into six subgroups based on expression profiles of sAML splice isoform signature transcripts that mapped to UCSC identifiers in TCGA isoform datasets from RNA-Seq studies performed on unsorted AML leukemic cells. *p=0.0045 (log rank test for trend). (J) All significantly differentially expressed genes in sAML versus normal age-matched HPC were probed for human transcription factors, and the most common families are shown. Differential expression of additional transcription factors is provided in Table S3. (K) LncRNA signature of sAML (FPKM>1, p<0.05, L2FC>1). See also Figures S2, S3 and Tables S1–S4.

Figure 3. Splice Isoform Switching Distinguishes Malignant from Normal Progenitor Aging

Gene and isoform expression data in FPKM were obtained from sAML LSC, MDS progenitors, and normal aged and young HPC RNA-Seq datasets using Cufflinks. GSEA was performed using all KEGG pathways plus custom gene sets including genes associated with the top differentially expressed transcript signatures in aged versus young HPC, and sAML versus aged HPC. Specifically, the sets of genes associated with isoforms that were enriched (AGED_VS_YOUNG_SPLICE_ISOFORM_SIGNATURE_GENES_AGED_UP) or depleted (AGED_VS_YOUNG_SPLICE_ISOFORM_SIGNATURE_GENES_YOUNG_UP) in HPC aging were used to query the sAML versus aged normal progenitor datasets for GSEA. Similarly, the sAML signature was used to generate a custom gene set representing genes associated with isoforms enriched (SAML_VS_AGED_SPLICE_ISOFORM_SIGNATURE_GENES_SAML_UP) or depleted (SAML_VS_AGED_SPLICE_ISOFORM_SIGNATURE_GENES_AGED_UP) in sAML. (A) Enrichment plot showing disruption of HPC aging-associated transcript genes (AGED_VS_YOUNG_SPLICE_ISOFORM_SIGNATURE_GENES_AGED_UP) in sAML progenitors. (B) Principal components analysis showing separation of all samples on the basis of expression values (log2 (FPKM+1)) of aged versus young HPC splice isoform signature transcripts. (C) GSEA KEGG apoptosis pathway enrichment plot showing disruption of apoptosis regulatory genes in sAML. (D) RNA-Seq-based analysis (Log2 (FPKM+1)) showing increased expression of pro-survival BCL2L1-001 (BCL-XL) in AML (p<0.05 by two-tailed, unpaired Student’s t-test). (E, F) RNA-Seq-based (E) and splice isoform-specific qRT-PCR (F) quantification showing decreased expression of the pro-survival BCL2-001 long isoform (BCL2-L) in normal progenitor aging (p<0.01 by unpaired, two-tailed Student’s t-test). See also Tables S2–S4.

Figure 4. Selective Spliceosome Modulation Reverses sAML Splicing Deregulation In Vitro

(A) Chemical structures for FD-895 and 17S-FD-895. (B) Left: summary of the predicted fluorescence readout using a dual fluorescence (RFP and GFP) alternative splicing reporter (pFlare) assay in HEK293 cells. Middle and right: live-cell confocal microscopy images in reporter-transfected, 17S-FD-895-treated (10 µM) HEK293 cells. Scale bar=50 µm. (C) Time course of MOLM-13 (sAML, n=2) cells treated with 17S-FD-895 for 30 mins – 24 hrs and analyzed by qRT-PCR for DNAJB1 intron 2 retention (EC50 of the 1 µM treatment condition at 4.5 hrs was 3.2 – 6.5 hrs, with a 95% C.I.). (D) HEK293 (n=2), MOLM-13 (sAML, n=2) and KG1a (AML, n=3) cells treated with increasing doses of 17S-FD-895 for 4 hrs and analyzed by qRT-PCR for DNAJB1 intron 2 retention. (E, F) RT-PCR analysis of HEK293 and MOLM-13 cells using primers flanking DNAJB1 intron 2 (E) or MCL1 exon 2 (F) after 4 hrs of 17S-FD-895 treatment. 100-bp ladder (L) shows estimated length of PCR products; arrowhead = 500 bp. (G) MCL1-S isoform-specific qRT-PCR analysis of 17S-FD-895-treated HEK293, MOLM-13 and KG1a cells. (H) Splice isoform-specific qRT-PCR analysis of PTK2B-202 expression in MOLM-13 cells (n=2) and KG1a (n=3) cells after 17S-FD-895 treatment as for (D–G). PTK2B-202 was undetectable in HEK293 cells. *p=0.004 (unpaired, two-tailed Student’s t-test) for KG1a cells compared to DMSO-treated control at 1 µM. See also Figure S4 and Movie S1.

Figure 5. Splicing Modulation Impairs LSC Maintenance in Stromal Co-cultures

(A) Schematic diagram of co-culture assay using mouse SL/M2 bone marrow stromal cells that express human interleukin-3 (IL-3), granulocyte colony stimulating factor (G-CSF) and stem cell factor (SCF). (B, C) CD34⁺ AML (n=4), normal bone marrow (BM, n=3) or cord blood (CB, n=3) cells were co-cultured with SL/M2 stroma for 2 wks in the presence of FD-895 (B), 17S-FD-895 (C) or vehicle controls (DMSO), then plated in methylcellulose. Colony formation assays (upper) after treatment with FD-895 or 17S-FD-895 showed reduced AML LSC survival that was significantly lower with 17S-FD-895 than FD-895 at the 1 µM dose (p=0.020). Colony replating assays (lower) showed reduced AML LSC self-renewal that was significantly lower with 17S-FD-895 than FD-895 at the 0.1 and 1 µM doses (p=0.001). #p<0.001 for AML compared with 1 and 10 µM-treated normal bone marrow controls (one-way ANOVA). (D) Reduced LSC survival and self-renewal compared to normal controls in a validation cohort including relapsed de novo AML and sAML samples treated with 1 µM 17S-FD-895 (*p<0.001 by one-way ANOVA). See also Figure S5.

Figure 6. Splicing Modulation Impairs LSC Maintenance in AML Primagraft Models

(A) Schematic diagram showing in vivo 17S-FD-895 treatment regimen, tissues analyzed (spleen, bone marrow, peripheral blood), and analytical endpoints. (B–D) FACS analysis of human hematopoietic cell (CD45⁺, B), progenitor (CD34⁺CD38⁺ Lin⁻, C), and granulocyte macrophage progenitor (GMP, D) cell engraftment in hematopoietic tissues from mice transplanted with AML-37 and treated with vehicle (DMSO, n=5) or 17S-FD-895 (5 mg/kg, n=4; 10 mg/kg, n=5). (E) Human CD45⁺ cell engraftment in serial transplant recipients of CD34⁺ cells from 17S-FD-895-treated mice. For statistical analyses in all graphs, p<0.05 by unpaired, two-tailed Student’s t-test compared to vehicle-treated controls. See also Figure S6.

Figure 7. Splicing Modulation Reverses sAML Splicing Deregulation in Primagraft Models

(A–D) Quantification of DNAJB1 intron 2 retention (A) and MCL1-L/S (B), BCLX-L/S (C) and BCL2-L/S (D) expression ratios in CD34⁺ cells isolated from the spleens and bone marrows of individual AML-37 mice treated with vehicle or 17S-FD-895. p<0.05 by unpaired, two-tailed Student’s t-test compared to vehicle-treated controls. (E–G) Aliquots of pooled CD34⁺ cells prepared for serial transplantation studies were analyzed by RT-PCR to evaluate MCL1 exon 1–3 splicing patterns (E) or RNA-Seq-based splice isoform expression profiles (F, G). In E, 1000-bp ladder (L) shows estimated length of PCR products, arrowhead = 500 bp. (F) Cytoscape network analysis showing reversion of aberrant expression patterns of genes associated with sAML signature transcripts quantified by RNA-Seq in human CD34⁺ cells pooled from the bone marrow (BM) of 17S-FD-895 versus vehicle-treated mice (compare to Figure 2E showing sAML versus aged normal BM). (G) RNA-Seq-based analysis showing expression of sAML-associated splice isoforms (Table S4) in human CD34⁺ fractions pooled from hematopoietic tissues after in vivo treatment of AML-37 xenografted (X) mice with vehicle (Veh) or 17S-FD-895 (5 or 10 mg/kg, n=4–5 mice pooled per tissue, per condition). (H) Overall survival (OS) of AML patients (n=84) separated into two subgroups based on high (upper quartile of 168 samples) and low (bottom quartile of 168 samples) expression of PTK2B-001 (UCSC transcript uc003xfp.1, GRCh37) in publicly available TCGA isoform datasets from RNA-Seq studies performed on unsorted AML leukemic cells (*p<0.05 by Gehan-Breslow-Wilcoxon test). See also Figure S7.