Abstract
Despite the considerable efficacy observed when targeting a dispensable lineage antigen, such as CD19 in B cell acute lymphoblastic leukaemia1,2, the broader applicability of adoptive immunotherapies is hampered by the absence of tumour-restricted antigens3–5. Acute myeloid leukaemia immunotherapies target genes expressed by haematopoietic stem/progenitor cells (HSPCs) or differentiated myeloid cells, resulting in intolerable on-target/off-tumour toxicity. Here we show that epitope engineering of donor HSPCs used for bone marrow transplantation endows haematopoietic lineages with selective resistance to chimeric antigen receptor (CAR) T cells or monoclonal antibodies, without affecting protein function or regulation. This strategy enables the targeting of genes that are essential for leukaemia survival regardless of shared expression on HSPCs, reducing the risk of tumour immune escape. By performing epitope mapping and library screenings, we identified amino acid changes that abrogate the binding of therapeutic monoclonal antibodies targeting FLT3, CD123 and KIT, and optimized a base-editing approach to introduce them into CD34+ HSPCs, which retain long-term engraftment and multilineage differentiation ability. After CAR T cell treatment, we confirmed resistance of epitope-edited haematopoiesis and concomitant eradication of patient-derived acute myeloid leukaemia xenografts. Furthermore, we show that multiplex epitope engineering of HSPCs is feasible and enables more effective immunotherapies against multiple targets without incurring overlapping off-tumour toxicities. We envision that this approach will provide opportunities to treat relapsed/refractory acute myeloid leukaemia and enable safer non-genotoxic conditioning.
Subject terms: Immunotherapy, Acute myeloid leukaemia, Haematopoietic stem cells, Gene therapy
Epitope engineering of donor haematopoietic stem/progenitor cells endows haematopoietic lineages with selective resistance to CAR T cells or monoclonal antibodies, without affecting protein function or regulation, enabling the targeting of genes that are essential for leukaemia survival and reducing the risk of tumour immune escape.
Main
CAR T cells, bispecific antibodies and antibody–drug conjugates are promising adoptive immunotherapies that can overcome the limitations of conventional cancer treatments and have demonstrated considerable efficacy when targeting dispensable haematopoietic antigens1,2. Nonetheless, the absence of safely actionable tumour-restricted markers hampers their application to other haematological malignancies, such as acute myeloid leukaemia (AML)6,7. As AML shares most surface markers with normal HSPCs or differentiated myeloid cells, on-target/off-tumour toxicities would result in myeloid aplasia and impairment of haematopoietic reconstitution3,4,8. Furthermore, owing to AML intratumoural heterogeneity and plasticity9, targeting more than one surface antigen may be required, therefore exacerbating the risk of overlapping toxicity5,10. Despite this, a range of AML immunotherapies is currently under development11–16, but their role will probably be time restricted to bridge treatment before allogeneic HSPC transplantation (HSCT), decreasing the chances of AML eradication. Removal of the targeted antigen through CRISPR–Cas knockout or exon skipping from donor HSPCs used in HSCT has recently been proposed17–19 to reduce the adverse effects associated with anti-CD33 treatments and this approach is currently undergoing clinical testing (ClinicalTrials.gov: NCT04849910). However, although these studies provided evidence for the dispensable role of CD33 for engraftment and myeloid differentiation in non-human primates, the long-term effects of CD33 knockout on myeloid cell functionality in humans remain unclear20,21. Furthermore, targeting non-essential genes would facilitate tumour escape through antigen loss or downregulation, as observed in CD19-negative relapses after CD19 CAR T cell therapy22–24 or HLA-loss after haplo-HSCT25. Here we show that precise editing of the targeted epitope on FMS-like tyrosine kinase 3 (FLT3; also known as CD135), KIT (also known as CD117) and the α subunit of the IL-3 receptor (IL-3RA; also known as CD123) in HSPCs results in loss of antibody binding without gene knockout, therefore preserving physiological protein expression, regulation and intracellular signalling. Critically, this strategy enables targeting one or more genes that are fundamental for leukaemia survival, resulting in potent anti-leukaemia efficacy with minimal on-target/off-tumour toxicity.
Base editing generates stealth receptors
FLT3 and proto-oncogene KIT are class III receptor tyrosine kinase expressed, either in the wild type or a mutated form, in 93% and 85% of AML cases, respectively26–28. CD123 is a type I cytokine receptor that is found in more than 75% of AML cases, including leukaema stem cells5,29. These genes are expressed at various stages of normal haematopoietic development and their overexpression on AML cells is associated with a higher incidence of relapse after HSCT and lower overall survival28,30,31. To develop our approach, we selected monoclonal antibodies currently studied for the development of AML immunotherapies: clone 4G8 (FLT3)16,32, Fab-79D (KIT)15,33 and 7G3 (CD123)34,35. 4G8 recognizes the FLT3 extracellular domain 4 (ECD4), whereas BV10A4—which binds to an unrelated epitope within ECD2—was used as control for FLT3 surface expression32. As 4G8 was generated by immunizing BALB/c mice with human FLT3-transfected cell lines, we reasoned that 4G8 recognizes a human-specific epitope, despite the high degree of homology and FLT3 ligand (FLT3L) cross-reactivity between human and mouse FLT3. We confirmed that the substitution of FLT3 ECD4 with its mouse orthologue (16 codon changes) results in a loss of 4G8 binding with preservation of FLT3L binding and kinase phosphorylation (Fig. 1a,b (left) and 1c (top)). To identify the minimal number of residues involved in 4G8 binding, we designed a Sleeping Beauty combinatorial library with human or mouse codons at the 16 mismatched positions (Fig. 1d (left)). Flow cytometry analysis of library-transduced cells revealed a 4G8−BV10A4+ population (Fig. 1d (centre)). Comparison of the relative codon abundance, by targeted deep sequencing of sorted 4G8− and 4G8+ cells, revealed enrichment for a single amino acid substitution (N399D) (Fig. 1d (right) and Extended Data Fig. 1a). To validate this result, we transduced K562 cells with FLT3N399D and confirmed the loss of 4G8 binding despite FLT3 expression comparable to the wild type (Extended Data Fig. 1b). We next evaluated gene-editing strategies to introduce the N399D mutation. To easily evaluate the outcomes of genome engineering on cells that do not depend on FLT3 signalling, we generated K562 reporters expressing FLT3 from the endogenous locus by targeted integration of a EEF1A1 promoter upstream of the transcriptional start site (Extended Data Fig. 1c). We next confirmed that N399D can be inserted by homology-directed repair (HDR) using either Streptococcus pyogenes Cas9 (SpCas9) or Acidaminococcus Cas12a (AsCas12a) nucleases and 200 bp single-stranded oligodeoxynucleotide templates. Notably, the use of Cas nucleases resulted in FLT3 knockout in a large proportion of non-edited cells (Extended Data Fig. 1d). As epitope engineering can be achieved by point mutations, we reasoned that base editing could be a suitable and safer option for epitope editing by avoiding double-stranded breaks (DSBs). The FLT3 asparagine residue at position 399, encoded by an AAC codon, can be converted to aspartate (GAC) or glycine (GGC) by adenine base editing (ABE). We tested this hypothesis by electroporating FLT3 reporters with sgRNAs in a 1 bp staggered manner (target A in position 3–9 of the spacer) in combination with advanced-generation TadA-8e deaminase, linked with SpCas9 nickase (NGG PAM) or variants with relaxed PAM specificity (NG-SpCas9n and SpRY-Cas9n) (Fig. 1f). Flow cytometry showed successful epitope editing with the loss of 4G8 recognition, with the highest efficiency achieved by SpRY-ABE8e + FLT3-sgRNA-18 (66.3%) (Fig. 1f). In contrast to the HDR-based strategy, non-edited cells retained FLT3 expression without significant knockout (Fig. 1f). As both N399D and N399G are potential outcomes of our base editing, we included these mutations in all further validation analysis.
A similar strategy was applied to the epitope mapping of Fab-79D, which binds to KIT ECD4 and blocks its ligand-induced dimerization33. We first confirmed the loss of Fab-79D binding by introducing ten orthologue amino acid changes at predicted contact points33 (F316S, M318V, I319K, V323I, I334V, E360K, P363V, E366D, E376Q, H378R) and verified the preservation of stem cell factor (SCF) binding and ligand-induced kinase phosphorylation (Fig. 1b,c). To comprehensively screen the interaction between ECD4 and Fab-79D, we used a degenerated codon library in which each position of KIT ECD4 was substituted by a random amino acid (Fig. 1e (left)). We transduced HEK293T cells with the library and sorted KIT-expressing cells with either reduced or preserved Fab-79D staining (Fig. 1e (centre)). We found several mutations enriched in the Fab-79D− fraction (Fig. 1e (right)) and selected those that could be introduced by base editing at ten positions identified by the library (Met318, Ile319, Val323, Asp332, Ile334, Asp357, Glu360, Glu376 and His378). We selected H378R for further development, as it showed the best reduction in Fab-79D binding while preserving SCF affinity (Extended Data Fig. 1e). Furthermore, H378R can be inserted by ABE, similarly to FLT3N399D, potentially enabling dual-epitope engineering. We next screened three sgRNAs aimed at His378 (A in position 5 to 7) in combination with SpRY-ABE8e and identified sgRNA KIT-Y as the best-performing candidate (Extended Data Fig. 1c,f).
For CD123, the epitope and amino acid substitutions affecting the binding of clone 7G3 (or its humanized counterpart, CSL362, talacotuzumab) had been previously reported35. We therefore designed a targeted base-editing screening on K562 reporter cells (Extended Data Fig. 1e) by testing sgRNAs aimed at positions Glu51, Tyr58, Ser59, Arg84, Pro88 and Pro89 of the CD123 N-terminal domain and rationally combined them with CBE (evo-APOBEC1-BE4 with NGG-, NG- and SpRY-Cas9) and ABE (NG- and SpRY-ABE8e) (Extended Data Fig. 1h). While several base-editing combinations reduced the affinity of 7G3 to CD123, only gRNAs CD123-N and -R with SpRY-ABE8e and gRNA CD123-L with SpRY-BE4 completely abrogated 7G3 binding (Extended Data Fig. 1h). Sequencing of edited cells revealed that ABE and CBE resulted in S59P and S59F, respectively. A bystander mutation (Y58H) was also introduced at higher efficiency with CD123-R than with -N by SpRY-ABE8e, and was therefore included in further validations. Owing to the possibility of editing multiple targets with ABE, we selected CD123-gRNA-R (S59P) for further development. Finally, by testing base-edited CD123 reporter cells with two additional CD123 monoclonal antibodies, we found that cytidine base editing of Pro88/Pro89 residues (giving rise to combinations of P88S/P89S or P88L/P89L) resulted in a loss of recognition by clones 6H6 and S18016E, therefore widening the pool of monoclonal antibodies with potential epitope-engineering applications (Extended Data Fig. 1i). To precisely quantify the monoclonal antibody affinity to epitope-edited receptors, we transduced K562 or NIH-3T3 cells with receptor variants and stained them with therapeutic monoclonal antibodies. We observed a near complete loss of binding of all of the tested variants up to saturating concentrations (>5,000 ng ml−1) (Fig. 1g). Notably, FLT3N399G showed a reduction in 4G8 binding comparable to N399D. Similarly, addition of the Y58H bystander to CD123(S59P) had similar effects as S59P alone, without affecting CD123 surface expression.
We next assessed whether our engineering procedure could alter receptor functionality. Using fluorescent FLT3L, SCF and IL-3, we confirmed comparable ligand binding to WT or engineered receptors across all of the tested concentrations (1 to >1,000 ng ml−1) (Fig. 1h). CD123 variants were co-expressed with the common β subunit (CSF2RB, also known as CD131) to form the heterodimeric IL-3 receptor and allow signal transduction (Extended Data Fig. 1j). Activation of intracellular signalling by FLT3 and KIT was confirmed by western blotting, which showed ligand-dependent kinase phosphorylation (Extended Data Fig. 2a,b). For CD123, we confirmed ligand-induced activation by measuring downstream phosphorylation of STAT5, which was equally activated in WT and engineered variants at all of the tested concentrations (Extended Data Fig. 2c). Finally, to confirm the preservation of the ligand-induced proliferative response, we performed a kinase complementation assay on BaF3 cells, which require mouse IL-3-mediated signalling for survival. We confirmed comparable and dose-dependent rescue of cell proliferation by WT and epitope-engineered receptors after exposure to human FLT3L, SCF and IL-3 during mouse IL-3 starvation (Extended Data Fig. 2d). Overall, we concluded that epitope engineering of functional FLT3, KIT and CD123 receptors is feasible and can be achieved with high efficiencies by selecting appropriate combinations of gRNAs and base-editing enzymes.
Stealth receptors are resistant to CAR T cells
Recent preclinical studies have shown that CAR T cells generated from clones 4G816, Fab-79D15 or CSL36212,35 have considerable efficacy against AML. To assess the resistance of epitope-engineered cells to CAR T cell therapy, we cloned the 4G8, Fab-79D and CSL362 single-chain variable fragments in second-generation CAR constructs with a CD28 costimulatory domain and used a bidirectional lentiviral vector to co-express an optimized36 truncated EGFR selection/depletion marker (tEGFR) (Extended Data Fig. 2e). For CAR T cell production, we used stimulation with CD3–CD28 beads in the presence of IL-7 and IL-15 to impart a T stem memory phenotype37 (Extended Data Fig. 3a,b). We obtained higher than 85% CAR transduction efficiency with greater than twentyfold in vitro expansion (Extended Data Fig. 2f). By performing in vitro killing assays with K562 reporters as targets, we found that, although the majority of cells expressing unmodified FLT3, KIT or CD123 were killed (<2% survival at an effector to target ratio (E:T) = 10 versus E:T = 0), cells expressing epitope-engineered variants were resistant to CAR-mediated killing (both in absolute counts and relative viability) and survived up to experiment termination (Fig. 2a–c and Extended Data Fig. 3c,d). T cell activation (CD69) and degranulation (surface CD107a) were significantly higher in co-cultures with cells expressing WT genes, consistent with a lack of recognition of epitope-edited variants by the CAR (Fig. 2a–c and Extended Data Fig. 3c,d). Moreover, surviving target cells still expressed the receptors at levels comparable to the untreated controls (Fig. 2a–c (right)). Untransduced T cells did not show target killing or CD69 upregulation across all of the target conditions (Extended Data Fig. 3d). To further confirm epitope-specific killing by CAR T cells, we co-cultured mixed populations of dual-expressing cells (FLT3+CD123+ K562), either unmodified or base edited, and observed selective resistance of the epitope-edited populations when plated with the corresponding CAR T cells (Extended Data Fig. 3e). These data provide a stringent validation that cells overexpressing epitope-engineered FLT3, KIT and CD123 variants are resistant to CAR T cell recognition and killing.
Efficient epitope editing of human HSPCs
To effectively introduce our variants into human HSPCs, we optimized a base-editing protocol on mobilized-peripheral-blood-derived CD34+ cells based on co-electroporation of sgRNAs and in vitro transcribed (IVT) SpRY-ABE8e mRNA (Fig. 3a and Extended Data Fig. 4a,b). After optimization of the mRNA IVT, culture, electroporation conditions and editing timepoint (Extended Data Fig. 4c–e), we achieved up to 86.6%, 78.6% and 78.9% editing efficiency of the target adenines within the windows of FLT3-18, KIT-Y and CD123-R sgRNAs (Fig. 3a). Contrary to previous observations with HDR-mediated editing38, base-editing efficiencies were similar in bulk cells and in more primitive, HSC-enriched subsets (Fig. 3b). Analyses of edited cells showed no skewing of the composition of phenotypically identified progenitors (lymphoid-primed multipotent progenitor (LMPP)-like, CD90−CD45RA+; multipotent progenitor (MPP)-like, CD90−CD45RA−; haematopoietic stem cell (HSC)-like, CD90+CD45RA−) compared with the control during in vitro culture (Fig. 3c and Extended Data Fig. 4f,h). To assess the functionality of the receptors, edited HSPCs were cultured with increasing concentrations of the respective ligand without other cytokines. We observed dose-dependent HSPC expansion for all three targets, with no difference between receptor-edited and controls except for CD123S59 cells at IL-3 concentrations of 1–10 ng ml−1 (Extended Data Fig. 4i). Nonetheless, we did not observe counter-selection of base-edited HSPCs, confirming uniform expansion of CD34+ cells regardless of CD123 editing (Extended Data Fig. 4j). To confirm the resistance of FLT3N399 and CD123S59 engineered HSPCs, we performed killing assays by plating edited HSPCs with 4G8 and CSL362 CARs. Specific killing by FLT3- and CD123-CAR was most pronounced within the CD45RA+ and the CD90+ subsets, respectively, which were therefore used to evaluate the outcome of these experiments. Whereas cells edited at a control site (AAVS1 safe genomic harbour) were eliminated by CAR T cell co-culture, epitope-edited cells showed higher viability and absolute counts (Fig. 3d,e). As KIT has known extrahaematopoietic expression in humans39 we focused on the use of monoclonal antibodies instead of CAR T cells, which might result in less severe on-target toxicities. By plating edited HSPCs with increasing concentrations of the dimerization-blocking Fab-79D monoclonal antibody, we observed preserved expansion of KITH378 HSPCs in response to SCF, whereas cells expressing base-edited AAVS1 (AAVS1BE) were inhibited in a dose-dependent manner (Fig. 3f). These data show that epitope-engineered HSPCs can efficiently be generated by ABE and become resistant to targeted immunotherapies.
Epitope editing preserves HSPC function
To evaluate the transcriptional changes associated with epitope editing, we performed RNA-sequencing (RNA-seq) analysis of CD34+ HSPCs edited for FLT3, CD123, KIT or AAVS1, either stimulated or unstimulated with the respective ligands. We found 78, 2,667 and 7,944 differentially expressed genes associated with FLT3L, SCF and IL-3 stimulation, respectively (Extended Data Fig. 5a–e). By comparing receptor-edited conditions with AAVS1BE, we confirmed the absence of transcriptional differences both at the baseline and after stimulation (Fig. 3g and Supplementary Tables 3–5). Phospho-proteomic profiling by mass spectrometry analysis of edited CD34+ HSPCs showed concordant changes of differentially phosphorylated sites after ligand stimulation between the receptor-edited cells and the AAVS1 control cells (Extended Data Fig. 5f), again confirming in an unbiased manner the activation of downstream signalling by epitope-modified receptors. To further corroborate the minimal impact on the differentiation ability of receptor-edited HSPCs, we performed expansion culture and a colony-forming assay and observed comparable absolute cell counts and numbers of myeloid and erythroid colonies (Fig. 3h and Extended Data Fig. 4g). In vitro differentiation of CD34+ HSPCs towards myeloid, macrophage, classical dendritic, granulocytic and megakaryocytic lineages was similar irrespective of the editing condition and did not result in counterselection of edited cells (Extended Data Fig. 6a). Functional assays of lineage-differentiated cells showed similar results across all of the conditions, including reactive oxygen species production by myeloid cells, Escherichia coli phagocytosis by macrophages, M1/M2-like macrophage polarization, phospho-flow profiling of IL-4-, PMA/ionomycin-, GM-CSF-, IFN type-I-, IL-6- and LPS-stimulated myeloid cells, HLA class II/CD86 upregulation by dendritic cells, induction of granulocyte NETosis and generation of hyperdiploid megakaryocytes (Extended Data Fig. 6b–i). Xenotransplantation of FLT3N399 HSPCs into female immunodeficient mice (NBSGW) showed preserved engraftment, repopulation and multilineage differentiation capacity (Fig. 3i and Extended Data Fig. 4k), similar to the AAVS1 controls. FLT3 editing of engrafted cells was comparable to input cells (35.5%) and stable up to 13 weeks after transplant (Fig. 3l), confirming successful editing of the most primitive HSPC subset. Transplantation of bone marrow (BM) cells into secondary recipients resulted in high human engraftment and no differences in lineage distribution up to 17 weeks (Extended Data Fig. 7a,b). Again, FLT3 editing levels remained comparable to primary recipients (Fig. 3i). As mouse FLT3L is cross-reactive with human FLT3, these results further confirm the functionality of FLT3N399 HSPCs. Similarly, in vivo repopulating ability and multilineage differentiation of KITH378 and CD123S59 edited HSPCs was comparable to AAVS1BE controls in both primary and secondary recipients, with no skewing of lineage differentiation or counterselection of edited cells (Extended Data Fig. 7c–k). Overall, these data confirm that epitope editing in HSPCs does not affect receptor signalling, stem cell differentiation ability and the functionality of their lineage-specific progenies.
Off-target effects of epitope editing
As the use of SpRY-Cas9 might lead to potential off-target effects, we performed a specificity analysis by combining genome-wide, unbiased identification of off-target sites (GUIDE-seq) and in silico off-target prediction. We performed genome-wide, unbiased identification of DSBs enabled by sequencing (GUIDE-seq) using SpRY-nuclease (Extended Data Fig. 8a) and found that, for gRNA mismatches + bulge < 6, all identified off-target sites were in non-coding genomic regions (12 intronic and 11 intergenic) (Extended Data Fig. 8a and Supplementary Table 16). As a complementary approach, we characterized the top exonic and intronic in silico predicted off-target sites for FLT3 (n = 12), CD123 (n = 9) and KIT (n = 12) sgRNAs and assessed the levels of undesired deamination on base-edited HSPCs using targeted deep sequencing (Extended Data Fig. 8b and Supplementary Tables 17–19). No significant deamination was observed at CD123 or KIT off-target sites (Extended Data Fig. 8b), whereas four FLT3-sgRNA-18 loci showed comparatively higher deamination. Only one of these off-target sites was in an exonic region, but the affected gene, SNTG1 (syntrophin-γ1), encodes a brain-specific syntrophin-family protein with no expression (Supplementary Table 22) or known functional role in haematopoietic tissue40,41. Despite this generally safe profile, we found that an alternative gRNA (2 nucleotides upstream of FLT3-18, AGA PAM) in combination with a more PAM-restricted Cas variant (SpG) avoids deamination at predicted off-target sites while preserving around 90% of on-target activity compared with the FLT3-18 sgRNA (70.6% versus 81% editing in HSPCs) (Extended Data Fig. 8b and Supplementary Table 20). To assess the occurrence of non-gRNA-dependent deamination, we examined our RNA-seq dataset generated on CD34+ HSPCs and observed no significant A>G conversion on transcripts with high sequencing coverage (top 5%) compared with the controls (Extended Data Fig. 8c). Finally, the rate of on-target indels was estimated to be ≤1.2% for all target loci (0.6, 1.2 and 0.2% for FLT3, CD123 and KIT, respectively) (Extended Data Fig. 8d), consistent with previously reported data for ABE42,43. Overall, these data support a generally safe genotoxicity profile of FLT3, CD123 and KIT epitope editing in CD34+ HSPCs.
FLT3BE confers resistance to 4G8 CAR in vivo
To assess whether FLT3 CAR T cells can effectively eliminate AML while sparing FLT3-edited haematopoiesis, we sequentially engrafted NBSGW mice with CD34+ HSPCs (either FLT3BE or AAVS1BE) and a human patient-derived AML xenograft (PDX-1), characterized by MLL-AF9 and FLT3-ITD, previously transduced with a reporter gene to facilitate its detection within the mixed haematopoiesis (Extended Data Fig. 7l,m and Supplementary Fig. 4c). Then, 10 days after the PDX challenge, the mice were treated with 4G8 CAR T cells and, after an additional 14 days, the composition of haematopoietic organs was analysed using flow cytometry (BM and spleen) (Fig. 4a). As observed in previous experiments, with no AML or CAR T cell administration, both editing groups showed similar peripheral blood composition (Extended Data Fig. 9a), editing levels comparable to input cells (around 85%), which remained stable over time, and a lack of differences within the BM myeloid and lymphoid subsets (sorted CD33+ and CD19+ cells, respectively) (Fig. 4b). Mice treated with 4G8-CAR showed robust CAR T cell engraftment (13.7% and 20.4% CD3+ in the FLT3BE and AAVS1BE groups, respectively) and AML eradication in the BM and spleen (Fig. 4c,e,f and Extended Data Fig. 9b). As expected, we observed a significant increase in the fraction of FLT3N399 cells in the BM of CAR-treated mice (88% versus 90% within myeloid and 89% versus 94% within lymphoid cells) (Fig. 4b). Multiparametric flow cytometry analysis of the BM revealed relative depletion of CD19+ subsets (precursor B (pre-B) cells and progenitor B (pro-B) cells) only in the AAVS1BE group treated with 4G8-CAR, whereas mice engrafted with FLT3N399 HPSCs were protected (Fig. 4d,g,h). Within differentiated myeloid cells (excluding AML), the proportion of immature granulocytes was reduced in AAVS1BE compared with in FLT3N399 mice (Extended Data Fig. 9c). FLT3N399 cells conferred selective resistance to lineage-negative progenitor cells (Extended Data Fig. 9d,e) and in particular to granulo-mono progenitors (GMPs) and LMPPs, which were nearly completely eliminated in the AAVS1BE group (1.4% versus 26.6% GMP and 4.8% versus 43.3% LMPP in AAVS1BE versus FLT3N399, respectively) (Fig. 4i,j,m,n). To more precisely identify haematopoietic subsets depleted by 4G8-CAR—and selectively protected by epitope engineering—we compared the absolute counts of BM lineages between treated groups. Absolute counts of common myeloid progenitors (CMPs), classical dendritic cells (cDCs) and GMPs were reduced in AAVS1BE mice (CMPs (0.48×), GMPs (0.01×) and cDCs (0.41×), in AAVS1BE versus FLT3N399 mice) (Fig. 4k) compared with in FLT3N399 mice. Within lymphoid subsets, LMPPs, pre-B/natural killer (NK) cells and downstream populations (B cell prolymphocytes (B-prolymphocytes), pro-B cells and pre-B cells) were protected by FLT3N399 (LMPPs (0.02×), pre-B/NK cells (0.19×), pro-B cells (0.2×) and pre-B cells (0.18×) in AAVS1BE versus FLT3N399 mice) (Fig. 4o). A decrease in HSC number that was more pronounced in the AAVS1BE group (0.32× compared with FLT3N399 mice), and an increase in the number of mature B cells were observed in CAR-treated versus untreated conditions. These differences probably reflect a non-CAR-specific effect, as humanized mice treated with untransduced T cells showed a similar reduction in HSC numbers, an increase in monocytes and a trend towards mature B cell expansion (Extended Data Fig. 7n). The FLT3 median fluorescence intensity (MFI) of persisting pre-B/NK cells, B-prolymphocytes, pro-B cells and pre-B cells, monocytes and myeloblasts in AAVS1BE mice exposed to 4G8-CAR was lower than in the same populations of FLT3N399 edited mice (Extended Data Fig. 9f), providing additional evidence that FLT3N399 cells can retain FLT3 expression while avoiding CAR-mediated killing. Notably, while CAR T cells detected at the end of the experiment showed a similar phenotype (mostly effector and central memory) and CD69 expression in all groups, those exposed to FLT3N399-cell haematopoiesis displayed a reduced expansion and a significant reduction in PD-1 expression compared with the AAVS1BE group (Extended Data Fig. 9g), suggesting an overall decrease in activation/exhaustion associated with the lower antigen burden to which the CAR T cells were exposed. Importantly, FLT3N399 epitope editing provided the same protection against 4G8-CAR killing regardless of the presence of human PDXs, highlighting the possibility of selectively eliminating AML cells while preserving haematopoietic reconstitution. Overall, these data confirmed that, in the NBSGW model, FLT3+ CAR T cells preferentially deplete B cells and progenitor subsets (GMPs, LMPPs), while FLT3N399 epitope editing confers protection to these subpopulations.
CD123BE haematopoiesis is resistant to CD123 CAR T
As done for FLT3 editing, we transplanted CD123S59 HSPCs into NBSGW mice and confirmed engraftment and multilineage repopulation comparable to AAVS1BE HSPCs, with a high and stable fraction of edited cells (Extended Data Figs. 9i and 10a,c). Transplanted mice were then injected with PDX-1—which also expresses CD123 (Extended Data Fig. 7m)—and, after 10 days, were treated with CSL362 CAR T cells. Similar to 4G8-CAR therapy, CSL362 CAR T cells nearly completely eradicated AML cells and displayed higher expansion in mice that were engrafted with AAVS1BE HSPCs compared with CD123S59 HSPCs (Extended Data Fig. 10b,d,e). Flow cytometry analysis of BM highlighted significantly lower absolute counts of human haematopoietic cells (after exclusion of AML and CAR T cells) (Extended Data Fig. 10f) and depletion of myeloid cells, including mature and immature granulocytes, in mice that were transplanted with AAVS1BE HSPCs, while the progeny of CD123S59 HSPCs was protected (Extended Data Fig. 10h–k). Differently from the killing pattern observed with 4G8-CARs, within the lymphoid lineage, only the percentage of pro-B cells showed a trend towards a reduction in CSL362 CAR-T-cell-treated AAVS1BE mice (Extended Data Fig. 10g). Dendritic cells, including CD123high plasmacytoid dendritic cells (pDCs), were depleted by CSL362 CAR T cell treatment, while they were preserved in the CD123S59 group (Extended Data Fig. 10l–n). Similar to FLT3 editing, lineage−CD34+ progenitors were relatively protected in the CD123S59 group (Extended Data Fig. 10o,p). Absolute counts of myeloid populations (CMPs, GMPs, myeloblasts, granulocytes and dendritic cells) were significantly reduced in the AAVS1BE group versus the CD123S59 group (Extended Data Fig. 10q,r). Among lymphoid cells, when comparing AAVS1BE versus CD123S59, we observed a partial depletion of B-prolymphocytes to mature B cells. As observed for FLT3, the CD123 MFI of persisting GMPs, myeloblasts, monocytes, cDCs and pDCs was higher in CD123S59 versus AAVS1BE mice treated with CAR T cells (Extended Data Fig. 11a). Overall, these data show a reduction in on-target toxicity induced by CD123 CAR T cells on the haematopoiesis of CD123S59 epitope-edited cells, which would otherwise result in depletion of myeloid subsets and dendritic cell and an overall reduction in absolute counts of haematopoietic cells.
Multiplex editing enables AML eradication
We reasoned that editing two or more epitopes might enable more effective immunotherapies by enabling simultaneous targeting of multiple AML antigens. To obtain proof-of-concept of protection from multiple CAR T cell therapies, we co-edited two (FLT3 and CD123) or three (FLT3, KIT and CD123) targets on dual- or triple-reporter K562 cells and co-cultured sorted single-, double- or triple-edited cells with bi- or tri-specific CAR T cells, respectively. After 2 days, only the double- or triple-epitope-edited cells survived dual- or triple-specificity CAR-mediated killing without inducing T cell activation (CD69), degranulation (CD107a), proliferation (CellTrace dilution) or cytokine secretion (IFNγ and TNF), while still expressing FLT3, CD123 and KIT (Fig. 5b,c and Extended Data Figs. 11e and 12a–c). To assess the feasibility of multiplex HSPC base editing, we electroporated CD34+ cells with SpRY-ABE8e and two sgRNAs and observed editing efficiencies comparable to single-base editing, without an increase in cellular toxicity (Fig. 5a and Extended Data Fig. 4e,g). To model the increased efficacy conferred by dual-targeting therapies, we identified an AML PDX (PDX-2) (Extended Data Fig. 7l,m) that is not effectively eradicated by 4G8-CAR (Extended Data Fig. 11b,c). When PDX-2 was transplanted in haematochimeric mice, 4G8-CARs showed a trend towards higher anti-leukaemia efficacy in FLT3N399-HSPC-engrafted mice compared with the control (Extended Data Fig. 11c), suggesting a detrimental role of off-tumour CAR activation for therapeutic success. We next tested whether combinations of CAR T cells targeting two antigens could provide superior elimination of PDX-2 and found that both 4G8-CAR plus Fab-79D or CSL362-CAR were more effective (Extended Data Fig. 11d). To obtain formal proof that dual-edited HSPCs are resistant to a combined CAR T cell therapy, we transplanted dual FLT3N399CD123S59 epitope-edited HSPCs or AAVS1BE controls into NBSGW mice. After confirmation of multilineage engraftment (Extended Data Fig. 11f) and injection of the AML PDX-2 cells, we treated the mice with 4G8 and CSL362 CAR T cells (1:1 co-infusion) (Fig. 5d). As suggested by our CAR-combination experiment, dual CAR T cell therapy was able to fully eradicate AML cells from the BM and spleen (Fig. 5d,e). FLT3N399CD123S59 edited cells persisted until the end point and were equally detected within lymphoid and myeloid cells (Fig. 5f). The selective pressure imposed by CAR T cell treatment increased the fraction of FLT3BE cells within the lymphoid and myeloid subsets and, to a lesser degree, of CD123BE cells within myeloid cells (as expected by the expression pattern of their respective target). By analysing the non-malignant haematopoiesis, we observed widespread reduction in myeloid and lymphoid haematopoietic lineages in CAR-T-cell-treated AAVS1BE mice compared with in the FLT3N399CD123S59 group, with the strongest depletion of GMPs, granulocytes, cDCs, LMPPs and pre-B/NK, pro-B and pre-B cells and a less pronounced but significant reduction across nearly all of the other subpopulations, suggesting a depletion pattern in between what we had observed with 4G8 and CSL362 CARs alone (Fig. 5g). Overall, these data provide a proof of concept that multiplex epitope editing can be efficiently obtained in HSPCs and enables potent multitarget immunotherapies with reduced overlapping on-target/off-tumour haematopoietic toxicities.
Discussion
Our studies show that tumour-associated antigens shared by normal tissue can be safely targeted by precisely modifying the epitope recognized by adoptive immunotherapies in healthy cells, endowing them with selective resistance and generating artificial leukaemia-restricted antigens. Different from approaches aimed at the removal or truncation of the target molecule, which can be applied only to dispensable genes (such as CD33 and CLEC12A (also known as CLL-1)), epitope engineering enables the targeting of genes that are essential for leukaemia survival regardless of their function in healthy haematopoiesis. Cytokine receptors are important signalling mediators in AML and their constitutive activation or overexpression is a major driver of leukaemia expansion and self-renewal. Tyrosine kinase inhibitors have demonstrated therapeutic efficacy in patients with AML (particularly for FLT3-ITD AML)6, underscoring the relevance of these receptors for leukaemia persistence. Although several agents targeting FLT3, CD123 or KIT are under development, preclinical and clinical reports have highlighted the risk of myelosuppression due to on-target/off-tumour toxicity, and this issue is expected to be exacerbated when a prolonged anti-leukaemia effect is desirable or when multiple antigens are targeted. Here we show that the introduction of a single amino acid substitution within FLT3, CD123 and KIT ECDs is sufficient to abrogate their recognition by monoclonal antibodies and CAR T cells without affecting protein function. Although we cannot exclude that these point mutations could be immunogenic, in the allo-HSCT setting, the immunosuppressive graft-versus-host disease (GvHD) prophylaxis is expected to confer tolerance towards the engineered proteins. The innovative tools developed in this study could increase the therapeutic index of AML immunotherapies and enable long-term anti-leukaemia maintenance, thanks to CAR T cell persistence or repeated administration of antibody–drug conjugates or bi-specific T cell engagers. Moreover, by restricting the on-target activity to leukaemia cells, epitope-editing can reduce the antigen burden to which CAR T cells are exposed. This has the additional benefit of decreasing undesired CAR T cell stimulation and possibly excessive cytokine release or exhaustion, as suggested by the reduced PD-1 levels observed in vivo. Finally, we show that epitope engineering by base editing can easily be multiplexed to enable combination therapies with synergistic on-tumour effects while avoiding overlapping toxicities. Targeting multiple molecules could eliminate AML with heterogeneous or low-level antigen expression and further reduce the risk of relapse. One possible caveat of this approach could be the reduction in haematopoietic clonality after immunotherapy-mediated elimination of non-edited cells. We have shown that mRNA electroporation enables highly efficient editing of HSCs, with no overt effect on stem cell functionality. On the basis of previous observations of clonality in gene therapy trials44, we expect that these efficiencies will provide a safe margin for effective engraftment and multilineage reconstitution. Moreover, base editing, which does not require DSBs nor template delivery, is inherently safer compared with nuclease-mediated gene inactivation or HDR, which have been associated with the induction of translocations or other genomic abnormalities45. Similarly, exogenous DNA templates bear the risk of unspecific genomic insertion46 and require strategies to overcome the detrimental activation of DNA damage responses47,48. Our data show minimal risk of introducing bystander mutations or converting a fraction of the DNA nick into DSBs. Although we cannot formally exclude that base editing could induce additional unpredicted on-target mutations, the regions affected by base editing are distant from mutational hotspots of cancer-associated variants (that is, the tyrosine kinase domain for KIT and FLT3 or the juxta-membrane domain for FLT3). Thus, the main concern would be a possible loss of function in a fraction of the treated cells, which are likely to be spontaneously counter-selected due to reduced fitness. Despite their overall safety, the potential for off-target deamination by base-editing tools warrants consideration. Although we observed a relatively safe profile for our sgRNAs coupled with PAM-relaxed base editors, a more comprehensive assessment of the specificity of gRNA- and non-gRNA-dependent off-target activity will be required before moving towards clinical translation. Nevertheless, the risk of single-base mutation in relevant coding regions is relatively minor, especially if compared with the genotoxic risk of conventional chemotherapy or the risk of disease progression itself. Risk–benefit evaluation will probably require case-by-case considerations, including the probability of relapse and the availability of alternative therapeutic options. Overall, we envisage a straightforward path to clinical translation, given the growing clinical experience with HSPCs genome editing strategies and the fact that immunotherapies based on our selected antibodies have already reached clinical testing (NCT02789254; NCT02642016; talacotuzumab). HSCT is widely used for the treatment of patients with AML, but their long-term survival hinges entirely on the remission status before HSCT, disease biology and the delicate balance between graft versus leukaemia and graft-versus-host disease. In the presence of post-transplant minimal residual disease or relapse, these patients are left with few treatment options. Our epitope-editing strategy could be rapidly implemented in current allo-HSCT protocols to enable minimal residual disease eradication or anti-leukaemia maintenance after HSCT. Moreover, using HSPCs from healthy donors will avoid the risk of inadvertently editing residual host AML. A paradigmatic setting for first-in-human testing could involve CAR T cell administration at the time of haematopoietic reconstitution after HSCT of relapsed/refractory or high-risk patients to prevent early relapse. Another option, also proposed by others49, would take advantage of the CAR T cell therapy itself as myeloablative conditioning before HSCT to concomitantly kill leukaemic cells and free the BM niche. Eliminating chemotherapy or radiotherapy could also minimize the risk of graft-versus-host disease due to reduced tissue damage and release of pro-inflammatory mediators. While we focused on advancing AML treatment, we expect that epitope engineering can be applied to other haematological malignancies characterized by the paucity of safely actionable antigens, such as T lymphoblastic leukaemia or relapsed CD19− B cell acute lymphoblastic leukaemia. Moreover, as KIT mutations have been implicated in small-cell lung carcinoma, melanoma, colorectal cancer and more than 80% of gastrointestinal stromal tumours39, autologous transplant of engineered HSPCs could theoretically improve the therapeutic index of KIT-directed therapies also for non-haematological malignancies. Finally, immunotherapies aimed at HSPC-specific markers have recently been proposed as non-genotoxic conditioning for non-malignant diseases50. In this setting, epitope-edited HSPCs would remove the limitations imposed by drug pharmacokinetics and reduce the time of aplasia while increasing HSPC engraftment. Moreover, if epitope editing is coupled with other therapeutic base-editing approaches, progressive in vivo enrichment of genetically modified autologous cells can be achieved by infusion of monoclonal antibodies, antibody–drug conjugates or bi-specific T cell engagers. In conclusion, epitope editing of HSPCs can enable safer and more effective immunotherapies when on-target/off-tumour toxicities are the key limiting factor to successful clinical translation.
Methods
Sleeping Beauty transgene overexpression
Bidirectional Sleeping Beauty plasmids encoding the codon-optimized cDNA of the gene of interest (FLT3, KIT, CD123) or their mutated variants were cloned downstream of an EEF1A1 promoter. The same construct included a reporter cassette comprising a fluorescent protein (mCherry or mTagBFP2 for FLT3 and KIT, respectively) and puromycin-acetyl-transferase (PAC) separated by a P2A peptide downstream a RPBSA chimeric promoter. CD123 constructs included human CSF2RB (cytokine receptor common subunit β) cDNA in place of the reporter cassette. dsDNA inserts used for cloning were synthesized by Genewiz or IDT (IDT gBlocks). Human (K562, HEK293T) or mouse (NIH-3T3 or BaF3) cell lines were electroporated using the Lonza 4D-Nucleofector system according to the manufacturer instructions with 500 ng pSB100x transposase plasmid (Addgene, 34879) and 100 ng of the transfer plasmid, unless stated otherwise. Cells underwent puromycin selection (1–5 μg ml−1) starting 3 days after electroporation and were analysed by flow cytometry 7–14 days after the start of the selection.
FLT3 combinatorial library
We designed a combinatorial plasmid library in which the human or the mouse codons were randomly selected at each of 16 positions (Asn354, Ser356, Asp358, Gln363, Glu366, Gln378, Thr384, Arg387, Lys388, Lys395, Asp398, Asn399, Asn408, His411, Gln412, His419) within FLT3 ECD4 (GenScript). The library was cloned to allow a total theoretical complexity of 65,536 (covered at least 10 times). Single-colony sequencing showed a rate of n = 4 mutations in 68.75% of the colonies, and n = 5 mutations in 18.75% of colonies. NGS sequencing of K562 cells transduced with the library, among 9,166 filtered reads, detected 4,375 unique amino acid sequences with a frequency of >0.0001. K562 cells were electroporated with 5–15 ng of library plasmid as described above and selected with 1 μg ml−1 puromycin. Cells were then stained with anti-FLT3 control antibodies (BV10A4, BioLegend, 313314) and FLT3 therapeutic antibodies (4G8, BD, 563908). The single-positive (BV10A4+4G8−) and double-positive (BV10A4+4G8+) populations were sorted using the BD FACS Aria sorter and expanded in culture. After gDNA extraction, the library region was PCR-amplified with primers bearing Illumina partial adapters for NGS sequencing (250 bp paired-end, Genewiz, Azenta Life Sciences). Amplification of the integrated transgene was ensured using exon-spanning primers specific for the codon-optimized sequence. Read alignment and trimming was performed by Genewiz (Azenta), after which the sequences were translated to protein sequence and indels were discarded. After aggregation of reads encoding the same protein variant, the sequences were filtered by minimum abundance >0.001 within single-positive and double-positive samples, and the relative representations of each amino acid at each position within the sorted fractions were plotted as sequence logos. Analysis was performed using R Studio 2022.07.0 (R v.4.1.2).
KIT degenerated codon library
We cloned a bidirectional Sleeping Beauty plasmid encoding a codon-optimized human KIT cDNA bearing unique restriction sites flanking each ECD and a reporter cassette composed of mTagBFP2-P2A-puromycin N-acetyl-transferase. To introduce random amino-acids, we generated a library insert by PCR amplification of 350-bp-long pooled ssDNA oligos (IDT oPools), each encoding for the KIT ECD4 bearing degenerated bases (NNN) at each amino-acid position, flanked by homology arms for the backbone plasmid. The gel-purified insert was then cloned into the Sleeping Beauty transfer by HiFi cloning (NEB, E2621L) and plated onto ten 15 cm agar dishes to estimate the library complexity. Electroporation of HEK293T cells with low plasmid doses (50–250 ng) enabled us to select cells transduced with (~5–10%) efficiency to obtain approximately 1 variant integration per cell. Sorting of cells positive for KIT control antibodies (104D2 or Ab55 clones) and negative for the therapeutic Fab-79D clone, and cells positive for both antibodies was performed using the BD FACS Melody sorter. gDNA of the sorted fractions was extracted and the library region was amplified by PCR using primers bearing Illumina partial adapters for NGS sequencing (Genewiz, Azenta Life Sciences). Read alignment and trimming was performed by automated analysis (Genewiz), after which the read sequences were translated into protein and indels were discarded. After aggregation of reads encoding the same protein variant, the relative abundance of each protein sequence within each sample and the enrichment in single-positive versus double-positive samples was calculated as the log[fold change]. Sequences were filtered by log[fold change] > 0.5, and the minimum-abundance fraction within single-positive samples >0.0015 of each amino acid substitution was plotted as a sequence logo. Analysis was performed using R Studio 2022.07.0 (R v.4.1.2).
Gene overexpression through promoter integration
Reporter K562 cells overexpressing FLT3, KIT and CD123 from the endogenous genomic locus were generated using CRISPR–Cas nuclease and HDR integration of a full EEF1A1 promoter upstream of the gene reading frame. A dsDNA donor template encoding the EEF1A1 promoter flanked by 50 bp homology arms (HA) was prepared by PCR amplification (Promega GoTaq G2, M7845) of plasmid DNA templates using primers with 5′-tails encoding the sHA. K562 cells were electroporated with 100 pmol SpCas9 gRNAs (for FLT3 and KIT) or AsCas12a sgRNAs (CD123) complexed with 12 μg of corresponding Cas protein (IDT; SpCas9 V2, 1081058; or AsCas12a, 10001272) to form RNPs, along with 0.5 to 10 μg donor template using the Lonza 4D-Nucleofector system. Edited cells were evaluated by flow cytometry 3–5 days after electroporation and FACS-sorted (BD FACS Melody) to select cells expressing the gene of interest. Single-cell cloning was performed by limiting dilution for FLT3 and CD123 to isolate clones with the highest expression for subsequent editing experiments.
Fluorescent ligand-binding assay
Human FLT3L (Peprotech, 300-19), hSCF (Peprotech, 300-07) or hIL-3 (200-03) were resuspended at 1 mg ml−1 and conjugated with AF488 or AF647 (Invitrogen AF-488 or AF-647 Antibody Labeling Kit, A20181 and A20186) for 1 h at room temperature. The reaction was quenched with 1% 1 M Tris-HCl pH 8. Cell lines transduced with the respective cytokine receptor by Sleeping Beauty transposase were incubated in the presence of AF488-conjugated FLT3L, SCF or IL-3 for 15 min at room temperature, washed with PBS + 2% FBS and analysed by flow cytometry.
Modelling of antibody–ligand affinity curve
The data obtained from the antibody and ligand affinity assays at different concentrations were analysed using the drc R package (v.3.0). The LL.4 model was used to fit the affinity curves and estimate the parameters of interest, including the maximum response (ED50), the slope of the curve at the inflection point (Hill slope), the lower asymptote (lower limit) and the upper asymptote (upper limit). The difference among affinity curves was tested using a likelihood ratio test in which the parameters were fixed (model0) or antibody-dependent (model1).
Western blotting
K562 (for FLT3) or NIH-3T3 (for KIT) cells overexpressing the receptor of interest by Sleeping Beauty transposase-mediated integration were starved overnight in IMDM without FBS. The next day, cells were collected, washed, counted and stimulated with the respective ligand at different concentrations (0, 0.1, 1, 10, 100 ng ml−1) for 15 min at 37 °C. Cells were then collected, washed twice in ice-cold PBS and lysed in cell extraction buffer (Thermo Fisher Scientific, FNN0011) + protease inhibitor (1 mM PMSF) for 30 min on ice. Protein-containing supernatants were then collected, and the concentration was measured using the BCA assay. A total of 35 µg of protein lysate was then mixed with Laemmli loading buffer (Bio-Rad, 161-0747) supplemented with 0.1× volumes of 2-mercaptoethanol and incubated for 5 min at 95 °C. Denatured protein samples and a prestained protein ladder (Bio-Rad Precision Plus Kaleidoscope, 1610375) were loaded onto 4–12% Tris-Glycine gel (Invitrogen Novex WedgeWell, XP04125BOX), run at 150 V for 90 min and transferred onto PDVF membranes by blotting at 100 mA for 60 min. Washed membranes were then blocked with TBST + 5% BSA, washed three times with TBST and cut at the 75 kDa mark to separate FLT3 and KIT (110–140 kDa) and actin (42 kDa) bands and incubated with primary antibodies at 1:1,000 concentration (pKIT Tyr568/570, Cell Signalling, 48347S; pFLT3 Y589-591, Cell Signalling, 3464S) for 1 h at room temperature. The membrane was then washed three times and incubated with the appropriate secondary antibodies (anti-rabbit IgG, HRP-linked Antibody, Thermo Fisher Scientific, 31460), washed and developed with Thermo Scientific SuperSignal West Femto substrate (34095). The membranes were imaged using the ImageQuant LAS 4000 system. To reprobe the same samples with anti-FLT3 or anti-KIT antibodies, the membranes were incubated for 30 min with Thermo Scientific Restore Stripping Buffer (Thermo Fisher Scientific, 21059), blocked, washed and incubated with primary antibodies (anti-FLT3, Origene, TA808157; anti-KIT, Invitrogenc MA5-15894). The membrane was then washed and incubated with secondary antibodies (anti-mouse IgG, HRP-linked antibodies, Thermo Fisher Scientific, 62-6520; or anti-rat IgG, HRP-linked antibodies, Thermo Fisher Scientific, 31470) and reimaged as described above.
Base editing of cell lines
Human K562 cells were cultured in IMDM supplemented with 10% FBS, 1% penicillin–streptomycin (10,000 U ml−1), 2% l-glutamine (200 mM). For base-editing experiments, K562 cells were collected and resuspended in electroporation solution51 supplemented with 500 ng base editor plasmid and 150–360 pmol of sgRNA (IDT) in 20 μl. Cells were electroporated using the Lonza 4D-Nucleofector system and cultured for 72 h before evaluation by flow cytometry or gDNA collection.
Base-editing evaluation by Sanger sequencing
Genomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, 69506) or QuickExtract solution (Lucigen, QE0905T). PCR amplification of the 450–600 bp region surrounding the target residue was performed using GoTaq G2 polymerase (Promega, M7848) and purified using the SV-Wizard PCR Clean-Up kit (Promega, A9282) or sparQ PureMag magnetic beads (MagBio, 95196). Sanger sequencing was performed by Genewiz (Azenta Life Sciences) and base-editing outcomes were estimated using the editR v.1.08 R package52.
CAR T cell generation
Peripheral blood mononuclear cells were isolated by ficoll density-gradient centrifugation from healthy blood donor samples (non-HLA-matched with CD34+ HSPCs). Total PBMCs or purified T cells (Pan T Cell Isolation Kit, Miltenyi, 130-096-535) were cultured in IMDM supplemented with 10% FBS, 1% penicillin–streptomycin (10,000 U ml−1), 2% l-glutamine (200 mM), 5 ng ml−1 IL-7 (Peprotech, 200-07) and 5 ng ml−1 IL-15 (Peprotech, 200-15) in the presence of CD3-CD28 Dynabeads (Gibco, 11141D) at a 3:1 bead:cell ratio. On day 2–3 of culture, cells were counted and transduced with a third-generation bidirectional lentiviral vector encoding the CAR and an optimized version36 of the truncated EGFR53 marker at a multiplicity of infection (MOI) of 5 to 10. Dynabeads were removed at day 7 and the culture was continued for a total of 14 days before either vital freezing or in vitro/in vivo experiments. The CAR T cell phenotype and transduction efficiency was evaluated by flow cytometry.
CAR T cell co-culture assays
Target cells (K562 cells, AML PDXs or CD34+ HSPCs) were plated in flat-bottom 96-well plates, 10,000 cells per well in IMDM supplemented with 10% FBS. Freshly cultured or thawed CAR T or unstransduced T cells were marked with CellTrace Yellow or Violet reagent (Invitrogen, C34567 and C34557) for 15 min at 37 °C and then washed with complete medium. T cells then were added to the target cells at several effector:target ratios (10, 5, 2.5, 1.25, 0.625, 0) in a total volume of 200 μl in triplicate or quadruplicate. The plates were incubated at 37 °C in a humidified incubator with 5% CO2 for 6 h (early timepoint) or 48 h (late timepoint). For analysis, cells were transferred to V-bottom plates supplemented with 10–15 μl CountBeads (BioLegend, 424902), centrifuged and resuspended in staining mix containing human Fc-blocking reagent (Miltenyi, 130-059-901), and CD3 (or CD4/CD8 cocktail), CD69 and CD107a antibodies unless stated otherwise. In K562 cell experiments, FLT3 BV10A4 and/or CD123 9F5 or KIT 104D2 antibodies were included in the staining mix to evaluate the residual target expression at the end of co-culture. In CD34+ HSPC experiments, cells were stained for CD34, CD90 and CD45RA. After incubation at room temperature for 15 min, the cells were washed and resuspended in annexin V binding buffer (BioLegend, 422201) containing annexin V FITC or PacificBlue (BioLegend, 640945 or 640918) and 7-AAD. The plates were analysed using the BD Fortessa high-throughput system (HTS) and acquired using BD FACS Diva (v.6). Absolute counts of live (Annexin V−7-AAD−) target cells were calculated using CountBeads and normalized to the E:T = 0 (no T cells) condition (absolute count of target cells divided by the median of the target cells in E:T = 0 replicates). The MFI of target genes (FLT3, CD123, KIT) was normalized in the same manner to account for slight differences in transgene expression.
Base editor mRNA in vitro transcription
Base editor mRNA was prepared by T7 run-off in vitro transcription using a custom plasmid template encoding for a T7 promoter, a minimal 5′-UTR, the base editor reading frame, 2× HBB 3′-UTR and a 110–120 bp poly(A) sequence54. The plasmid template was linearized by BbsI-HF restriction digestion (NEB, R3539) and purified by phenol–chloroform extraction (Sigma-Aldrich, P2069). mRNA IVT was performed using the NEB HiScribe T7 kit (E2040S) and co-transcriptionally capped with ARCA (3′-O-Me-m7G(5′)ppp(5′)G RNA cap analogue, NEB, S1411L) or CleanCap AG (Trilink, N-7113). Partial (75–85%) or total UTP substitution with N1-methyl-pseudo-UTP (Trilink, N-1103) or 5-methoxy-UTP (Trilink, N-1093) was performed as indicated. Dephosphorylation with QuickCIP (NEB, M0525L) or DNase treatment (NEB, M0303L) was added after IVT reaction (30 min at 37 °C). IVT mRNA was purified using the NEB Monarch RNA Clean up kit (T2050L) or sparQ PureMag magnetic beads (MagBio, 95196) and resuspended in RNase-free water. mRNA was quantified using the Nanodrop-8000 spectrophotometer and quality control was performed using the Agilent Fragment Analyzer with RNA Kit-15NT (Agilent, DNF-471).
CD34+ base editing
Mobilized peripheral-blood-derived human CD34+ HSPCs were cultured and electroporated as previously described55. In brief, cryopreserved cells were thawed and cultured in StemSpan SFEMII medium (StemCell, 09655) supplemented with 1% penicillin–streptomycin (10,000 U ml−1), 1% l-glutamine (200 mM), 125 ng ml−1 hSCF (Peprotech, 300-07), 125 ng ml−1 hFLT3L (Peprotech, 300-19), 62.5 ng ml−1 hTPO (Peprotech, 300-18), 0.75 μM StemRegenin-1 (SR1, StemCell, 72344) and 35 nM UM171 (Sellekchem, S7608) unless stated otherwise. Then, 48 h after thawing (unless stated otherwise), cells were collected and electroporated using the Lonza 4D-Nucleofector system by resuspending the cells in P3 solution (Lonza, V4XP-3024) supplemented with 100–250 nM base editor mRNA, 15–20 μM sgRNA (IDT) and 1.2 U μl−1 RNase inhibitor (Promega RNAsin Plus, N2611) or 1.5–3% (v/v) glycerol. After electroporation, cells were then counted and transplanted after 24 h for in vivo experiments, or cultured for an additional 5–7 days in the same medium described above at 0.5 M ml−1 for in vitro experiments.
Transduction and expansion of human AML xenografts
Human patient-derived AML xenografts (PDX) were thawed and cultured in StemSpan SFEMII medium (StemCell, 09655) supplemented with 1% penicillin–streptomycin (10,000 U ml−1), 1% l-glutamine (200 mM), 50 ng ml−1 hSCF (Peprotech, 300-07), 50 ng ml−1 hFLT3L (Peprotech, 300-19), 25 ng ml−1 hTPO (Peprotech, 300-18), 10 ng ml−1 IL-3, 10 ng ml−1 G-CSF, 0.75 μM StemRegenin-1 (SR1, StemCell, 72344), 35 nM UM171 (Sellekchem, S7608) and 10 μM PGE2. Cells were transduced with a third-generation LV vector expressing mNeonGreen under a hPGK promoter (titre, ~2×1010 TU ml−1) at an MOI of 100 and cultured overnight at 37 °C in a humidified incubator under 5% CO2. Cells were collected the next day and transplanted into 4–8-week-old NBSGW female mice by tail-vein injection. Engraftment was monitored by peripheral blood collection and FACS analysis. Mice were euthanized when PDX AML cells exceeded around 20% of total white blood cells. BM and spleen were collected and either vitally frozen or FACS-sorted (BD FACS Melody sorter) to isolate mNeonGreen+ cells and retransplanted into new NBSGW recipients to obtain fully transduced PDXs.
In vivo xenotransplantation experiments
All of the animal experiments were performed in accordance with regulations set by the American Association for Laboratory Animal Science and Institutional Animal Care and Use Committee (IACUC) approved protocol (DFCI, 21-002). Mice were housed in sterile individually ventilated cages and fed autoclaved food and water, under a standard 12 h–12 h day–night light cycle. Female NOD.Cg-KitW-41JTyr+PrkdcscidIl2rgtm1Wjl/ThomJ mice (aged 4–8 weeks; termed NBSGW, Jackson, 026622) were xenotransplanted with 1 million human CD34+ HSPCs per mouse by tail-vein injection. Human engraftment was monitored by peripheral blood FACS analysis at 7–9 weeks. In AML co-engraftment experiments, mice then received 0.75 M human patient-derived AML xenograft cells (PDX), previously transduced with a mNeonGreen, by tail-vein injection. Then, 10 days after AML transplantation, mice received 2.5 M CAR T cells by tail-vein injection unless stated otherwise. The mice were monitored by peripheral blood analysis for a period of 2 weeks after CAR T cell administration and then euthanized. At euthanasia, the bone marrow, spleen and peripheral blood were collected for FACS and genomic analysis.
Colony-forming assays
CFU assays were performed by plating 1,000 CD34+ cells per well for in vitro CD34+ HSPCs experiments, or 25,000 total bone marrow cells per well for xenotransplanted BM-derived assays, unless stated otherwise. Cells were resuspended in Methocult H4034 medium (StemCell, 04034) and plated into SmartDish meniscus-free six-well plates. Wells were imaged and analysed after 2 weeks using the StemCell STEMvision system and STEMvision software using the 14 day bone marrow setting. For flow cytometry analysis, methylcellulose medium was softened with prewarmed PBS, collected and washed twice before analysis. The percentage of leukaemia cells within the BM-derived CFU was determined by mNeonGreen fluorescence.
Lineage differentiation cultures from CD34+ HSPCs
CD34+ HSPCs were thawed and edited as previously described. At day 7 after electroporation, cells were placed into differentiation media as follows: myeloid culture (SFEMII, penicillin–streptomycin 1%, Q 1%, FLT3L 100 ng ml−1, SCF 100 ng ml−1, TPO 50 ng ml−1, GM-CSF 100 ng ml−1, IL-6 50 ng ml−1 and IL-3 10 ng ml−1), macrophage (RPMI, FBS 10%, M-CSF 100 ng ml−1 and GM-CSF 50 ng ml−1) on non-tissue-treated plates, DC (RPMI, FBS 10%, GM-CSF 50 ng ml−1 and IL-4 20 ng ml−1), megakaryocyte (SFEMII, penicillin–streptomycin 1%, Q 1%, SCF 25 ng ml−1, TPO 100 ng ml−1, IL-6 10 ng ml−1, IL-11 5 ng ml−1), granulocyte (SFEMII, penicillin–streptomycin 1%, Q 1%, GM-CSF 100 ng ml−1, then transitioned to RPMI + FBS 20% + G-CSF 50 ng ml−1 after 7 days). The immunophenotype of differentiated cells was evaluated by flow cytometry.
Flow cytometry
Cell lines were collected, washed in PBS + 2% FBS, resuspended in 100 μl, incubated with human or mouse Fc-blocking reagent (Miltenyi 130-059-901 and 130-092-575) and then stained with the indicated antibodies for 20–30 min at 4 °C and washed. Viability was assessed by LiveDead yellow (Invitrogen, L34959) 7-AAD (BioLegend, 420404) or propidium iodide (PI, BioLegend, 421301) staining. Immunophenotyping of in vitro base-edited CD34+ HSPCs was evaluated by staining for 30 min at 4 °C with CD34 BV421, CD45RA APC-Cy7, CD90 APC, CD133 PE and, in some experiments, CD49f PE-Cy7 antibodies after incubation with Fc-blocking reagent (Miltenyi, 130-059-901). Viability was assessed by 7-AAD or PI staining.
For assessment of reactive oxygen species production, cells from the myeloid differentiation culture were collected and incubated with PMA (5 ng μl−1) for 15 min at 37 °C, then CellROX Green reagent (Invitrogen, C10444) was added at a final concentration of 500 nM and the sample was kept for an additional 30 min at 37 °C (ref. 17). Cells were then stained with hFC block and HLA-DR PacificBlue, CD11b APC-Fire750, CD14 BV510, CD33 PE-Cy7 and CD15 AF700 antibodies, and washed and analysed. PI was included for viability. To test phagocytosis, in vitro differentiated macrophages were incubated with pHrodo green E. coli bioparticles (Thermo Fisher Scientific, P343666) for 60 min at 37 °C, washed and analysed by flow cytometry17. To assess signal transduction of in vitro differentiated myeloid culture, cells were collected, cytokine-starved overnight in SFEMII (not supplemented with cytokines) and then stimulated with GM-CSF 50 ng ml−1, IL-4 50 ng ml−1, IL-6 100 ng ml−1, type I IFNα A/D 5,000 U ml−1, PMA 100 ng ml−1 plus ionomycin 1 μg ml−1 or LPS 100 ng ml−1 for 30 min at 37 °C. Cells were then fixed with BioLegend fixation buffer (420801) and permeabilized with True-Phos Perm Buffer (425401) according to the manufacturer’s instructions. Cells were then aliquoted into two fractions, stained with the following antibody panels and analysed using flow cytometry: (1) pSTAT1 PE-Cy7, pSTAT3 BV421, pSTAT5 PE and pSTAT6 AF647; (2) pERK1/2 BV421, pCREB AF647, p38/MAPK PE and pRPS6 PE-Cy7. In vitro differentiated dendritic cells were stained with hFC block, CD1c APC, CD33 PE-Cy7, CD303 APC-Cy7, CD141 KiraviaBlue520 and FLT3 PE antibodies and 7-AAD for viability. To assess upregulation of co-stimulatory molecules, dendritic cells were incubated with 100 ng ml−1 LPS for 30 min at 37 °C and stained with hFC block, CD1c APC, CD303 APC-Cy7, CD11c BUV661, FLT3 PC7, HLA-DR Pacific Blue, CD80 BV605 and CD86 FITC antibodies, plus PI for viability. To evaluate macrophage differentiation towards an M1-like (CD80+CD86+HLA-DR+) or M2-like (CD86−CD200R+CD206+HLA-DR−) phenotype, in vitro differentiated macrophages were plated in a 96-well plate and stimulated overnight with LPS 100 ng ml−1 or IL-4 20 ng ml−1, respectively. Macrophages were then detached and stained with hFC block, CD11b APC-F750, CD33 BB515, CD200R PE-Cy7, CD206 PE, CD209 AF647, HLA-DR PacificBlue, CD80 BV605 and CD86 AF700 antibodies, and PI for viability and analysed by flow56. Neutrophil extracellular trap (NETosis) induction in differentiated granulocytes was performed as previously described56. In brief, cells were seeded in a 96-well plate, stimulated with PMA 50 ng ml−1 for 4 h at 37 °C and then stained with hFC block and CD15 FITC, CD66b PE-Cy7 and CD33 APC antibodies for 15 min at room temperature. Granulocytes were then washed and fixed with PFA, supplemented with DAPI and PI staining solution to stain exocytosed nucleic acid, and analysed by flow cytometry. In vitro differentiated megakaryocytes were analysed by staining for 30 min at 4 °C with hFC block and CD41 PE, CD42b AF647, CD61 FITC, CD34 BV421 and CD45 BV786 antibodies. For megakaryocyte ploidy, cells were stained with stained with hFC block and CD42b AF647 and CD61 FITC antibodies, and then washed and resuspended in 500 μl FxCycle PI/RNase staining solution (Molecular Probes, F10797) and analysed by flow cytometry57.
Peripheral blood collected from in vivo experiments was lysed twice for 10 min at room temperature using ACK reagent (StemCell, 07850), washed, incubated with human and mouse Fc-blocking reagent (Miltenyi, 130-059-901 and 130-092-575), and stained at room temperature for 15 min with hCD45 BV421, mCD45 PE, CD3 AF647, CD19 BV605 and CD33 PE-Cy7 antibodies, unless stated otherwise. 7-AAD was included as viability stain. To comprehensively identify haematopoietic populations in BM samples58, collected cells were lysed using ACK reagent, washed twice and supplemented with CountBeads for absolute quantification. The samples were then incubated with human and mouse Fc-blocking reagent and stained for 30 min on ice with hCD45 BV786, mCD45 PerCP-Cy5.5, CD3 PE-Cy5, CD7 AF700, CD10 BUV737, CD11c BUV661, CD14 BV510, CD19 BV605, CD33 PE-Cy7, CD38 BUV396, CD45RA APC-Cy7, CD56 BUV496, CD90 APC, FLT3 PE or BV711, and either KIT BV711 or CD123 PE antibodies (depending on the experimental design) (Supplementary Table 2), with the addition of 50 μl per sample Brilliant Stain buffer (BD, 659611). A T-cell-focused panel was applied to the spleen and BM samples by staining for 30 min on ice with hCD45 BV786, mCD45 PerCP-Cy5.5, EGFR AF488, CD3 BUV396, CD4 APC, CD8 PacificBlue, CD62L BV605, CD69 PE-Cy7, CD45RA APC-Cy7, PD1 BUV737 and LAG3 PE antibodies. Propidium iodide (3.5 μl per 100 μl) was included as a viability stain shortly before analysis. Cells were acquired on the 5-laser BD Fortessa flow cytometer using BD FACS Diva (v.6) and analysed using FCS Express 6 (DeNovo Software).
Gene expression analysis of edited CD34+ HSPCs
CD34+ cells from healthy donors were either base edited for FLT3, CD123, KIT or AAVS1 or untreated (electroporation only). Then, 4 days after editing, to enable complete turnover of the edited receptors, cells were starved overnight in SFEMII without cytokines. On day 5 after editing, CD34+ HSPCs were stimulated with either FLT3L 500 ng ml−1, SCF 500 ng ml−1 or IL-3 100 ng ml−1 according to the editing condition. AAVS1 and the electroporation-only conditions were divided into three separate wells and incubated with the three cytokines, to provide appropriate controls for each donor and stimulation. After 24 h of stimulation, cells were collected and RNA was extracted using the Qiagen RNAeasy Mini kit (74104) with on-column DNase I treatment (Qiagen, 79254). RNA was quantified using the Nanodrop and sent for sequencing (DNBSEQ stranded mRNA library, BGI). Variations in the gene expression profile in response to specific ligand stimulation were compared among FLT3-, KIT- and CD123-edited samples, control AAVS1-edited samples and electroporation-only samples. Raw sequencing files were filtered for quality control and aligned to the reference human genome hg38 using STAR workflow59, obtaining, as a result, the gene-based count matrices. On the basis of a preliminary principal component analysis, we identified one sample (HD-2, KIT-edited, unstimulated) as an outlier and removed it from the subsequent analysis. We also filtered poorly detected genes with cumulative read counts smaller than 10 or measured (read count > 0) in less than two samples. Statistical analysis was performed in R software, using the DEseq2 package60. Gene counts were modelled using a negative binomial model, the significance of specific coefficients was tested using the Wald test, and P values were adjusted for multiplicity using the Holm method61. We analysed the expression pattern for each gene by using the full model configuration: FullModel: GeneExpr_i = b_0 + b_Don. Donor + b_Edit. Editing+ b_Stim Stimulation + b_(Edit.Stim) Editing x Stimulation. We then tested the significance of the following parameters: b_Edit(UT VS AAVS1): genes differentially expressed in UT with respect to the reference group AAVS1; b_Edit(FLT3 | KIT | CD123 VS AAVS1): genes differentially expressed in sample edited using FLT3, KIT or CD123 sgRNA with respect to the reference group AAVS1; b_Stim: genes differentially expressed in stimulated samples (FLT3L, SCF or IL-3) with respect to the unstimulated group; b (Edit.Stim): genes differentially expressed in an editing group-specific manner after stimulation.
The expression values for the significant genes identified in each comparison are displayed as heat maps in Extended Data Fig. 5c,e. The figures were generated using the R package ggplot262.
Phosphoproteome analysis by mass spectrometry
CD34+ cells were base edited for FLT3, CD123, KIT or AAVS1. Four days after editing, cells were starved overnight in SFEMII without cytokines. On day 5 after editing, CD34+ HSPCs were incubated at 37 °C with FLT3L (500 ng ml−1), SCF (500 ng ml−1) or IL-3 (100 ng ml−1) according to the editing condition (AAVS1 was stimulated independently with each of the three cytokines). After 6 h, cells were collected and dry pellets were flash-frozen in liquid nitrogen. Cell pellets were lysed in 5% SDS, 50 mM TEAB pH 8.5 with protease inhibitors (Roche) and phosphatase inhibitors (Sigma-Aldrich, phosphatase inhibitor cocktails 2 and 3) for 30 min with shaking. The protein concentration was determined by BCA (Thermo Fisher Scientific). Aliquots of protein (30 µg) were treated with 250 U of benzonase and incubated for 5 min at room temperature. Proteins were then reduced with DTT (final concentration, 10 mM; 56 °C for 30 min), alkylated with iodoacetamide (25 mM) for 30 min at room temperature, acidified 1:10 with 12% phosphoric acid, further diluted 6× with binding buffer (90% methanol and 100 mM TEAB) and then applied to S-trap micro columns (Protifi). The columns were washed four times with 150 µl binding buffer, and proteins digested with trypsin overnight at 37 °C. Peptides were extracted from the column by centrifugation after applying elution buffers (1–50 mM TEAB, 2–0.2% formic acid and 3–50% acetonitrile), and dried by vacuum centrifugation. Peptides were resuspended in 100 mM HEPES, pH 8.0, and treated with TMT-Pro isobaric labelling reagents for 1 h at room temperature using 8 out of the 16 possible reagents (126, 127N, 128N, 129N, 130N, 131N, 132N and 133N, or 127C, 128C, 129C, 130C, 131C, 132C, 133C and 134N). Each experiment included proteins from target and control edits ± ligand stimulation for 2 separate donors. After labelling, the reactions were quenched with 5% hydroxylamine in 50 mM TEAB, combined, dried, reconstituted in 0.1% TFA and desalted by C18. Phosphopeptides were then enriched by Fe3+-NTA-IMAC as described previously63 and analysed by nanoflow liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS)64. Peptides were loaded onto an analytical column with an integrated ESI emitter tip (75 µm internal diameter, fused, packed with 25 cm of 1.9 µm Reprosil C18, ESI Source Solutions), desalted and resolved with an LC gradient (1–5% B in 5 min, 5–18% B in 115 min, 18–27% B in 50 min, 27–39% B in 30 min, 39–80% B in 15 min, where A is 0.1% formic acid in water and B is 0.1% formic acid in acetonitrile). Peptides were analysed on the TimsTOF Pro II mass spectrometer with 6 PASEF ramps (mobility range 0.8–1.4 V s cm−2, ramp time 100 ms), MS/MS stepping enabled, a 1.5 Da isolation width and a target intensity of 17,500. Active exclusion was enabled with a release time of 30 s. Data files were searched against a forward reverse human protein database (UCSG) using MSFragger with precursor and product ion tolerances of 25 ppm and 0.05 Da, respectively. Search parameters specified variable oxidation of methionine, variable phosphorylation (STY), fixed carbamidomethylation of cysteine and fixed TMT-Pro modification of peptide N termini and lysine residues. Custom Python scripts65 were used to filter search results to 1% FDR, as well as extract TMT reporter ion intensities and correct for isotopic impurities. After filtering out PSMs with missing TMT values, reporter ion evidence was summed for peptides of the same modification and charge state, with values of less than 20 counts imputed to the estimated noise level (20 counts). Data were then quantile normalized after removing peptides with a median reporter ion intensity of less than 100 counts. We analysed the phosphorylation pattern for each peptide using the full model configuration: FullModel: PhosphoPeptide_i = b_0 + b_Don. Donor + b_Edit. Editing+ b_Stim Stimulation; and tested the significance of b_Edit and b_Stim using a likelihood ratio test and the following reduced models: ReducedModel for testing b_Edit: PhosphoPeptide_i = b_0 + b_Don. Donor + b_Stim Stimulation; ReducedModel for testing b_Stim: PhosphoPeptide_i = b_0 + b_Don. Donor + b_Edit. Editing.
Given the high signal-to-noise ratio of this methodology and the limited sample size, the results were analysed descriptively. We calculated the average (across donor) fold change between the phosphorylation levels in the unstimulated and stimulation conditions for both editing settings.
Off-target evaluation
GUIDE-seq was conducted in HEK293T cells66. The GUIDE-seq dsODN was prepared by annealing PAGE-purified oligos GUIDE-seq dsODN sense and antisense (a list of the sequences is provided in Supplementary Table 1) using the following conditions: 95 °C for 2 min, slow ramp down to 25 °C with an increment of −0.1 °C per cycle for 700 cycles. HEK293T cells were electroporated with 4 µg SpRY Cas9 mRNA, 200 pmol of dsODN and 600 pmol of guide RNA using the SF Cell Line 4D Nucleofector X Kit S (Lonza, V4XC-2024). The dsODN concentration was selected after a titration to identify the maximal dose permitting at least 60% cell viability 24 h after delivery by electroporation. Genomic DNA was collected 4–6 days after electroporation. We performed GUIDE-seq library construction with some modifications as previously described67. Adapter oligos (from IDT) were annealed using the following conditions: 95 °C for 2 min, slow ramp down to 25 °C with an increment of −0.1 °C per cycle for 700 cycles. We added Tn5-transposase (86% reaction volume) to pre-annealed oligo (14% reaction volume) for transposome assembly at room temperature for 1 h. We prepared tagmentation reactions by titrating the assembled transposome into 200 ng of genomic DNA (gDNA) in the presence of 1× TAPS-DMF, incubating at 55 °C for 7 min. 1× TAPS-DMF buffer contains 50 mM TAPS-NaOH at pH 8.5 (at room temperature; Thermo Fisher Scientific, J63268AE), 25 mM magnesium chloride (MgCl2; Thermo Fisher Scientific, AM9530G), 50% dimethylformamide (Thermo Fisher Scientific, 20673). The transposome was inactivated with 0.2% SDS at room temperature for 5 min. Tagmentation conditions were selected that yielded DNA fragment size distribution concentrated at 500–1,000 bp. After the tagmented DNA was isolated, we performed a two-step PCR amplification with the first step (PCR 1) for GUIDE-seq oligo-specific amplification and the second step (PCR 2) for indexing. The PCR 1 reaction mix contained 50 mM magnesium chloride (MgCl2; Thermo Fisher Scientific, AM9530G), 2× Platinum SuperFi PCR Master mix (Thermo Fisher Scientific, 12358050), 0.5 M tetramethylammonium chloride solution (TMAC; Sigma-Aldrich, T3411), 10 µM GUIDE-Seq oligo-specific primer (GSP, IDT) (Supplementary Table 1), 10 µM i5 amplification primer (IDT, 5′-AATGATACGGCGACCACCGAGATC-3′) and tagmented DNA. The PCR 2 reaction mix was the same as for PCR 1, but without 50 mM MgCl2, and we added 10 µM i7 barcode primer (IDT) instead of GSP. The PCR 1 product was size-selected with 45 µl (0.9× original reaction) of AMPure XP Beads (Agencourt, A63882) and eluted in nuclease-free water before proceeding to the next step. The PCR 2 amplified product was double-size selected with 25 µl (0.5× original reaction) of AMPure XP Beads and then with 17.5 µl (0.35× original reaction) of the beads to isolate PCR products in the range of 200–500 bp, quantified using the Qubit dsDNA High-Sensitivity Assay (Thermo Fisher Scientific, Q32851), checked for size and purity using the TapeStation (Agilent 4200 System) and KAPA qPCR (KAPA Library Quantification Kit Illumina Platform), before samples were equimolar pooled and sequenced. The libraries were deep-sequenced as a pool using a paired-end 150 bp run on the Illumina MiniSeq system (~1.5 million reads per sample) with the following parameters: 25–35% PhiX and 151|8|17|141 (Read1|Index1|Index2|Read2). Deep sequencing data from the GUIDE-seq experiment were analysed using GS-Preprocess (https://github.com/umasstr/GS-Preprocess) and Bioconductor Package GUIDEseq (v.1.4.1)68. The GS-Preprocessing script demultiplexed the raw Illumina FASTQ files based on the index information and mapped the sequence files to the reference genome. It generated the input files (BAM files of plus and minus strands, gRNA fasta files and a unique molecular index (UMI) for each read) for Bioconductor Package GUIDEseq. The window size for peak aggregation was set to 50 bp. Off-target site identification parameters were set for SpRY-Cas9 as follows: PAM.pattern = “NNN”, max.mismatch = 10, includeBulge = TRUE, max.n.bulge = 2L, upstream = 50, downstream = 50, max.mismatch = 6 and allowed.mismatch.PAM = 3. To filter false-positive signals, we called the peaks and calculated their frequencies in the control group. These background peaks were used to filter out potential hot spots or sequencing noise in Cas9–sgRNA treatment groups. This analysis provided a list of potential off-target sequences that were ranked with regard to unique reads on the basis of the incorporated UMI and the adapter ligation position, which has been correlated with the activity of SpRY-Cas9 at each sequence (Supplementary Table 16). The results were filtered by the number of mismatch + bulge < 6 to identify high-risk off-target sites.
Candidate in silico predicted off-target sites were selected on the basis of the CRISPOR online tool (top six intronic and top six exonic sites based on sequence homology for FLT3, CD123 and KIT sgRNAs when using a SpRY-Cas9 PAM (NRN). We amplified the off-target sequences by PCR on genomic DNA extracted from edited or control CD34+ cells, using three different healthy donors for each condition. PCR products were designed to be <400 bp in size. PCR amplicons for each condition were pooled together and sent for library preparation and NGS sequencing (300 bp paired-end reads, Azenta). One exonic and two intronic sites for CD123 sgRNA-R were eliminated from the final analysis owing to an insufficient number of reads. Reads were processed using the CRISPResso2 tool (CRISPRessoPooled), setting as the quantification window the amplicon interval overlapping with the sgRNA. We modelled the proportion of EditedReads over TotalReads (unedited + edited) using a binomial response generalized linear model (logistic regression) including the following covariates: DonorID, factor distinguishing cell donor; treatment, a binary variable having value treatment = 1 for edited and treatment = 0 for mock samples. Standard logistic regression estimation frameworks have convergence issues when the input data table is sparse (multiple samples with EditedReads = 0). To address this issue, we adopted a bias-reduction approach69, a second-order unbiased estimator that returns finite standard errors for the coefficients under all conditions. We performed the regression and analysed the results for each locus separately and calculated: (1) EditedReads proportion estimation and related 95% confidence interval for all samples; (2) the significance of the treatment coefficient (Wald test for the null hypothesis β = 0 and α = 0.05). Suppose the coefficient estimate is positive and significant. In that case, the expected EditedReads proportion in the edited samples (treatment = 1) is greater than in the mock samples, and therefore bona fide, off-target, base editor activity is detected. For the quantification of on-target indels proportion, a similar procedure was used by selecting from the edited reads (Unedited=“FALSE”) only those having either n_deleted > 0 or n_inserted > 0. The analysis was performed using R software. For the estimation of the logistic model, we used the R package brglm2 (ref. 70).
RNA-editing analysis
The presence and extent of guide-independent RNA editing activity caused by ABE have been verified using the data generated in the RNA-seq experiment described in the ‘Gene expression analysis of edited CD34+ HSPCs’ section. Each sample’s R1 and R2 reads files were processed using REDItools271 using a base quality filtering threshold (-bq 30) and a hg38 transcript annotation database. On the basis of the output generated by REDItools2, for each sample, we selected the adenine nucleotides in the reference genome with the highest coverage (top 5%) by transcriptomics reads. We then calculated the percentage of detected A>G transition events (number of A>G transition)/A coverage). The distribution of A>G transition rates across all A nucleotides considered for each sample is graphically represented using box plots in Extended Data Fig. 8c. The sample-specific coverage threshold and the total number of A nucleotides considered is reported in Supplementary Table 21.
Statistical analyses
The n values indicate the number of biologically independent samples, animals or experiments. Data are summarized as mean ± s.d. Inferential techniques were applied in presence of adequate sample sizes (n ≥ 5), otherwise only descriptive statistics are reported. Comparisons between two groups were performed using unpaired t-tests. FDR-adjusted P values are reported when appropriate. When one variable was compared among more than two groups, one-way ANOVA was used. When multiple variables were compared among more than two groups, two-way ANOVA was used. The P values of the row or column effect is reported as appropriate to describe the significance of the selected parameter on the measured variable. Multiple comparisons between groups are performed with two-stage step-up procedure of Benjamini, Krieger and Yekutieli to control the FDR (Q = 0.05) when appropriate. In all of the analyses, the significance threshold was set at 0.05; NS, not significant. Analyses were performed using GraphPad Prism v.9.4 (GraphPad). The methods for the statistical analyses of genomic off-target sites, RNA-seq, phosphoproteomic profiling by MS, RNA editing and antibody/ligand affinity curves are reported in each Methods section.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06496-5.
Supplementary information
Acknowledgements
We thank D. A. Williams, G. Schiroli and all of the members of the Genovese’s laboratory for discussions; L. Sereni, G. Ceglie, A. Martinuzzi, V. Cinella, S. Rizzato, A. Bianchi and M. Freschi (Genovese laboratory) for help with experiments; A. Nguyen, N. Neri and A. Verma (Bauer laboratory) for the help in performing and analysing GUIDE-seq analysis; L. J. Zhu, T. Rodriguez and S. A. Wolfe for support during the analysis and supplying the Tn5 transposase (UMass Med); F. Perner and C. Marinaccio (Armstrong laboratory) for providing the PDXs and BaF3 cell line. G.C. conducted this study as partial fulfilment of his PhD in Translational and Molecular Medicine—DIMET, Milano-Bicocca University (Italy). This work was supported by grants to P.G. from the Children’s Cancer Research Fund (Emerging Scientist Award), the Leukemia Research Foundation (New Investigator Blood Cancer Research Grant Program), the National Blood Foundation (NBF2021GRANT-PG), the American Cancer Society (Discovery Boost Grants, DBG-23-1039598-01-IBCD), the National Institutes of Health (NIH) (grant R01AI155796) and the Boston Children’s Hospital (Gene Therapy Program startup package and Pilot Research Award from the Office of Sponsored Programs). J.A.M. acknowledges support from the Mark Foundation for Cancer Research, the NIH (grants CA233800, CA247671 and U24DK116204) and the Massachusetts Life Science Center. S.A.A. was supported by NIH (grants CA176745, CA259273 and CA066996). G.C. and A.C. were partially supported by fellowships from the American Italian Cancer Foundation (AICF).
Extended data figures and tables
Source data
Author contributions
G.C. designed and performed research, interpreted data and wrote the manuscript. A.C., A.M., M.S.M., I.U.Z. and D.K. performed experiments and interpreted data. J.Z. and D.E.B. performed and interpreted GUIDE-seq analysis. S.B.F. and J.A.M. performed, analysed and interpreted phospho-proteomic profiling. D.P. oversaw statistical analyses and performed bioinformatic analyses of RNA-seq, phospho-MS and NGS off-target data. C.B., A.R., J.R., J.A.M., D.E.B. and S.A.A. provided discussion and some primary samples. P.G. designed and supervised research, interpreted data, wrote the manuscript and coordinated the work.
Peer review
Peer review information
Nature thanks Elvin Wagenblast, Depei Wu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Data availability
RNA-seq datasets have been deposited with links to BioProject accession number PRJNA986596 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). Targeted amplicon sequencing datasets of off-target sites have been deposited at BioProject (PRJNA986845). All MS data files are available for download (ftp://massive.ucsd.edu/MSV000092272). All other data are available in the Article and its Supplementary Information. Source data are provided with this paper.
Competing interests
G.C. and P.G. are listed as inventors on patent applications related to this work that are owned and managed by the Boston Children’s Hospital and Dana-Farber Cancer Institute. S.A.A. has been a consultant and/or shareholder for Neomorph Inc, Imago Biosciences, Cyteir Therapeutics, C4 Therapeutics, Nimbus Therapeutics and Accent Therapeutics. S.A.A. has received research support from Janssen and Syndax. S.A.A. is named as an inventor on a patent application related to MENIN inhibition WO/2017/132398A1. J.A.M. is a founder, equity holder and advisor to Entact Bio, serves on the scientific advisory board of 908 Devices and receives sponsored research funding from Vertex, AstraZeneca, Taiho, Springworks and TUO Therapeutics. The other authors declare no competing interests.
Footnotes
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Extended data
is available for this paper at 10.1038/s41586-023-06496-5.
Supplementary information
The online version contains supplementary material available at 10.1038/s41586-023-06496-5.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
RNA-seq datasets have been deposited with links to BioProject accession number PRJNA986596 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). Targeted amplicon sequencing datasets of off-target sites have been deposited at BioProject (PRJNA986845). All MS data files are available for download (ftp://massive.ucsd.edu/MSV000092272). All other data are available in the Article and its Supplementary Information. Source data are provided with this paper.