Tumor-induced dendritic cell deregulation perturbs T cell proliferation and predicts clinical outcome in acute lymphoblastic leukemia

Anil Kumar; Kory Hamane; Caroline Duault; Joao Rodrigues Lima-Junior; Da Hae Jung; Hanjun Qin; Sheyla Salcido; Min Huang; Xinying Guo; Adeleh Taghi Khani; Ashly Sanchez Ortiz; Lucy Ghoda; Guido Marcucci; Norman J Lacayo; Kathleen M Sakamoto; Christian Hurtz; Martin Carroll; Sarah K Tasian; Huimin Geng; Lingyun Ji; Saro Armenian; Shai Izraeli; Xiwei Wu; Holden T Maecker; Srividya Swaminathan

doi:10.1016/j.xcrm.2026.102945

. 2026 Jul 28;7(8):102945. doi: 10.1016/j.xcrm.2026.102945

Tumor-induced dendritic cell deregulation perturbs T cell proliferation and predicts clinical outcome in acute lymphoblastic leukemia

Anil Kumar ¹, Kory Hamane ¹, Caroline Duault ², Joao Rodrigues Lima-Junior ¹, Da Hae Jung ¹, Hanjun Qin ³, Sheyla Salcido ⁴, Min Huang ⁵, Xinying Guo ¹, Adeleh Taghi Khani ¹, Ashly Sanchez Ortiz ¹, Lucy Ghoda ⁴, Guido Marcucci ^4,⁶, Norman J Lacayo ⁵, Kathleen M Sakamoto ⁵, Christian Hurtz ⁷, Martin Carroll ⁸, Sarah K Tasian ^9,¹⁰, Huimin Geng ¹¹, Lingyun Ji ¹², Saro Armenian ¹³, Shai Izraeli ¹⁴, Xiwei Wu ³, Holden T Maecker ², Srividya Swaminathan ^1,^13,^15,^16,^∗

PMCID: PMC13522792 PMID: 42520804

Summary

Perturbations in dendritic cells (DCs) in B/T cell acute lymphoblastic leukemia (ALL), their cause(s) and consequence(s) on antileukemia immunity, and patient outcomes remain poorly explored. We find that maturation of DC1-6 subsets is disrupted in children and adults with ALL. Conventional DC1 and DC2 subpopulations are reduced at the expense of the progenitors and the DC4 fractions in ALL. The potential to mature, present antigens, and produce cytokines for initiating T cell surveillance appears to be impaired in every ALL DC subset. Such DC subsets are accordingly unable to induce T cell proliferation compared to DCs from healthy donors. MYC overexpression in ALL cells disrupts DC homeostasis and reduces the ability of DCs to induce T cell proliferation. Predominance of cells with transcriptional signatures typical of “stimulated DCs” predicts favorable clinical outcomes in B-ALL, while it is associated with unfavorable outcomes in T-ALL. Phenotyping DC subsets at ALL diagnosis could thus be valuable in informing treatment outcomes.

Keywords: acute lymphoblastic leukemia, dendritic cell phenotype, clinical prognosis

Graphical abstract

Highlights

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Maturation of dendritic cells (DCs) into distinct subsets is impaired in ALL
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DCs in ALL have impaired T cell priming potential
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Overexpression of MYC oncogene in leukemia cells partly drives DC dysfunction
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DC phenotype at ALL diagnosis predicts patient outcomes on standard chemotherapy

Kumar et al. show that specialization of dendritic cells (DCs) into distinct functional subsets is disrupted in acute lymphoblastic leukemia (ALL). This disruption impairs the ability of DCs to prime T cells. DC phenotype in patient peripheral blood and bone marrow at ALL presentation is an indicator of their prognosis.

Introduction

Acute lymphoblastic leukemia (ALL) is a blood cancer of B/T cells that occurs across the age spectrum.¹ Remission rates drop with age.¹ Around 10%–15% of pediatric and 50%–60% of adult ALL subtypes are refractory to, or recur after, standard chemotherapy, targeted therapies, and/or immunotherapies.²^,³^,⁴ We found that many high-risk ALL subtypes overexpress difficult-to-drug c-MYC oncoprotein.⁵ Thus, approaches that reliably predict clinical outcome at initial diagnosis of high-risk ALL and apply treatments to ensure their sustained remission are urgently needed.

Treatment response and remission in patients with ALL are largely governed by the body’s natural anticancer defenses mediated by non-malignant immune cells.⁶^,⁷ In ALL, malignancy arises from B- or T-immune cells, which proliferate thereby crowding out and reducing the representation of potentially antileukemic, non-malignant immune cells. Why residual non-malignant immune cells (non-B in B-ALL and non-T in T-ALL) do not recognize and mount an attack on the malignant cells is poorly understood. We⁸ and others,⁹ respectively, identified perturbed homeostasis of innate immune natural killer (NK) cells and monocytes in ALL. However, perturbations in non-malignant immune cells that work together with NK cells and monocytes to modulate antileukemia immune response need characterization.

Among non-malignant immune cells, dendritic cells (DCs) are rare but critical antileukemic immune cells as they initiate the generation of long-term anticancer immunologic memory by bridging innate and adaptive immune responses.¹⁰ Importantly, we showed that DC-derived cytokines, type I interferons (IFN-Is) and interleukin (IL)-15, are reduced and thereby impair NK-cell tumoricidal activity in the highest-risk ALLs.⁵^,¹¹

Perturbations in DC homeostasis in ALL were previously reported.¹²^,¹³ However, what was lacking was a comprehensive molecular characterization of defects in this compartment at protein and mRNA levels and the cause and impact of such defects on long-term immunity and clinical outcome. Using high-dimensional single-cell RNA sequencing (scRNA-seq) and cytometry, here, we characterize the transcriptome and proteome of the DC compartment ex vivo in diagnosis samples from patients with ALL and in vivo in primary transgenic mouse models of high-grade B-/T-ALL. Our studies provide a granular characterization of the scale, cause, and consequences of DC dysfunction in B-/T-ALL and underscore the importance of DC-specific immunophenotyping at ALL diagnosis to guide therapeutic decisions.

Results

Homeostasis of non-malignant myeloid subsets is perturbed in patients with B- and T-ALL

Monocytes and DCs are functionally similar and plastic.¹⁴ We characterized phenotypic perturbations in these myeloid subsets in peripheral blood mononuclear cells (PBMCs) and bone marrow mononuclear cells (BMMCs) obtained from 8 children and 31 adults with B/T-ALL (Table S1; 30 B-ALL and 9 T-ALL), using high-dimensional flow cytometry (Figure S1). Our samples were from patients with high- and intermediate-risk ALL subtypes (KMT2A, Ph-like, BCR-ABL1, MYC/BCL2, and Notch), shown by us⁵ and others¹⁵^,¹⁶ to express high c-MYC levels. Within the non-malignant immune fraction, CD14⁺ monocytes (Mo) were significantly reduced in PBMCs and nearly absent in BMMCs of patients with B-/T-ALL compared to healthy donors (Figures 1A and S2A). Pro-inflammatory CD14^HighCD16⁻ classical monocytes (CMos) were significantly reduced at the expense of CD14^HighCD16⁺ intermediate monocytes (IMos) and CD14^LowCD16⁺ non-classical monocytes (NCMos) in ALL patient PBMCs (Figure 1B). Frequencies of CD14⁺CD209⁺ monocyte-derived DCs (Mo-DCs), known to have an inflammatory profile¹⁷ and impair T cell surveillance in patients with advanced solid tumors,¹⁸ were increased in ALL patient PBMCs compared to healthy controls (Figure 1C).

Within the non-B/non-T/non-NK (termed here, lineage negative [Lin⁻]), non-monocytic (CD14⁻), HLA-DR⁺ fraction of PBMCs and BMMCs, CD123^Low/−CD11c⁻ cells (termed “non-cDC, non-pDC” fraction) were increased at the expense of CD11c⁺CD123^Low/− conventional/myeloid DC (cDC) and CD123^HighCD11c⁻ plasmacytoid DCs (pDCs) in ALL (Figures 1D and S2B). As the CD123^Low/−CD11c⁻ non-cDC, non-pDC fraction is known to harbor the DC progenitors (DCPs),¹⁹ our results suggest an expansion of DCP at the expense of the more mature cDC and pDC subsets in ALL. To confirm this, we applied transcriptional signatures of monocytes, cDCs, pDCs, and DCPs¹⁹ in CIBERSORT²⁰ to estimate their relative proportions in PBMCs from healthy donors and patients with B-ALL²¹ and T-ALL.²² Consistent with Figures 1A–1D, PBMCs with transcriptional signature of DCP were aberrantly expanded in ALL, whereas more mature monocyte and DC subsets were reduced (Figure 1E).

We asked whether monocyte and DC subset frequencies in the non-malignant ALL PBMC fraction are influenced by leukemia cell type and patient age or sex. Classical monocytes correlated positively with disease type, whereas intermediate monocytes inversely correlated with patient age. However, DC subset frequencies showed no significant association with the type of malignant lymphoblast, age, and sex of the patient (Figure S2C), suggesting that such perturbations in ALL are likely driven by factors other than patient’s demographics or disease type.

DCs bridge innate and adaptive immunity by presenting antigens to T cells and inducing T cell proliferation.²³ Therefore, to investigate whether altered DC subset compositions coincide with their altered functionality, we co-cultured magnetically sorted naive pan T cells from healthy donor PBMCs (Figure S3A) with DC fraction of healthy donor and ALL patient PBMCs, enriched magnetically by negative selection (Figure S3B). Although healthy donor and ALL patient PBMC post-sort fractions are both enriched in cDCs, the purity is lower in ALL. Reduced purity of patient-derived CD11c⁺ DCs reflects the increased CD11c⁻ DC fraction in ALL in Figures 1D and 1E. Notably, healthy donor PBMC T cells, when co-cultured with DC fraction of ALL patient PBMCs in vitro, failed to proliferate as effectively as their counterparts that were co-cultured with healthy donor DCs (Figure 1F). Thus, perturbed DC homeostasis in ALL could impair the ability of DCs to induce T cell-mediated immunity, supporting further characterization of residual DC defects in the ALL microenvironment, their causes, and prognostic significance.

Residual cDCs in patients with ALL lose their specialization into distinct functional lineages

We interrogated defects in cDCs, the most abundant DC subset, which we found to be dysfunctional in ALL. cDCs were found to harbor five distinct sub-lineages (cDC1– cDC5).¹⁹ Of these, CD141^HighCD1c⁻ cDC1 and CD141^Low/−CD1c⁺ cDC2–3 were reduced and CD141^Low/−CD1c⁻ cDC4–5 subsets were concomitantly increased in ALL patient PBMCs compared to healthy counterparts (Figure 2A). Further resolution of the cDC subset characterization by high-dimensional flow cytometry revealed that cDC1 and cDC2 are reduced at the expense of cDC4 in ALL (Figure S4). C-type lectin domain family 9 member A (CLEC9A), a protein that is normally highly expressed on cDC1, was not expressed on this subset but was abnormally elevated in the cDC2–3 and cDC4–5 fractions in ALL PBMCs (Figures 2B and S5). In ALL patient BMMCs, only cDC1 was significantly reduced at the expense of cDC2–3 and/or cDC4–5 (Figure S6). However, unlike in PBMCs, no change was observed in the frequency of CLEC9A⁺ DC subsets in BMMCs of ALL patients (Figure S7).

The above results suggest that the transcriptomic specialization of DCs into distinct sub-lineages is abrogated in human ALL. To evaluate this, we compared the transcriptome of DC subsets in PBMCs of healthy donors and ALL patients (n = 6 each) using 5′-scRNA-seq (Figures 2C and S8). Unsupervised clustering identified distinct DCP, cDC1–5 (cDC), and DC6 (pDC) populations in healthy PBMCs, but such distinction was lost in ALL (Figures 2D and S9). Importantly, the expression of markers that define major DC families [HLA-DR (all DCs), CD11c (cDC1–5), and CD123 (pDC/DC6)] and their sub-lineages¹⁹^,²⁴ [CD100, CCR7 (DCP); CD141, CLEC9A and XCR1 (cDC1); CD1c (cDC2–3), CD5 (cDC2) and CD163 (cDC3); CD16/FCGR3A and SIGLEC10 (cDC4); Siglec6 and AXL (cDC5)] was aberrant in ALL (Figures 2E and S10A). Analysis of the top genes that identify each DC subset in healthy donors (Figure 2F) and in ALL patients (Figure S10B), as well as volcano plots (Figure S11) revealed transcriptomic abnormalities in every DC subset in ALL. We infer that residual DC subsets in ALL are not normal and hence may not be functionally specialized.

Maturation of DCs into functional lineages is impaired in patients with B- and T-ALL

To determine whether patients with ALL fail to specialize into distinct functional subsets, we analyzed scRNA-seq data from ALL patients and healthy donors using the computational trajectory inference algorithm, CellRank.²⁵ CellRank integrates RNA velocity and pseudotime analysis to robustly infer the directional progression and fate probabilities of cellular states. PBMC DC differentiation trajectory in healthy humans starts at a central node containing the DCP (shown by Villani et al.¹⁹ to give rise to cDC1–3) and has a subset of potentially primitive cells that overlap with DC4 (Figure 3A, central node population within blue dotted outline). Notably, overlap of the central node containing primitive DCs with cDC4 is consistent with our earlier findings showing that the DCPs express significant levels of the cDC4 markers CD16/FCGR3A and Siglec10 (Figures 2D, 2E, and S10A). In healthy donors, each differentiated DC sub-lineage (DC1-6) arises from the central node like well-separated branches of a tree (see directionality of arrows in Figure 3A healthy donor DC panel). In contrast, the number of distinct branches as well as their proximity and relationship is different when comparing the ALL-differentiation trajectory to that of healthy donors. Unlike healthy DCP, ALL DCPs fail to give rise to a cDC4 that is independent from cDC1–3; in ALLs, cDC1–3 are all merged into a single cluster that leads to cDC4 (see directionality of arrows in Figure 3A ALL DC panel). Additionally, the distance between pDCs and DCP is shorter in ALL than in healthy donors where pDCs are closer to intermediate cDC2–3 (Figure 3A compares left and right panels).

Using DC differentiation trajectory diffusion maps (Figure 3A), we then visualized the expression of transcription factors/surface receptors known to be involved in DC development²⁶^,²⁷^,²⁸ using Markov-affinity-based graph imputation of cells (MAGIC).²⁹ We found genes characteristic of primitive DCs,²⁸^,³⁰ including NFIL3, CBFB, CEBPA, and BATF3, to be expressed most highly in the central node in healthy donors, although their expression is perturbed in ALL (Figures 3B and S12A, left panel). SPI1 (PU.1), a transcription factor expressed predominantly by DC-restricted progenitors and all cDCs, was aberrantly upregulated only in the PBMC DC4 subset of ALL patients (Figure S12A, middle panel). Another factor, ZBTB46, expressed predominantly by cDCs and their committed progenitors,³¹ was downregulated in these fractions in ALL (Figure S12A, right panel). Consistent with this trend, the expression of other DCP-associated mRNAs,¹⁹ including CD100, CCR7, ID2, TNFRSF18, and RUNX2, was reduced in ALL DCPs compared to their healthy donor counterparts (Figure S12B). As with the directionality of differentiation in Figure 3A, expression of genes needed for DC development is disrupted both in level and localization to specific DC populations in ALL (Figures 3B and S12A).

Expression of cDC1³²^,³³^,³⁴ markers, CLEC9A, TLR3, and WDFY4, was downregulated in ALL (Figure S12C). Transcripts expressed mostly by cDC2-3,²⁶ CD1c, CLEC4A, and CLEC10A were abnormally downregulated in ALL (Figure S13A). Among cDC4-specific markers, FCGR3A, LST1, and TCF7L2,¹⁹ LST1 was aberrantly expressed in ALL (Figure S13B). Among genes expressed highly by pDCs (TCF4, BCL11A, and ZEB2),³⁵^,³⁶^,³⁷ expression of TCF4 and BCL11A was reduced in ALL compared to healthy counterparts (Figure S13C).

To understand how transcriptional regulators are dynamically modulated during healthy DC development, and how this dynamic is altered in ALL patients, we analyzed their expression trajectories across each DC cluster (DCP and DC1–DC6) using pseudotime. Transcription factors and receptors in each healthy PBMC DC fate followed the expected trajectory and expression levels. However, this normal dynamic was disrupted in ALL. For example, the expected pattern of expression of THBD, IRF8, ID2, CLEC9A, TLR3, and WDFY4 along the DC1 differentiation trajectory; cDC1c, CLEC4A, CLEC10A, and IRF4 along the DC2-3 differentiation trajectory; SIGLEC6 along the DC5 differentiation trajectory; and IRF8, ZEB2, and TCF4 along the pDC commitment trajectory was absent in ALL (Figures 3C, 3D, S14, S15A, and S15B).

Overall, our results confirm the impaired ability of DCs in ALL patients to mature into functional subsets.

Proliferating DC-precursor-like cells are increased in patients with B- and T-ALL

We examined the potential cause(s) for loss of specialized DC subsets in patients with ALL using scRNA-seq and high-dimensional cytometry. First, we asked whether the balance in proliferation of pre-mature vs. mature DCs is disturbed in ALL. Frequencies of cells expressing the proliferation marker, Ki67, increased in total PBMC DCs; in the non-pDC, non-cDC fraction that harbors the DCP; and in pDC fraction. Proliferating PBMC cDC fraction was concomitantly reduced in ALL patients (Figures 4A, 4B, and S16). Alterations in proliferation of total DCs and cDC subset in ALL BMMCs mirrored those seen in PBMCs (Figure S17). Thus, cDCs fail to proliferate while other DC subsets may exhibit compensatory or dysregulated proliferative responses in ALL.

To further explore the mechanisms underlying defective DC homeostasis in ALL, we compared proportions of cDCs positive for cytokine, cytokine receptors, and intracellular transcription factors associated with DC maturation. In ALL patients compared to healthy donors, a higher proportion of PBMC cDCs expressed CD45RA, a marker widely expressed by both myeloid and common lymphoid progenitors (Figure 4C). Similarly, frequencies of cDCs expressing CD115/M-CSFR (monocyte-specific) and CD135/FLT3-R (common to both cDCs and pDCs)³⁸ were aberrantly increased in PBMCs of patients with ALL (Figure 4C). Percentages of cDCs expressing key cytokine receptors that drive their differentiation into functional subsets,³⁸^,³⁹^,⁴⁰ including the CD116/granulocyte-macrophage colony-stimulating factor receptor (GM-CSFR) (expressed on monocytes and cDCs) and the CD117/SCF-R/c-Kit (expressed on cDC1 and most cDC2-3), were reduced in ALL patient PBMCs (Figure 4C). Unlike the PBMC cDC fraction, the non-pDC, non-cDC fraction exhibited no changes in proportions of CD45RA⁺, CD115⁺, and CD117⁺ cells. However, increased percentages of CD135⁺ and decreased percentages of CD116⁺ cells seen in PBMC cDCs were replicated in the PBMC non-cDC, non-pDC fraction (Figure S18A). ALL patient PBMCs also had reduced frequencies of CD45RA⁺ pDCs (Figure S18B).

Some changes in makers indicative of DC maturation status that occurred both in ALL PBMCs and BMMCs include increased frequencies of CD115⁺ and reduced frequencies of CD116⁺ and CD117⁺ cDCs and reduced percentages of CD116⁺ and CD117⁺ non-cDC, non-pDC cells (Figures S19A and S19B). In BMMC pDCs, only frequencies of CD135⁺ cells were significantly reduced (Figure S20).

We compared the frequencies of cells expressing lineage-commitment transcription factors IRF4 and IRF8⁴¹^,⁴²^,⁴³ (IRF8^HighIRF4⁻ for cDC1 and IRF8^LowIRF4⁺ for cDC2–3) between healthy donor and ALL patient PBMCs. Consistent with the extensive disruption of the cDC1–3 compartments seen in ALL in Figure 2, frequencies of IRF8^HighIRF4⁻ and IRF8^LowIRF4⁺ cDCs were significantly reduced in ALL (Figure 4D). We then compared the frequencies of IRF4/IRF8 subsets in pDCs of healthy donors and ALL patients because both these factors contribute to the development of and are highly expressed in pDCs.⁴¹ ALL patient pDCs exhibit a shift toward an IRF4-dominant, IRF8-deficient expression pattern, unlike their canonical IRF4^HighIRF8^High profile (Figure S21). These results, together with reduced expression of IRF8 transcripts in ALL pDCs (Figure S14), support deregulation of pDC identity and function, in addition to that of cDCs, in ALL.

We next investigated whether the commitment signals, i.e., cytokines, that are involved in DC differentiation, are perturbed in ALL. DCs respond to GM-CSF, interleukin (IL)-4, and/or tumor necrosis factor (TNF), maturing into functional subsets⁴⁴^,⁴⁵ and then producing TNF, IL-6, and IL-1β.⁴⁶ To compare the levels of these cytokines in healthy and ALL microenvironments, we employed complementary strategies to stimulate DCs: (1) PMA + ionomycin to assess cytokine production independent of pattern recognition receptor (PRR) signaling or (2) lipopolysaccharide (LPS) stimulation to measure cytokine production by cells including DCs that express the PRR, Toll-like receptor 4 (TLR4). Cytokines needed for DC maturation were all reduced in samples from ALL patients compared to healthy donors (Figure S22). Overall, the impaired proliferation of ALL DC subsets and alterations in the expression of transcription factors and cytokines needed for their differentiation prove that ALL microenvironments are not conducive for specialization of DCs into distinct subsets.

Antigen presentation potential of DC subset is markedly impaired in human ALL

Specialization of cDCs into functional lineages enables them to get activated in response to antigens and present these antigens on human leukocyte antigens (HLAs) or major histocompatibility complexes (MHCs) to T cell subsets, leading to T cell proliferation and establishment of immunological memory.⁴⁷ After LPS antigen stimulation, cDCs in PBMCs of patients with ALL could not upregulate HLA-DR to the extent seen in their healthy donor counterparts (Figure 5A), suggesting that ALL DC may have impaired antigen sensing and presentation potential.

Antigen presentation potential of DC subsets is markedly impaired in human ALL

(A and B) Flow cytometry analysis of PBMCs of healthy donors (n = 18) and patients with ALL (n = 8 B-ALL, n = 5 T-ALL), comparing (A) the LPS-induced increase in surface HLA-DR expression in the PBMC cDC fraction; one representative histogram for each group is shown; (B) percentages of CD11c^HighHLA-DR^High and CD11c^LowHLA-DR^Low/− cells within the Lin⁻CD14⁻CD11c⁺ fraction.

(C and D) Flow cytometry depicting relative frequencies of DQ-OVA⁺ cDCs and median fluorescence intensity (MFI) of HLA-DR in CD11c⁺DQ-OVA⁺ cDC fraction from healthy donors (n = 4) and patients with ALL (n = 3 B-ALL, n = 1 T-ALL). For (A–D), median ± interquartile range; p values: Mann-Whitney U test.

(E–H) Gene ontology gene set enrichment analysis by scRNA-seq (GEO: GSE304657) showing the topmost differentially enriched pathways in total DC and cDC1–3 subsets in PBMCs of healthy donors (n = 6) and patients with ALL (n = 4 B-ALL, n = 2 T-ALL). Dot color indicates the adjusted p values generated by a permutation-based test. NES, normalized enrichment score.

Antigen-primed mature DCs exhibit higher expression of CD11c and HLA-DR compared to their precursors and act as potent antigen-presenting cells.⁴⁸ We observed that Lin⁻CD14⁻CD11c⁺ cells in the healthy donors comprise of two distinct fractions: CD11c^HighHLA-DR^High and CD11c^LowHLA-DR^Low/−. Consistent with the inability of DCs in ALL patients to upregulate HLA-DR (Figure 5A) as well as their inability to induce T cell proliferation (Figure 1F), we found a reduction in CD11c^HighHLA-DR^High and a concomitant increase in CD11c^LowHLA-DR^Low/− in the PBMC DC fraction in patients with ALL (Figure 5B). This alteration was not observed in ALL BMMCs (Figure S23A) possibly because BMMC-derived DCs are immature and exhibit a less immune-activating profile compared to their PBMC counterparts.⁴⁹

To investigate whether antigen processing and/or presentation is defective in ALL DCs, we pulsed PBMCs of ALL patients and healthy donors with DQ-Ovalbumin (DQ-OVA) antigen. While frequencies of DQ-OVA⁺ DCs were unchanged between healthy donors and ALL patient PBMCs (Figure 5C), DQ-OVA-pulsed DCs in ALL had lower HLA-DR expression compared to their healthy counterparts (Figure 5D). Hence, we conclude that the DCs in patients with ALL lack the ability to present antigens to T cells although they take them up and process them as effectively as healthy DCs. Overall, the observed phenotypic alterations indicate perturbed antigen presentation capacity and potentially impaired immune stimulatory function.

We then interrogated whether the composition of cDC subsets within the CD11c^HighHLA-DR^High and CD11c^LowHLA-DR^Low/− is altered in human ALL. Frequencies of cDC1, cDC2, and cDC3 were reduced in the HLA-DR^HighCD11c^High PBMC fraction in ALL compared to healthy controls. Conversely, the percentage of cDC2-cDC3 was increased in the HLA-DR^LowCD11c^Low/− fraction of PBMCs and BMMCs of patients with ALL (Figures S23 and S24). Our results suggested that ALL cDC1–3 may have defects in pathways associated with antigen presentation. To confirm this, we conducted gene ontology (GO) gene set enrichment analysis (GSEA) to compare differentially regulated pathways in total DCs, DCPs, and DC1-6 subsets in scRNA-seq data of healthy donors and ALL patient PBMCs (Figures 2D–2F). Multiple gene sets associated with enhanced antigen presentation were enriched in total DCs and cDC1–3 derived from healthy donors while pathways involved in vesicular trafficking, active mitosis, granulocyte-specific lysosomal granules (azurophil granule lumen), and metabolic processes related to hydrogen peroxide are enriched in DCs from ALL patients (Figures 5E–5H). Pathways indicating impaired antigen presentation were also enriched in cDC4-5 subsets of patients with ALL (Figures S25A and S25B). Compared to their counterparts in ALL, pDC6 of healthy donors were enriched in β-catenin signaling, a crucial pathway⁵⁰ required for their differentiation (Figure S25C). Enrichment of cell proliferation pathways in total DCs and DCP subsets in ALL patient PBMCs (Figures 5E and S25D) is also consistent with our previous results in Figures 4A and 4B showing enhanced proliferation of these subsets in ALL. Despite their ability to proliferate and actively endocytose and process antigens, immature DCs have been found to be inferior in their capacity to prime naive T cells.⁵¹ Based on this and our results showing that ALL DCs express low CD11c and HLA-DR and are unable to enhance HLA-DR expression in response to antigenic challenge, we conclude that DCs in ALL remain in an immature, non-immunostimulatory state.

We compared ALL patients and healthy donors for their expression of immunostimulatory DC-derived cytokines (IL-12, TNF-α, IL-1β, and IL-6) that bridge antigen presentation to T cell proliferation and establishment of T cell-mediated immunity by flow cytometry of ALL and healthy PBMC samples before and after stimulation with LPS. Unstimulated PBMCs in patients with ALL have higher frequencies of IL-1β and TNF-α-expressing DCs (Figure S26A). Despite this, upon stimulation with LPS, the frequencies of DCs producing these cytokines were not increased beyond that seen in healthy donors (Figure S26B). In fact, after LPS stimulation, the frequencies of PBMC IL-1β⁺ cDCs were significantly reduced in ALL patients (Figure S26B). Thus, following LPS stimulation, DCs from ALL patients are not only unable to increase HLA-DR expression (Figure 5A) but also less efficient at upregulating cytokine production in response to antigenic stimulation compared to healthy controls.

We then compared the proportion of cDCs that express molecules involved in T cell activation, including CD86 and CD40 (co-stimulatory molecules), as well as CD83, which is a marker of maturation of DCs into effective antigen-presenting cells,⁵² between healthy controls and ALL patients. We also analyzed CD40L ligand associated with DC activation.⁵³ Among these, only the proportion of CD83⁺ cDCs was significantly increased in PBMCs of patients with ALL (Figure S27). The elevated proportion of cytokine-positive and CD83⁺ DCs at baseline, together with impaired upregulation of HLA-DR upon antigenic stimulation, suggest that DCs in ALL exist in an aberrant semi-mature state and may have tolerogenic potential.⁵⁴

Overexpression of MYC in malignant lymphoblasts drives the disruption of DC homeostasis

We previously showed that overexpression of MYC in leukemia cells suppresses endogenous immune responses mediated by NK cells.⁵^,¹¹ Whether leukemic MYC overexpression also impairs DC homeostasis and function in ALL is unknown. To determine whether MYC overexpression in malignant lymphoblasts prevent DCs in the ALL microenvironment from inducing T cell proliferation, we co-cultured an MYC-repressible Burkitt leukemia (BL) cell line with or without cDCs and/or pan T cells derived from healthy donor PBMCs. BL is a relevant model for addressing this question because MYC-translocated B-ALL resembles BL both molecularly and pathologically in patients.²^,⁵⁵^,⁵⁶ MYC^ON/High BL reduced the ability of cDCs to induce T cell proliferation in co-culture, while MYC^OFF/Low BL had the opposite effect (Figure 6A; compare DC + T vs. DC + T + MYC^ON BL vs. DC + T + MYC^OFF BL). Compared to their MYC^ON/High counterparts, co-culture of MYC^OFF/Low BL with T cells alone did not enhance the latter’s proliferation, proving that presentation of endogenous antigens by BL cells after MYC inactivation cannot induce T cell proliferation (Figure 6A; compare T + MYC^ON BL vs. T + MYC^OFF BL). Thus, leukemia-intrinsic MYC suppresses T cell proliferation that is specifically induced by cDCs. Reduction of MYC expression in BL cells did not cause cell death (Figure S28) but clearly enhanced the ability of DCs to induce T cell proliferation (Figure 6A), showing that the cell-intrinsic anti-apoptotic and pro-proliferative oncogenic functions of MYC are independent from its immunomodulatory functions. These data also suggest that the ability of DCs to induce T cell proliferation is dependent on the level of MYC in the malignant lymphoblasts and is not simply a function of the presence or absence of these lymphoblasts.

To examine the association between MYC overexpression and suppression of cDC homeostasis in primary ALL in vivo, we compared the relative proportions of MHCII/CD11c splenic cDC subsets in Eμ-MYC mice that developed B-ALL⁵⁷ and age-, strain-, and sex-matched healthy controls. As in ALL patient PBMCs (Figure 5B), percentages of the MHCII^HighCD11c^High DCs were reduced and those of MHCII^Low/−CD11c^Low DCs were concomitantly increased in spleens of mice bearing overt B-ALL compared to normal mice (Figure 6B). Next, we investigated whether leukemic MYC overexpression drives the disruption in DC homeostasis in the SRα-tTa-Tet-O-MYC transgenic mouse model that develops MYC-repressible T-ALL.⁵⁸ The concomitant reduction and increase, respectively, in percentages of MHCII^HighCD11c^High and MHCII^Low/−CD11c^Low splenic cDCs in mice bearing overt MYC^ON T-ALL was restored to normal levels after MYC inactivation in malignant T-lymphoblasts (Figure 6C). The percentages of splenic CD11c^High DCs expressing cKit (CD117), a receptor required for DC homeostasis, is reduced, and those of CD11c^HighCD117⁻ DCs is increased in SRα-tTa-Tet-O-MYC mice bearing overt MYC^ON T-ALL compared to their normal and MYC^OFF T-ALL counterparts (Figure 6D). Thus, oncogenic signaling (e.g., MYC overexpression) in leukemia cells perturbs DC phenotypes in ALL.

Because NK cells have been shown to mediate DC homeostasis,⁵⁹ we hypothesized that leukemic MYC-driven reduction in NK cell numbers and function⁵^,¹¹ could at least partially drive DC alterations. We co-cultured PBMC samples derived from three patients with ALL with the cytotoxic NK-92 cell line and measured changes in DC maturation phenotypes by flow cytometry. Co-culture with NK cells increased the frequencies of CD11c⁺ cells within the DC fraction, at least partly restoring the distribution of cDCs to homeostatic levels in PBMCs of patients with ALL (Figure 6E). MYC-induced reduction in NK cell number and function in ALL microenvironments⁵^,¹¹ thus may partly suppress homeostatic DC maturation.

DC phenotype at disease presentation predicts clinical outcome in patients with B- and T-ALL

We investigated whether frequencies and the phenotype of residual DCs at presentation of ALL have clinical relevance. First, using CIBERSORT,²⁰ we estimated the relative frequencies of cells with the transcriptional signature of “stimulated/active DCs” (predicted as more differentiated) and those with signature of “unstimulated/inactive DCs” (predicted as less differentiated) DCs⁶⁰ in PBMCs of healthy donors and patients with B-ALL from COG P9906 trial (GEO: GSE11877)²¹ and those with T-ALL from the COG AALL0434 trial (Synapse: syn54032669)⁶¹ (Figure 7A). While normal PBMC DC signatures were those of “stimulated DCs,” a significant proportion of patients with B- and T-ALL had no cells with signature of DCs or had cells with signature of only unstimulated DCs (collectively termed as “no/unstimulated DCs”) (Figure 7B).

To confirm that the signature of “stimulated DCs” indeed resembles that of differentiated DCs that can prime T cells and induce their proliferation, we conducted GSEA on PBMCs and BMMCs from ALL samples after segregating 207 COG P9906 B-ALL patients and 1,309 COG AALL0434 T-ALL patients into two groups based on their predominance of “stimulated DC” or “no/unstimulated DC”, as estimated by CIBERSORT. In both datasets, patients having cells with transcriptional signatures typical of “stimulated DC” were enriched in pathways associated with DC differentiation compared to their counterparts having “no/unstimulated DC” (Figure 7C). Patients with signatures typical of “no/unstimulated (less differentiated) DC” had moderate enrichment of MYC and E2F target genes compared to their “stimulated (more differentiated) DC” counterparts, although such enrichment did not reach statistical significance (Figures S29A and S29B).

Next, we asked if the predominance of cells with the transcriptional signatures of “stimulated (more differentiated) DCs” or “no/unstimulated (less differentiated) DCs” predicts prognosis of ALL. COG P9906 B-ALL patients with “stimulated DCs” tended to have longer relapse-free survival (RFS) times than their “no/unstimulated DCs” counterparts, although not statistically significant. In contrast, COG AALL0434 T-ALL patients with “no/unstimulated DCs” showed significantly better outcomes than their counterparts having more “stimulated DCs” (Figure 7D). We further examined whether this prognostic association was maintained across clinically defined high-risk ALL subgroups. Notably, “stimulated DCs” predicted favorable clinical outcome in B-ALL patients with the highest risk leukemias, including those with central nervous system (CNS) involvement of malignant lymphoblasts (termed CNS⁺), those with high white blood cell (WBC) count of ≥50,000/μL, those with minimal residual disease (MRD⁺) on day 29 after induction therapy, and those belonging to racial groups associated with poor leukemia prognosis such as Black, Hispanic, American Indian, and Alaskan Natives. Conversely, in T-ALL, predominance of cells with a transcriptional signature of “stimulated DCs” was associated with unfavorable outcomes, most notably among MRD-positive patients (Figures 7E–7I). Therefore, while the predominance of a “stimulated DC” signature predicts favorable clinical outcomes in patients with B-ALL, in patients with T-ALL, such a signature is associated with significantly adverse outcomes.

Discussion

Although patients with ALL experience severe immunosuppression from both the disease and treatments (chemotherapy), perturbations in endogenous, anti-leukemic immune cells remain poorly defined. Characterizing these baseline perturbations is needed for predicting long-term response to existing treatments and for developing approaches to sustain remission of refractory/relapsed leukemia subtypes. Here, we characterize perturbations in DCs in ALL, demonstrate that MYC-driven oncogenic signaling underlies some of these perturbations, and show that these defects impair antileukemia T cell surveillance and predict clinical outcome.

The plasticity of myeloid cells is evident from the existence of multiple DC subsets.¹⁹^,⁶² The trajectory of differentiation into these subsets, even in healthy humans, is less understood. Surprisingly, in peripheral blood of healthy donors, we found primitive cells that overlap with the cDC4 fraction that may be able to give rise to all specialized cDC (cDC1–cDC5) and pDC (DC6) lineages. The aberrant increase in cells with a DCP transcriptional signature and in cDC4 at the expense of cDC1–3 provides evidence for the severe disruption of the homeostatic myeloid architecture in lymphoid malignancies.

Role of myeloid cells in promoting lymphoid malignancies has gained appreciation.⁶³^,⁶⁴ Witkowski et al. and colleagues discovered that CMos are reduced and NCMos are concomitantly increased in children with ETV6-RUNX1 and BCR-ABL1 B-ALL compared to healthy donors.⁹ Here, we analyzed primary B-ALL and T-ALL patient samples across the age span from several cytogenetic subtypes (e.g., KMT2A-rearranged, hypodiploid, Philadelphia chromosome-like, NOTCH1-mutant, and others). We observed that the IMo, which is closely related to the NCMo, is trending toward an increase in patients with ALL. Furthermore, our findings suggest that the extent of monocytic perturbation in ALL may be dependent on the age of the patient or on the malignant lymphocyte type; this remains to be formally tested. Unlike monocytes, we find that changes in DC subset frequencies do not associate with patient age or ALL type (Figure S2C). Consistent with this, overexpression of universal oncogenes such as MYC, common to multiple high-risk subgroups of ALL,⁵^,¹¹ drive DC dysfunction. Therefore, our findings are broadly relevant to predict outcomes across different age groups and ALL subtypes.

We find that the defects in DC maturation and antigen presentation to T cells may be driven by leukemic MYC-driven suppression of NK cells. We demonstrated that MYC overexpression in malignant lymphoblasts suppresses NK cell maturation by blocking the production of IFN-Is and IL-15.⁵^,¹¹ Whether the reduction in these and other cytokines that drive DC maturation (e.g., GM-CSF, TNF, and IFN-γ), as well as whether aberrant expression of cytokine receptors and transcription factors in DC subsets in ALL are consequences of leukemic MYC overexpression remains to be explored. Similarly, whether aberrations in DC cytokine production result from or are a side effect of the inflammatory environment induced by ALL propagation remains to be determined.⁶⁵^,⁶⁶

The divergence in the association of DC phenotype with clinical outcome in B- and T-ALL may stem from the different role of T cells in these leukemia forms: while endogenous T cells form a critical antileukemic subset in B-ALL, they are the malignant subset in T-ALL. Consistent with their role in presenting antigens and priming T cells, stimulated DCs in B-ALL patients may thus promote anti-leukemic immunity, likely through the activation and expansion of T cells that eradicate leukemic blasts. In contrast, stimulated DCs in T-ALL may fuel expansion of malignant T cell clones. This theory, however, requires formal investigation. Nevertheless, our findings highlight the importance of considering malignancy cell-of-origin when evaluating the prognostic implications of DC phenotype and suggest that DC-based immunotherapeutic strategies may need tailoring based on ALL type and subtype.

MYC-translocated B-ALL²^,⁵⁵^,⁶⁷ and those recently shown by us to overexpress MYC⁵ have the poorest clinical outcomes.² Unfortunately, even revolutionary treatments including the bispecific T cell engager blinatumomab⁶⁸ have been ineffective in inducing long-term remission of these high-risk malignancies. Suppression of endogenous DC-mediated T cell proliferation by leukemic MYC, and the resultant absence of DC-primed endogenous T cells, may explain why CAR T cells and blinatumomab are less effective in curing MYC-driven ALL.

Our studies lay down the rationale for comprehensively profiling DCs at presentation of ALL to predict prognosis and inform treatment strategy. The association between DC phenotype and clinical outcome of patients who received induction therapy suggests that DC profiling at diagnosis may help predict chemotherapy response. Whether this prediction extends to patients on other Food and Drug Administration (FDA)-approved therapies such as blinatumomab and CAR T cells for B-ALL is unknown. Future studies comparing DC profiles of blinatumomab responders and non-responders could determine whether defective DCs, by impairing the endogenous T cell pool, contribute to treatment failure. Unlike blinatumomab, CAR-T cells do not need DC-mediated antigen presentation or co-stimulation, as these signals are encoded within the CAR construct itself. Nevertheless, examining DC phenotypes in ALL patients may still be informative, as DC compartment alterations could reflect the abundance and fitness of the endogenous T cells, a key determinant of successful CAR-T cell manufacturing. Overall, we predict that patients with poor T cell quality and blinatumomab non-responsiveness may benefit from strategies that enhance antileukemic DC maturation or function including allogeneic NK-cell-based treatments as findings from us (Figure 6E) and others⁶⁹ suggest.

Limitations of the study

The rarity of DCs in ALL prevented us from conducting additional functional assays to determine whether genetically manipulating them would restore their ability to induce T cell proliferation. We focused on DC defects in the highest- and standard-risk pediatric and adult ALL cytogenetic groups; how these defects compare with the lowest-risk ALL subtypes, such as ETV6-RUNX1 and DUX4² remains to be determined. We focused on the role of leukemic MYC but not other driver oncogenes in perturbing DC homeostasis because the MYC oncogene has been historically difficult to target⁷⁰ and recent studies from others and us demonstrate MYC’s critical role in host immune evasion.¹¹^,⁷¹^,⁷² However, other oncogenic drivers may play causal roles in suppressing DC homeostasis in ALL, and this could be a subject for future studies. In addition, although our findings suggest altered antigen presentation and reduced T cell stimulatory function in ALL-derived DCs, studies measuring the phenotypic status (maturation/activation) and ability of ALL DCs to induce T cell proliferation in response to leukemia-specific antigens remain to be conducted. Our study had two technical limitations: (1) healthy donor samples are provided to us as pre-enriched buffy coats preventing us from calculating absolute numbers of heathy immune subsets and from comparing these with those of ALL patients. Hence, as in our previous studies of other non-malignant immune cells in ALL,⁵^,⁸^,¹¹ we adopted stringent gating in cytometry to measure changes within the DC fraction. In scRNA-seq, we identified changes within DC subsets by applying DC transcriptomic framework from Villani et al.¹⁹ (2) We could not procure BMMC and PBMC from healthy children; nonetheless, altered proportions of the major DC fractions were found to not correlate with patient age.

Resource availability

Lead contact

Further information and resource requests should be directed to and will be fulfilled by the lead contact, Srividya Swaminathan (sswaminathan@coh.org).

Materials availability

This study did not generate new unique reagents.

Data and code availability

•
scRNA-seq data of the non-malignant immune cell fraction of healthy donor and ALL patient PBMCs are deposited in the Gene Expression Omnibus (GEO) under accession number GSE304657.
•
No custom code was developed for this study.
•
Publicly available transcriptomic datasets analyzed in this study include GEO datasets GSE65136 (healthy donors), GSE94820 (myeloid transcriptomic signature), GSE11877 (patients with B-ALL), GSE62156 (patients with T-ALL), and the Synapse dataset syn54032669 (https://doi.org/10.7303/syn54032669) of patients with T-ALL. All analyses were performed using publicly available software packages, with names and versions provided in the key resources table. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Acknowledgments

Schematics were created using BioRender.com. The following grants to S. Swaminathan supported this work: Special Fellow (LLS 3366-17) and Translational Research Program (LLS 6624-21) Awards from Blood Cancer United (formerly the Leukemia and Lymphoma Society); American Society of Hematology Scholar Award and Supplement; PhRMA Foundation Research Starter Grant in Drug Discovery; Andrew McDonough B+ Foundation Childhood Cancer Research Grant; Leukemia Research Foundation New Investigator Award; St. Baldrick's Foundation Scholar Award; Norman and Sadie Lee Foundation Pediatric Grant; Dake Wilson Family Pediatric Accelerator Funding; City of Hope Shared Resources Pilot Grant; and the City of Hope Chancellor’s Grant. Other support includes 1U24CA224309 from NIH/NCI (H.T.M.); P30CA033572 from NIH/NCI to the Integrative Genomics Core (X.W.); the Hematopoietic Tissue Biorepository (S. Salcido, L.G., and G.M.) and the Analytical Cytometry Shared Resource Cores at City of Hope; Alex's Lemonade Stand Foundation ‘A’ Award (C.H.); and NIH/NCI 1U01CA232486, 1U01CA24307, and 1R01CA293587, as well as a Department of Defense Translational Team Science Award, a Pennsylvania Department of Health Commonwealth Universal Research Enhancement Program Award, and a Blood Cancer United Scholar Award (all to S.K.T.). S.K.T. holds the Joshua Kahan Endowed Chair in Pediatric Leukemia Research at the Children’s Hospital of Philadelphia. S.I. was supported by The Israel Cancer Research Foundation (ICRF) and the Nevzlin Foundation. Research was also supported by NCTN Operations Center Grant U10 CA180886 and NCTN Statistics & Data Center Grant U10 CA180899 of the Children’s Oncology Group from NIH/NCI. This content is solely the responsibility of the authors and does not represent the official views of the NIH.

Author contributions

A.K. developed experimental methods, performed experiments, and analyzed and interpreted the results. K.H. analyzed scRNA-seq data. C.D. performed and analyzed mass cytometry experiments. J.R.L.-J. assisted with the development of the high-dimensional flow cytometry panels. H.Q. conducted the scRNA-seq experiments. D.H.J., A.T.K., A.S.O., and X.G. performed wet lab experiments. S. Salcido, M.H., L.G., G.M., N.J.L., K.M.S., C.H., M.C., and S.K.T. provided primary B-ALL and T-ALL patient samples. L.J. and H.G. assisted with interpretation and analysis of clinical data from the Children’s Oncology Group Clinical P9906 dataset. S.I. provided critical scientific input. S. Swaminathan, S.A., H.T.M., and X.W. provided administrative, technical, and material support. S. Swaminathan conceived and supervised the study, developed the scientific approach, interpreted the results, and wrote the original draft of the manuscript. A.K. further edited the manuscript after review by all authors.

Declaration of interests

The authors declare no competing or financial interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

Anti-human Lineage Cocktail (CD3, CD19, CD20, CD56), APC	Biolegend	Cat# 363601; RRID: AB_2916117
Anti-human CD14, Brilliant Violet 510	Biolegend	Cat# 301842; RRID: AB_2561946
Anti-human CD123, PE/Cyanine7	Biolegend	Cat# 306010; RRID: AB_493576
Anti-human CD1c, Brilliant Violet 711	Biolegend	Cat# 331536; RRID: AB_2629760
Anti-human CD370 (CLEC9A/DNGR1), PE	Biolegend	Cat# 353804; RRID: AB_10965546
Anti-human CD209 (DC-SIGN), Alexa Fluor 647	Biolegend	Cat# 330112; RRID: AB_1186092
Anti-human CD83, PE/Cyanine5	Biolegend	Cat# 305310; RRID: AB_314518
Anti-human CD45RA, FITC	Biolegend	Cat# 304148; RRID: AB_2564157
Anti-human XCR1, BV421	Biolegend	Cat# 372610; RRID: AB_2687373
Anti-(mouse/human) IRF4, Alexa Fluor 594	Biolegend	Cat# 646410; RRID: AB_2728476
Anti-human CD40L, PE/Cy5	Biolegend	Cat# 310808; RRID: AB_314831
Anti-human CD34, PE/Dazzle 594	Biolegend	Cat# 343534; RRID: AB_2564012
Anti-human CD39, PerCP/Fire 806	Biolegend	Cat# 328250; RRID: AB_3083311
Anti-mouse NK-1.1, Brilliant Violet 605	Biolegend	Cat# 108740; RRID: AB_2562274
Anti-mouse CD45, PerCP	Biolegend	Cat# 103130; RRID: AB_893339
Anti-mouse CD11c, PE/Cyanine7	Biolegend	Cat# 117318; RRID: AB_493568
Anti-human CD115 (CSF-1R), R718	BD Biosciences	Cat# 752001; RRID: AB_2917119
Anti-human CD117 (c-Kit), RB705	BD Biosciences	Cat# 757999; RRID: AB_3690155
Anti-human CD135, BV650	BD Biosciences	Cat# 752999; RRID: AB_2917953
Anti-human CD116, BUV805	BD Biosciences	Cat# 749387; RRID: AB_2873757
Anti-human CD100 (Sema4D), BUV661	BD Biosciences	Cat# 750139; RRID: AB_2874345
Anti-human HLA-DR, BUV737	BD Biosciences	Cat# 748339; RRID: AB_2872758
Anti-human CD11c, BV605	BD Biosciences	Cat# 563929; RRID: AB_2744276
Anti-human CD141, BUV395	BD Biosciences	Cat# 740312; RRID: AB_2916908
Anti-human CD1c, RB545	BD Biosciences	Cat# 756389; RRID: AB_3688621
Anti-Ki-67, BUV805	BD Biosciences	Cat# 569636; RRID: AB_3685192
Anti-human IRF8, PE	BD Biosciences	Cat# 566373; RRID: AB_2739716
Anti-human CD16, BUV805	BD Biosciences	Cat# 569165; RRID: AB_3684833
Anti-human CD5, BB515	BD Biosciences	Cat# 564647; RRID: AB_2744434
Anti-human CD45RA, BUV563	BD Biosciences	Cat# 612926; RRID: AB_2870211
Anti-human AXL, RB705	BD Biosciences	Cat# 758110; RRID: AB_3690265
Anti-human CD163, RB744	BD Biosciences	Cat# 758090; RRID: AB_3690245
Anti-human Siglec-6, BV786	BD Biosciences	Cat# 747909; RRID: AB_2872371
Anti-human HLA-DR, APC-H7	BD Biosciences	Cat# 561358; RRID: AB_10611876
Anti-human CD86, PE-Cy5	BD Biosciences	Cat# 555659; RRID: AB_396014
Anti-human CD40, APC-R700	BD Biosciences	Cat# 565179; RRID: AB_2739094
Anti-human CD80, BUV805	BD Biosciences	Cat# 751733; RRID: AB_2875715
Anti-human CD89, BUV737	BD Biosciences	Cat# 749244; RRID: AB_2873622
Anti-human FcεR1α, RY703	BD Biosciences	Cat# 771819; RRID: AB_3693640
Anti-human CD88, RB670	BD Biosciences	Cat# 771767; RRID: AB_3693593
Anti-human IL-12 (p40/p70), BV421	BD Biosciences	Cat# 565023; RRID: AB_2739045
Anti-human TNF-α, PerCP-Cy5.5	BD Biosciences	Cat# 560679; RRID: AB_1727579
Anti-human IL-1β, Alexa Fluor 647	BD Biosciences	Cat# 567774; RRID: AB_2916731
Anti-mouse CD335 (NKp46), PE	BD Biosciences	Cat# 560757; RRID: AB_1727466
Anti-mouse I-A[b], BUV805	BD Biosciences	Cat# 748905; RRID: AB_2873308
Anti-mouse CD317 (BST2), BV750	BD Biosciences	Cat# 747608; RRID: AB_2872189
Anti-mouse CD3, BUV395	BD Biosciences	Cat# 569614; RRID: AB_3095858
Anti-mouse CD19, APC	BD Biosciences	Cat# 550992; RRID: AB_398483
Anti-human IL-6, Alexa Fluor 700	Thermo Fisher Scientific	Cat# 56-7069-42; RRID: AB_1548814
Anti-human GM-CSF (159Tb)	Standard BioTools	Cat#: 3159008B RRID: AB_2864732
Anti-human IL-4 (144Nd)	Standard BioTools	Cat#: 3144010B RRID: AB_3677799
Anti-human TNF-α (152Sm)	Standard BioTools	Cat#: 3152002B RRID: AB_2895145
Anti-mouse CD11c (142Nd)	Standard BioTools	Cat# 3142003; RRID: AB_2814737
Anti-mouse MHCII (174Yb)	Standard BioTools	Cat# 3174003B; RRID: AB_2922924
Anti-mouse NKp46 (153Eu)	Standard BioTools	Cat# 3153006B; RRID: N/A
Anti-mouse CD49b (170Er)	Standard BioTools	Cat# 3170008B; RRID: AB_2814741
Anti-mouse CD3 (152Sm)	Standard BioTools	Cat# 3152004B; RRID: AB_3076460
Anti-mouse CD19 (149Sm)	Standard BioTools	Cat# 3149002B; RRID: AB_2814679
Anti-mouse cKit (166 Er)	Standard BioTools	Cat# 3166004B; RRID: AB_2801435

Biological samples

Healthy donors	This paper	Hematopoietic Tissue Biorepository of the City of Hope
ALL patients samples	This paper (Table S1)	Stem Cell and Xenograft Core of the University of Pennsylvania, and the tissue repository at the Bass Center for Childhood Cancer and Blood Diseases at Stanford University
Healthy Bone marrow	STEMCELL TECHNOLOGIES	Cat#70001; RRID: N/A

Chemicals, peptides, and recombinant proteins

Lipopolysaccharides (Salmonella enterica)	Sigma-Aldrich	Cat# L7770-1MG; RRID: N/A
CellTrace™ CFSE Cell Proliferation Kit, for flow cytometry	Thermo Fisher Scientific	Cat# C34554; RRID: N/A
R848 (Resiquimod) - TLR7/8 Agonist	InvivoGen	Cat# tlrl-r848-1; RRID: N/A
Cell-ID™ Intercalator-Ir—125 μM	Standard BioTools	Cat# 201192A; RRID: N/A
Ghost Dye UV450	Cytek Biosciences	Cat# 3-0868-T100; RRID: N/A
Live/dead fixable blue dead cell stain kit	Thermo Fisher Scientific	Cat# L23105; RRID: N/A
PMA (Phorbol 12-myristate 13-acetate)	Millipore Sigma	Cat# P8139-1MG RRID: N/A
Ionomycin	Millipore Sigma	Cat# I0634-1MG RRID: N/A
10x saponin-based permeabilization buffer	Thermo Fisher Scientific (eBioscience)	Cat# 00-8333-56; RRID: N/A
Brefeldin A	Millipore Sigma	Cat# B7651-5MG; RRID: N/A
Monensin sodium salt	Millipore Sigma	Cat# M5273; RRID: N/A
Paraformaldehyde, 16% w/v aq. soln., methanol free	Alfa Aesar	Cat# 43368; RRID: N/A
DQ-Ovalbumin (BODIPY-FL)	Thermo Fisher Scientific	Cat# D12053; RRID: N/A
Human IL-2 Recombinant Protein, PeproTech	Thermo Fisher Scientific	Cat# 200-02-500UG; RRID: N/A

Critical commercial assays

Myeloid Dendritic Cell Isolation Kit, human	Miltenyi Biotec	Cat# 130-094-487; RRID: N/A
NK Cell Isolation Kit, mouse	Miltenyi Biotec	Cat# 130-115-818; RRID: N/A
Pan T Cell Isolation Kit, human	Miltenyi Biotec	Cat# 130-096-535; RRID: N/A
Pan B Cell Isolation Kit, human	Miltenyi Biotec	Cat# 130-101-638; RRID: N/A
eBioscience Foxp3 / Transcription Factor Staining Buffer Set	Thermo Fisher Scientific	Cat# 00-5523-00; RRID: N/A
BD Cytofix/Cytoperm Fixation/Permeabilization Kit	BD Biosciences	Cat#554714; RRID: N/A
eBioscience Protein Transport Inhibitor Cocktail (500X)	Thermo Fisher Scientific	Cat# 00-4980-03; RRID: N/A
Library Construction Kit, 16 rxns	10X Genomics	Cat# PN-1000190; RRID: N/A
Chromium Single Cell Human TCR Amplification Kit, 16 rxns	10X Genomics	Cat# PN-1000252; RRID: N/A
Chromium Single Cell Human BCR Amplification Kit, 16 rxns	10X Genomics	Cat# PN-1000253; RRID: N/A

Deposited data

Dendritic cell profiling data	This paper	GEO accession: GSE304657
Gene expression data, B-ALL patients	Kang et al.²¹	GEO accession: GSE11877
Gene expression data, T-ALL patients	Peirs et al.²²	GEO accession: GSE62156
Gene expression data, T-ALL patients	Pölönen et al.⁶¹	Synapse accession: syn54032669; https://doi.org/10.7303/syn54032669
Gene expression data, Healthy donors	Newman et al.²⁰	GEO accession: GSE65136
Myeloid transcriptomic signature data	Villani et al.¹⁹	GEO accession: GSE94820

Experimental models: Cell lines

P493-6	DSMZ	Cat# ACC 915; RRID: N/A

Experimental models: Organisms/strains

B6.Cg-Tg(IghMyc)22Bri/J (Common name: Eμ-myc)	The Jackson Laboratory	Cat# 002728; RRID: IMSR_JAX:002728
B6.FVB-Tg(tetO-MYC)36Bop/DwfJ (Common name: tet-o-MYC)	The Jackson Laboratory	Cat# 034532; RRID: IMSR_JAX:034532
C57BL/6J (Common name: B6)	The Jackson Laboratory	Cat# 000664; RRID: IMSR_JAX:000664
FVB/NJ (Common name: FVB)	The Jackson Laboratory	Cat# 001800; RRID: IMSR_JAX:001800

Software and algorithms

CellRanger (9.0.1)	https://doi.org/10.1038/ncomms14049	https://github.com/10XG enomics/cellranger
Samtools (1.16.1)	https://doi.org/10.1093/bioinformatics/btp352	https://github.com/samto ols
Velocyto (0.17.16)	https://doi.org/10.1038/s41586-018-0414-6	https://velocyto.org/
Seurat (5.2.0)	https://doi.org/10.1038/nbt.4096	https://satijalab.org/seura t/
Doubletfinder (2.0.0)	https://doi.org/10.1016/j.cels.2019.03.003	https://github.com/chris-mcginnis-ucsf/DoubletFinder
Limma (3.64.3)	https://doi.org/10.1093/nar/gkv007	https://bioconductor.org/packages/release/bioc/ht ml/limma.html
ClusterProfiler (4.16.0)	https://doi.org/10.1089/omi.2011.0118	https://www.bioconductor .org/packages/release/bi oc/html/clusterProfiler.ht ml
scMRMA (1.0.0)	https://doi.org/10.1093/nar/gkab931	https://github.com/JiaLiV UMC/scMRMA
SingleR (2.11.3)	https://doi.org/10.1038/s41590-018-0276-y	https://github.com/dvirara n/SingleR
SeuratExtend (1.2.4)	N/A	https://github.com/huayc 09/SeuratExtend
Reticulate (1.43.0)	N/A	https://rstudio.github.io/re ticulate/
Pheatmap (1.0.13)	N/A	https://cran.r-project.org/web/package s/pheatmap/pheatmap.p df
CellRank (1.5.1)	https://doi.org/10.1038/s41592-021-01346-6	https://cellrank.readthedo cs.io/en/latest/
Matplotlib (3.10.5)	https://doi.org/10.1109/MCSE.2007.55	https://matplotlib.org/
ggplot2 (3.5.2)	https://doi.org/10.1002/wics.147	https://ggplot2.tidyverse.org/
Tidyverse (2.0.0)	https://doi.org/10.21105/joss.01686	https://www.tidyverse.org /
CIBERSORTx	Newman et al.²⁰	https://cibersortx.stanford .edu/
GraphPad Prism Software	Dotmatics	https://www.graphpad.co m
FlowJo (v10) Software	BD Biosciences	https://www.flowjo.com/
SpectroFlo Software	Cytek Biosciences	https://cytekbio.com/pag es/spectro-flo

Other

RPMI-1640	Thermo Fisher Scientific	Cat# 11875119; RRID: N/A
FBS Premium	Thermo Fisher Scientific	Cat# A5670701; RRID: N/A
Penicillin-Streptomycin	Thermo Fisher Scientific	Cat# 15140122; RRID: N/A
2-Mercaptoethanol	Thermo Fisher Scientific	Cat# 21985023; RRID: N/A
MEM Non-essential Amino Acids (100X)	Thermo Fisher Scientific	Cat# 11140050; RRID: N/A
GlutaMAX (100X)	Thermo Fisher Scientific	Cat# 35050061; RRID: N/A
DPBS, no calcium, no magnesium	Thermo Fisher Scientific	Cat# 14190144; RRID: N/A
Sodium pyruvate	Thermo Fisher Scientific	Cat# 11360070; RRID: N/A

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Experimental model and study participant details

Patient samples

Clinically annotated and coded peripheral blood and bone marrow primary pediatric and adult ALL samples without protected health information were procured from the Hematopoietic Tissue Biorepository of the City of Hope (IRB protocol # 18067), the Stem Cell and Xenograft Core of the University of Pennsylvania (IRB protocol # 703185), and the tissue repository at the Bass Center for Childhood Cancer and Blood Diseases at Stanford University (IRB protocols # 11062 and 45458). These samples were collected via Institutional Review Board (IRB)-approved research protocols following informed consent in accordance with the Declaration of Helsinki. Only ALL samples confirmed by the banking sites to have CD19 in B-ALL blasts and CD3 in T-ALL blasts were used to allow for accurate gating out of malignant cells and subsequent analysis of cell frequencies within the non-malignant, myeloid fraction. Healthy donor bone marrow mononuclear cells (BMMCs) were acquired from STEMCELL Technologies (Vancouver, Canada). Healthy donor peripheral blood mononuclear cells (PBMCs) were isolated from the buffy coat obtained from the City of Hope Michael Amini Transfusion Medicine Center. The patient samples analyzed include a newly assembled cohort (Table S1) and those from patient cohorts described in our previous publications.⁸ This research is classified as non-human subjects research by City of Hope IRB 19373.

Murine models and in vivo studies

Animal studies were approved by the Institutional Animal Care and Use Committee (IACUC) at the City of Hope (protocol #19032) and were adherent to institutional and national guidelines. The following strains of mice were used: Eμ-MYC (males and females, 8–14 weeks), SRα-tTA-Tet-O-MYC (males, 8–12 weeks), C57BL/6J (males and females, 8–14 weeks), and FVB/N (males, 4–8 weeks). The timeline for B- and T-ALL development in Eμ-MYC and SRα-tTA-Tet-O-MYC, respectively, and the processing of immune cells from spleen and bone marrows of these strains is described in our previous publications.⁵^,¹¹

Cell lines and culture

The NK-92 cell line was purchased from ATCC, routinely tested, and maintained under mycoplasma-free conditions. Cells were cultured in complete RPMI-1640 medium (10% fetal bovine serum, 100 U/mL penicillin, and 100 μg/mL streptomycin) and supplemented with Gibco non-essential amino acids (1X), L-glutamine (2 mM), sodium pyruvate (1 mM), and 100 IU/mL of recombinant human IL-2 (Peprotech).

Method details

Flow cytometry

ALL and healthy donor PMBC and BMMC cells were thawed and washed twice with complete RPMI-1640 medium. Cells were counted and resuspended in Flow Staining Buffer [Dulbecco’s Phosphate-Buffered Saline (DPBS), 5% FBS and 0.09% sodium azide] at a concentration of 3 × 10⁶ cells per 100 μL. For surface staining, antibody cocktails (Key Resource Table) were prepared in Flow Staining Buffer containing 10 μL/100 μL of Brilliant Stain Buffer Plus and Fc blocking antibody. Cells were incubated with the antibody mixture for 30 min at 4°C, followed by two washes before analysis on a BD Symphony or Cytek Aurora flow cytometer. For intracellular staining of transcription factors, cells were first surface stained and washed twice. They were then fixed and permeabilized using the eBioscience FoxP3 Staining Buffer Set for 45 min at room temperature (RT). After washing with Permeabilization Buffer (1X), intracellular antibodies were added and incubated for 45 min at RT. Stained cells were analyzed using the Cytek Aurora flow cytometer.

To induce cytokine production in myeloid cells, cells were stimulated with LPS (5 μg/mL) for 2 h at 37°C, followed by the addition of eBioscience Protein Transport Inhibitor Cocktail (1X) for 16 h. After 18 h of total incubation, cells were washed, surface stained, and fixed/permeabilized using the BD Fix/Perm Buffer Kit. Cells were then stained with cytokine-specific antibodies for 45 min at room temperature, washed with 1X permeabilization buffer, and analyzed using the BD Symphony flow cytometer.

Mass cytometry

Mass cytometry was carried out using the approaches and panels described in our recent studies.⁸ After thawing, cells were rested overnight and next day they were stimulated with phorbol myristate acetate (PMA) + ionomycin along with protein transport inhibitors brefeldin A, and monensin. Cells were labeled with Cell-ID Cisplatin-195Pt (Standard BioTools) prior to surface staining and fixed with 2% paraformaldehyde, followed by intracellular and DNA staining with Cell-ID Intercalator-Ir (Standard BioTools). Cells were washed with Milli-Q water and resuspended in a 1X solution of EQ Four Element Calibration Beads (Standard BioTools). Data were normalized using MATLAB normalizer before analyzing with Cytobank.

DQ-OVA uptake and processing assay

PBMCs from healthy donors and patients with ALL were thawed and were resuspended at a concentration of 5 × 10⁶/mL. Cells were rested at 37°C for 2 h to allow recovery. Following this, PBMCs were incubated with 5 μg/mL DQ-OVA at 37°C for 1 h to assess antigen uptake and proteolytic processing. After incubation, cells were washed twice with ice-cold PBS supplemented with 2% FBS and 0.09% sodium azide to halt further antigen processing. Cells were subsequently stained with fluorochrome-conjugated surface antibodies (listed in the Key Resource Table) to identify cDCs. Samples were then analyzed by Cytek Aurora spectral flow cytometer.

T cell proliferation assay

cDCs were isolated from PBMCs of healthy donors or patients with ALL using magnetic cell separation kits (Miltenyi Biotec# 130-094-487), following the manufacturer’s protocols. To induce maturation, DCs were stimulated overnight with the Toll-like receptor (TLR) 7/8 agonist R848 (2.5 μg/mL) and LPS (100 ng/mL) as previously described.¹⁹ The following day, naive pan T cells were isolated and labeled with CFSE (2.5 μM) for 20 min at 37°C in phosphate-buffered saline (PBS). Labeled T cells were then washed thoroughly with pre-warmed complete RPMI-1640 medium. Concurrently, cDCs were washed twice to remove residual activation stimuli, counted, and resuspended in complete RPMI-1640 medium. CFSE-labelled naive T cells were cultured alone or co-cultured with cDC at a 1:5 ratio (1 DC: 5 T cells) in 96-well round-bottom plates for 5 days at 37°C in a humidified incubator with 5% CO₂. For experiments involving co-culture of healthy donor DCs and/or T cells with P493-6 BL cells, the following ratios were used: 1:5 T cell: BL and 1:5:5 T cell: cDC: BL. Proliferation of T cells was subsequently assessed by flow cytometry.

NK-92 and ALL PBMC coculture assay

5 × 10⁵ NK-92 cells were co-cultured with PBMCs derived from ALL patients at 1:1 for 24 h in complete RPMI medium. After 24 h of coculture, cells were washed and surface stained with NK- and DC- cell specific antibody cocktail (Key Resource Table) for 30 min, followed by washing with Flow Stain Buffer, cells were then analyzed on BD Symphony flow cytometer.

Preparation of PBMC samples for scRNA sequencing

Only ALL samples verified to have malignant lymphoblasts that are CD19⁺ or CD3⁺ (by flow cytometry) were used for scRNA-seq experiments. To enrich non-malignant immune cells from PBMCs, CD19 and CD3 MicroBeads (Miltenyi Biotec) were respectively used to remove malignant cells from B-ALL and T-ALL samples, as well as from their corresponding healthy controls. The non-malignant fraction was processed using the 10x Genomics Chromium Single Cell 5′ Reagent Kit (v3) according to the manufacturer’s protocol. For each sample, approximately 40,000 Gel Beads-in-Emulsion (GEMs) were generated, with each GEM encapsulating a single cell along with a uniquely barcoded oligonucleotide bead. Following cell lysis and reverse transcription within GEMs, barcoded cDNA was amplified, and sequencing libraries were constructed. Libraries were sequenced on an Illumina NovaSeq X Plus to a target depth of 50,000 reads per cell.

Analysis of scRNA-seq

FASTQ files were generated from raw BCL files by Illumina’s BCL Convert (v4.1.7). Raw human FASTQ file reads were aligned to GRCh38 genome assembly, using 10x Genomic’s Cell Ranger (v7.2.0). Spliced and unspliced mRNA counts were extracted from subsequent BAM files using samtools (v1.9.0) and velocyto (v0.17.17) to generate individual loom files from each sample. Individual loom files from each sample were combined using Loompy (v3.0.8). Doublets were identified and removed scDblFinder R package (v2.0.0). Further quality control and subsequent analysis was performed using Seurat (v4.5.0). Initial automated annotation was performed using scMRMA (v1.0.0) to identify the myeloid subset while allowing for removal of non-myeloid subsets. Subsequent annotation was performed using singleR (v2.10.0) and Villani et al. (2017) reference database¹⁹ to identify classical monocytes, intermediate monocytes, non-classical monocytes, DC1-6, and cDC progenitors. All cell annotations were validated using differential gene expression analysis through Seurat’s FindAllMarkers. DC1-6 and cDC progenitors were isolated and diffusion maps (UMAPs based on multiscale space) were created using Palantir (v1.4.0) through utilization of SeuratExtend (v1.2.0) and reticulate (v.1.43.0) for Python dependencies. Spliced and unspliced mRNA counts from the combined loom files were appended to Seurat metadata and reductions using SeuratExtend and, in conjunction with scVelo (v0.3.0), starting and terminal states of DCs were identified. These starting and terminal states were used as input to predict cell fates and create pseudotime using Palantir via SeuratExtend with the Palantir Pseudotime function. The scVelo-informed pseudotime values were run with CellRank Compute and Cellrank Plot functions by CellRank via SeuratExtend to show directionality of differentiation among DC subsets. Additionally, with the scVelo-informed pseudotime, gene trajectories of various well known DC markers were evaluated through a Generalized Additive Model approach using Log_e normalized counts by Seurat’s NormalizeData and Markov Affinity-based Graph Imputation of Cells (MAGIC, v3.0.0) denoising.

CIBERSORT

CIBERSORT analysis was performed using the CIBERSORTx platform (https://cibersortx.stanford.edu/).²⁰ Using transcriptomic signatures of myeloid subsets i.e., cDC, pDC, DCP and Monocyte from GEO: GSE94820, bulk gene expression datasets of healthy donors (GEO: GSE65136), B-ALL (GEO: GSE11877) and T-ALL (GEO: GSE62156) patients were deconvoluted to estimate the relative proportions of these subsets within total myeloid fraction. Similarly, transcriptomic signatures of stimulated or no/unstimulated DCs were obtained from GEO: GSE22886 to perform CIBERSORTx on healthy (GEO: GSE65136), B-ALL (GEO: GSE11877) and T-ALL (Synapse: syn54032669) patient datasets to assess the relative proportion of stimulated or no/unstimulated DCs.

Gene set enrichment analysis (GSEA)

GSEA for comparing scRNA-seq data between any two groups was performed using Seurat’s FindMarkers between different subgroups generating a ranked differential gene expression list utilizing log₂ fold change values and test.use = limma. For COG bulk RNA-seq, the limma package (v3.64.3) was used directly to generate a rank gene list after characterizing each patient sample as being enriched for ‘stimulated’ or ‘no/unstimulated DC’ by CIBERSORT. The ranked gene lists were imported into either the Broad Institute’s GSEA tool or clusterProfiler R package to observe different pathways within the Gene Ontology (GO) gene sets. The GO pattern used was a combination of molecular function, biological process, and cellular component pathways.

Quantification and statistical analysis

Unless otherwise noted, statistical analysis was performed using unpaired two-tailed Student’s t- and Mann-Whitney U-tests (for comparisons of two groups), using GraphPad Prism Software. For comparisons of multiple groups, the Kruskal-Wallis test followed by Dunn’s post-hoc test was applied. Correlation analyses were performed using Pearson’s r, and two-tailed p-values were calculated using a simple linear regression t test. The numbers of samples utilized in each experiment were calculated using G∗power function to detect an effect size of 0.6 with at least 85% power (α = 0.05) and based on variation within each group known from our previous studies.⁵^,⁸^,¹¹ Results are expressed as median ± interquartile range (to include non-normally distributed datasets) or mean ± s.e.m. (for normally distributed datasets), p ≤ 0.05 is considered statistically significant, 0.05 ≤ p ≤ 0.1 is considered as trending to significance.

Published: July 28, 2026

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2026.102945.

Supplemental information

Document S1. Figures S1–S29

mmc1.pdf^{(4.4MB, pdf)}

Table S1. List of ALL patient samples used in the study

mmc2.xlsx^{(25.8KB, xlsx)}

Document S2. Article plus supplemental information

mmc3.pdf^{(20.8MB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S29

mmc1.pdf^{(4.4MB, pdf)}

Table S1. List of ALL patient samples used in the study

mmc2.xlsx^{(25.8KB, xlsx)}

Document S2. Article plus supplemental information

mmc3.pdf^{(20.8MB, pdf)}

Data Availability Statement

•
scRNA-seq data of the non-malignant immune cell fraction of healthy donor and ALL patient PBMCs are deposited in the Gene Expression Omnibus (GEO) under accession number GSE304657.
•
No custom code was developed for this study.
•
Publicly available transcriptomic datasets analyzed in this study include GEO datasets GSE65136 (healthy donors), GSE94820 (myeloid transcriptomic signature), GSE11877 (patients with B-ALL), GSE62156 (patients with T-ALL), and the Synapse dataset syn54032669 (https://doi.org/10.7303/syn54032669) of patients with T-ALL. All analyses were performed using publicly available software packages, with names and versions provided in the key resources table. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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PERMALINK

Tumor-induced dendritic cell deregulation perturbs T cell proliferation and predicts clinical outcome in acute lymphoblastic leukemia

Anil Kumar

Kory Hamane

Caroline Duault

Joao Rodrigues Lima-Junior

Da Hae Jung

Hanjun Qin

Sheyla Salcido

Min Huang

Xinying Guo

Adeleh Taghi Khani

Ashly Sanchez Ortiz

Lucy Ghoda

Guido Marcucci

Norman J Lacayo

Kathleen M Sakamoto

Christian Hurtz

Martin Carroll

Sarah K Tasian

Huimin Geng

Lingyun Ji

Saro Armenian

Shai Izraeli

Xiwei Wu

Holden T Maecker

Srividya Swaminathan

Summary

Graphical abstract

Highlights

Introduction

Results

Homeostasis of non-malignant myeloid subsets is perturbed in patients with B- and T-ALL

Figure 1.

Residual cDCs in patients with ALL lose their specialization into distinct functional lineages

Figure 2.

Maturation of DCs into functional lineages is impaired in patients with B- and T-ALL

Figure 3.

Proliferating DC-precursor-like cells are increased in patients with B- and T-ALL

Figure 4.

Antigen presentation potential of DC subset is markedly impaired in human ALL

Figure 5.

Overexpression of MYC in malignant lymphoblasts drives the disruption of DC homeostasis

Figure 6.

DC phenotype at disease presentation predicts clinical outcome in patients with B- and T-ALL

Figure 7.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

STAR★Methods

Key resources table

Experimental model and study participant details

Patient samples

Murine models and in vivo studies

Cell lines and culture

Method details

Flow cytometry

Mass cytometry

DQ-OVA uptake and processing assay

T cell proliferation assay

NK-92 and ALL PBMC coculture assay

Preparation of PBMC samples for scRNA sequencing

Analysis of scRNA-seq

CIBERSORT

Gene set enrichment analysis (GSEA)

Quantification and statistical analysis

Footnotes

Supplemental information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK