Therapeutic cancer vaccination seeks to elicit activation of tumor-reactive T cells capable of recognizing tumor-associated antigens (TAA) and eradicating malignant cells. Here, we present a cancer vaccination approach utilizing myeloid-lineage reprogramming to directly convert cancer cells into tumor-reprogrammed antigen-presenting cells (TR-APC). Using syngeneic murine leukemia models, we demonstrate that TR-APCs acquire both myeloid phenotype and function, process and present endogenous TAAs, and potently stimulate TAA-specific CD4+ and CD8+ T cells. In vivo TR-APC induction elicits clonal expansion of cancer-specific T cells, establishes cancer-specific immune memory, and ultimately promotes leukemia eradication. We further show that both hematologic cancers and solid tumors, including sarcomas and carcinomas, are amenable to myeloid-lineage reprogramming into TR-APCs. Finally, we demonstrate the clinical applicability of this approach by generating TR-APCs from primary clinical specimens and stimulating autologous patient-derived T cells. Thus, TR-APCs represent a cancer vaccination therapeutic strategy with broad implications for clinical immuno-oncology.

Significance:

Despite recent advances, the clinical benefit provided by cancer vaccination remains limited. We present a cancer vaccination approach leveraging myeloid-lineage reprogramming of cancer cells into APCs, which subsequently activate anticancer immunity through presentation of self-derived cancer antigens. Both hematologic and solid malignancies derive significant therapeutic benefit from reprogramming-based immunotherapy.

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Therapeutic generation of tumor-eradicating immunity critically depends on the instruction and activation of tumor-reactive T cells (1, 2). As a therapeutic modality, cancer vaccination leverages the well-characterized ability of antigen-presenting cells (APC) to take up and present tumor-associated antigens (TAA) in order to stimulate potent antitumor T-cell responses (3–5). Early preclinical studies in the 1990s were seminal in establishing dendritic cells (DC) as safe and effective in generating CD4+ and CD8+ T cell–mediated anticancer immunity (6–8). Further work characterizing the maturation of monocyte-derived DCs paved the way for the first clinical implementation of a DC-based cancer vaccine (9). Sipuleucel-T, a cancer vaccine consisting of enriched APCs pulsed with prostatic acid phosphatase fused to granulocyte–macrophage colony-stimulating factor (GM-CSF), gained FDA approval for use in castration-resistant prostate cancer (10). Despite having a limited impact on overall survival, evidence of immunity elicited by the immunizing agent was observed. Following these initial lackluster results, significant effort has been aimed at increasing the potency of cancer vaccination. Favorable results have been obtained with diverse approaches, including fusion of DCs to tumor cells, transfection of DCs with mRNA encoding neoantigens, and pulsing of DCs with neoantigenic peptides (11–13). However, clinical successes with therapeutic cancer vaccination remain limited. Ongoing efforts seek to characterize and predict immunogenic tumor antigens for use as cancer vaccines, optimize the delivery of those antigens, and elicit more robust tumor-reactive T-cell activity. Despite recent advances in each of these areas, no consensus regarding an optimal cancer vaccination strategy has been reached (14). Thus, novel methodologies and technological innovations are needed to offer rapid improvement to the cancer vaccination platform.

Lineage reprogramming, or the direct induction of a functional cell lineage from an alternate lineage, has been a long-standing goal within the field of regenerative medicine with clear implications for the clinical advancement of cellular therapeutics. Since the conversion of fibroblasts into myoblasts via overexpression of the MyoD gene, transcription factors have been of keen interest to the lineage reprogramming field given their ability to instruct cell-fate decisions in a lineage-specific manner (15, 16). Since these initial studies, enforced expression of lineage-specific transcription factors (TF) has been used to generate a wide array of functionally diverse cells from unrelated lineages, including induced pluripotent stem cells, neurons, cardiomyocytes, and hepatocytes (17). Importantly, the induction of neuronal cells from fibroblasts demonstrated that lineage reprogramming could even be conducted between different germ layers (18).

Lineage plasticity within the hematopoietic hierarchy has also been studied following exogenous cytokine stimulation, as well as overexpression of lineage-instructing TFs. Indeed, the lineage-restricted common lymphoid progenitor can be redirected to generate myeloid progeny through in vitro stimulation with exogenous IL2 and GM-CSF (19). Subsequent studies further demonstrated that ectopic expression of the granulocyte and macrophage–restricted TF CCAAT/enhancer-binding protein alpha (C/EBPα) is capable of reprogramming both immature and mature antibody-producing lymphoid-lineage B cells into myeloid-lineage macrophages (20, 21). Since this initial identification of C/EBPα as a myeloid-reprogramming factor, myeloid-lineage cells have been successfully generated from more distantly related, nonhematopoietic cell lineages through the combined activity of C/EBPα and other master regulators of myeloid-lineage differentiation, including PU.1 (Spi1; refs. 22, 23).

Importantly, we and others have also shown that malignant B-lineage cells, including those with a block in B-cell differentiation, remain amenable to reprogramming into nonmalignant macrophage-like cells (20, 24–26). Irrespective of the cellular origin and reprogramming methodology used, the resulting cells following myeloid-lineage reprogramming exhibit increased expression of myeloid phenotypic markers and adopt myeloid function. Indeed, myeloid-reprogrammed cells are capable of phagocytosis and generation of reactive oxygen species at levels similar to normal macrophages. Interestingly, these reprogrammed cells also exhibit increased expression of MHC molecules and T-cell costimulatory signals, suggesting they could function as APCs. However, the therapeutic potential of reprogramming cancer cells into APCs has not been reported. Coupled with the observation that myeloid-reprogrammed cells retain all of the genetic abnormalities present in the cell of origin (24), we hypothesized that reprogramming cancer cells into myeloid-lineage APCs could function as a novel cancer vaccination modality. Here, we demonstrate APCs generated from cancer cells via myeloid-lineage reprogramming are capable of processing and presenting self-derived TAAs, which potently activate tumor-reactive T cells and effectively extended overall survival in multiple diverse preclinical cancer models.

Ectopic TF Expression Generates TR-APCs

To investigate the therapeutic potential of reprogramming cancer cells into APCs, we constructed an inducible system to drive the ectopic expression of two master myeloid TFs, C/EBPα and PU.1 (Fig. 1A), which have previously demonstrated myeloid-lineage reprogramming activity in a wide variety of cell types (20, 22, 24, 25, 27). Via dual transduction of these lentiviral constructs into two murine leukemia cell lines (RAW-112 and 2F3), we generated stable, transplantable, syngeneic models of B-cell lineage acute leukemia with doxycycline-inducible C/EBPα and PU.1 expression (Fig. 1B; refs. 28, 29).

Tumor-reprogrammed APCs (TR-APC) were efficiently generated from both RAW-112 and 2F3 leukemia cells following 5 days of ectopic C/EBPα and PU.1 expression (Fig. 1C). Reprogramming of tumor cells was accompanied by cell-surface expression of myeloid markers (CD11b, CD14, Ly6C, CD115, and SIRPα) and included the loss of cell-of-origin marker (CD19; Fig. 1C and D). This immunophenotype is consistent with murine monocytic differentiation concomitant to loss of B-lineage identity as previously reported (20, 24, 25). Morphologic assessment by May–Gruenwald–Giemsa stain of sorted CD11b+CD14+ TR-APCs further revealed nuclear condensation and a corresponding decrease in the nuclear–cytoplasmic ratio, consistent with monocytic differentiation (Fig. 1E). Importantly, RAW-112–derived TR-APCs adopted not only a myeloid phenotype, but also myeloid functionality, as evidenced by an increased phagocytic capacity compared with uninduced RAW-112 leukemia cells (Fig. 1F).

To test the efficiency of tumor reprogramming in vivo, inducible RAW-112 leukemia cells were transplanted subcutaneously into immunodeficient NOD/SCID/IL2Rγnull (NSG) mice. Following 1 week of subcutaneous tumor growth, palpable tumors were injected with doxycycline every other day for a total of three injections. Histologic analysis of tumors revealed significant morphologic alterations, indicating widespread generation of TR-APCs throughout the tumor mass (Fig. 1G). Myeloid reprogramming was accompanied by increased staining of the myeloid marker CD11b and diminished expression of the proliferative marker Ki-67. Tumor digestion and cytometric analysis further confirmed that in vivo TR-APC generation resulted in widespread upregulation of myeloid antigens (Supplementary Fig. S1A–S1C). Thus, we generated a stable, transplantable model of B-cell leukemia for the investigation of the therapeutic potential associated with TR-APC generation. Unlike previously reported systems of C/EBPα-induced B-lymphoid-to-myeloid reprogramming, both the RAW-112 and 2F3 models are derived from inbred BALB/c mice and thus allow for investigation of syngeneic antitumor immune responses (25, 26, 28, 29).

TR-APCs Stimulate T-cell Activation

Because the role of antigen presentation and costimulation machinery is well appreciated in the efficacy of DC vaccination (30–33), we evaluated whether TR-APCs express elevated levels of MHC-I (H-2Dd), MHC-II (I-A/I-E), and costimulatory molecules (CD40, CD80, and CD86) using the inducible RAW-112 B-cell leukemia cells. With the exception of MHC-I, RAW-112 cells expressed low levels of all of these molecules prior to TR-APC induction (Fig. 2A). However, RAW-112 TR-APCs upregulated MHC-II in response to myeloid reprogramming. Expression of both MHC-I and MHC-II was further enhanced in response to activation with the inflammatory stimulus LPS, consistent with the known behavior of APCs (34). The same trend was observed with the expression of the costimulatory molecules CD40, CD80, and CD86, each of which was poorly expressed on RAW-112 cells, more highly on TR-APCs, and highest on LPS-activated TR-APCs (Fig. 2A; Supplementary Fig. S1D).

Given the importance of soluble APC-derived factors in the recruitment and instruction of tumor-infiltrating leukocytes, we characterized the chemokines and cytokines that were secreted by RAW-112 tumor cells and RAW-112 TR-APCs. Luminex-based detection of the soluble factors in the supernatant of RAW-112 TR-APC cultures revealed significant production of inflammatory mediators, particularly following LPS stimulation (Fig. 2B). Specifically, the proinflammatory and Th1-polarizing mediators IL6, IL1β, IL12p70, IFNγ, and TNFα were detected at significantly higher levels in LPS-stimulated TR-APC cultures compared with all other conditions (Fig. 2C; Supplementary Fig. S1E). Crucial mediators of lymphocyte recruitment, including CXCL10, were also found to be expressed at higher levels by TR-APCs, whereas protumorigenic factors, like VEGF, were reduced in TR-APC cultures (Fig. 2B; Supplementary Fig. S1E). Thus, TR-APCs express both surface-bound and soluble immunostimulatory molecules crucial for the development of potent antitumor immunity.

APCs routinely process and present TAAs through phagocytosis of tumor cell–derived debris within the tumor microenvironment. In order to understand if TR-APCs could similarly process extracellular antigens from the tumor milieu, we leveraged the DQ-OVA reagent, which represents a surrogate unprocessed tumor antigen (35). Incubation of DQ-OVA with RAW-112 cells and RAW-112 TR-APCs revealed that TR-APCs exhibit an increased capacity for processing of exogenous antigens (Fig. 2D). To further examine if TR-APCs are capable of processing and presenting not only exogenous antigen, but also self-derived TAAs, we utilized the chicken ovalbumin (OVA) model antigen system. Reprogrammable RAW-112 cells were transduced with full-length OVA (RAW-112-OVA) to serve as a model TAA, and transgenic CD4+ T cells capable of recognizing OVA in an MHC class II (I-Ad)–restricted manner were isolated from DO11.10 mice (36). Coculture of DO11.10 CD4+ T cells with RAW-112-OVA cells stimulated little T-cell proliferation, but TR-APCs generated from RAW-112-OVA cells caused marked DO11.10 T-cell proliferation, consistent with processing self-derived OVA protein into the OVA323–339 peptide, presentation of OVA323–339 in MHC-II (I-Ad), and activation of DO11.10 T cells (Fig. 2E and F).

We next evaluated the ability of TR-APCs to process and present endogenous TAAs by coculturing RAW-112 cells and RAW-112 TR-APCs with syngeneic T cells from naive BALB/c mice. Compared with coculture with tumor cells alone, RAW-112 TR-APCs stimulated significant activation of both CD4+ and CD8+ syngeneic T cells (Fig. 2G and H). This T-cell stimulation activity could be further enhanced through the addition of LPS and R848. Thus, TR-APCs are capable of processing and presenting both endogenous TAAs and exogenous antigens derived from the tumor microenvironment. Further, the presentation of TAAs by TR-APCs does not require prior knowledge of the tumor mutational burden or prediction of immunogenic epitopes, but instead is agnostic to the TAAs that are targeted.

TR-APCs Stimulate Antitumor Immunity

To understand the potential therapeutic benefit of generating TR-APCs in the context of tumor progression, we induced in vivo TR-APC formation in the RAW-112 transplantable model of B-cell acute leukemia (36). Intravenous engraftment of reprogrammable, but uninduced, RAW-112 cells into immunodeficient NSG mice resulted in rapid disease progression and mortality (Fig. 3A and B). Notably, induction of TR-APCs from RAW-112 cells during leukemic development significantly delayed median mortality in NSG mice, but only by 3 days, presumably by effectively reducing the leukemogenicity of the engrafted cells. However, because induction of TR-APCs is not complete (Fig. 1C), all animals ultimately succumbed to progressive disease. This result demonstrates that forced myeloid reprogramming in the absence of an immune compartment does not result in meaningful disease control or eradication.

However, when immunocompetent syngeneic BALB/c mice were engrafted with the same reprogrammable RAW-112 cells, induction of TR-APCs during disease progression drove eradication of leukemic cells and long-term overall survival (Fig. 3A and B), with histologic regression of disease (Fig. 3C). In addition to no evidence of leukemia in the bone marrow, in vivo TR-APC induction largely eliminated leukemic infiltration of the central nervous system. Further, these mice were protected from subsequent rechallenge with the parental RAW-112 tumor cell line 100 days after the initial tumor inoculation, demonstrating robust TR-APC–mediated generation of antileukemic immunity capable of persistent immunologic memory (Fig. 3D). Importantly, by rechallenging with the parental RAW-112 cell line, rather than the transgenic reprogrammable RAW-112 cells, we demonstrate that the antileukemic immunity was indeed targeted to bona fide TAAs. In vivo TR-APC induction exhibited no observable off-target toxicity following histologic analysis of numerous organs after treatment (Supplementary Fig. S2A). Furthermore, TR-APC induction did not lead to permanent B-cell aplasia or statistically significant alterations to complete blood counts throughout treatment (Supplementary Fig. S2B and S2C), further supporting the conclusion that TR-APCs direct antileukemic immunity to TAAs.

In order to gain insight into the efficiency of TR-APC induction required to elicit a therapeutic benefit, we titrated the proportion of reprogrammable RAW-112 cells injected intravenously into syngeneic animals. Thus, upon exposure to doxycycline-containing chow, we effectively limited the overall efficiency of in vivo reprogramming to defined ratios. Although increased duration of overall survival was strongly correlated with increasing reprogramming efficiency, it was notable that some animals completely eradicated the engrafted leukemia when as little as 25% of the leukemic cells underwent TR-APC induction (Fig. 3E).

We further probed the ability of local TR-APC induction to elicit a systemic antitumor response by using a dual-flank tumor strategy. Subcutaneous transplantation of inducible RAW-112 cells into the flank of syngeneic BALB/c mice was accompanied by transplantation of unmodified parental RAW-112 cells into the contralateral flank of the same animal (Fig. 3F). The systemic effect of local TR-APC activity was assessed by monitoring the growth of the contralateral parental tumor. Strikingly, induction of TR-APCs in a single flank was sufficient to drive regression of parental tumors in the contralateral flank of most animals (Fig. 3G and H). These data demonstrate that local TR-APC–dependent immune priming elicits potent, systemic immunity capable of trafficking to and eradicating distant metastatic sites.

TR-APCs Elicit Antitumor T-cell Effects

Given the ability of TR-APCs to stimulate T cells in vitro and the inability of TR-APCs to protect against leukemia in immunodeficient mice, we hypothesized that the in vivo immunologic activity of TR-APCs was dependent on T-cell activation. Indeed, immunologic profiling revealed relative increases in activated and memory T-cell populations and fewer immunosuppressive regulatory T cells (Treg) during TR-APC generation and leukemia eradication (Supplementary Fig. S3A–S3C). Further, in vitro coculture of T cells purified from surviving leukemia-eradicated mice with a panel of tumor cell lines demonstrated enhanced activation and cytokine secretion against RAW-112 cells and only minimal reactivity to unrelated tumor cells, consistent with the establishment of tumor-specific memory T cells (Supplementary Fig. S4A and S4B). Consistent with this finding, depletion of either CD4+ or CD8+ T cells completely abrogated the therapeutic benefit of TR-APC generation (Fig. 4A and B), indicating that the establishment of a tumor-specific T-cell response is necessary for TR-APC–mediated eradication of leukemia.

To further investigate how TR-APCs modulate T cells in vivo during leukemia progression, we profiled TCRVβ gene usage as a surrogate marker of clonal expansion (37, 38). TR-APCs elicited consistent oligoclonal expansion of TCRVβ14+CD8+ T cells, with only minimal alteration to the frequency of this T-cell population observed in mice in which reprogramming was not initiated (Fig. 4C; Supplementary Fig. S5A–S5D). CD4+ TCRVβ gene usage was less consistent between animals, but still revealed a higher degree of clonal expansion among the animals in which TR-APCs were generated (Fig. 4D; Supplementary Fig. S5E and S5F). FACS purification of TCRVβ14+ T cells from mice that had successfully eradicated the leukemia revealed that this oligoclonally expanded T-cell population was enriched for antileukemic activity (Fig. 4E). These data highlight that the therapeutic efficacy of TR-APCs is derived from in vivo activation of both CD4+ and CD8+ T cells, which clonally expand and drive tumor regression.

To comprehensively investigate how in vivo TR-APC induction modulates the immunologic status of a tumor, inducible RAW-112 cells were implanted subcutaneously into syngeneic BALB/c mice. Five and 10 days after implantation, unmanipulated and TR-APC–induced subcutaneous tumors were excised and subjected to single-cell RNA sequencing (scRNA-seq) to examine infiltrating immune cell activity and in vivo TR-APC phenotype. Tumor-derived cells and endogenous immune cells were deconvoluted by scoring of genomic instability (39), and cell identity was classified with SingleR (ref. 40; Supplementary Fig. S6A–S6C; Fig. 4F). Analysis of tumor-infiltrating immune cells across all conditions revealed a stark increase in immune infiltrate following TR-APC induction, including an increase in the absolute number as well as relative frequency of T and NK cells (Fig. 4G; Supplementary Fig. S6C). To further analyze the activity of these tumor-infiltrating lymphocytes, we used ProjecTIL to annotate T-cell phenotypes by projecting individual T-cell gene expression signatures onto published reference phenotypes (Supplementary Fig. S6D; ref. 41). At day 5 after tumor implantation, we observed increased numbers of almost all T-cell phenotypes within tumors undergoing reprogramming (Fig. 4H). Importantly, naive CD8+ and Th1-polarized CD4+ phenotype T cells were among the most frequent T-cell populations, indicative of the formation of a productive immune response (Fig. 4H; Supplementary Fig. S6E). At day 10 after tumor implantation, the total number of infiltrating T cells was similarly increased in tumors undergoing reprogramming compared with those that remained unmodified. Naive and effector memory CD8+ T cells, as well as Th1-polarized CD4+ T cells, dominated the T-cell infiltrate of TR-APC–induced tumors, whereas unmanipulated tumors exhibited higher relative frequencies of exhausted CD8+ T cells and immunosuppressive Treg populations.

We further analyzed the phenotype of tumor-infiltrating myeloid-lineage immune cells by scoring the expression of inflammatory gene signatures in these populations. Intriguingly, TR-APC induction resulted in increased myeloid activation and inflammatory cytokine production at day 5 after implantation, consistent with a more rapid orchestration of productive antitumor immunity (Supplementary Fig. S6F).

We next interrogated the phenotype of RAW-112 tumor–derived cells within our dataset. Although SingleR cell classification revealed somewhat modest frequencies of complete reprogramming at these early time points, tumor-derived cells were nonetheless enriched for gene signatures corresponding to antigen processing and presentation, myeloid activation, and cytokine production (Fig. 4I; Supplementary Fig. S6G). This effect was particularly pronounced among tumor-derived cells classified as monocytes, generating a shoulder of increased enrichment within the overall gene module scores. Importantly, tumor cells derived from TR-APC–induced samples did not show increased expression of coinhibitory gene modules that would indicate TR-APC differentiation into immunosuppressive macrophages or myeloid-derived suppressor cells. We functionally confirmed our scRNA-seq observations through in vivo depletion of various immune populations (Supplementary Fig. S6H). Intriguingly, although B cells and macrophages were largely dispensable for TR-APC efficacy, the absence of NK cells and granulocytes diminished the therapeutic efficacy of TR-APC induction. Together, these data are consistent with TR-APC–dependent rewiring of the tumor microenvironment, promoting T-cell infiltration and activation of inflammatory antitumor immunity.

Next, we probed the underlying mechanism of in vivo leukemia eradication with a particular emphasis on understanding the collaboration of TR-APCs with endogenous APCs and the relative contribution of each to activation of antileukemic T-cell immunity through presentation of TAAs. To facilitate this investigation, we used BALB/c-Langerin-DTR+/+ mice bred to BALB/c-Ptprca animals yielding BALB/c-Langerin-DTR+/−-Ptprca/b progeny in which Langerin-expressing APCs could be conditionally ablated by Diphtheria toxin (DT) administration (42). Importantly, type 1 conventional DCs (cDC1), the APCs most associated with cross-presentation of antigens to CD8+ T cells, express sufficiently high levels of Langerin to be susceptible to DT-mediated depletion in this model (Supplementary Fig. S7A and S7B). After confirming depletion of endogenous cross-presenting cDC1s in the DT-treated Langerin-DTR mice, we engrafted inducible RAW-112 intravenously as before and commenced TR-APC induction. Strikingly, endogenous cDC1s were not required for leukemia eradication, as TR-APC induction resulted in similar overall survival with and without DT treatment (Fig. 4J).

In order to characterize the necessity of direct antigen presentation by TR-APCs to T cells in vivo, we designed CRISPR guides targeting exon 2 of Tap1 to block the presentation of TAAs while maintaining some level of MHC-I on the cell surface so as to avoid NK cell–mediated lysis. Cas9-mediated editing of the Tap1 locus resulted in downregulation of surface MHC-I on RAW-112 cells, enabling bulk purification of edited cells (Supplementary Fig. S7C). In order to elucidate the distinct contribution of TR-APCs and endogenous cDC1s to antigen presentation, we again used the Langerin-DTR+/−-Ptprca/b DT depletion model. Although TAP1 knockout resulted in slower kinetics of leukemia progression, ultimately all animals that did not undergo TR-APC induction succumbed to their disease irrespective of the presence or absence of endogenous cDC1s (Fig. 4K). As previously observed, TR-APC induction resulted in leukemia eradication in cDC1-deficient animals at similar frequencies to cDC1-competent animals. Interestingly, when antigen presentation by TR-APC is inhibited by TAP-1 knockout in the presence of endogenous cDC1s, a proportion of the animals are nonetheless capable of eradicating the leukemia, indicating that TR-APCs likely cooperate with endogenous APCs to present tumor antigens, prime tumor-reactive T cells, and ultimately cause disease regression. However, when both TR-APC–mediated antigen presentation and endogenous antigen presentation are inhibited by simultaneous use of TAP1 knockout TR-APCs and DT depletion of cDC1s, much of the therapeutic benefit of TR-APC induction is lost, and all animals ultimately succumb to disease progression. Together, these data demonstrate cooperation between TR-APCs and endogenous APCs, show that differentiation of leukemia cells alone is insufficient for protection from leukemic progression, and underscore the necessity of antigen presentation in this model to prime leukemia-specific T-cell responses.

Finally, we sought to benchmark our TR-APC induction strategy against alternative DC vaccination strategies. We generated DC fusion vaccines by fusing in vitro differentiated murine DCs to mitomycin C–treated RAW-112 cells using PEG as previously described (Supplementary Fig. S7D; ref. 43). DC:RAW-112 fusions were FACS isolated and cocultured in vitro with syngeneic BALB/c T cells as a direct comparison with the stimulatory capacity of in vitro–generated RAW-112 TR-APCs (Supplementary Fig. S7E). Interestingly, DC:RAW-112 fusion cells stimulated higher levels of both CD4+ and CD8+ T-cell activation on a per-cell basis in vitro, possibly due to the increased surface area of these fusion cells (Supplementary Fig. S7E). We further compared the performance of these cells as prophylactic cancer vaccination strategies in vivo. Naive BALB/c mice were injected intravenously with equal numbers of RAW-112 TR-APCs or DC: RAW-112 fusion cells as ex vivo vaccination strategies or were engrafted with inducible RAW-112 cells and placed on doxycycline chow as an in situ vaccination strategy. All mice were subsequently challenged with the unmodified parental RAW-112 cells 1 week after vaccination. Despite the inferior in vitro T-cell activation potential of RAW-112 TR-APCs, prophylactic vaccination with either cell product resulted in protection from subsequent challenge in a minority of mice (Supplementary Fig. S7F). Interestingly, the in situ reprogramming vaccination strategy was superior to both vaccination strategies using ex vivo–generated cell products. Together, these data demonstrate that TR-APC induction is noninferior to other conventional DC vaccination strategies, and that in situ reprogramming may drive more potent immune reactivity than vaccination with cells generated in vitro.

Solid Tumor TR-APCs Prolong Survival

Seeking to extend this therapeutic modality beyond hematologic malignancies, we screened a number of tumor cell lines for the capacity to reprogram into TR-APCs. Notably, we identified multiple solid tumor models that were amenable to myeloid reprogramming via ectopic expression of CEBP/α and PU.1, including murine models of fibrosarcoma (K-BALB), osteosarcoma (K7M2), and mammary carcinoma (4T1). Importantly, these models represent diverse modes of transformation, as well as growth characteristics and immunologic features (44–46). Despite this inherent diversity, marked levels of myeloid reprogramming and increased levels of MHC class II expression were observed on TR-APCs generated from each of these tumor models (Fig. 5AC).

We next evaluated the ability of these solid tumor–derived APCs to eradicate disease in vivo. In all three models, in vivo induction of TR-APC reprogramming led to significantly improved overall survival (Fig. 5DF; Supplementary Fig. S8A and S8B). Strikingly, induction of TR-APCs in intrafemoral K7M2 tumors also resulted in durable tumor eradication in a significant proportion of the animals (Fig. 5F). Additionally, TR-APC formation in subcutaneous K-BALB and 4T1 tumors led to significant necrosis of the tumors, forcing us to euthanize the animals for humane reasons. However, given the degree of necrosis observed in these tumors, TR-APCs seemingly can effectively induce targeting of solid tumor cells. As with our earlier studies of systemic leukemic disease, induction of TR-APCs from K-BALB tumors increased the survival of mice not only with localized tumors but also with a model of metastatic disease (Supplementary Fig. S8C). Together, these data suggest diverse solid tumors respond to myeloid reprogramming into TR-APCs with the development of therapeutic antitumor T-cell responses.

Primary Patient TR-APCs Activate T Cells

Finally, we explored the potential applicability of the generation of TR-APCs to human B-cell acute lymphoblastic leukemia (B-ALL). We have previously shown that in vitro stimulation of purified CD34+CD19+ B-ALL blasts with exogenous myeloid cytokines drives myeloid reprogramming into nonleukemic macrophage-like cells (24). Briefly, CD34+CD19+ leukemic blasts were sorted and cultured in media supplemented with 20% human serum to avoid exposure to xenogeneic antigens. TR-APC reprogramming efficiencies from primary sorted B-ALL blasts ranged from 10% to 80% depending on the patient specimen (Supplementary Fig. S9A and Supplementary Table S1). In order to understand the role of TR-APC culture conditions on TR-APC cell state, we used SU453 to generate macrophage-like TR-APCs via stimulation with GM-CSF, M-CSF, IL3, IL7, and FLT3L and DC-like TR-APCs with GM-CSF, IL4, IL7, and FLT3L, or maintained the leukemic blasts with IL7 and FLT3L alone. We conducted scRNA-seq on sorted B-ALL blasts prior to in vitro reprogramming and bulk unsorted B-ALL–derived TR-APC cultures following 7 days of cytokine-induced reprogramming (Fig. 6A). As expected, B-ALL TR-APCs increased their expression of key myeloid genes (SPI, ITGAX, and CEBPA) and downregulated essential B-ALL genes (CD34) relative to the unmanipulated B-ALL blasts (Supplementary Fig. S9B and S9C). Indeed, gene module score analysis demonstrated robust upregulation of gene modules associated with myeloid-mediated immune processes, consistent with a successful commitment to the myeloid lineage and formation of APCs. Notably, we observed significant upregulation of gene sets specifically corresponding to programs necessary for antigen processing and presentation, as well as myeloid cell activation (Fig. 6B).

In order to further understand the phenotype of primary B-ALL–derived TR-APCs, we used two independent cell annotation tools to classify the cells according to gene signatures identified from published RNA-seq of hematopoietic cells (Fig. 6C and D; Supplementary Fig. S9D and S9E). Using either reference, B-ALL blasts were predominantly classified as early B-cell progenitors or other primitive hematopoietic cells, whereas B-ALL–derived TR-APCs were predominantly characterized as myeloid-lineage cells. Furthermore, although GM-CSF and IL4 stimulation generated TR-APCs that largely resembled DCs, M-CSF, GM-CSF, and IL3 stimulation led to more heterogeneous TR-APCs that were categorized as DCs, macrophages, and monocytes. Importantly, the vast majority of TR-APCs that were classified as macrophages exhibited hallmarks of inflammatory M1-polarized macrophages and were classified as such (Fig. 6D). Expression of coinhibitory gene modules (CD274, PDCD1LG2, LGALS9, and CD276) was heterogeneous, but overall slightly elevated upon TR-APC formation (Supplementary Fig. S9F–S9G).

We then assayed the ability of primary TR-APCs to stimulate allogeneic T cells obtained from healthy donors. Primary B-ALL–derived TR-APCs stimulated allogeneic T-cell proliferation at levels comparable with monocyte-derived DCs and significantly better than the B-ALL blasts themselves (Supplementary Fig. S10A).

Next, we analyzed the ability of these primary TR-APCs to stimulate autologous, unmanipulated T cells using previously described methods (47). Coculture of M-CSF–, GM-CSF–, and IL3-stimulated TR-APCs generated from primary B-ALL with bulk unmanipulated autologous T cells significantly increased T-cell activation as compared with T cells cocultured with purified leukemic blasts (Fig. 6EG). Importantly, the T-cell activation was inhibited in the presence of MHC-blocking antibodies, indicating that this T-cell activation is MHC restricted (Supplementary Fig. S10B). As in our murine B-ALL models, activation of primary B-ALL–derived TR-APCs augmented MHC and costimulatory molecule expression (Supplementary Fig. S10C), and further enhanced autologous CD4+ and CD8+ T-cell activation (Supplementary Fig. S10D). Thus, these data indicate that primary patient-derived TR-APCs can stimulate autologous, leukemia-specific T-cell activation.

We have developed a novel immunotherapeutic approach to cancer vaccination, leveraging direct myeloid reprogramming of cancer cells into APCs. We identified hematologic malignancies, as well as solid tumors, that are amenable to TR-APC reprogramming, and further demonstrated the immunologic activity of TR-APCs both in vitro and in vivo. TR-APCs potently enhanced the activation of antigen- and tumor-specific CD4+ and CD8+ T cells, drove clonal expansion, and ultimately led to the generation of tumor-eradicating, systemic, and durable immunity. Finally, using primary patient specimens, we demonstrated the potential clinical utility of TR-APCs in the induction of autologous, tumor-specific T-cell activation. These results represent a novel and significant advance to current immunotherapeutic cancer vaccine approaches.

TR-APCs resemble inflammatory myeloid cells with enhanced expression of immunostimulatory genes, including cytokines, chemokines, and antigen presentation machinery. Notably, TR-APC induction showed significant clinical benefit in the absence of homogeneous tumor reprogramming. Indeed, reprogramming only a small fraction of the tumor burden resulted in significant remodeling of the tumor immune microenvironment, eliciting enhanced recruitment and activation of tumor-infiltrating T cells and NK cells, as well as endogenous myeloid populations. Importantly, the immunostimulatory activity of TR-APCs did not rely on the presence of endogenous cross-presenting cDC1s, indicating that TR-APCs are indeed sufficient to activate downstream effector populations including T cells, NK cells, and neutrophils, which are crucial to the success of the therapy. Conversely, disruption of direct antigen presentation by TR-APCs to tumor-specific T cells significantly impaired therapeutic efficacy in preclinical murine models, as well as with primary human specimens. Finally, we demonstrated that TR-APC therapy deployed as a vaccination strategy performs similarly to other DC vaccination modalities, whereas in situ TR-APC induction is more efficacious and stimulates superior immunologic activity.

To date, most attempts to generate cancer vaccines have focused on the identification or prediction of immunogenic neoantigens, and the delivery of these neoantigens to the patient with either chemical or cellular adjuvants. Despite numerous improvements in tumor antigen selection, adjuvant activity, and vaccine delivery, these efforts have not resulted in significant antitumor activity in patients. However, the success of T cell–based cancer immunotherapies suggests novel approaches to cancer vaccination could still provide significantly enhanced clinical benefit (3, 5). Our approach of leveraging lineage reprogramming as a modality to generate cancer vaccines offers numerous advantages over previously reported methodologies. Primarily, the TR-APC cancer vaccination approach requires no previous knowledge of the genetic makeup of the malignancy and, correspondingly, does not require prediction of potentially immunogenic peptides. Similarly, TR-APCs are not limited to a defined selected subset of antigens, but have the potential to present a myriad of self-derived tumor antigens directly encoded in their genome, as well as exogenous antigens processed from the tumor microenvironment. As such, the risk of tumor immune escape is decreased relative to administration of a defined pool of antigenic peptides. Additionally, many tumor antigens are simply overexpressed, or aberrantly expressed self-antigens, which could also be presented by TR-APCs, but are commonly overlooked by neoantigen prediction methodologies (48–52). Finally, we have previously shown that lineage reprogramming of primary B-ALL cells is durable with no observable reversion to a malignant state (24). Clinical experience with various cancer vaccination strategies further indicates that this class of immunotherapeutics is well tolerated. Therefore, this modality allows for therapeutic vaccination against both neo- and tumor-associated antigens, both of which appear to be important targets for cancer immunotherapy while maintaining a robust safety profile.

Although the survival benefit observed in these studies is encouraging, these results also underscore the need for further investigation to advance this approach into the clinic. The role of myeloid pioneer TFs in regulating lineage reprogramming has been established, but little is known about the breadth of cancers that may be amenable to this approach (53–56). Further efforts will also need to be directed at elucidating the optimal mode of gene delivery for TR-APC induction in vivo. Recent reports of nanoparticle-mediated mRNA delivery have demonstrated safe and effective in vivo induction of gene expression, but optimization of delivery to different target cells is still needed (57, 58). Finally, whereas there is heterogeneity in reprogramming efficiency between different tumor models, our studies demonstrate that only a subset of cancer cells need to be reprogrammed into TR-APCs, as the therapeutic efficacy is derived from the T cells activated by the TR-APCs, not the TR-APCs themselves.

The tumor-eradicating efficacy observed in these preclinical models raises the prospect of generating TR-APCs in situ. However, the potential clinical utility of TR-APCs is not restricted to in vivo TR-APC induction. Rather, the potential clinical applications of TR-APCs also include ex vivo TR-APC induction and subsequent autologous vaccination, as well as adoptive transfer of TR-APC–activated and ex vivo–expanded tumor-reactive T cells. Additionally, TR-APCs could be used as a platform for screening, activating, and expanding tumor-reactive autologous T cells, which could subsequently be used for adoptive T-cell therapy. TCR sequencing and peptide identification from TR-APCs could further augment immuno-oncology through identification of novel tumor-reactive TCRs and novel antigenic targets in a wide array of cancers. Thus, TR-APCs represent a novel contribution to cancer vaccine immunotherapeutics, with broad reaching implications for both clinical oncology and basic immunology research.

Viral Vector Construction, Lentivirus Production, and Transduction

Codon-optimized cDNA encoding murine Myc-tagged C/EBPα (Cebpa) and FLAG-tagged PU.1 (Spi1), linked by a P2A cleavage sequence, was synthesized by IDT and cloned into an inducible-expression lentiviral response plasmid (pLVX-TRE3G; Takara) to generate pLVX-TRE3G-MCPPF (Myc-C/EBPα-P2A-PU.1-FLAG). The lentiviral inducible regulator vector (pLVX-EF1α-Tet3G) was also acquired from Takara. Full-length chicken OVA was cloned into the constitutive lentiviral vector pCDH-CMV-GFPz-P2A-OVA and was a gift of G. Kaber (Stanford University).

Lentivirus was produced in the 293T packaging cell line. Briefly, 5 × 106 293T cells were seeded in 10 mL of complete DMEM in a 10-cm dish. The following day, 293T cells were cotransfected with 10 μg lentivirus vector, 8 μg psPAX2, and 5 μg VSV-G envelope plasmid using 293 fectin (Invitrogen). Media were replaced 12 hours after transfection. Following media replacement, viral supernatant was collected every 12 hours and transferred to plated target cells for a total of four viral supernatant transfers. Twelve hours following the final viral supernatant addition, target cell media were replaced and cultured for an additional 24 hours to allow for transgene expression. Transduced cells were selected for lentiviral integration by exposure to puromycin (InvivoGen; pLVX-TRE3G), Geneticin (Thermo Fisher; pLVX-EF1α-Tet3G), or Zeocin (Thermo Fisher; pCDH-CMV-GFPz).

Cell Lines and Media Conditions

RAW-112 B-cell leukemia and 4T1 (RRID:CVCL_0125) mammary carcinoma cells were obtained from I. Weissman (Stanford University). 2F3 BCR–ABL+ B-cell leukemia cells were originally generated by and obtained from R. Levy (Stanford University; ref. 29). K-BALB fibrosarcoma (RRID:CVCL_4350) and K7M2 osteosarcoma (RRID:CVCL_V455) cells were obtained from ATCC. RAW-112, 2F3, and 4T1 cells were maintained in complete RPMI-based media (Gibco) supplemented with 10% Tetracycline-free FBS (Omega Scientific), 2 mmol/L GlutaMAX (Gibco), and 100 U/mL penicillin and 100 μg/mL streptomycin (Gibco). K-BALB and K7M2 cells were maintained in complete DMEM-based media (Gibco) supplemented with 10% Tetracycline-free FBS (Omega Scientific), 2 mmol/L GlutaMAX (Gibco), and 100 U/mL penicillin and 100 μg/mL streptomycin (Gibco). Cultures were incubated at 37°C in a 5% CO2 humidified atmosphere with at least three culture splits per week.

RAW-112-MCPPF, 2F3-MCPPF, K-BALB-MCPPF, 4T1-MCPPF, and K7M2-MCPPF cells were generated by dual transduction of each cell line with pLVX-TRE3G-MCPPF and pLVX-Tet3G followed by puromycin and geneticin selection. RAW-112-MCPPF cells were further single-cell sorted to achieve a clonal cell line that reprograms into TR-APCs with a high efficiency. OVA-expressing RAW-112 cells were generated by transduction of RAW-112 and RAW-112-MCPPF cells with pCDH-CMV-GFPz-P2A-OVA, followed by Zeocin selection. TAP1 knockout RAW-112 cells were generated by nucleofection of precomplexed Cas9 and single-guide RNA (sequence: CATATG TTGCGGGTGAAGCT) into RAW-112-MCPPF cells. Five days after nucleofection, MHC-I low–expressing cells were purified by FACS. Disruption of the TAP1 locus was confirmed in the purified cell by PCR amplification of the region, followed by TIDE analysis (59). Before using for in vivo experiments, cell lines were tested with the MycoAlert detection kit (Lonza). All cell lines tested negative.

TR-APC Induction from Cell Lines

Unless otherwise noted, TR-APCs were generated from transduced cell lines by in vitro culture with 1 μg/mL doxycycline hyclate (Sigma) for 5 days. Complete cell culture media were supplemented with 25 ng/mL rmM-CSF (PeproTech), 20 ng/mL rmGM-CSF (PeproTech), and 20 ng/mL rmIL3 (PeproTech) to support long-term survival of TR-APCs. Complete media changes were completed 3 times per week, with addition of fresh doxycycline and cytokines. Where indicated, TR-APC cultures were stimulated with 1 μg/mL LPS (Thermo Fisher) or 1 μg/mL R848 (InvivoGen) for 24 hours prior to the indicated assay.

Immunoblotting

Whole-cell protein lysates were obtained from RAW-112-MCPPF cells 48 hours after vehicle or 1 μg/mL doxycycline (Sigma) treatment in RIPA buffer [150 mmol/L NaCl, 0.1% Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS, 50 mmol/L Tris-HCl (pH 8.0)] and 1× HALT Protease Inhibitor Cocktail (Thermo Fisher). Lysates were extracted via sample agitation at 4°C for 30 minutes, followed by 4°C centrifugation at 14,000 RPM for 10 minutes. Protein concentration was determined with Pierce BCA Protein Assay Kit (Thermo Fisher). Lysates were denatured in NuPage LDS sample buffer (Thermo Fisher) supplemented with 2.5% β-mercaptoethanol by incubation at 95°C for 3 minutes. Denatured samples were loaded onto a NuPAGE 4% to 12% Bis-Tris polyacrylamide gel (Thermo Fisher) with a Novex prestained protein ladder (Thermo Fisher) and electrophoresed in MES running buffer (Invitrogen). Gels were transferred to PVDF membranes with the iBlot system (Thermo Fisher), blocked with 2% w/v dry milk in TBST, and incubated with primary antibodies according to the manufacturers’ directions. Primary antibodies, TetR (9G9; Clontech), C/EBPα (15C8, RRID:AB_2077903; Thermo Fisher), PU.1 (9G7, RRID:AB_2186909; Cell Signaling Technology), and β-Actin (8H10D10, RRID:AB_11001306; Cell Signaling Technology) were detected with HRP-conjugated secondary antibodies purchased from Cell Signaling Technology [Anti-mouse IgG HRP-linked Antibody (7076, RRID:AB_330924) and Anti-rabbit IgG HRP-linked Antibody (7074, RRID:AB_2099233)] and Clarity Western ECL substrate (Bio-Rad). Membranes were imaged with the Bio-Rad Gel Dox system.

Flow Cytometry and FACS

Mouse cell-surface immunophenotypes were assessed using the following antibodies:

From BioLegend: CD3-BV605 (clone 17A2, RRID:AB_2562039), CD8α-PE/Cy7 (clone 53-6.7, RRID:AB_312760), CD14-APC/Cy7 (clone Sa14-2, RRID:AB_10900813), CD40-PacBlue (clone 3/23, RRID:AB_2561475), CD44-APC/Cy7 (clone IM7, RRID:AB_830784), CD69-BV421 (clone H1.2F3, RRID:AB_10900250), CD69-A488 (clone H1.2F3, RRID:AB_492845), CD80-PE (clone 16-10A1, RRID:AB_313128), CD86-APC/Fire750 (clone GL-1, RRID:AB_2629769), CD115-BV605 (clone AFS98, RRID:AB_2562760), CD152-PE (clone UC10-4B9, RRID:AB_313254), CD279-PE/Cy7 (clone 29F.1A12, RRID:AB_10696422), F4/80-PE/Cy7 (clone BM8, RRID:AB_893490), FoxP3-A647 (clone MF-14, RRID:AB_1089116), H-2Dd-FITC (clone 34-2-12, RRID:AB_313487), I-A/I-E-A488 (clone M5/114.15.2, RRID:AB_493524), I-A/I-E-BV510 (clone M5/114.15.2, RRID:AB_2561397), IFNγ-BV605 (clone XMG1.2, RRID:AB_2561438), Ly-6C-PerCP/Cy5.5 (clone HK1.4, RRID:AB_1659242), and SIRPα-PE (clone P84, RRID:AB_2563549).

From Thermo Fisher: CD4-APC (clone GK1.5, RRID:AB_469320), CD8α-FITC (clone 53-6.7, RRID:AB_464915), CD11b-APC (clone M1/70, RRID:AB_469343), CD19-PE (clone 1D3, RRID:AB_657659), CD25-PE (clone PC61.5, RRID:AB_465607), CD25-SB436 (clone PC61.5, RRID:AB_2734937), and CD223-PerCP/efluor710 (clone C9B7W, RRID:AB_11151334).

From BD Biosciences: CD8α-V500 (clone 53-6.7, RRID:AB_1937329), TCRVβ14-FITC (clone 14-2, RRID:AB_394738), and TCRVβ Screening Panel (RRID:AB_647180).

Human cell-surface immunophenotypes were assessed using the following antibodies:

From BioLegend: HLA-A,B,C-APC/Cy7 (clone W6/32, RRID:AB_10708421) and HLA-DR-BV605 (clone L243, RRID:AB_11219187).

From Thermo Fisher: CD4-APC-efluor780 (clone RPA-T4, RRID:AB_1272044), CD8-A700 (clone RPA-T8, RRID:AB_11218688), CD69-SB436 (clone FN-50, RRID:AB_2688204), and CD134-PE (clone OX-86, RRID:AB_465854).

From BD Biosciences: CD11b-PE/Cy7 (clone ICRF44, RRID:AB_396849), CD14-BV605 (clone M5E2, RRID:AB_2687593), CD19-BB700 (clone SJ25C1), CD34-PE/Cy7 (clone 8G12, RRID:AB_400389), CD40-BB515 (clone 5C3, RRID:AB_2739137), CD45-V450 (clone HI30, RRID:AB_1645573), and CD86-APC (clone FUN-1, RRID:AB_398608).

Dead cells were excluded from use of one of the following viability dyes: DAPI (Thermo Fisher), PI (Life Technologies), Zombie Red (BioLegend), Live/Dead Fixable Violet (Thermo Fisher), Ghost Dye Violet 510 (Tonbo), Ghost Dye Violet 540 (Tonbo), or Ghost Dye Red 780 (Tonbo).

Wash steps were performed using FACS buffer (PBS with 2% FBS and 2 mmol/L EDTA). Prior to analysis or sorting, cells were incubated with TruStain FcX anti-mouse CD16/32 (BioLegend), or Human TruStain FcX Fc Receptor Blocking Solution (BioLegend) on ice for 15 minutes, followed by the desired antibody combinations on ice for 30 minutes, and then washing. Intracellular staining was performed using the Cytofix/Cytoperm Kit (BD Biosciences) or the FoxP3 Fix/Perm Buffer Set (BioLegend). Prior to intracellular cytokine staining, cells were cultured in the presence of monensin (GolgiStop, BD Biosciences) for 6 hours. Cell subpopulations were purified by FACS using a BD FACSAria II (BD Biosciences). Flow-cytometric analysis was completed using a BD FACSAria, BD Canto II, Beckman Coulter Cytoflex, BD A5 FACS Symphony, or a Cytek Aurora.

Cytospin and May–Gruenwald–Giemsa Stain

CD11b+CD14+ TR-APCs were FACS purified from RAW-112 cells and washed with PBS and resuspended at 1 × 106 cells/mL. The cell suspension (100 μL) was placed in a cytospin device and spun at 500 RPM for 5 minutes in a Cytospin Cytocentrifuge (Thermo Fisher). Slides were air-dried and subsequently fixed in absolute methanol for 5 minutes. Cells were stained for 5 minutes in 1:1 May–Gruenwald solution (Sigma):phosphate buffer (Sigma). Slides were washed in tap water and subsequently stained with 1:10 Giemsa solution (Sigma):phosphate buffer for 20 minutes. Slides were washed again, dried, and mounted with cover glass.

Phagocytosis Assay

Phagocytic capacity was measured using the pHrodo Red E. coli BioParticles (Thermo Fisher) according to manufacturer's instructions. Briefly, unmodified RAW-112 cells and RAW-112 TR-APCs were incubated at 37°C for 1 hour in 100 μL of complete media with 20 μL pHrodo Red E. coli BioParticles at a 20:1 particle:phagocyte ratio. For conditions containing the cytochalasin D phagocytosis inhibitor, 10 mmol/L cytochalasin D was added for the entirety of the 1 hour incubation. Following incubation, cells were washed twice with FACS buffer and analyzed by flow cytometry using a 685/42 nm bandpass filter.

Animal Studies

Immunocompromised NSG mice (RRID:IMSR_JAX:005557) were purchased from The Jackson Laboratory (JAX) and bred in-house. Immunocompetent BALB/c (RRID:IMSR_JAX:000651), congenic BALB/c-Ptprca (RRID:IMSR_JAX:006584), and DO11.10 (RRID:IMSR_JAX:003303) mice were purchased from JAX. BALB/c-Langerin-DTR−/− mice were a gift from Juliana Idoyaga (Stanford University) and bred to BALB/c-Ptprca animals yielding BALB/c-Langerin-DTR+/−-Ptprca/b. All mouse experiments were conducted in accordance with a protocol approved by the Institutional Animal Care and Use Committee (IACUC; Stanford Administrative Panel on Laboratory Animal Care #33085) and in adherence with the U.S. National Institutes of Health's Guide for the Care and Use of Laboratory Animals.

Six- to eight-week-old male or female age and sex-matched mice were inoculated with 1 × 106 RAW-112 leukemia cells either subcutaneously or intravenously, 1 × 106 K-BALB fibrosarcoma cells subcutaneously or intravenously, 1 × 106 4T1 mammary carcinoma cells subcutaneously, or 1 × 106 K7M2 osteosarcoma cells intrafemorally through the femoral epiphysis. Subcutaneous tumor progression was followed via caliper measurements. Mice were humanely euthanized when an IACUC-approved endpoint measurement was reached in the longest tumor dimension or when mice exhibited signs of morbidity and/or hind limb paralysis.

Models of in vivo TR-APC induction utilized intratumoral doxycycline injection, or doxycycline-containing chow as indicated. For intratumor injections, palpable tumors were allowed to develop and were subsequently injected with 100 μL of 1 mg/mL doxycycline hyclate solution every other day for a total of three injections. For TR-APC induction with chow, mice were placed on 625 mg/kg doxycycline hyclate containing chow (Envigo) immediately following tumor cell inoculation.

Immune cell depletions were achieved by intraperitoneal injections of 250 μg of depleting antibody beginning on day −3 prior to tumor inoculation, and continuing every third day onward until the conclusion of the study. CD4 T cells were depleted with anti-CD4 clone GK1.5 (Bio X Cell, RRID:AB_1107636), CD8 T cells were depleted with anti-CD8 clone 53–6.7 (Bio X Cell, RRID:AB_1107671), NK cells were depleted with anti-CD122 clone TM-Beta 1 (Bio X Cell), macrophages were depleted with anti-F4/80 clone Cl:A3-1 (Bio X Cell, RRID:AB_10949019), granulocytes were depleted with anti-Ly6G clone 1A8 (Bio X Cell, RRID:AB_1107721), and B cells were depleted with anti-CD20 clone SA271G2 (BioLegend, RRID:AB_2629619).

Depletion of cDC1 from BALB/c-Langerin-DTR+/−-Ptprca/b mice was achieved by DT administration intravenously at a dose of 50 ng/g on day −2 prior to RAW-112 engraftment, followed by maintenance doses of 500 ng intraperitoneally every other day throughout the experiment.

Histology and IHC

The indicated organs were dissected from NSG or BALB/c mice at the points indicated in the figure legends, rinsed, and fixed in 4% PFA at 4°C for 24 hours. Fixed organs were subsequently transferred to 70% ethanol. Paraffin embedding, sectioning, and tissue staining were completed by HistoWiz or the Stanford Human Pathology/Histology Service Center.

Complete Blood Counts

Blood was obtained by the collection of 20 μL of blood from the tail vein using an EDTA-coated capillary tube and analyzed by the HemaTrue Veterinary Hematology Analyzer (Heska).

T-cell Activation and Proliferation Assays

Prior to syngeneic T-cell assays, stimulator cells were washed out of complete media containing FBS and cultured for one passage in complete media supplemented with 10% BALB/c serum (Innovative Research) to remove the presence of xenogeneic antigens. As indicated, untouched CD4+ or CD8+ cells were isolated from the spleens of BALB/c or DO11.10 mice following tissue dissociation. Negative MACS selection via CD4+ T Cell Isolation Kit, CD8+ T Cell Isolation Kit, or Pan T Cell Isolation Kit II, mouse (all purchased from Miltenyi) was used to enrich for the desired cell population.

To examine DO11.10 T-cell proliferation, CD4+ T cells were isolated from the spleens of DO11.10 mice as described above and subsequently loaded with Cell Proliferation Dye eFluor450 (Thermo Fisher) according to the manufacturer's directions. DO11.10 T cells were cultured with RAW-112-OVA, RAW-112-OVA TR-APCs, or BALB/c bone marrow–derived dendritic cells (BMDC) pulsed with OVA 323-329 peptide (InvivoGen). BMDCs were generated from BALB/c bone marrow cells as previously described (60). Briefly, BALB/c mice were euthanized, and the femurs were dissected under sterile conditions. Bone marrow cells were retrieved by flushing of the femur cavity followed by ACK lysis of red blood cells. Bone marrow cells were cultured in complete RPMI-based media supplemented with 20 ng/mL rmGM-CSF for 6 days, with a media change on day 3. Nonadherent and loosely adherent immature BMDCs were collected from the culture while leaving behind strongly adherent macrophages. Immature BMDCs were subsequently pulsed with OVA peptide and matured with 1 μg/mL LPS for 24 hours prior to the T-cell proliferation assay. Stimulator cells and DO11.10 T cells were cocultured at a ratio of 1 × 105 T cells:2 × 103 stimulator cells for 72 hours prior to flow-cytometric analysis.

To examine BALB/c T-cell activation, CD4+ and CD8+ T cells were isolated from the spleens of naive BALB/c mice as described above and cocultured with RAW-112 cells or RAW-112 TR-APCs. Stimulator:T-cell ratios from 1:2 to 1:100 were used to examine the potency of T-cell stimulatory capacity. Cocultures were maintained for 24 hours prior to flow-cytometric analysis to preserve markers of early T-cell activation.

To generate RAW-112:DC fusions, DCs were generated as described above. RAW-112 cells were treated with mitomycin C to prevent continuous growth. On the day of fusion, treated RAW-112 cells and DCs were mixed in serum-free RPMI medium (Sigma), centrifuged, and washed twice in serum-free media. After centrifugation and aspiration of media, mixed cells were slowly treated with 1 mL room temperature (RT) 35% PEG 6,000 molecular weight (Sigma). RT serum-free media (1 mL) were then added, followed by 3 mL of media and then 5 mL of complete media in the same manner. After a 5-minute incubation at 37°C/5% CO2, PEG-fused cells were washed with RT complete media, centrifuged, and resuspended in complete media. Following a week of incubation, cell fusion products were evaluated and sorted by FACS; a double-positive population containing both CD19 (RAW-112) and CD11c (DC) was sorted and used for subsequent coculture experiments.

Luminex

RAW-112 cells and RAW-112 TR-APCs were treated with either LPS or unstimulated control for 24 hours. Following stimulation, media were completely replaced and cultures continued for 24 more hours. Media supernatant was collected and submitted for Luminex multiplex cytokine array (H80-Panel 1). Log2(fold change over media only) was visualized using pheatmap.

DQ-OVA Assay

DQ-OVA (Thermo Fisher) reagent was used to analyze the capacity for phagocytosis and processing of exogenous antigens by TR-APCs as previously described (35). Briefly, cells were pulsed with DQ-OVA for 15 minutes at 37°C, followed by extensive washing with PBS + 5% FBS at 4°C. Cells were transferred back to complete growth medium and cultured for 4 hours with monitoring of DQ-OVA fluorescence over time by flow-cytometric analysis using a 530/30 nm bandpass filter.

TCRVβ Usage Analysis

Mouse TCRVβ usage was calculated as a surrogate for clonal expansion as previously described (38, 61). BALB/c mice (5 mice per group) were retro-orbitally bled at day 0 prior to tumor inoculation. All mice were then inoculated intravenously with 1 × 106 RAW-112-MCPPF cells, and one group was placed on doxycycline chow, as discussed above, to induce TR-APC formation in vivo. Fourteen days after tumor inoculation, all mice were again bled retro-orbitally. Peripheral blood samples were ACK lysed to remove red blood cells and divided evenly into 15 staining cocktails, containing antibodies to detect the surface markers CD3, CD4, and CD8 and a single TCRVβ gene. Flow-cytometric analysis was completed, and the frequency of TCRVβ+ cells was calculated as a frequency of live CD4+ and CD8+ T cells for each individual mouse at each time point. The change in TCRVβ frequency was calculated for each gene family by subtracting the initial frequency from the frequency on day 14 after tumor inoculation.

Primary Human Specimens

Primary leukemia specimens were obtained according to the Administrative Panel on Human Subjects Research Institution Review Board (IRB)–approved protocols (Stanford IRB #6453 and #36560) with written informed consent, and studies conducted were done in accordance with the ethical guidelines set forth in the Belmont Report. Healthy donor peripheral blood mononuclear cells (PBMC) were commercially purchased from the Stanford Blood Center (Mountain View, CA).

Primary B-ALL blasts were isolated from bone marrow aspirates using FACS based on the expression CD19 and CD34 and the absence of CD11b and CD14. After sorting, B-ALL blasts were cultured in suspension at 5 × 105 cells/mL in Iscove's modified Dulbecco's medium (Gibco) supplemented with 20% human A/B serum (Gemini Bio), 2 mmol/L GlutaMAX, 25 mmol/L HEPES (Gibco), 1 mmol/L sodium pyruvate (Gibco), 55 μmol/L β-mercaptoethanol, and 100 U/mL penicillin and 100 μg/mL streptomycin. B-ALL blasts were maintained in blast maintenance media containing 100 ng/mL hrFLT3L (PeproTech) and 10 ng/mL rhIL7 (PeproTech) or TR-APC induction media containing either 20 ng/mL hrIL3 (PeproTech), 20 ng/mL rhGM-CSF (PeproTech) and 25 ng/mL rhM-CSF (PeproTech) or 50 ng/mL rhGM-CSF and 50 ng/mL rhIL4 (PeproTech) in addition to 100 ng/mL hrFLT3L and 10 ng/mL rhIL7, as indicated. Notably, after thawing, primary human specimens were never exposed to xenogeneic antigens. Primary TR-APCs were used for immunologic analysis following 14 days of culture in TR-APC induction media. As indicated, primary TR-APCs were activated with the following inflammatory stimuli for 24 hours prior to analysis: LPS (1 μg/mL), R848 (1 μg/mL), poly(I:C) high molecular weight (10 μg/mL, InvivoGen), rhIFNγ (100 U/mL, PeproTech), or rhTNFα (50 ng/mL, PeproTech).

Autologous T-cell Stimulation Assay

Autologous T-cell stimulation assays were performed as previously described (47). Briefly, autologous patient PBMCs were thawed and cultured in 1:1 AIM-V (Gibco):RPMI 1640 media supplemented with 10% human A/B serum + 55 μmol/L β-mercaptoethanol at 37°C overnight. T cells were subsequently purified from the PBMC culture via MACS isolation using the negative selection Pan T Cell Isolation Kit, human (Miltenyi). Purified T cells were subsequently cocultured with autologous B-ALL blasts or bulk unsorted TR-APCs generated with GM-CSF, M-CSF, and IL3 at a 1:1 T-cell:stimulator cell ratio for 24 hours. CD3/CD28 Dynabeads (Thermo Fisher) were used as a positive control for T-cell activation at a 1:1 T-cell:bead ratio. Analysis of T-cell activation was accomplished by flow-cytometric analysis of early T-cell activation markers, including CD69, CD134, and CD137. Crucially, antibody staining of the TCR complex was omitted to prevent T-cell stimulation during staining. T cells were identified by the presence of CD4 and CD8 staining and the lack of myeloid and B-lymphoid antigens.

scRNA-seq Sample Preparation and Library Construction

Murine tumors were dissociated and sorted by FACS to separate CD45.1+ RAW-112–derived cells and CD45.2+ endogenous immune cells. Tumor and immune cells were pooled at a 1:1 ratio following sorting to artificially increase the frequency of immune cells for subsequent analysis. Live samples were submitted to MedGenome for library construction with the 5′ gene expression 10x Genomics platform, targeting a capture of 10,000 cells/sample and 50,000 reads/cell. The library was subsequently sequenced on a NovaSeq with 500 million paired reads.

Human B-ALL TR-APCs were generated as described, with cytokine stimulation for 14 days in vitro. Twenty-four hours prior to sample harvesting, a fresh sample tube was sorted and rested overnight in blast retention media for the B-ALL blast condition. Bulk cultures of TR-APCs and B-ALL blasts were submitted as live samples to MedGenome for library construction with the 3′ gene expression 10x Genomics platform, targeting a capture of 10,000 cells/sample and 50,000 reads/cell. The library was subsequently sequenced on a NovaSeq with 500 million paired reads.

Murine scRNA-seq Processing and Analysis

Raw sequencing data fastq files were aligned to the transcriptome assembly mm10-2020-A, and counts were obtained using the cellranger count function and then analyzed using the Seurat R package. These included datasets for day 5 no Dox, day 5 Dox, day 10 no Dox, and day 10 Dox. Cells were filtered to have at least 200 unique genes. Data were normalized using the NormalizeData function using default parameters, and the top variable genes were identified using the FindVariableFeatures function with parameters of selection.method = “vst” and features = 2,500. This resulted in a total of 27,168 cells. The most variable genes were used for principal component analysis (PCA) dimensionality reduction, and the resulting clusters were visualized in a 2D Uniform Manifold and Approximation Projection (UMAP).

Genome Instability Score

To separate neoplastic from normal cells in our single-cell RNA-seq dataset, we performed genomic instability analysis as implemented in the GenomicInstability package (62). Briefly, a normalized feature count matrix was exported from Seurat, and the inferCNV() function was run using a loci-block size of 100 genes and a displacement between loci-blocks of 25 genes on all 19 autosomes and the X chromosome. Then, the Genomic Instability Score (GIS) was calculated using default parameters. The distribution of GIS for all cells was inspected, and a cutoff of the lowest 40% was picked and assigned as “likely normal.”

Cell Annotation

Automated cell type annotation was performed using SingleR and the celldex reference cell types (40). Specifically, SingleR was run using the mouse RNA-seq cell types derived from bulk RNA-seq (63) with minor modifications: Due to aberrant expression of erythrocyte genes in the neoplastic tumor cells, the “Erythrocyte” cell type was removed from the reference set. Other parameters were run with default settings.

Version Software

  • R version 4.0.2 (2020-06-22)

  • ggplot2_3.3.6

  • celldex_1.4.0

  • SingleR_1.8.1

  • SummarizedExperiment_1.24.0

  • Biobase_2.54.0

  • S4Vectors_0.32.4

  • BiocGenerics_0.40.0

  • MatrixGenerics_1.6.0

  • patchwork_1.1.1

  • SeuratObject_4.1.0

  • Seurat_4.1.1

  • genomicInstability 1.2.0

Primary Human B-ALL scRNA-seq Processing and Analysis

Raw sequencing data were converted to fastq files using 10x Cell Ranger Single Cell Software Suite version 6.1.2 (https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome) and aligned to transcriptome assembly hg38. scRNA-seq reads were obtained using the cellranger software count function. Cells were filtered according to the following parameters: those expressing fewer than 200 unique genes, according to features found in fewer than three single cells, and those expressing greater than 5% mitochondrial genes. The top 2,000 variable genes for each dataset were found using the FindVariableFeatures function with method = “vst” and nfeatures = 2,000, and the three datasets were integrated using anchors selected from these variable features. This resulted in a total of 17,987 cells. The 2,000 most variable genes were used for PCA dimensionality reduction, and the resulting clusters were visualized in a 2D UMAP.

Cell Annotation

Cells were annotated according to gene signatures identified from published RNA-seq of hematopoietic cells using SingleR. Briefly, the integrated Seurat Object was converted into a SingleCellExperiment. Then, Blueprint/ENCODE and Human Primary Cell Atlas (HPCA) immune cell expression data were retrieved using celldex. Cells were classified with SingleR using “label.fine” for Blueprint/ENCODE-derived annotations and “label.main” for HPCA-derived annotations.

Module Scores

Gene module scores were calculated using the AddModuleScore function. Features in each module were derived from the gene ontology gene lists “Antigen Processing and Presentation,” “Myeloid Leukocyte Mediated Immunity,” and “Myeloid Leukocyte Activation” or a curated coinhibitory molecule gene list comprised of CD274, PDCD1LG2, LGALS9, CD276, and VTCN1.

Data Accessibility

The datasets generated during this study are available at the Gene Expression Omnibus using the accession number GSE216559.

Quantification and Statistical Analysis

Unless otherwise noted, statistical analyses for significant differences between groups were conducted using unpaired two-tailed t tests without correction for multiple comparisons and without assuming consistent SD using the statistical analysis features of GraphPad Prism 8. Survival curves were compared using the log-rank Mantel–Cox test. Details regarding each individual statistical analysis are reported in the relevant figure legends. Significance measures are reported in each individual figure legend.

M.H. Linde reports grants from the Blavatnik Family Foundation during the conduct of the study; personal fees from Scribe Biosciences outside the submitted work; and a patent for lineage reprogramming as a cancer immunotherapy pending. T. Köhnke reports grants from The Leukemia & Lymphoma Society and other support from TenSixteen Bio outside the submitted work, and is a special fellow of The Leukemia & Lymphoma Society. R.G. Majzner reports personal fees from Link Cell Therapies, Syncopation Life Sciences, Lyell Immunopharma, NKarta, Immunai, Aptorum Group, Arovella Therapeutics, Zai Lab, Innervate Radiophar­maceuticals, Fate Therapeutics, Gamma Delta Therapeutics, Gadeta, and Waypoint Bio outside the submitted work. R. Majeti reports other support from Kodikaz Therapeutic Solutions and MyeloGene, personal fees from TenSixteen Bio, Roche, Cullgen, and 858 Therapeutics, personal fees and other support from Orbital Therapeutics, and grants and other support from Gilead and Pheast Therapeutics outside the submitted work. No disclosures were reported by the other authors.

M.H. Linde: Conceptualization, data curation, formal analysis, supervision, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. A.C. Fan: Conceptualization, data curation, software, formal analysis, methodology, writing–review and editing. T. Köhnke: Conceptualization, data curation, formal analysis, methodology. A.C. Trotman-Grant: Data curation, investigation, methodology. S.F. Gurev: Data curation, formal analysis. P. Phan: Data curation, formal analysis. F. Zhao: Methodology. N.L. Haddock: Formal analysis, visualization. K.A. Nuno: Formal analysis. E.J. Gars: Data curation, formal analysis, visualization. M. Stafford: Data curation, project administration. P.L. Marshall: Data curation, formal analysis. C.G. Dove: Conceptualization, data curation. I.L. Linde: Data curation, formal analysis. N. Landberg: Data curation. L.P. Miller: Data curation. R.G. Majzner: Conceptualization, resources, methodology, writing–review and editing. T.Y. Zhang: Conceptualization, data curation, investigation, methodology, writing–review and editing. R. Majeti: Conceptualization, resources, formal analysis, supervision, funding acquisition, methodology, project administration, writing–review and editing.

This work is supported by funding from the J. Benjamin Eckenhoff Fund, the Emerson Collective Cancer Research Fund, the New York Stem Cell Foundation, the Stinehart-Reed Award, and Ludwig Center for Cancer Stem Cell Research and Medicine (to R. Majeti). R. Majeti is a recipient of The Leukemia & Lymphoma Society Scholar Award. M.H. Linde is supported by a Blavatnik Family Fellowship. A.C. Fan is supported by the National Science Foundation Graduate Research Fellowship Program and a Stanford Graduate Fellowship. A.C. Trotman-Grant is supported by a Baker Fellowship. T. Köhnke is supported by Deutsche Forschungsgemeinschaft (DFG) KO 5509/1-1. P.L. Marshall is supported by the Stanford Bio-X Bowes Fellowship and the Stanford Medical Scientist Training Program. N. Landberg is supported by a Knut and Alice Wallenberg Foundation Postdoctoral Scholarship. P. Phan is supported by the Stanford Human Biology Research Exploration Program and a Stanford Undergraduate Advising and Research grant. C.G. Dove is supported by the Stanford Medical Scientist Training Program. I.L. Linde is supported by NIH F31CA196029. R.G. Majzner is the Taube Distinguished Scholar for Pediatric Immunotherapy at Stanford University School of Medicine. T.Y. Zhang is supported by NCI K08CA248940-1, an American Society of Hematology Research Training Award for Fellows, an A.P. Giannini Foundation Fellowship Award, and a Stanford Cancer Institute Fellowship Award. The authors thank P. Lovelace and C. Carswell-Crumpton for maintenance and management of the Stanford Institute for Stem Cell Biology and Regenerative Medicine FACS facility, Parveen Abidi for the maintenance of the Stanford University Division of Hematology Tissue Bank, and the patients of Stanford University Hematology Division who donated tissue samples to this effort.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/).

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