Combination IFNβ and Membrane-Stable CD40L Maximize Tumor Dendritic Cell Activation and Lymph Node Trafficking to Elicit Systemic T-cell Immunity

Distinct tumor immune phenotypes vary in their response to currently used immunotherapies (1–4). In general, the “inflamed” tumor phenotype is associated with high T-cell presence in tumors and positive responses to immunotherapies, especially immune-checkpoint inhibitors (ICI; refs. 1–4). In contrast, the “immune-desert” or “cold” phenotypes exhibit a virtual absence of T cells in tumor beds, possibly resulting from immune ignorance and/or lack of priming (1–3). Immune desert or immune-excluded phenotypes dominate in many cancer types, resulting in a large unmet need for patients who do not respond well to ICIs. One possible approach to treat these patients is through in situ delivered cancer therapies using intralesional/intratumoral (IT) approaches that incorporate stimulators of innate immunity such as STING and TLR agonists, as well as oncolytic viruses (OV; refs. 5–8). Such in situ vaccination approaches provide an opportunity to target conventional dendritic cells (cDC) in the tumor microenvironment (TME) and trigger the activation of a systemic T-cell response (7, 9, 10).

OVs have been developed for their ability to specifically replicate in cancer cells leading to cell lysis without cytotoxicity in normal cells. The first and currently only FDA-approved OV, herpes simplex virus type 1–based talimogene laherparepvec (T-VEC), is currently in use for the treatment of melanoma patients through IT administration (7, 11–14). Several studies indicate that T-VEC induces an increase in CD8⁺ T-cell density in noninjected melanoma tumors, consistent with an increase in systemic T-cell immunity (15, 16). T-VEC is engineered to express GM-CSF to modulate the activity of myeloid cells, including cDCs, suggesting that in addition to cancer cell lysis to release antigens, modulation of DC activity may also mediate response to T-VEC. However, GM-CSF is not a strong activator of DCs and functions as a general activator of myeloid cells, including those with suppressive functions (17). Hence, optimizing the ability of OVs to activate DCs could improve the therapeutic efficacy of this approach.

Conventional type 1 dendritic cells (cDC1) and conventional type 2 dendritic cells (cDC2) mediate the activation of T cells, with cDC1 playing a key role in both CD8⁺ and CD4⁺ T-cell activation, whereas cDC2 are more specific for CD4⁺ T cells in the absence of therapeutic intervention (18–23). cDC1 modulate T-cell activation in both tumor and lymph nodes (18, 21, 22, 24, 25), and their presence in tumors correlates with immunotherapy response (26, 27). Both cDC1 and cDC2 can differentiate into mature DC with high expression of costimulatory molecules, along with expression of T-cell regulatory molecules, and these cells are called mature DCs enriched in immunoregulatory molecules (mregDC; refs. 28, 29). Finally, the TME can impair DC function suggesting that strategies to activate tumor DCs may be of considerable therapeutic benefit (30, 31).

cDC activation is highly regulated by NF-κB and type 1 IFN–induced transcription factors (32, 33, 34). CD40 is a cell-surface receptor expressed on a variety of antigen-presenting cells (APC), including cDCs, B cells, and macrophages, and is a potent activator of NF-κB (35). Both CD40 and the type 1 IFN pathways have been shown to be crucial for the CD8⁺ T-cell cross-priming function of cDCs (35–39), and therefore activation of these pathways in the TME could be a promising approach for stimulating antitumor T-cell responses through modulation of cDC function. However, it is not clear which specific aspects of cDC function are regulated by these pathways, whether they have redundant or nonredundant functions in T-cell activation, and how these signals can be safely delivered to the TME without causing systemic toxicity. Herein, we report on the effects of delivering membrane-stable chimeric CD40L (MEM40; refs. 40–42), either alone or in combination with IFNβ, using an adenovirus vector to minimize replication suppression by type 1 IFNs (43). We demonstrate that combined activation of CD40 and type 1 IFN pathways in the TME triggered strong cDC activation and lymph node trafficking, which corresponded to potent induction of systemic CD8⁺ T-cell immunity and control of both injected and distant tumor growth. These findings demonstrate that the efficacy of OVs can be enhanced by the inclusion of cDC activators. Consistent with these preclinical data, early clinical studies indicate that MEM-288 OV expressing MEM40 + IFNβ transgenes induced tumor cell killing concomitant with a strong increase in T-cell density.

All mice were housed in the animal facility at Moffitt Cancer Center under specific pathogen-free conditions. C57BL/6 and SCID mice were obtained from Charles River, and 129S4/SvJaeJ were obtained from Jackson Laboratories. IFNAR1^−/−, CD40^−/−, BATF3^−/−, and CCR7^−/− mice were obtained from Jackson Laboratories and bred inhouse. All animal experiments were approved by the University of South Florida Institutional Animal Care and Use Committee.

The B16-F10 mouse melanoma cell line was from ATCC and was cultured in DMEM (cat no. 15-017-cv, Corning) with 10% FBS (cat no. A52568-01, GIBCO). Zs-Green overexpressing B16-F10 cells were kindly provided by Dr. Max Krummel (University of California, San Francisco). Mouse 344SQ cell line (kindly provided by Dr. J. Kurie, MD Anderson Cancer Center) with KRAS G12D and TP53 mutations (R172H) was maintained in RPMI-1640 (cat no. 10-040-cv, Corning) with 10% FBS. Human A549 lung cancer cell line was obtained from the Moffitt Lung Cancer Center of Excellent repository, authenticated by short tandem repeat analysis, and maintained in RPMI-1640 with 10% FBS. For tumor growth studies, cell lines were harvested in the logarithmic growth phase after being cultured for less than 2 weeks. Cell lines tested negative for Mycoplasma contamination (PlasmoTest, Mycoplasma Detection Kit from InvivoGen).

Replication-deficient ISF35 adenovirus expressing chimeric membrane-stable CD40L, MEM40 (Ad-MEM40) has been previously described (40–42). Replication-deficient adenoviruses expressing mouse IFNβ (Ad-mIFNβ) human IFNβ (Ad-hIFNβ), mouse GM-CSF (Ad-GMCSF), and control adenovirus Null (Ad-Null; Supplementary Fig. S1A) were developed with Vector Biolabs. The conditionally replicative OV type 5 adenovirus backbone that includes delta-24 (D24) E1, E1b 55kb, and E3 viral genome deletions (44, 45) was used to generate MEM-288 (Creative Biolabs). Briefly, an expression cassette for MEM40 including the cytomegalovirus (CMV) promoter and bovine growth hormone polyadenylation sequences (poly(A); ref. 46) was inserted upstream of the adenovirus E1 region, and an expression cassette for human IFNβ, including the simian virus 40 (SV40) promoter and poly(A) sequences, was inserted in the E3 region. Ad-GFP was generated with an expression cassette for GFP (including the CMV promoter and poly(A) sequences) inserted upstream of the adenovirus E1 region. All viruses were titered inhouse using the QuickTiter adenovirus titer immunoassay kit (VPK-109, Cell Biolabs) according to the manufacturer's protocol.

Cells were harvested in the logarithmic growth phase after being cultured for less than 2 weeks, washed once in an injection medium (phenol-free DMEM supplemented with 2% FBS), and counted. C57BL/6 mice were inoculated s.c. with 5×e5 B16-F10 cells on the primary site and with 2.5×e5 cells on the contralateral site. The mice were injected with oncolytic or replication-deficient adenoviruses on D12 and 16 into primary tumors or/and with anti-mouse CTLA-4 (Clone UC10-4F10-11, cat no. BE0032, Bio-X-Cell) and anti-mouse PD-1 (Clone RMP1-14, cat no. BE0146, Bio-X-Cell) i.p. on D16, D19, D23, and 27 along with matching isotype controls (cat no. BE0089 and cat no. BE0091, Bio-X-Cell). Tumors were monitored for growth by measurements 2 to 3 times per week. The tumor volume was determined as length × width²/2. Mice were sacrificed when s.c. tumors reached a diameter of 20 mm or when the animals showed signs of morbidity. Similarly, 129S4/SvJaeJ mice were inoculated s.c. with 5×e5 344SQ cells on the flank and injected with oncolytic or replication-deficient adenoviruses on D12 and 16 or/and with anti–PD-1 and CTLA-4 antibodies i.p. on D16, D19, D23, and 27. Mouse lungs were collected and used for H&E and IHC staining (47). SCID mice were inoculated s.c. with 5×e6 luciferase expressing A549, then injected with 2 doses of oncolytic 10e9 infectious units of Ad-GFP or MEM-288 on D21 and D28, and bioluminescence imaging (BLI) was used to detect tumor growth over 3 weeks.

Mice with tumors were cardiac-perfused with PBS containing 10 U/mL heparin to clear peripheral blood, and single-cell suspensions were prepared by incubating minced tumor in 1 mg/mL collagenase A (cat no. 11088793001, Roche) and 50 U/mL DNase I (cat no. 10104159001, Roche) and with the addition of collagenase D (11088882001, Roche) for lymph nodes, at 37°C for 20 minutes with agitation, followed by passing through a 70-μm cell strainer and lysis of red blood cells (cat no. BP10-548E, Lonza) for 2 minutes at room temperature. Immune populations were identified using antibodies described in Supplementary Table S1. Cells were incubated for 5 minutes at room temperature with Fc-block, 30 minutes with staining Abs on ice, and DAPI (or L/D near IR fixative viability dye) was added prior to analysis to assess viability. Flow-cytometric analysis was performed on BD FACSymphony and analyzed using FlowJo software (version 10.7.1, Tree Star). Smaller LNs were pooled and digested with collagenase A/D and DNase I, followed by passing through a 70-μm cell strainer and lysis of red blood cells. Intracellular staining or granyzme B (clone GB11) or TCF1 (clone S33-966) was performed on single-cell suspensions of tumors using BD Cytofix/Cytoperm Plus Kit (cat no. 555028, BD Biosciences) according to the manufacturer's instructions.

ELISA was performed using kits to detect mouse IFNβ (cat no. 42400, PBL Assay Science), human IFNβ (cat no. 41410, PBL Assay Science), human IL12 p70 (cat no. D1200, R&D Systems), and human TNFα (cat no. 430204, BioLegend). All results (n = 3) are expressed as the mean ± SEM.

Single-cell suspensions from pooled mouse spleens were subjected to magnetic bead isolation of CD8⁺ T cells according to the manufacturer's instructions (cat no.130-117-044, Miltenyi Biotec). Next, 3×10⁵/well purified CD8⁺ T cells and 1×10⁵/well of 50 Gy irradiated tumor cells were plated in triplicate wells and incubated in 96-well plates at 37°C for 24 hours. Tumor cells were stimulated with IFNγ (cat no. 315-05, PeproTech) to increase MHC expression 1 day before coculture. T cells were also cultured alone or with Concavalin A as negative or positive controls, respectively. Plates were washed 6 times with PBS + 0.05% Tween 20 and 100 μL/well of biotinylated anti-IFNγ (cat no. 13-7312-85, eBioscience) diluted to 1 μg/mL in PBS + 0.05% Tween 20 was added. Avidin-HRP (cat no. 554058, BD Biosciences) was used as a detection reagent. Spot counting was done using an ImmunoSpot ELISpot plate reader (Cellular Technologies, Ltd). The release of IFNγ by T cells was normalized to ConA treatment–induced release of IFNγ in the same sample T cells.

Single-cell RNA sequencing (scRNA-seq) was performed using the 10X Genomics Chromium System (10X Genomics) by the Molecular Genomics Core at the Moffitt Cancer Center. B16-F10 tumor–bearing mice were injected with Ad-Null, Ad-MEM40, Ad-mIFNβ, and the combination. Mice were cardiac-perfused with PBS containing 10 U/mL heparin to clear peripheral blood, and single-cell suspensions were prepared by incubating minced tumor in 1 mg/mL collagenase A and 50 U/mL DNase I at 37°C for 20 minutes with agitation, followed by passing through a 70-μm cell strainer and lysis of red blood cells for 2 minutes at room temperature. Each scRNA-seq sample was pooled from 3 different similarly treated tumors, CD11c⁺MHC-II⁺ sorted cells were sorted as in Supplementary Fig. S2 by flow cytometry and then washed twice with 1× PBS (calcium and magnesium-free) containing 0.04% weight/volume BSA and resuspended in the same buffer following the cell preparation guide from 10X Genomics. Cell viability and counts were obtained by AO/PI dual fluorescent staining and visualization on the Nexcelom Cellometer K2 (Nexcelom Bioscience LLC). Cells were then loaded onto the 10X Genomics Chromium Single-Cell Controller at a concentration of 1,000 cells/μL in order to encapsulate up to 10,000 cells per sample. Briefly, single cells, reagents, and 10X Genomics gel beads were encapsulated into individual nanoliter-sized Gelbeads in Emulsion (GEMs), and then reverse transcription of poly-adenylated mRNA was performed inside each droplet at 53°C. cDNA libraries were then completed in a single bulk reaction by following the 10X Genomics Chromium NextGEM Single-Cell 3′ Reagent Kit v3.1 user guide, and 50,000 sequencing reads per cell were generated on the Illumina NovaSeq 6000 instrument. Demultiplexing, barcode processing, alignment, and gene counting were performed using the 10X Genomics CellRanger v6.1.2 software, and analysis results were visualized using 10X Genomics Loupe browser v6.0.0.

Sequencing reads were mapped against mm10 mouse transcriptome and processed for a unique molecular identifier (UMI) counting and barcodes filtering using Cell Ranger (v3.0, 10X Genomics). Barcodes with UMI counts passing threshold for cell detection were imported to Seurat v4.0 (48) for analysis. Cells with fewer than 200 genes detected or with greater than 10% mitochondrial UMIs were further filtered out; genes detected in less than three cells were excluded. Doublets were detected using Scrublet (49), doubletCells implemented in scran (50), DoubletFinder (51), and scDblFinder, assuming 0.08% doublet rate for every 1,000 cells. Cells identified as doublets by at least two methods were removed. Raw UMI counts were then log-normalized, and the top 4,000 variable genes were detected in each sample independently. S and G₂–M cell-cycle phase scores were assigned to cells using the CellCycleScoring function. To remove batch effects between samples, samples were further integrated using the IntegrateData function (52) in Seurat with parameter anchor.features = 8,000. Scaled z-scores for each gene were calculated using ScaleData regressing against total reads count, mitochondrial UMI percentage, and cell-cycle phases. Principal component analysis was performed on the integrated data, and a shared nearest neighbor (SNN) graph was constructed based on the first 40 principal components. A total of 23 clusters were identified using the Louvain clustering (53) implemented in the FindClusters function at resolution = 0.8. Uniform manifold approximation and projection (UMAP) was used to visualize gene expression and clusters.

Differential expression analysis for each cluster was performed using the FindAllMarkers function in Seurat with default settings. Genes with Bonferroni-corrected P < 0.05 and an average log-fold change > 0.25 were considered differentially expressed. Clusters were further annotated by comparing differential genes with markers previously associated with melanocytes (Pmel, Mlana), T cells (Cd3e, Cd3d, Cd3g), fibroblasts (Col1a1, Col1a2, Nav1), cDC1 (Xcr1, Clec9a, Itgae, Batf3), cDC2 (Lilrb4a, Itgax, Csf1r, Mgl2), mregDC (Fscn1, Ccl22, Cacnb3, Ccr7, Fabp3), pDC (Siglech, Ccr9, Bst2, Pacsin1, Tcf4), and proliferation (Stmn1, Cdk1, Mki67). For the marker gene bubble plot, gene-level average expression was calculated for each cluster, and then Z-score was normalized.

Healthy donor PBMCs were obtained from Florida Blood Services, purified by Ficoll spin separation of buffy coats followed by purification of CD14⁺ cells (cat no. 130-050-201, Miltenyi Biotec) and culturing in RPMI-1640 with 10% FBS with 10 ng/mL GM-CSF and IL4 (Cat No. 215-GM and 204-IL, respectively, R&D Systems) for 6 days. On D5, A549 cells were infected with adenovirus at MOI = 10 for 24 hours and then washed three times with PBS to remove the free virus. On D6, monocyte-derived DCs (Mo-DCs) were plated at 1×10⁶ per mL in serum-free RPMI-1640 and cocultured with adenovirus-infected A549 tumor cells for 48 hours. Single-cell suspensions were prepared and subjected to flow cytometry analysis or sorting for RNA sequencing.

Cells were gated on strict forward and side scatter parameters to ensure single-cell separation. In addition, the L/DNIR negative population was used for viable cells. The target cells were gated on HLA-DR⁺CD11c⁺ DCs derived from monocytes of 3 healthy donors by using FACSAira SORP housed in BioProtect IV BSC (BD Biosciences). Total RNA was extracted (QIAGEN called RNeasy Plant Mini Kit, cat no. 74904) from human DCs after sorting HLA-DR⁺CD11c⁺ cells, and RNA was then quantitated using the Qubit fluorometer (Thermo Fisher Scientific) and screened for quality on the Agilent TapeStation 4200 (Agilent Technologies). The sequencing libraries were prepared using the Takara SMARTer Stranded Total RNA-seq Kit v2 Pico Input Mammalian kit (Takara Bio USA Inc.) by the Molecular Genomics Core at the Moffitt Cancer Center. Briefly, 2 ng of RNA was used to generate cDNA and a strand-specific library following the manufacturer's protocol. Library quality control steps were performed, including TapeStation size assessment and quantification using the Kapa Library Quantification Kit (cat no. 07960298001) Roche). The final libraries were normalized, denatured, and sequenced on the Illumina NextSeq 2000 sequencer with the P2-200 cycle reagent kit (cat no. 20046812) to generate 60 million 105-base read pairs per sample (Illumina, Inc.).

Paired-end RNA-seq fastq files were aligned to the hs37d5 reference genome using the STAR aligner v2.5.3a. Expression counts were summarized at the gene level against the gencode v19 gene model using featurecounts v1.5.3. Read counts were normalized to library size estimates using the R package DESeq2 v1.20.0. Differential gene expression for treatment effects was done in DESeq2 using a paired design. The ranked gene list was used to perform preranked gene set enrichment analysis (GSEA v4.0.2; ref. 54) to assess the enrichment of hallmarks, curated gene sets, and gene ontology (55) terms in MSigDB. The resulting normalized enrichment score (NES) and FDR controlled P values were visualized in bar plot.

Mouse tumors were directly fixed in 10% formalin in PBS and mouse lungs were insufflated with 10% formalin in PBS and fixed overnight. H&E and IHC were performed on fixed and paraffin-embedded samples. Tumor sections were cut on a microtome and stained according to standard protocols by Tissue Core, Moffitt Cancer Center. H&E- and IHC- (CD8 cat no. 98941S, Cell Signaling Technologies) stained slides were scanned using an Aperio AT2 digital pathology system (Leica Biosystems Inc.) with a 20 × 0.7 NA objective lens. Whole-slide H&E images were viewed with Aperio Imagescope software, and regions of interest (ROI) were annotated by an experienced digital pathology analyst to outline tumor regions. The total area of the tumor ROIs was calculated and normalized by total tissue area to determine the percentage of tumor burden for each sample. The CD8-stained IHC images were imported into Definiens Tissue Studio v 4.7 (Definiens Inc.), where a cell detection algorithm was used to enumerate CD8⁺ and CD8^– cells. The intensity threshold for positivity was set using the positive and negative staining controls as a guide. The CD8⁺ cell counts were normalized by total cell number (% positive cells) and area (number of positive cells per mm²). In some cases, heavy necrotic regions were manually omitted from the analysis to avoid false-positive staining due to excessive tissue debris.

Fresh deidentified lung cancer specimens were collected under an IRB-approved protocol (Advarra Inc.). Briefly, samples were obtained from 3 patients who provided written informed consent and who were undergoing standard-of-care surgical resection of lung cancer with excess tumor tissue beyond what was necessary for routine pathologic characterization (ideally at least 1 cm × 1 cm size). The studies were conducted in accordance with the Declaration of Helsinki. Freshly resected tumors were transported from the operating room to the pathology suite and initially stored in RPMI media. Tumor pieces were injected with PBS or 1 × 10⁹ MEM-288 and cultured for 2 days. After a 20-minute digestion with 2 mg/mL collagenase A in 40 mL DMEM + 80 μL DNase (50 U/mL), single-cell suspensions were processed and analyzed as described in the flow cytometry section. Tumor supernatants were used to determine levels of IFNβ by ELISA.

A phase I trial was activated on March 1, 2022, at the Moffitt Cancer Center in which single-agent MEM-288 is administered by intratumoral injection in patients with advanced/metastatic cancer (ClinicalTrials.gov trial registration ID: NCT05076760). This part Ia study titled “Phase I study of MEM-288 oncolytic virus in solid tumors including non–small cell lung cancer (NSCLC)” is sponsored by Memgen, Inc and is being conducted at two sites: Moffitt Cancer Center and Duke Cancer Institute. MEM-288 is administered as monotherapy to patients with advanced solid tumors, including NSCLC, refractory to standard therapies. Part Ia uses dose escalation using a BOIN design of 3 dose levels (1×10¹⁰, 3.3 × 10¹⁰, 1 × 10¹¹ viral particles) with the primary objective to determine the maximum tolerated dose (MTD) of MEM-288. Eligibility criteria were: patients (≥18 years old) with either advanced/metastatic NSCLC, cutaneous squamous-cell carcinoma (cSCC), Merkel cell, melanoma, triple-negative breast cancer, pancreatic cancer, or head and neck cancer, who progressed following previous anti–PD-1/PD-L1 therapy, with a tumor lesion which is accessible for injection. Intratumoral injection of MEM-288 is administered by a qualified interventional radiologist/clinician to a palpable cutaneous/subcutaneous lesion, or under CT or ultrasound guidance of a percutaneously accessible tumor. Only one tumor lesion is injected per treatment. Injected tumor should be ≥1 cm³ in volume and should not encase, or be adjacent to, vital neurovascular structures. Patients receive a minimum of 2 and a maximum of 6 administrations of MEM-288. Tumor biopsies from the injected lesions are obtained at two time points: prior to first treatment and again at 3 weeks after initiation of treatment. For each tumor biopsy, 6 passes with 18- to 20-gauge needles are used to obtain tumor tissue before and after 1 MEM-288 injection. Three passes are formalin-fixed and were used for mIF studies. The other three passes are snap-frozen and used for generating nucleic acids (RNA and DNA). Serial peripheral blood collections are at screening and during treatment. PBMC DNA is used to determine the systemic presence of potential tumor-reactive T cells by TCRβ sequencing using the ImmunoSEQ assay [see T-cell receptor beta chain sequencing (ImmunoSEQ assay)]. The results reported herein are based on the first two enrolled patients, both NSCLC, and treated with 1 × 10¹⁰ MEM-288. In both patients, MEM-288 was injected in skin-palpable tumor lesions. The studies were conducted in accordance with the Declaration of Helsinki and performed after approval by an institutional review board (Advarra Inc.) and in accordance with an assurance filed with and approved by the U.S. Department of Health and Human Services. Informed written consent was obtained from each subject.

FFPE tissue samples from biopsies of patients enrolled in the MEM-288 clinical trial were immunostained using the PerkinElmer OPAL 7-Color Automation IHC kit (cat no. NEL821001KT) on the BOND RX autostainer (Leica Biosystems). The OPAL 7-color kit uses tyramide signal amplification (TSA) conjugated to individual fluorophores to detect various targets within the multiplex assay. Sections were baked at 65°C for 1 hour and then transferred to the BOND RX (Leica Biosystems). All subsequent steps (e.g., deparaffinization and antigen retrieval) were performed using an automated OPAL IHC procedure (PerkinElmer). OPAL staining of each antigen occurred as follows: slides were blocked with PerkinElmer blocking buffer for 10 minutes and then incubated with primary antibody at optimized concentrations followed by OPAL HRP polymer and one of the OPAL fluorophores. Primary antibodies used for multispectral immunofluorescence (mIF) are listed in Supplementary Table S1. Individual antibody complexes are stripped after each round of antigen detection. After the final stripping step, DAPI counterstain is applied to the multiplexed slide and is removed from BOND RX for coverslipping. Autofluorescence slides (negative control) were included, which use primary and secondary antibodies omitting the OPAL fluors and DAPI. All slides were imaged with the Vectra3 Automated Quantitative Pathology Imaging System.

Multilayer TIFF images were exported from InForm (AKOYA) and loaded into HALO (Indica Labs) for quantitative image analysis. The tissue was segmented into individual cells using the DAPI marker that stains cell nuclei. For each marker, a positivity threshold within the nucleus or cytoplasm was determined per marker based on published staining patterns and intensity for that specific antibody. After setting a positive fluorescent threshold for each staining marker, the entire image set was analyzed with the created algorithm. The generated data include positive cell counts for each fluorescent marker in the cytoplasm or nucleus and percentage of cells positive for the marker. The markers shown are PCK, CD3, CD8, CD68, TCF1, and DAPI. These studies were performed in our Advanced Analytical and Digital Pathology laboratory under the Pathology Department at Moffitt Cancer Center.

Genomic DNA of biopsy tissue or PBMCs was isolated using the DNeasy Blood and Tissue Kit (Qiagen cat no. 69506) according to the manufacturer's recommendations. TCR repertoire analysis was performed using the Adaptive Biotechnologies ImmunoSEQ assay v3. The CDR3 locus of sorted T cells was amplified by ImmunoSEQ hs T-cell receptor beta (TCRβ) kit (ImmunoSEQ hsTCRβ kit v3; Adaptive Biotechnologies, cat no. ISK10101) and sequenced on the Illumina NextSeq 500 to a targeted depth of 2 million sequencing reads per sample. The data were analyzed using the Adaptive Biotechnologies ImmunoSEQ Analyzer software, to identify the V, D, and J genes, filter nonproductive sequences, and report and track T-cell clones. Productive clones and their frequencies were exported for further analysis. Shared clones between any tumors and any blood samples at both pretreatment and on-treatment time points were visualized by R package eulerr. The proportional frequencies of top 10 most abundant clonotypes in tumors were tracked across all samples at all time points and visualized by the trackClonetypes function in R package immunarch (56), where T cells with identical CDR3 amino acid sequence and V gene were considered as the same clonotype.

Tumor-bearing mice were randomly assigned to the different treatment groups. Experimenters were not blinded to the treatment groups. In therapeutic studies with different treatments, 5 to 10 mice were used to have 80% power to detect a difference in tumor size of 50% between treatment and control groups with 95% confidence (42). A two-tailed Student t test with Welch correction was used to determine the significance of differences between samples as in previous studies (57). GraphPad Prism 6 software (GraphPad Software Inc.) was used to determine significance: P < 0.05. *, <0.05; **, <0.01; ***, <0.001. Relative tumor size difference between treatment groups was determined using two-way ANOVA followed by the Tukey multiple comparison test. Survival differences were determined using the Kaplan–Meier estimator method. P value was calculated by the Mantel–Cox test. Heat map analysis of bulk RNA-seq data was performed through Morpheus software (Broad Institute).

RNA-seq data from 12 human DC samples are available in GEO (accession number GSE223342). scRNA-seq data from 4 mouse DC samples are available in GEO (accession number GSE223344). Human TCR sequences are available at ImmuneACCESS through the manuscript PMID. All other data generated in this study are available within the article and its supplementary data files or from the corresponding author upon reasonable request.

Toward the goal of developing new therapeutic modalities, we investigated the impact of individual and combined activation of CD40 and type 1 IFN pathways on mouse tumor cDCs. To this end, we utilized replication-deficient adenoviruses expressing a membrane-stable CD40L (MEM40; refs. 40, 41), mouse IFNβ (mIFNβ), and a control null virus (Ad-Null). In vitro studies showed that these viruses yielded robust expression of MEM40 and IFNβ (Supplementary Fig. S1). Mice bearing B16-F10 tumors were injected intratumorally on day 12 after s.c. tumor cell inoculation with 10e⁹ Ad-Null (NULL), 5×10e⁸ Ad-Null + 5×10e⁸ Ad-MEM40 (MEM40), 5×10e⁸ Ad-Null + 5×10e⁸ Ad-mIFNβ (mIFNβ) or 5×10e⁸ Ad-MEM40 + 5×10e⁸ Ad-mIFNβ (Combo) infectious units (Fig. 1A). We evaluated an early treatment timepoint of 3 days after a single injection to determine the potential direct effects of MEM40 and IFNβ on cDCs in B16-F10 tumors. Both IFNβ and MEM40 + IFNβ,  but not MEM40 alone, significantly reduced numbers of cDC1 (CD11c⁺MHC-II⁺CD103⁺CD11b⁻) and cDC2 (CD11c⁺MHC-II⁺CD103⁻CD11b⁺; Fig. 1B; Supplementary Figs. S2 and S3). The combined expression of MEM40 + IFNβ induced the largest increase in the level of expression of the costimulatory molecules CD80 and CD86 and the lymph node homing receptor CCR7 in cDC1 (Fig. 1B–E). A similar but less significant stimulatory effect was seen in cDC2 (Supplementary Fig. S3). MHC-I expression also exhibited a trend toward increased expression in cDC1 and cDC2 after MEM40 + IFNβ treatment (Fig. 1F; Supplementary Fig. S3). These results suggest that MEM40 + IFNβ expression in the TME triggers upregulation of CD80 and CD86 costimulatory molecules and the lymph node trafficking receptor CCR7.

To provide further insight into the treatment impact on DCs, we sorted MHC-II⁺CD11c⁺ cells from tumors for scRNA-seq. The vast majority of MHC-II⁺CD11c⁺ cells were of the DC lineage, including cDC1, cDC2, and plasmacytoid DCs (pDCs) along with few contaminating fibroblasts and melanocytes (Fig. 1G). Clusters were annotated by comparing differential genes with markers previously associated with specific cell types (Fig. 1H; Supplementary Fig. S4). Subclusters combined into major cDC types showed that the strongest effect was in COMBO-treated tumors, most notably a 6-fold expansion compared with Ad-Null-treated tumors of DCs with gene-expression profile of previously described for mregDCs (Fig. 1I and J; ref. 29). The top differentially expressed genes in these cells include Ccr7 and Ccl5 (Fig. 1H), as well as Il12b, Cxcl9, Cd40, Cd80, and Cd86 (Supplementary Table S2). mregDCs can be derived from cDC1 and cDC2 subsets and function as key drivers of T-cell activation in lymph nodes (29). Together with the flow cytometry results, these findings indicate that MEM40 + IFNβ activate cDCs to generate mature DCs with a high activation and migratory phenotype.

We next determined treatment impact on cDC numbers and phenotype in tumor-draining inguinal lymph nodes. The main cDC1 types in lymph nodes are migratory CD103⁺ and resident CD8α⁺ cDC1, besides the more numerous CD11b⁺ cDC2 subset. MEM40 + IFNβ led to the highest proportion of the migratory CD103⁺ cDC1, suggesting increased migration to lymph nodes (Supplementary Figs. S5 and S6A). In addition, combined MEM40 + IFNβ led to the highest expression of costimulatory markers CD80 and CD86 by cDC1 and cDC2 (Supplementary Fig. S6B).

To further investigate the treatment impact on DCs, we determined whether the reduction in numbers of cDC1 in tumors was due to their migration to the draining lymph nodes. As CCR7 deficiency impairs cDC1 lymph node trafficking (20), we compared the effect of Ad-Null control virus and COMBO viruses in WT and CCR7^−/− mice. Although COMBO treatment reduced the numbers of cDC1 and cDC2 in tumors in WT mice, no similar reduction was seen in CCR7^−/− mice (Fig. 2A). Furthermore, migratory CD103⁺ cDC1 were virtually absent in draining lymph node in CCR7^−/− mice, and this did not change after COMBO treatment (Fig. 2B). Lymph node–resident CD8α⁺ cDC1 numbers were also substantially lower in CCR7^−/− mice, whereas cDC2 numbers were reduced to a lesser extent, suggestive of just partial dependence on CCR7 (Fig. 2B). These results suggest that reduction in the number of intratumoral cDC1 after COMBO treatment is likely due to their trafficking to lymph nodes.

To directly test the impact on DC migration, we used Zs-Green expressing B16-F10 (ref. 20; Fig. 2C). This stable fluorophore antigen resists lysosomal degradation and allows detection of tumor antigen uptake in lymph node DCs (20). Although COMBO treatment triggered a reduction in the number of cDCs in tumors, a similar percentage of Zs-Green⁺ cDC1 and cDC2 was seen in tumors, suggesting that treatment did not increase antigen uptake (Fig. 2D and E). Furthermore, both Zs-Green⁺ and Zs-Green^– tumor cDC displayed an upregulation of activation marker expression after the COMBO treatment (Supplementary Fig. S7). Conversely in lymph nodes, we observed a 2- to 3-fold increase in the percentage of Zs-Green⁺ total DCs, migratory CD103⁺ cDC1, cDC2, as well as resident CD8α⁺ cDC1 after the COMBO treatment (Fig. 2F–I). Tumor antigen acquisition by CD8α⁺ cDC1 is consistent with previous studies showing antigen “hand-over” by migratory CD103⁺ cDC1s to this lymph node–resident population (20). Both Zs-Green⁺ and Zs-Green^– lymph node DCs had increased activation marker expression after COMBO treatment, but Zs-Green⁺ DCs tended to have higher expression after COMBO treatment (Supplementary Fig. S8). Collectively, these results demonstrate that the COMBO treatment triggers strong cDC trafficking and antigen transport to draining lymph nodes, concomitant with acquisition of a robust activation phenotype in all major lymph node cDC subsets.

FDA approved OVs (T-VEC) and several OVs in development rely on the expression of encoded GM-CSF to increase DC levels and TME immune activation (58–60). However, to our knowledge, previous studies have not investigated localized and systemic effects of in situ GM-CSF administration versus bona fide CD40/type 1 IFN DC activators. Using GM-CSF expressing adenovirus (Ad-GMCSF; Supplementary Fig. S9A), a significant increase in cDC2 but not cDC1 was observed after intratumor injection (Supplementary Fig. S9B). However, unlike the stimulatory effect of COMBO, GM-CSF did not induce tumor cDC activation and had no stimulatory effect on DC migration to LNs (Supplementary Fig. S9C–S9E). These results indicate that tumor CD40 and type 1 IFN pathway activation induce a superior DC response than GM-CSF.

We next determined the impact of MEM40 and IFNβ on the normal human donor monocyte-derived DC (Mo-DC) phenotype. We used an experimental design where human A549 lung cancer cells were first infected with Ad-Null or adenoviruses expressing human IFNβ, MEM40, or MEM40 + IFNβ for 24 hours followed by removal of virus-containing supernatant and coculture with Mo-DCs for 48 hours (Fig. 3A). Although both MEM40 and IFNβ individually increased CD80, CD86, CCR7, and MHC-I (HLA-B) expression, the highest increase in these markers was evident in MEM40 + IFNβ-treated human DCs (Fig. 3B). We also observed that MEM40 expression dramatically reduced CD40 levels, possibly through ligand-induced receptor internalization, but IFNβ allowed retention of CD40 expression (Fig. 3B), which could serve to sustain CD40 signaling. MEM40 alone, but not IFNβ, increased the expression of Th1-promoting cytokines IL12-p70 and TNFα (Fig. 3C and D). As seen with cell-surface markers, the combination of MEM40 + IFNβ also led to the highest expression of these cytokines. Consistent with the mouse studies, these results indicate that combined MEM40 + IFNβ expression induced the highest-level activation of human DCs.

To define the individual and combined effects of MEM40 and IFNβ on human DC gene expression, we used the experimental design in Fig. 3A followed by the sorting of MHC-II⁺CD11c⁺ cells for bulk RNA-seq. DCs were derived from three independent healthy donors. Both MEM40 and IFNβ induced a significant change in the expression of a substantial subset of genes (Supplementary Table S3). Within the top ∼80 named genes induced by the COMBO treatment, we found that MEM40 and IFNβ enhanced the expression of distinct target genes (Fig. 3E). We next determined impact on genes associated with DC functionality. Genes engaged in the MHC-I antigen presentation pathway, HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2, were induced more strongly by IFNβ than by MEM40 (Fig. 3F). On the other hand, key DC activation–associated genes CD80, CD86, CD40, CCR7, IL12A, IL12B, and IL23A were induced by both MEM40 and IFNβ, but more strongly by MEM40 (Fig. 3F). Hallmark Pathway analysis revealed an increase in both NF-κB (TNFA-NF-κB) and IFN pathways by MEM40 and IFNβ (Fig. 3G); however, as expected, analysis of COMBO versus individual treatment demonstrated that MEM40 contributed to NF-κB activation, whereas IFNβ contributed to IFN pathway activation in the COMBO samples (Supplementary Fig. S10). Collectively, these results indicated that target gene expression and pathway activation in the COMBO treatment was due to individual effects of MEM40 and IFNβ, whereas for key DC functionality genes both MEM40 and IFNβ could independently induce expression.

We next determined whether cDC activation by MEM40 and IFNβ affected the antitumor T-cell response. For these studies, we used a different timeline than the above 3-day treatments to allow sufficient time for the development of an initial T-cell response. As multiple intralesional injections are not feasible for most cancer patients, we determined treatment efficacy after 2 virus injections administered 4 days apart. The analyses below were performed 7 days after the second virus injection (i.e., 23 days after tumor cell injection and 11 days after treatment initiation). Tumor growth until day 23 showed that whereas both MEM40 and IFNβ reduced tumor growth compared with Ad-Null, the most significant reduction in tumor growth was observed with the COMBO treatment (Fig. 4A). No effect on tumor growth was observed with Ad-Null compared with untreated controls. Furthermore, the COMBO treatment resulted in significantly reduced tumor growth compared with the individual MEM40 and IFNβ treatments. Day 23 tumors were then used to characterize the intratumoral lymphoid and myeloid cell types by flow cytometry. Compared with Ad-Null–injected tumors, CD8⁺ T cells were significantly increased by ∼3-fold in COMBO-injected tumors (Fig. 4B). CD4⁺ T cells, B cells, macrophages, and neutrophils were not significantly affected by treatment versus Null control arm (Supplementary Fig. S11). Notably, cDC1, but not cDC2, showed a significant reduction in numbers after MEM40, IFNβ, and COMBO treatment in comparison with Ad-Null treatment (Fig. 4C and D). This was different than at the day 3 timepoint (Fig. 1B; Supplementary Fig. S3), as cDC1 numbers were also reduced by MEM40, suggesting different kinetics of impact of these transgenes with IFNβ dominating at early time points. A possible explanation is that IFNβ, being a diffusible mediator of DC activation, may act more rapidly within the TME, whereas CD40 activation requires cell-to-cell interactions between adenovirus-infected MEM40-expressing cells and tumor DCs, which is likely to be a temporally slower process. Within the intratumoral CD8⁺ T-cell population, we noted an increase in TCF1⁺ CD8⁺ T cells as well as granzyme B⁺ CD8⁺ T cells in murine studies, indicating that the COMBO treatment enhanced both stemlike and effector T cells (Supplementary Fig. S11G and S11H). To determine whether intralesional virus injections led to a systemic increase in tumor-reactive CD8⁺ T cells, we performed IFNγ ELISPOT assays with splenic CD8⁺ T cells. Both Ad-MEM40 and Ad-mIFNβ significantly increased the number of tumor antigen–reactive T cells compared with Ad-Null, but the highest increase was observed after COMBO treatment (Fig. 4E; also see Supplementary Fig. S12A for additional assay information), consistent with the delay in tumor growth. These differences were observed in both ConA-normalized (Fig. 4E) and nonnormalized results (Supplementary Fig. S12A). Notably, consistent with the lack of a localized DC stimulatory effect of GM-CSF, Ad-GM-CSF administration did not activate a systemic antitumor T-cell response (Supplementary Fig. S9F).

To confirm antitumor activity in a different tumor model, we used the transplantable 344SQ model derived from a KRAS-G12D/TP53 (KP) mutant lung tumor (61). 344SQ tumor inoculation at the s.c. site results in metastasis to multiple sites, including lungs (61), which can be controlled by high-level T-cell activation (62). As with B16-F10 tumors, 344SQ tumors were injected IT on D12 and 16 with viruses following which we monitored s.c. tumor growth and numbers of lung metastases. Similar to B16-F10, we observed significant growth reduction after Ad-MEM40 and Ad-mIFNβ injection with the most significant reduction after the COMBO treatment (Fig. 4F). The COMBO treatment also resulted in significantly reduced tumor growth compared with the individual MEM40 and IFNβ treatments. Notably, these treatments significantly reduced lung metastases with the greatest reduction after the COMBO treatment (Fig. 4G and H). Furthermore, ELISPOT assays with splenic CD8⁺ T cells showed a significant increase in tumor antigen reactivity with Ad-MEM40 and Ad-mIFNβ and the highest elevation in the COMBO treatment (Fig. 4I). Together with the above findings, these results suggest that cDC activation by MEM40 and IFNβ empowers systemic T-cell responses to control tumor growth.

Although both CD40 and type 1 IFNs are crucial for the generation of antitumor T-cell responses, it is not clear whether they participate in the same or independent pathways to drive CD8⁺ T-cell cross-priming. To investigate this, we determined the impact of MEM40 and IFNβ on T-cell priming in mice lacking either CD40 or the IFNα/β receptor IFNAR1. Consistent with previous studies, no major developmental abnormalities in DC subsets in tumor and lymph nodes were noted in CD40^−/− and IFNAR1^−/− mice (19, 37). Although the MEM40-induced systemic T-cell response was eliminated in CD40^−/− mice, it was only slightly reduced in IFNAR1^−/− mice (Fig. 4J). Conversely, T-cell priming by IFNβ was eliminated in IFNAR1^−/− mice but was unaffected in CD40^−/− mice (Fig. 4J). Furthermore, T-cell priming by the COMBO treatment was only partially reduced in both the CD40^−/− and the IFNAR1^−/− mice (Fig. 4K). These findings suggest that CD40L and IFNβ stimulate T-cell priming independently of each other, explaining why the combination of these 2 agents leads to the highest level of T-cell activation. A key target of both CD40 and type I IFNs is cDC1 (35, 37). Using BATF3^−/− mice, which lack cDC1, we found that T-cell priming was eradicated after MEM40 and IFNβ expression, indicating a crucial role for this cDC subtype (Fig. 4L). After the COMBO treatment, small residual priming activity was observed, suggesting that other DC or APC subsets may play a role in T-cell priming (Fig. 4M).

Although our studies indicate that tumor activation of CD40 and type 1 IFN pathways promotes generation of tumor-reactive T cells, immune checkpoints such as PD-1 and CTLA-4 could dampen this response in both tumor and periphery. To test this, mice bearing B16-F10 primary tumors subjected to injection with viruses and noninjected contralateral (abscopal) tumors were treated systemically (intraperitoneal) with murine anti–PD-1/CTLA4 (Fig. 5A). Anti–PD-1/CTLA4 combination was only moderately effective against established B16-F10 tumors (Fig. 5B). Although the COMBO virus treatment was more effective than anti–PD-1/CTLA4, COMBO + anti–PD-1/CTLA4 led to the most significant reduction in tumor growth (Fig. 5B). Notably, COMBO + anti–PD-1/CTLA4 also led to the most significant reduction in contralateral tumor growth (Fig. 5C) and resulted in the most significant increase in mouse overall survival (Fig. 5D). A similar strategy was used in the 344SQ model. Treatment with COMBO + anti–PD-1/CTLA4 led to the most significant decrease in s.c. tumor growth whereas anti–PD-1/CTLA4 was minimally effective (Fig. 5E). Notably, lungs of mice revealed an essentially complete absence of metastases after COMBO + anti–PD-1/CTLA4 treatment (Fig. 5F). These results demonstrate that in two tumor models with minimal sensitivity to ICI, pairing COMBO with ICI treatment led to enhancement of efficacy against distant lesions.

The replication-deficient adenoviruses used above do not cause virus-mediated lysis of either mouse or human tumor cells. Furthermore, unlike human cells, mouse cells do not efficiently support wild-type or oncolytic adenovirus replication (63). OVs are designed for replication-induced oncolysis of cancer cells and tumor antigen release (7). We hypothesized that OV-induced tumor antigen release could synergize with MEM40 + IFNβ in amplifying antitumor T-cell responses. Toward the goal of clinical testing of the impact of MEM40 + IFNβ in cancer patients, we utilized a type 5 adenovirus backbone with an E1A Δ24 deletion that prevents Rb inactivation to allow the replication and lysis of cancer cells but not normal cells (44). This oncolytic adenovirus backbone was engineered to express MEM40 and human IFNβ and was designated as MEM-288 (Fig. 6A). As control, we used E1A Δ24 adenovirus that expresses GFP (Ad-GFP).

Infection of mouse B16-F10, 344SQ, and human A549 lung cancer cell line with MEM-288, but not Ad-GFP, led to high-level secretion of IFNβ into the culture supernatant (Fig. 6B). In addition, infection of these cell lines with MEM-288 induced high MEM40 expression whereas infection with Ad-GFP led to high GFP expression (Fig. 6C). Notably, MEM-288 injection in freshly resected human NSCLC tumors induced IFNβ expression and MEM40 expression, especially in the CD45^– population enriched in cancer cells (Fig. 6D and E). Neither Ad-GFP nor MEM-288 induced lysis of mouse B16-F10 and 344SQ (Supplementary Fig. S13). Adenovirus replication and ensuing cell lysis are relatively resistant to type 1 IFN (43). In fact, compared with Ad-GFP, we observed stronger oncolysis by MEM-288 in A549 cells in vitro (Fig. 6F) as well as in A549 tumors in vivo (Fig. 6G).

We next determined the in vivo activity of MEM-288 in the B16-F10 and 344SQ tumor models. Because MEM-288 expresses human IFNβ, we first determined the ability of human IFNβ to induce antitumor T-cell responses using the B16-F10 model. Previous studies have shown that human IFNβ has substantially lower activity in mice but that it can induce signaling in the mouse immune compartment (64). Unlike mouse IFNβ, human IFNβ expressed by a replication-deficient adenovirus did not enhance a T-cell response (Fig. 6H). However, the combination of human IFNβ and MEM40 significantly enhanced the T-cell response compared with MEM40 alone (Fig. 6H), suggesting that residual human IFNβ activity may be sufficient to enhance CD40 signaling response. Consistently, in comparison with Ad-GFP, MEM-288 induced a greater antitumor response as well as T-cell activation in B16-F10 (Fig. 6I and J). Because these viruses are not oncolytic in mouse cells, the observed antitumor responses are likely due to the immune stimulatory effect of the products encoded by the transgenes. To confirm replication-deficient virus findings with MEM-288, we determined the effect of tumor injection of MEM-288 on the growth of injected and noninjected contralateral B16-F10 tumors and synergy with ICI treatment. The combination of anti-CTLA4 + anti–PD-1 and MEM-288 significantly reduced the growth of both injected and contralateral tumors compared with individual treatments and significantly increased mouse survival (Fig. 6K–M). In addition, as observed with replication-deficient viruses in the 344SQ model, MEM-288 led to a significant decrease in injected tumor growth and lung metastasis (Fig. 6N and O). CD8⁺ IHC further revealed significantly higher TIL density in lung metastases of MEM-288–treated mice (Fig. 6P). Together, these results demonstrate the single-agent antitumor activity of MEM-288 that was enhanced when combined with ICIs.

We initiated a first-in-human phase IA dose-escalation study of MEM-288 in multiple solid tumors, including melanoma and NSCLC, with the primary objective to determine the safety and MTD (NCT05076760). Biopsy tissues from the first two enrolled patients, both NSCLC, were used to determine MEM-288 impact on the TME. Three tumor biopsy passes were used to sample the tumor prior to MEM-288 injection (pretreatment biopsy) followed by 3 passes 21 days after the first biopsy (on-treatment biopsy), which were used for mIF (Fig. 7A–D). mIF was conducted separately on each pass after pathologic confirmation of integrity of FFPE tissue H&E slides. Palpable or accessible tumors for imaging-guided injections were used to administer MEM-288 without preference for the orthotopic tumor site. We detected a significant reduction in the percentage and density of PCK⁺ malignant cells (Fig. 7A, B, E, and F; Supplementary Fig. S14). Patient 2 had a pronounced loss of viable tumor tissue after treatment resulting in higher variability in biopsy passes from different tumor areas. Statistically significant findings were therefore obtained only from patient 1 biopsy studies. These results mirrored clinical measurements of injected tumors that showed significant radiographic tumor shrinkage of −53% (patient 1) and −31% (patient 2) on day 22 after 1 MEM-288 injection. In addition, we saw a significant increase in the percentage of CD3⁺ T cells but not CD68⁺ macrophages (Fig. 7A, B, G, and H; Supplementary Fig. S14). We also saw a significant increase in the percentage of TCF1⁺ stemlike CD8⁺ T cells (Fig. 7C, D, I and J; and Supplementary Fig. S14). We surmise that MEM-288 can induce substantial remodeling of the TME, most notably evident by increased numbers of T cells and especially stem-like T-cell subsets that are known to be associated with disease control (65–72).

Patient 1 biopsies and blood draws were used for TCRβ sequencing. The combined T clonotypes in all 3 tumor passes were increased from 1,497 in pretreatment to 3,918 in on-treatment biopsies (Fig. 7K and L), indicating that MEM-288 likely increased the number of tumor antigen–specific T-cell clonotypes. In addition, shared clonotypes between tumor and blood increased from 922 to 1,691 (Fig. 7K and L). Tracking the top-10 tumor clonotypes (top-10 highest total abundance in all pre- and on-treatment biopsies) in blood revealed a substantial increase that peaked on day 22 after MEM-288 treatment initiation (Fig. 7M). These results suggest that MEM-288 increases T-cell numbers and clonotype diversity leading to an increase in potential tumor-reactive T cells in the peripheral blood.

In this study, we show that combined activation of CD40 and type 1 IFN pathways in the TME can strongly stimulate systemic antitumor CD8⁺ T-cell responses through robust activation of DCs. When combined with ICI, MEM40 + IFNβ induced a potent abscopal tumor response indicating that this strategy can be effective against the disseminated disease. For clinical translation of this approach, we developed an oncolytic virus (MEM-288) that expresses MEM40 and human IFNβ. Our clinical findings indicate that intratumoral MEM-288 administration in NSCLC patients was associated with loss of cancer cells, and increase in overall T cells and an increase in CD8⁺ stem-like T cells, which are known to be associated with clinical benefit from ICI treatment and adoptive cell therapy (65–71). Furthermore, MEM-288 administration increased the systemic presence of tumor T-cell clonotypes. These studies provide a strong rationale for the further development of MEM-288 as a single-agent treatment and in combination with ICIs.

Our preclinical results indicate that the combination of MEM40 and IFNβ led to the highest levels of cDC1 and cDC2 activation and induction of systemic T-cell immunity. A striking effect of this treatment was the reduction in the number of tumor DCs and increase in trafficking to draining lymph nodes, which we believe is crucial for the mechanism of action of this combination. Unlike the strong DC stimulatory effect of CD40 and type 1 IFN pathways, we found that GM-CSF expression did not increase DC activation, LN trafficking and had no effect on the systemic T-cell responses. As GM-CSF is widely used in OVs, our results suggest that OVs with strong activators of DCs are likely to be more therapeutically effective than those encoding GM-CSF. Furthermore, our results also demonstrate that strong activation of either CD40 or type 1 IFN can bypass the need for the other in T-cell activation. Thus, IFNβ-induced T-cell priming was impaired in IFNAR1^−/− but undiminished in CD40^−/− mice. Conversely, MEM40 retained substantial T-cell priming activity in IFNAR1^−/− mice but was abrogated in CD40^−/− mice. Although roles of CD40 and type 1 IFN pathways in cDC activation are established, their ability to act in an independent manner to stimulate T-cell priming helps explain why their dual activation induces the most potent cDC and T-cell activation. It is likely that these two activators provide specific inputs to DCs, e.g., CD40 may specifically control DC survival (73), which remain to be fully defined and likely impact T-cell responses in different ways. Our studies with human Mo-DC also showed that both MEM40 and IFNβ can upregulate key DC genes that regulate MHC-I antigen presentation and costimulation. However, the highest cell-surface expression of MHC-I and costimulation markers in human DCs was observed with the MEM40 and IFNβ combination, whereas in mice the combination also led to the highest levels of mregDC. Although both MEM40 and IFNβ enhanced antigen presentation and costimulation, they were also capable of inducing expression of unique transcriptomes that will likely affect DC functionality and the TME through mechanisms that remain to be defined. Analogous to the use of multiple therapeutics to maximize T-cell functionality (e.g., anti–PD-1 combined with anti-CTLA4 or LAG3), our results indicate that multimodal DC activation by MEM40 and IFNβ has strong potential to elicit robust antitumor T-cell immunity in cancer patients.

We believe our virus-based approach has significant advantages over strategies currently being tested to enhance CD40 and type 1 IFN signaling. Although recent studies in pancreatic cancer have shown a promising response rate of a combination of a systemically delivered CD40 agonistic antibody with ICI and chemotherapy, this was also associated with significant toxicity (74). Indeed, toxicity associated with systemic CD40 agonists has hindered approval of this otherwise promising therapeutic modality and consequently, approaches that induce more targeted CD40 activation are being developed (35, 75). We believe that intratumoral administration, combined with the unique stable cell-surface expression of recombinant CD40L (MEM40) in MEM-288 will limit systemic CD40 activation and thus provide patient benefit without significant toxicity. Indeed, in MEM-288 patients treated to date, we have not observed any dose-limiting toxicity. Strategies to stimulate innate immunity by type 1 IFN expression have also been clinically tested through i.t. injection of STING and TLR9 agonists (5, 6). TLR9 agonists have shown a promising response rate in ICI-refractory melanoma, and this was associated with induction of type I IFN expression (5, 76). However, the relatively labile nature of these agents can require repeat intratumoral injections (e.g., a median of 8 injections; ref. 76), making their use in cancer patients who do not have superficial lesions more challenging. We believe that IFNβ expression by OVs can provide a superior approach for sustained activation of type 1 IFN signaling, which may therefore require fewer administrations. Sustained and combined MEM40 + IFNβ expression by MEM-288 should therefore provide a superior signal for stimulating antitumor immunity than use of individual activators of these pathways currently being tested. To our knowledge, therapeutic approaches that combine activation of these two key pathways have not been tested in cancer patients.

We recently initiated a first-in-human phase IA dose-escalation study of MEM-288 in multiple solid tumors, including NSCLC. Studies of patient biopsies showed increase in T cells after treatment with MEM-288, replicating our murine studies. Notably, an increase in TCF1⁺ stem-like CD8⁺ T cells after MEM-288 administration may be especially significant as this population is strongly associated with immunotherapy benefit (65–71). Previous studies have shown that the presence of shared T-cell clonotypes in tumor and blood is associated with ICI benefit (77–84). In the first MEM-288-treated patient, we performed TCR sequencing on biopsy tissue and PBMCs. We found that after a single MEM-288 injection, shared clonotypes between tumor and blood were substantially increased and was especially notable when the top-10 tumor clonotypes were tracked in peripheral blood. Both the TME mIF and T-cell clonotype findings, however, need to be extended to additional NSCLC patients as well as patients with other tumor types before firm conclusions can be drawn on MEM-288 treatment impact on local and systemic responses. Nonetheless, if these changes after MEM-288 administration are confirmed, it would indicate that combining MEM-288 and ICIs has the potential to increase patient benefit compared with ICIs alone.

B. Ruffell reports personal fees from Omios Biologics, LLC, and Roche Farma, S.A., outside the submitted work. B.A. Perez reports grants and personal fees from Bristol Myers Squibb, personal fees from AstraZeneca, and G1 Therapeutics outside the submitted work. S.J. Antonia reports personal fees from Memgen during the conduct of the study; personal fees from Achilles, Shoreline, RAPT, Glympse, Xilis, Immutep, and Guardian outside the submitted work; in addition, S.J. Antonia has a patent 20210268090 issued, licensed, and with royalties paid from Memgen. A.N. Saltos reports grants from Memgen during the conduct of the study; grants and personal fees from Daiichi Sankyo, Eli Lilly, personal fees from Zymeworks, grants from Novartis, Mersana, Genmab, AstraZeneca, Genentech, Turning Point Therapeutics, and BioAtla outside the submitted work. M.J. Cantwell reports personal fees from Memgen, Inc. during the conduct of the study; in addition, M.J. Cantwell has a patent for oncolytic virus or antigen-presenting cell-mediated cancer therapy using type I interferon and CD40-ligand pending and licensed to Memgen, Inc. A.A. Beg reports grants and personal fees from Memgen during the conduct of the study; grants from Bristol Myers Squibb outside the submitted work; in addition, A.A. Beg has a patent 20210268090 pending, licensed, and with royalties paid from Memgen and a patent 20210128653 pending, licensed, and with royalties paid from Memgen; and Amer Beg is a Memgen Scientific Advisory Board member. No disclosures were reported by the other authors.

H. Zheng: Data curation, formal analysis, visualization, methodology, writing–original draft, writing–review and editing. X. Yu: Conceptualization, data curation, formal analysis, visualization, writing–original draft, writing–review and editing. M.L. Ibrahim: Methodology, writing–review and editing. D. Foresman: Methodology, writing–review and editing. M. Xie: Methodology, writing–review and editing. J.O. Johnson: Data curation, formal analysis, methodology, writing–original draft, writing–review and editing. T.A. Boyle: Formal analysis, visualization, methodology, writing–review and editing. B. Ruffell: Conceptualization, formal analysis, writing–original draft, writing–review and editing. B.A. Perez: Supervision, visualization, methodology, writing–review and editing. S.J. Antonia: Conceptualization, formal analysis, writing–review and editing. N. Ready: Conceptualization, methodology, writing–review and editing. A.N. Saltos: Conceptualization, methodology, writing–review and editing. M.J. Cantwell: Conceptualization, formal analysis, funding acquisition, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. A.A. Beg: Conceptualization, formal analysis, supervision, funding acquisition, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing.

These studies were supported by funds to A.A. Beg from Miles for Moffitt, Moffitt Foundation, Moffitt Lung Cancer Center of Excellence, Memgen Inc and NIH 5P50CA168536. We wish to thank Richard Staley and Jayne Goetze for their generous support for lung cancer research at Moffitt. We would like to acknowledge the Molecular Genomics, Cancer Informatics, Tissue Core, Analytic Microscopy, Advanced Analytical and Digital Pathology and Flow Cytometry shared facilities at Moffitt Cancer Center, an NCI-designated Comprehensive Cancer Center supported by NIH P30-CA076292. We thank Moffitt colleagues Drs. Jose Conejo Garcia-Sastre, Daniel Abate-Daga, and Shari Pilon-Thomas for helpful discussions on this work. We also thank Zhihua Chen for help with RNA-sequencing analysis, Chase D. Powell for help with human lung tumor studies, Wenjie Dai and Margaret Barlow for technical assistance, and Jonathan Nguyen and Carlos Moran Segura for assistance with mIF studies.

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.