Intratumoral radiation dose heterogeneity augments antitumor immunity in mice and primes responses to checkpoint blockade

Justin C Jagodinsky; Jessica M Vera; Won Jong Jin; Amanda G Shea; Paul A Clark; Raghava N Sriramaneni; Thomas C Havighurst; Ishan Chakravarthy; Raad H Allawi; KyungMann Kim; Paul M Harari; Paul M Sondel; Michael A Newton; Marka R Crittenden; Michael J Gough; Jessica R Miller; Irene M Ong; Zachary S Morris

doi:10.1126/scitranslmed.adk0642

. Author manuscript; available in PMC: 2024 Oct 30.

Published in final edited form as: Sci Transl Med. 2024 Sep 18;16(765):eadk0642. doi: 10.1126/scitranslmed.adk0642

Intratumoral radiation dose heterogeneity augments antitumor immunity in mice and primes responses to checkpoint blockade

Justin C Jagodinsky ^1,², Jessica M Vera ^3,⁴, Won Jong Jin ¹, Amanda G Shea ¹, Paul A Clark ¹, Raghava N Sriramaneni ¹, Thomas C Havighurst ³, Ishan Chakravarthy ¹, Raad H Allawi ¹, KyungMann Kim ³, Paul M Harari ¹, Paul M Sondel ^1,⁵, Michael A Newton ³, Marka R Crittenden ^6,⁷, Michael J Gough ⁶, Jessica R Miller ¹, Irene M Ong ^3,⁸, Zachary S Morris ^1,^*

¹Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.

²Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.

³Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA.

⁴Sage Bionetworks, 2901 Third Ave. Suite 330, Seattle, WA 98121, USA.

⁵Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA.

⁶Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Portland Medical Center, NE Glisan St., Portland, OR 97213, USA.

⁷Oregon Clinic, Portland, OR 97232, USA.

⁸Department of Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA.

Author contributions:

Article conceptualization was performed by J.C.J. and Z.S.M., and methodology was developed by J.C.J., W.J.J., J.M.V., M.R.C., and M.J.G. Investigation was performed by J.C.J., W.J.J., P.A.C., R.N.S., I.C., R.H.A., A.G.S., J.R.M., M.R.C., and M.J.G. Data generated in figures were acquired as follows: Fig. 1 experiments and analysis were performed by J.C.J., W.J.J., A.G.S., P.A.C., R.N.S., I.C., R.H.A., and J.R.M. Figure 2 experiments were performed by J.C.J., W.J.J., A.G.S., P.A.C., R.N.S., I.C., R.H.A., and J.R.M. with data analysis by J.M.V. Figure 3A experiments were performed by J.C.J., W.J.J., A.G.S., P.A.C., R.N.S., I.C., R.H.A., and J.R.M. with data analysis by J.M.V. Figure 3 (J and K) experiments and analysis were performed by M.R.C. and M.J.G. Figure 4 experiments and analysis were performed by J.C.J., J.M.V., I.M.O., and M.A.N. Figure 5 experiments and analysis were performed by J.C.J., J.M.V., I.M.O., and M.A.N. Figure 6 experiments and analysis were performed by J.C.J., W.J.J., A.G.S., P.A.C., R.N.S., I.C., R.H.A., and J.R.M. Figure 7 experiments and analysis were performed by J.C.J., J.M.V., W.J.J., P.A.C., and I.M.O. Visualization was performed by J.C.J., W.J.J., J.M.V., and M.J.G. Statistical analysis was performed by J.C.J., J.M.V., I.M.O., M.A.N., T.C.H., K.K., and M.J.G. Z.S.M. acquired funding, and supervision was provided by Z.S.M., P.M.S., K.K., P.M.H., M.A.N., J.R.M., and I.M.O. J.C.J. wrote the original draft, with reviewing and editing provided by J.C.J., P.M.H., P.M.S., and Z.S.M.

Corresponding author. zmorris@humonc.wisc.edu

PMCID: PMC11522033 NIHMSID: NIHMS2024148 PMID: 39292804

Abstract

Radiation therapy (RT) activates multiple immunologic effects in the tumor microenvironment (TME), with diverse dose-response relationships observed. We hypothesized that, in contrast with homogeneous RT, a heterogeneous RT dose would simultaneously optimize activation of multiple immunogenic effects in a single TME, resulting in a more effective antitumor immune response. Using high-dose-rate brachytherapy, we treated mice bearing syngeneic tumors with a single fraction of heterogeneous RT at a dose ranging from 2 to 30 gray. When combined with dual immune checkpoint inhibition in murine models, heterogeneous RT generated more potent antitumor responses in distant, nonirradiated tumors compared with any homogeneous dose. The antitumor effect after heterogeneous RT required CD4 and CD8 T cells and low-dose RT to a portion of the tumor. At the 3-day post-RT time point, dose heterogeneity imprinted the targeted TME with spatial differences in immune-related gene expression, antigen presentation, and susceptibility of tumor cells to immune-mediated destruction. At a later 10-day post-RT time point, high-,moderate-, or low-RT-dose regions demonstrated distinct infiltrating immune cell populations. This was associated with an increase in the expression of effector-associated cytokines in circulating CD8 T cells. Consistent with enhanced adaptive immune priming, heterogeneous RT promoted clonal expansion of effector CD8 T cells. These findings illuminate the breadth of dose-dependent effects of RT on the TME and the capacity of heterogeneous RT to promote antitumor immunity when combined with immune checkpoint inhibitors.

INTRODUCTION

Despite remarkable clinical successes, the majority of patients with cancer who are treated with immunotherapy will ultimately succumb to their disease (1). Immune checkpoint inhibitors [ICIs; e.g., anti– programmed death ligand 1 (PD-L1) and anti–cytotoxic T lymphocyte– associated protein 4 (CTLA-4)] modulate immune tolerance by blocking specific inhibitory receptor-ligand interactions on the surface of immune cells, thereby overcoming T cell inhibition or exhaustion (2). In patients with preexisting but exhausted antitumor immune responses, ICIs can restore antitumor activity, sometimes resulting in regression even in metastatic disease (3, 4). Although ICIs can benefit a variety of cancers (5), this benefit is limited in poorly immunogenic tumors with low amounts of T cell infiltration and few mutation-created neoantigens (3, 4). In addition, patients with immunogenic tumors that initially respond to ICIs often exhibit disease progression over time (6).

“In situ” tumor vaccination is an approach to expand the response to ICIs by converting a patient’s own tumor into a source of tumor-specific antigens (7). This approach takes advantage of “private antigens” induced by random, patient-specific mutations (8). By modulating tumor immune tolerance and functional immunogenicity at a targeted site, delivery of radiation therapy (RT) to the tumor microenvironment (TME) may serve as a method of in situ tumor vaccination. The dose of RT influences its immunologic effects in the TME (9–15). A high dose induces immunogenic tumor cell death and release of tumor-specific antigens (16, 17). A moderate dose drives induction of a cyclic guanosine monophosphate–adenosine monophosphate synthase/stimulator of interferon genes (STING)–dependent type I interferon (IFN) response and expression of immune susceptibility markers on tumor cells surviving RT (11, 18). A low dose promotes inflammatory cytokine release (19) and damage-associated molecular patterns, altering endothelial cell adhesion receptor expression and immune cell trafficking and activation (20).

To study the dose-dependent effects of RT and to simultaneously engage these diverse mechanisms in a single TME, we delivered a spectrum of doses, from low to high, using a high-dose-rate (HDR) brachytherapy (BT) ¹⁹²Ir source directly placed within the tumor using a catheter. BT is part of the standard treatment of prostate, cervical, endometrial, head and neck, and breast cancers (21). To date, clinical studies combining RT and ICIs have used external beam RT (EBRT) delivering a homogeneous dose, and a clinical benefit of this combination has not been clearly established (22). We hypothesized that a heterogeneous dose may confer immunogenic advantages over a homogeneous dose when priming an in situ vaccine effect.

RESULTS

RT dose heterogeneity enhances antitumor immunity

To induce RT dose heterogeneity within the TME, we used an HDR BT single dwell position treatment plan with a catheter placed within the distal/caudal edge of the tumor (Fig. 1A). We randomized mice bearing B78 melanoma tumors on the right flank (approximately 200 mm³, designated primary tumors) and left shoulder (approximately 150 mm³, designated secondary tumors) to catheter insertion alone (sham), BT alone [2 gray (Gy) prescribed to the proximal/cranial tumor edge; Fig. 1, B to D], ICI alone, EBRT (2 or 8 Gy) + ICI, or BT + ICI. Tumor ulceration precluded use of EBRT 20 Gy in this model. BT and EBRT were delivered only to the primary tumor. Mice receiving ICI were intraperitoneally injected with 200 μg of anti–PD-L1 and anti–CTLA-4 antibodies on days 3, 6, and 9 after RT. Compared to sham treatment, neither BT nor ICI alone reduced primary (Fig. 1E) or secondary (Fig. 1F) tumor growth. EBRT at 2 Gy + ICI also did not reduce primary or secondary tumor growth compared to controls, whereas 8 Gy EBRT + ICI reduced primary tumor growth (P = 0.011) but not secondary. Mice treated with BT + ICI demonstrated reduced primary tumor growth compared with 2 Gy EBRT + ICI (P = 0.024) and comparable reduction in tumor growth to 8 Gy EBRT + ICI (Fig. 1E). In agreement with our hypothesis, BT + ICI improved antitumor response compared with 8 Gy EBRT + ICI (P = 0.026) and 2 Gy EBRT + ICI (P = 0.012) at the secondary untreated tumor (Fig. 1F). Neither BT nor ICI alone improved survival compared to sham treatment (Fig. 1G). The 2 Gy EBRT + ICI improved survival compared with sham (P = 0.025) but not compared to ICI alone. Although overall survival was improved compared with sham and BT alone in the EBRT 8-Gy treatment (P < 0.05), the greatest increase in overall survival was seen in the BT + ICI treatment group (Fig. 1G). BT + ICI significantly (P < 0.05) improved survival over the sham, single-treatment controls and both 2 Gy EBRT + ICI and 8 Gy EBRT + ICI. Among mice treated with BT + ICI, 63% experienced complete responses compared with 0% in 2 Gy EBRT–treated mice and 13% in 8 Gy EBRT–treated mice (Fig. 1H).

Fig. 1. — (A and B) Brachytherapy catheter placement location in distal/caudal tumor edge (A) and treatment plan (B); 2 Gy was delivered to proximal/cranial tumor edge. (C) Dose-volume histogram (DVH) for BT 2-Gy treatment plan showing coverage of tumor volume by dose. (D) Treatment schematic for (E) to (H). Mice with right flank and left shoulder B78 tumors were treated with sham control, BT, ICI alone, EBRT (2 or 8 Gy) + dual ICI (anti–CTLA-4 and anti–PD-L1, 200 μg on days 3, 6, and 9), or BT + ICI. (E and F) Tumor size was measured for primary (E) and secondary (F) tumors. (G and H) Survival was measured for all mice (G), and the number of complete responders (H) was recorded. (I) Treatment schematic for MyC-CaP tumor–bearing mice, including additional injection of 1 × 10⁶ MyC-CaP cells on the left flank immediately after radiation. Secondary engraftment rate was calculated at D14. (J to M) Tumor response by group (J), secondary engraftment frequency (K), animal survival (L), and complete response rate (M) are shown. (N and O) Heterogeneous EBRT studies were performed in B78-bearing mice. SBRT arc beam treatment plan, with approximately 20 Gy delivered to distal edge of tumor and approximately 2 Gy delivered to proximal edge of tumor to mimic BT treatment plan, is shown (N) with tumor response by group (O). (P to R) BT dose escalation studies are shown for mice with a single MyC-CaP flank tumor. Tumor response by group (P), animal survival (Q), and complete response rates (R) are shown. N = 8 to 16 mice per group. Significance was determined by linear mixed effects regression analysis [(E), (F), (J), (O), and (P)] and two-way ANOVA with Tukey multiple comparisons testing (K) for tumor growth or by Kaplan-Meier with log-rank testing for survival analysis [(G), (L), and (Q)]. Significant differences, where P < 0.05, are demarcated by asterisks, with the color of the asterisk representing the group from which the listed group differs.

To determine the generalizability of our treatment, we tested antitumor potential in a separate model of prostate cancer. We randomized mice bearing MyC-CaP tumors to sham insertion, BT (2 Gy prescribed to the periphery of the tumor, 5-mm depth), ICI alone, EBRT (2, 8, or 20 Gy) + ICI, or BT + ICI. One day before BT, mice were engrafted with a secondary tumor to model occult metastatic disease characteristic of high-risk prostate cancer (Fig. 1I). Neither BT alone, nor ICI alone, nor EBRT 2 Gy + ICI slowed tumor growth (Fig. 1J), reduced secondary engraftment (Fig. 1K), or improved survival (Fig. 1L) compared to sham. EBRT at 8 Gy and 20 Gy + ICI and BT + ICI all reduced tumor growth compared with sham and ICI-only controls (Fig. 1J), but only BT + ICI reduced secondary engraftment (Fig. 1K). Overall survival between BT + ICI and EBRT 8 + ICI was comparable, reflecting that cause of death in either case was largely because of primary tumor growth (Fig. 1L). However, complete response rate increased after BT + ICI treatment (Fig. 1M), consistent with an effective systemic immune response. Together, these results suggest that BT may elicit a more robust in situ vaccine response compared with any homogeneous dose of EBRT and may prime a more effective adaptive antitumor immune response with improved survival and complete response rate when combined with systemic ICI.

We sought to determine whether heterogeneous RT dose delivery through EBRT could recapitulate the enhanced in situ vaccine effect observed with BT. To do this, we generated an arc beam treatment plan (subsequently referred to as SBRT) that delivered approximately 20 Gy to the distal/caudal tumor edge (high-dose region) and 2 Gy to the proximal/cranial tumor edge (low-dose region) with 8 to 10 Gy delivered to the center of the tumor (moderate-dose region) (Fig. 1N). The dose rate of the BT (6 to 36 Gy/min depending on the age of the ¹⁹²Ir source) approximated that of the EBRT (2 Gy/min). Mice bearing B78 flank tumors were randomized to receive sham control, ICI alone, SBRT alone, SBRT + ICI, EBRT (2 or 8 Gy) + ICI, or BT (2 Gy to proximal tumor edge) + ICI. ICI alone, SBRT alone, and EBRT 2 Gy and 8 Gy + ICI did not slow tumor growth compared to sham control (Fig. 1O). In both BT + ICI and SBRT + ICI treatment groups, tumor growth was significantly (P < 0.05) reduced compared with all other treatment groups (Fig. 1O). Together, these findings provide proof of concept that RT dose heterogeneity, whether delivered by BT or EBRT, generates more robust immune activation and subsequent antitumor response compared with homogeneous dose EBRT.

Low-, moderate-, and high-dose regions are required for antitumor efficacy

One of the clinical benefits of BT is the ability to escalate doses more safely than EBRT because of BT’s highly conformal dose distribution. To test whether dose escalation further enhanced the antitumor efficacy of BT + ICI, we randomized mice bearing single MyC-CaP flank tumors to receive sham control, BT 2 (2 Gy to proximal/cranial tumor edge), BT 8 (8 Gy to proximal/cranial tumor edge), BT 2 + ICI, BT 8 + ICI, or ICI alone. In the BT 8 + ICI group, the low-dose region was 8 Gy, the moderate-dose region was 32 Gy, and the high-dose region was 128 Gy, effectively eliminating the low-dose region of the original BT 2-Gy treatment plan (Fig. 1B). The BT 2 + ICI combination improved survival over sham, ICI alone, and BT 8 alone (P < 0.05), whereas the dose-escalated BT 8 + ICI increased survival only compared with sham (P = 0.035) and did not increase survival compared to single treatments (Fig. 1, P and Q). This resulted in a subsequent decrease in the complete response rate in the dose escalation group (four of eight CR mice in BT 2 + ICI versus one of eight CR mice in BT 8 + ICI) (Fig. 1R). These findings may suggest that all three dose regions [low (2 Gy), moderate (8 Gy), and high (20 Gy)] are required for optimal antitumor efficacy at the primary tumor but may not be required to address distant microscopic disease sites.

RT dose heterogeneity results in spatial heterogeneity in gene expression

We sought to determine the effects of RT dose heterogeneity on the TME. We developed a method for reliably labeling and processing tissue to allow determination of RT dose–dependent effects for tumors obtained from mice receiving BT. Using tattoo ink to mark the dwell position of the radioactive source and a dissection grid annotated with fixed distances, we accurately sampled tumor tissue within low (approximately 2 Gy)–, moderate (approximately 8 Gy)–, and high (approximately 20 Gy)–dose regions for gene expression and immune cell infiltration analysis (Fig. 2, A and B). Mice bearing B78 tumors were randomized to receive BT 2 Gy prescribed to proximal/ cranial edge of tumor (Fig. 2A), sham catheter insertion alone, or EBRT (2, 8, or 20 Gy) (Fig. 2C). In mice receiving BT, the catheter was placed within the distal/caudal edge of the tumor, and the location of the ¹⁹²Ir source was marked with tattoo ink (Fig. 2B). Tumors were harvested 3 days after RT, and in the case of BT, punch excisions from the dissected tumor were taken 1, 3, and 5 mm from the source location, corresponding to the 20-, 8-, and 2-Gy isodose lines. Analysis of γH2AX foci in the high (20 Gy), medium (8 Gy), and low (2 Gy) regions indicated DNA damage response corresponding to dose (fig. S1). Polymerase chain reaction (PCR) analysis revealed that H2-K1 [a gene encoding murine major histocompatibility complex class I (MHC-I)], Ifnb, and Vcam gene expression differed significantly (P < 0.05) between tissue locations in BT-treated tumors, with peak expression of H2-K1 observed in the high-dose region, Ifnb in the moderate-dose region, and Vcam in the low-dose region (Fig. 2D). Similar dose-response patterns were observed for these genes in separate tumors receiving 2-, 8-, or 20-Gy EBRT homogeneous doses. This suggests that these dose-response relationships are not altered by heterogeneity of dose in neighboring tumor cells and demonstrates that RT dose heterogeneity can imprint spatial heterogeneity on gene expression from tumor and stroma cells that do not migrate over time. These data illustrate how a highly heterogeneous RT dose distribution could optimally engage, within a single tumor, diverse immune-related mechanisms that exhibit distinct dose-response relationships, whereas any given homogeneous EBRT dose may only optimally engage mechanisms that share a common dose-response profile (Fig. 2D).

Fig. 2. — (A) B78 flank tumors were treated with BT or sham control. (B) Tattoo ink marking shows the position of the radioactive seed in a BT-treated tumor. (C) Treatment plan schematic demonstrating three EBRT doses used to control for single BT. (D to K) Three days postradiation, tumor tissue was collected for qPCR analysis (N = 5 samples per group) (D) and bulk RNA-seq on individual samples [(E) to (K)]. [(E) to (G)] Differentially expressed gene counts (E), gene set variation analysis (GSVA) differential pathway enrichment–identified numerous KEGG pathways (F), and GO term biological processes (BP) positively enriched in BT-treated samples compared with sham (G). [(H) to (K)] Per gene expression pattern of selected GO terms: cell adhesion (H), cell response to IFN-1 (I), T cell activation (J), and inflammatory response (K), where superscripted H, M, and L indicate FDR < 0.5 for positive differential enrichment of this GO term in BT high, moderate (mod), or low regions, respectively, relative to control sham; superscripted NA indicates GO terms not tested. One-way ANOVA was used to compare qPCR gene expression across radiation doses and dose regions.

To further characterize differential gene expression across BT-treated tumor dose regions, we conducted bulk RNA sequencing (RNA-seq) analysis. Across tissue locations, 1008 common genes were up-regulated, and 359 genes were down-regulated. Each tissue location, which corresponded to a different delivered RT dose, had a unique set of genes that were significantly [false discovery rate (FDR) < 0.05] up- or down-regulated (Fig. 2E). This was confirmed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Across dose regions, 32 common pathways were up-regulated, with each dose region altering expression of unique pathways (high dose, 11 pathways; moderate dose, 24 pathways; low dose: 14 pathways) (Fig. 2F). Similar patterns were observed with Gene Ontology (GO) term analysis (Fig. 2G). In agreement with our quantitative PCR (qPCR) findings, the low-dose region had increased expression of cell adhesion molecules (Fig. 2H). Evaluation of the expression of genes related to IFN response (Fig. 2I), T cell activation (Fig. 2J), and inflammation (Fig. 2K) across dose regions suggested some potential dose region–dependent trends, but conclusions from these data were limited because of variation between tumor specimens. For these immune-related gene signatures, this could result from individual variations in baseline or treatment-induced antitumor immunity, confounding variables in the host animal (e.g., stress), or dynamic processes that can be subject to intra- or intertumor variation based on vascularization, perfusion, hypoxia, or migration of immune cell populations within tumors over time. To better account for the latter possibility, we turned our attention to spatial transcriptomics and single-cell sequencing approaches using whole tumor specimens.

RT dose heterogeneity results in spatial heterogeneity in immune cell infiltration and trafficking to tumor-draining lymph nodes

Building on our bulk RNA-seq findings, we sought to interrogate the TME using spatial transcriptomics in a tumor 3 days after BT. H2-K1 spatial expression (Fig. 3A and fig. S2) corroborated focal PCR and focal bulk RNA-seq analyses of dose-dependent effects of BT at this same time point (Fig. 2). We observed the imprinting of heterogeneous radiation dose gradients onto the gene expression profile of cells in the TME, as illustrated by dose-dependent induction of H2-K1 expression (Fig. 3A), which showed a statistically significant (P < 0.05) increase in expression in the high-dose region compared with both moderate- and low-dose regions. Modeling of H2-K1 expression along the radiation dose gradient revealed a pattern of elevated expression that decreased as distance to the radiation source increased. We evaluated how this spatially static, dose-dependent imprinting of RT dose–dependent gene expression changes on tumor and stroma cells in the TME influenced the spatially dynamic distribution of tumor-infiltrating immune cells. We identified eight clusters (Fig. 3, A and B) that were each present throughout each BT dose region and were deconvoluted into cell types using our BT + ICI single-cell RNA sequencing (scRNA-seq) data as a reference. In clusters 1, 3, and 8, we observed an increase in macrophage infiltration in high-dose regions relative to those of low dose and moderate dose. In clusters 5 and 6, epithelial and fibroblast cells dominated. We observed an increase in CD8 T cell infiltration in moderate- and low-dose regions, with a reduction in the high-dose region (Fig. 3B). We confirmed and expanded on these findings using multiparameter immunofluorescence microscopy. We profiled the distribution of lymphoid and myeloid cells within the same dose regions as done previously in the gene expression studies described above (Fig. 3C). In agreement with our spatial transcriptomic data, CD8 T cells increased in the moderate-dose region compared with high and low (Fig. 3D). In addition, the number of regulatory T cells (CD4⁺FOXP3⁺) increased significantly in the high-dose region compared with moderate (P = 0.0015) and low (P < 0.0001) (Fig. 3E). No changes in B cells, macrophages, natural killer (NK) cells, or myeloid cells in general were detected across dose regions at this time point (Fig. 3, F to I). Conversely, when RT was delivered as a single, uniform dose of either 2, 8, or 20 Gy by EBRT, no change was observed in the number of tumor-infiltrating CD8⁺ or FOXP3⁺ cells, as compared to the 0 Gy control, at 3 days after radiation (fig. S3, A and B). Together, our data indicate that RT dose heterogeneity results in spatial heterogeneity in T cell infiltration of irradiated tumors at an early time point after treatment.

Fig. 3. — Tumors were collected 3 days postradiation from BT- or sham control–treated B78 tumor–bearing mice for spatial transcriptomics. (A) H&E stain with approximate dose region boundaries are shown on the top left; BayesSpace spatial clusters are shown on the top right; a violin plot of per-spot *H2-K1* expression grouped by dose region is shown on the bottom left (*Bonferroni-adjusted FDR < 0.001 by Wilcoxon test comparing high-dose *H2-K1* expression to moderate- or low-dose regions); a spline-based negative binomial generalized linear model of *H2-K1* expression relative to BT seed distance is shown on the bottom right. (B) Cell type deconvolution of spatial spots grouped by cluster and dose region. Teff, T effector; pDC, plasmacytoid dendritic cell; Tmem, memory T cell. (C) A separate cohort of tumors was collected for multiparameter immunofluorescence microscopy. Arrow indicates tattoo marking radioactive seed position. (D to I) Frequencies of intratumoral CD8 T cells (CD8⁺) (D), regulatory T cells (T_reg; CD4⁺FOXP3⁺) (E), B cells (CD19⁺) (F), macrophages (F4/80⁺) (G), natural killer cells (CD161⁺) (H), and myeloid cells (CD11b⁺) (I) were quantified across dose regions. Percentages shown are frequencies among all CD45⁺ cells. **P < 0.05 and ****P < 0.0001 by two-way ANOVA with Šidák’s correction for multiple comparisons. (J) Experimental setup of fluorescence tracking approach in Kaede photoconvertible mice. (K) TDLNs were analyzed for DC population abundance. The left shows representative flow cytometry plots. The right shows quantification. Bars represent mean ± SEM, *P < 0.05 by unpaired t test, ns = not significant. Res, resident DC; Mig, migratory DC; UV, ultraviolet; NT, no treatment. N = 4 mice per group.

To better understand the mechanisms linking dose heterogeneity to improved systemic antitumor immunity, we used a fluorescence tracking approach to evaluate the movement of immune cells from the tumor to the tumor-draining lymph node (TDLN) (23). To do this, we used Kaede photoconvertible mice, which express the Kaede–green fluorescent protein that can be converted into the Kaede–red fluorescent protein upon exposure to violet light (24). B16 tumors were established in Kaede mice, and then, using partial shielding, a portion of the tumor was exposed to ultraviolet light to photoconvert the tumor-infiltrating cells. Immediately after photoconversion, 2-Gy EBRT was delivered to the photoconverted region and 20 Gy to the unconverted region, or alternatively, 20-Gy EBRT was delivered to the photoconverted region and 2 Gy to the unconverted region (Fig. 3J). Treatment planning using computed tomography (CT)–guided radiation with two different doses to two different isocenters in the target tumor is shown in fig. S4. As controls, mice with partial photoconversion were left untreated, or tumors were left unconverted. To confirm tumor sublocation–specific photoconversion, untreated tumors were heterogeneously photoconverted and then harvested. The converted and unconverted regions were separately harvested for flow cytometry (fig. S5). Infiltrating immune cell types in the tumor were analyzed for the proportion photoconverted, demonstrating consistent expression of Kaede red in the various immune cell types in the photoconverted region and lack of Kaede red in the neighboring unconverted region of the same tumor (fig. S5). One day after photoconversion, the TDLN was examined for migration of photoconverted cells from the tumor. As we have previously demonstrated (23), photoconverted cells are found only in the migratory CD103⁺ type 1 conventional dendritic cell (cDC1) and migratory CD11b⁺ type 2 cDC (cDC2) populations but not in resident CD8α+ cDC1s or resident CD11b⁺ cDC2s (Fig. 3K). Low-dose radiation of 2 Gy to the photoconverted region did not affect the movement of photoconverted cells to the TDLN, but a high dose of 20 Gy to the photoconverted region significantly decreased the proportion of migratory CD103⁺ DCs (P = 0.037) and migratory CD11b⁺ DCs (P = 0.031) moving from the tumor to the TDLN (Fig. 3K). DCs are relatively radioresistant and have been shown to remain functional after 20-Gy of RT (25); however, it is possible that limited DC movement to the TDLN at high doses relates to lymphovascular damage in the 20-Gy treatment field (26–28). These data demonstrate that when heterogeneous RT dose is administered to a tumor, the movement of cross-presenting DC to the TDLN from the high-dose regions is impaired, but movement of these cells from the low-dose regions is unimpaired. Thus, homogeneous high-dose radiation may impair DC cross-presentation in the TDLN, but this can be preserved using heterogeneous-dose radiation that includes low-dose regions, thus permitting expansion of tumor-specific T cells in the TDLN of tumors treated with heterogeneous RT doses.

RT dose heterogeneity reprograms myeloid compartment to an antitumor phenotype

To gain insight into transcriptional and subpopulation differences among tumor-infiltrating immune cells, we performed scRNA-seq analysis. Mice bearing B78 flank tumors were randomized to receive sham control, BT (2 Gy to proximal tumor edge), BT + ICI, EBRT (2, 8, or 20 Gy) + ICI, or ICI alone. On day 10, tumors were harvested, and CD45⁺ cells were sorted from dissociated tumor single-cell suspensions for analysis. We identified a total of 72,637 cells across seven samples, with 48,740 to 62,376 mean reads per cell and 1662 to 2498 median genes per cell. After removing low-quality cells and performing cell type predictions, we grouped cells into myeloid and lymphoid compartments. Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction and graph-based clustering of cells in each compartment grouped cells with distinct transcriptional profiles.

Focusing first on the myeloid compartment, graph-based clustering identified 17 clusters with distinct transcriptional programs that could be readily assigned to known cell lineages using marker genes resulting in classification of nine macrophage clusters (C0, C1, C2, C3, C5, C6, C7, C10, and C14), four DC clusters (C9, C12, C15, and C16), two classical monocyte clusters (C4 and C11), two nonclassical monocyte clusters (C13 and C14), and one neutrophil cluster (C8) (Fig. 4, A to C). Statistical testing identified several clusters that were overrepresented across treatment groups. For example, BT + ICI increased neutrophils (C8) compared with other groups, whereas the homogeneous EBRT groups enriched monocyte populations (C4, C11, and C13) (Fig. 4D).

Fig. 4. — B78 tumor–bearing mice were treated with sham control, EBRT + ICI, BT, BT + ICI, or ICI alone. Day 10 postradiation, live CD45⁺ cells were analyzed by scRNA-seq. (A) UMAP plots of tumor myeloid scRNA-seq data derived from combination treatment groups. (B) Individual cluster abundance analysis is shown relative to sham. (C) Individual cell type identification and abundance by cluster is shown. (D) Cluster abundance overrepresentation by treatment group is shown; * FDR < 1 × 10⁻⁵ and ** FDR < 1 × 10⁻¹⁰ by proportions test. (E) Unique gene hits of BT + ICI differential gene expression analysis are shown for cluster 8. (F) Shown is a comparison of gene expression for pro- and antitumor neutrophil markers. * FDR < 0.05 (G) Classical activation scores are shown by treatment group for monocyte clusters. ****P < 1 × 10⁻¹⁵, ns = not significant using the Wilcoxon rank sum test. (H) JASMINE scores, a signature-scoring method optimized for single-cell cancer datasets, are shown for cluster analysis KEGG pathway positive enrichment hits identified from bulk RNA-seq data displayed in Fig. 2. cGMP-PKG, cyclic guanosine monophosphate-dependent protein kinase Ge; GnRH, gonadotropin-releasing hormone; CoA, coenzyme A.

Given these findings, we used differential gene expression to determine the activation status of the increased neutrophil infiltrate, specifically looking for genes that showed a synergistic effect from BT + ICI treatment (Fig. 4E). BT + ICI treatment synergized to increase expression of several antitumor-associated genes, including Ccl5 and Egr1 (29), as well as Il1rn, which has been demonstrated to abrogate the effects of chronic interleukin-1 (IL-1)–induced inflammation, including tumor promotion, metastasis, and suppression of adaptive immunity (Fig. 4E) (30). BT + ICI treatment also uniquely down-regulated several protumor genes, including Znfx1 and Fabp5, as well as Spp1, which is associated with development of neutrophil extracellular traps that promote tumor progression and metastasis (31, 32). Conversely, EBRT 20 + ICI selectively enriched for these very same protumor genes. We examined expression of pro- and antitumor genes (33) in neutrophils for treatment-specific differential expression relative to Sham. BT + ICI treatment led to significant up-regulation of the antitumor gene C3 (FDR = 0.019) and the protumor gene Cd14 (FDR = 0.002) (Fig. 4F).

We examined the three monocyte clusters, given our observation that homogeneous dose groups were enriched for these populations. The classical activation score measuring monocyte polarization revealed a greater increase in the BT + ICI group compared with low (P = 2.6 × 10⁻¹⁶) or moderate (P = 4.3 × 10⁻¹⁷) EBRT and comparable to high-dose EBRT (P = 0.15) (Fig. 4G), suggesting more robust polarization toward an antitumor phenotype after BT + ICI treatment. We next determined whether the myeloid-related gene pathways that were positively enriched at the early day 3 time point continued to be activated at day 10. Single-treatment and dual-treatment groups formed two unique clusters in samples collected at day 10, with the homogeneous RT groups more closely associated with each other than with the heterogeneous RT group (Fig. 4H). Together, RT dose heterogeneity promoted reprogramming of the TME to favor infiltration and polarization of both neutrophils and monocytes toward predominantly antitumor phenotypes compared with homogeneous RT.

RT dose heterogeneity promotes enrichment of effector-associated T cells

We next interrogated the lymphoid compartment of tumor-infiltrating immune cells using scRNA-seq. Using graph-based clustering, we identified 21 clusters with distinct transcriptional programs that could be readily assigned to known cell lineages using marker genes. This resulted in classification of eight effector memory CD8 T cell clusters (C0, C1, C2, C6, C9, C11, C12, and C18), three effector CD8 T cell clusters (C8, C14, and C19), three regulator T cell clusters (C3, C4, and C13), two memory CD8 T cell clusters (C15 and C17), two B cell clusters (C16 and C20), one CD4 T cell cluster (C10), one memory CD4 T cell cluster (C5), and one NK cell cluster (C7) (Fig. 5, A to C). Several clusters were overrepresented across treatment groups. For example, BT + ICI significantly increased CD4 T cells (FDR = 2 × 10⁻²⁴) (C5) and effector memory CD8 T cells (FDR < 6 × 10⁻¹⁰) (C18) compared with other groups (Fig. 5D). Low-dose homogeneous EBRT increased regulatory T cell populations (C3 and C4) (FDR < 1 × 10⁻⁵), and moderate- and high-dose homogeneous EBRT increased effector CD8 T cells (C2 and C14; and C1, C6, and C7, respectively) (FDR < 1 × 10⁻⁵). Given these findings, we then used the TIL-PRED package (34) to further classify the T cell clusters identified in Fig. 5 (C and D) (Fig. 5E). BT + ICI selectively increased infiltration of T helper 1 (T_H1) CD4 cells and unique effector memory CD8 T cell clusters. Moderate-dose EBRT + ICI increased naïve CD8 T cell populations (C2 and C14). Both BT + ICI and high-dose homogeneous EBRT + ICI increased effector memory CD8 T cell clusters (C0 and C11, FDR < 1 × 10⁻¹³); however, high-dose homogeneous EBRT + ICI also significantly increased more exhausted CD8 T cell subsets (CD8 Tex) (C6, FDR = 9 × 10⁻⁹; C8, FDR = 8 × 10⁻²⁷) than BT + ICI (C6, FDR = 0.33; C8, FDR = 3 × 10⁻⁹) (Fig. 5, D and E).

We sought to determine whether the lymphoid cell gene pathways that were positively enriched at the early day 3 time point continued to be activated at day 10 after treatment initiation. In contrast with the myeloid compartment, single-treatment and dual-treatment groups did not form two unique clusters. BT + ICI was strongly associated with high-dose homogeneous EBRT + ICI, whereas low- and moderate-dose homogeneous EBRT + ICI cluster together with BT monotherapy (Fig. 5F), which mirrors the cell cluster enrichment findings (Fig. 5D). Given the overlap in cell enrichment between BT + ICI and high-dose, homogeneous EBRT + ICI (clusters C0, C8, and C11) (Fig. 5D), we then determined whether there were differences in gene expression between the two groups (Fig. 5F). There was both overlap and some notable differences in gene enrichment on cluster 11, which is an effector CD8 T cell population equally enriched in both BT + ICI and high-dose homogeneous EBRT + ICI groups. For example, high-dose homogeneous EBRT + ICI up-regulated Tsc22d3 and Ptpn6, which are associated with inhibition of T cell activation and reduction in immunotherapy efficacy (35, 36), as well as Tox and Bst2, which are associated with T cell exhaustion (37). Conversely, BT + ICI up-regulated Lmna and mt-Nd1, which are associated with T cell activation (38, 39), as well as Kdm2b, which is critical for memory T cell formation (40). Together, BT + ICI and high-dose homogeneous EBRT + ICI activated CD8 T cells in a similar fashion. However, BT + ICI resulted in enrichment of T_H1 CD4 cells and depletion of exhausted CD8 T cells.

Using DESeq2, we performed differential expression testing of the top 7500 variable genes in each lymphoid cell cluster (Fig. 5G). Of the common ICI-targeted proteins, we performed differential expression testing for Ctla4, Lag3, Tigit, Cd274 (encoding PD-L1), and Havcr2 (encoding Tim3) comparing BT-only–treated cells with sham. Of these genes, Lag3 was most commonly up-regulated in lymphoid compartment cell clusters (N = 5 clusters), followed by Ctla4 and Tim3 (both N = 3). Cd274 was only found to be up-regulated in lymphoid cluster 1. Ctla4 was commonly up-regulated in lymphoid clusters predominated by regulatory T cells.

Antitumor immune memory is induced by heterogeneous RT and is dependent on both CD4 and CD8 T cells

Given the enrichment in both CD4 and CD8 T cell populations in our scRNA-seq analysis, we sought to determine which cell types were critical for the antitumor effect generated by BT + ICI. Mice bearing single MyC-CaP flank tumors were randomized to sham control, BT (2 Gy to proximal tumor edge), BT + ICI, BT + ICI + anti-CD4 (CD4 T cell depletion group), BT + ICI + anti-CD8 (CD8 T cell depletion group), or ICI alone. Immediately after BT, mice in all groups were engrafted with a secondary tumor on the opposite flank (Fig. 6A). For both CD4 (P = 0.004) and CD8 (P = 0.02) T cell depletion groups, tumor growth increased significantly compared with nondepleted BT + ICI (Fig. 6B). Depletion of CD4 T cells resulted in a significant (P = 0.0024) increase in secondary engraftment rate compared with nondepleted BT + ICI, which was comparable to sham control (Fig. 6C). Unlike depletion of CD4 T cells, depletion of CD8 T cells had no detectable effect on increasing the secondary engraftment rate compared to nondepleted BT + ICI (Fig. 6C). In addition, depletion of either CD4 (P = 0.002) or CD8 (P = 0.029) T cells significantly reduced overall survival and complete response rate compared with nondepleted BT + ICI (Fig. 6, D and E). In the absence of BT or ICI, neither CD4 nor CD8 T cell depletion affected tumor growth, secondary engraftment, or survival (fig. S6). These data suggested that both CD4 and CD8 T cells were critical for response at the primary tumor and that CD4 T cells, but not CD8 T cells, were required for rejection of the distant tumor.

Fig. 6. — (A) MyC-CaP tumor–bearing mice were treated with sham control, BT, BT + ICI, or ICI alone. Two separate cohorts of mice treated with BT + ICI were depleted of CD4 (αCD4) and CD8 (αCD8) T cells. Secondary engraftment followed immediately post-RT and was quantified on day 14. (B to E) Tumor response by group (B), secondary engraftment (C), animal survival (D), and complete response rates (E) are shown (n = 10 to 16 mice per group). (F) Spleens of a separate group of mice were harvested at day 10 post-RT (early activation) and day 90 post-RT (memory phase). Sorted CD4 and CD8 T cells were cocultured with tumor cells. After 24 hours of coculture, splenocytes were harvested for analysis of T cell activation markers. (G to J) Shown are flow cytometry quantifications for CD4 T cells isolated at the early activation (G) and memory (H) time points and for CD4 T cells isolated at the early activation (I) and memory (J) time points. Significance was determined by linear mixed effects regression analysis and two-way ANOVA with Tukey multiple comparisons testing for tumor growth and Kaplan-Meier with log-rank testing for survival analysis, where significant differences (P < 0.05) are demarcated by asterisks, with the color of the asterisk representing the group from which the listed group differs. One-way ANOVA was used to compare secondary engraftment and expression of T cell markers.

We also tested whether BT + ICI–treated B78-bearing mice increased activation of CD4 or CD8 T cells during either the early activation phase or memory phase of immune response. To that end, we collected spleens from B78-bearing mice that had been treated with sham, BT, BT + ICI, or ICI alone at day 10 after treatment. In addition, we collected spleens at day 90 after treatment initiation from mice that had borne a B78 tumor, experienced a complete response after BT + ICI, and rejected rechallenge of B78 engraftment. We isolated splenocytes from these mice for coculture with preplated B78 tumor cells for 24 hours and then used flow cytometry to analyze activation markers in these cocultured lymphocytes (Fig. 6F). In the CD4 T cell population, expression of IFN-γ increased significantly (P = 0.0029) in the early-phase mice treated with BT + ICI (Fig. 6G). This increase in IFN-γ was not sustained in the memory phase, although CD69 expression was significantly (P = 0.0438) increased (Fig. 6H). In the CD8 T cell population, expression of IFN-γ increased significantly (P = 0.0027) in the early-phase mice treated with BT + ICI (Fig. 6I) with sustained activation in the memory phase, when CD69 also increased (P = 0.0031) on these CD8 T cells (Fig. 6J). This response was specific for the B78 tumors, because no increase in IFN-γ or CD69 was detected when cocultured with the unrelated MyC-CaP prostate cancer line (fig. S7). Together, these data suggest that BT + ICI activates both CD4 and CD8 T cells in MyC-CaP– and B78-bearing mice, which are critical for overall treatment efficacy in MyC-CaP–bearing mice, and provide evidence for the formation of immune memory.

Heterogeneous RT selectively induces expression of effector-related cytokines and clonal expansion of CD8 T cells

Given evidence that RT dose heterogeneity promotes T cell immunity, we performed single-cell proteomic analyses to evaluate the cytokine secretome of CD8 T cells in blood during the observed systemic antitumor response. We compared CD8 T cells isolated from the blood of mice bearing a single flank B78 tumor and treated with sham control, BT (2 Gy to proximal/cranial edge of tumor), BT + ICI, EBRT (2, 8, or 20 Gy) + ICI, or ICI alone. On day 20 after treatment initiation, peripheral blood mononuclear cells (PBMCs) were isolated and subjected to single-cell cytokine profiling.

We detected secretion of 12 cytokines: (i) effector—granzyme B, IFN-γ, and macrophage inflammatory protein 1 alpha (MIP-1α); (ii) chemoattractive—IFN-γ–inducible protein 10 (IP-10) and regulated upon activation, normal T cell expressed and presumably secreted (RANTES); (iii) regulatory—IL-10, IL-13, IL-27, IL-4, soluble cluster of differentiation 137 (sCD137); (iv) inflammatory— IL-6; and transforming growth factor–β (TGF-β). The median signal intensity of each cytokine was comparable across treatment groups (Fig. 7A). Granzyme B and sCD137 were the most and second-most frequently secreted cytokine across all groups, respectively, with EBRT 20 + ICI having the highest overall secretion frequency of each cytokine (Fig. 7B). A polyfunctional heatmap revealed cell populations capable of secreting multiple cytokines (Fig. 7C). Across all treatment groups, the most abundant cell populations detected were those that singly produced granzyme B. The second most abundant were cells that produced both granzyme B and sCD137, which were highest with EBRT 2 + ICI and EBRT 20 + ICI treatment. Overall, the EBRT 20 + ICI had the highest polyfunctionality score (Fig. 7D), with 25 unique cell populations detected compared with 11 populations detected with BT + ICI treatment. However, 18 of those populations were secretors of regulatory cytokines, compared with only two populations with BT + ICI treatment. This was further confirmed when the overall polyfunctional strength index (PSI) was plotted. EBRT 20 + ICI treatment produced the highest overall PSI; however, approximately one-third of the PSI was associated with regulatory cytokine secretion (Fig. 7E). When comparing the ratio of effector to regulatory PSI scores, the BT + ICI had a markedly increased ratio (approximately 22:1) compared with all other treatment groups (Fig. 7F), suggesting that heterogeneous RT delivered by BT + ICI treatment selectively induces expression of effector-associated cytokines compared with homogeneous RT in combination with ICI.

Fig. 7. — B78 tumor–bearing mice were treated with sham control, EBRT + ICI, BT, BT + ICI, or ICI alone. On day 20, PBMCs were isolated for secretome analysis. (A) Signal intensity of the indicated secreted cytokines is shown. RFU, relative fluorescence units. (B) Shown is the secretion frequency by cytokine. (C) A polyfunctionality heatmap is shown by treatment group. (D) Polyfunctionality score was quantified and is presented by treatment group. (E) PSI is shown by treatment group. (F) Shown is the ratio of stimulatory to regulatory components of the PSI score shown in (E). (G to J) TCR sequencing of RNA extracted from B78 flank tumors of the same cohort of mice was used for Isoplexis analysis; shown are the counts of unique CDR3β sequences (G), the total number of CDR3β sequences (H), the Shannon diversity index (I), and the D50 value (J). n = 3 mice per group. One-way ANOVA with Tukey’s correction for multiple comparisons was used to compare average CDR3 counts [(G) and (H)], diversity index (I), and D50 score (J) across groups.

To further characterize the functional status of CD8 T cells after BT or EBRT, we performed T cell receptor (TCR) sequencing analysis. From the same cohort of mice used for secretome analysis, B78 flank tumors were harvested on day 20 after treatment initiation. RNA was extracted and subjected to TCR sequencing. Both BT + ICI (P = 0.032) and EBRT 8 + ICI (P = 0.0012) significantly increased the number of unique complementarity determining region 3 (CDR3) β sequences detected (Fig. 7G), consistent with an increase in the abundance of tumor-infiltrating T cells. However, only BT + ICI treatment resulted in a significant (P < 0.0001) increase in the total number of CDR3β sequences detected within the TME (Fig. 7H). The Shannon diversity index is an alpha diversity metric used to reflect how many unique CDR3 sequences are observed as well as the overall evenness in abundance of each CDR3 sequence. We did not observe any change in the Shannon diversity index across groups (Fig. 7I). However, we observed a reduction in the percentage of distinct clonotypes that account for greater than or equal to 50% of the total of sequencing reads (D50 scores) in the BT + ICI group (Fig. 7J). This is consistent with a role for BT + ICI treatment in promoting an activated adaptive T cell response with clonal expansion evident among tumor-infiltrating lymphocytes.

DISCUSSION

Here, we demonstrated the capacity for heterogeneous RT combined with ICI to generate a superior systemic antitumor T cell–mediated response compared with homogeneous RT combined with ICI across a range of doses. RT dose heterogeneity imprints spatial heterogeneity in gene expression across the TME, illuminating the breadth of dose-response effects induced by RT. At early time points, these effects led to dose-responsive spatial heterogeneities in the infiltration of immune cell lineages. Tumors treated with a heterogeneous RT dose exhibited more robust immune activation compared with any single examined homogeneous dose of RT, and this effect was more than additive, with multiple genes and immune activation pathways exhibiting up-regulation only in the setting of a heterogeneous RT dose and not in any of the examined homogeneous treatment doses.

The combination of BT + ICI generated CD4 and CD8 T cell memory populations, which were both required for antitumor efficacy, and BT selectively generated CD8 T cell populations with an effector phenotype. High PSI did not predict for response to treatment in this preclinical study, which contrasts with prior data from a clinical study of patients with melanoma receiving ICI-based immunotherapy (41). Here, the quality, rather than magnitude, of PSI predicted the response, with BT + ICI having the highest ratio of effector to suppressor PSI scores compared with other groups. TCR sequencing of tumor-infiltrating CD8 T cells showed an increase in the number of unique CDR3 sequences detected after treatment with BT + ICI and with EBRT 8 + ICI. The latter is consistent with prior reports demonstrating the capacity for moderate doses of EBRT to diversify the TCR repertoire in combination with ICI (42, 43). Only BT + ICI increased the total number of distinct tumor-infiltrating CDR3–expressing TCRs compared with other groups. These data suggest the potential for combination of BT + ICI to induce enrichment of activated CD8 T cells, with an increased number of unique and total CDR3 TCR sequences leading to a robust antitumor immune response and subsequent generation of CD8 T cell memory, consistent with in situ vaccination.

In addition to showcasing potential advantages that heterogeneous RT has over homogeneous RT in generating antitumor immunity, we also describe a role for BT to serve as a tool to study dose-dependent effects of RT on immune activation and immune cell infiltration. Several conflicting data exist regarding dose-response relationships in gene expression after RT. For example, preclinical work has described a role for Trex1 exonuclease to serve as a negative regulator of type 1 IFN production and that Trex1 exonuclease is up-regulated after high doses of RT (11). However, seminal work describing the potential for RT to induce type 1 IFN production used a high dose of radiation (18). When comparing findings such as gene expression across multiple studies, many confounding factors, including mouse strain, vendor difference, and variable tumor implantation depth affecting baseline immune microenvironment characteristics, can limit the ability to draw conclusions (44). In addition, variations in perfusion and oxygenation of the TME can alter radiobiological characteristics. By using BT to deliver a spectrum of doses across a single TME, we limited several of these confounding factors within an internally controlled experimental system that accurately maps the relationship of radiation dose to gene expressions within a tumor at an early time point after RT. Nevertheless, we still observed variation across individual tumor specimens from different mice in the same dose regions from BT-treated mice; this may relate to host variations or intra- and intertumor variation in parameters that modify responses, including tumor vascularization, perfusion, hypoxia, and migration of immune cell populations.

The irregular vasculature of a tumor can have differing responses to RT dose. Low and moderate doses can promote vascular normalization for differing lengths of time, whereas high-dose radiation can induce vascular damage and lack of perfusion (45). Vascular damage induced by high-dose radiation can trigger nutrient deprivation and hypoxia, which can in turn alter downstream signaling through hypoxia inducible factors, thus altering immune signaling and potentially promoting an immune-suppressive microenvironment (46). Given the effects of tumor oxygenation on RT response and immune cells and the varied downstream signaling pathways that result from differing radiation doses, it will be valuable in future studies to evaluate how heterogeneous doses affect and are affected by tumor perfusion, hypoxia, and vascular endothelial cell responses to RT.

Our findings of favorable immune activation after RT dose heterogeneity may contribute to the efficacy of other treatment strategies. The dose heterogeneity evaluated in this manuscript is that of a macroscopic gradient oriented from a point of high dose to low dose at the contralateral aspect of a tumor. We expect that other distribution patterns of dose heterogeneity may achieve similar effects. Heterogeneous radiation dose can be delivered clinically using spatially fractionated EBRT in the form of GRID/lattice therapy or at submillimeter widths using microbeam and minibeam RT. These therapies can deliver high-dose and high-dose-rate therapy with three-dimensional dose heterogeneity by dividing a target volume into small fields of high or low dose with steep dose gradient transitions. In preclinical models, these approaches have been shown to produce positive immunological effects on tumors (47–53), and GRID has shown promise when used in combination with ICI (54). In future studies, the ability to control the spatial distribution of dose heterogeneity with these approaches may enable further understanding and optimization of the interaction between RT dose heterogeneity and antitumor immunity. It is unclear from our data whether the amplified immune response with dose heterogeneity requires a gradient of heterogeneity across a tumor or whether microscopically distributed dose heterogeneity might achieve similar effects. It may even be possible to optimize the immunogenic effects of heterogeneous radiation by modifying the distribution of dose heterogeneity. It will be valuable to further investigate this possibility in future studies with GRID or microbeam EBRT and with advanced micro-dosimetry in conjunction with radiopharmaceuticals. Radiophar-maceuticals generate an inherently heterogeneous distribution of RT dose because of the short range of RT emitted and varied target expression and perfusion. This may contribute to a capacity for promoting antitumor immunity in combination with ICIs in preclinical models (55). Last, some nanoparticle formulations, including those delivering immune or inflammatory agents, can function as radiosensitizers, and it is possible that combining certain nanoparticles with RT may create microscopic dose heterogeneity and thereby augment RT effects on antitumor immune response. A deeper understanding of the mechanisms underlying immune responses to RT is of critical importance, as highlighted by the recent announcement that the phase 3 PACIFIC-2 trial for stage III non–small cell lung cancer did not achieve improved progression-free survival with concurrent duvalumab administered with chemoradiation when compared to adjuvant durvalumab (56). Further insight into dose selection and timing is needed to access the combinatorial potential of RT and immunotherapy, and understanding the effects of dose heterogeneity is a necessary step in that direction.

We demonstrated an important role for low-dose RT in optimizing in situ vaccination at a site also receiving high-dose RT. These findings may share underlying mechanisms with separate studies that have demonstrated a benefit of delivering low-dose RT to distant tumor sites to propagate immune response from a focal higher-dose in situ vaccination at one tumor site (57, 58). A combination of high- and low-dose RT at different tumor sites can be particularly effective in combination with ICI, where low-dose RT has been shown to promote T cell clonal expansion and propagation of anti-tumor immunity (55, 59). Putting together these findings, we now posit that it may be even more effective to pair a heterogeneous dose of high- to low-dose RT at one tumor site to activate in situ vaccination with low-dose RT to all tumor sites to propagate response at all tumor sites. Recent evidence demonstrated loss of antitumor response after irradiation of the TDLN (60), which suggests a possible additional explanation for our observed requirement for the low-dose region for optimal antitumor effect of BT + ICI, because it would result in reduced RT to the TDLN. Although a potentially confounding source, this is also a strength of our treatment approach because, in addition to its advantages when combined with ICI, dose heterogeneity can also be used to spare the TDLN and other at-risk organs.

Our results demonstrate the capacity of RT dose heterogeneity to enhance the antitumor immune response in combination with ICI therapy. This suggests a unique opportunity to exploit RT dose heterogeneity to augment tumor response to ICIs and potentially other immunotherapies. This combination treatment strategy can be facilitated by the existence of specially designed BT catheters that can enable intratumoral injection and BT with a single needle insertion (61). It will be critical in future studies to evaluate the effects of dose rate, fractionation, and sequencing/timing of therapies on tumor response to the combination of heterogeneous dose radiation and ICBs. Comparing the effects of dose rate may be possible using a low dose rate compared with HDR or EBRT. However, interrogation of such time-dependent effects is not easily conducted in syngeneic murine tumor models because the rapid pace of tumor growth in these models confounds time-dependent treatment effects by differences in tumor size at the times when treatments are initiated. It may be necessary to examine such effects in early-phase clinical studies or in companion canines with cancers.

Moreover, in future studies, it will be valuable to evaluate effects of heterogeneous RT in combination with single agent and alternative combinations of ICIs including anti–programmed cell death 1/PD-L1, anti–CTLA-4, and anti–lymphocyte activation gene 3 (LAG-3). We have started with a focus here on a combination of ICIs that, at least in some diseases such as melanoma, confers the highest rates of response, reasoning that improving response in this context may convey the greatest clinical benefit because patients not responding to monotherapy ICIs are often standardly escalated to dual therapy, whereas those not responding to dual therapy often presently have limited treatment options. We observed by scRNA-seq that Lag3 was the most commonly up-regulated ICI-targeted receptor on lymphoid cell clusters after BT, and it will be important to explore combinations of heterogeneous RT with anti–LAG-3 ICIs in future studies.

Evaluating mechanisms of escape from our in situ vaccination regimen will be critical, and future mechanistic studies will enable rational combination with other immunotherapies that may limit the development of such resistance. The tumor models used in this study are moderate to poorly immunogenic as gauged by limited response to ICIs alone (shown here) and few, but some, baseline tumor-infiltrating lymphocytes (13, 62). In such settings, we expect that immune priming of adaptive antitumor immune response may be limiting, and this is where an optimized in situ vaccine approach using heterogeneous dose RT may confer a benefit. In this setting and with more immunogenic tumors where antigen presentation may not be limiting, we expect that delivering low-dose RT to all tumor sites may be beneficial to enable effective propagation of adaptive immune response to all tumor sites. In truly “cold” tumors such as certain pediatric and translocation-driven cancers, benefit may not be observed with these approaches because of an absence of recognizable tumor-associated neoantigens.

Several limitations of this study exist, including the use of syngeneic murine tumor models that may not be representative of the human immune microenvironment. Although our findings suggest that combination BT and ICI is a promising treatment strategy in two separate syngeneic tumor models of melanoma and prostate cancer, syngeneic tumor models can have preexisting immunity that is critical for the response to RT and ICIs (63). Therefore, additional studies in spontaneously developing murine tumor models would further support the potential for successful clinical translation. Because of the small size of the mouse, RT delivered by BT extends beyond the tumor border. Although this dose is very low, we recognize the potential for this to be a confounding factor particularly in the two-tumor model, because low-dose RT may immunomodulate distant TMEs and increase overall antitumor response when combined with high-dose RT delivered to the primary tumor (55, 57, 59, 64). To separate the effects of low-dose RT as part of a heterogeneous dose to the primary tumor from exposure of metastatic tumors to low-dose RT, we tested the systemic immune response to microscopic disease that had been injected after BT delivery.

Early-phase clinical studies are now warranted to evaluate the capacity of RT dose heterogeneity to augment rates or duration of response to ICIs. In the design of these studies, we would favor a focus on patients who exhibit moderate to poorly immunogenic tumors. This may be supported by a low frequency of tumor-infiltrating lymphocytes or prior response to ICI therapy, albeit incomplete or nondurable. Working within standards of care for radiotherapy, one approach could be to evaluate patients already receiving high-dose BT in cancers such as high-risk prostate cancer or cervical cancer, although it would be important to limit combination with EBRT to TDLN in this approach (60). Another testable approach could engage heterogeneous EBRT for palliation in patients receiving standard-of-care ICI therapies and with an indication for palliative RT (65). Although the safety and feasibility of such approaches may first need to be demonstrated alone, ultimately, we expect that an immune-priming effect of heterogeneous dose RT at a single site will optimally be delivered in combination with low-dose RT to all tumor sites, with the latter perhaps implementing a radiopharmaceutical.

MATERIALS AND METHODS

Study design

The objectives of this work were to use HDR BT as a tool to investigate dose-dependent effects of RT on the TME and subsequently determine whether RT dose heterogeneity was superior to a homogeneous dose in generating immune activation leading to induction of systemic antitumor immune responses when combined with ICI. Tumors were established subcutaneously in mice. When the desired tumor size was reached, mice were randomly separated, and rehoused mice were labeled with individual numbers. Each treatment was given with individual number recording to produce a blinded treatment record. All randomized mice were included in the subsequent analyses, and no data were excluded. EBRT or HDR BT was delivered, phosphate-buffered saline (PBS) or dual ICI (anti–CTLA-4 and anti–PD-L1) was injected intraperitoneally, and tumor growth and overall survival were recorded. The primary tumor was collected to evaluate immune infiltration and activation. Analyses included bulk RNA-seq, scRNA-seq, immunohistochemistry, and TCR sequencing. In addition, PBMCs were collected for single-cell secretome analysis. Mice were randomized to experimental groups the day before treatment initiation. In general, experimental groups consisted of at least five or six mice, but in some experiments up to 10, were used to provide greater statistical power when pilot studies indicated a modest effect size difference between comparison cohorts. To determine whether mice rendered disease-free generated memory T cells, we isolated CD4 and CD8 T cells from spleens for coculture with tumor cells and subsequently measured markers of activation using flow cytometry. To test whether T cell populations were critical for antitumor efficacy, we used antibody-mediated depletion of CD4 and CD8 T cells.

Cell lines

The cell lines used in this study were murine melanoma B78-D14 (B78), derived from B16 melanoma as previously described (66); B16 melanoma; and murine prostate MyC-CaP. Cells were grown in RPMI 1640 (B78) or Dulbecco’s modified Eagle’s medium (DMEM) (B16 and MyC-CaP) supplemented with 10% fetal bovine serum (FBS), penicillin (100 U/ml), and streptomycin (100 μg/ml). The murine melanoma B78-D14 (B78) cell line, derived from B16 melanoma as previously described, was obtained from R. Reisfeld (Scripps Research Institute) (66). The B16 melanoma cell line was obtained from W. Redmond (Earle A. Chiles Research Institute). The murine prostate cancer MyC-CaP cell line was obtained from the American Type Culture Collection (ATCC). B78 cells were grown in RPMI 1640 and were supplemented with 10% FBS, penicillin (100 U/ml), and streptomycin (100 μg/ml). MyC-CaP and B16 cells were grown in DMEM and were supplemented with 10% FBS, penicillin (100 U/ml), and streptomycin (100 μg/ml). Cell line authentication was performed per ATCC guidelines using morphology, growth curves, and Mycoplasma testing within 6 months of use.

Murine tumor models

Mice were housed and treated under a protocol (number M005670) approved by the Institutional Animal Care and Use Committee at the University of Wisconsin–Madison. Female C57BL/6 and male FVBn mice were purchased at age 6 to 8 weeks from Taconic. B78 and MyC-CaP tumors were engrafted by subcutaneous flank injection of 2 × 10⁶ and 1 × 10⁶ tumor cells, respectively. To model bulky metastatic disease while minimizing RT dose delivered to the secondary tumor, 2 weeks after primary engraftment a secondary tumor was engrafted on the left shoulder. To model micrometastatic disease, a secondary tumor was engrafted on the opposite flank immediately after primary tumor RT. Tumor size was determined using calipers, and volume was approximated as (width² × length)/2. Mice were randomized immediately before treatment when tumors were well established (150 to 200 mm³), approximately 4 weeks after tumor implantation for B78 and 3 weeks for MyC-CaP. For spatially resolved tumor analyses, mice were treated when tumors were approximately 1 cm in greatest dimension, approximately 5 weeks after tumor implantation for B78 tumors. The day of radiation was defined as day 1 of treatment. Anti–CTLA-4 (IgG2c, clone 9D9, NeoClone) and anti–PD-L1 (IgG2b, clone 10F.9G2, Bio X Cell) were administered at a dose of 200 μg by intraperitoneal injection on days 3, 6, and 9. T cell depletion was performed as previously described (67). Depletion was confirmed on day 15 of treatment (fig. S6, A and B). Mice were euthanized by CO₂ asphyxiation followed by cervical dislocation when tumor size exceeded 15 mm in the longest dimension or whenever recommended by an independent animal health monitor for morbidity or moribund behavior. Complete response of tumors or tumor engraftments was assessed by physical examination at time of death or at 60 days after treatment initiation and was defined as no palpable mass or nodularity in the region of original tumor engraftment.

Radiation

Delivery of homogeneous RT in vivo was performed using an x-ray biological cabinet irradiator X-RAD 320 (Precision X-Ray Inc.). Mice were placed near the central axis of the field using the full field (20 cm by 20 cm) and with shielding by custom lead surface collimation to cover nontargeted portions of the mouse. Dose was prescribed to approximately mid-tumor depth (approximated at 5-mm depth), resulting in an estimated variation of dose across the tumor of ≤10% of prescription dose. EBRT was prescribed to deliver 2, 8, or 20 Gy. These were chosen to reflect the clinically relevant range of RT dose, with 2 Gy representing conventional fractionation, 8 Gy representing a stereotactic body RT dose [typical range of 7.25 to 12 Gy (68, 69)] that has also been demonstrated to be optimal for inducing immune activation (11), and 20 Gy representing a typical stereotactic radiosurgery dose.

Heterogeneous EBRT (referred to as SBRT) was delivered using the Small Animal Radiation Research Platform (SARRP Xstrahl). The distal edge of the tumor was defined as the isocenter, and 20 Gy was delivered to isocenter using an arc beam such that the opposite edge of the tumor received ~2-Gy dose. The 20-Gy dose was not used for in vivo tumor response studies for the B78 model because of toxicity, which was not observed in the MyC-CaP model, likely because of the difference in mouse strain between models. The dose rate for EBRT delivery in all experiments was approximately 2 Gy/min.

Mice receiving BT were placed under isoflurane anesthesia, and a flexi-needle (Best Medical) was inserted into the distal edge of the tumor. The flexi-needle was cleansed between applications using 70% ethanol wipe. The ¹⁹²Ir high-dose-rate source was dispensed by a Flexitron remote afterloader (Elekta) into the flexi-needle placed at the caudal end of the tumor. BT was prescribed as 2 Gy to the proximal or cranial edge of the tumor (depth range from 5 to 10 mm depending on tumor size and dose rate of 6 to 36 Gy/min depending on the age of the ¹⁹²Ir source). Dosimetric calibration and quality assurance checks were performed on these irradiators by University of Wisconsin Medical Physics staff. For all experiments in which BT was performed in any cohort, all other mice in the experiment received a sham needle insertion/mock BT to control for effects of this procedure. BT at 2 Gy delivered a 4.96-Gy mean tumor dose with the maximum dose volume (DMax) to a tumor volume of 0.03 cm³ equal to 15.70 Gy, the DMax to a tumor volume of 0.1 cm³ equal to 11.19 Gy, the minimum dose volume (DMin) to a tumor volume of cm³ equal to 0.88 Gy, and the DMin to a tumor volume of 0.1 cm³ equal to 1.53 Gy. BT at 8 Gy delivered a 19.84-Gy mean tumor dose with the DMax to a tumor volume of 0.03 cm³ equal to 62.84 Gy, the DMax to a tumor volume of 0.1 cm³ equal to 44.86 Gy, the DMin to a tumor volume of 0.03 cm³ equal to 3.48 Gy, and the DMin to a tumor volume of 0.1 cm³ equal to 6.16 Gy.

For in vivo photoconversion, B16 tumors were implanted in Kaede mice (24), and once established, animals were completely covered in aluminum foil except for half of the tumor, which was exposed to a 405-nm light-emitting diode light source using a collimator for 5 min (Prizmatix). To deliver differential doses to the photoconverted versus unconverted regions, mice received CT-guided radiation using the SARRP from Xstrahl. Dosimetry was performed using Murislice software from Xstrahl. The SARRP delivered a single dose of 2 Gy to an isocenter within the photoconverted region and 20 Gy to an isocenter in the unconverted region or vice versa, using a collimator to restrict dose to neighboring tumor region and normal tissue. One day after photoconversion, the TDLN was harvested and digested as previously described for flow cytometry (70).

RNA extraction, cDNA preparation, and real-time qPCR

For analysis of tumor tissue, tumor samples were homogenized in TRIzol reagent (Thermo Fisher Scientific, catalog no. 15596026) using a bead mill homogenizer (Bead Ruptor Elite, Omni International, catalog no. 19–040E). Total RNA was extracted using an RNeasy Mini Kit (QIAGEN, Germany, catalog no. 74106) according to the manufacturer’s instructions. Extracted RNA was subjected to complementary cDNA synthesis using the QuantiTect Reverse Transcription Kit (QIAGEN, Germany, catalog no. 205314) according to the manufacturer’s instructions. Quantitative real-time PCR was performed using Taqman Fast Advanced qPCR Master Mix. A complete list of Taqman probes is in table S1. Thermal cycling conditions (Quantstudio 6, Applied Biosystems) included uracil-DNA glycosylase activation at 50°C for 2 min, followed by the Dual-Lock DNA polymerase activation stage at 95°C for 2 min and then 40 cycles of each PCR step (denaturation) 95°C for 1 s and (annealing/extension) 60°C for 20 s. For data analysis, the Ct values were exported to an Excel file, and fold change was normalized to untreated control samples and calculated using the ΔΔCt method. Hypoxanthine phosphoribosyltransferase (Hprt) was used as an endogenous control.

Bulk RNA-seq

RNA-seq was performed on samples collected from high-, moderate-, and low-dose regions on day 3 after BT. For library and RNA-seq, on day 3 after BT, tumors were collected, and tissue samples were taken from the high-, moderate-, and low-dose regions. The samples were homogenized in TRIzol reagent (Thermo Fisher Scientific) using a Bead Ruptor Elite (OMNI). RNA was isolated using Qiagen’s RNeasy Mini Kit according to the manufacturer’s instructions. RNA (1 μg) was used for library preparation (Illumina) with unique dual (UD) indexing (Illumina) according to the manufacturer’s instructions. The prepared libraries were sequenced on an Illumina NOVAseq 6000. Skewer (71) was used to trim adapter sequences from reads and to remove low-quality bases from read 3′ ends before aligning to GRCm38.p6 using Spliced Transcripts Alignment to a Reference (STAR) (72).

For RNA-seq differential expression and pathway enrichment, the RNA-seq by expectation-maximization (RSEM) software package was used to calculate expected gene counts from aligned reads (73). Principal components analysis (PCA) and Pearson correlation between vectors of gene counts that belong to biological replicates were used to detect outlier libraries. Any samples with an interreplicate Pearson correlation less than 0.9 or that did not cluster with replicates by PCA were dropped from downstream analyses. Features representing ribosomal RNA and tRNA and genes with an average gene count of less than 20 across all remaining samples were removed from differential expression analysis. Using R 4.0.5, gene count normalization and differential expression testing were performed using DESeq2 v1.30.1 (44). ClusterProfiler v18.1 was used to perform gene set enrichment analysis of KEGG immune system pathways (74). ComplexHeatmap (75) was used to generate heatmaps of z score–normalized gene expression calculated from DESeq2-normalized gene counts.

Spatial transcriptomics

For tumor harvest and sample preparation, mice bearing B78 tumors were treated with BT (2 Gy to opposite tumor edge). Three days after BT, tumors were harvested, formalin-fixed, and paraffin-embedded. Ten-micrometer-thick tissue sections were cut and placed onto a Visium Gene Expression slide (10x Genomics). Slides were subsequently stained with hematoxylin and eosin (H&E, Millipore Sigma) according to the manufacturer’s instructions and imaged by a Vectra multispectral slide scanner (Akoya). For library preparation and sequencing, tissue permeabilization, reverse transcription, second-strand synthesis, and cDNA amplification were performed according to the manufacturer’s instructions (10x Genomics). Twenty-five percent of amplified cDNA was used for gene expression library preparation; libraries were sequenced on an Illumina NextSeq 500 (Illumina). For analysis of spatial transcriptomic data, space Ranger (v2.0.0) (10x Genomics Visium Spatial Software Suite) was used to perform sample demultiplexing, barcode processing, and gene counting. Reads were aligned to the mouse mm10 reference genome from 10x Genomics. Raw count data were adjusted for spot-swapping using SpotClean (v 1.1.1) (76). Spots were filtered manually for outlier feature counts, unique molecular identifier counts, and nontissue association. Spots were clustered using BayesSpace (v1.8.2), and Seurat (4.3.0) was used to perform normalization and variance stabilization of adjusted count data using the SCTransform v2 (77, 78) method while “regressing out” cell cycle– related variation before subsequent transfer of cell type predictions from condition-matched scRNA-seq data to deconvolute spots by cell types. SCTransform-normalized expression values were used for spatial transcriptomic visualization. To calculate distance to BT seed, a single spot was manually annotated as a point source on the basis of the tattoo dwelling position in the tissue. The distance from the center of each spot was calculated, in Euclidean fashion, relative to the center of the point source spot. scLANE (https://github.com/jr-leary7/scLANE) was used to build spline-based negative binomial generalized linear models of SCTransformed gene counts relative to BT distance.

Multiplex immunohistochemistry

Multiplex immunohistochemistry was performed using the OPAL 7-color fIHC kit (PerkinElmer) according to the manufacturer’s instructions. Antibodies and specific staining parameters used include F4/80 (Ab6640, Abcam)—Opal 520, 1:50 for 45 min at room temperature; CD11b (Ab133357, Abcam)—Opal 540, 1:1500 for 15 min at room temperature; CD4 (Ab183685, Abcam)—Opal 650, 1:500 for 15 min at room temperature; CD8 (Ab217344, Abcam)—Opal 690, 1:250 for 15 min at room temperature; FOXP3 (Invitrogen, 17–5773-82)—Opal 520, 1:50 overnight at 4°C; CD161 (Ab234107, Abcam)—Opal 540, 1:4000 for 5 min at room temperature; and CD161 (Ab234107 Abcam)—Opal 540, 1:4000 for 5 min at room temperature. Slides were imaged using the Nuance Multispectral microscope (PerkinElmer), and images were analyzed using inForm V2.0.2 (PerkinElmer) batch analysis.

Single-cell RNA sequencing

For tumor harvest and dissociation, tumors were minced and digested using the Miltenyi Biotec tumor dissociation kit (mouse, tough tumor dissociation protocol) for 40 min at 37°C. Cells were then strained through a 70-μm filter and washed with fluorescence-activated cell sorting (FACS) buffer [PBS (Gibco) with 5 mM EDTA (Sigma-Aldrich) and 2% FBS (Gibco)]. Red blood cells were lysed with ammonium-chloride-potassium lysis buffer (Lonza), washed again with FACS buffer, and strained through a 40-μm filter. Cells were then washed and stained for cell sorting.

For FACS of CD45⁺ cells for scRNA-seq, single-cell suspensions of tumors were blocked with 1 μg of purified rat anti–mouse CD16/CD32 (BioLegend) for 10 min at room temperature and then stained with 0.5 μl of Live/Dead dye (Ghost Red Dye 780, Tonbo Biosciences) and 1 μg of anti–mouse CD45 phycoerythrin (BioLegend) for 30 min on ice. Live CD45⁺ cells were isolated for scRNA-seq using a FACSAria (BD Biosciences) sorter and resuspended in PBS with 0.04% bovine serum albumin at a concentration of 1000 cells/ μl for scRNA-seq.

Single-cell library preparation and sequencing and analysis of scRNA-seq data were performed as described (79). Briefly, single-cell suspensions from sorted live CD45⁺ cells were loaded on a Chromium iX single cell instrument (10x Genomics) to generate single-cell beads in emulsion, and scRNA-seq libraries were prepared using the Chromium single cell 3′ reagent kits (v3), including Single Cell 3′ Library and Gel Bead Kit v3 (PN-1000075), Single Cell 3′ Chip Kit v3 (PN-1000154), and i7 Multiplex Kit (PN-120262) (10x Genomics) following the Single Cell 3′ Reagent Kits (v3) User Guide. Single-cell barcoded cDNA libraries were quantified by qPCR (Kappa Biosystems) and sequenced on an Illumina NextSeq 500 (Illumina) according to 10x Genomics recommendations (26 cycles read 1, 8 cycles i7 index read, and 91 cycles read 2). Cells were sequenced to greater than 50,000 reads per cell as recommended by the manufacturer. The CellRanger single-cell software suite (v6.1.2) was used to perform sample demultiplexing, barcode processing, and single-cell 3′ gene counting. Reads were aligned to the mouse (mm10) reference genome. Cell-containing droplets defined in the CellRanger filtered feature matrix were further filtered to remove cells with <100 detected features and to remove features detected in fewer than 10 cells. Poor-quality cells with >10% mitochondrial gene content were removed, and doublets were predicted with scDblFinder (1.4.0) and also removed. Cell type predictions were made on remaining cells using SingleR (1.4.1) with mouse ImmGenData from the celldex (1.0.0) package. Cells with <350 features with additional removed and remaining cells were grouped into myeloid and lymphoid classes on the basis of SingleR predictions to identify compartment-specific subclusters. For each compartment, Seurat (4.1.0) was used to perform normalization and variance stabilization of molecular count data from the SCTransform v2 method while regressing out cell cycle and mitochondrial-related variation before subsequent integration, with canonical correlation analysis reduction, of cells across samples followed by Louvain graph–based cell clustering with multilevel refinement (77, 78). We selected cluster resolution according to Seurat recommendations and used UMAP visualization to confirm appropriate clustering. Graphics were generated using Seurat and ggplot2 R packages.

DESeq2 (80) (1.20.1) nbinomLRT, along with computeSumFactors (81) from scran (1.18.7) and glmGamPoi (82) (1.2.0) packages, was used to perform scRNA-seq differential expression testing from raw gene counts. The Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE) (83) odds ratio method was used for gene set activation scoring of individual cells. Secondary T cell subtype and activation states for lymphoid cells were predicted using the ProjecTILs (84) (2.2.1) package and mouse tumor-infiltrating lymphocyte (TIL) atlas.

In vitro coculture

Tumor cells were plated in 96-well plates (40,000 cells per well) containing RPMI 1640 medium supplemented with 10% FBS, penicillin (100 U/ml), and 780 streptomycin (100 μg/ml). Fresh medium was exchanged after 5 days of culture, and splenocytes harvested from treated, naïve, or disease-free mice were added (400,000 cells per well; 10:1 effector to target ratio). Splenocytes were collected from spleens of mice mechanically digested using a syringe plunger in the presence of RPMI 1640 (4 ml, Corning), filtered through a 70-μm strainer (Falcon), and then centrifuged (300g, 10 min, 25°C). The cell pellet was resuspended in red blood cell (RBC) lysis buffer (BioLegend) for 10 min at room temperature. After 24 hours of coculture, splenocytes were harvested for analysis of activation markers using flow cytometry.

Flow cytometry

Flow cytometry was performed as previously described (85), with fluorescent beads (UltraComp Beads eBeads, 176 Invitrogen, #01–222-42) to determine compensation and fluorescence minus one methodology to determine gating and analyzed in FlowJo (Tree Star, v10.7). For in vitro analysis, nonadherent CD4 and CD8 cells were collected from culture plates and washed with PBS twice. In either case, total cells were treated with CD16/32 antibody (BioLegend) to prevent nonspecific binding. Live cell staining was performed using Ghost Red Dye 780 (Tonbo Biosciences) according to the manufacturer’s instructions. After live-dead staining, a single-cell suspension was labeled with the surface antibodies at 4°C for 60 min and washed three times using flow buffer (2% FBS + 2 mM EDTA in PBS). For intracellular staining, the cells were fixed and stained for internal markers with permeabilization solution according to the manufacturer’s instructions (BD Cytofix/Cytoperm). Flow cytometry was performed using an Attune NxT Flow Cytometer (Thermo Fisher Scientific).

Analysis of photoconverted DCs in TDLNs was performed as previously described (23). Briefly, capsules were cut open and incubated with enzymatic mix [collagenase IV (250 U/ml; #LS004188; Worthington Biochemical), deoxyribonuclease I (30 U/ml; #4536282001; Millipore Sigma), 5 mM CaCl₂, 5% heat-inactivated FBS, and Hanks’ balanced salt solution] at 37°C for 15 min with agitation. Enzyme mix containing TDLN was then vigorously pipet-mixed and incubated at 37°C for an additional 15 min. Enzymatic reactions for both the tumor and TDLN were quenched using ice-cold RPMI containing 10% FBS and 2 mM EDTA. Single-cell suspensions were then filtered through 100-μm (tumor) or 40-μm (TDLN) nylon cell strainers to remove macroscopic debris. Cells were washed and counted. Live cells were identified using Zombie Aqua Viability Dye from BioLegend (#423102) for 10 min on ice, Fc receptors were blocked with α-CD16/CD32 antibodies from BD Biosciences (2.4G2) for 10 min on ice, and cells were stained with a surface antibody cocktail in FACS buffer for 20 min on ice. After surface staining, cells were washed in FACS buffer, fixed for 20 min on ice with fixation/permeabilization buffer from BD Biosciences (#554722), resuspended in FACS buffer, and acquired on a BD Fortessa flow cytometer.

Gating strategies for flow cytometry were as follows: Migratory CD103⁺ cDC1s were gated as leukocytes/single cells/live/CD45⁺/ CD90.2⁻CD19⁻/Ly-6C⁻/MHC-II⁺ CD11c⁺/CD8α−/CD103⁺, resident CD8α+ cDC1s were gated as leukocytes/single cells/live/CD45⁺/ CD90.2⁻CD19⁻/Ly-6C⁻/MHC-II⁺ CD11c⁺/CD103⁻/CD8α+, migratory CD11b⁺ cDC2s were gated as leukocytes/single cells/live/CD45⁺/ CD90.2⁻CD19⁻/Ly-6C⁻/MHC-IIhigh CD11c⁺/CD8α−/CD103⁻/CD11b⁺, and resident CD11b⁺ cDC2s were gated as leukocytes/single cells/live/CD45⁺/CD90.2⁻CD19⁻/Ly-6C⁻/MHC-IIint CD11c⁺/CD8α−/CD103⁻/CD11b⁺. A complete list of antibody targets, clones, and fluorophores is provided in table S2.

Single-cell multiplex cytokine profiling of murine PBMCs

Single-cell multiplex cytokine profiling was performed using the Isoplexis platform on PBMCs cryopreserved on day 20 after treatment. They were thawed, enriched for CD4 and CD8 T cells, stimulated, stained with Alexa Fluor 647–conjugated anti–mouse CD4 or CD8 (BioLegend), and then loaded onto an IsoCode chip. Single-cell multiplex cytokine profiling was performed with the Isoplexis platform. PBMCs were collected on day 20 after treatment and cryopreserved (90% FBS + 10% dimethyl sulfoxide). PBMCs were thawed and recovered in complete RPMI 1640 medium (Thermo Fisher Scientific) with IL-2 (10 ng/ml; BioLegend) at 37°C and 5% CO₂. CD4 T cells and CD8 T cells were separated by anti-CD4 or anti-CD8 microbeads (Miltenyi Biotec) per the manufacturer’s instructions. Enriched CD4 and CD8 T cells were resuspended in fresh complete RPMI 1640 medium at 1 × 10⁶/ml and activated with immobilized anti–mouse CD3 (10 μg/ml; Thermo Fisher Scientific) and soluble anti–mouse CD28 (5 μg/ml; Thermo Fisher Scientific) in a 96-well flat-bottom plate (Corning Life Science) at 37°C and 5% CO₂ for 48 hours. After stimulation, cells were stained with Alexa Fluor 647–conjugated anti–mouse CD4 or CD8 (BioLegend) at room temperature for 20 min and loaded onto an IsoCode chip. Each IsoCode chip contains approximately 12,000 microchambers prepatterned with a full copy of 28-plex antibody array including effector: granzyme B, tumor necrosis factor–α, IFN-γ, and MIP-1α; stimulatory: granulocyte-macrophage colony-stimulating factor, IL-2, IL-5, IL-7, IL-12p70, IL-15, IL-18, and IL-21; chemoattractive: B cell attracting chemokine, C-C motif chemokine 11, IP-10, and RANTES; regulatory: Fas, IL-4, IL-10, IL-13, IL-27, and sCD137; inflammatory: IL-6, IL-17A, monocyte chemoattractant protein 1, and IL-1β; and other: keratinocyte-derived chemokine and TGF-β1. The polyfunctional profile (2+ proteins per cell) of single cells was evaluated by IsoSpeak software.

TCR sequencing

On day 20 after treatment initiation, B78 flank tumors were harvested from mice treated with sham control, BT (2 Gy to proximal tumor edge), BT + ICI, EBRT (2, 8, or 20 Gy) + ICI, or ICI alone. Total RNA was isolated as previously described followed by sample library preparation with the SMARTer Mouse TCR a/b Profiling Kit (634402, Takara). Pooled libraries were sequenced with miSeq (Illumina). After initial filtering and alignment with the MiXCR v3.0.9 analysis platform, the total CDR3 of TCR α and β chains for each sample was determined along with the total number of unique CDR3 sequences. TCR repertoire diversity was determined using the Shannon diversity index, and clonal expansion was assessed by calculation of the D50 score, which represents the percentage of distinct clonotypes that account for greater than or equal to 50% of the total of sequencing reads.

Statistical analysis

Individual-level data for experiments where n < 20 are presented in data file S1. Prism 8 (GraphPad Software) and R version 4.2.1 (R Foundation) were used for all statistical analyses. One-way analysis of variance (ANOVA) with Tukey’s post hoc test to adjust for multiple comparisons was used to assess statistical significance of observed mean differences in gene expression and immune cell quantification. For comparisons between two groups, Student’s t test was performed. For tumor growth analysis, a linear mixed model after log transformation of tumor volume was fitted on treatment and day. Day and the interaction between treatment and day were fixed effects. When testing differences in slopes of log-transformed tumor volume, Tukey’s adjustment for multiplicity was used. The Kaplan-Meier method was used to estimate the survival distribution for the overall survival. A Cox regression model was fitted, and pairwise comparison of the overall survival was made using a log-rank test with Benjamini-Hochberg adjustment of P values between levels of factors. A chi-square test was used to compare complete response. All data where applicable were confirmed to have a normal distribution by Shapiro-Wilk testing. All data presented are reported as mean ± SEM. For all graphs, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001, and ns indicates no significant difference.

Supplementary Material

Figure S1

NIHMS2024148-supplement-Figure_S1.pdf^{(8.5MB, pdf)}

Figure S2

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Figure S3

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Figure S4

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Figure S5

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Figure S6

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Figure S7

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Supplemental Tables

NIHMS2024148-supplement-Supplemental_Tables.pdf^{(212.1KB, pdf)}

Supplementary Material

NIHMS2024148-supplement-Supplementary_Material.docx^{(48KB, docx)}

Reproducibility Checklist

NIHMS2024148-supplement-Reproducibility_Checklist.docx^{(68.3KB, docx)}

Data File S1

NIHMS2024148-supplement-Data_File_S1.xlsx^{(208.6KB, xlsx)}

Acknowledgments:

We would like to thank the University of Wisconsin Carbone Cancer Center (UWCCC) for support of this project, colleagues A. Erbe and A. Rakhmilevich for many helpful discussions, and T. Berg and S. McIlwain for assistance with editorial revisions. We would also like to acknowledge the University of Wisconsin Small Animal Imaging and Radiotherapy Facility. We thank the University of Wisconsin Translational Research Initiatives in Pathology laboratory (TRIP), supported by the UW Department of Pathology and Laboratory Medicine, UWCCC (P30 CA014520) and the Office of the Director-NIH (S10 OD023526) for use of its facilities and services. We thank the University of Wisconsin-Madison Biotechnology Center Gene Expression Center and DNA Sequencing Facility for providing library preparation and next-generation sequencing services. We would like to acknowledge the University of Wisconsin Biotechnology Center DNA Sequencing Facility and Bioinformatics Resource Center for providing Illumina sequencing and data processing services, the University of Wisconsin- Madison Center for High Throughput Computing for computational resources, and the UWCCC Cancer Informatics Shared Resource for support.

Funding:

This work was funded by NIH grant P30 CA014520 Cancer Center Core grant applicable to J.C.J., J.M.V., W.J.J., A.G.S., P.A.C., R.N.S., T.C.H., I.C., R.H.A., K.K., P.M.H., P.M.S., M.A.N., J.R.M., I.M.O., and Z.S.M. This work was also supported by NIH grants P50 DE026787, 1DP5OD024576, and P01CA250972 (to Z.S.M.); NIH grants T32GM140935, TL1TR002375, and F30CA250263 (to J.C.J.); NIH grant R35CA197078 (to P.M.S.); and NIH grant R01GM102756 (to M.A.N.).

Footnotes

Competing interests:

Z.S.M. is a member of the scientific advisory boards for Archeus Technologies, Seneca Therapeutics, Cali Biomedical, and NorthStar Medical Isotopes. Z.S.M. has a contract research agreement paid to University of Wisconsin from Telix Pharmaceuticals and a research agreement paid to the University of Wisconsin by Point Biopharmaceuticals. Z.S.M. has received consulting fees from Johnson & Johnson. Z.S.M. has stock options with Archeus Technologies, NorthStar Medical Radioisotope, Cali Biomedical, and Seneca Therapeutics and has received drugs for research from XRD Therapeutics, Bayer, Hibercell, AstraZeneca, and Nektar Therapeutics. Z.S.M. and P.M.S. are inventors on or filed patents managed by the Wisconsin Alumni Research Foundation relating to mAb-related immunotherapies and the interaction of targeted radionuclide therapies and immunotherapies. Patents including Z.S.M. and P.M.S. are US 10,736,949 (Radiohalogenated agents for in situ immune modulated cancer vaccination), US 10,751,430 (Targeted radiotherapy chelates for in situ immune modulated cancer vaccination), US 11,730,834 (Targeted radiotherapy chelates for in situ immune modulated cancer vaccination), US 11,633,506 (Using targeted radiotherapy to drive anti-tumor immune response to immunotherapies), and US 11,096,899 (Bacterial membrane nanoparticles as an immunotherapy system for cancer treatment). Z.S.M. has filed US Patent no. 11,672,956 (Multi-purpose catheter for brachytherapy and intratumoral injection), application number (app. no.) 63/282987 (A nanoparticle to potentiate the in situ vaccine effect of RT and its anticancer efficacy in combination with immunotherapies), app no. 63/408610 (Compositions and methods to enhance therapeutic efficacy of cancer therapies), and app no. 63/508578 (Treatment of solid tumors with targeted radionuclide therapy and CAR T cell therapy). P.M.S. has filed patent no. 4,265,873 (Method for typing human leukocyte antigens), patent no. 5,443,983 (Method of culturing lymphocytes and method of treatment using such lymphocytes), app. no. US17/904,706 (BiSpecific GD2 and B7-H3 binding molecules), and app. no. US17/148,33 (Multispecific Treg binding molecules). J.C.J. has filed US patent no. 11,672,956 (Multi-purpose catheter for BT and intratumoral injection). J.M.V. owns shares in Teva Pharmaceutical.

Data and materials availability:

All data associated with this study are present in the paper or the Supplementary Materials. All sequencing data presented in this manuscript have been deposited in publicly accessibly databases. The bulk RNA-seq counts are available in Zenodo with the following DOI: 10.5281/zenodo.8006631. The scRNA-seq data and spatial transcriptomic data are available in the NCBI Gene Expression Omnibus (GEO) database as a SuperSeries under the accession code GSE234161. The Kaede mice [B6. Cg-Tg(CAG-tdKaede)15Utr, RBRC05737] are available from RIKEN BioResource Research Center,3-1-1 Koyadai, Tsukuba, Ibaraki, Japan, under a material transfer agreement with the originator. Correspondence and requests for materials should be addressed to Z.S.M.

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Associated Data

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

Supplementary Materials

Figure S1

NIHMS2024148-supplement-Figure_S1.pdf^{(8.5MB, pdf)}

Figure S2

NIHMS2024148-supplement-Figure_S2.pdf^{(1MB, pdf)}

Figure S3

NIHMS2024148-supplement-Figure_S3.pdf^{(3MB, pdf)}

Figure S4

NIHMS2024148-supplement-Figure_S4.pdf^{(1.9MB, pdf)}

Figure S5

NIHMS2024148-supplement-Figure_S5.pdf^{(686.8KB, pdf)}

Figure S6

NIHMS2024148-supplement-Figure_S6.pdf^{(473KB, pdf)}

Figure S7

NIHMS2024148-supplement-Figure_S7.pdf^{(499.3KB, pdf)}

Supplemental Tables

NIHMS2024148-supplement-Supplemental_Tables.pdf^{(212.1KB, pdf)}

Supplementary Material

NIHMS2024148-supplement-Supplementary_Material.docx^{(48KB, docx)}

Reproducibility Checklist

NIHMS2024148-supplement-Reproducibility_Checklist.docx^{(68.3KB, docx)}

Data File S1

NIHMS2024148-supplement-Data_File_S1.xlsx^{(208.6KB, xlsx)}

Data Availability Statement

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PERMALINK

Intratumoral radiation dose heterogeneity augments antitumor immunity in mice and primes responses to checkpoint blockade

Justin C Jagodinsky

Jessica M Vera

Won Jong Jin

Amanda G Shea

Paul A Clark

Raghava N Sriramaneni

Thomas C Havighurst

Ishan Chakravarthy

Raad H Allawi

KyungMann Kim

Paul M Harari

Paul M Sondel

Michael A Newton

Marka R Crittenden

Michael J Gough

Jessica R Miller

Irene M Ong

Zachary S Morris

Abstract

INTRODUCTION

RESULTS

RT dose heterogeneity enhances antitumor immunity

Fig. 1. Radiation dose heterogeneity enhances response to immune checkpoint inhibition.

Low-, moderate-, and high-dose regions are required for antitumor efficacy

RT dose heterogeneity results in spatial heterogeneity in gene expression

Fig. 2. Radiation dose heterogeneity imprints spatial heterogeneity in gene expression within a tumor.

RT dose heterogeneity results in spatial heterogeneity in immune cell infiltration and trafficking to tumor-draining lymph nodes

Fig. 3. Radiation dose heterogeneity results in spatial heterogeneity in immune cell infiltration and trafficking to TDLNs.

RT dose heterogeneity reprograms myeloid compartment to an antitumor phenotype

Fig. 4. Radiation dose influences immune infiltration signature.

RT dose heterogeneity promotes enrichment of effector-associated T cells

Fig. 5. Heterogeneous radiation promotes enrichment of TH1 CD4 cells and effector-associated CD8 T cells.

Antitumor immune memory is induced by heterogeneous RT and is dependent on both CD4 and CD8 T cells

Fig. 6. CD4 and CD8 T cells are required for antitumor response generated by heterogeneous radiation therapy and immune checkpoint inhibition.

Heterogeneous RT selectively induces expression of effector-related cytokines and clonal expansion of CD8 T cells

Fig. 7. Radiation dose heterogeneity promotes effector-associated cytokine secretion among circulating CD8 T cells as well as tumor infiltration and clonal expansion of CD8 T cells.

DISCUSSION

MATERIALS AND METHODS

Study design

Cell lines

Murine tumor models

Radiation

RNA extraction, cDNA preparation, and real-time qPCR

Bulk RNA-seq

Spatial transcriptomics

Multiplex immunohistochemistry

Single-cell RNA sequencing

In vitro coculture

Flow cytometry

Single-cell multiplex cytokine profiling of murine PBMCs

TCR sequencing

Statistical analysis

Supplementary Material

Acknowledgments:

Funding:

Footnotes

Data and materials availability:

REFERENCES AND NOTES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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