# Sustained Macrophage Reprogramming Is Required for CD8+ T cell-Dependent Long-Term Tumor Eradication

Carolina Jardim, Marta Bica, Mariana Reis-Sobreiro, Afonso Teixeira da Mota, Raquel Lopes, Miguel Ferreira Pinto, Neuza Sousa, Sofia Mensurado, Henning Boekhoff, Tommaso Scolaro, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Carolina Jardim, Marta Bica, Mariana Reis-Sobreiro, Afonso Teixeira da Mota, Raquel Lopes, et al. "Sustained Macrophage Reprogramming Is Required for CD8+ T cell-Dependent Long-Term Tumor Eradication. Cancer Immunology Research, 2025, 13 (8), pp.1207-1225. 10.1158/2326-6066.CIR-24-0797. hal-05416980

HAL Id: hal-05416980

https://amu.hal.science/hal-05416980v1

Submitted on 15 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
Sustained macrophage reprogramming is required for CD8 T cell-
dependent long-term tumor eradication

Carolina Jardim¹#, Marta Bica¹#, Mariana Reis-Sobreiro¹, Afonso Teixeira da Mota¹,
Raquel Lopes¹, Miguel Pinto¹, Neuza Sousa¹, Sofia Mensurado¹, Henning Boekhoff¹,
Tommaso Scolaro¹, Maud Reugebrink¹, Natacha Gonçalves-Sousa¹, Hiroshi Kubo¹,
Catarina Monteiro Gomes²,³, Catarina Brito²,³, Rafael Arguello⁴, Elvira P. Leites¹, Vanessa
A. Morais¹, Bruno Silva-Santos¹, Nuno L. Barbosa-Morais¹* and Karine Serre¹,⁵*

1 Gulbenkian Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

2 iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal

3 Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal

4 Aix Marseille Université, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France

5 GIMM-CARE, Gulbenkian Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

# These authors contributed equally.

* Corresponding authors. Email: Karine Serre karine.serre@gimm.pt; Nuno L. Barbosa-Morais nuno.morais@gimm.pt

**Competing interests:** The authors declare no conflicts of interest.

Total word count (introduction, results, discussion, figure legends, notes): 7724 ; total number of
figures: 6; total number of supplementary figures: 9.

Running title: **Repeated TAM programming boosts antitumor response**

**Abstract**

Tumor-associated macrophages (TAMs) exhibit a dual role in tumor progression and antitumor immunity. However, understanding the functional states and molecular mechanisms of antitumor TAMs remains a challenge. Here, we show that intratumoral administration of combined TLR3 and CD40 agonists (hereafter termed myeloid cell treatment, MCT) reprogram TAMs *in situ* to adopt a protective antitumor phenotype in an orthotopic mouse breast cancer model, leading to tumor regression. Single-cell RNA sequencing of TAMs from different tumor stages and post-MCT revealed a transient antitumor TAM phenotype, present at 12h post-MCT, characterized by markers such as iNOS and CD38, which was replaced by TAMs co-expressing tumor-limiting and promoting features by 72h post-MCT. Maintenance of antitumor TAMs required repeated MCT administrations, and ROS and TNF-α were pivotal molecular mechanisms in TAM-mediated tumor control. Importantly, repeated MCT promoted the activation of CD8 T cells and long-term tumor eradication. Our findings uncover the vulnerability of transient TAM reprogramming which can be overcome by repeated MCT administrations to sustain efficient immune responses.
44

**Introduction**

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

Tumor-associated macrophages (TAMs) dominate the immune landscape in solid tumors, and their multifaceted phenotypes influence tumor progression (1). TAMs promote tumor growth, angiogenesis, invasion, metastasis, and immune evasion, complicating therapeutic responses and often leading to poor clinical outcomes (2, 3). Yet, in certain cancers, such as ovarian, HER2+ breast cancer and colorectal cancer, TAMs are associated to improved survival (2, 4, 5).

Historically, macrophages have been segregated into M1 and M2 phenotypes, with M1 macrophages driving antitumor responses and M2 macrophages fostering tumor progression. According to the immune contexture, in various solid cancer types, M1 features are associated with favorable prognosis, while M2 features correlate with poor outcomes (6). However, this simplistic classification fails to capture the full spectrum of macrophage diversity and plasticity in the tumor microenvironment (TME) (7). Single-cell omics assays have uncovered a remarkable heterogeneity among macrophages, unexpectedly revealing the coexistence of M1 and M2 signatures within macrophage clusters (8). This highlights a complexity beyond simple dichotomy underscoring the need to study TAMs longitudinally *in situ* to understand the contextual dynamic diversity of their functional states in the TME.

Most literature focuses on describing TAMs from progressing tumors and emphasizes TAMs that support cancer progression, thus overshadowing that macrophages can also limit tumor growth. Our incomplete understanding of the complex diversity and plasticity of TAMs has hindered the development of monocyte/macrophage-targeting strategies for clinical use. A key challenge is deciphering the molecular mechanisms of antitumor TAM dynamics and phenotypic heterogeneity. A limited number of single-cell RNA sequencing (scRNA-seq) studies have explored antitumor TAMs *in vivo* (9, 10), and efforts are underway to standardize functional annotations (11, 12). Naturally occurring antitumor TAMs are difficult to capture, as they may operate principally in early asymptomatic developing tumors (13, 14). Consequently, the molecular determinants of protective TAM functions, and their dynamics and maintenance mechanisms remain poorly defined.

Here, we demonstrate the potential of combined TLR3 and CD40 agonists, referred to as myeloid cell-treatment (MCT), to induce tumor-killing macrophages *in vitro* and to promote
77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

robust tumor regression *in vivo*, in mice orthotopically transplanted with E0771 breast tumor cells. Notably, TAMs controlled tumor growth within the first 3 days post-MCT, and complete and durable tumor eradication required three consecutive MCT injections. To dissect the underlying dynamics and heterogeneity of TAMs, we isolated them from early-stage not-treated tumors (NT), late-stage progressing tumors (PT), and 12h and 72h post-MCT treatment (MCT-12h and MCT-72h, respectively). Using scRNA-seq, we identified an antitumor phenotype in MCT-activated TAMs, characterized by enhanced expression of inducible nitric oxide (NO) synthase (iNOS) and CD38. These antitumor TAMs were transiently present in the TME and required MCT reinjection for phenotypic and functional maintenance. Furthermore, reactive oxygen species (ROS) and tumor necrosis factor-alpha (TNF-α) acted as potent antitumor effector molecules of MCT-activated TAMs *in vivo*. MCT-induced TAMs promoted tumor immunogenicity and self-antigen presentation through IFN-α/β-dependent mechanisms. Finally, MCT also induced intratumor accumulation of effector CD8 T cells, which were essential for long-term tumor eradication. Overall, our data support a framework strategy for sustaining antitumor TAMs *in vivo* as to enable durable tumor elimination in cooperation with cytotoxic T lymphocytes.

## Materials and Methods

97

98

### Mice and tumor cell line

99

100

101

102

103

104

105

106

107

108

109

110

C57Bl/6J (B6) wild-type mice, B6.Rag2<sup>-/-</sup>γc<sup>-/-</sup>, B6.Jhl<sup>-/-</sup>, B6. Il15r<sup>-/-</sup>, B6.Tcrα<sup>-/-</sup> and B6.Tcrδ<sup>-/-</sup> mice were purchased from Instituto Gulbenkian de Ciencia (Oeiras, Portugal) or Charles River Laboratories International Inc.. Batf3<sup>-/-</sup> mice were kindly provided by Dr. Caetano Reis e Sousa (Francis Crick Institute, London, UK) with permission from Dr. Ken Murphy (Washington University School of Medicine, Washington, US). Nos2<sup>-/-</sup> mice were kindly provided by Dr. Margarida Correia Neves (University of Minho, Braga, Portugal). Mice were housed under specific pathogen-free conditions and the genetically modified animals were bred at the Instituto de Medicina Molecular – João Lobo Antunes (iMM-JLA), Lisbon, Portugal, under conventional conditions. Standard food and water were given *ad libitum*. Animals were females 5–15 weeks of age, aged-matched within 4 weeks. All experimental procedures, followed European Union guidelines, and were performed in compliance with the relevant laws and institutional guidelines and approved by the institutional animal welfare
body – ORBEA-iMM-JLA – and by the Portuguese national authority for animal health –
Direcção-Geral de Alimentação e Veterinária (DGAV).

B16F10 (RRID:CVCL_0159), CT26 (RRID:CVCL_7254), E0771 (RRID:CVCL_GR23) and
MC38 (RRID:CVCL_B288) cell lines were purchased from Tebu-Bio, ATCC or Kerafast
companies, and maintained in our laboratory. LKR cell line was a kind gift from Dr. Zvi
Fridlender (The Hebrew University of Jerusalem). All cell lines were grown in Dulbecco's
modified Eagles' medium (DMEM) with 10% (v/v) FCS (Gibco; Thermo Fisher Scientific)
and 1% (v/v) penicillin/streptomycin (Sigma/Merck). Stock vials from lower passages were
kept at -80 °C until thaw for each experiment. Tumor cells were tested for mycoplasma
contamination regularly (Mycoalert Mycoplasma Detection kit from VWR). After thawing
cells were kept at 37°C in a humidified incubator in the presence of 5% CO2 and passed only
once before mice implantation. For tumor cell injections, E0771 cells at ~70% confluence
were detached using TrypLE Express (Gibco; Thermo Fisher Scientific) for 5 min at 37°C, and
cells were resuspended in ice-cold PBS with 50% Matrigel® Growth Factor Reduced
(Corning, ref: 356231). For in vitro analysis, E0771 tumor cells were prepared as a single-cell
suspension for flow cytometry staining, in vitro killing assays, in vitro MHC-I and self-antigen
presentation quantification.

For experiment of self-antigen presentation, E0771 cell line was transduced with a GFP-OVA
retroviral vector as described here (15) or a simple GFP pMIG retroviral vector. For the
transduction, 2x10⁵ E0771 cells were plated in a 48-well plate and 8 µg/mL of polybrene (TR-
1003-G, Merck Life Science) with 40 µL of retroviral suspension. Plate was centrifuged at
974g for 1h at 31°C and incubated at 37°C for more 8h. The E0771-GFP-OVA cell line and
the E0771-GFP cell line were then washed with cDMEM and sorted by flow cytometry to
guarantee >98% GFP+ cells.

136

In vivo tumor transplantation and treatments

For orthotopic breast cancer model, anesthetized mice received 1x10⁶ E0771 cells, 1:1 mixture
of PBS with Matrigel (Corning), subcutaneously (s.c.) in the mammary fat pad of the left flank
(16). B16F10 (5 × 10⁴), CT26 (5 × 10⁵), MC38 (1 × 10⁶) or LKR (1 × 10⁶), were injected s.c.
in the flank of mice, in PBS with 50% Matrigel (except B16F10), in a volume of 50 µL.
Tumor growth was assessed every 2-3 days using calipers and calculated as (length × width ×

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142
width)/2. Mice were sacrificed for analysis, by CO2 narcosis, at different time-points or when tumor size reached a humane endpoint (length of 10mm or ulceration). Mice that did not develop visible tumors were excluded from the study. When tumors reached 50-100mm3 of volume, mice were treated every 3 days with 50µl of intra-tumoral (i.t.) solution of 50 µg of the TLR3 agonist poly I:C (InvivoGene; tlrl-picw) and 15 µg of an agonistic anti-CD40 mAb (Bio X Cell Cat# BE0016-2, RRID:AB_1107647). Mice were randomized based on their tumor sizes to homogenize the groups.

For *in vivo* monocyte/macrophage depletion, 100 µl of clodronate liposomes (Liposoma B.V.) were injected s.c. and intraveinously (i.v.) one day before, one day after and then concurrently with MCT at day 3 (T3) and 6 day 6 (T6). For *in vivo* CD8 T cell depletion, 100 µg anti-CD8α (clone YTS169) + 100 µg anti-CD8α (clone YTS156) (kindly provided by Luis Graça (iMM, Portugal)) were injected intraperitoneally (i.p.), and 30µg directly in the tumor (i.t.) one day before the first MCT administration (T0) and then every 5 days. For *in vivo* NK cell depletion, 200 µg anti-NK1.1 (Bio X Cell Cat# BE0036, RRID:AB_1107737) was injected i.p., and 50µg i.t. one day before the first MCT administration (T0) and then every 5 days. For ROS inhibition (Fig. S5B), N-acetyl cysteine (NAC; Sigma/Merck) resuspended in PBS (pH = 7) was administrated i.t., at a concentration of 15 mg/kg/mice, one day before MCT and then every other day. For iNOS inhibition, 280µg and 30µg of 1400W (ab120165; Abcam) plus 1mg and 50µg of Aminoguanidine Hydrochloride (AG; ab120123; Abcam) were injected i.p. and i.t., respectively. For TNFα blockade (Fig. S5B), 250µg and 30µg of anti-mouse TNFα antibody (Bio X Cell Cat# BE0058, RRID:AB_1107764) were injected i.p. and i.t., respectively. iNOS inhibitors and anti-TNFα were administered one day before, concurrently with MCT, one day after and then only i.t. every other day.

## Tumor tissue processing and cell isolation

Tumors resected from mice were cut in small pieces and digested with 1mg/mL collagenase Type I (Worthington), 0.4 mg/mL collagenase Type IV (Worthington) and 10µg/mL DNase I (Sigma-Aldrich) for 30 min at 37°C, in Eppendorf tubes. Cell suspensions were then filtered through a 100µm nylon cell strainer (Corning). Red blood cells were lysed using RBC Lysis Buffer (Biolegend) for 10 min at RT in the dark. Cells were centrifuged and resuspended in cRPMI (RPMI + Sodium Pyruvate (100x) + Non Essential Acids Amines (100x) + Hepes
174

(100x) + Peni-Strep (100x) + βMercaptoethanol (1000x) + FCS 10%) and immediately processed for the specific readout.

175

176

177

## Generation of bone marrow-derived macrophages (BMDMs)

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

BMDMs were derived from haematopoietic progenitors flushed from two femurs with 10ml syringe and 0,5mm needles filled with RPMI 1640 supplemented with 10% (v/v) FCS, 0,2% (v/v) P/S and 50 ng/ml macrophage-colony stimulating factor (M-CSF, Peprotech) (BM-medium hereafter). BM cell suspension was filtered through a 100 µm nylon cell strainer (Falcon/Corning) and $4 \times 10^6$ BM cells were seeded in petri dishes (1.6502055, PS estéril, 90mm, Normax) with BM-medium for 6 days, at 37°C with 5% CO2. On day 6 BMDM were mechanically detached, using a cell scraper (P21040, ABDOS LifeScience), from the petri dishes and $2 \times 10^6$ cells were distributed per well in a 6-well plate. On day 7, BMDMs were polarized for 6 or 24h, at 37°C with 5% CO2, with the following conditions: M0 – unstimulated; M1 – LPS (50ng/ml, (InvivoGene) + IFNγ (50ng/ml, PeproTech); M2a –IL-4 (50ng/ml, PeproTech) + IL-13 (50ng/ml, PeproTech) and MCT – Poly I:C (10µg/ml, InvivoGene; tlrl-picw) + agonistic anti-CD40 mAb (5µg/ml, InvivoMab, FGK4.5, Cat#: BE0016-2). Supernatants of these BMDMs were collected and stored at -20C⁰ until further use. For analysis, BMDMs were mechanically detached from 6-well plate, washed with PBS and $1 \times 10^5$ macrophages were distributed per well in a 96-well V-bottom plate. Cell viability and phenotype were assessed by flow cytometry.

194

195

## Flow cytometry and cell sorting

196

197

198

199

200

201

202

203

204

205

Prior to staining, cells were incubated with mouse FcγIII/II receptors (CD16/CD32) blocking antibody (clone 93; eBioscience) at 1:200 in cRPMI for 10 min. Cell viability was assessed by Zombie Aqua™ Fixable Viability Kit at 1:400 (Biolegend, 423101), or 7-AAD (Biolegend) at 1:1000 for 20 min at 4 °C. Reduced glutathione (GSH) intracellular level was assessed using ThiolTracker violet (Invitrogen;T10095). For surface staining, cells were incubated in the dark, 30 min at RT, with fluorophore conjugated antibodies: anti-Ly6C (HK1.4; Thermo Fisher Scientific Cat# 53-5932-82, RRID:AB_2574427; 1:400), anti-F4/80 (BM8; Thermo Fisher Scientific Cat# 25-4801-82, RRID:AB_469653; 1:200), anti-CD3ε (17A2; Thermo Fisher Scientific Cat# 47-0032-82, RRID:AB_1272181; 1:200), anti-CD3ε (145-2C11; BioLegend Cat# 100327, RRID:AB_893320; 1:100), anti-CD4 (RM4-5; BioLegend Cat#
206 100547, RRID:AB_11125962; 1:200), anti-CD8a (53-6.7; Thermo Fisher Scientific Cat# 67-
207 0081-82, RRID:AB_2662351; 1:300), anti-CD45 (30-F11; BioLegend Cat# 103146,
208 RRID:AB_2564003; 1:500), anti-CD11b (M1/70; BioLegend Cat# 101222,
209 RRID:AB_493705; 1:400), anti-NK1.1 (PK136; BioLegend Cat# 108723, RRID:AB_830870;
210 1:200), anti-CD19 (6D5; BioLegend Cat# 115530, RRID:AB_830707;1:200), anti-CD38
211 (T10; BioLegend Cat# 102719, RRID:AB_10613289; 1:100), anti-Ly6G (1A8; BioLegend
212 Cat# 127639, RRID:AB_2565880; 1:200), anti-CD11c (N418; BioLegend Cat# 117349,
213 RRID:AB_2563905; 1:200), anti-I-A/I-E (MS/114.15.2; BioLegend Cat# 107645,
214 RRID:AB_2565977; 1:2000), anti-PDL1 (10F.9G2; BioLegend Cat# 124319,
215 RRID:AB_2563619; 1:200), anti-PDL2 (TY25; BioLegend Cat# 107210, RRID:AB_2566345;
216 1:500).

217 For intracellular staining, cell suspensions were re-stimulated in the presence of 50ng/ml LPS
218 (InvivoGene; tlrl-pb5lps) and 50ng/ml mouse recombinant IFN-γ (Peprotech) for myeloid cell
219 analysis; or with with 200ng/ml phorbol 12-myristate 13-acetate (PMA; Sigma/Merck) and
220 1µg/mL ionomycin (Sigma/Merck) for lymphoid cell analysis, during for 3-4 hours at 37°C,
221 5% CO2 in the presence of 5 µg/ml brefeldin-A (Sigma/Merck) and 2 µM monensin
222 (eBioscience/Thermo Fisher Scientific). Note that, for iNOS detection, similar results were
223 obtained by intracellular staining with or without myeloid re-stimulation.

224 Fc receptor blockade, cell surface staining and cell viability was performed as described in the
225 flow cytometry section. Then cells were fixed for 1h with fixation/permeabilization buffer
226 from Foxp3/transcription factor staining Buffer (eBioscience™) at 4°C, followed by
227 intracellular staining in permeabilization buffer (eBioscience™) for 45 min at RT or overnight
228 at 4°C, with the following fluorophore conjugated antibodies anti-IFN-γ (XMG1.2; 25-7311-
229 82; 1:100); anti-TNF-α (MP6-XT22; 48-7321-82; 1:100), anti-NOS2 (CXNFT; 12-5920-82;
230 1:100); all from eBioscience™ or Biolegend. Finally, cells were washed twice with
231 permeabilization buffer (eBioscience™), filtered with 70µm nylon strainers and immediately
232 analyzed.

233 For Annexin V staining, cells were washed with PBS and resuspended in annexin V binding
234 buffer and APC Annexin V Apoptosis Detection kit (Biolegend) following the manufacturer's
235 instructions.
236

Cells were analyzed by flow cytometry were acquired on LSR-Fortessa (BD Bioscience) using FACS Diva software (BD Bioscience), sorted on a FACS Aria III or FACS Aria Fusion (BD Biosciences) and data were analyzed using FlowJo software (BD Biosciences).

237

238

239

240

## Sequencing data analysis

241

### Single cell library preparation

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

Live CD45+CD19-CD3-NK1.1- cells were isolated by flow cytometry, from not-treated and MCT-treated tumor-bearing animals. Cells were isolated in 4 independent experiments. Each group (NT, MCT-72h, PT) included a minimum of three samples from distinct biological replicates, except for MCT-12h, which had two biological replicates. To integrate the four groups and mitigate potential batch effects, we included an NT sample in each FACS-sorting session. For each condition, isolated tumor-infiltrating immune cells were derived from two to four independent animals. Cell concentration and viability of each sample were determined using 0.4% trypan blue (Gibco™: 15250061). Cells were washed and resuspended in 1x PBS (calcium and magnesium free) containing 0.04% BSA, to a final concentration of 1000 cells/µl. In brief, 8,000 to 10,000 cells per condition were loaded onto a 10X Genomics Chromium platform for Gel Bead-in-Emulsion (GEM), complementary DNA (cDNA) generation was performed, and sequencing libraries created with cell- and transcript-specific barcodes, using the Chromium Next GEM Single Cell 3' Kit v3.1 (10x Genomics: PN-1000130), Chromium Next GEM Chip G Single Cell Kit (10x Genomics: PN-1000127) and the Dual Index Kit TT Set A (10x Genomics: PN-1000215), following the supplier's protocol. Libraries were sequenced using the Illumina NovaSeq6000 PE150 targeting 50,000 reads per cell, using services at Novogene (Cambridge, UK).

259

### Data processing

260

261

262

263

264

265

266

Feature-barcode matrices were generated by aligning reads to the mouse genome (GENCODE vM23/Ensembl 98) using CellRanger software. Specifically, CellRanger v3.1.0 was used for samples S1 through S9, and CellRanger v7.0.1 was used for samples MCT1, MCT3, and NT1. A total of 48,934 cells were imported into R (version 4.1.2), merged, and processed collectively. Quality filtering was performed by retaining only cells that met the following criteria: a minimum library size of 3,162 UMIs, a minimum of 3,000 expressed genes, and mitochondrial gene content below 25%. We identified and removed doublets using the
267 scDblFinder package (version 1.8.0, 268 https://bioconductor.org/packages/release/bioc/html/scDblFinder.html). The final dataset, 269 encompassing the entire tumor-infiltrating immune cell populations 40,446 cells. We 270 employed the scran package (version 1.22.1, 271 https://bioconductor.org/packages/release/bioc/html/scran.html) to normalize library size 272 biases resulting from variations in sequencing depth per cell. We utilized the limma package 273 (version 3.50.3, https://bioconductor.org/packages/release/bioc/html/limma.html) for batch 274 effect correction. Specifically, we employed the removeBatchEffect function to mitigate 275 unwanted batch effects associated with the technical variable "dataset". 276

276 Dimensionality reduction and clustering analysis

277 We performed the following analysis using the Seurat package (version 4.1.1, 278 https://satijalab.org/seurat/) unless mentioned otherwise. We selected the top 4,000 highly 279 variable genes (HVGs) using the FindVariableFeatures function with the selection method set 280 to "vst". We performed principal component analysis (PCA) using the selected highly variable 281 genes (HVGs). We then utilized the top 25 principal components (PCs) to construct t-SNE and 282 UMAP plots. We conducted the clustering analysis using shared nearest neighbor (SNN) 283 modularity optimization based on the top 25 principal components (PCs). To identify 284 biologically meaningful clusters, we employed a range of resolutions from 0.1 to 2, with a step 285 size of 0.1. We utilized the clustree package (version 0.5.0, https://cran.r- 286 project.org/web/packages/clustree/index.html) to generate clustering trees, illustrating the 287 relationship between clusters across different resolutions (Fig. S6B). This facilitated the 288 identification of clearly distinct clusters and those that were unstable. Final cluster assessment 289 was conducted by examining the expression of specific markers in each cluster. 290

290 Macrophage subset

291 In the analysis of the full dataset, we identified two major clusters of macrophages by 292 inspecting the differentially expressed genes against the remaining cells. These clusters 293 exhibited upregulation of canonical markers associated with monocytes/macrophages. This 294 group comprised 23,453 cells. Subsequently, we repeated the upstream analysis pipeline for 295 this subset with the following modifications: we selected the top 3,000 HVGs, and we 296 corrected the batch effect associated with the technical variable "sorting". 297

297 Differential expression analysis
298

299

300

301

We conducted differential gene expression analysis on the normalized dataset using the FindAllMarkers/FindMarkers function from the Seurat package. Specifically, we employed the MAST differential expression test for differential gene expression of the clusters (cluster-based analysis) on each subset.

302

## Gene Set Enrichment Analysis

303

304

305

306

307

308

309

310

311

312

313

314

315

316

We used the fgsea package (version 1.20.0, https://github.com/ctlab/fgsea) for pathway enrichment analyses. Each analysis involved the following steps: i) we conducted the MAST test for differential gene expression implemented in the Seurat package. ii) To minimize exclusions from the analysis, we set the threshold for log2-fold change (logfc(threshold) to 0. iii) Genes were ranked based on their log2-fold change values. iv) We provided this ranked gene list alongside the hallmark pathways from the Mouse MSigDB Collections (https://www.gsea-msigdb.org/gsea/msigdb/mouse/collections.jsp) as inputs to the fgsea function to compute the normalized enrichment score (NES) for each hallmark pathway. Briefly, we ranked the genes based on their absolute log2-fold change values. This approach ensured the inclusion of all genes involved in the pathway, regardless of whether they were up- or down-regulated in the group of interest. After performing gene set enrichment analysis (GSEA), we identified the leading edge genes, i.e., the genes that contributed to the normalized enrichment score for each hallmark pathway of interest.

317

## Data availability

318

319

320

321

322

323

324

325

326

327

328

329

The raw scRNA-seq FASTQ files generated in this study were deposited in the NCBI Sequence Read Archive (SRA) under accession PRJNA1156755 and made available by the publication date. Additionally, raw Market Exchange Format (MEX) files as returned by Cell Ranger, for each individual sample, along with batch effect corrected count matrices in log2(counts+1) for both the full dataset and the macrophage subset, are accessible via the NCBI Gene Expression Omnibus (GEO) under accession GSE276345. Processed Seurat .rds objects, including count matrices, metadata and clustering analysis results, are hosted on Zenodo (https://zenodo.org/records/13122751). Any further information about the scRNA-seq dataset generated or analyzed in this study is available from the corresponding authors upon request. All analysis scripts used in this study are available on GitHub (https://github.com/DiseaseTranscriptomicsLab/MCT).
330

## Tumor killing assay

331

332

333

To evaluate direct killing property of BMDM, E0771 tumor cells were stained with CellTrace™ CFSE Cell Proliferation Kit (Life Technologies) 1uM for 15 min at RT, and washed twice with PBS.

334

335

336

337

BMDM of each phenotype (M0, M1, M2 and MCT) were seeded with CFSE E0771 tumor cells at an effector-to-target ratio of 5:1 (BMDM:E0771) in a 96-well flat bottom plate at 37°C, 5% CO2 for 48h. Cells were stained with annexin V, 7-AAD, and fluorescent anti-CD11b as described in the Flow cytometry section.

338

339

340

341

342

343

344

To evaluate indirect killing property, 100µl of each BMDM supernatant (M0, M1, M2 and MCT) was added on E0771 tumor cells previously seeded at 1x10⁴ cells in cDMEM in 96-well flat bottom plate for 2/3h to allow adherence. After 72h incubation, the medium was removed and replaced with 80µl of CellTiter-Blue® Cell Viability Assay (ref: G8080; Promega) diluted 1:20 in cDMEM and cells were incubated for 1h. Cell viability was read using Microplate Reader Tecan Infinite M200, with the following parameters: 10s orbital shaking, 590nm excitation and 560nm emission of fluorescence.

345

346

347

348

In another type of experiment, 5x10⁴ E0771 cells were seeded in cDMEM in 24-well plate overnight, then the medium was replaced with BMDM supernatants (M0, M1, M2, and MCT). After 48h cells were stained with annexin V and 7-AAD as described in the Flow cytometry section.

349

350

## Tumor immunogenicity Assay

351

352

353

354

355

356

357

Ten thousand GFP-pMIG or GFP-OVA-vector E0771 cells were incubated in 96-well flat bottom plate in the presence of 100µl of BMDM supernatant (M0, M1, M2 and MCT) with or without 5 µg/ml of anti-TNFα (XT3.11; Invitrogen), 5 µg/ml of anti-IFNγ (XMG1.2) or 5 µg/mL of anti-IFNα/βR (MAR1-5A3; Selleckchem) for 24h. Then, cells were detached and surface staining with fluorophore conjugated anti-H-2Kb (AF6-88.5; 1:100) or anti-SIINFEKL H-2Kb (25-D1.16; 1:100), from Biolegend, was performed as described in the flow cytometry section.

358

359

## SCENITH™

360

361

One hundred thousand polarized BMDMs were seeded in 96-well plates and treated during 30 min at 37°C, 5% CO2, with control (BM-medium), 2-deoxy-D-glucose (2-DG, 100mM,
362

Sigma-Aldrich), Oligomycin (Oligo, 1 µM, Sigma-Aldrich) or the combination 2-DG + Oligomycin (for this last condition 2-DG is added for 15 min and Oligo supplemented for the next 15 min). Puromycin (Puro, 10 µg/mL, Sigma-Aldrich) was added during the last 15 min. Cells were washed in cold PBS and stained with a combination of mouse Fc receptor blockade and fluorophore conjugated antibodies against surface markers for 30 min at 4°C in flow cytometry Buffer (PBS 1X, 5% FCS, 2mM EDTA). Intracellular staining against puromycin was performed with Foxp3/Transcription factor staining kit (eBioscience), using an anti-puromycin monoclonal antibody (12D10, 1:600, Sigma-Aldrich), as described in the flow cytometry section. Cell dependencies and capacities were obtained as previously described (18).

363

364

365

366

367

368

369

370

371

372

373

## Metabolite and type I IFN analysis

374

To quantify ATP, polarized BMDMs were plated on a 6-well plate, washed with PBS without Ca²⁺ and Mg²⁺ and stored at -20°C. Next, cells were thawed for 30 min on ice and lysed with 50µl/well of extraction buffer 6M guanidine-HC (Sigma-Aldrich, G3272), 100mM Tris/HCl pH7.8 (Sigma-Aldrich, B9755), 4mM EDTA (Sigma-Aldrich, ED2SS). Cell lysates were incubated on ice for 5 min, snap-frozen and then incubated at 95°C for 3 min. Lysates were centrifuged at max speed for 10 min at 4°C and supernatant collected. Protein quantification was performed using the Bradford Protein Assay Kit (5000001, BioRad), according to the manufacturer's instructions. Luciferase-based ATP Determination Kit (Molecular Probes/ThermoFisher Scientific, catalogue nº A22066) was used according to the manufacturer's instructions and ATP content was determined based on a concentration standard curve.

375

376

377

378

379

380

381

382

383

384

385

pH quantification was performed with pH strips (Sigma). One drop was applied on the strip and the yielded color code was compared with a standard scale provided by the manufacturer. Glucose and lactate quantification was performed using Cedex Bio Analyzer 7100 (Roche).

386

387

388

IFN-α and IFN-β were assessed with the Mouse IFN 2-Plex Discovery Assay by the Eve Technologies (Calgary, Canada).

389

390

391

## RNA Isolation, cDNA reverse transcription, and Real-Time PCR

392

mRNA was prepared from CD11b+ FACS-sorted myeloid cell populations using High Pure RNA Isolation kit (Roche). cDNA and relative quantification was performed as described

393
394

395

396

397

398

399

400

401

402

403

404

405

previously (19). Briefly, reverse transcription was performed with random oligonucleotides (Invitrogen) using Moloney murine leukemia virus reverse transcriptase (Promega) for 1 h at 42°C. Relative quantification of specific cDNA was performed with power SYBR® Green PCR master mix (ref:4367659; applied Biosystems™) on ViiA™ 7 Real Time PCR system (applied biosystems™). The Ct values were calculated on QuantStudio™ Real-Time PCR Software v1.7.2 (applied biosystems™). Exported values of Ct for the target gene was subtracted from the Ct for endogenous reference gene (β2 microglobulin), and the relative amount was calculated as 2−ΔCT. Primer sequence: b2m fwd, CTCGGTGACCCTGGTCTTTC, b2m rev, GGATTTCAATGTGAGGCGGG, cxcl9 fwd, CGAGGCACGATCCACTACAA, cxcl9 rev, GAGTCCGGATCTAGGCAGGT.

405

## Kaplan-Meier survival curves

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

Plots of survival curves for human patients were based on normalized gene expression data for 1985 primary breast tumors from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (20) and respective metadata (including information on survival and hormone receptors), retrieved from European Genome-Phenome Archive (EGAC00001000484). Gene expression was profiled with Illumina HT-12 v3 microarrays, with probe-level intensity values being mean-summarized per gene. Relative tumor-associated macrophage (TAM) enrichment was determined, for each tumor sample, by summing the relative abundances of M0, M1, and M2 macrophages, as estimated by CIBERSORTx (21) based on gene expression. Samples were divided into “TAM rich” and “TAM poor” based on the median value of that enrichment. For all plots, stratification of patients into “High” and “Low” was based on the median value of the classifying feature. Plots were generated in R (v. 4.3.1), running function autoplot, from package ggplot2 (v. 3.4.2) (22), on curves generated with functions Surv and survfit from package survival (v. 3.5-5) (23). Function survdiff, also from package survival, was used to test for the differences between the curves, with significance given by the p-value corresponding to the Chi-square statistic.

422

423

424

425

426

## Statistical analysis

Statistics were done in GraphPad Prism v8.4.3 (GraphPad Software) using non-parametric two-tailed Mann-Whitney test. Unless otherwise indicated, individual values and mean are plotted or standard deviation. *p < 0.05; **p < 0.01; ***p < 0.001, ****p < 0.0001. Graphs show data from at least two independent experiments unless otherwise stated.
## Results

TLR3 and CD40 agonists (MCT) induce tumoricidal phenotype and activity in bone marrow-derived macrophages

Tumoricidal M1 macrophages are typically induced *in vitro* by lipopolysaccharide (LPS), a pathogen-associated TLR4 ligand, and interferon-gamma (IFN-γ), which mimics “help” from activated T cells. However, the combination of LPS and IFN-γ exhibits high toxicity, causing fever and sterile septic shock, which limits its use *in vivo* (24). To mimic the collaborative effects of innate and adaptive immune signals, we tested the combination of polyI:C, a TLR3 ligand, with an agonist anti-CD40 antibody – referred to from hereon as myeloid cell treatment (MCT) on bone-marrow derived macrophages (BMDMs) (Fig. 1A). We first compared putative tumoricidal properties of the MCT-phenotype, with that of M0, M1 and M2 polarization phenotypes as references (Fig. 1B). Approximately 30% and 47% of MCT-BMDMs expressed the molecules associated with tumoricidal properties, TNF-α and iNOS, respectively. In contrast, approximately 95% and 93% of M1-BMDMs expressed these effector molecules, respectively. We also looked at the expression of molecules involved in cell-cell interaction and T cell activation. MCT induced similar proportions of ICAM-1 and MHC-II expressing BMDM as M1 polarization (42% versus 26% for MHC-II, and 74% versus 65% for ICAM-1, respectively). Additionally, MCT induced PD-L1 expression to a similar extent as M1 polarization (50% versus 67%), while PD-L2 was not induced, unlike in M2 and M1 polarizations (53% versus 19%, respectively).

We next questioned if MCT-BMDMs could affect tumor cell survival. Mouse E0771 breast cancer cells, stained with CFSE, were co-cultured with BMDMs for 48h and then analyzed by flow cytometry, using the M0 condition for normalization (Fig. 1C). Both MCT-BMDMs and M1-BMDMs significantly impaired E0771 tumor cell survival, resulting in about 57% and 19% live cells, respectively. As expected, M2-BMDMs had no impact on E0771 tumor cell numbers. Direct contact was not required as supernatants from MCT- and M1-BMDMs also reduced E0771 tumor cell viability to about 70% and 45%, respectively (Fig. 1D). Notably, the conditioning MCT medium, by itself, did not affect E0771 tumor cell viability (Fig. S1A). Live dead staining with annexin V and 7-AAD (Fig. S1B) confirmed that conditioned medium
461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

from MCT- and M1-BMDMs induced on average 23% and 26% of E0771 tumor cell death, respectively (Fig. 1E). Altogether, our data showed that MCT induced tumor-killing macrophages.

We noticed that the supernatants of the different induced BMDM phenotypes exhibited different colors, likely indicative of different metabolic activities. Further analysis revealed higher glucose and lower lactate concentrations, with higher medium pH in MCT-BMDMs (7.7 and 7.1 mmol/L respectively and pH 7.6) compared to M1-BMDMs (5.8 and 11.1 mmol/L respectively and pH 6.1) (Fig. 1F). Consequently, ATP content was higher in MCT-BMDM than in M1-BMDM (0.11 versus 0.04 uM/20ug protein respectively) (Fig. 1F). Using SCENITH, a flow cytometry-based method, relying on protein synthesis, for profiling energy metabolism at single-cell resolution (18), we found that MCT-BMDMs displayed a similar energetic profile compared to M0 and M2-BMDMs, and that only M1-BMDMs exhibited lower mitochondrial dependence and higher glycolytic capacity (Fig. 1G). This metabolic distinction correlated with higher viability of MCT-BMDMs compared to M1-BMDMs, with on average 46% versus 21% live BMDM post polarization, respectively (Fig. 1H). Overall, MCT effectively programs BMDMs to be tumoricidal together with higher viability, lower local lactate concentration, and lack of PD-L2 expression, when compared to their M1-BMDM counterparts, potentially reducing local immunosuppression.

**Repetitive injections of MCT are necessary for macrophage-dependent tumor regression in vivo**

We investigated whether MCT could reprogram TAMs and induce tumor regression *in vivo*. Mice received $1 \times 10^6$ E0771 breast tumor cells orthotopically in the mammary fat pad. When tumors reached 50-100 mm³, mice were treated with MCT (Fig. 2A). MCT was administered via intratumoral injections every 3 days until tumor elimination or upon reaching a humane endpoint (1000mm³). In not-treated (NT) and single-agent (Poly:IC or anti-CD40 only) treated animals, tumors grew steadily over 3 days (T3/T0 ratio > 1), while MCT-treated tumors were stable or regressed within 3 days post-treatment (T3/T0 ratio ≤ 1) (Fig. 2B). To further validate the therapeutic efficacy of MCT, we tested this treatment in additional tumor models, including MC38, CT26, B16F10, and LKR, which represent various cancer types (colorectal, melanoma, and lung) from different genetic backgrounds, and observed similarly sustained

484

485

486

487

488

489

490

491

492

493
tumor control (Fig. S2A-D). Notably, the effect on tumor growth was rapid (within 3 days) for highly immunogenic cell lines (E0771 and MC38), while it was delayed (7 days) in low immunogenic cell lines (LKR, B16F10, and CT26). We then focused on the E0771 cell line and assessed if tumor size at the start of MCT was an important parameter governing treatment response rate. Indeed, regression was more effective for smaller tumors, with response rates >90% for tumors under 80 mm³, ~50% in the 80-100 mm³ range, and <30% for larger than 100 mm³ tumors (Fig. 2C). Complete tumor regression occurred in 70% of the animals that received 3 MCT injections, compared to 10% and 20% after 1 and 2 injections, respectively (Fig. 2D-E). This underscores the necessity of at least 3 consecutive injections to effectively halt tumor growth. The median survival of NT mice was about 30 days, while approximately 71% of mice receiving 3 MCT injections were alive 45 days post-treatment (Fig. 2F). To confirm the key role of macrophages in the MCT response, we used clodronate-containing liposomes which induce macrophage apoptosis. The clodronate-containing liposomes induced approximately a threefold reduction of the F4/80⁺Ly6C⁻ TAMs (Fig. S3A-C). They were administered one day before and one day after MCT, followed by additional doses at T3 and T6, concurrently with MCT (Fig. 2G), which abolished the antitumor effect of the treatment (Fig. 2H). These results indicate that macrophages play a crucial role early post-MCT as the primary immune cell subset exerting direct antitumor functions.

512

513

Single-cell RNA sequencing reveals MCT-activated TAMs linked to tumor regression in vivo

514 515

We next sought to delineate the antitumor TAM phenotype. We analyzed TAMs at 12h and 72h post-MCT to capture early dynamics and later stages of tumor regression (Fig. S4A). For the 72h time point, we selected responders with a tumor size ratio (T3/T0) ≤ 1 (Fig. S4B). To differentiate between protumor and antitumor inflammation originating from TAMs, we collected tumor-infiltrating myeloid cells from untreated mouse progressing tumors (PT, >500 mm³) (Fig. S4A-B). Myeloid cells were sorted using a permissive live CD45+CD19-CD3-NK1.1- cell gating approach (Fig. S4C).

Using 10X Genomics, we obtained quality-controlled profiles from 40,446 cells across the four conditions (NT, MCT-12h, MT-72h, PT). Ten tumor-associated subsets were identified based on marker genes (Fig. S5A). Macrophages were the main population, expressing *Itgam*, *Csf1r*, *Fcgr1*, *Cd14*, *Adgre1*, *Cebpb*, *Fcgr3*, *Maf*, *Mafb*, and *Cd68*, and segregated into *Lyz2*

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526
527 #0Mac and *Nos2* #3Mac clusters (Fig. S5A-B). Myeloid populations also included dendritic cells (DCs), identified by *Flt3*, *Batf3*, and *Cd83*. Subsets were monocyte-derived *Cd209a* #1cDC2 (*Cd209a*, *H2-dmb2*), conventional *Ccl22* #4cDC1 (*Ccl22*, *Ccr7*), and *Clec9a* #6cDC1 (*Clec9a*, *Xcr1*, *Itgae*), consistent with published datasets (9). Additionally, we identified *Siglech* #7 plasmacytoid DCs (*Siglech*, *Ccr9*, *Klk1*, *Tcf4*), and *Cpa3* #8 mast cells (*Cpa3*, *Gata2*, *Mrgprb2*, *Mcpt4*). Other clusters included CD8 T cells, CD4 T cells, and NK cells, identified within clusters #2 and #5 (*Cd3e*, *Cd8a*, *Cd4*, *Gzma*, *Gzmb*, *Prf1*), and *Col6a1* #9Fibroblasts (*Col6a1*, *Col3a1*, *Sparc*, *Aebp1*) (Fig. S5A-B).

535 Myeloid subsets (clusters #0, #1, #3, #4, #6, #7, #8) were analyzed across conditions (Fig. 3A). Macrophages (clusters #0 and #3) dominated, constituting approximately 58-84% of myeloid cells, while DCs (clusters #1, #4 and #6) comprised approximately 15-37% (Fig. 3A-B). Strikingly, NT, MCT-72h, and PT were primarily composed of *Lyz2* #0Mac and *Cd209a* #1cDC2, whereas MCT-12h predominantly contained *Nos2* #3Mac and *Ccl22* #4cDC1.

540 Given the enrichment in *Ccl22* #4cDC1 at MCT-12h and their role in antitumor CD8 T cell responses, we assessed DCs in tumor control using *Batf3-/-* mice, which lack cDC1 (25, 26). These mice, injected with E0771 cells followed by MCT, showed faster tumor growth in NT conditions (*T3/T0* ratio 1.25 in WT vs. 2 in *Batf3-/-*, Fig. 2E vs. 3C), confirming the key role of cDC1 in anti-tumor immunity. Although MCT did not induce complete tumor eradication in *Batf3-/-* mice, it delayed tumor growth, particularly in the first 3 days post-treatment (Fig. 3C). This suggested that the initial antitumor effects of MCT, might be driven by the therapy-reprogrammed TAM compartment.

548

549

## TAM clusters mainly segregate by condition

550 We then questioned which TAMs, *Lyz2* #0Mac or *Nos2* #3Mac, carried direct antitumor functions within the first 3 days post-MCT. Analyzing 23,453 cells, hierarchical clustering of pseudo-bulk RNA-seq data revealed two branches: one grouping MCT-12h and PT, and the other grouping MCT-72h with NT samples (Fig. S6A). Unsupervised graph-based clustering of single-cell data yielded 9 clusters (Fig. S6B), with the first 5 clusters comprising 86% of the cells (Fig. S6C). Using published signatures (9, 27), we identified *Ly6c2* #1Mono as inflammatory classical monocytes and *Nr4a1* #4Mono and *Itgal* #7Mono as patrolling/non-classical monocytes (Fig. S6D). The remaining clusters (#0, #2, #3, #5, #6, #8) were identified as macrophages. Hereafter, we will refer to macrophages as TAMs and monocytes as
TAMonos. The distribution of these clusters across the four conditions (Fig. 4A) showed that TAMs from clusters #0, #1, #2, and #3 made up >40% of the total TAMs in NT, PT, MCT-72h, and MCT-12h, respectively, though cluster #1 monocytes were also present in PT (Fig. 4A-B). This distribution suggested dynamic changes in the TME, where stimuli like MCT or a progressing tumor influence macrophage phenotype and function.

565

566

## TAM clusters in NT and PT tumors associate with selective TAM functions

We identified distinct TAM clusters in NT and PT tumors with specific functions. *C1qa* #0TAMs, predominantly found in NT tumors, expressed high levels of *C1qa/b/c*, *H2-Eb1*, *H2-Aa*, *H2-Ab1*, and *Apoe*, along with lower levels of *Trem2* and *Mrc1* (CD206) (Fig. 4C). These TAMs align with lipid-associated TAMs (LA-TAMs) (12), known for immunosuppressive functions (28, 29).

*Cxcl10* #1TAMonos, present in PT and NT, were identified as inflammatory classical monocytes, and expressed *Ifit3*, *Isg15*, *Rsad2*, *Cxcl10* and tumor progression markers *Chil3* and *Vcan* (Fig. 4C). This cluster displayed various functional activities of reactive oxygen species (ROS), IFN-α/βIFN-γ/inflammatory responses, allograft rejection, complement and TNF-α signaling pathways, along with protumor TGF-β response and angiogenesis (Fig. 4D, S6E).

*Spp1* #5TAMs, enriched in PT, expressed *Spp1*, *Arg1*, *Vegfa*, and *Hmox1* (Fig. 4C). This cluster lacked hallmarks associated with IFN-α/βIFN-γ responses, but displayed glycolysis, hypoxia and angiogenesis features (Fig. 4D, S6E), was referred to as pro-angiogenic Angi-TAMs (9, 12).

*S100a9/8* #6TAMs, have been proposed to belong to monocyte (30) or M-MDSC (31) subsets, but they lacked classical monocyte identity genes (*Fcn1*, *Fn1*, *Lyz2*, *Hp*, *Sell*) (Fig. 4C, S6D). We categorized them as inflammatory / immunosuppressive macrophages, as previously proposed (32). *S100a9/8* #6TAMs showed the most diverse functionalities, displaying the greatest range of activities across hallmark pathways notably, the highest activity in TGF-β, estrogen early and late responses (Fig. 4D, S6E), which are associated with robust immunosuppression (33) (Fig. 4D, S6E). Consistent with their exclusive presence in PT, *S100a9/8* #6TAMs shared epithelial-mesenchymal transition (EMT), glycolysis, hypoxia and angiogenesis features with *Spp1* #5TAMs (Fig. 4D, S6E). Unlike *Spp1* #5TAMs, *S100a9/8*
592

593

594

595

596

597

598

599

600

#6 TAMs exhibited high levels of inflammatory signatures. Revealing their duality, S100a9/8
#6 TAMs are enriched in both M1- or M2-associated genes (34) (Fig. 4E, Fig. S6F).

We also observed three monocyte clusters: inflammatory classical monocytes *Cxcl10+*
*#1TAMonos* (in NT and PT), patrolling non-classical monocytes *Hp+* #4TAMono, and
*Nedd9+* #7TAMono (Fig. 4C, Fig. S6D). Given their broad distribution (Fig. 4B), these
monocytes could differentiate into various TAM types, supporting the idea that *C1qa* #0TAMs
and *Spp1* #5TAMs likely derive from tumor-infiltrating monocytes (9).

TAM clusters in MCT tumors show transient antitumor features

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

After MCT injection, TAMs displayed unique functional profiles. At 12h post-MCT, tumors
were composed of approximately 80% of *Nos2* #3TAMs, and to a lesser extent, *C1qa*
#0TAMs, *Nedd9* #7TAMono, and *Ccr7* #8TAMs. Notably, *Nos2* #3TAMs and *Ccr7* #8TAMs
did not clearly match any previously published signatures, indicating that they differed from
TAMs seen in progressing tumors or those induced by CSF1R blockade or anti-CD40
treatment (Fig. S6D) (9, 27)(9). *Nos2* #3TAMs were characterized by the expression of *Ccl5*,
*Nos2*, *Slc7a2*, *Slc7a11*, *Prdx5* and *Dhfr* genes (Fig. 4C). Additionally, they co-expressed
*Cd38*, *Bst1* and *Bst2* that are all members of the ADP-ribosyl cyclase family and catalyze the
synthesis and hydrolysis of cyclic ADP-ribose. *Bst2* is associated with increased survival in
patients with BC (Fig. S6G), while *Bst1* and *Ccl5* are associated with survival in patients with
TNBC (Fig. S6H) in a TAM-enriched manner. *Nos2* #3TAMs displayed activities of ROS,
IFN-α/IFN-γ responses, allograft rejection, inflammatory response, complement and TNF-α
signaling pathways (Fig. 4D, S6E). Remarkably, these *Nos2* #3TAMs expressed lower levels
of immunosuppressive genes or hallmark pathways, compared to *Spp1* #5TAMs and *S100a9/8*
#6 TAMs (Fig. 4D, S6F), and we classified them as antitumor subsets. *Ccr7* #8TAMs
expressed high levels of *Ccr7*, *Ccl22*, *Fscn1*, *Stat4* and *Il12b* suggesting that they act as
migratory Th1-inducer TAMs (Fig. 4C-D). Overall, MCT-12h resulted in the unique
accumulation of specific TAMs with pro-inflammatory and antitumor properties.

By contrast, at 72h post-MCT, tumors were composed of approximately 58% of *Cxcl9*
#2TAMs, followed by *Hp* #4TAMono, *C1qa* #0TAMs and to a lesser extent *Cxcl10*
#1TAMono and *Spp1* #5TAMs (Fig. 4B). *Cxcl9* #2 TAMs showed high expression of *Cxcl9*,
an antitumor cytokine induced by IFN-γ that recruits CXCR3-expressing T and NK cells (35),
alongside with *Kcnn4*, *Pf4* (CXCL4) (Fig. 4C), which have been associated with protumor functions. *Cxcl9* #2TAMs mainly displayed high Myc targets pathway activities. The Myc gene family and its products promote cell proliferation, immortalization, dedifferentiation, and transformation, suggesting that *Cxcl9*+ #2TAMs or their precursors had undergone a recent adaptive change, possibly reflecting a shift towards a more pro-tumorigenic phenotype. This aligns with the proposed role of MYC in macrophages to limit glycolysis and reduce the production of inflammatory cytokines (36). Strikingly, while *Nos2* #3TAMs were enriched in M1 genes, they were also the cluster with the lowest expression of M2 genes (Fig. 4E, Fig. S6F). Conversely, *Cxcl9* #2TAMs exhibited the lowest expression of M1 genes but displayed high expression of M2 genes (Fig. 4E, Fig. S6F).

When compared to *Nos2* #3TAMs, *Cxcl9* #2TAMs were characterized by Myc target and oxidative phosphorylation (Fig. S6I). This was consistent with *Cxcl9* #2TAMs expressing high levels of mitochondria-associated genes such as *mt-Nd3*, *mt-Cytb*, *mt-Co2* (Fig. 4C), suggesting an involvement in mitochondria biogenesis. *Nos2* #3TAMs display a more active glycolytic metabolism (Fig.4E, Fig. S6F), whereas *Cxcl9* #2TAMs upregulated genes belonging to the 5 complexes of the electron transport chain, indicating mitochondrial activity (Fig.4E, Fig.S6F). Taken together, these results suggest that TAMs were equipped with antitumor properties by 12h post-MCT but were unable to sustain these antitumor functions, as visualized at 72h post-MCT by the disappearance of *Nos2* #3TAMs and the appearance of *Cxcl9* #2TAMs, which accumulated tumor-promoting features and displayed different metabolic responses.

Antitumor inflammation is a hallmark of MCT and distinct from protumor inflammation

To define the specific characteristics of the antitumor *Nos2* #3TAMs, we compared them with the pro-inflammatory TAMs that accumulated in the NT and PT conditions, namely *C1qa* #0TAMs, *Cxcl10* #1TAMono and *S100a9/8* #6TAMs (Fig. S6I-L). *Nos2* #3TAMs exhibited heightened antitumoral pathways than the *C1qa* #0TAMs (Fig. S6J). By contrast, *Nos2* #3TAMs shared *IFNα/γ* pathways with *Cxcl10* #1TAMono and *S100a9/8* #6TAMs (Fig. 4D, S6E, S6K). To discern differences between *Nos2* #3TAMs and those two subsets, we analysed the leading-edge genes that contributed to the upregulation of inflammatory pathways in each cluster relatively to the remaining macrophages. This approach revealed distinct leading-edge genes for *IFNα/γ* pathways between *Nos2* #3TAMs and *Cxcl10* #1TAMono, for example
excluding *Cxcl9* and *Cxcl10* between the two subsets in the IFN-γ pathways (Fig. S6M). This is consistent with *Cxcl9*, but not *Cxcl10*, being associated with increased survival in patients with TNBC in a TAM-enriched manner (Fig. S6H). Similarly, *Nos2* #3TAMs uniquely expressed genes involved in MHC protein binding and antigen presentation (*B2m*, *Tap1*, *Psme2*, *Psmb9*, *Psme1*, *Psmb8*, *Psma3*, *Psmb2*, *H2-Q7*) for IFNα/γ pathways distinguishing them from *S100a9/8* #6TAMs (Fig. S6N). This suggests that *Nos2* #3TAMs were likely better equipped to interact with, and activate T cells in the post-MCT TME. Consistently, *Nos2* #3TAMs and *Ccr7* #8TAMs displayed a higher score for lymphocyte activation signature, whereas *S100a9/8*+ #6TAMs scored high for myeloid activation signature (Fig.4E, Fig. S6F). *Nos2* #3TAMs shared the expression of genes involved in antigen processing and presentation with *C1qa* #0TAMs, *Cxcl10* #1TAMono, *S100a9/8*+ #6TAMs and *Ccr7* #8TAMs (Fig.4E, Fig.S6F).

Moreover, *Nos2* #3TAMs shared the TNF-α pathway with *S100a9/8* #6TAMs (Fig. 4D, S6E). The leading-edge genes expressed by *S100a9/8*+ #6TAMs uniquely highlighted the IL-17 signaling pathway that comprises genes involved in myeloid recruitment (*Il1b*, *Ccl2*, *Ccl4*, *Ccl7*, *Cxcl2*, *Cxcl3*, *Nfkb1*, *Ptgs2*, *Jun*, *Irak2*, besides *S100a8* and *S100a9*) a feature absent from *Nos2*+ #3TAMs (Fig. S6O). This underscored the multifaceted role of TNF-α in the TME. Under certain circumstances, TNF-α can induce IL-17, a cytokine known to promote angiogenesis, enhance tumor cell survival and proliferation, and modulate the antitumor response (37). Altogether, this supports the idea that the antitumor functions of *Nos2* #3TAMs in part lies on their properties to foster a TME favorable to T cell recruitment and activation.

**Protective processes against oxidative stress specifically characterize antitumor MCT TAMs**

Some of the most differentially expressed genes (DEGs) in *Nos2* #3TAMs were associated with oxidative stress, including nitric oxide (NO) metabolism (*Nos2*, *Slc7a2*, *Slc7a11* and *Dhfr*) (Fig. 4C). *Slc7a2* mediates the cellular uptake of arginine, a substrate for iNOS responsible for NO production. Dihydrofolate reductase (*Dhfr*) was also found to participate in NO bioavailability (38). Increased NO production is proposed to lead to augmented ROS and therefore to enhanced cytotoxic function (39). This aligns with *Slc7a11*, which imports cystine, the precursor of glutathione, that acts as a co-factor for enzymes responsible for scavenging ROS. We thus assessed whether gene encoding enzymes involved in antioxidant processes, such as those maintaining the reduced state of glutathione (GSH), detoxifying ROS
689

690

691

with reduced glutathione, and scavenging hydrogen peroxide, would be specifically expressed by *Nos2* #3TAMs. Indeed, *Nos2* #3TAMs expressed a higher signature of cell defense against oxidative stress (Fig. 4E, Fig. S6F).

692

693

694

695

696

697

698

699

700

701

To validate this signature, we measured intracellular GSH by flow cytometry. GSH neutralizes ROS by acting as an electron donor, directly reducing oxidative stress within cells. Thus, it serves as a key indicator of the cell's ability to defend itself against oxidative damage. We found that TAMs harvested from tumor-bearing mice treated with MCT (24h or 72h earlier) displayed significantly higher GSH levels compared to TAMs from NT animals (Fig. S7A-B). This result confirms that the increased oxidative stress protection gene signature in *Nos2* #3 TAMs is associated with elevated GSH levels, highlighting an intrinsic mechanism of self-protection against oxidative stress. Altogether, these results suggested that the production of NO and ROS could be participating in the antitumor mechanisms by which *Nos2* #3TAMs control tumor growth.

702

703

## Secondary MCT injection boosts expression of iNOS, TNF-α and CD38 in TAMs

704

705

706

707

708

709

710

711

712

713

714

715

716

717

718

719

720

Next, we sought to validate the findings of the scRNA-seq data at the protein level, particularly the hypothesis that prolonged stimulation is required for TAMs to maintain their antitumor phenotype. Thus, we used flow cytometry to investigate whether two consecutive MCT injections could sustain specific local antitumor phenotypes and functions of TAMs. Tumor-bearing mice were either left untreated or administered MCT either once or twice with a 3-day interval (Fig. 5A). Tumor samples were collected at 24h, 3 days and 4days post-MCT, some mice having received one injection (D4 1x) and others two (D4 2x). We collectively gated on myeloid cells identified as CD3e-CD19-NK1.1- cells, successively excluding CD11c-CD11b double-negative cells, then Ly6G+ cells, and finally Ly6C-F4/80- double-negative cells (TAMs + TAMonos) (Fig. S8A). We evaluated the expression levels of iNOS, TNF-α, CD38 and MHC-II, identified as specific markers of the *Nos2* #3TAMs (Fig. 4C, S8A). MCT increased the proportion of iNOS+, TNF-α+, CD38+ and MHC-II+ cells among TAMs + TAMonos as early as one day post-injection (Fig. 5B). By D3 (and D4 x1) post-MCT, the proportions of iNOS+, CD38+, and MHC-II+ mono-macs decreased compared to D1, while TNF-α+ cells were maintained. A second MCT injection boosted the proportions of iNOS+ and CD38+ cells increasing their levels between D4 x1 and D4 x2. MHC-II+ expressing mono-macs were not significantly affected by the second injection (Fig. 5B).
Altogether, the comparison of TAM responses after one or two injections showed that maintenance of antitumor TAMs required regular local reprogramming.

723

724

## ROS and TNF-α are potent antitumor effector mechanisms of TAMs early after MCT injection

The elevated expression of genes involved in detoxifying ROS, along with glutathione-precursor transporter (Slc7a11) expression (Fig. 4C, S6F), suggested that MCT-responding TAMs were actively producing and protecting themselves against ROS. Furthermore, the confirmation of iNOS and TNF-α protein production by TAMs *in vivo* in response to MCT (Fig. 5B, S8A) prompted an assessment of their antitumor role. Tumor-bearing mice received systemic and intratumoral injections of the antioxidant N-acetylcysteine (NAC) or iNOS inhibitors or blocking anti-TNF-α antibody (Fig. S8B). Despite using a combination of two known iNOS inhibitors (1400W, aminoguanidine hydrochloride), MCT effectively prevented tumor growth (Fig. 5C). To further validate these data, *Nos2-/-* mice received E0771 tumor cells followed by MCT. Consistent with the lack of effect of the iNOS inhibitors, MCT effectively induced tumor eradication in *Nos2-/-* mice, and limited tumor growth as early as 3 days post treatment (Fig. S8C). This suggested that iNOS and NO production were not involved in the antitumor properties of MCT-TAMs. By contrast, antioxidant NAC (Fig. 5D) and the blocking TNF-α antibody (Fig. 5E) partially inhibited the effect of MCT while showing no impact in MCT-untreated tumors. NAC is a potent antioxidant that works by replenishing intracellular levels of GSH, as it serves as a precursor to cysteine, a key amino acid required for glutathione synthesis. *Nos2 #3TAMs* are likely controlling tumor growth through high production of ROS. These ROS have the properties to damage tumor cells directly by causing oxidative stress, which can lead to apoptosis or inhibit tumor cell proliferation. By reducing ROS levels, NAC impairs the antitumor effector functions of the *Nos2 #3TAMs*. This demonstrated the importance of ROS and TNF-α in MCT-induced antitumor effector functions.

727

728

729

730

731

732

733

734

735

736

737

738

739

740

741

742

743

744

745

746

747

748

749

## MCT-TAMs enhance tumor self-antigenic complexes via an IFN-α/β-dependent mechanism

IFNα/γ pathways were present in *Nos2 #3TAMs*, *Nedd9 #7TAMono* and *Ccr7 #8TAMs* which represent over 90% of the TAMs within the MCT-12h tumors (Fig. 4B) whereas absent from

750

752
the MCT-72h TAMs (Fig. 4D, S6E). The IFNα/γ pathways were also expressed in C1qa #0TAMs, Cxcl10 #1TAMono, S100a9/8+ #6TAMs (Fig. 4D, S6E), which represent about 70% and 65% of the TAMs in NT and PT tumors, respectively (Fig. 4B). Given the antitumor functions of the type I and II IFN, we sought to determine whether MCT-BMDMs could secrete IFN-α/β. Both M1- and MCT-BMDMs secreted IFN-α and IFN-β, unlike M0- and M2a-BMDMs (Fig. 6A). Moreover, the concentration of IFN-β was over tenfold higher than that of IFN-α. We then questioned whether IFN-α/β could boost tumor cell immunogenicity. Ovalbumin-expressing E0771 (OVA-E0771) cells and control cells transfected with an empty GFP-vector (GFP-E0771) were incubated overnight with supernatants of M0-, M1-, M2-, or MCT-BMDMs. The supernatants of MCT- and M1-BMDMs induced upregulation of H-2Kb molecules on both GFP- and OVA-E0771 cells (Fig. 6B – left graphs). As expected, tumor self-antigenic complex expression assessed by the generation of OVA-derived peptide – SIINFEKL – complexing with H-2Kb molecules, was only up-regulated on OVA-E0771 cells by the supernatants of MCT- and M1-BMDMs (Fig. 6B – right graphs). The conditioning MCT-polarization medium (PolyI:C and anti-CD40) had no impact on expression of H-2Kb-SIINFEKL complexes whereas M1-polarization medium (IFN-γ and LPS) medium alone induced expression of these complexes (Fig. S9A).

This suggested that MCT-BMDMs produced IFN type I responsible for the self-antigen MHC complexes expression by tumor cells. Blocking IFN-α/β receptor (IFN-α-R) abolished the effect of MCT-BMDM supernatant, whereas blocking IFN-γ or TNF-α did not impair up-regulation of H-2Kb-SIIN complexes by OVA-E0771 cells (Fig. 6C). Anti-IFN-α-R or anti-IFN-γ failed to prevent self-presentation by tumor cells in the presence of M1-BMDM supernatant, likely due to redundant effects of these cytokines (with IFN-γ being part of the polarizing M1-condition). Thus, MCT-induced TAMs may also contribute to tumor eradication by inducing local production of IFN-α/β and thereby sensitizing tumor cells to CD8 T cell recognition in vivo.

779

780

MCT induces intra-tumoral accumulation of CD8 T cells, which are required for long-term tumor eradication

One of the top DEGs in TAMs at 72h is Cxcl9, an antitumor chemokine known to recruit cytotoxic CD8 T cells (Cxcl9 #2TAMs, Fig. 4C, S9B). We examined the kinetics of Cxcl9

754

755

756

757

758

759

760

761

762

763

764

765

766

767

768

769

770

771

772

773

774

775

776

777

778

779

780

781

782

783

784
mRNA expression by RT-PCR on CD45+CD11b+ CD3e-CD19-NK1.1-Ly6G- cells, from tumor samples collected from NT, or 1, 3, 4 and 7 days post-MCT animals. Cxcl9 mRNA levels steadily increased after one (day 3), two (day 4) and three (day 7) MCT administrations (Fig. 6D).

The essential role of T cells in long-term tumor eradication was underscored by MCT administration in tumor-bearing *Rag2*-/-*γc*-/- or *Tcrα*-/- mice (Fig. 6E). In the absence of conventional αβ T cells, MCT-stimulated TAMs induced only tumor growth delay (T3/T0 ratio lower than NT but close to 2), failing to stop tumor growth. By contrast, γδ T cells (*Tcrδ*-/-), NK cells (*Il15r*-/- or depleted using the anti-NK1.1 antibody) and B cells (*Jht*-/-) were dispensable for MCT efficacy (Fig. S9C). Then, we evaluated the kinetics of T cell response post-MCT. Tumor samples were collected from NT or 3 days and 7 days post-MCT. We focused on CD8 T cells that we gated as CD45+CD8+CD11b- cells, and quantified effector CD8 T cells based on IFN-γ production (Fig. S9D). Effector IFN-γ-producing CD8 T cells increased from about 8% in NT tumors to 21% at 3 days and up to 40% at 7 days post-MCT (Fig. 6F). Depletion of CD8 T cells (Fig. S9E) did not impair early (day 3) but inhibited late (day 7) MCT effects (Fig. 6G). Importantly, MCT-treated mice were resistant to rechallenge on the contralateral mammary fat pad as late as 50 days post-primary inoculation of E0771 cells, suggesting that MCT induced an effective memory response (Fig. 6H). Collectively, these findings suggest that MCT relies on macrophages as key innate effectors in the early antitumor response, setting the stage for adaptive CD8 T cells to enable long-term tumor control.

## Discussion

TAMs play critical roles in tumor progression but can also contribute to antitumor immunity. To date, most macrophage-targeted strategies have focused on the blockade of CSF-1/CSF1R-signalling, which regulates macrophage differentiation and survival, or the CCL2/CCR2 axis, which regulates monocyte recruitment. *In situ* TAM reprogramming (40), although benefiting from diverse options for TLR agonists (41), presents the challenge of inducing protective inflammation rather than its more often associated protumor inflammation (42). Consequently, although macrophage-targeted therapy holds much promise, it has not yet reached the standard
817

of care in the clinic as have T lymphocyte-targeted therapies, in the form of immune checkpoint blockers (ICB) or CAR T cells.

818

819

820

The omics era has exposed the extensive heterogeneity among TAMs in human cancers (9, 43). ScRNA-seq experiments have revealed distinct TAM subsets, and different approaches have been proposed to categorize the diverse single-cell clusters based on highly upregulated genes and combining information regarding their function (11, 12, 44, 45) or by integrating various public data sets (27, 30). These efforts are crucial to allow the community to standardize the data across studies. However, somewhat surprisingly, there is a lack of description of clear antitumor clusters of TAMs, which represents a limitation overcome by our study.

821

822

823

824

825

826

827

828

829

Most of the TAM subsets that display pro-inflammatory features show an increased IFN-stimulated gene (ISG) signature, which are also associated with an elevated expression of protumor or immunosuppressive functions. Based on the nomenclature proposed by Ma *et al.*, the tumor-infiltrating monocytes and macrophages including IFN-TAMs, Inflam-TAMs, Reg-TAMs and LA-TAMs, all show local signs of response to IFN-γ concomitantly associated with cancer cell motility and suppression of adaptive immunity (12). *Nos2+ #3TAMs* resemble IFN-TAMs as well as IL411_Mac(#6) cells from the MoMac-VERSE (30). They share with them several DEGs, including *Cd38*, *Cxcl9*, *ISG20*, although *Nos2* appears unique to *Nos2+ #3TAMs*. Conversely, they lack expression of *Ido1/2* or *IL411* suggesting that *Nos2+ #3TAMs* do not promote local tryptophan degradation that may in turn suppress T cells and attract Treg cells into the tumor (46). Moreover, *Nos2+ #3TAMs* are also closest to the macrophages harvested from inflamed tissues (colitis and lupus nephritis patients) that display specific pathways related to oxidative phosphorylation and cellular stress (30). Our data suggest that the absence of protumor features in the macrophage compartment may be a critical determinant impairing cancer progression as supported by the recent study revealing that macrophage polarity, defined by CXCL9 and SPP1 expression, had a strong prognostic association (47).

830

831

832

833

834

835

836

837

838

839

840

841

842

843

844

845

846

847

Surprisingly, within *Nos2+ #3TAMs* most TAMs expressed *Ccl5*, *Nos2*, *Slc7a2*, *Slc7a11*, *CD38*, *H2-q6*, *Prdx5*, *Dhfr*, *Bst1* and *Bst2*. We confirmed the specific expression of iNOS and CD38 in this population. Although the relevance of iNOS for tumor killing has long been

848

849
850

851

852

853

854

855

856

857

recognized both *in vitro* and *in vivo* (48), we do not demonstrate that iNOS is a key antitumor factor in our system. Nevertheless, it is likely being responsible for local production of NO, which can alter the TCA cycle and reroute pyruvate away from mitochondrial metabolism, encouraging glycolysis, and orchestrating macrophage metabolic rewiring towards an inflammatory phenotype (49) in *Nos2* #3TAMs (MCT-12h), but not in *Cxcl9* #2TAMs (MCT-72h). This is consistent with iNOS being expressed in a higher proportion of TAMs at day 1 compared to day 3 post-MCT. Notwithstanding, iNOS remains, along with CD38, a biomarker of antitumor features of TAMs.

858

859

860

861

862

863

864

865

866

867

868

869

870

871

872

873

874

875

876

877

878

Our findings further identified that *Nos2*+ #3TAMs likely carry diverse antitumor functions, with pivotal roles for ROS and TNF-α as potent *in vivo* antitumor effector molecules. Both molecules are known to have dual roles. ROS mediate key macrophage functions such as phagocytosis, antigen presentation and recognition, cytolysis, and phenotypic differentiation. However, under certain circumstances, they can exert immunosuppressive effects on T cells (50). TNF-α is a critical cytokine capable of blocking protumoral gene expression in TAMs (51) and enhancing CD8 T cell antitumor responses (52), but it can also foster cancer-promoting type of inflammation (53). Although both ROS and TNF-α signaling have complex activities in the TME, we hypothesize that, at early time points of tumor progression, they are beneficial. In addition, MCT-TAMs enhanced tumor immunogenicity and self-antigen presentation via an IFN-α/β-dependent mechanism. MCT induced the expression of *Ccl5* by *Nos2*+ #3TAMs and *Cxcl9* by *Cxcl9*+ #2TAMs, which in turn can participate in the accumulation of effector CD8 T cells that were responsible for tumor eradication and long-term protection. To date, the description of efficient antitumor macrophages is lacking as few other studies have performed scRNA-seq on tumor-infiltrating myeloid cells after administration of reprogramming agents. In the model of B16F10 melanoma, systemic treatment with TLR3 agonist was associated with a type-1 interferon signature, increased *Ccl5*, and macrophage antigen presentation gene expression (10). More data are needed to further understand the distribution and local impact of antitumour macrophages, such as *Nos2*+ #3TAMs, on tumors and T cells and their capacity to reshape the TME.

879

880

881

In large tumors, the TME undergoes discrete steps that contribute to tumor progression, such as the "angiogenic switch" (54), local production of tumor survival factors, and strong
882

883

884

885

886

887

888

889

890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

914

immunosuppression, likely limiting the impact of MCT. Angio-TAMs, which accumulate in PT, are resistant to therapeutic treatment with anti-CSF1R blockade (9) and likely insensitive to MCT. The composition of TAMs in the tumor is a probable determinant of the response to MCT. In early NT tumors, the TME is mainly constituted by *C1qa+ #0TAMs and Cxcl10+ #1TAMono*, which are potentially capable of being converted into *Nos2+ #3 TAM*. Strikingly, the protective *Nos2+ #3TAMs* were identified at 12h post-MCT but were replaced by *Cxcl9+ #2TAMs* by 72h post-MCT, suggesting that this subset failed to be sustained in TME. Our work identified a therapeutic vulnerability and proposes a better strategy to sustain antitumor TAMs. Indeed, the TME 72h post-MCT is permissive to generation of new iNOS+ CD38+ TAMs upon new administration of MCT. Only repetitive MCT injections would support a TME capable of maintaining *Nos2+ #3TAMs*. It is plausible that, once the local T cell response is ongoing, there is a positive feedback loop between intratumoral effector T cells and TAMs. The IFN-γ produced by T cells polarizes *Nos2+ #3TAMs*, which, in turn, reshape the TME to facilitate T cell infiltration, immune function, and tumor rejection (31, 55). This is consistent with our hypothesis that macrophages have the potential to boost every step of the cancer-immunity cycle (41).

While immune check point blockers have revolutionized the treatment of solid cancers, most patients fail to benefit from this immunotherapy due to primary and acquired drug resistance. ICOS co-stimulation and anti-CTLA-4 blockade therapy profoundly remodeled both lymphoid and myeloid compartments (55). The crosstalk between antitumor TAM and cytotoxic T cells has been shown critical to create a positive loop that supports effective immunotherapy to promote tumor control (31). Thus, the adaptability of TAMs to respond to therapies should be considered during tumor treatment(s). Moreover, simultaneous targeting of TAMs to enhance the efficacy of certain therapies (56), for instance, is a combination strategy to enhance antitumor immunity of irradiation or CAR T cells, (57, 58). In this context, our results should accelerate translational research as they advocate for a strategy based on *in situ* repeated reprogramming of macrophages. The new era of single-cell transcriptomics and subsequent integration of various datasets and information will pave the way to help the scientific community to advance innovative and more specific macrophage-based immunotherapy strategies.

## Limitation of the study
We have used an orthotopic breast cancer mouse model in this study, and this may explain why we failed to identify tissue-resident macrophages (typically expressing *Lyve1*, *Retnla*, *Marco*, *Folr2*, *Hes1*) (59, 60) or proliferating macrophages (typically expressing *mki67*, *Top2a*, *Tubb*, *Tuba1b*). This suggests that our mouse model of implanted syngeneic E0771 tumor cell line primarily recruited monocyte-derived macrophages. This aligns with the heterogeneity within the *Nos2* #3TAMs and the clear segregation of the antitumor functions concentrated in one sub-cluster, while the other displayed a monocyte-signature. Although monocyte-derived TAMs are the most prominent (61, 62) and TAMs predominantly manifest monocyte-derived phenotypic features in trajectory analysis (63, 64), orthotopic models failed to recapitulate a role for tissue-resident macrophages (65). It will thus be important to determine the impact of reprogramming-type of treatments on tissue-resident macrophage-derived TAM in spontaneous tumor mouse models.

**Acknowledgments:** We are grateful to the staff of the Flow Cytometry and Bioimaging Platforms as well as Animal Facilities of Instituto de Medicina MolecularJoão Lobo Antunes (iMM-JLA) Lisboa and to the staff of Genomics Facility from Instituto Gulbenkian de Ciência (IGC) Oeiras, for the valuable technical assistance and helpful discussions. We thank Caetano Reis e Sousa (Crick Institute, London, UK), Luis Graça (iMM-JLA) and Mark Veldhoen (iMM-JLA) for providing key mouse models and depletion antibodies. We thank Jocelyne Demengeot and Catarina Moita who provided the OVA-GFP plasmid. We want to thank deeply Julie Darrigues, Ângelo Chora and Pedro Papotto for captivating scientific discussions, and all the Ribot_Silva-Santos' lab for the professional, efficient and also joyful work environment!

**Funding:** This work was sponsored by: LISBOA-01-0145-FEDER-030948, with the project cofunded by Fundo Europeu de Desenvolvimento Regional (FEDER), through POR Lisboa 2020 (Programa Operacional Regional de Lisboa, PORTUGAL 2020) and Fundação para a Ciência e a Tecnologia (FCT); national funds through FCT, under the project UIDP/50005/2020. Personal research contracts to K.S. (CEECIND/00697/2018) and N.L.B.-M. (CEECIND/00436/2018), and PhD fellowships to C.J. (SFRH/BD/144792/2019) and M.B. (2020.05627.BD) were supported by FCT. This study also benefited from Fundo iMM-Laço award grant, and was supported by GIMM-CARE. GIMM-CARE is funded by the European Union's Horizon Europe research and innovation programme under grant agreement No.
948

949

950

951

952

953

954

955

956

957

958

959

960

961

962

963

964

965

966

967

968

101060102. GIMM-CARE is co-funded by the Portuguese Government, the National Foundation for Science and Technology (FCT), the Francisco Manuel dos Santos Society Group (ARICA – Investimentos, Participações e Gestão and Jerónimo Martins), iMM and CAML - Lisbon Academic Medical Centre. This work also benefited from active interactions with the European Cooperation in Science and Technology (COST) Actions BM1404 Mye-EUNITER and CA20117 - *Converting molecular profiles of myeloid cells into biomarkers for inflammation and cancer (Mye-InfoBank)*.

**Author contributions:** C.J., M.B., M.R.S., A.T.M., K.S., performed most experiments and analyzed the data; E.P.L., H.B., H.K., M.R., N.G-S., N.S., S.M., T.S., and V.M. helped performing experiments and/or analyze data, and participated in result discussions and critical suggestions; M.P. and R.L. obtained the preliminary data that initiated this study; R.A. provided scientific support and reagents for SCENITH™ protocol and contributed to fruitful discussions; K.S., designed experiments and wrote the manuscript; K.S., B.S.S. and N.L.B.-M. supervised the study.

**Competing interests:** The authors declared no financial conflicts of interest.
1. Cassetta, L., and J. W. Pollard. 2023. A timeline of tumour-associated macrophage biology. *Nat. Rev. Cancer* 23: 238–257.

2. Zhang, Q., L. Liu, C. Gong, H. Shi, Y. Zeng, and X. Wang. 2012. Prognostic Significance of Tumor-Associated Macrophages in Solid Tumor: A Meta-Analysis of the Literature. *PLoS One* 7.

3. Bingle, L., N. J. Brown, and C. E. Lewis. 2002. The role of tumour-associated macrophages in tumour progression: Implications for new anticancer therapies. *J. Pathol.* 196: 254–265.

4. Honkanen, T. J., A. Tikkanen, P. Karihtala, M. Mäkinen, J. P. Väyrynen, and J. P. Koivunen. 2019. Prognostic and predictive role of tumour-associated macrophages in HER2 positive breast cancer. *Sci. Rep.* 9: 1–9.

5. Macciò, A., G. Gramignano, M. C. Cherchi, L. Tanca, L. Melis, and C. Madeddu. 2020. Role of M1-polarized tumor-associated macrophages in the prognosis of advanced ovarian cancer patients. *Sci. Rep.* 10: 1–8.

6. Bruni, D., H. K. Angell, and J. Galon. 2020. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. *Nat. Rev. Cancer* 20: 662–680.

7. Murray, P. J., J. E. Allen, S. K. Biswas, E. A. Fisher, D. W. Gilroy, S. Goerdt, S. Gordon, J. A. Hamilton, L. B. Ivashkiv, T. Lawrence, M. Locati, A. Mantovani, F. O. Martinez, J. L. Mege, D. M. Mosser, G. Natoli, J. P. Saeij, J. L. Schultze, K. A. Shirey, A. Sica, J. Suttles, I. Udalova, J. A. vanGinderachter, S. N. Vogel, and T. A. Wynn. 2014. Macrophage Activation and Polarization: Nomenclature and Experimental Guidelines. *Immunity* 41: 14–20.

8. Azizi, E., A. J. Carr, G. Plitas, A. E. Cornish, C. Konopacki, S. Prabhakaran, J. Nainys, K. Wu, V. Kiseliovas, M. Setty, K. Choi, R. M. Fromme, P. Dao, P. T. McKenney, R. C. Wasti, K. Kadaveru, L. Mazutis, A. Y. Rudensky, and D. Pe'er. 2018. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. *Cell* 174: 1293-1308.e36.

9. Zhang, L., Z. Li, K. M. Skrzypczynska, Q. Fang, W. Zhang, S. A. O'Brien, Y. He, L. Wang, Q. Zhang, A. Kim, R. Gao, J. Orf, T. Wang, D. Sawant, J. Kang, D. Bhatt, D. Lu, C. M. Li, A. S. Rapaport, K. Perez, Y. Ye, S. Wang, X. Hu, X. Ren, W. Ouyang, Z. Shen, J. G. Egen, Z. Zhang, and X. Yu. 2020. Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer. *Cell* 181: 442-459.e29.

10. Thomas, G., L. Micci, W. Yang, J. Katakowski, C. Oderup, P. Sundar, X. Wang, K. G. Geles, S. Potluri, and S. Salek-Ardakani. 2021. Intra-Tumoral Activation of Endosomal TLR Pathways Reveals a Distinct Role for TLR3 Agonist Dependent Type-1 Interferons in Shaping the Tumor Immune Microenvironment. *Front. Oncol.* 11: 1–10.

11. Wang, J., N. Zhu, X. Su, Y. Gao, and R. Yang. 2023. Novel tumor-associated macrophage populations and subpopulations by single cell RNA sequencing. *Front. Immunol.* 14: 1–13.

12. Ma, R. Y., A. Black, and B. Z. Qian. 2022. Macrophage diversity in cancer revisited in the era of single-cell omics. *Trends Immunol.* 43: 546–563.

13. Nalio Ramos, R., Y. Missolo-Koussou, Y. Gerber-Ferder, C. P. Bromley, M. Bugatti, N. G. Núñez, J. Tosello Boari, W. Richer, L. Menger, J. Denizeau, C. Sedlik, P. Caudana, F. Kotsias, L. L. Niborski, S. Viel, M. Bohec, S. Lameiras, S. Baulande, L. Lesage, A. Nicolas, D. Meseure, A. Vincent-Salomon, F. Reyal, C. A. Dutertre, F. Ginhoux, L. Vimeux, E. Donnadieu, B. Buttard, J. Galon, S. Zelenay, W. Vermi, P. Guermonprez, E. Piaggio, and J. Helft. 2022. Tissue-resident FOLR2+ macrophages associate with CD8+ T cell infiltration in human breast cancer. *Cell* 185: 1189-1207.e25.

14. Joshi, S., L. López, L. G. Morosi, R. Amadio, M. Pachauri, M. Bestagno, I. P. Ogar, M. Giacca, G. M. Piperno, D. Vorselen, and F. Benvenuti. 2024. Tim4 enables large peritoneal macrophages to cross-present tumor antigens at early stages of tumorigenesis. *Cell Rep.* 43.

15. Almeida-Santos, J., M.-L. Bergman, I. A. Cabral, and J. Demengeot. 2021. Interruption of Thymic Activity in Adult Mice Improves Responses to Tumor Immunotherapy. *J. Immunol.* 206: 978–986.

16. Lopes, N., C. McIntyre, S. Martin, M. Raverdeau, N. Sumaria, A. C. Kohlgruber, G. J. Fiala, L. Z. Agudelo, L. Dyck, H. Kane, A. Douglas, S. Cunningham, H. Prendeville, R. Loftus, C. Carmody, P. Pierre, M. Kellis, M. Brenner, R. J. Argüello, B. Silva-Santos, D. J. Pennington, and L. Lynch. 2021. Distinct metabolic programs established in the thymus control effector functions of γδ T cell subsets in tumor microenvironments. *Nat. Immunol.* 22: 179–192.

17. Subramanian, A., P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub, E. S. Lander, and J. P. Mesirov. 2005. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. *Proc. Natl. Acad. Sci. U. S. A.* 102: 15545–15550.

18. Argüello, R. J., A. J. Combes, R. Char, J. P. Gigan, A. I. Baaziz, E. Bousiquot, V. Camosseto, B. Samad, J.
1026 Tsui, P. Yan, S. Boissonneau, D. Figarella-Branger, E. Gatti, E. Tabouret, M. F. Krummel, and P. Pierre.
1027 2020. SCENITH: A Flow Cytometry-Based Method to Functionally Profile Energy Metabolism with Single-
1028 Cell Resolution. *Cell Metab.* 32: 1063-1075.e7.

1029 19. Mensurado, S., M. Rei, T. Lanca, M. Ioannou, N. Gonçalves-Sousa, H. Kubo, M. Malissen, V.
1030 Papayannopoulos, K. Serre, and B. Silva-Santos. 2018. Tumor-associated neutrophils suppress pro-tumoral
1031 IL-17+ γδ T cells 1 through induction of oxidative stress. *PLoS Biol* 16: 1-21.

1032 20. Curtis, C., S. P. Shah, S. F. Chin, G. Turashvili, O. M. Rueda, M. J. Dunning, D. Speed, A. G. Lynch, S.
1033 Samarajiwa, Y. Yuan, S. Gráf, G. Ha, G. Haffari, A. Bashashati, R. Russell, S. McKinney, S. Aparicio, J. D.
1034 Brenton, I. Ellis, D. Huntsman, S. Pinder, L. Murphy, H. Bardwell, Z. Ding, L. Jones, B. Liu, I.
1035 Papatheodorou, S. J. Sammut, G. Wishart, S. Chia, K. Gelmon, C. Speers, P. Watson, R. Blamey, A. Green,
1036 D. MacMillan, E. Rakha, C. Gillett, A. Grigoriadis, E. De Rinaldis, A. Tutt, M. Parisien, S. Troup, D. Chan,
1037 C. Fielding, A. T. Maia, S. McGuire, M. Osborne, S. M. Sayalero, I. Spiteri, J. Hadfield, L. Bell, K. Chow, N.
1038 Gale, M. Kovalik, Y. Ng, L. Prentice, S. Tavaré, F. Markowetz, A. Langerød, E. Provenzano, A.
1039 Purushotham, A. L. Børresen-Dale, and C. Caldas. 2012. The genomic and transcriptomic architecture of
1040 2,000 breast tumours reveals novel subgroups. *Nature* 486: 346-352.

1041 21. Newman, A. M., C. B. Steen, C. L. Liu, A. J. Gentles, A. A. Chaudhuri, F. Scherer, M. S. Khodadoust, M. S.
1042 Esfahani, B. A. Luca, D. Steiner, M. Diehn, and A. A. Alizadeh. 2019. Determining cell type abundance and
1043 expression from bulk tissues with digital cytometry. *Nat. Biotechnol.* 37: 773-782.

1044 22. Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer Book.

1045 23. Therneau, T. M., and P. M. Grambsch. 2000. Modeling Survival Data: Extending the Cox Model. Springer
1046 Book.

1047 Sun, L., T. Kees, A. S. Almeida, B. Liu, X. Y. He, D. Ng, X. Han, D. L. Spector, I. A. McNeish, P. Gimotty,
1048 S. Adams, and M. Egeblad. 2021. Activating a collaborative innate-adaptive immune response to control
1049 metastasis. *Cancer Cell* 39: 1361-1374.e9.

1050 25. Theisen, D. J., S. T. Ferris, C. G. Briseño, N. Kretzer, A. Iwata, K. M. Murphy, and T. L. Murphy. 2019.
1051 Batf3-dependent genes control tumor rejection induced by dendritic cells independently of cross-presentation.
1052 *Cancer Immunol. Res.* 7: 29-39.

1053 26. Hildner, K., B. T. Edelson, W. E. Purtha, M. Diamond, H. Matsushita, M. Kohyama, B. Calderon, B. U.
1054 Schraml, E. R. Unanue, M. S. Diamond, R. D. Schreiber, T. L. Murphy, and K. M. Murphy. 2008. Batf3
1055 Deficiency Reveals a Critical Role for CD8 + Dendritic Cells in Cytotoxic T Cell Immunity. *Science* (80- ).
1056 322: 1097-1100.

1057 27. Sanin, D. E., Y. Ge, E. Marinkovic, A. M. Kabat, A. Castoldi, G. Caputa, K. M. Grzes, J. D. Curtis, E. A.
1058 Thompson, S. Willenborg, S. Dichtl, S. Reinhardt, A. Dahl, E. L. Pearce, S. A. Eming, A. Gerbaulet, A.
1059 Roers, P. J. Murray, and E. J. Pearce. 2022. A common framework of monocyte-derived macrophage
1060 activation. *Sci. Immunol.* 7.

1061 28. Revel, M., C. Sautès-Fridman, W. H. Fridman, and L. T. Roumenina. 2022. C1q+ macrophages: passengers
1062 or drivers of cancer progression. *Trends in Cancer* 8: 517-526.

1063 29. Werba, G., D. Weissinger, E. A. Kawaler, E. Zhao, D. Kalfakakou, S. Dhara, L. Wang, H. B. Lim, G. Oh, X.
1064 Jing, N. Beri, L. Khanna, T. Gonda, P. Oberstein, C. Hajdu, C. Loomis, A. Heguy, M. H. Sherman, A. W.
1065 Lund, T. H. Welling, I. Dolgalev, A. Tsirigos, and D. M. Simeone. 2023. Single-cell RNA sequencing reveals
1066 the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment. *Nat.
1067 Commun.* 14: 1-16.

1068 30. Mulder, K., A. A. Patel, W. T. Kong, C. Piot, E. Halitzki, G. Dunsmore, S. Khalilnezhad, S. E. Irac, A.
1069 Dubuisson, M. Chevrier, X. M. Zhang, J. K. C. Tam, T. K. H. Lim, R. M. M. Wong, R. Pai, A. I. S. Khalil, P.
1070 K. H. Chow, S. Z. Wu, G. Al-Eryani, D. Roden, A. Swarbrick, J. K. Y. Chan, S. Albani, L. Derosa, L.
1071 Zitvogel, A. Sharma, J. Chen, A. Silvin, A. Bertoletti, C. Blériot, C. A. Dutertre, and F. Ginhoux. 2021.
1072 Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease. *Immunity* 54:
1073 1883-1900.e5.

1074 31. van Elsas, M. J., J. Middelburg, C. Labrie, J. Roelands, G. Schaap, M. Sluijter, R. Tonea, V. Ovcinnikovs, K.
1075 Lloyd, J. Schuurman, S. J. Riesenfeld, T. F. Gajewski, N. F. C. C. de Miranda, T. van Hall, and S. H. van der
1076 Burg. 2024. Immunotherapy-activated T cells recruit and skewlate-stage activated M1-like macrophagesthat
1077 are critical for therapeutic efficacy. *Cancer Cell* 1-19.

1078 32. Kwak, T., F. Wang, H. Deng, T. Condamine, V. Kumar, M. Perego, A. Kossenkov, L. J. Montaner, X. Xu,
1079 W. Xu, C. Zheng, L. M. Schuchter, R. K. Amaravadi, T. C. Mitchell, G. C. Karakousis, C. Mulligan, B. Nam,
1080 G. Masters, N. Hockstein, J. Bennett, Y. Nefedova, and D. I. Gabrilovich. 2020. Distinct Populations of
1081 Immune-Suppressive Macrophages Differentiate from Monocytic Myeloid-Derived Suppressor Cells in
1082 Cancer. *Cell Rep.* 33: 108571.

1083 33. Chakraborty, B., J. Byemerwa, J. Shepherd, C. N. Haines, R. Baldi, W. Gong, W. Liu, D. Mukherjee, S.
1084 Artham, F. Lim, Y. Bae, O. Brueckner, K. Tavares, S. E. Wardell, B. A. Hanks, C. M. Perou, C. Y. Chang,
1085 and D. P. McDonnell. 2021. Inhibition of estrogen signaling in myeloid cells increases tumor immunity in melanoma. *J. Clin. Invest.* 131.
1086

1087 34. Martinez, F. O., S. Gordon, M. Locati, and A. Mantovani. 2006. Transcriptional Profiling of the Human Monocyte-to-Macrophage Differentiation and Polarization: New Molecules and Patterns of Gene Expression. *J. Immunol.* 177: 7303-7311.
1088
1089

1090 35. Marcovecchio, P. M., G. Thomas, and S. Salek-Ardakani. 2021. CXCL9-expressing tumor-associated macrophages: New players in the fight against cancer. *J. Immunother. Cancer* 9: 1-7.
1091

1092 36. Bae, S., P. S. U. Park, Y. Lee, S. H. Mun, E. Giannopoulou, T. Fujii, K. P. Lee, S. N. Violante, J. R. Cross, and K. H. Park-Min. 2021. MYC-mediated early glycolysis negatively regulates proinflammatory responses by controlling IRF4 in inflammatory macrophages. *Cell Rep.* 35: 109264.
1093
1094

1095 37. Rei, M., N. Gonçalves-Sousa, T. Lança, R. G. Thompson, S. Mensurado, F. R. Balkwill, H. Kulbe, D. J. Pennington, and B. Silva-Santos. 2014. Murine CD27(-) Vγ6(+) γδ T cells producing IL-17A promote ovarian cancer growth via mobilization of protumor small peritoneal macrophages. *Proc. Natl. Acad. Sci. U. S. A.* 127: 3562-3570.
1096
1097
1098

1099 38. Chalupsky, K., and H. Cai. 2005. Endothelial dihydrofolate reductase: Critical for nitric oxide bioavailability and role in angiotensin II uncoupling of endothelial nitric oxide synthase. *Proc. Natl. Acad. Sci. U. S. A.* 102: 9056-9061.
1100
1101

1102 39. Nathan, C., and M. U. Shiloh. 2000. No TitleReactive oxygen and nitrogen intermediates in the relationship between mammalian hosts and microbial pathogens. *Proc. Natl. Acad. Sci. U. S. A.* 97: 8841-8848.
1103

1104 40. Guiducci, C., A. P. Vicari, S. Sangaletti, G. Trinchieri, and M. P. Colombo. 2005. Redirecting in vivo elicited tumor infiltrating macrophages and dendritic cells towards tumor rejection. *Cancer Res.* 65: 3437-3446.
1105

1106 41. Reis-Sobreiro, M., A. Teixeira da Mota, C. Jardim, and K. Serre. 2021. Bringing Macrophages to the Frontline against Cancer: Current Immunotherapies Targeting Macrophages. *Cells* 10: 2364.
1107

1108 42. Bell, C. R., and S. Zelenay. 2022. COX-2 upregulation by tumour cells post-chemotherapy fuels the immune evasive dark side of cancer inflammation. *Cell Stress* 6: 76-78.
1109

1110 43. Cheng, S., Z. Li, R. Gao, B. Xing, Y. Gao, Y. Yang, S. Qin, L. Zhang, H. Ouyang, P. Du, L. Jiang, B. Zhang, Y. Yang, X. Wang, X. Ren, J. X. Bei, X. Hu, Z. Bu, J. Ji, and Z. Zhang. 2021. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. *Cell* 184: 792-809.e23.
1111
1112

1113 44. Yang, S., M. Wang, Y. Hua, J. Li, H. Zheng, M. Cui, N. Huang, Q. Liu, and Q. Liao. 2024. Advanced insights on tumor-associated macrophages revealed by single-cell RNA sequencing: The intratumor heterogeneity, functional phenotypes, and cellular interactions. *Cancer Lett.* 584: 216610.
1114
1115

1116 45. Nasir, I., C. McGuinness, A. R. Poh, M. Ernst, P. K. Darcy, and K. L. Britt. 2023. Tumor macrophage functional heterogeneity can inform the development of novel cancer therapies. *Trends Immunol.* 44: 971-985.
1117
1118

1119 46. Sadik, A., L. F. Somarribas Patterson, S. Öztürk, S. R. Mohapatra, V. Panitz, P. F. Secker, P. Pfänder, S. Loth, H. Salem, M. T. Prentzell, B. Berdel, M. Iskar, E. Faessler, F. Reuter, I. Kirst, V. Kalter, K. I. Foerster, E. Jäger, C. R. Guevara, M. Sobeh, T. Hielscher, G. Poschet, A. Reinhardt, J. C. Hassel, M. Zapatka, U. Hahn, A. von Deimling, C. Hopf, R. Schlichting, B. I. Escher, J. Burhenne, W. E. Haefeli, N. Ishaque, A. Böhme, S. Schäuble, K. Thedieck, S. Trump, M. Seiffert, and C. A. Opitz. 2020. IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression. *Cell* 182: 1252-1270.e34.
1120
1121
1122
1123
1124

1125 47. Bill, R., P. Wirapati, M. Messemaker, W. Roh, B. Zitti, F. Duval, M. Kiss, J. C. Park, T. M. Saal, J. Hoelzl, D. Tarussio, F. Benedetti, S. Tissot, L. Kandalaft, M. Varrone, G. Ciriello, T. A. McKee, Y. Monnier, M. Mermod, E. M. Blaum, I. Gushterova, A. L. K. Gonye, N. Hacohen, G. Getz, T. R. Mempel, A. M. Klein, R. Weissleder, W. C. Faquin, P. M. Sadow, D. Lin, S. I. Pai, M. Sade-Feldman, and M. J. Pittet. 2023. CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers. *Science* (80-.). 381: 515-524.
1126
1127
1128
1129
1130

1131 48. Klug, F., H. Prakash, P. E. Huber, T. Seibel, N. Bender, N. Halama, C. Pfirschke, R. H. Voss, C. Timke, L. Umansky, K. Klapproth, K. Schäkel, N. Garbi, D. Jäger, J. Weitz, H. Schmitz-Winnenthal, G. J. Hämmerling, and P. Beckhove. 2013. Low-Dose Irradiation Programs Macrophage Differentiation to an iNOS+/M1 Phenotype that Orchestrates Effective T Cell Immunotherapy. *Cancer Cell* 24: 589-602.
1132
1133
1134

1135 49. Palmieri, E. M., M. Gonzalez-cotto, W. A. Baseler, L. C. Davies, B. Ghesquière, N. Maio, C. M. Rice, T. A. Rouault, T. Cassel, R. M. Higashi, A. N. Lane, T. W. Fan, D. A. Wink, and D. W. Mcvicar. 2020. Nitric oxide orchestrates metabolic rewiring in M1 macrophages by targeting aconitase 2 and pyruvate dehydrogenase. *Nat. Commun.* .
1136
1137
1138

1139 50. Kotsafti, A., M. Scarpa, I. Castagliuolo, and M. Scarpa. 2020. Reactive oxygen species and antitumor immunity—from surveillance to evasion. *Cancers (Basel)*. 12: 1-16.
1140

1141 51. Kratochvill, F., G. Neale, J. M. Haverkamp, L. A. Van de Velde, A. M. Smith, D. Kawauchi, J. McEvoy, M. F. Roussel, M. A. Dyer, J. E. Qualls, and P. J. Murray. 2015. TNF Counterbalances the Emergence of M2 Tumor Macrophages. *Cell Rep.* 12: 1902-1914.
1142
1143
1144 52. Johansson, A., J. Hamzah, C. J. Payne, and R. Ganss. 2012. Tumor-targeted TNFα stabilizes tumor vessels and enhances active immunotherapy. *Proc. Natl. Acad. Sci. U. S. A.* 109: 7841–7846.

1145
1146 Moore, R. J., D. M. Owens, G. Stamp, C. Arnott, F. Burke, N. East, H. Holdsworth, L. Turner, B. Rollins, M. Pasparakis, G. Kollias, and F. Balkwill. 1999. Mice deficient in tumor necrosis factor-α are resistant to skin carcinogenesis. *Nat. Med.* 5: 828–831.

1149 54. Bergers, G., and L. E. Benjamin. 2003. Tumorigenesis and the angiogenic switch. *Nat. Rev. Cancer* 3: 401–410.

1151 55. Sharma, N., X. Fan, O. T. Atolagbe, Z. Ge, K. N. Dao, P. Sharma, and J. P. Allison. 2024. ICOS costimulation in combination with CTLA-4 blockade remodels tumor-associated macrophages toward an antitumor phenotype. *J. Exp. Med.* 221.

1154 56. Cassetta, L., and T. Kitamura. 2018. Targeting tumor-associated macrophages as a potential strategy to enhance the response to immune checkpoint inhibitors. *Front. Cell Dev. Biol.* 6: 1–6.

1156 57. Salvagno, C., M. Ciampricotti, S. Tuit, C. S. Hau, A. van Weverwijk, S. B. Coffelt, K. Kersten, K. Vrijland, K. Kos, T. Ulas, J. Y. Song, C. H. Ooi, D. Rüttinger, P. A. Cassier, J. Jonkers, J. L. Schultze, C. H. Ries, and K. E. de Visser. 2019. Therapeutic targeting of macrophages enhances chemotherapy efficacy by unleashing type I interferon response. *Nat. Cell Biol.* 21: 511–521.

1160 58. Sánchez-Paulete, A. R., J. Mateus-Tique, G. Mollaoglu, S. R. Nielsen, A. Marks, A. Lakshmi, J. A. Khan, C. M. Wilk, L. Pia, A. Baccarini, M. Merad, and B. D. Brown. 2022. Targeting Macrophages with CAR T Cells Delays Solid Tumor Progression and Enhances Antitumor Immunity. *Cancer Immunol. Res.* 10: 1354–1369.

1163 59. Sharma, A., J. J. W. Seow, C. A. Dutertre, R. Pai, C. Blériot, A. Mishra, R. M. M. Wong, G. S. N. Singh, S. Sudhagar, S. Khalilnezhad, S. Erdal, H. M. Teo, A. Khalilnezhad, S. Chakarov, T. K. H. Lim, A. C. Y. Fui, A. K. W. Chieh, C. P. Chung, G. K. Bonney, B. K. P. Goh, J. K. Y. Chan, P. K. H. Chow, F. Ginhoux, and R. DasGupta. 2020. Onco-fetal Reprogramming of Endothelial Cells Drives Immunosuppressive Macrophages in Hepatocellular Carcinoma. *Cell* 183: 377-394.e21.

1168 60. Blériot, C., S. Chakarov, and F. Ginhoux. 2020. Determinants of Resident Tissue Macrophage Identity and Function. *Immunity* 52: 957–970.

1170 61. Franklin, R. a, W. Liao, A. Sarkar, M. V Kim, M. R. Bivona, K. Liu, E. G. Pamer, and M. O. Li. 2014. The cellular and molecular origin of tumor-associated macrophages. *Science* 344: 921–5.

1172 62. Zhu, Y., J. M. Herndon, D. K. Sojka, K. W. Kim, B. L. Knolhoff, C. Zuo, D. R. Cullinan, J. Luo, A. R. Bearden, K. J. Lavine, W. M. Yokoyama, W. G. Hawkins, R. C. Fields, G. J. Randolph, and D. G. DeNardo. 2017. Tissue-Resident Macrophages in Pancreatic Ductal Adenocarcinoma Originate from Embryonic Hematopoiesis and Promote Tumor Progression. *Immunity* 47: 323-338.e6.

1176 63. Kim, N., H. K. Kim, K. Lee, Y. Hong, J. H. Cho, J. W. Choi, J. Il Lee, Y. L. Suh, B. M. Ku, H. H. Eum, S. Choi, Y. La Choi, J. G. Joung, W. Y. Park, H. A. Jung, J. M. Sun, S. H. Lee, J. S. Ahn, K. Park, M. J. Ahn, and H. O. Lee. 2020. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. *Nat. Commun.* 11.

1180 64. Chen, Y. P., J. H. Yin, W. F. Li, H. J. Li, D. P. Chen, C. J. Zhang, J. W. Lv, Y. Q. Wang, X. M. Li, J. Y. Li, P. P. Zhang, Y. Q. Li, Q. M. He, X. J. Yang, Y. Lei, L. L. Tang, G. Q. Zhou, Y. P. Mao, C. Wei, K. X. Xiong, H. B. Zhang, S. Da Zhu, Y. Hou, Y. Sun, M. Dean, I. Amit, K. Wu, D. M. Kuang, G. B. Li, N. Liu, and J. Ma. 2020. Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. *Cell Res.* 30: 1024–1042.

1185 65. Laviron, M., M. Petit, E. Weber-Delacroix, A. J. Combes, A. R. Arkal, S. Barthélémy, T. Courau, D. A. Hume, C. Combadière, M. F. Krummel, and A. Boissonnas. 2022. Tumor-associated macrophage heterogeneity is driven by tissue territories in breast cancer. *Cell Rep.* 39.

1188

1189

1190
1191 **Figure Legends**

1192

1193 **Fig. 1. Phenotype and function of BMDM activated with MCT.**

1194 (A) Scheme of *in vitro* polarization of bone marrow-derived macrophages (BMDM) to M0, M1, M2, and MCT phenotypes. (B) Proportion of polarized BMDM expressing TNF-α, iNOS, ICAM-1, MHC-II, PD-L1, and PD-L2. Note that TNF-α, was assessed 6h post-polarization while all others were assessed 24h post-polarization. (C) BMDM and E0771 tumor cells co-cultured for 48h, then analyzed by flow cytometry to identify live E0771 tumor cells. (D) Viability, assessed by cell tracker blue, and (E) cell death, assessed by 7-AAD staining, of E0771 tumor cells incubated with supernatants of the four BMDM phenotypes for 72h. (F) Quantification of glucose, lactate, ATP, and pH from the supernatants of the four BMDM phenotypes. (G) Metabolic dependencies or capacities of the four BMDM phenotypes analyzed by SCENITH™. (H) Viability of the four BMDM phenotypes assessed by flow cytometry using a live-dead dye. Data are representative of two to ten independent experiments.

1206

1207 **Fig. 2. Sustained MCT treatment induces tumor regression in a macrophage-dependent manner.**

1208
1209 (A) Scheme of *in vivo* experiment setup with the MCT schedule. (B) Individual tumor growth curves after E0771 cell transplantation in WT animals, with or without treatment with TLR3 ligand (Poly I:C) and anti-CD40 agonist, alone or in combination. The right graph represents the ratio of tumor size at the time of treatment (T0) and three days later (T3/T0) for each individual mouse. (C) Numbers of responders (orange) versus non-responders (black) as a function of the size of the tumor at the time of treatment. The numbers in the bars represent the total number of mice in each group. (D) Individual tumor growth curves from NT (black), or animals treated once (1x, yellow), twice (2x, orange), or three times (3x, red) every three days. (E) Ratio of tumor size at the time of MCT (T0) and three days (T3/T0), six days (T6/T0), or nine days (T9/T0) later, as a function of the number of MCT injections, for each individual mouse. (F) Kaplan-Meier survival graph for E0771-bearing mice receiving one, two, or three MCT injections. (G) Scheme of *in vivo* experiment setup with the treatment and macrophage depletion using clodronate-liposome injection schedule. (H) One representative experiment of individual tumor growth curves after E0771 cell transplantation and MCT

1210

1212

1214

1216

1218

1220

1222
(orange) or MCT with clodronate-liposome (blue) injections. The bottom graph represents the tumor size ratio (T3/T0) and includes animals receiving MCT with control PBS-liposomes (red). Data compile two to three independent experiments, except data in panel (B), which was done only once.

1227

<caption>**Fig. 3.** ScRNA-seq analysis with myeloid cell annotation and distribution across conditions.</caption>

1228

1229

(A) tSNE visualization of the selected myeloid cell subsets (clusters *Lyz2 #0Mac*, *Cd209a #1cDC2*, *Nos2 #3Mac*, *Ccl22 #4cDC1*, *Clec9a #6cDC1*, *Siglech #7* plasmacytoid, and *Cpa3 #8* mast cells identified in Fig. S3A-B). tSNE plots represent total (across all groups) and myeloid cells selected from each individual group: tumors without treatment (NT), tumors collected after 12 or 72 hours post-treatment (MCT-12h and MCT-72h, respectively), and progressive tumors without treatment (PT). Cells are colored by a shared nearest neighbor (SNN) modularity optimization-based clustering algorithm. (B) Proportion of the main myeloid clusters identified across samples from the four conditions (NT, MCT-12h, MCT-72h, and PT). (C) Individual tumor growth curves after E0771 cell transplantation in *Batf3* KO animals, with or without MCT. The right graph represents the ratio of tumor size at the time of treatment (T0) and three days later (T3/T0) for each individual *Batf3* KO mice.

1241

<caption>**Fig. 4.** Macrophage characterization and transient anti-tumor features.</caption>

1242

(A) tSNE visualization of the selected tumor-infiltrating macrophage and monocyte subsets (clusters *Lyz2 #0Mac* and *Nos2 #3Mac* identified in Fig. S3A-B). tSNE plots represent total (across all groups) and macrophages selected from each individual group (NT, MCT-12h, MCT-72h, and PT). (B) Proportion of the main tumor-infiltrating macrophage and monocyte clusters identified across samples from the four conditions (NT, MCT-12h, MCT-72h, and PT). (C) Dot plot representing scaled average gene expression (blue color intensity) and percentage of cells (dot size) expressing featured markers across clusters represented in 4A. (D) Heatmap representing a pseudobulk analysis of tumor-infiltrating macrophage and monocyte subset clusters as shown in 4A. Colors indicate the scaled gene set scores for each hallmark pathway. (E) Dot plot representing scaled average gene signature scores (color) and percentage of cells (dot size) with a positive score across clusters represented in 4A. The violin plots and the gene signatures are depicted in the table in Fig. S4F.

1243
<caption>**Fig. 5. Kinetics of in vivo antitumor determinants induced by MCT in TAMs.**</caption>

(A) Scheme of in vivo experiment setup with the MCT schedules. (C) Proportion of iNOS, CD38, TNF-α, or MHC-II positive cells within TAMs + TAMonos assessed by flow cytometry as depicted in Fig. S5A. Tumor-infiltrated macrophages and monocytes were isolated from WT animals left NT, or after one MCT injection at day 1, 3, and 4 (D4 x1), or two MCT injections at day 4 (D4 x2). WT mice received E0771 cell transplantation followed by MCT in the presence of iNOS inhibitors (D), the antioxidant N-acetyl cysteine (NAC) (E), or blocking anti-TNF-α antibody (F), as described in Fig. S5B. The plots on the right side shows the ratio of tumor size at the time of treatment and three (T3/T0), six (T6/T0), and nine (T9/T0) days later for each individual mouse. The left side shows the Kaplan-Meier survival graph for E0771-bearing mice receiving MCT in the presence of each inhibitor type: iNOS inhibitors (D), NAC (E), and blocking anti-TNF-α antibody (F). Data in B-E compile at least two independent experiments for each condition.

<caption>**Fig. 6. Self-antigen presentation by tumor cells and T cell activation in vivo.**</caption>

(A) Representative dot plots showing the concentration of IFN-α (left) and IFN-β (right) in the supernatant of BMDM polarized in various conditions. (B) Representative flow cytometry plots of H-2Kb molecules (left plots) and H-2Kb-SIINFEKL complexes (right plots) on OVA-E0771 cells and GFP-E0771 control cells incubated overnight with supernatants of M0-, M1-, M2-, or MCT-BMDMs. (C) Expression of H-2Kb-SIINFEKL complexes in OVA-E0771 cells induced by M0, M1, M2, and MCT supernatants in the presence of blocking anti-IFN-α receptor, anti-IFN-γ, or anti-TNF-α antibodies. (D) Expression of Cxcl9 mRNA measured by qPCR in FACS-sorted CD45+CD11b+CD3e-CD19-NK1.1-Ly6G+ cells from tumor samples collected from NT, or 1, 3, 4, and 7 days post-MCT animals. (E) Ratio of tumor size three days post-MCT (T3/T0) for each individual Rag2 KO x γc KO mice (left plot) and TCRα KO mice (right plot). (F) Frequency of tumor-infiltrating IFN-γ+ CD8 T cells from NT animals and from three and seven days post-MCT animals. (G) WT mice received E0771 cell transplantation followed by MCT in the presence of depleting anti-CD8 antibodies as depicted in Fig. S6E. (H) Kaplan-Meier survival graph for E0771-bearing mice treated with MCT and rechallenged 50 days later on the contralateral mammary fat pad with a new inoculation of E0771 cells.
<figure><img src="image_3.png" /><figcaption>Figure 1 – Phenotype and function of BMDM activated with MCT.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 2** – Sustained MCT treatment induces tumor regression in a macrophage-dependent manner.</figcaption></figure>
A

<figure><img src="image_5.png" /></figure>

B

<figure><img src="image_6.png" /></figure>

C

<figure><img src="image_7.png" /><figcaption>**Figure 3:** ScRNA-seq analysis with myeloid cell annotation and distribution across conditions.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 4 – Macrophage characterization and transient anti-tumor features.</figcaption></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 5** – Kinetics of *in vivo* antitumor determinants induced by MCT in TAMs.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>A</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>E Rag2-/-γc-/-</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>F Tcra-/-</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 6** – Self-antigen presentation tumor cells and T cell activation *in vivo*.</figcaption></figure>