FOLR2+ macrophages and antitumor immunity

Abstract

The present invention relates to tumor-associated FOLR2+ macrophages and their gene signatures as biomarkers of favorable outcome and anti-tumor immunity useful for prognosis and monitoring of cancer patients.The present invention also relates to FOLR2+ macrophages as therapeutic targets for enhancing T cell immunity in the prevention and treatment of cancer and infectious diseases.

Description

[Technical field]

[0001] The present invention relates to the field of immunological biomarkers and immunotherapy.The present invention relates to tumor-associated FOLR2+ macrophages and their gene signatures as a biomarker of favorable outcome and anti-tumor immunity, useful for prognosis and monitoring of cancer patients.The present invention also relates to FOLR2+ macrophages as a therapeutic target for enhancing T cell immunity in the prevention and treatment of cancer and infectious diseases. [Background technology]

[0002] Macrophage infiltration is a hallmark of solid cancers, and overall macrophage infiltration correlates with poorer patient survival and resistance to therapy. For example, macrophages are one of the most abundant immune cell populations in the human breast tumor microenvironment (TME) (Cassetta and Pollard, 2018). Macrophage infiltration in breast tumors correlates with poor prognosis and higher tumor grade (Zhao et al., 2017) (Ruffell and Coussens, 2015) (Ramos et al., 2020). Tumor-associated macrophages (TAMs) play a tumor-promoting role by providing growth factors to tumors, enhancing tumor cell motility and invasion, and promoting angiogenesis and metastasis (Lewis and Pollard, 2006) (Engblom et al., 2016) (Caux et al., 2016). In addition, TAMs exert immunosuppressive functions, thereby preventing tumor cell destruction by NK and T lymphocytes. Therefore, targeting TAM recruitment, survival and function has become a major therapeutic goal (Ries et al., 2014) (Mantovani et al., 2017) (Binneies et al., 2018).

[0003] Although the current paradigm presents TAMs as pro-tumorigenic cells in most cases, some studies have highlighted their protective role in certain disease stages or organs (Ruffell and Coussens, 2015). Thus, targeting TAMs in preclinical cancer models can have detrimental effects on tumor progression and metastasis (Bonapace et al., 2014) (Cassetta and Pollard, 2018) (Hanna et al., 2015). As previously described (Mantovani et al., 2004), different populations of macrophages with opposing pro- and anti-tumor functions can coexist within the same tumor (Mantovani et al., 2002) (Ali et al., 2016). Tumor-associated macrophages are phenotypically and functionally heterogeneous. Specific tumor-associated macrophage subsets can be endowed with antagonistic roles on cancer progression and the development of anti-tumor immunity. Establishing the degree of heterogeneity in the macrophage compartment is therefore a prerequisite for the rational design of macrophage-targeted therapies.

[0004] Macrophage heterogeneity could potentially result from i) alternative activation states (Mantovani et al., 2017), ii) imprinting by tissue- or tumor-associated cues that define the macrophage niche (Cassetta et al., 2019) (Guilliams and Scott, 2017), iii) distinct TAM cell origins (adult monocytes vs. embryonic progenitors) (Franklin et al., 2014) (Ginhoux et al., 2010) (Loyher et al., 2018) (Zhu et al., 2017), and iv) tumor-induced systemic modifications of circulating monocytes (Gallina et al., 2006) (Veglia et al., 2018) (Cassetta et al., 2019) (Ramos et al., 2020).

[0005] In human breast cancer, macrophage infiltration has been assessed with markers such as CD14 or CSF1R (Ruffell et al., 2012) (Cassetta et al., 2019) and CD68 (Leek et al., 1996) (Yuan et al., 2014). However, CD68 expression among phagocytes has not been fully characterized, although CD14 and CSF1R also mark undifferentiated monocytes. Other markers such as CD163, TIE2, MRC1 / CD206 or MARCO have been implemented to assess TAM phenotypic heterogeneity (Cassetta and Pollard, 2018). Pioneering single-cell RNA sequencing (scRNAseq) studies have invalidated alternative activation as the main mechanism explaining TAM heterogeneity (Azizi et al., 2018). In summary, the phenotypic and functional diversity of TAM-infiltrating human breast cancer remains to be elucidated. Summary of the Invention [Problem to be solved by the invention]

[0006] Therefore, reliable markers specific for distinct subsets of tumor-associated macrophages (TAMs) that reflect the antagonistic roles of TAM subsets are lacking in cancer prognosis and treatment.

[0007] The present invention fulfills this need. [Means for solving the problem]

[0008] To understand the phenotypic and functional heterogeneity of macrophage populations in human breast cancer, we created a single-cell atlas of myeloid cells infiltrating human luminal breast tumors. They identify two phenotypically distinct TAM subsets, TREM2, which express the triggering receptor expressed by myeloid cells-2 (TREM2), osteopontin (SPP1), and cell adhesion molecule 1 (CADM1) genes. +macrophages; and FOLR2 expressing folate receptor 2 (FOLR2), selenoprotein P (SEPP1), solute carrier family 40 member 1 (SLC40A1), hyaluronan receptor (LYVE-1), and mannose receptor type C 1 (MRC1 / CD206) genes + Identify macrophages.

[0009] Here, the inventors have + We show that macrophages are tissue-resident macrophages (TRM) that are evolutionarily conserved across species and that proliferate healthy mammary glands before cancer onset.

[0010] TREM2, whose invasion has previously been associated with the worst clinical outcomes (Molgora et al., 2020) + In complete contrast to macrophages, we found that FOLR2 + Showing that TAM abundance predicts better clinical outcomes. + The specific gene signature that defines macrophages is an independent prognostic factor that positively correlates with patient survival across breast cancer and at least six other types of cancer.

[0011] Therefore, FOLR2 + Macrophages are CD8 + It positively correlates with signatures of key cellular players in anti-tumor immunity, including T cells, NK cells and dendritic cells (DCs).

[0012] It was further shown that FOLR2+ and TREM2+ macrophages are spatially segregated within the tumor microenvironment (TME). FOLR2+ macrophages are specifically located in the stromal regions surrounding the tumor, including perivascular areas. Furthermore, FOLR2 + TAMs colocalize with lymphoid aggregates containing CD8+ T cells in breast cancer and 10 other cancer types. + Macrophage / CD8 + T cell colocalization correlates with favorable clinical outcomes suggesting an antitumor role for this newly characterized macrophage subset.

[0013] Furthermore, we have shown that FOLR2+ macrophages are more capable of activating T cells than TREM2+ (CADM1+) macrophages. Moreover, vaccination with anti-FOLR2-targeting antibodies coupled to model antigens induces specific CD8+ T cell responses.

[0014] This study highlights the antagonistic roles of tumor-associated macrophage subsets and paves the way for subset-specific therapeutic intervention in macrophage-based cancer treatment. Based on these results, we propose to use FOLR2+ tumor-associated macrophages as a biomarker for cancer prognosis and monitoring, and to target antigens to FOLR2-expressing macrophages to enhance T cell immunity to prevent or treat cancer and infectious diseases.

[0015] The present invention provides a method for prognosing and monitoring cancer in a patient, comprising: - determining the level of FOLR2+ macrophages in a tumor sample from a patient, wherein the level of tumor-associated FOLR2+ macrophages positively correlates with the patient's cancer disease or treatment outcome; - predicting therefrom whether an outcome of the cancer disease or treatment is likely to be favorable or unfavorable in the patient.

[0016] In some embodiments of the method, an elevated level of FOLR2+ macrophages in a patient tumor sample compared to a reference indicates a higher likelihood of a favorable outcome of the cancer disease or treatment in the patient.

[0017] In some embodiments of the methods, a favorable outcome of the cancer disease comprises increased survival time or rate, decreased recurrence rate, increased time to recurrence, and / or decreased tumor incidence or metastasis.

[0018] In some embodiments, the method comprises determining the density of FOLR2+ cells in a patient tumor sample, preferably by immunohistochemistry using an anti-FOLR2 antibody, and preferably wherein said FOLR2+ cells are furthermore TREM2- or TREM2 低 and / or CADM1-.

[0019] In some other embodiments, the method comprises determining the expression level of the FOLR2 gene in a patient tumor sample, preferably determining the level of FOLR2 protein.

[0020] In some other embodiments, the method comprises determining the level of expression of a gene signature of tumor-associated FOLR2+ macrophages comprising or consisting of the FOLR2, SEPP1 and SLC40A1 genes, preferably comprising determining the level of mRNA expressed by said genes.

[0021] In some preferred embodiments, the methods include determining the levels of the FOLR2 gene or the mRNA expressed by the FOLR2, SEPP1 and SLC40A1 genes by RNA-Seq.

[0022] In some embodiments, the method further comprises classifying the patient into a favorable prognosis group and a poor prognosis group based on the level of tumor-associated FOLR2+ macrophages determined in the patient's tumor sample.

[0023] The present invention also relates to a gene signature of tumor-associated FOLR2+ macrophages comprising or consisting of FOLR2, SEPP1 and SLC40A1 genes and its in vitro use as a biomarker for the prognosis or monitoring of cancer in a patient.

[0024] In some embodiments of the above methods and uses according to the invention, the cancer is selected from the group consisting of breast cancer, kidney cancer, lung cancer, liver cancer, skin cancer, uterine cancer and adrenal cancer, preferably breast cancer. Alternatively, the cancer is selected from the group consisting of breast cancer, kidney cancer, lung cancer, liver cancer, skin cancer, uterine cancer, brain cancer, thyroid cancer and adrenal cancer, preferably breast cancer.

[0025] The present invention also relates to a targeted antigen delivery system comprising a FOLR2-binding ligand associated with an antigen of interest or a nucleic acid encoding the antigen in an expressible form.

[0026] In some embodiments of the targeted antigen delivery system, the antigen of interest is a vaccine antigen, preferably selected from tumor antigens and antigens derived from pathogens, in particular viral, bacterial, fungal, and parasitic antigens.

[0027] In some embodiments of the targeted antigen delivery system, the FOLR2-binding ligand comprises an anti-FOLR2 antibody or a fragment thereof that comprises an antigen-binding site.

[0028] In some embodiments of the targeted antigen delivery system, the FOLR2-binding ligand and its antigen or nucleic acid are associated in a conjugate, fusion protein or particle, preferably the particle is selected from the group consisting of a lipophilic particle, a nanoparticle, a virus-like particle, a viral vector particle and combinations thereof, more preferably the particle incorporates the antigen or its nucleic acid and presents the FOLR2-binding ligand on its surface.

[0029] The present invention also relates to a pharmaceutical composition comprising the antigen delivery system according to the present disclosure and at least one pharma- ceutically acceptable vehicle, adjuvant and / or carrier, and its use for stimulating antigen-specific T cell immune responses in the prevention and treatment or in cancer and infectious diseases. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] The present invention provides the abundance of tumor-associated FOLR2+2 positive (FOLR2+) macrophages as a biomarker of favorable outcome and anti-tumor immunity in cancer patients.The present invention further provides gene signatures that define FOLR2+ macrophages, which are useful for measuring biomarkers.The present invention provides various uses of biomarkers and gene signatures for cancer prognosis and monitoring.The present invention also provides antigen delivery systems that target FOLR2+ macrophages, and their use for stimulating T cell immune responses in the prevention and treatment of cancer and infectious diseases.

[0031] definition Macrophages are a type of white blood cell of the immune system that are mononuclear phagocytes. Macrophages play an important role in innate and adaptive immunity and tissue homeostasis. Macrophages differentiate from embryonic precursors or circulating monocytes and reside in different tissues, including tumors. Macrophages present in healthy tissues are called tissue-resident macrophages (TRM). Macrophage-infiltrating tumors are called tumor-associated macrophages or TAM. Macrophages can be defined by various combinations of markers as disclosed in this example. These include, among others, CD3E-, CD19 / 20-, CD66-, XCR1-, CD1C-, CCR2-, CD64+, CD11c+, HLA-DR+, CD14+ and APOE+, APOE+, APOC1+, C1QA+ and C1QB+.

[0032] FOLR2 + Macrophages refer to a distinct subset of tumor-associated macrophages or TAMs. FOLR2+ macrophages are characterized by differential expression of specific genes (gene signatures) that are TREM2+, as shown in the examples and figures of this application. + They are distinct from other subsets of TAMs, such as macrophages.

[0033] As used herein, <<gene signature>>, <<gene expression signature>>, <<molecular signature>> refers to a single or combined group of cellular genes that have a unique characteristic pattern of gene expression that arises as a result of an altered or unaltered biological process or a pathogenic medical condition.

[0034] As used herein, "biomarker" refers to a characteristic biological or biologically derived indicator of a process, event, or condition. Biomarkers include "molecular markers" that refer to specific genes or gene products (mRNA or protein).

[0035] As used herein, anti-tumor immunity refers to tumor immunity, particularly CD8 + It refers to an immune response mediated by immune cells present in the tumor environment, including T cells, NK cells, and B cells. These cells can organize into proinflammatory lymphoid structures called tertiary lymphoid structures.

[0036] As used herein, an antigen refers to any substance that can be specifically recognized by the immune system, in particular by antibodies and cells of the immune system (B lymphocytes, CD4+ T lymphocytes, CD8+ T lymphocytes). An antigen according to the present invention refers to an immunogenic substance that can induce a specific immune response, such as the production of antibodies and / or the induction of a T helper response (activation of CD4+ T lymphocytes) and / or a cytotoxic T response (activation of CD8+ T lymphocytes) specific for said antigen.

[0037] As used herein, the term "cancer" refers to any member of a class of diseases or disorders characterized by the uncontrolled division of cells and the ability of these cells to invade other tissues, either by direct growth into adjacent tissues by invasion or by transplantation to distant sites by metastasis. Metastasis is defined as the stage where cancer cells are transported through the bloodstream or lymphatic system. The term cancer according to the present invention also includes cancer metastasis and cancer recurrence. Cancers are classified according to the type of cells that the tumor resembles and therefore the tissue from which the tumor is presumed to originate. For example, carcinomas are malignant tumors derived from epithelial cells. This group represents the most common cancers, including common forms of breast, prostate, lung and colon cancer. Lymphomas and leukemias include malignant tumors derived from blood and bone marrow cells. Sarcomas are malignant tumors derived from connective tissue or mesenchymal cells. Mesotheliomas are tumors derived from mesothelial cells that line the peritoneum and pleura. Gliomas are tumors derived from glia, the most common type of brain cell. Germinomas are tumors originating from germ cells and are usually found in the testes and ovaries. Choriocarcinomas are malignant tumors originating from the placenta. As used herein, "cancer" refers to any type of cancer, including solid and liquid tumors.

[0038] As used herein, an infectious disease refers to any disease caused by a pathogen or microorganism, such as a virus, bacteria, fungus, parasite, etc.

[0039] As used herein, the term "subject" refers to humans and non-human animals, particularly mammals, including but not limited to rodents, cats, dogs, cows, sheep, horses and primates. "Patient" refers to a subject suffering from a disease. Preferably, the subject or patient according to the present invention is a human. The patient is preferably a cancer patient.

[0040] As used herein, the term "tumor sample" of a patient refers to any biological sample that contains cancer cells of said patient. For example, the tumor sample can be a sample from a primary tumor, a metastasis, and / or a tumor-draining lymph node (TDLN). Preferably, it is a tumor biopsy.

[0041] Samples include direct samples and processed samples. Processed samples are processed by standard methods used to prepare biological samples for analysis. In particular, processed samples include samples processed by standard methods used to prepare tissue for immunohistochemical analysis, or to isolate and purify nucleic acid or protein for analysis, such as those described in the examples.

[0042] The term "treating" or "treatment" as used herein means reversing, alleviating, inhibiting the progression, or preventing the disorder or condition to which such term applies, or reversing, alleviating, inhibiting the progression, or preventing one or more symptoms of the disorder or condition to which such term applies. As used herein, the term "treatment" or "treating" refers to both prophylactic or preventive treatment, as well as curative or disease-ameliorating treatment, including treatment of patients at risk of or suspected of having a disease, as well as patients diagnosed with a disease or condition, including suppression of clinical recurrence. Treatment may be given to patients who have a medical disorder or who may eventually develop a disorder to prevent, cure, delay the onset of, reduce the severity of, or ameliorate one or more symptoms of a disorder or recurrent disorder, or to extend the patient's survival beyond that expected in the absence of such treatment.

[0043] "Treating cancer" includes, but is not limited to, reducing the number of cancer cells or the size of a tumor in a patient, reducing the progression of cancer to a more aggressive form (i.e., maintaining the cancer in a form that is sensitive to a therapeutic agent), reducing the proliferation of cancer cells or slowing the growth rate of a tumor, killing cancer cells, reducing the metastasis of cancer cells, or reducing the likelihood of cancer recurrence in a subject. Treating a subject as used herein refers to any type of treatment that benefits a subject suffering from cancer, at risk of developing cancer, or facing cancer recurrence. Treatment includes improving the subject's condition (e.g., in one or more symptoms), slowing the progression of the disease, delaying the onset of symptoms, slowing the progression of symptoms, etc.

[0044] As used herein, "drug" or "therapeutic agent" refers to a compound or agent that, when administered to a human or animal, produces a desired biological or pharmacological effect, particularly an intended therapeutic effect or response on the body to treat or prevent a condition or disease. Therapeutic agents include any suitable biologically active compound or biologically derived component.

[0045] As used herein, "therapeutic response" or "response to treatment with a drug" refers to a positive medical response characterized by objective parameters or criteria, such as objective clinical signs of disease, patient self-reported parameters and / or increased survival rate.The objective criteria for evaluating response to drug treatment vary from disease to disease and can be easily determined by those skilled in the art by using clinical scores.Positive medical response to a drug can be easily verified in appropriate animal models of disease well known in the art.

[0046] "A," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, the terms "a" (or "an"), "one or more," or "at least one" can be used interchangeably herein. Unless otherwise specified, "or" means "and / or."

[0047] Cancer Prognosis and Monitoring The present invention provides biomarkers and derived molecular diagnostic tests useful for predicting cancer disease and treatment outcomes in patients.

[0048] The present invention shows that the abundance or level of tumor-associated FOLR2+ macrophages positively correlates with cancer outcome and anti-tumor immunity.Therefore, the level of tumor-associated FOLR2+ macrophages is a useful biomarker for cancer prognosis to predict the outcome of cancer disease in patients before or during cancer treatment.In addition, anti-tumor immunity is a predictor of the effectiveness of cancer treatment.Therefore, the level of tumor-associated FOLR2+ macrophages is also considered to be a useful biomarker for monitoring cancer treatment to predict the response to treatment in cancer patients, particularly to treatment including immunotherapy such as checkpoint blockade therapy.

[0049] The present invention provides a method for prognostic and monitoring of cancer in patients, comprising measuring the level of FOLR2+ macrophages in tumor samples of patients, and the level of tumor-associated FOLR2+ macrophages is positively correlated with the outcome of cancer disease or treatment of patients.This means that the higher the level of tumor-associated FOLR2+ macrophages in patient samples, the more likely the outcome of cancer disease and treatment is in the patient.Therefore, according to the method of the present invention, the increased level of FOLR2+ macrophages in tumor samples of patients indicates that the outcome of cancer disease or treatment is more likely to be in the patient.

[0050] In some embodiments, the method according to the present invention further comprises: - determining the level of FOLR2+ macrophages in a tumor sample from a patient, wherein the level of tumor-associated FOLR2+ macrophages positively correlates with the patient's cancer disease or treatment outcome; -then predicting whether an outcome of the cancer disease or treatment is likely to be favorable in the patient.

[0051] A favorable outcome of cancer disease may include one or more of the following: increased survival time or survival rate, decreased recurrence rate, increased time to recurrence, decreased tumor development or metastasis.A favorable outcome of cancer treatment refers to a positive medical response characterized by objective parameters or criteria, such as decreased tumor growth, decreased tumor marker expression, and others known in the art.

[0052] The level of FOLR2+ macrophages in a patient's tumor sample (e.g., the level of tumor-associated FOLR2+ macrophages) can be determined directly by measuring the level of FOLR2+2 positive (FOLR2+) cells in the patient's tumor sample, or can be determined indirectly by measuring the expression level of the FOLR2+2 gene in the patient's tumor sample, alone or in combination with other genes specific to FOLR2+ macrophages, to form a gene signature of tumor-associated FOLR2+ macrophages.

[0053] According to the method of the present invention, the presence of elevated levels of FOLR2+ macrophages in a patient's tumor sample can be determined by comparison with a reference. The reference can be a reference sample that contains known levels of FOLR2+ cells, FOLR2+2 mRNA or protein, mRNA or protein expressed by a signature gene. Alternatively, the reference is a predetermined value. The predetermined value can be a threshold value or can be a range. The reference value refers to a value established by statistical analysis of values ​​obtained from a panel of representative individuals. The reference value can be obtained, for example, by measuring FOLR2+ macrophage levels as disclosed above in samples from a panel of cancer patients with favorable prognosis (e.g., increased survival rate) and a panel of cancer patients with unfavorable prognosis (e.g., no increased survival rate), as disclosed in this example. Then, a cut-off value capable of distinguishing between favorable and poor cancer prognosis is determined. The panel of cancer patients is preferably of the same type of cancer as the tested patient.

[0054] In some embodiments, the level of tumor-associated FOLR2+ macrophages is determined directly by measuring the level of FOLR2+2 positive (FOLR2+) cells in a patient tumor sample. The level of FOLR2+ cells in a patient tumor sample can be measured by immunohistological techniques using anti-FOLR2+2 antibodies according to well-known methods such as those disclosed in the present Examples. The method can include determining the density of FOLR2+ cells in the patient tumor sample, which means the number of FOLR2+ cells per surface unit of the tumor sample, measured in square millimeters (mm 2 In some preferred embodiments, FOLR2 positive (FOLR2+) cells may be further characterized as TREM2 negative or "low" (TREM2- or TREM2 低 ) and / or CADM1 negative (CADM1-).

[0055] In some other embodiments, the level of tumor-associated FOLR2+ macrophages is indirectly determined by measuring the expression level of FOLR2 gene in the tumor sample of the patient.The method can include measuring the level of mRNA or protein expressed by FOLR2 gene.Preferably, the method includes measuring the level of FOLR2 protein.

[0056] In some preferred embodiments, the level of tumor-associated FOLR2+ macrophages is indirectly determined by measuring the expression level of a gene signature of tumor-associated FOLR2+ macrophages that includes or consists of FOLR2, SEPP1 and SLC40A1 genes. The cut-off value for stratifying patients with high or low expression of the signature is determined by calculating all possible cut-offs and selecting the cut-off with the best p-value. Alternatively, the cut-off can be determined as 25% of patients in the cohort with the highest expression of the signature compared to 75% of patients with lower expression of the same signature. As shown in the examples (Figures 4A-B), this gene signature is specific for FOLR2+ macrophages and allows for distinguishing FOLR2+ macrophages from other TAMs and other leukocyte lineages. Preferably, the method includes measuring the level of mRNA expressed by the signature genes.

[0057] In some embodiments, TAM-FOLR2 + Macrophages express at least the folate receptor 2 (FOLR2), selenoprotein P (SEPP1) and solute carrier family 40 member 1 (SLC40A1) genes, but do not express the osteopontin (SPP1) and cell adhesion molecule 1 (CADM1) genes, i.e., the TAM-FOLR2+ macrophage gene signature includes FOLR2, SEPP1 and SLC40A1, but does not include SPP1 and CADM1. In some embodiments, the TAM-FOLR2 +Macrophages further express one or more or all of the following genes: hyaluronan receptor (LYVE-1), mannose receptor type C (MRC1 / CD206), CD163, and MAF; i.e., the TAM-FOLR2+ macrophage gene signature includes one or more or all of LYVE-1, MRC1 / CD206, C163, and MAF. In some embodiments, TAM-FOLR2 + Macrophages further express low levels of triggering receptor expressed by myeloid cells 2 (TREM2), i.e., the TAM-FOLR2+ macrophage gene signature is TREM2-low. In some embodiments, FOLR2 + Macrophages do not further express triggering receptor expressed by myeloid cells-2 (TREM2), i.e., the TAM-FOLR2+ macrophage gene signature does not include TREM2. In some embodiments, TAM-FOLR2 + Macrophages further do not express one or more or all of the following genes: FN1, FABP5, MSR1, CD9, IFI27, HSPB1 and HSPA1, i.e., the TAM-FOLR2+ macrophage gene signature does not include one or more or all of FN1, FABP5, MSR1, CD9, IFI27, HSPB1 and HSPA1. In some specific embodiments, the TAM-FOLR2+ macrophage gene signature does not include SPP1, C3 and CD9.

[0058] As used herein, the term "gene expression level" or "expression level of a gene" refers to the amount or concentration of a transcription product (or transcript), such as an mRNA, or a translation product, such as a protein or polypeptide. Typically, the level of mRNA expression can be expressed in units such as nanograms of transcript per cell or nanograms per microgram of tissue. The level of a polypeptide can be expressed, for example, as nanograms per microgram of tissue. Alternatively, relative units can be used to describe the gene expression level.

[0059] As used herein, the expression "measuring the expression level of a gene" encompasses measuring the amount of a transcription product, preferably an mRNA, obtained via transcription of said gene, and / or measuring the amount of a translation product, preferably a protein, obtained via translation of said gene.

[0060] Typically, gene expression levels can be determined according to routine techniques well known to those skilled in the art. For example, the measurement can include contacting a patient's tumor sample with a selective reagent, such as a probe, primer, ligand or antibody, thereby detecting the presence of the nucleic acid or protein of interest originally present in the sample.

[0061] Methods for determining the amount of mRNA are well known in the art. For example, the mRNA contained in the sample is first extracted according to standard methods, for example with lytic enzymes or chemical solutions, or by nucleic acid binding resins according to the manufacturer's instructions. The extracted mRNA present in the sample is then detected by any suitable method, such as, without limitation, spectrophotometry, for example hybridization, for example, Northern blotting, microarray, in situ hybridization, for example, RNAscope; sequencing, such as next generation sequencing (NGS), single molecule sequencing; micro- and nanosensor-based electrochemical, electrical, mechanical or optical detection and nucleic acid amplification techniques. Nucleic acid amplification methods include isothermal and polymerase chain reaction (PCR)-based techniques, such as reverse transcription PCR (RT-PCR), quantitative PCR (Q-PCR), in particular real-time Q-PCR, RT-qPCR, droplet digital PCR (ddPCR), PCR-HM (high resolution DNA melting, PCR combined with ligase detection reaction based on fluorescent microspheres (Luminex® microspheres).

[0062] In some embodiments, the mRNA present in the sample is detected by nucleic acid amplification, nucleic acid hybridization or nucleic acid sequencing assay or a combination thereof. The mRNA may be amplified using any suitable nucleic acid amplification technique or combination thereof, as described above. The nucleic acid amplification assay uses at least one oligonucleotide primer specific to the mRNA, usually a pair of forward primer (sense primer) and reverse primer (antisense) specific to the mRNA, and preferably also an oligonucleotide probe specific to the mRNA for detecting any amplification product. The mRNA is subjected to a reverse transcription reaction with the reverse primer before amplification. Preferably, the amplification is a reverse transcription polymerase chain reaction (RT-PCR), more preferably a real-time reverse transcription polymerase chain reaction (RT-qPCR). The amplification product or hybridization product is detected using a label suitable for nucleic acids, which is well known in the art and includes a fluorescent label, a chemiluminescent label, a radioactive label, an enzyme label, etc.

[0063] RNA-Seq, also called Whole Transcriptome Shotgun Sequencing (WTSS), is a technique that allows the investigation of the amount and sequence of RNA in a sample using next-generation sequencing (NGS) (reviewed in Wang et al., Nat. Rev. Genet., 2009, 10, 57-63). It analyzes the transcriptome for gene expression patterns encoded within RNA. RNA-seq has been adapted for single-cell analysis, and single-cell RNAseq was first reported by Tang et al. (Nat. Methods 2009, 6, 377-382) and reviewed in Wang et al., Nature Reviews Genetics, 2009, 10, 57-63 and Svensson et al. (Nat Protoc. 2018 Apr;13(4):599-604).

[0064] In a preferred embodiment, mRNA expression levels are measured by RNA-Seq.

[0065] The level of protein can be determined by any suitable method known to those skilled in the art. Usually, these methods include contacting a cell sample, preferably a cell lysate, with a binding partner that can selectively interact with the protein present in the sample. The binding partner is generally a polyclonal or monoclonal antibody, preferably a monoclonal antibody. The method generally includes a suitable label, such as a fluorescent, chemiluminescent, radioactive, enzyme label or dye molecule, or other method for detecting the amount of complex formed between the protein and the antibody or the antibody reacting therewith. The amount of protein can be measured, for example, by semi-quantitative Western blot, enzyme-labeled and mediated immunoassays such as ELISA, biotin / avidin type assays, radioimmunoassays, immunoelectrophoresis, mass spectrometry, immunoprecipitation, or by protein or antibody arrays.

[0066] The folate receptor beta (FOLR2) gene (also known as BETA-HFR, FBP, FBP / PL-1, FOLR1, FR-BETA, FR-P3, FR beta) encodes a member of the folate receptor (FOLR) family that has high affinity for folate and several reduced folate derivatives and mediates the delivery of 5-methyltetrahydrofolate to the interior of cells. The gene is expressed in placenta and hematopoietic cells. Expression is increased in malignant tissues. Human FOLR2 corresponds to gene ID: 2350. Human FOLR2 protein corresponds to the amino acid sequence UniProtKB / Swiss-Prot accession number P14207. Four transcript variants encoding the same protein have been found for this gene (GenBank / NCBI accession numbers: NM_803.5 (variant 1); NM_1113534.2 (variant 2); NM_1113535.2 (variant 3); NM_1113536.2 (variant 4), all accessed 22 March 2021).

[0067] The triggering receptor expressed on myeloid cells 2 (TREM2) gene (also known as PLOSL2, TREM-2, Trem2a, Trem2b, Trem2c) encodes a membrane protein that forms a receptor signaling complex with the TYRO protein tyrosine kinase binding protein. The encoded protein functions in the immune response and may be involved in chronic inflammation by triggering the production of constitutive inflammatory cytokines. Alternative splicing results in multiple transcript variants that encode different isoforms. TREM2 is widely expressed in the brain, lung and 14 other tissues. Human TREM2 corresponds to gene ID: 54209. Human TREM2 protein corresponds to the amino acid sequence UniProtKB / Swiss-Prot accession number Q9NZC2. Two transcript variants encoding the same protein have been found for this gene (GenBank / NCBI accession numbers: NM_18965.4 (variant 1); NM_1271821.2 (variant 2); all accessed March 22, 2021).

[0068] The cell adhesion molecule 1 (CADM1) gene (also known as BL2; ST17; IGSF4; NECL2; RA175; TSLC1; IGSF4A; Necl-2; SYNCAM; sgIGSF; sTSLC-1; synCAM1) encodes a cell adhesion molecule that is widely expressed in the lung, thyroid, and 23 other tissues. Human CADM1 corresponds to gene ID: 23705. Human CADM1 protein corresponds to the amino acid sequence UniProtKB / Swiss-Prot accession number Q9BY67. Five transcript variants encoding the same protein have been found for this gene (GenBank / NCBI accession numbers: NM_14333.4 (variant 1); NM_1098517.2 (variant 2); NM_1301043.2 (variant 3); NM_1301044.2 (variant 4); NM_1301045.2 (variant 5); all accessed 29 March 2021).

[0069] The selenoprotein P (SEPP1) gene (also known as SeP, SELP, SEPP, SELENOP) encodes a selenoprotein that is primarily expressed in the liver and secreted into plasma. This selenoprotein is unique in that it contains multiple selenocysteine ​​(Sec) residues per polypeptide (10 in humans), accounting for the majority of plasma selenium. It is involved as an extracellular antioxidant and in the transport of selenium to extrahepatic tissues via the apolipoprotein E receptor-2 (apoER2). The human SEPP1 gene corresponds to gene ID: 6414. The human selenoprotein P corresponds to the amino acid sequence UniProtKB / Swiss-Prot accession number P49908. Three transcript variants encoding the same protein have been found for this gene (GenBank / NCBI accession numbers NM_5410.4 (variant 1); NM_1085486.3 (variant 2); NM_1093726.3 (variant 3) all accessed March 22, 2021).

[0070] The solute carrier family 40 member 1 (SLC40A1) gene (also known as FPN1, HFE4, MTP1, IREG1, MST079, MSTP079, SLC11A3) encodes a cell membrane protein that may be involved in the transport of iron from duodenal epithelial cells. Defects in this gene are the cause of hemochromatosis type 4 (HFE4). This gene is expressed in the placenta, intestine, muscle and spleen. It is also detected (at the protein level) in red blood cells. The human SLC40A1 gene corresponds to gene ID: 30061. The human SLC40A1 protein corresponds to the amino acid sequence UniProtKB / Swiss-Prot accession number Q9NP59. The transcript corresponds to the nucleotide sequence GenBank / NCBI accession number NM_14585.6, accessed on 20 February 2021.

[0071] Various antibodies specifically directed to FOLR2, TREM2, CADM1, SEPP1 or SLC40A1 proteins have been disclosed and are publicly available, see in particular the antibodies used in the present examples or other antibodies disclosed in antibody-related databases or portals such as, but not limited to, antibodypedia, Antibody Group (ABG), Antibody Central, The hybridoma Databank, European Collection of Cell Cultures, Monoclonal Antibody Index, SCOP, Validated antibody database (VAD), Antibody Chemically Defined (ABCD).

[0072] As used herein, the term "cancer" refers to any cancer that may affect any one of the following tissues or organs: breast; liver; kidney; heart, mediastinum, pleura; floor of the mouth; lips; salivary glands; tongue; gums; oral cavity; palate; tonsils; larynx; trachea; bronchi, lungs; pharynx, hypopharynx, oropharynx, nasopharynx; esophagus; digestive tract, such as stomach, intrahepatic bile duct, biliary tract, pancreas, small intestine, colon; rectum; urinary tract, such as bladder, gallbladder, ureters; rectosigmoid junction; Anus, anal canal; skin; bones; joints, articular cartilage of the limbs; eyes and adnexa; brain; peripheral nerves, autonomic nervous system; spinal cord, cranial nerves, meninges; and various parts of the central nervous system; connective tissue, subcutaneous tissue and other soft tissues; retroperitoneum, peritoneum; adrenal glands; thyroid gland; endocrine glands and associated structures; female reproductive organs, such as ovaries, uterus, cervix; corpus of the uterus, vagina, vulva; male reproductive organs, such as penis, testes and prostate; hematopoietic and reticuloendothelial systems; blood; lymph nodes; thymus.

[0073] The term "cancer" according to the present invention includes leukemia, seminoma, melanoma, teratoma, lymphoma, non-Hodgkin's lymphoma, neuroblastoma, glioma, adenocarcinoma, mesothelioma (including pleural mesothelioma, peritoneal mesothelioma, pericardial mesothelioma, and end-stage mesothelioma), rectal cancer, endometrial cancer, thyroid cancer (including papillary thyroid carcinoma, follicular thyroid carcinoma, medullary thyroid carcinoma, anaplastic thyroid carcinoma, multiple endocrine neoplasia type 2A, multiple endocrine neoplasia type 2B, familial medullary thyroid carcinoma, pheochromocytoma, and paraganglioma), skin cancer (including malignant melanoma, basal cell carcinoma, squamous cell carcinoma, Kaposi's sarcoma, keratoacanthoma, mole, dysplastic nevi, lipoma, hemangioma, and dermatofibroma), nervous system cancer, brain cancer (astrocytoma, medulloblastoma, glioma, low-grade glioma, ependymoma, , germinoma (pinealoma), glioblastoma multiforme, oligodendroglioma, schwannoma, retinoblastoma, congenital tumors, spinal neurofibroma, glioma or sarcoma), skull cancer (including osteoma, hemangioma, granuloma, xanthomas or osteitis deformans), meningeal cancer (including meningioma, meningeal sarcoma or gliomatosis), head and neck cancer (including head and neck squamous cell carcinoma and oral cancer (e.g. oral cancer, lip cancer, tongue cancer, oral cavity cancer or pharyngeal cancer), lymph node cancer, gastrointestinal cancer, liver cancer (including hepatocellular carcinoma, hepatocellular carcinoma, cholangiocarcinoma, hepatoblastoma, hemangiosarcoma, hepatocellular adenoma and hemangioma), colon cancer, stomach cancer, esophageal cancer (including squamous cell carcinoma, laryngeal, adenocarcinoma, leiomyosarcoma or lymphoma), colorectal cancer, intestinal cancer, small intestine or small intestine bowel or large intestine cancer (e.g. adenocarcinoma, lymphoma, carcinoid tumor, Kaposi's sarcoma, leiomyoma, hemangioma, lipoma, neurofibroma or fibroma), colon or large intestine cancer (e.g. adenocarcinoma, tubular adenoma, villous adenoma, hamartoma or leiomyoma), pancreatic cancer (including ductal adenocarcinoma, insulinoma, glucagonoma, gastrinoma, carcinoid tumor or vipoma), ear, nose and throat (ENT) cancer, breast cancer (including HER2 enriched breast cancer, luminal A breast cancer, luminal B breast cancer and triple negative breast cancer), uterine cancer (endometrial cancer, e.g. endometrial carcinoma, endometrial stromal sarcoma and malignant mixed Müllerian tumor, uterine sarcoma, leiomyosarcoma and gestational trophoblastic disease), ovarian cancer (dysgerminoma,granulosa-theca cell tumors and Sertoli-Leydig cell tumors), cervical cancer, vaginal cancer (including squamous cell vaginal carcinoma, vaginal adenocarcinoma, clear cell vaginal adenocarcinoma, vaginal germ cell tumor, vaginal sarcoma, and vaginal melanoma), vulvar cancer (including squamous cell vulvar carcinoma, verrucous vulvar carcinoma, vulvar melanoma, basal cell vulvar carcinoma, Bartholin's gland carcinoma, vulvar adenocarcinoma, and Queyrat's erythrocytosis), genitourinary tract cancer, kidney cancer (including clear cell renal cell carcinoma, chromophobe renal cell carcinoma, papillary renal cell carcinoma, adenocarcinoma, Wilms' tumor, nephroblastoma, lymphoma, or leukemia), adrenal gland cancer, bladder cancer, urethral cancer (e.g., squamous cell carcinoma , transitional cell carcinoma or adenocarcinoma), prostate cancer (e.g., adenocarcinoma or sarcoma) and testicular cancer (e.g., seminoma, teratoma, embryonal carcinoma, teratocarcinoma, choriocarcinoma, sarcoma, stromal cell carcinoma, fibroma, fibroadenoma, adenomatous tumor or lipoma), lung cancer (including small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC) including squamous cell lung carcinoma, lung adenocarcinoma (LUAD), and large cell lung carcinoma, bronchogenic carcinoma, alveolar carcinoma, bronchiolar carcinoma, bronchial adenoma, pulmonary sarcoma, chondromatous hamartoma, pleural mesothelioma), sarcomas (Askin's tumor, Botryoid sarcoma, chondrosarcoma, Ewing's sarcoma, malignant hemangioendothelioma, malignant schwannoma, osteosarcoma, soft tissue sarcoma), soft tissue sarcomas (including alveolar soft part sarcoma, angiosarcoma, cystosarcoma phyllodes, dermatofibrosarcoma protuberans, desmoid tumor, linear small round cell tumor, epithelioid sarcoma, extraskeletal chondrosarcoma, extraskeletal osteosarcoma, fibrosarcoma, gastrointestinal stromal tumor (GIST), hemangiopericytoma, angiosarcoma, Kaposi's sarcoma, leiomyosarcoma, liposarcoma, lymphangiosarcoma, lymphosarcoma, malignant peripheral nerve sheath tumor (MPNST), neurofibrosarcoma, reticular fibrohistiocytic tumor, rhabdomyosarcoma, synovial sarcoma and undifferentiated pleomorphic sarcoma, cardiac cancer (e.g. sarcomas, such as angiosarcoma, fibrosarcoma, rhabdomyosarcoma or liposarcoma, sarcoma, myxoma, rhabdomyoma, fibroma, lipoma, teratoma), bone cancer (including osteogenic sarcoma, osteosarcoma, fibrosarcoma, malignant fibrous histiocytoma, chondrosarcoma, Ewing's sarcoma, malignant lymphoma and reticulum cell sarcoma, multiple myeloma, malignant giant cell tumor chordoma, osteochondroma, osteochondral exostosis, benign chondroma, chondroblastoma, chondromyxoid fibroma, osteoid osteoma and giant cell tumor), blood and lymphatic system cancer, blood cancer (including acute myeloid leukemia, chronic myeloid leukemia, acute lymphoblastic leukemia, chronic lymphocytic leukemia, myeloproliferative disorders, multiple myeloma and myelodysplastic syndromes), Hodgkin's disease,Non-Hodgkin's lymphoma and hairy cell and lymphatic diseases, including metastases thereof.

[0074] In some embodiments, the cancer is selected from the group consisting of breast cancer, kidney cancer, lung cancer, liver cancer, skin cancer, uterine cancer, and adrenal cancer. In some embodiments, the cancer is selected from the group consisting of breast cancer, kidney cancer, lung cancer, liver cancer, skin cancer, uterine cancer, brain cancer, thyroid cancer, and adrenal cancer. Kidney cancer includes renal cell carcinoma (KIRC). Lung cancer includes lung adenocarcinoma (LUAD). Liver cancer includes hepatocellular carcinoma (LIHC). Uterine cancer includes cervical cancer, particularly cervical squamous cell carcinoma and endocervix (CESC). Skin cancer includes melanoma (SKCM). Adrenal cancer includes adrenocortical carcinoma (ACC). Breast cancer includes estrogen receptor positive (ER+), progesterone positive (PR+), HER2 positive (HER2+), and triple negative (ER-, PR-, HER2-) breast cancer. "Triple-negative breast cancer" refers to any breast cancer that does not overexpress the estrogen receptor (ER), progesterone receptor (PR) and HER2 / Neu genes. This subtype of breast cancer is clinically characterized as more aggressive and less responsive to standard treatments, and is associated with poor overall patient survival. Breast cancer includes, in particular, luminal carcinoma (ER / PR+; HER2-). Brain cancer includes gliomas, such as Brain Lower Grade Glioma (LGG). Thyroid cancer includes thyroid carcinoma (THCA). In certain embodiments, the cancer is selected from the group including luminal breast cancer, renal cell carcinoma (KIRC), lung adenocarcinoma (LUAD), hepatocellular carcinoma (LIHC), cervical squamous cell carcinoma, cervical adenocarcinoma (CESC), melanoma (SKCM), gliomas, such as brain low grade glioma (LGG) and thyroid carcinoma (THCA). In particular, it is selected from the group including luminal breast cancer, renal cell carcinoma (KIRC), lung adenocarcinoma (LUAD), hepatocellular carcinoma (LIHC), cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC) and melanoma (SKCM). In some preferred embodiments, the cancer is breast cancer, in particular luminal breast cancer.

[0075] The methods of the invention are useful for establishing a cancer prognosis at an early stage of the disease, thereby adapting a patient's cancer treatment according to the early prognosis determined in an untreated patient. The methods of the invention are also useful for monitoring cancer during the course of treatment, and adjusting cancer treatment according to the prognosis determined for a treated patient.

[0076] The method of the present invention is also useful for predicting the response to cancer therapy, particularly immunotherapy such as checkpoint blockade therapy.Patients with higher levels of FOLR2+ macrophages are more likely to be good responders to immunotherapy such as checkpoint blockade therapy, because they have increased levels of immune cell infiltration in tumor or tumor environment.Therefore, patients with higher levels of FOLR2+ macrophages are treated with immunotherapy, particularly checkpoint blockade therapy.

[0077] Interestingly, breast cancer patients, especially ER / PR+ patients with high FOLR2 gene signature, have improved survival from endocrine therapy compared to chemotherapy alone or chemotherapy + endocrine therapy, which is not the case for patients with low FOLR2 gene signature. FOLR2 high patients will benefit from endocrine therapy alone and do not need chemotherapy. FOLR2 low patients can benefit from endocrine therapy combined with chemotherapy. In some advantageous embodiments, the method further comprises classifying patients into favorable prognosis group and poor prognosis group based on the level of tumor-associated FOLR2+ macrophages determined in the patient's tumor sample.

[0078] Another aspect of the invention relates to a composition for use in a method of treating a cancer patient, wherein the composition is administered to a patient previously diagnosed as having a favorable prognosis according to the prognostic method of the invention. In some embodiments, the composition comprises an immunotherapeutic agent, particularly an immune checkpoint inhibitor. In some embodiments, the composition comprises an endocrine therapeutic agent, particularly for treating breast cancer patients.

[0079] Another aspect of the invention relates to a composition for use in a method of treating a cancer patient, wherein the composition is administered to a patient previously diagnosed with an unfavorable prognosis according to a prognostic method according to the invention.

[0080] The composition may include agents for immunotherapy or chemotherapy or a combination thereof. In some embodiments, the composition includes at least a combination of endocrine therapy and a chemotherapeutic agent, particularly for treating breast cancer patients.

[0081] Patients who are diagnosed as having a good prognosis using the prognostic method of the present invention can benefit from less invasive cancer treatment, both in terms of treatment type and treatment regimen, thereby reducing side effects and improving patient comfort and well-being. Conversely, patients who are diagnosed as having a poor prognosis using the prognostic method of the present invention can benefit from more aggressive cancer treatment, both in terms of treatment type and treatment regimen, thereby increasing the effectiveness of treatment.

[0082] The present invention also relates to a method of treating cancer, comprising determining the level of FOLR2+ macrophages in a patient's tumor sample according to the method of prognosticating or monitoring cancer according to the present disclosure, and providing the patient with an appropriate treatment depending on whether the patient is likely to have a favorable outcome of the cancer disease or treatment. In some aspects, the method comprises administering an immunotherapy agent, particularly an immune checkpoint inhibitor, if the patient is diagnosed with a favorable prognosis. In some aspects, the method comprises administering an endocrine therapy agent, preferably the patient is a breast cancer patient as disclosed herein, if the patient is diagnosed with a favorable prognosis. In some other aspects, the method comprises administering a chemotherapeutic agent, or a combination of a chemotherapeutic agent and an immunotherapy agent, if the patient is diagnosed with an unfavorable prognosis. In some other embodiments, the method comprises administering a chemotherapeutic agent, or a combination of a chemotherapeutic agent and an endocrine therapy agent, if the patient is diagnosed with an unfavorable prognosis, preferably the patient is a breast cancer patient as disclosed herein.

[0083] Yet another aspect of the present invention relates to the use of tumor-associated FOLR2+ macrophages, their gene signature, and FOLR2 gene in vitro as a biomarker of favorable prognosis of cancer disease and treatment outcome in patients.FOLR2 gene and gene signature of FOLR2+ macrophages include gene products (mRNA, protein).Preferably, gene signature of FOLR2+ macrophages comprises or consists of FOLR2, SEPP1 and SLC40A1 genes.The biomarker is used to determine the level of tumor-associated FOLR2+ macrophages in tumor samples of patients. Preferably, the biomarkers are used to determine (i) the density of tumor-associated FOLR2+ cells, preferably FOLR2+ and TREM2- and / or CADM1- cells, (ii) the level of FOLR2 mRNA or protein in the tumor sample, preferably the level of protein, preferably the level of FOLR2 protein, (iii) the level of mRNA or protein expressed by a gene signature of FOLR2+ macrophages comprising or consisting of FOLR2, SEPP1 and SLC40A1 genes, preferably the level of mRNA expressed by the signature genes.

[0084] The present invention also relates to a gene signature of FOLR2+ macrophages comprising or consisting of FOLR2, SEPP1 and SLC40A1 genes.The present invention also relates to an in vitro use of the gene signature of FOLR2+ macrophages as a biomarker for the prognosis or monitoring of cancer.

[0085] treatment The present invention also provides antigen delivery systems that target FOLR2+ macrophages and their use to stimulate T cell immune responses in the prevention and treatment of cancer and infectious diseases.

[0086] One aspect of the invention relates to a targeted antigen delivery system comprising a FOLR2-binding ligand associated with an antigen or a nucleic acid encoding the antigen in an expressible form.

[0087] The antigen can be any antigen of interest, particularly a vaccine antigen. Vaccine antigens are well known in the art and include tumor antigens and pathogen-derived antigens, such as viral, bacterial, fungal, parasitic and other antigens. The antigen can be a complete antigen, an antigen fragment or part, an antigen construct, particularly a natural antigen, including those derived from several antigens, a recombinant antigen or a synthetic antigen. The antigen is specific for a tumor or a pathogen. It can include one or more epitopes, including B cell, CD4+ T cell and / or CD8+ T cell epitopes. Any known vaccine antigen is suitable for incorporation into the vaccine delivery system of the present invention, and the delivery system according to the present invention can incorporate any known vaccine antigen. The targeted antigen delivery system can be used to stimulate antigen-specific CD4+ and / or CD8+ T cell immune responses, including antigen-specific effector T cell and cytotoxic T cell immune responses.

[0088] The nucleic acid encoding the antigen may be composed of recombinant, synthetic or semi-synthetic nucleic acid that can be expressed in the target cell or tissue of an individual. The nucleic acid may be DNA, RNA, mixed, and may be further modified. The nucleic acid construct may be a mammalian expression cassette, preferably a human expression cassette, in which the coding sequence is operably linked to suitable regulatory sequences for their expression in the target cell or tissue of an individual, such as promoters, introns, enhancers, terminators, etc. The nucleic acid may be incorporated into suitable vectors for gene delivery to the target cell or tissue of an individual, which are well known in the art and include, but are not limited to, plasmids and viral vectors such as adenovirus, lentivirus, adeno-associated virus (AAV), poxvirus such as vaccinia virus, replication-defective alphavirus replicons, and cytomegalovirus.

[0089] The FOLR2 binding ligand binds to cell surface FOLR2, particularly cell surface human FOLR2. This refers to the FOLR2 binding ligand as having sufficient affinity for the FOLR2 extracellular domain to form a stable complex under standard conditions.

[0090] In some embodiments, the FOLR2 binding ligand comprises or consists of an anti-FOLR2 antibody or a fragment thereof that comprises the antigen binding site.

[0091] As used herein, the term "antibody" refers to a protein that comprises at least one antigen-binding region of an immunoglobulin. The antigen-binding region may comprise one or two variable domains, such as a VH domain and a VL domain, or a single VHH or VNAR domain. The term "antibody" encompasses full-length immunoglobulins of any isotype, functional fragments thereof comprising at least the antigen-binding region, and derivatives thereof. Antigen-binding fragments of antibodies include, for example, Fv, scFv, Fab, Fab', F(ab')2, Fd, Fabc, and sdAb (V HH, V-NAR). Antibody derivatives include, but are not limited to, multivalent or polyvalent antibodies, intrabodies and immunoconjugates. Intrabodies are antibodies that bind their antigens intracellularly after being produced in the same cell (for review, see, e.g., Marschall AL, Dubel S and Boldicke T “Specific in vivo knockdown of protein function by intrabodies”, MAbs.2015;7(6):1010-35). The antibody may be glycosylated. The antibody is preferably non-functional for antibody-dependent cellular cytotoxicity and complement-mediated cytotoxicity. The antibody may contain a mutation in the Fc domain that prevents binding to high affinity Fc-gamma receptors. Such mutants known in the art include, for example, N297A. The antibody may be a chimeric antibody that includes an Fv or scFv from an anti-FOLR2 monoclonal antibody and constant domains, particularly CH2 and CH3, from another antibody. Antibodies are prepared by standard methods known in the art, such as hybridoma technology, selected lymphocyte antibody assay (SLAM), transgenic animals, recombinant antibody libraries, or synthetic production.

[0092] The antibody is directed against the extracellular domain of FOLR2. A variety of anti-FOLR2 antibodies are known in the art and are publicly available. In particular, see the antibodies used in this example, or other antibodies disclosed in antibody-related databases or portals, including but not limited to: antibodypedia, Antibody Group (ABG), Antibody Central, The hybridoma Databank, European Collection of Cell Cultures, Monoclonal Antibody Index, SCOP, Validated antibody database (VAD), Antibody Chemically Defined (ABCD) database. For example, anti-FOLR2 antibodies are disclosed in US Patent Application Publication No. 2008 / 0260812, US Patent Application Publication No. 2014 / 0010756, and WO 2012 / 033987. A disulfide stabilized Fv anti-FOLR2 is disclosed in Nagai et al., Arthritis and Rheumatism, 2006, 54, 3126-3134.

[0093] Antigen and FOLR2 binding ligand can be directly or indirectly associated. Direct association refers to conjugate and fusion protein in particular. Fusion protein can include N-ter to C-ter, that is, anti-FOLR2 Fv, antibody CH domain, such as CH2 and CH3, and antigen fused to C-terminus of fusion protein. Antigen can be polyepitope polypeptide.

[0094] Indirect association refers specifically to non-covalent complexes and particles. Non-covalent complexes can be formed using binding interaction partners such as streptavidin / biotin. Particles refer to any particle capable of delivering a therapeutic agent to a cell. Particles include lipoparticles, microparticles, nanoparticles, exosomes, virus-like particles, viral vector particles, and combinations thereof, such as lipid nanoparticles (LNPs). Folate-modified liposome complexes are disclosed in Tie et al. (Signal transduction and targeted therapy, 2020, 5, 6). In some embodiments, the particles incorporate an antigen or its nucleic acid and present a FOLR2-binding ligand, in particular a FOLR2 antibody or a fragment thereof, on their surface.

[0095] The targeted antigen delivery system is advantageously used in the form of an immunogenic or vaccine composition comprising an antigen as an active substance and at least one pharma- ceutically acceptable vehicle, adjuvant and / or carrier.

[0096] Pharmaceutical compositions are formulated for administration by several routes, including, but not limited to, oral, parenteral and topical. Pharmaceutical vehicles are appropriate for the planned route of administration and are well known in the art.

[0097] The pharmaceutical composition contains a therapeutically effective amount of the antigen sufficient to stimulate a specific T cell immune response in the subject to which it is administered, particularly an anti-tumor or protective immune response against a pathogen.

[0098] The pharma- ceutically effective dose will depend on the composition used, the route of administration, the type of mammal (human or animal) being treated, the physical characteristics of the particular mammal under consideration, concurrent medications, and other factors that will be recognized by those skilled in the medical arts.

[0099] The pharmaceutical compositions of the present invention are generally administered according to known procedures at a dosage and for a period of time effective to induce a beneficial effect in an individual. Administration can be by injection or local administration. Injection can be subcutaneous or intramuscular.

[0100] In some embodiments, the pharmaceutical composition comprises another active agent, such as an immunomodulatory agent, an anti-cancer agent, an anti-viral agent, an anti-bacterial agent, an anti-fungal agent, or an anti-parasitic agent, among others.

[0101] The pharmaceutical composition of the present invention is advantageously used in combination with additional cancer therapies, such as immunotherapy, including but not limited to immune checkpoint therapy and immune checkpoint inhibitors, immune co-stimulatory antibodies, CAR-T cell therapy, anti-cancer vaccines, chemotherapy and / or radiation therapy. The combination therapy may be separate, simultaneous and / or sequential.

[0102] In some embodiments, the pharmaceutical compositions are used for treating humans.

[0103] The present invention encompasses targeted antigen delivery systems and pharmaceutical compositions for use in stimulating T cell immune responses in the prevention and treatment of cancer and infectious diseases.

[0104] The practice of the present invention will employ, unless otherwise indicated, conventional techniques that are within the skill of those in the art, and such techniques are fully explained in the literature.

[0105] The invention will now be illustrated by the following non-limiting examples with reference to the accompanying drawings. [Brief description of the drawings]

[0106] [Figure 1] APOE expression defines tumor-associated macrophages in human breast cancer. [Figure 1A] Flow cytometric quantification of CD1c+CD14-, CD1c+CD14+ and CD1c-CD14+ cells among CD11c+HLA-DR+ myeloid cells in matched non-metastatic and metastatic lymph nodes. [Figure 1B] Flow cytometric quantification of CD14+ cells in metastatic lymph nodes stratified by CD45-EPCAM+ tumor cell frequency. [Figure 1C] Experimental design of untreated luminal breast cancer patient samples (PBMCs, metastatic lymph nodes and primary tumors) and gating strategy for FACS sorting of bone marrow cells for scRNAseq droplet-based assay (10x Chromium). [Figure 1D] Dimensionality reduction of fused scRNAseq data from blood, metastatic lymph nodes and primary tumors was performed using Louvain graph-based clustering identifying six clusters. Each dot represents an individual cell (n=18008). Representative genes from each cluster are shown. [Figure 1E] Bubble map showing the top 5 most significantly expressed genes across the 6 defined clusters. Circles are sized to represent the percentage of cells in the cluster expressing the gene, and color represents the average expression of each gene. [Figure 1F] Violin plots showing the expression distribution of selected genes of interest in cluster 0 (CD14+ monocytes), cluster 1 (CD1C+ DCs), cluster 2 (macrophages) and cluster 4 (CD16+ monocytes). [Figure 1G] Flow cytometry quantification of CD14+CCR2+ monocytes versus CD14+APOE+ macrophages in healthy or metastatic lymph nodes and breasts. (*P≦0.05; **P≦0.01; ****P<0.0001). [Diagram 2] Single-cell RNA sequencing reveals two major subsets of APOE+ macrophages. [Figure 2A] UMAP plot visualization of APOE+ macrophages (cluster 2 in Figure 1D). Each dot represents an individual cell (n=3762). [Figure 2B] Hierarchical clustering of clusters 0, 1 and 2 based on average gene expression (1200 genes). [Figure 2C] Heatmap and hierarchical clustering of distinct predicted transcriptional regulons in clusters 0, 1 and 2 defined using the SCENIC pipeline. [Figure 2D]Volcano plot showing DEGs among each cluster. Genes selected from the top 25 are shown. [Figure 2E] UMAP plot showing mutually exclusive expression of TREM2 and FOLR2 in APOE+ macrophages. [Figure 2F] Violin plot expression distribution of FOLR2, TREM2 and CADM1 across APOE+ macrophage clusters [Figure 2G] Representative flow cytometry contour plots and cytospin images from FACS-sorted FOLR2+ and CADM1+ macrophages isolated from metastatic lymph nodes and primary tumors. [Figure 2H] Hierarchical clustering using the 100 most variable genes from bulk RNA sequencing of FOLR2+TAMs, CADM1+TAMs and CCR2+ monocytes isolated by FACS sorting from metastatic lymph nodes and primary tumors of untreated luminal breast cancer patients. [Figure 2I] Heatmap of DEGs between FOLR2+ (c2) and TREM2 high (c1) macrophages selected from the scRNAseq dataset and applied to bulk RNAseq of FOLR2+ macrophages, CADM1+ macrophages and CCR2+ monocytes isolated by FACS sorting from metastatic lymph nodes and primary tumors of untreated luminal breast cancer patients. [Figure 2J] Heatmap of DEGs between FOLR2+ (c2) and TREM2 high (c1) macrophages selected from the scRNAseq dataset and applied to bulk RNAseq of FOLR2+ macrophages, CADM1+ macrophages and CCR2+ monocytes isolated by FACS sorting from metastatic lymph nodes and primary tumors of untreated luminal breast cancer patients. [Figure 2K] Protein expression analysis (CyTOF) of monocyte / macrophage and dendritic cell markers in CD14+CCR2+ monocytes, CD14+CCR2-CD68+LYVE1+ macrophages and CD14+CCR2-CD68+CADM1+ macrophages from metastatic lymph nodes. [Diagram 3] FOLR2+ macrophages are tissue-resident macrophages. [Figure 3A] Representative contour plots (upper panels) and quantification (lower panels) of FOLR2+APOE+ and FOLR2-APOE+ macrophages across different breast cancer patient tissues using flow cytometry. [Figure 3B] FOLR2 and TREM2 mRNA expression in tumor microarrays from breast cancer patients in the TCGA database (tumor: n = 1093; normal breast: n = 112; basal subtype, n = 190; Her2 subtype, n = 82; and luminal subtype, n = 781). [Figure 3C] UMAP plot visualization of Fcgr1+ macrophages (n=4095) isolated from mammary tumors of 23-week-old MMTV-PyMT mice (n=2). Violin plot showing the expression distribution of Cadm1, Folr2 and Mrc1 across Fcgr1+ macrophages. [Figure 3D] Heatmap showing gene orthologs that are similarly expressed across mouse and human macrophage subsets. [Figure 3E] Similarity scores defined using Seurat v3 reference labelled integration (Stuart et al., 2019). Graph plot showing prediction scores for each mouse macrophage cluster (defined in Figure 2A) applied to each human macrophage cluster. Each dot represents a cell. Heatmap of mean prediction scores. [Figure 3F] Quantification of Folr2+ and Cadm1+ macrophages by flow cytometry during tumor development in the MMTV-PyMT mouse model. The top row shows representative contour plots of macrophage subsets in the mammary glands of tumor-free and tumor-bearing mice (20-week-old PyMT mice). The bottom row shows quantification of macrophage subsets in WT or PyMT mice during tumor development. [Figure 4] FOLR2+ macrophages correlate with increased survival in breast cancer patients. [Figure 4A]Venn diagram showing specific and common differentially expressed genes (DEGs) for each APOE+ macrophage cluster (defined in Figure 2A). [Figure 4B] Heatmap showing the average expression of specific genes identifying tumor-infiltrating immune cell populations in a published scRNAseq dataset performed on CD45+ cells isolated from breast cancer patients (Azizi et al., 2018). [Figure 4C] Kaplan-Meier survival curves generated for the macrophage gene signature (C1QA / C1QB / C1QC) and FOLR2+TAM gene signature (FOLR2 / SEPP1 / SLC40A1) in the METABRIC luminal breast cancer (BC) cohort (n=1309). Kaplan-Meier survival curves generated for FOLR2 protein expression in the CPTAC luminal breast cancer cohort (n=49). Patients were divided into high and low expressing groups based on the 75% quantile of signature expression. [Figure 4D] Kaplan-Meier survival curves generated for the FOLR2+ macrophage gene signature (FOLR2 / SEPP1 / SLC40A1) in Wang et al.'s luminal breast cancer cohort (n=209 patients). Patients were divided into high and low expression groups based on the 70% quantile of signature expression. [Figure 4E] To test the prognostic power of the FOLR2 signature in different treatment groups, univariate analysis was performed in luminal BC patients classified according to the treatment received. Kaplan-Meier survival curves generated for the FOLR2+macrophage gene signature (FOLR2 / SEPP1 / SLC40A1) in the METABRIC dataset of luminal A / B patients stratified by the treatment received. The FOLR2 gene signature has significant prognostic value in patients receiving endocrine therapy only. The FOLR2 gene signature did not have significant prognostic power in patients receiving endocrine therapy + chemotherapy or chemotherapy alone. Patients were divided into high and low expression groups based on the 70% quantile of signature expression. The number of patients in each group is shown. [Figure 4F]Kaplan-Meier survival curves generated for FOLR2+ macrophage density calculated by multispectral analysis of tumors (Cohort 1: n=122; Cohort 2: n=126). The graph shows the quantification of FOLR2+ macrophage density in tumors. Patients were divided into high and low cell density groups based on the best p-value cutoff (**P≦0.01). [Diagram 5] The FOLR2 gene signature is an independent prognostic factor that correlates with better survival [Figure 5A] FOLR2 mRNA expression in luminal (ER / PR) breast tumors of different grades and stages from the METABRIC dataset. [Figure 5B] Multivariate analysis of the prognostic value of the FOLR2 signature as a continuous variable (left) or with a 75% cutoff, adjusted age, histological grade, tumor size, histology (reference + duct) and number of disease-positive lymph nodes. Hazard ratios with 95% confidence intervals are shown. Asterisks refer to P values ​​from the Wald test for each variable. [Figure 6] FOLR2+ macrophages are enriched in CD8+ T cell-infiltrated tumors and co-localize with lymphoid aggregates throughout the cancer. [Figure 6A] Correlation map (A) and heat map (B) analyze the association of FOLR2 and TREM2 genes to immune cell gene signatures in the METABRIC dataset. [Figure 6B] Correlation map (A) and heat map (B) analyze the association of FOLR2 and TREM2 genes to immune cell gene signatures in the METABRIC dataset. [Figure 6C] Density of CD8+ T cells in tumors with high or low FOLR2+ cell density as determined by immunofluorescence images of tissue microarrays. [Figure 6D] Graph of the mean velocity of endogenous CD8+ T cells in contact or not with FOLR2+ macrophages as determined by live imaging. Mann-Whitney test. [Figure 7]FOLR2 macrophages can promote T cell effector differentiation. [Figure 7A] Volcano plot showing DEGs among FOLR2+ macrophages isolated from healthy MG or breast tumors. [Figure 7B] SIINFEKL OVA peptide-pulsed FOLR2+ or CADM1+ macrophages were co-cultured with CTV-labeled naïve OTI CD8+ T cells. T cell proliferation and activation was assessed after 3 days by flow cytometry. Data in the right panel represent OTI T cell numbers and % of IFN-γ+TNF-α+CD8+ T cells after 3 days of co-culture with separate macrophage populations pulsed with 1 nM SIINFEKL. Data are the mean + / - SD of triplicate wells and are representative of two experiments. Unpaired t-test. [Figure 7C] FOLR2 macrophages were purified from mouse breast tumors and loaded with OVA peptide. After washing, FOLR2 macrophages were co-cultured with anti-OVA naive CD8 T cells for 3 days. T cell activation and proliferation were measured on the third day of culture. FOLR2 macrophages show higher T cell activation capacity than CADM1 macrophages or OVA alone. [Figure 8] Vaccination with anti-FOLR2-targeting antibodies coupled to model antigens induces specific CD8+ T cell responses

[0107] Scheme of anti-FOLR2 ScFv linked to SIINFEKL peptide. WT mice were immunized with anti-FOLR2-SIINFEKL or anti-hCD19-SIINFEKL as control. Four days later, CFSE-labeled CD45.1 OTI naïve CD8 + T cells were adoptively transferred into immunized mice and their proliferation (CFSE dilution) and activation (CD44 expression and CD62L downregulation) were assessed 3 days later in lymph nodes draining the immunization site.

[0108] Working Example A. Materials and Methods 1. Details of the experimental model and subjects Human tumor samples Primary tumors, paratumoral tissues, metastatic and non-metastatic tumor-draining LNs were surgically resected from patients with luminal breast cancer at the Institut Curie Hospital (Paris, France) according to the institutional ethical guidelines and with informed consent (CRI-0804-2015, Institut Curie Ethics Committee approved). Lymph node metastases were assigned by the Pathology Department of the Institut Curie and further confirmed by using pan-cancer / CD45 immunodetection by flow cytometry. FOLR2 and TREM2 immunohistochemistry were performed on tumor sections retrieved from the archives of the Pathology Unit, ASST Spedali Civili di Brescia.

[0109] mouse Transgenic PyMT mice (MMTV-PyMT 634Mul ) (Davie et al., 2007) were maintained on a C57Bl / 6 background and bred and maintained specific pathogen-free in the Institut Curie animal facility according to Institut Curie guidelines. Healthy C57BL / 6J female mice were obtained from Charles River Laboratories, maintained in a non-barrier facility, and included at 8–12 weeks of age for experimental procedures. The care and use of animals for this study was carried out in accordance with the European Community recommendations for the care and use of laboratory animals (2010 / 63 / UE). Experimental procedures were specifically approved by the Swiss Federal Government (authorization number 2016-06.150) in accordance with international guidelines.

[0110] 2. Method Details Human sample processing Patient samples were processed as previously described (Nunuez et al., 2020). Briefly, freshly resected human samples were cut into small pieces and digested with 0.1 mg / ml Liberase TL (Roche) and 0.1 mg / ml DNase (Roche) in CO2-independent medium (GIBCO) + 0, 4 g / l human albumin (Vialebex) for 30 min at 37 °C. Single cell suspensions of dissociated tissue were filtered through a 40 μm cell strainer (BD Biosciences), washed in CO2-independent medium + 0.4 g / l human albumin, and resuspended in medium for cell number estimation.

[0111] Flow cytometry analysis After tissue processing and cell counting, cell suspensions were stained with the Live / Dead Fixable Aqua Dead Cell Stain Kit (Life Technologies) in 1X phosphate-buffered saline (PBS) for 10 min at room temperature. After incubation, cells were washed with cold PBS + 0.5% bovine serum albumin (BSA) + 2 mM EDTA and stained with fluorescent antibodies (Table) in the presence of Fc receptor blocking reagent (Miltenyi) for 30 min at 4 °C. Cell suspensions were then washed and subjected to intracellular staining using a fixation / permeabilization kit (eBiosciences / Thermo Fischer) according to the manufacturer's instructions. Data acquisition was performed using an LSR-Fortessa (BD) and compensation and analysis were performed using FlowJo software (TreeStar).

[0112] Mass cytometry staining and data analysis For mass cytometry, pre-conjugated or purified antibodies were obtained from Invitrogen, Fluidigm (pre-conjugated antibodies), Biolegend, eBioscience, Becton Dickinson or R&D Systems as listed in the table. For some markers, fluorophore-conjugated or biotin-conjugated antibodies were used as primary antibodies, followed by secondary labeling with anti-fluorophore metal-conjugated antibodies (e.g., anti-FITC clone FIT22) or metal-conjugated streptavidin, produced as previously described (Becher et al., 2014). Briefly, patient lymph node cell suspensions (approximately 30 × 10 cells in U-bottom 96-well plates) were cultured in 1000 mL of 1000 mM NaCl. 6Cells / well; BD Falcon, Cat. No. 3077) were washed once with 200 mL FACS buffer (4% FBS, 2 mM EDTA, 0.05% azide in 1× PBS) and then stained with 100 mL 200 mM cisplatin (SigmaAldrich, Cat. No. 479306-1G) for 5 min on ice to exclude dead cells. Cells were then washed with FACS buffer and once with PBS before being fixed overnight or longer with 200 mL 2% paraformaldehyde (PFA; Electron Microscopy Sciences, Cat. No. 15710) in PBS. After fixation, cells were pelleted and resuspended in 200 μL 1× permeabilization buffer (Biolegend, Cat. No. 421002) for 5 min at room temperature to allow intracellular labeling. Bromoacetamidobenzyl-EDTA (BABE)-conjugated metal barcodes were prepared by dissolving BABE (Dojindo, Cat. No. B437) in 100 mM HEPES buffer (GIBCO, Cat. No. 15630) to a final concentration of 2 mM. Isotopically purified PdCl2 (Trace Sciences) was then added to the 2 mM BABE solution to a final concentration of 0.5 mM. Similarly, DOTA-maleimide (DM)-conjugated metal barcodes were prepared by dissolving DM (Macrocyclics, Cat. No. B-272) in L buffer (MAXPAR, Cat. No. PN00008) to a final concentration of 1 mM. RhC3 (Sigma) and isotopically purified LnCl3 were then added to the DM solution at a final concentration of 0.5 mM. Six metal barcodes were used, namely BABE-Pd-102, BABE-Pd-104, BABE-Pd-106, BABE-Pd-108, BABE-Pd-110 and DMLn-113. All BABE and DM metal solution mixtures were immediately snap frozen in liquid nitrogen and stored at 80 °C. A unique duplicate combination of barcodes was selected to stain each tissue sample. Barcode Pd-102 was used at a 1:4000 dilution, Pd-104 at a 1:2000 dilution, Pd-106 and Pd-108 at a 1:1000 dilution, and Pd-110 and Ln-113 at a 1:500 dilution.Cells were incubated in 100 mL barcode in PBS for 30 min on ice, washed with permeabilization buffer, and then incubated in FACS buffer for 10 min on ice. Cells were then pelleted and resuspended in 100 mL nucleic acid Ir-intercalator (MAXPAR, Cat. No. 201192B) in 2% PFA / PBS (1:2000) at room temperature. After 20 min, cells were washed twice with FACS buffer and twice with water, then resuspended in water. In each set, cells were pooled from all tissue types, counted, and diluted to 0.5 × 106 cells / mL. Prior to acquisition, EQ Four Element Calibration Beads (DVS Science, Fluidigm) were added at a concentration of 1%. Cell data were acquired and analyzed using a CyTOF mass cytometer (Fluidigm). CyTOF data were exported in conventional flow cytometry file (.fcs) format and normalized using previously described software (Finck et al., 2013). Events with a value of 0 were randomly assigned a value between 0 and -1 using a custom R script used in a previous version of the mass cytometry software (Newell et al., 2012). Cells for each barcode were deconvoluted using a Boolean gating algorithm within FlowJo. CD45 in PBMCs. + Lin(CD3 / CD19 / CD20) - HLA-DR + Populations were gated using FlowJo and exported as .fcs files.

[0113] Human and mouse sample processing for single cell sequencing Tumors from mice from PyMT or wild-type littermate controls were cut into small pieces and digested for 1 h at 37 °C in CO2-independent medium (Gibco) containing 150 μg / mL DNAse and 75 μg / mL Liberase TL (Roche). Cell suspensions were then obtained by filtering through a 40 μM cell strainer (BD). Red blood cells were then lysed with red blood cell lysis buffer. Finally, cells were resuspended in cold PBS containing 2 mM EDTA (Invitrogen) and 5% bovine serum albumin (BSA; Sigma) for further assays.

[0114] Human tumors were processed according to previously published methods (Leruste et al., 2019) (Bourdely et al., 2020). Briefly, after tissue processing, dissociation and cell counting, cell suspensions were kept on ice and stained for FACS sorting with the antibodies and DAPI indicated in the table. Cells were isolated using a FACS-ARIA III (BD) cell sorter and collected in cold PBS + 0.04% BSA for cell counting. PBMCs were obtained from fresh blood samples by density gradient centrifugation using Lymphoprep (Stemcell Technologies) according to the manufacturer's instructions, then washed and resuspended in CO2-independent medium + 0.4 g / l human albumin before FACS sorting. Blood monocytes were also collected in cold PBS + 0.04% BSA before cell counting. All tissues were processed within 1 h after tumor resection and sorted cells were placed into the 10× Chromium chip instrument within 6 h.

[0115] Single-cell RNA sequencing Human and mouse cell suspensions (3000 to 8000 cells) were loaded onto a 10x Chromium Controller (10x Genomics) according to the manufacturer's protocol based on 10x GEMCode's proprietary technology. Single-cell RNA-Seq libraries were prepared using the Chromium Single Cell 3'v2 or v3 Reagent Kit (10x Genomics) according to the manufacturer's protocol. Briefly, the first step consisted of performing an emulsion, where individual cells were isolated in droplets with gel beads coated with a unique primer carrying the 10x cell barcode, unique molecular identifier (UMI) and poly(dT) sequence. A reverse transcription reaction was performed to generate barcoded full-length cDNA, followed by breaking the emulsion using a recovery agent and cleaning up the cDNA with DynaBeads MyOne Silane. Bulk cDNA was amplified using a GeneAmp PCR System 9700 with a 96-Well Gold Sample Block Module (Applied Biosystems) (98°C for 3 min; cycle 11 / 12×: 98°C for 15 s, 63°C for 20 s, and 72°C for 1 min; hold at 4°C). Amplified cDNA products were cleaned up with the SPRI Select Reagent Kit (Beckman Coulter). Reagents from the Chromium Single Cell 3'v3 Reagent Kit were used to construct indexed sequencing libraries following the following steps: (1) fragmentation, end repair, and tailing, (2) size selection with SPRI selection, (3) adapter ligation, (4) post-ligation cleanup with SPRI selection; (5) sample index PCR and cleanup with SPRI selection beads. Quantification and quality assessment of the libraries was performed using the dsDNA HS (High Sensitivity) Assay Kit with a High Sensitivity DNA chip (Agilent) and the Qubit fluorescent assay with a Bioanalyzer Agilent 2100 (Invitrogen).Indexed libraries were pooled according to cell number and sequenced on a NovaSeq 6000 (Illumina) using paired-end 28 × 91 bp, resulting in a depth of approximately 50,000 reads per cell.

[0116] Processing and analysis of single-cell RNA sequencing data Alignment and construction of raw expression matrices. Human and mouse raw sequencing data were aligned on the reference genomes GRCh38 / 84 and GRCm38 / 84 (genome assembly / ENSEMBL release) using 10X software CellRanger (version 3.0.2) with default parameters, respectively. Gene expression counts for individual cells were generated using CellRanger counts. Cell selection, filtering and normalization. For both human and mouse raw count matrices, cells expressing at least 200 genes were retained. Cells with mitochondrial content above 10% were removed. After this quality control, data were normalized by total counts following the Seurat 3 R pipeline. Cells identified as contaminants of the gating strategy or with high content of heat shock proteins or ribosomal coding genes were excluded. Selection of variable genes and fusion of samples For fusion of samples, the VST (variance stabilizing transformation) method of selecting the most variable genes was applied. The Seurat V3 integration pipeline was performed using the most abundant 8000 genes for the 10 human samples. The 3000 most variable genes were used to fuse the two mouse samples. Default parameters of the Seurat V3 function were used for both anchor selection and integration steps. Dimensionality reduction and visualization. The data were scaled by applying a regression using as variation factors the total UMI count, the proportion of mitochondrial genes expressed, the sample and tissue of origin of each cell, and the version of the CellRanger chemistry kit used for sequencing. The heatmap shows the z-score of this scaled matrix. UMAP visualization was constructed using the 50 and 30 most informative components of the human and mouse PCA, respectively. Clustering and differential gene expression analysis. Clustering was handled by constructing a shared nearest neighbor (SNN) graph. The 20 neighbors of each cell were first determined. The resulting KNN graph was used to construct the SNN graph by calculating the neighborhood overlap (Jaccard index) between every cell and its 20 nearest neighbors.Clustering was then applied to this graph using an algorithm based on the Louvain graph. Differential gene expression analysis was applied to each sample log-normalized matrix. The Seurat function FindAllMarkers was used with a logistic regression test, adding as variables the sample and tissue of origin of each cell, as well as the version of the CellRanger sequencing kit used. Only genes expressed in more than 10% of the cells in the cluster and with a log fold change of at least 0.10 between the comparison groups were tested. Low signals generated by dropped genes could be detected. For the Volcano plot, only the first condition was kept. Finally, only genes with significant adjusted p-values ​​(pv<0.05, false discovery rate (FDR) adjusted p-value) were kept and used to define each cluster. Human-mouse fusion and Seurat label metastasis prediction score. To compare mouse and human macrophages, orthologous genes conserved between both species were selected. The corresponding gene symbol table was provided by the Mouse Genome Informatics database. For genes with two or more corresponding orthologs in other species, the average expression of all orthologs was taken. The Seurat label metastasis scoring algorithm was applied from mouse (as query) to human dataset (as reference). For the anchor search step, the 10000 most variable genes of the reference dataset were used. The following steps of the Seurat pipeline were applied with default parameters: Processing of published datasets. We downloaded the dataset from Azizi et al., Cell 2018 (GSE 114725). We used the classical pipeline for single cell analysis of Seurat V3 (without integral correction) from the raw counting matrix (supplementary file GSE114725_rna_raw.csv.gz). We then performed a clustering based on the Louvain graph. At a resolution of 0.9, we found 39 clusters, namely 14 clusters of T cells, 6 clusters of B cells, 5 clusters of NK cells, pDC, DC1, DC2, CD16 + Monocytes, CD14 +One cluster of monocytes, neutrophils or mast cells, three clusters of macrophages, and four clusters of contaminating cells were obtained. Clusters of the same immune cell type were fused, except for the macrophage cluster and the contaminating cluster. The dataset from Han et al., Cell 2018 (GSE 108097) was downloaded. Two samples of virgin mammary glands and one sample of pregnant mammary glands were merged from the raw data (supplementary file GSE 108097_RAW.tar). The same pipeline as above, including the merge step, was used.

[0117] Bulk RNA sequencing After tissue processing and cell counting, cell suspensions were washed with cold PBS + 0.5% BSA + 2 mM EDTA and subjected to surface antibody staining (Table) for 30 min at 4 °C in the presence of Fc receptor blockade (FcR blocking reagent; Miltenyi). Myeloid cell subsets were isolated using a FACS-ARIA III (BD) cell sorter and collected directly onto lysis TCL buffer (QIAGEN) containing 1% β-mercaptoethanol before storage at -80 °C. RNA was extracted and isolated using a Single Cell RNA Purification Kit (Norgen, Cat. No. 51800) according to the manufacturer's instructions. After extraction, total RNA was analyzed on an Agilent 2100 Bioanalyzer System using an Agilent RNA 6000 Pico Kit. RNA quality was estimated based on capillary electrophoresis profiles using the RNA Integrity Number (RIN). RNA sequencing libraries were prepared using the SMARTer Stranded Total RNA-Seq Kit v2-Pico Input Mammalian (Clontech / Takara). The input amount of total RNA ranged from 1 ng to 22 ng. The protocol includes a first step of RNA fragmentation using a proprietary fragmentation mixture at 94 °C. The incubation time was set for each sample according to the manufacturer's recommendations, based on the RNA quality. After fragmentation, indexed cDNA synthesis was performed. Then, a ribodepletion step was performed using a probe specific for mammalian rRNA. Finally, PCR amplification was achieved to amplify the indexed cDNA library, with a number of cycles set according to the input amount of tRNA. Quantification and quality assessment of the libraries was performed using the dsDNA HS (High Sensitivity) Assay Kit using a High Sensitivity DNA chip (Perkin Elmer) and the Qubit Fluorescence Assay (Invitrogen) using LabChip GX Touch. Libraries were then pooled in equimolar amounts and quantified by qPCR using the KAPA Library Quantification Kit (Roche).Sequencing was performed on a NovaSeq 6000 (Illumina) using paired-end 2 × 100 bp targeting 10M–15M reads per sample.

[0118] Processing and analysis of bulk RNA sequencing data Raw sequencing data were first aligned to the human reference genome hg19 using the STAR aligner (v2.5.3a) (Dobin et al., 2013). A raw read count matrix was also generated in STAR (using the parameter --quantMode GeneCounts). Quality control of FastQ files was applied with FastQC (removal of adapters and low quality bases). Unexpressed genes (sum of counts in all samples < 2) and low expressed genes (background; log2 of the average of raw counts in all samples < 2) were removed from the raw read count matrix. For differential expression analysis, the R package DESeq2 (version 1.24.0) (Love et al., 2014) was used with p-value correction. The DESeq matrix was designed using patient and cell type information (following formula design = approximately batch + condition,). The median of proportions method (Anders and Huber, 2010) was used for normalization, and the rlog transform for visualization and clustering as suggested in the DESeq 2 tutorial (Love et al., 2014).

[0119] immunohistochemistry Paraffin-embedded tissue blocks were cut into 3-micron fine slivers on a microtome. Immunohistochemistry was processed on a Bond RX automated (Leica) using the Bond Polymer improved detection kit (Leica, DS 9800). Antigen retrieval was performed in BOND Epitope Retrieval Solution 1 (Leica, AR 9961). Primary antibody APOE (Abcam; ab52607) was incubated for 30 minutes at room temperature. Slides were counterstained with hematoxylin and then mounted with resin. Images were acquired using a Digital Pathology slide scanner (Ultra Fast Scanner 1.8, Philips). FOLR2 expression was examined in human tissues by using immunohistochemistry. Samples included reactive lymph nodes (n=7) from different primary sites (breast, bladder, gastrointestinal, lung, kidney and skin) retrieved from the archives of the Pathology Unit, ASST Spedali Civili di Brescia, primary carcinomas (n=47), metastatic tumor-draining lymph nodes (n=7) and distant metastases to the lung (n=8) and liver (n=11).Briefly, anti-FOLR2 (clone OTI4G6, 1:100, ThermoFisher SCIENTIFIC) and anti-TREM2 (clone D814C, 1:100, Cell Signaling Technology) antibodies were revealed using Novolink Polymer (Leica) followed by DAB. For double staining, FOLR2 was combined with anti-CD3 (clone LN10, 1:70, Leica Biosystem), anti-CD20 (clone L26, 1:200, Leica Biosystem), anti-CD31 (clone PECAM-1, 1:50, Leica) and anti-TREM2. Briefly, after completing the first immune reaction, the second immune reaction was visualized using Mach 4 MR-AP (Biocare Medical) followed by Ferangi Blue. FOLR2 in tertiary lymphoid structures (TLS) + Cellular localization was confirmed in duplicate for the B cell marker CD20 and the T cell marker CD3.

[0120] Immunofluorescence on fixed tumor sections Biopsies were fixed overnight at 4 °C in periodate-lysine-paraformaldehyde solution (0.05 M phosphate buffer containing 0.1 M L-lysine [pH 7.4], 2 mg / ml NaIO4, and 10 mg / ml paraformaldehyde). Fixed tumors were then embedded in 5% low gelling temperature agarose (type VII-A, Sigma-Aldrich) and cut into 400 μm-thick slices as previously described (Peranzoni et al., 2018). Tumor sections were stained with the antibodies shown in the table for 15 min at 37 °C. All antibodies were diluted in PBS and used at a concentration of 5 μg / ml, except for anti-FOLR2 and CD31 antibodies, which were used at 10 μg / ml. Z-stack images of 5 × 5 fields of view were captured using a 10× water-immersion objective (10x / 0.3NA) on an inverted spinning disk confocal microscope (IXplore, Olympus). Virtual slices were reconstructed and analyzed with ImageJ software.

[0121] Multispectral immunofluorescence in paraffin-embedded tissues Paraffin-embedded tissue blocks were cut into 5-micron fine slivers on a microtome. Immunostaining was performed using the Opal™ 7-Color IHC Kit (Akoya Biosciences, NEL821001KT) on a Bond RX automated (Leica) according to the manufacturer's instructions, using the antibodies shown in the table. Tissue sections were coverslipped with Proong™ Diamond Antifade Mountant (ThermoFisher) and stored at 4°C. Slides were subsequently scanned using a Vectra® 3 automated quantitative pathology imaging system (Vectra 3.0.5; Akoya Bioscience). Multispectral images were unblended and analyzed using inForm Advanced Image Analysis Software (inForm 2.4.6; Akoya Bioscience).

[0122] Tissue Microarray (TMA) Multispectral Immunofluorescence Paraffin-embedded tissue microarrays (n=122 spots) from breast cancer patients were obtained commercially (AMSBIO, UK). Immunostaining was performed using the Opal™ 7-Color IHC kit (Akoya Biosciences, NEL821001KT) on a Bond RX automated (Leica) according to the manufacturer's instructions (Table). After immunostaining, slides were subjected to DAPI staining, washed and coverslipped with Proong™ Diamond Antifade Mountant (ThermoFisher). Slides were subsequently scanned using a Vectra® 3 automated quantitative pathology imaging system (Vectra 3.0.5; Akoya Bioscience). Multispectral images were unmixed using inForm Advanced Image Analysis Software (inForm 2.4.6; Akoya Bioscience) and analyzed by HALO software for immune subset quantification.

[0123] METABRIC data analysis METABRIC (METABRIC Group et al., 2012) gene expression data, as well as clinical and sample-level metadata, were downloaded from cBioPortal. Patient breast cancer subtypes were annotated by defining TNBC as having negative ER and HER2 status. HER2-positive patients were defined as any patient with a HER2-positive status variable. ER / PR-positive patients were defined as HER2-negative but either ER or PR status was positive. TNBC with PR IHC-positive status were removed. Patients who died from other non-disease-related causes, as well as patients with breast sarcoma, were removed. TNBC expression data were subjected to the TNBC type (Chen et al., 2012) algorithm, which excluded six additional patients from the TNBC cohort (MB-3297, MB-7269, MB-5008, MB-6052, MB-0179, MB-2993). The final cohort consisted of 1339 samples (168 TNBC, 204 HER2, and 967 ER / PR). To score individual samples for gene signatures of interest, gene expression data were Z-score normalized and then the signatures were calculated. The mean levels of expression across neutrophil genes were calculated. MCPcounter (1.2.0) was used to infer the abundance of immune and stromal cell populations in each sample. To stratify patients according to gene signature score, a cutoff of 75% was used, thus defining 25% of patients as having a "high" score. Survival analysis was performed using the survival (3.1-12) and serveminer (0.4.6) packages in R. Pairwise Spearman correlations were calculated between signatures of interest and analyzed using corrplot The data were plotted as correlation heatmaps using the R package (0.84). Correlations that were not statistically significant (p<0.05) were marked with a dash (-). MCPs were inversely estimated immune cell abundance. To compare MCP-opposed cell types between patients stratified into high and low groups by specific signatures, Wilcoxon tests were used with P values ​​adjusted using the Benjamini-Hochberg method. The ggplot2 (3.3.0), pheatmap (1.0.12) and corrplot (0.84) packages were used to plot the data.

[0124] CPTAC Data Analysis The data used in this publication were generated by the Clinical Proteomic Tumor Analysis Consortium (NCI / NIH). Log-ratio normalized proteomic data, including clinical information and RNA sequencing data, from the CPTAC BRCA study were downloaded (http: / / linkedomics.org / data_download / TCGA-BRCA / ). Samples from BRCA patients annotated as either ER-positive or PR-positive and HER2-negative were selected for downstream analysis. Patients were then stratified according to the 25% cutoff by FOLR2 protein expression and univariate Cox regression analysis was performed. Log-rank test scores were plotted in Kaplan-Meier plots.

[0125] Comparison of GTEx and TCGA To compare TREM2 and FOLR2 mRNA expression between non-disease healthy tissues, tumor-adjacent normal tissues, and tumor tissues, we utilized datasets from the GTEx and TCGA consortia. For breast, colon, and lung studies, we downloaded normalized transcriptome data from Github (https: / / github.com / mskcc / RNAseqDB / tree / master / data / normalized). A non-parametric, Wilcoxon T-test was used to compare TREM2 and FOLR2 expression between tissue types. The Genotype-Tissue Expression (GTEx) project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, as well as NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.

[0126] Density of FOLR2+ macrophages Paraffin-embedded tissue microarrays (n=122 spots) from breast cancer patients were obtained commercially (AMSBIO, UK). Immunostaining was processed using the Opal™ 7-Color IHC kit (Akoya Biosciences, NEL821001KT) on a Bond RX automated (Leica) according to the manufacturer's instructions. After immunostaining, slides were subjected to DAPI staining, washed and coverslipped with Proong™ Diamond Antifade Mountant (ThermoFisher). Slides were subsequently scanned using a Vectra® 3 automated quantitative pathology imaging system (Vectra 3.0.5; Akoya Bioscience). Multispectral images were unmixed using inForm Advanced Image Analysis Software (inForm 2.4.6; Akoya Bioscience) and analyzed by HALO software for immune subset quantification.

[0127] Polyepitope-linked recombinant anti-FOLR2 IgG construct A recombinant mouse IgG2a anti-mouse FOLR2 construct linked to a polyepitope containing the well-characterized CD8+ T cell epitope SIINFEKL (SEQ ID NO: 1) derived from ovalbumin (OVA) was derived from the anti-FolR2 scFv disclosed in Nagai et al. (Arthritis and Rheumatism, 2006, 54, 3126-3134). The polyepitope-linked scFv was fused to an IgG2a Fc mutant (N297A) such that it does not bind to the high affinity Fc-gamma receptor. The polynucleotide construct (SEQ ID NO: 2; 1812 nt) contains the following elements: -Kozak Consensus: 1st to 6th place -Signal peptide: positions 7 to 63 -XhoI site: positions 64 to 69 -Anti-mouse FOLR2 VL: 70 to 408 -(G4S)4 linker: 409 to 468 -Anti-mouse FOLR2 VH: 469 to 831 -G4S(A)3 linker: positions 832 to 855 -NotI-Bgl II site: positions 856 to 870 -FcIgG2A*N297A: positions 871 to 1569 -srfI site: positions 1570 to 1578 -Polyepitope: positions 1579 to 1770 -AscI site: positions 1771 to 1779 -FlagTAG: 1780 to 1803 -Stop codon: positions 1804 to 1806 - NheI site: positions 1807 to 1812.

[0128] The recombinant anti-FOLR2 IgG protein construct corresponds to SEQ ID NO:3.

[0129] statistical analysis Tests used for statistical analysis are described in the legend of each relevant figure and were performed using GraphPad Prism v8 or R v3.4. Significance symbols: ns, non-significant; *, <0.05, **, <0.01; ***, <0.001; ****, <0.0001. For each experimental group, n represents the number of subjects in each group.

[0130] B. Results We demonstrated that tumor-associated CD14 isolated from metastatic LNs and primary breast tumors + HLA-DR + We performed scRNAseq on cells to identify CD14 + We assessed cellular heterogeneity within the compartments. They consisted of two phenotypically distinct macrophage populations, TREM2, which express the triggering receptor cell-2 (TREM2), osteopontin (SPP1), and cell adhesion molecule 1 (CADM1) genes. + Macrophages; FOLR2 expresses folate receptor 2 (FOLR2), selenoprotein P (SEPP1), solute carrier family 40 member 1 (SLC40A1), hyaluronan receptor (LYVE-1) and mannose receptor type C 1 (MRC1 / CD206) genes +This study identifies tumor-associated TREM2 + and FOLR2 + We show that macrophages are evolutionarily conserved between human and mouse breast cancers. + Macrophages are poorly represented in healthy breast tissue but increase with tumor development. In contrast, studies have shown that FOLR2 + We show that FOLR2 is a tissue-resident macrophage (TRM) that proliferates in healthy mammary glands prior to the onset of cancer development. + A specific gene signature defining macrophages correlates with better recurrence-free survival in breast cancer patients. + Macrophages are CD8 + It positively correlates with the signatures of key cellular players in antitumor immunity, including T cells, NK cells, and dendritic cells (DCs). This study demonstrated that FOLR2 expression in tumor resection samples + Macrophages are located near blood vessels and express CD8 + We further show that FOLR2 forms clusters with T cell aggregates. + Macrophage / CD8 + T cell colocalization correlates with favorable clinical outcomes suggesting an antitumor role for this newly characterized macrophage subset.

[0131] 1. APOE expression defines tumor-associated macrophages in human breast cancer Motivated by the idea of ​​clearly identifying macrophages within breast tumors, we investigated breast cancer macrophage populations and infiltrating CD14 + CD1c - Monocytes, CD14 + CD1c + Inflammatory DC / DC3 or CD14 - CD1c + ( Bourdely et al., 2020 ) attempted to define common features that would allow for a distinction between cDC2 ( Villani et al., 2017 ) and mitochondrial DC2 ( Duttertre et al., 2019 ).

[0132] In the first approach, we quantified mononuclear phagocytes in matched primary tumors, non-metastatic and metastatic lymph nodes (LNs) from a cohort of 13 treatment-naive luminal breast cancer patients. - CD14 + The monocyte / macrophage population was found to be the most significantly increased in metastatic LNs compared to corresponding non-metastatic LNs (Figure 1A). + This positive correlation between cell infiltration and tumor infiltration was confirmed in a second cohort of patients. + Cellular infiltration correlated with the degree of tumor infiltration in the LNs (Figure 1B).

[0133] Next, we used an unbiased method to identify tumor-infiltrating CD14 + We attempted to characterize the heterogeneity within the whole cell population. To this end, we analyzed metastatic LNs, primary tumors and blood from untreated luminal breast cancer patients (Figure 1C). + HLA-DR + We isolated all mononuclear phagocytes by FACS sorting the cell fraction and performing scRNA-Seq using a droplet-based approach. Data were processed using the SEURAT pipeline (Figure 1D). Contaminating lymphocytes (B / T / NK cells), XCR1 + DC and CCR7 + LAMP3 + DCs (Zhang et al., 2019) were excluded from the analysis. Approximately 18,000 remaining bone marrow cells from all patients were then fused (Figure 1D). Clustering based on the Louvain graph identified four clusters of mononuclear phagocytes as well as populations of cycling cells (e.g., mKI67, TOP2A, CDC20) and "stressed" cells (e.g., HSPA1A, HSPB1). Cluster 0 was CD14 + CD16 - Cluster 1 was characterized by selective and high expression of monocyte-defining markers (S100A8, S100A9, S100A12, VCAN) (Fig. 1D, E). + Cluster 3 is characterized by genes that define DC (including the DC2 and DC3 subsets), whereas cluster 4 is characterized by genes that define CD14- CD16 + The first cluster was identified as monocytes (Villani et al., 2017) (Duttertre et al., 2019) (Bourdely et al., 2020). Both monocyte clusters (c0 and c4) were found in blood, tumors and metastatic LNs. The remaining cluster 2 was identified as TAMs, as they selectively expressed high levels of the TAM signature (Figure 1F) (Azizi et al., 2018). Cluster 2 expressed high levels of APOE, APOC1, C1QA and C1QC, allowing their distinction from monocytes (Figure 1E). The uniform expression of APOE was consistent with the CD14 + Monocytes and CD1c + It has been shown that CD68 selectively distinguishes TAMs from DCs (Figure 1F). Other commonly used markers (CSF1R, CD68, CD14) did not achieve this distinction. + Monocytes, CD16 + CD14 is expressed on monocytes and CD1c DCs. + Expressed by a subset of DCs, CSF1R is promiscuous (Figure 1F).

[0134] Next, CD1c - CD14 + It was desirable to validate the protein expression that distinguishes macrophages / TAMs from monocytes intracellularly. The intracellular proteins S100A8 and S100A9-CD14 + APOE- was found to be highly and specifically expressed in monocyte cluster 0-, which followed the protein expression pattern of cell surface CCR2, which was not detected in this scRNAseq dataset. The best monocyte / macrophage discrimination was obtained by staining with APOE and CCR2 (Figure 1G). Then, CD14 within luminal breast tumor lesions (primary tumors or highly infiltrated metastatic LNs) was stained with APOE- and CCR2-. + CCR2 to cellular compartments + Monocyte vs. APOE + Macrophage contribution was analyzed relative to tumor-free tissues (blood, healthy breast tissue, tumor-free and minimally invasive metastatic LNs or paratumor tissues). +The frequency of macrophages increased with tumor burden and expressed CCR2 + We found that the frequency of monocytes was decreased (Figure 1G). + The cells were found to be located near and within tumor lesions in metastatic LNs and primary tumors. Collectively, these results establish APOE as a specific marker for identifying macrophages in primary and metastatic luminal breast cancer.

[0135] 2. Single-cell RNA Sequencing of APOE + Identifying two major subsets of macrophages Next, APOE + We investigated the heterogeneity of TAMs (cluster 2). Clustering based on the Louvain graph identified three clusters of TAMs (Figure 2A). Hierarchical clustering showed that clusters 0 and 1 were closer to each other compared to cluster 2 (Figure 2B). APOE + To further explore the transcriptional heterogeneity found within macrophages, Single Cell Regulatory Network Inference and Clustering (SCENIC) was performed to study the gene regulatory network (GRN) of each macrophage cluster. GRN analysis and hierarchical clustering based on their activity revealed that clusters 0 and 1 shared roughly half of their transcriptional regulons, including CEBPB and BHLHE41, while cluster 2 presented a mostly unique transcriptional regulon, such as NR1H3 and MAF (Figure 2C). Analysis of differentially expressed genes revealed that FOLR2, SEPP1, SLC40A1, MRC1, LYVE1 discriminated cluster 2 from both cluster 0 and cluster 1 (Figure 2D). Conversely, TREM2, SPP1, ISG15 discriminated both cluster 0 and cluster 1 from cluster 2 (Figure 2D). Taken together, FOLR2 is shown to be a marker defining cluster 2, while TREM2 defines clusters 0 and 1 (Figure 2E). APOE + TAMs are classified into two distinct populations: TREM2 + Macrophages and FOLR2 +It can be concluded that it contains macrophages.

[0136] We then sought to validate this finding by prospectively isolating these populations for bulk transcriptome analysis. To this end, we searched for surface proteins that were differentially expressed between the two populations, thus allowing their isolation by flow cytometry. TREM2 was not detected on the cell surface of TAMs after tissue dissociation. Alternatively, only cluster 1 (TREM2 高 TAMs) were found to specifically express and stain positive for CADM1 (Fig. 2F, G).

[0137] Flow cytometry analysis of FOLR2 and CADM1 expression revealed mutually exclusive expression patterns (Figure 2G). + CADM1 + and FOLR2 低 CADM1 + TAMs were isolated from both primary tumors and metastatic LNs by FAC sorting. + CADM1 - TAMs exhibited a typical macrophage morphology and were filled with vacuoles. 低 CADM1 + TAMs were small in size and resembled monocytes in morphology ( Fig. 2G ).

[0138] Next, bulk RNAseq was performed using FOLR2 + CADM1 - TAM, FOLR2 低 CADM1 + TAMs and CD14 + CCR2 + Hierarchical clustering was performed on monocytes (Figure 2H). Hierarchical clustering revealed that FOLR2 from both primary tumor and infiltrated LN clusters was significantly higher in monocytes than in primary tumors (Figure 2H). + CADM1 - Macrophages express FOLR2 低 CADM1 + Macrophages or CD14 + CCR2 +The scRNAseq results confirmed that FOLR2 leaves monocytes together (Figure 2H). + Macrophages express FOLR2 低 CADM1 + TAMs and CD14 + CCR2 + Compared to monocytes, primary tumor-derived FOLR2 expressed higher levels of SEPP1, SLC40A1, and LYVE1 (Figure 2I). Overall, primary tumor-derived FOLR2 低 CADM1 + Macrophages are CD14 + CCR2 + They clustered together with monocytes (Figure 2H). 低 CADM1 + Macrophages specifically expressed TREM2, and the gene was found to be overexpressed in cluster 1 (C3, FN1, SPP1) in the scRNAseq analysis ( Figure 2J ).

[0139] Some of these phenotypic differences were due to infiltrated LN-derived CD14 + CCR2 - CyTOF profiling of macrophages confirmed that co-expression of LYVE1, MRC1 / CD206 and CD163 was associated with CADM1 + was found in a macrophage population distinct from the expressing macrophages ( Fig. 2K ).

[0140] Taken together, these results indicate that breast TAMs comprise two populations separable by the mutually exclusive expression of TREM2 / CADM1 and FOLR2.

[0141] 3.FOLR2 + Macrophages are tissue-resident macrophages Recent studies have shown that TREM2 + Macrophage infiltration has been identified as an event associated with cancer development (Molgora et al., 2020). These data support the notion that CD14 + CCR2 +These results establish their transcriptional proximity to monocytes (Figure 2H). + CADM1 + We suggest that TAMs arise from the infiltration of circulating monocytes during tumor progression.

[0142] FOLR2 + The origin of macrophages is unknown. + Macrophages are either mammary TRM (i.e., present in healthy breasts) or TREM2 + The question was whether FOLR2 corresponds to tumor-recruited monocyte-derived macrophages such as TAMs. To address this question, we investigated the effect of FOLR2 in healthy tissues (healthy breast, breast tissue adjacent to tumor lesions - paratumor -, tumor-free or low-invasive metastatic LNs) versus luminal breast tumor lesions (primary tumors and highly invasive metastatic LNs). + Macrophages were quantified by flow cytometry. + In macrophages, FOLR2 + Macrophages were found to be enriched in healthy and paratumor tissues (Figure 3A). In contrast, FOLR2 - Proportion of TAM (TREM2 + including TAMs) were dramatically increased in both primary and metastatic tumor lesions compared to healthy tissues (Figure 3A).

[0143] FOLR2 in adjacent normal tissue versus tumor lesions + Macrophage enrichment was also confirmed at the transcriptional level by analyzing breast cancer samples from the Cancer Genome Atlas (TCGA) database (Figure 3B). FOLR2 transcripts were enriched in normal adjacent tissues compared to breast cancer tumor lesions of different subtypes (Her2 + , TNBC, luminal). FOLR2 transcripts were also enriched in non-diseased healthy tissues compared to breast cancer tumors; in contrast, TREM2 transcripts were enriched in breast tumor lesions compared to tumor-adjacent normal tissues and non-diseased healthy tissues (Figure 3B). Importantly, immunohistochemistry (IHC) analysis demonstrated that FOLR2 +We found that macrophages are present in the peritumoral area in all subtypes of breast cancer. + Bulk RNAseq (Figure 2J) and CyTOF (Figure 2K) analyses of macrophages revealed that FOLR2 + We show that macrophages specifically express the hyaluronan receptor LYVE1 and the mannose receptor (MRC1 / CD206), both markers of perivascular macrophages ((Lin et al., 2006) Lim et al., 2018) (Chakarov et al., 2019). Thus, FOLR2 + The question was whether macrophages were located near blood vessels. Confocal imaging of tumor surgical resection specimens revealed that CD31 cells were expressed in both tumor and adjacent tissues. + FOLR2 located near blood vessels + CD206 + Overall, these results suggest that FOLR2 + We show that macrophages are perivascular TRM associated with healthy mammary gland and LNs.

[0144] We next analyzed mammary macrophages in a mouse model that allows us to perform a longitudinal analysis of immune populations at steady state and during tumor progression. First, we analyzed a published scRNAseq dataset performed on hematopoietic cells from healthy mammary glands (Han et al., 2018). + We identified a subset of TRMs that co-express macrophage-like Folr2, Mrc1 and Lyve1. These cells are the previously described MRC1 + LYVE1 + We then aligned the CD45 TRM (Franklin et al., 2014) (Jappinen et al., 2019) (Wang et al., 2020) isolated from the MMTV-PyMT autologous luminal-like breast tumor model (Franklin et al., 2014). + CD3 - CD19 - B220 - NKP46 -We performed scRNAseq on these cells (Davie et al., 2007) (Figure 3C). These cells were excluded from the analysis. Contaminating lymphocytes (not shown), Ly6c2 + Monocytes (c3), Ly6c2 - Nr4a1 高 Monocytes (c6), cycling cells (c4) and cells with high ribosomal gene content (c1). The remaining Fcgr1 + Among the cells, three clusters of TAMs were identified in 23-week-old PyMT mouse tumors (Figure 3C). Among these, two expressed Cadm1 (Cx3cr1 int Cluster 0 and Cx3cr1 高 Cluster 1, Fig. 3C). Furthermore, Folr2 + Mrc1 + A distinct population of macrophages was identified (cluster 2, Figure 3C). + TAMs share expression of FOLR2, MRC1, SLC40A1, and MAF (Figure 3D). + Cx3cr1 + Mouse macrophages (clusters 0 and 1) express human CADM1 + TREM2 高 They are similar to TAMs (cluster 1, Figure 2A) and share the expression of CADM1, HAVCR2, and IFI44 (Figure 3D). Notably, the expression pattern of Trem2 is more pronounced in mouse compared to human macrophages. + To examine the similarities between macrophages, we performed a similarity analysis across the whole transcriptome at the level of each cell (Figure 3E). This unbiased analysis revealed that mouse Folr2 + Macrophages and human FOLR2 + Marker-based alignment of macrophages was confirmed. Conversely, Cadm1 + Cx3cr1 + Mouse macrophages (clusters 0 and 1) express CADM1 + TREM2 + It showed high similarity to human macrophages. +It is concluded that macrophages are evolutionarily conserved between mouse and human luminal breast tumors.

[0145] Next, FOLR2 during breast cancer progression in mice + (and CADM1 + ) macrophage dynamics were analyzed longitudinally. To this end, mammary macrophages were analyzed in healthy littermates (WT), pre-lesion PyMT mice (8 weeks old), neoplastic lesions (12 weeks old mice), early cancer (14 weeks old mice) and advanced cancer (20 weeks old mice). In healthy mammary glands (WT), FOLR2 + Macrophages were total macrophages (CD45 + Lin - Ly6C - F4 / 80 + CD64 + ) was found to account for approximately 90% of the + The frequency of macrophages gradually decreased during cancer progression, reaching a minimum of 10-20% in advanced cancer lesions of 20-week-old PyMT mice (Figure 3F). In contrast, carcinoma development was associated with a decline in the number of CADM1 macrophages, which accounted for up to 80% of total macrophages in 20-week-old PyMT mice. + This was accompanied by de novo proliferation of macrophages (Figure 3F). + We conclude that macrophages represent an evolutionarily conserved TRM subset that persists in advanced cancers.

[0146] 4.FOLR2 + Macrophages correlate with increased survival in patients with breast cancer and at least six other cancer types TAMs are generally believed to promote tumor growth and inhibit antitumor immunity. This is particularly well established in mouse models where macrophages exhibit excessive tumor-promoting functions (Lin et al., 2006) (Qian et al., 2011) (Franklin et al., 2014) (Linde et al., 2018). In humans, TAMs generally correlate with poor prognosis and higher tumor grade. However, clinical studies have explored the association of TAMs with patient survival by using markers that are often shared by monocytes and macrophages (e.g., CSF1R, MRC1 / CD206, CD163, etc.) or with other immune cell types such as pDCs (e.g., CD68) (Colonna et al., 2004). Thus, FOLR2 + The question was whether macrophages might be similarly associated with poorer survival in breast cancer patients.

[0147] To this end, total macrophage or FOLR2 within bulk tumor RNA sequencing + We defined a gene signature that allows us to infer the abundance of macrophage subsets. Three genes (C1QA, C1QB, C1QC) define a core macrophage signature shared by the three macrophage clusters identified in this study (Figure 2A, Figure 4A) and are sufficient to distinguish macrophages from other leukocyte lineages (Figure 4A, B). Three genes (FOLR2, SEPP1, SLC40A1) define a core macrophage signature shared by the three macrophage clusters identified in this study (Figure 2A, B). + It uniquely distinguishes macrophages from other TAMs and other leukocyte lineages (Figure 4A,B) (Azizi et al., 2018). LYVE1 was not considered for signature analysis due to its endothelial expression.

[0148] The expression of these signatures was analyzed in the bulk transcriptome of luminal breast cancer (1339 patients, METABRIC cohort) (METABRIC Group et al., 2012). Survival probability was calculated among the highest signature expressors (top 25%) versus the remaining patients (Figure 4C). In accordance with previous reports (Ruffell and Coussens, 2015), the highest levels of macrophage infiltration were found to correlate with poorer overall survival (p=0.036). In complete contrast, the highest levels of the FOLR2 signature correlated with increased overall survival (p=0.0013) (Figure 4C). The association between the FOLR2 gene signature and patient clinical outcome was confirmed in an independent BC patient cohort using the same cutoffs (Wang et al., 2005a) (Figure 4D). The association between FOLR2 protein and patient prognosis was also confirmed in 49 ER-positive patients from the CPTAC dataset (Figure 4C). + / HER2 - Among patients, FOLR2 protein abundance was found to be positively correlated with better survival (Figure 4C). FOLR2-high signature breast cancer patients had longer time to recurrence. FOLR2 signature is a prognostic factor independent of CD8 status in ER+ patients.

[0149] Patients treated with endocrine therapy were strongly stratified by the FOLR2 gene signature, but this was not true for patients treated with other treatment regimens including chemotherapy or neither chemotherapy nor endocrine therapy. Interestingly, ER / PR+ patients with a high FOLR2 gene signature had improved survival from endocrine therapy compared with chemotherapy alone or chemotherapy + endocrine therapy, which was not true for patients with a low FOLR2 gene signature (Figure 4E). Thus, FOLR2 high patients would benefit from endocrine therapy alone and would not require chemotherapy. FOLR2 low patients could benefit from a combination of endocrine therapy and chemotherapy.

[0150] In the TCGA database, the FOLR2 signature as a continuous variable positively correlates with better outcomes in at least six other cancer types: KIRC: renal cell carcinoma, LUAD: lung adenocarcinoma, LIHC: hepatocellular carcinoma, CESC: cervical squamous cell carcinoma and endocervix, MSKCM: melanoma, and ACC: adrenocortical carcinoma.

[0151] FOLR2 + As macrophages are a tissue-resident population in healthy mammary glands, FOLR2 mRNA abundance may be associated with smaller, less invasive tumors. To verify whether this could be a confounding factor in univariate survival analysis, FOLR2 mRNA expression levels were analyzed for breast tumors of different stages and grades. No significant differences in FOLR2 expression between grades were found, with a slight increase in later stage tumors (Figure 5A). Furthermore, multivariate analysis of the prognostic value of the FOLR2 gene signature, adjusted for age, histological grade, tumor size, histology and number of positive LNs in the disease, showed that the FOLR2 gene signature was an independent prognostic factor that correlated with better survival in patients with luminal breast cancer (Figure 5B).

[0152] Next, FOLR2 + We wished to directly assess whether macrophage density is associated with favorable clinical outcome. To this end, we used multispectral imaging to analyze tissue microarrays containing tumors from a retrospective cohort of 122 breast cancer patients. Tumors were stained for FOLR2, cytokeratin and DAPI, and FOLR2 + Macrophage cell density was calculated using the best performing threshold as the cutoff, FOLR2 + We found that macrophage density positively correlated with patient survival (Figure 4F). This result was confirmed in a second independent cohort of 126 breast cancer patients (Figure 4F). Overall, these results support the finding that FOLR2 +We show that macrophage abundance is associated with a better prognosis in breast cancer patients. Moreover, in patients with low CD8+ T cell infiltration, high density of FOLR2+ cells also correlates with better survival probability. This identifies the density of FOLR2+ cells as a prognostic value independent of CD8+ T cell density.

[0153] 5.FOLR2 + Macrophages express TREM2 throughout cancer + Spatially separated from macrophages. TREM2 + TAMs have been shown to translocate across cancers and infiltrate into tumor nests (Molgora et al., 2020). + Macrophages have been shown to be mammary TRMs. Furthermore, others have recently shown that macrophages expressing FOLR2 are found in healthy human tissues (Samaniego et al., 2014) (Sharma et al., 2020) (Thomas et al., 2021). Thus, FOLR2 + The question was whether macrophages could be detected across cancer types. To address this question, FOLR2 expression was analyzed in 80 histological sections of primary and metastatic tumors across different cancers (oral, liver, bladder, brain, kidney, skin, colon, lung, ovarian, stomach, and breast) and FOLR2 was detected in 80 histological sections of primary and metastatic tumors across different cancers (oral, liver, bladder, brain, kidney, skin, colon, lung, ovarian, stomach, and breast). + Macrophages were found across all of these cancers. Co-staining for FOLR2 and TREM2 confirmed the mutually exclusive expression of the two markers in different cells. Staining for FOLR2 and TREM2 on serial sections of various cancer types revealed that FOLR2 + and TREM2 + We showed that macrophages are spatially segregated. As described by Molgora et al., TREM2 + Macrophages infiltrated the tumor nests. In contrast, FOLR2 + Macrophages were consistently found within the peritumoral stromal region. This spatial segregation was seen in a variety of cancer types, including melanoma, lung cancer, hepatocellular carcinoma, oral squamous cell carcinoma, and pancreatic cancer. Collectively, these results support the finding that FOLR2 + and TREM2+ We show that macrophages associate with specific locations within the TME in various cancer types.

[0154] The prognostic value of FOLR2 gene alone or FOLR2 gene signature mRNA levels was validated in the TCGA whole tumor transcriptome dataset. High FOLR2 expression (or FOLR2 gene signature expression) was found to be associated with better survival in multiple cancer types, including: -Thyroid cancer (THCA) (HR=0.527;logRank p=0.005) - Brain low-grade glioma (LGG) (HR=0.7; logRank p=2.7×10 -7 ) - Adrenocortical carcinoma (ACC) (HR=0.776; logRank p=0.005) - Cervical squamous cell carcinoma and endocervical cancer (CESC) (HR=0.849; logRank p=0.025) -Lung adenocarcinoma (LUAD) (HR=0.9;logRank p=0.031) - Cutaneous melanoma (MSKCM) (HR=0.912; logRank p=0.043). Furthermore, the FOLR2 gene signature (but not FOLR2 expression alone) is associated with better survival in: -KIRC: Kidney clear cell carcinoma -LIHC: Liver hepatocellular carcinoma.

[0155] 6. FOLR2 + Macrophages are CD8 + It is enriched in T cell infiltrated tumors and co-localizes with lymphoid aggregates throughout the cancer. To gain further functional insight, we next used the previously defined FOLR2 gene signature (FOLR2 / SLC40A1 / SEPP1) or FOLR2 expression alone to characterize FOLR2 + Macrophage abundance was correlated with other immune and stromal cell types in the TME (Figure 6A). The FOLR2 gene signature (or FOLR2 expression alone) was associated with increased expression of CD8+ It was found to positively correlate with known players of antitumor immunity, such as T cells, DCs, B cells and tertiary lymphoid structures (Figure 6A). In contrast, CADM1 + The TREM2 gene signature (TREM2 / SPP1) or TREM2 expression alone is a predictor of T cell, CD8 + It did not correlate significantly with T cells, NK or B cells (Figure 6A). Moreover, the highest levels of FOLR2 expression in bulk tumor transcriptomes coincide with the gene signature of coordinated infiltration of multiple lymphocyte lineages and tertiary lymphoid structures in the same tumor (Figure 6B). In contrast, no correlation was found when patients were stratified according to the levels of TREM2 transcripts (Figure 6B). We analyzed the gene pathways represented in all genes positively correlated with FOLR2 (or TREM2) expression in the whole tumor transcriptome. Supporting our previous findings, we found a strong correlation between FOLR2 expression in tumors and various immune pathways, including TCR signaling, antigen processing and PD-1 signaling. CADM1 + TREM2 + FOLR2 compared to TAM + TAM-enriched gene pathways were also analyzed in this bulk RNAseq dataset. + Gene pathways enriched in TAMs were found to be involved in chemotaxis and functional modules of immune response regulation. Overall, these results support the finding that FOLR2 + These findings suggest that macrophages are part of the immune landscape underlying the development of antitumor immunity.

[0156] CD8 + FOLR2 T cells have been shown to be associated with better survival in various cancer types, including breast cancer (DeNardo et al., 2011), (Ali et al., 2014), (Pages et al., 2018), and + Tumor-infiltrating CD8 macrophages + We investigated whether CD31 T cells could be found to interact with CD31 T cells using confocal microscopy on tumor resection samples. + FOLR2 located near blood vessels +Macrophages are CD8 + It was found to be closely associated with T cell aggregates. + Macrophages and CD8 + To confirm the spatial association between T cells, a previous tissue microarray patient cohort was stained for both CD8 and FOLR2 and the respective cell densities were calculated. 高 Tumor lesions are characterized by FOLR2 macrophages 低 Significantly higher CD8 + T cell density (Figure 6C). Importantly, FOLR2 was found within the peritumoral stroma. + Macrophages were repeatedly enriched in lymphoid aggregates across various cancer types and could also be detected within tertiary lymphoid structures. Collectively, these results support the notion that stromal-associated FOLR2 + Macrophages are conserved throughout cancers and represent structural components of lymphoid structures near tumor nests that are likely associated with ongoing immune responses.

[0157] FOLR2 + Macrophages are CD8 + To further investigate whether CD4+ T cells can productively engage with BC tumors, we performed confocal live imaging on fresh human BC lesions. + T cells, FOLR2 + Macrophages and EPCAM + Tumor cells were stained with fluorescently conjugated antibodies against CD8, FOLR2 and EPCAM, and then cell dynamics were imaged by time-lapse microscopy (Figure 6D). + Macrophages were observed to localize within the tumor stroma and form a network of adherent cells with active membrane ruffles. + Quantification of the migration speed of T cells showed a heterogeneous behavior with more or less motile cells. Importantly, CD8 + T cells slow them down and FOLR2 +We reproducibly found that FOLR2-deficient tumors established long-term contacts with macrophages, which is consistent with CD8+ / - cells in FOLR2-deficient tumor regions. + This contrasted with the higher motility of CD8 T cells. + T cells express FOLR2, a behavior that likely promotes T cell activation. + It was concluded that FOLR2 establishes a long-term interaction with macrophages. Consistent with this result, expression of FOLR2 in the whole tumor transcriptome was positively correlated with genes controlling cytotoxic function in T cells (GZMA, GZMB, GZMK, GFR1, KLRB1, KLRD1) but not with genes of T cell dysfunction such as LAG3. TREM2 expression was associated with a strong correlation with CD8 + No significant correlation was observed with genes controlling the cytotoxic function of T cells.

[0158] It has previously been proposed that TAMs in the tumor stroma of the lung or in the pleural and peritoneal cavities may sequester T cells from reaching the tumor, negatively impacting antitumor immunity (Peranzoni et al., 2018) (Chow et al., Cancer Cell, 2021, 39, 973-988). Other studies have shown that long-term persistent interactions between antigen-presenting cells and T cells may precede T cell activation and thus promote T cell immunity (Hugues et al., Nat. Immunol, 2004, 5, 1235-1242) (Mempel et al., Nature, 2004, 427, 154-159). CD8 + To evaluate whether T cell / macrophage interactions lead to favorable clinical outcomes in BC, we investigated CD8 + A quantitative spatial analysis of cellular interactions between T cells and subsets of macrophages was performed in T cell-infiltrated tumors. To this end, CD8 + T cell density is 50CD8 + T cells / mm 2 We performed multiplex imaging of tumors from approximately 60 BC patients, which is superior to FOLR2. + Macrophages or TREM2 + CD8 in close proximity (<30 μm) to any of the macrophages +Quantify the percentage of T cells and CD8 + Patients were stratified according to their T cell percentage: high or low. + CD8 in close contact with macrophages + A high percentage of T cells is associated with a better outcome in BC patients, which is due to the TREM2 + CD8 interacts with macrophages + This was not found to be the case for tumor-infiltrating CD8 T cells. + T cells are FOLR2 + It actively engages macrophages, and this interaction is mediated by CD8 + It was concluded that FOLR2 positively correlated with T cell activation and patient survival. + It was concluded that macrophages represent a defined macrophage population involved in the development of anti-tumor immune responses.

[0159] 7. FOLR2+ macrophages promote T cell effector differentiation Nascent tumors have been shown to engage in cellular crosstalk with TRMs, which in turn promote tumor growth, motility, and invasiveness (Cassetta and Pollard, 2018). Using the PyMT BC mouse model, we demonstrated that FOLR2 + We investigated the functional characteristics of macrophages. First, we investigated the functional characteristics of FOLR2 in healthy mammary glands. + We asked whether macrophages respond to developing tumors. To this end, we used bulk RNAseq to characterize FOLR2 expression in macrophages isolated from mammary glands of healthy versus small or medium-sized tumor-bearing mice. + Macrophages were profiled. Principal component (PC) analysis using the 10,000 most variable genes identified FOLR2 isolated from breast tumors. + We showed that macrophages changed their transcriptome profile depending on the size of the tumor as seen by the variation of the PC2 axis. + Macrophages and CADM1 +6,139 DEGs were found between macrophages and FOLR2. + Macrophages respond to developing tumors but not CADM1 + It was concluded that FOLR2 remains a cellular entity separable from macrophages. + FOLR2 in macrophages and healthy mammary glands + There were 1,356 DEGs between macrophages and breast tumors (Figure 7A). + Macrophages expressed genes involved in the positive regulation of immune system processes, including B and T cell chemoattractants (Ccl6 to 9, Ccl12, Cxcl2, Cxcl13, Cxcl14, Cxcl16), adhesion molecules (Icam1, Vcam1, Fn1), and lysosomal proteins (Ctse, Rab32). In contrast, FOLR2 isolated from healthy mammary glands + Macrophages were enriched for genes that regulate metabolic processes (Igf1, Srebf2, Abcd2). These data therefore support the idea that FOLR2 + This indicates that TRM is responsive to tumor development.

[0160] Macrophage activation in tumors is often referred to as "pro-inflammatory / M1" versus "anti-inflammatory / M2" (Mantovani et al., 2002) (Murray et al., Immunity, 2014, 41, 14-20). Breast tumor FOLR2 + and CADM1 + To verify whether macrophage subsets have such functional specialization, the expression of genes defining M1 or M2 gene signatures was analyzed in the two macrophage subsets (Azizi et al., 2018). + and CADM1 + Both macrophages were found to simultaneously express individual M1 and M2 genes. For example, FOLR2 + Macrophages expressed Cd80, Cd40 and Il6 (M1 genes) and Cd163, Mrc1, Il10 (M2 genes). +Macrophages expressed Cd86, Cxcl9, and Il12b (M1 genes) and Vegfa, Cd276, and Tgfb3 (M2 genes). This indicates that macrophage activation in the tumor microenvironment does not fit with the in vitro M1 / M2 polarization model, revealing the complexity of macrophage activation in the tumor microenvironment. Interestingly, breast tumor FOLR2 + and CADM1 + Macrophage subsets expressed distinct sets of functional genes that may be related to T cell activation. Therefore, to directly verify the T cell activation potential of macrophage subsets, we isolated macrophage subsets from the same tumor and + FOLR2 co-cultured with T cells + and CADM1 + Two assays were set up using macrophages. First, purified TAMs were transfected with polyclonal activated CD8 + We set up a T cell suppression assay in which FOLR2 was co-cultured with T cells. + Macrophages did not show any suppressive activity. Instead, FOLR2 + Macrophages are CD8 + Improved T cell proliferation and differentiation (loss of CD62L and upregulation of CD44 and CD25). CADM1 + Macrophages are also CD8 + Although FOLR2 did not suppress T cell activation, their ability to promote effector T cell differentiation + In previous studies (Ruffell et al., Cancer Cell, 2014, 26, 623-627) (Katzenelenbogen et al., Cell, 2020, 182, 872-885), the immunosuppressive activity was weaker than that of macrophages. NEG Instead, MHCII was restricted to tumor-associated myeloid cells. + TAMs did not exert T cell inhibition. Here, CADM1 + and FOLR2 + Both TAMs have been shown to express high levels of MHCII, consistent with a lack of immunosuppressive activity. + and CADM1+ Since macrophages also did not strongly suppress T cell expansion, we set up an antigen-specific T cell priming assay. + and CADM1 + Macrophages were loaded with OTI-specific SIINFEKL peptide, washed, and subsequently transfected with naïve OTI CD8 + T cells were co-cultured with CADM1 cells isolated from the same tumor. + Compared with macrophages, FOLR2 + Macrophages showed a higher ability to induce the activation of naive T cells, their proliferation, their multi-functionality (IL-2, IFN-γ, TNF-α) and their cytotoxic function (expression of granzyme B) (Figure 7B). Moreover, FOLR2 isolated from healthy mammary glands + Macrophages are OTI CD8 + FOLR2 isolated from breast tumors does not efficiently activate T cells + It was found that macrophages could induce the expansion and differentiation of T cells (Figure 7B). These results are consistent with those of FOLR2. + Macrophages are activated during tumor development and express CD8 + In conclusion, these results confirm that FOLR2 acquires the ability to prime T cells. + We provide evidence that macrophages do not behave like immune suppressor cells. Instead, tumor-associated FOLR2 + Macrophages are CD8 + They are potent antigen-presenting cells that exhibit the functional capacity to trigger T cell activation.

[0161] FOLR2 macrophages were purified from mouse breast tumors and loaded with OVA peptide. After washing, FOLR2 macrophages were co-cultured with anti-OVA naive CD8 T cells for 3 days. On the third day after culture, T cell activation and proliferation were measured. FOLR2 macrophages show higher T cell activation capacity than CADM1 macrophages or OVA alone (Figure 7C).

[0162] 8. Vaccination with anti-FOLR2-targeting antibodies coupled to model antigens induces specific CD8+ T cell responses Tumor antigen FOLR2 + For delivery to macrophages, CD8 + We generated an anti-FOLR2 antibody linked to a T cell-specific OVA peptide (SIINFEKL) (Figure 8). Preliminary data show that immunization with anti-FOLR2-SIINFEKL suppresses the proliferation and proliferation of adoptively transferred OVA-specific OTI CD8 + We show that it induces T cell activation, validating the feasibility of this approach (Figure 8).

[0163] Consideration To understand the phenotypic and functional heterogeneity of macrophage populations in human breast cancer, we created a single-cell atlas of myeloid cells infiltrating human luminal breast tumors. Two phenotypically distinct TAM subsets, TREM2 + CADM1 + Macrophages and FOLR2 + Macrophage infiltration was previously associated with the worst clinical outcomes (Molgora et al., 2020) and TREM2 + In stark contrast to macrophages, FOLR2 + TAM abundance has been shown to predict better clinical outcomes. + It has been established that TAMs are spatially associated with sites of lymphocytic infiltration within the TME across cancers.

[0164] FOLR2 + Macrophages: A subset of TRM associated with favorable clinical outcome. In this study, we identified the developmentally conserved, tissue-resident FOLR2 gene present in both human and mouse healthy mammary glands. + A macrophage population was identified. In mice, mammary TRM regulates postnatal mammary gland development and remodeling (Gouon-Evans et al., 2002) (Van Nguyen and Pollard, 2002). Human FOLR2 +Macrophages express MRC1 and LYVE1, and express mouse MRC1, which has been reported in the adult mammary gland of healthy nulliparous mice. + Aligns to TRM (Jappinen et al., 2019) (Wang et al., 2020). MRC1 + TRM is Fartmapper Csf1r Mer-iCre-Mer Mouse or Cx3cr1 Cre-ERT2 They arise from fetal precursors, as demonstrated by genetic labeling at E8.5 or E13.5 in mice ( Jappinen et al., 2019 ). Furthermore, MRC1 + TRMs exhibit self-renewal capabilities (Wang et al., 2020). + TRMs have non-redundant functions in mammary gland development: Plvap - / - MRC1 in mice + Inhibition of TRM development significantly impairs ductal morphogenesis during puberty (Jappinen et al., 2019). In mice, MRC1 + TRM expresses CX3CR1 with intraductal localization 高 MRC1 - Colocalizes with a minor fraction of TRM (Dawson et al., 2020). 高 MRC1 - Macrophages develop from adult monocytes and expand during tissue remodeling imposed by lactation (Dawson et al., 2020). Interestingly, most breast tumor-infiltrating murine TAMs express homeostatic intraductal CX3CR1 高 MRC1 - transcriptionally align to TRMs (Dawson et al., 2020). Altogether, these findings highlight the ontogenetic and functional diversity within mammary TRM subsets (Ginhoux and Guilliams, 2016).

[0165] Here, FOLR2 + Macrophages are mouse MRC1 + Identified as the human orthologue of breast TRM. Human FOLR2 + Macrophages were found to represent the major macrophage population in healthy breast tissue. This is due to the expression of FOLR2 +Macrophages are defined as true mammary resident macrophages. In contrast, CADM1 + TREM2 + Macrophages have been shown to be low in healthy tissues and increased in metastatic LNs and primary tumors. + TREM2 + Macrophages express mouse CX3CR1 高 MRC1 - These findings support the role of TREM2 in multiple cancer types. + This is consistent with a recent study that identified macrophages as CADM1 (Molgora et al., 2020). + TREM2 + Macrophage infiltration is likely dependent on the influx of circulating monocytes that are locally attracted to tumor lesions. In support of this view, human CADM1 + TREM2 + This bulk RNAseq analysis of macrophages identified CCR2 + Demonstrating transcriptional proximity to monocytes.

[0166] Do TRMs have specific functions during carcinogenesis? Recent studies have reported the pro-tumorigenic activity of mouse TRMs. For example, depletion of embryo-derived pro-fibrotic TRMs delays tumor lesion progression in a mouse model of pancreatic ductal adenocarcinoma (Zhu et al., 2017). However, it is not known whether this impacts overall survival. Also, the depletion of CD163 in ovarian cancer + Depletion of TRM reduces epithelial to mesenchymal transition and overall tumor growth (Etzerodt et al., 2020). In mouse PyMT mammary cancer, Franklin et al. found that MRC1, which is present in healthy mammary glands, is downregulated by TRM1. + We have shown that TRMs persist in developing mouse mammary adenocarcinomas despite dilution by incoming monocyte-derived TAMs (Franklin et al., 2014). The pro-tumorigenic function of TRMs in pancreatic and ovarian cancers is related to MRC1. +This does not appear to apply to breast cancer, where TRMs are less immunosuppressive than monocyte-derived NOTCH-dependent TAMs (Franklin et al., 2014) (Kitamura et al., 2018). + TRM depletion had no effect on tumor growth in autologous MMTV-PyMT or MMTV-Her2 mouse models (Franklin et al., 2014)(Linde et al., 2018)(Linde et al., 2018), despite its effect on early cancer cell dissemination.

[0167] In humans, the lack of specific markers distinguishing monocyte-derived TAMs from TRMs has so far hindered the evaluation of their functions. To address this knowledge gap, specific gene signatures and surface markers that allow the careful identification of macrophage subsets within tumors have been defined. FOLR2 in breast cancer + FOLR2 in breast cancer bulk transcriptomes to explore the association between macrophages and clinical outcomes + We designed a gene signature that allows us to infer macrophage abundance. + Macrophage abundance is associated with better prognosis, but CADM1 + TREM2 + Macrophage abundance was not found to be associated. The FOLR2 signature predicts better survival in at least six other cancers. FOLR2 in a retrospective cohort of 122 breast cancer patients + We obtained validation of this in silico finding showing that macrophage density correlates with increased survival. Collectively, these results support the use of FOLR2 as a biomarker of favorable outcome. + Establish macrophages.

[0168] Functional diversity of perivascular macrophages. FOLR2 + Macrophages were observed to exhibit the transcriptional signature of steady-state perivascular (PV) macrophages (LYVE1, MRC1, TIMD4, MAF). Thus, some FOLR2+ Macrophages express CD31 + They were found to be located in close proximity to blood vessels in mice. In mice, various studies have identified PV macrophages across organs including the lung and skin (Chakarov et al., 2019), brain (Goldmann et al., 2016) (Sg et al., 2020), arterial walls (Lim et al., 2018), mammary gland (Jappinen et al., 2019) and spleen (Mebius and Kraal, 2005). However, little is known about the phenotype of human PV macrophages (Lapenna et al., 2018).

[0169] In mouse models, a pro-tumorigenic subset of PV macrophages expressing the angiopoietin receptor TIE2 has been implicated in angiogenesis and tumor expansion (Lewis et al., 2016) (De Palma et al., 2005) (Pucci et al., 2009). Mechanistically, TIE2 + PV macrophages release VEGFA, which promotes tumor cell intravasation and metastasis by reducing tight junctions in tumor vessels, promoting permeabilization of the vascular wall (Harney et al., 2015). + PV macrophages respond to endothelium-derived angiopoietin 2 (ANG2) by engaging the TIE2 receptor, thereby supporting PV positioning and proangiogenic function (Mazzieri et al., 2011). + PV macrophages are TIE2 + Whether FOLR2 aligns with PV macrophages remains unclear. This hypothesis was not supported for two reasons. First, FOLR2 + It was not possible to demonstrate expression of TIE2 in single-cell and bulk RNAseq analyses of macrophages. + PV macrophage ontogeny is regulated by the proangiogenic TIE2 + It depends on the progressive infiltration of specialized subsets of monocytes (De Palma et al., 2005) (Pucci et al., 2009) (Coffelt et al., 2010) (Arwert et al., 2018). This is due to the FOLR2 +In contrast to these data demonstrating that macrophages are TRMs, further experimental efforts are therefore required to disentangle the heterogeneity of PV macrophages.

[0170] FOLR2 in promoting lymphocyte infiltration and antitumor immunity + The role of macrophages Recent studies have demonstrated that FOLR2 is associated with human hepatocellular carcinoma and resembles fetal liver macrophages. + A subset of TRMs has been identified (Sharma et al., 2020). + CD86 expression in macrophages indicates that they are CTLA4 + These findings suggest that FOLR2 may interact with regulatory T cells. + We used this gene signature to investigate macrophage function by expressing FOLR2 in bulk tumor microarrays. + We estimated macrophage abundance and correlated it with infiltration of other immune cells. + Macrophage infiltration is associated with TREM2 + In contrast to macrophage infiltration, CD8 + It was found to be positively correlated with tumor-infiltrating lymphocytes, including T cells, B cells, and DCs. Furthermore, confocal imaging revealed that FOLR2 + Macrophages express CD8 + FOLR2 and CD8 were found to colocalize with T cell aggregates. + The correlation between T cell abundance and FOLR2 was verified by multispectral imaging. + Tumor lesions with high macrophage infiltration had significantly higher CD8 + The T cell density was CD8 + These results suggest that FOLR2 is a novel therapeutic target for cancer cell proliferation, as T cell infiltration correlates with better survival in many cancers, including breast cancer (DeNardo et al., 2011) (Ali et al., 2014). + These results suggest that macrophages are involved in the development of antitumor immunity. +Macrophages have been reported in human tissues, including fetal liver, placenta, and colon (Samaniego et al., 2014) (Sharma et al., 2020) (Thomas et al., 2021). Here, these observations were extended in healthy breast and breast cancer as well as multiple other cancer types. Importantly, FOLR2 + Association of macrophages with peritumoral stromal lymphoid aggregates is seen in multiple cancer types. In some instances, FOLR2 infiltrating organized tertiary lymphoid structures containing B and T cells. + Macrophages are reported herein. Murine PV macrophages have been described as potential regulators of lymphocyte infiltration during inflammation (Natsuaki et al., 2014) and autoimmunity (Mohan et al., 2017). + Macrophages recruit CD8 T cells by various mechanisms: directly by delivering chemokines that attract T cells (Dangaj et al., 2019), or indirectly by delivering inflammatory cytokines to endothelial cells or growth factors to pericytes (Minutti et al., 2019). + It can be hypothesized that FOLR2 can regulate T cell infiltration. + Further studies are required to identify the mechanisms by which macrophages regulate lymphocyte infiltration in tumors, key events for the development of efficient antitumor immune responses. This study highlights the antagonistic roles of tumor-associated macrophage subsets and paves the way for subset-specific therapeutic interventions in macrophage-based cancer treatment.

[0171] [Table 1]

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Claims

1. 1. A method for monitoring cancer in a patient, comprising: - determining the level of FOLR2+ macrophages in a tumor sample from a patient, wherein the level of tumor-associated FOLR2+ macrophages positively correlates with the outcome of the cancer disease or treatment in said patient; - then predicting whether an outcome of the cancer disease or treatment is likely to be favorable or unfavorable in said patient.

2. 2. The method of claim 1, wherein an elevated level of FOLR2+ macrophages in the patient's tumor sample compared to a reference indicates a high likelihood of a favorable outcome of the cancer disease or treatment in the patient.

3. 2. The method of claim 1, wherein the favorable outcome of a cancer disease comprises an increased survival time or survival rate, a decreased recurrence rate, an increased time to recurrence, and / or a decreased incidence of tumors or metastasis.

4. determining the density of FOLR2+ cells in the patient's tumor sample, preferably by immunohistochemical techniques using an anti-FOLR2 antibody, and preferably wherein said FOLR2+ cells are further characterized as TREM2- or TREM2+. 低 and / or CADM1-.

5. 2. The method of claim 1, comprising determining the expression level of the FOLR2 gene in a tumor sample of the patient, preferably determining the level of FOLR2 protein.

6. 6. The method of claim 5, comprising determining the level of expression of a gene signature of tumor-associated FOLR2+ macrophages comprising or consisting of the FOLR2, SEPP1 and SLC40A1 genes, preferably comprising determining the level of mRNA expressed by said genes.

7. The method of claim 5, comprising determining the levels of the FOLR2 gene or the mRNA expressed by the FOLR2, SEPP1 and SLC40A1 genes by RNA-Seq.

8. The method of claim 1, further comprising classifying the patient into a favorable prognosis group and a poor prognosis group based on the level of tumor-associated FOLR2+ macrophages determined in the patient's tumor sample.

9. A gene signature of tumor-associated FOLR2+ macrophages comprising or consisting of the FOLR2, SEPP1 and SLC40A1 genes.

10. 10. In vitro use of the gene signature of claim 9 as a biomarker for the prognosis or monitoring of cancer in a patient.

11. 11. The method according to claim 1 or the use according to claim 10, wherein the cancer is selected from the group consisting of breast cancer, kidney cancer, lung cancer, liver cancer, skin cancer, uterine cancer, brain cancer, thyroid cancer and adrenal cancer, preferably breast cancer.

12. A targeted antigen delivery system comprising a FOLR2 binding ligand associated with an antigen of interest or a nucleic acid encoding said antigen in an expressible form.

13. The targeted antigen delivery system according to claim 12, wherein the antigen of interest is a vaccine antigen, preferably selected from tumor antigens and antigens derived from pathogens, in particular viral, bacterial, fungal and parasitic antigens.

14. The targeted antigen delivery system of claim 12 , wherein the FOLR2-binding ligand comprises an anti-FOLR2 antibody or a fragment thereof comprising an antigen-binding site.

15. The targeted antigen delivery system of claim 12, wherein the FOLR2-binding ligand and its antigen or nucleic acid are associated in a conjugate, fusion protein or particle, preferably the particle is selected from the group consisting of a lipophilic particle, a nanoparticle, a virus-like particle, a viral vector particle and combinations thereof, more preferably the particle incorporates the antigen or its nucleic acid and presents the FOLR2-binding ligand on its surface.

16. A pharmaceutical composition comprising the antigen delivery system of claim 12 and at least one pharma- ceutically acceptable vehicle, adjuvant and / or carrier.

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