WO2024218260A1 - Detecting tumor-associated-macrophages in pancreatic cancer - Google Patents

Abstract

The invention pertains to isolated population of tumor-associated macrophage cell populations, their use in prognosing the malignancy and the outcome of solid tumors, as well as compounds which inhibit the expression of certain genes in these macrophage cells, in tumor-associated macrophage populations, for use for treating a solid cancer in a subject in need thereof.

Description

Detecting Tumor-associated-Macrophages in pancreatic cancer

FIELD OF THE INVENTION

The invention pertains to isolated population of tumor-associated macrophage cell populations, their use in prognosing the malignancy and the outcome of solid tumors, as well as compounds which inhibit the expression of certain genes in these macrophage cells, in tumor-associated macrophage populations, for use for treating a solid cancer in a subject in need thereof.

BACKGROUND OF THE INVENTION

The relatively recent consideration of tumor microenvironment (TME) including immune cells when studying tumor biology has led to the development of immunotherapies, offering new therapeutic opportunities for patients who failed to respond to conventional treatments (1 , 2). Yet, curative potency of current anti-PD-1 and CTLA4-based protocols is heterogeneous, being for example efficient in treating melanoma (3), while showing no beneficial effects for patients with pancreatic ductal adenocarcinoma (PDAC)

(4). This high specificity of available immunotherapies has sparked interest in the mode of action for such treatments, calling for a deeper understanding of TME biology, specifically within the immune compartment.

In cancer, macrophages have emerged as potential targets for future immunotherapies, because of their vastly documented pleiotropic functions in maintenance of tissue homeostasis, immune tolerance, and wound healing (5). These cells have been shown to be the most abundant immune cells within majority of tumors and their abundance has been clinically associated to worse prognosis for patients (6). So called tumor-associated macrophages (TAM) exhibit diverse pro-tumoral functions, these cells being implicated in angiogenesis, extracellular matrix remodelling, immune modulation or metastatic spread to cite only few examples (7). Furthermore, the recent rise of single cell-based technologies has revealed various TAM subpopulations, potentially harbouring distinct locations and assuming these different functions within the TME (8, 9). As integral components of the tissue, macrophages are strongly influenced by their local environment and have been shown to establish dense relationships with neighbouring cells (10). The last decade has also clarified macrophage ontogeny; it is now clear that macrophage can be either of embryonic origin or derived from adult circulating monocytes, with each tissue being inhabited by a specific proportion of these two ontogenically-distinct macrophage populations (11 ). Of note, it has been shown that most TAM were of monocytic origin (12), and that these major monocyte-derived TAM exhibit distinct functions than the embryonic-derived TAM in a murine PDAC model (13). Furthermore, differential location of TAM according to their origin has been revealed more recently in non-small cell lung carcinoma (14).

Besides origin and location, another emerging parameter shaping macrophage identities is time of residency (15). Indeed, tumors constitute a very dynamic environment from their emergence to metastatic dissemination; TAM support this evolution while also being impacted by it. Considering their continuous infiltration in response to tumor-induced inflammation, monocytes face a dynamic TME and can give rise to phenotypically and functionally distinct populations of TAM. As macrophage heterogeneity and their diverse functions in tissues are critical topics in immunology, it is needed to better determine, beside their local environment and ontogeny, the dynamism of their identities and their evolution in a changing environment such as a progressive disease like cancer. The present invention fulfils this need.

SUMMARY OF THE INVENTION

The present inventors identified three different macrophage signatures that can be used in various methods, as defined below.

In a first aspect, the present invention targets an isolated population of tumor-associated macrophage cells that are CD45⁺ Ly6G' CD11 b⁺ SiglecF' CCR2⁺ MHCH⁺ CD64⁺ (= murine “pre-Mac” I human “Hu.TAMI ” or “Hu. TAM a” or “Hu. Mac1 ”). These cells are also CD73^|OW CD11 c^l0W. They can express the MHC Class II protein at their surface.

Human “Hu.TAMI ” or “Hu. TAM a” can also be detected in immunofluorescent means by detecting the cell surface expression of CCR2 and CD64 (figure 7F). In human: “pre-mac” = Hu.TAMI CCR2+FCGR1A+ITGAM+CD14

Also, the present invention targets an isolated population of tumor-associated macrophage cells that are CD45⁺ Ly6G' CD11 b⁺ SiglecF' CD73⁺ MHCH⁺ CD11 c (= murine “TAM2” or “Mac-2” I human “Hu.Mac2” or “Hu.TAM2” or “Hu. TAM b” and “Hu.Mac3” or “Hu.TAM3” or “Hu. TAM c” = Hu.TAM b/c). The Hu.TAMb/c cell population can be identified by using the following markers: NT5E (CD73), KLRC1 (CD159a), ITGAV (CD51 ) and ENTPD1 (CD39. I CD39, CD51 , CD159a

It is also possible to detect these cells by assessing the level of the CD73 surface marker by immunofluorescence (figure 7H).

Also, the present invention targets an isolated population of tumor-associated macrophage cells that are CD45⁺ Ly6G' CD11 b⁺ SiglecF' CD73' MHCH⁺ CD11 c⁺ (= murine “TAM3” or “Mac3” I human “Hu.Mac4” or “Hu.TAM4” or “Hu.TAM d” and “Hu.Mac5” or “Hu.TAM5” or “Hu.TAM e” = Hu.TAM d/e). The Hu.TAMd/e cell population can be identified by using the following markers: CD36, ITGAX (CD11 c), VTCN1 (B7- H4) and TNFRSF10B (CD262). The present inventors propose to assess the macrophage signature in a solid tumor in order to prognose the malignancy and the outcome of said tumor. In a second aspect, the present invention targets a method to prognose the evolution a solid cancer in a subject in need thereof, said method comprising the step of analysing the macrophage signature in this tumor, and preferably the presence and localisation of the population of the tumor-associated macrophage cells defined above, i.e., the murine pre-Mac, TAM2 or TAM3 cells or the human TAM1 , TAM2, TAM3, TAM4 or TAM5 cells.

This prognostic method can also comprise the step of comparing said frequency to a reference value.

In this case, it can be concluded that the solid cancer has a bad prognostic if the frequency of the said population of macrophages is superior to said reference value. In a particular embodiment, it can be concluded the solid cancer has a bad prognostic if the frequency of the said population of TAM1 macrophages (CD45⁺ Ly6G' CD11 b⁺ SiglecF' CCR2⁺ MHCIT CD64⁺) is superior to said reference value. This method can in particular comprises the step of analyzing the spatial repartition of said population of tumor-associated macrophages, e.g. by immunohistochemistry or by transcriptomics, for instance special transcriptomics.

In a third aspect, the present invention relates to a method to treat a solid cancer in a subject in need thereof, said method comprising the step of administering in said subject a compound that inhibit the transition or differentiation between the human TAM1 macrophages (CD45⁺ Ly6G' CD11 b⁺ SiglecF' CCR2⁺ MHCH⁺ CD64⁺) into the human TAM2/TAM3 macrophages population (CD45⁺ Ly6G' CD11 b⁺ SiglecF' CD73⁺ MHCH⁺ CD11 c).

Said compound can for example inhibit the differentiation of the human TAM1 macrophages (CD45⁺ Ly6G' CD11 b⁺ SiglecF' CCR2⁺ MHCH⁺ CD64⁺) into the human TAM2/TAM3 macrophages (CD45⁺ Ly6G' CD11 b⁺ SiglecF CD73⁺ MHCIP CD11 c).

In this case, it is preferred to use a compound that is able to enhance, to interfere with or to block the expression of the FosL2 transcription factor or the expression of the Statl transcription factor, as these transcription factors have been found to be implicated in this transition or differentiation (see figure 5A).

The present invention also relates to a method to treat a solid cancer in a subject in need thereof, said method comprising the step of administering in said subject a compound that inhibit the transition between the human TAM2/TAM3 macrophage population (CD45⁺ Ly6G' CD11 b⁺ SiglecF CD73⁺ MHCIP CD11 c) into the human TAM4/TAM5 (CD45⁺ Ly6G' CD11 b⁺ SiglecF CD73' MHCIP CD11 c⁺).

In this case, it is preferred to use a compound that is able to enhance, to interfere with or to block the expression of any of the following transcription factors: Ddit3; Creb5; Zmizl; Junb; Ets2; Statl; Stat3; Fos; Egr1; Irf8; or Maf as these transcription factors have been found to be implicated in this transition (see figure 5A).

The present invention also relates to a method to treat a solid cancer in a subject in need thereof, said method comprising the step of administering in said subject a compound that block the tumor-associated macrophage population in the human TAM4/TAM5 (CD45⁺ Ly6G' CD11 b⁺ SiglecF CD73' MHCIP CD11 c⁺). In this case, it is preferred to use a compound that is able to enhance, to interfere with or to block the expression of any of the following transcription factors: Atf3, Bhlhe40, Bhlhe41, Jund or Jun as these transcription factors have been found to be implicated in this transition (see figure 5A).

In a fourth aspect, the present invention relates to a method to treat a solid cancer in a subject in need thereof, said method comprising the step of administering in said subject a compound that inhibit the expression of the gene CLEC4E in tumor-associated macrophage populations, in particular in the human TAM1 macrophages (CD45⁺ Ly6G' CD11 b⁺ Siglec-F' CCR2⁺ MHCIT CD64⁺), because CLEC4E was identified as a bad prognostic factor that could regulate the expression of the TAM1 program.

Alternatively, the invention relates to a method to treat a solid cancer in a subject in need thereof, said method comprising the step of administering in said subject a compound that inhibit the expression of the gene CYP27A1E in tumor-associated macrophage populations, in particular in the human TAM4/TAM5 (CD45⁺ Ly6G' CD11 b⁺ Siglec-F' CD73' MHCIT CD11 c⁺) because CYP27A1E was identified as a bad prognostic factor that could regulate the expression of the TAM4/5 program.

Also, the invention relates to a method to treat a solid cancer in a subject in need thereof, said method comprising the step of administering in said subject a compound that inhibit the expression of the gene PPARG in tumor-associated macrophage populations, in particular in the human TAM4/TAM5 (CD45⁺ Ly6G' CD11 b⁺ Siglec-F' CD73' MHCH⁺ CD11 c⁺) because PPARG was identified as a bad prognostic factor that could regulate the expression of the TAM4/5 program.

It would be also possible to use any compound inhibiting the expression of the genes listed in Figure 7F as differentially expressed in all the macrophage populations herein identified.

Also, the present invention relates to a method to treat a solid cancer in a subject in need thereof, said method comprising the step of administering in said subject a compound that inhibit the expression of the gene Hifla in tumor-associated macrophage populations, in particular in the human TAM2/TAM3 macrophages (CD45⁺ Ly6G' CD11 b⁺ SiglecF' CD73⁺ MHCH⁺ CD11c), because Hifla was identified as a transcription factor that could regulate the expression of the TAM 2 program.

Preferably, the compounds used in these treating methods are anti-sens or complementary oligonucleotides or chemical drugs that are known to impair the transition between these different macrophage stages or to block the migration of same within the tumor (e.g., to block the localisation of the macrophages TAM2-TAM5 macrophages into hypoxic regions of the tumor).

In all the methods of the invention, the cancer can be chosen in the group consisting of: squamous cell carcinoma, small-cell lung cancer, non-small cell lung cancer, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, brain cancer, central nervous system cancer, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, malignant hepatoma, breast cancer, colon carcinoma, head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, rhabdomyosarcoma, Ewing’s sarcoma, osteosarcoma, soft tissue sarcoma, sinonasal NK/T-cell lymphoma, myeloma, melanoma, multiple myeloma.

In particular, these solid tumors can be specifically lung cancer, malignant mesothelioma, bladder cancer, kidney cancer, testicular cancer, breast cancer, cancer of the upper aerodigestive tract, liver cancer, pancreas cancer, stomach cancer; colon cancer or ovarian cancer.

In a particular embodiment, said solid tumor is a lung adenocarcinoma, such as a NonSmall Cell Lung Cancer. In a particular embodiment, said solid tumor is a breast cancer, such as triple-negative breast cancer (TNBC).

In a particular embodiment, said solid tumor is pancreatic ductal adenocarcinoma. DETAILLED DESCRIPTION OF THE INVENTION

In the examples presented below, the inducible Ms4a3^CreERT2 x Rosa^tdTomato timestamping mouse model was studied to monitor and understand the monocyte-to-macrophage transition in the pancreatic ductal adenocarcinoma (PDAC). Multi-omics analyses were also used to track monocytes entering PDAC and precise the dynamism of their differentiation. By doing so, an intermediate population was surprisingly identified in the transition from monocytes to macrophages in this specific context, both in mouse and human. This population of particular Tumor-Associated Macrophages (TAMs) was called “PreMac” or “TAM1” or “Mac1 ”.

Furthermore, high-dimensional flow cytometry was used to identify markers for each tumor-associated macrophage population and assess their abundance throughout tumor growth. By transcriptional profiling and spatial transcriptomics, tumor-associated macrophage distinct identities were dissected and their functions revealed, according to their distinct locations in the tumor microenvironment.

It was herein found that, once PreMac are within the tissue for approximately two weeks, they integrate the phenotype of their environment and become a different cell type (TAM 2 or TAM 3 in PDAC). Remarkably, these phenotypes are directly associated with the metabolic phenotype of the microenvironment in which they are located. Given these observations, it is proposed here that a major determinant of macrophage phenotype in cancer is the availability of nutrients.

The present work highlights PreMac differentiation to TAM 3. Interestingly, it seems that the abundance of TAM 3 can be reduced in TREM2 knockout mice which in combination with anti-PD-1 treatment contributes to beneficial responses to PDAC. PreMac differentiation to TREM2 macrophage appears to be a result of the availability of cellular respiration and lipid metabolism.

Differentiation from a PreMac to a TAM 2 is likely governed by the presence of hypoxia. Based on the above spatial observations, PreMac that are in hypoxic tissue will become TAM 2. By gene expression and transcription factor expression, Hif1 a was identified as a transcription factor, in particular associated to hypoxia, that could regulate the expression of the TAM 2 program. PreMac exhibit a proinflammatory phenotype (expression of IL-10, IL-6, and TNF). It is herein proposed that targeting the inflammatory capacity of PreMac in cancer could promote their inflammatory potential and prevent tumorigenesis. These observations position PreMac as an integral target for cancer treatment.

Importantly, the existence of these various tumor-associated macrophage populations was validated in patients suffering from pancreatic ductal adenocarcinoma.

Thus, this work combines the aspect of time-dependant macrophage heterogeneity and spatial distribution to resolve the heterogeneity of macrophage in pancreatic cancer.

Prognosis methods

As disclosed herein, the terms “in vitro" and “ex vivo” are equivalent and refer to studies or experiments that are performed using biological components (e.g. cells or population of cells) that have been isolated from their usual host organisms (e.g. animals or humans). In contrast, the terms “in vivo’’ or “in situ’’ refer to studies that are conducted on whole living organisms (e.g., humans), after administration of the composition of the invention in a living subject.

In the context of the invention, the term “tumor sample” or “solid cancer sample” means a sample containing a detectable amount of tumor cells. Such solid cancer sample allows the skilled person to perform any type of measurement of the level of the macrophages of the invention. In some cases, the methods according to the invention may further comprise a preliminary step of taking a solid cancer sample from the patient. By a “solid cancer sample”, it is referred to a tumor tissue sample. Even in a cancerous patient, the tissue which is the site of the tumor still comprises non tumor healthy tissue. The “cancer sample” should thus be limited to tumor tissue taken from the patient. Said “cancer sample” may be a biopsy sample or a sample taken from a surgical resection therapy. These samples are preferably maintained in appropriate conditions so that they are not altered after their collect from the subject’s body. In particular, for flow cytometry, following cell isolation using conventional enzymatic digestion, isolated cells can be maintained in freezing conditions and I or suspended immediately analyzed by flow cytometry to evaluate their frequency among CD45+ leukocytes. Alternatively, the cancer sample can be embedded in paraffin or snap-frozen followed by analysis of macrophages cells by immunohistochemistry. In a preferred embodiment, the tissue is a diseased tissue. In a preferred embodiment of the method, the tissue is a tumor or a biopsy thereof. In a preferred embodiment of the method, a tissue or a biopsy thereof is first excised from a patient, and the levels of the cells of the invention in the tissue or biopsy are then determined in an immunoassay with the antibodies or antibody fragments described below.

A “subject in need thereof”, as herein meant, is therefore a mammal, preferably a human being, that is suffering from cancer. Said cancer can be a liquid or a solid cancer such as, without limitation, a lymphoma, a leukemia, a carcinoma, a melanoma, a glioblastoma, a sarcoma, a myeloma, colon rectal tumors, etc. as primary or metastatic cancers. In a particular embodiment, said “subject in need thereof” is a human or another mammal suffering from pancreatic cancer, e.g. from a pancreatic ductal adenocarcinoma.

The methods of the invention enable to establish a prognosis on the patient fate as far as its cancer is concerned, or to monitor the evolution of the cancer disease, or to determine the most probable outcome of the cancer disease, or to assess or predict the metastasis associated risk.

The methods may require the comparison of the frequency of the macrophages identified herein with a reference value.

The “frequency” of a particular cell population in a given sample is herein understood as being the proportion of this particular cell population among the cells present in said sample. It can be measured by calculating the percentage of cells of this particular population (i.e., displaying the markers known to be shared by the cells of this population) present in said sample, among the total number of cells present in the tested sample, or among the cells of another particular cell population (e.g., in the context of the invention, among all macrophage cells or among CD45+ leukocytes). It can also be the number of cells belonging to the target population divided by the number of other cells, provided that said number of other cells is normalized between samples, so as to be comparable. In that sense, the “frequency” of the cells of the invention can be herein assimilated to the “concentration” or the “abundance” of the cells belonging to the target population of the invention, within a particular category of cells. The term “frequency” as meant herein is therefore synonymous of the terms “proportion”, “percentage” or “concentration” which can be used interchangeably. The term "reference value", as used herein, refers to the expression level of a prognosis marker under consideration in a reference sample. A "reference sample", as used herein, means a solid cancer sample obtained from subjects, preferably two or more subjects, known to be suffering from solid cancer with a good prognosis. The suitable reference expression levels can be determined by measuring the expression levels of said prognosis marker in several suitable subjects, and such reference levels can be adjusted to specific subject populations. The reference value or reference level can be an absolute value; a relative value; a value that has an upper or a lower limit; a range of values; an average value; a median value, a mean value, or a value as compared to a particular control or baseline value. A reference value can be based on an individual sample value such as, for example, a value obtained from a sample from the subject being tested, but at an earlier point in time. It can also be based on a sample from the subject being tested, taken from a non-cancerous tissue (i.e., a normal tissue of the same subject, adjacent to the tumor or not). The reference value can be based on a large number of samples, such as from population of subjects of the chronological age matched group, or based on a pool of samples including or excluding the sample to be tested.

“Prognosis” herein means the prediction/determination/assessment of the risk of disease (in particular a cancer and/or a tumor) progression (or evolution, or development) in an individual. Prognosis includes the assessment of the future development of the subject’s condition and the possible chances of cure. The prognosis can be determined on the basis of observations and/or measurements, carried out using various tools.

“Monitoring” or “Follow-up” herein refers to the identification/assessment of the progression (or evolution, or development) of a disease (in particular a cancer and/or a tumor) in a subject. Monitoring may be carried out on the basis of observations and/or measurements, using different tools, at different time intervals. Intervals may be regular or irregular. Their frequency depends on the cancer but also on the stage of cancer progression. It can range from a few days (e.g. in case of severe/advanced/severe disease and/or rapidly progressing cancer and/or exacerbation phase) to a few years (e.g. in case of early, mild or moderate cancer and/or slowly progressing cancer).

As used herein, “determining the outcome” or “outcome determination”, herein means the prediction/determination/assessment of the most probable evolution (or progression or development) of a cancer (in particular a cancer and/or a tumor) in a subject. “Determining the outcome” of the disease thus includes at least assessing the next stages that are most likely to be undergone by the subject, in terms of probability. More specifically, “determining the outcome of a solid tumor” includes, but is not limited to, the assessment of the probabilities (or chances), for a subject of switching to a metastatic form; and/or the assessment of the probabilities (or chances), for a subject of having or progressing towards a tumor and/or a metastatic tumor.

As used herein, “predicting the metastasis-associated risk” or “assessing the risk of switching to a metastatic form” means the prediction/determination/assessment of the probabilities (or the chances) of exacerbation (or aggravation, or intensification) of a cancer and/or a tumor, in particular the prediction/determination/assessment of the probabilities (or the chances) to switch from a non-metastatic to a metastatic form.

In a preferred embodiment, the pronostic methods of the invention are achieved by flow cytometry by assessing the relative frequency of the different TAM cells among CD45⁺ leukocytes on a sample of the tumor.

Flow cytometry is a powerful technology that allows researchers and clinicians to perform complex cellular analysis quickly and efficiently by analysing several parameters simultaneously. The amount of information obtained from a single sample can be further expanded by using multiple fluorescent reagents. The information gathered by the flow cytometer can be displayed as any combination of parameters chosen by the skilled person. Cells pass single-file through a laser beam. As each cell passes through the laser beam, the cytometer records how the cell or particle scatters incident laser light and emits fluorescence. Using a flow cytometric analysis protocol, one can perform a simultaneous analysis of surface molecules at the single-cell level.

More preferably, the detection of the cell surface antigens in the methods of the invention is performed by an exclusion gating strategy by flow cytometry.

In the context of the present invention, the expression of the cell surface antigens may also be assessed using well known technologies such as cell membrane staining using biotinylation or other equivalent techniques followed by immunoprecipitation with specific antibodies, flow cytometry, western blot, ELISA or ELISPOT, antibodies microarrays, or tissue microarrays coupled to immunohistochemistry. Other suitable techniques include FRET or BRET, single cell microscopic or histochemistry methods using single or multiple excitation wavelength and applying any of the adapted optical methods, such as electrochemical methods (voltametry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g. multipolar resonance spectroscopy, confocal and non-confocal, detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, and birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry), cell ELISA, radioisotopic, magnetic resonance imaging, analysis by polyacrylamide gel electrophoresis (SDS-PAGE); HPLC-Mass Spectroscopy; Liquid Chromatography/Mass Spectrometry/Mass Spectrometry (LC-MS/MS)).

In another preferred embodiment, the prognostic methods of the invention require assessing the frequency or the density of the TAMs by immunohistochemistry on a sample of the tumor.

Immunohistochemistry is a well-known technique. In the context of the invention, it is possible to use conventional steps so as to fixate, dehydrate, embedded and section the tumor sample that has been previously collected and stored under appropriate freezing conditions. In particular, it is possible to preserve the tumor tissues embedded in paraffin (FFPE) and to perform histology directly on such samples.

Immunostainings can be performed on 3 pm thick whole sections prepared from FFPE blocks of tumor.

In another preferred embodiment, the prognostic methods of the invention are performed by measuring the expression level of the different genes in a nucleotide sample of the tumor, preferably in a RNA sample of the tumor, e.g., by measuring the quantity of the different mRNAs present in same.

In the context of these methods, the term “nucleotide sample” means a sample containing a detectable amount of RNA extracted from the cells of interest. The nucleotide sample may be obtained from any tumor sample, and, in particular, from a biopsy of a tumor tissue. The method of the invention can include the steps consisting of obtaining a tumor sample (e.g., a tissue biopsy) from said subject and extracting the nucleotide fraction from said tumor sample.

The nucleotide fraction can be extracted using any known method in the state of the art. In particular, the skilled person well knows how to prepare a tumor sample (that has been previously collected and stored under appropriate freezing conditions) in order to be used in gene analysis. Usually, the samples are washed with appropriate buffers and put in a lysis buffer so as to isolate the RNA. RNA is preferably extracted from said sample by using a convenient commercial extraction protocol such as those proposed by MOBIO, Qiagen or Zymo.

As used herein, the term “tumor nucleotide sequence” designates the sequence of the oligonucleotides contained in a tumor sample. Preferably, this sample contains all the mRNAs present in the tumor sample.

A number of widely used procedures exist for detecting and determining the abundance of a particular mRNA in nucleotide samples: Northern blot analysis, nuclease protection assays (NPA), in situ hybridization, and reverse transcription-polymerase chain reaction (RT-PCR), next generation RNA sequencing (RNAseq), scRNAseq, etc. In the context of the invention, the expression level of the target genes is preferably measured by RT- qPCR or by RNAseq. RT-qPCR is a well-known technology whose conditions are thoroughly explained in the notice of commercial kits (SIGMA-ALDRICH, QIAGEN, ...).

In the prognosis methods of the invention, if the frequency of the detected TAMs is increased in a sample of the tumor, as compared to the reference value, then this means that the prognosis of the subject is bad.

Conversely, if the frequency of the detected macrophages is decreased in a sample of the tumor, as compared to the reference value, then this means that the prognosis of the subject is good.

By “bad prognosis”, it is herein meant that the outcome of the tested patient is likely to be a short survival, typically a survival of less than one year, two years, or five years. Patients having such Overall Survival is also called a “bad responder” if the patient was receiving a therapy.

By “good prognosis”, it is herein meant that the outcome of the tested patient is likely to be a long survival, typically longer than 5 years, or more than 7 years. Treatment methods

It is be understood that the present invention also covers the use of the compounds of the invention (chemical drugs or anti-sense oligonucleotides) for manufacturing pharmaceutical compositions that can be used for treating cancer. Also, the invention covers the compounds of the invention (chemical drugs or anti-sense oligonucleotides) for their use for treating cancer.

As used herein, the terms “treat”, “treating”, “treatment”, and the like refer to reducing or ameliorating the symptoms of a disorder (e.g., cancer), and/ or symptoms associated therewith. It will be appreciated that, although not precluded, treating a disorder or condition does not require that the disorder, condition or symptoms associated therewith be completely eliminated.

Effective doses of the therapeutic entity of the present invention, e.g. for the treatment of cancer, vary depending upon many different factors, including means of administration, target site, physiological state of the patient, whether the patient is human or an animal, other medications administered, and whether treatment is prophylactic or therapeutic. Treatment dosages can be titrated to optimize safety and efficacy.

For prophylactic applications, pharmaceutical compositions or medicaments are administered to a patient susceptible to, or otherwise at risk of disease in an amount sufficient to eliminate or reduce the risk, lessen the severity, or delay the outset of the disease, including biochemical, histologic and/or behavioral symptoms of the disease, its complications and intermediate pathological phenotypes presenting during development of the disease. In these prophylactic applications, a relatively low dosage may be administered at relatively infrequent intervals over a long period of time. Some patients continue to receive treatment for the rest of their lives.

Conversely, in therapeutic applications, a relatively high dosage at relatively short intervals (typically each week) is sometimes required until progression of the disease is reduced or terminated, and preferably until the patient shows partial or complete amelioration of symptoms of disease.

The present invention encompasses treating methods in which the inhibitory compounds of the invention are administered to said subject in need thereof by injection, preferably by intravenous injection. A systemic injection may be also carried out by perfusion. These injections are harmless for the treated subject.

The present invention concerns pharmaceutical compositions containing the inhibitory compounds of the invention (oligonucleotides or chemical compounds).

These pharmaceutical compositions usually also contain a pharmaceutically acceptable excipient.

The term “pharmaceutically acceptable excipient" means an excipient that is useful in preparing a pharmaceutical composition that is generally safe, non-toxic, and desirable, and includes excipients that are acceptable for veterinary use as well as for human pharmaceutical use. Such excipients can be solid, liquid, semisolid, or, in the case of an aerosol composition, gaseous. Compositions for the treatment of cancer can usually be administered by parenteral, topical, intravenous, intratumoral, oral, subcutaneous, intraarterial, intracranial, intraperitoneal, intranasal or intramuscular means. A typical route of administration is intravenous or intratumoral, although other routes can be equally effective.

For intravenous administration, the compositions of the invention will be under liquid form. They will thus contain a pharmaceutically-acceptable diluent that does not affect the biological activity of the compounds of the invention. Example of such diluents are physiological phosphate-buffered saline, Ringer's solutions, dextrose solution, and Hank's solution. In addition, the pharmaceutical composition or formulation may also include other carriers, adjuvants, or nontoxic, nontherapeutic, nonimmunogenic stabilizers and the like.

The pharmaceutical compositions of the invention can be administered alone or combined with another pharmaceutical composition or another anti-cancer treatment. In particular, the pharmaceutical compositions of the invention can contain the inhibitory compounds of the invention as well as another anti-cancer treatment, combined in the same container.

As used herein, the term “combined” does not imply that the compounds of the invention and the other active principles are necessarily administered simultaneously. It also extends to any use or presentation involving their administration at different time intervals, or in separate containers. "Concomitant administration" of said active principles with the pharmaceutical composition of the present invention means administration at such a time that both the active principle and the composition of the present invention will have a therapeutic effect. Such concomitant administration may involve concurrent (i.e. at the same time), prior, or subsequent administration of the active principle with respect to the administration of a compound of the invention. A person of ordinary skill in the art would have no difficulty determining the appropriate timing, sequence and dosages of administration for particular drugs and compositions of the present invention.

As used herein, the term “anti-cancer treatment” or “anti-cancer therapy” or “therapy” designates any chemical or biochemical drug that can be used to treat a solid cancer. It is meant a substance which, when administered to a patient, treats or prevents the development of cancer in the patient. By way of non-limiting example for such agents, mention may be made of an antitumor/cytotoxic antibiotic, alkylating agents, antimetabolites, a topoisomerase inhibitor, a mitotic inhibitor, a platin based component, a specific kinase inhibitor, a hormone, a cytokine, an antiangiogenic agent, an antibody, a DNA methyltransferase inhibitor, a cancer vaccine, and a vascular disrupting agent.

Said antitumor agent or cytotoxic antibiotic can for example be selected from an anthracycline (e.g. doxorubicin, daunorubicin, adriamycine, idarubicin, epirubicin, mitoxantrone, valrubicin), actinomycin, bleomycin, mitomycin C, plicamycin and hydroxyurea.

Said alkylating agent can for example be selected from mechlorethamine, cyclophosphamide, melphalan, chlorambucil, ifosfamide, temozolomide busulfan, N- Nitroso-N-methylurea (MNU), carmustine (BCNU), lomustine (CCNU), semustine (MeCCNU), fotemustine, streptozotocin, dacarbazine, mitozolomide, thiotepa, mytomycin, diaziquone (AZQ), procarbazine, hexamethylmelamine and uramustine.

Said antimetabolite can for example be selected from a pyrimidine analogue (e.g. a fluoropyrimidine analog, 5-fluorouracil (5-FU), floxuridine (FUDR), cytosine arabinoside (Cytarabine), Gemcitabine (Gemzar®), capecitabine); a purine analogue (e.g. azathioprine, mercaptopurine, thioguanine, fludarabine, pentostatin, cladribine, clofarabine); a folic acid analogue (e.g. methotrexate, folic acid, pemetrexed, aminopterin, raltitrexed, trimethoprim, pyrimethamine). Said topoisomerase inhibitor can for example be selected from camptothecin, irinotecan, topotecan, amsacrine, etoposide, etoposide phosphate and teniposide.

Said mitotic inhibitor can for example be selected from a taxane [paclitaxel (PG-paclitaxel and DHA-paclitaxel) (Taxol ®), docetaxel (Taxotere ®), larotaxel, cabazitaxel, ortataxel, tesetaxel, or taxoprexin]; a spindle poison or a vinca alkaloid (e.g. vincristine, vinblastine, vinorelbine, vindesine or vinflunine); mebendazole; and colchicine.

Said platin based component can for example be selected from platinum, cisplatin, carboplatin, nedaplatin, oxaliplatin, satraplatin and triplatin tetranitrate.

Said specific kinase inhibitor can for example be selected from a BRAF kinase inhibitor such as vemurafenib; a MAPK inhibitor (such as dabrafenib); a MEK inhibitor (such as trametinib); and a tyrosine kinase inhibitor such as imatinib, gefitinib, erlotinib, sunitinib or carbozantinib.

Tamoxifen, an anti-aromatase, or an anti-estrogen drug can also typically be used in the context of hormonotherapy.

A cytokine usable in the context of an immunotherapy can be selected for example from IL-2 (lnterleukine-2), IL-11 (lnterleukine-11 ), IFN (Interferon) alpha (IFNa), and Granulocyte-macrophage colony-stimulating factor (GM-CSF).

Said anti-angiogenic agent can be selected for example from bevacizumab, sorafenib, sunitinib, pazopanib and everolimus.

Said antibody, in particular the monoclonal antibody (mAb) can be selected from a anti- CD20 antibody (anti-pan B-Cell antigen), anti-Her2/Neu (Human Epidermal Growth Factor Receptor-2/NEU) antibody; an antibody targeting cancer cell surface (such as rituximab and alemtuzumab); a antibody targeting growth factor (such as bevacizumab, cetuximab, panitumumab and trastuzumab); a agonistic antibody (such as anti-ICOS mAb, anti-OX40 mAb, anti-41 BB mAb); and an immunoconjugate (such as 90Y- ibritumomab tiuxetan, 1311-tositumomab, or ado-trastuzumab emtansine).

Said DNA methyltransferase inhibitor can for example be selected from 2'-deoxy-5- azacytidine (DAC), 5-azacytidine, 5-aza-2'- deoxycytidine, 1 -[beta]-D-arabinofuranosyl- 5-azacytosine and dihydro-5-azacytidine. Said vascular disrupting agent can for example be selected from a flavone acetic acid derivative, 5,6-dimethylxanthenone-4- acetic acid (DMXAA) and flavone acetic acid (FAA).

Other chemotherapeutic drugs include a proteasome inhibitor (such as bortezomib), a DNA strand break compound (such as tirapazamine), an inhibitor of both thioredoxin reductase and ribonucleotide reductase (such as xcytrin), and an enhancer of the Thl immune response (such as thymalfasin).

Said immune checkpoint blocker is typically an antibody targeting an immune checkpoint. Such an immune checkpoint blocker can be advantageously selected from anti-CTLA4 (ipilimumab and Tremelimumab), anti-PD-1 (Nivolumab and Pembrolizumab), anti-PD- L1 (Atezolizumab, Durvalumab, and Avelumab), anti-PD-L2 and anti-Tim3. Specifically, said patients have been treated or will be treated with immunotherapy drugs such as anti-PD-1 and/or anti-PD-L1 drugs.

Said cancer vaccine can for example be selected from a vaccine composition comprising (antigenic) peptides; a Human papillomavirus (HPV) vaccine (such as Gardasil®, Gardasil9®, and Cervarix®); a vaccine stimulating an immune response to prostatic acid phosphatase (PAP) sipuleucel-T (Provenge®); an oncolytic virus and talimogene laherparepvec (T-VEC or Imlygic®).

The treatment which can include several anticancer agents is selected by the cancerologist depending on the specific cancer to be prevented or treated.

The term “anti-cancer treatment” or “anti-cancer therapy” or “therapy” also designates any other treatment that was proven beneficial for treating cancer, namely radiotherapy, immunotherapy, or surgery. The radiotherapy typically involves rays selected from X-rays (“XR”), gamma rays and/or UVC rays.

The present inventors also propose to target cytotoxic agents to the TAM1/TAM2 or TAM3 cells in order to deplete or destroy them and to increase the prognosis of the patients and eventually treat them.

Said agent can be, for example, bicyclic peptides, antibodies, or antibody fragments like diabodies, Fab, or scFV. In a particular embodiment, the present invention proposes a cytotoxic antibody that specifically binds to the macrophage cells of the invention, for use for depleting said cells from the tumor core of a solid tumor or in specific regions thereof.

This antibody is herein called “antibody of the invention”. It can be a chimerized or a humanized antibody, as defined below. It can be multispecific, and in particular bispecific. As such, it can be chosen in the group consisting of: bispecific IgGs, lgG-scFv2, (scFv)4- IgG, (Fab')2, (scFv)2, (dsFv)2, Fab-scFv fusion proteins, (Fab-scFv)2, (scFv)2-Fab, (scFv-CH2-CH3-scFv)2, bibody, tribody, bispecific diabody, disulfide-stabilized (ds) diabody, 'knob-into whole' diabody, single-chain diabody (scDb), tandem diabody (TandAb), flexibody, DiBi miniantibody, [(scFv) 2-Fc] 2, (scDb-CH3)2, (scDb-Fc)2, Di- diabody, Tandemab., etc.

Preferably, the cytotoxic agent of the invention is an antibody that specifically binds to the TAM macrophages of the invention.

The antibodies of the invention would be preferably conjugated to a potent cytotoxic compound such as a radioisotope, a chemotherapeutic drug or a toxin, so as to provide an “Antibody-Drug Conjugate” (ADC). Examples thereof are given below.

In particular embodiments, the antibodies of the invention comprise changes in amino acid residues in the Fc region that lead to improved effector function including enhanced complement-dependent cytotoxicity (CDC) and/or antibody-dependent cellular cytotoxicity (ADCC) function and eventually DC-cell killing (also referred to herein as DCcell depletion). In particular, three mutations have been identified for improving CDC and ADCC activity: S298A/E333A/K334A (also referred to herein as a triple Ala mutant or variant; numbering in the Fc region is according to the EU numbering system). This may be achieved by introducing one or more amino acid substitutions in an Fc region of an antibody.

"Antibody-dependent cell-mediated cytotoxicity" or "ADCC" refers to a form of cytotoxicity in which secreted Ig bound onto Fc receptors (FcRs) present on certain cytotoxic cells (e.g., Natural Killer (NK) cells, neutrophils, monocytes and macrophages) enable these cytotoxic effector cells to bind specifically to an antigen-bearing target cell and subsequently kill the target cell with cytotoxins. The antibodies “arm” the cytotoxic cells and are absolutely required for such killing. The primary cells for mediating ADCC, NK cells, express Fc[gamma]RIII only, whereas monocytes express Fc[gamma]RI, Fc[gamma]RII and Fc[gamma]RIII. To assess ADCC activity of a molecule of interest, an in vitro ADCC assay, such as that described in U.S. 5,500,362 or 5,821 ,337 can be performed. Useful effector cells for such assays include peripheral blood mononuclear cells (PBMC) and Natural Killer (NK) cells. Alternatively, or additionally, ADCC activity of the molecule of interest can be assessed in vivo, e.g., in a animal model such as that disclosed (15) To promote ADCC, cysteine residue(s) may be introduced in the Fc region of the antibodies of the invention, thereby allowing interchain disulfide bond formation in this region. The homodimeric antibody thus generated may have improved internalization capability and/or increased complement-mediated cell killing and antibody-dependent cellular cytotoxicity (ADCC). Homodimeric antibodies with enhanced anti-tumor activity may also be prepared using heterobifunctional cross-linkers as described (16). Alternatively, an antibody can be engineered which has dual Fc regions and may thereby have enhanced complement lysis and ADCC capabilities.

“Complement dependent cytotoxicity” or “CDC” refers to the lysis of a target cell in the presence of complement. Activation of the classical complement pathway is initiated by the binding of the first component of the complement system (C1q) to antibodies (of the appropriate subclass) which are bound to their cognate antigen. To assess complement activation, a CDC assay can be performed (17).

Antibody variants with altered Fc region amino acid sequences and increased or decreased C1 q binding capability are described in U.S. Pat. No. 6,194,551 B1 and WO99/51642. The contents of those patent publications are specifically incorporated herein (18).

In particular embodiments, the antibody of the invention would be conjugated to a potent cytotoxic drug so as to mediate ADC, or would be modified so as to mediate efficient ADCC or CDC, as detailed above.

This cytotoxic antibody would be administered intratumorally in a sufficient amount for treating solid cancer in a subject in need thereof or for preventing metastasis to develop. Methods for selecting treatments

The present prognostic tool may also assist physicians in identifying patients who are likely to progress towards even more serious form of solid cancer and thus may suggest those patients require heavier or more aggressive treatment.

Thus, in a preferred embodiment, the methods of the invention can also be used to aid the skilled cancerologist in the selection of appropriate treatments for maximizing the survival of the patients. Appropriate treatments are for example chemotherapeutic treatments, immunotherapeutic treatments, radiotherapeutic treatments and/or surgery (as defined above). Preferably, the macrophage signature of the invention is assessed before and after a treatment, to see if said signature is significantly changed by the treatment.

The macrophage signature of the invention is preferentially generated before initiating a treatment or before changing a treatment.

The methods of the invention thus enable to generate a personalized treatment plan. The personalized treatment plan is based on the high or low level of the macrophages of the invention in the tumor sample. The personalized treatment plan may include a new therapeutic recommendation, a new therapeutic schedule, a new therapeutic dosage, a follow up treatment schedule, or other action. The personalized treatment plan may include information that facilitates developing a precision treatment plan for a patient. For example, upon determining that a tumor is classified as a responder, the method of the invention may control the personalized cancer treatment system to generate a first personalized treatment plan that indicates a first type of therapy. Upon determining that the tumor is classified as a non-responder to this first therapy, the method may generate a second, different personalized treatment plan that proposes a second, different type of therapy.

By increasing the accuracy with which response to a therapy is predicted, the methods of the invention produce the concrete, real-world technical effect of reducing the amount of unnecessary biopsies or other invasive procedures for patients who are unlikely to benefit from immunotherapy treatment. Additionally, these methods reduce the expenditure of time, money and therapeutic resources on patients who are unlikely to benefit from the treatment. They thus improve on conventional approaches to predicting response to therapy in a measurable, clinically significant way. In a preferred embodiment, the methods of the invention can be used: to evaluate the response of a patient to an anti-cancer treatment, to select an anti-cancer therapy for a patient, to assess the efficacy of an anti-cancer therapy for a patient, to adapt an anti-cancer therapy for a patient and/or to identify if a tumor or a patient is responding to the therapy, generate a personalized treatment plan.

In the context of the invention, the response to a therapy is preferably defined according to RECIST 1.1 criteria. A Complete Response (CR) is defined as a disappearance of all target lesions. Any pathological lymph nodes (whether target or non-target) must have reduction in short axis to<10 mm. A Partial Response (PR) is defined as at least a 30% decrease in the sum of diameters of target lesions, taking as reference the baseline sum diameters. A Progressive Disease (PD) is defined as at least a 20% increase in the sum of diameters of target lesions, taking as reference the smallest sum on study (this includes the baseline sum if that is the smallest on study). In addition to the relative increase of 20%, the sum must also demonstrate an absolute increase of at least 5 mm. (Note: the appearance of one or more new lesions is also considered progression). A Stable Disease (SD) is defined as neither sufficient shrinkage to qualify for PR nor sufficient increase to qualify for PD, taking as reference the smallest sum diameters while on study.

Time of the evaluation of the resistance or response to the therapy often depends of the disease (it is usually comprised between 6 weeks and 3-6 months). A “non-responder” is considered as a patient with a progression disease or a stable disease as defined according to RECIST 1.1 criteria.

As used herein, “selecting a therapy” or “selecting a treatment” or “selecting a drug” refers to the process of selecting (choosing, or deciding for, or opting for) the most appropriate therapy for a subject, in view of the symptoms (or signs) detected (observed and/or measured) in the subject, and/or in view of the subject himself, and the general knowledge in the medical field (preferably the medical field closest to the disease). Selecting a therapy include selecting the most appropriate therapy and may include also selecting the most appropriate administration mode and/or the most appropriate posology.

As used herein, the terms “assessing the efficacy of a therapy” or “assessment of treatment efficacy” refers to the determination of the clinical status of a subject undergoing treatment (i.e. receiving a therapy). The treatment may be preventive, for example in the case of predisposition to a disease, or it may be curative, for example in the case of a diagnosed disease. For example, the effectiveness of the treatment can be assessed by determining the condition of the subject at different time intervals. In particular, the condition of the subject can be assessed before the first dose of treatment and then at regular (or irregular) intervals after the first dose (e.g. after each new dose of treatment). A comparison of the subject’s condition at these different intervals can then be made to identify any changes. The patient’s condition can be assessed on the basis of observations and/or measurements made with different tools.

FIGURE LEGENDS

Figure 1 : Contribution of ontogeny to macrophage heterogeneity in murine PDAC.

A) Dot plots of top differentially expressed genes from neutrophils, mononuclear phagocytes, T cells, B cells, and plasmablasts.

B) UMAP representation of the scRNAseq data generated on mouse PDAC tumors 35 days after orthotopic injection of 50,000 KPC (Pdx1^Cre Kras^G12D/+ Trp53^R172H/+). Only filtered monocytes, macrophages, and dendritic cells are displayed.

C) Dot plot showing leading differentially expressed genes from monocytes, macrophages, and dendritic cells.

D) Proportions of monocytes, macrophages, and dendritic cells labeled with Tomato and expression of specific genes in Tomato and Tomato' macrophages extracted from PDAC tumors in Ms4a3^Cre x Rosa^W7°^mato rnice.

E) Gating strategy of monocytes and macrophages used for scRNAseq and relative proportion of monocytes and macrophage labeling of Tomato. *** p < 0.001 Mann-Whitney test.

Figure 2: Timestamping reveals temporal relationships of TAM populations

A) Experimental design of scRNAseq experiment involving Ms4a3^CreERT2 x Rosa^tdTomato mice orthopically injected with KPC-derived PDAC and pulsed with tamoxifen at day 18. CD45⁺ Tomato' and Tomato tumor cells are then analysed at day 35.

B) Dot plots of top differentially expressed genes from monocytes, Mac 1 , Mac 2, Mac 3, RTM1 , RTM2, cDC2, cDC1 , Mreg DC, pDC, and dividing cells.

C) UMAP representation of the scRNAseq data filtered on monocytes, TAM 1 , TAM 2, and TAM 3.

D) UMAP with arrows showing the Velocity analysis of monocyte-to-macrophage differentiation. Arrows were predicted using the velocyto package.

E) UMAP showing clustering and pseudotime along the UMAP of monocytes, TAM 1 , TAM 2, and TAM 3, generated by using the Slingshot package.

F) Heatmap of ordered Pseudotime analysis from slingshot and signature density of Monocytes, TAM 1 , TAM 2, and TAM 3.

Dot plots of top differentially expressed genes from Monocytes, Mac 1 , Mac 2, Mac 3, RTM1 , RTM2, cDC2, cDC1 , Mreg DC, pDC, and dividing cells.

Figure 3: High-throughput dynamic timestamping identifies markers of the TAM populations

A) Experimental design of scRNAseq-LegendScreen experiment involving Ms4a3^CreERT2 x Rosa^tdTomato mice orthopically injected with KPC-derived PDAC and pulsed with tamoxifen at day -21 , -14, and -7 before sacrifice.

B) UMAP of Tomato labeled mononuclear cells from Legendscreen analysis labeled as Monocytes, TAM 1 , TAM 2, and TAM 3.

C) Gene expression projected on UMAP of scRNA sequencing.

D) Protein expression projected on UMAP of scRNA sequencing.

E) Protein signatures of monocytes, TAM 1 , TAM 2, and TAM 3.

F) Ordered Pseudotime analysis from Slingshot analysis.

G) Gating strategy identifying monocytes, TAM 1 , TAM 2, and TAM 3 in conventional flow cytometry.

Figure 4: Dynamics of the TAM differentiation in PDAC

A) Proportion of monocytes, TAM 1 , TAM 2, and TAM 3 in control, tumor adjacent, and PDAC tissue.

B) Gating strategy of monocytes, TAM 1 , TAM 2, and TAM 3 at different timepoints in tumor growth. C) Expression of GFP from KPC- Trem2^cre LSL-EGFP mice orthopically injected with KPC-derived PDAC and analysed at day 35.

Figure 5: TAM subpopulations exhibit distinct functional identities

A) SCENIC analysis predicting transcription factor activity in monocytes, TAM 1 , TAM 2, and TAM 3.

B) Alluvial plot representing the gene ontology of specific programs in TAM1 , TAM 2, and TAM 3.

C) Gene score associated with the gene ontologies Chemotaxis, Response to hypoxia, and response to lipoprotein.

D) RNA magnet analysis predicting the preferential cell to cell interactions.

Figure 6: Spatial transcriptomics and imaging reveal driving factors of macrophage identity

A) Spatial transcriptomic data obtained on a FFPE sample of mouse PDAC tumors 35 days after orthotopic injection KPC (10X Visium). The different clusters are colored and projected on the UMAP representation.

B) Spatial arrangement of monocytes, TAM 1 , TAM 2, and TAM 3 by projecting gene signatures of each population.

C) Abundance of monocytes and TAM populations across different spatial clusters.

Figure 7: Identification of TAM subpopulations in human PDAC

A) Integration of several human PDAC scRNAseq datasets from the literature and UMAP representation of the different monocytes and TAM populations.

B) Differentially expressed genes specific to human TAM populations.

C) Proportion of monocytes and TAM populations in healthy pancreas and PDAC.

D) Label transfer of murine monocytes, TAM 1 , TAM 2, and TAM 3 on human PDAC monocyte and TAM populations.

E) Gene expression in murine and human monocyte and TAM populations.

F) Mean expression of monocyte and human TAM population genes corresponding to cell surface markers used to identify different TAM populations.

G) Gene ontology associated with distinct TAM populations in human PDAC. H) Signatures from the different human TAM populations projected on a FFPE sample of human PDAC. The different clusters are colored and projected on the UMAP representation.

I) Survival curve representing the impact of the Hu.TAM 2 signature on patient survival (upper line = low-group) (data from the TCGA).

EXAMPLES

To refine the understanding of the dynamism of monocyte-to-macrophage transition in tumors, the inventors took advantage of their previously reported monocyte fate mapping model Ms4a3^Cre x Rosa^tdTomat° and its derivative inducible timestamping model Ms4a3^CreERT2 x Rosa^tdTomat° (19). By coupling these with the orthotopic PDAC Pdx1^Cre, !_SI_KrasGi2D/+^ [_ [_Tr_P53Ri 72H/+ (KPC) model , they were able to precise the time of monocyte recruitment and track their fate within PDAC TME. Thus, they have observed that monocytes give rise first to a transient TAM population that they called ‘PreMac’ which then differentiate to more stable TAM populations, with specific location and functions. Finally, they explored clinical data to validate their findings and proposed a refined description of TAM from patients with PDAC.

Material and Methods

Mice

Ms4a3^cre and Ms4a3^creERT2 mice were generated as previously described (79). Rosa^tdTomat° mice were purchased from Jackson Laboratory. C57BL/6 mice were housed at the animal facility of Institute Gustave Roussy. Trem2^cre LSL-EGFP mice were generated at the Shanghai Institute of Immunology by the following gene editing procedure.

Orthotopic injection of KPC tumor cells

The murine pancreatic ductal adenocarcinoma cancer cell line Pdx1^Cre Kras^G12D/+ Trp53^R172H/+ (KPC) was described previously (20). Before using KPC cells for experiments, they were passaged more than six times. Briefly, 50,000 cells were resuspended in 20pl of ice-cold phosphate-buffered saline (PBS) and 25% Matrigel (Sigma) and were orthotopically injected into the pancreas of isoflurane-anaesthetized healthy 7-10 week animals as previously described (27). Immediately following surgery, buprenorphine (10 mg/kg) was subcutaneously administered to mice, which were then monitored for the three subsequent days. All mice were sacrificed within six weeks of surgery. Additionally, KPC cells used for these experiments expressed the luciferase gene; therefore, tumor growth was measured weekly by injecting luciferin (Thermo), waiting for seven minutes and imaging isoflurane-anaesthetized mice with an Ivis Specturm Bioimager (Perkin Elmer).

Preparation of single cell suspensions

Mice were sacrificed and organs were harvested immediately. Tumors were visually identified, separated from other tissues (peritoneal membrane and spleen), and preserved in ice-cold PBS until digestion. Freshly harvested pancreas/tumors were cut into small pieces and digested in 2 ml of Dulbecco’s Modified Eagle Media (DMEM) supplemented with 10% fetal bovine serum and Collagenase type IV (Sigma) at a working concentration of 0.2mg/ml. Following 30 minutes of incubation at 37°C with 5% CO2, tissues were homogenized by passing multiple times through a 5ml syringe with an 18G needle. Cell suspensions were finally passed through a 70-micron filter before downstream analysis.

Flow cytometry FACS and Infinityflow

Cells were incubated with a cocktail of antibodies as listed in the key resource table and according to supplier recommendations. Data on labeling of cell suspensions were acquired on a 5-laser Aurora spectral flow cytometer (Cytek), unmixed on SpectroFlo (Cytek) and analyzed with FlowJo (BD). Cell sorting was performed on an Aria Fusion sorter (BD) after magnetic selection for CD45⁺ cells (Automacs, Miltenyi Biotec).

Infinity Flow was conducted as previously described (22); cells from mice treated with tamoxifen at days 14/15, 21/22, and 28/29 were labeled with a specific anti-CD45 barcoding antibody: CD45-BUV395, CD45-BUV661 , and CD45-APC-Cy7. Barcoded cells were pooled and incubated with a ‘backbone’ flow cytometry panel including antibodies recognizing markers that discriminated monocytes, macrophages, and neutrophils. The antibodies used for the backbone were specific for: Ly6G (1 A8), Siglec- F (E50-2440), CD101 (Moushi101 ), Gr-1 (RB6-8C5), Ly6C (HK1.4), CD11 b (M1/70), I- A/l-E (M5/114.15.2), B220 (53-6.7), CD90.2 (53-2.1 ), NK1.1 (PK136), CD11c (N418), CD45 (30-F11 ) and CD43 (S7). Labeled and barcoded cells were placed into a 96-well U-bottom plate and to each well was added a unique PE-conjugated antibody, as per the LEGENDScreen™ kit (BioLegend). Fully labeled cells were acquired on a 5-laser LSR II (BD) using Diva software (BD). Each subsequent .fcs file with a unique PE-conjugated antibody and the backbone was analyzed using FlowJo (BD). Marker expression across cell types was predicted using the learning algorithm XGBoost implemented in Infinity Flow pipeline as previously described (22). Briefly, input data were subjected to logicle transformation, calculating a z-score for all backbone markers, before implementing the XGBoost R package, which trains a multivariate regression model and imputes the intensity expression of each marker on each cell into one concatenated .fcs file. The generated .fcs file is then used for downstream analysis. All downstream analysis was performed using FlowJo (BD) and the R package Seurat (23). Dimensional reduction (PCA, UMAP) was performed, and clustering using nearest-neighbor analysis by the Seurat package. Statistical analysis was performed by identifying differentially expressed proteins using a Wilcoxon ranked sum test. P values <0.05 were considered statistically significant.

Single cell RNA sequencing

Tomato⁴⁷' cell populations were sorted from Ms4a3^cre x Rosa^tdTomat° and Ms4a3^creERT2 x Rosa^tdTomat° mice using the indicated gating strategies. The cells were processed using the Chromium Single Cell 3’ (v3 Chemistry) platform (10X Genomics). Tomato⁴ and Tomato' cells were sequenced separately using the NovaSeq sequencer (Illumina). Briefly, cells were loaded onto a Chromium Next GEM Chip G, and single cell suspensions in gel beads-in-emulsion (GEMs) were generated using the chromium controller (10X genomics). Reverse transcription (RT) was performed to generate cDNA, which was amplified, cleaned, and fragmented. Finally, libraries were subjected to standard quality control (QC) steps before sequencing.

Analysis of scRNA transcriptomic data

Raw counts were aligned to the GRCm38 mm10 Mus musculus genome from the Genome Reference Consortium using STAR 2.5.3a (24). Counts were log normalized, scaled, and dimensionality reduction was performed (PCA, UMAP). Nearest-neighbor analysis was used for stable clustering. A bimodal likelihood-ratio test was performed, comparing each cluster to all other clusters to identify differentially expressed genes with an adjusted p-value < 0.05 and a logFC > 0.25 (25). To integrate cells from different human PDAC datasets, we applied ten rounds of clustering and correction steps using Harmony with parameters “epsilon. harmony = -Inf, max.iter.harmony =10”. To cluster single cells by their expression profiles, we used the functions FindNeighbors and FindClusters with an appropriate number of Harmony reduction (10-15) and resolution (0.6-0.8) to perform unsupervised graph-based clustering. To compare monocytes and TAMs from human PDAC and murine PDAC, we used SciBet (version 0.1.0) (26), a supervised cell type identifier based on E-test to predict cell identities for mouse PDAC using cells from human PDAC as references. The SelectGene function in SciBet selected the marker genes for each subset. Pseudotime and trajectory analysis were performed using the R packages Slingshot (27), scVelo (28). Transcription factor activity prediction was performed using SCENIC (29). Gene ontology analysis was performed by uploading DEGs from scRNA sequencing analysis to Metascape (30). Interactome analysis was performed using CellChat (31 ) and RNA Magnet (32). Alluvial plots were made using ggplot2 and circus plots were made using the circlize package in R (33).

10X Visium Spatial Transcriptomics

Two tumor tissue sections were collected at day 14 and two at day 35 and were fixed, and frozen using the specifications listed by 10X recommendations. Tissue sections were made by cutting frozen tissues with a cryostat (CM3050S, Leica) at 10pm thickness and mounted on Visium slides (10X Genomics). RNA was extracted following the 10X protocol. QC was performed using the RNA 6000 Pico kit (Agilent). Sequenced reads were aligned in Cell Ranger software (10X Genomics) using the mm 10 genome and underwent standard QC. The output was then analyzed further as a single-cell object using the BayesSpace R package (34). Briefly, spatial clusters were assigned using the mclust function, and spot enhancement was performed. Cell type signatures were selected using the top 10 differentially expressed genes (DEGs) (adjusted p-value < 0.05 and a logFC superior or equal to 0.25) identified by scRNA sequencing scored by a sum of the log counts and then projected onto the spatial transcriptomics data.

Nanostrinq GeoMx analysis

Human PDAC GeoMx (Nanostring) count matrices were downloaded from GSE199102. Next, single-sample gene set enrichment analysis was applied to score programs of Hu. TAM a-e, HALLMARK_HYPOXIA and

GOBP_GLYCEROLIPID_METABOLIC_PROCESS for each DSP sample within each ROI, using gsva from the GSVA package (version 1.34.0) (35) with parameters “method = ssgsea, kcdf = Gaussian”. Correlation coefficients were calculated using the Pearson correlation between Hu. TAM 1-5 program scores within ROI immune segments (CD45) and hypoxia and lipid metabolism program scores within ROI tumor segments (PanCK). CODEX Multiplex imaging and analysis

5pm slices of embedded murine PDAC (day 35 post-orthotopic injection) were prepared and used for CODEX labeling following manufacturer instructions. Briefly, sections were retrieved from the freezer, rehydrated, and photobleached as described in (36). Following photobleaching, sections were blocked and incubated with a 21-plex CODEX antibody panel overnight at 4°C. After washing, samples were fixed with ice-cold methanol, washed again with PBS, and fixed for 20min with BS3 fixative (Thermo Fisher). Samples were subseguently washed with PBS and stored at 4°C for a maximum of one week before imaging. Sections were eguilibrated at room temperature before imaging. Antibody detection was performed in a multicycle experiment, following manufacturer instructions. Images were acguired with a Zeiss Axio Observer widefield fluorescence microscope using a 40x objective (NA 0.85) and z-spacing of 1 .5pm. The 405, 488, 568, and 647 nm channels were used for acguisition. Images were exported using the CODEX Instrument Manager (Akoya Biosciences) and processed with CODEX Processor v1.7 (Akoya Biosciences). Processing steps included background subtraction, deconvolution, shading correction, stitching, and cell segmentation. DAPI counterstain was used for object detection, whereas sodium Potassium ATPase antibody labeling was used to mark membranes for delineating cell shape. Our defined gating strategy identified imaged cells as distinct cell types (monocytes, PreTAMs, TAM 2 and TAM 3). Cell types were projected onto the tissue section, and distance from hypoxic/necrotic tissue was calculated for each cell type.

Immunofluorescence imaging

Murine PDAC samples were freshly collected for immunofluorescence imaging. Briefly, tissue was fixed in 4% paraformaldehyde (PFA) overnight at 4°C. After, fixed tissue was incubated in 20% sucrose overnight at 4°C and frozen in Tissue-Tek Optimum Cutting Temperature compound for cryo-sectioning. Samples were then labeled with the indicated antibodies. FFPE blocks of human PDAC (Institut Gustave Roussy, Villejuif) and of human tonsil used as control (France tissue bank) were cut into 4 mm thick sections. Antigen retrieval was carried out on a PT-link (Dako) using the EnVision FLEX Target Retrieval Solutions at High pH (Dako, K8004). Endogenous peroxidase activity and non-specific Fc receptor binding were blocked with H2O23% (Gifrer, 10603051 ) and Antibody Diluent I Block (Akoya Biosciences, ARD1001 EA) respectively. The CD73/CD68/TREM2/CCR2 4-plex labeling was performed manually using the OPAL tyramide system amplification (TSA) (Akoya Biosciences). Antibodies and TSA used are listed in the key resource table. Nuclei were stained with DAPI Solution (Thermofisher, 62248) at 2pg/ml for 5 minutes. After mounting with Prolong™ Gold Antifade Mountant (Thermofisher, P36934), the slides were scanned at 20X magnification using a Vectra Polaris device.

Quantification and analysis of the immunofluorescence labelling

Tumoral area and other tissues areas (excluded from analysis) were defined from H&E images with the help of a pathologist. Necrotic, folded, and blurred areas were excluded from image analyses. Density of positive cells/mm2: CD68+CCR2+CD73-TREM2-, CD68+CD73+CCR2-TREM2- and CD68+TREM2+CD73-CCR2- cells in tumor areas and proximity between cell populations were quantified and plotted with Haloid software (Indica labs) using the fitting counting algorithms.

Survival data

The Cox proportional hazards model implemented in the R package survival was used to perform survival analyses. The R function ggsurvplot was used to plot Kaplan-Meier survival curves using data from 178 patients with primary pancreatic adenocarcinomas from TCGA, which were used to evaluate the impact of Hu.TAM 1-5 signatures on overall survival (OS). Differences in age and gender were corrected for in the Cox model.

Survival data

The Cox proportional hazards model implemented in the R package survival was used to perform survival analyses. The R function ggsurvplot was used to plot Kaplan-Meier survival curves using data from 178 patients with primary pancreatic adenocarcinomas from TCGA, which were used to evaluate the impact of Hu.TAM 1-5 signatures on overall survival (OS). Differences in age and gender were corrected for in the Cox model.

Statistical analysis

All statistical analyses were performed with Prism 5.0 (GraphPad Software). All p values are two-tailed. Other statistical analysis was performed using R as described in detail for each dataset, depending on the approach. Results

Macrophage ontogeny in pancreatic ductal adenocarcinoma

In order to quantitively assess the relative contribution of embryonic precursors and circulating monocytes to the pool of TAM in PDAC, the granulocyte-monocyte progenitor cell (GMP) fate-mapping Ms4a3^Cre x Rosa^tdTomat° mouse model (19) was used, allowing notably the exhaustive tagging of all GMP-derived circulating monocytes. These animals were challenged with KPC cells and the tumors formed in the pancreas were analysed after 35 days. A scRNA sequencing experiment was designed using Chromium technology (1 OX Genomics) in which tumor CD45⁺ GMP-derived Tomato and non GMP- derived Tomato' were FACS-sorted and sequenced. After QC filtering, cells were clustered and projected on a uniform-manifold approximation map (UMAP). Clusters were further annotated as the different immune cell populations based on differentially expressed gene (DEG) analysis (Figure 1A). As expected, GMP-derived neutrophils were all Tomato while T cells, B cells and plasmablasts were exclusively Tomato' (19). Mononuclear phagocytes on the other hand had a mixed Tomato labelling indicating cells with distinct ontogenies. The analysis was then focused by clustering only mononuclear phagocytes. By this approach, the cells were identified as monocytes (expression of Ly6c2, Hp, S100a4 and Plac8), macrophage (expression of Pf4, C1qc, Arg1, and C1qa) or dendritic cells (expression of CleclOa, H2-Aa, H2-Dmb2, and Ckb) (Figure 1 B and 1 C). The expression of Tomato was then looked further, identifying that all monocytes, majority of macrophages, and a minority of dendritic cells were labelled as Tomato¹ (not shown). Of note, transcriptional differences were observed in Tomato' and Tomato¹ macrophage, with notably Tomato' cells expressing genes specific from resident tissue macrophage such as Folr2 or Timd4. On the other hand, Tomato¹ macrophage displayed a more inflammatory phenotype expressing genes like 111b or 116 (Figure 1 D). The functional comparison of monocyte- and embryonic-derived macrophage has been explored previously in PDAC (13). Accordingly, using the Ms4a3^Cre x Rosa^tdTomat° mice it was possible to recapitulate the association of embryonic macrophages with extracellular matrix remodeling activities and monocyte-derived macrophage with inflammation. Finally, gene expression data were validated by using flow cytometry, confirming that the majority of macrophages were expressing the Tomato protein and therefore were of monocytic origin in PDAC (Figure 1 E). Time-dependent heterogeneity of TAM in murine PDAC

While these observations confirmed previous findings, it was aimed at exploring monocyte-to-macrophage transition and notably assess the dynamics of such a process in the specific context of PDAC. Thus, it was taken advantage of the inducible Ms4a3^CreERT2 x Rosa^tdTomat° time-stamping model which allows for temporary labelling of granulocyte monocyte progenitors (GMP) upon injection with tamoxifen, resulting in a transient labelling of circulating monocytes. In a similar approach that was adopted for the Ms4a3^Cre x Rosa^tdTomat° mice (cf. Figure 1 ), Ms4a3^CreERT2 x Rosa^tdTomat° mice were challenged with a KPC-derived PDAC for 35 days. Tamoxifen was administered at day 17 & 18 to induce GMP labelling at a midpoint of tumor growth. Of note, with these settings, short-lived circulating Ly6C^hi monocytes were tagged for 6 days and were then replaced in the blood by newly generated Tomato' cells deriving from non labelled GMP progenitors after washout of tamoxifen (79). Therefore, Tomato cells observed at the time of tumor collection (day 35) were macrophage deriving from Tomato monocytes recruited within the specific timeframe of tamoxifen induction. Tumors were analysed by scRNA sequencing (figure 2A), together with tumor-free pancreas harvested from similarly tamoxifen-induced Ms4a3^CreERT2 x Rosa^tdTomat° control mice.

It was focused on mononuclear phagocytes, clustered and projected cells from healthy pancreas and PDAC on the same UMAP space. Cells were annotated by identifying DEG (figure 2B). Putting aside dividing cells in which the cell division program masks transcriptomic features, it was focused on only populations expanding in cancer, which were monocytes and macrophage populations 1-3 (TAM 1-3) excluding further resident tissue macrophage, dendritic cells, and dividing cells from the downstream analysis; a hypothesis corroborated by the specific expression of markers such as Arg1, Spp1, and Mmp12 (37-39) (Figure 2C).

Using the splicing analytical tool velocity (40), it was found that monocytes were predicted to first give rise to TAM 1 which then give rise to TAM 2 or TAM 3 (Figure 2D). This temporal assertion was further explored by using the cell lineage and pseudotime inference tool slingshot (27), also suggesting that TAM 1 derived from monocytes and then subsequentially differentiate into TAM 2 and TAM 3 (Figure 2E). Therefore, TAM 1 appears as a discrete but transient transcriptional state which could represent an intermediate between monocytes and TAM, each population harboring its own signature (Figure 2F). Therefore, by coupling scRNA sequencing to an accurate monocyte time-stamping model, the present results suggest the existence of a transient cell state in the monocyte to macrophage transition.

High-throughput screen of surface markers for monocyte and TAM populations in murine PDAC

Transcriptomics give insight into the identity and putative functions of macrophage populations. However, it was aimed at validating these findings at the protein level to offer a reliable and more convenient way to analyse these TAM subpopulations. PDAC- bearing Ms4a3^CreERT2 x Rosa^tdTomato mice were tamoxifen-induced at different timepoints to cover the dynamics of monocyte recruitment over tumor development. All mice were analysed at day 35 but were induced at early (day 14-15), intermediate (day 21-22) or late (day 28-29) timepoints. To circumvent any potential batch effect, cells from each group were barcoded with a CD45 antibody conjugated with different fluorophores before being pooled and analysed together. The infinityflow pipeline (22, 41) was then used to screen surface marker expression and to identify the best ones to accurately define monocytes and TAM 1-3. Briefly, all pooled and barcoded cells were stained with a backbone flow cytometry panel to identify major immune populations including monocytes and macrophage. Cells were then split into 270 wells containing unique PE- complexed antibodies. Cells from each well were then analysed by flow cytometry and individual files were subjected to XGboost to predict the combined expression of these 270 markers (Figure 3A) (22, 41). It was focused only on Tomato monocytes and macrophage, generated a UMAP and identified clusters (Figure 3B). 4 clusters were retrieved, that were annotated as monocytes and TAM 1-3 populations based on the similarity between gene and protein expression (Figure 3C & 3D). Therefore, monocytes were defined as Ly6C⁺, TAM 1 as CCR2⁺ CD64⁺, TAM 2 as CD73⁺ and TAM 3 as CD36⁺ CD11 c⁺ cells (Figure 3E). The trajectory of monocytes to TAM 2 and TAM 3 through a transient TAM 1 state was also confirmed by applying the slingshot analysis to these surface marker data (Figure 3F). These results were in accordance with the ones generated thanks to gene expression and confirmed at the protein level the time relationship between monocytes and the different TAM subpopulations.

Using this approach, a gating strategy was setup to reliably define monocytes, TAM 1 , TAM 2, and TAM 3 (Figure 3G) Of note, this strategy has been established by compiling information from hundreds of surface markers but is based ultimately on the most specific markers. The analysis panel is therefore limited and should be advantageously applied on conventional cytometers with no need of the extended panels allowed by mass or spectral cytometry. Furthermore, because the TAM 1 population constitute a transient precursor of more stable TAM 2 and TAM 3 macrophage populations, the nomenclature “PreMac” was proposed for these cells.

Phenotypic characterization of macrophage populations in murine PDAC

Using the gating strategy established thanks to the infinityflow approach, the presence of monocyte and macrophage populations were looked at in control, adjacent tumor, and tumor tissue along tumor development. In agreement with previous analysis, monocytes, PreMac, TAM 2, and TAM 3 expanded in tumor tissue (Figure 4A). To measure the abundance of macrophage populations overtime monocytes, PreMac, TAM 2, and TAM 3 were looked at weekly timepoints. It was found that PreMac expanded early in tumor development and were reduced at week 5 (Figure 4B). Interestingly, TAM 2 and TAM 3 were more abundant at later timepoints, furthermore suggesting a sequential differentiation of PreMac to TAM 2 or TAM 3. Additionally, Trem2^Cre x LSL^EGFP mice were used and it was observed a specific labelling of the TAM 3 population (Figure 4C), arguing for the stability of this population once Trem2 expression is acquired and an absence of conversion from TAM 3 to TAM 2 or PreMac population.

TAM subpopulations exhibit distinct functional identities

This analysis was extended by using the SCENIC analytical tool (29) which revealed distinct regulons in monocytes, PreMac and TAM 2-3 with notably the preferential use of Irf1- and /f2-related genes in monocytes, Fos/2-related in PreMac, Statl- and Irf8- related in TAM 2 and Bhlhe47-related in TAM 3 (Figure 5A). A gene ontology analysis was then performed using MetaScape (30). Among others, chemotaxis was significantly associated to PreMac identity, hypoxia to TAM 2 and the response to lipoproteins to TAM 3 (Figure 5B). Considering the previous observations regarding the temporal relationship, PreMac could acquire the chemotaxis-associated program when differentiating from monocytes, which is then lost in TAM 2 and TAM 3. By contrast, the hypoxia-associated identity of TAM 2 and response to lipoproteins-associated from TAM 3 could result from a gradual specialization (Figure 5C).

Finally, the scRNA-seq data were used to predict interactions of the different subpopulations with the other components of the tumor microenvironment by using CellChat and RNA magnet (31, 32). Distinct incoming and outgoing interaction strengths were observed for monocytes, PreMac, TAM 2, and TAM 3 and revealed preferential interactions with specific cell populations notably endothelial cells for PreMac, pericytes for TAM 2 and cancer cells for TAM 3 (Figure 5D). Together these results suggest a spatial relationship with the phenotype of macrophage identified in PDAC.

Spatial transcriptomics and imaging reveal driving factors of macrophage identity After having defined the different TAM populations in PDAC according to the time dimension, it was focused on the spatial organisation of these populations to better define their niches and validate their different interactions and functions. A spatial transcriptomics approach (Visium, 10X Genomics) was used to analyse murine PDAC at day 42. Using BeyesSpace clustering (34), 5 distinct clusters were identified, corresponding to five different functional areas within tumors (Figure 6A). Namely, cluster 1 was associated with hypoxia, cluster 2 and 4 with cellular respiration, cluster 4 with inflammatory response, cluster 5 with cell cycle. Using BeyesSpace spot deconvolution, the presence of monocytes, PreMac, TAM 2 and TAM 3 were revealed in the tumor (Figure 6B) and their abundance was quantified in its functionally distinct areas (Figure 6C). Monocytes and PreMac were distributed evenly throughout the tissue, arguing for their relative immaturity and their transient nature while TAM 2 were more restricted to the cluster 1 and TAM 3 to the cluster 2. Therefore, these results suggest that more differentiated TAM 2 and TAM 3 populations inhabit defined niches within the tumor, arguing for their specific functions within this environment.

Identification of human TAM subpopulations in PDAC patients

Finally, after having exploited high-throughput -omics approaches combined with fatemapping and time-stamping murine models, it was time to explore macrophage populations from PDAC patients to adopt a more translational approach. To establish a robust dataset, it was taken advantage of publicly available human scRNA-seq data (42- 45). Mononuclear phagocytes were extracted and these datasets were integrated to identify one population of monocytes and 8 populations of human TAM (Hu.TAM) (Figure 7 A), with distinct DEG defining their identities (Figure 7B). Hu.TAM 1-6 were notably enriched in the tumor area in comparison to the adjacent healthy tissue (Figure 7C). It was then performed a label transfer pairing analysis (46) and similarities between monocytes, murine PreMac and Hu.TAM 1 , murine TAM 2 and Hu. TAM 2 and Hu. TAM 3, and murine TAM 3 and Hu. TAM 4 and Hu. TAM 5 were revealed (Figure 7D). Additionally, projecting DEG across monocytes and macrophage in mouse and human reveal similar gene expression and clustering (Figure 7E). Of note, human Hu. TAM 6 could not be identified by label transfer in murine PDAC suggesting a species specificity. Comparing the gene expression to protein markers from the murine analysis, an association was observed between the distinct human macrophage populations with markers which could be used for identifying populations in human samples (Figure 7F).

Regarding the functions, pathways between paired populations were also conserved (Figure 7G).

Based on this transcriptional homology, we annotated regions of human PDAC tissues and identified Hu.TAM 1 (CCR2⁺ CD68⁺), Hu.TAM 2/3 (CD68⁺ CD73⁺), and Hu.TAM 4/5 (CD68⁺ TREM2⁺). In tumor sections, these populations were localized within different areas, with notably Hu.TAM 2/3 enriched in the necrotic tumor (Fig. 7H).

Finally, to gain insights into the impact of each population on patient prognosis, the 30 most DEG from each TAM population were screened in the Cancer genome annotation (TCGA) database to monitor their impact on patient survival (the upper lines corresponding to the group-low), and TAM2 was the only population significantly associated with a worst overall patient survival (Figure 71).

BIBLIOGRAPHY

1. Y. Iwai et al., Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade. Proc Natl Acad Sci U S A 99, 12293-12297 (2002).

2. D. R. Leach, M. F. Krummel, J. P. Allison, Enhancement of antitumor immunity by CTLA- 4 blockade. Science 271 , 1734-1736 (1996).

3. C. Robert et al., Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med 372, 320-330 (2015).

4. J. Bian, K. Almhanna, Pancreatic cancer and immune checkpoint inhibitors-still a long way to go. Transl Gastroenterol Hepatol 6, 6 (2021 ).

5. M. D. Park, A. Silvin, F. Ginhoux, M. Merad, Macrophages in health and disease. Cell 185, 4259-4279 (2022).

6. L. Cassetta, J. W. Pollard, Targeting macrophages: therapeutic approaches in cancer. Nat. Rev. Drug Discov. 17, 887-904 (2018).

7. A. Mantovani, F. Marchesi, A. Malesci, L. Laghi, P. Allavena, Tumour-associated macrophages as treatment targets in oncology. Nat Rev Clin Oncol 14, 399-416 (2017).

8. K. Mulder et al., Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease. Immunity 54, 1883-1900 e1885 (2021 ).

9. S. Cheng et al., A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792-809 e723 (2021 ).

10. J. C. Rieckmann et al., Social network architecture of human immune cells unveiled by quantitative proteomics. Nat. Immunol. 18, 583-593 (2017).

11. F. Ginhoux, M. Guilliams, Tissue-Resident Macrophage Ontogeny and Homeostasis. Immunity 44, 439-449 (2016).

12. R. A. Franklin et al., The cellular and molecular origin of tumor-associated macrophages. Science 344, 921-925 (2014).

13. Y. Zhu et al., Tissue-Resident Macrophages in Pancreatic Ductal Adenocarcinoma Originate from Embryonic Hematopoiesis and Promote Tumor Progression. Immunity 47, 323- 338 e326 (2017).

14. M. Casanova-Acebes et al., Tissue-resident macrophages provide a pro-tumorigenic niche to early NSCLC cells. Nature 595, 578-584 (2021 ).

15. R. Clynes, Y. Takechi, Y. Moroi, A. Houghton, J. V. Ravetch, Fc receptors are required in passive and active immunity to melanoma. Proc Natl Acad Sci U S A 95, 652-656 (1998).

16. E. A. Wolff, G. J. Schreiber, W. L. Cosand, H. V. Raff, Monoclonal antibody homodimers: enhanced antitumor activity in nude mice. Cancer Res. 53, 2560-2565 (1993).

17. H. Gazzano-Santoro, P. Ralph, T. C. Ryskamp.A. B. Chen, V. R. Mukku, A non-radioactive complement-dependent cytotoxicity assay for anti-CD20 monoclonal antibody. J. Immunol. Methods 202, 163-171 (1997). 18. E. E. Idusogie et al., Mapping of the C1q binding site on rituxan, a chimeric antibody with a human lgG1 Fc. J. Immunol. 164, 4178-4184 (2000).

19. Z. Liu et al., Fate Mapping via Ms4a3-Expression History Traces Monocyte-Derived Cells. Cell 178, 1509-1525 e1519 (2019).

20. H. L. Penny et al., Targeting Glycolysis in Macrophages Confers Protection Against Pancreatic Ductal Adenocarcinoma. Int J Mol Sci 22, (2021).

21. N. M. Aiello, A. D. Rhim, B. Z. Stanger, Orthotopic Injection of Pancreatic Cancer Cells. Cold Spring Harb Protoc 2016, pdb prot078360 (2016).

22. C. A. Dutertre et al., Single-Cell Analysis of Human Mononuclear Phagocytes Reveals Subset-Defining Markers and Identifies Circulating Inflammatory Dendritic Cells. Immunity 51 , 573-589 e578 (2019).

23. Y. Hao et al., Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587 e3529 (2021).

24. A. Dobin et al., STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013).

25. A. McDavid et al., Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics 29, 461-467 (2013).

26. C. Li et al., SciBet as a portable and fast single cell type identifier. Nat Commun 11 , 1818 (2020).

27. K. Street et al., Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

28. V. Bergen, M. Lange, S. Peidli, F. A. Wolf, F. J. Theis, Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408-1414 (2020).

29. S. Aibar et al., SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083-1086 (2017).

30. Y. Zhou et al., Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10, 1523 (2019).

31. S. Jin et al., Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, 1088 (2021).

32. C. Baccin et al., Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat. Cell Biol. 22, 38-48 (2020).

33. Z. Gu, L. Gu, R. Eils, M. Schlesner, B. Brors, circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811-2812 (2014).

34. E. Zhao et al., Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375-1384 (2021).

35. S. Hanzelmann, R. Castelo, J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013). 36. Z. Du et al., Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging. Nat Protoc 14, 2900-2930 (2019).

37. R. E. Menjivar et al., Arginase 1 is a key driver of immune suppression in pancreatic cancer. Elife 12, (2023).

38. L. Zhang et al., Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer. Cell 181 , 442-459 e429 (2020).

39. E. Kerkela et al., Metalloelastase (MMP-12) expression by tumour cells in squamous cell carcinoma of the vulva correlates with invasiveness, while that by macrophages predicts better outcome. J. Pathol. 198, 258-269 (2002).

40. G. La Manno et al., RNA velocity of single cells. Nature 560, 494-498 (2018).

41. E. Becht et al., High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning. Sci Adv 7, eabg0505 (2021).

42. W. L. Hwang et al., Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment. Nat. Genet. 54, 1178- 1191 (2022).

43. K. Chen et al., Single cell RNA-seq reveals the CCL5/SDC1 receptor-ligand interaction between T cells and tumor cells in pancreatic cancer. Cancer Lett. 545, 215834 (2022).

44. N. G. Steele et al., Multimodal Mapping of the Tumor and Peripheral Blood Immune Landscape in Human Pancreatic Cancer. Nat Cancer 1 , 1097-1112 (2020).

45. J. Peng et al., Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res. 29, 725-738 (2019).

46. T. Stuart et al., Comprehensive Integration of Single-Cell Data. Cell 177, 1888-1902 e1821 (2019).

Claims

1. An isolated population of tumor-associated macrophage cells that are CD45⁺ Ly6G’ CD11 b⁺ Siglec-FCCR2⁺ MHCIP CD64⁺

2. An isolated population of tumor-associated macrophage cells that are CD45⁺ Ly6G’ CD11 b⁺ Siglec-FCD73⁺ MHCIP CD11c’

3. An isolated population of tumor-associated macrophage cells that are CD45⁺ Ly6G’ CD11 b⁺ Siglec-FCD73- MHCIP CD11c⁺

4. A method to prognose the evolution a solid cancer in a subject in need thereof, said method comprising the step of analysing the localisation and the frequency of the population of tumor-associated macrophage cells as defined in claim 1, 2 or 3.

5. The method of claim 4, further comprising the step of comparing the said frequency to a reference value.

6. The method of claim 5, wherein the solid cancer has a bad prognostic if the frequency of the population as defined in claim 1 is superior to said reference value.

7. The method of claim 5, wherein the solid cancer has a bad prognostic if the frequency of the population as defined in claim 2 is superior to said reference value.

8. The method of claim 5, wherein the solid cancer has a bad prognostic if the frequency of the population as defined in claim 3 is superior to said reference value.

9. The method of any of claims 4-8, comprising the step of analyzing the spatial repartition of said population of tumor-associated macrophages, preferably by immunohistochemistry or by transcriptomics, yet preferably spatial transcriptomics.

10. A compound that inhibits the differentiation between the tumor-associated macrophage population as defined in claim 1 into the tumor-associated macrophage population as defined in claim 2, for use for treating a solid cancer in a subject in need thereof.

11 . The compound for use of claim 10, wherein said compound is able to enhance, interfere with or block the expression of the FosL2 transcription factor or the expression of the Stat 1 transcription factor.

12. A compound that inhibits the transition between the tumor-associated macrophage population as defined in claim 2 into the tumor-associated macrophage population as defined in claim 3, for use for treating a solid cancer in a subject in need thereof.

13. The compound for use of claim 12, wherein said compound is able to enhance, interfere with or block the expression of any of the following transcription factors: Ddit3; Creb5; Zmizl; Junb; Ets2; Statl; Stat3; Fos; Egr1; Irf8; or Maf.

14. A compound that blocks the tumor-associated macrophage population in a state as defined in claim 3, for use for treating a solid cancer in a subject in need thereof.

15. The compound for use of claim 14, wherein said compound is able to enhance, interfere with or block the expression of any of the following transcription factors: Atf3; Bhlhe41; Jund; or Jun.

16. A compound that inhibits the expression of the gene Hifla in tumor-associated macrophage populations, in particular in the macrophage population of claim 2, for use for treating a solid cancer in a subject in need thereof.

17. The compounds for use according to claims 10-16, wherein said compounds are anti-sens oligonucleotides or chemical drugs.

18. The methods of claims 4-9 or the compounds for use according to claims 10-17, wherein said cancer is chosen in the group consisting of: squamous cell carcinoma, small-cell lung cancer, non-small cell lung cancer, glioma, gastrointestinal cancer, renal cancer, ovarian cancer, liver cancer, colorectal cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, neuroblastoma, brain cancer, central nervous system cancer, pancreatic cancer, glioblastoma multiforme, cervical cancer, stomach cancer, bladder cancer, malignant hepatoma, breast cancer, colon carcinoma, head and neck cancer, gastric cancer, germ cell tumor, pediatric sarcoma, rhabdomyosarcoma, Ewing’s sarcoma, osteosarcoma, soft tissue sarcoma, sinonasal NK/T-cell lymphoma, myeloma, melanoma, multiple myeloma. In particular, these solid tumors can be specifically lung cancer, malignant mesothelioma, bladder cancer, kidney cancer, testicular cancer, breast cancer, cancer of the upper aero-digestive tract, liver cancer, pancreas cancer, stomach cancer; colon cancer or ovarian cancer.

19. The methods of claims 4-9 or the compounds for use according to claims 10-17, wherein said cancer is pancreatic ductal adenocarcinoma.