EA046327B1 - MODULATION OF STIMULATING AND NON-STIMULATING MYELOID CELLS - Google Patents

Description

Cross reference to related applications

This application claims benefit from U.S. Provisional Patent Application No. 62/056569, filed September 28, 2014, and U.S. Provisional Patent Application No. 62/129883, filed March 8, 2015, each of which is hereby incorporated herein into in its entirety by reference in all respects.

Application for Federal Research or Development Funding

This invention was made with government support under grants U01 CA141451 and U54 CA163123 awarded by the National Institutes of Health. The government has certain rights to this invention.

State of the art

The immune system plays an important role in preventing tumor growth. A complex microenvironment can be created within the lesion and, despite the recruitment of T cells, there is often a lack of effective control of cell proliferation. Understanding the balance between tumor removal and tumor avoidance may come from understanding the different roles myeloid cells play in the tumor microenvironment.

Myeloid populations in the tumor microenvironment largely include monocytes and neutrophils (sometimes more broadly grouped as myeloid suppressor cells), macrophages, and dendritic cells. Although intratumoral myeloid populations in general have long been considered nonstimulatory or suppressive, it has recently been recognized that not all tumor-infiltrating myeloid cells are created equal.

In normal tissues, many of these myeloid cells are essential for both innate and adaptive immunity and, in particular, for wound healing. However, significant excess of macrophages and dysfunctional or distorted populations of these and other cell types are commonly described in the setting of cancer. If the overall population is considered to be defined by individual markers such as CD68 or CD163, macrophage infiltration correlates in subjects with poorer outcomes across multiple tumor types ((de Visser, Cancer Immunol Immunother 2008;57:1531-9); (Hanada et al., Int J Urol 2000; 7:263-9); (Yao et al., Clin Cancer Res, 520, 2001; 7:4021-6); (Ruffell et al., PNAS, 523 2012; 109: 2796-801)). But identifying the phenotypic and functional subpopulations of macrophages from the tumor microenvironment is complicated by the similarities between macrophages and dendritic cells, and is a challenge in tumor biology. To solve the problem, a morphological criterion is often used; one approach when attempting to differentiate dendritic cells from macrophages is based on the more spiky or dendritic morphology of the former and the smoother or convex morphology of the latter (Bell et al., J Exp Med 555, 1999; 190:1417-26). Other groups of researchers are trying to differentiate based on genetic and cell surface markers.

Within tumors, there is diversity in the antigen-presenting compartment and T cells can differentiate into antigen-presenting cell (APC) characteristics. Due to the fact that T cells represent the main source of immunity against tumors, it will be important to gain insight into the precise characteristics of recognized APCs. Myeloid cells are distinguished among cells by their ability to present tumor antigens to T cells and thereby maintain the latter in an activated state. Antigen presentation occurs within tumors themselves and likely influences the functions of tumor cytotoxic T lymphocytes (CTLs). Activation of T cells by antigen presenting cells (APCs) is an important component of antigen-specific immune responses and tumor cell killing. Because these myeloid populations represent the primary T cell and antigen presenting cell interacting partners for incoming tumor-reactive cytotoxic T lymphocytes, gaining insight into their characteristics can shape treatment directions.

All patents, patent applications, publications, documents and articles referenced herein are incorporated herein in their entirety by reference.

The essence of the invention

Described herein is a method of killing, blocking, or depleting non-stimulating myeloid cells present in cancer tissue of a subject, comprising contacting the non-stimulating myeloid cells with an antibody or antigen-binding fragment thereof that binds to the non-stimulating myeloid cells and is present in an amount effective to kill, block or depletion of non-stimulating myeloid cells in the subject's cancer tissue. In some aspects of the invention, non-stimulatory myeloid cells are found in a population of immune cells containing stimulatory myeloid cells and non-stimulatory myeloid cells. In some aspects of the invention, killing, blocking, or depleting non-stimulating myeloid cells cures the subject by reducing the number or volume of cancer tissue. In some aspects of the invention, contacting increases the ratio of stimulatory myeloid cells to non-stimulatory myeloid cells in the immune cell population. In some aspects of the invention, bringing into contact reduces the ratio of non-stimulating mie

- 1,046,327 loid cells to stimulating myeloid cells in the immune cell population. In some aspects of the invention, contact enhances the immune response in the subject. In some aspects of the invention, contacting does not substantially kill, block, or deplete myeloid cells present outside the cancer tissue and/or stimulate myeloid cells present within the cancer tissue.

Also described herein is a method of treating cancer in a subject, comprising administering an antibody or an antigen-binding fragment thereof that binds to nonstimulatory myeloid cells present in the cancer and is present in an amount effective to kill, block, or deplete nonstimulatory myeloid cells. In some aspects of the invention, non-stimulatory myeloid cells are found in a population of immune cells containing stimulatory myeloid cells and non-stimulatory myeloid cells. In some aspects of the invention, killing, blocking, or depleting non-stimulating myeloid cells cures the subject by reducing the number or volume of cancer tissue. In some aspects of the invention, contacting increases the ratio of stimulatory myeloid cells to non-stimulatory myeloid cells in the immune cell population. In some aspects of the invention, contacting reduces the ratio of non-stimulatory myeloid cells to stimulatory myeloid cells in the immune cell population. In some aspects of the invention, contacting does not substantially kill, block, or deplete myeloid cells present outside the cancer and/or stimulate myeloid cells present within the cancer. In some aspects of the invention, a subject's cancer is treated by inducing or enhancing an immune response to the cancer.

In some aspects of the invention, the antibody or antigen-binding fragment thereof binds to the extracellular domain of a target protein expressed on non-stimulating myeloid cells selected from the group consisting of TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37 , MERTK and TMEM119, wherein the non-stimulatory myeloid cells are CD45+, HLA-DR+, CD11c+, CD14+ and BDCA3 ^{- cells} , wherein the antibody or antigen-binding fragment thereof destroys, blocks or depletes non-stimulatory myeloid cells through antibody-dependent cell-mediated cytotoxicity (ADCC) or complement-dependent cytotoxicity (CDC) to a level that is less than the level of non-stimulatory myeloid cells present in the cancer tissue prior to bringing the non-stimulatory myeloid cells into contact with an antibody or antigen-binding fragment thereof, wherein the non-stimulatory myeloid cells are present in a population of immune cells containing stimulatory myeloid cells that represent are CD45+, HLA-DR+, ^CD14- , CD11c+, ^BDCA1- and BDCA3+ cells, and non-stimulating myeloid cells, wherein contact or administration does not substantially destroy, block or deplete myeloid cells present outside the cancer tissue, and/ or stimulating myeloid cells present in cancer tissue, while destroying, blocking or depleting non-stimulating myeloid cells cures cancer by enhancing the immune response to cancer tissue.

In some aspects of the invention, the non-stimulating myeloid cells are at least one of: tumor-associated macrophages; tumor-associated dendritic cells; CD45+, HLA-DR+, CD11c+, CD14+, and BDCA3 ^- cells; CD45+, HLA-DR+, and CD14+; CD45+, HLADR+, CD14+, BDCA3- ^, CD11b+, and CD11c+; CD45+, HLA-DR+, CD14 ^- , CD11c+ and BDCA1+; but not BDCA3 ⁺ cells, for example, as determined by flow cytometry analysis or equivalent analysis. In some aspects of the invention, the non-stimulating myeloid cells are positive for at least one of: C5AR1, LYVE1, ABCC3, MRC1, SIGLEC1, STAB1, C1QB, C1QA, TMEM37, MERTK, C1QC, TMEM119, MS4A7, APOE, CYP4F18, TREM2, TLR7, and LILRB4; and/or are negative for one of: KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, BDCA3 and XCR1, for example, as determined by polymerase chain reaction (PCR), gene chip, flow cytometry , RNA sequencing or equivalent analysis.

In some aspects of the invention, an antibody or antigen-binding fragment thereof has at least one of antibody-dependent cell-mediated cytotoxic (ADCC) activity, complement-dependent cytotoxic (CDC) activity, and antibody-mediated phagocytosis activity. In some aspects of the invention, the antibody is at least one of a monoclonal antibody, an antagonistic antibody, a polyclonal antibody, an IgGi antibody, an IgG3 antibody, an afucosylated antibody, a bispecific antibody, a human antibody, a humanized antibody, a chimeric antibody, a full-length antibody, and an antigen-binding fragment. In some aspects of the invention, the antibody or antigen binding fragment thereof is not an IgG ₂ antibody, or the antibody or antigen binding fragment thereof is not an IgG4 antibody. In some aspects of the invention, the antibody or antigen-binding fragment thereof is conjugated. In some aspects of the invention, the antibody or antigen-binding fragment thereof is conjugated to at least one therapeutic agent selected from the group consisting of a radionuclide, a cytotoxin, a chemotherapeutic agent, a drug, a prodrug,

- 2 046327 toxin, enzyme, immunomodulator, anti-angiogenic agent, pro-apoptotic agent, cytokine, hormone, oligonucleotide, antisense molecule, siRNA, second antibody and second antibody fragment. In some aspects of the invention, the antibody or antigen binding fragment thereof selectively binds to at least one of TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK, and TMEM119. In some aspects of the invention, the antibody or antigen binding fragment thereof does not selectively bind LILRB4.

In some aspects of the invention, contact or administration induces at least one of: death of non-stimulating myeloid cells, apoptosis of non-stimulating myeloid cells, lysis of non-stimulating myeloid cells, phagocytosis of non-stimulating myeloid cells, and growth arrest of non-stimulating myeloid cells.

In some aspects of the invention, stimulatory myeloid cells include cells that are at least one of: CD45+, HLA-DR+, ^CD14- , CD11c+, BDCA1- ^and BDCA3+; CD45+, HLA-DR+, CD14+, CD11c+ and BDCA3+; CD45+, HLA-DR+ and BDCA3+; CD45+, HLA-DR+, CD14 ^- and BDCA3+, and CD45+, HLA-DR+, CD11c+ and BDCA3+, for example, as can be determined by flow cytometry analysis or equivalent analysis. In some aspects of the invention, the stimulating myeloid cells are negative for at least one of: C5AR1, LYVE1, ABCC3, MRC1, SIGLEC1, STAB1, C1QB, C1QA, TMEM37, MERTK, C1QC, TMEM119, MS4A7, APOE, CYP4F18, TREM2, TLR7, and LILRB4; and/or positive for at least one of: KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, BDCA3 and XCR1, for example, as can be determined by polymerase chain reaction (PCR), on a genetic chip, flow cytometry, RNA sequencing or equivalent analysis.

In some aspects of the invention, non-stimulatory myeloid cells are found in a population of immune cells containing stimulatory myeloid cells and non-stimulatory myeloid cells.

In some aspects of the invention, the cancerous tissue is a solid cancer or hematologic malignancy. In some aspects of the invention, the cancer is selected from the group consisting of: melanoma, kidney cancer, hepatobiliary cancer, head and neck squamous cell carcinoma (SCC), pancreatic, colon, bladder, glioblastoma, prostate, lung and breast cancer.

In some aspects of the invention, the subject is a human. In some aspects of the invention, the subject has previously received, is currently receiving, or will subsequently receive immunotherapy. In some aspects of the invention, the immunotherapy is at least one of: an immunotherapy that inhibits a checkpoint inhibitor; immunotherapy, which inhibits T-cell checkpoint inhibitor; which uses anti-PD1 antibodies; anti-PDL1; anti-CTLA4; adoptive T-cell therapy; CAR-T cell therapy; application of a vaccine based on dendritic cells; monocytic vaccine; antigen binding protein, which binds both T cells and antigen presenting cells; antigen binding protein BiTE; toll-like receptor ligand and cytokine.

In some aspects of the invention, the methods enhance an immune response in a subject. In some aspects of the invention, the immune response is an immune response based on the use of immunotherapy. In some aspects of the invention, the immune response based on the use of immunotherapy is directed against cancerous tissue.

In some aspects of the invention, the method further includes administering an agent that enhances the activity or increases the number of stimulating myeloid cells. In some aspects of the invention, the agent is FLT3L.

In some aspects of the invention, the methods cure cancer in a subject.

In some aspects of the invention, contact or administration increases the ratio of stimulatory myeloid cells to non-stimulatory myeloid cells in the immune cell population. In some aspects of the invention, contact or administration results in the subject receiving more than 1-4, 1-2, 1, 1.37, 1.6, 2, 3, or 4% of the stimulating myeloid cells present in the tumor from all CD45 cells ⁺ , HLA-DR ⁺ present in the tumor.

In some aspects of the invention, the method further includes determining the number of stimulatory myeloid cells and/or non-stimulatory myeloid cells in a biological sample taken from the subject. In some aspects of the invention, the determination step is used to determine whether a subject may benefit from administration of a given antibody or antigen binding fragment thereof. In some aspects of the invention, the detection step is used to monitor the effectiveness of administration of the antibody or antigen-binding fragment thereof.

In some aspects of the invention, the method further includes determining the expression level of at least one of C5AR1, LYVE1, ABCC3, MRC1, SIGLEC1, STAB1, C1QB, C1QA, TMEM37, MERTK, C1QC, TMEM119, MS4A7, APO E, CYP4F18, TREM2, TLR7, and LILRB4 in a biological sample taken from a subject. In some aspects of the invention, the method further includes determining the expression level of at least one of KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, BDCA3, and XCR1 in a biological sample taken from the subject.

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In some aspects of the invention, the antibody or antigen-binding fragment thereof is a pharmaceutical composition comprising the antibody or antigen-binding fragment thereof and a pharmaceutically acceptable excipient. In some aspects of the invention, the composition is sterile.

In some aspects of the invention, the antibody or antigen-binding fragment thereof binds to the extracellular domain of a target protein expressed on non-stimulating myeloid cells selected from the group consisting of TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37 , MERTK and TMEM119, wherein the non-stimulatory myeloid cells are CD45+, HLA-DR+, CD11c+, CD14+ and BDCA3 ^{- cells} , wherein the antibody or antigen-binding fragment thereof destroys, blocks or depletes non-stimulatory myeloid cells through antibody-dependent cell-mediated cytotoxicity (ADCC) or complement-dependent cytotoxicity (CDC) to a level that is less than the level of non-stimulatory myeloid cells present in the cancer tissue prior to bringing the non-stimulatory myeloid cells into contact with an antibody or antigen-binding fragment thereof, wherein the non-stimulatory myeloid cells are present in a population of immune cells containing stimulatory myeloid cells that represent are CD45+, HLA-DR+, ^CD14- , CD11c+, ^BDCA1- and BDCA3+ cells, and non-stimulating myeloid cells, and killing, blocking or depleting non-stimulating myeloid cells cures cancer.

Also described herein is a method of enhancing a subject's immune response to a tumor, comprising administering to the subject an effective amount of a drug that increases the excess of stimulatory myeloid cells in a tumor or reduces the excess of non-stimulatory myeloid cells in a tumor, wherein the drug enhances the immune response to the tumor and optionally the immune response. reduces tumor volume.

Also described herein is a method of improving the effectiveness of an anticancer immunotherapy drug in a subject having a tumor, comprising administering to the subject an effective amount of a drug that increases the excess of stimulatory myeloid cells in the tumor or reduces the excess of non-stimulatory myeloid cells in the tumor, wherein the subject has previously received, is concurrently receiving, or will be receiving subsequently receive anticancer immunotherapy.

In some aspects of the invention, the method includes systemically administering or enhancing FLT3L. In some aspects of the invention, the method includes systemically administering one or more antibodies that result in the removal or reduction of non-stimulatory myeloid cells without selectively affecting stimulatory myeloid cells. In some aspects of the invention, the method includes treating autologous bone marrow or blood cells of the subject with FLT3L while simultaneously blocking the expression or action of CSF1. In some aspects of the invention, the method includes increasing the expression of IRF8, Mycl1 or BATF3, or ZBTB46 in populations of bone marrow or blood progenitor cells.

Also described herein is a method for determining the presence or absence of non-stimulatory myeloid cells in a sample taken from a subject, comprising: contacting a population of immune cells comprising non-stimulatory myeloid cells and stimulatory myeloid cells with an antibody or antigen-binding fragment thereof that binds to non-stimulatory myeloid cells. cells; determining the presence of complexes indicating binding of the antibody to non-stimulating myeloid cells; optionally determining the number of non-stimulating myeloid cells in the population and optionally treating the subject with an antibody or antigen-binding fragment thereof that binds to non-stimulating myeloid cells.

Also described herein is a method for determining the presence or absence of stimulatory myeloid cells in a sample taken from a subject, comprising: contacting a population of immune cells comprising stimulatory myeloid cells and non-stimulatory myeloid cells with an antibody or antigen-binding fragment thereof that binds to stimulatory myeloid cells. cells; determining the presence of complexes indicating binding of the antibody to stimulating myeloid cells; optionally determining the number of stimulating myeloid cells in the population and optionally treating the subject.

Also described herein is a method for quantifying non-stimulatory myeloid cells in a tumor sample, comprising measuring the number of cells that are at least one of: tumor-associated macrophages; tumor-associated dendritic cells; CD45+, HLA-DR+, and CD14+ cells; CD45+, HLA-DR+, CD14+, BDCA3 ^- , CD11b+ and CD11c+; CD45+, HLA-DR+, CD14 ^- , CD11c+ and BDCA1+; but not BDCA3+.

Also described herein is a method for quantifying stimulatory myeloid cells present in a tumor sample, comprising measuring the number of cells that are at least one of CD45+, HLA-DR+, ^CD14- , CD11c+ and BDCA3+ cells; CD45+, HLADR+, and BDCA3+; CD45+, HLA-DR+, CD14 ^- and BDCA3+, and CD45+, HLA-DR+, CD11c+ and BDCA3+.

In some aspects of the invention, cells are quantified by cell sorting. In some aspects of the invention, the cell sorting method is selected from the group consisting of:

- 4 046327 Fluorescence activated cell sorting, flow cytometry, magnetic cell sorting, microraft sorting and affinity based cell separation.

Also described herein is a method for quantifying non-stimulating myeloid cells in a tumor sample, comprising measuring the expression of at least one of the following non-stimulating myeloid cell markers: C5AR1, LYVE1, ABCC3, MRC1, SIGLEC1, STAB1, C1QB, C1QA, TMEM37, MERTK, C1QC, TMEM119, MS4A7, APOE, CYP4F18, TREM2, TLR7 and LILRB4.

Also described herein is a method for quantifying stimulatory myeloid cells present in a tumor sample, comprising measuring the expression of at least one of the stimulatory myeloid cell markers: KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, BDCA3, and XCR1.

In some aspects of the invention, expression of markers is measured by quantitative PCR. In some aspects of the invention, quantitation of marker gene expression is performed using an oligonucleotide chip containing immobilized probes for marker gene sequences.

In some aspects of the invention, a tumor sample is obtained from the tumor by needle biopsy, punch biopsy, or surgical excision of the tumor.

Also described herein is a method for assessing the cancer status of a patient, comprising the steps of obtaining a tumor sample from the subject and measuring the excess of stimulatory myeloid cells in the tumor sample obtained from the subject.

In some aspects of the invention, the assessed cancer status represents the likelihood of cancer recurrence, and wherein an increased excess of stimulating myeloid cells in the tumor sample is an indicator of a reduced likelihood of cancer recurrence. In some aspects of the invention, the assessed cancer status represents the subject's responsiveness to immunotherapy treatment, wherein an increased excess of stimulatory myeloid cells in the tumor sample is indicative of an increased likelihood that the subject will respond favorably to the immunotherapy treatment. In some aspects of the invention, the assessed cancer status represents the effectiveness of the immunotherapy treatment, and wherein an increased excess of stimulatory myeloid cells in the tumor sample is an indicator of the effectiveness of the immunotherapy treatment. In some aspects of the invention, the estimated cancer status is the expected duration of cancer survival, and wherein the increased excess of stimulating myeloid cells in the tumor sample is an indicator of the expected duration of cancer survival.

In some aspects of the invention, the increased excess of stimulatory myeloid cells is an excess that is greater than the median or average excess of stimulatory myeloid cells observed in a pool of representative tumor samples. In some aspects of the invention, the excess of stimulatory myeloid cells is measured as the ratio of stimulatory myeloid cells to non-stimulatory myeloid cells present in the sample. In some aspects of the invention, the excess of stimulatory myeloid cells is measured as the ratio of stimulatory myeloid cells to the total number of myeloid cells present in the sample. In some aspects of the invention, the excess of stimulatory myeloid cells is measured as the ratio of stimulatory myeloid cells to the total number of HLA-DR ⁺ cells present in the sample. In some aspects of the invention, an increased excess of stimulatory myeloid cells is defined as greater than 1.37 stimulatory myeloid cells per 100 HLA-DR+ cells. In some aspects of the invention, the immunotherapy treatment is an anti-PD1 antibody treatment. In some aspects of the invention, the anti-PD1 antibody treatment is the administration of nivolumab or pembromisulab. In some aspects of the invention, the subject has melanoma.

Also described herein is a method for assessing the effectiveness of a treatment to increase the excess of stimulatory myeloid cells in tumors, comprising the steps of: administering a drug to one or more subjects with cancer, and measuring the excess of stimulatory myeloid cells in one or more tumor samples obtained from one or more subjects, wherein an increased excess of stimulatory myeloid cells in one or more tumor samples is an indication that the agent is effective.

In some aspects of the invention, an increased excess of stimulatory myeloid cells is defined as a greater excess of stimulatory myeloid cells in one or more tumor samples than the excess that was observed in one or more tumor samples obtained from one or more subjects before drug administration. In some aspects of the invention, an increased excess of stimulatory myeloid cells is defined as a greater excess of stimulatory myeloid cells in one or more tumor samples taken from treated subjects than the excess that was observed in a pool of representative tumor samples taken from untreated subjects.

An anti-LILRB4 antibody or an antigen-binding fragment thereof that contains one or more of the sequences shown in table. BB, or a variant thereof, having at least 80, 90 or 95% sequence identity with the sequence shown in table. BB, optional

- 5 046327 specifically, the antibody or antigen binding fragment thereof contains each of the CDRs of the CDR sequences shown in table. BB, and optionally the antibody or antigen-binding fragment thereof contains each of the variable domains shown in table. EV, and optionally the antibody or antigen-binding fragment thereof contains the full-length sequences shown in table. BB.

An antibody or antigen-binding fragment thereof that binds the LILRB4 protein and is capable of specifically killing, depleting or blocking non-stimulating myeloid cells.

In some aspects of the invention, an antibody or an antigen-binding fragment thereof binds to the extracellular domain of LILRB4, wherein the antibody or antigen-binding fragment thereof kills, blocks, or depletes non-stimulating myeloid cells via antibody-dependent cell-mediated cytotoxicity (ADCC) or complement-dependent cytotoxicity (CDC).

In some aspects of the invention, an antibody or antigen-binding fragment thereof has at least one of antibody-dependent cell-mediated cytotoxic (ADCC) activity, complement-dependent cytotoxic (CDC) activity, and antibody-mediated phagocytosis activity. In some aspects of the invention, the antibody is at least one of a monoclonal antibody, an antagonistic antibody, a polyclonal antibody, an IgGi antibody, an IgG ₃ antibody, an afucosylated antibody, a bispecific antibody, a human antibody, a humanized antibody, a chimeric antibody, a full-length antibody, and an antigen-binding fragment. In some aspects of the invention, the antibody or antigen binding fragment thereof is not an IgG ₂ antibody, or the antibody or antigen binding fragment thereof is not an IgG ₄ antibody. In some aspects of the invention, the antibody or antigen-binding fragment thereof is conjugated. In some aspects of the invention, the antibody or antigen binding fragment thereof is conjugated to at least one therapeutic agent selected from the group consisting of a radionuclide, cytotoxin, chemotherapeutic agent, drug, prodrug, toxin, enzyme, immunomodulator, anti-angiogenic agent, pro-apoptotic agent, cytokine, hormone, oligonucleotide, antisense molecule, siRNA, second antibody and second antibody fragment. In some aspects of the invention, the antibody or antigen binding fragment thereof contains a sequence that is at least 95% identical to the sequence set forth in Table. BB.

A pharmaceutical composition comprising an anti-LIBR4 antibody or an antigen-binding fragment thereof described herein and a pharmaceutically acceptable excipient.

In some aspects of the invention, the composition is sterile.

In some aspects of the invention, an anti-LILBR4 antibody or antigen binding fragment thereof described herein is used in a method described herein.

Brief description of drawings

Fig. 1. Individual DCs and redundant macrophages in murine and human tumors.

(Fig. 1A) Flow cytometry results and gate distribution of tumor APC populations obtained from disaggregated and CD45-enriched PyMTchOVA tumors. AC: Representative results from more than 5 independent experiments. (Figure 1B) Cytometry results of tumor APC populations in ectopic B78ChOVA tumors. (Figure 1C) Histogram of tumor mCherry fluorescence from tumor-infiltrating immune cells in B78chOVA. (Fig. 1D) Representative results from a disaggregated metastatic human melanoma biopsy revealing additional DC and TAM populations defined by CD45+ Lin ^- (CD3e, CD56, CD19) HLA-DR+ and separated by CD14, BDCA1 and BDCA3. Double-negative cells likely reflect B cell avoidance of lineage gates, immature monocytes, or pDCs. (Figure 1E) Relative proportions of tumor-infiltrating myeloid cells as % of total CD45+ cells for PyMTchOVA and B78chOVA models. Pooled data obtained from individual tumors, presented as mean ± standard error of mean ± SEM from (n=5) mice. (Figure 1F) Frequency of DC and TAM populations infiltrating human metastatic melanoma, presented as % of total CD45+ cells. Pooled data obtained from individual tumors, presented as mean ±SEM from (n=4) mice. In fig. 1C, Histogram for each cell type from anterior to posterior plot: T cells, Νφ, DC2, DC1, Mono, TAM2, TAM1, and Tumor, respectively.

Fig. 2. Determination of surface and transcriptional profiles highlights different lineages of tumor DCs and macrophages. All data (Fig. 2A-G) are from the ectopic B78chOVA tumor model. Cell lineages are defined according to Fig. 1. (Fig. 2A) Expression of a set of specific DC markers, compared with the corresponding isotype (shaded in gray). Black boxes indicate CD103+DC2 populations showing unique expression. (Fig. 2B) Differential expression of specific macrophage markers (colored) with the corresponding isotype (gray). Black boxes indicate the CD11b ⁺ DC1, TAM1, and TAM2 populations showing unique expression. (Fig.

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2C) Specific expression of DC-T _h 2 markers (colored) by the CD11b ⁺ DC1 population compared to the corresponding isotype (colored). Black boxes indicate CD11b ⁺ DC1 that exhibit unique expression. (Fig. 2D) Overall transcriptional profile revealed by RNA sequencing of FACS-purified populations from biological samples in triplicate. The data are plotted as a map of maximum Log ₂ fold changes relative to the overall mean of the top 1000 expressed genes by maximum variation between DC1, DC2, TAM1 and TAM2. (Fig. 2E) PCA populations from DC1, DC2, TAM1, and TAM2 based on overall transcriptional profiles obtained from RNA sequencing. (Fig. 2F) Results of quantitative real-time PCR (qRT-PCR) analysis of Irf4, Irf8, Myb, and Zbtb46 (zDC) expression from sorted APC populations. Data are presented as the mean ΔCt±SEM value calculated from biological assays in triplicate (n=3), (ND - not detected). (Fig. 2G) Intracellular staining for Irf4 and Irf8 in APC tumor populations compared to the corresponding isotype (gray). In fig. 2E, each cluster of three points from left to right represents: CD103+DC2, CD11b+DC1, F4/80+ TAM1 and F4/80+ TAM2, respectively.

Fig. 3. Different requirements of Irf4, Irf8 and Batf3 for tumor-infiltrating APC populations. All data are representative of flow cytometric analysis of the CD11b'DC1 and CD103+DC2 populations (gated into CD45 ⁺ , ^Ly6C- , MHCII+, and CD24+). Data are shown as mean±SEM. Statistical significance is indicated as *p<0.05, **p<0.01, ***p<0.001; u=not statistically significant. (Fig. 3A) Ectopic PyMT-VO tumors from IrJ8 ^-/- (KO) compared to controls (wild type - WT). Relative cell ratios as % of total MHCII+ cells. Data pooled for individual mice (n=6) from 2 independent experiments. (Fig. 3B) Ectopic B78chOVA tumors in an Irf4 ^f/f xCD11c-CRE ⁺ host compared to Cre-negative animals of the same litter. Relative cell ratios as % of total MHCII+ cells. Data pooled for individual mice (n=7) from 2 independent experiments. (Fig. 3C) Ectopic B78chOVA tumors in Batf3 KO versus WT. Relative cell ratios are depicted as % of total MHCII+ cells. Data pooled for individual mice (n=6). (Fig. 3D) Ectopic B78chOVA tumors in Zbtb46-DTR mice acutely starved for 24 h with DT or PBS. Relative cell ratios are depicted as % of total MHCII+ cells. Data pooled for individual mice (n=6) from 2 independent experiments.

Fig. 4. Different possibilities for the synthesis of M-CSF and GM-CSF cytokines by tumor-infiltrating APC populations. (Fig. 4A) qPCR results of CSF1R, CSF2Rb and CSF3R expression from sorted APCs. Data are presented as the mean ΔCt±SEM calculated from biological assays in triplicate (n=3) for individual B78chOVA tumors, (ND - not detected). (Fig. 4B) Cytometry results of tumor APCs after 3 days in animals with aCSF-1 blocked (filled with dots) compared with isotype (solid) animals treated with tumors. Quantification results as % of total CD45+ tumor cells pooled for individual mice (n=6) from 2 independent experiments are shown as mean±SEM. Statistical significance is indicated as *p<0.05, **p<0.01, ***p<0.001; th = not statistically significant. (Fig. 4C) Schematic of adoptive transfer of BM precursors and entry into BM, spleen, and tumor. (Fig. 4D) Representative cytometry results of congenic cells arriving in tumors. Distributed to gates by CD45. 2 and the subsequent gate determination strategy from FIG. 1A. (Fig. 4E) Competitive adoptive transfer of CM for WT versus GMCSFR KO GMP precursors into recipients of B78chOVA tumors. Repopulation efficiency, reported as % of total cells transferred. Representative results of the distribution by gate of cells arriving in the tumor GMP, WT (left column for each data type for BM, spleen and tumor), KO (right column for each data type for BM, spleen and tumor). Results of quantification of tumor-incoming DCs identified by CD24 ⁺ CD11c ⁺ . Data pooled from 2 independent experiments and reported as mean ±SEM for individual tumors (n=6). (Figure 4F) Cytometry data for the CD11b'DC1 and CD103+DC2 populations (gated into CD45 ⁺ , Ly6C ^- MHCII ⁺ , CD24+) between ectopic, cytokine-expressing tumors B16-F10, B16-GMCSF, and B16-FLT3L. Populations for each tumor are presented as % of total MHCII+ cells. Data pooled from 3 independent experiments and reported as mean ±SEM for individual tumors (n=6). For each group in Fig. 4F (right image) the columns from left to right are: DC2, DC1, TAM1 and TAM2, respectively.

Fig. 5. Unique antigen processing and presentation characteristics of CD103+DC2. All data (Fig. 5A-G) are from the ectopic B78chOVA tumor model. (Fig. 5A) Map of maximum Log ₂ -transformed expression values obtained from RNA sequencing across populations for selected genes involved in cross-presentation, cytokine and chemokine production, and costimulation. Color scaling is defined as: light gray=lower ^20th percentile, dark gray=upper ^80th percentile, with scaled and priced

- 7 046327 three ^{20th -80th} percentiles ( ^50th percentile). Sorted cell data from biological assays in triplicate. (Figure 5B) Cytometry data of surface protein ligand levels for T cell regulatory molecules (dark line) compared to the corresponding isotype (shaded in gray). (Fig. 5C) Cytometry data for MCHI and MHCII expression (dark lines; front four lines) compared to the corresponding isotype (shaded; back four lines). In fig. In Figure 5C, each line colored from anterior to posterior represents CD103+ DC2, CD11b ⁺ DC1, F4/80+ TAM1, and F4/80+ TAM2, respectively. In fig. In Figure 5C, each shaded line from anterior to posterior represents CD103+ DC2, CD11b ⁺ DC1, F4/80+ TAM1, and F4/80+ TAM2, respectively. (Figure 5D) Cytometric data of ex vivo dextran uptake among populations. Gray=no dextran, light histogram=dextran binding at 4C and dextran uptake at 37C=dark histogram, shown in triplicate. Change in geometric mean fluorescence intensity (gMFI) for each population, reported as mean±8EM. Data are representative of 2 independent experiments (n=6). (Fig. 5E) Cytometric analysis of the relative pH of endocytic compartments among populations. B78 tumor cells were transfected with constructs for measuring pH ratios, N1-mCherry-pHlourin. The pHluorin protein is a pH-sensitive derivative of GFP (green fluorescent protein) that quenches fluorescence at acidic pH. Representative histograms show pHluorin fluorescence in mCherry ⁺ cells, where the lower value for pH-GFP represents a more acidic environment. Gray histograms represent the corresponding populations from the pHluorin-non-expressing control tumor (original B78). Data are summarized as the ratio of gMFI values between GFP and mCherry fluorescence. Data are presented as mean ±SEM ratios pooled from 3 independent experiments. (Fig. 5F) Intracellular staining in IL12 cytokine populations. % of IL12 ⁺ cells quantified in each population, data pooled from 2 independent experiments, (n=3), reported as mean±SEM. Statistical significance is indicated as *p<0.05. In fig. 5F, each line from anterior to posterior represents CD103+ DC2, CD11b ⁺ DC1, F4/80+ TAM1, and F4/80+ TAM2, respectively. (Fig. 5G) Transcript levels measured by qPCR for IL12b and IL10 cytokine transcripts. Data are presented as the mean ΔCt±SEM calculated from biological assays in triplicate (n=3) for individual tumors (ND - not detected).

Fig. 6. CD103+ DCs are the best T cell stimulators for naïve and activated CD8 ⁺ T cells. All data (Fig. 6A-G) were obtained from the ectopic B78chOVA tumor model. T cells+KMDC (shaded in grey; mostly last), T cells+KMOS+ SL8 (unshaded in grey; mostly penultimate), T cells+tumor APCs (corresponding color histograms). The plates were plated with 20,000 T cells:APC ratio of 4000. Representative flow-through assay graphs for 4 independent experiments unless otherwise noted. (Fig. 6A) Flow cytometry results of early activation markers, Nur77 and CD69 (12 h) on naïve or pre-activated OT-ICD8' T cells. cultured on sorted APC populations obtained directly from tumors. In fig. 6A, each line from anterior to posterior represents F4/80+ TAM2, F4/80+ TAM1, CD11b ⁺ DC1, and CD103+ DC2, respectively. (Fig. 6B) Representative proliferation cytometry results of naïve OT-I CD8 ⁺ T cells measured by eFluor670 dye dilution marked against Nur77 (as measured by TCR stimulation) 72 h after coculture with a population of tumor APCs. The graphs above list the total cell yield values. (Fig. 6C) The histogram covers data on the proliferation of naïve T cells among tumor APCs. In fig. 6C, each line from the first to the last represents F4/80+ TAM1, F4/80+ TAM2, CD11b ⁺ DC1 and CD103+ DC2, respectively. (Fig. 6D) Representative T cell proliferation cytometry results measured by eFluor670 dye dilution marked against Nur77 at 72 h for pre-activated OT-I CD8 ⁺ T cell progenitors cultured on tumor APC populations. The graphs above list the total cell yield values. (Fig. 6E) The histogram captures the proliferation of preactivated OT-I CD8 ⁺ T cells among tumor APCs. In fig. 6E, each line from anterior to posterior represents F4/80+ TAM1, F4/80+ TAM2, CD11b ⁺ DC1, and CD103+ DC2, respectively. (Fig. 6F) Representative T cell proliferation cytometry results measured by eFluor670 dye dilution at 72 h for naïve OT-II CD4 ⁺ T cells cultured on tumor APC populations. Representative plots of flow assays for 2 independent experiments. (Fig. 6G) The histogram covers the proliferation of naïve RT-iI CD4 ⁺ T cells among tumor APCs. In fig. 6G, each line from anterior to posterior represents F4/80+ TAM1, F4/80+ TAM2, CD11b ⁺ DC1, and CD103+ DC2, respectively.

Fig. 7. Intravital images and sections revealed CD11b'DC1 and CD103+DC2, which are scattered near the borders of tumors, but can interact with T cells that are present there. (Fig. 7A) Quantification of proximal/distal location of APC within the tumor. Data pooled for 4 independent imaging analyses, presented in

- 8 046327 as the mean value ±SEM. In fig. 7A columns for each group from left to right: TAM2, TAM1 and DC1/2, respectively. (Figure 7B) APC-T cell contacts in vivo were observed as % of total T cell connections. Cumulative data from 4 different positions, as images acquired over 30 minutes in 2 independent intravital 2-photon imaging assays. Contacts were scored manually by counting the physical contact made between T cells and red, yellow, and green APCs. The column arrangement represents the APC of the contact (top: CD103+, CD11b ⁺ DC1, bottom: TAM1, middle: TAM2). (Fig. 7C) Binding assay of ex vivo T cells, positively selected for CD45 + cells from a disaggregated tumor, with pre-activated OT-I CD8 ⁺ T cells. Data were calculated as % T cell junctions within each population (left) and as total % of T cell junctions (right). Data pooled from 2 independent experiments and reported as mean±SEM. In fig. 7C, each column of each group from left to right represents: DC2, DC1, TAM1 and TAM2, respectively.

Fig. 8. Effective adoptive CTL therapy requires a rare CD103 DC2 population in the tumor. (Fig. 8A) Tumor growth curve plotted as change in tumor area (mm ² ) over time for EG7. 1 in zDC-DTR hosts. The arrows indicate the time in. b. introduction of D.T./FSB and V. V. transfer of 5x10° pre-activated OT-I CD8 ⁺ T cells, respectively, on days 4 and 5. Subsequently, DT/PBS was administered every 3 ^days , and FTY-720/saline was subsequently administered every other day for the duration of treatment. The light gray dashed line (top) shows the growth of EG7. 1 in a zDC-DTR host without T cell transfer. EG7 regression. 1 with activated CD8 ⁺ T cell transfer and treatment with FTY-720 (black line; bottom) or with additional DT-mediated DC depletion (dark gray line; middle). Representative data are presented as mean tumor area ±SEM (n=4) from 2 independent experiments. Statistical significance is indicated as *p<0.05. (Fig. 8B) Comparison of the predictive value of the ratio of CD103+/CD103 ^- gene signals compared with individual genes (either CD103+ specific genes, green, or TAM1/TAM2/Cd11b DC1 specific genes, red) for TCGA samples obtained from humans, in multivariate survival analysis with Cox proportional hazards, adjusting for cancer type as a covariate. Data are expressed as hazard ratios (HRs) with 95% confidence intervals, where a value <1 indicates increased overall survival (OS); >1 indicates decreased OS for genes with p-values <0.05 for VL. (Fig. 8C) Comparison of the prognostic value of the CD103+/CD103 ⁻ gene signal ratio compared with some published prognostic gene signatures for TCGA samples from humans in a multivariate Cox proportional hazards analysis, adjusting for cancer type as a covariate. Data are expressed as hazard ratios (HRs) with 95% confidence intervals, where a value <1 indicates increased overall survival (OS); >1 indicates reduced OS for genes with p-values <0.05 for VL. (Figure 8D) K-M plot among all 12 cancers in the human TCGA dataset, adjusted for cancer type. Data are split into HIGH CD103+/CD103 ^- gene ratio (black, n=1801) and LOW CD103+/CD103 ^- expressor ratio (gray, n=1801) with p-value=1.76e-07. (Fig. 8E) K-M plot of overall survival of breast cancer patients in the TCGA dataset. Data are split into HIGH CD103+/CD103 ^- gene ratio (black, n=422) and LOW CD103+/CD103 ^- expressor ratio (gray, n=423) with p-value=0.0255. (Fig. 8F) K-M plot of overall survival of patients with head and neck squamous cell carcinoma in the TCGA dataset. Data are split into HIGH CD103+/CD103 ^- gene ratio (black, n=151) and LOW CD103+/CD103 ^- expressor ratio (gray, n=152) with p-value =0.000207. (Fig. 8G) K-M plot of overall survival of patients with lung adenocarcinoma cancer in the TCGA dataset. The data are split into HIGH CD103 ⁺ /CD103 ^- gene ratio (black, n=177) and LOW CD103 + /CD103 ^- expressor ratio (gray, n=178) with p-value=0.000874.

Fig. 9. Transcriptional excess associations of the CD103+ and BDCA3+ genes with increased post-relapse survival in metastatic melanoma. (Figure 9A) Comparison of prognostic value for CD103+ genes, CD103 ^+/− ratio, and individual genes using the metastatic melanoma dataset in a Cox proportional hazards survival analysis. Data are expressed as hazard ratios (HR) with 95% confidence intervals, where a value <1 indicates increased overall survival (OS) from the time of metastasis (post-relapse survival) and >1 indicates decreased OS from the time of metastasis, VL-adjusted p-values <0 .05. (Fig. 9B) Kaplan-Meier plot of post-relapse survival of local myeloma patients for the CD103+ gene expression list. The data are categorized into high (light gray; top line of each graph) and low (black; bottom line of each graph) categories using stringent cutoffs of 33%, 50%, and 66% for CD103+ gene expression levels. (Fig. 9C) Kaplan-Meier plot of post-relapse survival of local myeloma patients for CD103 ^+/- gene expression ratio. Data are divided into high (light gray; top line of each graph) and low (black; bottom line of each graph) categories at strict thresholds

- 9 046327 values of 33%, 50% and 66% for the expression levels of the CD103 ^+/- gene ratio. (FIG. 9D-E) Measurements based on TIL category class definition and FIG. 9F-G with histological measurements of peritumoral CD3 ⁺ T cell numbers from {Bogunovic et al., 2009} were noted in comparison with the SDC gene signature (Fig. 9D,F) and the SDC/NSM ratio (Fig. 9E,G).

Fig. 10. Quantitative determination by flow cytometry of tumor-infiltrating APC populations in human metastatic myeloma. (Fig. 10A) Data table for biopsies from human metastatic myeloma patients. The table lists the patient ID, age, sex, and location of the tumor biopsy for each patient, as well as prior treatment (if known). All patients listed received anti-PD-1 immunotherapy at UCSF. Previous treatment history is coded as untreated, 0, or treated, 1. ** indicates rapid progression that was resolved with additional scans. (Figure 10B) Representative data for a flow cytometry gate assignment strategy for human metastatic melanomas to identify tumor-infiltrating myeloid subsets. Data are representative of a patient with a significant population of BDCA3+ and BDCA1+ DCs gated into singlets and live cells. (pDC, CD14+ TAM, BDCA1+ DC, BDCA3+, CD14 ^- TAM). (Figure 10C) Representative data for a flow cytometry gate assignment strategy for human metastatic melanomas to identify tumor-infiltrating myeloid subsets. Data are representative of a patient without a significant BDCA3+ DC population gated into singlets and live cells (pDC, CD14 ⁺ , BDCA1+DC, BDCA3+ DC, CD14 ^- TAM). (Fig. 10D) Frequencies of CD45+ cells (black; left column of each group) and HLA-DR'(gray; right column of each group) as a percentage of total viable cells among patient biopsies. (Fig. 10E) Frequencies of occurrence of tumor-infiltrating immune populations among patient biopsies when determined by gate allocation strategy. Data are presented as frequencies of total CD45+ cells, mean ±S. E.M. among patients. pDC, CD14+TAM, BDCA1+ DC, BDCA3+ DC, CD14 ^- TAM, lineage markers (CD3e, CD56, CD19).

Fig. 11. Excess of BDCA3+ DC cells in human melanoma tumor predicted by susceptibility to anti-PD1 antibody. Patients were categorized as either treatment responders (gray, including partial or complete responses) or nonresponders (black, including stable disease and progressive disease). (FIG. 11A) Waterfall plot of the percentage of CD45+ cells out of total living cells in individual patients divided into responders and non-responders. (Fig. 11B) Quantification of the frequency of responders (gray) and non-responders (black) to treatment for the percentage of CD45+ cells of total living cells in the tumor. Data are pooled across patients and presented as mean ±SEM, ns=not significant. (C) Waterfall plot of the percentage of tumor BDCA3+ DC cells relative to total CD45+ cells in individual patients divided into responders and nonresponders. (Fig. 11D) Quantification of the frequency of responders (gray) and non-responders (black) to treatment for the percentage of BDCA3+ DC cells out of total CD45+ cells in tumors. Data are pooled across patients and presented as mean ±S. E.M., **p=0.0056. (Fig. 11E) Scatterplot of the frequencies of BDCA3+ DCs and CD14+ TAMs as a proportion of the total number of HLA-DR+ cells in the tumor, responders to treatment are shown as open circles, and non-responders to treatment are shown as filled circles. (Fig. 11F) Scatter plot of the frequencies of BDCA3+ DC and BDCA1+ DC as a proportion of the total number of HLA-DR+ cells in the tumor, responders are shown as open circles and non-responders are shown as filled circles. (FIG. 11G) Scatter plot of BDCA3+ DC and CD14 ^- TAM frequencies as a proportion of the total number of HLA-DR+ cells in the tumor, responders are shown as open circles and non-responders are shown as filled circles.

Fig. 12. CD103+ DC requirements for anti-PD1 antibody efficacy in a mouse model of melanoma. (Fig. 12A) Schematic of the use of a mouse and tumor model in a combination immunotherapy treatment regimen. (Fig. 12B) Tumor area (mm ² ) of individual B78chOVA tumors in control B6 mice (leftmost graph) treated with 100 μg control Armenian hamster IgG and 100 μg control rat IgG2a c. b. on days 5, 8 and 11 of tumor growth. Data pooled from 2 independent experiments, n=8. (Fig. 12C) Tumor area (mm ² ) of individual B78chOVA tumors in Zbtb46-DTR KM chimeric mice (middle graph) treated with 100 μg anti-CTLA-4 and 100 μg anti-PD-1 on days 5, 8, and 11 of growth tumors with PBS injection on days 4, 7 and 10. Data combined from 2 independent experiments, n=8. (Fig. 12D) Tumor area (mm ² ) of individual B78chOVA tumors in Zbtb46-DTR KM chimeric mice (rightmost graph) treated with 100 μg anti-CTLA-4 and 100 μg anti-PD-1 on days 5, 8, and 11 tumor growth with DT injection on days 4, 7 and 10. Data pooled from 2 independent experiments, n=8.

In fig. Figure 13 shows that progressive distribution over gates identified as ass

- 10 046327 lation of NSM and SDC populations in all human tumor types analyzed by flow cytometry displayed (metastatic melanoma, head and neck squamous cell carcinoma (HNSCC), and colon carcinoma).

In fig. 14 shows the labeling of subpopulations of these cells in the tumor microenvironment with various NSM markers, including TREM2, among the B16-F10 and MC38 ectopic mouse tumor models, as well as MS467, LILRB4, and CD88 staining analyzed by flow cytometry. Staining patterns for all NSM markers reveal high specificity for the NSM population (TAM, Ly6C ⁺ monocytes, CD11b ⁺ DC) with no SDC staining (CD103 + DC). Populations pre-allocated to gates according to the gate allocation strategy as in FIG. 1A, B. Secondary controls for each population are represented as gray shading, while NSM marker coloring for each population is overlaid as a solid black histogram.

In fig. 15 shows the expression of CCR7 on human SDC. Populations pre-allocated to gates according to the gate allocation strategy in FIG. 13.

In fig. Figure 16 shows the specific expression of SDC, CCR7 and XCR1 gene products on SDCs (CD103+ DCs) and the lack of SDC protein expression on NSMs (TAMs, Ly6C ⁺ monocytes, CD11b ⁺ DCs) in the tumor microenvironment of ectopic tumors of B16-F10 mice. Populations pre-allocated to gates according to the gate allocation strategy as in FIG. 1A, B. Secondary controls for each population are represented as gray shading, while SDC marker coloring for each population is overlaid as a solid black histogram.

In fig. Figure 17 shows the absence of staining for NSM, MS4A7 and TREM2 markers in healthy tissues of the BM and spleen of male wild-type C57BL/6 mice, analyzed by flow cytometry. Populations pre-allocated to gates according to the gate allocation strategy as in FIG. 1A, B. Secondary controls for each population are represented as gray shading, while MS4A7 and TREM2 staining for each population (top figure) are overlaid as a solid black histogram. The bottom half of the figure shows the lack of specific staining in healthy mouse BM and spleen tissue by titrated concentrations (0 nM, 2 nM, 20 nM, and 200 nM) of TREM2 antibody clones 2, 5, and 7 among immune populations.

In fig. 18 shows that anti-TREM2 and anti-LILRB4 antibodies specifically deplete TREM2- and LILRB4-bearing cells, respectively, in vivo. Control and TREM2- or LILRB4-expressing EL4 transfectant cells were mixed at a 1:1 ratio and IP was injected into WT B6 male mice. Three hours later, animals were injected with antibody (anti-TREM2 or anti-LILRB4) or control human IgG1 or PBS. After 36 h, mice were sacrificed and cells isolated from the peritoneal cavity, collected by peritoneal lavage, were counted by flow cytometry.

In fig. 19 indicated that anti-NSM antibodies reduced tumor growth relative to controls to the same extent as anti-PD-1 antibody therapy. MC38 colon carcinomas were injected into 6-week-old male B6 mice. Mice randomly assigned to treatment groups were treated with the indicated antibodies on days 5, 7, 11, 15 by IP injection. Data are shown as 75% rank (discarding the largest outlier and showing the smaller data from 3 of the 4 mice). Dosage: Injections of 200 μg/day PD-1 and Fc control, 40 μg, 20 μg, 20 μg, and 40 μg anti-Pi1.2 antibody (aTREM2) were given on days 5, 7, 11, and 15. Tumors were measured with calipers and shown volume of tumors.

In fig. 20 shows an analysis of true to false detection ratios (ROC) of BDCA3' in human melanoma. Staining data for %BDCA3 and anti-PD1 antibody treatment outcome were used to perform (Figure 20A) ROC analysis of BDCA3+ versus CD45+ ratio versus outcome, and (Figure 20B) ROC analysis of BDCA3+ versus HLADR ⁺ ratio compared to the outcome.

In fig. 21 demonstrated limited TREM2 protein expression relative to NSM (CD14+ TAM) populations, with little or no expression on CD14-negative CD11c-positive cells that include SDCs (BDCA3+ DC) in human HNSCC primary tumor tissue. TREM2-specific commercial antibody staining (RnD, clone 237920) was performed on disaggregated human HNSCC tumor tissue compared to a secondary staining control (rat IgG antibody, Jackson Immunoresearch) and analyzed by flow cytometry. This figure shows the specific expression of NSM gene products on NSM cells and the lack of expression on SDC cells in human tumor tissue. Populations were gated into live, CD45+, lineage negative, HLA-DR+, CD11c ⁺ and stratified by CD14 expression.

These and other aspects and advantages of the present invention will become apparent from the following detailed description and claims. It should be understood that one, more, or all of the features of the various embodiments described herein may be combined to form other embodiments of the invention.

Detailed Description of the Invention

For purposes of explanation, the following definitions will be used in this description and, where appropriate, terms used in the singular will also include the plural

- 11 046327 number and vice versa. To the extent that any definition provided below conflicts with any document incorporated herein by reference, the definition provided will govern.

It should be understood that aspects and embodiments of the invention described herein include aspects and embodiments of the invention defined by the terms comprising, consisting of, and/or consisting essentially of.

With respect to all compositions described herein and all methods using the composition described herein, the compositions may either contain listed components or steps or may consist essentially of listed components or steps. When a composition is described as consisting substantially of the listed components, the composition contains the listed components and may contain other components that have substantially no effect on the condition requiring treatment, but does not contain any other components that have a practical effect on the condition requiring treatment. , different from the explicitly listed components; or if the composition does not contain additional components other than those listed that have a practical effect on the condition requiring treatment, then the composition does not contain a sufficient concentration or amount of additional components to have a practical effect on the condition requiring treatment. When a method is described as comprising substantially the steps listed, the method contains the steps listed and may contain other steps that have substantially no effect on the condition requiring treatment, but the method does not contain any other steps that have substantially impact on the condition requiring treatment. treatments that differ from the steps explicitly listed. As a non-limiting specific example, when a composition is described as consisting essentially of a component, the composition may further contain any amounts of pharmaceutically acceptable carriers, placebo or diluents and other such components that have substantially no effect on the pathological condition requiring treatment.

The term optionally, when used sequentially, means to include from one to all numbered combinations and implies all subcombinations.

An effective amount or therapeutically effective amount, as used herein, refers to the amount of a therapeutic compound, such as an anti-NSM antigen-binding agent or an anti-NSM antibody, administered to an individual, either as a single dose or as part of a series of doses, that is effective to produce or promoting the desired therapeutic effect, either alone or in combination with other therapeutic effects. Examples of the desired therapeutic effect are enhancing the immune response, slowing or delaying tumor development; stabilization of the disease; relief of one or more symptoms. An effective amount may be given in one or more dosages.

The term treatment, as used herein, refers to slowing or reversing the progression of a pathological condition, such as cancer. The term treatment, as used herein, refers to the action of treating a pathological condition, such as cancer.

Individual or subject, as used herein, refers to any animal belonging to the class of mammals, including humans, domestic and farm animals, as well as zoo animals, sporting animals or pets such as dogs, horses, rabbits , cattle, pigs, hamsters, gerbils, mice, ferrets, rats, cats and the like. In some embodiments of the invention, the individual is a human. In some embodiments, the individual is a mouse.

The term about, as used herein, refers to the typical error range for the corresponding value, well known to one skilled in the art. As used herein, reference to about a value or parameter includes (and describes) embodiments of the invention that pertain to that value or parameter in essence.

It should be noted that, as used in this specification and the accompanying claims, the singular includes a reference to the plural unless the context clearly indicates otherwise.

With respect to any of the structural and functional characteristics described herein, methods for determining these characteristics are known in the art.

Non-stimulating myeloid cells (NSM)

Methods and compositions provided herein for blocking and/or identifying non-stimulating myeloid cells (NSM) include the use of an anti-NSM antibody. Also provided herein are methods and compositions for targeting and/or identifying non-stimulatory myeloid cells expressing NSM protein.

Also provided herein are methods and compositions for blocking and/or identifying non-stimulating myeloid cells, comprising the use of an antibody directed to a non-human homologue of the human NSM protein in a non-human subject.

As used herein, non-stimulating myeloid cells are

- 12 046327 myeloid cells that are not as effective at stimulating an immune response (eg, not as effective at stimulating an antitumor response in the tumor microenvironment compared to stimulating myeloid cells). In some embodiments, non-stimulatory myeloid cells are not as effective at presenting antigen (eg, a tumor antigen) to T cells or are not as effective at promoting tumor-specific T cell responses compared to stimulatory myeloid cells. In some embodiments, non-stimulatory myeloid cells may display a reduced ability to uptake, process, and/or present tumor-associated antigens to T cells compared to stimulatory myeloid cells. Nonstimulatory myeloid cells may have a reduced ability or lack the ability to reprime cytotoxic T lymphocytes or, in some cases, fail to stimulate effective tumor cell killing. Nonstimulatory myeloid cells may display lower expression of genes and cell surface markers involved in antigen processing, antigen presentation, and/or antigen costimulation, including, but not limited to, CD80, CD86, MHCI, and MHCII, compared to stimulatory myeloid cells.

Non-stimulatory myeloid cells, when compared to stimulatory myeloid cells, may display lower expression of genes associated with cross-presentation, costimulation and/or stimulatory cytokines, including, without limitation, any one or more of TAP1, TAP2, PSMB8, PSMB9, TAPBP, PSME2, CD24a, CD274, BTLA, CD40, CD244, ICOSL, ICAM1, TIM3, PDL2, RANK, FLT3, CSF2RB, CSF2RB2, CSF2RA, IL12b, XCR1, CCR7, CCR2, CCL22, CXCL9 and CCL5, and increased expression of the anti-inflammatory cytokine IL -10. In some embodiments, non-stimulatory myeloid cells are dependent on the transcription factor IRF4 and the cytokines GM-CSF or CSF-1 for differentiation and survival. In some embodiments, non-stimulating myeloid cells can promote tumor angiogenesis by secreting vascular endothelial growth factor (VEGF) and nitric oxide synthase (NOS) and supporting tumor growth by secreting epidermal growth factor (EGF).

In some embodiments, the non-stimulatory myeloid cells are tumor-associated macrophages (TAMs) or dendritic cells (DCs). In some embodiments, the non-stimulating myeloid cells are not dendritic cells (DC).

In some embodiments, the non-stimulating myeloid cells are tumor-associated macrophages (TAMs). TAMs are macrophages present near or within a malignant tumor, and they are derived from circulating monocytes or resident tissue macrophages.

In some embodiments, non-stimulatory myeloid cells and stimulatory myeloid cells are distinguished based on the markers they express or the markers they selectively express. Expression of cell surface markers can be described as + or positive. The absence of cell surface markers can be described as - or negative. Expression of cell surface markers can be further described as high (cells expressing high levels of markers) or low (cells expressing low levels of markers), indicating the relative expression of each marker on the cell surface. The level of markers can be determined using various methods known in the art, for example immunostaining and FACS analysis or gel electrophoresis and Western blotting.

In some embodiments, the non-stimulating myeloid cells are dendritic cells (DC). In some embodiments, dendritic cells can be distinguished by their spiky or dendritic morphology. In one embodiment, the non-stimulating dendritic cell is at least a CD45+, HLADR+, ^CD14- , CD11c ⁺ , and BDCA1+ (also referred to as a DC1 cell). In one embodiment, the non-stimulating dendritic cell is non-CD45+, HLA-DR+, ^CD14- , CD11c ⁺ and BDCA3 ⁺ (also referred to as a DC2 cell). In one embodiment of the invention, the dendritic cell that is CD45+, HLA-DR+, CD14 ^- , CD11c ⁺ and BDCA3+ is a stimulatory myeloid cell.

In some embodiments, the non-stimulating myeloid cells are tumor-associated macrophages. In some embodiments, for example in humans, the non-stimulatory tumor-associated macrophages are at least CD45 ⁺ , HLA-DR ⁺ , CD14 ⁺ . In some embodiments, the non-stimulatory tumor-associated macrophages are at least CD45+, HLA-DR+, CD14+, CD11b ⁺ . In some embodiments, the non-stimulatory tumor-associated macrophages are at least CD45 ⁺ , HLA-DR ⁺ , CD14 ⁺ , CD11c ⁺ . In some embodiments, the non-stimulatory tumor-associated macrophages are at least CD45 ⁺ , HLA-DR ⁺ , CD14 ⁺ ,

- 13 046327

BDCA3 ^- . In some embodiments, the non-stimulatory tumor-associated macrophages are at least CD45+, HLA-DR+, CD14+, ^BDCA3- , CD11b ⁺ . In some embodiments, the non-stimulatory tumor-associated macrophages are at least CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- , CD11c ⁺ . In some embodiments, the non-stimulatory tumor-associated macrophages are at least CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11b ⁺ , and CD11c ⁺ . In some embodiments, the non-stimulatory tumor-associated macrophages are at least CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- , CD11b ⁺ , and CD11c ⁺ .

In some embodiments, the methods and compositions of the invention are useful for targeting TAMs and DCs in other mammals, such as mice. In such embodiments, murine TAMs and DCs are contacted with an anti-NSM antibody. In one embodiment, for example in mice, the tumor-associated macrophage is at least CD45 ⁺ , HLA-DR+, CD14+, CDN™™ and CD11c™ ³ ™ (also called TAM1). In one embodiment, for example in mice, the tumor-associated macrophages are at least CD45 ⁺ , HLA-DR + , CD14 ⁺ , CD11b™ ³ ™ and CDUc ™ ⁰ ™ (also called TAM2). The term macrophage CD11b ^{β high} as used herein refers to macrophages expressing high levels of CD11b. The term CD11b ^LOW macrophage, as used herein, refers to macrophages that express on their surface levels of CD11b that are substantially lower than those of CDUb™ ¹ ” macrophages. The term CDUc™™™, as used herein, refers to to macrophages expressing high levels of CD 11c. The term CDIIc™ ³ ™ macrophage, as used herein, refers to macrophages that express on their surface levels of CD 11c that are substantially lower than those of macrophages

CD 11c high

In some embodiments, the non-stimulatory myeloid cells of the invention include one or more of TAM and DC1 cells.

In some embodiments, such as in mice, the non-stimulatory myeloid cells of the invention include one or more of TAM1, TAM2, and DC1 cells. In such embodiments, non-stimulating myeloid cells of the invention are contacted with an anti-NSM antibody.

In some embodiments, non-stimulating myeloid cells are located within the boundaries of tumor lesions or in transformed tumor ducts, where they are brought into contact with cognate T cells. In one embodiment of the invention, the localization of non-stimulating myeloid cells is modified such that the cells are no longer localized within tumors or no longer come into contact with T cells.

In some embodiments, the non-stimulatory myeloid cells are found in a population of immune cells comprising stimulatory myeloid cells and non-stimulatory myeloid cells. In some embodiments, the non-stimulatory myeloid cells are present in a population of immune cells that contains only non-stimulatory myeloid cells. The immune cell populations of the present invention may be pure, homogeneous, heterogeneous, derived from a variety of sources (eg, diseased tissue, tumor tissue, healthy tissue, cell banks), maintained in primary cell cultures, maintained in immortalized cultures, and/or maintained in cultures ex vivo.

In some embodiments, the non-stimulating myeloid cells are tumor-associated macrophages.

In some embodiments, the non-stimulating myeloid cells are dendritic cells.

In some embodiments, the non-stimulating myeloid cells are CD45 ⁺ , HLA-DR+, ^CD14- , CD11c ⁺ , and BDCA1+. In some embodiments, the non-stimulating myeloid cells comprise cells that are CD45 ⁺ , HLA-DR+, ^CD14- , CD11c ⁺ , and BDCA1+. In some embodiments, non-stimulating myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR+, ^CD14- , CD11c ⁺ , and BDCA1+. In some embodiments, non-stimulatory myeloid cells consist essentially of cells that are CD45 ⁺ , HLA-DR+, ^CD14- , CD11c ⁺ , and BDCA1+.

In some embodiments, the non-stimulating myeloid cells are CD45 ⁺ , HLA-DR + , CD14 ⁺ , BDCA3 ^- . In some embodiments, the non-stimulating myeloid cells comprise cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- . In some embodiments, non-stimulating myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR + , CD14 ⁺ , BDCA3 ^- . In some embodiments, non-stimulatory myeloid cells are composed essentially of cells that are CD45 ⁺ , HLA-DR + , CD14 ⁺ , BDCA3 ^- .

In some embodiments, the non-stimulating myeloid cells are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11b ⁺ . In some embodiments of the invention, non-stimulant

- 14 046327 myeloid cells contain cells that are CD45+, HLA-DR+, CD14+, CD11b ⁺ . In some embodiments, non-stimulating myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11b ⁺ . In some embodiments, non-stimulatory myeloid cells consist essentially of cells that are CD45 ⁺ , HLA-DR+, CD14+, CD11b ⁺ .

In some embodiments, the non-stimulating myeloid cells are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11c ⁺ . In some embodiments, the non-stimulating myeloid cells comprise cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11c ⁺ . In some embodiments, non-stimulating myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11c ⁺ . In some embodiments, non-stimulatory myeloid cells are composed essentially of cells that are CD45 ⁺ , HLA-DR+, CD14+, CD11c ⁺ .

In some embodiments, the non-stimulatory myeloid cells are CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- and CD11c ⁺ . In some embodiments, the non-stimulating myeloid cells comprise cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- and CD11c ⁺ . In some embodiments, non-stimulating myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- and CD11c ⁺ . In some embodiments, non-stimulatory myeloid cells consist essentially of cells that are CD45 ⁺ , HLA-DR + , CD14 ⁺ , BDCA3 ^- and CD11c ⁺ .

In some embodiments, the non-stimulatory myeloid cells are CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- and CD11b ⁺ . In some embodiments, the non-stimulating myeloid cells comprise cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- and CD11b ⁺ . In some embodiments, non-stimulatory myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR + , CD14 ⁺ , BDCA3 ^- and CD11b ⁺ . In some embodiments, non-stimulatory myeloid cells consist essentially of cells that are CD45 ⁺ , HLA-DR + , CD14 ⁺ , BDCA3 ^- and CD11b ⁺ .

In some embodiments, the non-stimulatory myeloid cells are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11b ⁺ , and CD11c ⁺ . In some embodiments, the non-stimulating myeloid cells comprise cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11b ⁺ and CD11c ⁺ . In some embodiments, non-stimulating myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11b ⁺ and CD11c ⁺ . In some embodiments, non-stimulatory myeloid cells consist essentially of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11b ⁺ , and CD11c ⁺ .

In some embodiments, the non-stimulatory myeloid cells are CD45 ⁺ , HLA-DR + , CD14 ⁺ , BDCA3 ^- , CD11b ⁺ and CD11c ⁺ . In some embodiments, the non-stimulating myeloid cells comprise cells that are CD45 ⁺ , HLA-DR+, CD14+, ^BDCA3- , CD11b ⁺ , and CD11c ⁺ . In some embodiments, non-stimulating myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , ^BDCA3- , CD11b ⁺ , and CD11c ⁺ . In some embodiments, non-stimulatory myeloid cells are composed of essentially cells that are CD45 ⁺ , HLA-DR + , CD14 ⁺ , BDCA3 ^- , CD11b ⁺ and CD11c ⁺ .

In some embodiments, the non-stimulating myeloid cells are non-CD45 ⁺ , HLA-DR+, ^CD14- , CD11c ⁺ , and BDCA3+. In some embodiments, the non-stimulating myeloid cells comprise cells that are not CD45 ⁺ , HLA-DR+, ^CD14- , CD11c ⁺ , and BDCA3+.

In some embodiments, for example in mice, the non-stimulatory myeloid cells are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CDUfe™™ and CD11c™ ³ ™. In some embodiments, for example in mice, the non-stimulating myeloid cells contain cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CDUfe™™ and CD11c™ ³ ™. In some embodiments, for example in mice, non-stimulating myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CDUb™ ⁰ ™ and CD11c™ ^3K ™. In some embodiments, for example in mice, non-stimulating myeloid cells cells are composed essentially of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CDUb™ ¹ ”™ and CDUc™ ³ ™. In such embodiments, non-stimulating mouse myeloid cells are contacted with an anti-NSM antibody.

In some embodiments, for example in mice, the non-stimulatory myeloid cells are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CDUb™ ³ ™ and CDUc™™. In some embodiments, for example in mice, the non-stimulating myeloid cells comprise cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CDUb™ ³ ™ and CDIIc™ ¹ ™. In some embodiments, for example in mice, non-stimulatory myeloid cells are composed of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CD11b™ ³ ™ and CDUc™™. In some embodiments, for example in mice, non-stimulatory myeloid cells consist essentially of cells that are CD45 ⁺ , HLA-DR+, CD14 ⁺ , CDUb™ ³ ™ and CDIIc™ ¹ ™. In such embodiments, non-stimulating mouse myeloid cells are contacted with an anti-NSM antibody.

- 15 046327

In some embodiments, the non-stimulating myeloid cells are found in cancerous tissue.

In some embodiments, the population of immune cells is located in cancerous tissue.

In some embodiments, non-stimulatory myeloid cells and stimulatory myeloid cells are present in cancer tissue.

In some embodiments, the biological sample comprises a population of immune cells comprising non-stimulatory myeloid cells and stimulatory myeloid cells.

NSM cells may collectively refer to DC1, TAM1 and TAM2 cells present in tumor tissues, which may be distinguished from other cell types by their expression of NSM cell markers. For example, genes and associated proteins that are expressed or translated in greater abundance in NSM cells than SDCs may function as NSM markers. The typical NSM marker is CD11b. Additional typical NSM markers are listed in Table. A. NSM cells can express TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK, and TMEM119 on their cell surface. In some aspects of the invention, NSM cells do not express at least one of KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, BDCA3, and XCR1.

In one embodiment of the invention, NSM cells are tumor-infiltrating myeloid cells that express one or more NSM marker genes listed in table. A. In another embodiment of the invention, NSM cells are tumor myeloid cells that express three or more NSM markers listed in table. A. In another embodiment, the NSM cells are tumor myeloid cells that express most or all of the NSM markers listed in table. A. In another embodiment, NSM cells are identified as tumor myeloid cells expressing MRC1, mS4A7, C1QC, APO E, C1QB, C1QA and C5AR1.

Table A

SDC markers NSM markers KIT C5AR1 CCR7 LYVE1 BATF3 ABCC3 FLT3 MRC1 ZBTB46 SIGLEC1 IRF8 STAB1 BTLA C1QB MYCL1 C1QA CLEC9A TMEM37 BDCA3 MERTK XCR1 C1QC TMEM119 MS4A7 APOE CYP4F18 TREM2 TLR7 LILRB4

Stimulating myeloid cells

As used herein, stimulatory myeloid cells (also called SDCs in certain aspects) are myeloid cells that are effective

- 16 046327 in stimulating an immune response (eg, more effective in stimulating an antitumor response in the tumor microenvironment compared to non-stimulated myeloid cells). In some embodiments, stimulatory myeloid cells are effective at presenting an antigen (eg, a tumor antigen) to T cells or effective at promoting tumor-specific T cell responses compared to non-stimulatory myeloid cells. In some embodiments, stimulatory myeloid cells may display an increased ability to uptake, process, and/or present tumor-associated antigens to T cells compared to non-stimulatory myeloid cells. Stimulatory myeloid cells may have an increased ability to reprime cytotoxic T lymphocytes or, in some cases, stimulate efficient tumor cell killing relative to nonstimulatory myeloid cells.

Nonstimulatory myeloid cells may display higher expression of genes and cell surface markers involved in antigen processing, antigen presentation, and/or antigen costimulation, including, but not limited to, CD80, CD86, MHCI, and MHCII compared to nonstimulatory myeloid cells.

Typical stimulating myeloid cell markers are listed in Table. A. For example, in human SDC, the expression of Xcr1, Clec9a, and BDCA3 (CD141) is a marker of SDC identity. It should be noted that in mice, CD 103 can also be used as a strong marker of SDC identity, although it is not expressed in human SDC.

In one embodiment, SDCs are tumor-infiltrating myeloid cells having dendritic cell identity that also express one or more of the SDC markers listed in Table 1. A. In another embodiment, SDCs are tumor-infiltrating myeloid cells having dendritic cell identity that also express two, three, four, five, six, seven, eight, nine, or all of the SDC markers listed in Table. A. In another embodiment, SDC are identified as tumor-infiltrating myeloid dendritic cells expressing BDCA3, KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, XCR1 and CLEC9A. SDC cells may express at least one of KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, BDCA3, and XCR1. In some embodiments, SDCs express substantially no TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK, and/or TMEM119 on their cell surface. In some embodiments, SDCs express substantially no C5AR1, LYVE1, ABCC3, MRC1, SIGLEC1, STAB1, C1QB, C1QA, TMEM37, MERTK, C1QC, TMEM119, MS4A7, APO E, CYP4F18, TREM2, TLR7, and/or LILRB4. Flow cytometry and PCR, among other assays recognized in the art, can be used to assess the expression of the marker described herein.

Stimulating myeloid cells can be CD45+, HLA-DR+, ^CD14- , CD11c+ and BDCA3+. Stimulating myeloid cells can be CD45+, HLA-DR+ and BDCA3+. Stimulating myeloid cells can be CD45+, HLA-DR+, CD14 ^- and BDCA3+. Stimulating myeloid cells can be CD45+, HLA-DR+, CD11c ⁺ and BDCA3+.

Antibodies

This application provides antibodies and compositions containing an antibody that binds the NSM protein, including antibodies that block non-stimulating myeloid cells.

This application provides antibodies and compositions containing an antibody that binds the NSM protein, including antibodies that block non-stimulating myeloid cells.

As used herein, the term antibody or immunoglobulin refers to a polypeptide that is substantially encoded by an immunoglobulin gene or a set of immunoglobulin genes or analyte-binding fragments thereof that specifically bind and recognize an analyte (eg, an antigen). All recognized immunoglobulin genes include the constant regions of the kappa, lambda, alpha, gamma, delta, epsilon and mu genes, as well as many immunoglobulin variable region genes. Light chains are classified as either kappa or lambda chains. An antibody or immunoglobulin class refers to the type of constant domain or constant region contained in its heavy chain. There are five main classes of antibodies: IgA, IgD, IgE, IgG and IgM, and some of them can be further divided into subclasses (isotypes), for example, IgGi, IgG2, IgG ₃ , IgG ₄ , IgAi and IgA ₂ . The heavy chain constant domains that correspond to different classes of immunoglobulins are called α, δ, ε, γ and μ, respectively.

The structural unit of a typical immunoglobulin (antibody) consists of two pairs of polypeptide chains, each pair having one light chain (about 25 kDa) and one heavy chain (about 50-70 kDa). The N-terminal domain of each chain defines a variable region consisting of about 100 to 110 or more amino acids primarily responsible for antigen recognition. The terms light chain variable region (VL) and heavy chain variable region (VH) refer to these light and heavy chain domains, respectively. The IgG1 heavy chain consists of VH, CH1, CH2 and CH3 domains, respectively, from N- to C-terminus. The light chain consists of VL and CL domains from the N- to the C-terminus. Harder

- 17 046327 The IgG1 chain contains a hinge between the CH1 and CH2 domains. In some embodiments, the immunoglobulin constructs comprise at least one immunoglobulin domain of IgG, IgM, IgA, IgD, or IgE linked to a therapeutic polypeptide. In some embodiments, the immunoglobulin domain found in an antibody provided herein is derived from or derived from an immunoglobulin-based construct, such as a diabody or nanobody. In some embodiments, the immunoglobulin constructs described herein comprise at least one immunoglobulin domain from an antibody heavy chain, such as a camel antibody. In some embodiments, the immunoglobulin constructs provided herein comprise at least one immunoglobulin domain from a mammalian antibody, such as a bovine antibody, a human antibody, a camel antibody, a mouse antibody, or any chimeric antibody.

The term hypervariable region or HVR, as used herein, refers to each of the regions of the variable domain of an antibody that have a hypervariable sequence and/or form structurally defined loops (hypervariable loops). Typically, native four-chain antibodies contain six HVRs; three in VH (H1, H2, H3) and three in VL (L1, L2, L3). HVRs typically contain amino acid residues from hypervariable loops and/or from complementarity determining regions (CDRs), the latter having the highest variability and/or being involved in antigen recognition. With the exception of CDR1 in VH, CDRs typically contain amino acid residues that form hypervariable loops. Hypervariable regions (HVRs) are also called complementarity determining regions (CDRs), and these terms are used interchangeably herein to refer to portions of the variable region that form the antigen binding regions. This particular site was described by Kabat et al., U.S. Dept. of Health and Human Services, Sequences of Proteins of Immunological Interest (1983) and Chothia et al., J Mol Biol 196:901-917 (1987), where the definitions include overlap or subsets of amino acid residues when compared with each other. However, the use of any term in relation to an antibody CDR or variants thereof is intended to be covered by the term as defined and used herein. The exact number of residues that comprise a particular CDR will vary depending on the sequence and size of the CDR. Those skilled in the art can determine by standard techniques which residues are contained in a particular CDR providing the amino acid sequence of an antibody variable region.

As used herein, the term single-chain refers to a molecule containing amino acid monomers linearly linked by peptide bonds. In this particular embodiment, the C-terminus of the Fab light chain is connected to the N-terminus of the Fab heavy chain in a single-chain Fab molecule. As described in more detail herein, scFv has a light chain variable domain (VL) connected from its C-terminus to the N-terminus of a heavy chain variable domain (VH) by a polypeptide chain. In an alternative embodiment, the scFv contains a polypeptide chain in which the VH terminal end is connected to the VL N-terminal end by a polypeptide chain.

The Fab fragment (also called antigen binding fragment) contains a light chain constant domain (CL) and a heavy chain first constant domain (CH1) along with VL and VH variable domains located on the light and heavy chains, respectively. Variable domains contain complementarity determining loops (CDRs, also called hypervariable regions) that are involved in antigen binding. Fab' fragments differ from Fab fragments by an additional few residues at the carboxyl terminus of the heavy chain CH1 domain, including one or more cysteines from the antibody hinge region.

A single chain Fv or scFv includes the VH and VL domains of an antibody, these domains being contained in a single polypeptide chain. In one embodiment, the Fv polypeptide further comprises a polypeptide linker between the VH and VL domains, allowing the sFv to form the desired structure for antigen binding. For a review of ScFv, see Pluckthun in The Pharmacology of Monoclonal Antibodies, vol. 113, Rosenburg and Moore eds., Springer-Verlag, New York, pp. 269-315 (1994). HER2 antibody scFv fragments are described in WO 93/16185; US Patent No. 5,571,894 and US Pat. No. 5,587,458.

The single domain antibody form, or sdAb, is a single immunoglobulin domain. Sdab are quite stable and are readily expressed as fusion partners with the Fc chain of antibodies (Harmsen MM, De Haard HJ (2007). Properties, production, and applications of camelid single-domain antibody fragments. Appl. Microbiol Biotechnol. 77(1 ): 13-22).

The term Fc domain or Fc region is used herein to refer to the C-terminal region of an immunoglobulin heavy chain that contains at least a portion of a constant region. This term covers native sequence Fc regions and variant Fc regions. Unless otherwise specified herein, numbering of amino acid residues in the Fc region or constant region is according to the EU numbering system, also referred to as the EU index, as described in Kabat et al., Sequences of Proteins of Immunological Interest, ^5th Ed. Public Health Service, National Institutes of Health, Bethesda, MD, 1991. As used herein, the term Fc

- 18 046327 dimeric Fc polypeptide refers to one of the two polypeptides that form the dimeric Fc domain, i.e. to a polypeptide containing the C-terminal constant regions of the immunoglobulin heavy chain, capable of stable self-association. For example, the Fc polypeptide of a dimeric IgG Fc contains the constant domain sequence of an IgG CH2 and an IgG CH3. Fc can belong to the class IgA, IgD, IgE, IgG and IgM, and some of them can be further divided into subclasses (isotypes), for example, IgGi, IgG ₂ , IgG ₃ , IgG ₄ , IgAi and IgA ₂ .

The term Fc receptor and FcR are used to describe a receptor that binds to the Fc region of an antibody. For example, the FcR may be a human FcR with a native sequence. Typically, an FcR is a receptor that binds an IgG antibody (gamma receptor) and includes the receptor subclasses FcyRI, FcyRII, and FcyRIII, including allelic variants and alternative splicing forms of these receptors. FcyRII receptors include FcyRIIA (activating receptor) and FcyRIIB (inhibitory receptor), which have similar amino acid sequences that differ mainly in their cytoplasmic domains. Immunoglobulins of other isotypes may also be bound by certain FcRs (see, for example, Janeway et al., Immuno Biology: the immune system in health and disease, (Elsevier Science Ltd., NY) ( ^4th ed., 1999)). The activating receptor FcyRIIA contains an immunoreceptor tyrosine inhibitory motif (ITAM) in its cytoplasmic domain. The inhibitory receptor FcyRIIB contains an immunoreceptor tyrosine inhibitory motif (ITIM) in its cytoplasmic domain (reviewed in Daeron, Annu. Rev. Immunol. 15:203-234 (1997)). A review on FcR is contained in Ravetch and Kinet, Annu. Rev. Immunol 9:457-92 (1991); Capel et al., Immunomethods 4:25-34 (1994) and de Haas et al., J. Lab. Clin. Med. 126:330-41 (1995). The term FcR as used herein covers other FcRs, including those to be identified in the future. The term also includes the neonatal receptor, FcRn, which is responsible for the transfer of maternal IgG to the fetus (Guyer et al., J. Immunol. 117:587 (1976) and Kim et al., J. Immunol. 24:249 (1994)).

Modifications in the CH2 domain may affect the binding of FcR to Fc. A number of amino acid modifications in the Fc region are known in the art to selectively alter the affinity of the Fc for various Fc gamma receptors. In some aspects, Fc contains one or more modifications to promote selective binding of Fc gamma receptors.

Typical mutations that alter FcR binding to Fc are listed below:

S298A/E333A/K334A, S298A/E333A/K334A/K326A (Lu Y, Vernes JM, Chiang N, et al. J

Immunol Methods. 2011 Feb 28;365(l-2): 132-41);

F243L/R292P/Y300L/V305EP396L, F243L/R292P/Y300L/L235V/P396L (Stavenhagen JB, Gorlatov S, Tuaillon N, et al. Cancer Res. 2007 Sep 15;67(18):8882-90; Nordstrom JL, Gorlatov S, Zhang W, et al Breast Cancer Res 2011 Nov 30;13(6):R123);

F243L (Stewart R, Thom G, Levens M, et al. Protein Eng Des Sei. 2011 Sep;24(9):671-8.

), S298A/E333A/K334A (Shields RL, Namenuk AK, Hong K, et al. J Biol Chem. 2001 Mar 2;276(9):6591604);

S239D/I332E/A330L, S239D/I332E (Lazar GA, Dang W, Karki S, et al. Proc Natl Acad Sci USA.

2006 Mar I4;I03(II):4005-I0);

S239D/S267E, S267E/L328F (Chu SY, Vostiar I, Karki S, et al. Mol Immunol. 2008

Sep;45(15):3926-33);

S239D/D265S/S298A/I332E, S239E/S298A/K326A/A327H, G237F/S298A/A330L/I332E, S239D/I332E/S298A, S239D/K326E/A330L/I332E/S298A, G236 A/S239D/D270L/I332E, S239E/ S267E/H268D, L 234F/S267E/N325L, G237F/V266L/S267D and other mutations listed in WO2011/120134 and WO2011/120135, incorporated herein by reference. Therapeutic Antibody Engineering (by William R. Strohl and Lila M. Strohl, Woodhead Publishing series in Biomedicine No. 11, ISBN 1 907568 37 9, Oct 2012) lists mutations on p. 283.

In some embodiments, an antibody described herein includes modifications to improve the ability to mediate effector function. Such modifications are known in the art and include afucosylation or affinity engineering

- 19 046327 Fc affinity to the activating receptor, mainly FCGR3a for ADCC, and to C1q for ADCC. In the table below. The various constructs described in the literature for constructing effector function are summarized.

Methods for producing antibodies with little or no fucose at the Fc glycosylation site (Asn 297 EU numbering) without changing the amino acid sequence are well known in the art. GlymaX® technology (ProBioGen AG) is based on the introduction of an enzyme gene that disrupts the cellular mechanism of fucose biosynthesis in cells used for the production of antibodies. This prevents antibody-producing cells from adding the sugar fucose to the N-linked carbohydrate moiety of the antibody (von Horsten et al. (2010) Glycobiology. 2010 Dec; 20 (12): 1607-18. Another approach for producing antibodies with reduced levels of fucosylation can be found in the patent US 8409572, which outlines the selection of cell lines for antibody production based on their ability to produce lower levels of antibody fucosylation.Antibodies may be fully afucosylated (meaning they contain no detectable fucose) or they may be partially afucosylated, meaning the isolated antibody contains less than 95%, less than 85%, less than 75%, less than 65%, less than 55%, less than 45%, less than 35%, less than 25%, less than 15% or less than 5% of the amount of fucose typically found in a similar antibody, produced by the mammalian expression system.

Thus, in one embodiment of the invention, an antibody described herein may contain a dimeric Fc that contains one or more amino acid modifications, as indicated in table. B, imparting improved effector function. In another embodiment, the antibody may be afucosylated to improve effector function.

Table B

CH2 domains and effector function engineering

Literary source Mutations Action Lu, 2011, Ferrara 2011, Mizushima 2011 Afucosylated Enlarged AZKTS Lu, 2011 S298A/E333A/K334A Enlarged AZKTS Lu, 2011 S298A/E333A/K334A/K326A Enlarged AZKTS Stavenhagen, 2007 F243L/R292P/Y3 00L/V3 05I/P3 96L Enlarged AZKTS Nordstrom, 2011 F243L/R292P/Y300L/L235V/P396L Enlarged AZKTS Stewart, 2011 F243L Enlarged AZKTS Shields, 2001 S298A/E333A/K334A Enlarged AZKTS Lazar, 2006 S239D/I332E/A330L Enlarged AZKTS Lazar, 2006 S239D/I332E Enlarged AZKTS Bowles, 2006 AME-D, unspecified mutations Enlarged AZKTS Heider, 2011 37. 1, mutations not described Enlarged AZKTS Moore, 2010 S267E/H268F/S324T Enlarged KZTs

Fc modifications that reduce FcyR and/or complement binding and/or effector functions are known in the art. Recent publications describe strategies that have been used to construct antibodies with reduced or suppressed effector activity (see Strohl, WR (2009), Curr Opin Biotech 20:685-691 and Strohl, WR and Strohl LM, Antibody Fc engineering for optimal antibody performance in Therapeutic Antibody Engineering, Cambridge:Woodhead Publishing (2012), pp 225-249). These strategies include reduction of effector functions through modification of glycosylation, use of IgG ₂ /IgG ₄ scaffolds, or introduction of mutations in the hinge or CH2 regions of the Fc. For example, US Patent Application Publication No. 2011/0212087 (Strohl), International Patent Application Publication No. WO 2006/105338 (Xencor), US Patent Application Publication No. 2012/0225058 (Xencor), US Patent Application Publication No. 2012 /0251531 (Genentech) and Strop et al. ((2012) J. Mol. Biol. 420:204-219) describe specific modifications to reduce FcyR or complement binding to Fc.

Specific, non-limiting examples of known amino acid modifications to reduce FcyR or complement binding to Fc include those listed in the following table: Table C

Modifications to reduce FcyR or complement binding to Fc

Company Mutations GSK N297A Ortho Biotech L234A/L235A Protein Design labs IGG2 V234A/G237A Wellcome Labs IGG4 L235A/G237A/E318A GSK IGG4 S228P/L236E Alexion IGG2/IGG4combo Merck IGG2 H268Q/V309L/A330S/A331S Bristol-Myers C220S/C226S/C229S/P238S Seattle Genetics C226S/C229S/E3233P/L235V/L235A Amgen Products in E. coli, without glyco Medimune L234F/L235E/P331S Trabion Hinge mutant, probably C226S/P230S

In some embodiments, the antibody has antibody-dependent cellular cytotoxicity (ADCC) activity. ADCC can occur when antibodies bind to antigen on the surface of pathogenic or tumorigenic target cells. Effector cells bearing the Fc gamma receptor (FcyR or FCGR) on their cell surface, including cytotoxic T cells, natural killer (NK) cells, macrophages, neutrophils, eosinophils, dendritic cells or monocytes, recognize and bind the Fc region of antibodies bound with the target cell. Such binding may trigger the activation of intracellular signaling pathways leading to cell death. In specific embodiments, the immunoglobulin Fc region subtypes (isotypes) include human IgG ₁ and human IgG ₃ . As used herein, ADCC refers to a cell-mediated response in which nonspecific cytotoxic cells that express Fc receptors (FcR) (eg, natural killer (NK) cells, neutrophils, and macrophages) recognize binding of an antibody on a target cell and subsequently causing lysis of the target cell. The primary cells for mediating ADCC, NK cells, express only FcyRIII, whereas monocytes express FcyRI, FcyRII, and FcyRIII. Data on FcR expression on hematopoietic cells are summarized in Table. 3 on p. 464 publications Ravetch and Kinet, Annu. Rev. Immunol 9:457-92 (1991). To assess the ADCC activity of a molecule of interest, an in vitro ADCC assay, such as that described in US Pat. No. 5,500,362 or 5,821,337, can be performed. Effector cells suitable for such assays include peripheral blood mononuclear cells (PBMCs) and natural killer (NK) cells. Alternatively or in addition

- 21 046327 Alternatively, the ADCC activity of a molecule of interest can be assessed in vivo, for example, in an animal model such as described in Dynes et al., Proc. Natl. Acad. Sci. (USA) 95:652-656 (1998).

In some embodiments, the antibody has complement-dependent cellular cytotoxicity (CDC) activity. Antibody-induced CDC is mediated through proteins of the classical complement activation pathway and is triggered by the binding of the complement protein C1q to the antibody. Binding of the Fc region of an antibody to C1q can induce activation of the complement cascade. In specific embodiments, the immunoglobulin F region subtypes (isotypes) include IgG| and human IgG ₃ . As used herein, CLC refers to the ability of a molecule to lyse a target in the presence of complement. Activation of the complement pathway is initiated by the binding of the first component of the complement system (C1q) to a molecule (eg, polypeptide, (eg, antibody)) complexed with the recognized antigen. To assess complement activation, a CDC assay can be performed, for example, as described in Gazzano-Santoro et al., J. Immunol. Methods 202:163 (1996).

The targets of anti-NSM antibodies may be at least 1, 2, 3, 4 or more of TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK and TMEM119. TREM2 may be a target for anti-NSM antibodies. Anti-NSM antibody may target MS4A7. In some aspects of the invention, the targets of anti-NSM antibodies are not one or more of TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK and/or TMEM119. In some aspects of the invention, the target of anti-NSM antibodies is not LILRB4. In some aspects of the invention, anti-NSM antibodies do not bind TREM2. In some aspects of the invention, anti-NSM antibodies do not bind MS4A7. In some aspects of the invention, anti-NSM antibodies do not bind C5AR1. In some aspects of the invention, anti-NSM antibodies do not bind LYVE1. In some aspects of the invention, anti-NSM antibodies do not bind ABCC3. In some aspects of the invention, anti-NSM antibodies do not bind LILRB4. In some aspects of the invention, anti-NSM antibodies do not bind MRC1/CD206. In some aspects of the invention, anti-NSM antibodies do not bind SIGLEC1. In some aspects of the invention, anti-NSM antibodies do not bind STAB 1. In some aspects of the invention, anti-NSM antibodies do not bind TMEM37. In some aspects of the invention, anti-NSM antibodies do not bind MERTK. In some aspects of the invention, anti-NSM antibodies do not bind TMEM119. In some aspects of the invention, anti-NSM antibodies do not bind TLR7.

In some embodiments, the antibody has antibody-dependent cellular phagocytosis (ADCP) activity. ADCF can occur when antibodies bind to an antigen on the surface of pathogenic or tumorigenic target cells. Phagocytes bearing Fc receptors on their cell surface, including monocytes and macrophages, recognize and bind the Fc region of antibodies bound to target cells. When the Fc receptor binds to target cells with bound antibodies, phagocytosis of the target cell can be initiated. ADCF can be considered a form of ADCC.

In some embodiments, the antibodies are capable of forming an immune complex. For example, the immune complex may be a tumor cell coated with antibodies.

In some aspects of the invention, the anti-NSM antibody substantially does not bind myeloid cells present outside the cancer tissue. In some aspects of the invention, the anti-NSM antibody substantially does not bind stimulatory myeloid cells present in cancer tissue.

In some embodiments, the antibodies are monoclonal antibodies. In some embodiments, the antibodies are polyclonal antibodies.

In some embodiments, the antibodies are produced by hybridomas. In other embodiments, antibodies are produced by recombinant cells engineered to express the desired variable and constant domains.

In some embodiments, the antibody may be a single chain antibody or other antibody derivative that retains antigen specificity and a downstream hinge region or variant thereof.

In some embodiments, the antibodies may be multifunctional antibodies, recombinant antibodies, human antibodies, humanized antibodies, fragments or variants thereof. In specific embodiments, the antibody fragment or derivative thereof is selected from a Fab fragment, a Fab'2 fragment, a CDR, and a ScFv.

Human antibody includes all antibodies having variable and constant regions derived from human immunoglobulin sequences. In one embodiment of the invention, all of the variable and constant domains are derived from human immunoglobulin (fully human antibody) sequences. These antibodies can be produced in a variety of ways, including by immunizing a mouse with the antigen of interest that has been genetically modified to express antibodies derived from human genes encoding severe and/or

- 22 046327 light chains.

A humanized antibody has a sequence that differs from that of an antibody originating from a non-human species by one or more amino acid substitutions, deletions and/or insertions such that the humanized antibody is less likely to elicit an immune response and/or elicits a less severe immune response compared to non-human ones. types of antibody when it is administered to a person. In one embodiment, certain amino acids in the framework and constant domains of the heavy and/or light chains of a non-human antibody are altered by mutation to produce a humanized antibody. In another embodiment, the constant domain(s) from a human antibody is fused to the variable domain(s) of a non-human species. In another embodiment, one or more amino acid residues in one or more CDR sequences of a non-human antibody are altered to reduce the likely immunogenicity of the non-human antibody when administered to a human, wherein the altered amino acid residues are either not critical for immunospecific binding of the antibody to its antigen, or the alterations are created in the amino acid sequence are conservative changes such that binding of a humanized antibody to an antigen is not significantly inferior to binding of a non-human antibody to an antigen. Examples of how to create humanized antibodies can be found in US Patent Nos. 6,054,297, 5,886,152, and 5,877,293.

A chimeric antibody refers to an antibody that contains one or more regions from one antibody and one or more regions from one or more other different antibodies.

In some embodiments, the antibodies are specific for surface antigens, such as the NSM protein. In some embodiments, therapeutic antibodies are specific for tumor antigens (eg, molecules specifically expressed by tumor cells). In specific embodiments, the therapeutic antibodies may have the Fc portions of human or non-human primate IgG ₁ or IgG ₃ .

In some embodiments, the antibody is bound or conjugated to an effector molecule. In specific embodiments, the antibody is conjugated to at least one therapeutic agent selected from the group consisting of a radionuclide, cytotoxin, chemotherapeutic agent, drug, prodrug, toxin, enzyme, immunomodulator, anti-angiogenic agent, pro-apoptotic agent, cytokine , hormone, oligonucleotide, antisense molecule, siRNA, second antibody and second antibody fragment.

In some embodiments, the antibody is an agonistic antibody. An agonist antibody can induce (eg, increase) one or more NSM activities or functions after the antibody binds an NSM protein expressed on a cell. An agonistic antibody can bind to and activate NSM, causing changes in cell proliferation or modification of antigen-presenting abilities. An agonistic antibody can bind to and activate NSM, triggering intracellular signaling pathways that lead to modified cell growth or apoptosis.

In some embodiments, the antibody is an antagonistic antibody. An antagonistic antibody can block (eg, reduce) one or more NSM activities or functions after the antibody binds an NSM protein expressed on a cell. For example, an antagonistic antibody may bind to and block ligand binding to one or more NSM proteins, preventing cell differentiation and proliferation or modification of antigen presenting properties. The antagonist antibody can bind to and prevent activation of the NSM protein through its ligand, modifying intracellular signaling mechanisms that are involved in cell growth and survival.

In some embodiments, the antibody is a depleting antibody. A depleting antibody is an antibody that can destroy a non-stimulating myeloid cell upon contact through interaction of the antibody with other immune cell molecules. For example, antibodies, when binding to cells carrying NSM proteins, can engage proteins of the complement system and cause complement-dependent cell lysis. Antibodies, when bound to NSM protein-bearing cells, can also stimulate neighboring Fc receptor-bearing cells to kill them through antibody-dependent cellular cytotoxicity (ADCC).

In some embodiments, the antibody is a neutralizing antibody and the antibody neutralizes one or more biological activities of NSM. In some embodiments, the NSM protein is expressed on the surface of non-stimulating myeloid cells and the antibody recognizes the extracellular domain of the NSM protein.

In some embodiments, the antibody is selective for NSM (preferably binds to NSM). In some embodiments, an antibody that selectively binds to NSM has a dissociation constant (K _d ) ranging from 0.0001 nM to 1 μM. In some embodiments, the antibody specifically binds to an epitope on an NSM protein that is conserved among proteins from different species. In another embodiment of the invention, selective binding includes, but does not require, exclusive binding.

- 23 046327

In one embodiment, an anti-NSM antibody bound to its target is responsible for causing in vivo depletion of non-stimulating myeloid cells to which it is bound. In some embodiments, effector proteins induced by clustered antibodies can trigger a variety of responses, including the release of inflammatory cytokines, regulation of antigen production, endocytosis, or cell killing. In one embodiment, the antibody is capable of recruiting and activating complement or mediating antibody-dependent cellular cytotoxicity (ADCC) in vivo, or mediating phagocytosis by binding Fc receptors in vivo. The antibody can also deplete non-stimulating myeloid cells by inducing apoptosis or necrosis of non-stimulating myeloid cells upon binding.

In some embodiments, the antibody is an IgG ₁ antibody.

In some embodiments, the antibody is an IgG ₃ antibody.

In some embodiments, the antibody is not an IgG ₂ antibody.

In some embodiments, the antibody is not an IgG ₄ antibody.

In some embodiments of the invention, blocking of non-stimulating myeloid cells occurs in vitro and is achieved by: a) destruction of non-stimulating myeloid cells; b) magnetic bead depletion of non-stimulating myeloid cells; or c) sorting of non-stimulating myeloid cells by fluorescence activated cell sorting (FACS).

In some embodiments, an antibody provided herein has a dissociation constant (Kd) ranging from 0.0001 nM to 1 μM. For example, the Kd of an antibody may be about 1 μM, about 100 nM, about 50 nM, about 10 nM, about 1 nM, about 500 pM, about 100 pM, or about 50 pM to any value of about 2 pM, about 5 pM, about 10 pM, about 15 pM, about 20 pM, or about 40 pM.

In some embodiments of the invention for in vivo administration of anti-NSM antibodies described herein, normal dosage sizes may range from about 10 ng/kg up to about 100 mg/kg of individual body weight or more per day, preferably from about 1 mg/kg/day. kg/day to 10 mg/kg/day, depending on the route of administration. If repeated administration is required over several days or longer, depending on the severity of the disease or disorder requiring treatment, treatment is continued until the desired symptom suppression is achieved. A typical dosing regimen involves an initial dose of anti-NSM antibody of approximately 2 mg/kg followed by a weekly maintenance dose of approximately 1 mg/kg every other week. Other dosage regimens may be used depending on the pharmacokinetic disruption pattern that the clinician wishes to achieve. For example, this document assumes dosing to an individual from one to twenty-one times per week. In some embodiments, a dosage range of from about 3 μg/kg to about 2 mg/kg (such as about 3 μg/kg, about 10 μg/kg, about 30 μg/kg, about 100 μg/kg, about 300 µg/kg, about 1 mg/kg and about 2 µg/kg). In some embodiments, the dosing frequency is three times a day, twice a day, once a day, once every two days, once a week, once every two weeks, once every four weeks, once every five weeks, once every six weeks, once every seven weeks, once every eight weeks, once every nine weeks, once every ten weeks or once a month, once every two months, once every three months or longer. The progress of therapy is easy to monitor using traditional methods and tests. The dosage regimen, including the anti-NSM antibody administered, may vary over time regardless of the dose administered.

In some embodiments, the antibody is conjugated to a drug, such as a toxin, chemotherapeutic agent, immunomodulator, or radioisotope. Some methods for preparing ADCs (antibody drug conjugates) are known in the art and are described, for example, in US patents 8624003 (pot method), 8163888 (one-step) and 5208020 (two-step method). The antibody or antigen-binding fragment thereof may be conjugated to at least one agent, including a radionuclide, cytotoxin, chemotherapeutic agent, drug, prodrug, toxin, enzyme, immunomodulator, antiangiogenic agent, pro-apoptotic agent, cytokine, hormone, oligonucleotide, antisense molecule , siRNA, a second antibody, and a fragment of a second antibody that is antigen binding.

Proteins, nucleotides and homologues

Provided herein are methods and compositions for blocking and/or identifying non-stimulating myeloid cells that express NSM proteins. In some embodiments, the invention is directed to blocking and/or identifying non-stimulatory myeloid cells from non-human mammalian cells that express a homologue of the NSM protein. For example, NSM proteins in the mouse may express a comparable restricted expression pattern to its human homolog. Thus, provided herein are methods and compositions for blocking and/or identifying non-stimulating myeloid cells that express NSM protein. This document also provides similar methods and compositions for blocking and/or

- 24 046327 identifying non-stimulating cells from the body of any individual that express a homolog of the NSM protein with a similar expression pattern, in which the cells exhibit a comparable expression pattern as for the NSM protein.

NSM proteins or nucleotides may include at least one or more of C5AR1, LYVE1, ABCC3, MRC1, SIGLEC1, STAB1, C1QB, C1QA, TMEM37, MERTK, C1QC, TMEM119, MS4A7, APO E, CYP4F18, TREM2, TLR7 and LILRB4, and their homologues. SDC proteins or nucleotides may include at least one or more of KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, BDCA3 and XCR1, and homologues thereof. NSM cell surface proteins may include at least one or more of TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK, and TMEM119. NSM cell surface proteins can be targets of one or more anti-NSM antibodies, alone or in combination. In general, NSMs are positive for NSM proteins or nucleotides and negative for SDC proteins or nucleotides; in contrast, SDCs are generally positive for SDC proteins or nucleotides and negative for NSM proteins or nucleotides.

The antigen binding constructs described herein contain at least one polypeptide. Also described are polynucleotides encoding the polypeptides described herein. Antigen binding constructs are typically isolated.

As used herein, the term isolated means an agent (eg, a polypeptide or polynucleotide) that has been identified and separated and/or isolated from a component of its natural cell culture environment. Contaminants in its natural environment are substances that may interfere with diagnostic or therapeutic applications of the antigen-binding construct, and may include enzymes, hormones, and other protein or non-protein solutes. The term isolated also refers to a product that has been produced synthetically, such as through human intervention.

As used herein, the terms polypeptide, peptide and protein are used interchangeably to refer to a polymer of amino acid residues. Namely, a description regarding a polypeptide applies equally to a description of a peptide and a description of a protein, and vice versa. These terms apply to naturally occurring amino acid polymers, as well as to amino acid polymers in which one or more amino acid residues are a non-naturally occurring amino acid. As used herein, the terms cover amino acid chains of any length, including full-length proteins, where the amino acid residues are linked by covalent peptide bonds.

The term amino acid refers to naturally occurring and non-naturally occurring amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to naturally occurring amino acids. Naturally occurring amino acids are the 20 common amino acids (alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine and valine) and pyrrolysine and selenocysteine. Amino acid analogues refer to compounds that have a basic chemical structure identical to a naturally occurring amino acid, i.e. a carbon that is bonded to hydrogen, a carboxyl group, an amino group and an R group, for example homoserine, norleucine, methionine sulfoxide, methionine methylsulfonium. Such analogues may have modified R groups (eg, norleucine) or modified peptide backbones, but retain the same basic chemical structure as the naturally occurring amino acid. Reference to amino acids includes, for example, proteogenic L-amino acids of natural origin; D-amino acids, chemically modified amino acids such as amino acid variants and derivatives; non-proteogenic amino acids of natural origin, such as β-alanine, ornithine, etc., and chemically synthesized compounds having properties known in the art to be characteristic of amino acids. Examples of non-naturally occurring amino acids include, but are not limited to, α-methyl amino acids (e.g., amethylalanine), D-amino acids, histidine-like amino acids (e.g., 2-aminohistidine, β-hydroxyhistidine, homohistidine), amino acids having an additional methyl group in the side chain (homo amino acids), and amino acids in which the functional group on the carboxylic acid side chain is replaced by a sulfonic acid functional group (for example, cysteic acid). The inclusion of unnatural amino acids, including synthetic negative amino acids, substituted amino acids, or one or more D-amino acids, in the proteins of the present invention can be advantageous in a number of different ways. Peptides containing D-amino acids exhibit increased stability in vitro or in vivo compared to analogues containing L-amino acids. Thus, in the design of peptides and the like, the inclusion of D-amino acids may be particularly useful where greater intracellular stability is desired or required. More specifically, D-peptides and the like are resistant to endogenous peptidases and proteases, thereby providing improved bioavailability of the molecule and increased in vivo lifetime, in the case

- 25 046327 if such properties are desired. Additionally, D-peptides and the like cannot be efficiently processed for MHC class II-restricted presentation to T helper cells and are therefore less likely to induce a humoral immune response in the entire body.

Amino acids may be designated herein by their commonly used three-letter symbols or by the single-letter symbols recommended by the IUPAC-IUB Commission on Biochemical Nomenclature. Likewise, nucleotides can be represented by their conventional single-letter codes.

Also described herein are polynucleotides encoding polypeptides of antigen binding constructs. The term polynucleotide or nucleotide sequence refers to a sequential stretch of two or more nucleotide molecules. The nucleotide sequence may be of genomic, cDNA, RNA, semi-synthetic or synthetic origin, or any combination thereof.

The term nucleic acid refers to deoxyribonucleotides, deoxyribonucleosides, ribonucleosides or ribonucleotides and polymers thereof in either single- or double-stranded form. Without limitation, the term covers nucleic acids containing known analogues of naturally occurring nucleotides that have similar binding properties to the reference nucleic acid and are metabolized in a similar manner to naturally occurring nucleotides. Without regard to other special limitations, the term also refers to oligonucleotide analogs, including PNA (peptidonucleic acid), DNA analogs used in antisense technology (phosphorothioates, phosphoroamidates and the like). Unless otherwise indicated, a specific nucleotide sequence also implicitly covers conservatively modified variants thereof (including but not limited to degenerate codon substitutions) and complementary sequences, and also explicitly designates the sequence. In particular, substitution with degenerate codons can be achieved by creating sequences in which the third position of one or more (or all) selected codons is replaced with mixed base residues and/or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)).

The term conservatively modified variants applies to both amino acid sequences and nucleotide sequences. With respect to specific nucleotide sequences, conservatively modified variants refer to those nucleic acids that encode identical or substantially identical amino acid sequences or, in the case of a nucleic acid that does not encode an amino acid sequence, substantially identical sequences. Due to the degeneracy of the genetic code, a large number of functionally identical nucleic acids encode any given protein. For example, the codons GCA, GCC, GCG, and GCU all code for the amino acid alanine. Thus, at each position at which alanine is specified by a codon, that codon can be changed to any of the corresponding codons described without changing the encoded polypeptide. Such nucleic acid variations are silent variations, which are one type of conservatively modified variation. Each nucleotide sequence that encodes a polypeptide also describes each possible silent nucleic acid variation. One of ordinary skill in the art will appreciate that every codon in a nucleic acid (except AUG, which is typically the only codon for methionine, and TGG, which is typically the only codon for tryptophan) can be modified to produce a functionally identical molecule. Accordingly, in each sequence described, each silent nucleic acid variation that encodes a peptide is meant.

With regard to amino acid sequences, one of ordinary skill in the art will understand that single substitutions, deletions or insertions in a nucleic acid, peptide, polypeptide or protein sequence that result in a change, insertion or deletion of one amino acid or a small percentage of amino acids in the encoded sequences refer to a conservatively modified variant in which the change results in the deletion of an amino acid, the insertion of an amino acid, or the replacement of an amino acid with an amino acid with similar chemical properties. Conservative substitution tables that list amino acids with similar functional properties are known to one of ordinary skill in the art. Such conservatively modified variants further include, and do not exclude, polymorphic variants, cross-species homologs, and alleles described herein.

Conservative substitution tables that list amino acids with similar functional properties are known to one of ordinary skill in the art. Each of the following eight groups contains amino acids that are conservative substitutions for each other: 1) Alanine (A), Glycine (G); 2) Aspartic acid (D), Glutamic acid (E); 3) Asparagine (N), Glutamine (Q); 4) Arginine (R), Lysine (K); 5) Isoleucine (I), Leucine (L), Methionine (M), Valine (V); 6) Phenylalanine (F), Tyrosine (Y), Tryptophan (W); 7) Serine (S), Threonine (T) and [0139] 8) Cysteine (C), Methi

- 26 046327 onine (M) (see, for example, Creighton, Proteins: Structures and Molecular Properties (W H Freeman &Co.;2nd edition (December 1993).

The terms identical or percentage identity in the context of two or more nucleic acid sequences or polypeptide sequences refer to two or more sequences, or subsequences, that are the same. Sequences are substantially identical if they contain a certain percentage of amino acid residues or nucleotides that are the same (i.e., about 60% identical, about 65% identical, about 70% identical, about 75% identical, about 80% identical, about 85% identical, about 90% or about 95% within a particular region) when compared and aligned for best fit in the comparison window or designated region when evaluated using one of the sequence comparison algorithms (or other algorithms available to those of ordinary skill in the art) or by alignment manually and visually. This definition also applies to the sequence complementary to the sequence of interest. The identity may extend to a region that is at least about 50 amino acids or nucleotides in length, or to a region that is 75-100 amino acids or nucleotides in length, or, unless otherwise indicated, to the entire polynucleotide or polypeptide sequence. A polynucleotide encoding a polypeptide of the present invention, including homologues belonging to species other than humans, can be obtained by a method comprising the steps of screening a library under stringent hybridization conditions using a labeled probe containing a polynucleotide sequence described herein, or a fragment thereof, and isolating full-length cDNA and genomic clones containing the specified polynucleotide sequence. Such hybridization technologies are well known to one skilled in the art.

To compare sequences, one sequence is usually considered as a reference sequence to which the sequences under study are compared. When using an algorithm to compare sequences, the test and reference sequences are entered into the computer, the coordinates of the subsequences are designated, if necessary, and the software parameters of the sequence comparison algorithm are set. You can use the program's default settings, or you can set alternative settings. The sequence comparison algorithm then calculates the percent identity for the sequences of interest relative to the reference sequence based on program parameters.

The term comparison window as used herein includes reference to a segment, any of a plurality of contiguous positions selected from the group consisting of from 20 to 600, typically about 50 to about 200, more often from about 100 to about 150 positions. in which a sequence can be compared to a reference sequence from the same number of adjacent positions after the two sequences are optimally aligned. Methods for aligning sequences for comparison are well known to those of ordinary skill in the art. Optimal alignment of sequences for comparison can be performed including, but not limited to, the use of the local homology algorithm from Smith and Waterman (1970) Adv. Appl. Math. 2:482c, homology alignment algorithm from Needleman and Wunsch (1970) J. Mol. Biol. 48:443, ), the similarity search method from Pearson and Lipman (1988) Proc. Nat'l. Acad. Sci. USA 85:2444, computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics software package, Genetics Computer Group, 575 Science Dr., Madison, Wis.), or by manual alignment and visual inspection (see, for example, Ausubel et al., Current Protocols in Molecular Biology (1995 supplement)).

One example of an algorithm that is useful for determining percent sequence identity and sequence similarity is the BLAST and BLAST 2.0 algorithms, which are described in Altschul et al. (1997) Nuc. Acids Res. 25:3389-3402 and Altschul et al. (1990) J. Mol. Biol. 215:403-410, respectively. The BLAST analysis software is publicly available from the National Center for Biotechnology Information on the World Wide Web at the ncbi website. nlm. nih. gov. The BLAST algorithm parameters W, T and X determine the sensitivity and speed of the alignment. The BLASTN program (for nucleotide sequences) uses as default parameters a word length (W) of 11, an expected value (E) of 10, M=5, N=-4, and comparison of both strands. For amino acid sequences, BLASTP uses as default parameters a word length of 3, an expected value (E) of 10, and a BLOSUM62 scoring matrix (see Henikoff and Henikoff (1992) Proc. Natl. Acad. Sci. USA 89:10915) alignments (B) equal to 50, expected value (E) equal to 10, M=5, N=-4 and comparison of both chains. The BLAST algorithm is typically run with the low complexity filter turned off.

The BLAST algorithm also performs statistical analysis of the similarity between two sequences (see, for example, Karlin and Altschul (1993) Proc. Natl. Acad. Sci. USA 90:5873-5787). The single similarity value provided by the BLAST algorithm is the smallest probability sum (P(N)), which is a measure of the probability with which a match between two nucleotide or amino acid sequences will occur by chance. For example, a nucleic acid is considered similar to a reference sequence if the smallest sum of probabilities when comparing the test nucleic acid to the reference nucleic acid is less than about 0.2, or less than about 0.01, or less than about 0.001.

The phrase selectively (or specifically) hybridizes with refers to the binding, duplexing, or hybridization of a molecule only to a specific nucleotide sequence under stringent hydridation conditions when that sequence is contained in a complex mixture (including, but not limited to, total cellular DNA or RNA, or DNA or RNA libraries) .

The phrase stringent hydridation conditions refers to the hybridization of DNA, RNA or other nucleic acid sequences, or combinations thereof, under conditions of low ionic strength and high temperature, as is known in the art. Typically, under stringent hybridization conditions, a probe will hybridize to its target sequence in a complex mixture of nucleic acids (including, without limitation, total cellular DNA or RNA, or DNA or RNA libraries), but will not hybridize to other sequences contained in the complex mixture. The stringency conditions depend on the sequences and will vary under different circumstances. Specific hybridization of longer sequences occurs at higher temperatures. For a comprehensive guide to nucleic acid hybridization, see Tijssen, Laboratory Techniques in Biochemistry and Molecular Biology-Hybridization with Nucleic Probes, Overview of principles of hybridization and the strategy of nucleic acid assays (1993).

As used herein, the terms construct, engineered, engineering are intended to include any manipulation of the peptide backbone or post-translational modification of a naturally occurring or recombinant polypeptide, or fragment thereof. Design involves modifications to the amino acid sequence, glycosylation profile or side chain group of individual amino acids, as well as combinations of these approaches. Expression and production of engineered proteins is carried out using standard molecular biology methods.

By isolated nucleic acid molecule or polynucleotide is meant a nucleic acid molecule, DNA or RNA, that has been isolated from its natural environment. For example, a recombinant polynucleotide encoding a polypeptide contained in a vector is considered isolated. Additional examples of isolated polynucleotide include recombinant polynucleotides maintained in heterologous host cells or purified (partially or substantially) polynucleotides in solution. An isolated polynucleotide includes a polynucleotide molecule contained in cells that normally contain the polynucleotide molecule, but the polynucleotide molecule is located outside the chromosome or in a position on the chromosome that is different from its natural position on the chromosome. Isolated RNA molecules include RNA transcripts produced in vivo or in vitro, as well as positive-strand, negative-strand and double-stranded forms. Isolated polynucleotides or nucleic acids, as used herein, further include such molecules obtained synthetically, for example, by PCR or chemical synthesis. In addition, the polynucleotide or nucleic acid in certain embodiments of the invention includes a regulatory element, such as a promoter, a ribosome binding site, or a transcription terminator.

The term polymerase chain reaction or PCR generally refers to a method for amplifying a desired nucleotide sequence in vitro, as described, for example, in US Pat. No. 4,683,195. In general, the PCR method involves repeating multiple rounds of primer extension synthesis using oligonucleotide primers, capable of preferentially hybridizing with the template nucleic acid.

By a nucleic acid or polynucleotide that has a nucleotide sequence that is at least, for example, 95% identical to a reference nucleotide sequence of the present invention, it is meant that the nucleotide sequence of the polynucleotide is identical to the reference sequence, except that the polynucleotide sequence may include up to up to five point mutations for every 100 nucleotides of the reference nucleotide sequence. In other words, to produce a polynucleotide having a nucleotide sequence that is at least 95% identical to a reference nucleotide sequence, up to 5% of the nucleotides in the reference sequence may be deleted or replaced with a different nucleotide, or a number of nucleotides may be inserted into the reference sequence up to 5% of the total number of nucleotides contained in the reference sequence. These reference sequence changes may occur at the 5' or 3' terminal positions of the reference nucleotide sequence or anywhere between these terminal positions, dispersed either individually between residues in the reference sequence or occurring as part of one or more successive groups within the reference sequence. sequences. In practice, in order to determine whether any particular polynucleotide sequence is at least 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% identical to the nucleotide after

- 28 046327 Traditional methods using known computer programs, such as those discussed above for polypeptides (eg, ALIGN-2), can be used with this invention.

A derivative or variant of a polypeptide is considered to have homology or is homologous to a peptide if the amino acid sequences of the derivative or variant are at least 50% identical to the 100 amino acid amino acid sequence of the original peptide. In some embodiments, the derivative or variant has at least 75% similarity to either a peptide or peptide fragment having the same number of amino acid residues as the derivative. In some embodiments, the derivative or variant has at least 85% similarity to either a peptide or peptide fragment having the same number of amino acid residues as the derivative. In some embodiments, the amino acid sequence is a derivative having at least 90% similarity to a peptide or peptide fragment having the same number of amino acid residues as the derivative. In some embodiments, the amino acid sequence is a derivative having at least 95% similarity to a peptide or peptide fragment having the same number of amino acid residues as the derivative. In some embodiments, the derivative or variant has at least 99% similarity to either a peptide or peptide fragment having the same number of amino acid residues as the derivative.

The term modified, as used herein, refers to any changes made to a given polypeptide, such as changes in polypeptide length, amino acid sequence, chemical structure, co-translational modification, or post-translational modification of the polypeptide. The wording of the term (modified) means that the polypeptides in question are not necessarily modified, but that the polypeptides in question may be modified or unmodified.

In some aspects of the invention, an antibody or protein described herein contains an amino acid sequence that is at least 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% identical to the corresponding amino acid sequence or fragment thereof as set forth in the table(s) or used under the accession number(s) described herein. In some aspects, an isolated antibody or protein described herein comprises an amino acid sequence encoded by a polynucleotide that is at least 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 % identical to the corresponding nucleotide sequence or fragment thereof set forth in the table(s) or used under the accession number(s) described herein.

Pharmaceutical compositions

This application provides compositions containing antibodies, including pharmaceutical compositions containing any one or more antibodies described herein with one or more pharmaceutically acceptable excipients. In some embodiments of the invention, the composition is sterile. Pharmaceutical compositions typically contain a therapeutically effective amount of an antibody.

In addition to one or more antibodies described herein, these compositions may contain a pharmaceutically acceptable excipient, carrier, buffer, stabilizer, or other substances well known to those skilled in the art. Such substances must be non-toxic and must not interfere with the effectiveness of the active ingredient. The exact nature of the carrier or other substance may depend on the route of administration into the body, such as oral, intravenous, dermal or subcutaneous, nasal, intramuscular, and intraperitoneal.

Pharmaceutical compositions for oral administration may be prepared in tablet, capsule, powder or liquid form. The tablet may include a solid carrier such as gelatin or an adjuvant. Liquid pharmaceutical compositions typically include a liquid carrier such as water, petrolatum, animal or vegetable oils, mineral oil or synthetic oil. They may also include physiological saline, dextrose or other saccharide solution, or glycols such as ethylene glycol, propylene glycol or polyethylene glycol.

For intravenous, dermal, subcutaneous or intralesional injection, the active ingredient must be in the form of a parenterally acceptable aqueous solution with non-pyrogeic properties, and have suitable pH, isotonicity and stability. Those skilled in the art are well capable of preparing suitable solutions using, for example, isotonic vehicles such as sodium chloride injection, Ringer's injection, lactated Ringer's injection. Also, if necessary, the composition may include preservatives, stabilizers, buffering agents, antioxidants and/or other additives.

In the event that a polypeptide, antibody, nucleic acid, small molecule substance or other pharmaceutically usable compound is to be administered to an individual, administration is preferable

- 29 046327 is specifically given in a therapeutically effective amount or a prophylactically effective amount (optionally, although prophylaxis may be considered therapy) that is sufficient to provide benefit to the individual. The actual amount to be administered, as well as the rate and dynamics of administration, will depend on the nature and severity of the disease due to protein aggregation requiring treatment. The administration of treatment, for example, decisions regarding dosage, etc., are within the competence of general practitioners and other medical doctors and, as a rule, take into account the nature of the disorder to be treated, the state of health of the individual subject, the site and method of administration, and as well as other factors known to medical practitioners. Examples of the techniques and protocols mentioned above can be found in Remington's Pharmaceutical Sciences, ^16th edition, Osol, A. ed., (ed), 1980.

The composition may be administered alone or in combination with other treatments, either simultaneously or sequentially, depending on the pathological condition being treated.

Methods

Methods of application

In one aspect of the invention, the present application provides methods for contacting non-stimulatory myeloid cells with an anti-NSM antibody, such as a human antibody, that results in blocking non-stimulatory myeloid cells.

In another aspect, the present application provides methods for contacting non-stimulatory myeloid cells with a murine anti-NSM antibody that results in blocking the non-stimulatory myeloid cells.

In some embodiments, the non-stimulatory cells are one or more of DC1 cells and TAM cells.

In some embodiments, the present application provides methods for blocking non-stimulatory myeloid cells, comprising contacting the non-stimulatory myeloid cells with an anti-NSM antibody, thereby killing the non-stimulatory myeloid cells. Blocking refers to exerting an effect that results in partial or complete loss of cell functionality. In some embodiments, blocking non-stimulating myeloid cells results in the cells inducing growth arrest. In some embodiments, blocking non-stimulating myeloid cells causes the cells to undergo apoptosis. In some embodiments, blocking non-stimulating cells results in cell lysis, such as through complement-dependent cytotoxicity (CDC) or antibody-dependent cellular cytotoxicity (ADCC). In some embodiments, blocking non-stimulating myeloid cells causes the cells to necrosis. In some embodiments, blocking non-stimulating myeloid cells results in the cells inducing growth arrest. In some embodiments, blocking non-stimulating myeloid cells results in cell inactivation. In some embodiments, blocking non-stimulating myeloid cells results in neutralization of NSM protein activity in the cells. In some embodiments, blocking non-stimulating myeloid cells causes the cells to decrease proliferation. In some embodiments, blocking non-stimulating myeloid cells causes the cells to differentiate. In some embodiments, blocking non-stimulatory myeloid cells results in a decrease in the ability of the cells to act as inhibitory antigen presenting cells or results in an increase in the ability of the cells to act as activating antigen presenting cells. In some embodiments, blocking non-stimulating myeloid cells results in impaired cell localization within tumor tissue or tumor microenvironment (TME). In some embodiments, blocking non-stimulating myeloid cells results in altered partial spatial organization of cells within tumor tissue or tumor microenvironment. In some embodiments, blocking non-stimulatory myeloid cells results in altered transient expression of cells within the tumor tissue or TME. In some embodiments, the method further includes removing non-stimulating myeloid cells.

In any and all aspects of blocking non-stimulating myeloid cells as described herein, any increase or decrease or change in aspect of property(s) or function(s) is compared to the cell not exposed to the anti-NSM antibody.

In another aspect, the present application provides methods for contacting non-stimulatory myeloid cells with an anti-NSM antibody, which results in modulation of the function of the non-stimulatory myeloid cells. The modulation may be any one or more of the following. In some embodiments, the non-stimulatory cells are one or more of DC1 cells, TAM1 cells, and TAM2 cells. In some embodiments, modulation of function results in the blocking of non-stimulating myeloid cells. In some embodiments, modulation of the functioning of non-stimulatory myeloid cells results in an increase in the cells' ability to stimulate both native and

- 30 046327 activated CD8+ T cells, for example, by increasing the ability of non-stimulatory cells to cross-present tumor antigen on MHCI molecules to naïve CD8 ⁺ T cells. In some embodiments, modulation increases the T cell stimulatory function of non-stimulatory myeloid cells, including, for example, the cells' ability to trigger T cell receptor (TCR) signaling, T cell proliferation, or T cell cytokine production. In one embodiment of the invention, the survival of the non-stimulating cell is reduced or the proliferation of the non-stimulating cell is reduced. In one embodiment of the invention, the ratio of stimulatory myeloid cells to non-stimulatory myeloid cells is increased.

In any and all aspects of decreased function of non-stimulatory myeloid cells as described herein, any increase or decrease or change in aspect of property(s) or function(s) is compared to the cell not exposed to the anti-NSM antibody.

In some embodiments, the present application provides methods for killing (also referred to as inducing cell death) non-stimulating myeloid cells, comprising contacting the non-stimulating myeloid cells with an anti-NSM antibody, thereby killing the non-stimulating myeloid cells. In some embodiments, killing is increased for non-stimulating myeloid cells that have not been contacted with the anti-NSM antibody. In some embodiments, contacting induces apoptosis in non-stimulated myeloid cells. In some embodiments, contacting induces apoptosis in non-stimulated myeloid cells. In some embodiments, the non-stimulatory myeloid cells are present in a population of immune cells comprising non-stimulatory myeloid cells and stimulatory myeloid cells. In some embodiments, the method further includes removing non-stimulating myeloid cells. In some embodiments, 10% to 80% of the cells die. In some embodiments, at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, or 80% of the cells die.

In some embodiments, the present application provides methods for increasing the ratio of stimulatory myeloid cells to non-stimulatory myeloid cells in an immune cell population comprising stimulatory myeloid cells and non-stimulatory myeloid cells, comprising contacting the population of immune cells with an anti-NSM antibody. In some embodiments, the ratio is increased with respect to the population of cells that were not contacted with the anti-NSM antibody. In some embodiments, the ratio of DC2 cells to DC1 cells is increased. In some embodiments, the ratio of DC2 cells to TAM1 cells is increased. In some embodiments, the ratio of DC2 cells to TAM2 cells is increased. In some embodiments, the ratio of DC2 cells to TAM1 + TAM2 cells is increased. In some embodiments, the ratio of DC2 cells to TAM1+DC1 cells is increased. In some embodiments, the ratio of DC2 cells to DC1 + TAM2 cells is increased. In some embodiments, the ratio of DC2 cells to DC1 + TAM1 + TAM2 cells is increased. In some embodiments, the ratio is increased by at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%.

In some embodiments, the ratio of stimulatory myeloid cells to non-stimulatory myeloid cells before contacting is in the ranges of 0.001:1 to 0.1:1. In some embodiments, the ratio of stimulatory myeloid cells to non-stimulatory myeloid cells after contacting is in the ranges of 0.1:1-0.100:1.

In some embodiments, the number of non-stimulating myeloid cells is reduced. In some embodiments, the stimulating myeloid cells are DC2 cells. In some embodiments, non-stimulating myeloid cells are killed, for example, by necrosis or apoptosis. In some embodiments, non-stimulating myeloid cells are induced to cause growth arrest. In some embodiments, the non-stimulating myeloid cells no longer proliferate. In some embodiments, the spatial localization of non-stimulating myeloid cells is changed and the ratio is increased in a particular region of the TME. In some embodiments, the spatial localization of non-stimulating myeloid cells changes and the ratio increases over a specific period of time during tumor development.

In some embodiments of the invention, contacting occurs in vitro. In some embodiments of the invention, contacting occurs in vivo. In some specific embodiments of the invention, contacting occurs in vivo in a human body. In some embodiments, contacting is accomplished by administering an anti-NSM antibody. In some embodiments, the individual receiving the antibody

- 31 046327 (such as a person), suffers from cancer.

In another aspect, the invention provides methods for treating an immune system-related condition (eg, cancer) in an individual, comprising administering to the individual an effective amount of a composition comprising an anti-NSM antibody. In another aspect, the invention provides methods for enhancing an immune response in an individual comprising administering to the individual an effective amount of a composition comprising an anti-NSM antibody. In some embodiments, these methods are further provided in combination with other concomitant therapies, such as PDL blockade therapy, CTLA4 blockade therapy, generalized checkpoint blockade therapy that blocks inhibitory molecules on T cells, adoptive T cell therapy -cells, CAR T-cell therapy, dendritic cell or other cell therapies, as well as traditional chemotherapy.

In some embodiments, the method further includes determining the level of NSM protein expression in a biological sample taken from an individual. In some embodiments, the biological sample includes, but is not limited to, body fluid, tissue sample, organ sample, urine, feces, blood, saliva, CSF, and any combination thereof. In some embodiments, the biological sample is derived from tumor tissue. In some embodiments, the expression level includes the expression level of the NSM protein-coding mRNA. In some embodiments, the NSM protein expression level includes the NSM protein expression level. In some embodiments, the level of NSM protein expression is detected in a sample using a method selected from the group consisting of FACS, Western blot, ELISA, immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay, dot blot, immunodetection methods, HPLC, surface plasmon resonance , optical spectroscopy, mass spectrometry, HPLC, qPCR, qRT-PCR, multiplex qPCR or qRT-PCR, RNA sequencing, microarray analysis, SAGE, MassARRAY and FISH technology, and combinations thereof.

In another aspect of the invention, the present application provides methods for determining the presence or absence of non-stimulating myeloid cells in general, or determining the presence or absence of specific non-stimulating myeloid cells (for example, DC1 cells, TAM1 cells and/or TAM2 cells), comprising: contacting a population of cells containing non-stimulating myeloid cells, with anti-NSM antibody, and determining the number of non-stimulating myeloid cells. In another aspect of the invention, the present application provides methods for determining the presence or absence of non-stimulatory myeloid cells, comprising: contacting a population of immune cells comprising non-stimulatory myeloid cells and stimulatory myeloid cells with an anti-NSM antibody; identifying a complex or fragment indicating antibody binding to a cell and optionally determining the number of non-stimulating myeloid cells in the population. In another aspect of the invention, methods are provided for determining the relative ratio of non-stimulatory myeloid cells to stimulatory myeloid cells, comprising: contacting a population of immune cells comprising non-stimulatory myeloid cells and stimulatory myeloid cells with an anti-NSM antibody; determining the number of stimulating myeloid cells and non-stimulating myeloid cells and determining the relative ratio of non-stimulating myeloid cells to stimulating myeloid cells.

In embodiments described herein, for detection and/or quantitation, the anti-NSM antibody binds to the NSM protein, but does not necessarily affect a biological response, such as ADCC, although it may affect the biological response.

In another aspect, the present invention provides methods for identifying an individual who may respond to immunotherapy (eg, with an anti-NSM antibody) for treatment of an immune-related disease condition (eg, cancer), comprising: detecting the level of NSM protein expression in a biological sample taken from the individual, and determining, based on the level of NSM protein expression, whether the individual may respond to immunotherapy, wherein an increased level of NSM protein in the individual relative to a healthy individual indicates that the individual may respond to immunotherapy. In some embodiments, these methods can also be used to diagnose an immune-related condition (eg, cancer) in an individual and are based on the level of NSM protein expression, wherein an increased level of NSM protein in an individual relative to a healthy individual indicates that the individual is an individual suffers from cancer. In some embodiments, the expression level includes the expression level of the NSM protein-coding mRNA. In other embodiments, the NSM protein expression level includes a protein expression level of the NSM protein. In some embodiments, the level of NSM protein expression is detected in a sample using a method selected from the group consisting of FACS, Western blot, ELISA, immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay, dot blot, immunodetection methods, HPLC, surface plasmon resonance ,

- 32 046327 optical spectroscopy, mass spectrometry, HPLC, qPCR, qRT-PCR, multiplex qPCR or qRT-PCR, RNA sequencing, microarray analysis, SAGE, MassARRAY and FISH technology, and combinations thereof. In these embodiments, the anti-NSM antibody binds to the NSM protein but does not necessarily have an effect on a biological response such as ADCC. In some embodiments, the biological sample is derived from tumor tissue. In some embodiments, the biological sample includes, but is not limited to, body fluid, tissue sample, organ sample, urine, feces, blood, saliva, CSF, and any combination thereof.

Also described herein is a method of enhancing a subject's immune response to tumors or enhancing the effectiveness of immunotherapeutic treatments. In general, treatments that increase excess SDC will improve the outcome for the subject, such as the length of survival without recurrence, and will enhance the effectiveness of anticancer immunotherapies. Treatment may increase the relative or absolute excess of SDC cells in the subject's tumor. Treatment may reduce the relative or absolute excess of NSM cells in the subject's tumor.

Typical general treatment strategies include increasing the number of SDCs by systemically administering Flt3L. Another method is to treat the subject's autologous bone marrow cells or blood cells with Flt3L while simultaneously blocking CSF1. Expression, for example by retrovirus, of SDC transcription factors such as IRF8, MycII or BATF3, or ZBTB46 in bone marrow or blood progenitor cell populations can also be used to drive SDC development. Another treatment strategy involves systemic removal of NSM cells while selectively sparing SDC. This action may create an overall beneficial change in the ratio of these populations. Removal of NSM cells can be accomplished by any means, including administration (systemic or tumor localized) of antibodies against NSM surface proteins.

In some embodiments, SDC-improving treatments are used as therapeutic treatments to enable the subject's native immune system to better fight or get rid of cancer. In another embodiment, the SDC-enhancing treatments of the present invention are used in combination with a therapeutic treatment, such as an immunotherapy treatment (such use before, concurrently, or after the immunotherapy treatment), wherein the SDC-improving treatments act as adjuvant or adjuvant treatments to increase efficacy of this therapeutic treatment.

Methods of administration

In some embodiments, a method provided herein is useful for treating an immune system-related condition in an individual. In one embodiment of the invention, the subject is a human and the antibody is an anti-NSM antibody. In another embodiment, the subject is a mouse and the antibody is an anti-NSM antibody.

In some embodiments, the methods provided herein (such as methods for enhancing an immune response or influencing the blocking of non-stimulating myeloid cells) are useful for treating cancer and, as such, an individual receiving an NSM antibody or an anti-NSM antibody has cancer. In some embodiments, the cancer is a solid cancer. In some embodiments, the cancer is a hematologic malignancy. In some embodiments, the cancer evades recognition by the immune system. In some embodiments, the cancer is immunoreactive. In specific embodiments, the cancer is selected from the group consisting of melanoma, kidney cancer, hepatobiliary cancer, head and neck squamous cell carcinoma (HNSCC), pancreatic, colon, bladder, prostate, lung, glioblastoma, and breast cancer.

In some embodiments, the immune-related condition is an immune-related condition associated with expression of NSM protein on non-stimulating myeloid cells (in humans) or expression of a homolog of NSM protein in non-human species. In some embodiments, the immune-related condition is an immune-related condition associated with overexpression of NSM protein on non-stimulatory myeloid cells compared to stimulatory myeloid cells. In some embodiments, the overexpression of NSM mRNA or NSM protein is about at least 2-fold, 5-fold, 10-fold, 25-fold, 50-fold, or 100-fold higher compared to stimulating myeloid cells.

In some embodiments, the antibody is administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intraorbitally, by implantation, by inhalation, intrathecally, intraventricularly, or intranasally. An effective amount of anti-NSM antibody can be administered to treat cancer. The appropriate dosage of anti-NSM antibody can be determined based on the type of cancer being treated, the type of anti-NSM antibody

- 33 046327 tive NSM, the severity and course of the cancer, the individual's clinical condition, the individual's medical history and response to treatment and the discretion of the treating physician.

Detection and quantification of SDC and NSM cells

The invention covers any methodology for the quantification of SDC and/or NSM cells. Various embodiments of the invention are directed to identifying and quantifying SDC and/or NSM cells, for example, in a tumor sample. The tumor sample may be any tissue containing tumor cells obtained from a patient as known in the art, for example, a portion of the tumor removed by biopsy (e.g., needle biopsy or punch biopsy, e.g., 4 mm punch biopsy) or substantially entire tumors that are surgically removed, including primary or local cells. It should be noted that the excess of SDC is greater in distal areas of the tumor than in marginal areas. In a typical sample, this difference must be averaged so that it does not affect the results. However, if an excess amount of marginal material is included in the sample, this may bias the result toward under-accounting for excess SDC.

In an embodiment of the invention, the number of SDC and NSM cells in a sample is directly quantified by fluorescence-activated cell sorting (FACS), similar flow cytometry techniques, magnetic bead cell sorting, microraft sorting, cell affinity-based separation methods, and other methods for isolating specific types cells from a mixed population of cells. For example, a tumor sample can be disaggregated using enzymes to form a single cell suspension, as is known in the art. The cells can then be labeled with antibodies specific to protein or carbohydrate markers unique to each cell type. Various label-based gating protocols can then be used to separate cell fractions by FACS, or similar methodologies, as is known in the art. For example, as described in the Examples, labeling single cell suspensions from disaggregated tumors with fluorescently labeled antibodies allows FACS separation of the dendritic cell fraction from other cell types within the sample and separation of the SDC and NSM cell pools. As is known in the art, such methodologies may use tagging of either extracellular or intracellular marker proteins.

For example, in one FACS protocol, different cell types of the myeloid compartment of a tumor can be sorted as follows. Those cells that express CD 11b and Ly6C are monocytes and neutrophils and can be removed based on such expression. High expression of CD24 and low expression of F4/80 can be used to distinguish dendritic cells (SDC and DC1 cells) from tumor macrophages (TAM1 and TAM2). The two populations of tumor macrophages can be separated from each other by their differential expression of CD11b and CD11c, resulting in TAM1 cells being CD11b high, MHC class II low and CD11c low, whereas TAM2 cells are CD11b low, MHC class II high and CD11c high. Tumor macrophages in humans tend to express CD 14, whereas DCs typically do not. Examples of distinct surface markers that help distinguish the two dendritic populations from each other and from macrophages include CD103, XCR1, Clec9a and CD11b in mouse or CD14, BDCA3, XCR1 and Clec9a in humans. For example, in mouse tumors, two dendritic cell populations can be separated by their differential expression of CD11b (absent in SDC, present in NSM cells) and CD103 (absent in NSM cells, present in SDC). Similarly, SDCs in humans express BDCA3, XCR1, and Clec9a, while nonstimulatory DCs express BDCA1 and macrophages express CD14.

In another embodiment, direct histological analysis of tissue samples is used to determine the presence, frequency, and relative abundance of SDC compared to other cell types. For example, tissue sections can be stained with labeled antibodies against SDC markers and the antigens they contain, and labeled antibodies against NSM markers and the antigens they contain. Quantitative fluorescence microscopy analysis of labeled tissue sections and visualization of the relative physical distribution and localization of such cells in the sample can then be used to quantify SDC and NSM cells in a sample.

In another embodiment, the excess of SDC and/or the excess of NSM cells in a sample is determined indirectly by monitoring gene expression patterns throughout the bulk of the tumor sample, eliminating the need to separate the sample into cell fractions. Quantitative analysis of SDC and NSM genetic markers within the tumor tissue volume is used as a surrogate measure of the ratio of SDC to NSM cells present in the tumor. For example, in one embodiment, the entire transcriptome of cells within a tumor sample is analyzed to determine the ratio of SDC marker gene expression to NSM marker gene expression. It should be understood that such quantitative determinations of marker gene expression can be performed using any number of tools known in the art for analyzing gene expression.

- 34 046327

In one embodiment of the invention, assessment of marker gene expression in a tumor sample is performed using quantitative PCR techniques as known in the art. Such techniques can be performed using whole transcriptome protocols from a tumor, as is known in the art, and primer pairs capable of exclusively amplifying marker gene sequences. Having known marker gene sequences, it is easy to design primers for this within the knowledge of one skilled in the art. It should be understood that, as is known in the art, reference to the gene sequence of a particular gene in the context of quantitating expression will refer to all or part of the nucleotide sequence corresponding to or complementary to the mRNA (or cDNA derived therefrom) produced by such gene when expressed .

In another embodiment, the expression of marker genes can be measured using DNA chip technology, as is known in the art. Probes that bind marker gene transcripts can be easily generated by one skilled in the art using known marker gene sequences and can be used in any number of gene expression chips and analytical platforms.

In another embodiment of the invention, quantification of SDC and NSM cells is accomplished by monitoring the presence or activity of downstream genes and proteins, defined as genes and proteins that are regulated by the marker gene or translation product thereof, and such activity varies predictably in response to expression of the marker gene . In another embodiment of the invention, functional assays are used to identify or quantify SDC and NSM cells, for example, due to different immunological activity. For example, SDCs are effective in cross-presenting tumor antigens and are potent activators of CD8 T cells, whereas NSM cells are not. Similarly, while NSM cells exhibit a high degree of phagocytic behavior, SDCs are comparatively less persistent phagocytes.

Determination of excess and screening

Various embodiments of this invention are directed to detect excess SDC in a tumor. The excess of such cells can be assessed in various ways. Such measurements may include relative measurements or absolute measurements and may include direct or indirect measurement. For example, the relative abundance can be determined by measuring the ratio of SDC to NSM cells in a sample; measuring within a sample the ratio of SDC to the number of DC1 cells or measuring within a sample the ratio of SDC to all cell types, measuring within a sample the ratio of SDC to total myeloid cells or measuring within a sample the ratio of SDC to HLA-DR+ cells. In some embodiments, the absolute excess of SDC is determined, for example, by measuring the total amount of SDC per ml of tumor tissue or per microgram of tumor tissue. Indirect measurements include assessing SDC marker gene expression levels, alone or in comparison with marker expression levels with other cell types, such as NSM cells.

In various embodiments, the excess of SDC in a subject's tumor is a prognostic indicator, a diagnostic indicator, or an indicator for treatment selection.

Various embodiments of the invention are directed to comparative measurements, such as increased or increased excess SDC. Such comparative measurements can be made by comparing a sample taken from a subject with representative samples. Representative samples may include, for example, samples obtained from the same subject at an earlier point in time, samples obtained from similar subjects (e.g., matched for age, gender, health status, cancer progression, cancer type, etc.), or samples , obtained from similar tumors (for example, matching tumor type, stage of tumor development and other parameters of tumor progression). In some embodiments, such as those in which the effectiveness of a treatment is assessed, an increased excess is defined as an excess greater than that observed in representative samples taken from untreated subjects. In some embodiments, a measurement of a typical or average excess SDC is used as a baseline to determine what the elevated or increased excess is, for example, the average, median, or similar statistical value of the SDC in representative samples may be used. Various statistical methodologies known in the art can be used to determine amounts that are significantly elevated or increased.

In one embodiment, the excess of SDC in the subject's tumor includes a prognostic indicator in which an outcome for the subject is expected based on the excess of SDC in the subject's tumor, wherein an increased excess is indicative of a greater likelihood of a positive outcome. Typical outcome measures include probability of recurrence-free survival, expected duration of recurrence-free survival, expected duration of overall survival, risk of relapse, quality of life indicators, etc. For example, in one embodiment of the invention, the ratio of SDC to NSM cells in a tumor sample, measured either directly or indirectly, is used as an indicator of excess SDC and the expected duration

- 35 046327 recurrence-free survival rate, which is an outcome measure for the subject. For example, the mean or median ratio of SDC marker gene expression to NSM marker gene expression observed in a pool of similar tumor samples may serve as a threshold value and, if the measured ratio for an individual subject exceeds the specified threshold, then the subject is considered to have an increased likelihood of recurrence-free survival.

In one embodiment, an excess of SDC in a subject's tumor is an indicator of the likelihood that the subject will respond positively to immunotherapy treatment, wherein an increased excess of SDC in a tumor sample obtained from the subject is an indicator of a greater likelihood of a positive outcome of the immunotherapy. For example, in one embodiment of the invention, the ratio of SDC to NSM cells in a tumor sample, measured directly or indirectly, is used as an indicator of excess SDC. For example, the mean or median ratio of SDC marker gene expression to NSM marker gene expression observed in a pool of similar tumor samples may serve as a threshold value and, if the measured ratio for an individual subject is below this threshold, then the subject is considered to have a reduced likelihood of responding to immunotherapy.

Anticipating which subjects will be more likely to respond to treatment with an immunotherapeutic method advantageously allows the selection of the appropriate therapeutic intervention. Subjects considered less likely to respond to immunotherapy treatment may be referred to other treatment modalities, while a subject more likely to respond to immunotherapy may be treated accordingly. The treatment selection indicators of this invention are used to predict responsiveness or nonresponsiveness to any anticancer immunotherapy known in the art. For example, one class of cancer immunotherapies is known as checkpoint blockade treatments. The mammalian immune system includes various checkpoints, which are self-limiting inhibitory mechanisms that normally act to dampen immune responses and prevent autoimmune reactions. Tumors have been shown to bypass these mechanisms in such a way that the immune response against the tumor is suppressed. Various therapeutic strategies, known as checkpoint blockades, involve the use of ligands such as antibodies to block these checkpoints and inhibit immune responses to suppress the tumor and avoid an immune response against the tumor. Another class of immunotherapies covers cell-based strategies, for example, the removal of cells, such as dendritic cells, from the body of a subject and the proliferation and ex-vivo activation of such cells against tumor antigens. The activated cells are then reintroduced into the body to enhance the subject's immune response against the tumor.

This description contained herein presents the field of technology with new relationships between excess SDC and outcome for the subject and/or the subject's responsiveness to immunotherapy treatment. This invention broadly covers any application of these concepts. It is within the knowledge of one skilled in the art to implement the teachings of the invention by developing predictable relationships between specific measures of SDC excess and specific measures of outcome for a subject, or responsiveness to particular immunotherapy treatments, for any particular type or subtype of cancer. Such predictable relationships may encompass any statistical pattern that embodies the phenomenon whereby increased SDC excess is indicative of improved outcome for a subject or increased responsiveness to immunotherapy treatment.

In one embodiment, the invention includes a predictive relationship in which the ratio of SDC marker gene expression to NSM marker gene expression is used as an indicator of excess SDC relative to excess NSM immune cells, and the increased proportion of SDC is predictive of a subject's greater likelihood of survival. For example, as described in the Examples, a large pool of gene expression data from 3602 tumor samples representing 12 different cancer types (data from the TCGA pan-cancer project) was analyzed, in which subject-associated survival data were obtained for each tumor sample. For each sample, the average observed expression levels (measured in relative intensity units from the normalized data set) of all SDC marker genes in Table 1 were calculated. And they were taken as an indicator of an excess of dendritic immune cells SDC. Similarly, the observed mean expression levels of all NSM marker genes in Table 1 were calculated for each sample. And they were taken as an indicator of an excess of NSM dendritic immune cells. The ratio of SDC and NSM cell excess signals for each sample was then calculated, log-transformed, and the Z-score normalized to make the median 0 and standard deviation 1 across the entire data set. For each cancer type, the median value of the ratio of gene expression of SDC marker genes to NSM was calculated. For each cancer type, the pool of samples was divided into a high ratio of SDC to NSM or a low ratio of SDC to NSM, where the high pool covers all samples with a standardized ratio above the median value for the entire population of similar samples (matched by

- 36 046327 based on cancer type), and the low pool covers all samples with a standardized ratio below the median value. The Kaplan-Meier method was used to assess the association between overall survival and the SDC/NSM signature ratio as a dichotomous variable (dividing the median by cancer type). As depicted in the figures, the data clearly show that overall survival rates are significantly increased in the high pool compared to the low pool.

Accordingly, the ratio of SDC marker gene expression to NSM marker gene expression in a sample is predictive of overall survival time for the subject from which the sample is obtained, with an increased ratio being indicative of increased overall survival. In this case, elevated is defined as a ratio greater than the median ratio for that type of cancer. Accordingly, in one embodiment, the invention includes a method of predicting greater overall survival time for a subject (relative to subjects with a low SDC/NSM signature ratio) by: (1) calculating the z-scored and log-transformed ratio of mean SDC marker gene expression to mean marker gene expression NSM in a tumor sample obtained from the subject; and then comparing the observed ratio for the subject with a threshold value representing the ratio of median expression of SDC marker genes to expression of NSM genes for a given cancer type of the tumor sample; wherein the longer average overall survival time is indicated if the ratio value for the sample exceeds the threshold value.

It should be noted that the typical methods for calculating excess SDC presented in this document are illustrative and can be modified in various ways. For example, you can use subsets of genes from Table. A. For example, in one embodiment of the invention, SDC markers BDCA3, KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, and CLEC9A, and NSM markers MRC1, MS4A7, C1QC, APO E, C1QB, C1QA are used and C5AR1.

Likewise, the specific mathematical operations used to calculate gene expression ratios can be modified, e.g., log-transformed values are better summed and averaged than log-transformed averages, etc. Because a particular methodology represents the ratio of SDC marker gene expression to level of expression of NSM marker genes in a sample, then the exact nature and sequence of mathematical operations used to calculate this ratio are not significant.

In alternative embodiments, excess NSM can be measured as the number of NSM cells per total number of living tumor cells, the number of NSM cells per total number of tumor immune cells, the number of NSM cells per unit volume of a tumor sample, or the number of NSM cells per unit mass (e.g., mg) tumor sample. Similarly, excess SDC can be measured as the number of SDC cells per total number of living tumor cells, the number of SDC cells per total tumor immune cells, the number of SDC cells per unit volume of a tumor sample, or the number of SDC cells per unit mass (eg, mg) of a tumor sample.

The total myeloid cell content in a sample can be estimated as the total number of CD11b ⁺ cells in the sample. The total myeloid cell content in a sample can be defined as all cells expressing CD14, plus all cells expressing CD16, plus all cells expressing HLA.

In one embodiment of the invention, the excess of SDC cells is assessed as the percentage of HLA-DR positive myeloid cells in the sample expressing the SDC marker by flow cytometric methods. For example, the SDC marker may be BDCA3 or XCR1 or Clec9a.

It has been advantageously discovered by the present disclosure that excess CD14 expressed by (CD14 ⁺ ) macrophages and monocytes can be used as an indicator of excess non-stimulatory cells, either to stratify subject pools to more accurately predict cancer outcome or evaluate treatment efficacy. CD14 ⁺ macrophages can compete with stimulatory dendritic cells and thus achieving an equilibrium of CD14 ⁺ cells and SDC cells in the myeloid pool may be an important determinant of cancer outcome. The proportion of CD14 ⁺ cells may modulate the effects of excess SDC. In one embodiment, an alternative measure of NSM excess is defined as the excess of CD14 ⁺ myeloid cells in a sample. In one embodiment, the excess CD14 ⁺ myeloid cells is measured as a percentage of the total number of CD11b ⁺ myeloid cells expressing CD14. In another embodiment of the invention, the percentage of HLA-DR positive cells that express CD14 is used as an indicator of the excess of CD14 ⁺ cells. In another embodiment, the excess of CD14 ⁺ myeloid cells is measured as a percentage of the total number of immune cells expressing CD45. In another embodiment, the excess CD14 ⁺ myeloid cells is measured as the number of CD14 ⁺ cells per gram of tissue sample or the like.

In one embodiment of the present invention, SDC excess is measured as the percentage of HLA-DR ⁺ -expressing myeloid cells that also express the marker

- 37 046327

SDC, for example BDCA3 or XCR1. In one embodiment of the invention, the excess SDC measured in this way is used as a prognostic indicator, an indicator of treatment effectiveness, or an indicator of a subject's responsiveness to a particular treatment, wherein an increased excess of SDC is an indicator of a positive prognosis, a positive response to treatment, or a greater likelihood of responsiveness to a particular treatment, and a lower excess SDC is an indicator of poor prognosis, treatment failure, or lower likelihood of responsiveness to a particular treatment. For example, an excess of SDC (eg, BDCA3+ cells) of 1-4, 1-2, 1, 2, 3, 4, 5, or 1.37% or more of HLA-DR+ cells may be considered an increased excess of SDC. For example, subjects having tumors with 1-5, 1-4, 1-2, 1, 2, 3, 4, 5, or 1.37% or more HLA-DR+ SDC cells have a greater than 50% chance of responding immunotherapy treatments such as anti-PD1 antibodies, for example, have a greater than 85% chance of a positive response.

In one embodiment of the invention, the treatment is assessed as an immunostimulating or immunotherapeutic treatment. For example, the treatment may include treatment that targets programmed cell death protein 1 (PD1) or programmed cell death ligand 1 (PD-L1). For example, anti-PD1 antibody treatments include nivolumab (Opdivo™), pembromizulab (Keytruda™). Other similar treatments target CTLA-4, such as ipilimumab (Yervoy™). In one embodiment, the likelihood that a subject will respond to anti-PD1 therapy is assessed by measuring the excess SDC cells in the subject's tumor, for example, wherein the excess SDC cells is assessed as the percentage of HLA-DR+ myeloid cells that express an SDC marker (e.g. BDCA3), with an increased excess of SDC cells being an indication that the subject will respond favorably to anti-PD1 antibody treatment and a lower excess of SDC cells in the tumor being an indication that the subject will respond poorly to anti-PD1 antibody treatment. In one example, an increased excess of SDC cells is defined as 4% or more HLA-DR+ myeloid cells, and a lower excess of SDC cells is defined as less than 4% of HLA-DR+ myeloid cells in a tumor sample. In one embodiment of the invention, the subject is a subject with melanoma. The above methodology can be used in a similar manner to evaluate the effectiveness of anti-PD1 antibody treatment, the observed increase in excess SDC cells after treatment is an indication that the treatment is effective. Likewise, putative PD1 inhibitors can be identified as agents that increase the excess of SDC cells, for example by measuring the percentage of HLA-DR+ cells that are BDCA3+ cells.

In one embodiment, the invention contains a screening method for identifying a composition (or other type of treatment) that enhances excess SDC. For example, an animal having a tumor can be exposed to a putative SDC excess enhancer, and then, using the tools and methods of the present invention, the SDC excess can be assessed, the increase in SDC excess compared to, for example, untreated controls or samples obtained from that the same subject before treatment is an indication that the treatment is an effective SDC excess enhancer. Such SDC excess may include any measure of SDC excess, including relative and absolute measures of SDC, such as the ratio of SDC to NSM cells, or indirect measures thereof.

Sets and products

This application provides kits containing one or more of the antibody compositions described herein. In some embodiments, the kits further comprise a component selected from any secondary antibody, immunohistochemical assay reagents, a pharmaceutically acceptable excipient and instructions for use, and any combination thereof. In one specific embodiment of the invention, the kit contains a pharmaceutical composition containing any one or more antibody compositions described herein with one or more pharmaceutically acceptable excipients.

In one aspect of the invention, the kit can be composed of reagents, biological materials and other components to facilitate the measurement of SDC and NSM cell counts. For example, in one embodiment, the kit may include antibodies, including fluorescently labeled antibodies, that target myeloid cells, common dendritic cell markers, SDC cell markers, and NSM cell markers to facilitate FACS or other cell sorting techniques or flow cytometry for quantitative determination of SDC and/or NSM cells within the sample. For example, to facilitate FACS separation of SDC and NSM fractions, a kit containing differently labeled antibodies to mouse CD45, CD11c, Ly6C, MHCII, CD24, F4/80, CD11b, and CD103 may be used for mice, whereas for humans, antibodies to CD45, CD11c, CD14, HLADR, BDCA1 and BDCA3.

In another embodiment, the kit may contain a number of PCR primers for amplifying one or more SDC gene markers or one or more NSM gene markers. In one embodiment of the invention, the PCR primer set contains a number of primers capable of

- 38 046327 exclusive amplification of one, two, three, four, five, six, seven, eight, nine or all SDC markers listed in table. A, and one, two, three, four, five, six, seven, eight, nine or more of the nine NSM markers listed in Table. A. In another embodiment of the invention, the PCR primer set contains a number of primers exclusively capable of amplifying BDCA3, KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, MRC1, MS4A7, C1QC, APO E, C1QB, C1QA and C5AR1.

The kit may include two or more probes containing subsequences of SDC or NSM gene markers for binding and/or tagging SDC or NSM transcripts or cDNAs. In another embodiment, the kit contains a microarray or other solid substrate carrying immobilized probes containing one or more SDC or NSM marker gene sequences for binding and quantitating tumor-infiltrating SDC or NSM transcripts, or cDNA derived therefrom. In one embodiment of the invention, the chip contains a number of probes corresponding to one, two, three, four, five, six, seven, eight, nine, or all of the SDC markers listed in table. A, and one, two, three, four, five, six, seven, eight, nine, or more than nine NSM markers listed in Table. A. In another embodiment, the chip contains a number of probes corresponding to BDCA3, KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, CLEC9A, MRC1, MS4A7, C1QC, APO E, C1QB, C1QA and C5AR1.

The kit may contain a number of PCR primers that exclusively amplify the gene sequences KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1 and CLEC9A in the sample. The kit may contain a number of PCR primers capable of exclusively amplifying the gene sequences C5AR1, LYVE1, ABCC3, MRC1, SIGLEC1, STAB1, C1QB, C1QA, TMEM37, MERTK, C1QC, TMEM119, MS4A7, APO E, CYP4F18, TREM2 and TLR7 in a sample. The kit may contain a range of PCR primers capable of exclusively amplifying the MRC1, MS4A7, C1QC, APO E, C1QB, C1QA and C5AR1 gene sequences in a sample. The oligonucleotide chip may contain immobilized probes capable of binding the gene sequences KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL1, BDC3A, XRC1 and CLEC9A. The oligonucleotide chip may contain immobilized probes capable of binding the gene sequences C5AR1, LYVE1, ABCC3, MRC1, SIGLEC1, STAB1, C1QB, C1QA, TMEM37, MERTK, C1QC, TMEM119, MS4A7, APO E, CYP4F18, TREM2 and TLR7. The oligonucleotide chip may contain immobilized probes capable of binding the MRC1, MS4A7, C1QC, APOE, C1QB, C1QA and C5AR1 gene sequences.

This application also provides articles containing one or more of the antibody compositions or kits described herein. Examples of articles include a vial (including sealed vials).

Examples

The following are examples of specific embodiments for carrying out the present invention. The examples are presented for illustrative purposes only and are in no way intended to limit the scope of the present invention. Efforts have been made to ensure accuracy in terms of the numbers used (e.g. quantity, temperature, etc.), but of course some experimental errors and deviations must be tolerated.

In the practice of the present invention, unless otherwise indicated, conventional methods of protein chemistry, biochemistry, recombinant DNA technology and pharmacology appropriate to the art will be used. Such technologies are described in detail in the literature. See, for example, T. E. Creighton, Proteins: Structures and Molecular Properties (WH Freeman and Company, 1993); A. L. Lehninger, Biochemistry (Worth Publishers, Inc., current addition); Sambrook, et al., Molecular Cloning.A Laboratory Manual (2nd Edition, 1989); Methods In Enzymology (S. Colowick and N. Kaplan eds., Academic Press, Inc.); Remington's Pharmaceutical Sciences, ^18th Edition (Easton, Pennsylvania: Mack Publishing Company, 1990); Carey and Sundberg Advanced Organic Chemistry ^3rd Ed. (Plenum Press) Vols A and B (1992).

Example 1. Materials and methods for examples 1-9.

Tumors of mice

Transgenic C57BL/6 PyMT-ChOVA founder mice as described ( Engelhardt et al., 2012 ) and their offspring were screened for the presence of the PyMT-ChOVA transgene by PCR, monitored for tumor development, and used at 20–30 weeks of age. B78ChOVA, which is a variant of B78 ( Graf et al., 1984 ), was generated as described in Supplementary Methods. All additional information on lines can be found in additional methods. All mice were maintained under SPF (specimen free) conditions in accordance with NIH and American Association for Laboratory Animal Care standards, and in accordance with UCSF animal care guidelines.

Flow cytometry

All antibodies were purchased from BD Pharmingen, eBioscience, Invitrogen, Biolegend, UCSF hybridoma core, or obtained from the Krummel Lab. For surface staining, cells were incubated with anti-Fc receptor antibody (clone 2.4G2) and stained with antibodies in PBS + 2% FCS for 30 min on ice. Viability was assessed by staining with Zombie Live/Dead Cell Removable Dye (Biolegend) or DAPI. For intracellular staining in mice 6 hours before

- 39 046327 collection, 10 μg/gram body weight of Brefeldin A (Cayman) was injected, the cells were stained with antibodies against surface markers, then fixed with 2% PFA for 10 min at 25°C and soaked in 0.2% saponin, then stained with the target antibody . All flow cytometry analyzes were performed on a BD Fortessa flow cytometer. Flow cytometry data analysis was done using Flowjo (Treestar). Cell sorting was performed using a BD FACS Aria II.

Bioinformatic analysis TCGA

Clinical expression assays use genome-wide mRNA levels (Illumina mRNA sequencing) obtained from 3602 patient tumor samples representing 12 cancer types (845 breast, 265 ovarian, 303 head and neck squamous cell carcinoma, 122 urinary bladder, 168 glioblastoma, 190 colon, 173 AML, 72 rectal, 355 lung adenocarcinoma, 259 squamous cell lung cancer, 480 kidney and 370 uterine cancers), normalized and combined into one data set by the TCGA PanCancer working group , as published (Cancer Genome Atlas Research et al., 2013; Hoadley et al., 2014) (data located in the TCGA data portal https://tcga-data.nci.nih.gov/tcga/ and available as syn1715755 at https://www.synapse.org/). The CD103+/CD103− signature ratio ^was calculated as the log of mean CD103+DC gene expression divided by mean CD103−DC gene expression, followed by z-score standardization (mean=0, co=1; gene list in Figure 8C) .

The authors also assessed published data for T cell signatures (Palmer et al., 2006), proliferation (Wolf et al., 2014), CSR/healing (Chang et al., 2005) and interferon gamma (Viigimaa et al., 2010 ) according to publications along with the CD8/CD68 expression ratio (DeNardo et al., 2011). Overall survival data were obtained from the TCGA portal (downloaded 6/2013) (Cancer Genome Atlas Research et al., 2013), and survival analysis was performed using Cox proportional hazards modeling in a multivariate model adjusting for cancer type. Log ranks of p values were used to assess significance after adjustment for multiple comparisons using the Benjamini-Hochberg method ( Benjamini and Hochberg, 1995 ). Kaplan-Meier survival plots are generated using the Survival Package in R. In the CM plot with all data (Fig. 8E), the authors adjusted for cancer type by classifying each sample as high or low using the median of those cancer types by ratio CD103+/CD103 ^- signatures.

Mouse strain information

OT-I mice, specific for the ovalbumin peptide SIINFEKL (SL8) in the context of H-2Kb (Hoquist et al., 1994), were crossed with CD45 mice. 1 Nur77-eGFP (Moran et al., 2011) and Cd2-RFP mice (VeigaFernandes et al., 2007) or actin-CFP mice (Hadjantonakis et al., 2002) to produce genome-encoded, fluorescently or congenically labeled T -cells for adoptive transfer, imaging and activation experiments.

The following mouse strains were used to modulate myeloid cell populations in tumors: C57BL/6, purchased from Simonsen, Irf4 ^f/f xCD11c-Cre (Klein et al., 2006), (Williams et al., 2013), kindly provided by Anne Sperling from University of Chicago, Irf8 ^-/- FVBN (Ouyang et al., 2011), kindly provided by Scott Kogan from UCSF, Zbtb46 - DTR (Meredith et al., 2012), Csf2rb ^-/- (Robb et al., 1995) and Csf3r ^-/- (Liu et al., 1996).

To visualize APCs within tumors, PyMT-ChOVA transgenic mice were crossed with Cx ₃ crl-eGFP (Jung et al., 2000) and CD11c-mCherry (Khanna et al., 2010) mice and the resulting F1 generation carrying both transgenes.

Cell lines, cell culture, plasmids and transfection

Before injection into mice, the following tumor cell lines were cultured under standard conditions. B16-F10 (Fidler, 1975), B16-GMCSF (Dranoff et al., 1993), B16-FLT3L (Curran and Allison, 2009), kindly contributed by Larry Fong, B78-original (Graf et al., 1984), B78chOVA , parental B78 transfected with a Ch-OVA fusion construct identical to that used in PyMTChOVA (Engelhardt et al.) using standard methods, EL4 (Hyman et al., 1972), EG7 (Moore et al., 1988), EG7-chOVA, ^-PyMT-luciferase ^AB, kindly provided by Zena Werb of UCSF (Halpern et al., 2006) and LLC, kindly provided by Lewis Lanier of UCSF (Bertram and Janik, 1980). B78pmCherry-pHlourin was produced by transfecting parental B78 cells with Lipofectamine according to the manufacturer's instructions from the N1-mCherry-pHlourin construct (Koivusalo et al., 2010; Webb et al., 2011; Choi et al., 2013).

Briefly, adherent cells were cultured at 37°C/5% CO ₂ in DMEM plus 10% FCS with Pen/Strep/Glut on tissue culture-treated plastic plates and subcultured every other day. Suspension cells were cultured in RPMI-1640 plus 10% FCS and pen/strep/glut in flasks and subcultured every other day.

Injection of ectopic tumors

Tumor cell lines were collected and washed 3 times with PBS, then mixed 1:1 with growth factor-reduced Matrigel matrix (BD Biosciences) to obtain a final injection volume of 50 μl. 100,000 tumor cells (unless otherwise stated) were injected into the right flank

- 40 046327 shaved mice subcutaneously and allowed to grow for 14-21 days before use.

Tumor disaggregation

PyMT-chOVA tumors and ectopic B78chOVA tumors were excised from mice, and the total mass of the removed tumor tissue was determined. Tumors were then minced using scalpels and disaggregated with 500 U/ml collagenase IV (Sigma), 100 U/ml collagenase I (Worthington) and 200 mg/ml DNase I (Roche) per 0.3 gram tumor mass over 3 intervals for 30 minutes in a 25 ml Erlenmeyer flask with a magnetic stirrer and placed on a stirring device in an incubator at 37°C with 5% CO2. Tumors were then passed through a 70-μm cell strainer at 30-min intervals to remove large pieces of undisaggregated tumor, and the remaining flocs were reintroduced for disaggregation while the isolated cells were kept on ice. Positive selection for CD45-6uotuh with magnetic beads (StemSep) was then performed at 4°C to enrich the total tumor immune infiltrate.

Samples from the human body

The tissue was intensively minced with surgical scissors and transferred to a 25 ml Erlenmeyer flask with a magnetic stirrer, with 3 mg/ml collagenase A (Roche) and 50 U/ml DNase I (Roche) per 0.3 g of tissue for 1 hour at 37°C C/5% CO2 with constant stirring. Samples were then filtered through a 70-μm filter, centrifuged, and resuspended for staining (Ruffell et al., 2012). For all human specimens, informed consent was obtained from all subjects and work was performed in accordance with IRB approval (IRB number 13-12246, 06/12/201312/05/2014).

Cell isolation

OT-I naïve CD8+ T cells were isolated from the lymph nodes and spleens of 6- to 12-week-old mice. Selection was performed using a CD8 negative cell isolation kit (Stemcell Technologies) following the manufacturer's instructions. KMDC were obtained by culturing bone marrow cells for 8-11 days, seeding 1-2x10 ⁶ cells/ml on plates in IMDM with 10% FCS and GM-CSF (granulocyte-macrophage colony-stimulating factor). IL-4 was added in the last 2 days of culture, and LPS was added 12 hours before using fully mature KMDC cells.

eFlour670 labeling of T cells

OT-I CD8+ T cells were incubated in RPMI without FCS with 2 μM efluor670 (eBioscience) for 15 min at 37°C. The eFluor670 dye was then quenched with 2 ml FCS and washed in RPMI with 10% FCS 3 times before use.

General T cell activation (CTL acquisition)

Transgenic lymph node OT-I TCR cells were stimulated with B6 splenocytes activated with 100 ng/ml SL8 peptide for 30 min, then washed 3x. Cells were propagated in the presence of 2 U/ml recombinant human IL-2 two days after stimulation and again four days after stimulation, and were used for experiments 2-3 days later.

T cell proliferation assays

Lymph node cells were isolated from OT-I TCR transgenic mice and enriched for naïve CD8 ⁺ T cells using the StemSep CD8 enrichment method (Stemcell technologies) and/or general CTL culture activation was used. Lymph node cells were isolated from OT-iI TCR transgenic mice and enriched for naïve CD4+ T cells using the StemSep CD4 enrichment method (Stemcell technologies). 20,000 enriched naive CD8 cells or 5-day pre-activated OT-1 T cells, or naive CD4 cells labeled with 2 µM eFluor670, were mixed with either 4000 KMDC, activated or not with 25 ng/ml SL8 peptide (OT-I) or 1 μg/ml pOVA 323-339 peptide (OT-iI), or with 4000 non-activated tumor APCs (unless otherwise stated), in 96-well V-bottom plates at any time of 12, 48, or 72 h at 37°C/5% CO ₂ , and the activation time was measured by increasing the expression of CD69/Nur77 and diluting efluor670 by flow cytometry.

Binding Analysis

Binding assays were performed as described (Friedman et al., 2006). Briefly, labeled T cells were mixed with a single stained cell suspension from tumor disaggregates for 30 min 1 h and then fixed with 2% PFA for flow cytometry. Percentage binding values were calculated as the number of T cell aggregates to the total number of T cells.

In vivo multiphoton microscopy imaging and surgery

Animals were maintained under isoflurane anesthesia on a heated microscopy stage, and the depth of anesthesia was monitored at regular intervals according to institutional guidelines. Before surgical intervention in animals. V. injected 100 μg of Evans Blue dye in PBS and 1 ml of lactated Ringer's solution. b. The breasts were surgically exposed and tumors were visualized using a modified version of the absorption window previously described by the authors (Thornton et al.). Intravital imaging was performed using

- 41 046327 specially manufactured two-photon attachment equipped with two infrared lasers (MaiTai: Spectra Physics, Chameleon: Coherent). The MaiTai laser was set to 870 nm or 910 nm to simultaneously excite CFP and GFP or GFP alone, respectively. The Chameleon laser excitation was set at 1030 nm to excite mCherry. The emitted light was detected using 25 x 1.2NA water lenses (Zeiss) coupled to a 6-color detector grating (custom; using Hamamatsu H9433MOD detectors), alternating laser excitation was used to produce 12 detection channels. The following emission filters were used: violet 417/50, blue 475/23, green 510/42, yellow 542/27, red 607/70, infrared 675/67. The microscope was controlled by the MicroManager software package, z-axis images of sections were obtained using 4-fold averaging, and z-axis intensities were 3 μM. Data analysis was performed using the Imaris software package (Bitplane).

Ex vivo tumor section staining and multiphoton imaging

Animals were sacrificed and tumors were collected. The covering fat was removed and the tumors were embedded in 2% low-melting agarose in PBS (SeaPlaque, Lonza). Sections were prepared at 300 μm thickness using a Compresstome VF-200 tissue microtome, Precisionary Instruments Inc. Sections were mounted to plastic coverslips using Vetbond (3M) and stained with Alexa647-labeled rat anti-CD11b antibody in RPMI supplemented with 5% rat serum for 2 h at 37°C/5% CO2. Sections were washed in RPMI and imaged using a Nikon AIR confocal microscope.

RNA extraction, Fluidigm analysis and RNA sequencing

4000-20000 cells were sorted directly into 300 μl of Trizol LS, quickly frozen and immediately stored at -80°C until isolation. RNA was isolated using the phenol chloroform method and ethanol precipitation. Samples were then treated with DNase I and cDNA was synthesized using Superscript III (Invitrogen). For Fluidigm analysis using nanoliter qPCR, cDNA was preamplified (12 cycles) by target-specific amplification with a 2x Taqman PreAmp master kit (Applied Biosystems) and then treated with exonuclease I to remove unincorporated primers. Samples and primers (target primers were designed using the Harvard primer bank: http://pga.mgh.harvard.edu/primerbank/) were then applied to a Dynamic Array 48. 48 chip with 2x SsoFast EvaGreen Super Mix (Bio-Rad) and analyzed on BioMark HD. For RNA sequencing, samples were isolated using the Arcturus Picopure RNA Isolation Kit (Life Technologies), bioanalyzed, and submitted to the UCSF Genomics Core. The library was prepared using the Nugen Ovation kit and further sequenced on an Illumina HiSeq 2500. 50-base reads were obtained from one end, yielding ~405 million reads with an average depth of 33.7 million reads/sample. Reads were aligned to mouse genomes (USCS mm 10) and those mapped exclusively to known mRNAs were used to assess differential expression. To analyze and evaluate differential expression, the Tophat method ( Trapnell et al., 2009 ) was used to perform alignments, and DESeq sequencing ( Anders and Huber, 2010 ) was used to perform differential expression analysis.

Tumor growth

To obtain tumor growth curves over the indicated time periods, the area of the tumors (mm ² ) was measured using a caliper as tumor width x tumor height.

Treatment with diphtheria toxin, FTY-720 and aCSF-1

We purchased D. T. from Sigma-Aldrich. To determine the rapid removal of D. T. in DTR mice. b. were injected with 20 ng of D.T. per gram of body weight and for analysis, mice were sacrificed 24 hours after injection of D.T. To determine long-term removal, mice were first i.c. b. 20 ng/gram DT was injected and then maintained at 4 ng/gram every three days thereafter.

FTY720 was purchased from Cayman, dissolved in saline, and stored in 1 mg/mL aliquots at -20°C. For every next day after the specified periods of time. b. injected with 200 μl of FTY at a final concentration of 100 μg/ml in saline.

aCSF-1 neutralizing antibody, clone 5A1, and rat IgG2a isotype were purchased purified from the UCSF Antibody Core. Animals were initially treated with 1 mg of antibody by i.v. b. injections and analyzed after 3 days. After this, to maintain exhaustion for a long time, mice were subsequently given c. b. doses of 0.5 mg every 5 days.

Production of GMP precursors and adoptive transfer

All bones (including femurs, tibias, humeri, ulnas, radii and pelvis) were collected in sorting buffer (PBS+2% FCS), crushed using a mortar and pestle and washed again with HBSS and then passed through a filter with a size pores 70 microns. RBCs were then lysed among the cells using 175 mM ammonium chloride solution for 5 min at 37°C and then washed and separated on a ficcol with 3 ml of Histopaque-1110 support (Sigma) to select live cells and

- 42 046327 bone debris separation. Bone marrow cells were enriched for CO117-positive cells using SP117 microbeads (Miltenyi Biotec) and selected for positivity on AutoMACS. Cells were then stained with a lineage cocktail of unconjugated rat antibodies (CD4, CD8, Mac1, Gr-1, CD5, Ter119, and CD3) for 30 min on ice, followed by washing and staining with fluorescently conjugated anti-rat secondary antibody for 30 minutes on ice. Cells were then washed and stained with the progenitor master kit (Ckit-APC-cy7, Sca1-PB, CD34-FITC and FcgR-PerCPCy5.5) for 30 min on ice, washed and sorted for GMP live, cKit+ Sca1 -, CD34+ FcgR using BD FACs AriaII.

Dextran Absorption Assay

Tumors were disaggregated and OO45-positive cells were selected as described above and then plated at 1 × ¹⁰ cells per well in 96-well round-bottom plates and incubated with or without 1 mg/ml Pacific Blue dextran dye solution (MM 10000) or at 4°C or at 37°C for 30 minutes in triplicate. The plates were shaken every 5 minutes. The plates were then washed 3 times, stained with surface antibodies, and immediately analyzed by flow cytometry. Dextran uptake was measured as the geometric mean fluorescence intensity for dextran uptake at 4°C, which was subtracted from the geometric mean fluorescence intensity for dextran uptake at 37°C.

Cell tracking and imaging analysis

Data were visualized and analyzed using Imaris software (Bitplane). Individual T cells were identified and tracked using Imaris. CD11c mcherry DCs were calculated using isosurfaces of masked DCs from MATLAB segmentation. Contact duration was determined by calculating the path duration of masked Tcell-DC aggregates, which were monitored using Imaris.

Antibody clones

Mouse Ab clones: CD45 clone 30-F11, CD45. 1 clone A20, CD45. 2 clone 104, CD11b clone M1/70, CD 11c clone N418, CD103 clone 2E7, CD24 clone M1/69, CD90. 2 clone 30-H12, Ly6C clone HK1. 4, MHCII clone N22, F4/80 clone BM8, CD69 clone H1. 2F3, CD135 cone A2F10, CD117 clone 2B8, CD26 clone H194112, CD206 clone C068C2, CD64 clone X54-5/7. 1, MerTK clone Y323, CD301b clone 11A10-B7-2, kindly provided by Akiko Iwasaki from Yale University, PDL2 clone TY25, IRF4 clone M17 and IRF8 clone T14, kindly provided by Roger Sciammas from the University of Chicago, IL12 clone C17.8, CD80 clone 16-10A1, CD86 clone GL1, 2B4 clone t2B4 and PDL1 clone 10F. 9G2.

Human Ab clones: CD45 clone HI30, CD3e clone OKT3, HLADR clone L243, CD56 clone CMSSB, CD19 clone H1B19, CD14 clone 61D3, CD16 clone CB16, CD11c clone 3.9, BDCA1 clone L161 and BDCA3 clone AD5-14H12.

Statistical analysis

Statistical analyzes were performed using GraphPad Prism software. Unless specifically stated otherwise, all data are representative of >3 separate experiments. Error values are presented as SEM calculated using Prism and obtained under triplicate experimental conditions. Specific statistical tests were with one-tailed and two-tailed T-tests, and all p-values less than 0.05 were considered statistically significant.

Example 2: Surface markers differentiate rare tumor DC subpopulations and excess macrophages.

To isolate tumor-infiltrating myeloid populations, an 11-color flow cytometry panel and advanced gating strategy were developed using a spontaneous breast tumor model, PyMTChOVA (Engelhardt et al., 2012), engineered together with an initiating oncogene to independently co-express the fluorescent protein mCherry and ovalbumin. We profiled the tumor compartment of CD45 ⁺ cells, many of which had phagocyte-ingested tumor antigen and thus exhibited mCherry fluorescence (Fig. 1A). Subgating of all hematopoietic cells using the myeloid-specific marker CD11b and the monocyte marker Ly6C allowed the removal of neutrophils and monocytes (data not shown). Among MHCII+ cells, DCs were distinguished from macrophages based on CD24 ^high and F4/80'expression; generally, using one of these parameters alone was not sufficient to provide such discrimination. Subsequently, DCs were found to segregate into two populations based on differential expression of CD11b and CD103, as observed for healthy peripheral tissues (Hashimoto et al., 2011). We found these populations in two mouse models of melanoma (B78ChOVA (B16 variant expressing mCherry and OVA), Fig. 1B and BRAF V600E, data not shown), among mouse strains (eg, FVB RuMT; data not shown) and in ectopic tumors (Leiss lung carcinoma; data not shown). The authors hereafter refer to these DC populations as CD11b+DC1 and CD103+DC2 for ease of distinction and discussion.

Division of the F4/80 ^high CD24™ ^B compartment also revealed two types of macrophages, identified

- 43 046327 data on differential expression of CDllc and CD11b. CD11c' CD11b ^BbIC (previously TAM1) and CD11c ^BbIC CDUb™ ⁵ (TAM2) cells were found to broadly correspond to similarly demarcated populations of MHC11 ^high and MHC11 ^low (Movahedi et al., 2010) (see also FIG. 5c). While CD 11c, otherwise the prototypical DC marker, had the highest abundance on DCs, it was highly expressed in TAM2 and to a lesser extent in TAM1 (data not shown). These populations existed among all models examined, although the frequency of each and their ability to uniquely differ varied slightly (Fig. 1A-B and data not shown). Thus, the authors applied their lineage and function studies to one example spontaneous tumor (PyMTChOVA) and ectopic tumor (B78ChOVA) model, except where noted.

Tumor-derived mCherry load and retention were assessed for each of these populations. These analyzes revealed that ^hijacking cells, which were localized at the tumor margin as shown in the authors' previous report and then identified only by CD11c (Engelhardt et al., 2012), were best captured at the TAM1 and TAM2 gates (Fig. 1c and data not shown). CD11b+DC1 and CD103+DC2 took up or retained relatively less mCherry, while some monocytes and individual neutrophils showed evidence of moderate antigen load.

CD11b ⁺ and CD103 + DC subpopulations are found in many mouse peripheral tissues, and their counterparts have been identified in human peripheral tissues, as determined by the expression of BDCA1 and BDCA3, respectively (Dzionek et al., 2000; Haniffa et al., 2012). We found that equivalent TAM/DC discrimination is also available in human metastatic melanoma samples using these markers (Fig. 1D). CD16 - HLADR + CD11c ⁺ CD14 ⁺ cells, representing all TAMs, differed from CD16 ^- HLADR ⁺ CD11c ⁺ CD14 ^- DC populations, which in turn were divided by differential expression of BDCA1 (DC1) and BDCA3 (DC2). Common among mouse models ( Fig. 1E ) and human melanoma biopsies ( Fig. 1F ) is the presence and low abundance of the CD11b/BDCA1 DC1 and CD103 ⁺ /BDCA3 DC2 populations, with DC2 being particularly rare.

Example 3: Protein and transcriptional discrimination of tumor DCs and macrophages.

To validate gate partitioning strategies, the authors used sets defined by the ImmGen consortium (Gautier et al., 2012; Miller et al., 2012). As defined by the authors, CD103+DC2 DCs expressed CD135 (Flt3), CD117 (cKit), and CD26, while both TAM populations were not found in the B78chOVA and PyMTchOVA models. (Fig. 2A and data not shown). Surprisingly, CD11b ⁺ DC1 did not express detectable levels of DC markers and in fact cosegregated more with TAM1 and TAM2 due to the expression of several macrophage markers, including CD206, CD64, and MerTK (Fig. 2B and data not shown). However, CD11b+DC1 weakly expressed CD301b and PDL2, both of which were used to define IRF4-dependent _Th 2 DC populations found in the skin (Figure 2C and data not shown) (Gao et al., 2013; Kumamoto et al., 2013 ).

To further distinguish between these APCs, the authors used gene expression profiles of sorted cells from the B78chOVA tumor using RNA sequencing. As shown in FIG. 2D, gene blocks clearly segregate into four populations, with TAM1, TAM2, and CD11b ⁺ DC1 being the most similar by PCA analysis (Fig. 2E), and CD103 + DC2 being the most divergent. Among the most differentially expressed genes, the DC lineage-determining transcription factors Irf8 (Tamura et al., 2005) and Zbtb46 (zDC) (Meredith et al., 2012) were specific for only CD103+DC2 or both DCs, respectively, while while Irf4 was moderately enriched in CD11b+DC1 and all of them were validated by RT-qPCR ( Figure 2F ). This was also confirmed at protein levels by intracellular flow cytometry for IRF4/8 (Fig. 2G and data not shown). All Myb-expressing populations indicated an origin from hematopoietic stem cells, which is the opposite of an origin from tissue progenitors sampled from the yolk sac (Schulz et al., 2012). In the PyMTchOVA model, unique surface phenotypes were replicated and key transcription factors were expressed (data not shown).

Because these intratumoral populations can arise through various tumor-specific mechanisms and are not driven by these transcription factors, as occurs in some traditional tissues, we examined the dependence of Irf8, Irf4, Batf3, and zDC using knockout or transcription factor-driven diphtheria toxin receptor (DTR) mice. The authors took advantage of the different ectopic tumors due to the distortions when crossing these alleles in a spontaneous model. Using the ectopic RuMT breast tumor model, we found that loss of Irf8 exclusively removes CD103+DC2 but does not affect TAM1 or TAM2, and it is slightly enriched in the percentage of CD11b ⁺ DC1, likely as a result of compensation (Fig. 3A). In contrast, conditional Irf4 deletion driven by CD11c-Cre (Williams et al., 2013) resulted in a specific reduction in CD11b ⁺ DC1 with little change in others in the B78chOVA model (Figure 3B). Consistent with RNA sequencing data, Batf3-deficient animals also lacked CD103+DC2 tumor populations in the B78chOVA model, with no effect on CD11b+DC1, TAM1, or TAM2 proportions ( Fig. 3C ). Finally, when the zDC-driven DTR allele was used, the authors somewhat unexpectedly found a specific and significant reduction in CD103 ⁺ DC2 with no

- 44 046327 significant or no changes in CDllb'DCl or TAM1/TAM2 populations in the B78chOVA tumor (Fig. 3D). This may represent DTR allele distortions or subtle but significant variations in zDC expression. Taken together, the authors conclude that CD103+DC2 represent a distinct APC lineage compared to CD11b'DC1. and TAM1/TAM2 are highly abundant in the tumor.

Example 4. CD103+DC2 programmed with various cytokines.

APCs are derived from bone marrow (BM) progenitors and their differentiation is dependent on specific cytokines. To determine cytokine-driven differentiation in these populations, we examined colony-stimulating factor (CSF) receptor expression among models using qPCR. Whereas Csflr (M-CSFR) was found exclusively in TAM1, TAM2 and CD11b ⁺ DC1, Csf2rb (GM-CSFR) was exclusively expressed in the DC1 and DC2 subpopulations, and Csf3r (G-CSF) was absent from all (Fig. 4A). Using either neutralizing antibody treatment or cytokine receptor-deficient mice bearing ectopic tumors, the authors functionally examined the CSF-cytokine dependence of APC in the tumor.

Because TAM1 and TAM2 cells were critically dependent on CSF-1 for their maintenance, as previously shown (Wyckoff et al., 2004), the CD11b'DC1 and CD103+DC2 populations were exclusively CSF1-independent (Fig. 4B). To use cytokine receptor-deficient mice, we developed a congenic adoptive transfer model whereby granulocytic macrophage progenitors (GMPs) were transferred into ectopic tumor-bearing hosts and monitored for repopulation in the BM, spleen, and tumor ( Fig. 4C ). In tumor GMP-derived cells, all myeloid compartments expanded, confirming the GMP origin of CD11b'DC1, CD103+DC2, TAM1, and TAM2 (Fig. 4D). By using a GMP-adoptive competitive transfer system, the authors found a selective inability of Csf2rb ^−/− cells to regenerate tumor DCs, here defined as the sum of DC1/DC2 using the CD24 ⁺ CD11c ⁺ gate. The authors found no effect on TAM1 and TAM2 repopulation, suggesting a sole requirement of CSF2 (GM-CSF) for DC tumorigenesis (Fig. 4E), while no requirement for CSF-3 was found for any of the four APCs (data not shown).

Because DCs are prototypically driven by GM-CSF or FLT3 ligand (FLT3L), we assessed the sufficiency of cytokines to drive tumor DC populations using B16 melanoma tumor models engineered to express GMCSF or FLT3L. Because tumor expression of GMCSF fundamentally shifts the proportion of CD11b ⁺ DC1, FLT3L-expressing tumors exclusively drive the expansion of rare CD103' DC2 within the tumor ( Fig. 4F ).

Example 5: Unique antigen processing and presentation characteristics of CD103' DC2.

Having established the lineage requirements of various APCs, the authors then assessed their ability to initiate, recruit, and sustain T cell responses. To separate cells with respect to antigen processing, presentation, and co-stimulation, we analyzed transcript and protein levels of genes involved in these mechanisms using RNA-seq data from Fig. 2. The magnitudes of the difference were significant across a wide range of potential APC function coverage (Figure 5A). Notably, while surface levels of molecules involved in regulating T cell responses, including CD80, CD86, and 2B4, were comparable between populations, CD103+ DC2 showed distinct transcriptional signatures consistent with increased precross presentation, increased co-stimulation, and increased expression of chemokines, which are known to would be expected to enhance T cell interactions (Fig. 5A,B and data not shown). There are no major differences in MHCI and MHCII expression between APCs, with the exception of slightly reduced MHCI on CD103+DC2 (Fig. 5C). However, significant differences in phagocytic capacity were observed in CD103+DC2 compared with TAM1/TAM2, measured exogenously by ex vivo dextran uptake assay by ectopic tumors (Fig. 5D).

Because DC maturation and phagocytic capacity are often inversely correlated, we hypothesized that increased phagocytic capacity of CD103' DC2 may correspond to more mature DCs with increased antigen cross-presentation (Guermonprez et al., 2002). Efficient antigen cross-presentation to DCs is mediated by Nox2 to regulate phagosomal pH, which thereby prevents destruction of T cell peptides. Prior to this work, the relative intracellular fluorescence intensity ratios of pH-sensitive and pH-insensitive fluorophores following phagocytosis were previously determined using an analysis to measure the ratios (Savina et al., 2006). Thus, the authors created the B78 (melanoma) tumor line expressing a fusion of a pH-sensitive GFP (pHluorin, quenched at pH values below 6.5) of a pH-insensitive fluorophore (mCherry). By analyzing the self-intensity for pHluorin within the mCherry ⁺ compartment of each population, the authors found that only DC populations maintained pHluorin in an alkaline (fluorescent) environment; comparison of the ratio of pHluorin to mCherry signals showed that CD103+ DC2 maintained the most basic endocytic compartment, whereas the TAM1 and TAM2 populations displayed highly acidic and therefore destructive phagocytic mechanisms (Figure 5E). In addition to increased alkaline fa

- 45 046327 gosomal light flux CD103+DC2, these cells showed differential expression of the pro-inflammatory cytokine IL-12 and the absence of anti-inflammatory IL-10 (Fig. 5F, G and data not shown). Together, all of these features suggest that CD103+DC2 is highly poised for efficient antigen cross-presentation to CD8+ T cells.

Example 6: CD103+ DC2 are the best stimulators of naïve and activated CD8+ T cells.

Previously, the authors found that the myeloid compartment absorbing antigen aggregates could stimulate naive, but not early activated CD8+ T cells, when entering directly into tumors (Engelhardt et al., 2012). However, based on the unique cross-presentation phenotype of f CD103+ DC2, we sought to examine the stimulatory capacity of T cells from each population recently isolated from tumors. After 12 hours of co-culture with ovalbumin-specific OT-I CD8+ T cells, the CD103+ DC2 population was the only population able to robustly induce TCR signaling, as confirmed by increased expression of the early activation markers Nur77 and CD69 of T cells in both naïve and early activated T cells. OT-I CD8+ T cells. Importantly, this was consistent with both ectopic and spontaneous mouse models (Fig. 6A and data not shown). Extended coculture of OT-I dye-labeled CD8+ T cells revealed that the CD11b'DC1 and CD103+DC2 populations were the most robust stimulators of naïve CD8+ T cell proliferation and demonstrated that almost all of the stimulatory capacity previously identified in phagocytic tumor myeloid cells , resides exclusively in these DCs (Fig. 6B-C and data not shown). Interestingly, CD103+ DC2 were exclusively capable of inducing strong proliferation of established CTLs that were not stimulated by other populations, indicating that CD103+ DC2 were excellent cross-presenting CTL stimulators in the tumor (Fig. 6D-E and data not shown, respectively).

Finally, at its usual low frequency in the general tumor isolate, CD103+DC2 remains unable to drive CTL proliferation (data not shown (Engelhardt et al., 2012)). In addition, none of the APC subsets induced proliferation of CD4+ T cells directly from the tumor (Fig. 6F-G and data not shown). However, the exogenous peptide did not restore the ability of DC1 and DC2 to stimulate proliferation, suggesting that these DCs may not be themselves incapable of stimulating CD4 T cells (data not shown). It is important that this determines the unique ability of CD103+DC2 within the tumor to absorb, process and cross-present tumor antigen for persistent stimulation of CTL. This challenges the simple concept that tumors contain only weak or suppressive myeloid populations.

Example 7. CD103+ DC2 localization and interaction of T cells identified by intravital imaging.

With the unique ability of rare CD103+DC2 to stimulate T cells, we sought to understand the spatial organization of these cells within the tumor and their dynamics of interaction with T cells both in vivo and in vitro. To differentiate these populations in living spontaneous tumors in vivo, the PyMTchOVA allele was crossed with the Cx ₃ crl-eGFP and CD11c-mCherry alleles, creating three unique fluorescent populations in the myeloid compartment (data not shown). Both DC subpopulations (DC1/DC2) were specifically labeled with red dye (CDIIc-mCherry only), whereas the TAM1 and TAM2 populations were green (Cx ₃ crle-only eGFP) and yellow (CD11c-mCherry and Cx ₃ crleGFP), respectively. Using this fluorescence approach with 2-photon intravital imaging, the authors observed that the TAM1 and TAM2 populations were preferentially slightly adjacent to tumor lesions. This zone is the zone in which the authors previously found preferential T cell uptake (Engelhardt et al., 2012). In contrast, red DC subpopulations were typically found in discrete collagen-free zones distal to tumor sites, accounting for up to nearly 70% of all distally localized APCs (Fig. 7A and data not shown).

While this approach does not fully differentiate CD11b+DC1 and CD103+DC2 cells among cells located at the margins of tumor foci, we sought to determine whether new red DCs might preferentially represent exclusively one or the other subpopulation. To distinguish these subpopulations in situ, the authors used imaging of live tumor sections using anti-CD11b antibody staining. Using this approach, the authors were able to distinguish CD11b + DC1 and CD103 + DC2 subpopulations in situ in the presence of red/green fluorescent reporters and found that both CD11b ⁺ and CD11b ⁻ DCs were present at these sites (data not shown). The authors conclude that since TAMs typically represent the dominant cell type at the tumor margin, pro-CTL stimulating APCs may still be found there, albeit in very small quantities.

The authors' previous data demonstrated that incoming CTLs are involved in suppression at tumor margins, and the authors sought to determine whether they might colocalize with DCs or TAMs, or all of them. We analyzed the in vivo dynamics of T cells in the red/green system

- 46 046327 porters by adoptive transfer of actin-CFP-labeled OT-I CD8+ T cells into spontaneous mammary tumor-bearing mice by either intravital imaging or live section imaging. The authors observed stable T-cell interactions largely confined to tumor boundaries, as previously described (Boissonnas et al., 2013; Engelhardt et al., 2012) (Fig. 7B and data not shown). Although TAM1 interactions dominated all interactions noted, DCs and TAM2 were also well represented in T cell suppression. This finding demonstrates that DC1/2 at tumor-proximal sites are not physically excluded from recruiting T cells within tumors, but it raises the fundamental question of which is more likely to recruit T cells.

To answer this question, the authors overrode APC selection due to physical tissue limitations and tumor disaggregation to obtain a single cell suspension, introduced in vitro activated OTI CTLs and allowed them to form antigen-specific pairs. The authors then determined the percentage of each APC population that bound to T cells by flow cytometry. This technique revealed that OT-I T cells paired preferentially with the CD103+DC2 and TAM1/2 subsets (Fig. 7C, left panel). However, due to the high frequency of TAM1/2, the majority of T cell-APC pairs are formed with TAM1/2 cells (Fig. 7C, right image). The authors conclude that DC2 are involved in T-cell interactions in tumors and, when present near the border, are able to compete for T-cell engulfment.

Example 8. Rare tumor CD103+ DC2 provide the opportunity to conduct effective adoptive T-cell therapy.

The authors were surprised to find that the proportions of CD11b'DC1 and CD103+DC2 were almost opposite in the spontaneously regressing tumor cell line EG7, hereinafter referred to as EG7. 2, compared to the very aggressive and expanding EG7 line. 1. Because the aggressively growing lineage maintained the relative proportions of DCs that the authors observed in all other aggressive tumors (data not shown), the spontaneously regressing model contained unusually large numbers of CD103+DC2 (data not shown). The authors also observed increased tumor growth in an Irf8 KO tumor model that lacked CD103+ DC2, but not in a conditional Irf4 KO model (data not shown). Taken together, it was suggested that tumor DC2 abundance may play an important role in tumor control, but differences in proliferation may represent many variations in these lineages beyond their myeloid cell populations and their ability to stimulate CTLs. Therefore, to formally test whether CD103+DC2 are required for efficient CTL-mediated tumor regression, the authors moved to the EG7 expanding tumor model. 1 and performed adoptive T cell therapy with activated tumor-specific T cells for regression analysis (Helmich and Dutton, 2001). We performed these experiments in zDC-DTR mice, which allowed tumor-specific ablation of CD103+DC2 ( Fig. 3D ). To isolate the effect of CD103+DC2 on tumor site, and exclude any influence of LN priming, the authors specifically designed the experiment to include two strategies: (1) use of activated OT-I CD8 ⁺ CTL blast cells that did not require priming into the LN and generally did not migrate there, and (2) treating the animals with the SIP ₁ antagonist, FTY-720, which prevented LN escape from the rare transferred CTL T cells that migrated to the LN. The effect of FTY-720 alone had minimal effects on transferred CTLs to mediate tumor regression (data not shown). However, the authors found that deletion of CD103+ DC2 in the context of FTY-720 had a significant impact on the ability of CTLs to mediate effective tumor regression, massively slowing down T cell-mediated tumor regression (Figure 8A).

Example 9 Intratumoral excess CD103+DC2 signatures predict human cancer outcome.

To determine whether the critical role of excess CD103+DC2 is transferred to human tumors, we took advantage of the TCGA dataset (Cancer Genome Atlas Research et al., 2013; Hoadley et al., 2014), which quantifies the relative gene expression of numerous types of cancers. human tumors that were consistent with the data obtained. The authors used their RNA-seq data to select high-level transcripts that characterized CD103+DC2 and also selected a subset of genes that characterized TAM1/TAM2/CD11b'DC1 cells. but were deficient in CD103+DC2 (top and bottom genes in Fig. 8B, respectively). The authors identified human homologs of these mouse genes and analyzed the expression of these signatures among TCGA patient data across all cancer types to evaluate predicted associations. In proportional hazards survival analyses, adjusting models for cancer type as covariates, the authors observed that individual genes from these populations had only modest prognostic benefits (expressed as hazard ratios (HRs)). However, a highly significant association with increased survival (VL p=0.00019) was observed when we determined the ratio of CD103+ to ^CD103− gene expression data and used them as continuously variable within the Cox analysis (Fig. 8B).

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This analysis showed that the cell type identified by the authors when determining the relationship with its functional opposite provides very strong prognostic value for outcome among human cancers. Comparison of this signature with other previously described immune assessments indicates that the CD103+/ ^CD103- gene ratio provides the strongest proimmune survival signal compared to other existing TCGA assays, including those based on total T cell excess (Palmer et al., 2006). and derived from the overall CD8 T cell to macrophage ratio (CD8/CD68 DeNardo et al., 2011) (Figure 8C). The authors' assessment also favors contrastive prediction comparisons for those immune scores associated with poor outcome. It is also noteworthy that CSF-1 expression in tumors from these patients was also inversely correlated with the CD103/BDCA3 gene ratio, although it was similarly inversely correlated with total tumor Flt3L levels (data not shown).

Finally, the authors sought to analyze TCGA data within individual cancer types. Adjusted for cancer type, the Kaplan-Meier (K-M) plot for all 12 cancers in this data set shows an overall advantage in tumors with a high CD103+/ ^CD103− gene expression profile (Fig. 8D and data not shown). The extent of this association is particularly significant in breast cancer, squamous cell carcinoma of the head and neck, and lung adenocarcinoma (Fig. 8E-G). In summary, this fact represents an unexpectedly strong immune signature, all the more significant since it derives entirely from the empirical acquisition of immune profiles in mouse tumor models.

Example 10. Materials and methods for Example 11.

Bioinformatic analysis of melanomas

The GSE19234 melanoma dataset (n=44) was preprocessed by quantile normalization in R before signature evaluation and assessment of associations with survival (Cox proportional hazards). Bogunovic, D., et al. The immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. Proc Natl Acad Sci USA 106, 20429-20434 (2009). The SDC/NSM signature ratio was calculated as the log of mean SDC gene expression divided by mean NSM gene expression, followed by z-score standardization (mean=0, co=1; gene list in Fig. 1A). Survival analysis was performed using Cox proportional hazards modeling. Log-ranks of p-values were used to assess significance after adjustment for multiple comparisons using the Benjamini-Hochberg method. ²⁰ Kaplan–Meier survival plots were generated using the survival package in R, and the authors classified each sample as high or low using the 33%, 50% (median), or 66% SDC value or SDC/NSM signature ratio.

Patients and samples

Patients were included in this study if they had histologically confirmed stage IV or III unresectable metastatic melanoma. Patients consented to tissue collection according to an approved UCSF IRB protocol (UCSF CHR #13-12246). The study enrollment period was from December 2012 to February 2015. Patients were treated with the following PD-1/PDL-1 axis blocking monoclonal antibodies: pembrolizumab (Keynote 001, 002, 006 either expanded access program or commercial supply) or nivolumab ( commercial supply). Cutaneous/subcutaneous tumors were biopsied with any punch (4 mm or 6 mm), surgically removed (sample K10), and all other tumor biopsies were collected exclusively by core biopsy (16g or 18g). An additional sample was sent for pathological examination. Biopsies were collected (n=21) before anti-PD1 antibody infusion. Fresh biopsy specimens were immediately placed in a sterile container on saline-soaked gauze, placed on melting ice in the container, and transported to the laboratory for evaluation. All response assessments were performed by radiological imaging using Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST). Complete response was defined as complete regression of all target and non-target lesions, partial response was defined as >30% regression of target lesions without the appearance of new lesions, and stable disease was defined as <30% reduction or <20% reduction in the size of target lesions. Progressive disease was defined as >20% increase in target lesions or the appearance of new lesions >1 cm in size. Patients with complete or partial response were classified as responders, whereas patients with stable disease or progressive disease were classified as non-responders. In cases where progression was determined clinically (eg, new lesions) without the benefit of follow-up scanning, the patient was categorized as a responder and marked x in RECIST.

Disaggregation of human tissues

The tissue was minced intensively with surgical scissors and transferred to a 25 ml Erlenmeyer flask with a magnetic stirrer and 3 mg/ml collagenase A (Roche) and 50 U/ml DNase I (Roche) per 0.3 g.

- 48 046327 tissue for 1 hour at 37°C with 5% CO ₂ with constant stirring. Samples were then filtered through a 70-μm filter, centrifuged, and resuspended for staining. Ruffell, B., et al., Leukocyte composition of human breast cancer. Proc Natl Acad Sci USA 109, 2796-2801 (2012).

Flow cytometry and Ab clones

All antibodies were purchased from BD Pharmingen, eBioscience, Invitrogen, BioLegend, UCSF hybridoma core, or obtained from the Krummel Lab. For surface staining, cells were incubated with a cocktail of antibodies against the human Fc receptor (clones 3G8, FUN-2 and 10.1, BioLegend) and stained with antibodies in PBS + 2% FCS + 2 mM EDTA for 30 min on ice. Viability was assessed by staining with Zombie NIR or Aqua live/dead cell removable dye (Biolegend). All flow cytometry analyzes were performed on a BD Fortessa flow pitometer. Flow cytometry data analysis was done using FlowJo software (Treestar). Cell sorting was performed using a BD FACS Aria II.

Mouse antibodies against human antigens: CD45 clone H13O, CD3e clone OKT3, HLA-DR clone L243, CD56 clone CMSSB, CD19 clone H1B19, CD14 clone 61D3, CD16 clone CB16, CD11c clone 3.9, BDCA1 clone L161 and BDCA3 clone AD5- 14H12.

Cell lines and cell culture

B78ChOVA is a B78 variant line generated by standard transfection procedures with a Ch-OVA fusion construct identical to that used to generate the PyMTchOVA cell line. Graf, L. H., Jr., Kaplan, P. & Silagi, S. Efficient DNA-mediated transfer of selectable genes and unselected sequences into differentiated and undifferentiated mouse melanoma clones. Somatic cell and molecular genetics 10, 139-151 (1984) and Engelhardt, JJ, et al., Marginating dendritic cells of the tumor microenvironment cross-present tumor antigens and stably engage tumor-specific T cells. Cancer Cell 21, 402-417 (2012). Briefly, adherent cells were cultured at 37°C with 5% CO ₂ in DMEM plus 10% FCS with penicillin, streptomycin and glutamine on tissue culture-treated plastic plates and subcultured every other day. Suspension cells were cultured in RPMI-1640 plus 10% FCS and pen/strep/glut in T25 and T75 tissue culture flasks and subcultured every other day.

Tumors of mice

All mice were maintained under SPF conditions in accordance with NIH and American Association for Laboratory Animal Care standards, and according to UCSF IACUC protocols.

Tumor cell lines were collected and washed 3 times with PBS, then mixed 1:1 with growth factor-reduced Matrigel matrix (BD Biosciences) to a final volume of 50 μl. One hundred and fifty thousand tumor cells were injected subcutaneously into the right shaved side and allowed to grow for 14-21 days.

Mouse lines

To modulate myeloid cell populations in tumors, wild-type C57BL/6 mice were purchased from Simonsen and Zbtb46 -DTR C57BL/6 mice were generated from SImonsen. Meredith, M. M., et al. Expression of the zinc finger transcription factor zDC (Zbtb46, BtbcW) defines the classical dendritic cell lineage. J Exp Med 209, 1153-1165 (2012). Zbtb46-DTR KM chimeras were generated according to the following standard procedures using 8-week-old C57BL/6 male recipients treated with lethal irradiation (2 doses of 5.5 Gy) and 2-5x10 ⁶ Zbtb46-DTR KM cells. Mice were kept on antibiotic water for 4 weeks and used for experiments at 8 weeks after recovery.

Tumor growth

To obtain tumor growth curves over the indicated time periods, the tumor area (mm ² ) was measured using an electronic caliper as tumor width x tumor height.

Treatment with diphtheria toxin, anti-PD-1 antibody and anti-CTLA-4 antibody

DT was purchased from Sigma-Aldrich. To determine the transient removal of DT in DTR mice. b. were injected with 20 ng of DT per gram of body weight and mice were sacrificed 24 h after DT injection for analysis. To determine long-term removal, mice first c. b. 20 ng/gram DT was injected and then maintained at 4 ng/gram every three days thereafter for up to 15 days.

Purified anti-PD-1 (clone RMPI-14) and anti-CTLA-4 (clone 9H10) antibodies were purchased from BioXcell and injected into the. b. using 100 μg of each antibody in the form of combination therapy with three stages of treatment on days 5, 8 and 11 of tumor growth.

Statistical analysis

Statistical analyzes were performed using GraphPad Prism software. Unless specifically stated otherwise, all data are representative of >3 independent experiments. Error values are presented as S.E.M. calculated using Prism and obtained from triplicate experimental conditions. Specific statistical tests were with one-tailed and two-tailed T-tests, and all p-values <0.05 were considered statistically significant.

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Example 11 Intratumoral BDCA3+ DC predict outcome to anti-PD1 checkpoint therapy for human melanoma.

Intratumoral APCs are extremely diverse in their phenotype, and the myeloid system provides multiple populations similar to both macrophages and dendritic cells. However, it has long been suspected that because macrophages are inhibitory to tumor progression (DeNardo et al., and Hanada et al.), intratumoral dendritic cells are thought to be stimulatory (Sandel et al.). Efforts to understand this statement explicitly have been hampered greatly by the lack of clear distinction between these cell types. Recently, the authors performed high-resolution flow cytometry coupled with RNA sequencing to differentiate intratumoral myeloid cells. The authors found that, in fact, a small population of cross-presenting dendritic cells was highly stimulatory for CTL, but other subpopulations of true dendritic cells, as well as a highly abundant population of macrophages, failed to stimulate tumor antigen-reactive CD8 T cells. Rare stimulatory dendritic cells (SDCs) were defined in mice by CD103 integrin expression and in humans by CD141/BDCA3 expression (Broz et al.).

Solely to elucidate the role of these rare immunostimulatory DC populations in human melanoma, we first took advantage of two sets of signature genes, the RNAs of each of which were significantly enriched in mouse SDCs relative to the remaining nonstimulatory myeloid (NSM, containing macrophages and nonstimulatory DCs) subpopulations of antigen presenting cells or vice versa set of genes that were predominantly expressed in NSM (Fig. 9A). The authors then queried a well-documented dataset of 44 local myeloma patients that covered RNA expression analysis, tumor mitotic index, clinical staging, and local myeloma patient survival (Bogunovic et al). The authors undertook RNA abundance analysis by analyzing either individual SDC genes, the average of all SDC genes, or the ratio of SDC/NSM genes. The latter two could be considered indicators of total SDC excess and relative SDC/NSM excess, respectively (Broz et al.).

Of the 9 SDC gene signatures, 7 had a significant prognostic benefit expressed as hazard ratio (HR) (Table 1, below) and both the SDC signature and the SDC/NSM signature ratio were highly predictive.

Gene/signature Coefficient p-value VN p-value MYCL -1763 6 20E-05 0.0008519 All SDC genes -1.379 8.52E-05 0.0006519 BTLA -1.114 0.0002445 0.001834 Ratio SDC/NSM -326 0.0005465 0.002742 FLT3 -1.176 0.0008321 0.003492 CCR7 •0.4115 0.0009313 0.003492 IRF8 -0.373 0.005329 0.01776 BATF3 -0 6643 0.02956 0.08062 XCR1 -0732 0.0372 0.093

To better visualize these prognostic associations, Kaplan-Meier (KM) plots were generated for either SDC genes or the SDC/NSM ratio, dividing patients into high or low signature expression with increasing cutoff stringency at any of 33%, 50%, or 66% of grouped expression thresholds (Figure 9B,C). These analyzes demonstrated an increase in the odds of survival when the highest expression levels were selected; the top 33% of SDC or SDC/NSM tumors have the greatest statistically significant increase in survival from the onset of metastasis. This finding supports the hypothesis that an increased abundance of BDCA3+ stimulating DCs in the tumor better ensures survival even in the absence of therapy.

When considering the relationship between this association and T-cell immunity, the authors further took advantage of selected information on TIL excess in tumors with defined profiles relative to SDC and SDC/NSM signatures to disaggregate data by TIL infiltration. This analysis revealed (Fig. 9D-G) that TIL category class-based measures and peritumor CD3+ T cell counts were all highly correlated with the SDC gene signature and less, but still highly correlated, with the ratio SDC/NSM.

These relationships suggested a relationship between SDC excess, T cell function, and overall survival. The authors sought to clarify this relationship, how

- 50 046327 may be related to the blockade of control points. But since the SDC and SDC/NSM signatures are merely imitations of the populations themselves, the authors sought to directly measure these populations from melanoma biopsies in the context of clinical trials of breakpoint-based therapies to see how related they were.

To investigate this point, the authors analyzed tumor biopsies from 21 biopsy samples of metastatic melanoma patients using flow cytometry, and then tracked their treatment progress in response to anti-PD-1 therapy. Among the 21 patients, 5 were women and 16 were men with a mean age of 61.6 years, and biopsies were collected from a variety of sites and tissues (Fig. 10A). For each of these patients, biopsies were disaggregated enzymatically and analyzed by flow cytometry to quantify the proportion of immune cell infiltrates into the tumor. We developed a comprehensive flow panel for isolating human myeloid infiltrates using markers CD45, HLA-DR, CD3, CD19, CD56, CD11c, CD11b, CD85g, CD14, BDCA1 and BDCA3. Using these markers, we were able to consistently gate the immune compartment of these tumors, identifying subpopulations of BDCA3+ DC, BDCA1+ DC, CD14+ TAM, and CD14 ^- TAM (Fig. 10B,C). And the authors found patients with significant populations of BDCA3+ SDCs (Figure 10B) as well as patients with significantly smaller populations (Figure 10C). Tumors also varied widely in the total amount of CD45+ cell infiltration and in the proportion of cells expressing HLA-DR (Fig. 10D). Most melanomas were highly enriched in total lymphocyte lineage excess (Figure 10E). And when HLA-DR+ cells were separated as in FIG. 10C/D, on myeloid subpopulations, they also showed quite significant heterogeneity among patient biopsies (Fig. 10E).

To examine the association of these immune myeloid infiltrates with patient responses, we categorized patients as either nonresponders, defined as patients with stable or progressive disease, or responders, defined as partial or complete responses to anti-PD-1 therapy (see Fig. 10A and methods). According to this approach, there was no significant difference in the percentage of total CD45 ⁺ immune cell infiltrates between responders and nonresponders, and both groups actually had similar mean percentages of CD45 ⁺ cells with similar variability around the mean (Fig. 11A, B and data not shown ). Likewise, there was no apparent relationship between overall lymphocytic cell frequency and outcome (data not shown). In comparisons where BDCA3+ DCs among the responding group were quantified as either a proportion of total immune cells (CD45+ cell gate, Figure 11C-D) or total APCs (HLA-DR ⁺ cell gate, data not shown) , responding patients to anti-PD-1 therapy had statistically significant higher frequencies of BDCA3+ DCs in their tumors. Taken together, these results suggested that because overall tumor immune cell infiltration does not predict susceptibility to immunotherapy, perhaps because the overall CD45 ⁺ population contains both types of pro and con antitumor players in the TME, the proportion of BDCA3+ DC stimulating may, in fact, predict the effectiveness of anti-PD-1 immunotherapy. Because there are clear examples of responders with relatively low amounts of BDCA3, an absolute cutoff of greater than 2% HLA-DR+ provides 95% confidence for responder status using this sample size.

The authors sought to further examine these data to understand whether the positive relationship could be further improved by accounting for the precise identity of the remaining myeloid populations; For this purpose, an excess of either CD14+ TAMs or alternative DC populations by BDCA1 or CD14 ^- TAMs was specially marked. The authors ranked individual patients by their percentage of BDCA3+ cells within each population and coded each according to responder status. Because responders still remained separated into high BDCA3+ regions in this plot, the authors did not find obvious patterns with other populations (Fig. 11 EG). Again, these results indicate that the presence of BDCA3 is a strong indicator of the ability to elevate the antitumor response in solid tumors, but other factors may also play a role in allowing individual patients to respond despite the presence of low levels. Future studies should focus on the intratumoral localization of BDCA3 ⁺ cells as a possible explanation; Currently, antibodies for them did not work well in the authors' case and therefore this fact needs to be developed in future studies.

Having established a strong association between excess BDCA3+ DCs and responsiveness to immunotherapy, the authors asked whether this association represents a functional requirement for the presence of this stimulatory DC population for checkpoint blockade-based therapies or simply represents a signature of responsiveness. To investigate this question, the authors used a controllable mouse model of melanoma, the B78chOVA ⁷ cell line, which is a modified variant of B16 melanoma that expresses mCherry fluorescent protein and ovalbumin. In combination with this tumor model, the authors used Zbtb46DTR chimeric mice. KM, which were previously shown to preferentially remove (CD103+) SDCs (Broz et al.). In service

- 51 046327 According to the authors, monotherapy with anti-PD1 antibodies alone was ineffective for melanoma in mouse models. We therefore used an ablative model to examine whether deletion of CD103+ SDCs could prevent the effectiveness of a combination immunotherapy regimen with anti-PD-1 antibody and anti-CTLA-4 antibody (Fig. 12A). Zbtb46-DTR KM chimeric mice were either treated with anti-PD-1 or anti-CTLA-4 or isotype-matched control antibodies using three-dose regimens on days 5, 8, and 11, and with injections of either DT or PBS , starting at 24 hours, and the authors confirmed that treatment with DT alone had no effect on tumor growth in this model (data not shown). The authors found that while untreated melanoma tumors gradually grew, tumors treated with combination immunotherapies rapidly regressed after 7-8 days and were almost completely eliminated by day 15 (Fig. 12B, C). In comparison, when mice were depleted of CD103+ DCs in the context of this active immunotherapy, rapid tumor regression was lost and the efficacy of dual therapy was abolished, suggesting a functional requirement for CD103+ DCs for immunotherapeutic effect (Figure 12D).

In summary, the authors established a strong predictive and prognostic benefit of a specific SDC population (CD103+ or BDCA3+) for both mouse and human tumor tissues. Overall, these results further emphasized the importance of fully understanding the immune landscape of tumor tissues identified in previous studies (DeNardo et al., and Fridman et al.). Immunograding (Ascierto et al.) has begun to be accepted as a new diagnostic marker for some cancers; However, going beyond this, the authors' study highlighted the need for increasingly detailed immune profiling of human tumors to identify rare populations of immune cells that can modulate T cells in the microenvironment. Applying ongoing refinements to the signatures identified by preliminary studies will require stratification followed by refinement of biopsy-sensitive assays, and the authors' study will highlight this in the future; one such method may be represented by high-resolution imaging, such as mass ion beam technologies (Angelo et al.).

Example 12: Materials and methods for the following examples.

Tumor disaggregation

Tumors were excised from the mice and the total mass of the removed tumor tissue was determined. Tumors were then minced using scalpels and disaggregated with 20 U/ml Liberase TL (Roche) from 5 mg/ml stock solution and 200 mg/ml DNase I (Roche) per 0.3 g tumor mass for 30 min in a conical flask 50 ml and placed on a shaker with 37°C and 5% CO2. Then, after 30 min, the tumor mass was passed through a 70 μm cell sieve and live cells were enriched on a Ficoll Paque Plus (GE) density gradient. Live cells were collected from the surface and washed in staining buffer (PBS + 2% FCS + 2 mM EDTA).

Isolation of mouse bone marrow and spleen

The femurs and tibias were removed, and the bone marrow was extracted using PBS/2% FCS/2 mM EDTA and a 25-gauge syringe/needle. Spleens were removed from mice and minced using scalpels. Tissue fragments were disaggregated using 500 U/ml collagenase IV (Worthington), 100 U/ml collagenase I (Worthington), and 200 mg/ml DNase I (Roche) in a 50-ml Erlenmeyer flask on a shaker at 37°C. The disaggregated tissues were then filtered through a 70-μm cell strainer. Red blood cells were lysed in both bone marrow and spleen using 0.8% NHCU for 5 min and then washed in staining buffer (PBS + 2% FC + 2 mM EDTA).

Human tumor samples

The tissue was minced intensively using surgical scissors and transferred to a 50 ml conical flask with 20 μg/ml Liberase TL (Roche) from 5 mg/ml stock solution and 200 mg/ml DNase I (Roche) per 0.3 g tumor mass in for 30 min at 37°C and 5% CO2 with constant stirring. Samples were then filtered through a 70-μm filter, centrifuged, and resuspended for staining (Ruffell et al., 2012). Informed consent was obtained from all subjects for all human specimens and work was performed in accordance with IRB approval (IRB number 13-12246, 06/12/2013-05/12/2014).

Flow cytometry and Ab clones

All antibodies were purchased from BD Pharmingen, eBioscience, Invitrogen, BioLegend, Human Protein Atlas or obtained from Krummel Lab or Precision Immune Inc. For surface staining, cells were incubated with anti-Fc receptor antibody (clone 2.4G2) as well as 500 nM human IgG ₁ Fc and stained with primary antibodies in PBS + 2% FCS + 2 nM EDTA for 30 min on ice. Followed by 2 washes in PBS + 2% FC + 2 mM EDTA and staining with the appropriate secondary antibody for 30 min on ice. Viability was assessed by staining with the live/dead cell removable dye Zombie NIR or Aqua (Biolegend) or Zombie NIR or DAPI. All flow cytometry analyzes were performed on a BD Fortessa flow pitometer. Flow cytometry data analysis was done using FlowJo software (Treestar).

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Ab clones against mouse markers: CD45 clone 30-F11, CD11b clone M1/70, CD11c clone N418, CD103 clone 2E7, CD24 clone M1/69, CD90. 2 clone 30-H12, Ly6C clone NCL. 4, MHCII clone M5/114. 15. 2, F4/80 clone BM8, CD64 clone X54-5/7. 1.

NSM markers: MS4A7 (polyclonal, from Human Protein Atlas, product number: HPA017418), MS4A6A (polyclonal from Human Protein Atlas, product number HPA011391). Rat anti-mouse CD88 (C5aR) clone 20/70, anti-LILRB4 clone (Pi1.5 Clone 1), anti-Trem2 (Pi1.2 clone 2, 5 or 7), CD206 clone C068C2, MerTK clone Y323.

SDC: Antibody anti-CCR7 clone 4B12 (mouse), anti-CCR7 clone 3D12 (human), anti-XCR1 clone ZET (mouse), CD135 clone A2F10 (mouse), CD117 clone 2B8 (mouse).

Ab clones against human markers: CD45 clone H13O, CD3e clone OKT3, HLADR clone L243, CD56 clone CMSSB, CD19 clone H1B19, CD14 clone 61D3, CD16 clone CB16, CD11c clone 3.9, BDCA1 clone L161 and BDCA3 clone AD5-14H12. TREM2 (clone 237920).

Secondary antibodies: anti-human-Fab-A488, anti-rat-A488, and anti-goat A488 were all purchased from Jackson Immunoresearch.

To generate anti-TREM2 antibodies, purified protein antigens corresponding to the extracellular domains of human and mouse TREM2 were prepared as Fc fusions and purchased from R&D Systems (Minneapolis, MN). These antigens were diluted in phosphate-buffered saline (PBS) at pH 7.4 and immobilized in 96-well immunoplates by overnight adsorption at 4°C. The immunoplates were then blocked with bovine serum albumin (BSA) and incubated with a naïve synthetic Fab-phagemid library for at least 2 h at room temperature. Unbound phage was removed by extensive washing with PBS + 0.05% Tween-20. Bound phage was eluted using 0.1 N HCl. The eluted phage was neutralized with 1 M Tris-Cl pH 8.0 and amplified by transfer through a bacterial host using trans-complementation with helper phage M13KO7. The amplified phage was concentrated from the bacterial supernatant by precipitation by adding 1/5 volume of PEG-8000, 2.5 M NaCl, incubation on ice for 20 min and centrifugation at >17600xg for 20 min. The precipitated phage was resuspended in PBS containing 0.5% BSA and 0.05% Tween-20 and used in subsequent rounds of selection on adsorbed mouse, human, or both antigens. After three to five rounds of selection, phage obtained from individual clones grown in 96-well format and culture supernatants were used in a phage ELISA to identify specific binding clones. Clones that bound to antigen but not to bovine serum albumin or human Fc control were subjected to DNA sequence analysis. Pi1.2 clones 2, 5, and 7 were selected and examined. These clones were found to bind mouse and human extracellular TREM2 and not to bind mouse and human extracellular TREM1 (data not shown). TREM1 (initiation receptor expressed on myeloid cells) has accession number NM_018643. 3, accessed September 25, 2015 via the NCBI website.

The antibody library was obtained from the University of Toronto. See Persson et al., CDR-H3 Diversity is Not Required for Antigen Recognition by Synthetic Antibodies. J Mol Biol. 2013 February 22; 425(4):803-811, herein incorporated by reference for its express purpose and for all purposes. Various synthetic antibody libraries are also described in USPN 7985840 B2, incorporated herein by reference, and various book chapters (Fellouse and Sidhu, Making Antibodies in Bacteria in: Making and Using Antibodies, Howard and Kaser, eds. Taylor and Francis, 2007, incorporated herein by reference.

LILRB4 antibodies were generated using methods similar to those described above or described in application WO2013080055, incorporated herein by reference. The sequences of clone 1 are shown in table. BB.

Cell lines and cell culture

MC38 cells were cultured using standard cell culture techniques. Briefly, adherent cells were cultured at 37°C with 5% CO ₂ in DMEM plus 10% FCS with penicillin, streptomycin and glutamine on tissue culture-treated plastic plates and subcultured every other day. Suspension EL4 cells were cultured at 37°C with 5% CO ₂ in RPMI-1640 plus 10% FCS with penicillin, streptomycin and glutamine in tissue culture flasks and subcultured every other day.

Tumors of mice

All mice were maintained under SPF (pathogen free) conditions in accordance with NIH and American Association for Laboratory Animal Care standards, and according to UCSF IACUC protocols.

Tumor cell lines were collected, washed 3 times with PBS and injected in a final volume of 50 μl. One hundred fifty thousand tumor cells were injected subcutaneously into the right shaved flank and allowed to grow for 14-21 days in 6-8 week old male C57BL/6 mice.

Tumor growth

To obtain tumor growth curves, tumors were measured with

- 53 046327 using an electronic caliper as the tumor widthxtumor height and calculated the tumor volume (mm ³ ) as

V=(LxWxW)/2.

Antibody treatment

Purified anti-PD-1 antibody (clone RMPI-14) was purchased from BioXcell, human IgG ₁ Fc (purchased from BioXcell) or self-produced antibody clones were injected with 200 μg of WB in four treatments on days 5, 8, and 11 and 14 of tumor growth with the exception of anti-TREM2 (Pi1.2) antibody (clone 2), which was injected at 40, 20, 20 and 40 μg on the corresponding days.

Creation of transductants

Cell lines were generated by lentiviral transduction using GeneCopoeia lentiviral vectors and LentiPack HIV packaging systems. Following the manufacturer's instructions, infected cell lines were cultured in a selecting antibiotic (puromycin) and sorted for target protein expression by FACS.

Labeling cells with dyes

Cells were incubated in RPMI without FCS with 0.5 μM eFluor670 (eBioscience) or 0.5 μM CMTMR (Thermo) for 15 min at 37°C. The dyes were then quenched with 2 mL FCS and washed in RPMI 10% FCS 3 times before use.

Intraperitoneal depletion assay

2 x 10 ⁶ dye-labeled cells from each of the original and target expressing transduced cell lines were injected with IP into male WT B6 mice. Four hours later, animals were injected with 500 μg of depletion or control antibodies. After 24-36 hours, the transferred cells were isolated by peritoneal lavage and counted by flow cytometry.

Statistical analysis

Statistical analyzes were performed using GraphPad Prism software. Unless specifically stated otherwise, all data are representative of >3 independent experiments. Error values are presented as S.E.M. calculated using Prism and obtained from triplicate experimental conditions. Specific statistical tests were with one-tailed and two-tailed T-tests, and all p-values <0.05 were considered statistically significant.

Example 13 Presence of NSM and SDC in a variety of human tumors.

The next step was to determine whether NSM and SDC exist among many different types of human tumors. Using flow cytometry, the authors analyzed human tumor tissue biopsies from local melanoma, head and neck squamous cell carcinoma (HCC), and colon carcinoma for the presence of SDC and NSM populations. Representative flow cytometry plots show that a progressive gate distribution identified both the NSM and SDC populations across all human tumor types displayed (metastatic melanoma, head and neck squamous cell carcinoma (HCSC), and colon carcinoma). See fig. 13.

Example 14 NSM Protein Expression and Binding in a Mouse Tumor.

The next step was to determine whether certain NSM proteins were expressed on the cell surface of NSM and whether these NSM proteins could be bound by anti-NSM antibodies. It was also determined whether certain NSM proteins were expressed on SDCs.

Mouse tumors were stained with NSM markers to demonstrate the specificity of these markers for NSM, but not SDC or other cell types or subpopulations. In fig. 14 shows the labeling of these cell subpopulations with various NSM markers defining gates within these cell subpopulations. Staining for the indicated markers is shown in black histograms, whereas staining for isotype controls is shown in gray-shaded histograms. Top line: B16 melanoma stained with anti-TREM2 antibody (Pi1.2 clone 2); second row: MC38 stained with anti-TREM2 antibody (Pi1.2 clone 2); third row: MS38 stained with anti-MS4A7 antibody (commercial polyclonal antibody purchased from Human Protein Atlas, product code: HPA017418); fourth row: B16, stained with antibody against LILRB4 (Pi1.5 clone 1); fifth row: MC38 stained with anti-C5AR1 antibody. Data show strong binding to inflammatory DCs, Ly6c ⁺ monocytes and TAMs and significant binding to CD11b ⁺ DCs. Little or no staining was observed for CD103 ⁺ DCs, T cells, B cells, and tumor cells.

Binding of a given NSM protein by an anti-NSM antibody directed against a given NSM protein indicates that NSMs will be depleted or destroyed through known antibody-based depletion mechanisms, for example, by selecting the appropriate Fc domain to mediate ADCC.

Example 15. Expression of CCR7 in human SDC and NSM cells.

The specific expression of CCR7 was analyzed using flow cytometry in the SDC and NSM populations, respectively, in disaggregated tumor tissue. All data is obtained from the locality

- 54 046327 human melanoma cells. In fig. 15 shows the specific expression of CCR7 on human SDC relative to NSM and other immune cells.

Example 16 SDC Protein Expression and Binding in Tumor.

The next step was to determine whether specific SDC proteins were expressed on the cell surface of SDCs and whether these SDC proteins could be bound by anti-SDC antibodies. It was also determined whether certain SDC proteins were expressed on NSM.

The specific expression of SDC and NSM gene products was analyzed using flow cytometry in the SDC and NSM populations, respectively, in disaggregated tumor tissue. All data were obtained on the ectopic B78chOVA tumor model. Expression of SDC markers (CCR7 and XCR1; black line) compared with the corresponding isotype (shaded in gray) among cell populations in tumors. In fig. 16 shows specific expression of SDC gene products on SDC and lack of expression of SDC protein on NSM.

Example 17 Lack of NSM binding outside the tumor.

Bone marrow (BM) and spleens from healthy wild-type B6 mice were analyzed by flow cytometry for the expression of TREM2 (clone 237920, RnD) and MS4A7 (commercial polyclonal, Human Protein Atlas).

Representative histograms show TREM2 and MS4A7 staining levels among several healthy tissue populations. Secondary controls (anti-A488 antibody and anti-A488 antibody, respectively, from Jackson Immunoresearch) for each population are shown in gray, while target protein staining is represented by a histogram overlay with a solid black line for each population.

Bone marrow (BM) and spleens from healthy wild-type B6 mice were analyzed by flow cytometry for TREM2 expression (Pi1.2 clone 2, clone 5, and clone 7) by antibody staining across a range of staining concentrations (2, 20, 200 nM) versus with control (human IgG1 Fc labeled 0 nM) among immune populations.

In fig. Figure 17 shows the absence of significant staining for multiple NSM markers in healthy BM and spleen tissues. This suggests that the use of anti-NSM antibodies is unlikely to cause significant off-target effects, for example during tumor treatment.

Example 18 Depletion of NSM in a tumor using anti-TREM2 or anti-LILRB4 antibodies.

The next step was to determine whether anti-NSM antibodies could specifically deplete NSM-bearing cells in vivo.

TREM2: Control and TREM2 (TREM2 may also be called Pi 1.2) expressing EL4 transfectants were labeled with CMTMR and Elfour670 dye, respectively, and mixed in a 1:1 ratio. Mixtures of a total of 4x106 cells were injected with WB into male WT B6 mice. After 4 h, animals were injected with 500 μg of anti-Pi1.2 (anti-TREM2) or control human IgG1 antibody. After 36 h, mice were sacrificed and cells isolated from the peritoneal cavity, collected by peritoneal lavage, were counted by flow cytometry. In fig. 18 shows that anti-TREM2 antibodies specifically deplete TREM2-bearing cells in vivo, whereas the control antibody does not.

LILRB4 (ILT3): Control and LILRB4-expressing EL4 transfectant cells expressing TdTomato and GFP, respectively, were mixed 1:1 with 5x105 of each cell type and IP injected into WT B6 male mice. Two hours later, animals were injected with 100 μg of antibody against LILRB4, clone 1, or control PBS. After 24 h, mice were sacrificed and cells isolated from the peritoneal cavity, collected by peritoneal lavage, were counted by flow cytometry. In fig. 18 shows that antibodies against LILRB4 specifically deplete LILRB4-bearing cells in vivo.

Depletion of NSM by an anti-NSM antibody is directed against a given NSM protein, indicating that an anti-NSM antibody will reduce tumor growth when administered to a tumor-bearing subject.

Example 19. Reduced tumor growth after administration of antibodies against TREM2.

The next step was to determine whether anti-NSM antibodies could reduce tumor growth in vivo.

MC38 colon carcinomas were injected into 6-week-old male B6 mice at time T0. Mice randomly assigned to treatment groups were treated with the indicated antibodies on days 5, 7, 11, 15 by IP injection after tumor implantation. Dosage: injections of 200 μg/day of anti-PD-1 antibody and Fc control, 40 μg, 20 μg, 20 μg and 40 μg of anti-TREM2 antibody (Pi1.2 clone 2) were given on days 5, 7, 11 and 15. Tumors measured with a caliper and the volume of tumors is shown. For each of the TREM2 and PD-1 groups of data analysis, the largest outlier value was discarded. In fig. 19 indicated that anti-NSM antibodies reduced tumor growth relative to controls to the same extent as anti-PD-1 antibody therapy.

Example 20 Depletion of NSM in a tumor using anti-NSM antibodies. Control and NSM protein-expressing EL4 transfectant cells expressing TdTomato and

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GFP is mixed in a 1:1 ratio, for example, with 5x10 ⁵ of each cell type and injected with IP into male WT B6 mice. Two hours later, animals were injected with anti-NSM antibody (eg, 100 μg), control Fc, or control PBS. After 24 h, mice were sacrificed and cells isolated from the peritoneal cavity, collected by peritoneal lavage, were counted by flow cytometry. Antibodies against NSM specifically deplete NSM protein-bearing cells in vivo. Antibodies against NSM bind TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK and/or TMEM119. NSM proteins are selected from TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK and TMEM119.

Example 21. Reduced tumor growth after administration of anti-NSM antibodies.

Cancer cells (eg, MC38 colon carcinoma) are injected into 6-week-old male B6 mice at time T0. Mice randomly assigned to treatment groups are treated with control Fc or anti-NSM antibody for several days (eg, days 5, 7, 11, 15) by IP injection after tumor implant. Determine the dosage, for example, 200 μg/day of control Fc; 40 µg, 20 µg, 20 µg and 40 µg anti-NSM antibody every day (eg days 5, 7, 11 and 15). The tumors are measured with a caliper and the volume of the tumors is determined. Antibodies against NSM reduce tumor growth relative to controls. Antibodies against NSM enhance the immune response to tumors in experimental mice relative to controls. Antibodies against NSM bind TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK and/or TMEM119.

Example 22 Improvement of immunotherapy by co-administration of anti-NSM antibodies.

Cancer cells (eg, MC38 colon carcinoma) are injected into 6-week-old male B6 mice at time T0. Mice randomly assigned to treatment groups are treated with control Fc or anti-NSM antibody for several days (eg, days 5, 7, 11, 15) by IP injection after tumor implant. Mice have previously received, are currently receiving, or will subsequently receive immunotherapy. Types of immunotherapy may include immunotherapy that inhibits a checkpoint inhibitor; immunotherapy, which inhibits T-cell checkpoint inhibitor; which uses anti-PD1 antibodies; anti-PDL1; anti-CTLA4; adoptive T-cell therapy; CAR-T cell therapy; use of a vaccine based on dendritic cells; monocytic vaccine; antigen binding protein, which binds both T cells and antigen presenting cells; antigen binding protein BiTE; toll-like receptor ligand and/or cytokine. The dosage is determined using information available in the art and one of ordinary skill in the art, for example, 200 μg/day control Fc; 40 µg, 20 µg, 20 µg and 40 µg anti-NSM antibody every day (eg days 5, 7, 11 and 15). The tumors are measured with a caliper and the volume of the tumors is determined. Coadministration of anti-NSM antibodies with immunotherapy enhances the reduction of tumor growth relative to a non-coadministered control (eg, monotherapy). Coadministration of anti-NSM antibodies with immunotherapy enhances the immune response against tumors relative to a non-coadministered control (eg, monotherapy). Antibodies against NSM bind TREM2, MS4A7, C5AR1, LYVE1, ABCC3, LILRB4, MRC1/CD206, SIGLEC1, STAB1, TMEM37, MERTK and/or TMEM119.

Example 23 Analysis of SDC Threshold in Immunotherapy Responders versus Nonresponders.

The next step was to determine, through an ROC curve, what percentage SDC cut-off value is a statistical indicator of responder versus non-responder status before initiating immunotherapy in subjects with cancer.

Data from %BDCA3 as the CD45+ fraction or %BDCA3 as the HLA-DR ⁺ fraction collected from melanoma patients before treatment with anti-PD-1 antibody were used to construct the ROC curve. The curve displays the characteristics of the binary responder/nonresponder system in the form of varying %BDCA3+ values. The curve is constructed by plotting the true positive rate (TPR) versus false positive rate (FPR) values of the optimal threshold parameters. True Positive Frequency is also known as signal detection sensitivity. The false positive rate is also known as the outlier rate and can be calculated as (1 - specificity). Larger areas under the curve indicate a cutoff value with greater sensitivity relative to specificity and thus a greater predictive value.

Further validation of BDCA3 as a prognostic factor was obtained using the DeLong method implemented by the pROC software package in R. For the percentage DC/HLA+ value, a 95% confidence interval (CI) of the area under the curve (AUC) value was estimated to be [0.76-1 ] (AUC=0.905 [0.76-1]). Using the bootstrap method with 2000 stratified replications, the bootstrap result was CI=[0.73-1] (AUC=0.905 [0.73-1]). For pct/CD45+, the DeLong method estimates a 95% CI: [0.59-0.98] (AUC=0.786 [0.59-0.98]), and bootstrap - [0.57-0.95] (AUC= 0.786 [0.57-0.95]). In fig. 20 shows (A) ROC analysis of BDCA3+ versus CD45+ ratio versus outcome, and (B) ROC analysis of BDCA3+ versus HLA-DR+ ratio versus outcome.

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The figure shows that using a BDCA3+/HLADR+ percentage threshold of 1.347% represents one of: the probability of a high BDCA3+ result means that the patient is an 89% responder (called sensitivity), and the probability of a low BDCA3+ result means that that the patient is an 86% non-responder (called specificity). This indicates that increasing the percentage of SDC in the tumor to about 1.347% or greater among HLA-DR+ cells in the tumor likely results in increased response to anticancer immunotherapy, pre-challenged, current, targeted, or vice versa. Such an increase can be achieved, for example, by increasing the amount of SDC (for example, through the use of FLT3L) and/or reducing the amount of NSM (for example, through the use of anti-NSM antibodies).

Example 24: Expansion of SDC Populations into Tumors by FLT3-L Treatment.

Cancer cells (eg, MC38 colon carcinoma) are injected into 6-week-old male B6 mice at time T0. Mice randomly assigned to treatment groups are treated with injections of FLT3-L or control PBS for several days (eg, days 5, 7, 11, 15) by V.B., I.V. and/or P.C. injections after tumor implant. The dosage is determined using information available in the art and one of ordinary skill in the art (eg, 10 μg in 100 μl PBS). Tumor-growing animals are sectioned at multiple time points and excess SDC and NSM are analyzed by flow cytometry. Treatment with FLT3-L during tumor development enhances SDC excess in the tumor.

Example 25 Reduced tumor growth after FLT3-L treatments due to increased SDC populations.

Cancer cells (eg, MC38 colon carcinoma) are injected into 6-week-old male B6 mice at time T0. Mice randomized to treatment groups are treated with injections of FLT3-L or control PBS several days (eg, days 5, 7, 11, 15) by V.B., V.V., and or P.C. injections after tumor implant . Dosage is determined using information available in the art and one of ordinary skill in the art (eg, (10 μg in 100 μl PBS). Tumors are measured with calipers and tumor volume is determined. FLT3-L treatment reduces tumor growth relative to control. FLT3-L treatment increases excess SDC in the tumor, as well as SDC populations in the dLN, spleen and bone marrow (KM) FLT3-L enhances the immune response to the tumor in experimental mice relative to controls.

Example 26 Improvement of immunotherapy through co-administration with FLT3-L.

Cancer cells (eg, MC38 colon carcinoma) are injected into 6-week-old male B6 mice at time T0. Mice randomized to treatment groups are treated with injections of FLT3-L or control PBS several days (eg, days 5, 7, 11, 15) by V.B., V.V., and or P.C. injections after tumor implant . Mice have previously received, are currently receiving, or will subsequently receive immunotherapy. Types of immunotherapy may include immunotherapy that inhibits a checkpoint inhibitor; immunotherapy, which inhibits T-cell checkpoint inhibitor; which uses anti-PD1 antibodies; anti-PDL1; anti-CTLA4; adoptive T-cell therapy; CAR-T cell therapy; use of a vaccine based on dendritic cells; monocytic vaccine; antigen binding protein, which binds both T cells and antigen presenting cells; antigen binding protein BiTE; toll-like receptor ligand and/or cytokine. The dosage is determined using information available in the art and one of ordinary skill in the art (eg, 10 μg in 100 μl PBS). The tumors are measured with a caliper and the volume of the tumors is determined. Coadministration of FLT3-L with immunotherapy enhances tumor growth reduction relative to non-coadministered controls (eg, monotherapy). Co-administration of FLT3-L with immunotherapy enhances the immune response against tumors relative to a non-co-administered control (eg, monotherapy). See also Curran et al., Lynch et al., Chen et al., Peron et al., and Shurin et al.

Example 27 SDC-targeted stimulation via agonist antibodies against FLT3.

Cancer cells (eg, MC38 colon carcinoma) are injected into 6-week-old male B6 mice at time T0. Mice randomly assigned to treatment groups are treated with injections of anti-FLT3 antibodies or control Fc antibodies several days (eg, days 5, 7, 11, 15) by V.B. injection after tumor implant. Dosage is determined using information available in the art and of ordinary skill in the art (eg, 200 μg/day control Fc; 40 μg, 20 μg, 20 μg, and 40 μg anti-FLT3 antibodies). Tumor-growing animals are sectioned at multiple time points and excess SDC and NSM are analyzed by flow cytometry. Treatment with an FLT3 agonist antibody during tumor development enhances the excess of SDC in the tumor as well as SDC populations in the dLN, spleen, and BM.

Example 28 Reduced tumor growth after anti-FLT3 agonist antibody treatments due to increased SDC populations.

Cancer cells (eg, MC38 colon carcinoma) are injected into 6-week-old male B6 mice at time T0. Mice randomly assigned to treatment groups

- 57 046327 are treated with injections of agonist antibodies against FLT3 or control Fc for several days (for example, days 5, 7, 11, 15) by V.B. injection after the tumor implant. Dosage is determined using information available in the art and of ordinary skill in the art (eg, 200 μg/day control Fc; 40 μg, 20 μg, 20 μg, and 40 μg anti-FLT3 antibodies). The tumors are measured with a caliper and the volume of the tumors is determined. Anti-FLT3 antibody treatment reduced tumor growth relative to controls. Treatment with anti-FLT3 antibody enhances the excess of SDC in the tumor as well as the SDC population in the dLN, spleen and BM. Agonist antibodies against FLT3L enhance the immune response to tumors in experimental mice relative to controls.

Example 29 Improvement of immunotherapy by co-administration with anti-FLT3 agonist antibodies.

Cancer cells (eg, MC38 colon carcinoma) are injected into 6-week-old male B6 mice at time T0. Mice randomly assigned to treatment groups are treated with injections of anti-FLT3 antibodies or control PBS over several days (eg, days 5, 7, 11, 15) by V.B., V.V., and or P.C. injections after tumor implant . Mice have previously received, are currently receiving, or will subsequently receive immunotherapy. Types of immunotherapy may include immunotherapy that inhibits a checkpoint inhibitor; immunotherapy, which inhibits T-cell checkpoint inhibitor; which uses anti-PD1 antibodies; anti-PDL1; anti-CTLA4; adoptive T-cell therapy; CAR-T cell therapy; use of a vaccine based on dendritic cells; monocytic vaccine; antigen binding protein, which binds both T cells and antigen presenting cells; antigen binding protein BiTE; toll-like receptor ligand and/or cytokine. The dosage is determined using information available in the art and one of ordinary skill in the art (eg, 10 μg in 100 μl PBS). The tumors are measured with a caliper and the volume of the tumors is determined. Coadministration of anti-FLT3 antibodies with immunotherapy enhances tumor growth reduction relative to non-coadministered controls (eg, monotherapy). Coadministration of anti-FLT3 antibodies with immunotherapy enhances the immune response against tumors relative to a non-coadministered control (eg, monotherapy).

Example 30. Expression of NSM proteins on human cells.

In fig. 21 demonstrated limited TREM2 protein expression relative to NSM (CD14+ TAM) populations, with little or no expression on CD14-negative CD11c-positive cells that include SDCs (BDCA3+ DC) in human HNSCC primary tumor tissue. TREM2-specific commercial antibody staining (RnD, clone 237920) was performed on disaggregated human HNSCC tumor tissue against a secondary staining control (rat IgG antibody, Jackson Immunoresearch) and analyzed by flow cytometry. This figure shows the specific expression of NSM gene products on NSM cells and the lack of expression on SDC cells in human tumor tissue. Populations were gated into live, CD45+, lineage negative, HLA-DR+, CD11c ⁺ and stratified by CD14 expression.

Although the invention has been specifically illustrated and described with reference to the preferred embodiment of the invention and various alternative embodiments of the invention, those skilled in the art will understand that various changes can be made in terms of form and details without departing from the spirit and scope of this invention. inventions.

All references, published patents, and patent applications listed herein are hereby incorporated herein by reference in their entirety and to all intents and purposes.

Sequences

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Table AA

Name Myshynaya, NCBI accession no. Human, access no. NCBI cKIT feline Hardy-Zuckerman sarcoma viral oncogene homologue 4 v-kit (KIT) ΝΜ_001122733. 1 NM_000222. 2 CCR7 chemokine (mtoiv C-C) receptor 7 (CCR7) NM_001301713. 1 NM_001838. 3 BATF3 basic leucine zipper transcription factor ATF-like 3 (BATF3) NM_030060. 2 NM_018664. 2 FLT3 fms-related tyrosine kinase 3 (FLT3) ΝΜ_010229. 2 NM_004119. 2 ZBTB46 Zinc finger and BTB domain-containing protein 46 (ZBTB46) NM_027656. 2 NM_025224. 3 IRF8 interferon regulatory factor 8 (IRF8) NM_001301811. 1 NM_002163. 2 BTLA B- and T-lymphocyte-associated protein (BTLA) NM_001301811. 1 NM_001085357. 1 MYCL1 homologue derived from lung carcinoma viral oncogene avian myelocytomatosis, v-myc (MYCL) 1 NM_001303121. 1 NM_001033081. 2 Clec9A family 9 member A with a C-type lectin domain (CLEC9A) NM_001205363. 1 NM_207345. 3 BDCA3/THBD thrombomodulin (THBD) NM_009378. 3 NM_000361.2 XCR1 chemokine (motif C) receptor 1 (XCR1) NM_011798. 4 NM_001024644. 1

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C5AR1 complement component 5a receptor 1 (C5AR1) ΝΜ_001173550. 1 NM_001736. 3 LYVE1 endothelial hyaluronan lymphatic vessel receptor 1 (LYVE1) NM_053247. 4 NM 006691.3 ABCSS representative of subfamily C 3 (CFTR/MRP) with an ATP-binding cassette (ABCC3) NM_029600. 3 NM_001144070. 1 MRC1 mannose receptor C type 1 (MRC1) NM_008625. 2 NM_002438. 3 SIGLEC1 sialic acid binding 1g-like lectin 1, sialoadhesin (SIGLEC1) ΝΜ_011426. 3 NM_023068. 3 STAB1 stabilin 1 (STAB1) NM_138672. 2 NM_015136. 2 C1QA chain A subcomponent q of complement component 1 (C1QA) NM_007572. 2 NM_015991.2 C1QB chain B subcomponent q of complement component 1 (C1QB) NM_009777. 2 NM_000491. 3 TMEM37 transmembrane protein 37 (TMEM37) NM_019432. 2 NM_183240. 2 MERTK MER proto-oncogene tyrosine kinase (MERTK) NM_008587. 1 NM_006343.2 C1QC chain C subcomponent q of complement component 1 (C1QC) NM_007574. 2 NM_001114101. 1 TMEM119 transmembrane protein 119 (Tmemll9) NM_146162. 2 NM_181724. 2 MS4A7 4-domain membrane spanning subfamily A member 7 (MS4A7) NM_001025610. 4 NM_021201. 4 APOE apolipoprotein E (APOE) NM_001305819. 1 NM_000041. 3 CYP4F18/ CYP4F2 cytochrome P450 subfamily F family 4 polypeptide 2 (CYP4F2) NM_024444. 2 NM_001082. 4 TREM2 initiation receptor 2, expressed on myeloid cells (TREM2) NM_001272078. 1 NM_001271821. 1 TLR7 toll-like receptor 7 (TLR7) NM_001290755. 1 NM_016562. 3 LILRB4 representative of subfamily B (with TM and ITIM domains) of the leukocyte immunoglobulin-like receptor (LILRB4) NM_001013894. 1 NM_001278426. 3

•All NCBI accession numbers described in this document and associated sequences are as of September 25, 2015, available through public access on the NCBI website.

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BB table

CDR, variable and full-length sequences of each anti-LILRB4 antibody fragment, clone 1

Clone Hl H2 H3 LI L2 L3 Antibody against LILRB4, clone 1-1 GFNLSSSY (SEQ ID NO:1) ISSSYGST (SEQ ID NO:2) ARAHYVWYGSVYAH SYGGMDY (SEQ ID NO:3) QSVSSA (SEQ ID N0:4) SAS (SEQ ID N0:5) QQWSGGYSG LIT (SEQ ID N0:6) Ahth-LILRB4. clone1-1. vL- DIQMTQSPSSLSASVGDRVTITCRASQSVSSAVAWYQQKPGKAPKLLIYSASSLYSGVPSRFSGSRSGTDFTLTISSLQP EDFATYYCQQWSGGYSGLITFGQGTKVEIK (SEQ ID NO:7) Ahth-LILRB4. clone1-1. vH- EVQLVESGGGLVQPGGSLRLSCAASGFNLSSSYMHWVRQAPGKGLEWVASISSSYGSTYYADSVKGRFTISADTSKN TAYLQMNSLRAEDTAVYYCARAHYVWYGSVYAHSYGGMDYWGQGTLVTVSS (SEQ. ID NO:8) Ahth-LILRB4. clone1-1. hukappa- DIQMTQSPSSLSASVGDRVTITCRASQSVSSAVAWYQQKPGKAPKLLIYSASSLYSGVPSRFSGSRSGTDFTLTISSLQP EDFATYYCQQWSGGYSGLITFGQGTKVEIKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNAL QSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC (SEQ ID NO:9) Ahth-LILRB4. clone1-1. huIgGl- EVQLVESGGGLVQPGGSLRLSCAASGFNLSSSYMHWVRQAPGKGLEWVASISSSYGSTYYADSVKGRFTISADTSKN TAYLQMNSLRAEDTAVYYCARAHYVWYGSVYAHSYGGMDYWGQGTLVTVSSASTKGPSVFPLAPSSKSTSGGTAA LGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLG DI AVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK (SEQ ID NQ:10)

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Claims (19)

CLAIM 1. A method of treating cancer in a subject, comprising: A. detecting or detecting the presence of myeloid cells positive for initiation receptor 2 expressed on myeloid cells (TREM2+) from a tumor sample obtained from the subject; And b. administering to a subject an antibody or antigen binding fragment thereof comprising a human Fc domain, wherein the antibody or antigen binding fragment thereof binds to the extracellular domain of TREM2, wherein the antibody or antigen binding fragment thereof is present in an amount effective to kill, block or deplete TREM2+ myeloid cells via antibody-dependent cell-mediated mediated cytotoxic activity, antibody-dependent phagocytosis activity, complement-dependent cytotoxic activity or antibody-mediated phagocytosis activity. 2. The method according to claim 1, characterized in that the tumor sample is obtained by needle biopsy, punch biopsy or surgical excision of the tumor. 3. The method according to claim 1, wherein the detection step includes determining the presence of TREM2 mRNA expression or TREM2 protein expression. 4. The method according to claim 1, characterized in that the determination step is performed using at least one of polymerase chain reaction (PCR), quantitative PCR (qPCR), qRT-PCR, microchip, genetic chip, RNA sequencing and sorting method cells. 5. The method of claim 4, wherein the cell sorting method comprises at least one of fluorescence activated cell sorting, flow cytometry, magnetic bead cell sorting, microraft sorting, and cell affinity based separation. 6. The method of claim 1, wherein the antibody is at least one of a monoclonal antibody, an IgG ₁ antibody, an IgG ₄ antibody, an afucosylated antibody, a human antibody, a humanized antibody, a chimeric antibody, and a full-length antibody. 7. The method according to claim 6, characterized in that the human Fc is a human IgG1 Fc. 8. The method according to claim 6, characterized in that the antibody is an afucosylated antibody. 9. The method according to claim 6, characterized in that the antibody is a humanized antibody. 10. The method of claim 1, wherein administration induces at least one of TREM2+ myeloid cell death, TREM2+ myeloid cell apoptosis, TREM2+ myeloid cell lysis, TREM2+ myeloid cell phagocytosis, and TREM2+ myeloid cell growth arrest. 11. The method according to claim 1, characterized in that the cancer is a solid cancer or hematological malignancy. 12. The method according to claim 11, characterized in that the cancer is selected from the group consisting of: melanoma, kidney cancer, hepatobiliary cancer, squamous cell carcinoma of the head and neck, pancreatic cancer, colon cancer, bladder cancer, glioblastoma, prostate cancer, lung cancer and mammary gland. 13. The method according to claim 1, characterized in that the subject has previously received immunotherapy. 14. The method according to claim 13, characterized in that the immunotherapy includes at least one of pembromizulab, nivolumab and ipilimumab. 15. The method of claim 1, further comprising administering the immunotherapy to the subject simultaneously with an antibody or an antigen-binding fragment thereof, wherein the immunotherapy comprises at least one of pembromisulab, nivolumab and ipilimumab. 16. The method of claim 1, further comprising administering the immunotherapy to the subject after the antibody or antigen-binding fragment thereof, wherein the immunotherapy comprises at least one of pembromisulab, nivolumab and ipilimumab. 17. A method of killing, blocking or depleting TREM2+ myeloid cells, comprising: a. detecting or detecting the presence of TREM2+ myeloid cells from a tumor sample obtained from a subject with cancer; And b. contacting TREM2+ myeloid cells present in the subject's cancer tissue with an antibody or antigen binding fragment thereof comprising a human Fc domain, wherein the antibody or antigen binding fragment thereof binds an extracellular domain of TREM2; wherein the antibody or antigen-binding fragment thereof is present in an amount effective to kill, block, or deplete TREM2+ myeloid cells through antibody-dependent cell-mediated cytotoxic activity, antibody-dependent phagocytosis activity, complement-dependent cytotoxic activity, or antibody-mediated phagocytosis activity. 18. The method of claim 17, wherein the antibody is at least one of a humanized antibody, an IgG1 antibody, or an afucosylated antibody. 19. The method of claim 17, wherein contact induces at least one of TREM2+ myeloid cell death, TREM2+ myeloid cell apoptosis, TREM2+ myeloid cell lysis, TREM2+ myeloid cell phagocytosis, and TREM2+ myeloid cell growth arrest -