Patent Application
20240043539
The present disclosure relates to methods of inducing an immunomodulatory tumor response for the treatment of a subject having a tumor. The disclosure further relates to an organotypic tumor microenvironment culture system that can be utilized to screen and identify novel immunomodulatory cancer therapeutics.
Tumors rely on complex interactions between malignant and non-malignant stromal and immune cells to create a tumor microenvironment (TME) niche that promotes tumor growth and enhances immune evasion (Hanahan and Coussens, “Accessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment,” Cancer Cell 21:309-322 (2012) and Quail and Joyce, “Microenvironmental Regulation of Tumor Progression and Metastasis,” Nat. Med. 19:1423-1437 (2013)). In breast cancer, infiltrating macrophages represent a major immune component of the TME Cassetta and Pollard, “Targeting Macrophages: Therapeutic Approaches in Cancer,” Nat. Rev. Drug Discov. (2018). During the growth of mammary tumors, macrophages accumulate significantly and undergo phenotypic alterations to enhance invasive growth, matrix remodeling, angiogenesis, and immune suppression (DeNardo et al., “Macrophages as Regulators of Tumour Immunity and Immunotherapy,” Nat. Rev. Immunology 19:369-382 (2019); Lin et al., “Macrophages Regulate the Angiogenic Switch in a Mouse Model of Breast Cancer,” Cancer Res. 66:11238-11246 (2006). Consistent with their pro-tumorigenic phenotypes, the accumulation of macrophages in breast cancer patients is generally associated with poor prognosis, resistance to therapies, and disease recurrence (DeNardo et al., “Leukocyte Complexity Predicts Breast Cancer Survival and Functionally Regulates Response To Chemotherapy,” Cancer Discov. 1:54-67 (2011); De Palma and Lewis, “Macrophage Regulation of Tumor Responses to Anticancer Therapies,” Cancer Cell 23:277-286 (2013); and Shabo et al., “Breast Cancer Expression of CD163, a Macrophage Scavenger Receptor, is Related to Early Distant Recurrence and Reduced Patient Survival,” Int. J. Cancer 123:780-786 (2008)).
These tumor-supporting characteristics have highlighted macrophages as a promising target for therapeutic intervention primarily through CSF-1R targeting (Cassetta and Pollard, “Targeting Macrophages: Therapeutic Approaches in Cancer,” Nat. Rev. Drug Discov. (2018)). Consistent with their pro-angiogenic and immunosuppressive roles, depletion of macrophages in mouse tumor models resulted in reduced tumor vascularization (Keklikoglou et al., “Periostin Limits Tumor Response to VEGFA Inhibition,” Cell Rep. 22:2530-2540 (2018)), increased intratumoral influx of cytotoxic CD8+ T and NK cells (Ries et al., “Targeting Tumor-Associated Macrophages with Anti-CSF-1R Antibody Reveals a Strategy for Cancer Therapy,” Cancer Cell 25:846-859 (2014)), and reduction of tumor burden (Qian et al., “CCL2 Recruits Inflammatory Monocytes to Facilitate Breast-Tumour Metastasis,” Nature 475:222-225 (2011); Zhu et al., “CSF1/CSF1R Blockade Reprograms Tumor-Infiltrating Macrophages and Improves Response to T-Cell Checkpoint Immunotherapy in Pancreatic Cancer Models,” Cancer Res. 74:5057-5069 (2014)). Nonetheless, sustained macrophage depletion strategies have safety concerns, especially when employed systemically, because macrophages are critical for homeostasis. The cessation of certain macrophage-targeting interventions resulted in a significant metastatic resurgence in pre-clinical models, as in the case of CCL2 inhibition (Bonapace et al., “Cessation of CCL2 Inhibition Accelerates Breast Cancer Metastasis by Promoting Angiogenesis,” Nature 515:130-133 (2014)). These limitations suggest that rather than eliminating macrophages or blocking their recruitment, they need to be selectively targeted their tumor-promoting phenotypes (Beatty et al., “CD40 Agonists Alter Tumor Stroma and Show Efficacy Against Pancreatic Carcinoma in Mice And Humans,” Science 331:1612-1616 (2011); Pyonteck et al., “CSF-1R Inhibition Alters Macrophage Polarization and Blocks Glioma Progression,” Nat. Med. 19:1264-1272 (2013); and Tseng et al., “Anti-CD47 Antibody-Mediated Phagocytosis of Cancer by Macrophages Primes an Effective Antitumor T-Cell Response,” Proc. Natl. Acad. Sci. U.S.A. 110:11103-11108 (2013)).
A first aspect of the present disclosure involves a method of inhibiting an immunosuppressive phenotype in a population of macrophages. This method involves administering to a population of macrophages, an agent selected from a cyclin-dependent kinase 4 (Cdk4) inhibitor, a tumor necrosis factor related apoptosis-inducing ligand receptor 2 (TRAIL-R2) inhibitor, a protein tyrosine kinase 2 beta (Ptk2b) inhibitor, and a Notch-4 inhibitor under conditions effective to inhibit the immunosuppressive phenotype in the population of macrophages.
Another aspect of the present disclosure relates to a method of inhibiting macrophage proliferation in a population of cells comprising macrophages. This method involves administering a Notch-4 inhibitor to the population of cells under conditions effective to inhibit macrophage proliferation in a population of cells.
Another aspect of the present disclosure relates to a method of treating a tumor in a subject. This method involves administering, to a subject having a tumor, a Notch-4 inhibitor, where administering induces an anti-tumor immune response in the subject.
Another aspect of the present disclosure relates to a combination therapeutic involves a Notch-4 inhibitor and a checkpoint inhibitor.
Another aspect of the present disclosure relates to a combination therapeutic involves a Notch-4 inhibitor and a pro-inflammatory agent. Another aspect of the present disclosure relates to an organotypic tumor
microenvironment model (TME) culture system. The system involves an isolated population of cells, said population comprising tumor epithelial cells, mesenchymal stromal cells, and macrophages.
Identification of signals that drive macrophages toward pro-tumorigenic phenotypes (macrophage education) in non-scalable systems (such as tumor mouse models) poses a formidable barrier to target discovery through high-throughput screens. Therefore, the inventors developed a scalable organotypic TME (oTME) model from a murine mammary tumor (Lin et al., “Progression to Malignancy in the Polyoma Middle T Oncoprotein Mouse Breast Cancer Model Provides a Reliable Model for Human Diseases,” Am. J. Pathol. 163:2113-2126 (2003), which is hereby incorporated by reference in its entirety) that contains tumor epithelial cells and their supporting stromal cells and enables the application of high-throughput discovery platforms. The culture of macrophages in this system closely recapitulates their alteration toward pro-tumorigenic phenotype in vivo, via complex cellular interactions with tumor epithelial cells and stromal fibroblasts. The oTME platform was leveraged to dissect the macrophage education mechanisms using a genome-wide CRISPR/Cas9 screen in primary macrophages. The induction of Arginase-1 (Arg1) was utilized as a surrogate for educated macrophages and identified gene targets, including Cdk4 and Ptk2b, as druggable regulators that prevented the accumulation of Arg1+ macrophages. Second, using single-cell RNA-seq macrophage education time course, it was discovered that acquisition of proliferative and immunomodulatory phenotypes in macrophages followed an earlier transient activation of type-I interferons/STING that triggered proliferation in a subset of F4/80highLy6A+ macrophages that interact with stromal fibroblasts. Furthermore, it was demonstrated that macrophage localization and intra-cellular interactions in TME determined their phenotype. Using the murine model and human breast cancer specimens, macrophage-stroma interactions were found to give rise to proliferative, immunosuppressive and phagocytic phenotypes, while macrophage-tumor epithelial interactions resulted in pro-inflammatory phenotype. Finally, the Notch4 was identified as a targetable regulator of macrophage proliferation in pre-clinical models of breast cancer that effectively blocked tumor progression.
A first aspect of the present disclosure is directed to a method of inhibiting an immunosuppressive phenotype in a population of macrophages. This method involves administering to the population of macrophages, an agent selected from a cyclin-dependent kinase 4 (Cdk4) inhibitor, a tumor necrosis factor related apoptosis-inducing ligand receptor 2 (TRAIL-R2) inhibitor, a protein tyrosine kinase 2 beta (Ptk2b) inhibitor, Notch-4 inhibitor and combinations thereof under conditions effective to inhibit the immunosuppressive phenotype in the population of macrophages.
In accordance with this aspect of the disclosure, the population of macrophages comprises macrophages having an M2 phenotype. Macrophages exhibiting a type-2 (M2) phenotype are often characterized as being anti-inflammatory and immunosuppressive as they suppress T-cell responses and are involved in the Th2-type immune response. The type-2 macrophage phenotype facilitates tissue repair, wound healing, and is profibrotic. Type-2 macrophages often undesirably infiltrate and surround tumors, where they provide an immunosuppressive microenvironment that promotes rather than suppresses tumor progression. Type-2 macrophages are characterized by high surface expression of I1-4R, FecR, Dectin-1, CD136, CD206, and CD209A. Type-2 macrophages include IL-4/IL-13-stimulated macrophages, IL-10-induced macrophages, and immune complex-triggered macrophages. In some embodiments, the administering is carried out to a population of macrophages having an M2 phenotype in vitro. In some embodiments, the administering is carried out to a population of macrophages having an M2 phenotype in vivo.
Administering the Cdk4 inhibitor, the TRAIL-R2 inhibitor, the Ptk2b inhibitor, or combination thereof to the population of macrophages comprising an M2 phenotype induces a change in the macrophage phenotype. In some embodiments, the administering will induce an
M1 phenotype. Macrophages exhibiting a type-1 phenotype are pro-inflammatory, and are capable of either direct (pathogen pattern recognition receptors) or indirect (Fc receptors, complement receptors) recognition of pathogens and tumor antigens (i.e., they exhibit anti-tumor activity). Type-1 macrophages produce reactive oxygen species and secrete pro-inflammatory cytokines and chemokines, such as, for example, but without limitation, TNFα, IL-1, IL-6, IL-IL-18, IL-23, and iNOS. Thus, the conversion of type-2 macrophages to type-1 macrophages in accordance with the methods described herein can be monitored or assessed by assessing the levels of the aforementioned cytokines and chemokines. Type-1 macrophages can also be characterized by their expression of high levels of MHC, costimulatory molecules, and FCγR. The type-1 phenotype is triggered by GM-CSF and further stimulated by interferon-γ (IFN-γ), bacterial lipopolysaccharide (LPS), or tumor necrosis factor a (TNFα), and is mediated by several signal transduction pathways involving signal transducer and activator of transcription (STAT), nuclear factor kappa-light-chain-enhancer of activated B cells (NFKB), and mitogen-activated protein kinases (MAPK). These events enhance the production of agents such as the reactive oxygen species and nitric oxide (NO) and promote subsequent inflammatory immune responses by increasing antigen presentation capacity and inducing the Thl immunity through the production of cytokines such as IL-12.
In accordance with this aspect of the disclosure, suitable Cdk4 inhibitors for use in the method of inhibiting an immunosuppressive phenotype in a population of macrophages include, without limitation, palbociclib (6-acetyl-8-cyclopentyl-5-methyl-2-[(5-piperazin-1-ylpyridin-2-yl)amino]pyrido[2,3-d]pyrimidin-7-one), ribociclib (7-cyclopentyl-N,N-dimethyl-2-[(5-piperazin-1-ylpyridin-2-yl)amino]pyrrolo[2,3-d]pyrimidine-6-carboxamide), abemaciclib (N-[5-[(4-ethylpiperazin-1-yl)methyl]pyridin-2-yl]-5-fluoro-4-(7-fluoro-2-methyl-3-propan-2-ylbenzimidazol-5-yl)pyrimidin-2-amine), voruciclib (2-[2-chloro-4-(trifluoromethyl)phenyl]-5,7-dihydroxy-8-[(2R,3S)-2-(hydroxymethyl)-1-methylpyrrolidin-3-yl]chromen-4-one), and trilaciclib (4-[[5-(4-methylpiperazin-1-yl)pyridin-2-yl]amino]spiro[1,3,5,11-tetrazatricyclo[7.4.0.02,7]trideca-2,4,6,8-tetraene-13,1′-cyclohexane]-10-one).
In accordance with this aspect of the disclosure, suitable Ptk2B inhibitors for use in the method of inhibiting an immunosuppressive phenotype in a population of macrophages include, without limitation, PF-00562271 (N-methyl-N-[3-[[[2-[(2-oxo-1,3-dihydroindo1-5-yl)amino]-5-(trifluoromethyl)-4-pyrimidinyl]amino]methyl]-2-pyridinyl]methanesulfonamide is a member of indoles), conteltinib (2-[[2-[2-methoxy-4-[4-(4-methylpiperazin-1-yl)piperidin-1-yl]anilino]-6,7-dihydro-5H-pyrrolo[2,3-d]pyrimidin-4-yl]amino]-N-propan-2-ylbenzenesulfonamide), and NVP-TAE226 (2-[[5-chloro-2-(2-methoxy-4-morpholin-4-ylanilino)pyrimidin-4-yl]amino]-N-methylbenzamide).
In accordance with this aspect of the disclosure, the TRAIL-R2 inhibitor for use in the method of inhibiting an immunosuppressive phenotype in a population of macrophages is a monoclonal antibody inhibitor. In one embodiment, the TRAIL-R2 inhibitor is TRAIL-R2 or Tnfrsfl2a receptor (TWEAK) monoclonal antibody. Other genes and proteins involved in enhancing macrophage immunosuppressive
phenotype in the tumor environment are identified in Table 1. Accordingly, the method of inhibiting immunosuppressive phenotype in a population of macrophages can also involve administering an inhibitor of one or more of the genes and it encoded protein identified in Table 1. Known inhibitors of these modulators are also provided in Table 1. In any embodiment, one or more inhibitors identified in Tablel are used alone or in combination with each other or in combination with a Cdk4 inhibitor, a TRAIL-R2 inhibitor, or a Ptk2b inhibitor as described supra to inhibit the immunosuppressive phenotype in a population of macrophages. Such inhibition can be carried out in vitro or in vivo.
The method of inhibiting an immunosuppressive phenotype in a population of macrophages can be carried out in vivo to treat a variety of conditions in a subject where the immunosuppressive phenotype of macrophage plays a causative role in the progression of the condition or contributes to one or more symptoms of the condition. For example, the method of inhibiting an immunosuppressive phenotype in a population of macrophages can comprise administering Cdk4 inhibitor, the TRAIL-R2 inhibitor, the Ptk2b inhibitor, or combination thereof to a subject in need thereof Subjects who would benefit from inhibiting the immunosuppressive phenotype of macrophage using a Cdk4 inhibitor, the TRAIL-R2 inhibitor, the Ptk2b inhibitor, or combination thereof as described herein include those suffering endometriosis (see e.g., Hogg et al., “Endometriosis-associated Macrophages: Origin, Phenotype, and Function,” Front. Endocrinol. 11:7 (2020), which is hereby incorporated by reference in its entirety), systemic sclerosis (see e.g., Bhandari et al., “Profibrotic Activation of Human Macrophages in Systemic Sclerosis,” Arthritis & Rheumatology 72(7): 1160-69 (2020), which is hereby incorporated by reference in its entirety), and idiopathic pulmonary fibrosis (see e.g., Morse et al., “Proliferating SPP1/MERTK-expressing Macrophages in Idiopathic Pulmonary Fibrosis,” Eur. Respir J. 54(2): 1802441 (2019), which is hereby incorporated by reference in its entirety).
In any embodiment, the method is carried out in vivo to a subject having a tumor. In accordance with this method, the Cdk4 inhibitor, the TRAIL-R2 inhibitor, the Ptk2b inhibitor, or combination thereof is administered to macrophages within the tumor microenvironment to induce an immunomodulatory response to the tumor. Subjects that are suitable for such administration, are those subject having a cold tumor. A “cold tumor” is a tumor that contains few if any infiltrating T cells. Exemplary cold tumors that can be treated in accordance with this and other methods described herein include, without limitation, breast tumors, pancreatic tumors, ovarian tumors, prostate tumors, colon tumors, solid tumors, gliomas, myelomas, liver tumors, and kidney tumors.
The status of a subject's tumor (i.e., a hot tumor vs. a cold tumor) is typically determined by immunological parameters, in particular by assessing lymphocyte infiltration and IFN-γ status. This can be determined by employing known immunohistochemical methods to a core needle biopsy. Tumors with low lymphocyte infiltration, and commonly high infiltration of immunosuppressive myeloid cells, such as M2 macrophages are considered “cold tumors” and suitable for treatment in accordance with the methods described herein. M2 macrophage accumulation can be determined using immunohistological methods suitable for the detection of markers, including, but not limited to, CD163, CD68, CD206, or the combination of CD163 and PD-L1.
Administering the Cdk4 inhibitor, the TRAIL-R2 inhibitor, the Ptk2b inhibitor, or a combination thereof will induce an immunomodulatory response or immunomodulatory phenotype in the macrophages surrounding the tumor.
In some embodiments, the Cdk4 inhibitor, TRAIL-R2 inhibitor, or Ptk2b inhibitor is administered to the subject as a part of a combination therapy or therapeutic. In some embodiments, the combination therapeutic comprises the Cdk4 inhibitor, the TRAIL-R2 inhibitor, and/or the Ptk2b inhibitor in combination with a checkpoint inhibitor. In some embodiments, the combination therapeutic comprises the Cdk4 inhibitor, the TRAIL-R2 inhibitor, and/or the Ptk2b inhibitor in combination with a pro-inflammatory agent.
As used herein, the term “combination therapy” or “combination therapeutic” refers to the administration of two or more therapeutic agents, e.g., an agent that inhibits Cdk4, an agent that inhibits TRAIL-R2, and an agent that inhibits Ptk2b, a checkpoint inhibitor, a pro-inflammatory agent, and combinations thereof. In some embodiments, the combination therapy is co-administered in a substantially simultaneous manner, such as in a single capsule or other delivery vehicle having a fixed ratio of active ingredients. In some embodiment, the combination therapy is administered in multiple capsules or delivery vehicles, each containing an active ingredient. In some embodiments, the therapeutic agents of the combination therapy are administered in a sequential manner, either at approximately the same time or at different times. In all of the embodiments, the combination therapy provides beneficial effects of the drug combination in treating cancer, particularly in treatment-resistant cancers as described herein.
In some embodiments, the agents of the combination therapeutic are administered concurrently. In other embodiments, the agent that inhibits Cdk4, the agent that inhibits TRAIL-R2, and/or the agent that inhibits Ptk2b is administered prior to administering the checkpoint inhibitor and/or the proinflammatory agent.
In accordance with this and all aspects of the disclosure, suitable checkpoint inhibitors include, without limitation, a programmed death-ligand 1 (PD-L1) inhibitor, a programmed cell death protein 1 (PD-1) inhibitor, a cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor, and combinations thereof.
In some embodiments, the checkpoint inhibitor is a PD-1 inhibitor. Suitable PD-1 inhibitors include, without limitation, the monoclonal antibodies of Pembrolizumab (Merck), Nivolumab (Britsol Myers Squibb), Pidilizumab (Medivation), and Cemiplimab (Regeneron).
In some embodiments, the checkpoint inhibitor is a PD-L1 inhibitor. Suitable PD-L1 inhibitors include, without limitation, the monoclonal antibodies of Atezolizumab (Genentech), Avelumab (Pfizer), Durvalumab (AstraZeneca).
In some embodiments, the checkpoint inhibitor is a CTLA-4 inhibitor. A suitable CTLA-4 inhibitor is the monoclonal antibody, Ipilimumab (Bristol Myers Squibb).
In some embodiments, the subject having a cold tumor is administered a pro-inflammatory agent in combination with the Cdk4 inhibitor, the TRAIL-R2 inhibitor, and/or the Ptk2b inhibitor. In accordance with this embodiment, suitable pro-inflammatory agents, includes, without limitation, GM-CSF, an OX40 (CD134, TNFSRSF4) activation antibody, and a TREM2 (Triggering Receptor Expressed on Myeloid Cells) blocking antibody or inhibitory peptide.
Granulocyte-macrophage colony stimulating factor (GM-CSF) is an immunostimulatory monomeric glycoprotein secreted by macrophages, T cells, mast cells, and natural killer cells. Pharmaceutical analogs of GM-CSF suitable for use in the methods described herein include sargramostim and molgramostim.
OX40 is a co-stimulatory molecule expressed by activated immune cells. Agonist antibodies suitable for administration in accordance with the methods of the present disclosure include, without limitation, INCAGN01949 IgG (Gonzalez et al., “INCAGN01949: A Novel Anti-OX40 Agonist Antibody with the Potential to Enhance Tumor Specific T-cell Responsiveness, While Selectively Depleting Intratumoral Regulatory T Cells,” Cancer Res. 76(14 Suppl) 3204 (2016), which is hereby incorporated by reference in its entirety) and PF-4518600 (El-Khoueiry et al., “Analysis of OX40 Agonist Antibody (PF-4518600) in Patients with Hepatocellular Carcinoma,” J. Clin. Oncol. 38(4): (2020), which is hereby incorporated by reference in its entirety).
Another aspect of the present disclosure relates to a method of inhibiting macrophage proliferation in a population of cells comprising macrophages. This method involves administering a Notch-4 inhibitor or a TYK2 inhibitor to the population of cells under conditions effective to inhibit macrophage proliferation in said population of cells.
The method of inhibiting macrophage proliferation can be carried out in vivo to treat a variety of conditions in a subject where macrophage proliferation plays a causative role or in some way contributes to one or more symptoms of the condition. For example, the method of inhibiting macrophage proliferation in a population of cells can comprise administering the Notch-4 inhibitor or TYK2 inhibitor to a subject in need thereof Subjects who would benefit from inhibition of macrophage proliferation using a Notch-4 inhibitor or TYK2 inhibitor as described herein include those suffering from inflammatory conditions, such as asthma, atherosclerosis, arthritis (e.g., rheumatoid arthritis, osteoarthritis); metabolic diseases, such as diabetes and obesity related adipose inflammation (see e.g., Ponzoni et al., “Targeting Macrophages as a Potential Therapeutic Interventions Impact on Inflammatory Diseases and Cancer, Int. J. Mol. Sci. 19(7): 1953 (2018), which is hereby incorporated by reference in its entirety); autoimmune diseases, such as systemic lupus erythematosus, systemic sclerosis, primary biliary cholangitis, Sjögren's syndrome, and inflammatory bowel disease (see e.g., Ma et a., “The Role of Monocytes and Macrophages in Autoimmune Diseases: A Comprehensive Review,” Front. Immunol. 10:1140 (2019), which is hereby incorporated by reference in its entirety); chronic inflammation and age-related chronic inflammatory disease (see e.g., Oishi and Manabe, “Macrophages in Age-related Chronic Inflammatory Diseases,” Nature: Aging and Mechanisms of Disease 2:16018 (2016), which is hereby incorporated by reference in its entirety); and hyperinflammatory conditions associated with ‘cytokine storm’, such as infections (viral infections), sepsis, systemic juvenile idiopathic arthritis, adult onset Still disease, connective tissue diseases, cancer and cancer immunotherapy (see e.g., McGonagle et al., “Immune Cartography of Macrophage Activation Syndrome in the COVID-19 Era,” Nature Reviews Rheumatology 17:145-157 (2021), which is hereby incorporated by reference in its entirety).
Another condition that would benefit from a decrease in macrophage proliferation is cancer. Accordingly, another aspect of the present disclosure relates to a method of treating a tumor in a subject. This method involves administering, to a subject having a tumor, a Notch-4 inhibitor or a TYK2 inhibitor, wherein said administering induces an anti-tumor immune response in the subject.
In accordance with this aspect of the present disclosure, the subject having a tumor may have a tumor selected from the group consisting of a breast tumor, pancreatic tumor, ovarian tumor, prostate tumor, lung tumor, colon tumor, solid tumor, glioma, melanoma, myeloma, liver tumor, and kidney tumor. In some embodiments, the subject has a cold tumor as described above. In some embodiments, the tumor is characterized by tumor cells overexpressing Notch-4.
Suitable Notch-4 inhibitors include, without limitation protein or peptide Notch-4 inhibitors, e.g., anti-Notch-4 antibody-based molecules; nucleic acid molecule inhibitors, e.g., a Notch-4 antisense oligonucleotide inhibitor; and small molecule inhibitors of Notch-4.
In any embodiment, a suitable Notch-4 inhibitor for use in inhibiting macrophage proliferation or inducing an anti-tumor immune response in a subject having a tumor, is an anti-Notch-4 antibody-based molecule, including, for example, a Notch-4 antibody, Notch-4 binding fragment thereof, or a Notch-4 antibody derivative.
Human Notch-4 has the amino acid sequence the of SEQ ID NO: 1 (UniProt Accession No. Q99466) as provided below.
Suitable Notch-4 antibody-based molecules for use in the methods disclosed herein bind to one or more epitopes in the Notch-4 amino acid sequence. Suitable Notch-4 antibody-based molecules include those known in the art. In some embodiments, the Notch-4 antibody or antibody-based molecule is the Notch-4 antibody or a derivative thereof disclosed in U.S. Pat. No. 9,527,921 to Sakamoto et al., which is hereby incorporated by reference in its entirety, having a heavy chain CDR1 (H-CDR1) sequence of SYGMS (SEQ ID NO: 2); a H-CDR2 sequence of GFTESSYGMS (SEQ ID NO: 3) or a HCDR-2 sequence of TINSNGGRTYYPDSVKG (SEQ ID NO: 4), or a HCDR-2 sequence of TINSNGGRTY (SEQ ID NO: 5); and a HCDR-3 sequence of DQGFAY (SEQ ID NO: 6). In some embodiments, the Notch-4 antibody comprises a light chain CDR 1 (LCDR-1) sequence of KASQDVGTAVA (SEQ ID NO: 7); a LCDR-2 sequence of WASTRHT (SEQ ID NO: 8); and a LCDR-3 sequence of QQYSSYPWT (SEQ ID NO: 9).
In some any embodiment, the Notch-4 antibody-based molecule for use in the methods disclosed herein has a heavy chain variable region amino acid sequence of SEQ ID NO: 10 as provided below or a humanized version thereof as disclosed in U.S. Pat. No. 9,527,921 to Sakamoto et al., which is hereby incorporated by reference in its entirety.
In any embodiment, the Notch-4 antibody-based molecule for use in the methods disclosed herein has a heavy chain variable region amino acid sequence that is at least 80%, 85%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the amino acid sequence of SEQ ID NO: 10. Suitable variant heavy chain variable region amino acid sequences are disclosed in U.S. Pat. No. 9,527,921 to Sakamoto et al., which is hereby incorporated by reference in its entirety.
In some embodiments, the Notch-4 antibody-based molecule for use in the methods disclosed herein has a light chain variable region amino acid sequence of SEQ ID NO: 11 as provided below or a humanized version thereof as disclosed in U.S. Pat. No. 9,527,921 to Sakamoto et al., which is hereby incorporated by reference in its entirety.
In any embodiment, the Notch-4 antibody-based molecule for use in the methods disclosed herein has a light chain variable region amino acid sequence that is at least 80%, 85%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the amino acid sequence of SEQ ID NO: 11. Suitable variant light chain variable region amino acid sequences are disclosed in U.S. Pat. No. 9,527,921 to Sakamoto et al., which is hereby incorporated by reference in its entirety. Notch-4 antibody-based molecules suitable for use in the methods described
herein include, without limitation full antibodies, epitope binding fragments of whole antibodies, and antibody derivatives. An epitope binding fragment of an antibody can be obtained through the actual fragmenting of a parental antibody (for example, a Fab or (Fab)2 fragment). Alternatively, the epitope binding fragment is an amino acid sequence that comprises a portion of the amino acid sequence of such parental antibody. As used herein, a molecule is said to be a “derivative” of an antibody (or relevant portion thereof) if it is obtained through the actual chemical modification of a parent antibody or portion thereof, or if it comprises an amino acid sequence that is substantially similar to the amino acid sequence of such parental antibody or relevant portion thereof (for example, differing by less than 30%, less than 20%, less than 10%, or less than 5% from such parental molecule or such relevant portion thereof, or by 10 amino acid residues, or by fewer than 10, 9, 8, 7, 6, 5, 4, 3 or 2 amino acid residues from such parental molecule or relevant portion thereof).
In any embodiment, the Notch-4 antibody-based molecule suitable for use in the methods described herein is an intact immunoglobulin or a molecule having a Notch-4 epitope-binding fragment thereof As used herein, the terms “fragment”, “region”, and “domain” are generally intended to be synonymous, unless the context of their use indicates otherwise. Naturally occurring antibodies typically comprise a tetramer, which is usually composed of at least two heavy (H) chains and at least two light (L) chains. Each heavy chain is comprised of a heavy chain variable (VH) region and a heavy chain constant (CH) region, usually comprised of three domains (CH1, CH2 and CH3 domains). Heavy chains can be of any isotype, including IgG (IgG1, IgG2, IgG3 and IgG4 subtypes), IgA (IgA1 and IgA2 subtypes), IgM and IgE. Each light chain is comprised of a light chain variable (VL) region and a light chain constant (C L) region. Light chains include kappa chains and lambda chains. The heavy and light chain variable regions are responsible for antigen recognition, while the heavy and light chain constant regions may mediate the binding of the immunoglobulin to host tissues or factors, including various cells of the immune system (e.g., effector cells) and the first component (Clq) of the classical complement system. The VH and VL regions can be further subdivided into regions of hypervariability, termed “complementarity determining regions,” or “CDRs,” that are interspersed with regions of more conserved sequence, termed “framework regions” (FR). Suitable Notch-4 heavy chain and light chain CDRs are described supra. Each VH and VL region is composed of three CDR domains and four FR domains arranged from amino-terminus to carboxy-terminus in the following order: FR1-CDR1-FR2-CDR2-FR3-CDR3-FR4. Suitable Notch-4 VH and VL regions are described supra. The variable regions of the heavy and light chains contain a binding domain that interacts with an antigen. Of particular relevance are antibodies and their epitope-binding fragments that have been “isolated” so as to exist in a physical milieu distinct from that in which it may occur in nature or that have been modified so as to differ from a naturally-occurring antibody in amino acid sequence.
Fragments of antibodies (including Fab and (Fab)2 fragments) that exhibit Notch-4 epitope-binding ability are also suitable for use in the methods described herein. Notch-4 epitope binding fragments can be obtained, for example, by protease cleavage of intact antibodies. Single domain antibody fragments possess only one variable domain (e.g., VL or VH). Examples of the epitope-binding fragments encompassed within the present invention include (i) Fab′ or Fab fragments, which are monovalent fragments containing the VL, VH, CL and CH1 domains; (ii) F(ab′)2 fragments, which are bivalent fragments comprising two Fab fragments linked by a disulfide bridge at the hinge region; (iii) Fd fragments consisting essentially of the VH and CH1 domains; (iv) Fv fragments consisting essentially of a VL and VH domain, (v) dAb fragments (Ward et al. “Binding Activities Of A Repertoire Of Single Immunoglobulin Variable Domains Secreted From Escherichia coli,” Nature 341:544-546 (1989) which is hereby incorporated by reference in its entirety), which consist essentially of a VH or VL domain and also called domain antibodies (Holt et al. “Domain Antibodies: Proteins For Therapy,” Trends Biotechnol. 21(11):484-490 (2003), which is hereby incorporated by reference in its entirety); (vi) nanobodies (Revets et al. “Nanobodies As Novel Agents For Cancer Therapy,” Expert Opin. Biol. Ther. 5(1):111-124 (2005), which is hereby incorporated by reference in its entirety), and (vii) isolated complementarity determining regions (CDR). A suitable Notch-4 epitope-binding fragment for use in the methods described herein may contain 1, 2, 3, 4, 5 or all 6 of the CDR domains of the Notch-4 antibody described supra (i.e., SEQ ID NOs: 2-9).
Such antibody fragments may be obtained using conventional techniques known to those of skill in the art. For example, F(ab′)2 fragments may be generated by treating a full-length antibody with pepsin. The resulting F(ab′)2 fragment may be treated to reduce disulfide bridges to produce Fab′ fragments. Fab fragments may be obtained by treating an IgG antibody with papain and Fab′ fragments may be obtained with pepsin digestion of IgG antibody. A Fab′ fragment may be obtained by treating an F(ab′)2 fragment with a reducing agent, such as dithiothreitol. Antibody fragments may also be generated by expression of nucleic acids encoding such fragments in recombinant cells (see e.g., Evans et al. “Rapid Expression Of An Anti-Human C5 Chimeric Fab Utilizing A Vector That Replicates In COS And 293 Cells,” J. Immunol. Meth. 184:123-38 (1995), which is hereby incorporated by reference in its entirety). Nucleic acid molecules encoding heavy chain and light chain regions of the Notch-4 antibodies described supra are disclosed in U.S. Pat. No. 9,527,921 To Sakamoto et al., which is hereby incorporated by reference in its entirety. For example, a chimeric gene encoding a portion of a F(ab′)2 fragment could include DNA sequences encoding the CH1 domain and hinge region of the heavy chain, followed by a translational stop codon to yield such a truncated antibody fragment molecule. Suitable fragments capable of binding to a desired epitope may be readily screened for utility in the same manner as an intact antibody.
Notch-4 antibody derivatives suitable for use in the methods described herein include those molecules that contain at least one epitope-binding domain of an antibody, and are typically formed using recombinant techniques. One exemplary antibody derivative includes a single chain Fv (scFv). A scFv is formed from the two domains of the Fv fragment, the VL region and the VH region, which may be encoded by separate genes. Such gene sequences or their encoding cDNA are joined, using recombinant methods, by a flexible linker (typically of about 10, 12, 15 or more amino acid residues) that enables them to be made as a single protein chain in which the VL and VH regions associate to form monovalent epitope-binding molecules (see e.g., Bird et al. “Single-Chain Antigen-Binding Proteins,” Science 242:423-426 (1988); and Huston et al. “Protein Engineering Of Antibody Binding Sites: Recovery Of Specific Activity In An Anti-Digoxin Single-Chain Fv Analogue Produced In Escherichia coli,” Proc. Natl. Acad. Sci. (U.S.A.) 85:5879-5883 (1988), which are hereby incorporated by reference in their entirety). Alternatively, by employing a flexible linker that is not too short (e.g., not less than about 9 residues) to enable the VL and VH regions of a different single polypeptide chains to associate together, one can form a bispecific antibody, having binding specificity for two different epitopes.
In another embodiment, the antibody derivative suitable for use in the methods described herein is a divalent or bivalent Notch-4 single-chain variable fragment, engineered by linking two scFvs together either in tandem (i.e., tandem scFv), or such that they dimerize to form diabodies (Holliger et al. “‘Diabodies’: Small Bivalent And Bispecific Antibody Fragments,” pu Proc. Natl. Acad. Sci. (U.S.A.) 90(14), 6444-8 (1993), which is hereby incorporated by reference in its entirety). In yet another embodiment, the antibody is a trivalent single chain variable fragment, engineered by linking three scFvs together, either in tandem or in a trimer formation to form triabodies. In another embodiment, the antibody is a tetrabody of four single chain variable fragments. In another embodiment, the antibody is a “linear antibody” which is an antibody comprising a pair of tandem Fd segments (VH-CH1-VH-CH1) that form a pair of antigen binding regions (see Zapata et al. Protein Eng. 8(10):1057-1062 (1995), which is hereby incorporated by reference in its entirety). In another embodiment, the antibody derivative is a minibody, consisting of the single-chain Fv regions coupled to the CH3 region (i.e., scFv-CH3).
Antibody fragments and derivatives suitable for use in the methods described herein also include antibody-like polypeptides, such as chimeric antibodies and humanized antibodies, and antibody fragments retaining the ability to specifically bind to the Notch-4 (epitope-binding fragments) provided by any known technique, such as enzymatic cleavage, peptide synthesis, and recombinant techniques.
An antibody as generated herein may be of any isotype. As used herein, “isotype” refers to the immunoglobulin class (for instance IgG1, IgG2, IgG3, IgG4, IgD, IgA, IgE, or IgM) that is encoded by heavy chain constant region genes. The choice of isotype typically will be guided by the desired effector functions, such as antibody-dependent cellular cytotoxicity (ADCC) induction. Exemplary isotypes are IgG1, IgG2, IgG3, and IgG4. Either of the human light chain constant regions, kappa or lambda, may be used. If desired, the class of a Notch-4 antibody of the present invention may be switched by known methods. For example, an antibody of the present invention that was originally IgM may be class switched to an IgG antibody of the present invention. Further, class switching techniques may be used to convert one IgG subclass to another, for instance from IgG1 to IgG2. Thus, the effector function of the antibodies of the present invention may be changed by isotype switching to, e.g., an IgG1, IgG2, IgG3, IgG4, IgD, IgA, IgE, or IgM antibody for various therapeutic uses.
In one embodiment, the Notch-4 antibody-based molecules suitable for use in the methods described herein are “humanized,” particularly if they are to be employed for therapeutic purposes. The term “humanized” refers to a chimeric molecule, generally prepared using recombinant techniques, having an antigen-binding site derived from an immunoglobulin from a non-human species and a remaining immunoglobulin structure based upon the structure and/or sequence of a human immunoglobulin. The antigen-binding site may comprise either complete non-human antibody variable domains fused to human constant domains, or only the complementarity determining regions (CDRs) of such variable domains grafted to appropriate human framework regions of human variable domains. The framework residues of such humanized molecules may be wild-type (e.g., fully human) or they may be modified to contain one or more amino acid substitutions not found in the human antibody whose sequence has served as the basis for humanization. Humanization lessens or eliminates the likelihood that a constant region of the molecule will act as an immunogen in human individuals, but the possibility of an immune response to the foreign variable region remains (LoBuglio, A. F. et al. “Mouse/Human Chimeric Monoclonal Antibody In Man: Kinetics And Immune Response,” Proc. Natl. Acad. Sci. USA 86:4220-4224 (1989), which is hereby incorporated by reference in its entirety). Another approach focuses not only on providing human-derived constant regions, but modifying the variable regions so as to reshape them as closely as possible to human form. When non-human antibodies are prepared with respect to a particular antigen, the variable regions can be “reshaped” or “humanized” by grafting CDRs derived from non-human antibody onto the FRs present in the human antibody to be modified. Suitable methods for humanizing non-human antibodies, including those, described herein are known in the art see e.g., Sato, K. et al., Cancer Res 53:851-856 (1993); Riechmann, L. et al., “Reshaping Human Antibodies for Therapy,” Nature 332:323-327 (1988); Verhoeyen, M. et al., “Reshaping Human Antibodies: Grafting An Antilysozyme Activity,” Science 239:1534-1536 (1988); Kettleborough, C. A. et al., “Humanization Of A Mouse Monoclonal Antibody By CDR-Grafting: The Importance Of Framework Residues On Loop Conformation,” Protein Engineering 4:773-3783 (1991); Maeda, H. et al., “Construction Of Reshaped Human Antibodies With HIV-Neutralizing Activity,” Human Antibodies Hybridoma 2:124-134 (1991); Gorman, S. D. et al., “Reshaping A Therapeutic CD4 Antibody,” Proc. Natl. Acad. Sci. USA 88:4181-4185 (1991); Tempest, P. R. et al., “Reshaping A Human Monoclonal Antibody To Inhibit Human Respiratory Syncytial Virus Infection In Vivo,” Bio/Technology 9:266-271 (1991); Co, M. S. et al., “Humanized Antibodies For Antiviral Therapy,” Proc. Natl. Acad. Sci. USA 88:2869-2873 (1991); Carter, P. et al., “Humanization Of An Anti-p185her2 Antibody For Human Cancer Therapy,” Proc. Natl. Acad. Sci. USA 89:4285-4289 (1992); and Co, M.S. et al., “Chimeric And Humanized Antibodies With Specificity For The CD33 Antigen,” J. Immunol. 148:1149-1154 (1992), which are hereby incorporated by reference in their entirety. In some embodiments, humanized Notch-4 antibodies of suitable for use in the methods described herein preserve all CDR sequences (for example, a humanized antibody containing all six CDRs from the mouse antibody). In other embodiments, humanized Notch-4 antibodies suitable for use in the methods described herein have one or more CDRs (one, two, three, four, five, six) which are altered with respect to the original antibody. Suitable humanized Notch-4 antibodies for use in the methods disclosed herein are disclosed in U.S. Pat. No. 9,527,921 To Sakamoto et al., which is hereby incorporated by reference in its entirety.
In another embodiment, the Notch-4 inhibitor is a small molecule Notch-4 inhibitor. Suitable Notch-4 inhibitors include, without limitation, RO4929097 (RG-4733) having the following structure.
Another small molecule Notch-4 inhibitor that can be utilized in accordance with the methods of the present disclosure, i.e., to inhibit macrophage proliferation and self-renewal in the tumor microenvironment, is Nirogacestat (PF-030840140) having the following structure,
In another embodiment, the method of inhibiting macrophage proliferation in a population of cells comprising macrophages involves administering a TYK2 inhibitor to the population of cells under conditions effective to inhibit macrophage proliferation in said population of cells. Suitable TYK2 inhibitors for use in accordance with this method of the disclosure include, without limitation, PF-06826647 (3-(cyanomethyl)-3-[4-[6-(1-methylpyrazol-4-yl)pyrazolo[1,5-a]pyrazin-4-yl]pyrazol-1-yl]cyclobutane-1-carbonitrile), NDI-031407 (Gracey et al., J. Clin. Invest. 130(4): 1863-78 (2020), which is hereby incorporated by reference in its entirety), Deucravacitinib (BMS-986165; 6-(cyclopropanecarbonylamino)-4-[2-methoxy-3-(1-methyl-1,2,4-triazol-3-yl)anilino]-N-(trideuteriomethyl)pyridazine-3-carboxamide), and others known in the art.
In some embodiments, the method of treating a tumor in a subject as disclosed herein further comprises administering to the selected subject a checkpoint inhibitor in combination with the Notch-4 inhibitor and/or TYK2 inhibitor. Suitable checkpoint inhibitors are described supra, and include, without limitation a PD-L1 inhibitor, a PD-1 inhibitor, a CTLA-4 inhibitor, and combinations thereof
In some embodiments, the method of treating a tumor in a subject as disclosed herein further comprises administering to the selected subject a pro-inflammatory agent in combination with the Notch-4 inhibitor. Suitable pro-inflammatory agent include those described supra, including without limitation, GM-CSF, an OX40 activation antibody, and a TREM2 blocking antibody.
In accordance with this method and all methods described herein that involve administration of one or more therapeutic agents as described herein to a subject having a tumor, said administration of the agent(s) alone or in combination is carried out by systemic or local administration. Suitable modes of systemic administration of the therapeutic agents and/or combination therapeutics disclosed herein include, without limitation, orally, topically, transdermally, parenterally, intradermally, intrapulmonary, intramuscularly, intraperitoneally, intravenously, subcutaneously, or by intranasal instillation, by intracavitary or intravesical instillation, intraocularly, intra-arterially, intralesionally, or by application to mucous membranes. In certain embodiments, the therapeutic agents of the methods described herein are delivered orally. Suitable modes of local administration of the therapeutic agents and/or combinations disclosed herein include, without limitation, catheterization, implantation, direct injection, dermal/transdermal application, or portal vein administration to relevant tissues, or by any other local administration technique, method or procedure generally known in the art. The mode of affecting delivery of agent will vary depending on the type of therapeutic agent and the type of cancer to be treated.
A therapeutically effective amount of the therapeutic agent(s) alone or in combination in the methods disclosed herein is an amount that, when administered over a particular time interval, increases the subject's immune response to the tumor, which further leads to a slowing or halting of cancer growth, cancer regression, cessation of symptoms, etc. The therapeutic agents for use in the presently disclosed methods may be administered to a subject one time or multiple times. In those embodiments where the therapeutic agents are administered multiple times, they may be administered at a set interval, e.g., daily, every other day, weekly, or monthly. Alternatively, they can be administered at an irregular interval, for example on an as-needed basis based on symptoms, patient health, and the like. For example, a therapeutically effective amount may be administered once a day (q.d.) for one day, at least 2 days, at least 3 days, at least 4 days, at least 5 days, at least 6 days, at least 7 days, at least 10 days, or at least 15 days. Optionally, the status of the cancer, e.g., presence of an immune response, or the regression of the cancer is monitored during or after the treatment, for example, by a multiparametric ultrasound (mpUS), multiparametric magnetic resonance imaging (mpMRI), and nuclear imaging (positron emission tomography [PET]) of the subject. The dosage of the therapeutic agents administered to the subject can be increased or decreased depending on the status of the cancer or the regression of the cancer detected.
The skilled artisan can readily determine this amount, on either an individual subject basis (e.g., the amount of a compound necessary to achieve a particular therapeutic benchmark in the subject being treated) or a population basis (e.g., the amount of a compound necessary to achieve a particular therapeutic benchmark in the average subject from a given population). Ideally, the therapeutically effective amount does not exceed the maximum tolerated dosage at which 50% or more of treated subjects experience side effects that prevent further drug administrations.
A therapeutically effective amount may vary for a subject depending on a variety of factors, including variety and extent of the symptoms, sex, age, body weight, or general health of the subject, administration mode and salt or solvate type, variation in susceptibility to the drug, the specific type of the disease, and the like.
The effectiveness of the methods of the present application in increasing the immune response or decreasing immune-tolerance can be assessed, for example, by assessing changes in cancer burden and/or disease progression following treatment with the therapeutic agents as described herein according to the Response Evaluation Criteria in Solid Tumours (Eisenhauer et al., “New Response Evaluation Criteria in Solid Tumours: Revised RECIST Guideline (Version 1.1),” Eur. J. Cancer 45(2): 228-247 (2009), which is hereby incorporated by reference in its entirety). In some embodiments, cancer burden and/or disease progression is evaluated using imaging techniques including, e.g., X-ray, computed tomography (CT) scan, magnetic resonance imaging, multiparametric ultrasound (mpUS), multiparametric magnetic resonance imaging (mpMRI), and nuclear imaging (positron emission tomography [PET]) (Eisenhauer et al., “New Response Evaluation Criteria in Solid Tumours: Revised RECIST Guideline (Version 1.1),” Eur. J. Cancer 45(2): 228-247 (2009), which is hereby incorporated by reference in its entirety). Cancer regression or progression may be monitored prior to, during, and/or following treatment with one or more of the therapeutic agents described herein.
In some embodiments, the effectiveness of the methods described herein may be evaluated, for example, by assessing immunological parameters, such as lymphocyte infiltration and IFN-y status. In some embodiments, the methods described are suitable for increasing the subject's immune response to the tumor, are effective to inhibit disease progression, inhibit cancer growth/spread, relieve cancer-related symptoms, inhibit tumor-secreted factors (e.g., tumor-secreted hormones), delay the appearance of primary or secondary cancer tumors, slow development of primary or secondary cancer tumors, decrease the occurrence of primary or secondary cancer tumors, slow or decrease the severity of secondary effects of disease, arrest tumor growth, and/or achieve regression of cancer in a selected subject.
In some embodiments, the methods described herein reduce the rate of cancer growth in the selected subject by at least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or more. In certain embodiments, the methods described herein reduce the rate of cancer invasiveness in the selected subject by at least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or more. In specific embodiments, the methods described herein reduce the rate of cancer progression in the selected subject by at least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or more. In various embodiments, the methods described herein reduce the rate of cancer recurrence in the selected subject by at least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or more. In some embodiments, the methods described herein reduce the rate of metastasis in the selected subject by at least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or more.
Another aspect of the present disclosure relates to a combination therapeutic. In some embodiments, the combination therapeutic comprises a Notch-4 inhibitor and a checkpoint inhibitor. Suitable Notch-4 antibodies and checkpoint inhibitors of the combination therapeutic are described supra.
In some embodiments, the combination therapeutic a Notch-4 inhibitor and a pro-inflammatory agent. Suitable Notch-4 antibodies and pro-inflammatory agents of the combination therapeutic are described supra.
Another aspect of the present disclosure is directed to an organotypic tumor microenvironment model (TME) culture system. The TME culture system comprises an isolated population of cells, said population comprising tumor epithelial cells, mesenchymal stromal cells, fibroblasts. In one embodiment, the fibroblasts are immortalized fibroblasts.
The immortalization of fibroblasts can be induced or arise spontaneously as a result of the cultures system. To induce fibroblast immortalization, the fibroblasts can be transformed with one or more reagents to facilitate immortalization. Suitable methods of immortalizing cells are well known in the art and suitable for use in accordance with this embodiment. For example, the cells can be transformed with a viral gene (e.g., large T-antigen of the simian virus (SV-40), HPV E6 or E7 genes) to promote viral gene overexpression which in turn suppresses expression of the endogenous cell cycle modulators (e.g., retinoblastoma and p53 genes) to induce uncontrolled proliferation. Alternatively, fibroblasts can be immortalized by inducing expression of genes that confer immortality, e.g., telomerase (hTERT). hTERT expression prevents normal telomere shortening to abate the senescence process and enables the cell to undergo infinite cell division. In another embodiment, the fibroblasts become immortalized as a result of being in culture with tumor epithelial cells. In either embodiment, the fibroblasts of the TME cell culture system described herein are different from fibroblasts which exist naturally in the tumor environment because they exhibit an immortalized phenotype.
In some embodiments, the TME culture system further comprises one or more additional cell types. Suitable cell types include, without limitation, macrophages, endothelial cells, T cell, NK cells, and dendritic cells. The TME cell culture system, which replicates the tumor environment, is useful for examining and delineating cell-to-cell interactions in the tumor environment as well as test and screen candidate agents and compounds for manipulating these interactions.
In one embodiment, the TME culture system comprises macrophages having an M1 phenotype. In any embodiment, these macrophages of the model have an expression profile of Ki67NegCD11chiArg1Neg indicating a non-proliferative, pro-inflammatory phenotype. In another embodiment, the TME culture system comprises macrophages having an M2 phenotype. In any embodiment, these macrophages of the model have an expression profile of
Ki67+ CD11clowArgl+indicating a proliferative, immunosuppressive phenotype.
In some embodiments, the TME culture system comprises a population of tumor epithelial cells and mesenchymal cells derived from a breast tumor as described herein. In accordance with this embodiment, the tumor epithelial cells are characterized by EpCAM30/CD49fhigh/CD24high/CD61− expression.
In any embodiment, the population of tumor epithelial cells, mesenchymal stromal and any of the other one or more cell types present in the cell culture are derived from a tumor or tumor environment. In any embodiment, the cells are derived from the tumor or tumor environment associated with a breast tumor, pancreatic tumor, ovarian tumor, prostate tumor, lung tumor, colon tumor, solid tumor, glioma, melanoma, myeloma, liver tumor, and kidney tumor.
In any embodiment, the cells of the TME culture system are primary cells, isolated directly from a tumor or the surrounding tumor environment. In some embodiments, the population of cells in the TME cultures is a syngeneic population of cells. In some embodiments, the population of cells is a population of human cells. In some embodiments, the population of cells is a population of rodent cells, such as murine or rat cells.
In any embodiment, one or more cell populations of the TME culture are modified to express a detectable label. For example, in any embodiment, cells of the model can be modified to express a detectable label, such as fluorescent protein, where the expression of the detectable label is controlled by a cell specific promoter or an inducible promoter system (e.g., cre-recombinase), and useful to identify or track the presence of a particular cell type (e.g., allow the tracking of cell differentiation). In other embodiments, expression of the detectable label can be coupled to the expression of any protein of interest. Such modification may involve transient transfection or stable transductions of the cells of the culture with an expression vector comprising a nucleic acid molecule encoding the detectable label.
The in vitro TME culture model described herein is cultured or maintained using standard tissue culture procedures. Appropriate growth and culture conditions for various mammalian cell types are well known in the art. The cells in the in vitro TME culture model may be seeded onto and/or within a substrate from a suspension so that they are evenly distributed at a relatively high surface and/or volume density. The cell suspensions may comprise approximately about 1×104 to about 5×107 cells/ml of culture medium, or approximately about 2×106 cells/ml to about 2×107 cells/ml, or approximately about 5×106 cells/ml. The optimal concentration and absolute number of cells will vary with cell type, growth rate of the cells, substrate material, and a variety of other parameters. The suspension may be formed in any physiologically acceptable medium, preferably one that does not damage the cells or impair their ability to adhere to the substrate. Appropriate mediums include standard cell growth media (e.g., DMEM with 10% FBS).
Cells of the in vitro TME culture model are cultured in a media that generally includes essential nutrients and, optionally, additional elements such as growth factors, salts, minerals, vitamins, etc., that may be selected according to the cell type(s) being cultured. A standard growth media includes Dulbecco's Modified Eagle Medium, low glucose (DMEM), with 110 mg/L pyruvate and glutamine, supplemented with 10-20% fetal bovine serum (FBS) or calf serum and 100 U/ml penicillin. The culture media may also contain particular growth factors selected to enhance cell survival, differentiation, secretion of specific proteins, etc. In some embodiments, the culture of the TME culture model is performed in a sterile environment under standard culture conditions, e.g., incubation at 37° C. in an incubator containing a humidified atmosphere of 95% air and 5% CO2.
As described herein, the TME culture system can be utilized to identify targetable mediators of the tumor microenvironment for the development of innate immune cell targeted immunotherapies. In some embodiments, the step of identifying targetable mediators of the tumor microenvironment can further be used to test the efficacy of a therapeutic agent or combination of therapeutic agents. Such assessment comprises performing at least one test or multiple tests to evaluate (qualitatively or quantitatively) a modification of the morphology and/or composition of the organotypic culture at the tissue level (for example at the level of the vessels), at the cellular level and/or at the molecular level (protein, DNA, RNA, etc.) by immunohistochemistry or western Blot, by flow cytometry, by a microscopy technique or by protein analysis. Depending on the results obtained, an anti-cancer therapy is identified. In some embodiments, the method can be adapted to identify a personalized anti-cancer therapeutic regimen by developing a TME culture system utilizing TME cells of a cancer patient. Since the organotypic TME culture model obtained by the methods described herein is more representative of the in vivo situation than a single cell line or a primary cell culture, it is an excellent model for the screening of therapeutic compounds.
Thus, in another aspect, the present disclosure relates to the use of an organotypic TME culture system described herein for the screening compounds which may have immunotherapeutic properties against the tumor. In one embodiment, the disclosure encompasses a method of screening for compounds capable of having immunotherapeutic properties against cancer where the method involves the steps of incubating an organotypic TME culture as described herein in the presence of a test compound, and observing the effects of the test compound on the organotypic culture. In any embodiment, the TME culture is representative of tumor environment selected from a lung tumor, esophagus tumor, stomach tumor, liver tumor, pancreas tumor, colon tumor, breast tumor, ovary tumor, cervix tumor, prostate tumor, testes tumor, skin tumor, thyroid tumor, adrenal gland tumor, etc. As noted supra, the TME culture comprises tumor epithelial cells, immortalized fibroblasts, and stromal cells of the tumor of interest. Further cell types of interest (e.g., macrophages, NK cells, T cells, etc.) are added to the model to examine cell specific contribution to the tumor environment and identify druggable targets of immunotherapeutics.
Accordingly, another aspect of the present disclosure is directed to a method of identifying candidate compounds or agents capable of modulating the tumor microenvironment. In one aspect, the method involves identifying a candidate compounds or agent capable of modulating macrophage activity in a tumor environment. This method involves providing the organotypic tumor microenvironment model (TME) culture system as described herein, wherein said system comprises macrophages, in addition to the fibroblasts, tumor epithelial cells, and stromal cells. The system can optionally comprise one or more other cell types. The method further involves administering the candidate compound to the culture system and assessing one or more markers of macrophage activity in the culture systems before and after said administering. A candidate compound capable of modulating macrophage activity in the tumor environment is identified based on said assessing.
As described herein, the TME culture system was utilized to identify compounds that modulate macrophage phenotype in the tumor environment. Macrophages in the tumor environment often exhibit a type-2 phenotype, which is characterized as being anti-inflammatory and immunosuppressive as they suppress T-cell responses and are involved in the Th2-type immune response. Type-2 macrophages are characterized by high surface expression of Il-4R, FeϵR, Dectin-1, CD136, CD206, and CD209A. The induction of Arg1 is considered as one of the bona fide hallmarks of M2-like macrophages and associated with anti-inflammatory and tissue repair phenotypes. Therefore, as described herein, Arg1 induction is a useful surrogate for TME-education. Type-2 macrophages include IL-4/IL-13-stimulated macrophages, IL-10-induced macrophages, and immune complex-triggered macrophages. Thus, a candidate agent capable of modulating macrophage immunosuppressive tumor phenotype is one that can decrease the expression of the aforementioned proteins and/or promote the expression of markers of Type-1 macrophages. Gene involved in modulating macrophage tumor phenotype and candidate agents identified using this screen are provided in Table 1 below.
In another aspect, the method involves identifying a candidate compound capable of modulating NK cell activity in a tumor environment. This method comprises providing the organotypic TME culture system, wherein said system further comprises NK cells, in addition to the fibroblasts, tumor epithelial cells, and stromal cells. The system can optionally comprise one or more other cell types. The method further involves administering the candidate compound to the culture system and assessing one or more markers of NK cell activity in the culture systems before and after said administering. A candidate compound capable of modulating NK cell activity in the tumor environment is identified based on said assessing. For example, in one embodiment, this method is used for identifying a candidate compound capable of modulating NK cell exhaustion in a tumor environment. NK cell exhaustion is marked by a decrease in the expression of several cytokines (e.g., IFN-γ) and cytolytic molecules (e.g., granzymes, perforin, FasL and TRAIL). Thus, the screening method described herein is useful for identifying genes/proteins involved in modulating NK cell exhaustion as well as drug candidates for reversing such exhaustion.
In another aspect, the method involves identifying a candidate compound capable of modulating T cell activity in a tumor environment. This method comprises providing the organotypic TME culture system, wherein said system further comprises T cells, in addition to the fibroblasts, tumor epithelial cells, and stromal cells. The system can optionally comprise one or more other cell types. The method further involves administering the candidate compound to the culture system and assessing one or more markers of T cell activity in the culture systems before and after said administering. A candidate compound capable of modulating T cell activity in the tumor environment is identified based on said assessing. For example, in one embodiment, this method is utilized for identifying a candidate compound capable of modulating T cell exhaustion in a tumor environment. T cell exhaustion is marked by a decrease in the expression of several surface markers (e.g., CD127 and CD62L) and effector molecule activity (e.g., decrease in IL-2, TNF-α, IFN-γ, and GranB), as well as an increase in inhibitory receptor expression (e.g., PD-1, Tim-3, and Lag-3). Thus, the screening method described herein is useful for identifying genes/proteins involved in modulating T cell exhaustion as well as drug candidates for reversing such exhaustion.
In another aspect, the method involves identifying genetic changes in one or more cell types of the tumor environment. This method comprises providing the organotypic TME culture system, wherein said system may further comprise one or more of macrophages, NK cells, T cells, dendritic cells, and/or endothelial cells in addition to the fibroblasts, tumor epithelial cells, and stromal cells. The method further involves subjecting the cells of the TME culture system to genetic analysis after time in culture. Genetic analysis involves employment of any art known method of analyzing gene expression in the system (e.g., qPCR, RNA-sequence analysis, CRISPR/Cas9 genomic screen, etc.) as well as analysis of genetic changes in any one or more of the cell types (i.e., detection of nucleotide deletions, substitutions, and/or additions that lead to mutations in the resulting protein). This analysis is useful for identifying target genes/proteins of the tumor environment contributing to the tumorigenic process. Further, administering a candidate compound to the culture system and assessing changes in the expression or function of one or more identified genetic elements in the culture system can be used to identify new tumor therapeutics.
The following examples are provided to illustrate embodiments of the present disclosure but are by no means intended to limit its scope
Pooled CRISPR sgRNA Libraries Construction: CRISPR single-guide RNAs (sgRNAs) were optimized for on-target activity with minimal off-targets in the human genome using a similar approach as done previously (Meier et al., “GUIDES: sgRNA Design for Loss-of-Function Screens,” Nat. Methods 14(9):831-832 (2017), which is hereby incorporated by reference in its entirety). CRISPR sgRNAs were targeted within —200 nt upstream of the gene's transcription start site (TSS). Four guides per gene were designed including 1000 non-targeting sgRNAs as control. Custom ssDNA oligos were synthesized by Twist Bioscience and cloned into the lentiCRISPRV2 plasmid (Sanjana et al., “Improved Vectors and Genome-Wide Libraries for CRISPR Screening,” Nat. Methods 11:783-784 (2014), which is hereby incorporated by reference in its entirety) using a method similar to the one described in (Shalem et al., “Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells,” Science 343:84-87 (2014), which is hereby incorporated by reference in its entirety). Individual sgRNAs were cloned as previously described (Parnas et al., “A Genome-Wide CRISPR Screen in Primary Immune Cells to Dissect Regulatory Networks,” Cell 162:675-686 (2015), which is hereby incorporated by reference in its entirety).
Library Lentivirus Production: HEK-293T cells were plated (8×106) in T225 flasks and transfected the day after with a DNA mix consist of: 25 μg lentiCRISPRv2 sgRNA construct, 14 μg pMD2.G, and 20μg psPAX2 in 2.5 mL Opti-MEM+PEI (polyethylenimine) at the ratio of 3 μL per 1 μg DNA mix. Day after transfection, the growth medium was refreshed and cells were incubated for another 48 hrs to allow accumulation of viruses. Supernatants were collected, cell debris were removed by centrifugation at 1000 g for 10 min at 4 C. Virus-containing supernatants were tested for virus titer, aliquoted and stored in −80 C.
Pooled Whole Genome CRISPR Screen of Arg1-EYFP Macrophages: Bone marrow-derived macrophages (BMDMs) (3.2×108) were generated from Arg1-EYFP mice (n=45), seeded at 50% confluency on RetroNectin-coated (50μg/mL) 10cm plates, and infected with pooled lentiviral library at an MOI of 1. The inventors aimed for 500 cells per guide in order to achieve a 500× library representation after —20% infection efficacy puromycin selection. Transduced BMDMs were incubated for 48hrs with puromycin (1 μg/mL) and M-CSF (long/mL). Puromycin-resistant BMDMs were pooled together and a batch equivalent to 1.0× representations of sgRNA libraries (500× per gene) was snapped-frozen as “library representation”, while rest of BMDMs were co-cultured with oTME cells for 10 days to initiate M2-education. Educated Arg1-EYFP BMDMs were collected, immunostained for CD45, CD11b, F4/80, and FACS-sorted into two groups according to EYFP-Arg1: (i) the top 10% of EYFP+ (M2) and (ii) EYFPneg BMDMs (“M2-resistant”). To detect the sgRNAs and gene identities, genomic DNA (gDNA) was purified using Qiagen DNeasy Blood & Tissue Kit according to the manufacturer's instruction. The integrated sgRNA cassettes were amplified by PCR (PCR1), and Illumina sequencing adapters were attached by nested PCR (PCR2) as previously described (Shalem et al., “Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells,” Science 343:84-87 (2014), which is hereby incorporated by reference in its entirety). The resulting gDNA amplicon library was sequenced at ˜50-75 reads per guide on an Illumina MiSeq 150v3 kit.
Chromium 10× Single-Cell RNA-Seq: BMDMs were co-cultured with oTME cells for 2 (early) and 10-days (late), or left alone with M-CSF for same time intervals. Cells were collected, clumps were removed through a 40 μm filter, and cells (n=4000 per condition) were loaded on Chromium platform to generate cDNA following the manufacturer's protocol. Single-cell cDNA libraries were prepared using the Chromium Single Cell 3′ Library & Gel Bead Kits v2 (PN-120237, PN-120236, PN-120262) according to the manufacturer's instructions. Samples were sequenced at an average of 50,000 reads per cell.
RNA Isolation for qRT—PCR: RNA from cells was extracted using TRIzol™ Reagent (Thermo Fisher Scientific; 15596026) and 1-5ug RNA were used to prepare cDNA using RNA to cDNA EcoDryT™ kit (Clontech; 639549). RNA from FACS-sorted cells were extracted using TRIzol™ LS Reagent (Thermo Fisher Scientific; 10296010) and processed according to manufacturer's protocol. For qPCR reactions, mouse Taqman probes (Applied
Biosystems) were used for quantifying expression of Cdh1 (Mm01247357_m1), Csf1 Mm00432686_m1, Csf2 (Mm01290062_m1), Csf3 Mm00438334_m1, Vim (Mm01333430_m1). Expression values were normalized to both Hprt (Mm03024075_m1) and Gapdh (Mm99999915_g1) as housekeeping genes.
Mouse Work and Strains: Animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of the Research Animal Resource Center (RARC) at Weill Cornell Medicine (Protocol: 2016-0058). MMTV-PyMT (Stock No: 022974), Arg1-EYFP (Stock No: 015857), and wild-type C57BL/6J (Stock No: 000664) were purchased from The Jackson Laboratory (USA). Rosa26mTmG (Stock No: 007676, mTmG) mice were a gift from Dr. Johhan Joyce (MSKCC).
Tumor Engraftment and Processing: Females of the MMTV-PyMT model developed spontaneous tumors after 100-120 days post birth. To generate orthotopic tumor transplants, tumor cells (1.0×106) were injected in 50 μL of 50% Matrigel (Corning) in serum-free DMEM, into the 4th mammary gland of 7-10 weeks of wild-type (WT) C57BL/6 female mice. Prior to injection, mice were anesthetized with 2% isoflurane and cells were injected under the nipple into the fat pad. Tumor dimensions (width and length) were measured using a digital caliper and tumor volumes were calculated as V=(L×W2)/2.
Neutralization of NOTCH4 in vivo: To target NOTCH4 in mammary tumors, wild-type C57BL/6 female mice were engrafted with tumor epithelial cells in the 4th mammary glands and allowed to establish palpable tumors. Then, mice were pooled and randomized into two arms: vehicle-treated (PBS) or Notch4-treated with anti-Notch4 monoclonal antibodies (BioXcell, clone HMN4-14). Mice were dosed every 3 days intraperitoneally with 15 μg/kg body weight.
Mouse Bone Marrow Derived Macrophages (BMDMs): Mouse BMDMs were obtained from femurs and tibias of 6-8-week-old B6 mice. Bone marrow cells were flushed onto a 40 μM strainer using a 25G×⅝ needle washed with RPMI. Bone marrow cells were gently meshed through the 40 μm strainer using a 1 ml plunger. After centrifugation at 300 g 4° C., for 5 minutes, cells were resuspended in DMEM medium, 10% FBS and 1% Pen-strep supplemented with 10 ng/mL recombinant murine M-CSF. Growth medium was replenished every other day with fresh 10 ng/mL M-CSF. On day-7 >95% of the cells were CD11b+ Ly6C/Ly6G− (Gr1−) F4/80+ macrophages.
Mouse Bone Marrow Monocytes Isolation: Mouse bone marrow was obtained from femurs and tibias as previously described with differentiation of BMDMs. Bone marrow cells were resuspended with 5 mL of RBC Lysis Buffer (ThermoFisher Scientific) for five minutes incubation on ice to remove red blood cells, washed with serum-free RPMI, and monocytes are purified using negative selection monocytes isolation kit (130-100-629; Miltenyi).
Chemicals and Biological Reagents: 99LN-parental cells were generated as previously described (Quail et al., “Obesity Alters the Lung Myeloid Cell Landscape to Enhance Breast Cancer Metastasis Through IL5 and GM-CSF,” Nat. Cell Biol. 19:974-987 (2017), which is hereby incorporated by reference in its entirety). Isolations of tumor epithelial or stromal cells were performed by FACS-sort using EpCAM and PDGFRA antibodies. Cells were maintained in DMEM-GlutaMAX (Gibco; 10566016) supplemented with 10% FBS (Gibco), and 1% Pen-strep (ThermoFisher Scientific). Cells were routinely verified to be mycoplasma-free using commercial kit (Lonza). M-CSF, IL-4, and IL-13 recombinant proteins were from Peprotech.
EdU Incorporation: Cells were incubated with 10μM EdU (5-ethynyl-2′-deoxyuridine) without changing conditioned growth medium for the required duration and analyzed by flow cytometry according to the manufacturer's instructions (A10202; Thermo Fisher Scientific). For EdU imaging, cells were plated on coverslips, incubated with EdU (10 μM), fixed with 4% PFA, and immunostained with desired antibodies prior to EdU staining protocol (C10337).
Immune Cell Isolation for Flow Cytometry from Mammary Gland or Mammary Tumors: MMTV-PyMT tumors were collected from euthanized female mice, washed in cold PBS and digested by mechanical dissociation, using gentleMACSTM Dissociator (Miltenyi Biotec) and mouse tumor dissociation kit (Miltenyi Biotec) according the manufacturer's instructions. To remove cell clumps and undigested tissues, cell suspensions were passed through 70 μm and then 40 μm filters, and even cell numbers were analyzed by flow cytometry. To isolate cells from murine mammary glands, the 4th and 5th glands were collected and digested with 4 mg/ml collagenase type 4 (porcine origin, Sigma), 4 μg/mlDNase I (Sigma) at 37° C. with periodic vortexing. Cells were further mashed through 100 μm filters, and then passed through 40 μm. Cells were collected, counted, and analyzed by flow cytometry.
Whole Mount Imaging: For whole mount immunofluorescence imaging, approximately 3 mm3 pieces of inguinal fat-pad from 12 weeks old PyMT-MMTV females were incubated in 4% paraformaldehyde (PFA) (Electron Microscopy) diluted in PBS for 30 min at room temperature with agitation, permeabilized with PBS Triton 0.3% (Sigma T8787) 4% BSA for 1 hour at room temperature. Samples were stained with directly conjugated antibodies in PBS Triton for 1 hour. Samples were rinsed with PBS 3X and mounted on cavity slides (Sigma) with Fluoromount G (eBioscience). The antibodies used are the following: Anti-mouse F4/80 eFluor570 (eBioscience, clone BM8 (1/200), anti-mouse EpCAM Alexa Fluor 488 (Biolegend clone G8.8, 1/100), Anti-mouse CD140a Alexa Fluor 647 (eBioscience: APAS; PDGFR-a, 1/100). Z-stacks of 30 μm to 60 μm with 0.8 μm consecutive intervals and tile scans were acquired using LSM880 Zeiss microscope with 40×/1.3 objective(oil).
3D Primary Mammary Gland Culture and Time-Lapse Imaging: After sacrifice, inguinal fat pads were dissected from 12 week old PyMT-MMTV Rosa26mTmG females.
Tissues were minced into small pieces and digested in PBS containing Collagenase II (sigma, ref: C6885-1g), prepared at 0.8 mg/mL in PBS+0.5% BSA+CaCl2 5 mmol/L for 20 min at 37° C. Once processed, all samples were filtered through 100 mm strainers and approximately 2×105 total cells were resuspended in 1001iL drop of Growth Factor Reduced Matrigel on ice, incubated 30 min at 37° C. and cultivated in DMEM F12 10%FBS with Alexa Fluor 647 anti-mouse F4/80 (Biolegend 123122, 1/200) in Ibidi 24 wells microplate. Imaging was performed on day 3 of culture using LSM-880 in imaging chambers maintained at 37° C. 5% CO2, 20% O2 and 90% relative humidity. Seven consecutives stacks of 3 mm intervals were captured every 5 min per position using 20× objective for >12 hours. At endpoint, cell cultures were fixed for 10 min in 4% PFA after imaging, stained as indicated, and imaged as for whole mount imaging.
Flow Cytometry and Fluorescence-Activated Cell Sorting (FACS): Flow cytometry data were collected using BD LSRFortessa, BD FACSCanto II, and BD FACSAria III was used for FACS-sort. FlowJo X was used for data analysis and generation of flow plots for figures. For analyzing live cells from tissues by flow cytometry, mice were anaesthetized with ketamine/xylazine cocktail and perfused with 30 mL cold PBS using cardiac puncture. Cells from dissociated tissues were filtered through 70 μm then 40 μm filters to generate a single-cell suspension. Cells (1-2×106) were then incubated with 2× Fc Block solution (1:50, CD16/32 BD Biosciences) in FACS-buffer (2% FBS-PBS, 2 mM EDTA for 20 min in 8° C.). Conjugated antibodies were added to cells and incubated for 30 min in ° C. in the presence of the FC-blocking solution. Stained cells were washed twice and resuspended with a FACS-buffer containing DAPI (1 m/mL) to exclude dead/compromised cells. Mammary gland eosinophils were defined as CD45+CD11b+F4/80low SiglecF+ and excluded together with DAPI+ dead cells by staining with BV421-SiglecF.
CFSE T-Cell Proliferation Assay: T-cell proliferation was measured using CFSE assay (ThermoFisher Scientific). CD3+ T-cells were negatively isolated (EasySep Mouse T Cell Isolation Kit) from spleen of WT mouse, labeled with 5μM CSFE and stimulated with CD3e (1:100) and CD28 (1:500) activating antibodies (ThermoFisher Scientific) in serum-free RPMI for 20 minutes, 37 C. Activated CFSE-labeled T cells were seeded either alone or with: (i) M-CSF-treated BMDMs, (ii) oTME CM-educate BMDMs, or (iii) co-cultured with oTME/BMDMs in DMEM growth media contained 10% FBS (Gibco), 1% P/S. Five days later, CD8+ and CD4+ cell division were analyzed by flow cytometry by quantifying the CFSE dye dilutions.
Immunohistochemistry: Mice were anesthetized with xylazine/ketamine and transcardially perfused with 30 mL cold PBS, followed by 10 mL cold 4% PFA. Tissues were collected and fixed in 4% PFA overnight, washed, transferred to 70% Ethanol, and embedded in paraffin blocks. Paraffin sections (10-12 μm) were mounted on plus slides, de-waxed in xylene and hydrated into graded alcohol solutions. Endogenous peroxidase activity was quenched by immersing the slides in 1% hydrogen peroxide in PBS for 15 minutes, room temp (RT). Antigen retrieval pretreatment was performed in a steamer using the appropriate buffer for 30 minutes. Sections were incubated overnight with primary antibody in 4° C. For DAB staining, sections were washed with PBS and incubated with the appropriate secondary antibody followed by avidin-biotin complexes (Vector Laboratories, Burlingame, CA, Cat. No. PK-6100). Antibody reaction was visualized with 3-3′ Diaminobenzidine (Sigma, Cat. No. D8001) followed by counterstaining with hematoxylin. Tissue sections were dehydrated in graded alcohols, cleared in xylene and sealed with coverslips. For immunofluorescence staining, slides were stained fluorescently-labelled secondary antibodies (Invitrogen) for 1 hr at room temperature, counterstained with DAPI (5 m/mL) for 5 min, washed and sealed with VECTASHIELD® Antifade Mounting media.
Immunofluorescence and Image Processing: Cells were grown on coverslips for 48 hours. After treatment, cells were washed, permeabilized using 0.02% Triton X-100 and fixed for 20 min with 4% PFA. Cells were blocked with 5% BSA and incubation with primary antibodies was overnight in 4° C. Probing with AlexaFluor-488, AlexaFluor-555, or AlexaFluor-647 fluorescent secondary antibodies (Invitrogen) was carried out for 1 hr at room temperature. Slides were sealed (VECTASHIELD® Antifade; H-1000) and images were taken using an inverted fluorescence Nikon microscope, or LSM880 Zeiss confocal microscope. Images were processed using Photoshop (version 21.1.2) and analyzed by Fiji/ImageJ (version 2.1.0) software.
Immunoblotting Analysis: Cell lysates were collected, cleared and processed as previously described (Ben-Chetrit et al., “Synaptojanin 2 is a Druggable Mediator of Metastasis and the Gene is Overexpressed and Amplified in Breast Cancer,” Sci. Signal. 7:8 (2015), which is hereby incorporated by reference in its entirety). Samples were loaded on Mini-PROTEAN®Precast 4-15% gradient gels (BioRad; 456-1083). Antibodies against ARG1 (#79404), Notch Activated Targets Antibody Sampler Kit (#68309), phospho-SMAD2/3 (#8685), CSF-1R (#3152), phospho-AKT Ser473 (#4060), AKT (#9272), phospho-ERK (#9101) were purchased from Cell Signaling.
Cytokine Arrays: Mouse XL Cytokine Arrays were purchased from R&D Systems and used according to the manufacturer's instructions. Briefly, similar volumes of conditioned media were collected, cell debris were removed by centrifugation (2000 g, 10 min 4C), and cleared supernatants were loaded on spotted membranes for 16 hrs, 4° C. with tilting. Secreted cytokines, chemokines and growth factors (111 in total) were probed in duplicated along with positive and negative controls.
Parameters such as sample size, precision (mean±SD or SEM), and statistical test significance are reported in the Examples, Figures, and Figure Legends. Statistical significance was calculated by one-way ANOVA when comparing two groups or two-way ANOVA when comparing three or more groups. A p<0.05 was considered statistically significant. Prism v8 software was used for statistical data analysis.
Bulk RNA-Seq Analysis: BMDMs were co-cultured with oTME cells (educated) or left alone with M-CSF for 10 days (n=3 replicates, each). Then, BMDMs were FACS-sorted and RNA was extracted using Trizol-LS. RNA-sequencing libraries were generated with the SMART-Seq preparation kit (CloneTech) with the Nextera XT kit (Illumina), and pair-end 150-bp sequencing was performed by GeneWiz (South Plainfield, New Jersey, USA) on an Illumina HiSeq 2500. FASTQ files were mapped to the mouse genome (mm 10) using STAR (version 2.5.3a) with default parameters (Dobin et al., “STAR: Ultrafast Universal RNA-Seq Aligner,” Bioinformatics 29:15-21 (2013), which is hereby incorporated by reference in its entirety). Transcript count was quantified using STAR -quantMode option with Gencode mouse release M21 annotation GTF files (https://www.gencodegenes.org/mouse/release_M21.html). The resulting count matrix was analyzed and normalized using DESeq2 v1.18.1 (Love et al., “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2,” Genome Biol. 15:550 (2014), which is hereby incorporated by reference in its entirety). Differential gene expression was assessed with default parameters. Differentially expressed genes were defined as any gene with an absolute log-fold change (logFC) larger than 1 at a false discovery rate (FDR) of 0.01. To generate the volcano plot in
sequenced guide abundances after gDNA extraction and PCR of the guide cassette on an Illumina MiSeq 150 cycle v3 with ˜25 million reads. The inventors aimed for 50 reads per sgRNA within each time-point. Resulting FASTQs were trimmed using cutadapt (version 0.12.0) to result in 20 bp sgRNA sequences in two steps. First, the 5′ flanking regions of the 20 bp sgRNA were trimmed off using the recognition sequence GACGAAACACCG (SEQ ID NO: 12) directly 5′ of the sgRNA with a maximum allowable error rate of 0.2 or 20% of base pairs. The trimmed sequences were trimmed again for the 3′ sequence flanking the 20 bp sgRNA using GTTTAAGAGCTA (SEQ ID NO:13) as a recognition sequence, a maximum allowable error rate of 0.2, and a minimum read length of 5 bp. This yielded 20 bp sgRNA sequences. To derive a read count frequency for each of the sgRNAs in the CRISPR library, the reads were aligned to the library using bowtie. A bowtie index for the CRISPR library was constructed using the bowtie-build command on a FASTA file where each sgRNA sequence was a FASTA entry. Bowtie was used to align the trimmed reads to the reference, with arguments −v 1 to allow for only 1 bp mismatch and −m 1 to only keep unique alignments. Unique sgRNAs were counted from the alignment to produce a sgRNA read count table, which is used for all downstream analysis. To ensure an evenly distributed sgRNA representation at the initial, unselected time-point, the inventors calculated a skew, where skew is defined by dividing the 90% read count quantile by the 10% read count quantile, and aimed for a skew no greater than 2. The samples which were taken from the initial library representation were close to 2, while the Arg1-EYFP+ and Arg1-EYFPNeg sorted groups were close to 20. This was expected as sgRNAs that were abundant in Arg1-EYFP+ are expected to have lower abundance or absence in Arg1-EYFPNeg. Two biological replicates (RepA and RepB) of the M2-education CRISPR screen were generated from two batches of mice and each biological replicate was further split into two technical replicates. The inventors sequenced and processed them separately and combined the technical replicates together to generate one raw count matrix containing the two biological replicates.
Analysis of M2-Education CRISPR Screen: The MAGeCK v0.5.8 software (Li et al., “MAGeCK Enables Robust Identification of Essential Genes From Genome-Scale CRISPR/Cas9 Knockout Screens,” Genome Biol. 15:554 (2014), which is hereby incorporated by reference in its entirety) test module was used to identify negatively and positively enriched screen hits. Briefly, the sgRNA counts were normalized using the size factor estimated from the 1000 control (non-targeting) guides. The p-value for each gene is tested by 50,000 rounds of randomized permutation, and adjusted with Benjamini—Hochberg procedure. Log2 fold change (LFC) for each gene is calculated using the second-best sgRNA. MAGeCK produced both guide-level and gene-level enrichment scores using the alpha-robust rank aggregation (RRA). The RRA was specified to consider the top 0.1 percentile of the guides as successful targeting sgRNA and genes that have at least one successful targeting sgRNAs were selected.
Chromium 10× Single-Cell RNA-Seq: Single-cell RNA-sequencing data generated with 10× Genomic Chromium Single Cell 3′ Kit v2 (10× Genomics) and were processed using Cell Ranger (v1.3.1) with default parameters. Samples were sequenced at an average of 50,000 reads per cell. Raw sequencing data were demultiplexed and post-processed following the custom pipelines provided by 10× Genomics. Briefly, raw base calls were demultiplexed into fastq files using the cellranger mkfastq command, followed by alignment to the selected reference mm10 genome. Barcode and UMI counting were performed using the cellranger count command with default parameters.
R markdown HTML documents for the scRNA-seq analysis are provided as the Supplementary Note and corresponding scripts can be downloaded from the Github repository. All six samples were analyzed together as described in the following sections. The oTME cells that were cultured alone (2 and 10-days) were excluded for visualization purposes in this paper.
Preprocessing: The count matrices of all six samples were pooled and normalized using log-transformed transcript per million (logTPM). Specifically, the inventors denoted the UMIcount of jth gene in ith cell as Counti,j, and the logTPM was calculated as
The Seurat v2.3.4 R package (Butler et al., “Integrating Single-Cell Transcriptomic Data Across Different Conditions, Technologies, and Species,” Nat. Biotechnol. 36, 411-420 (2018), which is hereby incorporated by reference in its entirety) was used for downstream analysis. Low-quality cells with less than 500 genes detected or mitochondrial gene percentage >10% were filtered out. Genes that were expressed in less than 50 cells were also removed. After filtering, the 19,280 cells in total were retained with an average of 13,013±56.49 UMIs per cell (mean±s.e.m.) and an average of 2,947±7.49 (mean±s.e.m.) genes detected across all cells. A set of 1,000 highly variable genes were identified using the FindVariableGenes function with default parameters, which finds variable genes while controlling for the strong relationship between variability and average expression.
Dimensionality reduction: The normalized count data was scaled and centered using ScaleData function with default parameters to calculate z-score for each gene. Principal Component Analysis (PCA) was performed using the RunPCA function with the 1,000 highly variable genes identified from the preprocessing step. The first 15 PCs were selected for downstream clustering and dimensionality reduction analysis based on the observation of the PC “elbow” using the PCElbowPlot function. tSNE (Maaten and Hinton, “Visualizing Data Using t-SNE,” J. Mach. Learn. Res. 9:2579-2605 (2008), which is hereby incorporated by reference in its entirety) was used to visualize the scRNA-seq data. The top 15 PCs were selected and used the RunTSNE function to perform tSNE dimensionality reduction to embed the data into two dimensions. Clustering: The modularity based shared nearest neighbor (SNN) clustering
algorithm was implemented in Seurat's FindClusters function with resolution=1 and other default parameters using the top 15 PCs. Initially the 19 clusters were retrieved. The clustering result were validated by constructing a phylogenetic tree of the 19 clusters using their average expressions. The BuildClusterTree function with the top 15 PCs was used as input to calculate the distance between clusters. The clustering quality was assessed using the AssessNodes function to calculate the out of bag error for a random forest classifier trained on the bottom 25% of the nodes. Nodes with an out of bag error bigger than 0.03 were merged together. The above steps were repeated until all the nodes have an out of bag error below 0.03. One of the clusters expresses heterogeneous lineage markers of epithelial (Epcam), CAF (Fn1, Acta2), and basal-like (Cd24a) cells. It also exhibits higher UMI counts per cell compared with the non-cycling cells. These features are common to doublet/multiplet cells that are considered as artifacts of droplet-based scRNA-seq technologies (DePasquale et al., “DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data,” Cell Rep. 29(8):1718-1727 (2019) and McGinnis et al., “DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors,” (2018), which are hereby incorporated by reference in their entirety). Therefore, this cluster was annotated as doublets and removed it from downstream analysis. Macrophage clusters were taken from the data for further analysis. Cells from one of the clusters expressing Mki67 are the cycling macrophages. They are heterogeneous in that they contain both cycling M-CSF-treated and cycling educated macrophages, but this biological difference was masked by the strong cell cycle effect. Therefore, this cycling population was dissected by following the same procedures as described in the Preprocessing, Dimensionality reduction, and Clustering sections with the top 10 PCs and an out of bag error of 0.07. In addition, the inventors further identified and removed a group of doublet cells of macrophages and CAFs. The macrophages were clustered mainly by education status (M-CSF-treated vs educated), time points (early vs late), and cell cycle phases (non-cycling vs cycling). Thus, they were annotated accordingly as M-CSF-treated CD11c+, M-CSF-treated, M-CSF-treated-cycling, early, early-cycling, late, late-cycling macrophages. In total, 2 clusters of Epcam+tumor cells, 3 clusters of Cd24a+ Epcam− Basal-like cells, 3 clusters of Acta2+ CAFs, and 7 clusters of Cd68+ M-CSF-treated and educated macrophages were identified.
Differential Expression: Differential expression analysis was performed using the Wilcoxon Rank Sum test implemented in Seurat's FindAllMarkers function. Two sets of differential expressed genes were defined. First, 4 cell lineages were defined based on known markers and grouped the clusters into Epcam+ tumor epithelial cells, Acta2+ CAF cells, Cd24+ Epcam− basal-like cells, and Cd68+ macrophages lineages. Differential expression analysis was conducted among these 4 lineages with the parameter min.pct=0.5 which only tests genes that were detected in a minimum percentage of 50% of the cells in each lineage in order to get a consensus list of differential expressed markers. Genes with a log fold change bigger than 1 and a p-value of less than 0.01 are considered as lineage markers. Second, a set of cell-type markers were defined by comparing the clusters within each lineage (i.e., cycling tumor epithelial cells vs all tumor epithelial cells). In addition, non-cycling and cycling macrophage clusters were separately analyzed for differential expression. The parameter min.pct=0.4 was used in order to get subtler differences between similar cell types. Genes with a log fold change greater than 0.75 and a p-value of less than 0.01 are considered as cell-type markers. All the p-values are adjusted in Seurat using Bonferroni correction. To generate the volcano plot in
Education Trajectory: A diffusion map (Coifman and Lafon, “Diffusion Maps,”Appl. Comput. Harmon Anal. 21:5-30 (2006), which is hereby incorporated by reference in its entirety), a non-linear dimensionality reduction technique, was used to capture the continuous transitions (aka pseudotime) during macrophage education in the scRNA-seq data. Specifically, the differentially expressed markers of M-CSF-treated (Itgax; CD1 1c+) and educated (late) non-cycling macrophage clusters were first identified as described in the Differential expression section but using the MAST method (Finak G., et al., “MAST: A Flexible Statistical Framework for Assessing Transcriptional Changes and Characterizing Heterogeneity in Single-Cell RNA Sequencing Data,” Genome Biol. 16:278 (2015), which is hereby incorporated by reference in its entirety). The inventors then used the scaled and centered macrophage scRNA-seq data of these 74 marker genes as input for the DiffusionMap function in the destiny v2.6.2 R package (Angerer et al., “Destiny: Diffusion Maps for Large-Scale Single-Cell Data in R,” Bioinformatics 32:1241-1243 (2016), which is hereby incorporated by reference in its entirety) with default parameters. The first two eigenvectors (DM1 and DM2) captured the continuous transition between M-CSF-treated and educated macrophages. Therefore, these were used to represent the macrophage education trajectory. Then a principal curve (Hastie and Stuezle, “Principal Curves,” J. Am. Stat. Assoc. 84:502-516 (1989), which is hereby incorporated by reference in its entirety) was fitted using the princurve v2.1.4 R package to the education trajectory (DM1 and DM2). The inventors defined the education pseudotime as projection of each cell onto this principal curve (the arc-length from the beginning of the curve), and normalized the pseudotime into 0-1 range as described previously (Wang et al., “A Single-Cell Transcriptional Roadmap for Cardiopharyngeal Fate Diversification,” Nat. Cell Biol. 21:674-686 (2019), which is hereby incorporated by reference in its entirety). To identify the gene expression dynamics, the gene expression profiles of the 1,000 highly variable were genes smoothed as described above on the education pseudotime by fitting a local polynomial regression, using loess R function with the smoothing parameter span=0.7. The mutual information between the smoothed gene expression profiles and the pseudotime was calculated using the discretize and mutinformation functions in the infotheo v1.2.0 R package (Meyer, “Information-Theoretic Variable Selection and Network Inference From Microarray Data,” Universite Libre de Bruxelles (2008), which is hereby incorporated by reference in its entirety) with default parameters. The results were filtered by removing the genes that have lower mutual information than the 25% quantile of the total mutual information calculated, and retained 750 genes. K-means clustering was applied using the kmeans R function to cluster these gene expression dynamics into 3 clusters. The 3 clusters of genes correspond to M-CSF-treated, transient (early day 2) and educated (late day 10) macrophage signatures according to their expression dynamics on the education pseudotime. To visualized the gene expression dynamics during macrophage education as shown in
Gene Signature Scoring: For a given gene set, the gene signature score in single cells was calculated as described previously (Tirosh et al., “Dissecting the Multicellular Ecosystem of Metastatic Melanoma by Single-Cell RNA-Seq,” Science 352:189-196 (2016), which is hereby incorporated by reference in its entirety). The AddModuleScore function in Seurat was used to calculate the gene signature score. Briefly, signature genes are splitted into 10 bins based on their average expression levels. For each gene, 100 control genes were selected at random within the same expression bin to serve as control sets. The gene signature score was calculated as the differences between the aggregated expression of signature genes and the controls.
Human Breast Cancer scRNA-Seq: Human breast cancer scRNA-seq data was obtained from GSE114725. The Final Annotation based on bulk combined with differential expressed genes was used as described by Azizi et al., “Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment,” Cell 174(36):1293-1308 (2018), which is hereby incorporated by reference in its entirety, to select all the monocytic populations including macrophages, monocytes, monocyte precursors, pDCs and mDCs. The inventors normalized the count data to logTPM as described in the Preprocessing section. The inventors defined the ex-vivo education signature as differentially expressed genes between M-CSF-treated (CD11c+) and educated (late) non-cycling macrophages using Wilcoxon Rank Sum test implemented in Seurat's FindAllMarkers function with parameter min.pct=0.4 . Genes with a log fold change bigger than 0.75 and a Bonferroni corrected p-value of less than 0.01 are selected. Similarly, the human M2-signature was defined by performing differential expression analysis between the three TAM clusters (23, 25, 28) with other monocytic populations using the same procedure. To qualitatively visualize the data, the Principal Component Analysis using only the ex-vivo murine signature was performed as shown in
The phenotypic transition of macrophages towards a pro-tumorigenic and immune-tolerant phenotype in breast cancer relies on complex interactions with both tumor cells and their supporting stroma (DeNardo et al., “Macrophages as Regulators of Tumour Immunity and Immunotherapy,” Nature Reviews Immunology 19:369-382 (2019), which is hereby incorporated by reference in its entirety). To comprehensively investigate the macrophage-tumor-stroma interactions that fuel this transition, syngeneic breast cancer cells derived from a C57BL/6 MMTV-PyMT tumor model were utilized (
Tissue fibroblasts are heterogeneous cells that arise from diverse origins. During wound-healing responses, including breast cancer, bone marrow-derived mesenchymal/progenitor cells (MSCs) infiltrate the primary tumors and differentiate into a distinct subpopulation of cancer-associated fibroblasts.
The oTME cells were isolated from a primary tumor of the MMTV-PyMT model that carries the PyMT viral oncogene under the mouse mammary tumor virus (MMTV) promoter. Although the MMTV promoter is active primarily in mammary gland epithelium, the data herein suggest a substantial “leakiness” leading to activation of MMTV promoter in other tissues. MMTV-Cre mice were crossed with LSL-tdTomato reporter mice and the expression of tdTomato in various tissues was analyzed by flow cytometry (
PyMT expression in oTME subpopulations was compared by qPCR and the highest levels were detected in tumor epithelial cells (EpCAM+), but detectable levels were also found in stromal-like cells (PDGFRA+CD24neg) (
Importantly, unlike other highly tumorigenic fibroblast lines like the NIH-3T3, the PDGFRA+CD24neg stromal-like cells are not transformed (
Next, the inventors sought to evaluate whether macrophages cultured in the oTME would recapitulate the spatial localization and tumor-supportive phenotypes observed in breast cancer including, promotion of angiogenesis, matrix remodeling (Azizi et al., “Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment,” Cell 174(36):1293-1308 (2018); DeNardo et al., “Macrophages as Regulators of Tumour Immunity and Immunotherapy,” Nature Reviews Immunology 19:369-382 (2019); Wagner et al., “A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer,” Cell 177(18)1330-1345 (2019), which are hereby incorporated by reference in their entirety). To enable visualization of spatial localization, purified Ly6Chigh bone marrow (BM) monocytes from Rosa26mTmG mice that express membrane-tagged tdTomato (mT) were added (Muzumdar et al., “A Global Double-Fluorescent Cre Reporter Mouse,” Genesis 45:593-605 (2007), which is hereby incorporated by reference in its entirety) (
To further interrogate the changes in macrophage phenotypes driven by the oTME system, RNA-seq analysis on bone marrow-derived macrophages (BMDMs) cultured for ten days in oTME was performed and compared to M-CSF-treated macrophages left unperturbed as control cells. Differential expression analysis (
To identify genes essential for TME education of macrophages (often termed M2-like), the inventors leveraged the scalability of the oTME model and performed a genome-wide CRISPR/Cas9 screen in BMDMs. The induction of Arg1 is considered as one of the bona fide hallmarks of M2-like macrophages and associated with anti-inflammatory and tissue repair phenotypes (Biswas ad Mantovani, “Macrophage Plasticity and Interaction with Lymphocyte Subsets: Cancer as a Paradigm,” Nat. Immunol. 11:889-896 (2010); Bosurgi et al., “Macrophage Function in Tissue Repair and Remodeling Requires IL-4 Or IL-13 with Apoptotic Cells,” Science 356:1072-1076 (2017); Colegio et al., “Functional Polarization of Tumour-associated Macrophages by Tumour-derived Lactic Acid,” Nature 513:559-563 (2014), which are hereby incorporated by reference in their entirety). Therefore, Arg1 induction was used as a surrogate for TME-education and screen readout, by utilizing BMDMs from reporter mice that express a yellow fluorescent protein (EYFP) under the control of the Arg1 promoter (Arlauckas et al., “Arg1 Expression Defines Immunosuppressive Subsets of Tumor-Associated Macrophages,” Theranostics 8:5842-5854 (2018) and Reese et al., “Chitin Induces Accumulation in Tissue of Innate Immune Cells Associated with Allergy,” Nature 447:92-96 (2007), which are hereby incorporated by reference in their entirety). First, the induction of EYFP in Arg1-EYFP BMDMs following ten days of culture with oTME cells and their acquired ability to suppress CD8 T cell growth was confirmed (
Notably, CDK4/6 dual inhibitors have been shown to be effective in patients with metastatic breast cancer (Goel et al., “CDK4/6 Inhibition Triggers Anti-Tumour Immunity,” Nature 548:471-475 (2017); Im et al., “Overall Survival with Ribociclib plus Endocrine Therapy in Breast Cancer,” N. Engl. J. Med. 381:307-316 (2019), which are hereby incorporated by reference in their entirety) , acting not only on cancer cells (through cell-cycle arrest) but also unleashing an anti-tumor T-cell-mediated response in mouse models (Deng et al., “CDK4/6 Inhibition Augments Antitumor Immunity by Enhancing T-cell Activation,” Cancer Discov. 8:216-233 (2018), which is hereby incorporated by reference in its entirety). The data herein suggests that CDK4/6 inhibitors may in part trigger anti-tumor immune responses through the disruption of the immunosuppressive phenotype of macrophages in tumors. To further validate CDK4/6 signaling in oTME macrophages, the inventors measured gene signatures that were recently reported with CDK4/6 inhibition (Jerby-Arnon et al., “A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade,” Cell 175(24):984-997 (2018), which is hereby incorporated by reference in its entirety) in the bulk RNA-seq data from oTME vs. M-CSF-treated macrophages. The CDK4/6-associated gene module was predominantly expressed in oTME macrophages, while the CDK4/6 inhibition gene module (generated through treatment with abemaciclib) was enriched in M-CSF-treated macrophages (
Another screen candidate and druggable mediator of macrophage education was Ptk2b (Pyk2), a tyrosine kinase previously shown to regulate macrophage inflammasome activation and phagocytosis (Chung et al., “Pyk2 Activates the NLRP3 Inflammasome by Directly Phosphorylating ASC and Contributes to Inflammasome-Dependent Peritonitis,” Scientific Reports 6 (2016); Paone et al., “The Tyrosine Kinase Pyk2 Contributes to Complement-Mediated Phagocytosis in Murine Macrophages,” J. Innate Immun. 8:437-451 (2016); and Rhee et al., “Macrophage Fusion is Controlled by the Cytoplasmic Protein Tyrosine Phosphatase PTP-PEST/PTPN12,” Mol. Cell. Biol. 33:2458-2469 (2013), which are hereby incorporated by reference in their entirety) . Similar to CDK4/6 inhibition, treatment with PTK2B inhibitors (PF-431396) results in robust suppression of Arg1 expression in oTME and IL-4/IL-13-treated macrophages (
Additional mediators of macrophage education identified in this screen are provided in Table 1 below. These are all druggable mediators. Accordingly, known inhibitors that can be utilized in the methods described herein to inhibit macrophage immunosuppressive phenotypes are also provided.
Collectively, these data show that the organotypic TME model described herein can be leveraged for high-throughput target discovery to reveal novel and druggable mediators of the immunosuppressive phenotype in TME macrophages.
The oTME offers a unique opportunity to study the dynamics of macrophage education. The inventors therefore performed time-course single-cell RNA sequencing (scRNA-seq), comparing macrophages educated in the oTME at two (“Early”) or ten days (“Late”) time-points, vs. control macrophages maintained with M-CSF for the same intervals (“M-CSF-treated”) (
To evaluate the phenotypic fidelity of macrophages in this model to human breast cancer macrophages, the inventors first generated an education signature by comparing differential expression between control and late educated macrophages, and projected this signature (
To study the dynamics of transcriptional alterations in macrophages during TME education, the inventors reconstructed an “education trajectory” by overlaying controls, early, and late single-cell transcriptomes onto a pseudo-temporal projection (
To investigate the cell-cycle activation mechanisms in oTME macrophages, the inventors further annotated single-cell macrophage transcriptomes (from
A non-cell cycle gene, Ly6A (Stem Cell Antigen-1; Sca-1), was associated with the cycling subset of oTME macrophages (
The induction of Ly6A in macrophages from mammary tumors and oTME
cultures, particularly when cultured with CD24neg cells was confirmed (
To determine if there are spatio-functional differences between the F4/80high and F4/80int populations, the inventors immunostained oTME mTmG macrophages for F4/80 along with Fibronectin (FN1) to highlight stromal areas (
Given the close association between F4/80high Ly6Ahigh and macrophage proliferation in mammary tumors, these findings suggest that SAMs are predominantly responsible for replenishing macrophages in the TME. To identify the cellular components in the oTME that drive macrophage proliferation, the inventors plated quiescent macrophages (Ki67-) together with FACS-sorted tumor epithelial cells (EpCAM+), stromal-like cells (CD24negPDGFRA+), oTME cells, or M-CSF alone as control. After 10 days, macrophage proliferation was significantly enhanced by CD24negPDGFRA+ cells compared with tumor epithelial cells or M-CSF alone (
Distinct cytokine production was observed between purified stromal-like and EpCAM+ epithelial cultures, including CCL-2 and POSTN specific production from stromal-like cells, while G-CSF, IL-23, and CCL-5 were derived specifically from tumor epithelial cells (
Next, the inventors sought to determine whether macrophage proliferation is licensed by cell contact. The inventors compared EdU incorporation in macrophages that were either allowed physical contact with tumor epithelial, CD24negPDGFRA+ cells, or treated individually with their conditioned media. EdU incorporation rates were similar in response to either CM, but when physical contact was allowed, SAMs displayed significantly higher EdU incorporation (
Macrophage long-term accumulation in tumors was previously shown to be unaffected by genetic ablation of monocyte recruitment (Ccr2-KO MMTV-PyMT) (Franklin et al., “The Cellular and Molecular Origin of Tumor-associated Macrophages,” Science 344:921-925 (2014a), which is hereby incorporated by reference in its entirety). Therefore, it was hypothesized that macrophage proliferation would start at the early stages of mammary gland transformation and persist as tumors grow. To address this hypothesis, Ki67 macrophages in mammary tissues from normal, early (hyperplasia), and late (adenocarcinoma) stages of tumor progression were scored in the MMTV-PyMT model (
Characterization of the SAM subpopulation in mammary tumors was carried out. It was hypothesized that orthotopic transplantation of tumor epithelial cells enriched with PDGFRA+CD24neg stromal-like cells (40:60% of EpCAM+:CD24neg) would skew the relative proportions of SAMs versus TEMs in these tumors. Accelerated tumor growth was observed in stroma-enriched tumors as previously reported (Orimo and Weinberg, “Stromal Fibroblasts in Cancer: A Novel Tumor-Promoting Cell Type,” Cell Cycle 5:1597-1601 (2006) and Orimo et al., “Stromal Fibroblasts Present in Invasive Human Breast Carcinomas Promote Tumor Growth and Angiogenesis Through Elevated SDF-1/CXCL12 Secretion,” Cell 121:335-348 (2005), which are hereby incorporated by reference in their entirety), in comparison with tumors that originated from tumor epithelial cells-only (
Immunohistochemistry (IHC) staining for vimentin and IBA1 confirmed a substantial enrichment of stromal cells in stroma-enriched tumors and increased accumulation of IBA1+ macrophages (
To further confirm the localization of F4/80high and F4/80int subpopulations in mammary tumors, tumor sections were immunostained with F4/80, IBA1 (pan macrophages marker), and Ki67 to highlight the tumor regions. In agreement with the flow data, tumor nests macrophages (TEMs) displayed a lower signal ratio of F4/80:IBA1, while stromal macrophages were associated with a higher ratio (
The spatial localization of macrophages in solid tumors confers a significant prognostic value in cancer patients. Clinical data from breast cancer patients showed that infiltration of CD163+ macrophages in tumor stroma (rather than in tumor epithelial nests) correlated with aggressive histopathological characteristics and adverse outcomes (Gwak et al., “Prognostic Value of Tumor-Associated Macrophages According to Histologic Locations and Hormone Receptor Status in Breast Cancer,” PLoS One 10:e0125728 (2015); Medrek et al., “The Presence of Tumor Associated Macrophages in Tumor Stroma as a Prognostic Marker for Breast Cancer Patients,” BMC Cancer 12:306 (2012); Salmi et al., “The Number and Localization of CD68 and CD163 Macrophages in Different Stages of Cutaneous Melanoma,” Melanoma Research 29:237-247 (2019); and Yang et al., “Stromal Infiltration of Tumor-Associated Macrophages Conferring Poor Prognosis of Patients with Basal-Like Breast Carcinoma,” J. Cancer 9:2308-2316 (2018), which are hereby incorporated by reference in their entirety), which further suggests that cellular interactions within the microenvironment may shape their tumor-supportive functions.
To address this aspect, monocytes were differentiated on tumor epithelial or stromal-like cells and interrogated for functional and immunomodulatory markers (
The immunomodulatory and scavenging phenotypes that associate with immune tolerance were further examined (Baghdadi et al., “TIM-4 Glycoprotein-Mediated Degradation of Dying Tumor Cells by Autophagy Leads to Reduced Antigen Presentation and Increased Immune Tolerance,” Immunity 39:1070-1081 (2013); Biswas ad Mantovani, “Macrophage Plasticity and Interaction with Lymphocyte Subsets: Cancer as a Paradigm,” Nat. Immunol. 11:889-896 (2010); Kratochvill et al., “TNF Counterbalances the Emergence of M2 Tumor Macrophages,” Cell Rep. 12:1902-1914 (2015), which are hereby incorporated by reference in their entirety). Immunomodulatory (CD206, LGALS3, PD-L1) and inflammatory markers (CD11a, IL-1b), as well as scavenging mediators (TIM-4) were probed following exposure to GFP-labeled NK cells. Consistent with immunomodulatory impact as a function of spatial localization, the expression of immunosuppressive markers such as CD206, LGALS3, and TIM4 were strongly associated with SAMs, whereas inflammatory markers such as CD11a and IL-1β were associated with TEMs (
To confirm and visualize the immunomodulatory phenotype of SAMs in vivo, the expression of PD-L1, CD206, and CD11a in macrophages of MMTV-PyMT tumors was analyzed. As shown by flow cytometry, the expression of CD206 and PD-L1 was distinctly compartmentalized (
The spatially-defined immunosuppressive phenotype of SAMs was conserved in human breast cancer. As in murine tumors, CD206 was expressed exclusively on CD163+ stromal macrophages of adipose tissue, normal, and tumor stroma regions, but was absent in CD163+ macrophages that associated with normal and tumor epithelia (
Given that macrophage proliferation was significantly enhanced by cell contact, the relevance of contact-mediated signaling, such as the Notch pathway, to macrophage proliferation was examined. Indeed, activation of Notch signaling in macrophages was previously implicated with inflammation, wound-healing responses, mammary stem cell maintenance, and breast cancer (Palaga et al., “Notch Signaling in Macrophages in the Context of Cancer Immunity,” Front. Immunol. 9:652 (2018); Boniakowski et al., “Macrophage-Mediated Inflammation in Normal and Diabetic Wound Healing,” The Journal of Immunology 199:17-24 (2017); Franklin et al., “The Cellular and Molecular Origin of Tumor-associated Macrophages,” Science 344:921-925 (2014), which are hereby incorporated by reference in their entirety). Gene set enrichment analysis of RNA-seq data from oTME macrophages showed a significant enrichment of the Notch pathway (
To determine the effect of Notch signaling on macrophage proliferation the inventors scored for EdU incorporation and Ki67 in oTME macrophages following treatment with two protease inhibitors that target Notch activation: (i) metalloproteinase Adaml 7 inhibitors (A17Pro) that inhibit the extracellular cleavage of Notch receptors and macrophage inflammation (Wong et al., “Harnessing the Natural Inhibitory Domain to Control TNFa Converting Enzyme (TACE) Activity In Vivo,” Sci. Rep. 6:35598 (2016), which are hereby incorporated by reference in their entirety), and (ii) γ-secretase protease inhibitors (GSI; Compound-E) that inhibit the subsequent intracellular cleavage of activated Notch receptors. Both treatments resulted in a significant reduction of EdU incorporation (84.8%±3.9% mean±SEM; A17Pro), and in Ki67 positivity (89.6±3.8%; Compound-E) (
Among the upregulated Notch-related genes, Notch4 was identified as one of the top induced genes in oTME macrophages (
In this study, the inventors developed an organotypic TME system to define mechanisms of two fundamental aspects of macrophage biology in mammary tumors: (i) to adopt pro-tumoral phenotypes and (ii) their ability to accumulate through self-renewal. These findings were validated in murine models and primary human breast cancer specimens, demonstrating that the oTME model recapitulates the tumor-stroma-macrophage interactions with a high degree of phenotypic fidelity. First, the inventors leveraged this model's scalability and conducted a phenotypic CRISPR/Cas9 screen in primary macrophages to discover gene targets that disrupt the formation of immunosuppressive macrophages. Using the expression of Arg1 as a surrogate for the M2-like phenotype, known mediators of macrophage education (e.g., Stat3, Marco) were identified in addition to novel targets including Cdk4 and Ptk2b that were essential for adopting the Argl+immunosuppressive phenotype.
The underlying mechanisms that regulate macrophage proliferation in mammary tumors was identified. Although macrophages leave the cell cycle upon differentiation (Aziz et al., “MafB/c-Maf Deficiency Enables Self-renewal of Differentiated Functional Macrophages,” Science 326:867-871 (2009) and Klappacher et al., “An Induced Ets Epressor Complex Regulates Growth Arrest During Terminal Macrophage Differentiation,” Cell 109:169-180 (2002), which are hereby incorporated by reference in their entirety), they are able to proliferate again in response to pathological challenges including inflammatory response, tissue repair, obesity, and infection (Robbins et al., “Local Proliferation Dominates Lesional Macrophage Accumulation in Atherosclerosis,” Nat. Med. 19:1166-1172 (2013); Bosurgi et al., “Macrophage Function in Tissue Repair and Remodeling Requires IL-4 Or IL-13 with Apoptotic Cells,” Science 356:1072-1076 (2017); Minutti et al., “Local Amplifiers of IL-4Rα-mediated Macrophage Activation Promote Repair in Lung and Liver,” Science 356:1076-1080 (2017); Amano et al., “Local Proliferation of Macrophages Contributes to Obesity-Associated Adipose Tissue Inflammation,” Cell Metab. 19:162-171 (2014), which are hereby incorporated by reference in their entirety). To uncover the mechanisms that enable reactivation of cell-cycle in TME macrophages, the inventors performed scRNA-seq time-course analysis and followed the transcriptional changes associated with proliferative and non-proliferative cells. This revealed that macrophages first undergo a transient pro-inflammatory activation, typical of type-I interferons/STING signaling prior to the acquisition of anti-inflammatory phenotype. During this pro-inflammatory phase, activated macrophages engaged the cell-cycle and upregulated Ly6A (Sca-1) that marked a subset of proliferating TME macrophages. Dual EdU/BrdU pulse-chase experiments further revealed an expansion pattern typical of self-renewal, where this cycling subset entered a continuous proliferative mode. Pharmacological inhibition of the early type-I interferons signaling attenuated the onset of macrophage proliferation, suggesting that the transient activation of IFN receptors (IFNAR) was essential for their return into the cell cycle. Interestingly, analogous mechanisms were described during the reactivation of cell cycle in dormant HSCs (Essers et al., “IFNalpha Activates Dormant Haematopoietic Stem Cells in Vivo,” Nature 458:904-908 (2009); Ito et al., “Hematopoietic Stem Cell and Progenitor Defects in Sca-1/Ly-6A-null Mice,” Blood 101:517-523 (2003); Walter et al., “Exit From Dormancy Provokes DNA-Damage-Induced Attrition in Haematopoietic Stem Cells,” Nature 520:549-552 (2015), which are hereby incorporated by reference in their entirety). In response to systemic treatment of IFNα and IFNAR signaling, quiescent HSCs upregulated Ly6A that mediated their return back into the cell cycle.
The expression of Ly6A in TME macrophages revealed informative clues about their spatial localization. The inventors found that Ly6A is a novel marker for macrophages in proximity to stromal fibroblasts (SAMs) and further delineates Ly6AhighF4/80highCD11bhigh as the major proliferative macrophage subset in mammary tumors. Regulated self-renewal of tissue-resident macrophages is critical for their maintenance in healthy tissues, including the mammary glands (Hashimoto et al., “Tissue-Resident Macrophages Self-Maintain Locally Throughout Adult Life With Minimal Contribution From Circulating Monocytes,” Immunity 38:792-804 (2013), which are hereby incorporated by reference in their entirety). Although a substantial portion of TME macrophages can originate from circulating monocytes (Movahedi et al., “Different Tumor Microenvironments Contain Functionally Distinct Subsets of Macrophages Derived From Ly6C(high) Monocytes,” Cancer Research 70:5728-5739 (2010), which is hereby incorporated by reference in its entirety), their abundance in mammary tumors was unaffected by genetic ablation of monocyte recruitment (Franklin et al., “The Cellular and Molecular Origin of Tumor-associated Macrophages,” Science 344:921-925 (2014), which is hereby incorporated by reference in its entirety) further suggesting that mammary tumors hijack their intrinsic ability to self-renew. In agreement with these observations, it was shown that macrophage proliferation began at early stages of mammary gland transformation (hyperplasia) but continues predominantly in proliferative regions of late-stage carcinomas. The inventors validated a similar correlation between macrophage proliferation and tumor cell proliferation in human breast tumor specimens (
Although macrophage proliferation is critically dependent on M-CSF/CSF1R signaling, M-CSF availability was insufficient to license proliferation burst in resting cells (Aziz et al., “MafB/c-Maf Deficiency Enables Self-renewal of Differentiated Functional Macrophages,” Science 326:867-871 (2009), which is hereby incorporated by reference in its entirety), but was significantly enhanced through contact with activated (POSTN+) fibroblasts. Consistent with contact-mediated signaling, it was found that Notch signaling and particularly Notch4 regulate macrophage proliferation in mammary tumors. The inventors demonstrated an effective and significant inhibition of tumor growth in mouse models in response to Notch4 neutralization, coupled with reduction in Ki67+ TME macrophages. Since solid tumors critically rely on abundant presence of macrophages through local renewal, effective disruption of this process resulted in an attenuated expansion of tumors but without depleting the existing macrophages. Therefore, no detectable changes in tumor vascularization (CD31) were observed in Notch4 Abs-treated tumors. This mechanism of action may be clinically important given the fact that pan-macrophage depletion approaches have been associated with a substantial reduction in tumor vasculature (Keklikoglou et al., “Periostin Limits Tumor Response to VEGFA Inhibition,” Cell Rep. 22:2530-2540 (2018); Lobov et al., “WNT7b Mediates Macrophage-Induced Programmed Cell Death in Patterning of the Vasculature,” Nature 437:417-421 (2005); Qian et al., “CCL2 Recruits Inflammatory Monocytes to Facilitate Breast-Tumour Metastasis,” Nature 475:222-225 (2011), which are hereby incorporated by reference in their entirety), and cessation of such interventions led to increased angiogenesis and metastatic resurgence (Bonapace et al., “Cessation of CCL2 Inhibition Accelerates Breast Cancer Metastasis by Promoting Angiogenesis,” Nature 515:130-133 (2014), which is hereby incorporated by reference in its entirety). Therefore, neutralization of Notch4 may represent an effective but also safe strategy for therapeutic intervention in breast cancer patients by targeting macrophage self-renewal.
Phenotypic plasticity is a hallmark of the mononuclear phagocytes including monocytes, macrophages, and dendritic cells (Biswas ad Mantovani, “Macrophage Plasticity and Interaction with Lymphocyte Subsets: Cancer as a Paradigm,” Nat. Immunol. 11:889-896 (2010) and Mantovani et al., “Macrophage Polarization: Tumor-Associated Macrophages as a Paradigm for Polarized M2 Mononuclear Phagocytes,” Trends Immunol. 23:549-555 (2002), which are hereby incorporated by reference in their entirety). Macrophages are particularly susceptible to and shaped by signals in their microenvironment (Gautier et al., “Gene-Expression Profiles and Transcriptional Regulatory Pathways that Underlie the Identity and Diversity of Mouse Tissue Cacrophages,” Nat. Immunol. 13:1118-1128 (2012); Lavin et al., “Tissue-Resident Macrophage Enhancer Landscapes are Shaped by the Local Microenvironment,” Cell 159:1312-1326 (2014); Mass et al., “Specification of Tissue-Resident Macrophages During Organogenesis,” Science 353 (2016), which are hereby incorporated by reference in their entirety). Conventionally, macrophages of solid tumors are broadly termed as “tumor-associated macrophages; TAMs” by virtue of their gross association within solid tumors (Biswas ad Mantovani, “Macrophage Plasticity and Interaction with Lymphocyte Subsets: Cancer as a Paradigm,” Nat. Immunol. 11:889-896 (2010); Gordon, “Alternative Activation of Macrophages,” Nat. Rev. Immunol. 3:23-35 (2003); Wynn et al., “Macrophage Biology in Development, Homeostasis and Disease,” Nature 496:445-455 (2013), which are hereby incorporated by reference in their entirety). However, the inventors' results provide compelling evidence that within the TME, local interactions between macrophages and their neighboring cells such stromal cells (SAMs), or tumor epithelial cells (TEM), give rise to phenotypically and functionally-distinct subpopulations with a significant impact on breast cancer patient survival (Medrek et al., “The Presence of Tumor Associated Macrophages in Tumor Stroma as a Prognostic Marker for Breast Cancer Patients,” BMC Cancer 12:306 (2012) and Yang et al., “Stromal Infiltration of Tumor-Associated Macrophages Conferring Poor Prognosis of Patients with Basal-Like Breast Carcinoma,” J. Cancer 9:2308-2316 (2018), which are hereby incorporated by reference in their entirety). In murine tumor models, SAMs (Sca-1+F4/80highMHC-IIhighCD11bhigh) display granular morphology, highly proliferative, scavenging, and uniquely express immunosuppressive markers such as CD206, LGALS3, and PD-L1 (Baghdadi et al., “TIM-4 Glycoprotein-Mediated Degradation of Dying Tumor Cells by Autophagy Leads to Reduced Antigen Presentation and Increased Immune Tolerance,” Immunity 39:1070-1081 (2013); Gordon et al., “PD-1 Expression by Tumour-Associated Macrophages Inhibits Phagocytosis and Tumour Immunity,” Nature 545:495-499 (2017); Kim et al., “Immuno-Subtyping of Breast Cancer Reveals Distinct Myeloid Cell Profiles and Immunotherapy Resistance Mechanisms,” Nat. Cell Biol. 21:1113-1126 (2019); Zhu et al., “CSF1/CSF1R Blockade Reprograms Tumor-Infiltrating Macrophages and Improves Response to T-Cell Checkpoint Immunotherapy in Pancreatic Cancer Models,” Cancer Res. 74:5057-5069 (2014), which are hereby incorporated by reference in their entirety). On the other hand, TEMs (Sca-1negF4/80intMHC-IIhighCD11blow) have dendritic-like morphology and express inflammatory markers such as IL-1b, CD11ahigh, and CD11chigh. Importantly, the inventors validated these findings in specimens of human breast cancer and showed a comparable phenotypic compartmentalization in TME macrophages. As predicted by the murine models, the intraepithelial macrophages from normal and malignant tissues were non-proliferative, displayed dendritic morphology, and expressed inflammatory markers (CD163+, CD206neg, CD11chigh). On the other hand, macrophages in proximity to stromal regions were highly granular, proliferative and expressed surface markers that associate with immunosuppressive phenotype (CD163highCD206highCD11clow) (Ramos et al., “CD163+ Tumor-Associated Macrophage Accumulation in Breast Cancer Patients Reflects Both Local Differentiation Signals and Systemic Skewing of Monocytes,” Clin Transl Immunology 9:e1108 (2020), which is hereby incorporated by reference in its entirety). Collectively, these findings suggest that within the TME macrophages, the SAM population is a primary source for immunosuppressive signals in the TME. In addition, these findings also provide mechanistic insights into previous clinical data showing that accumulation of CD163+ macrophages in tumor stroma is associated with shorter patient survival and disease recurrence, as compared when accumulated in tumor nests (Medrek et al., “The Presence of Tumor Associated Macrophages in Tumor Stroma as a Prognostic Marker for Breast Cancer Patients,” BMC Cancer 12:306 (2012); Salmi et al., “The Number and Localization of CD68 and CD163 Macrophages in Different Stages of Cutaneous Melanoma,” Melanoma Research 29:237-247 (2019); Yang et al., “Stromal Infiltration of Tumor-Associated Macrophages Conferring Poor Prognosis of Patients with Basal-Like Breast Carcinoma,” J. Cancer 9:2308-2316 (2018), which are hereby incorporated by reference in their entirety).
Collectively, the findings described herein emphasize the therapeutic potential of modeling the tumor-stroma-macrophage interactions using an organotypic TME system to decipher the molecular mechanisms underlying macrophage education and proliferation. Thus, deconvolution cell-cell interactions of macrophages within their microenvironment may pave the way for the next generation of immunotherapies to harness the innate immunity against cancer.
NK cells. The oTME captures the suppressive interactions between NK cells and oTME macrophages and provides functional read-outs for high throughput screens to overcome NK suppression in solid tumors. It was found that in the absence of macrophages, NK cells (spleen-purified) were capable of killing tumor cells and unexpectedly proliferate; however, this response was abrogated by the presence of oTME macrophages (
T-cells. Similar to NK cells, oTME macrophages were able to suppress the growth of activated T-cells effectively but only when cell-cell contact was allowed (see
High throughput proteomics of intracellular and secreted proteins. Secreted cytokines and growth factors are critical mediators of immune responses. However, the ability to measure secreted proteins or identify their source in tumor tissues is limited. To overcome this limitation, a protein labeling strategy was developed that relies on heavy amino acid (AA) incorporation by Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) and enables the identification of secreted proteins and their secreting source by mass spectrometry (MS). For detection of intracellular proteins (
Although preferred embodiments have been depicted and described in detail herein, it will be apparent to those skilled in the relevant art that various modifications, additions, substitutions, and the like can be made without departing from the spirit of the disclosure and these are therefore considered to be within the scope of the disclosure as defined in the claims which follow.
This application claims the priority benefit of U.S. Provisional Patent Application Ser. No. 63/122,815, filed Dec. 8, 2020, which is hereby incorporated by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/062388 | 12/8/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63122815 | Dec 2020 | US |
