RNA aptamers and uses thereof

Abstract

To provide RNA aptamers that specifically bind to tumor-infiltrating myeloid cells.SOLUTION: Described herein are RNA aptamers that specifically bind to tumor-infiltrating myeloid cells and uses thereof.SELECTED DRAWING: None

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 62 / 815,142, filed March 7, 2019, the disclosure of which is incorporated herein by reference in its entirety.

[0002] U.S. Government Interest Statement This invention was made with Government support under BC084337 awarded by the Department of Defense. The Government has certain rights in this invention.

[0003] INCORPORATION BY REFERENCE OF MATERIAL SUBMITTED ELECTRONICALLY This application contains, as a separate part of this disclosure, a sequence listing in computer readable form (Filename: 53952A_Seqlisting.txt; Size: 13,241 bytes; Created: March 2, 2020), which is incorporated by reference in its entirety. [Background technology]

[0004] Chemotherapy using different cytotoxic agents is the standard of care for most human malignancies. However, these treatments are associated with moderate to severe toxicities that reduce the overall quality of life of patients with cancer and are often dose-limiting, thus causing modest therapeutic efficacy (1, 2). Drug delivery nanoplatforms aimed at concentrating chemotherapeutic agents at the tumor site have attracted interest as modalities to reduce systemic toxicity and increase efficacy (2-4). However, most nanoplatforms rely either on the physical properties of the tumor vasculature, such as the enhanced permeability and retention (EPR) effect (5), or on the presence of markers that characterize neoplastic cells. The EPR effect allows preferential targeting of primary tumors and established metastases, but is less effective against micrometastases that are not yet vascularized (6). Targeting appropriate tumor-associated antigens allows for disseminated drug delivery to neoplastic cells, but due to the genetic instability of neoplastic cells (9), may result in tumor editing rather than eradication (7, 8). An alternative and fast-growing strategy is the delivery of therapeutic agents to genetically stable tumor stromal cells that characterize primary tumors, metastases, and premetastatic niches (10). Myeloid cells in the tumor microenvironment are an interesting target because they represent a large proportion of the tumor population, promote cancer cell survival and metastasis (11-16), provide immune protection, and are recruited to primary tumors and premetastatic niches early during tumor progression (17). Importantly, tumor-infiltrating myeloid cells (TIMCs; including myeloid-derived suppressor cells-MDSCs, tumor-associated macrophages-TAMs, neutrophils, and monocytes) express a unique activated, tumor-promoting phenotype that distinguishes them from their systemic counterparts (18-22). Because markers expressed on TIMCs but not on their circulating counterparts remain poorly defined, we used an unsupervised approach to generate RNA aptamers against known and unknown TIMC-specific epitopes (23).

[0005] RNA aptamers penetrate deep into tissues, are non-immunogenic, and are easy to produce and engineer for improved pharmacokinetics and stability. Moreover, these reagents can be selected for rapid internalization into target cells (24), thus allowing specific drug (e.g., RNA therapeutics or small molecules) absorption into desired cells. Thus, the combination of aptamers, each binding to a different epitope in target cells, should increase overall specificity and maximize drug delivery in selected tissues. [Brief description of the drawings]

[0006] [Figure 1] Selection of polyclonal aptamers specific for tumor-infiltrating myeloid cells. HT-cell SELEX was performed on MSC2 cells that were treated with IL4 or left untreated as a surrogate for tumor-infiltrating and splenic myeloid cells, respectively. A) FACS analysis of IL4-treated MSC2 stained with Cy3-labeled RNA aptamers from cycle 0 as a control, or cycle 11. B) FACS analysis of MSC2 or IL4-treated MSC2 with Cy3-labeled polyclonal aptamers from the indicated libraries. C) Single cell suspensions from spleens or tumors of mice bearing CT26 colon carcinoma (0.5 cm diameter) were stained with the polyclonal aptamer library and counterstained with antibodies against CD11b, Gr1, F4 / 80, CD11c, CD19, CD49b, CD4, and CD8. Data were from one representative experiment of each. [Figure 2A]Identification of monoclonal aptamers specific for tumor-infiltrating myeloid cells. A) Single cell suspensions from mouse spleens and tumors pooled from three mice bearing 4T1 tumors (0.5 cm diameter) were labeled with antibodies against CD11b, Ly6C, Ly6G and 15 Cy3-labeled monoclonal aptamers identified by APTANI. Data are from n=5 biological replicates and from n=2 independent experiments. Aptamers 3, 6, 11, and 14 were selected for further analysis and are in bold. *=P<0.001 by one-way ANOVA and post hoc Holm-Sidak comparison against an unrelated aptamer. Only background staining was observed in CD11b- cells. B) Secondary structures of aptamers 3, 6, 11, and 14. Binding motifs identified by APTANI are underlined in black. Fluorinated nucleotides are highlighted in grey. C) Equimolar mixtures of aptamers increase specificity for tumor-infiltrating myeloid cells. Pooled single cell suspensions of tumors or spleens from 4T1-bearing mice were labeled with aptamers 3, 6, 11, 14, or an equimolar mixture of each aptamer and analyzed by FACS. Data are from n=3 independent experiments. [Figure 2B]Identification of monoclonal aptamers specific for tumor-infiltrating myeloid cells. A) Single cell suspensions from mouse spleens and tumors pooled from three mice bearing 4T1 tumors (0.5 cm diameter) were labeled with antibodies against CD11b, Ly6C, Ly6G and 15 Cy3-labeled monoclonal aptamers identified by APTANI. Data are from n=5 biological replicates and from n=2 independent experiments. Aptamers 3, 6, 11, and 14 were selected for further analysis and are in bold. *=P<0.001 by one-way ANOVA and post hoc Holm-Sidak comparison against an unrelated aptamer. Only background staining was observed in CD11b- cells. B) Secondary structures of aptamers 3, 6, 11, and 14. Binding motifs identified by APTANI are underlined in black. Fluorinated nucleotides are highlighted in grey. C) Equimolar mixtures of aptamers increase specificity for tumor-infiltrating myeloid cells. Pooled single cell suspensions of tumors or spleens from 4T1-bearing mice were labeled with aptamers 3, 6, 11, 14, or an equimolar mixture of each aptamer and analyzed by FACS. Data are from n=3 independent experiments. [Figure 2C]Identification of monoclonal aptamers specific for tumor-infiltrating myeloid cells. A) Single cell suspensions from mouse spleens and tumors pooled from three mice bearing 4T1 tumors (0.5 cm diameter) were labeled with antibodies against CD11b, Ly6C, Ly6G and 15 Cy3-labeled monoclonal aptamers identified by APTANI. Data are from n=5 biological replicates and from n=2 independent experiments. Aptamers 3, 6, 11, and 14 were selected for further analysis and are in bold. *=P<0.001 by one-way ANOVA and post hoc Holm-Sidak comparison against an unrelated aptamer. Only background staining was observed in CD11b- cells. B) Secondary structures of aptamers 3, 6, 11, and 14. Binding motifs identified by APTANI are underlined in black. Fluorinated nucleotides are highlighted in grey. C) Equimolar mixtures of aptamers increase specificity for tumor-infiltrating myeloid cells. Pooled single cell suspensions of tumors or spleens from 4T1-bearing mice were labeled with aptamers 3, 6, 11, 14, or an equimolar mixture of each aptamer and analyzed by FACS. Data are from n=3 independent experiments. [Diagram 3]The selected RNA aptamers preferentially recognized human TIMC over circulating bone marrow cells from patients with recurrent HNSCC. A) Single cell suspensions from blood and tumors of patients with recurrent HNSCC (n=3) were stained with AF647-labeled aptamers, anti-CD33, anti-CD14, anti-CD124 antibodies, and zombie vital dyes and analyzed by flow cytometry. B) Image cytometry: Paraffin-embedded tumor specimens from patients with HNSCC undergoing salvage surgery (n=5) were stained with an equimolar mixture of Cy3-labeled aptamers, FITC-anti-CD33 antibodies, and DAPI and analyzed by cell profiler and FCS-express V6 after gating on either the "tumor compartment" or the "healthy tissue compartment" and CD33+ or CD33- cells. Aptamer MFI was normalized to the MFI of all cells in the corresponding region of interest. Approximately 106 and 105 cells were analyzed in the "tumor compartment" or "healthy tissue compartment," respectively. Significant paired T-tests are reported. [Figure 4]Characterization of aptamers that preferentially recognize tumor-infiltrating myeloid cells. A) Aptamer affinity for IL4-treated MSC2 cells was determined by FACS. Putative targets (ANXA4 and VIM) were identified via aptamer-based immunoprecipitation, SDS page, and mass spectrometry. Kd for ligands was determined by FACS for recombinant proteins conjugated to epoxy beads. B) Epoxy beads conjugated with ANXA4 or an irrelevant protein were stained with Cy3-labeled aptamer 3 or an irrelevant aptamer and analyzed by FACS. C) Epoxy beads conjugated with vimentin or an irrelevant protein were stained with Cy3-labeled aptamer 11 or an irrelevant aptamer and analyzed by FACS. D) ANXA4 competition assay. 5x105 IL4-treated MSC2 were stained with aptamer 3 in the presence or absence of recombinant ANXA4. E) MCS2 cells were transfected via 4PD nanoparticles with shRNA against vimentin or scrambled shRNA. After 4 days, cells were stained with Cy3-labeled aptamer 11 and DAPI. Pa = p-value of one-way ANOVA. Data were from n = 2 independent experiments. [Diagram 5]Aptamers preferentially target tumor stroma in vivo. A) Aptamers target tumors in vivo. Mice bearing 4T1-luciferase breast cancer were intravenously injected with an equimolar mixture of biotinylated aptamers 3, 6, 11, and 14 conjugated with AF750 streptavidin. B) Biodistribution was assessed after 2 h by IVIS. C) Time course analysis or aptamer biodistribution performed by IVIS. D) Mice bearing 4T1 breast cancer (n=5) were injected iv with aptamers 3, 6, 11, and 14 loaded on AF-647 streptavidin. After 2 h, the indicated organs were harvested and counterstained with antibodies against CD11b, CD19, CD49b, CD11c, and CD3. E, F) Mice (n=5) were treated as in D and aptamer distribution was assessed by flow cytometry at different time points after counterstaining of single cell suspensions with antibodies against CD11b, F4 / 80, Ly6C, and Ly6G. *=1-way Anova p<0.001. [Figure 6]TIMC-specific aptamers increase doxorubicin concentrations at tumor sites. A) TIMC-specific aptamers were conjugated to doxorubicin by extending the 3' end with a GC-rich tail. B) Fluorescence spectra of doxorubicin solutions (1.5 μM) with increasing molar ratios (top to bottom: 0, 0.008, 0.016, 0.032, 0.062, 0.125, 0.25, and 0.5 equivalents) of an equimolar mixture of TIMC aptamers. Inset: Hill plot for aptamer titration (Kd=0.16 equivalents; 6.2 dox molecules / aptamer). C) BALB / c mice (n=5) were orthotopically (4T1_o; primary tumor: breast, metastasis: lung) or intravenously (4T1_iv; metastases in lung and liver) challenged with 4T1 breast cancer. An additional group of mice was orthotopically loaded with the non-metastatic 4T1-derived cell line 67NR (primary tumor only). 10 days later, mice were treated intravenously with doxorubicin conjugated to a TIMC-specific aptamer (0.35 mg / kg). Doxorubicin biodistribution was assessed 24 h later. Data are from one representative experiment each. D) Biodistribution of free doxorubicin or doxorubicin delivered via TIMC-specific or control aptamers. Mice (n=5) bearing 4T1 mammary tumors (0.5 cm diameter) in the third mammary gland were given iv free doxorubicin (Dox high 3.5 mg / kg), TIMC-aptamer conjugated doxorubicin (TIMC apt; 0.35 mg / kg) or irrelevant aptamer (Irr. Apt) conjugated doxorubicin. Doxorubicin in tissues was quantified 24 h later by spectroscopy after acid-alcohol extraction. Data are from one representative experiment each. *=p<0.05 in multiple pairwise comparisons (Holm-Sidak method) versus dox high group. [Figure 7]TIMC-specific aptamers increase the doxorubicin therapeutic index. A) Mice bearing 4T1 tumors in the third mammary gland were treated iv with free doxorubicin (3.5 mg / kg or 0.35 mg / kg), Doxil (0.35 mg / kg), or doxorubicin conjugated to a TIMC-specific aptamer mix (0.35 mg / kg). Additional controls included an irrelevant aptamer conjugated to doxorubicin, unconjugated TIMC-specific aptamer, and untreated mice. Treatment was repeated 2 and 6 days later. Mice were euthanized when the tumor index reached 1.2 cm2. Log-rank and post-hoc multiple comparison analyses (Holm-Sidak method) are reported. Weight loss is reported as a measure of toxicity. C) BALB / c mice were challenged with 4Tlwt breast cancer or the doxorubicin-resistant variant dox-resistant 4T1. When tumors reached 0.5 cm in diameter, mice were either treated with TIMC-specific aptamers or left untreated, and survival was monitored. [Figure 8] The aptamers effectively deliver CCR1 and 5 siRNA to tumor-infiltrating myeloid cells and delay tumor progression. A) Structure of the aptamer / siRNA chimera. B) Balb / c mice were orthotopically challenged with 4T1 breast cancer and treated intravenously with scrambled siRNA (black bars) or aptamers loaded with siRNA against CCR1 and CCR5 (30 pmoles, white bars) at days 5, 7, 9, 12, 14, 16, and 19 after challenge. qRT-PCR for CCR1 and CCR5 mRNA was performed 6 days after the last treatment on tumor-infiltrating myeloid cells. C) Tumor size 25 days after challenge. [Figure 9]Affinity of aptamers #3, #6, #11 and #14 to IL4-treated MSC2 or their putative ligands. A) 105 IL4-treated MSC2 were stained with different concentrations of the indicated Cy3-labeled aptamers. Binding was assessed by FACS. B) Different concentrations of Cy3-labeled aptamer #3 and aptamer #4 were used to stain epoxy beads conjugated with recombinant ANXA4 or recombinant VIM, respectively. Cy3-labeled aptamer against IL4Ra was used as an irrelevant control. Binding was assessed by FACS. Data were derived from three independent experiments. [Figure 10] Cluster analysis of 15 aptamers selected with APTANI. The variable regions of 15 monoclonal aptamers selected with APTANI were aligned using clustal-Ω and a neighbor-joining tree generated with Jailview using default parameters. [Figure 11] Sequences, frequencies, and motifs of 15 aptamers selected by APTANI based on an enrichment of at least 0.1% and the presence of at least three motifs or at least one motif with a frequency higher than 0.1%. [Figure 12] Oligonucleotides used in the examples. [Figure 13] List of mouse and human antibodies used in the examples. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] The present disclosure is based in part on the discovery of RNA aptamers that specifically recognize tumor-infiltrating myeloid cells (TIMCs) across different preclinical models and, more importantly, in humans.

[0008] Although systemic chemotherapy remains one of the most important treatments for cancer therapy, there are systemic side effects, such as cardiotoxicity and severe neutropenia, that impose limitations on the use of this treatment option.(38) Additionally, chemotherapy induces long-term morbidity that reduces the quality of life of cancer survivors.

[0009] Described herein is a new mode by which chemotherapeutic or other therapeutic agents (e.g., RNA therapeutics) can be concentrated in tumors and metastatic sites by physically targeting the tumor-promoting activated phenotype of tumor-infiltrating myeloid cells. Myeloid cells are the most abundant innate immune cells in the stroma of several types of mouse and human cancers (39-41). Their presence in human tumors correlates with increased vascular density, higher metastatic spread, and poorer clinical outcomes, and their presence is necessary for tumor progression (42, 43). At the tumor site, myeloid cells acquire unique antigenic profiles and functional characteristics that distinguish them from their systemic circulating cellular counterparts and are necessary for their immunosuppressive, tolerogenic, and tumor-promoting roles (20, 44, 45). Although many approaches have been tested to inhibit mechanisms that myeloid cells use to promote tumor progression or their interactions with neoplastic cells (46-48), to our knowledge, no reagents are yet available that can distinguish between tumor-infiltrating and circulating myeloid cells. Furthermore, aptamers and peptides capable of binding to both tumor-associated and circulating MDSCs in mice have been isolated (49-51) and used to deplete MDSCs (50, 51) or to enhance Doxil delivery (49), but these reagents do not preferentially recognize tumor-infiltrating myeloid cells, and indeed, when used to deliver Doxil, significant accumulation of Dox in the heart as well as in the spleen and liver was observed (49).

[0010] Four RNA aptamers (aptamers 3, 6, 11, and 14, Figure 2) were isolated that were specific for tumor-infiltrating myeloid cells but not their circulating counterparts, regardless of the tumor model or mouse strain used. Interestingly, aptamer 3 and aptamer 11 have ANXA4 and vimentin as their ligands, respectively. Vimentin plays a key role in monocyte differentiation and reactive oxygen species production, and under proinflammatory stimuli, it translocates to the membrane of activated macrophages (52-56). Indeed, vimentin is present in the tumor stroma of different cancers, but is poorly expressed in cultured tumor cells (54-56). Functionally, this protein is involved in epithelial-mesenchymal transition and metastasis (57). ANXA4 is a calcium-dependent phospholipid-binding protein that promotes membrane fusion and exocytosis (58). It is overexpressed in activated MDSCs, TIMCs, and activated M2 macrophages (44, 59). Although localized in the cytoplasm of resting macrophages, ANXA4 translocates to the membrane during activation (60, 61). Both proteins are expressed in human malignancies and their expression correlates with poorer prognosis in renal, breast and ovarian cancer according to TCGA data and the Protein Atlas. Therefore, both identified targets support the specificity of the aptamer for the TIMC activation phenotype.

[0011] The aptamers described herein preferentially cross-react with and recognize human MDSC in tumors of patients with HNSCC, but not in blood, suggesting their potential use for the detection of MDSC or as tumor targeting agents in human malignancies. Indeed, the combination of these four aptamers provides efficient delivery of chemotherapeutic agents, such as doxorubicin, or RNA therapeutics (e.g., siRNA against CCR1 and CCR5), to primary and metastatic tumor sites in our proof-of-principle experiments, with virtually no drug accumulation in other tissues. In a therapeutic context, our approach is far superior to free doxorubicin or Doxil (34, 35), the current gold standard for targeted delivery of doxorubicin, resulting in tumor regression in approximately 40% of treated mice without observed systemic toxicity.

[0012] Taken together, the data provided herein indicates that it is possible to enhance the therapeutic index of chemotherapeutic agents by targeting the surface protein profile of tumor-infiltrating myeloid cells and enabling bystander release of drugs within the tumor microenvironment.Although the combination of aptamers described herein is not required (i.e., the use of a single aptamer is envisioned), the combination of several different aptamers as a means of chemotherapeutic agent delivery, each of which targets a different TIMC-specific epitope, significantly increases therapeutic specificity, improves efficacy, and reduces the toxicity of chemotherapeutic agents compared to the systemic delivery of these agents in current clinically available treatment modalities.

[0013] The present disclosure provides an RNA aptamer conjugated to a therapeutic agent, which specifically binds to a target expressed on tumor-infiltrating myeloid cells. In various embodiments, the therapeutic agent is a nucleic acid molecule (e.g., DNA, RNA, shRNA, siRNA, or miRNA). In various embodiments, the therapeutic agent is a chemotherapeutic agent (e.g., doxorubicin). The target expressed on tumor-infiltrating myeloid cells is optionally annexin or vimentin. The present disclosure further provides a method of delivering a therapeutic agent to tumor-infiltrating myeloid cells, comprising contacting the cells with an aptamer. The present disclosure also provides a method of detecting the presence of tumor-infiltrating myeloid cells in a biological sample, comprising contacting the sample with an aptamer conjugated to a detectable label. The present disclosure additionally provides a method of treating cancer in a subject in need thereof, comprising administering an aptamer to the subject. The subject optionally suffers from breast cancer, colon cancer, or kidney cancer. EXAMPLES

[0014] Materials and Methods

[0015] Study design and rigor. The goal of this study was to identify RNA aptamers capable of discriminating tumor-infiltrating myeloid cells from those in the periphery. Aptamers were selected by unsupervised cell SELEX, bioinformatics analysis, and empirical testing with flow cytometry and immunofluorescence analysis. Each experiment was performed at least twice by two independent experimenters, and when possible, the same phenomenon was evaluated using two independent techniques to eliminate assay-specific artifacts. Flow cytometry was performed daily on a calibrated flow cytometer using titrated and validated antibody controls, vital dyes, automatic compensation using single cell colors, and FMO or control aptamers (cycle 0 or unrelated aptamers) as negative controls. For in vivo experiments, mice were randomized before treatment and tumor measurements were performed by an experimenter blinded to treatment. Unless otherwise specified, mice were euthanized when tumors reached a diameter of approximately 1.2 cm, or if they lost more than 20% of their initial body weight or showed clinical signs of treatment-related toxicity (i.e., lethargic mice, ruffled coats, etc.). Data are cumulative and were derived from 2-3 replicate experiments with 3-5 mice each per group. Group sizes were determined by power analysis using effect sizes from pilot experiments. Outliers were always included in the data. In one experiment, 2 mice were removed and euthanized for reasons unrelated to the study (i.e., wounds from fighting).

[0016] Cell lines and recombinant proteins: 4T1 (62), 4T1HAThy1.1luciferase (63), TS / A (64), MSC2 (26), CT26 (65), Renca (66), MCA203 (67), and 67NR (68) cell lines were previously described. All cell lines were maintained in complete medium (RPMI (Gibco) supplemented with HEPES (10 mM), streptomycin (150 U / mL), penicillin (200 U / mL), 10% heat-inactivated FBS (Invitrogen), and beta-mercaptol (20 μM).

[0017] DOXR-4T1 was selected from the parental cell line by culturing the cells with increasing doses of DOX, and the resulting DOXR-4T1 cell line was maintained in complete medium supplemented with doxorubicin (3.5 μg / ml).

[0018] 5 × 10 in T75 flasks (Falcon) as previously described ( 26 ). 5 MSC2 were treated with IL4 by incubating the cells with rmIL4 (100 ng / ml, Peprotech) in 20 ml of complete medium for 4 days. Annexin A4 and vimentin recombinant proteins were purchased from myBiosource.

[0019] MSC2 and CDllb +Cellular microarray gene expression profiling: After 4 days of culture with or without IL4, MSC2 were washed with PBS before RNA extraction. For each chip, 2.5 μg of total RNA extracted with Trizol and cleaned with RNeasy Qiagen columns was amplified to biotinylated complementary RNA (cRNA) as described in the Affymetrix GeneChip® Expression Analysis Technical Manual. All prehybridization quality controls were performed using an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA from five biological replicates of MSC2 and MSC2 cells treated with IL4 were then hybridized onto the Affymetrix Mouse Genome Expression MG-U74Av2 array. Microarray probe fluorescence signals were converted to log2 expression values ​​using the Robust Multiarray Average process of the affy Bioconductor package. Fluorescence intensities were background adjusted and normalized using quartile normalization, and expression values ​​were calculated using median polish summarization and custom chip definitions for mouse array MG-U74Av2 based on Entrez genes (mgu74av2_Mm_ENTREZG version 21.0.0; brainarray.mbni.med.umich.edu / Brainarray / Database / CustomCDF / 21.0.0 / entrezg.asp) for a total of 8,124 custom probe sets. Microarray data are available from Gene Expression Omnibus GSE110774.

[0020] CD11b from single suspensions of spleens or tumors of mice challenged 9 days prior with C26GM +Cells were magnetically isolated. Purity was >95% by flow cytometry analysis. For each chip, 2.5 μg of total RNA extracted with Trizol and cleaned using RNeasy Qiagen columns was amplified to biotinylated cRNA as described in the Affymetrix GeneChip® Expression Analysis Technical Manual. All pre-hybridization quality controls were performed using an Agilent 2100 Bioanalyzer (Agilent Technologies). CD11b from tumor and spleen + RNA from six biological replicates of cells was then hybridized onto Affymetrix Mouse Genome Expression MOE4302 arrays. Microarray probe fluorescence signals were converted to log2 expression values ​​using the Robust Multiarray Average process in the affy Bioconductor package. Fluorescence intensities were background adjusted and normalized using quartile normalization, and expression values ​​were calculated using median polish summaries and custom chip definitions for mouse array MOE4302 based on Entrez genes (mouse4302_Mm_ENTREZG version 21.0.0; brainarray.mbni.med.umich.edu / Brainarray / Database / CustomCDF / 21.0.0 / entrezg.asp) for a total of 18,139 custom probe sets. Microarray data are available from Gene Expression Omnibus GSE110774. The MSC2 and CD11b datasets were then merged using the 7,860 common Entrez gene identifiers, and the merged dataset was batch corrected using the ComBat function in the Bioconductor sva package. +To identify genes overexpressed in cells, the Significance Analysis of Microarray (SAM) algorithm coded in the samr R package was used to compare expression levels of IL4-treated MSC2 with those of untreated cells (and CD11b from tumors). + Expression levels of cells from the SAM were compared to those from cells from the spleen. In the SAM, the percentage of false positive predictions (i.e., false discovery rate, FDR) was estimated using 100 permutations and differentially expressed probe sets with FDR q-value ≦0.05 and absolute fold change ≧2 were selected. Global unsupervised clustering was performed using the function hclust from the R stats package with Pearson correlation as distance metric and the average agglomeration method. Statistical significance of clustering was calculated in terms of approximately unbiased p-values ​​using the cluster.bootstrap function from the R pvclust package. Gene expression heatmaps were generated using the function heatmap.2 from the R gplots package after column-wise normalization of expression values. All data analysis was performed in R version 3.3.3 using Bioconductor libraries and the R statistical package.

[0021] Aptamer selection: cDNA random libraries (previously described by Sullenger's group (69)) were synthesized by Eurofins technology and amplified by PCR using recombinant Taq (Invitrogen)) with the following cycle conditions: 95°C 5', 3x (94°C 30", 52°C 20", 72°C 25"), 20x (94°C 30", 54°C 20", 72°C 25"), 72°C 5' with SRlong and SF primers (Figure 12). Amplified DNA was purified using a PCR purification kit (Qiagen) and transcribed in vitro by Durascribe T7 RNA synthesis kit (Epicentre, Madison, WI, USA) or APT-GET T7 transcription kit. The resulting 2'fluoro-RNA aptamers were purified using an RNeasy kit (Qiagen). In the initial round of selection, 27 pmoles of RNA was resuspended in 450 μl of PBS (pH 7.4, Life Technologies, MO, USA), heated to 65°C for 5', and then cooled to room temperature (RT) for at least 10'. The RNA aptamer library was first incubated at 2 × 10 6 The ryCells were then incubated with 1000x1000 MSC2 cells in PBS (1 ml) at RT for 10 min. The ryCells were then centrifuged (514 g, 10 min), and the supernatant was filtered (0.2 μm supor membrane, Pall Corporation) to remove residual cells and to recover unbound aptamer. The unbound aptamer was then transferred to a rotator with IL4-treated MSC2 (0.5 × 10 6) and yeast RNA (10ug / ml, Ambion) in PBS at RT for 10'. Cells were spun down (514g, 10') and washed 3 times with 1ml PBS containing MgCl2 (0.5mM MgCl2, 1mM CaCl2) (3' wash). Total RNA was then isolated using Trizol (Invitrogen) and reverse transcribed into cDNA using the Superscript® III Reverse Transcriptase kit (Invitrogen) and SR oligonucleotides according to the manufacturer's instructions. The resulting cDNA was amplified by PCR (95°C, 5', 3x (94°C 30", 52°C 20", 72°C 25"), 15x (94°C 30", 54°C 20", 72°C 25"), 72°C 5) using SF and SRshort primers (Figure 12). The amplified DNA was purified using a PCR purification kit (Qiagen), transcribed using a Durascribe T7 kit, and the resulting RNA aptamers were purified using an RNeasy mini kit (Qiagen) and used for the next cycle of selection. To increase stringency, starting in the sixth round of selection, the incubation time with IL4-treated MSC2 was reduced 5' and five washes were performed. Additionally, a reduced number of cells (3x10 in cycles 6 and 7) was used. 5 , 2.5 × 10 in cycles 8 to 10 5 , and 2×l0 in cycle 11 5 ) was used as a positive selector to promote competition between aptamers with the same specificity and occasionally to isolate the most similar ones. Monoclonal aptamers were synthesized by PCR and T7 RNA polymerase using the DNA primers shown in Figure 12 or were synthesized with biotin on the 5' end by Boston Open Labs.

[0022] Flow cytometry: Aptamers were labeled with Cy3 using the Silencer siRNA Labeling Kit-Cy3 (Ambion) and 5 × 10 ng / ml of 5% PBS using 37 pmoles unless otherwise specified. 6 In some experiments, biotinylated aptamers were labeled with streptavidin conjugated to Alexa Fluor 647 or Alexa Fluor 750 and purified from unbound aptamers by Ultra-4 centrifugal filters, 50,000 kDa (Millipore).

[0023] The antibodies used are summarized in Figure 13. Dead cells were excluded by analysis using 4',6'-diamidino-2-phenylindole (DAPI; Sigma) or Live / Dead dye stains (Invitrogen). Samples were read on an LSR2 equipped with 405 nm, 488 nm, 532 nm, and 635 nm lasers (BD Bioscience). Data were analyzed using FCS6 express software (Denovo-Software).

[0024] Immunofluorescence microscopy and image cytometry. Fresh frozen tissues and tissue microarrays (AMSBIO) were fixed in 10% neutral buffered formalin (BDH) for 15' at RT, incubated with dextran sodium sulfate / PBS (1:2 m / V - Pharmacia biotech) for 30', and washed with PBS. Tissues were then stained with Cy3-labeled aptamers (10ug / ml) in PBS for 30', masked with 2% BSA, and counterstained with antibodies and / or DAPI.

[0025] Formalin-fixed tissues were deparaffinized with xylene (2x10' incubation) and first rehydrated with xylene / ethanol (1:1 v:v, 10' at RT), followed by 10' incubation with the following ETOH solutions: 100%, 100%, 95%, 90%, and 75%. Antigen retrieval was performed with citrate buffer (pH6 Thermo-Fisher) by microwaving the tissues for 30 minutes at 100W. Tissues were incubated in PBS O / N at RT, incubated with dextran sulfate sodium / PBS (1:2 m / V - Pharmacia biotech) for 30', washed with PBS, then stained with Cy3-labeled aptamer (10ug / ml) in PBS for 30', masked with 2% BSA, and counterstained with antibodies and / or DAPI.

[0026] Whole stained tissue sections were scanned at 20X using an Olympus VS120 equipped with DAPI CUBE 455 nm (12.941 ms), FITC CUBE 518 nm (410.271 ms) and TRITC CUBE 580 nm (592.6 ms). Images were exported as a single channel with a resolution of 5X using OlyVIA software. Images were evaluated using ImageJ (fiji.se / ), tiled at 600 × 600 pixels using ImageSlicer (www.coolutils.com / ) and processed using cell-profiler (www.cellprofiler.com / ) as follows: Nuclei were identified using the blue (DAPI) channel as primary objects using a 3-class Otsu adaptive threshold method (correction factor of 1; threshold 0.1-1.0) with diameters of 2-10 pixels. Declamping of objects was based on shape and size using default smoothing and distance parameters. Cells were segmented as secondary objects using the fluorescence from the three merged channels by a three-class Otzu adaptive threshold propagation method (threshold correction factor 0.0-1.0; regularization factor = 0.02). The cytoplasm was identified as a tertiary object as the compartment contained within the cell (secondary object) but not within the nucleus (primary object). The resulting cpout files were analyzed using FCS Express 6 plus (www.denovosoftware.com) by evaluating the fluorescence intensity of the aptamer in CD33+ or CD33- cells in tumor and surrounding healthy tissues identified on serial H&E sections by an experienced pathologist.

[0027] HNSCC specimens: Specimens and blood from patients (median age 59 years, range 49-72 years) with recurrent stage 3 or 4 HNSCC of the oral or oropharyngeal cavity undergoing salvage surgery were collected at the time of surgery or biopsy under a clinical protocol approved by the University of Miami.

[0028] shRNA transfection: 2 × 10 MDSCs were transfected with 1 μg of vimentin-specific shRNA (29) (GCG CAA GAU AGA UUU GGA AUA UUC AAG AGA UAU UCC AAA UCU AUC UUG CGC UU - SEQ ID NO: 47) or scrambled shRNA using the 4PD MDSCs transfection kit (Kerafast) according to the manufacturer's instructions (30). 5 MSC2 cells were transfected with 10 μg / ml of mitomycin C for 30 min, washed twice, and incubated with IL4 (100 ng / ml, Peprotech) in 6-well plates for 4 days (37° C., 5% CO2).

[0029] Preparation of polyclonal libraries for HT-sequencing: Two sequential PCRs were used to tag cDNAs from cycles 1, 6, 10, and 11. The first PCR reaction was performed in 100 μl water containing 1× PCR buffer, MgCl2 solution (1.5 mM), dNTPs (200 μM each), DNA template (5 ng / μl), recombinant Taq polymerase (5 U, Invitrogen), and PFA and PRA primers corresponding to each cycle above (supplementary Table 2). Reactions were performed in a GS482 thermocycler (G-STORM) using the following program: 95° C. 5′, 5× (95° C. 1′, 56° C. 30″, 72° C. 30″), 72° C. 10′. PCRs were purified via gel extraction using the QIAquick Gel Extraction Kit (QIAGEN) according to the manufacturer's instructions. The second PCR was performed using the same conditions as above but with UFB and PRB primers (Supplementary Table 2) and the following program: 95°C 5', 6× (95°C 30”, 65°C 30”, 72°C 30”), 72°C 10'. Products were purified by gel extraction and assessed for quality and quantity via bio-analyzer (Agilent). Library quantification and pooling were performed at the Hussman Institute for Human Genomics-Center for Genome Technology using the KAPA Library Quantification Kit for Illumina platform (part# KK4854). 10-13 pM of pooled samples were loaded onto an Illumina cBot for cluster generation according to the manufacturer's recommendations. Illumina TruSeq PE Cluster Kit v3 and reagents provided in the TruSeq SBS Kit-HS (200 cycle) kit were used to generate the clusters on an Illumina HiSeq 2000 / 2500 (HCS Sequencing was performed on HiSeq's Real Time Analysis (RTA) from Casava software.The base calling files were converted to zipped FASTQ files containing raw reads with base quality. These raw read files were then filtered by Illumina's internal filter to obtain two FASTQ files (one per read) containing all pass-filter reads. The FASTQ files were used as input to APTANI (27).

[0030] Bioinformatics selection of aptamers using APTANI: Aptamers were selected using APTANI (27), a computational method for the identification of target-specific aptamers from HT-SELEX data and secondary structure information. Briefly, APTANI first calculates the relative enrichment of each individual aptamer sequence generated by the HT-SELEX process, then for each aptamer with enrichment higher than a preselected threshold, it predicts all secondary structures within a characteristic energy range and extracts motifs appearing in these structures. As a result, APTANI returns a list of aptamers ranked by their abundance and the presence of structural motifs. Here, APTANI was used with default parameters on HT-SELEX data from cycles 1, 6, 10, and 11, with enrichment and motif frequency thresholds set to 0.01% and 0.05%, respectively. Since in each run APTANI randomly samples 20% of the library to reduce computational load, each analysis was replicated n = 5 times, and results were pooled to ensure complete coverage of the entire library.

[0031] Mice: All animal studies were approved by the Division of Veterinary Resources and the Institutional Animal Care & Use Committee at the University of Miami. BALB / c and C57Bl / 6J mice, 8-10 weeks of age, were purchased from Jackson Laboratories and maintained in the pathogen-free animal facility at the University of Miami on a chlorophyll-free diet. Mice were acclimated for at least 1 week prior to the experiment, ear-tagged, and randomized after tumor inoculation.

[0032] Aptamer-doxorubicin treatment: Aptamer-doxorubicin was prepared by creating a DNA template with the appropriate aptamer followed downstream by a GC-rich primer sequence (GC-rich Sul3', Figure 12). RNA was transcribed from this template and purified as described above. Doxorubicin (Sigma) was mixed with the RNA sequence as needed. Doxil (ALZA corporation) was purchased through the University of Miami pharmacy.

[0033] BALB / c mice were injected IV with the 4T1 luciferase thy1.1 tumor cell line. Treatment was initiated when tumors reached a diameter of 5 mm and repeated 2 and 6 days later. Tumor growth and mouse weights were reported. Mice were evaluated at least three times a week and were randomly assigned to receive a tumor size index of 150 mm. 2 Mice were humanely euthanized when they reached a body weight of 0.01 or lost >20% of their initial body weight due to chemotherapy or tumor growth in accordance with IACUC policy and animal protocol. Data are expressed as tumor size index, defined as the product of the main diameter and the perpendicular diameter.

[0034] IVIS analysis. Tumor-bearing mice anesthetized with isoflurane were analyzed by an In Vivo Imaging System (Xenogen IVIS Spectrum - Perkin Elmer) 2 h after iv injection of 5' biotinylated aptamer conjugated with Alexa Fluor-750 (AF750) streptavidin (13.6 pmol / g) using a 37°C heated imaging stage. Mice were injected intraperitoneally with D-luciferin (150 μg / g) 15' prior to imaging. AF750 fluorescence was read at 800 nm after excitation at 748 nm. Raw signals were subjected to spectral decomposition to remove background fluorescence signals, and AF750 fluorescence was quantified using living Image v4.3 software (Perkin Elmer).

[0035] Detection of doxorubicin in tissues: Doxorubicin was quantified in tumors, liver, spleen, and lungs using the fluorescent properties of doxorubicin as previously described (70). Briefly, tissues were harvested, weighed, homogenized in acid alcohol (0.3 N HCl solution, 70% EtOH), and doxorubicin was extracted overnight at 4°C. Homogenates were spun down, and supernatant samples were quantified by fluorometry (SpectraMax M5, Molecular Devices) using an excitation wavelength of 470 nm and measuring the intensity of emission at 590 nm, and plotted on a standard curve of doxorubicin serially diluted in acid alcohol. Results were normalized to tissue weight.

[0036] Statistical analysis: All values ​​depicted represent the mean ± standard deviation of biological replicates unless otherwise indicated in the figure legends. Statistical calculations were performed by a person blinded to the treatment groups using Sigmaplot 12.5 (Systat software). After normality was assessed by the Shapiro-Wilk test, statistical tests (one-way ANOVA followed by Holm-Sidak test or Student's T test for multiple pairwise comparisons) were applied as indicated in the figure legends in a two-tailed, unpaired fashion. Variances were similar between experimental groups in each experiment unless otherwise stated. In vivo experiments included cohorts of sizes indicated in each figure legend, but at least 6 mice / group. In vitro analyses and in vivo experiments were repeated 2-5 times to ensure reproducible conclusions. The exact number of replicates is stated in each figure legend. The log-rank test was used for survival analysis, followed by a full pairwise multiple comparison procedure (Holm-Sidak method). Data from multiple experiments were cumulative unless otherwise indicated in the figure legends. No experimental data points were excluded from the analysis. Sample sizes were selected by power analysis using effect sizes determined by the authors' pilot experiments or previous experience.

[0037] Example 1 –Identification of aptamers specific for tumor-infiltrating myeloid cells To identify TIMC-specific aptamers, unsupervised, high-throughput (HT) sequencing-cell SELEX (systematic evolution of ligands by exponential enrichment) ( 25 ) was performed using the MDSC-derived cell line MSC2 ( 26 ), followed by empirical selection of monoclonal aptamers that bind to TIMC but not their splenic counterparts.

[0038] MSC2 cells were considered to be a suitable TIMC surrogate for initial aptamer screening because these cells are available without requiring any manipulation of tumor-infiltrating cells that would cause artifacts, and they acquire suppressive activity only after treatment with IL4 (26). Moreover, MSC2 cells express genes shared with TIMC, as confirmed by genome-wide transcriptional analysis. Indeed, MSC2 cells treated with IL4 are transcriptionally similar to TIMC, both in terms of genome-wide expression levels and overexpressed genes, whereas untreated MSC2 cells express splenic CD11b + Share gene expression profiles of cells.

[0039] Aptamers were selected through 11 cycles of cell SELEX using untreated MSC2 as negative selector and IL4-treated MSC2 as positive selector, i.e., TIMC surrogate. The resulting polyclonal aptamer library showed increasing specificity with selection cycles only for IL4-treated MSC2 (Figure 1A), thus proving the overall efficacy of SELEX enrichment (Figures 1A and 1B). Furthermore, when tested against single cell suspensions obtained from either tumors or spleens of CT26 tumor-bearing mice, the aptamer library was able to discriminate TIMC from their splenic counterparts by preferentially recognizing MDSCs and macrophages from tumors, but not those from the spleens of the same tumor-bearing animals or tumor-free mice (Figure 1C).

[0040] Example 2 –Identification of four monoclonal aptamers specific for tumor-infiltrating myeloid cells Monoclonal aptamers that could be easily produced and manipulated were identified using a bioinformatics pipeline. Briefly, polyclonal aptamer libraries from cycles 1, 6, 10, and 11 were HT sequenced and the data was analyzed using HT-SELEX and APTANI (27), a computational tool for identifying target-specific aptamers from secondary structure information. Analysis on the library from cycle 11 resulted in 154 monoclonal aptamers that showed enrichment in the library of greater than 0.01% and contained 158 secondary RNA motifs with motif frequencies greater than 0.05% of the total number of motifs. From this set of aptamers, we focused on 15 sequences that had an enrichment of at least 0.1% and contained either a large number of different secondary RNA motifs (i.e., at least three motifs) or at least one motif with a frequency greater than 0.1%. Interestingly, frequency analysis across cycles indicates that when stringency was gradually increased, most of these aptamers began to emerge from cycle 6 (data not shown).

[0041] These 15 aptamers were tested by flow cytometry on single cell suspensions from tumors and spleens of mice challenged with 4T1 tumors (Figure 2A). Twelve of the 15 aptamers recognized macrophages, granulocytic MDSCs (gMDSCs), and / or monocytic MDSCs (mMDSCs) from tumors with statistically significant accuracy (compared to irrelevant aptamers; p<0.001) (Figure 1D), whereas only six of them (i.e., apts 3, 6, 11, 12, 14, 15) were specific for tumor-infiltrating myeloid cells and did not recognize any subset of splenic MDSCs or CD11b cells (p>0.05). Cluster and secondary structure analysis indicated that aptamers 15 and 11 were similar to aptamers 14 and 12, respectively (Figure 10), therefore only aptamers 3, 6, 11, and 14 (Figure 2B) were selected for further experiments.

[0042] Example 4 – Aptamers 3, 6, 11 and 14 recognize tumor-infiltrating myeloid cells from multiple mouse tumors The binding properties of monoclonal RNA aptamers 3, 6, 11 and 14 were evaluated in other tumor models. Aptamer 3, 6, 11 or 14 single cell suspensions were stained from tumors, bone marrow, spleen and lungs of BALB / c mice bearing 4T1 breast cancer, CT26 colon cancer or RENCA kidney cancer. Similar analyses were performed on single cell suspensions from C57Bl / 6 mice bearing B16LU8 melanoma, MCA203 fibrosarcoma or E0771 breast cancer. The selected aptamers were able to recognize TIMC from all tumors in both mouse strains, while no or low staining was observed in myeloid cells from either spleen or bone marrow and not in T and B cells. This finding indicates that in all analyzed tumor samples, aptamers 3, 6, 11 and 14 were able to recognize TIMC, regardless of mouse strain or tumor type. Interestingly, in different tumors, the selected aptamers recognize different tumor-infiltrating myeloid subsets.

[0043] Specificity was further evaluated by immunofluorescence microscopy using 4T1 tumors and tissue arrays from naive mice. All selected aptamers recognized cells in the tumor, but most of the tissues appeared to be negative for the aptamers with a few exceptions: aptamer 3 recognized hepatocytes with low intensity, aptamer 6 showed binding to epithelial cells in the uterus and low binding to pancreatic acinar tissue, and aptamer 14 recognized intestinal villi and appeared to show nuclear staining in the cerebellum, pancreatic acinar tissue, stomach, and spleen.

[0044] We next assessed whether an equimolar mixture of aptamers 3, 6, 11, and 14 would be more efficient in recognizing TIMC compared to each individual aptamer. FACS analysis on cells from spleens and tumors of 4T1-bearing mice revealed clear additive / synergistic effects when using a mixture of the four aptamers (Figure 2C).

[0045] The selected aptamers recognized human myeloid cells in tumors but not in the blood of patients with head and neck squamous cell carcinoma.

[0046] Since the selected aptamers were found to be able to discriminate between spleen and tumor-infiltrating myeloid cells in mice, we tested whether they could cross-react with human myeloid cells and whether these aptamers could discriminate human TIMCs from their circulating counterparts. Briefly, single cell suspensions from tumors or blood of patients with recurrent HNSCC were stained with antibodies against CD33, HLADR, CD11b, CD14, CD15, and IL4Ra, counterstained with PE-conjugated monoclonal aptamers and vital dyes, and analyzed by flow cytometry (Figure 3A). Although no or low staining was detected in PBMCs and non-myeloid cells, at least two aptamers recognized tumor-infiltrating myeloid cells from all analyzed patients to different degrees. Notably, at the tumor site, the selected aptamers were able to discriminate the majority of mMDSCs and CD14 - CD15 - The aptamers recognize subsets of macrophages / iMDSCs and gMDSCs (Figure 3). Interestingly, within each subset, the aptamers recognize IF4Ra, which we previously showed to be a suppressor myeloid cell and correlate with tumor recurrence in HNSCC (28). + Recognizes almost all cells.

[0047] Similar results were obtained by image cytometry. Briefly, paraffin-embedded tissues from patients with recurrent HNSCC were stained with an equimolar mixture of four Cy3-labeled aptamers and counterstained with DAPI and anti-CD33 antibody. Images were acquired by a high-resolution scanner and data were sent to cellprofiler and FCS express for final analysis. The aptamers correlated with CD33 in the tumor. - CD33 present in cells and surrounding healthy tissue + CD33 infiltrating tumors compared with cells +The aptamers showed higher binding to human myeloid cells (Figure 3B). Taken together, these data indicate that the selected aptamers cross-react with human myeloid cells and preferentially recognize myeloid cells infiltrating HNSCC.

[0048] Example 4 – Annexin A4 and vimentin are putative ligands for aptamers 3 and 11 Aptamer-based immunoprecipitation and mass spectrometry identified annexin A4 (ANXA4 isoform X1 P97495) and vimentin (VIM P20152) as putative targets for aptamers 3 and 11, respectively (Figure 4A). FACS analysis using beads loaded with related or unrelated recombinant proteins confirmed the specificity of these two aptamers for annexin and vimentin proteins and IF4-treated MSC2 (Figure 4B and Figure 4C). The specificity of aptamer 3 for ANXA4 and aptamer 11 for vimentin was tested by competition experiments (Figure 4D) and with validated shRNAs (29) (Figure 4E), respectively. No targets have been identified so far for aptamers 6 and 14.

[0049] Example 5 –Aptamers specifically recognize tumor-associated myeloid cells in vivo To assess whether the aptamers could accumulate at tumor sites in vivo, mice orthotopically implanted with 4T1-luciferase breast cancer or tumor-free mice were treated intravenously with a mixture of aptamers 3, 6, 11, and 14 conjugated with streptavidin-Alexa Fluor 750 or Alexa Fluor 647 (Figure 5A). These fluorescent dyes allow detection of aptamer biodistribution by either an in vivo imaging system (IVIS) or flow cytometry, respectively.

[0050] IVIS analysis revealed that the selected aptamer rapidly accumulated at the tumor site and in the liver (Figure 5B and Figure 5C), and subsequently in the bladder. Signal from the TIMC-specific aptamer was detectable in the tumor 8 days after injection, while the fluorescent signal in other tissues disappeared more rapidly, indicating preferential retention of the aptamer to the tumor microenvironment. Flow cytometry analysis (Figure 5D-5F) confirmed the IVIS results, showing that the aptamer in the tumor microenvironment bound to CD11b + aptamer-positive gMDSCs appeared at later time points in the tumors and spleens of treated mice (Figures 5E-5F), supporting previous evidence that mMDSCs can differentiate into gMDSCs (30, 31).

[0051] Example 6 – TIMC-specific aptamers maximize delivery of doxorubicin at tumor sites Since TIMC-specific aptamers can target the tumor microenvironment, their drug delivery capabilities were evaluated using doxorubicin (DOX) as a chemotherapeutic agent. Briefly, aptamers were extended at the 3' end with a GC-rich tail that promotes DOX intercalation (32, 33), and optimal loading conditions were evaluated by fluorescence spectroscopy (Figure 6A and Figure 6B). To evaluate whether DOX-aptamer conjugates can target primary and metastatic lesions, an equimolar mixture of the four aptamers was loaded with DOX and injected intravenously into mice previously loaded with i) 4T1 breast cancer injected into the mammary gland (primary tumor in the breast, metastases in the lung); ii) 4T1 administered intravenously (metastases in the liver and lung); or iii) the nonmetastatic 4T1 variant 67NR injected into the mammary gland (no metastases). Two hours after injection, the biodistribution of DOX-aptamer conjugates was evaluated by spectrophotometric analysis (Figure 6C). The aptamer was able to deliver DOX to the primary breast tumor and metastatic sites. In mice bearing non-metastatic 67NR tumors, DOX was found only in the diseased breast tissue. In mice bearing intravenously injected metastatic 4T1, DOX was found in the liver and lungs (sites of metastatic cancer), whereas in mice bearing mammary-injected 4T1, DOX accumulates at the primary tumor site and in the lungs, which are the primary metastatic site in this model. Additional experiments indicated that DOX concentrations at the tumor site of mice treated intravenously with 0.35 mg / kg ("low dose doxorubicin") DOX-aptamer complex were similar to those of mice treated with 3.5 mg / kg ("high dose") free DOX, while overall DOX concentrations in all other evaluated compartments were significantly lower in mice treated with DOX-aptamer complex (Figure 6D). These results indicate not only that doxorubicin conjugates do not affect aptamer specificity, but also that TIMC-specific aptamers are capable of delivering chemotherapeutic agents to primary and metastatic sites.

[0052] Example 7 – TIMC-specific aptamers increase the therapeutic index of doxorubicin We next compared the therapeutic efficacy of doxorubicin delivered by the TIMC-specific aptamer with that by PEGylated liposomes (Doxil, the first clinically available nanoparticle that became the gold standard for doxorubicin therapy (34, 35)) and as an unconjugated molecule. Briefly, 4T1-bearing BALB / c mice were treated with high or low doses of unconjugated DOX, or low doses of DOX conjugated to the TIMC-specific aptamer, or Doxil (low dose - 0.35 mg / kg). Treatment was repeated 2 and 6 days later. As additional controls, mice were treated with unconjugated aptamer, an irrelevant aptamer conjugated to doxorubicin, or left untreated. No significant antitumor effect was observed in mice treated with either high doses of free doxorubicin or Doxil (Figure 7A). Conversely, low doses of doxorubicin delivered via the TIMC-specific aptamer significantly delayed tumor progression, resulting in 40% of treated mice being free of clinically detectable tumors 60 days after treatment (Figure 7A). Furthermore, high doses of free doxorubicin resulted in high toxicity, as determined by significant weight loss and 15% treatment-related mortality, whereas no toxicity was observed with the DOX-loaded aptamer (Figure 7B).

[0053] Since depletion or inactivation of MDSCs can delay tumor progression (79) and doxorubicin has been reported to deplete MDSCs (36) or impair their function (37), we investigated whether the observed antitumor efficacy was due to bystander release of doxorubicin in the tumor microenvironment or rather to depletion / inactivation of MDSCs. To this end, we evaluated the efficacy of the treatment using a doxorubicin-resistant 4T1 cell line (DoxR-4Tl) and a doxorubicin-sensitive parental cell line. Although DOX-loaded aptamer treatment confirmed its efficacy against the doxorubicin-sensitive parental cell line, no therapeutic effect was observed in mice challenged with doxorubicin-resistant 4T1 tumors (Figure 7C).

[0054] Example 8 - Aptamers effectively deliver CCR1 and 5 siRNA to tumor-infiltrating myeloid cells and delay tumor progression. Balb / c mice were orthotopically challenged with 4T1 breast cancer and treated 5, 7, 9, 12, 14, 16, and 19 days after challenge with a) scrambled siRNA (black bars) or a mixture of aptamers 3, 6, 11, and 14 loaded with siRNA against CCR1 and CCR5 (30 pmoles, white bars) intravenously. qRT-PCR for CCR1 and CCR5 mRNA was performed on magnetically isolated tumor-infiltrating bone marrow cells from each group 6 days after the last treatment. The results show that the mixture of aptamers effectively delivered siRNA against CCR1 and CCR5, allowing in vivo silencing of both genes, whereas no effect was seen with the scrambled siRNA control (Figure 8B). Importantly, aptamer-mediated delivery of both siRNAs to tumor-infiltrating bone marrow cells induced a significant reduction in tumor size (Figure 8C). Taken together, these results indicate that aptamers can deliver therapeutic siRNA to tumor-infiltrating myeloid cells in vivo and influence tumor progression.

[0055] References:

[0056] 1. C. Carvalho, RX Santos, S. Cardoso, S. Correia, PJ Oliveira, MS Santos, PI Moreira, Doxorubicin: the good, the bad and the ugly effect. Current medicinal chemistry 16, 3267-3285 (2009).

[0057] 2. J. Kydd, R. Jadia, P. Velpurisiva, A. Gad, S. Paliwal, P. Rai, Targeting Strategies for the Combination Treatment of Cancer Using Drug Delivery Systems. Pharmaceutics 9, (2017).

[0058] 3. C. Kinnear, T. L. Moore, L. Rodriguez-Lorenzo, B. Rothen-Rutishauser, A. Petri-Fink, Form Follows Function: Nanoparticle Shape and Its Implications for Nanomedicine. Chemical reviews 117, 11476-11521 (2017).

[0059] 4. B. Bahrami, M. Hojjat-Farsangi, H. Mohammadi, E. Anvari, G. Ghalamfarsa, M. Yousefi, F. Jadidi-Niaragh, Nanoparticles and targeted drug delivery in cancer therapy. Immunology letters 190, 64-83 (2017).

[0060] 5. P. Tiet, J. M. Berlin, Exploiting homing abilities of cell carriers: Targeted delivery of nanoparticles for cancer therapy. Biochemical pharmacology, (2017).

[0061] 6. U. Prabhakar, H. Maeda, R. K. Jain, E. M. Sevick-Muraca, W. Zamboni, O. C. Farokhzad, S. T. Barry, A. Gabizon, P. Grodzinski, D. C. Blakey, Challenges and key considerations of the enhanced permeability and retention (EPR) effect for nanomedicine drug delivery in oncology. Cancer research 73, 2412-2417 (2013).

[0062] 7. M. Ruella, D. M. Barrett, S. S. Kenderian, O. Shestova, T. J. Hofmann, J. Scholler, S. F. Lacey, J. J. Melenhorst, F. Nazimuddin, J. Perazzelli, D. A. Christian, C. A. Hunter, D. L. Porter, C. H. June, S. A. Grupp, S. Gill, Treatment of leukemia antigen-loss relapses occurring after CD19-targeted immunotherapies by combination of anti-CD123 and anti-CD19 chimeric antigen receptor T cells. Journal for Immunotherapy of Cancer 3, O5-O5 (2015).

[0063] 8. S. Kelderman, T. N. M. Schumacher, J. B. A. G. Haanen, Acquired and intrinsic resistance in cancer immunotherapy. Molecular Oncology 8, 1132-1139 (2014).

[0064] 9. H. Attarwala, Role of antibodies in cancer targeting. Journal of Natural Science, Biology, and Medicine 1, 53-56 (2010).

[0065] 10. C. Szot, S. Saha, X. M. Zhang, Z. Zhu, M. B. Hilton, K. Morris, S. Seaman, J. M. Dunleavey, K.-S. Hsu, G.-J. Yu, H. Morris, D. A. Swing, D. C. Haines, Y. Wang, J. Hwang, Y. Feng, D. Welsch, G. DeCrescenzo, A. Chaudhary, E. Zudaire, D. S. Dimitrov, B. St. Croix, Tumor stroma-targeted antibody-drug conjugate triggers localized anticancer drug release. The Journal of Clinical Investigation 128, 2927-2943 (2018).

[0066] 11. T. Condamine, I. Ramachandran, J. I. Youn, D. I. Gabrilovich, Regulation of tumor metastasis by myeloid-derived suppressor cells. Annu Rev Med 66, 97-110 (2015).

[0067] 12. D. Marvel, D. I. Gabrilovich, Myeloid-derived suppressor cells in the tumor microenvironment: expect the unexpected. J Clin Invest 125, 3356-3364 (2015).

[0068] 13. A. Arina, L. Corrales, V. Bronte, Enhancing T cell therapy by overcoming the immunosuppressive tumor microenvironment. Semin Immunol, (2016).

[0069] 14. S. Ugel, F. De Sanctis, S. Mandruzzato, V. Bronte, Tumor-induced myeloid deviation: when myeloid-derived suppressor cells meet tumor-associated macrophages. J Clin Invest 125, 3365-3376 (2015).

[0070] 15. S. Zilio, P. Serafini, Neutrophils and Granulocytic MDSC: The Janus God of Cancer Immunotherapy. Vaccines 4, (2016).

[0071] 16. P. Serafini, Myeloid derived suppressor cells in physiological and pathological conditions: the good, the bad, and the ugly. Immunol Res 57, 172-184 (2013).

[0072] 17. T. Kitamura, B.-Z. Qian, J. W. Pollard, Immune cell promotion of metastasis. 15, 73 (2015).

[0073] 18. B. R. Achyut, A. S. Arbab, Myeloid cell signatures in tumor microenvironment predicts therapeutic response in cancer. OncoTargets and therapy 9, 1047-1055 (2016).

[0074] 19. S. Ugel, F. De Sanctis, S. Mandruzzato, V. Bronte, Tumor-induced myeloid deviation: when myeloid-derived suppressor cells meet tumor-associated macrophages. The Journal of Clinical Investigation 125, 3365-3376 (2015).

[0075] 20. S. Kusmartsev, D. I. Gabrilovich, STAT1 signaling regulates tumor-associated macrophage-mediated T cell deletion. J Immunol 174, 4880-4891 (2005).

[0076] 21. V. Kumar, S. Patel, E. Tcyganov, D. I. Gabrilovich, The nature of myeloid-derived suppressor cells in the tumor microenvironment. Trends in immunology 37, 208-220 (2016).

[0077] 22. A. A. Al-Khami, L. Zheng, L. Del Valle, F. Hossain, D. Wyczechowska, J. Zabaleta, M. D. Sanchez, M. J. Dean, P. C. Rodriguez, A. C. Ochoa, Exogenous lipid uptake induces metabolic and functional reprogramming of tumor-associated myeloid-derived suppressor cells. Oncolmmunology 6, el344804 (2017).

[0078] 23. S. Catuogno, C. L. Esposito, Aptamer Cell-Based Selection: Overview and Advances. Biomedicines 5, (2017).

[0079] 24. J. Zhou, J. Rossi, Aptamers as targeted therapeutics: current potential and challenges. Nature reviews. Drug discovery 16, 181-202 (2017).

[0080] 25. M. Hamada, In silico approaches to RNA aptamer design. Biochimie, (2017).

[0081] 26. E. Apolloni, V. Bronte, A. Mazzoni, P. Serafini, A. Cabrelle, D. M. Segal, H. A. Young, P. Zanovello, Immortalized myeloid suppressor cells trigger apoptosis in antigen-activated T lymphocytes. J Immunol 165, 6723-6730 (2000).

[0082] 27. J. Caroli, C. Taccioli, A. De La Fuente, P. Serafini, S. Bicciato, APTANI: a computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data. Bioinformatics 32, 161-164 (2016).

[0083] 28. D. T. Weed, J. L. Vella, I. M. Reis, A. C. De la Fuente, C. Gomez, Z. Sargi, R. Nazarian, J. Califano, I. Borrello, P. Serafini, Tadalafil reduces myeloid-derived suppressor cells and regulatory T cells and promotes tumor immunity in patients with head and neck squamous cell carcinoma. Clin Cancer Res 21, 39-48 (2015).

[0084] 29. M. Desclaux, M. Teigell, L. Amar, R. Vogel, Y. R. M. Gimenez, A. Privat, J. Mallet, A novel and efficient gene transfer strategy reduces glial reactivity and improves neuronal survival and axonal growth in vitro. PLoS One 4, e6227 (2009).

[0085] 30. S. Zilio, J. L. Vella, A. C. De la Fuente, P. M. Daftarian, D. T. Weed, A. Kaifer, I. Marigo, K. Leone, V. Bronte, P. Serafini, 4PD Functionalized Dendrimers: A Flexible Tool for In Vivo Gene Silencing of Tumor-Educated Myeloid Cells. J Immunol 198, 4166-4177 (2017).

[0086] 31. J.-I. Youn, V. Kumar, M. Collazo, Y. Nefedova, T. Condamine, P. Cheng, A. Villagra, S. Antonia, J. C. McCaffrey, M. Fishman, A. Sarnaik, P. Homa, E. Sotomayor, D. I. Gabrilovich, Epigenetic silencing of retinoblastoma gene regulates pathologic differentiation of myeloid cells in cancer. Nature immunology 14, 211-220 (2013).

[0087] 32. C. H. Stuart, D. A. Horita, M. J. Thomas, F. R. Salsbury, M. O. Lively, W. H. Gmeiner, Site-Specific DNA-Doxorubicin Conjugates Display Enhanced Cytotoxicity to Breast Cancer Cells. Bioconjugate Chemistry 25, 406-413 (2014).

[0088] 33. V. Bagalkot, O. C. Farokhzad, R. Langer, S. Jon, An aptamer-doxombicin physical conjugate as a novel targeted drug-delivery platform. Angew Chem Int Ed Engl 45, 8149-8152 (2006).

[0089] 34. S. M. Rafiyath, M. Rasul, B. Lee, G. Wei, G. Lamba, D. Liu, Comparison of safety and toxicity of liposomal doxorubicin vs. conventional anthracyclines: a meta-analysis. Experimental Hematology & Oncology 1, 10 (2012).

[0090] 35. U. Bulbake, S. Doppalapudi, N. Kommineni, W. Khan, Liposomal Formulations in Clinical Use: An Updated Review. Pharmaceutics 9, 12 (2017).

[0091] 36. N. N. Belyaev, N. Abdolla, Y. V. Perfilyeva, Y. O. Ostapchuk, V. K. Krasnoshtanov, A. Kali, R. Tleulieva, Daunombicin conjugated with alpha-fetoprotein selectively eliminates myeloid-derived suppressor cells (MDSCs) and inhibits experimental tumor growth. Cancer immunology, immunotherapy : CII 67, 101-111 (2018).

[0092] 37. D. Alizadeh, M. Trad, N. T. Hanke, C. B. Larmonier, N. Janikashvili, B. Bonnotte, E. Katsanis, N. Larmonier, Doxorubicin eliminates myeloid-derived suppressor cells and enhances the efficacy of adoptive T-cell transfer in breast cancer. Cancer Res 74, 104-118 (2014).

[0093] 38. L. Ansari, F. Shiehzadeh, Z. Taherzadeh, S. Nikoofal-Sahlabadi, A. A. Momtazi-Borojeni, A. Sahebkar, S. Eslami, The most prevalent side effects of pegylated liposomal doxorubicin monotherapy in women with metastatic breast cancer: a systematic review of clinical trials. Cancer gene therapy 24, 189-193 (2017).

[0094] 39. A. Sica, V. Bronte, Altered macrophage differentiation and immune dysfunction in tumor development. J Clin Invest 117, 1155-1166 (2007).

[0095] 40. N. Erez, L. M. Coussens, Leukocytes as paracrine regulators of metastasis and determinants of organ-specific colonization. Int J Cancer 128, 2536-2544 (2011).

[0096] 41. J. W. Pollard, Tumour-educated macrophages promote tumour progression and metastasis. Nat Rev Cancer 4, 71-78 (2004).

[0097] 42. C. Steidl, T. Lee, S. P. Shah, P. Farinha, G. Han, T. Nayar, A. Delaney, S. J. Jones, J. Iqbal, D. D. Weisenburger, M. A. Bast, A. Rosenwald, H. K. Muller-Hermelink, L. M. Rimsza, E. Campo, J. Delabie, R. M. Braziel, J. R. Cook, R. R. Tubbs, E. S. Jaffe, G. Lenz, J. M. Connors, L. M. Staudt, W. C. Chan, R. D. Gascoyne, Tumor-associated macrophages and survival in classic Hodgkin's lymphoma. N Engl J Med 362, 875-885 (2010).

[0098] 43. J. Zhang, L. Patel, K. J. Pienta, CC chemokine ligand 2 (CCL2) promotes prostate cancer tumorigenesis and metastasis. Cytokine Growth Factor Rev 21, 41-48 (2010).

[0099] 44. G. Gallina, L. Dolcetti, P. Serafini, C. De Santo, I. Marigo, M. P. Colombo, G. Basso, F. Brombacher, I. Borrello, P. Zanovello, S. Bicciato, V. Bronte, Tumors induce a subset of inflammatory monocytes with immunosuppressive activity on CD8+ T cells. J Clin Invest 116, 2777-2790 (2006).

[0100] 45. S. Kusmartsev, S. Nagaraj, D. I. Gabrilovich, Tumor-associated CD8+ T cell tolerance induced by bone marrow-derived immature myeloid cells. J Immunol 175, 4583-4592 (2005).

[0101] 46. V. Fleming, X. Hu, R. Weber, V. Nagibin, C. Groth, P. Altevogt, J. Utikal, V. Umansky, Targeting Myeloid-Derived Suppressor Cells to Bypass Tumor-Induced Immunosuppression. Frontiers in immunology 9, 398 (2018).

[0102] 47. S. Hamada, A. Masamune, T. Shimosegawa, Novel therapeutic strategies targeting tumor-stromal interactions in pancreatic cancer. Frontiers in physiology 4, 331 (2013).

[0103] 48. F. Veglia, M. Perego, D. Gabrilovich, Myeloid-derived suppressor cells coming of age. Nat Immunol 19, 108-119 (2018).

[0104] 49. H. Liu, J. Mai, J. Shen, J. Wolfram, Z. Li, G. Zhang, R. Xu, Y. Li, C. Mu, Y. Zu, X. Li, G. L. Lokesh, V. Thiviyanathan, D. E. Volk, D. G. Gorenstein, M. Ferrari, Z. Hu, H. Shen, A Novel DNA Aptamer for Dual Targeting of Polymorphonuclear Myeloid-derived Suppressor Cells and Tumor Cells. Theranostics 8, 31-44 (2018).

[0105] 50. H. Qin, B. Lerman, I. Sakamaki, G. Wei, S. C. Cha, S. S. Rao, J. Qian, Y. Hailemichael, R. Nurieva, K. C. Dwyer, J. Roth, Q. Yi, W. W. Overwijk, L. W. Kwak, Generation of a new therapeutic peptide that depletes myeloid-derived suppressor cells in tumor-bearing mice. Nature medicine 20, 676-681 (2014).

[0106] 51. F. Roth, A. C. De La Fuente, J. L. Vella, A. Zoso, L. Inverardi, P. Serafini, Aptamer-Mediated Blockade of IL4Ralpha Triggers Apoptosis of MDSCs and Limits Tumor Progression. Cancer Res 72, 1373-1383 (2012).

[0107] 52. S. Bilalic, A. Michlmayr, V. Gruber, E. Buchberger, C. Burghuber, G. A. Bohmig, R. Oehler, Lymphocyte activation induces cell surface expression of an immunogenic vimentin isoform. Transplant immunology 27, 101-106 (2012).

[0108] 53. A. Mitra, A. Satelli, X. Xia, J. Cutrera, L. Mishra, S. Li, Cell-surface Vimentin: A mislocalized protein for isolating csVimentin(+) CD133(-) novel stem-like hepatocellular carcinoma cells expressing EMT markers. International journal of cancer 137, 491-496 (2015).

[0109] 54. N. Mor-Vaknin, A. Punturieri, K. Sitwala, D. M. Markovitz, Vimentin is secreted by activated macrophages. Nat Cell Biol 5, 59-63 (2003).

[0110] 55. P. Benes, V. Maceckova, Z. Zdrahal, H. Konecna, E. Zahradnickova, J. Muzik, J. Smarda, Role of vimentin in regulation of monocyte / macrophage differentiation. Differentiation 74, 265-276 (2006).

[0111] 56. Y. Lou, O. Preobrazhenska, U. auf dem Keller, M. Sutcliffe, L. Barclay, P. C. McDonald, C. Roskelley, C. M. Overall, S. Dedhar, Epithelial-mesenchymal transition (EMT) is not sufficient for spontaneous murine breast cancer metastasis. Dev Dyn 237, 2755- 2768 (2008).

[0112] [ PubMed ] 57. Sarkar TR, Battula VL, Werden SJ, Vijay GV, Ramirez-Pena EQ, Taube JH, Chang JT, Miura N, Porter N, Sphyris N, Andreeff M, Mani, SA GD3 synthase regulates epithelial-mesenchymal transition and metastasis in breast cancer. Oncogene 34, 2958–2967.

[0113] [ PMC free article ] [ PubMed ] 58. Boye TL, Maeda K, Pezeshkian W, Spnder SL, Haeger SC, Gerke V, Simonsen AC, Nylandsted J. Annexin A4 and A6 induce membrane curvature and constriction during cell membrane repair. Nature Communications 8, 1623 (2017).

[0114] 59. F. O. Martinez, L. Helming, R. Milde, A. Varin, B. N. Melgert, C. Draijer, B. Thomas, M. Fabbri, A. Crawshaw, L. P. Ho, N. H. Ten Hacken, V. Cobos Jimenez, N. A. Kootstra, J. Hamann, D. R. Greaves, M. Locati, A. Mantovani, S. Gordon, Genetic programs expressed in resting and IL-4 alternatively activated mouse and human macrophages: similarities and differences. Blood 121, e57-69 (2013).

[0115] 60. M. Diakonova, V. Gerke, J. Ernst, J. P. Liautard, G. van der Vusse, G. Griffiths, localization of five annexins in J774 macrophages and on isolated phagosomes. Journal of Cell Science 110, 1199-1213 (1997).

[0116] 61. F. O. Martinez, L. Helming, R. Milde, A. Varin, B. N. Melgert, C. Draijer, B. Thomas, M. Fabbri, A. Crawshaw, L. P. Ho, N. H. Ten Hacken, V. Cobos Jimenez, N. A. Kootstra, J. Hamann, D. R. Greaves, M. Locati, A. Mantovani, S. Gordon, Genetic programs expressed in resting and IL-4 alternatively activated mouse and human macrophages: similarities and differences. Blood 121, e57-e69 (2013).

[0117] 62. B. A. Pulaski, S. Ostrand-Rosenberg, Mouse 4T1 breast tumor model. Current protocols in immunology Chapter 20, Unit 20.22 (2001).

[0118] 63. P. Serafini, K. Meckel, M. Kelso, K. Noonan, J. Califano, W. Koch, L. Dolcetti, V. Bronte, I. Borrello, Phosphodiesterase-5 inhibition augments endogenous antitumor immunity by reducing myeloid-derived suppressor cell function. J Exp Med 203, 2691-2702 (2006).

[0119] 64. P. Nanni, C. de Giovanni, P. L. Lollini, G. Nicoletti, G. Prodi, TS / A: a new metastasizing cell line from a BALB / c spontaneous mammary adenocarcinoma. Clinical & experimental metastasis 1, 373-380 (1983).

[0120] 65. M. G. Brattain, J. Strobel-Stevens, D. Fine, M. Webb, A. M. Sarrif, Establishment of mouse colonic carcinoma cell lines with different metastatic properties. Cancer Res 40, 2142-2146 (1980).

[0121] 66. G. P. Murphy, W. J. Hrushesky, A murine renal cell carcinoma. Journal of the National Cancer Institute 50, 1013-1025 (1973).

[0122] 67. R. J. Barth, S. N. Bock, J. J. Mule, S. A. Rosenberg, Unique murine tumor-associated antigens identified by tumor infiltrating lymphocytes. The Journal of Immunology 144, 1531-1537 (1990).

[0123] 68. C. J. Aslakson, F. R. Miller, Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Res 52, 1399-1405 (1992).

[0124] 69. J. M. Layzer, B. A. Sullenger, Simultaneous generation of aptamers to multiple gamma-carboxyglutamic acid proteins from a focused aptamer library using DeSELEX and convergent selection. Oligonucleotides 17, 1-11 (2007).

[0125] 70. M. Ahmed, W. E. Monsky, G. Girnun, A. Lukyanov, G. D'Ippolito, J. B. Kruskal, K. E. Stuart, V. P. Torchilin, S. N. Goldberg, Radiofrequency thermal ablation sharply increases intratumoral liposomal doxorubicin accumulation and tumor coagulation. Cancer Res 63, 6327-6333 (2003).

Claims

1. An RNA aptamer conjugated to a therapeutic agent, said aptamer specifically binding to a target expressed on tumor-infiltrating myeloid cells.

2. The aptamer of claim 1 , wherein the therapeutic agent is a nucleic acid molecule.

3. The aptamer of claim 1 , wherein the nucleic acid molecule is DNA, RNA, shRNA, siRNA or miRNA.

4. The aptamer of claim 1 , wherein the therapeutic agent is a chemotherapeutic agent.

5. The aptamer of claim 4 , wherein the chemotherapeutic agent is doxorubicin.

6. The aptamer of any one of claims 1 to 5, wherein the target expressed on tumor-infiltrating myeloid cells is annexin or vimentin.

7. A method for delivering a therapeutic agent to tumor-infiltrating myeloid cells, comprising contacting said cells with an aptamer according to any one of claims 1 to 6.

8. A method for detecting the presence of tumor-infiltrating myeloid cells in a biological sample, comprising contacting the sample with an aptamer according to any one of claims 1 to 6 conjugated to a detectable label.

9. A method of treating cancer in a subject in need thereof, comprising administering to the subject an aptamer according to any one of claims 4 to 6.

10. 11. The method of claim 10, wherein the subject is suffering from breast cancer, colon cancer, renal cancer, head and neck cancer, or melanoma.

Images