Synthetic glycopeptides and derivative compounds for detecting cancer biomarkers in early stage MUC1-positive tumors

Abstract

The mucin MUC1 has been shown to undergo aberrant glycosylation in various tumors. The tumor-associated (TA) form of MUC1 is immunogenic, and patients commonly demonstrate the presence of autoantibodies against it. Therefore, these antibodies serve as potential biomarkers for cancer detection. A structurally based non-native MUC1 epitope has been developed and can be used as a highly efficient agglutinating agent for patient-derived antibodies. Furthermore, to improve the sensitivity and specificity of quantitative assays using this agglutinating agent, gold nanoparticles functionalized with multiple copies of the non-native epitope have been generated.

Description

[Technical Field]

[0001] The present invention relates to the technical field of medicine, in particular to the technical field of oncology, and more particularly to synthetic glycopeptides suitable for the early detection of MUC1-expressing pancreatic tumors and other tumors. [Background technology]

[0002] MUC1 (mucin-1) is a glycoprotein complexed with glycans attached via α-O-glycosidic bonds to threonine and / or serine side chains. Mucins coat the apical surface of epithelial cells in the lung, stomach, intestine, eye, and other organs. They protect the body from infection by binding pathogens to oligosaccharides in their extracellular domains, preventing them from reaching and infecting cells. Overexpression and aberrant glycosylation of MUC1 are frequently associated with cancers of the colon, breast, ovary, lung, and pancreas, among others. Joyce Taylor-Papadimitriou has published a paper on breast and ovarian cancers. 1 In the course of this study, the most immunogenic fragments of the MUC1 glycoprotein were identified and characterized. MUC1 mucin has two subunits: C-terminal MUC and N-terminal MUC 2,3 The latter subunit can be shed from the surface and is therefore detected in the serum of cancer patients. 4,5 Aberrantly glycosylated MUC1, which displays simple glycans, is known as (TA)-MUC1 (tumor-associated MUC1). In this sense, TA-MUC1 is found at very low levels in human serum from healthy individuals and increases in cancer cases. 6,7 Furthermore, for other biomarkers, CA27-29 at levels above 40 U / ml 8 or CA 15-3 levels exceeding 25-30 U / ml 9 have been found to generally indicate the presence of malignant tumors. Many examples of antigen-based cancer biomarkers can be found in the literature, but most of them have not demonstrated satisfactory sensitivity and specificity. 5,10-13 . However, anti-TA-MUC1 antibodies produced by patients generally persist in the blood longer than the corresponding antigens, and are present early in the disease process, making them an attractive alternative. In particular, several studies using MUC1 as a tool to detect and measure these antibodies have been reported in the literature. In this context, particular attention should be paid to the system developed by Wang et al., who used a peptide containing six MUC1 repeat units to detect anti-MUC1 antibodies in human serum. 14 In this series of studies, Gheybi et al. developed a chimera consisting of MUC1 and breast cancer-associated growth factor receptor 2 (HER2). This combination can be used to simultaneously detect MUC1 and HER2 antibodies in human serum. 15 .

[0003] Pancreatic cancer is currently the 14th most common cancer in the world and the 8th leading cause of cancer-related deaths. 16 In Spain, pancreatic tumors were the third leading cause of cancer deaths in 2019. 17 The lethality of this disease is a result of the late detection of the tumor, which is generally at a very advanced stage. 18 Currently, surgery, chemotherapy, and radiation therapy are the most common treatments for pancreatic cancer, but patients diagnosed with this cancer have a life expectancy of only four to six months. 19 . It should be noted that in pancreatic cancer, overexpression of TA-MUC1 correlates with increased susceptibility to metastasis and poor prognosis. 20 In this regard, anti-MUC1 antibodies may play an important role in the early diagnosis of pancreatic cancer patients. 21 Most of the examples in the literature based on the detection of anti-MUC1 antibodies do not show changes in the native antigen. Currently CA19-9 22 and carcinoembryonic antigen (CEA) 23Two blood biomarkers, α, β, and β, are used in the clinical management of pancreatic cancer patients. However, these biomarkers lack sufficient sensitivity and specificity to be used as diagnostic markers, but are currently used to monitor the progress of certain patients. Therefore, the discovery of more sensitive and specific biomarkers would provide a means for early detection of pancreatic cancer, thereby providing an opportunity for early intervention and significantly increasing patient survival. Summary of the Invention

[0004] In summary, tumor-associated MUC1 (TA-MUC1) induces the production of antibodies in patients. These autoantibodies could be attractive biomarkers for the early detection of this disease and can be quantified using various immunoassay systems. All previously reported systems lack diagnostic performance due to low sensitivity and specificity. We propose the use of antigens consisting of short artificial glycopeptides conjugated to nanoparticles as a more efficient detection system for patient-specific autoantibodies that can be used for early clinical diagnosis of patients.

[0005] Brief description of the invention To overcome the shortcomings of the current state of the art, modification of the antigen structure may be a key point to enhance the interaction between the antigen and patient-produced TA-MUC1 antibodies, thus improving the selectivity and sensitivity of potential detection systems. 24,25 To this end, it is essential to understand the molecular interactions through which these antibodies recognize their targets. In this sense, our research group obtained the X-ray structure of a MUC1 glycopeptide in complex with the monoclonal antibody 5E5 (anti-MUC1), providing important information on how this antibody interacts with antigens, thus paving the way for the design of non-natural antigens with improved antigen-antibody recognition properties. 26 .

[0006] According to a first aspect of the present invention, there are provided non-natural synthetic glycopeptides represented by [GGGGPAPGST(GalNCOEt)APPA] or CGGGPAPGST(GalNCOEt)APPA] and derivative glycopeptides thereof for use as antigens in a detection system for TA-MUC1 antibodies as cancer biomarkers. Derivatives should be understood as other molecules that involve minor chemical modifications of these glycopeptide sequences and have higher affinity for anti-TA-MUC1 targeted antibodies than the native glycopeptides.

[0007] These glycopeptides were designed to be most effective in detecting the appropriate subset of TA-MUC1 antibodies that correlate with disease state, and our data indicate that they are useful as diagnostic reagents for detecting pancreatic cancer and possibly other cancers with aberrant glycosylation of MUC1. A synthetic non-natural glycopeptide antigen, or compound [GGGGPAPGST(GalNCOEt)APPA], has 6.5-fold higher affinity for anti-MUC1 antibodies than the native antigen, making this antigen (and its derivatives) an extremely useful tool for detecting cancers with aberrant MUC1 glycosylation.

[0008] According to a second aspect of the present invention, there is provided a detection system comprising nanoparticles and a covalently bound antigen comprising a synthetic non-natural glycopeptide represented by [CGGGGPAPGST(GalNCOEt)APPA] and its glycopeptide derivatives by minor chemical modifications. Although ELISA assays are widely used and exhibit good selectivity, several issues limit their application, such as low sample concentrations or the difficulty of performing measurements in a reproducible manner. In this regard, thanks to nanotechnology, appropriately functionalized nanomaterials have been shown to be highly effective in detecting various tumor markers, resulting in highly sensitive and selective tests. 27,28 .

[0009] Gold nanoparticle arrays [CGGGGPAPGST(GalNCOEt)APPA] conjugated to antigens can aid antigen presentation and increase the sensitivity of antibody detection systems, which can be extrapolated to any other combination of glycopeptides according to the first aspect of the invention with nanoparticles such as micelles, dendrimers, liposomes, hybrid and compact polymer nanoparticles, fullerenes, carbon dots, quantum dots, silica, metal nanoparticles, among others. Preferably, the nanoparticles are gold nanoparticles. Preferably, the synthetic glycopeptide antigen is [CGGGGPAPGST(GalNCOEt)APPA]. Analysis of clinical samples indicates that our non-natural glycopeptide conjugated to gold nanoparticles (AuNPs) provides higher sensitivity and specificity (AUC=0.918) than the currently used blood biomarkers CEA and CA19-9, thus demonstrating our system's competitive advantage in the early detection of pancreatic cancer and potentially other cancers with aberrant glycosylation of MUC1.

[0010] According to a third aspect of the invention, there is provided a kit for carrying out an assay for detecting and quantifying binding of patient-produced anti-MUC1 antibodies (biomarkers) in a sample to one of the detection systems of the first or second aspects, the kit comprising a detection system according to the first or second aspect of the invention. The assay may be selected from the group consisting of dot blot, Western blot, ELISA, SPR, liquid phase infrared, or any other suitable assay for detecting antigen-antibody interactions known in the art or hereafter developed.

[0011] The invention will now be described in more detail in the following paragraphs of this specification, by way of example only, without intending to limit the scope of the invention, and the following description can be better understood when read in conjunction with the accompanying drawings, in which: [Brief explanation of the drawings]

[0012] [Figure 1A-C] FIG. 1 shows the chemical formula of the MUC1 antigen, first the natural glycopeptide 21 (FIG. 1A); then, two synthetic glycopeptides according to one embodiment of the present invention are shown in FIGS. 1B (glycopeptide 24) and 1C (glycopeptide 33). [Figure 2A-B] 2A and 2B show the concentration-response curves obtained for the binding of glycopeptides 21 and 24, respectively, to the 5E5 (anti-MUC1) antibody at 20°C. [Figure 3] Figure 3 shows a schematic representation of the synthetic route for preparing glycopeptide-AuNP conjugates: (i) passivation of AuNPs with alkyl-PEG600 thiols, (ii) coupling of SM(PEG)2, and (iii) coupling of glycopeptides. [Figure 4] FIG. 4 shows the experimentally obtained dissociation constants (KD) for the glycopeptide-AuNP conjugates analyzed. [Figure 5] Figure 5 shows a graph depicting anti-MUC1 antibody levels in healthy volunteers (controls) and pancreatic cancer patients. Box plots represent interquartile ranges, and medians are horizontal lines. ****: p<0.0001 [Figure 6] Figure 6 shows the receiver operating characteristic (ROC) curve plot for the sensitivity and specificity of anti-MUC1 antibody levels in the subjects' serum. The area under the curve (AUC) was 0.918 (95% CI: 0.832-1.000), with a sensitivity of 85%, a specificity of 90%, and a discrimination threshold of 11.63 arbitrary fluorescent units (AFU). For comparison, published values ​​for CEA and CA19-9 are also plotted. See also Table 4. DETAILED DESCRIPTION OF THE INVENTION

[0013] Detailed Description of the Invention 1) Synthesis of glycopeptides Glycopeptides were synthesized by stepwise microwave-assisted solid-phase synthesis (MW-SPPS) on a Liberty Blue synthesizer (CEM®) using an Fmoc strategy on Rink Amide MBHA resin (0.1 mmol). Unnatural amino acids were synthesized according to the published protocol with minor modifications. 29The glycopeptide was synthesized according to the method described in the literature. The remaining O-acetyl groups of GalNAc were removed in a mixed solvent of NH2NH2 / MeOH (7:3). The glycopeptide was then released from the resin, and all acid-sensitive protecting groups were simultaneously removed using 95% TFA, 2.5% TIS (triisopropylsilane), and 2.5% HO, followed by precipitation with cold diethyl ether. The product was purified by HPLC using a Phenomenex Luna C18(2) column (10 μm, 250 mm × 21.2 mm) and dual absorbance detector at a flow rate of 20 ml / min. Following this methodology, various glycopeptides were synthesized using fully protected amino acids and purified by semipreparative HPLC, including the native sequence (glycopeptide 21 shown in Figure 1A), a modified glycopeptide (24) (shown in Figure 1B), and the same glycopeptide with an added cysteine ​​for conjugation to nanoparticles (glycopeptide 33) (shown in Figure 1C).

[0014] These are the specific characteristics of these glycopeptides: Glycopeptide 21 (GGGGPAPGST(GalNAc)APPA): Following MW-SPPS methodology, fully protected amino acids were used to obtain glycopeptide 21, which was purified by semi-preparative HPLC. Semi-preparative HPLC: Rt = 12.7 min (Phenomenex Luna C18(2), 10 μm, 21.2 × 250 mm, gradient: acetonitrile / water + 0.1% TFA (5:95) → (12.5:87.5), 15 min, 20 ml / min, λ = 212 nm). Analytical HPLC: Rt=15.8 min (Phenomenex Luna C18(2), 5 μm, 4.6 x 250 mm, gradient: acetonitrile / water + 0.1% TFA (5:95) → (15:85), 20 min, 1 ml / min, λ=212 nm). HRMS(ESI)m / z:[M+H]+C 54 H 87 N 16 O 21 Theoretical value: 1295.6226; measured value: 1295.6184.

[0015] Glycopeptide 24 (GGGGPAPGST(GalNCOEt)APPA): Following MW-SPPS methodology, fully protected amino acids were used to obtain glycopeptide 24, which was purified by semi-preparative HPLC. Semi-preparative HPLC: Rt = 14.9 min (Phenomenex Luna C18(2), 10 μm, 21.2 × 250 mm, gradient: acetonitrile / water + 0.1% TFA (5:95) → (12.5:87.5), 15 min, 20 ml / min, λ = 212 nm). Analytical HPLC: Rt=15.8 min (Phenomenex Luna C18(2), 5 μm, 4.6 x 250 mm, gradient: acetonitrile / water + 0.1% TFA (5:95) → (20:80), 30 min, 1 ml / min, λ=212 nm). HRMS(ESI)m / z:[M+H]+C 55 H 89 N 16 O 21 Theoretical value: 1309.6383; measured value: 1309.6327.

[0016] Glycopeptide 33 (CGGGGPAPGST(GalNCOEt)APPA): Semi-preparative HPLC: Rt=19, 1 min (Phenomenex Luna C18(2), 10 μm, 21.2×250 mm, gradient: acetonitrile / water + 0.1% TFA (5:95) → (16:84), 22 min, 20 ml / min, λ=212 nm). Analytical HPLC: Rt=15.8 min (Phenomenex Luna C18(2), 5 μm, 4.6 x 250 mm, gradient: acetonitrile / water + 0.1% TFA (5:95) → (20:80), 30 min, 1 ml / min, λ=212 nm). HRMS(ESI)m / z:[M+H] + C 60 H 96 N 17 O 23 Theoretical value of S: 1454,6580; measured value: 1454,6514.

[0017] 2) Antigen-antibody binding studies using surface plasmon resonance (SPR) Surface plasmon resonance (SPR) experiments were performed using a Biacore X-100 instrument (Biacore, GE) in HBS-EP buffer solution, pH 7.5 (containing 10 mM Hepes, 150 mM NaCl, 3 mM EDTA, 2% DMSO, and 0.05% Tween X100) at 25°C. This was the running buffer. 5E5 antibody (from Abcam) was coupled to the 5E5 antibody using standard amine coupling methods. 30 Protein was immobilized onto a CM5 sensor chip (Biacore, GE) according to the protocol described in [1]. Briefly, the carboxymethyl dextran surface of flow cell 2 was activated by a 7-minute injection of a 1:1 ratio of 0.4 M 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) aqueous solution and 0.1 M sulfo-N-hydroxysuccinimide. Thus, antibodies were coupled to the surface during 7-minute injections using various dilutions in 10 mM sodium acetate, pH 4.0. Unreacted active esters on the surface were inactivated by a 7-minute injection of 0.1 M ethanolamine-HCl in water (pH 8.0). The immobilization level was found to be around 5500 resonance units (RU). Flow cell 1 served as a reference and was treated identically to flow cell 2, but without protein (amine coupling method). Prior to use, a 50 mM peptide ligand stock solution was diluted to a final concentration in running buffer. A series of different compounds was then injected onto the sensor chip at a flow rate of 30 μl / min for 1 minute, followed by a 1-minute dissociation period. No regeneration was required. Figures 2A and 2B show the response concentration curves obtained for the binding of glycopeptides 21 and 24, respectively, to the 5E5 (anti-MUC1) antibody at 20°C in SPR experiments.

[0018] Sensogram data were acquired in duplicate and processed with Biacore Evaluation X100 software (Biacore, GE). Experimental data obtained from affinity measurements were fitted to a specific two-site binding model using Prism software. The resulting dissociation constants (Kd) are shown in Table 1. [Table 1] Thus, the unnatural glycopeptide (24) has 6.5 times higher affinity (=less dissociation) than the natural peptide (21).

[0019] 3) Nanoparticle synthesis and peptide conjugation All glassware used in the preparation of nanoparticles was cleaned with aquaregia (HCl (37%) / HNO3 (65%) 3:1). Deionized ultrapure water (Millipore Elix® 35 water purification system, 18.2 MΩ cm) was used to prepare all aqueous solutions. All solutions used in the preparation of nanoparticles were filtered through a 0.2 μm membrane filter (cellulose acetate, Whatman®). Gold nanoparticles (AuNPs) were characterized by dynamic light scattering (DLS) (Zetasizer, Malvern Instruments or Litesizer 500, Anton Paar). The average diameter of AuNPs was measured by TEM (JEOL JEM-1011 transmission electron microscope, operated at an accelerating voltage of 100 kV). UV / Vis measurements were performed using a TECAN Infinite M200 Pro plate reader. Gold concentrations were measured by inductively coupled plasma optical emission spectroscopy (Agilent 720 ICP-OES) or inductively coupled plasma mass spectroscopy (iCAP™ RQ ICP-MS - Thermo Scientific). Citrate-coated AuNPs with a diameter of 13.0 ± 1.0 nm were prepared using a published method. 31 The AuNPs were passivated by forming a self-assembled monolayer of alkyl-PEG600 thiol on their surfaces. 32 A mixture of carboxy-terminated and amino-terminated PEG600 thiols (I and II) was used, with the molar fraction of amino-terminated derivatives being χ NH2 = 0.06. Stock solutions of thiols in EtOH (5 mM) were prepared immediately before use and used as is.

[0020] The final concentrations of the passivation reaction were 80–90 nM AuNP, 25 mM NaHCO3, and 1 mM thiol (total), thus the reaction contained 20% v / v EtOH. After stirring at room temperature for 96 h, the PEG-AuNP was purified by ultrafiltration through an Amicon Ultra-15 filter (1 × 15 ml 25 mM NaHCO3, 1 × 15 ml 50 mM EtOH / NaHCO3 2:8 v / v, and 2 × 15 ml 50 mM NaHCO3 buffer solution). The purified PEG-AuNP was dispersed in HO to a final concentration of 200–400 nM.

[0021] The above peptides were conjugated to PEG-AuNPs according to a previously reported method. 33 Briefly, SM(PEG)2 derivatives were added to a sample of amino-functionalized AuNPs in 30 mM phosphate buffer at pH 8.2 at a final concentration of 5 mM. The AuNP concentration in the reaction mixture could be as high as 400 nM. The reaction mixture was stirred at 1 °C for 4 h. The functionalized AuNPs were purified by ultrafiltration (2 × 4 ml EtOH / 20 mM phosphate buffer pH 8.2 2:8, 2 × 4 ml HO), with the entire process performed on ice in a centrifuge tube cooled to 4 °C using cold washing solutions. The purified functionalized AuNP particles were dispersed in water. In parallel, 200 μl of a 1 mM aqueous dilution of the peptide was incubated with 10 mM tris(2-carboxyethyl)phosphine hydrochloride (TCEP) at 0 °C for 2 h to completely suppress the formation of disulfides from the glycopeptide derivatives. For the coupling reaction, the required amount of peptide, freshly treated with TCEP, was added to a cold solution of freshly prepared functionalized AuNPs and stirred at 500 rpm overnight at 4 °C. A typical reaction volume was 500 μl, with final concentrations in the reaction mixture of approximately 100 nM functionalized AuNPs, 15 mM phosphate buffer at pH 7.0, and 50 μM peptide. The excess unbound peptide was centrifuged, the supernatant removed, and resuspended in 1 ml of buffer (three times, 20,800 x g, 45 min, 18 °C), once in 1 ml of 25 mM NaHCO3 and twice in 10 mM NaHCO3. Finally, the purified peptide-conjugated AuNPs were treated with 350 μl of 10 mM NaHCO3 to a final concentration of approximately 100 nM and stored at +4 °C, protected from light. This method is shown schematically in Figure 3.

[0022] The hydrodynamic radius of AuNPs was determined by DLS. AuNPs were suspended in 10 mM NaHCO3. The zeta potential was measured in the same buffer. The values ​​are shown in Table 2. [Table 2]

[0023] 4) Dot blot assay AuNPs were loaded onto pre-wetted nitrocellulose membranes in triplicate using a capillary (AuNPs, 16 nM) and allowed to dry at room temperature (ta) for at least 2 h. For affinity testing (results shown in Figure 4), each membrane was blocked with 5% nonfat milk in Tris-buffered saline (TBS) for 1 h, washed (3 × 10 min) with TBS containing 0.1% Tween 20, and incubated overnight at 4 °C with mouse monoclonal anti-MUC1 5E5 antibody (1 μg / ml in 5% milk in TBS) provided by Dr. Clausen (University of Copenhagen). After washing (3 × 10 min) with TBS containing 0.1% Tween 20, the membrane was incubated for 1 h at room temperature with Dylight 800 mouse anti-IgG (H+L) secondary antibody (039610-145-121, Tebu-bio) from goat, diluted 1:2500 in TBS. Finally, the membrane was washed with TBS containing 0.1% Tween 20 (3 × 10 min) and TBS (2 × 10 min) and visualized using a LI-COR Biosciences Odyssey Infrared Imaging System. Mark intensity was quantified by densitometry using ImageJ, and the nadir value was subtracted.

[0024] 5) Serum analysis of pancreatic cancer patients and healthy volunteers This study was designed as a retrospective, observational, longitudinal clinical trial. All data were anonymized. Personal and clinical data collected for the study were in accordance with the Spanish Data Protection Law (Organic Law 3 / 2018 of December 5th for the Protection of Personal Data). The study adhered to all principles of the Declaration of Helsinki and was approved by the local review board (La Rioja Clinical Research Ethics Committee, Ref. CEICLAR Pl260). All subjects who signed informed consent underwent blood sampling, serum samples were collected, and immunoreactivity assays against the MUC1 antigen were performed using the dot blot technique. The study participants were 20 pancreatic cancer patients (65% male, 65.2±9.0 years old) and 20 age- and sex-matched healthy volunteers (65% male, 63.4±7.5 years old) (San Pedro Hospital, Logroño, Spain). No significant differences were observed between the two groups in terms of age or sex (Table 3). [Table 3]

[0025] For serum analysis, each membrane was functionalized with nanoparticles as described in Section 4 and blocked with 5% BSA in TBS for 1 h. After washing with TBS containing 0.1% Tween 20 (3 × 10 min), it was incubated overnight at 4 °C with patient serum (1:100 dilution in TBS containing 1% BSA). After washing with TBS containing 0.1% Tween 20 (3 × 10 min), the membrane was incubated with rabbit anti-human IgG (H+L) secondary antibody Dylight 800 (039609-445-002, Tebu-bio) diluted 1:5000 in TBS for 1 h at room temperature. Finally, the membrane was washed with TBS containing 0.1% Tween 20 (3 × 10 min) and TBS (2 × 10 min) and visualized using a LI-COR Biosciences Odyssey Infrared Imaging System. Mark intensity was quantified by densitometry using ImageJ, and the baseline value was subtracted. Each serum was analyzed in triplicate, and the assay was performed three times (three different membranes for each serum). Initial normalization was performed within each acquisition for cases and controls using the inter-array function in the Limma library (R main library). This was done to avoid the loss of disease effects. Next, the intensities of all acquisitions were normalized to the grand mean of all acquisitions. This was necessary to eliminate intensity biases related to differences in acquisitions by the fluorescence scanner. For statistical data analysis, the triplicate samples were combined, and the intensities of each tested peptide-AuNP were averaged.

[0026] For statistical analysis, the normality of data distribution (Shapiro-Wilk test) and homogeneity of variance (Levene test) were verified. The t-test was used when the data sets were normally distributed and homogeneous. The Welch test was applied when data were normally distributed but heterogeneous. For non-normally distributed data, the non-parametric Mann-Whitney U test was used. The significance level was set at p<0.05. The levels of anti-MUC1 antibodies detected in cancer patients (11.76 ± 0.14 arbitrary fluorescence units, AFU) were statistically significantly higher (p < 0.0001) than in healthy volunteers (11.38 ± 0.26 AFU) (Figure 5). To better understand the potential of the MUC1 immune response system as a diagnostic tool, a receiver operating characteristic (ROC) curve was constructed. The area under the curve (AUC) was 0.918 (95% IC: 0.832–1.000). The optimal threshold was calculated to be 11.63 AFU, which maximizes the sum of sensitivity (85%) and specificity (90%) (Figure 6). When these values ​​are compared with the values ​​of two tumor markers currently used for monitoring pancreatic cancer, the superiority of the inventors' system is extremely clear (FIG. 6, Table 4). [Table 4]

[0027] References JPEG2026504084000006.jpg125170 JPEG2026504084000007.jpg231170 JPEG2026504084000008.jpg61170

Claims

1. A synthetic non-natural glycopeptide represented by [GGGGPGPAPGST(GalNCOEt)APPA] and / or [CGGGGGPAPGST(GalNCOEt)APPA] and / or derivative synthetic glycopeptides thereof for use as a cancer biomarker detection agent in the detection of early stage tumors.

2. A biomarker conjugation platform comprising nanoparticles and a covalently attached synthetic non-natural glycopeptide antigen represented by [CGGGGGPAPGST(GalNCOEt)APPA] and / or its derivative glycopeptide antigen for use as a cancer biomarker detector in the detection of early stage tumors.

3. The binding platform of claim 2 , wherein the nanoparticles comprise gold nanoparticles.

4. The binding platform of claim 2 , wherein the nanoparticles include micelles, dendrimers, liposomes, hybrid and compact polymer nanoparticles, fullerenes, carbon dots, quantum dots, silica, and metal nanoparticles.

5. The binding platform of any one of claims 2 to 4, wherein the non-natural synthetic glycopeptide is [CGGGGPAPGST(GalNCOEt)APPA].

6. A kit for carrying out an assay for detecting and quantifying binding between anti-MUC1 antibodies produced by a patient from a sample and a binding platform according to any one of claims 1 to 5.

7. 7. The kit of claim 6, wherein the assay is selected from the group comprising dot blot, Western blot, ELISA, SPR and liquid phase infrared spectroscopy.

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