RU2750463C1 - Set of peptides capable of specifically bonding with circulating antibodies of blood plasma of patients for diagnosing breast cancer - Google Patents

Abstract

FIELD: biotechnology.

SUBSTANCE: invention relates to the field of biotechnology, specifically to a set of peptides, and can be used in medicine. The peptides included in the set are able to specifically bind with antibodies in the blood plasma of patients diagnosed with breast cancer (BC) while ensuring detection of antibodies in the blood plasma of patients with BC. By the presence of increased or reduced production of antibodies to the panel of 119 peptides presented in the invention, donors diagnosed with BC can be distinguished from donors without BC.

EFFECT: present invention can be used in immunology and oncology for diagnostics/screening of malignant breast tumours; the proposed invention can also be used in addition to methods for diagnostics of breast cancer for improving sensitivity and specificity, and in combination with other analytical systems for improving efficiency of diagnostics of BC.

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Description

FIELD OF TECHNOLOGY

The invention relates to a group of peptides with a random amino acid sequence, to which circulating antibodies of the blood plasma of patients diagnosed with breast cancer bind specifically. The peptides of the invention represent a fundamentally new strategy for assessing the repertoire of circulating antibodies, which allows the detection of breast cancer. In particular, the invention - a set (panel) of 119 peptides, in any combination, can be used in the development of test systems or tools for the diagnosis and screening of breast cancer in the early stages.

LEVEL OF TECHNOLOGY

In the structure of the causes of morbidity and mortality of the population of the countries of the world, according to WHO data for 2015-2019, cancer in recent years has taken a leading position [1, 2]. Newly diagnosed cancer cases increased from 14.1 million in 2012 to 18.1 million in 2018, and cancer deaths - from 8.2 million in 2012 to 9.6 million in 2018 [2, 3]. The most commonly diagnosed cancers are lung, breast, prostate and colorectal cancers. According to the statistics of 2018, 624709 cases of malignant neoplasms were registered in Russia, of which 285,949 were in male patients and 338,760 were female. It was found that the increase in this indicator in comparison with 2017 was 1.2% [4]. The average incidence of malignant neoplasms per 100,000 of the population of Russia is 425.4 cases, which is 1.2% higher than the 2017 level (23.1% higher than the 2008 level). The bulk of patients is formed from patients with malignant neoplasms of the mammary gland (18.4%). Thus, in the structure of the incidence of malignant neoplasms in the female population of Russia, breast cancer (BC) remains the leading oncological pathology [4, 5]. About 60% of breast cancer cases are diagnosed in the early stages, but approximately 20% of diagnoses are false negative [6]. The five-year survival rate in patients with stage I breast cancer is 93% and only 7% in patients with stage IV [7]. Thus, at present, the most urgent is the diagnosis of oncological diseases in the early stages, i.e. before the manifestation of clinical symptoms of the disease [8-11]. Analysis of indicators of active diagnostics of malignant neoplasms shows that in a number of regions there is almost no system of preventive and screening examinations of all categories of the population [12]. The development of early diagnosis of oncological diseases is of great importance in the selection of an effective therapeutic strategy to reduce the disability and mortality of the population.

Asymptomatic development of oncological diseases can occur over several years. Modern methods (methods) of clinical diagnosis of oncological diseases are aimed, to a greater extent, at determining circulating protein markers, which are almost impossible to detect in blood serum. Today the generally recognized biomarkers in the clinical diagnosis of breast cancer are CEA (CEA / CD66e), CA 15.3 (Mucin-1), p53, ER, PR, HER2, Ki 67, VEGF, TPA, CA 27.29, CA 125 [9, 13-19 ]. However, these markers can only be detected when a tumor is formed (up to 2.5 cm), available for biopsy or capable of producing high concentrations of proteins [9, 13]. In addition, some of these biomarkers do not provide sufficient sensitivity and specificity of diagnostic methods, which leads to a certain proportion of false positive and false negative diagnoses [8, 15, 20]. Other circulating markers, which are represented by circulating cancer cells (CTCs), DNA, RNA, exosomes, nucleosomes, have the potential to be used in the diagnosis of breast cancer [21-23]. However, all these markers are used either as single ones or as a set of a limited (small) number of markers, which affects the sensitivity and specificity. Technologies for detecting malignant neoplasms based on these markers have some drawbacks regarding sensitivity and specificity, limitations in determining the early stages of the disease, cost, rapid degradation of the marker, complexity of sample preparation and a large volume of biological material.

According to the data obtained in the last decade, in oncoimmunology, it became known that the immune response is of particular importance in antitumor protection, and the early stages of oncogenesis are accompanied by the production of antibodies against antigens associated with tumors of OAA (or TAA - tumor associated antigens) [10, 24 -27]. Antibodies, in contrast to existing cancer biomarkers, are more stable and specific, and are characterized by early production in response to a small amount of antigen. Thus, as an alternative to existing biomarkers, it was proposed to use antibodies (AT) against TAA [10, 24, 28, 29].

Most of the research into the development of anti-OAA antibody (TAA) -based diagnostic tools is aimed at assessing the interaction of circulating antibodies with 6 or fewer antigens associated with tumors. For reasons of limited immune response to any particular epitope in cancer patients and tumor heterogeneity, strategies and methods are required that allow the detection of antibodies against multiple (array) of antigenic epitopes. However, so far, it is difficult to isolate individual proteins / antigens to study changes in the set of circulating antibodies in cancer patients. The use of a large number of epitopes, in the form of short peptides, makes it possible to more adequately analyze the changes in the repertoire of circulating antibodies that accompany early tumor development.

The set of short peptides (with a random amino acid sequence) according to this invention, obtained on peptide microarrays, demonstrate the ability to determine the interaction of circulating antibodies with a huge number of potential epitopes and are of diagnostic value.

EXISTING ANALOGUES

During the information and patent search, patent RU 2504785 (dated 01.20.2014) was identified, which is close in meaning, but technically not a direct analogue of the present invention. Document RU 2504785 (dated January 20, 2014), contains a description of a method for diagnosing breast cancer, where glycans - polysaccharides or oligosaccharides - are used as epitopes with which the antibodies of the patient's blood interact. The technology has a number of advantages, but one of the disadvantages of this method is the use of a small panel of glycans (epitopes). It is important to note that the use of natural and frequently occurring glycans somewhat limits the completeness of the information obtained about the early development of the disease and potential intranosological subtypes of breast cancer, as well as other features.

This invention can be an alternative or complementary tool, since it uses many molecules of peptides (with a random amino acid sequence) as partial or complete similarity of epitopes (mimotopes) of antigens in oncogenesis, to assess the interaction with the maximum variety of antibodies to characterize the disease, in particular, cancer mammary gland [10, 30]. The present invention is directed to the development of a method for the diagnosis of malignant neoplasms of the mammary gland, which is based on the assessment of blood plasma antibodies, using a panel consisting of 119 peptides.

SUMMARY OF THE INVENTION

Existing tumor markers of breast cancer make it possible to identify already formed tumors that are available for biopsy or capable of producing high concentrations of proteins (Semiglazov, 2011; Tamkovich, 2014) [9, 13]. Antibodies, in comparison with breast cancer tumor markers, are more stable and specific, and are characterized by early production in response to a small amount of antigen [24, 30, 31]. Thus, antibodies (AT) against OAA can be used as an alternative to existing cancer markers. However, it is still difficult to isolate individual proteins / antigens to study changes in the set of circulating antibodies in cancer patients, while the use of a large number of epitopes, in the form of short peptides, makes it possible to more adequately analyze changes in the repertoire of circulating antibodies that accompany early tumor development.

The objective of the present invention is to develop a method for diagnosing malignant neoplasms of the mammary gland in the early stages. It is based on the evaluation of circulating blood plasma antibodies that accompany the development of a tumor in the early stages, using the proposed in the present invention, a combination of molecules from a large set of 119 peptides according to the invention, selected from 330034 peptides with a random amino acid sequence presented on microchips.

The key technical result of the present invention is a set of 119 peptides for the specific and sensitive determination of the repertoire of circulating antibodies, in the early stages, present in plasma and serum of individuals with breast cancer.

This invention is a set of peptides with the ability to specifically bind to circulating blood plasma antibodies of patients diagnosed with breast cancer, representing a combination of 119 amino acid peptides

This kit allows you to distinguish blood plasma samples from patients with breast cancer, from donors without breast cancer.

Thus, from a set in any combination of 119 peptides of the indicated amino acid composition, can be made for use in research, screening or diagnosis of breast cancer.

In one aspect of this invention, Example 1, there is provided a method that comprises incubating blood plasma antibodies with synthetic peptides (of the invention) and detecting the resulting complexes, said method comprising a protocol for using the peptide microchip for this.

In another aspect of this invention, Example 1, there is provided a method for detecting peptides to which antibodies in blood plasma of patients diagnosed with breast cancer and healthy donors described in this document bind specifically, said method comprising a protocol for using a peptide microchip and methods for determining peptide sequence.

In a further aspect of this invention, Example 1, an approach is proposed for assessing the profile of identified peptides for a group of patients diagnosed with breast cancer and a group of healthy donors in comparison.

In another aspect of this invention, example 1, provides a method for the preparation and synthesis of short peptides with a random amino acid sequence described in this document.

In a further aspect of this invention, Example 1, sets of informative peptides are provided that distinguish patients diagnosed with breast cancer from healthy donors.

In another aspect of this invention, Example 1, there is provided the use of a set of 119 peptides, in any combination, as a diagnostic panel in the development of test systems for the diagnosis of breast cancer.

DESCRIPTION OF FIGURES

FIG. 1 Shows the distribution of the luminescence intensity of peptides (Volcano plot). The left array contains peptides, the interaction with which is higher with the plasma of breast cancer patients. The right array contains peptides with which the blood plasma of BC patients has a stronger interaction, as well as another set of peptides, the interaction with which is reduced in BC patients. The dots above the dotted line represent peptides, the difference in the luminescence intensity of which in the groups of breast cancer patients and healthy donors is statistically significant (p <0.001)

FIG. 2 Presents cluster analysis and Heatmap of blood plasma of breast cancer patients and healthy donors. Individual peptides (119) are presented in horizontal rows, blood plasma samples from patients (41) and healthy (40) are presented in vertical columns. Gray signals (dots) show high interaction of antibodies with a particular peptide, black show low interaction. The clustering of peptides is shown on the left side of the figure. Plasma sample clustering is shown at the top of the figure. The serial numbers of the samples are indicated (below the diagram).

FIG. 3 Reflects the results of the classification of blood plasma samples from breast cancer patients and healthy donors by the principal component method (PCA) after selection of 119 informative peptides using the t-test. Patients diagnosed with breast cancer are black; control group is gray. Confidence level of analysis data at 0.95.

FIG. 4 Demonstrates the results of the ROC-analysis of the effectiveness of the CCP classifiers - Compound Covariate Predictor; DLDA - Diagonal Linear Discriminant Analysis; BCCP - Bayesian Compound Covariate Predictor for breast cancer data analysis based on the use of 119 peptide immunosignatures. Next to the curve is the threshold value with the corresponding indicators of specificity and sensitivity, as well as the AUC value with confidence intervals.

DETAILED DESCRIPTION OF THE INVENTION

Terminology

For clarification, "peptide", as used herein, refers to a polymer formed by α-amino acids linked in order by a peptide bond. The amino acids in the peptide according to the invention, depending on the position of the amino group on the α-carbon atom, belong to the L-series. Amino acids presented on microchips are natural amino acids, including rare amino acids.

Naturally occurring amino acids include aliphatic amino acids (glycine, alanine, valine, leucine, and isoleucine), hydroxylated amino acids (serine and threonine), sulfated amino acids (cysteine and methionine), dicarboxylic amino acids and their amides (aspartic acid, asparagine, glutamic acid, and glutamine) amino acids containing two main groups (lysine, arginine and histidine), aromatic amino acids (phenylalanine, tyrosine and tryptophan) and cyclic amino acids (proline).

The term "Specificity" in the text of the description of the present invention reflects the ability to selectively bind a "related" antigen (mimotope), which are peptides from a set of 119 pcs.

Use of the peptides of the present invention

The proposed invention can be used in addition to methods of clinical diagnosis of breast cancer to improve sensitivity and specificity. The set of peptides according to the invention can be used in the format of an independent test system, as well as in combination with other analytical systems to increase the efficiency of breast cancer diagnostics.

EXAMPLES

EXAMPLE 1. Evaluation of the repertoire of circulating antibodies in the plasma of patients diagnosed with breast cancer on peptide microarrays.

Obtaining peptides according to the invention

Peptides were synthesized on a microchip substrate by photolithography [10, 30]. Structural synthesis of peptides using masks was carried out on 200 mm silicon wafers with a thermal oxide coating, starting with a monolayer of aminosilane-glycine and building peptides through cycles of formation of structured acids in a photoresistor that removes Boc groups from the N-terminus of amines of the resulting peptides and the binding of the next amino acid. They are cut into rectangular plates with a micro-magnification size (75 × 25 mm), each of which contains 24 arrays with dimensions of 300 and 30,000, 8 microns.

Photolithographic method for the synthesis of peptides [10, 30]. The first amino acid (A) is covalently attached to the microchip support; step 1 - the chip is irradiated with light of a certain wavelength through a template (or mask), as a result of which the photosensitive protection is removed at certain points on the chip; step 2 - a solution containing amino acid (E) is added to the chip; step 3 - amino acids are attached only at points with the protective removed; Steps 4-10 illustrate the use of different patterns that open different positions on the chip for the next amino acid to attach. After all the putative amino acids of the first layer are bound, the cycle repeats. In total, it takes up to 20 cycles (depending on the number of amino acids used for synthesis) with the use of appropriate templates to remove photosensitive protection, followed by the addition of one of 20 amino acids [10]. All cycles are repeated until all peptides have reached the desired length and amino acid sequence. The amino acid sequence and location of each peptide on a microarray is known [30].

Initial identification of peptides of the invention

For the initial identification of the peptides of the invention, a search method based on the "peptide microarray" technology was used. This method makes it possible to identify peptides with which circulating antibodies of blood plasma of patients and donors bind with high specificity.

Microchip - is a silicon plate (25 × 75 × 1 mm), which contains 24 identical microarrays, each with 330034 peptides with "random" amino acid sequences. Peptides were synthesized on a microchip substrate by photolithography.

The amino acid sequence and location of each peptide per microarray is known. Thus, each microarray has an area of 0.5 cm ² , the point size of each peptide is about 8 µm in diameter, and the distance between adjacent peptides is about 1 nm.

For experimental work, preliminary preparation of microchips was carried out. Each microchip was separately placed in a Petri dish with distilled water for 60 min, then in phosphate buffered saline (PBS, Biolot) for 30 min, and incubated at a low speed of the Biosan OS 20 orbital shaker (Biosan). After that, the microchips were fixed in an EasyDip cuvette holder and rinsed in three fresh FSBT solutions (FSB + 0.25% Tween 20, Helicon) and distilled water.

After preliminary preparation, the microarrays dried by centrifugation for 5 min at 800 rpm were placed in a hybridization cassette (Arrait Corporation) with silicone pads that separated all 24 microarrays on the microchip. To each well of the cassette corresponding to the microarray of the microchip was added 150 μl of an incubation solution containing PSBT and 3% bovine serum albumin (BSA, Amresco). The surface of the cassette was covered with a film, the microchips were incubated for 18 h at 4 ° C.

After removing the contents of the cassette wells, 75 μl of the incubation solution was added. Samples of the studied blood plasma were diluted (1: 250) in the incubation solution and added to the wells of the hybridization cassette in a volume of 75 μl, the cassette surface was covered with a film and covered with a lid, incubated for 60 min (22-24 ° C) on an orbital shaker at 250 rpm. / min. After incubation using a BioTek ELx50 microplate washer (BioTek Instruments), the microchips were washed with three replicates of fresh FSBT and a wash solution (FSBT + 1% BSA).

Disassembly of cassettes and removal of microchips was carried out in a container with distilled water, avoiding drying. Microchips were placed in four-well plates filled with a solution of "secondary" antibodies against human IgG (5 ml / well) with a fluorescent label Alexa Flour 647 (Life Technologies) 75 pg / ml in an incubation solution.

Plates with microchips in a solution of "secondary" antibodies were covered with a light-blocking lid, incubated at a temperature of 22-24 ° C for 60 min at a low speed of an orbital shaker. After that, the microchips, fixed in EasyDip cuvettes, were rinsed in three fresh FSBT solutions, distilled water, and dried by centrifugation (800 rpm) for 5 min.

The dried microchips were scanned using a high-resolution dual-laser scanner "InnoScan 900 AL" ("Innopsys") at wavelengths of 632 and 535 nm. The position, size and fluorescence intensity for each peptide was assessed using Mapix software (v. 7.3.1). The digitized results were used in mathematical and statistical processing.

Digitization process

Obtaining digital data from a peptide microchip was carried out using Mapix software (v. 7.3.1), the fluorescence levels of all peptide-protein complexes on the microchip were taken into account. Data from a scanned array of 330034 peptides were converted to 16-bit TIFF images. To analyze fluorescence and determine the amino acid sequence of peptides, a GAL file (grid) was installed for each scanned microarray (image). During the analysis, from the data array obtained as a result of scanning and digitizing the 1st microarray (microarray) of the peptide microchip, peptides were determined for which the maximum level of fluorescence was noted. Such peptides were considered to interact with antibodies in the blood plasma of patients. The digitized results were transferred to Excel, they were used in mathematical and statistical processing followed by bioinformatics analysis.

Mathematical analysis and statistical processing

The data were analyzed using the analysis algorithms of the BRB-Array Tools v4.4.0 software (https://brb.nci.nih.gov/BRB-ArrayTools/). Each digitized microarray was analyzed using fluorescence intensity filters. The obtained fluorescence intensity data were normalized using the quantile normalization algorithm [32-33]. Additionally, a quality algorithm was used. The intensity levels for each sample were logarithm with subsequent averaging for class comparison.

Peptides, the difference in interaction with which in the two groups of patients' blood plasma is statistically significant, were selected using the Student's t-test for independent samples, followed by hierarchical cluster analysis [24, 33-35]. To construct dendrograms, the Ward's method was applied using the square of the Euclidean distance as a measure of the similarity of objects. The peptides were analyzed using the Compound Covariate Predictor, Diagonal Linear Discriminant Analysis, Bayesian Compound Covariate classifiers, the k-Nearest Neighbors classifier with the Euclidean metric, and the leave-one-out cross validation algorithm. Differences were considered statically significant if p <0.001. The predictive ability of the classifiers was analyzed - ROC-curve, and the area under the curve (AUC) was calculated.

All the results obtained during the analysis were presented graphically - in the format of histograms, heat maps, graphs, etc.

Assessment of the antibody repertoire in patients and donors.

The groups are represented - 41 healthy donors and 40 patients before the start of therapy, with a confirmed diagnosis of breast cancer. In the group diagnosed with breast cancer, 40% of patients were diagnosed with stage I of the disease, 60% with stage II. All patients were free of lymph node metastases and distant metastases. The average age of the patient group and the control group were similar in value. Plasma samples were obtained from patients prior to initiation of therapy. The study was approved by the ethics committee, all study participants (patients and donors) expressed their voluntary consent.

Capillary blood was taken from a finger into conical tubes of the Microvet type ("Firma Syntacon") containing K ₃ EDTA. Sample tubes were centrifuged at 1500 rpm at 4 ° C for 10 min. Samples of the resulting plasma were frozen for storage at -20 ° C and used for research.

Evaluation of the antibody repertoire was carried out using new generation microarrays, which contain 330034 peptides consisting of a random amino acid sequence.

The scanned images of the microarrays were digitized, and the fluorescence intensity of each peptide was estimated. The fluorescence intensity corresponds to the amount of circulating antibodies interacting with individual peptides. The median levels of fluorescence intensity for each of the analyzed microarrays had some differences. To align the median of all microarrays, we have applied a quantile normalization algorithm that allows us to smooth out the differences between microarrays.

The distribution of normalized fluorescence intensities of peptides interacting with antibodies in plasma of breast cancer patients and healthy donors is shown in Fig. 2. With the help of statistical analysis of the data, peptides have been identified that interact differently with AT of blood plasma of the control group and breast cancer patients. FIG. 2 shows synthetic peptides with which the blood plasma of breast cancer patients has a stronger interaction, as well as a different set of peptides, the interaction with which is reduced in patients with breast cancer.

As a result of evaluating circulating antibodies in healthy donors and breast cancer patients using peptide microarrays (a total of 330034 peptides), we identified 119 peptides, binding to which with plasma antibodies (IgG) showed statistically significant intergroup differences (p <0.001).

A hierarchical cluster analysis of blood plasma samples using the identified 119 peptides is shown in FIG. 3. The graph shows a clear division of the two groups into clusters depending on the presence or absence of a diagnosis of breast cancer. On closer examination of the heatmap, intragroup clusters can be seen among patients with breast cancer (Fig. 3). This may indicate an immune response associated with a certain molecular heterogeneity of tumors.

This specific reaction of the interaction of circulating antibodies with peptides within the same group possibly characterizes the molecular subtypes of this disease, but this requires additional study. It is interesting to note that 2 out of 40 patients diagnosed with breast cancer were assigned to the control group, with a fairly close profile to healthy donors, perhaps these patients have the initial stages of oncogenesis. However, this may indicate that the set of 119 peptides is insufficient to identify all molecular subtypes of breast cancer.

To assess and visualize the separation of the study groups diagnosed with breast cancer and conventionally healthy patients, the principal component method was additionally applied. FIG. 4, according to the results of the interaction of plasma antibodies with combinatorial peptides, a clear division into two groups is presented.

To analyze the accuracy of the classification of control groups and breast cancer patients, the k-Nearest Neighbors classifier with the Euclidean metric and the leave-one-out cross validation algorithm were used. The data in Table 1 below show that using a set of identified 119 peptides, it is possible to separate control samples from samples of patients with breast cancer with high sensitivity (0.951) and specificity (0.854).

To assess the reliability of the selected classifiers and the results obtained by dividing the study groups, an ROC analysis was carried out taking into account the AUC indicator, the results are presented in Fig. 5 (English area under ROC curve). The data obtained demonstrate a high predictive ability of the classifiers used and may indicate a high diagnostic value of a set of peptides in assessing the antibody repertoire in breast cancer patients.

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A set of peptides with the ability to specifically bind

with circulating antibodies of blood plasma of patients

for the diagnosis of breast cancer

application

Sequence listing

<110> Federal State Budgetary Educational Institution of Higher Education "Altai State University" (FGBOU VO "Altai State University")

<120> A set of peptides with the ability to specifically bind to circulating antibodies in the blood plasma of patients for the diagnosis of breast cancer

<210> SEQ ID NO: 1

<211> 11

<212> Peptide

<213> Synthetic construct

<223> Amino acid sequences of a set of synthetic peptides (mimotopes - antigens in oncogenesis), to assess the specific interaction with the maximum diversity (repertoire) of circulating antibodies, allowing to distinguish blood plasma samples from patients with breast cancer from donors without breast cancer.

<400> 1

ADSGVYVEGSG

<210> SEQ ID NO: 2

<211> 14

<213> Synthetic construct

<400> 2

QEGLKPRSQFEGSG

<210> SEQ ID NO: 3

<211> 10

<213> Synthetic construct

<400> 3

EGYLGDDGSG

<210> SEQ ID NO: 4

<211> 9

<213> Synthetic construct

<400> 4

VGGYEVGSG

<210> SEQ ID NO: 5

<211> 11

<213> Synthetic construct

<400> 5

EGLKRQFEGSG

<210> SEQ ID NO: 6

<211> 9

<213> Synthetic construct

<400> 6

EPPGRYGSG

<210> SEQ ID NO: 7

<211> 8

<213> Synthetic construct

<400> 7

LEGVKGSG

<210> SEQ ID NO: 8

<211> 13

<213> Synthetic construct

<400> 8

KEHNRPQWHEGSG

<210> SEQ ID NO: 9

<211> 13

<213> Synthetic construct

<400> 9

KNKRRLEEFEGSG

<210> SEQ ID NO: 10

<211> 12

<213> Synthetic construct

<400> 10

HDALLEFEYGSG

<210> SEQ ID NO: 11

<211> 9

<213> Synthetic construct

<400> 11

NGVGVPGSG

<210> SEQ ID NO: 12

<211> 11

<213> Synthetic construct

<400> 12

NLPRGDKWGSG

<210> SEQ ID NO: 13

<211> 10

<213> Synthetic construct

<400> 13

ALPVFKWGSG

<210> SEQ ID NO: 14

<211> 11

<213> Synthetic construct

<400> 14

QELNRGNEGSG

<210> SEQ ID NO: 15

<211> 10

<213> Synthetic construct

<400> 15

GRPVGDYGSG

<210> SEQ ID NO: 16

<211> 14

<213> Synthetic construct

<400> 16

KHDAEVLDFYYGSG

<210> SEQ ID NO: 17

<211> 11

<213> Synthetic construct

<400> 17

NGPGVYERGSG

<210> SEQ ID NO: 18

<211> 11

<213> Synthetic construct

<400> 18

EGLNRPSGGSG

<210> SEQ ID NO: 19

<211> 10

<213> Synthetic construct

<400> 19

GDNWGYQGSG

<210> SEQ ID NO: 20

<211> 7

<213> Synthetic construct

<400> 20

LNGDGSG

<210> SEQ ID NO: 21

<211> 12

<213> Synthetic construct

<400> 21

GASEVQDFKGSG

<210> SEQ ID NO: 22

<211> 8

<213> Synthetic construct

<400> 22

NLKQLGSG

<210> SEQ ID NO: 23

<211> 12

<213> Synthetic construct

<400> 23

QARKWQEWEGSG

<210> SEQ ID NO: 24

<211> 15

<213> Synthetic construct

<400> 24

NLVPGEWGLGNKGSG

<210> SEQ ID NO: 25

<211> 12

<213> Synthetic construct

<400> 25

QALPRFVPDGSG

<210> SEQ ID NO: 26

<211> 11

<213> Synthetic construct

<400> 26

EEQYEFFSGSG

<210> SEQ ID NO: 27

<211> 10

<213> Synthetic construct

<400> 27

ASDYFEYGSG

<210> SEQ ID NO: 28

<211> 11

<213> Synthetic construct

<400> 28

NLSGQERWGSG

<210> SEQ ID NO: 29

<211> 13

<213> Synthetic construct

<400> 29

HADVGLWRQRGSG

<210> SEQ ID NO: 30

<211> 9

<213> Synthetic construct

<400> 30

ANRVDGGSG

<210> SEQ ID NO: 31

<211> 12

<213> Synthetic construct

<400> 31

EAPYGEVWKGSG

<210> SEQ ID NO: 32

<211> 13

<213> Synthetic construct

<400> 32

ALPVGQFFKEGSG

<210> SEQ ID NO: 33

<211> 14

<213> Synthetic construct

<400> 33

QRPRGVEEEDKGSG

<210> SEQ ID NO: 34

<211> 12

<213> Synthetic construct

<400> 34

EGRVKEYQRGSG

<210> SEQ ID NO: 35

<211> 6

<213> Synthetic construct

<400> 35

HHHGSG

<210> SEQ ID NO: 36

<211> 11

<213> Synthetic construct

<400> 36

GVGELDRKGSG

<210> SEQ ID NO: 37

<211> 9

<213> Synthetic construct

<400> 37

GHKRDYGSG

<210> SEQ ID NO: 38

<211> 11

<213> Synthetic construct

<400> 38

QYLNRWDGGSG

<210> SEQ ID NO: 39

<211> 14

<213> Synthetic construct

<400> 39

QGDRSSWYYHYGSG

<210> SEQ ID NO: 40

<211> 9

<213> Synthetic construct

<400> 40

EADLYFGSG

<210> SEQ ID NO: 41

<211> 8

<213> Synthetic construct

<400> 41

GPSLWGSG

<210> SEQ ID NO: 42

<211> 7

<213> Synthetic construct

<400> 42

NGDYGSG

<210> SEQ ID NO: 43

<211> 11

<213> Synthetic construct

<400> 43

GPSGEYLGGSG

<210> SEQ ID NO: 44

<211> 12

<213> Synthetic construct

<400> 44

ASNRRGYKDGSG

<210> SEQ ID NO: 45

<211> 10

<213> Synthetic construct

<400> 45

QGLVEYVGSG

<210> SEQ ID NO: 46

<211> 17

<213> Synthetic construct

<400> 46

NKQEALLNHSWYEKGSG

<210> SEQ ID NO: 47

<211> 13

<213> Synthetic construct

<400> 47

ALPGWVYEPSGSG

<210> SEQ ID NO: 48

<211> 11

<213> Synthetic construct

<400> 48

QAPSGGRWGSG

<210> SEQ ID NO: 49

<211> 12

<213> Synthetic construct

<400> 49

EADSLPRYQGSG

<210> SEQ ID NO: 50

<211> 8

<213> Synthetic construct

<400> 50

EGLKYGSG

<210> SEQ ID NO: 51

<211> 8

<213> Synthetic construct

<400> 51

EYGVKGSG

<210> SEQ ID NO: 52

<211> 11

<213> Synthetic construct

<400> 52

AARLGEGKGSG

<210> SEQ ID NO: 53

<211> 8

<213> Synthetic construct

<400> 53

EGRKVGSG

<210> SEQ ID NO: 54

<211> 10

<213> Synthetic construct

<400> 54

YKPQDNDGSG

<210> SEQ ID NO: 55

<211> 8

<213> Synthetic construct

<400> 55

AGDGFGSG

<210> SEQ ID NO: 56

<211> 11

<213> Synthetic construct

<400> 56

DRSRPDGPGSG

<210> SEQ ID NO: 57

<211> 12

<213> Synthetic construct

<400> 57

NEDALEFVDGSG

<210> SEQ ID NO: 58

<211> 9

<213> Synthetic construct

<400> 58

EAVVGYGSG

<210> SEQ ID NO: 59

<211> 14

<213> Synthetic construct

<400> 59

QYRNRHVEYEDGSG

<210> SEQ ID NO: 60

<211> 12

<213> Synthetic construct

<400> 60

QYSPEVDRDGSG

<210> SEQ ID NO: 61

<211> 8

<213> Synthetic construct

<400> 61

EEGVKGSG

<210> SEQ ID NO: 62

<211> 12

<213> Synthetic construct

<400> 62

KGNRDERYVGSG

<210> SEQ ID NO: 63

<211> 11

<213> Synthetic construct

<400> 63

SVGGDRKWGSG

<210> SEQ ID NO: 64

<211> 12

<213> Synthetic construct

<400> 64

QHLPQEGNQGSG

<210> SEQ ID NO: 65

<211> 8

<213> Synthetic construct

<400> 65

KERNHGSG

<210> SEQ ID NO: 66

<211> 14

<213> Synthetic construct

<400> 66

QYKGYQDEGEYGSG

<210> SEQ ID NO: 67

<211> 8

<213> Synthetic construct

<400> 67

PEEFYGSG

<210> SEQ ID NO: 68

<211> 11

<213> Synthetic construct

<400> 68

EGLKHRSQGSG

<210> SEQ ID NO: 69

<211> 12

<213> Synthetic construct

<400> 69

EDPVRPYQFGSG

<210> SEQ ID NO: 70

<211> 10

<213> Synthetic construct

<400> 70

ALLQEVYGSG

<210> SEQ ID NO: 71

<211> 11

<213> Synthetic construct

<400> 71

EGNKRLDYGSG

<210> SEQ ID NO: 72

<211> 15

<213> Synthetic construct

<400> 72

YAAWVYQGFEWNGSG

<210> SEQ ID NO: 73

<211> 9

<213> Synthetic construct

<400> 73

EGNKVFGSG

<210> SEQ ID NO: 74

<211> 11

<213> Synthetic construct

<400> 74

ANHGEVKLGSG

<210> SEQ ID NO: 75

<211> 11

<213> Synthetic construct

<400> 75

NASHEWGPGSG

<210> SEQ ID NO: 76

<211> 11

<213> Synthetic construct

<400> 76

SVEGFRLDGSG

<210> SEQ ID NO: 77

<211> 10

<213> Synthetic construct

<400> 77

NVYQEPHGSG

<210> SEQ ID NO: 78

<211> 7

<213> Synthetic construct

<400> 78

LPRWGSG

<210> SEQ ID NO: 79

<211> 8

<213> Synthetic construct

<400> 79

QALHEGSG

<210> SEQ ID NO: 80

<211> 15

<213> Synthetic construct

<400> 80

NEGRKRHEDEVVGSG

<210> SEQ ID NO: 81

<211> 13

<213> Synthetic construct

<400> 81

KQEPELQDFKGSG

<210> SEQ ID NO: 82

<211> 11

<213> Synthetic construct

<400> 82

NKDANSVGGSG

<210> SEQ ID NO: 83

<211> 9

<213> Synthetic construct

<400> 83

EALKHWGSG

<210> SEQ ID NO: 84

<211> 12

<213> Synthetic construct

<400> 84

LRPGEWYQVGSG

<210> SEQ ID NO: 85

<211> 16

<213> Synthetic construct

<400> 85

QEALNHEYKDENQGSG

<210> SEQ ID NO: 86

<211> 15

<213> Synthetic construct

<400> 86

GLPVPGLENQVYGSG

<210> SEQ ID NO: 87

<211> 11

<213> Synthetic construct

<400> 87

GVRGGGYEGSG

<210> SEQ ID NO: 88

<211> 13

<213> Synthetic construct

<400> 88

NEALSVEYRLGSG

<210> SEQ ID NO: 89

<211> 14

<213> Synthetic construct

<400> 89

KEGYALSGGFVGSG

<210> SEQ ID NO: 90

<211> 10

<213> Synthetic construct

<400> 90

QALKWGEGSG

<210> SEQ ID NO: 91

<211> 11

<213> Synthetic construct

<400> 91

SLKVPELHGSG

<210> SEQ ID NO: 92

<211> 13

<213> Synthetic construct

<400> 92

KGHYRHGKEKGSG

<210> SEQ ID NO: 93

<211> 14

<213> Synthetic construct

<400> 93

NKHLSPSVYPYGSG

<210> SEQ ID NO: 94

<211> 11

<213> Synthetic construct

<400> 94

QAASHGWEGSG

<210> SEQ ID NO: 95

<211> 12

<213> Synthetic construct

<400> 95

QALLDYFPDGSG

<210> SEQ ID NO: 96

<211> 10

<213> Synthetic construct

<400> 96

NRPGQPDGSG

<210> SEQ ID NO: 97

<211> 13

<213> Synthetic construct

<400> 97

NYNVPDPYEDGSG

<210> SEQ ID NO: 98

<211> 9

<213> Synthetic construct

<400> 98

NRPGWHGSG

<210> SEQ ID NO: 99

<211> 12

<213> Synthetic construct

<400> 99

EGYLRVWELGSG

<210> SEQ ID NO: 100

<211> 11

<213> Synthetic construct

<400> 100

LPLKEFDLGSG

<210> SEQ ID NO: 101

<211> 11

<213> Synthetic construct

<400> 101

EGHRVLKGGSG

<210> SEQ ID NO: 102

<211> 11

<213> Synthetic construct

<400> 102

EDSGYERRGSG

<210> SEQ ID NO: 103

<211> 12

<213> Synthetic construct

<400> 103

KEDARVYEYGSG

<210> SEQ ID NO: 104

<211> 9

<213> Synthetic construct

<400> 104

SHPPRQGSG

<210> SEQ ID NO: 105

<211> 13

<213> Synthetic construct

<400> 105

EHLNPSGKFWGSG

<210> SEQ ID NO: 106

<211> 10

<213> Synthetic construct

<400> 106

NALGYQPGSG

<210> SEQ ID NO: 107

<211> 10

<213> Synthetic construct

<400> 107

NQEYAAYGSG

<210> SEQ ID NO: 108

<211> 14

<213> Synthetic construct

<400> 108

QEGAHRKDYLDGSG

<210> SEQ ID NO: 109

<211> 9

<213> Synthetic construct

<400> 109

QALLEVGSG

<210> SEQ ID NO: 110

<211> 10

<213> Synthetic construct

<400> 110

ASGWRFRGSG

<210> SEQ ID NO: 111

<211> 9

<213> Synthetic construct

<400> 111

NSGEYYGSG

<210> SEQ ID NO: 112

<211> 14

<213> Synthetic construct

<400> 112

NKQHSHGKEWDGSG

<210> SEQ ID NO: 113

<211> 14

<213> Synthetic construct

<400> 113

QEKRPWVGDQDGSG

<210> SEQ ID NO: 114

<211> 12

<213> Synthetic construct

<400> 114

EDLLRQDYEGSG

<210> SEQ ID NO: 115

<211> 11

<213> Synthetic construct

<400> 115

QALPGFFHGSG

<210> SEQ ID NO: 116

<211> 11

<213> Synthetic construct

<400> 116

GKVSLQWLGSG

<210> SEQ ID NO: 117

<211> 10

<213> Synthetic construct

<400> 117

NDLLRQEGSG

<210> SEQ ID NO: 118

<211> 9

<213> Synthetic construct

<400> 118

NRVRVDGSG

<210> SEQ ID NO: 119

<211> 11

<213> Synthetic construct

<400> 119

ELKPEWYEGSG

Claims (1)

A set of peptides with the ability to specifically bind to circulating antibodies of blood plasma of patients for the diagnosis of early breast cancer, which is a combination of 119 peptides of the amino acid composition: SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID N0: 11, SEQ ID NO : 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, SEQ ID NO: 20 , SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO : 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, SEQ ID NO: 42, SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45 , SEQ ID NO: 46, SEQ ID NO: 47, SEQ ID NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ ID NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117 , SEQ ID NO: 118, SEQ ID NO: 119.

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