Kit for predicting drug resistance of breast cancer patient to trastuzumab

Abstract

The invention discloses a kit for predicting drug resistance of a breast cancer patient to trastuzumab. The kit comprises a multi-element probe group; a drug resistance detection model and a UV-vis absorption spectrum detection reagent. According to the present invention, the four HOFS-enzyme sensing units interact with the exosome, the specific exosome fingerprint is generated through the multiple interactions, and the prediction of the trastuzumab treatment effect can be achieved without the immunohistochemical method and the gene detection. Compared with other methods, the HOFS enzyme array sensing method based on machine learning has the advantages of being fast in response, convenient to operate and relatively low in cost.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and specifically relates to a kit for predicting trastuzumab resistance in breast cancer patients. Background Technology

[0002] Breast cancer is the leading cause of cancer death worldwide, accounting for approximately 11.7% of new cancer cases. Human epidermal growth factor receptor 2 (Her-2) is overexpressed in 20%–30% of breast cancers. Her-2 positive breast cancers are highly invasive, and patients have short disease-free survival and poor prognosis.

[0003] Trastuzumab (trade name: Herceptin, a humanized monoclonal antibody targeting the Her-2 / neu protein) has shown good efficacy in the treatment of early and metastatic breast cancer, significantly improving the prognosis of Her-2 positive breast cancer patients. However, 14%-31% of early-stage breast cancer patients and almost all advanced breast cancer patients still develop trastuzumab resistance and disease recurrence. In fact, approximately 10% of patients develop primary resistance to trastuzumab at the start of treatment, and once resistance develops, the prognosis is extremely poor, whether primary or secondary. Therefore, there is an urgent need to develop methods for accurately predicting the efficacy of trastuzumab, screening for the best-benefiting population, timely intervention in treatment regimens for resistant patients, improving treatment efficacy, and enhancing the prognosis of Her-2 positive breast cancer patients. Currently, Her-2 expression status is the main predictive indicator for the efficacy of targeted therapy in Her-2 positive breast cancer.

[0004] The latest Her-2 testing guidelines recommend using immunohistochemistry (IHC) to detect Her-2 protein expression levels. However, the reliability of IHC results is affected by pre-analytical variables, including sample processing, fixation, storage, and staining. Pathologist subjectivity and a lack of inter-laboratory reproducibility also contribute to the heterogeneity of IHC results. Furthermore, in advanced cases, 25% of patients exhibit inconsistent Her-2 status between metastatic and primary lesions. Therefore, multiple Her-2 status tests of both primary and metastatic or recurrent lesions are necessary before developing a treatment plan. A comprehensive assessment of the patient's condition is crucial to determine whether trastuzumab treatment should be used, thus limiting the accuracy of trastuzumab efficacy prediction. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a kit for predicting trastuzumab resistance in breast cancer patients.

[0006] To achieve the above objectives, the present invention employs the following technical solution: a kit for predicting trastuzumab resistance in breast cancer patients, comprising:

[0007] (1) Multi-probe array;

[0008] (2) Drug resistance detection model;

[0009] (3) UV-vis absorption spectroscopy detection reagent.

[0010] Furthermore, the multi-probe set includes four HOFS@enzyme probes, each prepared using the following steps:

[0011] Dissolve 4,4',4'',4'''-methanetetrabenzamide tetrahydrochloride in H2O, dissolve tetra(4-carboxyphenyl)methane in AMH first, then add H2O to make up to volume, and mix the above two solutions with HRP solution to prepare HOFS@enzyme probe one;

[0012] Dissolve 4,4',4'',4'''-methanetetrabenzamide tetrahydrochloride in H2O, dissolve azobenzene-4,4-dicarboxylic acid in AMH first, then add H2O to make up to volume, and mix the above two solutions with HRP solution to prepare HOFS@enzyme probe II;

[0013] HOFS@enzyme probe III was prepared by dissolving tetra(4-carboxyphenyl)methane in H2O;

[0014] H4TBAPy was dissolved in DMF, and then HRP solution and H2O were added to prepare HOFS@enzyme probe four.

[0015] Furthermore, the drug resistance detection model was obtained using the following steps:

[0016] 2.1 Serum samples were collected from three groups: trastuzumab-sensitive individuals, trastuzumab-induced resistant individuals, and trastuzumab-induced resistant individuals. Exosomes were extracted from these samples and detected using a multivariate probe array. A dataset of absorption spectra was generated, and specific exosome fingerprints were created. Fingerprint databases were established for each of the three groups.

[0017] 2.2 A detection model was constructed using the fingerprint database as the training set, and the detection performance of the model was verified using cases with different known drug resistance results as the test set, thus obtaining a drug resistance detection model.

[0018] A method for predicting trastuzumab resistance in breast cancer patients involves obtaining serum samples from breast cancer patients, detecting UV-vis absorption spectra using the kit described in any one of claims 1 to 3, and inputting the detection results into a resistance detection model to obtain the corresponding type.

[0019] Compared to existing technologies, the advantages of this invention are as follows: Four HOFS@enzyme sensing units interact with exosomes, generating specific exosome fingerprints through multiple interactions, eliminating the need for immunohistochemical methods and gene detection, and can be used to predict trastuzumab efficacy. Compared to other methods, this machine learning-based HOFS@enzyme array sensing offers advantages such as rapid response, ease of operation, and relatively low cost. Through a multi-channel sensor array, the four sensing units can simultaneously mix and interact with each sample, achieving high efficiency, high stability, and high accuracy. Furthermore, due to its high sensitivity and excellent biological system recognition capabilities, this sensing platform may be well-suited for more complex biological systems. Future work will focus on simplifying operation and improving the ability to identify exosomes expressing similar phenotypes to broaden the applicability of this machine learning approach. We hope this method can provide a rapid detection platform for predicting trastuzumab efficacy, providing a basis for clinical treatment planning and advancing the development of precision medicine. Attached Figure Description

[0020] Figure 1 This is a flowchart of using HOF-based exosome fingerprinting for predicting trastuzumab efficacy. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product or device.

[0023] A method for predicting trastuzumab resistance in breast cancer patients. The following steps are employed:

[0024] S1: Construction of a multivariate sensing probe based on HOFs@enzymes;

[0025] S2: Extract exosomes;

[0026] S3: Construction of exosome fingerprinting;

[0027] S4: Machine Learning Model Building

[0028] S1: Synthesis of HOFs@enzyme multivariate sensing probes

[0029] HOF1: Dissolve 5 mg of 4,4',4'',4'''-methanetetrabenzamide tetrahydrochloride in 1.8 mL of H2O; dissolve 6 mg of tetra(4-carboxyphenyl)methane in 0.02 mL of AMH; then add H2O to bring the volume to 2 mL; mix the above solution with 0.2 mL of HRP solution.

[0030] HOF2: Dissolve 5 mg of 4,4',4'',4'''-methanetetrabenzamide tetrahydrochloride in 1.8 mL of H2O; dissolve 3 mg of azobenzene-4,4-dicarboxylic acid in 0.02 mL of AMH; then add H2O to bring the volume to 2 mL; mix the above solution with 0.2 mL of HRP solution.

[0031] HOF3: 3.7 mg of tetrakis(4-carboxyphenyl)methane was dissolved in 3.8 mL of H2O, followed by the addition of 3.8 mL of H2O;

[0032] HOF4: 2 mg H4TBAPy was completely dissolved in 0.2 mL DMF under sonication, followed by the addition of 0.2 mL HRP solution and 3.6 mL H2O;

[0033] All the above solutions were stirred at 1200 rpm for 5 minutes at room temperature, the mixture was centrifuged at 12000 rpm for 10 minutes, the precipitate was washed three times with ddH2O, dried at 45 °C, and stored away from light.

[0034] S2-1: Cell-derived exosome extraction

[0035] Once the cell density reaches 70%, the culture medium is replaced with serum-free medium, and the cells are cultured at 37°C for another 48 hours using an exosome-free serum-free culture method. Then, the supernatant of the culture medium is collected into a 100 kD ultrafiltration tube, centrifuged at 4000 g for 20 minutes, and the supernatant extract is collected and mixed with the ExoQuick-TC kit at a volume ratio of 5:1. The mixture is incubated at 4°C for at least 12 hours, followed by centrifugation at 1500 g for 30 minutes. The supernatant is discarded, and the cells are centrifuged again at 1500 g for 5 minutes to remove all liquid. The cells are resuspended in 100-500 μL of PBS for later use. The exosome solution is analyzed using NTA and TEM to determine its concentration, hydration diameter, and morphology.

[0036] S2-2: Serum-derived exosome extraction

[0037] Serum was extracted and transferred to new tubes. The tubes were centrifuged at 3000 g for 15 minutes at 4 °C to remove residual cell debris. 63 μL of the ExoQuick exosome precipitation solution kit was added to every 250 μL of supernatant, and the mixture was incubated at 4 °C for 30 minutes to 1 hour to allow for complete exosome precipitation. The exosomes were then centrifuged at 1500 g for 30 minutes at 4 °C to precipitate the exosomes. The supernatant was aspirated, and the exosomes were resuspended in 100 μL of PBS. The exosome solution was analyzed by NTA to determine the concentration and hydration diameter of the isolated exosomes.

[0038] S3 includes the following steps:

[0039] S3-1: Research based on array sensing;

[0040] Four HOFs were diluted with PBS to a final volume of 99 μL per tube to prepare detection solutions. Then, 1 μL of exosomes was added to each tube, mixed thoroughly, and transferred to a 96-well plate. After incubation for 30 minutes, TMB chromogenic buffer was added to each well, and the UV-vis absorption intensity at 650 nm was measured using a multi-mode microplate reader. Each well was tested six times in total.

[0041] S3-2: Fingerprint mapping;

[0042] Serum samples were collected from three groups of patients: trastuzumab-sensitive individuals, trastuzumab-antigen-induced resistant individuals, and trastuzumab-antigen-secondary resistant individuals. Exosomes were extracted from these samples and detected using array sensing to generate a dataset of absorption spectra. Specific exosome fingerprints were generated, and fingerprint databases were established for each of the three groups.

[0043] S4 Machine Learning Model Building

[0044] To improve the generalization ability of the model, we comprehensively considered three commonly used machine learning techniques—SVM, RF, and K-means—to model the data in order to further determine the efficacy of trastuzumab treatment.

[0045] Sixty patients who received trastuzumab treatment were tested, and an efficacy prediction model was constructed. The same three machine learning algorithms were used to analyze their exosome fingerprints. The accuracy rates of matching with clinical efficacy were 91.67%, 87.5%, and 81.67%, respectively. Therefore, when predicting the efficacy of trastuzumab, we used the discriminant model of SVM.

[0046] Figure 1This is a flowchart illustrating the use of HOF-based exosome fingerprinting for trastuzumab efficacy prediction. Because exosomes from different treatment groups differ in surface proteins, nucleic acids, and physicochemical properties (such as hydrophobicity, pH, and charge), their binding to HOFs@enzyme composites induces different signals from the internal enzyme molecules and active sites on the HOF framework to the substrate. Four different probes generate specific signals for each sample. Then, linear discrimination analysis, hierarchical cluster analysis, and deep learning algorithms are used to analyze the response patterns and convert them into specific fingerprints, thereby enabling the identification of exosomes from different treatment groups.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A kit for predicting trastuzumab resistance in breast cancer patients, characterized in that... include: (1) Multi-probe array; (2) Drug resistance detection model; (3) UV-vis absorption spectroscopy detection reagent.

2. The kit for detecting and evaluating the efficacy of trastuzumab according to claim 1, characterized in that: The multivariate probe set includes four HOFS@enzyme probes, which are prepared using the following steps: Dissolve 4,4',4'',4'''-methanetetrabenzamide tetrahydrochloride in H2O, dissolve tetra(4-carboxyphenyl)methane in AMH first, then add H2O to make up to volume, and mix the above two solutions with HRP solution to prepare HOFS@enzyme probe one; Dissolve 4,4',4'',4'''-methanetetrabenzamide tetrahydrochloride in H2O, dissolve azobenzene-4,4-dicarboxylic acid in AMH first, then add H2O to make up to volume, and mix the above two solutions with HRP solution to prepare HOFS@enzyme probe II; HOFS@enzyme probe III was prepared by dissolving tetra(4-carboxyphenyl)methane in H2O; H4TBAPy was dissolved in DMF, and then HRP solution and H2O were added to prepare HOFS@enzyme probe four.

3. The kit for detecting and evaluating the efficacy of trastuzumab according to claim 1, characterized in that: The drug resistance detection model was obtained using the following steps: 2.1 Serum samples were collected from three groups: trastuzumab-sensitive individuals, trastuzumab-induced resistant individuals, and trastuzumab-induced resistant individuals. Exosomes were extracted from these samples and detected using a multivariate probe array. A dataset of absorption spectra was generated, and specific exosome fingerprints were created. Fingerprint databases were established for each of the three groups. 2.2 A detection model was constructed using the fingerprint database as the training set, and the detection performance of the model was verified using cases with different known drug resistance results as the test set, thus obtaining a drug resistance detection model.

4. A method for predicting trastuzumab resistance in breast cancer patients, characterized in that: Serum samples from breast cancer patients were obtained, and the UV-vis absorption spectrum was detected using the kit described in any one of claims 1 to 3. The detection results were then input into a drug resistance detection model to obtain the corresponding type.

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