Plasma biomarker for predicting postoperative recurrence risk of non-small cell lung cancer

Abstract

The invention relates to a plasma biomarker for predicting postoperative recurrence risk of non-small cell lung cancer and application of the plasma biomarker. The biomarker for predicting the postoperative recurrence risk of the non-small cell lung cancer is thymus stromal lymphopoietin (TSLP). The plasma biomarker for predicting the postoperative recurrence risk of the non-small cell lung cancer can be detected by adopting an MSD electrochemiluminescence technology, and the postoperative recurrence risk of the non-small cell lung cancer of a patient is judged according to the concentration of the marker in plasma.

Description

Technical Field

[0001] This invention relates to plasma biomarkers for predicting the risk of postoperative recurrence of non-small cell lung cancer and their applications. Background Technology

[0002] Lung cancer is the leading cause of cancer-related morbidity and mortality. The two main histological subtypes are non-small cell lung cancer (NSCLC) and small cell lung cancer. Surgical resection is part of the standard treatment for patients with early or locally advanced NSCLC. However, postoperative recurrence is common. A systematic literature review and its meta-analysis showed that the recurrence-free survival (RFS) rate for stage I patients was 96% at 1 year post-surgery, decreasing to 82% at 5 years. Predicting the risk of recurrence within 5 years post-surgery is crucial for treatment decision-making, clinical trial design, and future research directions, ultimately aiming to improve the prognosis of NSCLC patients.

[0003] Cytokines play a crucial role in regulating the body's immune system. They can promote or inhibit tumor growth by modulating signaling pathways related to cell proliferation, metastasis, and apoptosis. Numerous studies suggest that various immune and inflammation-related proteins are involved in the development and progression of lung cancer.

[0004] Electrochemiluminescence (ECL) is a specific chemiluminescent reaction initiated by electrochemistry on an electrode surface, representing a perfect combination of electrochemical and chemiluminescent processes. In the history of immunoassays, the first generation was radioimmunoassay, the second generation was enzyme-linked immunosorbent assay (ELISA) / chemiluminescence / fluorescence immunoassay, and the third generation is electrochemiluminescence. Because electrochemiluminescence can replace the sensitivity of first-generation radioimmunoassay while inheriting the throughput convenience of second-generation ELISA, it has been widely recognized and used by a large number of users worldwide. Meso Scale Discovery (MSD) in the United States employs this ultrasensitive multi-factor electrochemiluminescence technology. Summary of the Invention

[0005] This invention provides a plasma biomarker for predicting the risk of recurrence after non-small cell lung cancer surgery. The plasma biomarker for predicting the risk of recurrence after non-small cell lung cancer surgery is thymic stromallymphopoietin (TSLP). Specifically, this invention provides a method for predicting the risk of recurrence after non-small cell lung cancer surgery, the method comprising the step of detecting the concentration of TSLP in the preoperative plasma of a subject.

[0006] In one or more embodiments, the detection method includes: ELISA, suspension chip, MSD electrochemiluminescence technology, or mass spectrometry. Preferably, the detection method is MSD electrochemiluminescence technology.

[0007] In one or more embodiments, the concentration of TSLP in the preoperative plasma of the subject is detected using MSD electrochemiluminescence technology.

[0008] In one or more embodiments, when the plasma concentration of the biomarker is detected using MSD electrochemiluminescence technology, if the concentration is greater than a threshold, the patient has a high risk of recurrence after treatment for non-small cell lung cancer, and if the concentration is less than or equal to the threshold, the patient has a low risk of postoperative recurrence after treatment for non-small cell lung cancer.

[0009] This invention also provides the application of TSLP as a biomarker or its detection reagent in the preparation of products for predicting the risk of postoperative recurrence of non-small cell lung cancer.

[0010] In one or more embodiments, the reagent detects the concentration of TLSP in a sample or detects the transcriptional level of the coding sequence of thymic stromal cell lymphopoietin.

[0011] In one or more embodiments, the product is a reagent kit or a detection chip.

[0012] In one or more embodiments, the sample to be tested is the patient's peripheral blood, plasma, or serum before surgery. Preferably, the sample to be tested is the patient's plasma.

[0013] In one or more embodiments, the reagent is a reagent used for detecting proteins using any of the following methods: ELISA, suspension chip, MSD electrochemiluminescence technology, or mass spectrometry. Preferably, the detection method is MSD electrochemiluminescence technology.

[0014] The present invention also provides a kit for predicting the risk of postoperative recurrence in non-small cell lung cancer.

[0015] In one or more embodiments, the kit includes: (1) Specific reagents for detecting lymphopoietin in thymic stromal cells, (2) Calibrators, wherein the calibrators are standards for thymic stromal cell lymphopoietin, and (3) Test instructions, which state that the risk of recurrence after surgery in patients with non-small cell carcinoma can be determined by detecting the concentration of lymphopoietin in thymic stromal cells in the patient's blood.

[0016] In one or more embodiments, the kit uses MSD electrochemiluminescence technology for detection.

[0017] In one or more implementation schemes, the procedure for using the kit includes: (1) Process the patient's preoperative plasma, centrifuge and collect the supernatant. (2) The concentration of lymphopoietin in thymic stromal cells of the sample was detected using the MSD electrochemiluminescence technique and the specific reagents described above. (3) The recurrence risk of non-small cell lung cancer patients after surgery is determined based on the concentration of lymphopoietin in thymic stromal cells: when the concentration is greater than the threshold, the recurrence risk is high; when the expression level is less than or equal to the threshold, the recurrence risk is low.

[0018] The present invention also provides a detection method for predicting postoperative recurrence of non-small cell lung cancer.

[0019] In one or more embodiments, the detection method includes the following steps: (1) Obtain the patient's preoperative plasma sample, centrifuge, and collect the supernatant. (2) The concentration of lymphopoietin in thymic stromal cells of the sample was detected using the specific reagent, and (3) The recurrence risk of non-small cell lung cancer patients after surgery is determined based on the concentration of lymphopoietin in thymic stromal cells: when the concentration is greater than the threshold, the recurrence risk is high; when the expression level is less than or equal to the threshold, the recurrence risk is low.

[0020] In one or more embodiments, the specific detection reagent uses MSD electrochemiluminescence technology to detect the concentration of TSLP in the sample.

[0021] The present invention also provides a device for predicting the risk of postoperative recurrence of non-small cell lung cancer.

[0022] In one or more embodiments, the apparatus includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, performs the following steps: (1) Obtain the concentration of lymphopoietin in thymic stromal cells in preoperative plasma samples, and (2) Predict the risk of recurrence after non-small cell lung cancer surgery based on the concentration of lymphopoietin in thymic stromal cells in plasma samples.

[0023] The present invention also provides a system for predicting the risk of postoperative recurrence in non-small cell lung cancer.

[0024] In one or more implementations, systems for predicting the risk of postoperative recurrence in non-small cell lung cancer include: (1) A collection device used to obtain the concentration of thymic stromal cell lymphopoietin in preoperative plasma samples from patients. (2) A data processing device for reading concentration, and (3) A determination device for predicting the risk of recurrence after non-small cell lung cancer surgery based on numerical values.

[0025] The present invention also provides a method for constructing a model to predict the risk of postoperative recurrence in non-small cell lung cancer.

[0026] In one or more implementations, a method for constructing a model of the risk of postoperative recurrence in non-small cell lung cancer includes the steps of: (1) Obtain the concentration of thymic stromal cell lymphopoietin in the patient's preoperative plasma sample, and (2) Calculate the ROC curve and the area under the curve to determine the threshold. Attached Figure Description

[0027] Figure 1 The difference in TSLP concentration between the relapse and non-relapse groups in the screening set.

[0028] Figure 2 ROC curve of the filtered set.

[0029] Figure 3 The difference in TSLP concentration between the relapse and non-relapse groups in the validation set.

[0030] Figure 4 ROC curve of the validation set. Detailed Implementation

[0031] It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form a preferred technical solution.

[0032] The inventors collected preoperative plasma from NSCLC patients and used the MSD electrochemiluminescence immunoassay to detect the concentration of TSLP in the patient's plasma to predict the risk of postoperative recurrence. Specifically, this invention discovered that TSLP can serve as a specific biomarker for predicting the risk of postoperative recurrence in non-small cell lung cancer. The risk of postoperative recurrence in non-small cell lung cancer can be predicted by detecting the concentration of TSLP in the patient's plasma.

[0033] Quantitative protein detection is a common technique in molecular biology, accurately determining the content of one or more proteins in a sample through specific technical methods. Quantitative protein detection is mainly performed from the perspectives of spectroscopic analysis, chemical analysis, immunological analysis, and chromatographic analysis. Specifically, methods for quantitative protein detection include, but are not limited to: ELISA, suspension microarrays, MSD electrochemiluminescence immunoassay, biuret method, BCA method, Lowry method, Bradford method, and ultraviolet spectroscopy. For example, in this invention, MSD electrochemiluminescence immunoassay is used to detect TSLP concentration in preoperative plasma of patients.

[0034] In this article, "postoperative recurrence risk" refers to the possibility that the primary disease will reappear anatomically or functionally after a patient undergoes surgical treatment. In an exemplary implementation plan, postoperative recurrence risk specifically refers to the possibility that residual tumor cells (including tiny residual lesions that cannot be identified by imaging or conventional detection methods) will proliferate again in the primary lesion area, regional lymph nodes, pleura, or distant organs (such as the brain, bone, liver, adrenal glands, etc.) after a non-small cell lung cancer patient has undergone tumor resection surgery (including lobectomy, wedge resection, pneumonectomy, and thoracoscopic minimally invasive surgery) and completed the prescribed range of lymph node dissection, forming detectable lesions. It is a core indicator for assessing the integrity of surgical treatment, guiding the formulation of postoperative adjuvant therapy plans, and judging the patient's prognosis.

[0035] In this article, terms such as "amount," "content," and "concentration" include both absolute and relative quantities. The terms "amount," "content," and "concentration" in this article can be standardized.

[0036] The term "marker" as used herein can refer to a nucleic acid molecule or its protein expression product. Methods for detecting protein levels are well known in the art, such as ELISA, suspension microarrays, MSD electrochemiluminescence, or mass spectrometry. In some embodiments, the present invention relates to the detection of marker protein concentrations from blood-derived samples.

[0037] Therefore, this invention relates to reagents for detecting protein expression levels. All reagents described in the methods for detecting protein concentrations are known in the art. In one or more embodiments, the reagent is a reagent from an ECL multifactor assay kit suitable for detection using the MSD electrochemical method.

[0038] In an exemplary embodiment, the present invention uses MSD electrochemiluminescence technology to detect protein concentration in a sample. The steps and required reagents of conventional MSD electrochemiluminescence technology are known in the art, including electrochemiluminescence detection reagents, MSD-specific microplates, and specific antibodies used for the quantitative detection of proteins in plasma samples using this technology. Designing and preparing specific antibodies (including capture antibodies and detection antibodies) against a protein based on its amino acid sequence is a conventional technique in the art.

[0039] Therefore, the present invention also relates to methods for pretreating samples. Samples derived from blood include, but are not limited to, peripheral blood, plasma, and serum. Those skilled in the art know methods for pretreating samples to obtain components containing the target biomarker (e.g., nucleic acids, proteins), such as centrifugation, plasma protein extraction kits, etc. Therefore, the reagents described herein also include reagents for separating proteins from plasma.

[0040] In this document, the sample is derived from a mammal, preferably a human. The sample may be derived from any organ (e.g., lung), tissue (e.g., epithelial tissue), cell (e.g., tumor cell), or bodily fluid (e.g., blood, plasma, serum, tissue fluid, urine). In the example embodiment, the sample is plasma.

[0041] This invention also relates to a kit for predicting the risk of postoperative recurrence in non-small cell lung cancer, comprising the reagents described herein. The kit may also contain the biomarkers described herein as internal standards or positive controls. In addition to the aforementioned reagents, the kit may contain other reagents required for protein detection. Exemplarily, the kit may contain one or more of the following: tools and / or reagents for collecting samples from subjects, tools and / or reagents suitable for long-term storage of samples and / or controls, and tools and / or reagents for pretreatment of samples and / or controls.

[0042] MSD electrochemiluminescence technology

[0043] The term "MSD electrochemiluminescence technology" refers to a highly sensitive quantitative detection technique for biomolecules based on the principle of electrochemiluminescence (ECL), combined with a microplate solid-phase support and a signal amplification system. Its core principle involves applying a voltage to a luminescent substance (such as ruthenium terpyridine labeled terpyridine) via electrodes, exciting the complex resulting from a specific binding reaction to generate an electrochemiluminescence signal. The signal intensity is positively correlated with the concentration of the target molecule, thus enabling precise quantitative analysis of the target substance. In protein quantification, this technology utilizes a sandwich immunoreaction model of "capture antibody-target protein-detection antibody" to specifically identify the target protein and amplify the luminescent signal, ensuring both specificity and sensitivity of the detection.

[0044] The core components of MSD electrochemiluminescence technology include three main parts: detection system, reaction system and signal acquisition system. (1) Detection system: The core is MSD-specific multi-well microplate (such as 96-well or 384-well plates). The working electrode and auxiliary electrode are built into the bottom of the plate. Some microplates support multi-array site design, which can realize the simultaneous detection of multiple target molecules in a single sample (multiplex detection). (2) Reaction system: It includes specific binding reagents (capture antibodies against target proteins, detection antibodies labeled with luminescent substances (such as ruthenium tripyridine), co-reactants (such as tripropylamine, as an electrochemiluminescence substrate), buffer, etc. Among them, the electrochemical redox reaction between the luminescent substance and the co-reactant under electrode voltage excitation is the key to signal generation. (3) Signal acquisition system: It is composed of MSD-specific electrochemiluminescence detector, which can accurately control electrode voltage and excitation time, and simultaneously collect luminescence signal intensity and convert it into digital data for subsequent quantitative analysis.

[0045] Based on detection mode, MSD electrochemiluminescence technology can be classified into single detection mode (single detection of a single target protein) and multiple detection mode (single detection of multiple target proteins); based on microplate type, it can be classified into standard multi-well plate detection, high-sensitivity multi-well plate detection, and rapid detection-specific plate detection; based on application scenario, it can be classified into research detection technology, clinical sample detection technology, and high-throughput screening detection technology; based on signal amplification level, it can be classified into conventional signal amplification detection and ultrasensitive signal amplification detection (suitable for low-abundance protein detection).

[0046] In the field of biomarker detection, there are also different detection schemes corresponding to this technology, such as direct sandwich detection schemes, competitive inhibition detection schemes, and bridging immunoassay schemes. Depending on factors such as the abundance level of the protein to be tested, sample type (e.g., plasma, serum, tissue homogenate), throughput requirements, and specificity requirements, an appropriate detection mode and scheme can be selected to obtain accurate quantitative results.

[0047] In this paper, the concentration of cytokines in plasma samples was detected using MSD electrochemiluminescence technology. The core procedure is as follows: plasma samples are added to MSD microplates coated with cytokine-specific capture antibodies. After incubation, the cytokines in the sample bind specifically to the capture antibodies. After washing to remove unbound contaminating proteins, cytokine detection antibodies labeled with electrochemiluminescent substances are added to form a sandwich complex of "capture antibody-cytokine-detection antibody". Subsequently, an electrochemiluminescent substrate is added, and a specific voltage is applied to the electrodes of the microplate using an MSD detector to excite the luminescent substances to generate a luminescent signal. By detecting the signal intensity and using a standard curve plotted with known concentrations of cytokine standards, the specific concentration of cytokines in the sample can be calculated.

[0048] Typically, the concentration of TSLP in a subject's plasma can be detected using MSD electrochemiluminescence technology. For example, peripheral blood of the subject can be obtained, and plasma can be separated using conventional methods. Blood cells can then be removed from the plasma. Methods for removing blood cells from plasma include, but are not limited to, centrifugation, filtration, natural sedimentation, or magnetic field separation. For example, centrifugation can be used, with a centrifuge speed of 3000 g, centrifuging for 10 minutes and then collecting the supernatant; or filtration can be used, employing a disposable plasma separation filter (such as a membrane filter or filter paper filter), pouring the anticoagulated whole blood into the filter, and collecting the filtered plasma by gravity or slight pressure (such as by injecting with a syringe); natural sedimentation can also be used, placing the anticoagulated whole blood in a test tube at room temperature or 4°C for several hours or even overnight, or adding sedimentation agents such as gelatin or dextran to accelerate sedimentation, collecting the upper plasma layer after the blood cells have settled to the bottom of the tube; or magnetic field separation can be used, adding anti-erythrocyte antibody-conjugated magnetic beads or other labeled magnetic particles to the anticoagulated whole blood, incubating for 10-15 minutes to allow the magnetic beads to bind to blood cells, then placing the test tube on a magnetic rack to attract blood cells, and finally pouring out and collecting the upper plasma layer.

[0049] After centrifugation, blood cells precipitate in the plasma, and the supernatant obtained can be used for MSD electrochemical detection. MSD electrochemical techniques known in the art can be used for detection. In the embodiments of this invention, an MSD electrochemical detection platform is used. The detection reaction plate can be a conventional MSD-specific reaction plate, such as the carbon electrode microplate provided by Meso Scale Discovery. Matching detection reagents (such as diluents, capture antibodies, detection antibodies, and luminescent substrates) can be selected according to different detection targets (or kit requirements). For example, in an embodiment of the present invention, a customized kit (K15067L-1, MSD) containing 24 factors (CCL2 / MCP1, CCL3 / MIP-1-alpha, CCL4 / MIP-1-beta, CSF3 / G-CSF, CXCL10 / IP-10, GM-CSF, IFN-gamma, IL-1 alpha, IL-1 beta, IL-2, IL-4, IL-6, IL-7, IL-8, IL-10, IL-16, IL-17A, IL-23, IL-27, IL-29 / IFN-λ1, IL-33, TNF-alpha, TSLP, VEGF / VEGF-A) is used for ECL multifactor detection. The amino acid sequence of thymic stromal lymphopoietin (TSLP) is shown in SEQ ID NO: 1.

[0050] Other MSD electrochemical detection procedures can be performed according to the operation manual. This allows the determination of the TSLP concentration in the sample plasma.

[0051] ROC curve

[0052] The term "ROC curve" refers to the Receiver Operating Characteristic Curve, a tool used to evaluate the performance of classification models, widely applied in bioinformatics and medicine. The ROC curve helps us understand the classifier's performance at different thresholds by plotting the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR). The True Positive Rate (TPR), also known as sensitivity, represents the proportion of correctly identified positive samples out of all actual positive samples, and its calculation formula is: TPR = TP / (TP + FN) Where TP (True Positive) is the number of samples that are actually positive and are predicted as positive by the model, and FN (False Negative) is the number of samples that are actually positive but are predicted as negative by the model.

[0053] The false positive rate (FPR) represents the proportion of samples that are incorrectly identified as positive out of all actual negative samples. It is calculated using the following formula: FPR = FP / (TP + FN) FP (False Positive) is the number of samples that are actually negative but are predicted as positive by the model, and TN (True Negative) is the number of samples that are actually negative and are predicted as negative by the model.

[0054] A graph plotting TPR versus FPR by varying the classification threshold shows that the closer the curve is to the top left corner, the better the classifier's performance. Ideally, the ROC curve will pass through the point (0,1), meaning the model can distinguish between positive and negative examples, i.e., FPR=0 (no false positives) and TPR=1 (all positives are correctly identified). If the ROC curve is a diagonal line from the origin (0,0) to the point (1,1), it indicates that the model's predictions are random and lack actual classification ability. In this case, for any given true positive rate, the false positive rate is the same, similar to random guessing.

[0055] ROC curves provide an intuitive way to evaluate the performance of classification models (such as classifying genes as disease-related genes or proteins as functional proteins in bioinformatics). By observing the curve's position on the coordinate plane, the model's quality can be quickly determined. For example, in cancer biomarker screening, ROC curves can be used to evaluate models that distinguish between cancer patients and healthy individuals based on the concentration or signal intensity of biomarkers in the blood. If the ROC curve is closer to the upper left corner, it means that a higher true positive rate can be obtained with a lower false positive rate, indicating that the model can effectively identify cancer patients. In practical applications, it is necessary to determine an appropriate classification threshold to divide samples into positive and negative examples. ROC curves can help researchers understand the performance at different thresholds. For example, in microbial identification, classification models are built based on the gene sequence characteristics of microorganisms. ROC curves can be used to observe the changes in the true positive and false positive rates of the model in identifying microbial species at different sequence similarity thresholds. Depending on the actual needs (such as the tolerance for false and true positives), an appropriate threshold can be selected from the ROC curve for classification.

[0056] AUC (Area Under the Curve) is the area under the ROC curve, a quantitative metric used to more intuitively measure model performance. AUC values ​​range from 0.5 to 1. The closer the AUC is to 1, the stronger the model's discriminative ability. For example, in gene regulatory network prediction, a high AUC value for a model predicting the interaction between transcription factors and target genes indicates that it better identifies truly interacting gene pairs and non-interacting gene pairs. After calculating a series of true positive rate (TPR) and false positive rate (FPR) data points to plot the ROC curve, the trapezoidal method can be used to approximate the area under the curve. This is based on dividing the region under the ROC curve into multiple small trapezoids and then calculating the sum of the areas of these trapezoids. It is now commonly used in conjunction with computers, employing statistical software or built-in functions in programming languages ​​(such as R and Python) to calculate AUC values.

[0057] In one or more embodiments, the concentration of the target sample increases or decreases when compared with a control sample. For the sample being tested, a positive result is determined when the concentration is greater than a threshold, indicating a higher risk of recurrence after non-small cell lung cancer surgery; otherwise, a negative result is determined, indicating a lower risk of recurrence after non-small cell lung cancer surgery. Conventional mathematical analysis methods and procedures for determining thresholds are known in the art; an exemplary method is to determine the threshold directly through an ROC curve. In addition, mathematical models such as logistic regression models, support vector machines, and random forest models can be used. Conventional methods for constructing logistic regression models are known to those skilled in the art. For example, for differentially expressed biomarkers, a logistic regression model, support vector machine model, or random forest model is constructed for two groups of samples, and the accuracy, sensitivity, and specificity of the detection results, as well as the area under the predictive value characteristic curve (ROC) (AUC), are statistically analyzed to calculate the predicted score for the test set samples.

[0058] In one or more experimental protocols, when using the MSD electrochemical technique to detect the plasma concentration of the biomarker, the threshold is 1.8538 pg / ml. If the concentration is greater than 1.8538 pg / ml, the non-small cell lung cancer patient has a high risk of recurrence after treatment; if the concentration is less than or equal to 1.8538 pg / ml, the non-small cell lung cancer patient has a low risk of recurrence after treatment.

[0059] When other detection methods are used to detect the absolute or relative concentration of proteins in plasma, the judgment threshold will change due to the different presentation of the detection results. However, the specific statistical relationship reflected by the model and the judgment result for the same patient will not change. When other methods are used to detect concentration or relative concentration, the threshold can be easily determined by those skilled in the art. For example, the threshold can be obtained by: a) collecting preoperative plasma samples from patients with known postoperative recurrence and those without recurrence of non-small cell lung cancer, and obtaining the relative concentration of the TSLP in the above samples; b) constructing a discriminant model for the postoperative recurrence risk of TSLP, at which point those skilled in the art will obtain a model containing specific parameters and a judgment threshold; the more samples, the greater the sensitivity and specificity of the obtained model, and the more accurate the diagnostic results of the model. Then, the postoperative recurrence risk of non-small cell lung cancer patients can be determined by the following steps: c) obtaining the relative concentration of the TSLP in the sample to be tested, the method may be the same as or different from a); d) judging the postoperative recurrence risk of non-small cell lung cancer based on the judgment threshold obtained in step b).

[0060] The model and parameters derived from the content measured in the same batch of experiments are unique. Moreover, based on the statistical relationships and statistical principles obtained in this application, it is known that the parameters (including coefficients, sensitivity, specificity, and accuracy) of the model derived from the content measured in different batches of experiments may vary slightly, but the specific statistical relationship reflected by the model will not change, that is, it has the ability to predict the risk of recurrence after non-small cell lung cancer surgery.

[0061] Once the biomarkers are known, those skilled in the art can construct ROC curves based on any preoperative plasma sample of non-small cell lung cancer to obtain a discriminant model, which can also predict the risk of postoperative recurrence of non-small cell lung cancer.

[0062] According to statistical principles, for a fixed biomarker, the statistical relationship of a model constructed by a person skilled in the art after testing any sample of non-small cell lung cancer that has recurred after surgery and non-small cell lung cancer that has not recurred after surgery using the above method is the same. Even if the parameters may vary slightly, the model can still predict the risk of recurrence after surgery for non-small cell lung cancer.

[0063] In this study, the inventors discovered a correlation between TSLP plasma concentration and the risk of recurrence after non-small cell lung cancer surgery. Further research led to the development of a mathematical model to determine the risk of recurrence after non-small cell lung cancer surgery.

[0064] The concentration of TSLP was determined by MSD electrochemiluminescence assay, which showed a relative concentration. ROC curve analysis of the screening groups is shown below. Figure 2 The AUC was 0.889, the threshold was 1.8538 pg / ml, the specificity was 88.9%, and the sensitivity was 75%. ROC curve analysis of the validation group is shown below. Figure 4 The AUC was 0.847, the threshold was 1.8538 pg / ml, the specificity was 93.3%, and the sensitivity was 80.0%.

[0065] The present invention also provides an apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the following steps: (1) obtaining the content of thymic stromal cell lymphopoietin in a preoperative plasma sample, and (2) predicting the risk of postoperative recurrence of non-small cell lung cancer based on the concentration of thymic stromal cell lymphopoietin in the plasma sample.

[0066] The present invention also provides a system for predicting the risk of recurrence after non-small cell lung cancer surgery, comprising: (1) a collection device for acquiring the content of thymic stromal cell lymphopoietin in a patient's preoperative plasma sample; (2) a data processing device for reading the content value; and (3) a judgment device for predicting the risk of recurrence after non-small cell lung cancer surgery based on the value.

[0067] The present invention also provides a method for constructing a model to predict the efficacy of immunotherapy for non-small cell lung cancer, comprising the steps of: (1) obtaining the content of thymic stromal cell lymphopoietin in a preoperative plasma sample of a patient, and (2) calculating the ROC curve and the area under the curve, and determining the threshold.

[0068] It should be understood that the risk of recurrence after non-small cell lung cancer surgery described in this invention is relative. If the detection threshold for a patient is less than or equal to 1.8538 pg / ml, it indicates that the patient has a low risk of recurrence after non-small cell lung cancer surgery; conversely, if the threshold is greater than 1.8538 pg / ml, it indicates that the patient has a high risk of recurrence after non-small cell lung cancer surgery. When the detection threshold for a patient is less than 1.8538 pg / ml, but the patient exhibits poor symptoms after surgery, other conventional efficacy testing methods in the art can be used as needed.

[0069] Furthermore, the calculation formulas and thresholds described above in this invention are obtained based on the supernatant sample obtained after centrifuging 25 μL of preoperative plasma. Therefore, the thresholds will differ when the sample volume used is different, but those skilled in the art can easily determine the corresponding thresholds using the methods disclosed in this application.

[0070] The present invention will be described below by way of specific embodiments. It should be understood that these embodiments are merely illustrative and are not intended to limit the scope of protection of the present invention. Unless otherwise stated, the methods and reagents used in the embodiments are conventional methods and reagents in the art.

[0071] Example 1: Screening of cytokines associated with postoperative recurrence of NSCLC in preoperative plasma

[0072] 1. Specimen preparation

[0073] Preoperative plasma samples from NSCLC patients were retrospectively collected at 3000 g for 10 min, and the supernatant was collected by centrifugation.

[0074] 2. ECL multifactor detection

[0075] ECL multifactor detection was performed using a customized kit (K15067L-1, MSD) containing 24 factors (CCL2 / MCP1, CCL3 / MIP-1-alpha, CCL4 / MIP-1-beta, CSF3 / G-CSF, CXCL10 / IP-10, GM-CSF, IFN-gamma, IL-1 alpha, IL-1 beta, IL-2, IL-4, IL-6, IL-7, IL-8, IL-10, IL-16, IL-17A, IL-23, IL-27, IL-29 / IFN-λ1, IL-33, TNF-alpha, TSLP, VEGF / VEGF-A). The procedure was performed according to the manufacturer's instructions, as follows: (1) Prepare the MSD board 1) Mix 300 μl of Linker with 200 μl of the corresponding biotin-labeled antibody, incubate at room temperature for 30 min; 2) Add 200 μl of stop solution, mix well, and incubate at room temperature for 30 min; 3) Take 600 μl of the conjugated antibody from each tube and bring the volume up to 6 ml with stop solution; 4) Coat U-plex MSD plates, add 50 μl of the prepared antibody to each well, and incubate at room temperature for 1 hour.

[0076] (2) Add 250 μl of diluent to the standard, shake well, and let stand at room temperature for 15-20 min. Take 50 μl from each bottle into another EP tube, and make up to 250 μl with diluent as the highest concentration point. Perform 4-fold dilutions in sequence to obtain a total of 7 gradient standards + 1 Blank. (3) Prepare antibody dilution solution: Take 60 μl of antibody and add dilution solution to make up to 6 ml; Prepare the washing solution: Prepare 1× PBS (0.05% Tween-20) for later use. Prepare reading solution: Prepare 2× reading solution (4) Clean the MSD plate three times, each time with 150 μl of cleaning solution; (5) Add 25 μl of diluent to each well, add 25 μl of specimen, standard or quality control sample to each well, seal with sealing film, and shake at room temperature for 1 hour; (6) Clean the MSD plate three times, each time with 150 μl of cleaning solution; (7) Add 50 μl of the prepared detection antibody to each well, seal with sealing film, and shake at room temperature for 1 hour; (8) Clean the MSD plate 3 times, each time with 150 μl of cleaning solution; (9) Add 150 μl of the prepared reading solution to each well and perform the test.

[0077] 3. Experimental Results

[0078] As shown in Table 1, 4 cases relapsed within five years post-surgery, while 36 cases remained relapse-free. Thymic stromal lymphopoietin (TSLP) levels showed a statistically significant difference between the two groups (p<0.05). Figure 1 ROC curve analysis can be found in [link to ROC curve analysis]. Figure 2 The AUC was 0.889, the interpretation criterion (threshold) was a concentration greater than 1.8538 pg / ml, 3 out of 4 cases in the relapse group were positive, and 4 out of 36 cases in the non-relapse group were positive, with a specificity of 88.9% and a sensitivity of 75.0%.

[0079] Table 1. Concentration of TSLP in the screening group

[0080] Example 2: Validation of preoperative plasma TSLP as a biomarker for postoperative recurrence in NSCLC.

[0081] The above-mentioned ECL detection technology was used to analyze another batch of retrospectively collected preoperative plasma samples from NSCLC. As shown in Table 2, the number of cases with recurrence within five years after surgery was 5, and the number without recurrence was 30. TSLP showed a statistically significant difference between the two groups (p<0.05). Figure 3 ROC curve analysis can be found in [link to ROC curve analysis]. Figure 4 The AUC was 0.847. According to the interpretation criteria provided by this invention (concentration greater than 1.8538 pg / ml), 4 out of 5 cases in the relapse group were positive, and 2 out of 30 cases in the non-relapse group were positive, with a specificity of 93.3% and a sensitivity of 80.0%. Table 2 shows the concentration of TSLP in the validation group.

[0082] sequence of this article

[0083] SEQ ID NO: 1

[0084] mfpfallyvlsvsfrkifilqlvglvltydftncdfekikaaylstiskdlitymsgtkstefnntvscsnrphclteiqsltfnptagcaslakemfamktkaalaiwcpgysetqinatqamkkrrkrkvttnkcleqvsqlqglwrrfnrpllkqq.

Claims

1. Use of thymic stromal cell lymphopoietin as a biomarker or its detection reagent in the preparation of products for predicting the risk of postoperative recurrence of non-small cell lung cancer.

2. The use as described in claim 1, characterized in that, The reagent is used to detect the concentration of thymic stromal cell lymphopoietin in a sample or to detect the transcriptional level of the coding sequence of thymic stromal cell lymphopoietin.

3. The use as described in claim 1, characterized in that, The product is a reagent kit or a detection chip.

4. The use as described in claim 1, characterized in that, The samples tested are the patient's peripheral blood, plasma, or serum before surgery; Preferably, the sample being tested is the patient's preoperative plasma.

5. The use as described in any one of claims 1-4, characterized in that, The detection methods employed include ELISA, suspension chip, MSD electrochemiluminescence, or mass spectrometry. Preferably, the detection employs MSD electrochemiluminescence technology.

6. A kit for predicting the risk of postoperative recurrence in non-small cell lung cancer, characterized in that, Include: (1) Specific reagents for detecting lymphopoietin in thymic stromal cells, (2) Calibrators, wherein the calibrators are standards for thymic stromal cell lymphopoietin, and (3) Test instructions, which state that the risk of recurrence of non-small cell lung cancer after surgery can be determined by detecting the concentration of thymic stromal cell lymphopoietin in the patient's preoperative plasma. Preferably, the kit uses MSD electrochemiluminescence technology for detection.

7. The kit according to claim 6, characterized in that, The usage process includes: (1) Process the patient's preoperative plasma, centrifuge and collect the supernatant. (2) The concentration of lymphopoietin in thymic stromal cells of the sample was detected using the MSD electrochemiluminescence technique and the specific reagents described above. (3) The recurrence risk of non-small cell lung cancer patients after surgery is determined based on the concentration of lymphopoietin in thymic stromal cells: when the concentration is greater than the threshold, the recurrence risk is high; when the expression level is less than or equal to the threshold, the recurrence risk is low.

8. An apparatus for predicting the risk of postoperative recurrence in non-small cell lung cancer, the apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it performs the following steps: (1) Obtain the concentration of lymphopoietin in thymic stromal cells in preoperative plasma samples, and (2) Predict the risk of recurrence after non-small cell lung cancer surgery based on the concentration of lymphopoietin in thymic stromal cells in plasma samples.

9. A system for predicting the risk of postoperative recurrence in non-small cell lung cancer, characterized in that, include: (1) A collection device used to obtain the concentration of thymic stromal cell lymphopoietin in preoperative plasma samples from patients. (2) A data processing device for reading concentration values, and (3) A determination device for predicting the risk of recurrence after non-small cell lung cancer surgery based on a threshold.

10. A method for constructing a model to predict the risk of postoperative recurrence in non-small cell lung cancer, comprising the steps of: (1) Obtain the concentration of thymic stromal cell lymphopoietin in the patient's preoperative plasma sample, and (2) Calculate the ROC curve and the area under the curve to determine the threshold.

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