Biomarkers for predicting the efficacy of immunotherapy in non-small cell lung cancer and their applications

Abstract

This invention relates to biomarkers for predicting the efficacy of immunotherapy for non-small cell lung cancer (NSCLC) and their applications. The biomarkers for predicting the efficacy of immunotherapy for NSCLC in this invention include phosphoglycerate kinase 1 (PGK1), chromogranin A (CHGA), elastin (ELN), heterologous ribonucleoprotein H3 (HNRNPH3), and SERPINE1 mRNA-binding protein 1 (SERBP1). These biomarkers can be detected using mass spectrometry, and the efficacy of immunotherapy for NSCLC in patients can be determined based on these biomarkers.

Description

Technical Field

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

[0002] Lung cancer is the leading cause of cancer-related morbidity and mortality in my country. Programmed cell death protein 1 (PD-1) or programmed cell death ligand 1 (PD-L)1 monoclonal antibodies combined with chemotherapy have been approved as first-line treatment for advanced non-small cell lung cancer (NSCLC). PD-1 monoclonal antibodies include pembrolizumab, camrelizumab, tislelizumab, sintilimab, toripalimab, slulilimab, and penprimab; PD-L1 monoclonal antibodies include atezolizumab and sugemalimab. While these treatments can lead to long-term survival in some patients, the clinically beneficial population is limited. How to identify a wider pool of patients who will benefit from immunotherapy remains a pressing clinical challenge.

[0003] Protein crown proteomics is a method developed by Seer Corporation for unbiased and in-depth detection of proteins in serum and plasma using the innovative Proteograph™ plasma proteomics high-depth analysis system combined with mass spectrometry. In proteomics research, parallel reaction monitoring (PRM) is often used for targeted detection or screening of target proteins. PRM is similar to multiple reaction monitoring (MRM), but the difference lies in that PRM uses a high-resolution mass spectrometer (Q-Orbitrap / timsTOF), which significantly reduces background interference and improves the selectivity and specificity of detection.

[0004] Currently, the main biomarkers for predicting the efficacy of NSCLC immunotherapy are PD-L1 expression and tumor mutational burden (TMB). While the former has been validated by prospective clinical studies, its predictive efficacy is limited due to factors such as tissue heterogeneity, detection platforms, and detection thresholds, making it less than ideal. Similarly, the latter lacks support from prospective data and is also unable to accurately predict immunotherapy efficacy due to difficulties in tissue acquisition, differences in detection panels, and threshold variations. Current clinical practice tells us that PD-L1 and TMB cannot effectively predict the efficacy of immunotherapy. Other biomarkers are needed clinically to expand the pool of patients who can benefit from immunotherapy. Summary of the Invention

[0005] This invention provides novel biomarkers for predicting the efficacy of immunotherapy for non-small cell lung cancer (NSCLC). The biomarkers for predicting the efficacy of immunotherapy for NSCLC include phosphoglycerate kinase 1 (PGK1), chromogranin A (CHGA), elastin (ELN), heterologous ribonucleoprotein H3 (HNRNPH3), and SERPINE1 mRNA-binding protein 1 (SERBP1).

[0006] Specifically, the present invention provides a method for predicting the efficacy of immunotherapy for non-small cell lung cancer, the method comprising the step of detecting the levels of PGK1, CHGA, ELN, HNRNPH3 and SERBP1 in the plasma of a subject.

[0007] In one or more embodiments, the detection method includes: ELISA, suspension chip, MSD electrochemiluminescence technology, or mass spectrometry. Preferably, the detection method is mass spectrometry.

[0008] In one or more embodiments, mass spectrometry is used to detect the signal intensity of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 in the plasma of the target.

[0009] In one or more embodiments, after detecting the signal strengths of PGK1, CHGA, ELN, HNRNPH3, and SERBP1, the signal strengths are substituted into the following formula:

[0010] Score = -0.011×PGK1 - 0.027×CHGA - 0.068×ELN + 0.039×HNRNPH3 + 0.018×SERBP1. In one or more embodiments, when using the PRM method to detect the plasma concentration of the biomarker, the threshold is -4.6851.

[0011] If the score is less than or equal to -4.6851, the patient is not responding to immunotherapy for non-small cell lung cancer; if the threshold is greater than -4.6851, the patient is responding to immunotherapy for non-small cell lung cancer.

[0012] The present invention also provides the use of PGK1, CHGA, ELN, HNRNPH3 and SERBP1 as biomarkers or their detection reagents in the preparation of reagents or kits for predicting the efficacy of immunotherapy for non-small cell lung cancer.

[0013] In one or more embodiments, the reagent detects the amount of the protein in the plasma in the sample.

[0014] In one or more embodiments, the predictive model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma content of the biomarker is as follows:

[0015] Score = -0.011×PGK1-0.027×CHGA-0.068×ELN+0.039×HNRNPH3+0.018×SERBP1.

[0016] In one or more embodiments, when using the PRM method to detect the plasma content of the biomarker, the threshold is -4.6851.

[0017] 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 mass spectrometry.

[0018] In one or more embodiments, the reagent is a mobile phase used for mass spectrometry detection, comprising mobile phase A and mobile phase B.

[0019] In one or more embodiments, the reagent comprises formic acid in mobile phase A and formic acid acetonitrile in mobile phase B.

[0020] In one or more embodiments, the concentration of the formic acid aqueous solution in mobile phase A is 0.1%, and the concentration of the formic acid acetonitrile solution in mobile phase B is 0.1%.

[0021] In one or more embodiments, the reagent is an antibody.

[0022] In one or more embodiments, the reagent is a target peptide used for protein quantification using the PRM method.

[0023] In one or more embodiments, the reagent further includes a reagent for separating proteins from plasma.

[0024] The present invention also provides an isolated protein combination that serves as a biomarker for the efficacy of immunotherapy for non-small cell lung cancer, the protein combination comprising PGK1, CHGA, ELN, HNRNPH3, and SERBP1.

[0025] In one or more embodiments, the amino acid sequence of PGK1 is shown in SEQ ID NO:1.

[0026] In one or more embodiments, the amino acid sequence of CHGA is shown in SEQ ID NO:2.

[0027] In one or more embodiments, the amino acid sequence of ELN is shown in SEQ ID NO:3.

[0028] In one or more embodiments, the amino acid sequence of HNRNPH3 is shown in SEQ ID NO:4.

[0029] In one or more embodiments, the amino acid sequence of SERBP1 is shown in SEQ ID NO:5.

[0030] The present invention also provides a reagent for detecting the levels of PGK1, CHGA, ELN, HNRNPH3 and SERBP1 in plasma.

[0031] 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 mass spectrometry.

[0032] In one or more embodiments, the reagent is a mobile phase used for mass spectrometry detection, comprising mobile phase A and mobile phase B.

[0033] In one or more embodiments, the reagent comprises formic acid in mobile phase A and formic acid acetonitrile in mobile phase B.

[0034] In one or more embodiments, the concentration of the formic acid aqueous solution in mobile phase A is 0.1%, and the concentration of the formic acid acetonitrile solution in mobile phase B is 0.1%.

[0035] In one or more embodiments, the reagent is an antibody.

[0036] In one or more embodiments, the reagent is a target peptide used for protein quantification using the PRM method.

[0037] In one or more embodiments, the reagent further includes a reagent for separating proteins from plasma.

[0038] The present invention also provides a reagent kit comprising the reagents described in any embodiment herein.

[0039] In one or more embodiments, the kit further comprises a combination of proteins as described in any embodiment herein.

[0040] In one or more embodiments, the kit further comprises a reagent for detecting an internal standard.

[0041] Application of the reagents described in any of the embodiments herein in the preparation of reagents or kits for predicting the efficacy of immunotherapy for non-small cell lung cancer.

[0042] Application of the reagents and protein combinations described in any embodiment of this document in the preparation of reagents or kits for predicting the efficacy of immunotherapy for non-small cell lung cancer.

[0043] In one or more embodiments, the kit is a PRM detection kit.

[0044] In one or more embodiments, the reagent comprises:

[0045] (1) Reagents required for mass spectrometry detection of PGK1, CHGA, ELN, HNRNPH3, and SERBP1, and optionally an internal standard; preferably, the reagents are mobile phases used for mass spectrometry detection, comprising mobile phase A and mobile phase B; more preferably, the reagents comprise an aqueous formic acid solution in mobile phase A and an acetonitrile formic acid solution in mobile phase B; and

[0046] (2)Optional, reagent for separating proteins contained in plasma, preferably from alcohol reagents and reagents used for salting out, such as methanol.

[0047] The present invention also provides an apparatus, characterized in that the apparatus includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to perform the following steps:

[0048] (1) Obtain the levels of the following proteins in the plasma sample: PGK1, CHGA, ELN, HNRNPH3, and SERBP1.

[0049] (2) A score is obtained by constructing a model using the content, and

[0050] (3) Predict the efficacy of immunotherapy for non-small cell lung cancer based on the score.

[0051] In one or more embodiments, the predictive model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma content of the biomarker is as follows:

[0052] Score = -0.011×PGK1-0.027×CHGA-0.068×ELN+0.039×HNRNPH3+0.018×SERBP1.

[0053] The present invention also provides a system for predicting the efficacy of immunotherapy for non-small cell lung cancer, characterized in that it comprises:

[0054] The collection device is used to obtain the levels of the following proteins in plasma samples: PGK1, CHGA, ELN, HNRNPH3, and SERBP1.

[0055] Data processing equipment is used to obtain scores by building models using content.

[0056] A scoring device for predicting the efficacy of immunotherapy for non-small cell lung cancer based on a score.

[0057] In one or more embodiments, the predictive model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma content of the biomarker is as follows:

[0058] Score = -0.011×PGK1-0.027×CHGA-0.068×ELN+0.039×HNRNPH3+0.018×SERBP1.

[0059] This invention also provides a method for constructing a model to predict the efficacy of immunotherapy for non-small cell lung cancer, comprising the following steps:

[0060] (1) Obtain the levels of the following proteins in the plasma sample: PGK1, CHGA, ELN, HNRNPH3, and SERBP1.

[0061] (2) Calculate the ROC curve and area under the curve of the protein combination, and establish a model using logistic regression statistical analysis. Attached Figure Description

[0062] Figure 1 The protein levels of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 differed significantly between the immunotherapy response and non-response groups.

[0063] Figure 2 The ROC curve generated for the model.

[0064] Figure 3 ROC curve for model validation. Detailed Implementation

[0065] 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 preferred technical solutions.

[0066] The inventors collected plasma from patients with advanced NSCLC and conducted research using non-targeted proteomics. Ultimately, they identified biomarkers that can predict the efficacy of immunotherapy for non-small cell lung cancer (NSCLC) using a machine learning-based algorithm. Specifically, this invention discovered that phosphoglycerate kinase 1 (PGK1), chromogranin A (CHGA), elastin (ELN), heterologous ribonucleoprotein H3 (HNRNPH3), and sERPINE1 mRNA-binding protein 1 (SERBP1) can serve as specific biomarkers for predicting the efficacy of immunotherapy for NSCLC. The efficacy of immunotherapy for NSCLC can be diagnosed by detecting the levels of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 in the patient's plasma.

[0067] 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 conducted 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 technology, biuret method, BCA method, Lowry method, Bradford method, and ultraviolet spectroscopy. For example, in this invention, multiple proteins are first screened, and then the signal intensities of the screened PGK1, CHGA, ELN, HNRNPH3, and SERBP1 proteins are measured by mass spectrometry. Finally, a proteomics database search is performed.

[0068] In this document, immunotherapy includes immune checkpoint inhibitor therapy. In an exemplary embodiment, the immune checkpoint inhibitor therapy is a PD-1 or PD-L1 inhibitor therapy. In an exemplary embodiment, the immunotherapy described herein includes the use of a PD-1 / PD-L1 inhibitor and a chemotherapy drug. Chemotherapy drugs include, but are not limited to, one or more of the following: pemetrexed, paclitaxel, albumin-bound paclitaxel, gemcitabine, and platinum-based drugs, such as: pemetrexed + platinum, paclitaxel / albumin-bound paclitaxel + platinum, or gemcitabine + platinum.

[0069] 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.

[0070] The "marker" as described herein can be 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 levels from blood-derived samples.

[0071] Therefore, this invention relates to reagents for detecting protein expression levels. Reagents used in the methods for detecting protein levels described above are well known in the art. In one or more embodiments, the reagent is a mobile phase used for mass spectrometry detection, such as formic acid in mobile phase A and formic acid acetonitrile in mobile phase B, at concentrations conventional in the art. The reagent for detecting protein levels can also be an antibody, such as specific antibodies against PGK1, CHGA, ELN, HNRNPH3, and SERBP1.

[0072] In an exemplary embodiment, the present invention uses the PRM method to detect plasma proteins. The steps and required reagents of conventional PRM methods, including mass spectrometry reagents and specific peptides used for protein quantification in PRM methods, are known in the art. Designing specific peptides based on protein sequences is a conventional technique in the art.

[0073] Therefore, this invention also relates to methods for pretreating samples. Samples derived from blood include, but are not limited to, whole blood, plasma, and serum. Those skilled in the art are familiar with methods for pretreating samples to obtain components containing the target biomarker (e.g., nucleic acids, proteins), such as plasma protein extraction kits, Proteograph workstations, etc. Therefore, the reagents described herein also include reagents for separating proteins from plasma.

[0074] 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.

[0075] This invention also relates to a kit for predicting the efficacy of immunotherapy for 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.

[0076] mass spectrometry

[0077] The term "mass spectrometry" refers to a mass spectrometer (MS), an instrument used to measure the "mass" of molecules. In proteomics research, the "mass" of molecules that mass spectrometry needs to measure generally includes the mass-to-charge ratio (m / z) of the precursor ion and the mass-to-charge ratio of the fragment ions obtained after breaking down the precursor ion. Mass spectrometers from different manufacturers and of different types vary in appearance, but their internal structure mainly consists of three parts: an ion source, a mass analyzer, and a detector. The role of the ion source is to charge the analyte molecules and allow them to enter the mass spectrometer for scanning. In proteomics research, the main ionization method used is electrospray ionization (ESI). The mass analyzer is the core component of the mass spectrometer, and its function is to distinguish the mass size (m / z) of the analyte molecules for separation or scanning. Common mass analyzers include quadrupole, ion trap, time-of-flight (TOF), and orbitrap. Among them, TOF and orbitrap are high-resolution mass analyzers. The detector's role is to convert and record the signal of the analyte molecules to obtain the raw mass spectrometry data.

[0078] Mass spectrometers can be classified in several ways: by ion source (electron impact ionization (EI) mass spectrometers, chemical ionization (CI) mass spectrometers, electrospray ionization (ESI) mass spectrometers, atmospheric pressure chemical ionization (APCI) mass spectrometers, and matrix-assisted laser desorption / ionization (MALDI) mass spectrometers; by mass analyzer (magnetic mass analyzer mass spectrometers, quadrupole mass analyzer mass spectrometers, ion trap mass analyzer mass spectrometers, time-of-flight (TOF) mass spectrometers, and Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers; by application field (organic mass spectrometers, inorganic mass spectrometers, and biological mass spectrometers); and by scanning mode (full scan mode, selected ion monitoring mode (SIM), multiple reaction monitoring mode (MRM), and parallel reaction monitoring mode (PRM).

[0079] In the field of mass spectrometry analysis, there are also data acquisition modes and types with different mechanisms, such as DDA (Data-Dependent Acquisition), DIA (Data-Independent Acquisition), SIM (Selected Ion Monitoring Data), MRM (Multi-Reaction Monitoring Data), and PRM (Parallel Reaction Monitoring Data). Depending on the analytical purpose, sample type, and data quality requirements, an appropriate data acquisition mode can be selected to obtain the corresponding data.

[0080] This paper utilizes an LC-MS / MS mass spectrometer, an analytical instrument that combines liquid chromatography (LC) with tandem mass spectrometry (MS / MS). Samples are separated by LC, which uses the differences in partition coefficients between the stationary and mobile phases to separate complex sample mixtures into individual components, allowing them to elute sequentially at different retention times. The components eluting from the LC are then ionized by the ion source in the mass spectrometer, producing ions with different charges and mass numbers. These ions enter the tandem mass spectrometry system and are separated by a mass analyzer according to their mass-to-charge ratios (m / z), resulting in mass spectra arranged in order of m / z. Qualitative and quantitative results of the sample can be obtained through analysis and processing of the mass spectra.

[0081] Mass spectrometry is typically used to detect the ion response abundance of phosphoglycerate kinase 1 (PGK1), chromogranin A (CHGA), elastin (ELN), heterologous ribonucleoprotein H3 (HNRNPH3), and SERPINE1 mRNA-binding protein 1 (SERBP1) in the plasma of a subject. For example, peripheral blood can be obtained from the subject, and plasma can be separated using conventional methods. Subsequently, proteins can be removed from the plasma. Methods for removing proteins from plasma include, but are not limited to, salting out, organic solvent precipitation, and pH adjustment. For example, neutral salts such as ammonium acetate can be used to dehydrate proteins, causing them to aggregate and precipitate. Alternatively, polar organic solvents can be used to lower the dielectric constant, causing proteins to coagulate and precipitate. Commonly used organic solvents include alcohols, such as monohydric alcohols with 1-4 carbon atoms, such as methanol and ethanol, as well as acetone and acetonitrile. In some embodiments, sulfosalicylic acid can also be used to precipitate proteins. When using sulfosalicylic acid, the pH value needs to be adjusted to be lower than the pI value of the plasma proteins, so that the proteins are positively charged and thus combine with salicylic acid ions to form insoluble salts and precipitate. Since plasma proteins all carry a negative charge, adjusting the plasma pH to below the lowest isoelectric point of the plasma proteins is a viable method. For example, the isoelectric point of plasma albumin is 4.7-4.9, so adjusting the pH to below 4.7 can achieve protein precipitation and separation.

[0082] After precipitating proteins from plasma, the supernatant is obtained and can be analyzed by mass spectrometry. Mass spectrometry techniques known in the art can be used for detection. In this embodiment of the invention, a TOF mass spectrometer is used for mass spectrometry detection. A conventional chromatographic column can be used, such as those provided by a mass spectrometry company. Different mobile phases can be selected depending on the type of column. For example, in this embodiment of the invention, an acetonitrile-water-formic acid system is used, wherein mobile phase A is a 0.1% formic acid aqueous solution and phase B is a 0.1% formic acid-acetonitrile solution. After the column is equilibrated with 100% phase A, the sample is directly loaded onto the column by an autosampler and then subjected to gradient separation at a flow rate of 300 nL / min for a gradient duration of 60 min. The proportions of mobile phase B are: 2% for 0 min, 2-22% for 45 min, 22-37% for 5 min, 37-80% for 5 min, and 80% for 5 min.

[0083] Other mass spectrometry operations can be performed according to the operation manual. The ion response abundances of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 can then be obtained.

[0084] In this paper, during the sample preparation stage, DDA data of the sample is first collected to obtain a large amount of full scan information, which includes information on the daughter ions of the selected parent ion, which helps in the structural identification and analysis of the compound.

[0085] Parallel reaction monitoring (PRM) is a targeted quantitative analysis technique based on high-resolution mass spectrometry. In PRM, the characteristic ions of the target compound, including precursor ions and daughter ions, must first be identified. The precursor ion is the initial ion formed after the target compound is ionized, while the daughter ions are fragment ions generated from the precursor ion through collision-induced dissociation (CID) and other processes in the collision chamber of the mass spectrometer. PRM monitors the transition from precursor ion to daughter ion of the target compound within a selected mass window. When the target compound in the sample enters the mass spectrometer, the instrument automatically monitors these specific ion reactions, thereby achieving specific detection of the target compound. This monitoring mode is similar to multiple reaction monitoring (MRM), but PRM uses a high-resolution mass spectrometer (Q-Orbitrap / timsTOF), which significantly reduces background interference and improves the selectivity and specificity of the detection. Typically, 1-3 specific peptides are selected for the analyte protein, and 3-6 fragment ions are selected for quantification of each target peptide.

[0086] The construction of the PRM method involves: analyzing the acquired DDA mass spectrometry data, then using SpectroDive software to generate a corresponding library to identify the specific peptides and fragment ions to be detected, thus completing the construction of the PRM method. Subsequently, PRM data is acquired. This data effectively eliminates matrix interference, accurately detects the target compound, and also possesses high resolution and mass accuracy, enabling the acquisition of more precise ion mass information.

[0087] In this study, proteins were first enriched from plasma, enzymatically digested, desalted, and concentrated. Then, nanoLC-MS / MS detection, DDA library search and identification, PRM method construction, and quantitative analysis were performed. The PRM method was constructed using SpectroDive software: 1) The collected DDA data were matched with the target peptide sequence, and a library was searched using SpectroDive software to generate the corresponding library; 2) Based on the library and target peptides, a panel was generated to screen peptides and confirm the secondary b / y ions for quantification. A maximum of three specific peptides were selected for each protein, and three to six fragment ions were selected for each target peptide for quantification. In an exemplary embodiment, in the PRM detection method, the PGK1 peptide is a fragment of PGK1 containing the sequence shown in SEQ ID NO:1, preferably as shown in SEQ ID NO:1; the CHGA peptide is a fragment of CHGA containing the sequence shown in SEQ ID NO:2, preferably as shown in SEQ ID NO:2; the ELN peptide is a fragment of ELN containing the sequence shown in SEQ ID NO:3, preferably as shown in SEQ ID NO:3; the HNRNPH3 peptide is a fragment of ELN containing the sequence shown in SEQ ID NO:4, preferably as shown in SEQ ID NO:4; and the SERBP1 peptide is a fragment of SERBP1 containing the sequence shown in SEQ ID NO:5, preferably as shown in SEQ ID NO:5.

[0088] Bioinformatics Analysis

[0089] The term "bioinformatics analysis" is a discipline that integrates mathematics, computer science, and biology to process, analyze, and interpret biological data. Biological data typically refers to data related to physiological and biochemical processes, such as genomic data, transcriptomic data, proteomic data, and metabolomic data. Performing sequence alignment, gene annotation, expression analysis, cluster analysis, or pathway analysis on this data contributes to disease research and diagnosis, drug development, and the advancement of medical devices.

[0090] Commonly used analytical methods in the field of bioinformatics include: (1) Principal Component Analysis (PCA): transforming multiple variables of the original data into a few principal components through linear transformation. These principal components are linear combinations of the original variables; (2) Hierarchical Clustering Analysis (HCA): constructing a clustering dendrogram based on the similarity or distance measure between data points (such as genes, proteins, or samples); (3) K-Means Clustering: this method is suitable for rapid classification of a large number of biological samples and classification of gene expression data; (4) Survival Analysis: in cancer research, by analyzing the clinical characteristics and gene expression data of patients, the survival prognosis of patients is assessed or the impact of different treatment methods on patient survival is compared; (5) Gene Enrichment Analysis: by comparing the number of genes observed in the gene set with the expected number, it is determined whether gene enrichment is statistically significant. It is usually used to identify disease-related biological pathways and functional categories and to help screen potential drug targets; (6) Support Vector Machine (SVM) VectorMachine (SVM) is a supervised machine learning algorithm used for classification and regression analysis.

[0091] ROC curve

[0092] 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:

[0093]

[0094] Where TP (True Positive) is the number of samples that were actually positive and were predicted as positive by the model, and FN (False Negative) is the number of samples that were actually positive but were predicted as negative by the model. The false positive rate (FPR) represents the proportion of samples incorrectly identified as positive out of all actual negative samples, and its calculation formula is:

[0095]

[0096] 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.

[0097] 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.

[0098] 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 cancer patients from 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.

[0099] 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.

[0100] In one or more embodiments, the expression level of the target sample increases or decreases when compared with a control sample. Mathematical analysis is performed on the expression of the measured protein to obtain a score. For the tested sample, if the score is greater than a threshold, the result is considered positive, indicating good efficacy of non-small cell lung cancer immunotherapy (responding to non-small cell lung cancer immunotherapy); otherwise, it is considered negative, indicating poor efficacy of non-small cell lung cancer immunotherapy (not responding to non-small cell lung cancer immunotherapy). Conventional mathematical analysis methods and procedures for determining thresholds are known in the art; exemplary methods include mathematical models such as logistic regression models, support vector machines, and random forest models. Those skilled in the art are familiar with conventional methods for constructing logistic regression models. 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.

[0101] An exemplary logistic regression model is shown below (where each protein name represents its plasma concentration):

[0102] Score = -0.011×PGK1-0.027×CHGA-0.068×ELN+0.039×HNRNPH3+0.018×SERBP1.

[0103] When using the PRM method to detect the plasma levels of biomarkers, the threshold for determining the efficacy of immunotherapy for non-small cell lung cancer (NSCLC) is -4.6851. That is, if the score is less than or equal to -4.6851, the patient is not responding to immunotherapy for NSCLC; if the score is greater than -4.6851, the patient is responding to immunotherapy for NSCLC.

[0104] When other detection methods are used to detect the absolute or relative content 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 the following method: a) collecting samples from patients in known non-small cell lung cancer immunotherapy response and non-response groups, and obtaining the relative content of the combination of the five proteins in the above samples; b) constructing an immunotherapy efficacy discrimination model for the protein combination using logistic regression statistical analysis. At this time, those skilled in the art will obtain a model with 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 patient's responsiveness to non-small cell lung cancer immunotherapy can be judged by the following steps: c) obtaining the relative content of the protein combination in the sample to be tested, the method may be the same as or different from a); d) calculating the model score of the sample according to the corresponding logistic regression model obtained in step b), and then judging the responsiveness to non-small cell lung cancer immunotherapy.

[0105] For logistic regression statistical analysis, the model and parameters derived from the content measured in the same batch of experiments are unique. Moreover, based on the statistical relationships and 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 distinguish the efficacy of immunotherapy for non-small cell lung cancer.

[0106] Once the gene combination is known, those skilled in the art can obtain a logistic regression model based on any non-small cell lung cancer immunotherapy sample, and this model also has the function of identifying the efficacy of non-small cell lung cancer immunotherapy.

[0107] According to statistical principles, for a fixed combination of genes, the statistical relationship of the model constructed by those skilled in the art after testing any non-small cell lung cancer immunotherapy response and non-response samples based on the above method is the same. Even if the parameters may vary slightly, the model can still distinguish the efficacy of non-small cell lung cancer immunotherapy.

[0108] In this study, the inventors discovered that plasma levels of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 are correlated with the efficacy of immunotherapy for non-small cell lung cancer (NSCLC). Further research led to the development of a mathematical model to determine the efficacy of NSCLC immunotherapy. In one or more embodiments, the logistic regression prediction model for NSCLC immunotherapy efficacy based on plasma levels of these biomarkers is as follows:

[0109] Score = 4.988 - 0.011 × PGK1 - 0.027 × CHGA - 0.068 × ELN + 0.039 × HNRNPH3 + 0.018 × SERBP1

[0110] The content of each protein was determined by the relative concentration detected by PRM. The peptide levels of these five proteins were analyzed using the formula above, and the scores for each sample were calculated (see Table 4). ROC curve analysis is shown below. Figure 2 The AUC was 0.884, the threshold was 0.3029, the specificity was 88.9%, and the sensitivity was 90.5%.

[0111] 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, characterized in that the processor executes the program to perform the following steps: (1) obtaining the content of the following proteins in a plasma sample: PGK1, CHGA, ELN, HNRNPH3 and SERBP1, (2) obtaining a score using the content by constructing the non-small cell lung cancer immunotherapy efficacy prediction model described herein, and (3) predicting the non-small cell lung cancer immunotherapy efficacy based on the score.

[0112] The present invention also provides a system for predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising: a collection device for acquiring the content of the following proteins in a plasma sample: PGK1, CHGA, ELN, HNRNPH3 and SERBP1; a data processing device for obtaining a score by constructing the immunotherapy efficacy prediction model for non-small cell lung cancer described herein; and a judgment device for predicting the efficacy of immunotherapy for non-small cell lung cancer based on the score.

[0113] 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 the following proteins in a plasma sample: PGK1, CHGA, ELN, HNRNPH3 and SERBP1, and (2) calculating the ROC curve and area under the curve of the protein combination, and establishing a model using logistic regression statistical analysis.

[0114] It should be understood that the efficacy of non-small cell lung cancer immunotherapy described in this invention is relative. If the threshold for detection in a patient is less than or equal to -4.6851, it indicates that the patient's non-small cell lung cancer immunotherapy efficacy is poor (non-responsive to immunotherapy), or that the non-small cell lung cancer immunotherapy efficacy is good (responsive to immunotherapy) compared to subjects with a threshold greater than -4.6851. When a patient's threshold for detection is greater than -4.6851, but the patient exhibits symptoms of poor efficacy, other conventional efficacy testing methods in the art may be used as needed.

[0115] Furthermore, the calculation formulas and thresholds described above in this invention are obtained using a 250 μL plasma sample. Therefore, when the amount of plasma sample used is different, the coefficients and thresholds of the above calculation formulas will be different, but those skilled in the art can easily determine the corresponding coefficients and thresholds according to the methods disclosed in this application.

[0116] 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.

[0117] Example

[0118] Example 1: High-depth screening of plasma protein coronapromics

[0119] 1. Sample pretreatment

[0120] Prepare a 250 μL plasma sample and place it in a sample tube. Start the Proteograph automated workstation. The machine will operate automatically and complete the pretreatment process automatically, including: 1) incubation of NP-plasma with nanoparticles and formation of protein crowns; 2) purification of NP protein crowns with magnetic beads; 3) digestion of proteins and purification of peptides.

[0121] 2. Mass spectrometry detection

[0122] The desalted lyophilized peptides were reconstituted in 0.1% formic acid aqueous solution and then analyzed by LC-MS / MS. The entire system was a timsTOF Pro 2 mass spectrometer (Bruker Daltonics) from a Thermo Fisher Scientific, MA, USA, in tandem with an UltiMate 3000 system. A total of 200 ng of sample was loaded (analytical column: 25 cm × 75 μm·d., IonOpticks), and the sample was separated using an 80-min gradient at a column temperature of 50 °C. Alternatively, 2.5 μL of sample was loaded, with the column flow rate controlled at 300 nL / min. The gradient started at 4% B phase, increasing to 28% within 45 min, 44% within 10 min, reaching 90% within 10 min, maintaining this level for 7 min, and then equilibrating at 4% for 8 min.

[0123] The mass spectrometer used diaPASEF mode for DIA data acquisition, with a scan range of 349-1229 m / z and an isolation window width of 40 Da. During the PASEF MSMS scan, the collision energy increased linearly with ion mobility, from 59 eV (1 / K0 = 1.6 Vs / cm). 2 The voltage rises to 20 eV (1 / K0 = 0.6 Vs / cm). 2 ).

[0124] 3. Library search identification and protein quantification

[0125] The raw data files were analyzed using the software DIA-NN (version 1.8.1).

[0126] Table 1 Database matching parameter settings

[0127]

[0128]

[0129] 4. Experimental Results

[0130] Baseline plasma samples from 5 patients with advanced NSCLC treated with PD-(L)1 monoclonal antibody in combination with chemotherapy (responder group, including complete response (CR) and partial response (PR)) and 5 nonresponder group (including stable disease (SD) and progressive disease (PD)) showed differences in the levels of up to 222 proteins between the two groups. Subsequent experiments were conducted using PGK1, CHGA, ELN, HNRNPH3, and SERBP1. Figure 1 ).

[0131] Example 2: Validation of PRM quantitative proteomics

[0132] 1. Sample preparation

[0133] 1.1 Protein enrichment

[0134] Protein enrichment and enzymatic digestion steps were performed using the EasyPeptide Blood Low Abundance Protein Enrichment and Pretreatment Kit (OSFP0002).

[0135] Before use, take out reagent H and disperse the magnetic beads evenly by blowing or sonicating. Transfer 20 μL of reagent H to a new EP tube (Note: 2 ml flat bottom tube is recommended), place it on a magnetic rack and let it stand for 2 min. Discard the supernatant after magnetic separation.

[0136] Add 50 μL of reagent G, place on a constant temperature mixer, shake and wash the magnetic beads for 5 minutes (1300 rpm, room temperature), place on a magnetic rack and let stand for 2 minutes, then magnetically separate and discard the supernatant.

[0137] Resuspend the magnetic beads in 50 μL of reagent G, add 50 μL of plasma, place on a constant temperature mixer, and incubate at 37°C with shaking for 1 h (1300 rpm).

[0138] Place the beads on a magnetic rack and let them stand for 2 minutes. After magnetic separation, discard the supernatant, add 150 μL of reagent G, and wash with shaking for 5 minutes. Repeat the washing process three times. The low-abundance protein enriched on the magnetic beads will then be present.

[0139] 1.2 Proteolytic activity

[0140] Add 20 μL of reagent A to the cleaned magnetic beads in the step and mix well by blowing.

[0141] Heat at 95℃ for 5 minutes (95℃, 1000 rpm);

[0142] After heating is complete and the sample has returned to room temperature, add 7.5 μL of reagent B, mix well, and enzymatically hydrolyze at 37°C for 2 h (37°C, 1300 rpm).

[0143] After enzymatic hydrolysis, add 1.5 μL of reagent C, mix well, and terminate the enzymatic hydrolysis reaction. A precipitate will appear at this time. Centrifuge at 17000g for 2 min, then collect the supernatant for subsequent desalting.

[0144] 1.3 Peptide Desalting

[0145] Desalting column activation: Add 100 μL of methanol to the desalting column and centrifuge at 700g for 1 min;

[0146] Desalting column for impurity removal: Add 100 μL of Condition buffer to the desalting column and centrifuge at 700 g for 1 min;

[0147] Desalination column equilibration: Add 100 μL of wash buffer to the desalination column, centrifuge at 700g for 1 min;

[0148] Loading the sample: Load the entire sample into the desalting column, centrifuge at 700g for 1 min, and repeat the operation once; (ensure that the pH is <3 before loading the sample, you can use 20% TFA to adjust the pH to <3 before loading the sample)

[0149] Desalting column cleaning: Add 100 μL of wash buffer to the desalting column, centrifuge at 700g for 1 min, and repeat once;

[0150] Sample elution: Add 30 μL of Elution buffer to the desalting column, centrifuge at 700g for 1 min, elute twice, and the final sample volume is 60 μL;

[0151] Vacuum freeze centrifuge concentrates samples.

[0152] 2. NanoLC-MS / MS detection

[0153] Three μL of total peptides from each sample were separated using a nano-UPLC system (nanoElute2) and then data were acquired using a mass spectrometer (timsTOF Pro2) equipped with a nanoliter ion source. Chromatographic separation was performed using a 75 μm ID × 15 cm reversed-phase column (PePSep C18, 1.9 μm, 75 μm × 15 cm, Bruker, Germany). The mobile phase was an acetonitrile-water-formic acid system, where mobile phase A was a 0.1% formic acid aqueous solution and phase B was a 0.1% formic acid-acetonitrile solution. After equilibration of the column to 100% phase A, the sample was directly loaded onto the column via an autosampler and then subjected to gradient separation at a flow rate of 300 nL / min for 60 min. The mobile phase B ratios were: 2% for 0 min, 2-22% for 45 min, 22-37% for 5 min, 37-80% for 5 min, and 80% for 5 min.

[0154] The mass spectrometer used DDA PaSEF mode for DDA data acquisition, with a scan range of 100-1700 m / z. During the PASEF MS / MS scan, the collision energy increased linearly with ion mobility, from 20 eV (1 / K0 = 0.6 Vs / cm). 2 The voltage rose to 59 eV (1 / K0 = 1.6 Vs / cm). 2 ).

[0155] 3. Library search identification and protein quantification

[0156] 3.1 DDA Database Search and Identification

[0157] The original data files were searched using the Pulsar search engine in SpectroDive (4.1.230421.52329; Biognosys AG 2013) software, and qualitative analysis was performed after the search was completed.

[0158] Table 2 Database Matching Parameter Settings

[0159]

[0160] 3.2 Construction and Quantitative Analysis of PRM Method

[0161] The PRM method was constructed using SpectroDive software: 1) The acquired DDA data was matched with the target peptide sequence, and a library was searched using SpectroDive software to generate the corresponding library. 2) Based on the library and target peptides, a panel was generated to screen peptides and confirm the secondary b / y ions for quantification. A maximum of 3 specific peptides were selected for each protein, and 3–6 fragment ions were selected for each target peptide for quantification.

[0162] 4. Experimental Results

[0163] 4.1 Model Construction

[0164] Baseline plasma samples from 21 patients in the response group and 9 patients in the nonresponder group of advanced NSCLC treated with PD-(L)1 monoclonal antibody combined with chemotherapy were analyzed for PRM targeting of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 (Table 3), and the model was constructed as follows:

[0165] Score = 4.988 - 0.011 × var1 - 0.027 × var2 - 0.068 × var3 + 0.039 × var4 + 0.018 × var5 Table 3: PRM Target Detection Results

[0166]

[0167]

[0168] The peptide levels of these five proteins were analyzed using the formula above, and the scores for each sample were calculated (see Table 4). ROC curve analysis is shown below. Figure 2 The AUC was 0.884, and the interpretation criterion was a score greater than 0.3029. 19 out of 21 patients in the response group were positive, and 1 out of 9 patients in the non-response group was positive. The specificity was 88.9%, and the sensitivity was 90.5%. Table 4 shows the scores of the combined use of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 in the construction of a predictive model for the efficacy of NSCLC immunotherapy.

[0169] Responder 3.6523 Responder 2.3789 Responder 0.7480 Responder 1.7738 Responder 1.8807 Responder 1.9432 Responder -0.9653 Responder 3.9640 Responder 0.4446 Responder 3.8758 Responder -0.2832 Responder 0.9094 Responder 0.8982 Nonresponder 0.0585 Responder 0.4875 Nonresponder 0.3029 Responder 2.8451 Nonresponder 0.2835 Responder 2.0797 Nonresponder 2.0484 Responder 1.5112 Nonresponder -2.3240 Responder 2.6530 Nonresponder -1.5421 Responder 1.1992 Nonresponder -0.5624 Responder 2.6182 Nonresponder -0.0990 Responder 3.4278 Nonresponder -0.8753

[0170] 4.2 Model Validation

[0171] Baseline plasma samples from 18 patients in the responder group and 12 patients in the nonresponder group of advanced NSCLC treated with PD-(L)1 monoclonal antibody combined with chemotherapy were analyzed for PRM targeting of PGK1, CHGA, ELN, HNRNPH3, and SERBP1. The peptide levels of these five proteins were analyzed using the formula above, and the scores for each sample were calculated (Table 5). ROC curve analysis is shown in [Table 5]. Figure 3 The AUC was 0.935. According to the interpretation criteria provided by this invention (score greater than 0.3029), 16 out of 18 cases in the response group were positive and 2 out of 12 cases in the non-response group were positive, with a specificity of 83.3% and a sensitivity of 88.9%.

[0172] Table 5 shows the scores for validating the combined use of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 in predicting the efficacy of NSCLC immunotherapy.

[0173]

[0174]

[0175] Sequence of this article:

[0176] SEQ ID NO:1PGK1

[0177] IQLINNMLDK

[0178] SEQ ID NO:2CHGA ELQDLALQGAK

[0179] SEQ ID NO:3ELN AGYPTGTGVGPQAAAAAAAK

[0180] SEQ ID NO:4HNRNPH3

[0181] DGMDNQGGYGSVGR

[0182] SEQ ID NO:5SERBP1

[0183] SAAQAAAQTNSNAAGK.

Claims

1. A combination of isolated peptides for predicting the efficacy of immunotherapy in non-small cell lung cancer (NSCLC), serving as a biomarker for NSCLC immunotherapy efficacy, wherein the combination of peptides comprises peptides of PGK1, CHGA, ELN, HNRNPH3, and SERBP1, and wherein the immunotherapy is a combination of PD-(L)1 monoclonal antibody and chemotherapy drugs. in, The amino acid sequence of the PGK1 peptide is shown in SEQ ID NO:

1. The amino acid sequence of the CHGA peptide is shown in SEQ ID NO:

2. The amino acid sequence of the ELN peptide is shown in SEQ ID NO:

3. The amino acid sequence of the HNRNPH3 peptide is shown in SEQ ID NO:4, and The amino acid sequence of the SERBP1 peptide is shown in SEQ ID NO:

5.

2. A kit for predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising the peptide combination of claim 1 and its detection reagent, wherein the detection reagent is an antibody, and the immunotherapy is a PD-(L)1 monoclonal antibody combined with a chemotherapy drug.

3. The kit according to claim 2, characterized in that, The kit also includes reagents for detecting internal standards.

4. The application of a detection reagent for a peptide combination of PGK1, CHGA, ELN, HNRNPH3, and SERBP1 in the preparation of reagents or kits for predicting the efficacy of immunotherapy for non-small cell lung cancer, wherein the detection reagent is an antibody, and the immunotherapy is a PD-(L)1 monoclonal antibody combined with chemotherapy drugs. in, The amino acid sequences of the PGK1 peptide are shown in SEQ ID NO:1, the CHGA peptide in SEQ ID NO:2, the ELN peptide in SEQ ID NO:3, the HNRNPH3 peptide in SEQ ID NO:4, and the SERBP1 peptide in SEQ ID NO:

5.

5. The application as described in claim 4, characterized in that, The predictive model for the efficacy of non-small cell lung cancer immunotherapy based on plasma content of the aforementioned peptide combination is as follows: Score = -0.011×PGK1 - 0.027×CHGA - 0.068×ELN + 0.039×HNRNPH3 + 0.018×SERBP1 When using the PRM method to detect the plasma content of the biomarker, the threshold is -4.6851.

6. An apparatus, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, performs the following steps: (1) Obtain the content of the following peptides in plasma samples: PGK1, CHGA, ELN, HNRNPH3 and SERBP1 peptides. (2) A score is obtained by constructing a model using the content, and (3) The efficacy of immunotherapy for non-small cell lung cancer is predicted based on the scoring, wherein the immunotherapy is a combination of PD-(L)1 monoclonal antibody and chemotherapy drugs. in, The amino acid sequence of the PGK1 peptide is shown in SEQ ID NO:

1. The amino acid sequence of the CHGA peptide is shown in SEQ ID NO:

2. The amino acid sequence of the ELN peptide is shown in SEQ ID NO:

3. The amino acid sequence of the HNRNPH3 peptide is shown in SEQ ID NO:4, and The amino acid sequence of the SERBP1 peptide is shown in SEQ ID NO:

5.

7. The apparatus as claimed in claim 6, characterized in that, The predictive model for the efficacy of non-small cell lung cancer immunotherapy based on plasma content of the aforementioned peptide combination is as follows: Score = -0.011×PGK1 - 0.027×CHGA - 0.068×ELN + 0.039×HNRNPH3 + 0.018×SERBP1 When using the PRM method to detect the plasma content of the biomarker, the threshold is -4.6851.

8. A system for predicting the efficacy of immunotherapy for non-small cell lung cancer, wherein the immunotherapy is a PD-(L)1 monoclonal antibody combined with chemotherapy drugs, characterized in that, include: The collection device is used to obtain the levels of the following peptides in plasma samples: PGK1, CHGA, ELN, HNRNPH3, and SERBP1 peptides. Data processing equipment is used to obtain scores by building models using content. A diagnostic device is used to predict the efficacy of immunotherapy for non-small cell lung cancer based on a scoring system. in, The amino acid sequence of the PGK1 peptide is shown in SEQ ID NO:

1. The amino acid sequence of the CHGA peptide is shown in SEQ ID NO:

2. The amino acid sequence of the ELN peptide is shown in SEQ ID NO:

3. The amino acid sequence of the HNRNPH3 peptide is shown in SEQ ID NO:4, and The amino acid sequence of the SERBP1 peptide is shown in SEQ ID NO:

5.

9. The system as described in claim 8, characterized in that, The predictive model for the efficacy of non-small cell lung cancer immunotherapy based on plasma content of the aforementioned peptide combination is as follows: Score = -0.011×PGK1 - 0.027×CHGA - 0.068×ELN + 0.039×HNRNPH3 + 0.018×SERBP1 When using the PRM method to detect the plasma content of the biomarker, the threshold is -4.6851.

10. A method for constructing a model to predict the efficacy of immunotherapy for non-small cell lung cancer, wherein the immunotherapy is a PD-(L)1 monoclonal antibody combined with chemotherapy drugs, the method comprising the steps of: (1) Obtain the content of the following peptides in plasma samples: PGK1, CHGA, ELN, HNRNPH3 and SERBP1 peptides, and (2) Calculate the ROC curve and area under the curve for the peptide combination, and establish a model using logistic regression statistical analysis. in, The amino acid sequence of the PGK1 peptide is shown in SEQ ID NO:

1. The amino acid sequence of the CHGA peptide is shown in SEQ ID NO:

2. The amino acid sequence of the ELN peptide is shown in SEQ ID NO:

3. The amino acid sequence of the HNRNPH3 peptide is shown in SEQ ID NO:4, and The amino acid sequence of the SERBP1 peptide is shown in SEQ ID NO:5.

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