Biomarker combination for predicting clinical benefit of non-small cell lung cancer immunotherapy and application thereof

Abstract

The invention relates to a biomarker combination for clinical benefit of non-small cell lung cancer immunotherapy and application of the biomarker combination. The biomarker for predicting the clinical benefit of the non-small cell lung cancer immunotherapy comprises an immune globulin kappa light chain variable region 1-27 (IGKV1-27), a human leukocyte antigen H (HLA-H), an S100 calcium binding protein A8 (S100A8), a chromogranin A (CHGA), an elastin (ELN) and a nephronectin (NPNT). The biomarkers for predicting the clinical benefit of the immunotherapy of the non-small cell lung cancer can be detected, and the clinical benefit effect of the immunotherapy of the non-small cell lung cancer of a patient is judged by the markers.

Description

Technical Field

[0001] The present invention relates to a biomarker combination for predicting clinical benefit of immunotherapy for non-small cell lung cancer and an application thereof. Background Art

[0002] Immunotherapy has become an important treatment modality for non-small cell lung cancer (NSCLC). Predictive markers for immunotherapy efficacy, such as programmed death-ligand 1 (PD-L1), tumor mutational burden (TMB), and T cell infiltration abundance, remain limited. Exploration of highly effective and accessible predictive markers is ongoing. Peripheral blood markers are widely used in disease diagnosis and treatment. Using peripheral blood markers to predict immunotherapy efficacy has enormous translational value.

[0003] Protein Crown Proteomics is an innovative plasma proteome high-depth analysis system ProteographTM developed by Seer, an American company, combined with a mass spectrometer to conduct unbiased and in-depth detection of proteins in serum plasma.

[0004] Parallel reaction monitoring (PRM) is a targeted detection method similar to multiple reaction monitoring (MRM) technology. The difference between the two is that PRM uses a high-resolution mass spectrometer (Q-Orbitrap / timsTOF), which can significantly reduce background impurity interference and improve the selectivity and specificity of detection. Summary of the Invention

[0005] The present invention provides a biomarker combination for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer. The biomarker combination for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer of the present invention includes immunoglobulin kappa light chain variable region 1-27 (IGKV1-27), human leukocyte antigen H (HLA-H), S100 calcium binding protein A8 (S100A8), chromogranin A (CHGA), elastin (ELN), and nephronectin (NPNT).

[0006] Specifically, the present invention provides a method for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer, which comprises the steps of detecting the levels of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT in the plasma of a subject.

[0007] In one or more embodiments, the detection method comprises: 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 intensities of IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT in the subject's plasma.

[0009] In one or more embodiments, after detecting the signal intensities of IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT, the signal intensities are substituted into the following formula:

[0010] Score = -0.0026 × IGKV1-27 + 3.2395 × HLA-H - 0.0001 × S100A8 - 0.0157 × CHGA - 0.0177 × ELN - 0.0118 × NPNT

[0011] In one or more embodiments, when the plasma level of the marker is measured using the PRM method, the threshold value is -2.2001.

[0012] If the score is greater than or equal to -2.2001, the patient has an efficacy or clinical benefit in immunotherapy for non-small cell lung cancer; if the score is less than -2.2001, the patient has no efficacy or no clinical benefit in immunotherapy for non-small cell lung cancer.

[0013] The present invention also provides the use of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT as biomarkers or their detection reagents in the preparation of reagents or kits for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer.

[0014] In one or more embodiments, the reagent detects the amount of the protein in plasma in the sample.

[0015] In one or more embodiments, the prediction model for the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer based on the plasma level of the marker is:

[0016] Score = -0.0026 × IGKV1-27 + 3.2395 × HLA-H - 0.0001 × S100A8 - 0.0157 × CHGA - 0.0177 × ELN - 0.0118 × NPNT

[0017] In one or more embodiments, when the plasma level of the marker is measured using the PRM method, the threshold value is -2.2001.

[0018] 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.

[0019] In one or more embodiments, the reagent is a mobile phase used for mass spectrometry detection, including mobile phase A and mobile phase B.

[0020] In one or more embodiments, the reagents include formic acid in mobile phase A and formic acid acetonitrile in mobile phase B.

[0021] In one or more embodiments, the concentration of the formic acid aqueous solution in the mobile phase A is 0.1%, and the concentration of the formic acid acetonitrile solution in the mobile phase B is 0.1%.

[0022] In one or more embodiments, the agent is an antibody.

[0023] In one or more embodiments, the reagent is a target polypeptide for quantifying protein using a PRM method.

[0024] In one or more embodiments, the reagents further include reagents for separating proteins from plasma.

[0025] The present invention also provides an isolated protein combination, which is a marker for the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer. The protein combination includes IGKV1-27 or a fragment thereof, HLA-H or a fragment thereof, S100A8 or a fragment thereof, CHGA or a fragment thereof, ELN or a fragment thereof, and NPNT or a fragment thereof.

[0026] In one or more embodiments, the Uniprot number of IGKV1-27 is A0A075B6S5.

[0027] In one or more embodiments, the Uniprot number for HLA-H is P01893.

[0028] In one or more embodiments, S100A8 has a Uniprot number of P05109.

[0029] In one or more embodiments, CHGA has a Uniprot number of P10645.

[0030] In one or more embodiments, the ELN has a Uniprot number of P15502.

[0031] In one or more embodiments, the Uniprot number of NPNT is Q6UXI9.

[0032] In one or more embodiments, the amino acid sequence of the fragment of IGKV1-27 is shown in SEQ ID NO:1.

[0033] In one or more embodiments, the amino acid sequence of the HLA-H fragment is shown in SEQ ID NO:2.

[0034] In one or more embodiments, the amino acid sequence of the fragment of S100A8 is shown in SEQ ID NO:3.

[0035] In one or more embodiments, the amino acid sequence of the CHGA fragment is shown in SEQ ID NO:4.

[0036] In one or more embodiments, the amino acid sequence of the fragment of ELN is shown in SEQ ID NO:5.

[0037] In one or more embodiments, the amino acid sequence of the fragment of NPNT is shown in SEQ ID NO:6.

[0038] The present invention also provides a reagent for detecting the contents of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT in plasma.

[0039] 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.

[0040] In one or more embodiments, the reagent is a mobile phase used for mass spectrometry detection, including mobile phase A and mobile phase B.

[0041] In one or more embodiments, the reagents include formic acid in mobile phase A and formic acid acetonitrile in mobile phase B.

[0042] In one or more embodiments, the concentration of the formic acid aqueous solution in the mobile phase A is 0.1%, and the concentration of the formic acid acetonitrile solution in the mobile phase B is 0.1%.

[0043] In one or more embodiments, the agent is an antibody.

[0044] In one or more embodiments, the reagent is a target polypeptide for quantifying protein using a PRM method.

[0045] In one or more embodiments, the reagents further include reagents for separating proteins from plasma.

[0046] The present invention also provides a kit comprising the reagents described in any embodiment herein.

[0047] In one or more embodiments, the kit further comprises a protein combination as described in any embodiment herein.

[0048] In one or more embodiments, the kit further comprises a reagent for detecting an internal standard.

[0049] Use of the reagent described in any embodiment of the present invention in the preparation of a reagent or kit for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer.

[0050] Use of the reagent described in any embodiment herein and the protein combination described in any embodiment herein in the preparation of a reagent or kit for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer.

[0051] In one or more embodiments, the kit is a PRM detection kit.

[0052] In one or more embodiments, the reagents include:

[0053] (1) using mass spectrometry to detect the reagents required for the IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT and the optional internal standard; preferably, the reagents are mobile phases used for mass spectrometry detection, including mobile phase A and mobile phase B; more preferably, the reagents include a formic acid aqueous solution in mobile phase A and a formic acid acetonitrile solution in mobile phase B; and

[0054] (2) Optionally, a reagent for separating plasma proteins from plasma, preferably selected from alcohol reagents and reagents used for salting out, such as methanol.

[0055] The present invention further provides a device, 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 when the processor executes the program, the following steps are implemented:

[0056] (1) Obtain the content of the following proteins in plasma samples: IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT,

[0057] (2) obtaining a score using the content by constructing a model, and

[0058] (3) Predict the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer based on the score.

[0059] In one or more embodiments, the prediction model for the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer based on the plasma level of the marker is:

[0060] Score = -0.0026 × IGKV1-27 + 3.2395 × HLA-H - 0.0001 × S100A8 - 0.0157 × CHGA - 0.0177 × ELN - 0.0118 × NPNT

[0061] The present invention also provides a method for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer, comprising the steps of:

[0062] (1) Obtain the content of the following proteins in plasma samples: IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT, and

[0063] (2) Calculate the ROC curve and area under the curve of the protein combination, and establish a model using logistic regression statistical analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 ROC curve plot constructed for the model.

[0065] Figure 2 ROC curve diagram for model validation. DETAILED DESCRIPTION

[0066] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a preferred technical solution.

[0067] The inventors collected plasma from patients with advanced NSCLC and used non-targeted proteomics for research. They eventually found biomarkers that can predict the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer through a machine learning-based algorithm. Specifically, the present invention found that immunoglobulin kappa light chain variable region 1-27 (IGKV1-27), human leukocyte antigen H (HLA-H), S100 calcium-binding protein A8 (S100A8), chromogranin A (CHGA), elastin (ELN) and nephronectin (NPNT) can be used as specific biomarkers to predict the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer. The efficacy or clinical benefit of immunotherapy for non-small cell lung cancer in a subject can be diagnosed by detecting the levels of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT in the subject's plasma.

[0068] Quantitative detection of proteins is a commonly used method in molecular biology. It uses specific technical methods to accurately determine the content of one or more proteins in a sample. Quantitative detection of proteins is mainly carried out from the perspectives of spectral analysis, chemical analysis, immunological analysis, chromatographic analysis, etc. Specifically, methods for quantitative detection of proteins include but are not limited to: ELISA, suspension chip, MSD electrochemiluminescence technology, biuret method, BCA method, Lowry method, Bradford method, and ultraviolet spectroscopy. For example, in the present invention, multiple proteins are first screened, and then the signal intensity of the screened IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT proteins is measured by mass spectrometry, and then the proteomics data is searched in the library.

[0069] Herein, immunotherapy includes immune checkpoint inhibitor therapy. In an exemplary embodiment, immune checkpoint inhibitor therapy is PD-1 or PD-L1 inhibitor therapy. In an exemplary embodiment, immunotherapy herein includes the use of PD-1 / PD-L1 inhibitors and chemotherapy drugs. Chemotherapy drugs include, but are not limited to, one or more of pemetrexed, paclitaxel, nab-paclitaxel, gemcitabine, and platinum drugs, for example, pemetrexed + platinum, paclitaxel / nab-paclitaxel + platinum, or gemcitabine + platinum.

[0070] As used herein, "efficacy" refers to the effect of a medical intervention (such as immunotherapy) in achieving the intended therapeutic goal. In some embodiments, efficacy includes clinical benefit (DCB), i.e., progression-free survival (PFS) ≥ 6 months; correspondingly, lack of efficacy or insignificant efficacy includes nonclinical benefit (NCB), i.e., progression-free survival (PFS) < 6 months.

[0071] Herein, "amount", "content", "concentration" and the like include absolute amount and relative amount. The "amount", "content" and "concentration" herein may be standardized.

[0072] As used herein, a "marker" can be a nucleic acid molecule or a protein expression product thereof. Methods for detecting protein levels are well known in the art, such as ELISA, suspension microarray, MSD electrochemiluminescence technology, or mass spectrometry. In certain embodiments, the present invention relates to detecting the level of a marker protein in a sample derived from blood.

[0073] Therefore, the present invention relates to reagents for detecting protein expression levels. The reagents used in the above-mentioned methods for detecting protein levels are well known in the art. In one or more embodiments, the reagent is a mobile phase for mass spectrometry detection, such as formic acid in mobile phase A and formic acid acetonitrile in mobile phase B, the concentrations of which are conventional in the art. The reagent for detecting protein levels can also be an antibody, such as specific antibodies for IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT.

[0074] In an exemplary embodiment, the present invention uses PRM to detect plasma proteins. The steps and reagents required for conventional PRM methods are known in the art, including mass spectrometry reagents and specific peptides used for PRM protein quantification. Designing specific peptides based on protein sequences is a routine technique in the art.

[0075] Therefore, the present invention also relates to a method for pre-treating a sample. Samples derived from blood include, but are not limited to, whole blood, plasma, and serum. Those skilled in the art are aware of methods for pre-treating a sample to obtain components (e.g., nucleic acids, proteins) containing markers to be measured, such as plasma protein extraction kits, proteograph workstations, etc. Therefore, the reagents herein also include reagents for separating proteins from plasma.

[0076] Herein, the sample is from a mammal, preferably a human. The sample can be from any organ (e.g., lung), tissue (e.g., epithelial tissue), cell (e.g., tumor cell), or body fluid (e.g., blood, plasma, serum, tissue fluid, urine). In an exemplary embodiment, the sample is plasma.

[0077] The present invention also relates to a kit for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer, comprising the reagents described herein. The kit may also contain the markers described herein as internal standards or positive controls. In addition to the reagents described, the kit may also 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 pre-treating samples and / or controls.

[0078] The term "mass spectrometer" refers to a mass spectrometer (MS), an instrument used to measure the "mass" of molecules. In proteomics research, the "mass" of molecules measured by mass spectrometry generally includes the mass-to-charge ratio (m / z) of the precursor ion and the mass-to-charge ratio of the fragment ions (fragment ions) after the precursor ion is broken down. Common mass analyzers include quadrupoles, ion traps, time-of-flight (TOF), and orbitraps. TOF and Orbitrap are high-resolution mass analyzers. The detector converts and records the signals of the molecules being measured to obtain raw mass spectrometry data. Based on the scanning method, these can be categorized into full scan mode, selected ion monitoring mode (SIM), multiple reaction monitoring mode (MRM), and parallel reaction monitoring mode (PRM).

[0079] This article uses an LC-MS / MS mass spectrometer, an analytical instrument that combines liquid chromatography (LC) with tandem mass spectrometry (MS / MS). Samples are separated by liquid chromatography, which exploits the differences in the distribution coefficients of different substances between the stationary and mobile phases to separate complex sample mixtures into individual components, which then elute sequentially with different retention times. Each component eluting from the liquid chromatography enters the ion source of the mass spectrometer where it is ionized, producing ions with distinct charges and mass numbers. These ions enter the tandem mass spectrometer system, where the mass analyzer separates them according to their mass-to-charge ratio (m / z), producing a mass spectrum arranged in order of mass-to-charge ratio. By analyzing and processing the mass spectrum, qualitative and quantitative results for the sample can be obtained.

[0080] The term "parallel reaction monitoring" (PRM) refers to a targeted quantitative analysis technique based on high-resolution mass spectrometry. In PRM, the characteristic ions of the target compound, including parent ions and daughter ions, must first be identified. The parent ion is the initial ion formed after the target compound is ionized, and the daughter ions are fragment ions produced by the parent ion through processes such as collision-induced dissociation (CID) in the collision chamber of the mass spectrometer. PRM monitors the transition from parent ions to daughter ions 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 can significantly reduce background impurity interference and improve the selectivity and specificity of the detection. Typically, one to three specific peptides are selected for the protein to be detected, and three to six fragment ions are selected for each target peptide for quantification.

[0081] PRM method development involves analyzing the acquired DDA mass spectrometry data and then using SpectroDive software to generate a library to identify the specific peptides and fragment ions to be detected, completing the PRM method development. PRM data is then collected. This data effectively eliminates matrix interference and accurately detects target compounds. It also provides high resolution and mass accuracy, enabling the collection of more precise ion mass information.

[0082] Typically, mass spectrometry methods can be used to detect the abundance of ion responses for immunoglobulin kappa light chain variable region 1-27 (IGKV1-27), human leukocyte antigen H (HLA-H), S100 calcium-binding protein A8 (S100A8), chromogranin A (CHGA), elastin (ELN), and nephronectin (NPNT) in a subject's plasma. For example, peripheral blood can be obtained from the subject and separated using conventional methods to obtain plasma. Subsequently, proteins can be removed from the plasma. Methods for removing proteins from plasma include, but are not limited to, salting out, precipitation with organic solvents, 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 protein aggregation and precipitation. Commonly used organic solvents include alcoholic organic solvents, such as monohydric alcohols with 1-4 carbon atoms, such as methanol and ethanol, as well as acetone and acetonitrile. In certain embodiments, sulfosalicylic acid can also be used to precipitate proteins. When using sulfosalicylic acid, the pH must be adjusted below the pI of plasma proteins, imparting a positive charge to the proteins, which then combine with salicylate to form insoluble salts and precipitate. Since plasma proteins are all negatively charged, the pH of the plasma should be adjusted below the lowest isoelectric point of the plasma proteins. For example, the isoelectric point of plasma albumin is between 4.7 and 4.9, so adjusting the pH to below 4.7 can achieve protein precipitation and separation.

[0083] After the proteins in the plasma are precipitated, the supernatant is obtained and can be subjected to mass spectrometry analysis. Detection can be performed using mass spectrometry techniques well known in the art. In an embodiment of the present invention, a TOF mass spectrometer is used for mass spectrometry analysis. Conventional chromatographic columns, such as those provided by mass spectrometry companies, can be used as chromatographic columns. Different mobile phases can be selected depending on the column. For example, in an embodiment of the present invention, an acetonitrile-water-formic acid system is used, where mobile phase A is a 0.1% formic acid solution in water and phase B is a 0.1% formic acid solution in acetonitrile. After the column is equilibrated with 100% phase A, the sample is loaded directly onto the column via an autosampler and then subjected to gradient separation on the column at a flow rate of 300 nL / min and a gradient duration of 60 min. The mobile phase B ratio is: 2% for 0 min, 2-22% for 45 min, 22-37% for 5 min, 37-80% for 5 min, and 80% for 5 min.

[0084] Other mass spectrometry operations can be performed according to the operating manual. Thus, the ion response abundance of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT can be obtained.

[0085] In this paper, during the sample preparation stage, the DDA data of the sample is first collected to obtain a large amount of full scan information, which includes the daughter ion information of the selected parent ion, which is helpful for the structural identification and analysis of the compound.

[0086] In this study, plasma proteins were first enriched, enzymatically digested, desalted, and concentrated. NanoLC-MS / MS analysis, DDA library search, PRM method development, and quantitative analysis were then performed. PRM method development was accomplished using SpectroDive software: 1) the acquired DDA data were matched to the target peptide sequence and library searched using SpectroDive software to generate a corresponding library; 2) based on the library and target peptides, secondary b / y ions were generated for panel screening peptides and confirmation of quantification. Up to 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 peptide segment of IGKV1-27 is a fragment of IGKV1-27 comprising the sequence shown in SEQ ID NO: 1, preferably as shown in SEQ ID NO: 1; the peptide segment of HLA-H is a fragment of HLA-H comprising the sequence shown in SEQ ID NO: 2, preferably as shown in SEQ ID NO: 2; the peptide segment of S100A8 is a fragment of S100A8 comprising the sequence shown in SEQ ID NO: 3, preferably as shown in SEQ ID NO: 3; the peptide segment of CHGA is a fragment of CHGA comprising the sequence shown in SEQ ID NO: 4, preferably as shown in SEQ ID NO: 4; the peptide segment of ELN is a fragment of ELN comprising the sequence shown in SEQ ID NO: 5, preferably as shown in SEQ ID NO: 5; the peptide segment of NPNT is a fragment of NPNT comprising the sequence shown in SEQ ID NO: 6, preferably as shown in SEQ ID NO: 6.

[0087] The term "bioinformatics analysis" refers to a discipline that integrates mathematics, computer science, and biology to process, analyze, and interpret biological data. Common analytical methods in bioinformatics include principal component analysis (PCA), hierarchical clustering analysis (HCA), K-means clustering, survival analysis, gene enrichment analysis, and support vector machine (SVM).

[0088] The term "ROC curve" refers to the Receiver Operating Characteristic Curve, a tool used to evaluate the performance of classification models and is widely used in bioinformatics and medicine. The ROC curve helps us understand the performance of a classifier 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 samples correctly identified as positive to all actual positive samples. It is calculated as follows:

[0089]

[0090] Where TP (True Positive) is the number of samples that are actually positive and predicted as positive by the model, and FN (False Negative) is the number of samples that are actually positive but predicted as negative by the model. The false positive rate (FPR) represents the ratio of samples that are incorrectly identified as positive to all actual negative samples. Its calculation formula is:

[0091]

[0092] Among them, FP (False Positive) is the number of samples that are actually negative examples but predicted as positive examples by the model, and TN (True Negative) is the number of samples that are actually negative examples and predicted as negative examples by the model.

[0093] Plotting TPR versus FPR by varying the classification threshold, the closer the curve is to the upper left corner, the better the classifier's performance. Ideally, the ROC curve passes through the (0,1) point, meaning the model is able to distinguish between positive and negative examples, with FPR = 0 (no false positives) and TPR = 1 (all positive examples correctly identified). If the ROC curve is a diagonal line running from the origin (0,0) to the point (1,1), this indicates that the model's predictions are random and lack true classification ability. In this case, for any given true positive rate, the false positive rate is the same, similar to random guessing. The ROC curve provides an intuitive way to evaluate the performance of classification models (such as classifying genes as disease-associated genes or proteins as functional proteins in bioinformatics). By observing the curve's position in the coordinate plane, one can quickly determine the quality of the model. For example, in cancer biomarker screening, ROC curves can be used to evaluate models that distinguish cancer patients from healthy individuals based on biomarker concentration or signal intensity in blood. If the ROC curve is close to the upper left corner, it means that a higher true positive rate can be obtained at a lower false positive rate, and the model can identify cancer patients well.

[0094] AUC (Area Under the Curve) is the area under the ROC curve. It is a quantitative indicator used to more intuitively measure model performance. The AUC value range is from 0.5 to 1. The closer the AUC is to 1, the stronger the model's discriminatory ability. For example, in gene regulatory network prediction, if a model predicting the interaction between transcription factors and target genes has a high AUC value, it means that it is better at distinguishing between 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 method is based on the principle of dividing the area under the ROC curve into multiple small trapezoids and then calculating the sum of the areas of these trapezoids. Nowadays, AUC values ​​are usually calculated in conjunction with computers, using built-in functions in statistical software or programming languages ​​(such as R and Python).

[0095] In one or more embodiments, when compared with the control sample, the expression level of the subject sample increases or decreases. The expression of the measured protein is mathematically analyzed to obtain a score. For the sample detected, when the score is greater than the threshold, the result is determined to be positive, that is, the patient has an efficacy or clinical benefit in the immunotherapy of non-small cell lung cancer, otherwise it is negative, that is, the patient has no efficacy or the efficacy is not significant or non-clinical benefit in the immunotherapy of non-small cell lung cancer. Conventional mathematical analysis methods and the process of determining the threshold are known in the art. An exemplary method is a mathematical model, such as a logistic regression model, a support vector machine, and a random forest model. Those skilled in the art are aware of conventional methods for constructing a logistic regression model. For example, for differentially expressed markers, a logistic regression model, a support vector machine model, or a random forest model is constructed for two groups of samples, and the accuracy, sensitivity and specificity of the test results and the area under the predictive characteristic curve (ROC) (AUC) of the model are statistically analyzed to calculate the test set sample prediction score.

[0096] An exemplary logistic regression model is as follows (wherein each protein name indicates its plasma content):

[0097] Score = -0.0026 × IGKV1-27 + 3.2395 × HLA-H - 0.0001 × S100A8 - 0.0157 × CHGA - 0.0177 × ELN - 0.0118 × NPNT.

[0098] When using the PRM method to measure plasma levels of markers, the threshold for determining efficacy or clinical benefit from immunotherapy for non-small cell lung cancer is -2.2001. That is, if the score is greater than or equal to -2.2001, the patient has an efficacy or clinical benefit from immunotherapy for non-small cell lung cancer; if the score is less than -2.2001, the patient has no efficacy or insignificant efficacy or no clinical benefit from immunotherapy for non-small cell lung cancer.

[0099] When other methods are used to measure absolute or relative plasma protein levels, the thresholds may vary due to differences in the presentation of test results. However, the specific statistical relationships reflected by the model and the results for the same patient remain unchanged. When other methods are used to measure concentrations or relative concentrations, the thresholds can be readily determined by those skilled in the art.

[0100] For example, the threshold value can be obtained by the following method: a) collecting samples from patients in the known non-small cell lung cancer immunotherapy clinical benefit group and non-clinical benefit group, and obtaining the relative content of the combination of the six proteins in the above samples; b) using the logistic regression statistical analysis method to construct an immune clinical benefit discrimination model for the protein combination. At this time, a person skilled in the art will obtain a model containing specific parameters and a judgment threshold value; the more samples there are, the higher 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 of the sample to be tested, the method can be the same as or different from step a); d) calculating the model score of the sample based on the corresponding logistic regression model obtained in step b), and then judging whether the non-small cell lung cancer immunotherapy has clinical benefit.

[0101] For the logistic regression statistical analysis method, the model and parameters derived from the content measured by the same batch of experimental results are unique. Moreover, based on the statistical relationships and statistical principles obtained in this application, it can be seen that the parameters (including coefficients, sensitivity, specificity, and accuracy) of the model derived from the content measured by different batches of experimental results may change slightly, but the specific statistical relationship reflected by the model will not change, that is, it has the ability to discriminate the clinical benefits of immunotherapy for non-small cell lung cancer.

[0102] After knowing the gene combination, technicians in this field can obtain a logistic regression model based on any non-small cell lung cancer immunotherapy sample, and the model also has the function of identifying the clinical benefits of non-small cell lung cancer immunotherapy.

[0103] According to statistical principles, for a fixed gene combination, the statistical relationship of the model constructed by technicians in this field after testing any samples with clinical benefits and non-clinical benefits of immunotherapy for non-small cell lung cancer based on the above method is the same. Even if the parameters may change slightly, the model can still identify the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer.

[0104] Herein, the inventors discovered that plasma levels of IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT are associated with the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer. Further research subsequently established a mathematical model for determining the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer. In one or more embodiments, the logistic regression prediction model for the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer based on the plasma levels of these markers is:

[0105] Score = 2.2198 - 0.0026 × IGKV1-27 + 3.2395 × HLA-H - 0.0001 × S100A8 - 0.0157 × CHGA - 0.0177 × ELN - 0.0118 × NPNT

[0106] The content of each protein is the relative concentration detected by PRM. The peptide levels of these five proteins were jointly analyzed using the above formula to calculate the scores of each sample. See Table 4. ROC curve analysis is shown in Figure 2 , the AUC was 0.865, the threshold was 0.0197, the specificity was 80.0%, and the sensitivity was 90.0%.

[0107] The present invention also provides a device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the following steps are implemented: (1) obtaining the content of the following proteins in a plasma sample: IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT; (2) obtaining a score using the content by constructing a prediction model for the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer as described herein; and (3) predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer based on the score.

[0108] The present invention also provides a system for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer, comprising: a collection device for obtaining the content of the following proteins in a plasma sample: IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT; a data processing device for obtaining a score using the content by constructing the non-small cell lung cancer immunotherapy efficacy or clinical benefit prediction model described in this article; and a determination device for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer based on the score.

[0109] The present invention also provides a method for constructing a model for predicting the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer, comprising the steps of: (1) obtaining the content of the following proteins in a plasma sample: IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT, and (2) calculating the ROC curve and area under the curve value of the protein combination, and establishing a model using a logistic regression statistical analysis method.

[0110] It should be understood that the efficacy or clinical benefit of immunotherapy for non-small cell lung cancer described herein is relative. If the score is greater than or equal to -2.2001, the patient has shown efficacy or clinical benefit from immunotherapy for non-small cell lung cancer; if the score is less than -2.2001, the patient has shown no efficacy, insignificant efficacy, or no clinical benefit from immunotherapy for non-small cell lung cancer. If the patient's detection threshold is greater than -2.2001, but the patient demonstrates poor efficacy or clinical benefit, other efficacy testing methods commonly used in the art may be used as needed.

[0111] Furthermore, the calculation formula and threshold values ​​described above were obtained using a 250 μL plasma sample. Therefore, the coefficients and threshold values ​​in the calculation formula will vary when using different plasma sample sizes. However, those skilled in the art can readily determine the corresponding coefficients and threshold values ​​using the methods disclosed in this specification.

[0112] The present invention will be described below by way of specific examples. It should be understood that these examples are merely illustrative and are not intended to limit the scope of the present invention. The methods and reagents used in the examples are, unless otherwise stated, conventional methods and reagents in the art.

[0113] Example

[0114] Example 1: High-depth screening of plasma protein corona proteomics

[0115] 1. Sample Pretreatment

[0116] Prepare 250ul / sample and place it in a sample tube. Start the Proteograph automated workstation, and the machine will automatically operate and complete the pre-treatment process, including: 1) nanoparticle NP-plasma incubation and protein corona formation; 2) magnetic bead purification of the NP protein corona; 3) protein digestion and peptide purification.

[0117] 2. Mass spectrometry detection

[0118] Desalted, lyophilized peptides were reconstituted in 0.1% formic acid in water and analyzed by LC-MS / MS. The system consisted of a timsTOF Pro 2 mass spectrometer (Bruker Daltonics) connected to an UltiMate 3000 system (Thermo Fisher Scientific, MA, USA). A total of 200 ng of sample was loaded onto an analytical column (25 cm × 75 μm d., Ion Opticks) and separated using an 80-min gradient at 50°C. A total of 2.5 μL of sample was loaded, and the column flow rate was controlled at 300 nL / min. The gradient started at 4% phase B, increased to 28% over 45 minutes, 44% over 10 minutes, and 90% over 10 minutes, held for 7 minutes, and then equilibrated at 4% for 8 minutes.

[0119] The mass spectrometer was used in 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 ) rises to 20eV (1 / K0=0.6Vs / cm 2 ).

[0120] 3. Library Search and Protein Quantification

[0121] The original data files were searched and analyzed using the software DIA-NN (version 1.8.1).

[0122] Table 1 Database matching parameter settings

[0123]

[0124]

[0125] 4. Experimental Results

[0126] By testing baseline plasma samples from 5 patients with advanced NSCLC in the immunotherapy clinical benefit group (DCB, progression-free survival PFS ≥ 6 months) and 5 patients in the non-clinical benefit group (NCB, progression-free survival PFS < 6 months) who received PD-(L)1 monoclonal antibody combined with chemotherapy, it was found that the levels of 222 proteins were different between the two groups, among which the protein levels of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT were significantly different between the two groups.

[0127] Example 2, PRM quantitative proteomics validation

[0128] 1. Sample Preparation

[0129] 1.1 Protein enrichment

[0130] The protein enrichment and enzymatic hydrolysis steps were performed using the EasyPeptide Blood Low Abundance Protein Enrichment and Pretreatment Kit (OSFP0002).

[0131] Before use, take out reagent H and disperse the magnetic beads evenly by pipetting or sonication. Pipette 20 μL of reagent H and transfer it to a new EP tube (Note: 2 ml flat-bottom tube is recommended). Place it on a magnetic stand and let it stand for 2 minutes. Discard the supernatant after magnetic separation.

[0132] 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 stand and let it stand for 2 minutes, magnetically separate and discard the supernatant;

[0133] Resuspend the magnetic beads with 50 μL of reagent G, add 50 μL of plasma, place on a thermomixer, and incubate at 37°C with shaking (1300 rpm) for 1 h;

[0134] Place on a magnetic stand and let it stand for 2 minutes. Discard the supernatant after magnetic separation. Add 150 μL of reagent G and shake and wash for 5 minutes. Repeat the washing three times. The low-abundance protein is enriched on the magnetic beads.

[0135] 1.2 Proteolysis

[0136] Add 20 μL of reagent A to the washed magnetic beads and mix thoroughly by pipetting;

[0137] Heating at 95°C for 5 min (95°C, 1000 rpm);

[0138] After heating, wait until the sample returns to room temperature, add 7.5 μL of reagent B, mix well, and perform enzymatic hydrolysis at 37°C for 2 h (37°C, 1300 rpm);

[0139] After the enzymatic hydrolysis is completed, add 1.5 μL of reagent C, mix well, and terminate the enzymatic hydrolysis reaction. At this time, a precipitate will appear. Centrifuge at 17000g for 2 minutes and then aspirate the supernatant for subsequent desalting.

[0140] 1.3 Peptide desalting

[0141] Desalting column activation: 100 μL of methanol was added to the desalting column and centrifuged at 700 g for 1 min;

[0142] Desalting column to remove impurities: 100 μL Condition buffer was added to the desalting column and centrifuged at 700 g for 1 min;

[0143] Desalting column equilibration: 100 μL of wash buffer was added to the desalting column and centrifuged at 700 g for 1 min.

[0144] Loading the sample: Load all the samples onto the desalting column, centrifuge at 700g for 1 minute, and repeat the operation once; (Before loading the sample, make sure the pH is <3. You can use 20% TFA to adjust the pH to <3 before loading the sample)

[0145] Desalting column washing: add 100 μL wash buffer to the desalting column, centrifuge at 700 g for 1 min, and repeat the operation once;

[0146] Sample elution: 30 μL of elution buffer was added to the desalting column, centrifuged at 700 g for 1 min, and eluted twice to a final sample volume of 60 μL.

[0147] Concentrate the samples using a vacuum refrigerated centrifugal concentrator.

[0148] 2. nanoLC-MS / MS detection

[0149] 3 μl of total peptide from each sample was separated using a nano-UPLC system (nanoElute2) and then coupled to a mass spectrometer (timsTOF Pro2) equipped with a nanoliter ion source for data acquisition. Chromatographic separation was performed on 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, with mobile phase A consisting of 0.1% formic acid in water and phase B consisting of 0.1% formic acid in acetonitrile. After the column was equilibrated with 100% phase A, the sample was loaded directly onto the column via an autosampler and separated by a gradient flow at a flow rate of 300 nL / min over 60 minutes. The mobile phase B ratio was: 2% for 0 minutes, 2-22% for 45 minutes, 22-37% for 5 minutes, 37-80% for 5 minutes, and 80% for 5 minutes.

[0150] The mass spectrometer was used in DDA PaSEF mode for DDA data acquisition, with a scan range of 100-1700 m / z. During the PASEFMS / MS scan, the collision energy increased linearly with the ion mobility, from 20 eV (1 / K0 = 0.6 Vs / cm 2 ) rises to 59eV (1 / K0=1.6Vs / cm 2 ).

[0151] 3. Library Search and Protein Quantification

[0152] 3.1DDA database search and identification

[0153] The raw data files were searched using SpectroDive (4.1.230421.52329; Biognosys AG 2013) software with the Pulsar search engine, and qualitative analysis was performed after the search.

[0154] Table 2 Database matching parameter settings

[0155]

[0156] 3.2 PRM method construction and quantitative analysis

[0157] PRM method development was performed using SpectroDive software: 1) The acquired DDA data was imported into SpectroDive and searched to generate a corresponding library. 2) Based on the library and target peptides, a panel was generated for peptide screening and secondary b / y ions for confirmation of quantification. A maximum of three specific peptides were selected for each protein, and three to six fragment ions were selected for quantification for each target peptide.

[0158] 4. Experimental Results

[0159] 4.1 Model Construction

[0160] Baseline plasma specimens from 17 patients with advanced NSCLC in the immunotherapy clinical benefit group (DCB) and 13 patients in the nonclinical benefit group (NCB) were subjected to PRM targeted detection of IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT (Table 3), and the model was constructed as follows:

[0161] Score = 2.2198 - 0.0026 × var1 + 3.2395 × var2 - 0.0001 × var3 - 0.0157 × var4 - 0.0177 × var5 - 0.0118 × var6

[0162] Table 3 PRM targeted detection results

[0163]

[0164] The peptide levels of these five proteins were jointly analyzed using the above formula to calculate the scores of each sample, see Table 4, and ROC curve analysis is shown in Figure 1 The AUC was 0.796, the interpretation standard was a score greater than 0.0197, 14 of the 17 clinical benefit groups were positive, and 3 of the 13 non-clinical benefit groups were positive, with a specificity of 76.9% and a sensitivity of 82.4%.

[0165] Table 4 Scores of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT combined for the construction of a prediction model for clinical benefit of NSCLC immunotherapy

[0166] Group Score Group Score DCB 0.7109 DCB 1.3388 DCB 0.4756 DCB 1.9226 DCB 1.6279 NCB -0.9597 DCB 1.2198 NCB 0.0197 DCB 29.6353 NCB -0.8761 DCB 0.3749 NCB 1.8859 DCB 0.0691 NCB 1.7116 DCB 6.4199 NCB -2.5023 DCB -1.6276 NCB -0.4913 DCB -2.1593 NCB -0.6679 DCB 0.7263 NCB -0.4196 DCB 0.3410 NCB -0.5919 DCB 35.9136 NCB -1.0207 DCB 14.3553 NCB 0.3260 DCB -0.2773 NCB -2.7443

[0167] 4.2 Model Validation

[0168] Baseline plasma samples of 20 patients with advanced NSCLC in the immunotherapy clinical benefit group (DCB) and 10 patients in the non-clinical benefit group (NCB) were subjected to PRM targeted detection of IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT. The peptide levels of these five proteins were jointly analyzed using the above formula to calculate the scores of each sample (see Table 5). ROC curve analysis is shown in Figure 2 , AUC was 0.865. According to the interpretation criteria provided by the present invention (score greater than 0.0197), 18 of the 20 clinical benefit groups were positive, and 2 of the 10 non-clinical benefit groups were positive, with a specificity of 80.0% and a sensitivity of 90.0%.

[0169] Table 5 Scores of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT combined for validation of the prediction model for clinical benefit of NSCLC immunotherapy

[0170]

[0171]

[0172] Sequence of this article:

[0173] SEQ ID NO: 1IGKV1-27

[0174] ASQGISNYLAWYQQK

[0175] SEQ ID NO:2HLA-H

[0176] GGSYSQAASSNSAQGSDVSLTA

[0177] SEQ ID NO:3S100A8

[0178] ALNSIIDVYHK

[0179] SEQ ID NO:4CHGA

[0180] ELQDLALQGAK

[0181] SEQ ID NO:5ELN

[0182] AGYPTGTGVGPQAAAAAAAK

[0183] SEQ ID NO:6NPNT

[0184] VTGLHSGTLQVFVR

Claims

1. An isolated protein combination, which is a marker for the efficacy of immunotherapy for non-small cell lung cancer, the protein combination comprising IGKV1-27 or a fragment thereof, HLA-H or a fragment thereof, S100A8 or a fragment thereof, CHGA or a fragment thereof, ELN or a fragment thereof, and NPNT or a fragment thereof, Preferably, The amino acid sequence of the fragment of IGKV1-27 is shown in SEQ ID NO: 1, and / or The amino acid sequence of the HLA-H fragment is shown in SEQ ID NO: 2, and / or The amino acid sequence of the fragment of S100A8 is shown in SEQ ID NO: 3, and / or The amino acid sequence of the CHGA fragment is shown in SEQ ID NO: 4, and / or The amino acid sequence of the ELN fragment is shown in SEQ ID NO: 5, and / or The amino acid sequence of the NPNT fragment is shown in SEQ ID NO:

6.

2. A reagent for detecting the levels of IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT in blood, Preferably, the reagent is a reagent used for detecting proteins using any of the following methods: ELISA, suspension chip, MSD electrochemiluminescence technology or mass spectrometry, More preferably, the reagent is a reagent used for quantifying protein using the PRM method, More preferably, the reagent further includes a reagent for separating proteins from blood.

3. A kit comprising the reagent according to claim 2, Preferably, the kit further comprises the protein combination according to claim 1, Preferably, the kit further comprises a reagent for detecting an internal standard.

4. Use of IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT 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; Preferably, the reagent detects the content of the protein or the transcription level of the coding sequence of the protein.

5. The use according to claim 4, characterized in that The prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma levels of the markers is: Score = -0.0026 × IGKV1-27 + 3.2395 × HLA-H - 0.0001 × S100A8 - 0.0157 × CHGA - 0.0177 × ELN - 0.0118 × NPNT.

6. A device, characterized in that The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the following steps are implemented: (1) Obtain the content of the following proteins in plasma samples: IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT, (2) obtaining a score using the content by constructing a model, and (3) Predict the efficacy of immunotherapy for non-small cell lung cancer based on the score.

7. The device according to claim 6, characterized in that The prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma levels of the markers is: Score = -0.0026 × IGKV1-27 + 3.2395 × HLA-H - 0.0001 × S100A8 - 0.0157 × CHGA - 0.0177 × ELN - 0.0118 × NPNT.

8. A system for predicting the efficacy of immunotherapy for non-small cell lung cancer, characterized in that: include: A collection device for obtaining the content of the following proteins in a plasma sample: IGKV1-27, HLA-H, S100A8, CHGA, ELN, and NPNT; A data processing device for obtaining a score by constructing a model using the content; A determination device is used to predict the efficacy of immunotherapy for non-small cell lung cancer based on a score.

9. The system according to claim 8, wherein The prediction model for the efficacy of immunotherapy for non-small cell lung cancer based on the plasma levels of the markers is: Score = -0.0026 × IGKV1-27 + 3.2395 × HLA-H - 0.0001 × S100A8 - 0.0157 × CHGA - 0.0177 × ELN - 0.0118 × NPNT.

10. A method for constructing a model for predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising the steps of: (1) Obtain the content of the following proteins in plasma samples: IGKV1-27, HLA-H, S100A8, CHGA, ELN and NPNT, and (2) Calculate the ROC curve and area under the curve of the protein combination, and establish a model using logistic regression statistical analysis method.

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