A combination of n-glycan markers for predicting the therapeutic status of ig a and ig g type mm and a prediction scoring system and use thereof

Abstract

A N-glycan marker combination for predicting the treatment effect state of IgA and IgG type MM, a prediction scoring system thereof and uses thereof, which is composed of 11 N-glycans: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb.(1) The present application first discovers that the N-glycans in the blood of IgA and IgG type MM patients in different treatment effect states are related to the treatment effect, and first proposes that N-glycans can be used as an index for predicting the treatment effect state of IgA and IgG type MM patients.(2) The N-glycan treatment effect prediction scoring system provided by the present application is used for predicting the treatment effect state of IgA and IgG type MM patients, and provides a new technical means for predicting the treatment effect of IgA and IgG type MM patients.

Description

Technical Field

[0001] This invention belongs to the field of molecular biomedical technology, specifically relating to a combination of N-glycan biomarkers for predicting the therapeutic status of IgG and IgA type MM patients, its predictive scoring system, and its applications. Background Technology

[0002] Multiple myeloma (MM) is a plasma cell tumor and the second most common hematologic malignancy, accounting for approximately 10% of all hematologic malignancies. It primarily affects the elderly and remains incurable. IgG-type MM accounts for about 45% of cases, while IgA-type MM accounts for about 20%. Despite conventional treatment, most MM patients eventually die from disease relapse, indicating the presence of residual lesions undetectable by routine serological methods, leading to relapse. Early detection of treatment efficacy, evaluation of treatment response and prognosis, and timely adjustment of treatment plans can significantly improve patients' quality of life and survival time. However, currently, there is a lack of biomarkers that can efficiently and independently predict the treatment efficacy of IgG and IgA-type MM patients. Therefore, discovering effective biomarkers for predicting the efficacy of treatment in IgG or IgA-type MM patients is a pressing clinical challenge.

[0003] Studies have found that IgG-type MM patients produce abnormal glycoprotein IgG, with significant alterations in the N-glycans on IgG. Similarly, IgA-type MM patients produce abnormal glycoprotein IgA, with significant alterations in the N-glycans on IgA. Changes in the structure and number of N-glycans on glycoproteins are closely related to the patient's treatment status. This invention reveals that N-glycans in blood samples from IgG and IgA-type MM patients at different disease states can predict patient treatment efficacy, providing a novel N-glycan biomarker and scoring system for predicting treatment status in clinical practice. Therefore, this invention focuses on exploring the application value of the N-glycan predictive efficacy scoring system in IgG and IgA-type MM patients. Summary of the Invention

[0004] Technical problem solved: In view of the limitations of the prior art, the present invention provides a combination of N-glycan biomarkers for predicting the efficacy status of IgA and IgG type MM, a predictive scoring system and its uses, which can be used to predict the efficacy status of IgA and IgG type MM patients, thereby providing a combination of biomarkers and a scoring system for predicting the efficacy of IgA and IgG type MM patients, and providing a solution for predicting the efficacy status of IgA and IgG type MM patients after treatment.

[0005] Technical solution: A combination of N-glycan biomarkers for predicting the treatment status of IgA and IgG type multiple myeloma (MM), consisting of the following 11 N-glycans: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, and NA4Fb.

[0006] A scoring system for predicting the therapeutic status of IgA and IgG type MM, comprising: a data input module for receiving the relative content detection values ​​of the 11 N-glycans as described in claim 1 in a sample; and an analysis and calculation module for storing classification models IgA-MM-GEES and IgG-MM-GEES, wherein the models are constructed using a logistic regression algorithm and the calculation formula is: IgA-MM-GEES=exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75). 33) / [1+exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+ 0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75.33)];

[0007] IgG-MM-GEES=exp(-0.026×NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2 F-2+0.006×NA2-0.403×NA2FB+1.697×NA3-4.837×NA4+2.644×NA4Fb-16.889) / [1+exp(-0.026× [NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2F-2+0.006×NA2-0.403×NA2FB+1.697×NA3-4.837×NA4+2.644×NA4Fb-16.889)]; Result output module: Outputs IgA-MM-GEES or IgG-MM-GEES scores.

[0008] In the above analysis and calculation module, the threshold values ​​for IgA-MM-GEES are set to 0.505, and the threshold values ​​for IgG-MM-GEES are set to 0.642.

[0009] The use of a reagent for detecting the N-glycan biomarker combination in the preparation of a kit for predicting the therapeutic status of IgA and IgG type MM.

[0010] The above detection was achieved using capillary electrophoresis.

[0011] A kit for predicting the therapeutic status of IgA and IgG type MM, comprising: (a) reagents for detecting the 11 N-glycans; and (b) a computing device storing the scoring system.

[0012] The aforementioned detection reagents include fluorescent labeling reagents and glycosidases used for capillary electrophoresis analysis.

[0013] A method for constructing the scoring system includes: (a) obtaining blood samples from patients with IgA and IgG type MM at different therapeutic statuses and detecting the relative content of 11 N-glycans; (b) using N-glycan data as independent variables and therapeutic status as dependent variables, and training classification models IgA-MM-GEES and IgG-MM-GEES and their coefficients respectively through logistic regression algorithm.

[0014] The above efficacy statuses include: complete remission: clinical evaluation meets the criteria for complete remission; incomplete remission: includes initial diagnosis and disease progression status.

[0015] The sample mentioned in step (a) includes venous blood, serum or plasma.

[0016] Beneficial effects: (1) This invention is the first to discover that N-glycans in the blood of patients with different therapeutic statuses of IgA and IgG type MM are correlated with therapeutic efficacy, and proposes for the first time that N-glycans can be used as an indicator to predict the therapeutic status of patients with IgA and IgG type MM. (2) The N-glycan therapeutic efficacy prediction scoring system provided by this invention is used to predict the therapeutic status of patients with IgA and IgG type MM, providing a new technical means for predicting the therapeutic efficacy of patients with IgA and IgG type MM. Attached Figure Description

[0017] Figure 1 The ROC curves of the efficacy assessment prediction scoring system for IgA type MM, which distinguishes between patients with complete remission and those with incomplete remission, are shown in the training and validation sets. The AUC values ​​are 0.969 and 0.889, respectively.

[0018] Figure 2 This is the N-glycan map of IgA type MM patients at initial diagnosis, in complete remission, and during disease progression.

[0019] Figure 3This is a comparison of the predicted values ​​of the N-glycan prediction scoring system for newly diagnosed, completely remitted, and disease-progressed IgA MM patients in the training set. The model's predicted values ​​for newly diagnosed and disease-progressed patients are significantly higher than those for those in complete remission.

[0020] Figure 4 This study compares the predictive values ​​of the N-glycan prediction scoring system for newly diagnosed, completely remitted, and disease-progressed IgA MM patients. The model predictive values ​​for newly diagnosed and disease-progressed patients are significantly higher than those for those in complete remission.

[0021] Figure 5 The ROC curves of the efficacy evaluation scoring system in the training and validation sets for IgG type MM distinguish between patients with complete remission and those with incomplete remission, with AUC values ​​of 0.971 and 0.986, respectively.

[0022] Figure 6 This is the N-glycan map of IgG type MM patients at initial diagnosis, when they reach complete remission, and when the disease progresses.

[0023] Figure 7 This is a comparison of the predicted values ​​of the N-glycan scoring system for newly diagnosed, completely remitted, and disease-progressed IgG MM patients in the training set. The model predicted values ​​for newly diagnosed and disease-progressed patients were significantly higher than those for patients in complete remission.

[0024] Figure 8 This study compares the predictive values ​​of the N-glycan scoring system for newly diagnosed, completely remitted, and disease-progressed IgG MM patients. The model predictive values ​​for newly diagnosed and disease-progressed patients are significantly higher than those for patients in complete remission. Detailed Implementation

[0025] The present invention will be further described in detail below with reference to embodiments and accompanying drawings. It should be noted that the following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application.

[0026] The implementation details of this application will be further described in detail below through specific embodiments.

[0027] A method for predicting the treatment status of IgA and IgG type MM patients using an N-glycan scoring system includes the following steps:

[0028] Step 1: Sample Collection

[0029] Blood, serum, and plasma samples were collected from venous blood or peripheral blood of IgA and IgG type MM patients in different treatment statuses.

[0030] Step 2: Detection and data acquisition of N-glycan chains in the sample

[0031] A technical platform for detecting N-glycans using capillary electrophoresis (CE method) was developed to detect N-glycans in blood samples, obtain N-glycan maps, and acquire N-glycan data. The specific detection method is as follows: First, add 2 μL of the subject's sample to a test tube, then add 5 μL of 5% SDS, mix thoroughly, heat at 95℃ for 5 min for high-temperature denaturation, and cool to 4℃ to obtain the sample; add 3 μL of 2.2 units / μL glycosaminoglycanase to the sample, mix and centrifuge, keep in an incubator at 37℃ for 3 h, then cool to 4℃, add 50 μL of deionized water to the cooled sample to terminate the reaction for 1 min, and store at -20℃; take 10 μL of the low-temperature stored sample and dry it in a metal bath for 90 min, then cool to 4℃, add 2 μL of a mixture of 100 mM trisodium trisulfonate fluorescent label and 1 M organic reducing agent (1:1 volume ratio) to the dried sample, centrifuge, keep in an incubator at 37℃ for 16 h for fluorescent labeling, then cool to 4℃, add 100 μL of deionized water to terminate the reaction for 1 min, mix and centrifuge, and store at -20℃; take 2 After the reaction was terminated by adding 2 μL of sialidase to the sample, mix and centrifuge, and keep in an incubator at 37℃ for 16 h. Then add 40 μL of deionized water to terminate the reaction for 1 min, mix and centrifuge, and take 10 μL of sample for oligosaccharide chain fragment separation and detection through a gene sequencer to obtain oligosaccharide chain detection values.

[0032] Step 3: Efficacy Assessment

[0033] Using N-glycan detection data as independent variables and disease status as dependent variables, a classifier was constructed to determine the N-glycan biomarker model related to therapeutic efficacy.

[0034] Preferably, the sample type is blood, serum, or plasma from venous blood or peripheral blood.

[0035] Preferably, the N-glycan markers include NGA2 (galactosylated biantennary N-glycan), NGA2F (galactosylated α-1,6-core fucosylated biantennary N-glycan), NGA2FB (galactosylated α-1,6-core fucosylated bi-segmented N-glycan), NG1A2F-1 (mono-branched galactosylated α-1,6-core fucosylated biantennary N-glycan), NG1A2F-2 (mono-branched galactosylated α-1,6-core fucosylated biantennary N-glycan), and NA2. (galactosylated two-antenna N-glycan), NA2F (galactosylated α-1,6-core fucosylated two-antenna N-glycan), NA2FB (galactosylated α-1,6-core fucosylated bisecting N-glycan), NA3 (galactosylated three-antenna N-glycan), NA3Fb (galactosylated α-1,3-branched fucosylated three-antenna N-glycan), NA4 (galactosylated four-antenna N-glycan), NA4Fb (galactosylated α-1,3-branched fucosylated four-antenna N-glycan).

[0036] Preferably, the classifier is a classifier constructed using a logistic regression algorithm.

[0037] Preferably, the therapeutic status refers to the clinical evaluation results after the patient receives treatment, including complete remission and disease progression.

[0038] The application of a composition in a reagent for predicting the therapeutic status of IgA and IgG type multiple myeloma (MM), the composition comprising NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, and NA4Fb in N-glycan chains, the composition being calculated using the classification model formula: IgA-MM-GEES=exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2). F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75.33) / [1+exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A 2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75.33)].

[0039] IgG-MM-GEES=exp(-0.026×NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2F-2+0. 006×NA2-0.403×NA2FB+1.697×NA3-4.837×NA4+2.644×NA4Fb-16.889) / [1+exp(-0.026×NGA2+0.688×N The scores of the IgA-MM-GEES model were used to predict the efficacy of treatment for IgA-type MM patients, while the scores of the IgG-MM-GEES model were used to predict the efficacy of treatment for IgG-type MM patients.

[0040] Preferably, the classification model uses the predicted value of the therapeutic effect status as the output value.

[0041] Preferably, the IgA-MM-GEES prediction score of the classification model is a predicted value of the therapeutic effect status. When the IgA-MM-GEES prediction score is ≥0.505, it indicates that the disease has not reached a complete remission state after treatment, and when the IgA-MM-GEES prediction score is <0.505, it indicates that the disease has reached a complete remission state after treatment. Similarly, the IgG-MM-GEES prediction score of the classification model is a predicted value of the therapeutic effect status. When the IgG-MM-GEES prediction score is ≥0.642, it indicates that the disease has not reached a complete remission state after treatment, and when the IgG-MM-GEES prediction score is <0.642, it indicates that the disease has reached a complete remission state after treatment.

[0042] Preferably, the treatment regimens used for disease treatment include those routinely recommended in clinical practice for patients with IgA and IgG MM.

[0043] A scoring system for predicting the efficacy of treatment in patients with IgA and IgG MM, comprising:

[0044] The N-glycan detection module is used to detect N-glycans in blood samples obtained from IgA or IgG type MM patients at different therapeutic stages.

[0045] The analysis module uses the values ​​of various N-glycans obtained from the detection module as independent variables and the therapeutic effect status as the dependent variable to construct a classifier, obtain a classification model, and then predict the therapeutic effect status based on the predicted values ​​output by the classification model.

[0046] Example 1

[0047] This invention provides, for the first time, a predictive scoring system for the relationship between N-glycans and therapeutic status in blood samples from IgA MM patients at different therapeutic statuses. This system can predict the therapeutic status of IgA MM patients.

[0048] This invention relates to the collection of blood samples from 85 patients with IgA-type multiple myeloma (MM) at different treatment statuses, including 32 newly diagnosed patients, 30 patients in complete remission, and 23 patients with disease progression. Patients achieving complete remission and those with disease progression had previously received standard clinical treatment. In this invention, treatment status refers to the clinical evaluation results after treatment, including complete remission and disease progression.

[0049] The detection method for N-glycans in blood samples is as follows: First, add 2 μL of the subject's sample to a test tube, then add 5 μL of 5% SDS. Mix thoroughly, heat at 95℃ for 5 min for high-temperature denaturation, and cool to 4℃ to obtain the sample. Add 3 μL of 2.2 units / μL glycosaminoglycanase to the sample, mix, centrifuge, and incubate at 37℃ for 3 h. Then cool to 4℃, add 50 μL of deionized water to the cooled sample to terminate the reaction for 1 min, and store at -20℃. Take 10 μL of the refrigerated sample, dry it in a metal bath for 90 min, and then cool to 4℃. Add 2 μL of a 1:1 mixture of 100 mM trisodium trisulfonate fluorescent label and 1 M organic reducing agent, centrifuge, and incubate at 37℃ for 16 h for fluorescent labeling. Then cool to 4℃ and add 100 μL of deionized water to terminate the reaction. After centrifugation for 1 min, the sample was stored at -20℃. 2 μL of the terminated sample was taken and 2 μL of sialidase was added. After centrifugation, the sample was kept at 37℃ for 16 h in an incubator. Then, 40 μL of deionized water was added to terminate the reaction for 1 min. After centrifugation, 10 μL of the sample was taken and the oligosaccharide chain fragments were separated and detected using a gene sequencer to obtain the oligosaccharide chain detection value.

[0050] Blood samples were subjected to protein denaturation, glycosidase treatment, fluorescent labeling, N-glycan mapping detection, and data acquisition to obtain the relative amounts of 12 N-glycans in each sample. The 12 N-glycans were NGA2 (galactosylated biantennary N-glycan), NGA2F (galactosylated α-1, 6-core fucosylated biantennary N-glycan), NGA2FB (galactosylated α-1, 6-core fucosylated biantennary N-glycan), NG1A2F-1 (monobranched galactosylated α-1, 6-core fucosylated biantennary N-glycan), and NG1A2F-2 (monobranched galactosylated biantennary N-glycan). NA2 (galactosylated two-antenna N-glycan), NA2F (galactosylated α-1,6-core fucosylated two-antenna N-glycan), NA2FB (galactosylated α-1,6-core fucosylated bisecting N-glycan), NA3 (galactosylated three-antenna N-glycan), NA3Fb (galactosylated α-1,3-branched fucosylated three-antenna N-glycan), NA4 (galactosylated four-antenna N-glycan), NA4Fb (galactosylated α-1,3-branched fucosylated four-antenna N-glycan).

[0051] Screening of N-glycans in the efficacy evaluation prediction scoring model;

[0052] Intergroup comparative analysis of N-glycan data in IgA type MM patients with different treatment statuses;

[0053] Data from 12 N-glycans in 85 patients with different treatment statuses were compared and analyzed. N-glycans with a p-value less than 0.05 were selected as biomarkers for the predictive scoring model, as shown in Table 1.

[0054] Table 1. Comparative analysis of 12 N-glycans in IgA MM patients at different disease states

[0055]

[0056] Dataset partitioning: The dataset was randomly divided into a training set and a validation set. The training set contained 16 newly diagnosed cases, 15 cases of complete remission, and 12 cases of disease progression. The validation set contained 16 newly diagnosed cases, 15 cases of complete remission, and 11 cases of disease progression.

[0057] Eleven N-glycans were selected, and logistic regression was used in the training set to calculate the predicted efficacy status score. An efficacy score prediction model was constructed, and the calculation formula is as follows: IgA-MM-GEES=exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×N A3Fb+0.419×NA4-1.202×NA4Fb-75.33) / [1+exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75.33)]. The validation set data validates the performance of the prediction model.

[0058] The ROC curves of the IgA-MM-GEES efficacy score prediction model, which differentiates between patients with complete remission and those with incomplete remission, are shown in the training and validation sets. Figure 1 As shown, the AUC values ​​are 0.969 and 0.889, respectively.

[0059] The threshold for the predictive scoring model was 0.505. An IgA-MM-GEES predicted score ≥ 0.505 indicated that the disease had not achieved complete remission after treatment, while an IgA-MM-GEES predicted score < 0.505 indicated that the disease had achieved complete remission after treatment. The overall concordance rate between the IgA-MM-GEES model and clinical evaluation results in the training set reached 90.70%, as shown in Table 2. The overall concordance rate between the IgA-MM-GEES model and clinical evaluation results in the validation set reached 83.33%, as shown in Table 3.

[0060] Table 2 shows the concordance rate between the efficacy prediction model in the training set and the clinical evaluation results.

[0061]

[0062] Table 3. Concordance rate between the validation-focused efficacy prediction model and clinical evaluation results.

[0063]

[0064] In IgA MM patients who were first diagnosed and then received treatment, significant changes were observed in their N-glycan profiles when they achieved complete remission; these changes also occurred when the disease progressed from complete remission. Figure 2 As shown.

[0065] The results of the training session comparing IgA-MM-GEES predictive values ​​in newly diagnosed, completely remitted, and disease-progressed patients are as follows: Figure 3 As shown, the model predictive values ​​for newly diagnosed and disease-progressing patients were significantly higher than those for patients in complete remission (p<0.0001).

[0066] The results of the validation comparison of IgA-MM-GEES predictive values ​​in patients with initial diagnosis, complete remission, and disease progression are as follows: Figure 4 As shown, the model predictive values ​​for newly diagnosed and disease-progressing patients were significantly higher than those for patients in complete remission (p<0.05).

[0067] Figure 1 The ROC curves of the efficacy evaluation scoring system in the training and validation sets distinguish between patients with complete remission and those with incomplete remission, with AUC values ​​of 0.969 and 0.889, respectively.

[0068] Figure 2 This is the N-glycan map of IgA MM patients at initial diagnosis, when they reach complete remission, and when the disease progresses.

[0069] Figure 3 This is a comparison of the predicted values ​​of the N-glycan scoring system for newly diagnosed, completely remitted, and disease-progressed IgA MM patients in the training set. The model predicted values ​​for newly diagnosed and disease-progressed patients were significantly higher than those for completely remitted patients.

[0070] Figure 4 This is a comparison of the predicted values ​​of the N-glycan scoring system for newly diagnosed, completely remitted, and disease-progressed IgA MM patients in the training set. The model predicted values ​​for newly diagnosed and disease-progressed patients were significantly higher than those for completely remitted patients.

[0071] Example 2

[0072] This invention provides, for the first time, a predictive scoring system for the relationship between N-glycan chains and therapeutic status based on N-glycan chains in blood samples from patients with different therapeutic statuses of IgG type MM. This system can predict the therapeutic status of patients with IgG type MM.

[0073] This invention relates to the collection of blood samples from 106 patients with IgG type MM at different therapeutic statuses, including 40 newly diagnosed cases, 45 cases of complete remission, and 21 cases of disease progression. Patients achieving complete remission and those with disease progression had previously received standard clinical treatment. In this invention, therapeutic status refers to the clinical evaluation results after treatment, including complete remission and disease progression.

[0074] For the detection of N-glycan chains in blood samples, the specific detection method is as described in Chinese Invention CN202311858738.7.

[0075] Blood samples were subjected to protein denaturation, glycosidase treatment, fluorescent labeling, N-glycan mapping detection, and data acquisition to obtain the relative amounts of 12 N-glycans in each sample. The 12 N-glycans were NGA2 (galactosylated biantennary N-glycan), NGA2F (galactosylated α-1, 6-core fucosylated biantennary N-glycan), NGA2FB (galactosylated α-1, 6-core fucosylated biantennary N-glycan), NG1A2F-1 (monobranched galactosylated α-1, 6-core fucosylated biantennary N-glycan), and NG1A2F-2 (monobranched galactosylated biantennary N-glycan). NA2 (galactosylated two-antenna N-glycan), NA2F (galactosylated α-1,6-core fucosylated two-antenna N-glycan), NA2FB (galactosylated α-1,6-core fucosylated bisecting N-glycan), NA3 (galactosylated three-antenna N-glycan), NA3Fb (galactosylated α-1,3-branched fucosylated three-antenna N-glycan), NA4 (galactosylated four-antenna N-glycan), NA4Fb (galactosylated α-1,3-branched fucosylated four-antenna N-glycan).

[0076] Screening of N-glycans in the efficacy assessment prediction scoring model.

[0077] Intergroup comparative analysis was performed on N-glycan data of IgG type MM patients with different treatment statuses.

[0078] Data from 12 N-glycans in 106 patients with different treatment statuses were compared and analyzed. N-glycans with a statistical p-value less than 0.05 were selected as biomarkers for inclusion in the predictive scoring model, as shown in Table 4.

[0079] Table 4. Comparative analysis of 12 N-glycans in IgG type MM patients at different disease states

[0080]

[0081] Dataset partitioning: The dataset was randomly divided into a training set and a validation set. The training set contained 20 newly diagnosed cases, 23 cases of complete remission, and 11 cases of disease progression. The validation set contained 20 newly diagnosed cases, 22 cases of complete remission, and 10 cases of disease progression.

[0082] Ten selected N-glycans were used in the training set to calculate predicted efficacy status scores using logistic regression equations. An efficacy score prediction model was constructed, and the calculation formula is shown below: IgG-MM-GEES=exp(-0.026×NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2F-2+0.006×NA2-0.403×NA2FB+1.697× NA3-4.837×NA4+2.644×NA4Fb-16.889) / [1+exp(-0.026×NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2F-2+0.006×NA2-0.403×NA2FB+1.697×NA3-4.837×NA4+2.644×NA4Fb-16.889)]. The validation set data validates the performance of the prediction model.

[0083] The ROC curves of the IgG-MM-GEES efficacy score prediction model, used in the training and validation sets, distinguish between patients with complete remission and those with incomplete remission are shown below. Figure 5 As shown, the AUC values ​​are 0.971 and 0.986, respectively.

[0084] The threshold for the predictive scoring model was 0.642. A predicted IgG-MM-GEES score ≥ 0.642 indicated that the disease had not achieved complete remission after treatment, while a predicted score < 0.642 indicated that the disease had achieved complete remission after treatment. The overall concordance rate between the IgG-MM-GEES model and clinical evaluation results in the training set reached 96.30%, as shown in Table 5. The overall concordance rate between the IgG-MM-GEES model and clinical evaluation results in the validation set reached 94.23%, as shown in Table 6.

[0085] Table 5 shows the concordance rate between the training set efficacy prediction model and clinical evaluation results.

[0086]

[0087] Table 6. Concordance rate between the validation-focused efficacy prediction model and clinical evaluation results

[0088]

[0089] In IgG type MM patients, significant changes were observed in the N-glycan profile when they achieved complete remission after initial diagnosis and treatment; similarly, significant changes also occurred in the N-glycan profile when transitioning from complete remission to disease progression. Figure 6 As shown.

[0090] The results of the training session comparing IgG-MM-GEES predictive values ​​in newly diagnosed, completely remitted, and disease-progressed patients are as follows: Figure 7 As shown, the model predictive values ​​for newly diagnosed and disease-progressing patients were significantly higher than those for patients in complete remission (p<0.0001).

[0091] The results of the validation comparison of IgG-MM-GEES predictive values ​​in patients with initial diagnosis, complete remission, and disease progression are as follows: Figure 8 As shown, the model predictive values ​​for newly diagnosed and disease-progressing patients were significantly higher than those for patients in complete remission (p<0.0001).

[0092] Figure 5 The ROC curves of the efficacy evaluation scoring system in the training and validation sets distinguish between patients with complete remission and those with incomplete remission, with AUC values ​​of 0.971 and 0.986, respectively.

[0093] Figure 6 This is the N-glycan map of IgG type MM patients at initial diagnosis, when they reach complete remission, and when the disease progresses.

[0094] Figure 7 This is a comparison of the predicted values ​​of the N-glycan scoring system for newly diagnosed, completely remitted, and disease-progressed IgG MM patients in the training set. The model predicted values ​​for newly diagnosed and disease-progressed patients were significantly higher than those for patients in complete remission.

[0095] Figure 8 This study compared the predictive values ​​of the N-glycan scoring system in newly diagnosed, completely remitted, and disease-progressed IgG MM patients. The model's predictive values ​​were significantly higher in newly diagnosed and disease-progressed patients than in patients with complete remission.

[0096] The above description, in conjunction with the accompanying drawings, provides a detailed explanation of the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention without creative effort should be included within the scope of protection of the present invention.

Claims

1. A combination of N-glycan markers for predicting the therapeutic efficacy status of IgA and IgG type MM, characterized by, Composed of 11 N-glycan chains: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb, wherein the N-glycan marker combination for predicting the efficacy status of IgA type MM is NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb; the N-glycan marker combination for predicting the efficacy status of IgG type MM is NGA2, NGA2F, NG2A2F, NG1A2F-1, NG1A2F-2, NA2, NA2FB, NA3, NA4, NA4Fb.

2. A scoring system for predicting the therapeutic efficacy status of IgA and IgG type MM, characterized by, Comprise: Data input module: for receiving the detection values of the relative contents of the 11 N-glycan chains in the sample; analysis and calculation module: storing the classification models IgA-MM-GEES and IgG-MM-GEES, which are constructed by a logistic regression algorithm and have the following calculation formula: IgA-MM-GEES = exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75.33) / [1+exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75.33)]; IgG-MM-GEES = exp(-0.026×NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2F-2+0.006×NA2-0.403×NA2FB+1.697×NA3-4.837×NA4+2.644×NA4Fb-16.889) / [1+exp(-0.026×NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2F-2+0.006×NA2-0.403×NA2FB+1.697×NA3-4.837×NA4+2.644×NA4Fb-16.889)]; result output module: outputting the IgA-MM-GEES or IgG-MM-GEES score value; the analysis and calculation module IgA-MM-GEES is set to have a threshold value of 0.505, and the analysis and calculation module IgG-MM-GEES is set to have a threshold value of 0.

642.

3. Use of a reagent for detecting the N-glycan marker combination of claim 1 in the preparation of a kit for predicting the therapeutic efficacy status of IgA and IgG MM.

4. Use according to claim 3, characterized in that, The detection is achieved by capillary electrophoresis.

5. A kit for predicting the therapeutic efficacy status of IgA and IgG type MM, characterized by, It comprises: (a) reagents for detecting 11 N-glycan chains of claim 1; wherein the N-glycan marker combination for predicting the efficacy status of IgA type MM is NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb; the N-glycan marker combination for predicting the efficacy status of IgG type MM is NGA2, NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2FB, NA3, NA4, NA4Fb; (b) a computing device storing the scoring system of claim 2.

6. The kit of claim 5, wherein The detection reagent includes a fluorescently labeled reagent for capillary electrophoresis analysis and a glycosidase.

Images