Biomarker for predicting curative effect of immune combined chemotherapy in lung cancer and application of biomarker

Abstract

The invention discloses a biomarker for predicting the curative effect of immune combined chemotherapy in lung cancer and application thereof, and the biomarker comprises any one of N-(3-indoleacetyl)-L-alanine and methomyl or a combination of N-(3-indoleacetyl)-L-alanine and methomyl. The biomarker can accurately predict the curative effect of immune combined chemotherapy in lung cancer. The biomarker provided by the invention is extracted from a serum sample of a lung cancer patient, so that compared with PD-L1 expression, the method provided by the invention has the advantages that a sample is convenient to obtain, and dynamic monitoring is also very easy.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technology, and in particular to a biomarker for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer and its application. Background Art

[0002] Lung cancer is a high-profile public health issue, and its mortality rate remains high, with non-small cell lung cancer accounting for the highest proportion of lung cancer. The emergence of immune checkpoint inhibitors (ICIs) has completely changed the treatment landscape for lung cancer. However, only about 20% of patients can show lasting and stable efficacy after immunotherapy, and some patients not only have no effect but even experience serious adverse reactions. Therefore, biomarkers that can predict the efficacy and prognosis of immunotherapy are particularly important.

[0003] There is evidence that immunotherapy combined with chemotherapy can improve the effectiveness of immunotherapy by activating the immune system compared to monotherapy, and we hope to find biomarkers that can predict the efficacy of immunotherapy combined with chemotherapy. Currently, the only validated predictive biomarker is PD-L1, but the results are not accurate.

[0004] Therefore, finding a biomarker that can accurately predict the effectiveness of combined immunotherapy and chemotherapy is of great clinical significance. Summary of the invention

[0005] The present invention provides a biomarker for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer and its application, so as to provide a reference for predicting the effectiveness of immunotherapy combined with chemotherapy in lung cancer patients.

[0006] According to a first aspect of the present invention, there is provided a biomarker for use in predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer, wherein the biomarker comprises any one of N-(3-indoleacetyl)-L-alanine and methomyl or a combination thereof.

[0007] Preferably, the biomarker is N-(3-indoleacetyl)-L-alanine.

[0008] In one embodiment of the present invention, the biomarkers are extracted from serum samples of lung cancer patients.

[0009] According to a second aspect of the present invention, a method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers is provided, comprising:

[0010] Detecting the content of biomarkers in serum samples of lung cancer patients; wherein the biomarkers include any one of N-(3-indoleacetyl)-L-alanine and methomyl or a combination thereof;

[0011] The efficacy of immunotherapy combined with chemotherapy for lung cancer patients is predicted based on the content of biomarkers detected in the serum samples of lung cancer patients, including:

[0012] If the content of the biomarker is lower than the first threshold, immunotherapy combined with chemotherapy is effective for the corresponding lung cancer patient;

[0013] If the content of the biomarker is higher than the second threshold, immunotherapy combined with chemotherapy is ineffective for the corresponding lung cancer patient.

[0014] In one embodiment of the present invention, the biomarker is N-(3-indoleacetyl)-L-alanine, and the first threshold value is: the relative content of the biomarker detected per 50uL serum is 1400-1500; the second threshold value is: the relative content of the biomarker detected per 50uL serum is 1500-1600.

[0015] In one embodiment of the present invention, the biomarker is methomyl, the first threshold is: the relative content of the biomarker detected per 50uL serum is 7200-7600; the second threshold is: the relative content of the biomarker detected per 50uL serum is 7600-7800.

[0016] In one embodiment of the present invention, the relative content is the relative content of the biomarker relative to the standard substance added to the serum.

[0017] In one embodiment of the present invention, the standard substance is L-2-chlorophenylalanine.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention provides an application of a biomarker in predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer, wherein the biomarker comprises any one of N-(3-indoleacetyl)-L-alanine and methomyl or a combination thereof. The biomarker can accurately predict the efficacy of immunotherapy combined with chemotherapy in lung cancer.

[0020] Furthermore, the biomarkers of the present invention are extracted from serum samples of lung cancer patients, so compared with PD-L1 expression, the method of the present invention is convenient for obtaining samples and is also easy to dynamically monitor. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a method for using biomarkers to predict the efficacy of combined immunotherapy and chemotherapy in lung cancer;

[0022] Figure 2A Kaplan-Meier progression-free survival curves for the chemotherapy group, discovery set, and validation set;

[0023] Figure 2B This is a comparison chart of the objective response rate of the chemotherapy group, discovery set, and validation set;

[0024] Figure 3A Principal component analysis plot for chemotherapy vs. discovery set;

[0025] Figure 3B Orthogonal partial least squares discriminant analysis plot for chemotherapy vs. discovery set;

[0026] Figure 3C To find the principal component analysis diagram of the concentrated NR group vs. R group;

[0027] Figure 3D To find the orthogonal partial least squares discriminant analysis diagram of the concentrated NR group vs. R group;

[0028] Figure 3E This is the principal component analysis diagram of NR group vs. R group in the validation set;

[0029] Figure 3F Orthogonal partial least squares discriminant analysis diagram of NR group vs. R group in the validation set;

[0030] Figure 4A Bar graph of the fold differences (FC) for the top 20 differences between chemotherapy groups and discovery set;

[0031] Figure 4B To find the top 20 differences between the NR group and the R group, the bar chart of the fold difference (FC) was used;

[0032] Figure 4C This is a bar chart of the top 20 fold differences between the NR group and the R group in the validation set (FC);

[0033] Figure 5 is a Venn diagram showing the number of differential metabolites in different comparisons;

[0034] Figure 6 The Kaplan-Meier progression-free survival curves were plotted according to the relative contents of N-(3-indoleacetyl)-L-alanine (A) and methomyl (B);

[0035] Figure 7 This is the multivariate Cox regression analysis diagram of PFS;

[0036] Figure 8 Statistical graph of objective response rate of patients grouped by relative content of N-(3-indoleacetyl)-L-alanine (A) and methomyl (B). DETAILED DESCRIPTION

[0037] The technical solution of the present invention is described in detail below in conjunction with the embodiments. In view of the fact that there is no accurate and convenient method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer in the prior art, the present invention provides an application of a biomarker in predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer, wherein the biomarker includes any one of N-(3-indoleacetyl)-L-alanine and methomyl or a combination thereof.

[0038] In a preferred embodiment of the present invention, the biomarker is N-(3-indoleacetyl)-L-alanine. Among them, N-(3-indoleacetyl)-L-alanine is an amino acid derivative, and amino acid metabolism plays an important role in promoting immune cell function. More and more studies have shown that amino acid metabolism is a potential therapeutic target for regulating immune responses in cancer, infection and autoimmunity. And N-(3-indoleacetyl)-L-alanine is an independent predictor, and the applicant has verified that low levels of N-(3-indoleacetyl)-L-alanine are significantly associated with prolonged PFS. The content related to verification will be explained later.

[0039] It should be noted that the use of any one of N-(3-indoleacetyl)-L-alanine and methomyl or their combination as biomarkers to predict the efficacy of immunotherapy combined with chemotherapy in lung cancer is proposed for the first time in this technical field and has significant innovation and clinical value. This application has greatly helped in determining the treatment plan for lung cancer patients, thereby helping to adopt more targeted treatment plans for lung cancer patients in the clinic; it can be said that it has epoch-making and significant value in a sense.

[0040] The biomarker is extracted from serum samples of lung cancer patients. Compared with PD-L1 expression, serum samples are easy to obtain and dynamic monitoring; therefore, they can be easily applied and promoted in clinical practice.

[0041] The present invention also provides a method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers. Figure 1 , the method comprising:

[0042] S10: Detecting the content of biomarkers in serum samples of lung cancer patients; wherein the biomarkers include any one of N-(3-indoleacetyl)-L-alanine and methomyl or a combination thereof;

[0043] S20: Predict the efficacy of immunotherapy combined with chemotherapy for lung cancer patients based on the levels of biomarkers detected in serum samples of lung cancer patients, including:

[0044] If the content of the biomarker is lower than the first threshold, immunotherapy combined with chemotherapy is effective for the corresponding lung cancer patient;

[0045] If the content of the biomarker is higher than the second threshold, immunotherapy combined with chemotherapy is ineffective for the corresponding lung cancer patient.

[0046] In one embodiment of the present invention, the biomarker is N-(3-indoleacetyl)-L-alanine, and the first threshold is: the relative content of the biomarker detected per 50uL serum is 1400-1500; the second threshold is: the relative content of the biomarker detected per 50uL serum is 1500-1600. For example, taking patients in clinical practice as an example, the relative content of N-(3-indoleacetyl)-L-alanine of patient A is 1088, and his PFS is as long as 17 months; while the relative content of patient B is 1637, and his PFS is 7 months.

[0047] In one embodiment of the present invention, the biomarker is methomyl, and the first threshold is: the relative content of the biomarker detected per 50uL serum is 7200-7600; the second threshold is: the relative content of the biomarker detected per 50uL serum is 7600-7800. For example, taking patients in clinical practice as an example, the relative content of methomyl in patient C is 6954, and his PFS is as long as 15 months; while the relative content of patient D is 7864, and his PFS is 8 months.

[0048] Wherein, the relative content is the relative content of the biomarker relative to the standard substance (standard product) added to the serum. In one embodiment of the present invention, the standard substance is L-2-chlorophenylalanine.

[0049] In order to fully and in detail describe the present invention, the present invention will systematically introduce the acquisition of the above-mentioned biomarkers and the verification of the prediction of the efficacy of immunotherapy combined with chemotherapy for lung cancer patients.

[0050] Unless otherwise specified, the reagents and biological materials used below are all commercial products. Among them, the instruments and reagents used in the present invention are shown in the following Tables 1 and 2:

[0051] Table 1: Instruments used in the present invention

[0052]

[0053]

[0054] Table 2: Reagents used in the present invention

[0055] name CAS Number purity brand Part Number Methanol 67-56-1 Chromatographic pure Merck 1.06007.4008 Acetonitrile 75-05-8 Chromatographic pure Merck 1.00030.4008 Acetic acid 64-19-7 Chromatographic pure Ron R009611-500ml Ammonium formate 540-69-2 Chromatographic pure Aladdin 516961-100G ammonia 1336-21-6 Chromatographic pure Aladdin 221228-100ML-A Formic acid 64-18-6 Chromatographic pure Aladdin 695076-100ML Standards - More than 98% isoreag / TRC / TCI / Sigma -

[0056] Example 1 Extraction and determination of biomarkers

[0057] 1. Screening samples

[0058] This study retrospectively enrolled 200 patients with stage IIIB to IV NSCLC who received first-line or second-line PD-1 inhibitor combined with chemotherapy and 50 patients with stage IIIB to IV NSCLC who received first-line cytotoxic chemotherapy at Shanghai Chest Hospital from January 2019 to December 2021. The 200 patients who received combined therapy were randomly divided into two groups: a discovery set of 50 patients and a validation set of 150 patients. The follow-up period ended on March 31, 2023. This study was approved by the Ethics Committee and Institutional Review Board of Shanghai Chest Hospital (Shanghai, China), and all patients provided written informed consent. Among them, based on PFS, progression-free survival (PFS) ≥ 12 months was the good prognosis (R) group, and PFS < 12 months was the poor prognosis (NR) group. Clinical data and laboratory parameters, including age, sex, smoking history, tumor histology, TNM stage, and PD-L1 expression, were obtained from medical records. Patients were followed up regularly during treatment, and their condition was assessed every 2-3 treatment cycles by laboratory tests, chest CT, abdominal ultrasound, and, if necessary, cranial MRI and bone scan or PET-CT. Patients receiving immunotherapy combined with chemotherapy received the following immunotherapy: 200 mg of pembrolizumab, 200 mg of sindilimab, or 200 mg of tislelizumab intravenously once every 3 weeks, followed by appropriate chemotherapy drugs according to their condition. Chemotherapy drugs included pemetrexed, docetaxel, paclitaxel / albumin-bound paclitaxel, liposome paclitaxel, vinorelbine, and gemcitabine. Patients in the chemotherapy group received chemotherapy drugs as above, also once every 3 weeks. Treatment continued until tumor progression, unacceptable drug toxicity, or death. Blood samples were collected from patients within 10 days before starting PD-1 inhibitor-based combination therapy or chemotherapy, and serum was used as samples for non-targeted metabolomics testing.

[0059] Of course, it should be appreciated that the above sample numbers are only examples and may be other values. 2. Extraction of biomarkers from sample serum

[0060] The extraction of biomarkers from sample serum specifically includes the following steps:

[0061] (1) Take out the sample from the -80℃ freezer and thaw it on ice until there is no ice in the sample (all subsequent operations are performed on ice);

[0062] (2) After the sample is thawed, vortex for 10 seconds to mix, and transfer 50 μL of the sample into the corresponding numbered centrifuge tube;

[0063] (3) Add 300 μL of 20% acetonitrile-methanol internal standard extract, vortex for 3 min, and centrifuge at 12,000 rpm for 10 min at 4 °C;

[0064] (4) After centrifugation, transfer 200 μL of the supernatant to another corresponding numbered centrifuge tube and place it in a -20°C refrigerator for 30 min;

[0065] (5) Centrifuge again at 12,000 rpm for 3 min at 4°C and transfer 180 μL of the supernatant into the corresponding injection bottle liner for analysis.

[0066] The chromatographic conditions are:

[0067] (1) Chromatographic column: Waters ACQUITY UPLC BEH C18 chromatographic column (model: 1.8 μm, 2.1 mm*100 mm);

[0068] (2) Mobile phase A: ultrapure water (0.1% formic acid); Mobile phase B: acetonitrile (0.1% formic acid);

[0069] (3) Instrument column temperature: 40°C; flow rate: 0.40 mL / min; injection volume: 2 uL.

[0070] The mass spectrometry conditions are shown in Table 3.

[0071] Table 3: Mass spectrometry conditions of the present invention

[0072]

[0073]

[0074] 3. Data Preprocessing

[0075] The raw data from the mass spectrometer was converted into mzXML format by ProteoWizard, and the XCMS program was used for peak extraction, alignment, and retention time correction. The "SVR" method was used to correct the peak area, and the peaks with a missing rate greater than 50% in each group of samples were filtered. After correction and screening, the metabolite identification information was obtained by searching the laboratory's self-built database, integrating the public library, the AI ​​prediction library, and the metDNA method. These practices are routine practices in the biomedical field, and existing operations are used, so they will not be introduced in detail.

[0076] Unsupervised PCA (Principal Component Analysis) was performed by the statistical function prcomp in R. Data were unit-variance scaled before unsupervised PCA. HCA (Hierarchical Cluster Analysis) results for samples and metabolites were presented as heatmaps with dendrograms, while Pearson correlation coefficients (PCC) between samples were calculated by the cor function in R and presented only as heatmaps. Both HCA and PCC were performed by the R package ComplexHeatmap. For HCA, normalized signal intensities (unit-variance scaled) of metabolites were displayed as color spectra. For two-group analysis, differential metabolites were identified by VIP (VIP>1) and P value (P value<0.05, Student t-test). VIP values ​​were extracted from OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) results, which also included score plots and arrangement plots, generated using the R package MetaboAnalystR. Data were log-transformed (log2) and mean-centered before OPLS-DA. Permutation tests (200 permutations) were performed to avoid overfitting. Among them, these practices are also routine practices in the biomedical field, using existing operations, and therefore will not be introduced in detail.

[0077] The data were subjected to clinical data analysis, wherein the chi-square test was used to examine the differences in baseline and patient characteristics and ORR between different groups. Median PFS was derived using the Kaplan-Meier method. Cox regression analysis of the hazard ratio (HR) of each factor for PFS. All statistical tests were performed using SPSS25.0 software (SPSS, Chicago, IL, USA) and GraphPad Prism software (Prism 8). It is believed that bilateral p values ​​<0.05 are statistically significant. Among them, these clinical data analysis and statistical methods are conventional clinical data analysis methods in the art, and the present invention does not improve these methods, so they are not expanded for introduction.

[0078] 4. Tracking Patient Data

[0079] The 250 screened patients received corresponding treatment according to the plan. The baseline characteristics of these patients are summarized in Table 4.

[0080] Table 4: Baseline characteristics of patients

[0081]

[0082]

[0083] Among them, some descriptions in the table are as follows:

[0084] *Nonsquamous cell tumors include adenocarcinoma, lymphoepithelioma-like carcinoma, and adenosquamous carcinoma.

[0085] Abbreviations: PD-L1: programmed cell death ligand 1; TPS: tumor proportion score.

[0086] Among them, 200 of the 250 patients subsequently received first-line or second-line combined therapy according to the treatment plan, and 50 subsequently received first-line chemotherapy. The patients who subsequently received combined therapy were further grouped, and the discovery set included 50 patients (20.0%) and the validation set included 150 patients (60.0%). According to Table 5, the patient characteristics were balanced among the three groups.

[0087] Table 5: Characteristics of patients in the chemotherapy group, discovery set, and validation set

[0088]

[0089]

[0090] Among them, some descriptions in the table are as follows:

[0091] *Nonsquamous cell tumors include adenocarcinoma, lymphoepithelioma-like carcinoma, and adenosquamous carcinoma.

[0092] Abbreviations: PD-L1: programmed cell death ligand 1; TPS: tumor proportion score.

[0093] Among them, the above 150 patients were continuously followed up during the subsequent treatment and PFS was calculated. For details, please see Figure 2A and Figure 2B ,in, Figure 2A 2A and 2B were obtained by statistical analysis of the survival curve in GraphPad Prism software (Prism8); PD indicates progressive disease, SD indicates stable disease, PR indicates partial remission, and CR indicates complete remission; *** indicates P < 0.05. Figure 2B It can be seen that the median PFS of the discovery set, validation set, and chemotherapy group were 15.0 months, 18.0 months, and 7.0 months, respectively. There was no significant difference in PFS and ORR between the discovery set and validation set (P>0.05). However, the PFS of the chemotherapy group was shorter than that of the discovery set and validation set, and the ORR was worse than that of the discovery set and validation set (P<0.05).

[0094] 5. Identify biomarkers

[0095] Based on the above analysis, it can be seen that the prognosis of the discovery set is significantly better than that of the chemotherapy group. Next, non-targeted metabolomics analysis was performed on the discovery set and chemotherapy group. The various comparison diagrams obtained by the analysis are shown in Figure 2. Figures 3A-3F As shown, Figure 3A , 3C , 3E is the principal component analysis diagram between different groups, which is a multidimensional data statistical analysis method for unsupervised pattern recognition. It converts a group of variables that may be correlated into a group of linearly unrelated variables through orthogonal transformation. The converted group of variables is called principal component. Usually, the mathematical processing is to linearly combine the original multiple indicators as a new comprehensive indicator, using the statistical function prcomp in R (www.r-project.org). The data was measured for unit variance before the unsupervised principal component analysis. Figure 3B , 3D , 3F are the orthogonal partial least squares discriminant analysis diagrams between different groups. Partial Least Squares Discriminant Analysis (PLS-DA) can solve the problem of insensitivity of variables with small correlation. PLS-DA is a multivariate statistical analysis method for supervised pattern recognition. The specific method is to extract the components of the independent variable X and the dependent variable Y respectively, and then calculate the correlation between the components. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) combines the orthogonal signal correction (OSC) and PLS-DA methods. When modeling, it can decompose the X matrix information into two types of information related to Y and unrelated to Y. The variable information related to Y is the predicted principal component, and the variable information unrelated to Y is the orthogonal principal component. The difference variables are screened by removing irrelevant differences. OPLS-DA logs the original data. 2 After conversion, the centering process is performed (the original data minus the mean of the variable). X is the sample quantitative information matrix, and Y is the sample grouping information matrix. Figure 3A-3F The method of obtaining is a prior art and will not be described in detail here. Figure 3A-3F It can be seen that the close clustering of QC samples in the chemotherapy group and the discovery group PCA can illustrate the reliability of this study ( Figure 3A ), because in order to obtain reliable and high-quality metabolomics data, quality control (QC) is required. QC samples are used for quality control in non-targeted metabolomics testing. In theory, QC samples are the same, but there will be systematic errors in the sample extraction, detection and analysis process, resulting in differences between QC samples. The smaller the difference, the higher the stability of the method and the better the data quality. This is reflected in the dense distribution of QC samples in the principal component analysis diagram, indicating that the data is reliable.

[0096] In addition, the analysis based on OPLS-DA showed significant differences between the two groups ( Figure 3B ).

[0097] Combining univariate statistical analysis and multivariate statistical analysis, 1475 differential metabolites (VIP>1&P<0.05) were initially screened out. As for the specific metabolites, further screening is required in the future, so they are not listed here one by one. After qualitative and quantitative analysis of the detected metabolites, combined with the specific grouping situation, the difference fold changes of the quantitative information of metabolites in each group were compared. The results of the top 20 metabolites in the comparison of each group were plotted into a difference fold (FC) bar chart. For details, please see Figures 4A-4C Among them, the fold change (FC value) analysis is to calculate the difference in the expression of a metabolite between the two groups based on the relative or absolute quantitative results of the metabolites. The fold change is used as a standard for up and down regulation. Assuming that the comparison group is AvsB, the calculation method is: FC = B / A, FC greater than 1 is up-regulated, and less than 1 is down-regulated. The horizontal axis is the log of the differential metabolite 2 FC, i.e., the logarithm of the difference multiple of the differential metabolites with base 2, the vertical axis is the differential metabolites. A horizontal axis greater than 0 represents an increase in the metabolite content, and a horizontal axis less than 0 represents a decrease in the metabolite content. The method of difference multiple is a prior art and will not be repeated here. The differential metabolites were further screened according to the FC value (FC<0.5 or FC>2), and 57 metabolites were screened out, especially 4-aminobenzoate, Phe4Cl-Tyr-OH and phenylbutazone, with the most significant differences ( Figure 4A ). We hypothesized that these metabolites would be associated with prognosis in response to immunotherapy.

[0098] We continue to explore potential biomarkers for the prognosis of patients with advanced NSCLC who receive ICI combined with chemotherapy. The discovery set was divided into the R group (PFS ≥ 12 months) and the NR group (PFS < 12 months) according to PFS, and the baseline characteristics of the patients were balanced, as shown in Table 6.

[0099] Table 6: Baseline characteristics of patients in the NR and R groups in the discovery and validation sets.

[0100]

[0101]

[0102] Among them, some descriptions in the table are as follows:

[0103] *Nonsquamous cell tumors include adenocarcinoma, lymphoepithelioma-like carcinoma, and adenosquamous carcinoma.

[0104] Abbreviations: PD-L1: programmed cell death ligand 1; TPS: tumor proportion score; PFS in the NR group was <12 months, and PFS in the R group was ≥12 months.

[0105] Among them, PCA and OPLS-DA still showed good grouping and reliable results ( Figure 3C and 3D ). Using the same criteria as above, 212 differential metabolites were obtained, of which 11 had FC>2 or FC<0.5. In addition, the metabolite difference of Arg-Gln-Tyr-Lys was the most significant ( Figure 4B ). In order to eliminate some interference from chemotherapy, the common differential metabolites obtained from the comparison between the chemotherapy group and the discovery set, and the comparison between the NR group and the R group in the discovery set were focused on. There are 57 differential metabolites in total, but whether these are biomarkers for predicting the efficacy of combined therapy needs further verification. The variable importance projection (VIP) obtained based on the OPLS-DA model (biological replication ≥ 3) can preliminarily screen out metabolites that differ between different varieties or tissues. At the same time, the P-value of the univariate analysis can be combined to further screen out differential metabolites. The screening criteria for differential metabolites in this figure are: select metabolites with VIP>1, and the influence intensity of VIP corresponding to each difference is different. It is generally believed that metabolites with VIP>1 are significantly different; select metabolites with P-value<0.05 (Student's t test). If there is a statistically significant difference in metabolites between different groups, the difference is considered to be significant. The differential metabolites that meet the above two conditions between different groups are drawn into a Venn diagram according to whether there is an intersection, and the result is obtained. Figure 5 ,in Figure 5 Figure 2 is a Venn diagram of the number of differential metabolites in different comparisons, where A is the comparison between the chemotherapy group and the discovery set, and the comparison between NR and R in the discovery set; B is the comparison between NR and R in the discovery set, and the comparison between NR and R in the validation set; C is the comparison between the chemotherapy group and the discovery set, the comparison between NR and R in the discovery set, and the comparison between NR and R in the validation set.

[0106] Next, the different metabolites in the discovery and validation sets were further compared. To validate the predictive and prognostic effects of these serum metabolites, 150 samples from another independent cohort (validation set) were used. As with the discovery set, the validation set was divided into R and NR groups according to PFS ≥ 12 months or < 12 months, and non-targeted metabolomics analysis was performed. First, PCA showed reliability, and OPLS-DA showed a clear separation between the R and NR groups without overfitting ( Figure 3E and 3F ). According to VIP>1 and P<0.05, 150 metabolites were considered to be significant differential metabolites. The difference fold bar shows the top 20 annotated metabolites with the most obvious FC expression in the NR group and the R group. Among them, 7-ketodeoxycholic acid changed the most, and only its FC>2 ( Figure 4C ).

[0107] Comparing the discovery and validation sets, combined with the control of the chemotherapy group, three common differential metabolites were found in the discovery and validation sets, namely 3,3'-diamino-4,4'-dihydroxydiphenyl sulfone, N-(3-indolacetyl)-L-alanine and methomyl. In addition, the trend of the relative contents of these three metabolites was studied, and it was found that low N-(3-indolacetyl)-L-alanine and methomyl were associated with better prognosis. However, in the discovery set, low 3,3'-diamino-4,4'-dihydroxydiphenyl sulfone predicted better outcomes, while in the validation set, low 3,3'-diamino-4,4'-dihydroxydiphenyl sulfone predicted worse outcomes (Table 7).

[0108] Table 7: Common differential metabolites among three comparisons.

[0109]

[0110] These results suggest that N-(3-indoleacetyl)-L-alanine and methomyl may have the potential to select patients with good prognosis from advanced NSCLC without oncogenic driver alterations before they receive first-line or second-line PD-1 inhibitor combined with chemotherapy.

[0111] The above is the extraction and confirmation of the biomarkers of the present invention. It should be appreciated that the corresponding methods and analyses used in the extraction and confirmation process described above are all conventional technical means in the field. The focus of the present invention is to creatively use the biomarkers in serum samples to predict the efficacy of immunotherapy combined with chemotherapy in lung cancer patients. This prediction method does not exist in the prior art and is a pioneering approach of the inventors of the present application in the field, which has great innovative significance and extremely valuable clinical effects.

[0112] Example 2 Clinical Validation Analysis

[0113] In order to explore the impact of the two metabolites identified above on the overall population, 200 patients receiving ICI combined chemotherapy were divided into low and high groups according to the median relative content of the metabolites N-(3-indoleacetyl)-L-alanine and methomyl. The patient characteristics were balanced between the low and high N-(3-indoleacetyl)-L-alanine groups and the low and high methomyl groups. The correlation between the substance content and clinical pathological characteristics is shown in Table 8.

[0114] Table 8: Correlation between substance content and clinical pathological characteristics.

[0115]

[0116]

[0117] Among them, some descriptions in the table are as follows:

[0118] *Nonsquamous cell tumors include adenocarcinoma, lymphoepithelioma-like carcinoma, and adenosquamous carcinoma.

[0119] Abbreviations: PD-L1: programmed cell death ligand 1; TPS: tumor proportion score.

[0120] The survival curves were analyzed by GraphPad Prism software (Prism 8) according to the median grouping of the substances. Figure 6 ,in, Figure 6 The Kaplan-Meier curve (survival curve) showed that the median PFS of the low N-(3-indoleacetyl)-L-alanine group and the high N-(3-indoleacetyl)-L-alanine group were 18.0 months (95% CI, 12.3-23.7) and 11.0 months (95% CI, 8.5-13.5), respectively. In addition, the median PFS of the low methomyl group and the high methomyl group were 18.0 months (95% CI, 14.0-22.0) and 10.0 months (95% CI, 7.7-12.3), respectively.

[0121] The univariate analysis of PFS was performed and Table 9 was obtained, in which there were no significant differences in patient age, gender, smoking history, histological type and TNM stage. However, low N-(3-indoleacetyl)-L-alanine indicated longer PFS (HR=0.59, 95%CI, 0.41-0.84, P=0.003). Low methomyl also indicated prolonged PFS (HR=0.67, 95%CI, 0.47-0.96, P=0.029). It was also found that the PFS of PD-L1 expression (TPS≥50%) was longer than that of PD-L1 expression (TPS<1%) (HR=0.33, 95%CI: 0.17–0.61, P<0.001).

[0122] Table 9: Univariate analysis of PFS

[0123]

[0124] Among them, some descriptions in the table are as follows:

[0125] * Non-squamous cell tumors include adenocarcinoma, lymphoepithelioma-like carcinoma, and adenosquamous carcinoma;

[0126] #Applicable only to patients with available PD-L1 expression data (patients with unknown PD-L1 expression are excluded);

[0127] Abbreviations: PFS, progression-free survival; HR, hazard ratio; PD-L1: programmed cell death ligand 1; TPS, tumor proportion score; **, P < 0.05 indicates statistical significance.

[0128] In addition, to identify independent predictive factors, Cox multivariate analysis was performed and the results were Figure 7 ,in, Figure 7 The symbols in are explained as follows:

[0129] * Non-squamous cell tumors include adenocarcinoma, lymphoepithelioma-like carcinoma, and adenosquamous carcinoma;

[0130] # Applicable only to patients with available PD-L1 expression data (patients with unknown PD-L1 expression are excluded);

[0131] Abbreviations: PFS, progression-free survival; HR, hazard ratio; PD-L1: programmed cell death ligand 1; TPS, tumor proportion score; **, P < 0.05 indicates statistical significance.

[0132] In multivariate analysis, low N-(3-indoleacetyl)-L-alanine was significantly associated with prolonged PFS (HR=0.61, 95%CI: 0.38-0.98, P=0.040). PFS was longer in patients with PD-L1 expression (TPS ≥ 50%) than in patients with PD-L1 expression (TPS < 1%) (HR=0.36, 95%CI: 0.19-0.70, P=0.002). No significant correlation was found between methomyl and PFS.

[0133] Obtained from stacked plot statistical analysis in GraphPad Prism software (Prism 8) Figure 8 , where the P value is obtained by student t test. Figure 8 PD in the table indicates progressive disease; SD indicates stable disease; PR indicates partial remission; CR indicates complete remission; *** indicates P < 0.05. Figure 8 It can be seen that in this study, 67 patients (33.5%) achieved objective remission. The ORRs in the low and high N-(3-indoleacetyl)-L-alanine groups were 43.0% and 24.0%, respectively ( Figure 8 A). In addition, the ORRs in the low and high methomyl groups were 42.0% and 25.0%, respectively ( Figure 8 B). The differences between the low and high N-(3-indoleacetyl)-L-alanine groups and the low and high methomyl groups (P<0.05) were significant.

[0134] Clinical validation analysis showed that, consistent with the results of non-targeted metabolomics, low N-(3-indoleacetyl)-L-alanine and methomyl were associated with better outcomes in patients with advanced NSCLC treated with PD-1 inhibitors plus chemotherapy. Moreover, N-(3-indoleacetyl)-L-alanine was an independent predictor that may have better predictive value than methomyl.

[0135] In summary, the embodiment of the present invention compares the difference metabolites of good and poor prognosis in advanced NSCLC patients treated with immunotherapy combined with chemotherapy, confirming that N-(3-indoleacetyl)-L-alanine and methomyl are metabolites with significant differences in both the discovery set and the validation set. In addition, they show the same trend in the two cohorts, and the relative content is more when the prognosis is poor. In addition, when COX multifactor analysis was performed in the total population of combined treatment, N-(3-indoleacetyl)-L-alanine was an independent predictor, and low levels of N-(3-indoleacetyl)-L-alanine were significantly associated with PFS extension, while methomyl was not an independent predictor, indicating that N-(3-indoleacetyl)-L-alanine may be more reliable than methomyl as a potential biomarker. Compared with PD-L1 expression, biomarkers in serum have the advantages of being non-invasive, convenient, easy to obtain, and can be dynamically monitored.

[0136] Furthermore, by establishing multiple independent cohorts including a chemotherapy group, a discovery group, and a validation group, the results of the present invention are made more reliable and accurate.

[0137] Among them, N-(3-indoleacetyl)-L-alanine is an amino acid derivative. Although there is no literature directly describing the role of N-(3-indoleacetyl)-L-alanine, amino acid metabolism plays an important role in promoting immune cell function. More and more studies have shown that amino acid metabolism is a potential therapeutic target for regulating immune responses in cancer, infection and autoimmunity. Methomyl is a heterocyclic compound that produces toxicity by inhibiting cholinesterase in the central nervous system. Poisoned people are prone to symptoms such as skeletal muscle tremor, spasm, and dyspnea. However, these two metabolites are rarely reported in immunotherapy, so the results of the present invention are pioneering and innovative.

[0138] The above are only some preferred embodiments of the present invention, and the present invention is not limited to the contents of the embodiments. For those skilled in the art, various changes and modifications can be made within the scope of the technical solution of the present invention, and any changes and modifications made are within the protection scope of the present invention.

Claims

1. Application of a biomarker in predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer, It is characterized in that The biomarker includes any one of N-(3-indoleacetyl)-L-alanine and methomyl or a combination thereof.

2. Use of the biomarker according to claim 1 in predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer, It is characterized in that The biomarker is N-(3-indoleacetyl)-L-alanine.

3. Use of the biomarker according to claim 1 or 2 in predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer, It is characterized in that The biomarkers are extracted from serum samples of lung cancer patients.

4. A method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers. It is characterized in that include: Detecting the content of biomarkers in serum samples of lung cancer patients; wherein the biomarkers include any one of N-(3-indoleacetyl)-L-alanine and methomyl or a combination thereof; The efficacy of immunotherapy combined with chemotherapy for lung cancer patients is predicted based on the content of biomarkers detected in the serum samples of lung cancer patients, including: If the content of the biomarker is lower than the first threshold, immunotherapy combined with chemotherapy is effective for the corresponding lung cancer patient; If the content of the biomarker is higher than the second threshold, immunotherapy combined with chemotherapy is ineffective for the corresponding lung cancer patient.

5. The method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers according to claim 4, It is characterized in that The biomarker is N-(3-indoleacetyl)-L-alanine, and the first threshold is: the relative content of the biomarker detected per 50uL serum is 1400-1500.

6. The method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers according to claim 5, It is characterized in that The second threshold value is: the relative content of the biomarker detected in every 50uL serum is 1500-1600.

7. The method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers according to claim 4, It is characterized in that The biomarker is methomyl, and the first threshold is: the relative content of the biomarker detected in every 50uL of serum is 7200-7600.

8. The method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers according to claim 7, It is characterized in that The second threshold value is: the relative content of the biomarker detected in every 50uL serum is 7600-7800.

9. The method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers according to any one of claims 5 to 8, It is characterized in that The relative content is the relative content of the biomarker relative to the standard substance added to the serum.

10. The method for predicting the efficacy of immunotherapy combined with chemotherapy in lung cancer using biomarkers according to claim 9, It is characterized in that The standard substance is L-2-chlorophenylalanine.

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