Application of Plasma Protein Markers in Prognosis Prediction of Targeted Therapy for EML4-ALK Positive Non-Small Cell Lung Cancer

Abstract

The present invention discloses the application of plasma protein markers in predicting the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, and the plasma protein is selected from at least one of SERPINA4, ATRN, APOA4, TF, MYOC, CRP, SAA1 and AHSG. The present invention can provide effective plasma markers for predicting the efficacy of ALK-TKI treatment, which is conducive to the selection of subsequent treatment methods for drug-resistant patients.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical detection, and particularly relates to blood molecular markers for tumor prognosis, especially the application of plasma proteins in predicting the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer. Background Art

[0002] Lung cancer is the most common malignant tumor with the highest incidence and mortality in China. Among them, non-small cell lung cancer (NSCLC) is the most common, accounting for about 85%. The anaplastic lymphoma kinase (ALK) gene and the echinoderm microtubule-associated protein-like 4 (EML4) gene fuse due to the inversion of the short arm of chromosome 2, forming the EML4-ALK fusion gene with a strong carcinogenic effect, which promotes cell carcinogenesis and accounts for about 3%-5% of NSCLC patients. With the progress of tumor molecular targeted therapy, the first-generation ALK tyrosine kinase inhibitor (ALK-TKI) crizotinib, the second-generation ALK-TKIs ceritinib, alectinib, conteltinib (CT-707), and the subsequent third-generation ALK-TKI lorlatinib have been gradually applied to NSCLC patients with EML4-ALK positivity, showing better efficacy compared with traditional chemotherapy.

[0003] However, patients will inevitably develop drug resistance after medication, leading to disease progression. The previously reported resistance mechanisms of ALK-TKIs mainly focus on drug target mutations (including ALK copy number amplification or kinase domain mutations) and abnormal activation of bypass pathways. For these resistance mechanisms, sequential use of new-generation ALK-TKIs targeting ALK mutation sites and combination with bypass inhibitors have a certain effect on reversing targeted therapy resistance. However, the use of new-generation ALK-TKIs will still lead to the gradual accumulation of new drug-resistant mutations in tumor cells, and patients will experience disease progression again. Finding prognostic markers for targeted therapy in NSCLC patients with EML4-ALK positivity will help guide treatment selection and dosing sequence and improve the prognosis of patients. However, different from the detection of PD-L1, TMB, and MSI in immunotherapy, there is currently a lack of efficacy markers for ALK-TKI targeted therapy that can be widely applied in clinical practice.

[0004] Due to the advantages of plasma protein markers, such as easy availability of test specimens, easy detection, ability to dynamically monitor treatment efficacy, and contribution to the discovery of new drug resistance mechanisms and treatment targets, they have great application potential in predicting the prognosis of tumor treatment efficacy. Summary of the Invention

[0005] To solve the above technical problems, the present invention used a data-independent acquisition mass spectrometry platform (DIA-MS) to detect the pre-treatment plasma proteome of EML4-ALK positive NSCLC patients receiving ALK-TKI treatment. According to the marker expression levels, a prognostic scoring model based on plasma markers that can predict the response to ALK-TKI treatment was constructed and verified; and grouping was performed according to the high or low marker expression levels to screen individual plasma protein markers related to the prognosis of EML4-ALK positive NSCLC patients receiving ALK-TKI treatment, and the predictive efficacy of individual plasma protein markers was verified in an independent cohort of patients receiving ALK-TKI treatment through a quantitative enzyme-linked immunosorbent assay (ELISA) experiment. The present invention can provide effective plasma markers for predicting the efficacy of ALK-TKI treatment, provide ideas for exploring drug resistance mechanisms, and facilitate the selection of subsequent treatment methods for drug-resistant patients.

[0006] The present invention provides the following technical solutions:

[0007] In one aspect, the present invention provides a plasma protein for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, and the plasma protein is selected from at least one of SERPINA4, ATRN, APOA4, TF, MYOC, CRP, SAA1, and AHSG.

[0008] In one aspect, the present invention provides the use of a detection reagent for plasma protein in the preparation of a kit for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, and the plasma protein is selected from at least one of SERPINA4, ATRN, APOA4, TF, MYOC, CRP, SAA1, and AHSG.

[0009] In one aspect, the present invention provides a kit for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, and the kit contains a detection reagent for plasma protein, and the plasma protein is selected from at least one of SERPINA4, ATRN, APOA4, TF, MYOC, CRP, SAA1, and AHSG.

[0010] In one aspect, the present invention provides a system for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, and the system includes:

[0011] An acquisition module for acquiring a sample from a subject; and

[0012] An evaluation module connected to the acquisition module for detecting plasma proteins in the sample, the plasma proteins including at least one of SERPINA4, ATRN, APOA4, TF, MYOC, CRP, SAA1, and AHSG.

[0013] In one aspect, the present invention provides a method for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, the method comprising detecting plasma proteins selected from at least one of SERPINA4, ATRN, APOA4, TF, MYOC, CRP, SAA1, and AHSG.

[0014] In some embodiments, the plasma proteins are selected from any combination of two or more of SERPINA4, ATRN, APOA4, TF, MYOC, CRP, SAA1, and AHSG.

[0015] In some embodiments, the plasma proteins are selected from at least one of SERPINA4, ATRN, APOA4, TF, and MYOC, preferably a combination of SERPINA4, ATRN, APOA4, TF, and MYOC.

[0016] In some embodiments, the plasma proteins are selected from at least one of SERPINA4, TF, CRP, SAA1, and AHSG, preferably a combination of SERPINA4, TF, CRP, SAA1, and AHSG.

[0017] In some embodiments, the plasma proteins are plasma proteins in peripheral blood.

[0018] In some embodiments, the detection reagent for plasma proteins includes a reagent capable of quantitatively detecting plasma proteins.

[0019] In some embodiments, the detection reagent for plasma proteins includes a substance (such as a specific antibody) capable of specifically binding to plasma proteins.

[0020] In some embodiments, the detection of plasma proteins can be performed based on known methods, such as DIA mass spectrometry and / or ELISA, etc.

[0021] In some embodiments, the targeted therapy includes ALK tyrosine kinase inhibitor (ALK-TKI) targeted therapy.

[0022] In some embodiments, the ALK tyrosine kinase inhibitor (ALK-TKI) targeted therapy includes crizotinib, ceritinib, alectinib, brigatinib, Conteltinib (CT-707), and lorlatinib.

[0023] In some embodiments, the targeted therapy prognosis refers to the progression-free survival (PFS) of EML4-ALK positive non-small cell lung cancer after ALK-TKI targeted therapy.

[0024] In some embodiments, the system further includes an output module, which is used to output results according to the detection data of the evaluation module.

[0025] In some embodiments, the evaluation module includes evaluating the targeted therapy prognosis by detecting the plasma protein expression level.

[0026] In some embodiments, the plasma protein expression level includes the plasma protein expression level before treatment.

[0027] In some embodiments, the detection of plasma proteins is performed by ELISA, where the plasma protein is at least one of CRP, SAA1, AHSG, SERPINA4, and TF, preferably a combination of CRP, SAA1, AHSG, SERPINA4, and TF.

[0028] In some embodiments, the detection of plasma proteins is performed by DIA mass spectrometry, where the plasma protein is at least one of SERPINA4, ATRN, APOA4, TF, and MYOC, preferably a combination of SERPINA4, ATRN, APOA4, TF, and MYOC.

[0029] In some embodiments, when the following plasma protein expression levels are detected by ELISA, the targeted therapy prognosis of the subject is good: CRP ≤ 15.72 μg / ml, and / or SAA1 ≤ 592.4 ng / ml, and / or AHSG > 538 μg / ml, and / or SERPINA4 > 12.5 μg / ml, and / or TF > 44 μmol / l; that is, the median PFS of the subject is longer than that of the subject with CRP > 15.72 μg / ml or SAA1 > 592.4 ng / ml or AHSG ≤ 538 μg / ml or SERPINA4 ≤ 12.5 μg / ml or TF ≤ 44 μmol / l. In some embodiments, when the plasma protein expression levels are detected by ELISA, the subject is suitable for receiving targeted therapy, such as ALK tyrosine kinase inhibitor (ALK-TKI) targeted therapy.

[0030] In some embodiments, the expression levels of SERPINA4, ATRN, APOA4, TF, and MYOC are detected by DIA mass spectrometry. When the prognostic score (exp(SERPINA4)*0.43 + exp(ATRN)*0.62 + exp(APOA4)*0.21 + exp(TF)*0.66 + exp(MYOC)*0.18) ≥ 42.4994, the prognosis of the subject is good; that is, the median PFS of the subject is longer than that of the subject with a prognostic score < 42.4994. In some embodiments, when the prognostic score is obtained by DIA mass spectrometry detection, the subject is suitable for receiving targeted therapy, such as ALK tyrosine kinase inhibitor (ALK-TKI) targeted therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Showing the process of LASSO screening for plasma protein biomarkers of the prognostic model

[0032] Figure 2 Showing the relationship between 5 proteins included in the training set model and PFS and the performance of the prognostic score model in the training set

[0033] Figure 3 Showing the performance of the prognostic score model in the validation set

[0034] Figure 4 Showing the Kaplan-Meier survival curve of ELISA validating the plasma prognostic biomarker for ALK-TKI treatment DETAILED DESCRIPTION OF THE INVENTION

[0035] The present invention will be further described below in conjunction with the accompanying drawings of the specification and specific embodiments. These embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. The experimental methods without specific conditions noted in the following embodiments are generally carried out according to the conventional conditions in the art or according to the conditions recommended by the manufacturer. Unless otherwise specified, they are all conventional methods. Unless otherwise defined, the professional and scientific terms used herein have the same meaning as those familiar to those skilled in the art.

[0036] Sources of the main reagents used in the embodiments of the present invention:

[0037]

[0038] Example 1. Construction and validation of a prognostic model based on plasma biomarkers

[0039] Baseline plasma samples of 59 ALK-positive NSCLC patients treated with crizotinib were collected, and plasma proteins were detected by DIA-MS. The specific experimental process is as follows:

[0040] 1.1 DIA-MS steps

[0041] 1.1.1 Library construction sample preparation: Take some equal amounts from each sample and mix them. Perform high-abundance depletion experiments separately (for specific steps, see the Thermo A36370 instruction manual of High Select Top14 Abundant Protein Depletion Mini Spin Columns), and use the BCA method for protein quantification. About 600 μg of the protein sample after high-abundance depletion is subjected to FASP digestion. The steps are as follows: Add an appropriate amount of 1 M DTT to each sample to a final concentration of 100 mM, heat in a boiling water bath for 5 min, and cool to room temperature. Add 200 μL of urea buffer (8 M urea, 150 mM Tris-HCl, pH 8.0), mix well, transfer to a 10 KD ultrafiltration centrifugal tube, and centrifuge at 12000 g for 15 min. Add 200 μL of urea buffer and centrifuge at 12000 g for 15 min, discard the filtrate. Add 100 μL of IAA (50 mM IAA in urea), shake at 600 rpm for 1 min, incubate at room temperature in the dark for 30 min, and centrifuge at 12000 g for 10 min. Add 100 μL of urea buffer and centrifuge at 12000 g for 10 min, repeat 2 times. Add 100 μL of NH4HCO3 buffer and centrifuge at 14000 g for 10 min, repeat 2 times. Add 40 μL of trypsin buffer (6 μg of trypsin in 40 μL of NH4HCO3 buffer), shake at 600 rpm for 1 min, and incubate at 37 °C for 16 - 18 h. Replace the collection tube, centrifuge at 12000 g for 10 min, collect the filtrate, add an appropriate amount of 0.1% TFA solution, and desalt the digested peptides using a C18 Cartridge, then vacuum freeze-dry. The dried peptides are then prepared for fractionated library construction. TM Top14 Abundant Protein Depletion Mini Spin ColumnsThermo, A36370 instruction manual), use the BCA method for protein quantification. About 600 μg of the protein sample after high-abundance depletion is subjected to FASP digestion. The steps are as follows: Add an appropriate amount of 1 M DTT to each sample to a final concentration of 100 mM, heat in a boiling water bath for 5 min, and cool to room temperature. Add 200 μL of urea buffer (8 M urea, 150 mM Tris-HCl, pH 8.0), mix well, transfer to a 10 KD ultrafiltration centrifugal tube, and centrifuge at 12000 g for 15 min. Add 200 μL of urea buffer and centrifuge at 12000 g for 15 min, discard the filtrate. Add 100 μL of IAA (50 mM IAA in urea), shake at 600 rpm for 1 min, incubate at room temperature in the dark for 30 min, and centrifuge at 12000 g for 10 min. Add 100 μL of urea buffer and centrifuge at 12000 g for 10 min, repeat 2 times. Add 100 μL of NH4HCO3 buffer and centrifuge at 14000 g for 10 min, repeat 2 times. Add 40 μL of trypsin buffer (6 μg of trypsin in 40 μL of NH4HCO3 buffer), shake at 600 rpm for 1 min, and incubate at 37 °C for 16 - 18 h. Replace the collection tube, centrifuge at 12000 g for 10 min, collect the filtrate, add an appropriate amount of 0.1% TFA solution, and desalt the digested peptides using a C18 Cartridge, then vacuum freeze-dry. The dried peptides are then prepared for fractionated library construction.

[0042] 1.1.2 Protein digestion: Take 5 μL from each serum sample, and add 50 μL of 8 M urea for denaturation treatment. Add an appropriate amount of 1 M DTT to each sample to a final concentration of 10 mM, incubate at 37 °C for 30 min, and cool to room temperature. Then add an appropriate amount of IAA to each sample to a final concentration of 50 mM, and incubate at room temperature in the dark for 30 min. Dilute each sample 6-fold with 50 mM NH4HCO3, then add 8 μg of trypsin, shake at 600 rpm for 1 min, and incubate at 37 °C for 20 h. Acidify the digested sample with an appropriate amount of TFA to terminate the digestion. Then desalt the digested peptides using a C18 Cartridge, and finally quantify the peptides using OD280.

[0043] 1.1.3 Peptide fractionation (high pH RP-library construction): Take about 300 μg of an equal peptide mixture of all samples and fractionate it using an Agilent 1260 infinity II HPLC system. Buffer A is 10 mM HCOONH4, 5% ACN, pH 10.0, and buffer B is 10 mM HCOONH4, 85% ACN, pH 10.0. The chromatographic column is equilibrated with buffer A, and the sample is loaded onto the column by an autosampler (Waters, XBridge Peptide BEH C18 column, 130A, 5 μm, 4.6 mm X 100 mm) for separation at a flow rate of 1 mL / min. The liquid phase gradient is as follows: Using a linear gradient, from 5% B to 45% B within 60 min, and the column temperature is maintained at 30 °C. 60 fractions are collected, and each fraction is dried in a vacuum concentrator for later use. After freeze-drying the sample, it is reconstituted with 6 μl of 0.1% formic acid aqueous solution and combined into 20 fractions.

[0044] 1.1.4 LC-MS / MS analysis (DDA library construction): Take 9 μL of peptides from each fraction and add 1 μL of 10X iRT peptides. After mixing, use a nano-flow Easy nLC 1200 chromatography system (Thermo Scientific) for chromatographic separation. Buffer: Solution A is 0.1% formic acid aqueous solution, and solution B is 0.1% formic acid acetonitrile aqueous solution (acetonitrile is 85%). The chromatographic column is equilibrated with 95% of solution A. The sample is injected into the Trap column (100 μm * 20 mm, 5 μm, C18, Dr. Maisch GmbH) and then separated by gradient through the analytical column (75 μm * 150 mm, 3 μm, C18, Dr. Maisch GmbH) at a flow rate of 300 nl / min. The liquid phase gradient is set as follows: 0 min - 2 min, the linear gradient of solution B is from 5% to 8%; 2 min - 90 min, the linear gradient of solution B is from 8% to 23%; 90 min - 100 min, the linear gradient of solution B is from 23% to 40%; 100 min - 108 min, the linear gradient of solution B is from 40% to 100%; 108 min - 120 min, solution B is maintained at 100%. After peptide separation, perform DDA (data-dependent acquisition) mass spectrometry analysis using a Q-Exactive HF-X mass spectrometer (Thermo Scientific). The analysis duration is 120 min, the electrospray voltage is 2.1 kV, the detection mode: positive ion, the parent ion scan range: 350 - 1500 m / z, the first-stage mass spectrometry resolution: 60,000 @ m / z 200, AGC target: 1e6, the first-stage maximum IT: 50 ms. The second-stage mass spectrometry analysis of peptides is collected according to the following method: After each full scan, trigger the collection of the second-stage mass spectrometry spectra (MS2 scan) of the 20 highest-intensity parent ions. The second-stage mass spectrometry resolution: 15,000 @ m / z 200, AGC target: 1e5, the second-stage maximum IT: 25 ms, MS2 activation type: HCD, separation window: 1.6 Th, normalized collision energy: 28.

[0045] 1.1.5 Database search (spectral library construction): Merge all mass spectrometry data through the software Spectronaut (version 15, Biognosys AG), analyze and search the library to establish a spectral database. The database is uniprot-Homo sapiens (Human)

[9606] -202249-20211020, sourced from the protein database at the website https: / / www.uniprot.org / taxonomy / 10090, with 202249 protein entries, and the download time: 2021 - 10.

[0046] 1.1.6 DIA Mass Spectrometry Data Acquisition: For each sample, 9 μL of peptides were taken and 1 μL of 10X iRT peptides were added. After mixing, 2 μg of the mixture was injected for chromatographic separation using a nano-flow rate Easy nLC 1200 chromatographic system (Thermo Scientific). Buffer: Solution A was 0.1% formic acid aqueous solution, and solution B was 0.1% formic acid acetonitrile aqueous solution (acetonitrile was 85%). The chromatographic column was equilibrated with 95% of solution A. After the sample was injected into the Trap column (100 μm * 20 mm, 5 μm, C18, Dr. Maisch GmbH), gradient separation was performed using a chromatographic analysis column (75 μm * 150 mm, 3 μm, C18, Dr. Maisch GmbH) at a flow rate of 300 nl / min. The liquid phase gradient was set as follows: from 0 min to 2 min, the linear gradient of solution B was from 5% to 8%; from 2 min to 90 min, the linear gradient of solution B was from 8% to 23%; from 90 min to 100 min, the linear gradient of solution B was from 23% to 40%; from 100 min to 108 min, the linear gradient of solution B was from 40% to 100%; from 108 min to 120 min, solution B was maintained at 100%. After peptide separation, DIA (data-independent acquisition) mass spectrometry analysis was performed using a Q-Exactive HF-X mass spectrometer (Thermo Scientific). The analysis duration was 120 min, the electrospray voltage was 2.1 kV, the detection mode was positive ion, the parent ion scan range was 350 - 1200 m / z, the primary mass spectrometry resolution was 60,000 @ m / z 200, the AGC target was 3e6, and the primary maximum IT was 30 ms. The secondary mass spectrometry resolution was 15,000 @ m / z 200, the AGC target was 1e6, the secondary maximum IT was 25 ms, the MS2 activation type was HCD, the isolation window was 20 Th, and the normalized collision energy was 32.

[0047] 1.1.7 Data Analysis:

[0048] 1) After removing high-abundance components from the mixed sample of all previous samples, DDA mass spectrometry data was acquired. Then, using the software Spectronaut Pulsar for library searching and identification, a spectral library was constructed through the Spectronaut software, and the self-built spectral library of human serum without removing high-abundance components was integrated as the database for subsequent data-independent acquisition (DIA) quantification. The threshold of peptide mass error was set to ±10 ppm to ensure the mass accuracy of the mass spectrometry; the screening criteria for library searching and qualitative analysis were set as peptide FDR ≤ 0.01 and protein FDR ≤ 0.01.

[0049] 2) Statistical analysis of DIA protein qualitative and quantitative results: For each sample in the project, sample preparation was carried out independently. After protein enzymatic digestion, DIA analysis was performed on the machine separately. The obtained original DIA mass spectrometry files were imported into Spectronaut for analysis, and the Q value <= 0.01 was used as the screening parameter.

[0050] 3) To ensure the effectiveness and accuracy of subsequent bioinformatics and statistical analyses, according to general principles, in the protein identification table, first, sample experimental data were screened to ensure that at least 50% of the data in the samples corresponding to the identified proteins had no null values. Then, the remaining null values were filled with data, and the data were standardized using the standardization function of the database search software. The standardized DIA-MS data were used for subsequent analysis.

[0051] 1.2 Experimental results

[0052] A total of 737 proteins were detected by DIA-MS. All the original intensity data obtained by DIA-MS were subjected to univariate Cox regression analysis (uni-Cox) after log2 transformation. The results showed that 99 plasma proteins were significantly correlated with the progression-free survival (PFS) of ALK-positive NSCLC patients after receiving crizotinib treatment (p < 0.05, Table 1).

[0053] Table 1 99 efficacy-related proteins identified by univariate Cox regression analysis

[0054]

[0055]

[0056]

[0057] Next, 59 baseline sample cohorts were randomly divided into a training set (n = 30) and a validation set (n = 29) at a ratio of 1:1. In the training set, 99 prognosis-related proteins were included in the LASSO analysis to screen for key prognosis proteins ( Figure 1 ). Finally, five proteins, kallistatin (SERPINA4), attractin (ATRN), apolipoprotein A-IV (APOA4), serotransferrin (TF), and MYOC (myocilin), were used to construct a multivariate Cox regression prognosis model. The C-index of this prognosis model was 0.812. Since the proteins included in the scoring were all protective markers ( Figure 2a), Calculate the prognostic scores of each sample based on the expression levels of 5 proteins and the absolute values of the corresponding regression coefficients β. The formula for the prognostic score is: Prognostic score = (exp(SERPINA4) * 0.43 + exp(ATRN) * 0.62 + exp(APOA4) * 0.21 + exp(TF) * 0.66 + exp(MYOC) * 0.18). According to the median, the cut-off value is determined, and 30 patients in the training set are divided into a high-score group (n = 15) and a low-score group (n = 15). The Kaplan-Meier curve shows that the PFS of patients in the low-score group is worse than that in the high-score group (HR = 4.01, 95% CI (1.69, 9.54), Figure 2 b), indicating that the prognostic score is positively correlated with the efficacy of the patients. The AUC of the time-dependent ROC can reach above 0.9 ( Figure 2 c).

[0058] In addition, to verify the predictive efficacy of the prognostic model, the prognostic scores of each sample in the validation set were calculated according to the above calculation formula. Using the median as the threshold, 29 patients were divided into a high-score group (n = 15) and a low-score group (n = 14). The Kaplan-Meier results further verified that the prognosis of low-score patients is worse than that of high-score patients (HR = 2.40, 95% CI (1.02 - 5.66), Figure 3 a). The AUCs of the time-dependent ROC curves at one year and two years are 0.74 and 0.75 respectively ( Figure 3 b)

[0059] Therefore, the DIA-MS results indicate that the baseline expression levels of plasma proteins SERPINA4, ATRN, APOA4, TF, and MYOC, as well as the prognostic scoring model constructed by the combination of 5 plasma proteins, have a predictive effect on the progression-free survival (PFS) of patients treated with ALK-TKI.

[0060] Example 2. Screening and verification of single plasma protein markers

[0061] As described in Example 1, a total of 737 proteins were detected by DIA-MS in the baseline plasma samples of 59 ALK-positive NSCLC patients treated with crizotinib. Among them, 99 proteins were significantly correlated with the progression-free survival (PFS) of ALK-positive NSCLC patients after treatment with crizotinib as shown by univariate Cox regression analysis (uni-Cox). Since many proteins lack corresponding ELISA quantitative detection kits, only the prognostic efficacy of some proteins was verified by ELISA. In the ELISA verification stage, baseline plasma samples of 52 ALK-positive NSCLC patients treated with CT-707 were collected for independent verification. The specific experimental process is as follows:

[0062] 2.1 ELISA procedure

[0063] According to the kit instructions, verify candidate protein markers in an independent cohort by ELISA technology: CRP (C-reactive protein, CUSABIO, CSB-E08617h), AHSG (α-2-HS-glycoprotein, CUSABIO, CSB-E12882h), SAA1 (serum amyloid A1, Abcam, ab100635), SERPINA4 (kallistatin, CUSABIO, CSB-EL021060HU), TF (serum transferrin, CUSABIO, CSB-E13093h).

[0064] Take the CRP detection as an example:

[0065] 1) Sample dilution

[0066] Normal human serum and plasma samples are diluted 1:1000 times with sample diluent before detection. The specific operation is as follows: Take 5 μl of the sample and add it to 95 μl of sample diluent (1:20 dilution), and mix well. Then take 5 μl from the above diluent and add it to 245 μl of sample diluent (1:50 dilution), and mix well. The sample after two-step dilution is the sample diluted 1:1000 times.

[0067] 2) Reagent preparation

[0068] Standard product: Take out a standard product from the kit and centrifuge it at 6000 - 10000 rpm for 30 seconds. Dissolve it with 1 ml of sample diluent, and repeatedly pipette it 5 times with the pipette tip against the bottom of the cryopreservation tube to assist dissolution, and mix well to obtain standard product S7, and set it aside for use. Take 7 1.5-ml centrifuge tubes (S0 - S6) and arrange them in sequence, and add 250 μl of sample diluent to each. Pipette 250 μl of standard product S7 into the first centrifuge tube (S6), and gently pipette and mix well. Pipette 250 μl from S6 into the second EP tube (S5), and gently pipette and mix well. And so on for the serial dilution of the standard product. S0 is the sample diluent.

[0069] Washing solution working solution: Dilute the concentrated washing solution 1:25 times with deionized water. For example, measure 240 ml of deionized water with a measuring cylinder, pour it into a beaker or other clean container, then measure 10 ml of concentrated washing solution, add it evenly, stir and mix well, and prepare it just before use. Salt will precipitate when the concentrated washing solution is stored at low temperature. It can be heated in a water bath to assist dissolution during dilution.

[0070] HRP-labeled working solution: Dilute the HRP-labeled reagent 100-fold with the HRP-labeled reagent diluent. For example, add 10 μl of the HRP-labeled reagent to 990 μl of the HRP-labeled reagent diluent, mix gently, and prepare it within 10 minutes before use.

[0071] 3) Transfer various reagents to room temperature (18 - 25 °C) and equilibrate for at least 30 minutes. Prepare the reagents according to the aforementioned method and set aside for use.

[0072] 4) Sample addition: Set up standard wells and wells for test samples respectively. Add 100 μl of the standard or test sample to each well. Mix gently by shaking, cover with a plate sticker, and incubate at 37 °C for 60 minutes.

[0073] 5) Discard the liquid in the wells, spin dry, and wash the plate 3 times. Immerse for 2 minutes each time, 200 μl per well, and spin dry.

[0074] 6) Add 100 μl of the HRP-labeled working solution to each well. Mix gently by shaking, cover with a plate sticker, and incubate at 37 °C for 60 minutes.

[0075] 7) Discard the liquid in the wells, spin dry, and wash the plate 5 times. Immerse for 2 minutes each time, 200 μl per well, and spin dry.

[0076] 8) Sequentially add 90 μl of the substrate solution to each well and develop color at 37 °C in the dark for 20 minutes.

[0077] 9) Sequentially add 50 μl of the stop solution to each well to terminate the reaction.

[0078] 10) Measure the optical density (OD value) of each well at a wavelength of 450 nm using an ELISA reader within 5 minutes after the reaction is terminated. Plot the standard curve and calculate the concentration of the test sample.

[0079] 2.2 Experimental results

[0080] Statistical analysis was performed on the ELISA test results of 52 patients, and the results are shown in Table 2.

[0081] Table 2 Plasma protein content of 52 patients

[0082]

[0083]

[0084] The optimal threshold for each protein was determined using the function "surv_cutpoint" in the R package "survminer", and the patients were divided into high-expression and low-expression groups. Finally, the baseline expression levels of 5 candidate genes were confirmed to be related to the prognosis of the patients. Kaplan-Meier survival curve analysis showed that the baseline expression levels of 5 plasma proteins, CRP, SAA1, AHSG, SERPINA4, and TF, could effectively distinguish the PFS of the patients ( Figure 4 ). The PFS of patients with CRP > 15.72 μg / ml or SAA1 > 592.4 ng / ml was worse than that of patients with low expression of CRP or SAA1, while patients with AHSG > 538 μg / ml, SERPINA4 > 12.5 μg / ml, or TF > 44 μmol / l had a better clinical response to CT-707 than the low-expression group. Baseline plasma levels of CRP, SAA1, AHSG, SERPINA4, and TF have the potential to be prognostic biomarkers for predicting the response to ALK-TKI treatment.

[0085] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, and these simple modifications all fall within the protection scope of the present invention.

Claims

1. Plasma proteins for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, wherein the plasma proteins are a combination of SERPINA4, ATRN, APOA4, TF and MYOC or a combination of SERPINA4, TF, CRP, SAA1 and AHSG.

2. Use of a detection reagent for plasma proteins in the preparation of a kit for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, wherein the plasma proteins are a combination of SERPINA4, ATRN, APOA4, TF and MYOC or a combination of SERPINA4, TF, CRP, SAA1 and AHSG.

3. A kit for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, wherein the kit contains a detection reagent for plasma proteins, and the plasma proteins are a combination of SERPINA4, ATRN, APOA4, TF and MYOC or a combination of SERPINA4, TF, CRP, SAA1 and AHSG.

4. A system for evaluating the prognosis of targeted therapy for EML4-ALK positive non-small cell lung cancer, the system comprising: an acquisition module for acquiring a sample from a subject; and an evaluation module connected to the acquisition module for detecting plasma proteins in the sample, and the plasma proteins are a combination of SERPINA4, ATRN, APOA4, TF and MYOC or a combination of SERPINA4, TF, CRP, SAA1 and AHSG.

5. The use according to claim 2 or the kit according to claim 3, wherein the detection reagent for plasma proteins comprises a reagent capable of quantitatively detecting plasma proteins.

6. The use according to claim 2 or the kit according to claim 3, wherein the detection reagent for plasma proteins comprises a substance capable of specifically binding to plasma proteins.

7. The use according to claim 2 or the kit according to claim 3, wherein the detection reagent for plasma proteins comprises a specific antibody.

8. The system according to claim 4, wherein the detection of plasma proteins in the sample is performed by mass spectrometry and / or ELISA.

9. The plasma proteins according to claim 1, the use according to claim 2, the kit according to claim 3 or the system according to claim 4, wherein the targeted therapy includes ALK tyrosine kinase inhibitor (ALK-TKI) drug therapy.

10. The plasma proteins, use, kit or system according to claim 9, wherein the ALK tyrosine kinase inhibitor (ALK-TKI) drug therapy includes crizotinib, ceritinib, alectinib, brigatinib, Conteltinib (CT-707) and lorlatinib.

11. The application according to claim 2, the kit according to claim 3 or the system according to claim 4, wherein the detection of the plasma protein is carried out by ELISA, and the plasma protein is a combination of CRP, SAA1, AHSG, SERPINA4 and TF.

12. The system according to claim 4, wherein the detection of the plasma protein is carried out by mass spectrometry, and the plasma protein is a combination of SERPINA4, ATRN, APOA4, TF and MYOC.

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