WO2023082820A1 - Marqueur pour le diagnostic d'un adénocarcinome pulmonaire et son application - Google Patents

Marqueur pour le diagnostic d'un adénocarcinome pulmonaire et son application Download PDF

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WO2023082820A1
WO2023082820A1 PCT/CN2022/118708 CN2022118708W WO2023082820A1 WO 2023082820 A1 WO2023082820 A1 WO 2023082820A1 CN 2022118708 W CN2022118708 W CN 2022118708W WO 2023082820 A1 WO2023082820 A1 WO 2023082820A1
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lung adenocarcinoma
phase
metabolites
diagnosing
metabolic
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娄加陶
王琳
郭巧梅
乔理华
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上海市第一人民医院
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/04—Preparation or injection of sample to be analysed
    • G01N30/06—Preparation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/26—Conditioning of the fluid carrier; Flow patterns
    • G01N30/28—Control of physical parameters of the fluid carrier
    • G01N30/34—Control of physical parameters of the fluid carrier of fluid composition, e.g. gradient
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G01N30/7233—Mass spectrometers interfaced to liquid or supercritical fluid chromatograph
    • G01N30/724—Nebulising, aerosol formation or ionisation
    • G01N30/7266—Nebulising, aerosol formation or ionisation by electric field, e.g. electrospray
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry

Definitions

  • the invention relates to the technical field of detection and diagnosis, in particular to a marker for lung adenocarcinoma diagnosis and its screening method and application.
  • lung cancer is mainly divided into two types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).
  • NSCLC non-small cell lung cancer
  • SCLC small cell lung cancer
  • the proportion of non-small cell lung cancer is as high as 85% to 90%.
  • NSCLC further includes lung adenocarcinoma, lung squamous cell carcinoma, and large cell carcinoma. Compared with small cell carcinoma, the growth and division of cancer cells are slower, and the spread and metastasis are relatively late.
  • the most common subtype of lung cancer is lung adenocarcinoma.
  • Clinically, non-small cell lung cancer is often diagnosed at an advanced stage. More than half of NSCLC patients die within 1 year after diagnosis, and the 5-year survival rate is less than 20%. However, the 5-year survival rate of patients with early lung cancer can be as high as 90%. Therefore, early diagnosis of lung cancer is an important method for lung cancer patients to obtain a good prognosis and reduce mortality.
  • the means of clinical diagnosis of lung cancer mainly rely on ultrasound imaging and lung puncture.
  • the sensitivity of ultrasound diagnosis is low, and lung puncture is harmful to the lungs of patients, which is risky and difficult to promote.
  • many patients are not diagnosed until the decompensated stage of lung cancer.
  • gene molecules can be used as markers for the diagnosis of lung cancer, but the sensitivity and specificity of single gene diagnosis need to be improved urgently.
  • LDCT low-dose computed tomography
  • Metabolomics is an emerging discipline after genomics and proteomics, and is an important part of systems biology. Metabolomics has developed and rapidly penetrated into many fields, and its purpose is to study the overall metabolic differences in biological systems by monitoring the levels of small molecule metabolites in biological fluids or tissues, and to find the relative relationship between metabolites and pathophysiological changes. The occurrence of tumors is bound to be accompanied by metabolic changes, but in the early stages, the changes of small molecule metabolites are very weak and not easy to be found (Pei-Hsuan, C., Ling, C., Kenneth, H. et al. Metabolic diversity in human non-small cell lung cancer cells. Molecular Cell. 2019, 76, 1-14.
  • Lung cancer diagnostic biomarkers such as Mathe, E.A., Patterson, A.D., Haznadar, M. et al.
  • Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res. 2014, 74:3259-3270. William, R.W.
  • the metabolic marker obtained through screening has great clinical application value, and is especially suitable for the prediction and diagnosis of early lung cancer.
  • the present invention adopts following technical scheme:
  • the present invention provides a marker for diagnosing or monitoring lung adenocarcinoma, wherein the metabolic marker is at least selected from D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L - at least one of proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine, kynuric acid.
  • the metabolic marker is at least selected from D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L - at least one of proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine, kynuric acid.
  • the present invention provides a marker for diagnosing or monitoring lung adenocarcinoma
  • the combination of metabolic markers is at least selected from D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy- At least one of L-proline, hexadecanedioic acid, and guanine.
  • the marker combination is also selected from at least one of glutamic acid, creatine, alanine, and kynuric acid.
  • the AUC value of the area under the ROC curve of a single metabolic marker in the present invention is 0.7-0.9.
  • the performance of multiple metabolite groups is significantly better than that of a single metabolite, and the area under the ROC curve AUC value is 0.86-0.99, which can effectively diagnose patients with early lung adenocarcinoma.
  • the present invention also provides a reagent product or kit, including the above-mentioned standard product of metabolic markers for early diagnosis or monitoring of lung adenocarcinoma.
  • the reagent product or kit also includes solvents and/or internal standards for extracting and enriching the metabolic markers.
  • the present invention also provides a method for screening metabolic markers for diagnosing or monitoring lung adenocarcinoma, comprising the following steps:
  • the samples of the lung adenocarcinoma group and the serum samples of the healthy group were collected separately, and the TNM staging of the patients in the lung adenocarcinoma group included stage I, stage II, stage III, and stage IV;
  • the screening criteria with a P value less than 0.05 were used to obtain candidate differential metabolites; the candidate differential
  • the 10 metabolic markers screened by the present invention can effectively diagnose lung adenocarcinoma patients.
  • the invention can realize the diagnosis of lung adenocarcinoma only by taking blood for detection, without additional collection of tissue samples, can well replace the existing tissue biopsy and imaging diagnosis modes, and reduce trauma and radiation risks.
  • Fig. 1 is an S-plot diagram of the metabolite OPLS-DA provided in Example 1 of the present invention.
  • the "local standard substance database” in the present invention refers to the mass spectrometric detection of a large number of related metabolite molecular standards in the present invention, and the collection of mass spectrometry information of these metabolites, thereby forming a localized standard substance database.
  • This embodiment provides a method for screening metabolic markers for lung adenocarcinoma diagnosis, comprising the following steps:
  • peripheral vein samples from 242 patients with lung adenocarcinoma (lung adenocarcinoma group, including 172 patients with early lung adenocarcinoma) and 150 healthy people (healthy group) were collected from Shanghai Chest Hospital. blood serum.
  • lung adenocarcinoma group including 172 patients with early lung adenocarcinoma
  • healthy people healthy group
  • the diagnostic standard for patients with lung adenocarcinoma is confirmed by postoperative pathology.
  • TNM staging patients with stage I and II lung adenocarcinoma were defined as patients with early-stage lung adenocarcinoma.
  • step S1 Take out the sample collected in step S1 from the -80°C refrigerator, and thaw it on ice until there are no ice cubes in the sample (subsequent operations are required to be carried out on ice);
  • Into a numbered centrifuge tube add 300 ⁇ L pure methanol internal standard extraction solution (containing 100 ppm L-phenylalanine internal standard); vortex for 5 min, let stand for 24 h, and then centrifuge at 12000 r/min, 4 °C for 10 min ; Take 270 ⁇ L of the supernatant and concentrate for 24 hours; then add 100 ⁇ L of acetonitrile and water in a complex solution with a volume ratio of 1:1 for LC-MS/MS analysis. 20 ⁇ L of each sample was mixed to form a quality control sample (QC), which was collected every 15 samples.
  • QC quality control sample
  • Chromatographic column Waters ACQUITY UPLC HSS T3 C18 1.8 ⁇ m, 2.1mm*100mm; column temperature is 40°C; injection volume is 2 ⁇ L.
  • Phase A is an aqueous solution containing 0.1% acetic acid
  • phase B is an acetonitrile solution containing 0.1% acetic acid.
  • the elution gradient program is: 0min, the volume ratio of phase A to B is 95:5; 11.0min, the volume ratio of phase A to B is 10:90; 12.0min, the volume ratio of phase A to B is 10 :90; 12.1min, the volume ratio of A phase and B phase is 95:5; 14.0min, the volume ratio of A phase and B phase is 95:5V/V.
  • Flow rate 0.4mL/min.
  • electrospray ionization temperature 500°C
  • mass spectrometer voltage 5500V (positive) or -4500V (negative)
  • ion source gas I GS I
  • gas II GS II
  • gas curtain Gas curtain gas, CUR
  • collision-induced ionization collision-activated dissociation, CAD
  • each ion pair is scanned in MRM mode according to the optimized declustering potential (DP) and collision energy (collision energy, CE).
  • DP declustering potential
  • CE collision energy
  • the samples were analyzed and detected according to the determined liquid chromatography conditions and mass spectrometry conditions: 20% of the samples in the lung adenocarcinoma group and the healthy group were randomly selected, and enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight mass spectrometry (TOF) were used.
  • MIM-EPI enhanced ion scanning mass spectrometry
  • TOF time-of-flight mass spectrometry
  • the local standard database was integrated to construct the lung adenocarcinoma serum metabolite database.
  • the collected serum samples were analyzed to obtain the original mass spectrometry data of each serum sample.
  • the metabolites of the samples were qualitatively and quantitatively analyzed by mass spectrometry. Metabolites of different molecular weights can be separated by liquid chromatography. The characteristic ions of each substance are screened out by the multiple reaction monitoring mode (MRM) of the triple quadrupole, and the signal intensity (CPS) of the characteristic ions is obtained in the detector.
  • MRM multiple reaction monitoring mode
  • CPS signal intensity
  • Use MultiQuant software to open the mass spectrum file of the sample off-machine, preprocess and correct the original mass spectrum data according to the mass-to-charge ratio and retention time, and perform the integration and calibration of the chromatographic peaks.
  • the peak area (Area) of each chromatographic peak represents the corresponding substance.
  • the repeatability of metabolite extraction and detection can be judged by overlaying and displaying the total ion chromatograms (TIC charts) of different quality control QC samples for mass spectrometry detection and analysis, that is, technical repetition.
  • the high stability of the instrument provides an important guarantee for the repeatability and reliability of the data.
  • the CV value is the coefficient of variation (Coefficient of Variation), which is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of dispersion of the data.
  • the Empirical Cumulative Distribution Function (ECDF) can be used to analyze the frequency of the CV of substances less than the reference value.
  • the proportion of substances with 0.5 is higher than 85%, indicating that the experimental data is relatively stable; the proportion of substances with QC sample CV value less than 0.3 is higher than 75%, indicating that the experimental data is very stable.
  • the change of the CV value of the internal standard L-phenylalanine during the detection process was monitored, and the change of the CV value of the internal standard was less than 20%, indicating that the instrument was stable during the detection process.
  • the peak area integration data were imported into SIMCA software (Version 14.1, Sweden) for multivariate statistical analysis.
  • OPLS-DA orthogonal-partial least squares discriminant
  • Ten differential metabolites screened by binary logistic regression forward stepwise method can diagnose and distinguish lung adenocarcinoma patients from healthy people: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy -L-proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine, kynuric acid. See Table 3 to Table 5 for specific information on metabolites:
  • ROC receiver operating characteristic curve
  • Table 7 The AUC value of any differential metabolite combined for the diagnosis of lung adenocarcinoma
  • Example metabolic marker combinations are as follows:
  • a further preferred combination of metabolic markers is: D-galactose, homocitrulline, and N6-acetyl-L-lysine to construct a model for diagnosing lung adenocarcinoma.
  • the AUC value of these three metabolites combined for the diagnosis of lung adenocarcinoma reached 0.925. Under the optimal cutoff value, the sensitivity and specificity were 90.8% and 90.3%, respectively.
  • a further preferred combination of metabolic markers is: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, and guanine to construct a diagnostic lung gland cancer model.
  • the AUC value of these 6 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.956. Under the optimal cutoff value, the sensitivity and specificity were 94.1% and 93.3%, respectively.
  • Table 8 The AUC value of a single metabolite for the diagnosis of early lung adenocarcinoma
  • Example metabolic marker combinations are as follows:
  • a further preferred combination of metabolic markers is: D-galactose, homocitrulline, and N6-acetyl-L-lysine to construct a model for diagnosing early lung adenocarcinoma.
  • the AUC value of these three metabolites combined to diagnose early lung adenocarcinoma reached 0.925. Under the optimal cutoff value, the sensitivity and specificity were 90.8% and 90.3%, respectively.
  • a further preferred combination of metabolic markers is: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, guanine, constructing an early diagnosis Lung adenocarcinoma model.
  • the combined AUC value of these 6 metabolites in the diagnosis of early lung adenocarcinoma reached 0.956. Under the optimal cutoff value, the sensitivity and specificity were 94.1% and 93.3%, respectively.
  • Example 2 Validation of diagnostic markers for lung adenocarcinoma
  • the subjects of this study included 266 serum samples of patients with lung adenocarcinoma from 2 independent medical centers, including 118 patients with early lung adenocarcinoma; and 149 serum samples of healthy people, which were from the same source as the feature screening samples (150 cases) .
  • the diagnostic standard of lung adenocarcinoma is lung adenocarcinoma diagnosed by postoperative pathology; healthy people are healthy people without lung diseases after physical examination. All lung adenocarcinoma patients and healthy samples had no history of any other malignant tumors, no other major systemic diseases, and no chronic medical history of long-term medication.
  • patients with stage I and II lung adenocarcinoma were defined as patients with early-stage lung adenocarcinoma.
  • the detection and data analysis methods of this example are the same as those of Example 1, and the differential metabolites detected and analyzed are the following 10 kinds: D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy - L-proline, hexadecanedioic acid, guanine, glutamic acid, creatine, alanine, kynuric acid for the diagnosis of lung adenocarcinoma.
  • These 10 differential metabolites are individually used to diagnose and distinguish between patients with lung adenocarcinoma and healthy people.
  • the ability of early lung adenocarcinoma patients and healthy people is relatively strong, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance;
  • AUC area under the ROC curve
  • the AUC is further improved, and the AUC value of 10 joint diagnosis of lung adenocarcinoma reaches 0.955.
  • the sensitivity and specificity are 91.5% and 93.6%, respectively; the diagnosis of early lung adenocarcinoma
  • the AUC of cancer was 0.983, and the sensitivity and specificity were 93.2% and 96.0% at the best cutoff value. See Table 11 to Table 14 for the AUC of a single and any combination of 2 to 9 metabolites used for diagnosis:
  • Table 12 The AUC value of any differential metabolite combined for the diagnosis of lung adenocarcinoma
  • D-galactose D-galactose, homocitrulline, N6-acetyl-L-lysine, 4-hydroxy-L-proline, hexadecanedioic acid, and guanine were used to construct a model for diagnosing lung adenocarcinoma.
  • the AUC value of these 6 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.931. Under the optimal cutoff value, the sensitivity and specificity were 90.2% and 91.4%, respectively.
  • Table 13 The AUC value of a single metabolite for the diagnosis of early lung adenocarcinoma
  • Table 14 The AUC value of any differential metabolite combined for the diagnosis of early lung adenocarcinoma
  • Metabolic markers D-galactose, homocitrulline, and N6-acetyl-L-lysine were further optimized to construct a model for diagnosing early lung adenocarcinoma.
  • the AUC value of these three metabolites combined to diagnose early lung adenocarcinoma reached 0.916. Under the optimal cutoff value, the sensitivity and specificity were 89.3% and 90.1%, respectively.
  • This embodiment provides a detection kit, comprising:
  • 50% acetonitrile in water can be used as a solvent to dissolve the standards.

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Abstract

La présente invention concerne un marqueur métabolique pour le diagnostic ou la surveillance d'un adénocarcinome pulmonaire, un procédé de dépistage associé et une application de celui-ci. Le système de marqueur métabolique est choisi parmi une ou plusieurs combinaison(s) de D-galactose, d'homocitrulline, de N6-acétyl-L-lysine, de 4-hydroxy-L-proline, d'acide hexadécanedioïque, de guanine, d'acide glutamique, de créatine, d'alanine et d'acide kynurénique. Le marqueur métabolique selon la présente invention peut diagnostiquer avec précision des patients atteints d'un adénocarcinome pulmonaire, peut distinguer des patients atteints d'un adénocarcinome pulmonaire précoce de personnes en bonne santé, présente une sensibilité élevée et une forte spécificité, peut bien remplacer les modes de diagnostic de biopsie tissulaire et d'imagerie existants, et réduit les risques traumatiques et radiologiques.
PCT/CN2022/118708 2021-11-09 2022-09-14 Marqueur pour le diagnostic d'un adénocarcinome pulmonaire et son application WO2023082820A1 (fr)

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