CN115326938A - Biomarker for predicting lung cancer immunotherapy curative effect and application thereof - Google Patents
Biomarker for predicting lung cancer immunotherapy curative effect and application thereof Download PDFInfo
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Abstract
The invention provides a biomarker for predicting the curative effect of immunotherapy of lung cancer and application thereof. Specifically, the invention can predict the curative effect of the lung cancer immunotherapy by detecting the concentration of the biomarker in the blood plasma after the lung cancer immunotherapy, has the characteristics of non-invasiveness, low cost and easy operation, and has important clinical value and significance for optimizing the treatment scheme and the curative effect detection of individual patients.
Description
Technical Field
The invention relates to the field of biomedicine, and more particularly relates to a biomarker for predicting lung cancer immunotherapy curative effect and application thereof.
Background
Lung cancer is the most serious malignant tumor with the highest incidence and mortality rate worldwide. According to cancer data published by the global cancer statistics report 2018, it is shown that lung cancer ranks first in tumors in both predicted new and dead cases of 2018, which is a significant public health problem. More than 80% of lung cancers belong to non-small cell lung cancer (NSCLC).
In recent years, the breakthrough of the immune checkpoint (such as CTLA-4, PD-1/PD-L1) inhibitor as a new strategy of tumor immunotherapy in clinical treatment has been achieved, the core of the breakthrough is that the inhibitor plays a role in inhibiting tumor growth by activating the anti-tumor immune response of a patient, and the excellent clinical curative effect of the inhibitor is expected to become one of the therapies which finally overcome cancers. FDA has approved the clinical use of immune checkpoint inhibitors in melanoma, lung and bladder cancer. However, current clinical research results show that the effective rate of the immune checkpoint inhibitor in patients with clinical audience is greatly different, and the total curative effect of the immune checkpoint inhibitor for treating lung cancer is only 25-30%. But once effective, long lasting with significant individual variation. The high treatment costs of immune checkpoint inhibitors also require effective indicators to identify suitable populations, thereby reducing the personal and social burden. It follows that the use of immune checkpoint inhibitors in the treatment of lung cancer still lacks effective biomarkers that guide clinical use.
Disclosure of Invention
The invention aims to provide a biomarker for predicting lung cancer immunotherapy curative effect and application thereof.
In a first aspect of the present invention, there is provided a use of a biomarker panel or a detection reagent thereof for the preparation of a kit for the prognostic evaluation of lung cancer immunotherapy, wherein the panel comprises biomarkers selected from the group consisting of: hypoxanthine, histidine, threonine, or a combination thereof.
In another preferred embodiment, the immunotherapy for lung cancer comprises treatment with a PD-1 inhibitor.
In another preferred embodiment, the PD-1 inhibitor comprises nivolumab.
In another preferred embodiment, the immunotherapy for lung cancer comprises: receive PD-1 inhibitor (nivolumab) as second or third line monotherapy.
In another preferred embodiment, said evaluating comprises the steps of:
(1) Providing a sample derived from a subject, and detecting the level of one or more biomarkers in said collection in the sample;
(2) Comparing the level measured in step (1) with a reference data set or a reference value.
In another preferred embodiment, said reference data set comprises the levels of individual biomarkers in said set derived from patients with an effective lung cancer immunotherapy and patients with an ineffective lung cancer immunotherapy.
In another preferred embodiment, the sample is selected from the group consisting of: blood, plasma, and serum.
In another preferred embodiment, the sample is a sample taken 10 to 20 days, preferably 13 to 15 days, more preferably 14 days after the first immunotherapy.
In another preferred embodiment, the detection is a mass spectrometry-based detection, and preferably, the data obtained from the detection is subjected to non-targeted metabolomic analysis.
In another preferred example, the prognostic assessment comprises predicting disease progression free survival and/or overall survival of the cancer.
In another preferred example, the prognostic assessment further comprises predicting the risk of recurrence of the cancer.
In another preferred embodiment, any one member of the set of biomarkers includes its nucleic acid form (e.g. DNA, mRNA), and/or protein form.
In another preferred embodiment, the set of biomarkers comprises one, two or three biomarkers.
In another preferred example, the detection reagent is a reagent for detecting the expression level of the biomarker panel.
In another preferred embodiment, the biomarkers in the kit are used as standards.
In another preferred embodiment, the kit is used for detecting blood samples.
In another preferred embodiment, the kit further comprises an instruction, and the instruction describes a method for prognosis evaluation.
In another preferred embodiment, the expression level of the biomarker is detected by mass spectrometry detection, PCR detection, expression profiling chip detection or high-throughput sequencing.
In a second aspect of the invention, there is provided a set of biomarkers comprising biomarkers selected from the group consisting of: hypoxanthine, histidine, threonine, or a combination thereof.
In another preferred embodiment, the set of biomarkers is derived from a blood sample.
In another preferred embodiment, the biomarker panel is used for prognostic assessment of lung cancer immunotherapy.
In a third aspect of the invention, there is provided a combination of reagents for prognostic assessment of immunotherapy of lung cancer, the combination of reagents comprising reagents for detecting each biomarker in a collection according to the second aspect of the invention.
In another preferred embodiment, the reagents comprise reagents for detecting the expression level of each of the biomarkers.
In another preferred embodiment, the reagent comprises a reagent for detecting the expression level of each biomarker by mass spectrometry, PCR detection, expression profiling chip detection or high-throughput sequencing. .
In a fourth aspect of the invention, there is provided a kit comprising a set of biomarkers according to the second aspect of the invention and/or a combination of reagents according to the third aspect of the invention.
In another preferred embodiment, the kit further comprises an instruction, and the instruction describes a prognosis evaluation method.
In a fifth aspect of the invention, there is provided a method for prognostic evaluation of lung cancer immunotherapy, comprising the steps of:
(1) Providing a sample derived from a subject, and detecting the level of one or more biomarkers in the collection according to the second aspect of the invention in the sample;
(2) Comparing the level measured in step (1) with a reference data set or a reference value.
In another preferred example, the method further comprises the steps of: the expression cutoff (Ym) for each biomarker bm was determined.
In another preferred example, the method further comprises the steps of: and drawing a ROC curve of the biomarkers, and determining the expression cut-off value of the biomarkers based on the ROC curve.
In another preferred embodiment, the sample is a blood sample.
In another preferred embodiment, the expression level of one or more biomarkers selected from said set is significantly increased when compared to a reference data set, indicating a better prognosis for immunotherapy of lung cancer patients.
In another preferred example, the "significantly increased" means that the ratio C1/C0 of the expression level C1 of the biomarker in the lung cancer patient to the reference value C0 is more than or equal to 1.1, preferably more than or equal to 1.2, and more preferably more than or equal to 1.3.
In another preferred embodiment, the expression level of one or more biomarkers selected from subset N is significantly reduced when compared to a reference data set, indicating a lung cancer patient with a poorer prognosis for immunotherapy.
In another preferred embodiment, the "significantly reduced" means that the ratio C0/C1 of the reference value C0 to the expression level C1 of the biomarker of the lung cancer patient is more than or equal to 1.1, preferably more than or equal to 1.2, and more preferably more than or equal to 1.3.
In another preferred embodiment, the expression level of each biomarker is detected by mass spectrometry detection, PCR detection, expression profiling chip detection or high throughput sequencing.
It is to be understood that within the scope of the present invention, the above-described features of the present invention and those specifically described below (e.g., in the examples) may be combined with each other to form new or preferred embodiments. Not to be repeated herein, depending on the space.
Drawings
FIG. 1 shows the scatter distribution of 3 metabolic markers in different therapeutic groups of lung cancer immunotherapy, and the corresponding molecular formulas of the metabolites.
Figure 2 shows the receiver operating characteristic curves (ROC) for the 3 metabolic markers and combinations thereof.
FIG. 3 shows a Kaplan-Merier disease Progression Free Survival (PFS) analysis curve showing prognostic performance of metabolic markers applied separately or in combination.
FIG. 4 shows a Kaplan-Merier Overall Survival (OS) analysis curve showing prognostic performance of metabolic markers applied separately or in combination.
Detailed Description
The inventor of the present invention has conducted extensive and intensive studies and unexpectedly found a biomarker for predicting the curative effect of immunotherapy for lung cancer for the first time. The biomarkers provided by the invention can be used for predicting the curative effect independently or in combination. The invention can predict the curative effect of anti-PD-1 treatment by detecting the metabolite concentration in the blood plasma after the lung cancer immunotherapy, has the characteristics of non-invasiveness, low cost and easy operation, and has important clinical value and significance for optimizing the treatment scheme and curative effect detection of individual patients. The present invention has been completed based on this finding.
Specifically, the inventor carries out qualitative and quantitative detection on a metabolite spectrum of blood plasma of a patient based on a series of patient blood plasma samples (including a control) receiving immune checkpoint treatment (anti-PD-1) through a non-targeted metabonomics method, carries out systematic verification and discovery on a biomarker related to the curative effect of an immune checkpoint inhibitor from the perspective of the metabolite by combining a univariate statistical method and a multivariate statistical method, constructs a molecular marker related to clinical medication, curative effect prediction and prognosis, further verifies the molecular marker on a prospective sample, develops a practical technology, and strives to use the discovered biomarker for the curative effect prediction of clinical immunotherapy, and achieves the aim of accurate medical treatment of lung cancer.
More specifically, in order to analyze the metabolite spectrum related to the curative effect of immunotherapy of lung cancer patients, the invention is based on the high performance liquid chromatography detection of plasma samples after immunotherapy of 74 Chinese lung cancer patients and the analysis of non-targeted metabonomics, and is associated with the disease progression and clinical indexes of the patients for research. To improve the reliability of the analysis results, the 74 patients were divided into one finding set and two validation sets, including clinical study group and real world study group. Meanwhile, although the anti-PD-1 immunotherapy is adopted by the two people, two different PD-1 antibody preparations are involved, and the universality of the prediction of the curative effect of the PD-1 blocking treatment is ensured. The inventor identifies and verifies 3 metabolic markers related to the curative effect of immunotherapy of lung cancer patients by a statistical method combining univariate and multivariate. To visually assess the predictive power and accuracy of these 3 plasma metabolites, the inventors characterized them by a characteristic curve ("ROC" curve) and further calculated correlations with disease progression and overall patient survival duration.
By adopting the technical scheme, the invention develops a related metabolic marker function analysis method for the curative effect of the lung cancer immunotherapy. The clinical application of these biomarkers helps to optimize the diagnosis of the therapeutic effect of immunotherapy and can provide some valuable clues for the subsequent lung cancer treatment strategy.
Term(s) for
The terms used herein have meanings commonly understood by those of ordinary skill in the relevant art. However, for a better understanding of the present invention, some definitions and related terms are explained as follows:
according to the present invention, the term "biomarker panel" refers to one biomarker, or a combination of two or more biomarkers.
According to the present invention, the level of the marker substance is determined by its expression level.
According to the invention, the term "individual" refers to an animal, in particular a mammal, such as a primate, preferably a human.
According to the present invention, terms such as "a," "an," and "the" do not refer only to a singular entity, but also include the general class that may be used to describe a particular embodiment.
As used herein, the term "about" when used in reference to a specifically recited value means that the value may vary by no more than 1% from the recited value. For example, as used herein, the expression "about 100" includes 99 and 101 and all values in between (e.g., 99.1, 99.2, 99.3, 99.4, etc.).
As used herein, the term "comprising" or "includes" can be open, semi-closed, and closed. In other words, the term also includes "consisting essentially of or" consisting of 823030A ".
It should be noted that the explanation of the terms provided herein is only for the purpose of better understanding the present invention by those skilled in the art, and is not intended to limit the present invention.
According to the present invention, the term "sample" or "specimen" refers to a material specifically associated with a subject from which specific information about the subject can be determined, calculated or inferred. The sample may be composed in whole or in part of biological material from the subject.
According to the present invention, the reference set refers to a training set.
According to the present invention, the training set and the validation set have the same meaning, as is known from the prior art. In one embodiment of the invention, a training set refers to a set of marker levels from biological samples from patients with effective lung cancer immunotherapy and patients with ineffective lung cancer immunotherapy. In one embodiment of the invention, a validation set refers to a data set used to test the performance of a training set. In one embodiment of the invention, the level of the marker may be represented as an absolute value or a relative value according to the method of determination. For example, when the level of the marker is determined by mass spectrometry, the intensity of the peak may represent the level of the marker, which is a relative value level; when PCR is used to determine the level of a marker, the copy number of a gene or copy number of a gene fragment may represent the level of the marker.
Biomarker panel
The invention provides a metabolic marker set for predicting the curative effect of lung cancer immunotherapy, which consists of three metabolites, namely hypoxanthine, histidine and threonine, wherein the curative effect of the immunotherapy of a subject is judged by detecting the concentration of the marker in the plasma of the subject.
In another preferred embodiment, the subject is tested for an early blood sample collected after administration of the PD-1 repressor.
In another preferred embodiment, the metabolic markers are mass spectrometry based detection, and non-targeted metabolomics based analysis methods.
In another preferred embodiment, the plasma concentrations of hypoxanthine, histidine and threonine are higher in the lung cancer patients in the anti-PD-1 treatment effective group, and are significantly higher in the lung cancer patients in the non-effective group.
ROC curve
The ROC curve is a short term of a characteristic curve (receiver operating characteristic curve) of a subject, is widely applied to the medical field, and is used for judging whether a certain factor has a diagnostic value for diagnosing a certain disease. The ROC graph reflects the relationship between sensitivity and specificity, the X axis of the abscissa is 1-specificity, also called false positive rate (false alarm rate), and the closer the X axis is to zero, the higher the accuracy rate is; the Y-axis on the ordinate is called sensitivity, also called true positive rate (sensitivity), with larger Y-axes representing better accuracy. The whole graph is divided into two parts according to the Curve position, the Area of the part below the Curve is called AUC (Area Under Current) and is used for representing the prediction accuracy, and the higher the AUC value, namely the larger the Area Under the Curve, the higher the prediction accuracy is. The closer the curve is to the upper left corner (the smaller X, the larger Y), the higher the prediction accuracy.
The main advantages of the present invention include:
(a) According to the application, metabolite spectrum detection is carried out on early plasma of a non-small cell lung cancer patient after anti-PD-1 treatment through a non-targeted metabonomics method, and metabolites such as hypoxanthine and the like are identified through a series of mass spectrometry and statistical analysis and can be used as biomarkers to predict the curative effect of immunotherapy. And the concentration of hypoxanthine has a significant correlation with clinical indicators of patients, including time to disease progression and overall survival rate.
(b) Although the prior art has retrospective research on biomarkers related to clinical efficacy of immune checkpoint inhibitors, there is no support of prospective clinical research data, and especially, research in chinese population has not been carried out in series. Compared with other existing detection methods, the method can realize noninvasive detection by detecting the metabolites through the blood plasma, and is convenient, rapid, economical and practical.
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Experimental procedures without specific conditions noted in the following examples, generally followed by conventional conditions, such as Sambrook et al, molecular cloning: conditions described in a Laboratory Manual (New York: cold Spring Harbor Laboratory Press, 1989), or according to the manufacturer's recommendations. Unless otherwise indicated, percentages and parts are percentages and parts by weight.
Universal materials and methods
The experimental reagents referred to in the examples include: HPLC grade ultrapure water, HPLC grade acetonitrile, HPLC grade methanol, ammonia water, ammonium acetate, 2-chloro-phenylalanine, ice, and the like, all of which are commercially available.
The instrument device involved in the embodiments comprises: the device comprises a centrifugal machine, a vortex oscillation instrument, a plasma collecting pipe, a centrifugal pipe, a chromatographic-mass spectrometer, an HPLC sample feeding bottle, a-80-degree refrigerator and a Feilomen amino column.
Plasma samples in the examples were obtained from lung cancer patients receiving immunotherapy (anti-PD-1) and were provided by the department of thoracic hospital of shanghai city.
The general experimental methods referred to in the examples include:
1. collection of plasma samples
1) Drawing appropriate amount of blood from venous blood vessel to a heparin lithium plasma collection tube (Greiner, cat. No. 455084)
2) The collected plasma was immediately centrifuged (4 ℃,3000g, 20min), and the supernatant was collected.
3) The supernatant was aliquoted (0.5 ml per tube), placed into centrifuge tubes (Greiner, cat. No. 122261/122263) and stored in a-80 ℃ freezer for future use.
2. Packet standard
After taking PD-1 immune suppressor, the patient is tested by CT or MRI regularly to diagnose the disease progress. If the disease symptoms of the patient are relieved or continuously stabilized within 6 months after the first administration, the patient is considered as an effective group patient; if the disease continues to progress within 6 months, it is divided into the null group.
3. Collecting a sample of a subject, and performing pretreatment
After blood samples of the subjects were collected according to a standard and reasonable procedure, they were immediately centrifuged at high speed (1500 g,10 minutes), and the supernatants were collected, dispensed and stored in a-80 ℃ freezer.
4. Treatment of plasma samples
1) Taking out plasma sample from refrigerator at-80 deg.C, and thawing on ice for 30-60min;
2) Place 100. Mu.l of plasma into a 2mL centrifuge tube (Fisher Scientific, cat. No. TUL-150-370W), add 100. Mu.l of internal standard solution (0.3 mg/mL 2-chloro-phenylalanine), then add 200. Mu.l of methanol HPLC grade, merck, cat # 1.06007.4000);
3) Configuring a QC sample: randomly selecting a group of plasma samples to be mixed into a tube, and processing the same as the above;
4) Preparing a blank sample: taking 100 mu l of 0.7% (wt/vol) NaCl, putting the NaCl into a 2ml centrifuge tube, adding 100 mu l of internal standard solution, and then adding 200 mu l of methanol;
5) Whirling the solution for 60s, and centrifuging at room temperature for 16000g 15min;
6) Mu.l of the supernatant was dispensed into two 2ml centrifuge tubes, 150. Mu.l of one (300. Mu.l of the other) and centrifuged again for 16000g 15min, and 120. Mu.l of the supernatant was used for LC-MS detection.
5. Detection of plasma samples
1) Preparing a chromatographic column mobile phase A, wherein the component is 20mM NH4Ac, and the pH is not less than 9.0; and (3) mobile phase B:100% of ACN; mobile phase 100mM NH4Ac, pH 6-7; mobile phase: 100% isopropanol.
2) Washing the Felmor amino column: (1) the HPLC was connected to a two-way valve, the flow rate was set at 2ml/min, the whole system was flushed with water and acetonitrile, respectively, for 5min, and the solution was let into waste. (2) Attach the column, first rinse with 80% H2O +20% ACN for 15min at a flow rate of 0.2 ml/min. (3) Then 80% of 4 Ac (100mM, pH 6-7) +20% ACN for 30min at a flow rate of 0.2 ml/min. (4) Wash with 15% A +85% B at a flow rate of 0.2ml/min for 60min.
3) Detection by chromatography-mass spectrometry: (1) the liquid phase portion used UPLC from waters: the sample injection volume is 10ul; the temperature of the chromatographic column is 15 ℃; the flow rate is 0.3ml/min; the gradient elution procedure was: 0min,85%;3min,30 percent; 12min,2%;15min,2%; 1695in, 85%;23min,85 percent. (2) Mass spectrometry section using Thermo's orbitrap combination mass spectrometry (Q active): the ion source is a heated electrospray ion source (ESI source) operating in either positive or negative mode. The main parameters are as follows: ionization voltage, +3.8kV/-3.8kV; sheath gas pressure, 35 arbitrary units; assist gas, 10 arbitrary units; the auxiliary gas heating temperature is 350 ℃; capillary temperature, 320 ℃; collision energy, 15-35eV. The mass spectrum acquisition range m/z is 70-1000, and the mass spectrum resolution is set to 70,000.
6. Statistical analysis
1) Processing mass spectrum data: (1) the MS raw data was converted to mzXML format using the ProteoWizard software. (2) Meanwhile, a mass spectrum data set of the sample is obtained through XCMS software, and comprises Retention Time (RT) of a metabolic characteristic peak, an m/z value and peak intensity. (3) The data were corrected by QC-RLSC machine learning method and metabolic peaks with RSD >20% were filtered out. (4) The total intensity of the peaks was used for normalization and normalized by the UV method.
2) The metabolites to which the metabolic signature peaks belong are annotated by identifying the primary and secondary spectra of the m/z and RT and the metabolic peaks using the Compound discovery software of Thermo corporation.
3) Identification of differential metabolites: (1) carrying out multi-parameter statistical analysis by SIMCA software, scoring each metabolite by an orthogonal bias least square discriminant analysis method, and screening possible differential metabolites by setting the standard of VIP > 1. (2) A single parameter statistical screening was performed for each metabolite and metabolites satisfying P <0.05 and FDR (false discovery rate) <0.05 were considered possible differential metabolites. (3) After the limited number of differential metabolites are obtained, a binary logistic regression model is constructed, and a possible biomarker model is established. (3) Predictive power of biomarkers was assessed by constructing a receiver characteristic curve (ROC).
4) Evaluation of clinical index: integrating the time to disease progression and overall survival of patients, analysis was performed using software SPSS, including: (1) carrying out univariate survival analysis by using Kaplan-Meier, and detecting the significance of the univariate survival analysis by using a Log Rank Test; (2) a Cox regression model was performed to assess the effect of clinical indices and metabolic markers on survival.
Example 1 screening of biomarkers
The experimental procedure was as follows:
1) The patient data is collated, and the early-stage blood sample of the lung cancer patient receiving the anti-PD-1 treatment is collected and is pretreated.
2) Metabolites in the blood sample were extracted and used for mass spectrometric detection.
3) And (3) identifying and identifying differential metabolites by combining multivariate and univariate statistical analysis methods, and constructing a model to determine the biomarkers.
As shown in FIG. 1, 3 biomarkers, i.e., hypoxanthine, histidine, threonine, were identified in this example, and the contents of the three amino acids were significantly different in both patients who received PD-1 treatment in the effective group and patients who did not receive PD-1 treatment in the ineffective group.
Example 2 the predictive power of biomarkers was evaluated.
The ability of the biomarkers to distinguish between different groups was assessed by constructing a receiver characteristic curve (ROC). ROC construction was performed according to a conventional method in the art.
The results are shown in fig. 2, and the ROC curves of the three biomarkers identified in this example all showed higher AUC values, indicating that they all could better distinguish between patients in the effective group and patients in the ineffective group; meanwhile, the combined AUC value of the three markers is obviously higher than that of a single marker, and better prediction capability is shown.
Example 3 correlation analysis of biomarkers
The concentration of the biomarker is analyzed for correlation with disease progression or patient survival, in conjunction with patient time to disease progression and overall survival data.
As shown in fig. 3 (disease progression-free survival) and fig. 4 (overall survival), the concentrations of the three biomarkers identified in this example all have significant correlation with disease progression and overall survival of patients, and the disease progression-free survival of patients with higher concentrations of the markers is significantly prolonged and the overall survival is significantly increased; meanwhile, the combination of the three markers has obvious synergistic effect, which indicates that the biomarkers have better prognostic performance.
All documents mentioned in this application are incorporated by reference in this application as if each were individually incorporated by reference. Furthermore, it should be understood that various changes and modifications of the present invention can be made by those skilled in the art after reading the above teachings of the present invention, and these equivalents also fall within the scope of the present invention as defined by the appended claims.
Claims (10)
1. Use of a set of biomarkers or detection reagents thereof for the preparation of a kit for the prognostic evaluation of immunotherapy of lung cancer, wherein said set comprises biomarkers selected from the group consisting of: hypoxanthine, histidine, threonine, or a combination thereof.
2. The use of claim 1, wherein the lung cancer immunotherapy comprises lung cancer immunotherapy with a PD-1 inhibitor.
3. The use of claim 1, wherein the prognostic assessment includes prediction of disease progression and/or overall survival of the cancer.
4. The use of claim 1, wherein the detection reagent is a reagent that detects the expression level of the set of biomarkers.
5. The use of claim 4, wherein the level of expression of a biomarker is detected by mass spectrometry detection, PCR detection, expression profiling chip detection, or high throughput sequencing.
6. The use of claim 1, wherein the biomarker in the kit is used as a standard.
7. The use of claim 1, wherein the kit is for the detection of a blood sample.
8. A set of biomarkers for prognostic evaluation of immunotherapy of lung cancer, wherein said set comprises biomarkers selected from the group consisting of: hypoxanthine, histidine, threonine, or a combination thereof.
9. A combination of reagents for prognostic assessment of immunotherapy for lung cancer, the combination of reagents comprising reagents for detecting each biomarker in the collection of claim 8.
10. A kit comprising a biomarker panel according to claim 8 and/or a combination of reagents according to claim 9.
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