CN111965236A

CN111965236A - Product, application and method for pheochromocytoma and paraganglioma related screening and evaluation

Info

Publication number: CN111965236A
Application number: CN201910419879.6A
Authority: CN
Inventors: 张华�; 郑杰; 钟晟
Original assignee: Shanghai Changtai Biotechnology Co ltd
Current assignee: Shanghai Changtai Biotechnology Co ltd
Priority date: 2019-05-20
Filing date: 2019-05-20
Publication date: 2020-11-20

Abstract

The invention provides a product, application and a method for relevant screening and evaluation of pheochromocytoma and paraganglioma, wherein body fluid samples of pheochromocytoma and paraganglioma patients and healthy people are mixed with nanosphere materials, metabolic molecule information in relevant body fluid is collected through matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS), and the metabolic molecule spectrum difference of the healthy people, the pheochromocytoma and paraganglioma patients is distinguished by using an artificial intelligent classification algorithm and is classified, so that the distinguishing effect of high accuracy, high specificity and high sensitivity is achieved, or the product becomes a tool for relevant screening and evaluation of the pheochromocytoma and paraganglioma.

Description

Product, application and method for pheochromocytoma and paraganglioma related screening and evaluation

Technical Field

The invention relates to the fields of biology and doctors, in particular to a product, application and a method for pheochromocytoma and paraganglioma related screening and evaluation.

Background

PPGL is a group of rare endocrine tumors originating in the neural crest, distributed mainly in the adrenal glands, or throughout the paraganglia of the whole body, about 90% of which are capable of producing and secreting catecholamines. For a long time, the diagnosis of PPGL has mainly relied on the detection of catecholamines, i.e. epinephrine, norepinephrine and dopamine, in the blood/urine, or vanillylmandelic acid (VMA) in the urine, and the coordination of several dynamic functional tests with poor sensitivity and specificity. The sensitivity of the above method is poor because catecholamines have a short half-life in blood and are rapidly metabolized, and the secretion of catecholamines by PPGL is often paroxysmal.

Disclosure of Invention

The invention aims to solve the technical problem of the prior art and provides a product, application and a method for relevant screening and evaluation of pheochromocytoma and paraganglioma.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

a product for pheochromocytoma and paraganglioma screening, early diagnosis, risk assessment, said product being an operable system, said product comprising:

(S1) the nanometer material used for selectively enhancing the resolution of the metabolic molecule of the measured body fluid, and the matrix-assisted laser desorption ionization time-of-flight mass spectrometer used for measuring the metabolic molecule signal of the body fluid are used for measuring different metabolic signals of the body fluids of patients with different sources and healthy people through a core artificial intelligence classification algorithm;

(S2) a module and/or processor for calculating the proportion of metabolic molecular composition and content of healthy humans to pheochromocytoma and paraganglioma;

(S3), optionally, a module and/or a processor for determining whether to suffer from pheochromocytoma and paraganglioma, or for assessing the risk of suffering from a tumor in a subject based on the composition and content of the metabolic molecules in the body fluid.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the body fluid is selected from serum, plasma, saliva and urine.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the reagent for determining body fluid metabolism in the step (S1) is a nano gold ball material, a matrix-assisted laser desorption ionization time-of-flight mass spectrum and a corresponding matched target plate.

The product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma further comprises one or more of the following components:

a. reagents and instruments for collecting and processing a sample of a subject's bodily fluid;

b. reagents and instruments for separating and purifying serum/or plasma/or saliva/or urine;

c. reagents and instruments for separating and purifying serum/plasma/saliva/urine;

d. reagents and instruments for separating, purifying, enriching serum/plasma/saliva/urine;

e. a database, module and/or processor for storing, processing metabolic molecular indicators of a subject's serum/plasma/saliva/urine;

f. a module and a processor for providing a decision threshold;

g. a module and a processor for comparing the metabolic molecular profile of the subject to a control to determine whether the subject suffers from pheochromocytoma and paraganglioma, for performing a prognostic assessment or risk assessment, disease monitoring or diagnostic assessment of suffering from pheochromocytoma and paraganglioma;

h. a module and processor for providing a diagnosis, test result report;

j. instructions or instructions for use in which one or more of the following applications and decisions are described:

(i) the kit is used for screening and detecting pheochromocytoma and paraganglioma: when all detected molecular weights are 100-3000 daltons and the ratio between the number of metabolic molecules with a signal-to-noise ratio greater than 2.5 reaches or is higher than a predetermined threshold, determining that the subject has or is at higher risk of developing pheochromocytoma and paraganglioma;

(ii) for judgment of health and malignancy: when the ratio of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 3.0 reaches or is higher than a preset threshold value, determining that the subject suffers from or is at risk of suffering from pheochromocytoma and paraganglioma;

(iii) for prognostic monitoring: when the detected molecular weights are 100-3000 daltons and the ratio of the number of the metabolic molecules with the signal-to-noise ratio greater than 2.6 reaches or is higher than a preset threshold value, the recurrence of the pheochromocytoma and the paraganglioma of the subject or the risk of suffering from the pheochromocytoma and the paraganglioma is prompted;

(iv) pheochromocytoma and paraganglioma development in a subject population is expected: when the detected molecular weights are 100-3000 daltons and the ratio of the number of the metabolic molecules with the signal-to-noise ratio greater than 2.5 reaches or is higher than a preset threshold value, the probability that the subjects have pheochromocytoma and paraganglioma is higher than that of other subjects;

(v) for the disease monitoring of patients with pheochromocytoma and paraganglioma: when the detected molecular weight is 100-;

(vi) for efficacy assessment: the ratio of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.7 is measured and calculated at different time points before and after diagnosis and treatment or in the treatment process, and when the difference of the result higher than the result obtained by the previous measurement reaches or is higher than a preset range, the treatment effect of the therapy and/or the medicament is poor.

In the products for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the preset threshold values in (i) - (iv) are determined by the optimal threshold values of 2.5-3.0, wherein the optimal threshold values are determined based on an artificial intelligence classification algorithm.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the preset threshold value in (i) - (iv) is 2.5-3.0.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, wherein in (vi), when the number of metabolic molecules with the signal-to-noise ratio of detected molecular signals being more than 2.7 is higher than that obtained in the previous test, the condition of the patient is shown to be developed or further worsened.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the ratio of the number of molecules with the signal-to-noise ratio higher than the optimal threshold value to the number of the selected detected metabolic molecules is calculated by adopting the following formula:

score ratio-number of molecules with signal to noise ratio above the optimal threshold/number of detected metabolic molecules.

The product provided by the invention is used for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, and is used in a detection method comprising the following steps:

1. determining the level of a metabolic molecule in a plasma/serum/saliva/urine sample of a subject, said level of a metabolic molecule comprising: a metabolic molecule having a molecular weight of 100-;

2. calculating the ratio of the number of molecules with signal-to-noise ratio higher than the optimal threshold value to the number of detected metabolic molecules;

3. judging whether the subject suffers from pheochromocytoma and paraganglioma according to the level of the metabolic molecules, carrying out prognosis evaluation on the subject or risk evaluation on the subject suffering from pheochromocytoma and paraganglioma, monitoring the disease condition or evaluating the curative effect.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the method further comprises one or more of the following steps:

1) collecting and/or processing a subject plasma/serum/saliva/urine sample;

2) separating and/or purifying serum/plasma/saliva/urine;

3) separating and/or purifying serum/plasma/saliva/urine;

4) separating, purifying, enriching serum/plasma/saliva/urine;

5) the metabolic molecule content ratio of the serum of the storage and/or processing object is used as an index for the relevant screening of pheochromocytoma and paraganglioma;

6) providing a judgment threshold value;

7) comparing the metabolic molecular composition content of the subject with a control to judge whether the subject suffers from pheochromocytoma and paraganglioma, carrying out prognosis evaluation on the subject or suffers from pheochromocytoma and paraganglioma, monitoring the condition of the subject or evaluating the curative effect of the subject;

8) providing diagnosis and detection result report;

9) and monitoring other pheochromocytoma and paraganglioma indexes, and combining the detection results of the other pheochromocytoma and paraganglioma indexes with the disease condition monitoring result.

The product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma further comprises a kit, a device, a system and/or a combination thereof for detecting other indexes of pheochromocytoma and paraganglioma.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the indexes are selected from the following items: metabolic molecules of serum/plasma/saliva/urine of a patient.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the basic condition of the patient is selected from the following: the number of molecules whose metabolic molecular signal-to-noise ratio is above an optimal threshold.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the clinical manifestation of the tumor is selected from one or more of the following: complete health/abnormal hypertension/definitive diagnosis of pheochromocytoma/cure of pheochromocytoma/recurrence of pheochromocytoma.

In the product for screening, early diagnosis and risk assessment of pheochromocytoma and paraganglioma, the specific physicochemical inspection index comprises a tumor biochemical index, wherein the tumor biochemical index is selected from one or more of the following: MN, NMN and 3-MT in plasma.

Use of an apparatus, module and/or processor for the manufacture of a product for screening, early diagnosis, prognosis assessment, risk assessment, condition monitoring and/or efficacy assessment of pheochromocytoma and paraganglioma in a subject, said product being a device, system and combination thereof, said apparatus, module and processor comprising:

A. reagents, instruments, modules and processors for determining the levels of serum/plasma/saliva/urine metabolic molecules for determining the composition and content of metabolic molecules;

B. optionally, a module or processor for calculating that the signal-to-noise ratio of the metabolic molecule is above a certain threshold;

C. optionally, a module or a processor for determining whether the subject suffers from pheochromocytoma and paraganglioma, performing prognostic evaluation on the subject, or performing risk evaluation, disease monitoring, or efficacy evaluation on the subject suffering from pheochromocytoma and paraganglioma based on the ratio of the signal-to-noise ratios.

The instrument, the module and/or the processor are applied to preparation of products for screening, early diagnosis, prognosis evaluation, risk evaluation, disease monitoring and/or curative effect evaluation of pheochromocytoma and paraganglioma of a subject, wherein the instrument, the module and/or the processor are used for determining blood metabolites, combining with a nanogold ball material, desorbing ionization time-of-flight mass spectrometry by using matrix-assisted laser, measuring signal-to-noise ratio data of the metabolites in a sample, and then giving a disease performance judgment and evaluation result based on an artificial intelligence algorithm.

Compared with the prior art, the invention has the advantages that: the invention develops a new method for diagnosing and parting PPGL based on a small amount or trace (less than 5 mL) of body fluid samples (including plasma, serum or urine and the like). The accuracy (sensitivity and specificity) of the method for diagnosing the PPGL is higher than that of the current clinical gold standard, the PPGL without catecholamine can be identified, the PPGL can be typed, and the method has prompting significance for the benign and malignant tumors and the pathogenic gene mutation carried by the tumors. In addition, compared with the existing mass spectrometry (LC-MS) for detecting three catecholamine metabolites, the mass spectrometry adopted in the invention has the advantages of shorter time consumption, smaller required sample size and greatly reduced cost.

Drawings

FIG. 1 is a schematic view of a sample collection device.

FIG. 2 is a schematic view of a sample storage device.

FIG. 3 is a schematic view of a sample preparation device.

FIG. 4 is a schematic diagram of a mass spectrometry data acquisition device.

FIG. 5 is a schematic diagram of the data generated by the mass spectrometric data acquisition apparatus.

FIG. 6 is a schematic diagram of a classification analysis of mass spectral data using an artificial intelligence algorithm.

FIG. 7 is a schematic diagram of the biological signal pathway for identifying relevant metabolite molecules that can be used for basic classification and studying the association of the metabolite molecules.

FIG. 8 is a flow chart of an algorithm for diagnosing, typing, and predicting which disease-causing gene mutations are carried by PPGL.

Detailed Description

The technical solution adopted by the present invention will be further explained with reference to the schematic drawings.

The invention needs to obtain a high-accuracy and high-sensitivity prediction classification model which is based on a large number of real biological samples and can be used for repeated detection application, and the specific method comprises the following steps:

1. mixing a biological sample (blood plasma, blood serum or urine, etc.) with a nano material for selectively enhancing the resolution of measuring the metabolism molecules of the body fluid, wherein the nano material comprises but is not limited to silicon and silicon compounds, and noble metals such as gold, silver, platinum, etc.;

2. preparing the mixed sample on a target plate of a mass spectrum;

3. obtaining data (with accuracy of 0.001-0.1 Dalton) by mass spectrometer, wherein the obtained data is relative number value of metabolic molecules above 100 Dalton, and we select data of all metabolic molecules with 100-3000 Dalton and signal-to-noise ratio greater than 2.5;

4. grouping and classifying the obtained data according to requirements, wherein the classification mode is as follows: diseased Vs healthy, metastatic Vs non-metastatic, types of gene mutations, etc.;

5. the data is classified using a machine learning algorithm, typically trained using 70% of the data taken randomly, tested using 30% of the data, and the model accuracy is cross-validated, where the purpose of data training and validation is: a high accuracy, high sensitivity predictive classification model based on a large number of real biological samples is obtained that can be used for repeated detection applications.

FIG. 1 represents the collection of serum or plasma or saliva or urine from a test person using a sample collection device; FIG. 2 is a schematic representation of the storage of collected samples using a sample storage device, the storage of collected samples at-80 ℃ and the transport to the corresponding detection laboratory; FIG. 3 is a schematic diagram of a sample preparation apparatus for mixing a sample with prepared nanomaterial to prepare the sample before processing; FIG. 4 is a schematic view of a mass spectrometry data acquisition device to obtain mass spectrometry data of a sample; FIG. 5 is a schematic diagram of the data generated by the mass spectrometric data acquisition device, which is a graph of the data generated by the mass spectrometric data acquisition device; FIG. 6 is a schematic diagram of a classification analysis of mass spectrometry data using an artificial intelligence algorithm; FIG. 7 is a schematic diagram of the biological signal pathway for identifying relevant metabolite molecules that can be used for basic classification and studying the association of the metabolite molecules. FIGS. 1-7 are experimental and analytical routes for pheochromocytoma and paraganglioma-related screening and evaluation.

The invention utilizes MALDI-TOF-MS (Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry) based on a new material to detect a large number of clinically confirmed PPGL patients and plasma of non-PPGL control population at high flux, and uses the obtained Mass data for machine learning.

The technology of the invention adopts laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) based on nanometer results (including but not limited to quantum dots, nanowires, nanospheres, nano hollow spheres, nano triangles and the like) of nanometer materials (including but not limited to silicon and silicon compounds, gold, silver, platinum and other precious metals), detects metabolites (substances with molecular weight below 3000 daltons) of plasma samples of a large number of patients with pheochromocytoma and paraganglioma which have been diagnosed, and develops a brand-new algorithm for diagnosing and typing PPGL and predicting which pathogenic gene mutation is carried by tumors by learning large data by using a machine, and the specific content of the algorithm is as follows in figure 8:

1) converting raw data derived from a mass spectrometer (namely the relative numerical value of the metabolic molecules above 100 daltons mentioned in the foregoing, we select 100-;

2) repeating the grouping of the data set processed in the step 1) for 10 times, wherein 30% of the groups are used as a test data set and 70% of the groups are used as a model training data set. A layered sampling method is adopted during grouping, so that the distribution of positive and negative samples in each group of data is consistent with the distribution of positive and negative samples in the total samples;

3) carrying out denoising and smoothing operation on the model training set data;

4) carrying out baseline removal work on the model training set data;

5) carrying out standardization operation on the model training set data to obtain an integral mean value and variance parameters;

6) aligning the data of the model training set;

7) and repeating the five-fold cross validation for 10 times on the model training set data in the step 6), and dividing the model training set data into 20% validation data set and 80% training data set. A layered sampling method is adopted during grouping, so that the distribution of positive and negative samples in each group of data is consistent with the distribution of positive and negative samples in the total samples;

8) training the data in the step 7) by using a classifier, outputting the average verification precision of the classifier model, and selecting the hyper-parameter of the classifier model when the verification precision is highest. The classifier model specifically adopts a Support Vector Machine (SVM), the SVM is a machine learning model utilizing hyperplane segmentation data, the SVM is generally applied to a two-classification problem under a supervised learning situation, a learning strategy is interval maximization, and SVM solution can be converted into a convex quadratic programming problem. The linear SVM is proposed by cortex and Vapnik in 1995, has great advantages in solving the problem of high-dimensional small sample classification, and can be popularized and applied to other machine learning problems such as function fitting and the like.

The idea of SVM classification is as follows: a hyperplane is found to segment the data (a straight line in the two-dimensional case) and then the segmentation of the data is maximized using the support vectors. The mathematical model is as follows:

simultaneously, the following requirements are met: y is_i(w^T·x_i+b)-1≥0，i＝1，2，…，n

Where the sample points of the data set T are hyperplanes. If the dataset T is linearly separable, then a maximum spaced separation hyperplane exists and is unique that can completely separate the samples in the dataset.

9) Putting the whole model training data set into the classifier model with the highest precision and the hyperparameter in the step 8) for training to obtain a final classifier model;

10) carrying out denoising and smoothing operation on the model test set data;

11) carrying out baseline removal work on the model test set data;

12) standardizing the test set data by using the standardized parameters of the model training set data obtained in the step 5);

13) aligning the data of the model test set;

14) putting the model test data set in the step 13) into the final classifier model in the step 9) for testing;

15) obtaining a predicted value of each sample in the test data set through the processing of the classifier in the step 14);

16) and comparing the predicted value obtained in the step 15) with the sample gold standard to obtain the average test error of the classifier model.

Steps 1) to 16) are detailed for the "high accuracy, high sensitivity predictive classification models based on large numbers of real biological samples that can be used for repeated detection applications" obtaining method.

Example 1: screening/screening of pheochromocytoma and paraganglioma by serum metabolism molecule flight time mass spectrum detection

The method comprises the following steps of (1) determining the content of 100-3000 Da metabolic molecules in a control serum sample and a pheochromocytoma and paraganglioma patient serum sample by adopting a nanogold ball material and a method, wherein the detection results are as follows:

1. by observing the metabolic molecular spectrogram of 100-3000 Da of serum samples of 500 patients in total, the number of the metabolic molecules with the signal-to-noise ratio of more than 2.5 can be found to be obviously different between pheochromocytoma and paraganglioma patients and controls.

2. Comparing the metabolic molecular maps of 500 healthy human serum samples of 100-3000 Da, and simultaneously carrying out comparison analysis on the number of metabolic molecules with the signal-to-noise ratio of a test subject being more than 2.5. The result shows that the metabolic molecular number with the signal-to-noise ratio of the metabolic molecules of 100-3000 Da being more than 2.5 is used as the screening index of pheochromocytoma and paraganglioma, and the corresponding sensitivity is up to 90% under the condition that the control specificity is 98.5%. The index is highly accurate as a diagnosis index by referring to the judgment standard of the puncture tissue pathology.

3. Numerically, the difference between the metabolic molecules with the signal-to-noise ratio of more than 2.5 in the 100-3000 Da metabolic molecules of pheochromocytoma and paraganglioma patients and the control sample is significant.

4. Meanwhile, the threshold of 2.5 is used as a criterion for judging that the subject has pheochromocytoma and paraganglioma or has a risk of having pheochromocytoma and paraganglioma, and the following table 1 is found:

TABLE 1

In the sample, the detection rate of pheochromocytoma and paraganglioma can reach about 90.2%;

the results show that the matrix-assisted laser desorption ionization time-of-flight mass spectrometry can be effectively used for screening pheochromocytoma and paraganglioma with high sensitivity and high specificity, and compared with the existing standard, the method and the index have higher accuracy. In addition, the method and the index of the invention can be used together with the existing standard to further improve the detection rate of pheochromocytoma and paraganglioma.

The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A product for pheochromocytoma and paraganglioma screening, early diagnosis, risk assessment, said product being an operable system, said product comprising:

(S3), optionally, a module and/or a processor for determining whether to suffer from pheochromocytoma and paraganglioma, based on the composition and content of metabolic molecules in the body fluid, and for performing a risk assessment of the subject for suffering from a tumor;

(S4), optionally, the product further comprises:

(vi) for efficacy assessment: measuring and calculating the proportion of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.7 at different time points before and after diagnosis and treatment or in the treatment process, and when the difference of the result higher than the result obtained by the previous measurement reaches or is higher than a preset range, indicating that the treatment effect of the therapy and/or the medicament is poor;

wherein the body fluid is selected from serum, plasma, saliva, urine.

2. The product of claim 1, further comprising one or more selected from the group consisting of:

f. a module and a processor for providing a decision threshold;

h. a module and a processor for providing diagnosis and detection result report.

3. The product of claim 1, wherein the predetermined threshold values in (i) - (iv) are determined by an optimal threshold value of 2.5-3.0, wherein the optimal threshold value is determined based on an artificial intelligence classification algorithm;

(vi) when the number of the metabolic molecules with the signal-to-noise ratio of the detected molecular signal of more than 2.7 is higher than that obtained in the previous test, the patient is indicated to have the disease developed or further worsened.

4. The product of claim 3, wherein the calculation of the ratio of the number of molecules having a signal-to-noise ratio above the optimal threshold to the number of selected detected metabolic molecules is performed using the formula:

5. The product of claim 1, wherein the product is used in a test method comprising the steps of:

6. The product of claim 5, wherein the method further comprises one or more of the following steps:

1) collecting and/or processing a subject plasma/serum/saliva/urine sample;

2) separating and/or purifying serum/plasma/saliva/urine;

3) separating and/or purifying serum/plasma/saliva/urine;

4) separating, purifying, enriching serum/plasma/saliva/urine;

6) providing a judgment threshold value;

8) providing diagnosis and detection result report;

7. The product of claim 1, further comprising a kit, device, system and/or combination thereof for detecting other pheochromocytoma and paraganglioma indicators.

8. The product of claim 7, wherein the indicator is selected from the group consisting of: metabolic molecules of serum/plasma/saliva/urine of the patient;

the patient's basic condition is selected from the following: the number of molecules whose metabolic molecular signal-to-noise ratio is above an optimal threshold;

the clinical presentation of the tumor is selected from one or more of the following: complete health/abnormal hypertension/definitive diagnosis of pheochromocytoma/cure of pheochromocytoma/relapse of pheochromocytoma;

the specific physicochemical examination index comprises tumor biochemical indexes, wherein the tumor biochemical indexes are selected from one or more of the following indexes: MN, NMN and 3-MT in plasma.

9. Use of an apparatus, module and/or processor for the manufacture of a product for screening, early diagnosis, prognosis evaluation, risk evaluation, condition monitoring and/or efficacy evaluation of pheochromocytoma and paraganglioma in a subject, wherein said product is a device, system and combination thereof, said apparatus, module and processor comprising:

10. The use of claim 9, wherein in (a), after the blood metabolites are determined by combining with the nanogold ball material, matrix-assisted laser desorption ionization time-of-flight mass spectrometry is used to determine the signal-to-noise ratio data of the metabolites in the sample, and then the disease performance judgment and evaluation result is given based on an artificial intelligence algorithm.