CN111235265A - Application method of composition for predicting generation probability of hemifacial short deformity - Google Patents

Abstract

The present disclosure provides the use of a marker panel, a method and a kit for determining the probability of a fetus suffering from hemifacial microsomia in the first trimester. The present disclosure is based in part on the discovery that biomarkers on cell-free DNA in maternal peripheral blood biological samples from first trimester fetuses, relative to matched controls, are predictive of future fetuses at risk for developing hemifacial microsomia. The present disclosure is also based on the important discovery that one or more combinations of these biomarkers can be used with relatively high sensitivity and high specificity in methods of determining the probability of a fetus suffering from hemifacial shortness. Determination of a panel of biomarkers disclosed herein is useful in compositions, uses and methods for classifying a test sample, predicting the probability of hemifacial microsomia, and monitoring fetal development in the first trimester.

Description

Method of application of a composition for predicting the probability of occurrence of hemifacial microsomia

technical field

The present invention relates generally to the field of personalized medicine and, in particular, to compositions and methods for predicting the probability of a fetus having hemifacial microsomia in the first trimester.

Background technique

Hemifacial microsomia (Craniofacial microsomia; MIM: 164210) is a group of congenital hereditary diseases including external and middle ear deformities, mandibular deformities, facial nerve and maxillofacial soft tissue deformities. About 90% of the patients with hemifacial brachymia have severe facial asymmetry. At the same time, the affected side also has symptoms such as external ear deformity, ear canal atresia, facial cleft, and occlusal surface inclination, which seriously affects the physical and mental health of patients. The cost of diagnosis and treatment and repair for each patient is as high as several hundred thousand yuan, which brings a heavy burden to the family and society.

Worldwide, the incidence of hemifacial brachymia is 1/3000-1/5600, and it is the second most common craniofacial deformity after cleft lip and palate. China is located in one of the three high-incidence regions (Central and South America, East Asia, and Northern Europe) with hemifacial microsomia. Data from the Newborn Birth Defect Monitoring Center shows that the incidence of hemifacial microsomia in my country is 3/10,000 newborns. More than 10,000 children with hemifacial microsomia are born.

Hemifacial brachymia can only reconstruct the outer ear and maxillofacial tissue through plastic surgery after the child is born, but plastic surgery can only restore part of the structure of the craniofacial surface, but such as depression of the temporal bone, middle ear deformity, and absence of the external auditory canal Other symptoms are still not recoverable by surgery or require huge costs to partially repair.

Early prediction of the risk of the disease and identification of fetuses at high risk for true hemifacial microsomia are the keys to reducing the incidence of this disease. Due to the limited loci currently used for risk assessment of hemifacial microsomia, the accuracy needs to be further improved, and a larger sample size and more genetic biomarkers are needed to achieve accurate risk prediction of the disease.

With the development of modern molecular biology techniques, free nucleotides have been widely studied and used as important molecular markers. Cell-free nucleic acids, also known as extracellular nucleic acids, are extracellular DNA (cell free DNA, cfDNA) and RNA that are widely present in plasma, saliva, bronchoalveolar lavage fluid, urine, semen, pleural and ascites fluids, and cell culture fluids. (cell free RNA, cfRNA). The study found that the plasma of pregnant women contains fetal-derived cfDNA and cfRNA, which enables the rapid development of non-invasive prenatal testing technology and avoids the risk of miscarriage and teratogenicity caused by traumatic prenatal testing.

A number of studies have confirmed that highly correlated biomarkers can be used to predict disease risk, especially genetic disease risk. However, so far, the accurate prediction of the risk of hemifacial microsomia is still accurate in predicting fetal risk in the Chinese population. Need to upgrade. However, due to the limited genome-wide association studies on the disease in China and even internationally, only the team of the inventor of this patent has carried out relevant studies. Therefore, we continue to increase the sample size to discover more risk loci, and clarify the effect of genetic factors on hemifacial shortness. The contribution to the risk of malformation is significant.

The present invention addresses this need, and provides related advantages, by providing compositions and methods for determining whether a fetus in the first trimester is at risk for hemifacial microsomia.

SUMMARY OF THE INVENTION

SUMMARY OF THE INVENTION

The present invention provides compositions and methods for predicting the probability of the onset of hemifacial microsomia in the first trimester.

The present invention provides a set of isolated biomarkers comprising the N biomarkers listed in Table 1. In some embodiments, N refers to a number selected from the group consisting of 2-15. In an embodiment, the biomarker panel comprises at least 2 isolated biomarkers selected from the group consisting of the biomarkers described in Table 1 and the biomarker panel described in Table 2.

In some embodiments, the present invention provides biomarker panels comprising at least 2 isolated biomarkers.所述生物标志物组选自rs3923380、rs56758397、rs10980575、rs17153925、rs17881412、rs1323689、rs754423、rs17802111、rs7420812、rs3754648、rs13089920、rs10905359、rs11263613、rs10459648和rs17090300。

In some embodiments, the present invention provides biomarker panels comprising at least 2 isolated biomarkers.所述生物标志物组选自人类基因组区域chr4:76968594-77968594、chr8:23057959-24057959、chr9:113085503-114085503、chr10:13797853-113585503、chr10:44870705-44870705、chr10:95040769-95040769、chr14:52527187 -52527187、chr2:46509657-46509657、chr2:206435709-206435709、chr2:237021346-237021346、chr3:78552232-78552232、chr10:8449891-8449891、chr11:69661334-69661334、chr15:74865440-74865440、chr13:74157451-74157451 Biomarkers with high linkage (r ² >0.6) at loci in and Table 1.

The present invention also provides a method of detecting the above-mentioned biomarkers, comprising determining each of the N biomarkers selected from the biomarkers listed in Tables 1 to 2 in a biological sample obtained from the mother of the first trimester fetus A measurable characteristic is analyzed and the measurable characteristic is analyzed to determine the probability of hemifacial microsomia in the fetus being conceived. The methods include chips, kits and sequencing products. Wherein, the chip includes a gene chip; the kit includes a gene detection kit, and the sequencing includes first-, second-, and third-generation methods for obtaining measurable features of biomarkers by obtaining DNA sequences, and the measurable features include The genotype of a biomarker, but is not limited to genotype, also includes measurable characteristics that can identify differences in the biomarker between samples.

In some embodiments, the present invention uses a gene chip to detect measurable characteristics of biomarkers, the gene chip comprising a solid support and oligonucleotide probes immobilized on the solid support, the oligonucleotide probes The needle includes a probe for detecting a measurable characteristic of at least 2 separate panels of biomarkers selected from among the N biomarkers of biomarkers listed in Tables 1 and 2 measurable characteristics of each.

In some embodiments, the present invention uses a genetic detection kit to detect a measurable characteristic of a panel of biomarkers, the genetic detection kit comprising primers for detecting the measurable characteristic of at least 2 separate panels of biomarkers or chip, the isolated biomarkers are selected from the measurable characteristics of each of the N biomarkers of biomarkers listed in Tables 1 and 2.

In some embodiments, the present invention uses sequencing methods to detect measurable characteristics of a panel of biomarkers comprising enriched probes for detecting measurable characteristics of at least 2 separate panels of biomarkers or Random probes, the isolated biomarkers are selected from the measurable characteristics of each of the N biomarkers of biomarkers listed in Tables 1 and 2.

The patent of the present invention also provides a method for calculating and determining the probability of a fetus suffering from hemifacial microsomia after obtaining the measurable characteristics of the biomarkers, which includes determining the biomarkers in N of the biomarkers listed in Table 1 and Table 2. After the characteristics were measured, they were analyzed using methods based on polygenic genetic score (PRS) and machine learning to predict the probability of fetal hemifacial microsomia.

In some embodiments, the present invention provides for predicting the probability of having a fetus with hemifacial microsomia, the method comprising: (a) dividing pregnant women into a training group and a validation group; (b) collecting data from subjects in the training group The measurable characteristics of the N biomarkers in the biomarker groups listed in Table 1 and Table 2 were determined in the samples of the group; (c) a prediction model was constructed based on the PRS and odds ratio (OR) values, and the validation group was used to predict the risk The model is validated to test the prediction accuracy of the established model; (d) Substitute the measurable features of the biomarkers of the sample to be tested into the model, calculate the PRS value of the sample and substitute it into the model to evaluate the probability of the affected hemifacial microsomia . It is characterized in that, described PRS score calculation formula is:

When the PRS value is less than 0.196, the fetus is normal. When the PRS value is greater than 0.196, it indicates that the fetus has the risk of hemifacial microsomia, and with the increase of the PRS value, the risk of the fetus with hemifacial microsomia also increases gradually.

In some embodiments, the present invention provides for predicting the presence or absence of a fetus with hemifacial microsomia, the method comprising: (a) dividing pregnant women into a training group and a validation group; (b) selecting subjects from the training group The measurable characteristics of N biomarkers in the biomarker groups listed in Table 1 and Table 2 were determined in the collected specimens; (c) a prediction model was constructed based on machine learning methods, and the risk prediction model was verified using the validation group, Test the prediction accuracy of the built model; (d) then substitute the measurable features of the biomarkers of the sample to be tested into the model, and calculate the possibility of the sample suffering from hemifacial microsomia. It is characterized in that, using the machine learning risk prediction model as elastic network algorithm, random forest algorithm, support vector machine algorithm and neural network algorithm, according to the function result to judge whether there is a risk of hemifacial shortness, when the judgment result is 1, it means the fetus Normal; when the result is 2, the fetus is at risk of hemifacial microsomia.

Other features and advantages of the present invention will be apparent from the detailed description and claims.

Detailed description of the invention

The present disclosure is based in part on the discovery that certain variant loci in fetus-derived nucleic acid material isolated from biological samples of the fetus' mother carry specific alleles in fetuses with a high risk of hemifacial microsomia, relative to controls combination. The present disclosure is also based, in part, on the discovery that groups admixing one or more of these variant loci can be used with high sensitivity and high specificity in methods for determining the probability of occurrence of fetal microsomia. These variant loci disclosed herein, alone or as a panel of biomarkers, are used to classify test samples and predict the probability of hemifacial micromyopathy.

The present disclosure provides biomarker panels, methods and kits for determining the probability of a fetus suffering from hemifacial microsomia. A major advantage of the present disclosure is that the risk of the fetus for hemifacial microsomia can be assessed early in pregnancy so that appropriate monitoring and clinical management can be initiated in a timely manner to address fetal developmental abnormalities. The present invention is particularly useful for fetuses that lack any risk factors for birth defects and will not be identified and treated.

For example, the present disclosure includes methods for producing results useful in determining the probability of having a fetus with hemifacial shortness by obtaining a data set associated with a sample, wherein the data set includes at least information about an identified hemifacial shortness Analytical data for malformation-indicating biomarkers and biomarker panels, and input of the dataset to an analytical method that uses the dataset to produce results useful in determining the probability of a fetus having hemifacial microsomia. As described further below, the quantitative analysis data may include single base variation, copy number variation, chromosomal structural variation, and amino acids, peptides, polypeptides, proteins, nucleotides, metabolites, antibodies, surrogates for biological macromolecules ROIs and their combinations.

In addition to specific biomarkers identified in the present disclosure by, for example, variant site numbers, sequences, or references in public databases, the present invention contemplates a high degree of linkage to the specific biomarkers exemplified and described above ( r ² >0.6) use of biomarker variants that are known or to be discovered later and that are useful for the methods described herein. These variants can represent polymorphisms, mutations, structural variations, and the like. In this regard, the present specification discloses a variety of variant sites known in the art in the context of the present invention and provides exemplary variant site numbers in relation to one or more public databases. In the context of the present invention, suitable samples include, for example, blood, plasma, serum, amniotic fluid. In some embodiments, the biological sample is selected from whole blood, plasma, and serum. In a specific embodiment, the biological sample is plasma. Biomarkers can be detected by a variety of assays and techniques known in the art, as described herein. As further described herein, these assays include, without limitation, chip, probe, sequencing-based assays.

Variant loci biomarkers associated with the probability of hemifacial microsomia in the first trimester fetus include, but are not limited to, one or more of the isolated biomarkers listed in Tables 1 and 2. In addition to specific biomarkers, the present disclosure also includes known or later discovered biomarkers that are highly linked (r ² >0.6) to the exemplified variant sites. Biomarkers and others as used herein include copy number variations, structural variations, protein variations, and the like.

Additional markers may be selected from one or more risk indicators including, but not limited to, maternal characteristics, medical history, past pregnancy, and marriage and childbearing. These other markers can include, for example, paternal smoking, maternal smoking, paternal drinking, maternal drinking, number of pregnancies, environmental pollution, medication during pregnancy, exposure to teratogenic agents during pregnancy. Demographic risk indicators for hemifacial microsomia can include, for example, maternal age, race, single marital status, low socioeconomic status, maternal age, occupational-related physical activity, occupational exposure, and environmental exposure and stress. Other risk indicators useful as markers can be identified using learning algorithms known in the art, such as PRS, elastic net algorithms, random forest algorithms, support vector machine algorithms, and neural network algorithms, which are of skill in the art known to the person and described further herein.

Provided herein are isolated biomarker panels comprising N biomarkers selected from the groups listed in Table 1, Table 2. In the disclosed panel of biomarkers, N can be a number selected from 2 to 15. In the disclosed methods, the number of biomarkers detected and their levels determined can be 1 or greater, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. In certain embodiments, the number of biomarkers detected and their levels determined may be 1 or greater, such as 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. The methods described in the present disclosure are useful for determining the probability of a fetus suffering from hemifacial microsomia.

In some embodiments, the isolated biomarker panel comprises one, two, three or more isolated biomarkers comprising rs3923380, rs56758397, rs10980575, rs17153925, rs17881412, rs1323689, rs754423, rs17802111, rs7420812, rs3754648, rs13089920, rs10905359, rs11263613, rs10459648, and rs17090300.

In some embodiments, the panel of isolated biomarkers comprises one, two, three or more isolated biomarkers comprising regions selected from the human genome chr4:76968594-77968594, chr8 :23057959-24057959、chr9:113085503-114085503、chr10:13797853-113585503、chr10:44870705-44870705、chr10:95040769-95040769、chr14:52527187-52527187、chr2:46509657-46509657、chr2:206435709-206435709、chr2:237021346 -237021346, chr3: 78552232-78552232, chr10: 8449891-8449891, chr11: 69661334-69661334, chr15: 74865440-74865440, ^chr13 : 74157451-74157451 and table 11: 2.0 in biological integrain height of linkage landmark.

It should be noted that, as used in this specification and the appended claims, "a" and "the" include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to a "biomarker" includes mixtures of two or more biomarkers, and the like. As used herein, the term "first trimester" refers to the onset of pregnancy through the 22nd week of pregnancy. The first trimester used in this study embodiment was 8-12 weeks of gestation. The scope of use of the patent of the present invention includes (but is not limited to) the first trimester, and the biomarkers and methods provided by the present invention can also be used in other periods of pregnancy to detect the probability of a fetus suffering from microtia.

As used herein, the terms "comprising", "including", "containing" and any variation thereof are intended to encompass a non-exclusive inclusion, such as a process, method, process product or combination of matter that includes, includes or contains an element or list of elements Matters include not only these ingredients, but may include other ingredients not expressly listed or inherent in the processes, methods, process products, or compositions of matter.

As used herein, the term "panel" refers to a composition, such as an array or collection, comprising one or more biomarkers. The term can also refer to a profile or index of the expression pattern of one or more of the biomarkers described herein. The number of biomarkers used in the biomarker panel is based on the sensitivity and specificity values for the particular combination of biomarker values.

As used herein and unless otherwise specified, the term "isolated" generally describes a composition of matter that is removed from its natural environment (eg, if it is naturally occurring, the natural environment) and is thus altered from its natural state by human hands . An isolated protein or nucleic acid is different from the way it occurs in nature.

The term "biomarker" refers to a biomolecule or biomolecule genotype, the variation of which is related to a sample characteristic. Throughout this disclosure, the terms "marker" and "biomarker" are used interchangeably. For example, the biomarkers of the present invention are associated with an increased likelihood of hemifacial microsomia. These biomarkers include, but are not limited to, variant sites, copy number variations, structural variations, protein variations.

The present invention also provides a method for determining the probability of hemifacial microsomia in the fetus carried by the mother of the fetus in the first trimester, the method comprising determining a biological sample obtained from the mother of the fetus in the first trimester selected from the group consisting of those listed in Table 1 and Table 2 measurable characteristics of each of the N biomarkers of the biomarkers, and analyzing the measurable characteristics to determine the probability that the fetus has hemifacial microsomia.

The present invention also provides a method for detecting the above-mentioned biomarkers, which comprises determining the N biomarkers selected from the biomarkers listed in Table 1 and Table 2 in a biological sample obtained from the mother of the fetus in the first trimester. measurable characteristics of each, and the measurable characteristics are analyzed to determine the probability of hemifacial microsomia in the fetus being conceived. The method includes chips, kits, capture sequencing, and whole genome sequencing products. Wherein, the chip includes a gene chip; the kit includes a gene detection kit, a capture kit, and the sequencing includes first- and second-generation methods for obtaining measurable features of biomarkers by obtaining DNA sequences, which can be measured Characteristics include, but are not limited to, the genotype of the biomarker, but also measurable characteristics that can identify differences in the biomarker between samples.

In some embodiments, the present invention uses a gene chip to detect measurable characteristics of biomarkers, the gene chip comprising a solid support and oligonucleotide probes immobilized on the solid support, the oligonucleotide probes The needle includes a probe for detecting a measurable characteristic of at least 2 separate panels of biomarkers selected from among the N biomarkers of biomarkers listed in Tables 1 and 2 measurable characteristics of each.

Biochips are used for the capture and detection of the biomarkers of the present invention. In the art, a variety of genetic biochips are known. These include, for example, genetic biochips produced by Illumina, Affymatix, Hualian (Taiwan), Boao. Generally, a genetic biochip includes a substrate having a surface. Attach capture reagents or adsorbents to the substrate surface. Typically, the surface includes a plurality of addressable addresses, each address having a capture reagent bound thereto. The capture reagents can be biomolecules, such as nucleic acids, probes that capture other biomarkers in a specific manner.

Measurement of mRNA in biological samples can be used as a surrogate for genotype detection of corresponding genetic biomarkers in biological samples. Accordingly, any biomarker or panel of biomarkers described herein can also be detected by detecting the appropriate RNA.

In some embodiments, the present invention uses a genetic detection kit to detect a measurable characteristic of a panel of biomarkers, the genetic detection kit comprising primers for detecting the measurable characteristic of at least 2 separate panels of biomarkers or chip, the isolated biomarkers are selected from the measurable characteristics of each of the N biomarkers of biomarkers listed in Tables 1 and 2.

In some embodiments, the present invention uses a kit to detect a measurable characteristic of a panel of biomarkers in combination with a sequencing method comprising enrichment for detecting the measurable characteristic of at least 2 separate panels of biomarkers A probe or random probe, the isolated biomarker is selected from the measurable characteristics of each of the N biomarkers of biomarkers listed in Tables 1 and 2.

The kit may include one or more reagents for detecting a biomarker, a container for containing a biological sample isolated from the mother of a first trimester fetus; and reacting the reagents with the biological sample or a portion of the biological sample to detect the biological Printed instructions for the presence or amount of the isolated biomarker in the sample. The reagents can be packaged in separate containers. The kit may also contain one or more control reference samples and reagents for performing genotyping.

The sequencing product kit may comprise one or more containers for the compositions contained within the kit. The composition may be in liquid form or may be freeze-dried. Suitable containers for the compositions include, for example, bottles, vials, syringes and test tubes. The container can be formed from a variety of materials, including glass or plastic. The kit may also include a package insert containing written instructions for the method of determining the probability of hemifacial microsomia.

A "measurable characteristic" is any property, characteristic or aspect that can be determined and correlated with the probability of having a fetus in a subject with hemifacial microsomia. For biomarkers, these measurable characteristics may include, for example, the presence of alleles of the biomarker or its genotype in the biological sample, such as nucleotide combinations, such as the presence or amount of nucleotide modifications, such as copies The number of numbers, such as the form of structural variation. In addition to biomarkers, measurable characteristics can include risk indicators including, for example, paternal smoking, maternal smoking, paternal drinking, maternal drinking, number of pregnancies, environmental pollution, medication during pregnancy, exposure to teratogenic agents during pregnancy. Demographic risk indicators for hemifacial microsomia can include, for example, maternal age, race, single marital status, low socioeconomic status, maternal age, occupational-related physical activity, occupational exposure, and environmental exposure and stress.

In the context of the present invention, the terms "sample", "biological sample" include any sample collected from the mother of the fetus in the first trimester and containing one or more of the biomarkers listed in Table 1, Table 2. In the context of the present invention, suitable samples include, for example, blood, plasma, serum, amniotic fluid. In some embodiments, the biological sample is selected from whole blood, plasma, and serum. In a specific embodiment, the biological sample is plasma. As will be understood by those of skill in the art, a biological sample can include any part or component of blood, without limitation, T cells, monocytes, neutrophils, red blood cells, platelets, and microcapsules, such as exosomes and exosomes like microcapsules. In a specific embodiment, the biological sample is plasma.

In the present invention, the term "probe" refers to a molecule capable of binding to a specific sequence or subsequence or other portion of another molecule. Unless otherwise indicated, the term "probe" generally refers to a polynucleotide probe capable of binding to another polynucleotide (often referred to as a "target polynucleotide") by complementary base pairing. Depending on the stringency of the hybridization conditions, a probe can bind to target polynucleotides that lack complete sequence complementarity to the probe. Probes can be labeled directly or indirectly, ranging from primers. Hybridization means, including, but not limited to, solution phase, solid phase, mixed phase, or in situ hybridization assays. Exemplary probes in the present invention include PCR primers and gene-specific DNA oligonucleotide probes, such as microarray probes immobilized on microarray substrates, quantitative nuclease protection assay probes, probes linked to molecular barcodes needles, and probes immobilized on beads.

In the present invention, the "kit" also contains a label for labeling the DNA sample, and a substrate corresponding to the label. In addition, the kit can also include various reagents required for DNA extraction, PCR, hybridization, color development, etc., including but not limited to: extraction solution, amplification solution, hybridization solution, enzyme, control solution , color developing solution, lotion, etc. In addition, the kit also includes instructions for use and/or chip image analysis software.

In the context of the present invention, the term "capture reagent" refers to a compound that can specifically bind to a target, in particular a biomarker. The term includes nucleic acids, antibodies, antibody fragments, nucleic acid-based protein binding reagents, nucleic acid capture reagents, small molecules or variants thereof. Capture reagents can be configured to specifically bind to a target, specifically a biomarker. Capture reagents can include, but are not limited to, organic molecules such as polypeptides, polynucleotides, and other non-polymeric molecules that can be identified by the skilled artisan. In the embodiments disclosed herein, capture reagents include any reagents that can be used to detect, purify, isolate or enrich a target, particularly a biomarker. Any affinity capture technology known in the art can be used to selectively isolate and enrich/concentrate biomarkers that are components of the complex mixture of biological media used in the disclosed methods.

The term "genotype" or "allele" refers to the composition of bases at base positions on chromosomal DNA, including any two of the four bases "A", "T", "C", and "G" composition form. When the biomarker is insertion or deletion, it can also be expressed as the specific sequence composition of the above four bases.

Some embodiments disclosed herein relate to diagnostic and prognostic methods for determining the probability of a fetus having hemifacial microsomia in a mother of a fetus in the first trimester. Genotyping of one or more biomarkers and/or determination of biomarker ratios can be used to determine the probability of having a fetus with hemifacial microsomia. These assays can be used, for example, for early assessment of the condition to determine whether a subject is susceptible to hemifacial microsomia, to monitor the development of hemifacial microsomia, or to progress treatment protocols to assess hemifacial microsomia The severity of the deformity to help determine appropriate treatment and preventive measures.

The genotype of a biomarker in a biological sample can be determined without limitation by the methods described above, as well as by any other method known in the art. The genotype data thus obtained are then subjected to an analytical taxonomy. In this method, the raw data is conditioned according to an algorithm, wherein the algorithm has been predefined by a training data set, eg, as described in the examples provided herein. The algorithm may use the training data set provided herein, or may use the guidance provided herein to generate the algorithm from a different data set.

In some embodiments, analyzing the measurable characteristics to determine the probability of having a fetus with hemifacial microsomia includes the use of a predictive model. In other embodiments, analyzing the measurable characteristic to determine the probability of the fetus having hemifacial microsomia includes comparing the measurable characteristic to a reference characteristic. As can be appreciated by those skilled in the art, this comparison may be a direct comparison with a reference feature or an indirect comparison where the reference feature has been introduced into the predictive model. In other embodiments, analyzing the measurable characteristics to determine the probability of having a fetus with hemifacial microsomia includes one or more of the following: PRS analysis model, elastic network algorithm, neural network algorithm, linear difference analysis model, support vector machine Classification algorithm, regression feature elimination model, microarray predictive analysis model, logistic regression model, multiple regression model, survival model, CART algorithm, flex tree algorithm, LART algorithm, random forest algorithm, MART algorithm, machine learning algorithm, penalized regression method and its combination.

Analytical Taxonomy Any of a variety of statistical analysis methods can be used to condition quantitative analysis data and prepare for sample classification. Examples of useful methods include linear difference analysis, regression feature elimination, microarray predictive analysis, logistic regression, CART algorithm, FIexTree algorithm, LART algorithm, random forest algorithm, MART algorithm, machine learning algorithm, and the like.

In order to construct a prediction model for fetal hemifacial microsomia, in a specific embodiment, the method is polygenic risk assessment and random forest. The polygenic genetic score (PRS) assigns corresponding weights to each genetic biomarker based on the results of genome-wide association analysis, and calculates the sum of all risk loci to evaluate the risk index of disease occurrence. Random Forest (RF) is one of the machine learning algorithms, and its advantage for disease risk prediction is that the interaction between non-additive models and genetic biomarkers can be considered, while the computational complexity and prediction accuracy are in the present invention. significantly outperforms other machine learning algorithms.

The samples to be tested can be classified according to a predictive model approach that sets a threshold that determines the probability that a sample belongs to a given class. The probability is preferably at least 60%, or at least 70%, or at least 80% or higher. Classification can also be performed by determining whether a comparison between the obtained dataset and the reference dataset yields a statistically significant difference. If generated, the samples for which this dataset was obtained are classified as not belonging to the reference dataset class. Conversely, if the comparison is not statistically significantly different from the reference dataset, the samples from which the dataset was obtained are classified as belonging to the reference dataset class.

The predictive ability of a model can be evaluated in terms of quality metrics that provide specific values or ranges of values, eg, AUROC (area under the ROC curve) or accuracy. The area under the curve measure is useful for comparing the accuracy of classifiers across the data range. A classifier with a larger AUC will have a larger ability to correctly classify the unknown between the two groups of interest. In some embodiments, the desired quality threshold is a prediction that classifies the sample with an accuracy of at least about 0.6, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or higher Model. As an alternative measure, the desired quality threshold may represent a predictive model that classifies the sample with an AUC of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

As is known in the art, the relative sensitivity and specificity of a predictive model can be adjusted in favor of a selectivity measure or a sensitivity measure, where the two measures have an inverse relationship. Depending on the specific requirements of the test being performed, the limits in the above models can be adjusted to provide a selected level of sensitivity or specificity. One or both of sensitivity and specificity can be at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

In order to generate a hemifacial dysmorphia prediction model, a robust dataset was used in the training set, which included known control samples and samples corresponding to the hemifacial dysmorphia classification of interest. The sample size can be selected using accepted criteria. As discussed above, different statistical methods can be used to obtain high accuracy predictive models.

In some embodiments, the present invention provides for predicting the probability of having a fetus with hemifacial microsomia, the method comprising: (a) dividing pregnant women into a training group and a validation group; (b) collecting data from subjects in the training group The measurable characteristics of the N biomarkers in the biomarker groups listed in Table 1 and Table 2 were determined in the samples of the group; (c) the prediction model was constructed based on the PRS and OR values, and the risk prediction model was verified by the validation group. Test the prediction accuracy of the built model; (d) Substitute the measurable features of the biomarkers of the sample to be tested into the model, calculate the PRS value of the sample and substitute it into the model to evaluate the probability of hemifacial microsomia. It is characterized in that, described PRS score calculation formula is:

When the PRS value is less than 0.196, the fetus is normal. When the PRS value is greater than 0.196, it indicates that the fetus has the risk of hemifacial microsomia, and with the increase of the PRS value, the risk of the fetus with hemifacial microsomia also increases gradually.

In some embodiments, the present invention provides for predicting the presence or absence of a fetus with hemifacial microsomia, the method comprising: (a) dividing pregnant women into a training group and a validation group; (b) selecting subjects from the training group The measurable features in the N biomarkers of the biomarker groups listed in Tables 1 and 2 were determined in the collected specimens; (c) a prediction model was constructed based on machine learning methods, and the risk prediction model was validated using the validation group, Test the prediction accuracy of the built model; (d) then substitute the measurable features of the biomarkers of the sample to be tested into the model, and calculate the possibility of the sample suffering from hemifacial microsomia. It is characterized in that, using the machine learning risk prediction model as elastic network algorithm, random forest algorithm, support vector machine algorithm and neural network algorithm, according to the function result to judge whether there is a risk of hemifacial shortness, when the judgment result is 1, it means the fetus Normal; when the result is 2, the fetus is at risk of hemifacial microsomia.

From the foregoing description, it will be apparent that modifications and variations of the invention described herein can be made to adapt it to various usages and conditions. Such embodiments are also within the scope of the following claims.

As used herein, the recitation of a list of elements in any definition of a variable includes the definition of the variable as any single element or combination (or subcombination) of the listed elements. Herein, the recitation of an embodiment includes that embodiment as any single embodiment or in combination with any other embodiment or portions thereof.

Table 1 Variation sites and information significantly associated with hemifacial microsomia

Table 2. Variation sites and related information significantly associated with hemifacial microsomia

Description of drawings

Figure 1. Model for predicting the risk of hemifacial microsomia based on PRS

Figure 2. Model for predicting the risk of hemifacial microsomia based on machine learning

Detailed ways

The present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The following examples are only used to illustrate the present invention and not to limit the scope of the present invention. The experimental methods for which specific conditions are not indicated in the examples are usually in accordance with conventional conditions or in accordance with the conditions suggested by the manufacturer.

Example 1

Samples: The samples used in this example are training samples: samples of patients with hemifacial brachymorphism (2165 cases) and samples of normal people (3909 cases). The patient samples were all from the Plastic Surgery Hospital of the Chinese Medical College, aged between 4 and 50 years old, with an average age of 11.2 years; the normal samples were from various medical testing centers in China.

Quality control: Quality control includes sample quality control and genetic biomarker quality control. The condition of sample quality control is that the detection rate of genetic biomarkers in a single sample is greater than 90%; the conditions of genetic biomarker quality control are that the detection rate of single SNP is greater than 95%, the minimum allele frequency is greater than 0.01, and the Hardy-Weinbergequilibrium is greater than 0.0001. The final number of samples and genetic biomarkers used for genome-wide association analysis were 6074 and 36, respectively.

Genome-wide association analysis: Plink (v1.9) software was used for genome-wide association analysis. The algorithm used was the logistic regression algorithm, and the geographic location information of the samples was used as a covariate in the algorithm to correct differences in population structure.

RESULTS: A total of 15 genetic loci (P value<5.0×10 ^-8 ) significantly associated with hemifacial brachymorphism were identified in this genome-wide association analysis, including rs17802111, rs7420812, rs3754648, rs13089920, rs3923380, rs56758397, rs10980575, rs10905359, rs17153925, rs17881412, rs1323689, rs11263613, rs17090300, rs754423, rs1059648 (Table 1).

Example 2

Prediction of the risk of hemifacial microsomia based on PRS

Samples: The samples used in this example are the patient samples (700 cases) and normal human samples (972 cases) with hemifacial brachymorphism.

Genotype determination of biomarkers: According to the genome-wide association analysis results, we designed Taqman probes for the 15 loci in Example 1 to obtain the genotype information of the verification samples.

Calculation and correlation analysis of PRS: The formula for calculating PRS is

where S _i is the logarithm of the genetic biomarker risk value (OR, odd ratio) with e as the base, G _ij is the type of genetic biomarker (0 is the non-risk locus genotype, 1 is the heterozygous genotype, 2 is the risk locus genotype. The association analysis between PRS and the occurrence of hemifacial microsomia was performed by logistic regression. In addition, the PRS was divided into 20 equal parts, and the 10th equal part was used as the reference value to calculate the different equal parts PRS Risk of developing hemifacial microsomia.

RESULTS: There were significant differences in PRS values between patients with hemifacial microsomia and normal subjects (Fig. 1A). There was a significant correlation between PRS and hemifacial microsomia (95%CI 2.74to 3.63, P=7.22×10 ^-58 ); the risk of hemifacial microsomia increased gradually with the increase of the PRS value, and the PRS was located in the front The 5% population had a 6.85-fold increased risk (OR=6.85) of hemifacial microsomia compared with the 50th percentile PRS population, which was a high-risk group (Fig. 1B).

Example 3

Prediction of the risk of hemifacial microsomia based on machine learning

Model construction: We select four machine learning algorithms for risk prediction model construction: elastic network algorithm, random forest algorithm, support vector machine algorithm and neural network algorithm. Use the R package caret of machine learning in R language to determine the optimal parameters of each algorithm model. Using 10×10 cross-validation, we select the optimal parameters of each model as: Elnet(alpha=.1, gamma=0.027), RF(mtry=2), SVM(cost=2, gamma=0.8), ANN (size=3, decay=0.1).

Model comparison: Using the constructed machine learning model, we applied it to the validation samples in Example 2, and calculated the subject characteristic curve (ROC) and AUC values to judge the pros and cons of each model ( Figure 2A).

Optimal model: According to the prediction accuracy of each machine learning model, the optimal threshold is calculated according to the ROC curve. Under the best machine learning model and the best threshold, we calculated its sensitivity (Sensitivity), specificity (Specificity), positive predictive value (PPV), negative predictive value (NPV) for predicting the risk of hemifacial microsomia ) (Fig. 2B).

RESULTS: The ROC curve and AUC values indicated that the random forest algorithm (RF) was optimal in predicting the risk of hemifacial microsomia. Taking the constructed random forest as the disease prediction model, its PPV and NPV can reach 63.6% and 77.0%, respectively.

Example 4

A kit for non-invasive prenatal risk assessment of congenital hemifacial microsomia.

It is characterized in that the kit includes a primer set, and the primer set is preferably 15 sets of Taqman probe primers, and the 15 sets of primers are as follows:

(1) Sample preparation:

Collection of maternal blood to extract fetal cf-DNA in blood

1) Prepare washing liquid (purchasing manufacturer: Changzhou Baidai Biotechnology Co., Ltd.), prepare washing liquid A and washing liquid B;

Washing solution A: take 21 ml of washing solution and add 9 ml of absolute ethanol; if take 42 ml of washing solution, add 18 ml of absolute ethanol.

Washing solution B: take 9 ml of washing solution and add 21 ml of absolute ethanol; if take 18 ml of washing solution, add 42 ml of absolute ethanol.

2) Take a 1.5ml centrifuge tube, add 200ul of the collected maternal blood sample, 4ul DNA Carrier (DNA carrier, purchase manufacturer: Changzhou Biotech Co., Ltd.), mix well, add 300ul lysis solution (purchase manufacturer: Changzhou Biotech Co., Ltd. Company), and 20ul of digestion solution (purchased manufacturer: Changzhou Baidai Biotechnology Co., Ltd.), shake and mix, and water bath at 56°C for 10 minutes.

3) Add 1000 ul absolute ethanol to the centrifuge tube in 2), invert and mix gently, if there is a translucent suspension, it will not affect the DNA extraction and subsequent experiments.

4) Put the adsorption column into the collection tube, transfer 760ul of the solution obtained in step S3 into the adsorption column, let stand for 2 minutes, centrifuge the adsorption column containing the collection tube at 12,000rpm and 4°C for 1 minute, and take out the adsorption column. , discard the waste liquid in the collection tube, put the adsorption column back into the collection tube, transfer the remaining 760ul solution to the adsorption column, and repeat this step once.

5) Remove the liquid obtained in the collection tube in the repeated steps, put the adsorption column back into the collection tube, add 500 ul of washing solution A to the adsorption column, centrifuge at 12,000 rpm at 4°C for 1 minute, discard the waste liquid in the collection tube, and put the adsorption The column is placed back into the collection tube.

6) Add 500ul of washing solution B to the adsorption column, centrifuge at 12,000rpm for 1 minute at 4°C, discard the waste liquid in the collection tube, put the adsorption column back into the collection tube, centrifuge at 12,000rpm for 2 minutes at 4°C, and leave the residual washing solution.

7) Take out the adsorption column, put it into a new 1.5ml centrifuge tube, add 30-50ul eluate, let stand for 3 minutes, centrifuge at 12,000rpm for 2 minutes at 4°C, and collect the DNA solution. The extracted DNA can be used in the next experiment or stored at -20℃.

(2) Real-time fluorescence quantitative q-PCR reaction

The real-time quantitative RT-PCR reaction system is: 0.5ul of 300-800nM primer set in Example 4, 1.5ul of enzyme mixture, 1ul of 20-50mM Tris-sulfuric acid at pH 8.5, and 10-20mM pH7.9 1ul of MOPS buffer, 1ul of 2~5mM sodium citrate, 1ul of 10~20mM ( _NH4 )2SO4, 1ul of 5~10mM _MgSO4 , 1ul of 0.1mg/ml acetylated BSA and 1ul of _420mM dNTP, Make up to 50ul of RNAase-free ddH ₂ O. Among them, the primer set includes 15 sets of primer sets in Example 4, the concentration of each set is in the range of 300-800nM, and the volume of the primer sets in the 15 sets is the same or the same; the enzyme mixture is: 0.5ul A mixture of Tfl DNA polymerase with a concentration of 0.5-1 unit and Stoffel fragment with a concentration of 0.5 ul at a concentration of 0.5-1 unit, or the enzyme mixture: 0.5 ul of Tfl DNA polymerase with a concentration of 0.5-1 unit, 0.5 ml of Stoffel with a concentration of 0.5-1 unit A mixture of fragments and 0.5ul of MMLV reverse transcriptase at a concentration of 0.5-1 unit.

The real-time quantitative RT-PCR reaction procedure is: the first step: 45°C, 20-45 minutes; 94-96°C, 2 minutes; the second step: 94-95°C, 15-30 seconds; 65-69°C, 30 seconds ～75 seconds; 68～72℃, 30～40 seconds; 6～9 cycles; Step 3: 93～95℃, 15～20 seconds; 60℃, 30 seconds; 68～72℃, 30 seconds; 8 cycles cycle; fourth step: 93-95°C, 15 seconds; 52-55°C, 30-60 seconds; 40 cycles; fluorescence collection at 55°C;

(3) Effect verification

The kit constructed by the above Taqman method was verified by sequencing as 20 normal human samples, 20 rs3923380 mutant samples, 20 rs56758397 mutant samples, 20 rs10980575 mutant samples, 10 rs13089920 mutant samples, and rs17802111 mutant samples. 10 samples including 5 mutant samples, a total of 100 samples (including mixed cases), the detection results are consistent with the Sanger sequencing results, which proves that the taqman probe kit of the present invention has specificity in detecting the hemifacial brachymycosis gene mutation.

(4) Result judgment:

For the final results displayed by the taqman probe, through the synergy study of each variant site and multiple variant sites and the verification of big data cases, it was found that the more risk allele sites were carried, the shorter the half of the face appeared. The greater the risk of malformed phenotypes, specifically, if the tested samples carry risk alleles at 15 loci at the same time, the risk of the fetus having at least one characteristic manifestation of hemifacial microsomia is more than 90%, with at least The risk of 2 hemifacial microsomia is above 80%. For example: when the tested sample carries 10-12 risk alleles at the same time, the risk of having at least one hemifacial brachymorphism feature is more than 80%; when the tested sample carries 7-9 risk alleles at the same time When the sample carries 4-6 risk alleles at the same time, the risk of having at least one hemifacial microscopic feature is at least 60-80%. At 40-60%; when the examined sample carries 1-3 risk alleles, the risk of having at least one hemifacial microsomia is less than 15%.

The above-mentioned hemifacial brachymorphism features include small ears, hemifacial brachymorphism, congenital heart disease, kidney malformation, or costal cartilage malformation, etc.

In the above-mentioned step (1), for pregnant women, screening of the disease can be carried out as early as 8-12 weeks of pregnancy, that is, through the kit of the present invention, the peripheral blood of pregnant women in 8-12 weeks of pregnancy can be collected to evaluate and judge the fetus There is a possibility of hemifacial shortness. If the test result is positive, the possibility of the fetus having hemifacial shortness is extremely high, and early intervention can be carried out.

In this embodiment, the probe sets of 15 compositions can be amplified simultaneously in one real-time quantitative RT-PCR, or can be amplified in multiple times, which can be determined according to experimental conditions and actual needs.

It should be noted that the primer probe sets and kits in this application are researched and developed based on 15 human biomarkers, and the specific information of the 15 biomarkers is shown in Table 1.

Example 5

Subjects: The peripheral blood of pregnant women at 8-12 weeks of gestation was collected, and the collected pregnant women were all with the phenotype of hemifacial brachymorphism or their close relatives had hemifacial brachymorphism;

Collection location: collected in nearly 11 hospitals nationwide; time: 2017.1-2017.10;

Quantity: 30;

Method: Detect according to the method described in Example 4.

Results: Thirteen samples were judged to be at high risk of hemifacial microsomia, and 11 fetuses were confirmed to be positive for hemifacial microsomia by three-dimensional color Doppler ultrasound. The 17 samples were judged to be at low risk of hemifacial microsomia, and one fetus was confirmed to be positive for hemifacial microsomia by 3D color Doppler ultrasound.

In this example, by collecting the peripheral blood of pregnant women in the early stage of pregnancy, the kit of the present invention is used for detection, and the positive rate thereof is counted; the follow-up detection in the later period shows that the actual results obtained are all within the range of the predicted results of the present application. From this, it can be further known that the kit of the present application has high accuracy and accurate prediction, which can be basically accurate to about 80%, so that pregnant women can know the situation of the fetus as soon as possible, which is helpful for early treatment or intervention, and is extremely important for prenatal and postnatal care. value.

More importantly, about 99% of pregnant women can be tested for this item at 8-12 weeks of pregnancy, and it can be detected at the latest 12 weeks, so that a more accurate estimate can be made; of course, it can be detected after 12 weeks. , but the sooner it is detected, the more meaningful it is.

The present invention is not limited to the above-mentioned best embodiment, and anyone can draw other various forms of products under the inspiration of the present invention, but no matter if any changes are made in its shape or structure, all products with the same or similar characteristics as those of the present application can be obtained. Similar technical solutions all fall within the protection scope of the present invention.

Claims (10)

1. Use of a method for measuring the measurable characteristics of a marker group for the manufacture of a composition and a kit for assessing the probability of hemifacial brachymorphism for an in vitro biological sample, characterized in that the marker of the tested biological sample Measurable characteristics of the object that differ from the control sample may indicate an increased probability of hemifacial microsomia. 2. The use according to claim 1, wherein the assay marker set obtained from the ex vivo biological sample can be selected from one of the group consisting of biomarkers described in Table 1, Table 2, or A measurable characteristic of each of the plurality of biomarkers, the measurable characteristic including, but not limited to, the genotype of the biomarker or the sequence characteristic comprising the marker. 3. The use of claim 1, wherein the assay for markers obtained from the ex vivo biological sample is further selected from the group consisting of one or more measurable characteristics of risk indicators, including paternal smoking, maternal smoking, Paternal alcohol consumption, maternal alcohol consumption, number of pregnancies, environmental pollution, drug use during pregnancy, teratogenic exposure during pregnancy, age, race/ethnicity, single marital status, low socioeconomic status, maternal age, occupational-related physical activity, occupational exposure, and environmental exposure and stress group. 4. The use of claim 1, further comprising communicating the probability to a biological sample provider and communicating subsequent treatment decisions to the sample provider. 5. The use according to claim 1, comprising an analytical method for assessing the probability of occurrence of hemifacial shortness, said analysis comprising the use of polygenic genetic scoring, elastic network algorithm, random forest algorithm, support vector machine algorithm and neural network algorithm One or more predictive models in compare the measurable characteristic to a reference characteristic to obtain a risk score or risk level. 6. The ex vivo biological sample of claim 1, wherein the ex vivo biological sample is selected from the group consisting of whole blood, plasma and serum of pregnant women. 7. A test kit, characterized in that, the test kit is used for evaluating or assisting in evaluating the probability of a fetus suffering from hemifacial microscopic deformity in the first trimester, and the test kit comprises the marker described in any one of claims 2-3 Group of reagents and probes, specifically including real-time quantitative PCR (qRT-PCR), capture sequencing kits, genotyping chips and protein profiling reagents and probes. 8. test kit according to claim 7, wherein, described qRT-PCR comprises Taqman probe method fluorescent quantitative PCR, described test kit is selected from oligonucleotide, probe, antibody, antibody fragment, nucleic acid-based protein binding A group of reagents, small molecules or variants thereof and based on the combination to perform chip detection, genotyping detection, target capture sequencing to obtain one or more of the group of biomarkers described in Table 1 and Table 2 Measurable characteristics of biomarkers. 9. A method for determining the probability of hemifacial shortness of a fetus in the first trimester, the method comprising: (a) comparing a biological sample selected from the group consisting of the biomarkers listed in Table 1 and Table 2 in a biological sample obtained from the mother of the fetus in the first trimester determination of the genotype of each of the N biomarkers of The individual products are added to obtain an overall risk score corresponding to the probability. 10. The method of claim 9, wherein the first trimester ex vivo biological sample is taken from 4-22 gestational weeks.

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