WO2023115662A1

WO2023115662A1 - Method for detecting variant nucleic acids

Info

Publication number: WO2023115662A1
Application number: PCT/CN2022/070974
Authority: WO
Inventors: 张之宏; 祝鹏飞; 吴帅来; 王晨阳; 邱福俊; 汉雨生
Original assignee: 广州燃石医学检验所有限公司
Priority date: 2021-12-24
Filing date: 2022-01-10
Publication date: 2023-06-29
Also published as: CN114292912A

Abstract

A method for detecting variant nucleic acids and the use thereof in the detection of samples. Specifically, the present invention relates to a method for detecting the presence and/or quantity of variant nucleic acids. The method comprises determining the presence and/or quantity of variant nucleic acids on the basis of a somatic mutation region and a background mutation region in a sample to be detected.

Description

A method for detecting variant nucleic acid

technical field

The present application relates to the field of biomedicine, in particular to a method for detecting variant nucleic acids.

Background technique

Detecting the presence and/or proportion of circulating tumor DNA (ctDNA, Circulating Tumor DNA) in peripheral blood is the main method for detecting minimal residual disease (MRD, Minimal Residual Disease). Minimal Residual Disease (MRD, Minimal Residual Disease) refers to a small amount of cancer cells remaining in the body after cancer treatment. It is a potential source of tumor recurrence and distant metastasis. have good prognostic value. At present, MRD positive or negative is mainly judged by detecting ctDNA content in peripheral blood after surgery. The first domestic "Consensus on the Detection and Clinical Application of MRD in Lung Cancer" stipulates that when used for MRD detection, the detection limit of ctDNA needs to be as low as 0.02%. . Therefore, there is an urgent need in the art for a method that can accurately detect the presence and/or proportion of ctDNA.

Contents of the invention

In one aspect, the application provides a method for detecting the presence and/or quantity of a variant nucleic acid, the method comprising determining the variant based on the somatic mutation site and the background mutation site in the region to be tested in the sample to be tested The presence and/or amount of nucleic acid, wherein said background mutation site is determined by removing said somatic mutation site from all mutation sites in said test sample.

In one aspect, the present application provides an analysis device for detecting the presence and/or quantity of a variant nucleic acid, the device comprising a judgment module for based on the somatic mutation site and the background mutation of the region to be tested in the sample to be tested site, determining the presence and/or quantity of the variant nucleic acid, wherein the background mutation site is determined by removing the somatic mutation site from all mutation sites in the sample to be tested.

In one aspect, the present application provides a storage medium, which records the program that can implement the method described in the present application.

In one aspect, the present application provides a device, the device comprising the storage medium described in the present application.

In one aspect, the present application provides the method according to the present application, which is used to detect and/or quantify circulating tumor DNA in a test sample obtained from a subject.

The present application provides a method for detecting a variant nucleic acid, for example, a method for detecting the presence and/or quantity of a variant nucleic acid, the method comprising a somatic mutation site and a background mutation site based on a region to be tested in a sample to be tested Determine the presence and/or amount of the variant nucleic acid, wherein the background mutation site is determined by removing the somatic mutation site from all mutation sites in the test sample. The detection method of the present application can accurately evaluate the proportion of sample ctDNA and the significance level of sample ctDNA.

Those skilled in the art can easily perceive other aspects and advantages of the present application from the following detailed description. In the following detailed description, only exemplary embodiments of the present application are shown and described. As those skilled in the art will appreciate, the content of the present application enables those skilled in the art to make changes to the specific embodiments which are disclosed without departing from the spirit and scope of the invention to which this application relates. Correspondingly, the drawings and descriptions in the specification of the present application are only exemplary rather than restrictive.

Description of drawings

The particular features of the invention to which this application relates are set forth in the appended claims. The features and advantages of the invention to which this application relates can be better understood with reference to the exemplary embodiments described in detail hereinafter and the accompanying drawings. A brief description of the accompanying drawings is as follows:

Figures 1A-1B show the observed signal frequency of insertion or deletion of 1 repeat unit for different repeat unit repeat numbers.

Figures 2A-2B show the frequency of observable signals for insertions or deletions of 1, 2 or 3 repeat units for different repeat unit repeat numbers.

Figures 3A-3B show the observed signal frequency of insertion or deletion of a repeating unit with a length of 1, 2 or 3 bases for different repeating numbers of the repeating unit.

Figure 4 shows the frequency of observable signals for random insertions or deletions of 1 or 2 bases.

Figures 5A-5B show the results of the evaluation dilution ratio based on the number of different sites, where the abscissa is the number of sites, the ordinate is the evaluation dilution ratio, and the dotted line represents the dilution ratio of the experiment.

Figure 6 shows the detection sensitivity results of different detection methods.

Figures 7A-7E show the results of sensitivity and specificity detected by the method of the present application for diluted samples of different cell lines. Figure 7A detects the detection of diluted samples for the H2009 cell line (human lung adenocarcinoma cells), including analysis based on 88 positive sites and 265 negative sites; Figure 7B detects the detection of HCC38 cell line (human breast ductal carcinoma cells ) detection of diluted samples, including analysis based on 41 positive sites and 312 negative sites; Figure 7C detects the detection of diluted samples for H1437 cell line (human non-small cell lung cancer cells), including analysis based on 48 positive sites and the analysis of 305 negative sites; Figure 7D has detected the detection of the diluted sample for HCC1395 cell line (human breast cancer cells), including the analysis based on 85 positive sites and 268 negative sites; Figure 7E has detected the analysis for H2126 Detection of diluted samples of a cell line (human lung cancer cell line), including analysis based on 91 positive loci and 262 negative loci. Among them, the abscissa 05pct represents the 5e-03 dilution, 01pct represents the 1e-03 dilution, 002pct represents the 2e-04 dilution, 0004pct represents the 4e-05 dilution, 00008pct represents the 8e-06 dilution, negative samples can represent the dilution is 0.

Detailed ways

The implementation of the invention of the present application will be described in the following specific examples, and those skilled in the art can easily understand other advantages and effects of the invention of the present application from the content disclosed in this specification.

Definition of Terms

In the present application, the term "variant nucleic acid" generally refers to a nucleic acid fragment after mutation such as insertion, addition, deletion and/or substitution at one or more positions of the nucleic acid sequence. For example, variant nucleic acid may comprise variant nucleic acid derived from tumor tissue, such as ctDNA. For example, variant nucleic acids may comprise variant nucleic acids derived from fetal tissue. For example, a variant nucleic acid may comprise a variant nucleic acid derived from a foreign tissue or organ.

In this application, the term "somatic mutation" generally refers to an acquired class of mutations that occur in non-embryonic cells. In the present application, the somatic mutation may include a genetic change occurring in a somatic tissue (eg, a cell outside the germline). In this application, the somatic mutations may include point mutations (for example, the exchange of a single nucleotide for another nucleotide (for example, silent mutations, missense mutations and nonsense mutations)), insertions and deletions (for example , addition and/or removal of one or more nucleotides (eg, indels), amplifications, gene duplications, copy number alterations (CNAs), rearrangements, and splice variants. The somatic mutations may be closely related to the processes of cell growth, programming, senescence and apoptosis. For example, the somatic mutations may be associated with alterations in signaling pathways in tumorigenesis, angiogenesis and/or tumor metastasis.

In the present application, the term "background mutation" generally refers to a mutation in a test sample that can be used for background reference. For example, a background mutation can be a heritable mutation in a subject, for example, a background mutation can be a mutation that both normal tissues as well as tumor tissues of the subject can have. For example, in order to determine more accurate background mutations, the method provided by this application can remove all mutations in the sample to be tested, somatic mutations detected in tumor tissue, and information corresponding to other sites that need to be excluded, so as to exclude definite mutations. Influence of points or regions on background calculations.

In the present application, the term "mutation site" generally refers to the site where there is a nucleotide difference compared with the nucleotide sequence of the control sequence. For example, the control sequence may be a reference sequence used in gene sequencing (for example, it may be a human reference genome). In the present application, the mutation site may include at least 1 (for example, 1, 2, 3, 4 or more) differences in the nucleotide sequence at the site (for example, the difference Nucleotide substitutions, duplications, deletions and/or additions may be included). For example, the mutation site may include a nucleotide mutation at at least one nucleotide position. The nucleotide mutation can be a natural mutation or an artificial mutation. The mutation site may comprise a single nucleotide variation (SNV).

In this application, the term "database" generally refers to an organized entity of related data, regardless of the manner in which the data or the organized entity is represented. For example, the organized entity of related data may take the form of a table, map, grid, group, datagram, file, document, list, or any other form. In this application, the database may include any data collected and stored in a computer-accessible manner.

In this application, the term "computing module" generally refers to a functional module for computing. The calculation module can calculate the output value or obtain a conclusion or result according to the input value, for example, the calculation module can be mainly used for calculating the output value. A computing module can be tangible, such as a processor of an electronic computer, a computer or electronic device with a processor, or a computer network, or it can be a program, command line or software package stored on an electronic medium.

In this application, the term "processing module" generally refers to a functional module for data processing. The processing module may be based on processing the input value into statistically significant data, for example, it may be a classification of data for the input value. A processing module may be tangible, such as an electronic or magnetic medium for storing data, and a processor of an electronic computer, a computer or electronic device with a processor, or a computer network, or it may be a program stored on an electronic medium, command line or package.

In this application, the term "judgment module" generally refers to a functional module for obtaining relevant judgment results. In this application, the judging module may calculate an output value or obtain a conclusion or a result according to an input value, for example, the judging module may be mainly used to obtain a conclusion or a result. The judging module can be tangible, such as a processor of an electronic computer, a computer with a processor or an electronic device or a computer network, or it can be a program, a command line or a software package stored on an electronic medium.

In this application, the term "sample obtaining module" generally refers to a functional module for obtaining said sample of a subject. For example, the sample obtaining module may include reagents and/or instruments required to obtain the sample (eg, tissue sample, blood sample, saliva, pleural effusion, peritoneal effusion, cerebrospinal fluid, etc.). For example, lancets, blood collection tubes, and/or blood sample transport boxes may be included. For example, the device of the present application may not contain or contain one or more of the sample obtaining modules, and may optionally have the function of outputting the measured value of the sample described in the present application.

In this application, the term "receiving module" generally refers to a functional module for obtaining said measured values in said sample. In this application, the receiving module may input the samples described in this application (such as tissue samples, blood samples, saliva, pleural effusion, peritoneal effusion, cerebrospinal fluid, etc.). In the present application, the receiving module may input the measured values of the samples described in the present application (such as tissue samples, blood samples, saliva, pleural effusion, peritoneal effusion, cerebrospinal fluid, etc.). The receiving module can detect the state of the sample. For example, the data receiving module may optionally perform the gene sequencing described in this application (eg, next-generation gene sequencing) on the sample. For example, the data receiving module may optionally include reagents and/or instruments required for the gene sequencing. The data receiving module can optionally detect sequencing depth, sequencing read length count or sequencing sequence information.

In this application, the terms "next-generation gene sequencing", high-throughput sequencing" or "next-generation sequencing" generally refer to the second-generation high-throughput sequencing technology and the higher-throughput sequencing methods developed thereafter. Next-generation sequencing Platforms include but are not limited to existing sequencing platforms such as Illumina. With the continuous development of sequencing technology, those skilled in the art can understand that the sequencing methods and devices of other methods can also be used for this method. For example, second-generation gene sequencing It can have the advantages of high sensitivity, high throughput, high sequencing depth, or low cost. According to the development history, influence, sequencing principles and technologies, there are mainly the following types: Massively Parallel Signature Sequencing (Massively Parallel Signature Sequencing, MPSS), Polony Sequencing, 454pyro sequencing, Illumina (Solexa) sequencing, Ion semi conductor sequencing, DNA nano-ball sequencing, Complete Genomics DNA nanoarrays and combined probe anchored ligation sequencing, etc. The second-generation gene sequencing can make it possible to analyze the transcriptome and genome of a species in detail, so it is also called deep sequencing (deep sequencing) For example, the method of the present application can also be applied to first-generation gene sequencing, second-generation gene sequencing, third-generation gene sequencing or single molecule sequencing (SMS).

In this application, the term "sample to be tested" generally refers to a sample that needs to be detected and determined whether there is a variant nucleic acid in one or more gene regions of the sample. For example, the sample to be tested or its data can be pre-stored in the memory before testing.

In this application, the term "human reference genome" generally refers to the human genome that can function as a reference in gene sequencing. The information of the human reference genome can refer to UCSC (University of California, Santa Cruz). The human reference genome can have different versions, for example, it can be hg19, GRCH37 or ensembl 75.

In this application, the term "sequencing depth" generally refers to the number of times a specific region (eg, a specific gene, a specific interval, or a specific base) is detected. The sequencing depth may refer to a base sequence detected by sequencing. For example, by comparing the sequencing depth to the human reference genome, and optionally removing duplicates, the number of sequencing reads on a specific gene, a specific interval, or a specific base position can be determined and counted as the sequencing depth. In some cases, sequencing depth can be correlated to sequencing depth. For example, sequencing depth can be affected by the mutation status of a gene.

In this application, the term "sequencing data" generally refers to data of short sequences obtained after sequencing. For example, the sequencing data includes the base sequence of a sequenced short sequence (sequencing read), the number of sequencing reads, and the like.

In this application, the term "significance test" generally refers to a way of judging whether the difference between a sample and a hypothesized distribution is significant. For example, through the significance test, it can be judged whether the somatic mutation of the sample to be tested is a significant difference.

In this application, the term "T-test" generally refers to a form of statistical hypothesis testing with a Student's t distribution. For example, the somatic mutation of a certain target gene in the sample to be tested can be confirmed to be significant by T-test.

In this application, the term "comprising" generally means including specifically specified features, but not excluding other elements.

In this application, the term "about" generally refers to a range of 0.5%-10% above or below the specified value, such as 0.5%, 1%, 1.5%, 2%, 2.5%, above or below the specified value. 3%, 3.5%, 4%, 4.5%, 5%, 5.5%, 6%, 6.5%, 7%, 7.5%, 8%, 8.5%, 9%, 9.5%, or 10%.

Detailed description of the invention

In one aspect, the application provides a method for detecting the presence and/or quantity of a variant nucleic acid, the method may comprise determining the variant based on the somatic mutation site and the background mutation site in the region to be tested in the sample to be tested The presence and/or amount of nucleic acid, wherein said background mutation site is determined by removing said somatic mutation site from all mutation sites in said test sample. For example, the region to be tested can be targeted and detected based on a probe or a combination of probes. For example, the region to be tested can be selected based on known mutation regions of tumors in the art. For example, the region to be tested can be selected according to the mutation region obtained after sequencing the tumor tissue. For example, somatic mutation sites can be selected based on sequencing data from a subject's tumor sample. For example, the somatic mutation site can be randomly selected based on the somatic mutation of the subject's tumor sample, or one or more sites with higher priority can be selected according to the ranking of somatic mutation frequency and the like. For example, select 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 15 or more, 20 or more , 25 or more, 30 or more, 40 or more, 50 or more, 60 or more or 100 or more sites for somatic mutation sites.

For example, the variant nucleic acid may be selected from the group consisting of circulating tumor nucleic acid, cell-free fetal nucleic acid (or may be referred to as circulating fetal nucleic acid), and circulating nucleic acid derived from allogeneic organs and/or tissues. For example, the variant nucleic acid can be circulating tumor DNA.

On the one hand, the method provided in the present application may also include performing base error correction on all mutation sites in the sample to be tested. For example, the correction of base errors can be a commonly used correction means in the art.

For example, the base error correction may comprise correcting the base type at each position of the sequencing reads originating from the same position based on a majority voting rule to determine a consensus sequence. For example, the correction includes adjusting the base quality of the site whose base type cannot be determined to 0. For example, the correction of the present application may include simultaneous correction of the sequencing reads of the sense strand and the antisense strand derived from the same site, that is, the sense strand and the antisense strand derived from the same nucleic acid fragment are corrected to retain a corrected consistency sequence. For example, the correction of the present application may include correcting the sequencing reads of the sense strand and the antisense strand derived from the same site separately, that is, after the sense strand and the antisense strand derived from the same nucleic acid fragment are corrected, two corrected consistent sequence.

For example, the base error correction may also include correcting the base type at each position of the sense strand and the antisense strand derived from the same site, retaining the respective consensus sequences of the sense strand and the antisense strand . For example, the correction may include adjusting the base quality of the sites of inconsistent bases derived from the same site to 0. For example, the correction may include deleting the base information of the positions of inconsistent bases derived from the same position. For example, the correction may include not using the base information of the positions of inconsistent bases derived from the same position for subsequent data analysis.

For example, the sequenced reads derived from the same locus may comprise sequenced reads that align to the same position in the human reference genome and comprise the same unimolecular signature (UMI). For example, the sequence reads derived from the same locus can comprise sequence reads that align to substantially the same position in the human reference genome.

For example, the method may include determining a mutation site in the sample to be tested based on the base error-corrected site. For example, the method of the present application may include selecting a mutation site in the sample to be tested from the base error-corrected sites.

For example, the method described in the present application may further comprise obtaining the background mutation sites by removing high-frequency mutation sites from all mutation sites in the sample to be tested. For example, the background mutation sites in the present application may include removing known tumor somatic mutation sites and high-frequency mutation sites from the mutation sites in the sample to be tested, and the remaining mutation sites.

For example, the high frequency mutation site may comprise a site with a mutation frequency of about 5e-03 or higher. For example, the high-frequency mutation sites can be adjusted according to factors such as sequencing accuracy and sample quality. For example, the high frequency mutation site may comprise a mutation frequency of about 1e-03 or higher, 5e-03 or higher, 1e-02 or higher, 5e-02 or higher, 1e-01 or higher, or 5e-01 or higher loci.

For example, the method of the present application may include removing sequence information that is unqualified for quality control from the sequence information of the sample to be tested. For example, the unqualified sequence information of quality control may include the unqualified sequence information determined by the sequencing quality control method commonly used in the art. For example, the sequence information of unqualified quality control may include sequence information of low-quality sequencing reads, sequence information of low-quality bases, and the like.

For example, the method may further include removing sequence information of low-quality sequencing reads (reads) from sequence information of the sample to be tested. For example, low-quality sequencing reads may contain alignment errors or difficult-to-align sequencing reads. For example, a low-quality sequencing read may be a sequencing read that, when the sequencing read is aligned to a human reference genome location, has a low probability value that the aligned position turns out to be correct. For example, the low quality sequencing reads may comprise sequencing reads having an alignment quality of less than 60. For example, for sequencing reads that are incorrectly aligned or difficult to align, the sequencing information of the sequencing reads may not be used as the sequence information of the alignment position. For example, the sequencing quality of the sequencing reads can be confirmed by a sequencing instrument and quality control methods commonly used in the art. For example, the low-quality sequencing reads can also include sequencing reads that include 8 or more base mismatches.

For example, the method further includes removing sequence information of low-quality bases from the sequence information of the sample to be tested. For example, sequence information of bases with small base masses is removed after correction. For example, the low-quality bases may include bases whose corrected base quality is less than 20. For example, for bases with a base quality of 20, the sequencing accuracy can be 99.99% or higher.

For example, the method described in the present application, the method may further comprise determining a mutation frequency selected from the group consisting of the somatic mutation frequency of the somatic mutation site and the background mutation frequency of the background mutation site for evaluating Site mutation significance level. For example, a mutation frequency derived from a somatic mutation site can be a somatic mutation frequency. For example, a mutation frequency derived from a background mutation site can be a background mutation frequency.

For example, in the method described in the present application, the mutation frequency may comprise multimer mutation frequency and/or insertion or deletion (INDEL) mutation frequency; for example, the model used to calculate the mutation frequency may be the multimer mutation frequency of sequencing data . For example, the model used to calculate mutation frequency can be the insertion or deletion (INDEL) mutation frequency of sequencing data. For example, INDEL can represent insertion or deletion.

For example, the multimer mutation frequency may include the frequency at which a base at a specific position in a specific contiguous base sequence is mutated into another base. For example, the frequency of single base mutation may include the frequency of mutation of a single base. For example, the multimer mutation frequency may include the frequency at which a base at an intermediate position is mutated in a consecutive sequence of bases.

For example, the contiguous base sequence may contain 2 or more bases contiguously. For example, the contiguous base sequence may comprise 2 or more, 3 or more, 5 or more, 7 or more, or 9 or more bases in a contiguous arrangement. For example, the contiguous base sequence may comprise 3 or 5 contiguous bases.

For example, the multimer mutation frequency may include the frequency at which the second base in a specific contiguous sequence is mutated to another specific base. For example, for the trimer mutation frequency, focus on the frequency of the second base mutating into another base in a specific arrangement environment of the first base and the third base.

For example, the INDEL mutation frequency may include the following groups: random INDEL mutation frequency, and base repeat region INDEL mutation frequency.

For example, the INDEL mutation frequency may comprise a random INDEL mutation frequency. For example, the random INDEL mutation frequency may comprise the frequency of insertion or deletion of one or more bases. For example, the random INDEL mutation frequency may comprise the frequency of insertion or deletion of one or more bases after a specified one or more bases. For example, the random INDEL mutation frequency may comprise the frequency of insertion or deletion of one or more bases after a specific one base; for example, the random INDEL mutation frequency may comprise the frequency of insertion or deletion of one or more bases after a specific The frequency with which one or more bases are subsequently inserted or deleted.

For example, when a base is inserted or deleted, the random INDEL mutation frequency may include the frequency of insertion or deletion of a specific base after a specific base. For example, when 2 or more bases are inserted or deleted, the random INDEL mutation frequency may include the frequency of insertion or deletion of a specific length of bases after a specific base. For example, when two or more bases are inserted or deleted, the specific base combination of the insertion or deletion may not be considered, and only the frequency of insertion or deletion of a specific length of bases after a specific one or more bases may be considered.

For example, the INDEL mutation frequency may include the frequency of INDEL mutations in base repeat regions. For example, the INDEL mutation frequency in the base repeating region may include the frequency of insertion or deletion of one or more base repeating units (Unit), and the length of the Unit is 1 or more.

For example, the INDEL mutation frequency in the base repeating region may include the frequency of insertion or deletion of one or more base repeating units (Unit), and the length of the Unit is 2 or more. For example, the Unit length is 2 bases or more, 3 bases or more, 4 bases or more, 5 bases or more, 6 bases or more, 7 bases or More, 8 bases or more, 9 bases or more, or 10 bases or more.

For example, the INDEL mutation frequency in the base repeat region may include the frequency of insertion or deletion of a specific number of Units in sequences with the same Unit length and the same Unit repetition number. For example, when the length of the Unit is 2 or more, the specific base combination of the Unit may not be considered, and only the frequency of insertion or deletion of one or more Units in a Unit with a specific number of repetitions may be considered. For example, determining the frequency of INDEL mutations in the base repeat region may include the frequency of insertion or deletion of a specific number of Units in sequences with the same Unit length and the same Unit repetition number, where Unit may include any sequence. For example, the Units of any combination of bases in this case can be combined for calculation.

For example, the method described in the present application may further comprise determining the presence of the variant nucleic acid in the sample to be tested and/or the significance level of the mutation at the somatic mutation site. For example, significantly mutated somatic mutation sites can be used to assess the presence of a variant nucleic acid. For example, when assessing the proportion of variant nucleic acids, only the data of somatic mutation sites with significant mutations can be used.

For example, the method may comprise measuring said level of significance by determining a cumulative probability of considering said somatic mutation site as a background mutation. For example, it is possible to estimate the cumulative probability that a candidate somatic mutation site is subject to background mutations. For example, the cumulative probability can be used to represent a significance level.

For example, the method may comprise determining the cumulative probability based on a Poisson distribution or a binomial distribution. For example, the method may comprise determining the cumulative probability based on a binomial distribution. For example, the method may comprise determining the cumulative probability based on a Poisson distribution.

For example, the method may comprise determining the cumulative probability based on the following formula:

Among them, P represents the cumulative probability, k is accumulated from 0 to x-1, x represents the coverage depth of the sequence after the mutation of the somatic cell mutation site, n represents the total coverage depth of the somatic cell mutation site, and p represents the somatic cell mutation site The background mutation frequency of the mutation site, e represents the natural logarithm.

Among them, P represents the cumulative probability, k is accumulated from 0 to x-1, x represents the coverage depth of the sequence after the mutation of the somatic cell mutation site, n represents the total coverage depth of the somatic cell mutation site, and p represents the somatic cell mutation site The background mutation frequency of the mutation site.

For example, the method may comprise determining the presence of a variant nucleic acid when the cumulative probability is less than a significance threshold. For example, the determination of the significance threshold can be adjusted by those skilled in the art according to the accuracy of the sequencing instrument and the quality of the sample to be tested. For example, the significance threshold is 0.05 or less. For example, the significance threshold is 0.05 or less, 0.01 or less, 0.005 or less, 0.001 or less, 0.0005 or less, or 0.0001 or less.

Such as the method described in the present application, the method may further comprise determining the presence and/or amount of the variant nucleic acid in the sample to be tested. For example, the method of the present application can be used to accurately determine the proportion of variant nucleic acid such as ctDNA in the sample to be tested. For example, the method of the present application for determining the proportion of variant nucleic acid and/or obtaining the significance level of the proportion can be evaluated based on the data of somatic mutation sites and background mutation sites.

For example, the method may comprise determining the presence and/or amount of the variant nucleic acid in the test sample by a likelihood estimation algorithm. For example, the method may comprise a likelihood estimation algorithm based on a Poisson distribution or a binomial distribution to determine the presence and/or amount of the variant nucleic acid in the test sample. For example, the quantity of the variant nucleic acid may comprise the proportion of circulating tumor DNA (ctDNA) in the total DNA of the test sample. For example, the maximum likelihood estimation value of ctDNA proportion π is determined by a maximum likelihood estimation algorithm.

For example, the method may comprise determining a maximum likelihood estimate of the ctDNA fraction π

When the value of π is the

, the function l(π) of the following formula takes the maximum value, for example, as known in the art, ln(x) represents the calculation symbol of the logarithm of x with the natural logarithm e as the base:

l(π)＝∑ _i w _i lnl _i (π; x _i ,n _i ,p _i ,q _i )

Among them, w _i is the weight of the mutation site in the i-th somatic cell, and l _i (π; x _i , ni _, p _i , q _i ) is calculated by the following formula:

Among them, f _i is calculated by the following formula:

f _i =πq _i +(1-π)p _i

n _i represents the total coverage depth of the i-th somatic mutation site, x _i is the coverage depth of the i-th somatic mutation site after mutation, q _i represents the i-th somatic mutation site in the tumor The mutation frequency in the tissue sample, p _i represents the background mutation frequency of the corresponding mutation in the sample to be tested, and e represents the natural logarithm.

When the value of π is the

When , the function l(π) of the following formula takes the maximum value:

l(π)＝∑ _i w _i lnl _i (π; x _i ,n _i ,p _i ,q _i )

Among them, f _i is calculated by the following formula:

f _i =πq _i +(1-π)p _i

n _i represents the total coverage depth of the i-th somatic mutation site, x _i is the coverage depth of the i-th somatic mutation site after mutation, q _i represents the i-th somatic mutation site in the tumor The mutation frequency in the tissue sample, _pi represents the background mutation frequency of the corresponding mutation in the sample to be tested.

For example, the method may comprise determining a significance level for the maximum likelihood estimate of the ctDNA proportion π. For example, the method may comprise determining the significance level by a likelihood ratio testing algorithm. For example, the method may comprise determining the significance level by a chi-square distribution based likelihood ratio testing algorithm. For example, the method described in this application, according to the likelihood ratio statistic

Values and a probability density function of the chi-square distribution with 1 degree of freedom determine the significance level.

For example, the method may comprise determining the likelihood ratio statistic by

value,

Among them, l(π)=∑ _i w _i lnl _i (π; x _i ,n _i ,p _i ,q _i )

Among them, w _i is the weight of the i-th individual cell mutation site, and the value of w _i is determined according to the somatic mutation frequency or sequencing coverage depth of the i-th individual cell mutation site, l _i (π; x _i , n _i , p _i ,q _i ) are calculated by the following formula:

Among them, f _i is calculated by the following formula:

f _i =πq _i +(1-π)p _i

For example, the method includes determining the likelihood ratio statistic by

value,

Among them, l(π)=∑ _i w _i lnl _i (π; x _i ,n _i ,p _i ,q _i )

Among them, f _i is calculated by the following formula:

f _i =πq _i +(1-π)p _i

n _i represents the total coverage depth of the i-th somatic mutation site, x _i is the coverage depth of the i-th somatic mutation site after mutation, and q _i represents the i-th somatic mutation site in the tumor The mutation frequency in the tissue sample, p _i represents the background mutation frequency of the corresponding mutation in the sample to be tested, and e represents the natural logarithm; for example, the value of each w _i can be the same, for example, the value of each w _i can be 1. For example, those skilled in the art can adjust the specific value of w _i from 0 to 1 according to the actual importance of the i-th somatic mutation site, such as the mutation frequency or sequencing coverage depth of the site.

In one aspect, the present application provides a method for detecting the presence and/or quantity of a variant nucleic acid, the method may include determining the mutation site based on the somatic mutation site and the background mutation site in the region to be tested in the sample to be tested. The presence and/or amount of somatic nucleic acid, wherein the background mutation site is determined by removing the somatic mutation site from all mutation sites in the test sample; optionally, the test sample can be All mutation sites in the sample are corrected for base errors; optionally, the background mutation sites in this application can include removing known tumor somatic mutation sites and high-frequency mutation sites from the mutation sites in the sample to be tested point, the remaining mutation site; optionally, the unqualified sequence information of the quality control can be removed from the sequence information of the sample to be tested; optionally, the type of mutation frequency evaluated by this application can be selected from single base mutation frequency, Multimer mutation frequency and insertion or deletion (INDEL) mutation frequency; optionally, the cumulative probability of considering the somatic mutation site as a background mutation can be determined based on a Poisson distribution or a binomial distribution; optionally , the likelihood estimation algorithm based on Poisson distribution or binomial distribution can be used to estimate the existence and/or quantity of the variant nucleic acid in the sample to be tested and determine the significance level of the estimated value of the proportion of the variant nucleic acid.

For example, the minimal residual disease detection method (PROPHET) of the present application can determine whether MRD is positive or negative by analyzing tumor somatic mutations in next-generation sequencing data generated by the amplicon method or hybridization capture method, and can belong to the tumor-informed method (tumor-informed method). -informed assay) strategy.

The detection method of the present application can use clear tumor somatic mutation information, for example, it can be obtained from tumor tissue, and used to detect tumor somatic mutation in peripheral blood. Specifically, it can be: 1) Perform whole-exome analysis on tumor tissue samples and paired samples group sequencing; 2) compare the sequencing results to the human reference genome based on commonly used comparison software such as bwa; 3) detect somatic mutations in tumor tissues based on commonly used somatic mutation analysis software such as mutect2; 4) detect somatic mutations Perform prioritization, and select a certain number of mutations based on the priority; 5) Based on the screened mutations, design hybridization capture probes, which are subsequently used for peripheral blood sample detection. Before sorting somatic mutations, mutations in high repeat regions, high GC regions, and regions homologous to other sequences can be filtered out to reduce the difficulty of hybridization capture. The priority of somatic cell sorting from high to low can be: 1) driver mutation (driver mutation), 2) mutations that cause amino acid sequence changes, including non-synonymous mutations, alternative splicing mutations, and in-frame/out- of-frame InDel et al., 3) synonymous mutations, each of these three types of mutations are arranged in descending order of mutation frequency.

The analysis of this application may specifically include the following steps: 1) data preparation, including correcting base errors based on specific molecular tags (UMI, Unique Molecular Identifier), and aligning the corrected reads to the human genome; 2) based on the read length ( reads) compare the results, and calculate the sample-specific background level; 3) calculate the mutation rate of the mutation site of the somatic cell to be tested; 4) evaluate it as a true mutation according to the background level of each mutation site of the somatic cell to be tested 5) Based on all the somatic loci screened, according to the background level, evaluate the proportion of sample ctDNA and the significance level of sample ctDNA.

data preparation

Since the probability of base errors in library construction and sequencing can be at the 1e-03 level, and MRD detection requires detection at the 1e-04 level, base correction can optionally be performed by UMI, or using methods commonly used in the art, such as Choose a more accurate method for library construction and sequencing to reduce base errors in library construction and sequencing. The data preparation step can generate sequence alignment files in BAM format after UMI deduplication and base correction. The principle of UMI base correction is to use the sequencing sequences of multiple PCR products from the same molecular source to correct base errors in the process of library construction and sequencing. The specific steps can be: 1) based on the commonly used next-generation sequencing comparison software bwa (version 0.7.10), compare the sequencing reads to the human reference genome; 2) use the comparison information and UMI information to compare the genome positions And all reads with the same UMI are regarded as reads from the same molecular source, and they are classified as one unit and the unit with the number of reads greater than a certain threshold is reserved; 3) Determine the base at each position in the unit based on the majority voting rule, and finally generate A consistent reads representing this unit; 4) Align the consistent reads to the genome to generate a BAM file.

In order to use the sequence information from the same molecular source to correct base errors, the applicant can optionally use the double-end UMI duplex library construction method during hybridization library construction. UMI duplex library construction can distinguish molecules from different strands of double-stranded DNA, and this information can be used to correct each other in the subsequent base correction. When performing base error correction, based on UMI and alignment position information, the reads from the same DNA chain can be corrected based on the majority voting rule, and the undetermined bases are set to N and the quality is 0, and other bases The quality can be set to the highest value to generate single-strand consensus sequences or SSCSs; and then correct the sequences derived from different strands of the same DNA, the quality of inconsistent bases in the double strand can be adjusted to 0, but these two can be retained SSCSs. Since the ctDNA molecule is only about 164bp, the sequencing read length can usually reach about 150bp. The method of the present application can optionally use the overlapping parts of the sequencing read lengths of R1 and R2 derived from the same DNA chain sequenced for recalibration, and adjust the inconsistent base quality in R1 and R2 to 0. The method provided in this application can optionally distinguish the reads from different strands of the same DNA, so that the loss of this part of the correction information can be avoided during the subsequent base correction.

Sample-specific SNV background

The sample-specific background is based on the alignment information of the sequencing target region BAM file. Various multimer mutation frequencies can optionally be calculated as the sample-specific background frequency. For example, when calculating the frequency of trimer mutations, we can pay attention to whether the second base is changed, and the remaining two bases are fixed. For example, a trimer composed of one base at a certain position in the target area and one base on the left and right is AGC, and the alignment result at this position includes 4 ACCs, 6 ATCs, 10 AACs and 99980 AGCs, then its AGC-> The ACC trimer transition frequency was 4e-05, the AGC->ATC transition frequency was 6e-05, and the AGC->AAC transition frequency was 1e-04. Here, the trimer can also be changed into oligomers of other lengths, and the calculation method can be similar to that of the trimer. The specific steps for sample-specific background calculation are: 1) Count the number of trimers corresponding to all sites in the sequencing target region; 2) Remove all somatic mutation sites and other sites that need to be excluded. 3) All trimer information corresponding to sites with a mutation frequency higher than a specific threshold such as 5e-03 can be removed to exclude other potential The interference of mutations to the background calculation; 4) The trimer information of the remaining sites was integrated, and the frequency of each mutation was calculated based on the trimer mutation type, which was used as the specific background mutation level of the sample. In order to exclude the impact of sequence alignment and low-quality bases on the background noise evaluation, when calculating the background, you can optionally filter the reads whose alignment quality is less than 60 or include 8 or more base mismatches, and in addition Trimers with lower base quality may also optionally be discarded. When calculating the sample-specific background, the sequencing data information of the sample to be analyzed can be used, and it does not rely on other normal samples or other samples of the same batch as controls, which is beneficial to eliminate background fluctuations caused by inter-sample factors or experimental batch factors. In addition, when calculating the sample-specific background, it makes full use of all the information of the sequencing target region, integrates the information belonging to the same trimer at different positions, and effectively solves the problem of inaccurate background assessment due to insufficient data.

Sample-specific InDel background

In order to make full use of the mutation information of the sample, in addition to the SNV, the method can also use the InDel mutation. When calculating the sample-specific InDel background, based on the InDel sequence characteristics, it is divided into two categories: 1) random InDel, 2) base repeat region InDel, represented by (Unit)n, where Unit represents a repeating unit, which can be Single base or multiple bases, n represents the number of repetitions, generally 2 or more. InDel in the base repeat region generally manifests as single or multiple indels of repeating units. The background steps for calculating InDel are similar to SNV, specifically: 1) For all sites in the sequencing target region, based on their reference sequences, count their sequencing The number of InDel signals and non-InDel signals; 2) Remove all somatic mutation sites and other information corresponding to other sites that need to be excluded, so as to exclude the impact of specific mutation sites or regions on the background calculation; 3) The mutation frequency can be removed All the information corresponding to the site above a specific threshold such as 5e-03 to exclude the interference of other potential mutations on the background calculation; 4) Integrate the information of the remaining sites together, and calculate the frequency of each mutation based on the InDel type, as Specific InDel background mutation level for this sample.

For random InDels, in background statistics, this application can count different types of InDel background values according to the type of the base before the InDel position and the length of the InDel insertion or deletion. When a single base is inserted or deleted, the previous base can be combined with the indel base, and the related frequency can be counted separately. For example, when a single base A is inserted or deleted, TA->T, GA-> can be counted separately Background frequency of G, CA->C, T->TA, G->GA, C->CA. When the indel has 2 or more bases, due to the excessive number of combinations and the small number of single type target sites, it is optional to calculate the background mean value of the same length bases without separate statistics.

For (Unit)n, where Unit is a single-base mutation, this application counts the background value based on the type of Unit in the reference sequence, the value of n, and the number of indels. For (Unit)n, when Unit is 2 bases, you can optionally ignore the specific sequence of Unit, merge all InDels with Unit length 2 and the same number of repetitions n, and calculate the corresponding background. For example, the mutations GATAT->GAT, CTGTG->CTG, all of which belong to Unit is 2, n is 2, and there is one missing mutation, will be combined and processed when calculating the background noise. When the Unit length is greater than 2, the processing method can be the same as when the Unit is 2.

For the length n of Unit, this application assumes that there is a correlation between the background error rate and n when the base type of Unit and the number of indels are the same, the specific connection is as follows:

Where p _n (Unit|n ₁ ) represents the background error rate of n ₁ insertions (deletions) when a specific Unit has n repetitions. Under this assumption, using the detection information of all sites that meet the conditions, it can be estimated

Then the error rate of repeating n times the site is:

somatic mutation site mutation signal

According to the comparison information of the BAM file, for the pre-selected target cell sites, the specific SNV mutation frequency is calculated based on the trimer pattern, or the corresponding mutation frequency is calculated based on the InDel type. For example, when the original trimer of a specific somatic cell to be tested is CAG, and the somatic cell mutation is A->G, the mutation frequency of CAG->CGG can be calculated. Likewise, the effects of low-quality alignments or low-quality bases are optionally excluded from the calculations.

Significance assessment of somatic mutation sites

The binomial distribution is to repeat n times independent Bernoulli experiments. In each experiment, there are only two possible results, and whether the two results occur are opposite to each other and independent of each other, which is in line with the description of the sample background mutation scene. In addition, when the n of the binomial distribution is large enough and the event probability p is small enough, the number of observed events approximately obeys the Poisson distribution (λ=np). Therefore, the method of the present application can adopt the assumption of Poisson distribution (x~Binom(n,p)) or binomial distribution (x~Poison(np)) to calculate the significance of somatic mutations. For example, the present application adopts the assumption of Poisson distribution for calculation, which can have higher evaluation result accuracy.

The method of the present application calculates the cumulative probability P value under the background condition according to the observed value of the specific mutation of the somatic cell site and the frequency of the mutation in the sample background. When the P value is less than a specific threshold, it can be considered that the mutation frequency of this site is significantly higher than the sample background, and this position is a true mutation. Assuming that the mutation type of the site to be detected is A->G, the original trimer is CAG, and the coverage depth of the site is observed to be n, where the number of CGGs is x, then the p value of the mutation detection at the site is:

or

where p(CAG→CGG) is the background frequency.

In addition to calculating SNV significance, the method is also applicable to calculating INDEL significance. If a certain INDEL to be detected is AGGG->AGG, the coverage depth of this point is n1, and the number of observed AGGs is x1, you can replace n in the above formula with n1, x with x1, and p(CAG→CGG) with p(AGGG→AGG) is enough. By analogy, all types of INDE1 or SNV mutations can use this method to calculate the significance of their sites.

Sample ctDNA significance level and ctDNA proportion evaluation

In practical applications, the effective sequencing depth of samples is limited due to the influence of peripheral blood sampling volume and testing cost. When the proportion of ctDNA is as low as 0.02% or less, if the average effective depth is 10000X, there are only about 2 or less mutation signals per point on average, so some somatic mutation sites may be difficult to detect in peripheral blood data Mutational signal, and taking into account the background level of various mutations, it may be difficult to directly calculate the proportion of ctDNA. The method of the present application can use the multi-site joint test method to determine whether ctDNA exists in the sample, and use the likelihood method to estimate the proportion of ctDNA in the sample. Assuming that the proportion of ctDNA is π, the frequency of the ith somatic cell mutation in the tumor tissue sample is q _i , and the background frequency of the corresponding mutation in the detection sample is p _i , then the expected somatic mutation frequency in the detection sample is f _i satisfies:

f _i =πq _i +(1-π)p _i

Using the likelihood method to estimate the parameter π, the log-likelihood function is:

l(π)＝∑ _i w _i lnl _i (π; x _i ,n _i ,p _i ,q _i )

Where w _i is the weight of the i-th somatic cell mutation to be tested. In actual analysis, the value of the weight can be set according to the type and reliability of the mutation. n _i represents the effective coverage depth of the i-th somatic cell mutation to be tested. x _i is the target mutation depth of the ith somatic cell mutation to be tested, and l _i (π) is the posterior probability of the ith somatic cell mutation to be tested:

or

Among them, f _i = πq _i + (1-π) p _i

The parameter π is estimated by the maximum likelihood estimation algorithm, and the maximum likelihood estimation value is obtained

Using the likelihood ratio test algorithm to test the null hypothesis π=0, the likelihood ratio statistic is:

use

The probability density function of the distribution allows the calculation of the P value.

On the other hand, the present application also provides a method for detecting the presence and/or quantity of a variant nucleic acid, the method may include determining the somatic mutation site based on the mutation priority of the somatic mutation site in the variant sample to be detected. Point set, the somatic mutation site set can be used to detect the presence and/or quantity of variant nucleic acid, and the mutation priority from high to low can include: driver mutations, non-synonymous mutations other than driver mutations, and synonymous mutations mutation.

For example, the sample of the variant to be tested may be derived from a sample obtained from the subject prior to receiving treatment. For example, the treatment may comprise tumor treatment.

For example, the somatic mutation site can be determined by comparing the variant sample to be detected with a negative sample.

For example, non-synonymous mutations other than the driver mutation may be selected from the group consisting of alternative splicing mutations, insertions or deletions that do not cause a shift in the reading frame of the gene (in-frame INDEL), and insertions or deletions that cause a shift in the reading frame of the gene. Missing (out-of-frame INDEL).

For example, the method may include sorting the somatic mutation sites according to mutation priority from high to low, wherein in the same mutation priority, the somatic mutation sites may be sorted according to mutation frequency from high to low.

For example, the method may include selecting the five or more highest-ranked mutation sites as the set of somatic mutation sites. For example, the method of the present application may include selecting the highest ranked 1 or more, the highest 2 or more, the highest 3 or more, the highest 4 or more, the highest 5 or more, Highest 6 or more, Highest 7 or more, Highest 8 or more, Highest 9 or more, Highest 10 or more, Highest 15 or more, Highest 20 or more, up to 25 or more, up to 30 or more, up to 40 or more, up to 50 or more, or up to 100 or more mutation sites As the set of somatic mutation sites.

For example, the method may further include determining a region to be tested in the sample to be tested based on the set of somatic mutation sites. For example, the method may further comprise determining nucleic acids that can bind to the region to be tested based on the set of somatic mutation sites. For example, the method of the present application may include designing probes for detecting the sample to be tested based on the set of somatic mutation sites.

On the other hand, the present application also provides an analysis device for detecting the presence and/or quantity of variant nucleic acid. mutation site and background mutation site, determining the presence and/or quantity of the variant nucleic acid, wherein the background mutation can be determined by removing the somatic mutation site from all mutation sites in the sample to be tested location. For example, the analytical device of the method for detecting the presence and/or quantity of variant nucleic acids of the present application may comprise a module for performing the method for detecting the presence and/or quantity of variant nucleic acids described in the present application.

On the other hand, the present application also provides a method for establishing a database, the database includes a collection of somatic mutation sites, and the method may include determining the mutation priority of the somatic mutation sites based on the variant sample to be tested. A set of somatic mutation sites, the set of somatic mutation sites is used to detect the presence and/or quantity of variant nucleic acids, and the priority of the mutations includes: driver mutations, non-synonymous mutations other than driver mutations, and synonymous mutation. For example, the method for establishing the database of the present application may include a method of determining a set of somatic mutation sites based on the mutation priority of the somatic mutation sites in the variant sample to be tested.

On the other hand, the present application also provides a device for establishing a database, the database includes a collection of somatic mutation sites, and the device includes a determination module for mutation of somatic mutation sites based on the variant sample to be detected Priority, determine the set of somatic mutation sites, the set of somatic mutation sites is used to detect the presence and/or quantity of variant nucleic acid, the mutation priority from high to low includes: driver mutation, non-driver mutation Synonymous mutations and synonymous mutations. For example, the device for establishing a database in this application may include a module for executing the method for establishing a database in this application.

On the other hand, the present application also provides a database, which can be established according to the database establishment method of the present application.

On the other hand, the present application also provides a storage medium, which records a program capable of running the method described in the present application. For example, the non-transitory computer readable storage medium may include a floppy disk, a flexible disk, a hard disk, a solid state storage (SSS) (such as a solid state drive (SSD)), a solid state card (SSC), a solid state module (SSM)), an enterprise high-grade flash drives, tape, or any other non-transitory magnetic media, etc. Non-transitory computer readable storage media may also include punched cards, paper tape, cursor sheets (or any other physical media having a pattern of holes or other optically identifiable markings), compact disc read only memory (CD-ROM) , Rewritable Disc (CD-RW), Digital Versatile Disc (DVD), Blu-ray Disc (BD) and/or any other non-transitory optical media.

On the other hand, the present application also provides a device, which includes the storage medium described in the present application. For example, the device of the present application may further include a processor coupled to the storage medium, and the processor is configured to execute based on a program stored in the storage medium to implement the method of the present application.

On the other hand, the present application also provides the method according to the present application, which can be used to detect and/or quantify circulating tumor DNA in a test sample obtained from a subject. In the present application, the method can be used to determine the presence and/or content of circulating tumor DNA in the test sample of the subject. For example, any one or more of the methods of the present application may be for non-diagnostic purposes. For example, any one or more of the methods of the present application may be for diagnostic purposes.

On the other hand, the present application also provides the method according to the present application, which can be used for the diagnosis, prevention and/or concomitant treatment of the disease or residual disease.

On the other hand, the present application also provides the method according to the present application, which can be used for prediction, selection and/or evaluation of disease treatment methods. For example, it can be determined or aided in determining the likelihood that a subject has cancer or has a recurrence of cancer that would benefit from anticancer therapy, including chemotherapy, immunotherapy, radiation therapy, surgery, or a combination thereof.

In this application, the method can be used in clinical practice by detecting the presence and/or content of circulating tumor DNA in the test sample (for example, it can be speculated whether certain specific tumor treatment methods are suitable for the subject) . In some cases, the presence and/or content of circulating tumor DNA in the test sample detected by the method can be used in clinical practice in combination with biomarkers known in the art.

Not intending to be limited by any theory, the following examples are only for explaining the methods and uses of the present application, and are not intended to limit the scope of the invention of the present application.

Example

Example 1

In this application, a total of 25 real samples were selected, and their InDel observable signals were analyzed to preliminarily evaluate the background mutation frequency. Based on the statistical results, it was found that in (Unit)n type InDel, when the number of repeat units inserted or deleted is the same, the observable signal frequency increases exponentially with the increase of n, as shown in Figure 1A-1B. Wherein, Unit represents the base length of the repeating unit, and n represents the number of repetitions of the repeating unit.

At the same (Unit)n, the observed signal frequency of 2 indels is lower than that of 1 indel, and the observable signal is weak when there are 3 or more indels, as shown in Figure 2A-2B.

Compared with base repeat units with a length of 1 base, the frequency of indel observable signals of repeat units with a length of 2-3 bases is equivalent or increased, as shown in Figures 3A-3B.

Compared with repeat unit indels, when random indels have 1-2 bases, the frequency of observable signals is very low, at the 1e-7 level, as shown in Figure 4.

When considering MRD detection, the proportion of ctDNA is low, such as 2e-4 or below. When the number of repeats n<=3 or random indels, the observable signal of InDel can be below 1e-5, so this type of mutation can be included in the MRD analysis, and for samples with a ctDNA ratio above 1e-5, it can be achieved for MRD accurate detection.

Example 2

This application selects 1 cell line to be tested and 1 background cell line as research materials, and dilutes them into 5 gradient dilution samples of 5e-03, 1e-03, 2e-04, 4e-05, 8e-06. Samples with different ctDNA ratios. From the cell lines to be tested, 88 mutation sites different from the background cell lines were selected to design probes and captured for sequencing. Finally, each diluted sample was sequenced three times, and a total of 15 diluted samples were obtained. The input amount for each sample was 30ng, and the average sequencing depth of the target area was 100,000X. Then, from these 88 sites, 5-60 mutation sites were randomly selected to analyze the ctDNA ratio, and the number of repetitions was 50 times, so a total of 150 analysis tests were performed for each dilution gradient. When the sample Pvalue<0.01 is selected as the threshold for sample detection, when the dilution gradient is 5e-3 or 1e-3, 5 mutation sites can complete the detection of 100% of the sample; when the dilution gradient is 2e-4 100% of samples can be detected with 15 mutation sites or more; when the dilution gradient is 4e-5, 100% of samples can be detected with 40 mutation sites or more, as shown in Table 1.

Table 1 Sample detection ratio results

When evaluating the results of the dilution ratio, it is found that when the dilution gradient is 5e-3 or 1e-3, 5 mutation sites can calculate the dilution ratio more accurately; when the dilution gradient is 2e-4, 15 mutation sites The dilution ratio can be calculated more accurately; when the dilution gradient is 4e-5, the dilution ratio can be calculated more accurately for 40 mutation sites or more, as shown in Figures 5A-5B.

Example 3

Compared the detection effect of the background construction method (PROPHET) used in the application method (PROPHET) and the background construction method (INVAR) of the 10 bp region before and after the mutation site, the parallel analysis of the two methods was carried out on the 15 sequencing samples in Example 2. Similarly, 5-60 sites were arbitrarily selected from 88 sites, and the number of repetitions was 50 times. In addition, 5-60 negative sites were randomly selected from the 88 sites, and the number of repetitions was also 50 times, in order to evaluate the specificity of the detection. When the sample Pvalue<0.01 is selected as the threshold, the detection sensitivity of the method of this application can be better than that of the INVAR method for the case of less than 40 sites, as shown in Table 2 and Figure 6.

Table 2 Comparison of detection effects of different methods

At the same time, the known INVAR method not only uses the sequencing information of 10 bp before and after the mutation site, but also uses the same target combination (panel) to capture and sequence the sequencing information of multiple samples. Therefore, in addition to the mutation site of the sample itself, the INVAR method also includes sequencing information of 10 bp before and after the mutation site of other samples sequenced at the same time, so the total number of optional mutation sites is relatively large. However, the method of this application is more suitable for the panel of a single sample, and can be applied to the detection environment with fewer total optional mutation sites.

Example 4

In order to measure the evaluation effect of INDEL and SNV on the proportion of ctDNA, this application selected a standard product data and a background cell line for dilution, and diluted them into 2.5e-3, 1.25e-3, 6.25e-4, 3.125e- 4. Seven gradients of 1.6e-4, 8e-5, and 4e-5 simulate samples with different ctDNA proportions. Within the scope of sequencing, the standard contains a total of 28 effective mutations, including 8 INDEL mutations and 20 SNV mutations. The average sequencing depth of the diluted products is 60000X. In this application, 8 INDELs, 8 SNVs (randomly selected), and 28 mutations were used to analyze the proportion of ctDNA, and the results are shown in Table 3.

Table 3 Standard product dilution sample analysis results

Based on the results, in the case of selecting 8 mutation sites, SNV or INDEL can be accurately estimated at the dilution level of 1.6e-4 and above when analyzed separately, and the significance pvalue<0.01 is satisfied, and at 8e-5 or below , INDEL calculation results can be better than SNV; when SNV and INDEL are used for combined analysis, the dilution gradient can be accurately calculated under all gradient dilutions of the experiment, and the significance pvalue<0.01 is satisfied.

Example 5

This application selected 5 cell lines to be tested and 1 background cell line as research materials, and each cell line to be tested and the background cell line were diluted to 5e-03, 1e-03, 2e-04, 4e-05 , 8e-06, a total of 5 gradient dilution samples, simulating samples with different ctDNA proportions. In each cell line to be tested, 40-100 unique germline mutations were selected as somatic mutation sites, and corresponding hybridization probes were designed for subsequent experiments. Finally, three repeated experiments were carried out on each diluted sample and background sample, and a total of 90 sample data were obtained. The input amount for each sample was 30ng, and the average sequencing depth of the target region was 100,000X. Then use the method of this application to analyze these sequencing data, and calculate the detection of the site and the detection of the sample. Table 4 shows the evaluation and significance level results of the mixing ratio (proportion of simulated ctDNA) of the sample, and Figure 7A-7E shows the detection status of the site when the pvalue<0.05.

Table 4 Analysis results of cell line dilution samples

Based on the sample analysis results, it can be seen that when the dilution gradient is 5e-03 to 4e-05, the method of the present application can evaluate the dilution level more accurately, and the sample significance pvalue is low. At the 8e-06 level, the dilution level estimation The value is quite different from the actual one. Based on the site analysis results, it can be known that when the dilution gradient is 5e-03 to 4e-05, the sensitivity drops from 100% to about 15%, but they are all significantly higher than (1-specificity). At the 8e-06 level, The sensitivity drops to about 5%, which is relatively close to (1-specificity). Therefore, it is verified that the detection method of this application can detect ctDNA as low as about 4e-05, which is lower than the level of ctDNA detection limit as low as 2e-04 given by the consensus, and provides data support and support for subsequent application in the detection of minimal residual lesions. auxiliary.

The foregoing detailed description has been offered by way of explanation and example, not to limit the scope of the appended claims. Variations on the presently recited embodiments of this application will be apparent to those of ordinary skill in the art and remain within the scope of the appended claims and their equivalents.

Claims

A method for detecting the presence and/or amount of variant nucleic acid, the method comprising determining the presence and/or quantity of the variant nucleic acid based on somatic mutation sites and background mutation sites in the region to be tested in the sample to be tested Quantity, wherein the background mutation site is determined by removing the somatic mutation site from all mutation sites in the sample to be tested.
The method according to claim 1, wherein the variant nucleic acid is selected from the group consisting of circulating tumor nucleic acid, fetal free nucleic acid and circulating nucleic acid derived from allogeneic organs and/or tissues.
The method according to any one of claims 1-2, further comprising performing any one or more of the following base error corrections on the mutation site of the sample to be tested, and based on the base error The corrected site is used to determine the mutation site in the sample to be tested;

1) The base error correction includes correcting the base type at each position of the sequencing reads from the same position based on the majority voting rule, and determining the consensus sequence;

2) The base error correction includes adjusting the base quality of the site where the base type cannot be determined to 0;

3) the base error correction comprises correcting the base type at each position of the sense strand and the antisense strand derived from the same site, and retaining the respective consensus sequences of the sense strand and the antisense strand;

4) The base error correction includes adjusting the base quality of the site of inconsistent bases in the sense strand and the antisense strand derived from the same site to 0;

The sequencing reads derived from the same locus include sequencing reads that align to the same position in the human reference genome and include the same unimolecular signature (UMI).
The method according to any one of claims 1-3, further comprising performing any one or more of the following filters from all mutation sites of the sample to be tested to obtain the background mutation site ;

1) Remove high-frequency mutation sites;

2) removing the unqualified sequence information from the sequence information of the sample to be tested;

3) removing the sequence information of low-quality sequencing reads (reads) from the sequence information of the sample to be tested;

4) The sequence information of low-quality bases is removed from the sequence information of the sample to be tested.
The method according to claim 4, wherein the high-frequency mutation site comprises a site with a mutation frequency of about 5e-03 or higher, and the low-quality sequencing reads comprise sequencing reads with an alignment quality less than 60 and/or Or sequence reads containing 8 or more base mismatches, and/or the low-quality bases include bases with a corrected base quality of less than 20.
The method of any one of claims 1-5, further comprising determining a mutation frequency selected from the group consisting of the somatic mutation frequency of the somatic mutation site and the background of the background mutation site Mutation frequency, used to assess the significance level of site mutations.
The method according to claim 6, wherein the mutation frequency comprises a single base mutation frequency, a multimer mutation frequency and/or an INDEL mutation frequency.
The method according to claim 7, wherein the multimer mutation frequency comprises the frequency of mutation of a base at a specific position in a specific continuous sequence of bases to another base, wherein the continuous sequence of bases Contains 2 or more bases arranged in a row, or the sequence of bases arranged in a row includes 3 bases arranged in a row.
The method according to any one of claims 7-8, wherein the multimer mutation frequency comprises the frequency at which the base at position 2 is mutated into another specific base in a specific contiguous sequence.
The method according to any one of claims 7-9, wherein said INDEL mutation frequency comprises random INDEL mutation frequency and/or base repeat region INDEL mutation frequency, or said random INDEL mutation frequency comprises insertion or deletion of one or more The frequency of a plurality of bases, or the random INDEL mutation frequency includes the frequency of insertion or deletion of one or more bases after a specific one or more bases, or the random INDEL mutation frequency is included in a specific How often one or more bases are inserted or deleted after one base.
The method according to any one of claims 1-10, further comprising determining the presence of the variant nucleic acid in the test sample and/or the significance level of the mutation at the somatic mutation site.
The method of claim 11 , comprising measuring said level of significance by determining a cumulative probability of background mutation frequency for said somatic mutation site.
The method of claim 12, said method comprising determining said cumulative probability based on the following formula:

Among them, P represents the cumulative probability, k is accumulated from 0 to x-1, x represents the coverage depth of the sequence after the mutation of the somatic cell mutation site, n represents the total coverage depth of the somatic cell mutation site, and p represents the somatic cell mutation site The background mutation frequency of the mutation site, e represents the natural logarithm;

And/or, the method comprises determining the cumulative probability based on the following formula:

Among them, P represents the cumulative probability, k is accumulated from 0 to x-1, x represents the coverage depth of the sequence after the mutation of the somatic cell mutation site, n represents the total coverage depth of the somatic cell mutation site, and p represents the somatic cell mutation site The background mutation frequency of the mutation site.
The method of any one of claims 12-13, comprising determining the presence of a variant nucleic acid when the cumulative probability is less than a significance threshold, wherein the significance threshold is 0.05 or less.
The method according to any one of claims 1-14, said method comprising a likelihood estimation algorithm based on Poisson distribution or binomial distribution to determine the presence and/or quantity of variant nucleic acid in the sample to be tested, wherein, The quantity of the variant nucleic acid includes the proportion of circulating tumor DNA (ctDNA) in the total DNA of the sample to be tested.
The method of claim 15, comprising determining a maximum likelihood estimate of the ctDNA fraction π
When the value of π is the
When , the function l(π) of the following formula takes the maximum value:

l(π)＝∑ i w i lnl i (π; x i ,n i ,p i ,q i )

Among them, w i is the weight of the mutation site in the i-th somatic cell, and l i (π; x i , ni , p i , q i ) is calculated by the following formula:

Among them, f i is calculated by the following formula:

f i =πq i +(1-π)p i

n i represents the total coverage depth of the i-th somatic mutation site, x i is the coverage depth of the i-th somatic mutation site after mutation, q i represents the i-th somatic mutation site in the tumor The mutation frequency in the tissue sample, pi represents the background mutation frequency of the corresponding mutation in the sample to be tested, and e represents the natural logarithm;

And/or, the method comprises determining a maximum likelihood estimate of the ctDNA proportion π
When the value of π is the
When , the function l(π) of the following formula takes the maximum value:

l(π)＝∑ i w i lnl i (π; x i ,n i ,p i ,q i )

Among them, w i is the weight of the mutation site in the i-th somatic cell, and l i (π; x i , ni , p i , q i ) is calculated by the following formula:

Among them, f i is calculated by the following formula:

f i =πq i +(1-π)p i

n i represents the total coverage depth of the i-th somatic mutation site, x i is the coverage depth of the i-th somatic mutation site after mutation, q i represents the i-th somatic mutation site in the tumor The mutation frequency in the tissue sample, pi represents the background mutation frequency of the corresponding mutation in the sample to be tested.
The method of claim 16, comprising determining the significance level of the maximum likelihood estimate of the ctDNA proportion π by a likelihood ratio test algorithm.
The method of claim 17, said method comprising determining said likelihood ratio statistic by
value,

Among them, l(π)=∑ i w i lnl i (π; x i ,n i ,p i ,q i )

Among them, w i is the weight of the i-th individual cell mutation site, and the value of w i is determined according to the somatic mutation frequency or sequencing coverage depth of the i-th individual cell mutation site, l i (π; x i , n i , p i ,q i ) are calculated by the following formula:

Among them, f i is calculated by the following formula:

f i =πq i +(1-π)p i

n i represents the total coverage depth of the i-th somatic mutation site, x i is the coverage depth of the i-th somatic mutation site after mutation, q i represents the i-th somatic mutation site in the tumor The mutation frequency in the tissue sample, pi represents the background mutation frequency of the corresponding mutation in the sample to be tested, and e represents the natural logarithm;

And/or, the method comprises determining the likelihood ratio statistic by
value,

Among them, l(π)=∑ i w i lnl i (π; x i ,n i ,p i ,q i )

Among them, w i is the weight of the i-th individual cell mutation site, and the value of w i is determined according to the somatic mutation frequency or sequencing coverage depth of the i-th individual cell mutation site, l i (π; x i , n i , p i ,q i ) are calculated by the following formula:

Among them, f i is calculated by the following formula:

f i =πq i +(1-π)p i

n i represents the total coverage depth of the i-th somatic mutation site, x i is the coverage depth of the i-th somatic mutation site after mutation, q i represents the i-th somatic mutation site in the tumor The mutation frequency in the tissue sample, p i represents the background mutation frequency of the corresponding mutation in the sample to be tested, and e represents the natural logarithm.
An analysis device for detecting the presence and/or quantity of a variant nucleic acid, the device comprising a judgment module for determining the variant nucleic acid based on the somatic mutation site and the background mutation site in the region to be tested in the sample to be tested The presence and/or quantity of somatic nucleic acid, wherein the background mutation site is determined by removing the somatic mutation site from all mutation sites in the sample to be tested.
A storage medium recording a program capable of executing the method according to any one of claims 1-18.