CN117604086A - Quantitative method for ctDNA level of blood plasma of subject - Google Patents

Quantitative method for ctDNA level of blood plasma of subject Download PDF

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CN117604086A
CN117604086A CN202311540699.6A CN202311540699A CN117604086A CN 117604086 A CN117604086 A CN 117604086A CN 202311540699 A CN202311540699 A CN 202311540699A CN 117604086 A CN117604086 A CN 117604086A
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mutation
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CN117604086B (en
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曾晓玲
高伟
曹务强
杜新华
管彦芳
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Beijing Jiyinjia Medical Laboratory Co ltd
Shenzhen Guiinga Medical Laboratory
Suzhou Jiyinjia Biomedical Engineering Co ltd
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Shenzhen Guiinga Medical Laboratory
Suzhou Jiyinjia Biomedical Engineering Co ltd
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Abstract

The present disclosure provides a method and apparatus for quantifying the level of ctDNA in the plasma of a subject, the method comprising: detecting somatic mutations in tumor tissue and the copy number of alleles of tumor cells, obtaining tumor cell content, obtaining CCF for each individual cell mutation, and obtaining the genotype of the somatic mutation. Wherein the allele-specific copy number includes a major allele copy number and a minor allele copy number. The ctDNA level quantification method provided by the invention can be used for detecting tiny residual lesions (Minimal Residual Disease, MRD).

Description

Quantitative method for ctDNA level of blood plasma of subject
Technical Field
The invention relates to the technical field of clinical auxiliary detection, in particular to a plasma ctDNA level quantification method.
Background
The liquid biopsy technology is a technology for detecting and monitoring diseases by means of cells, nucleic acid and protein in body fluid, and has the characteristics of convenience and low invasiveness. Circulating tumor DNA (ctDNA) is usually a DNA fragment actively secreted by tumor cells or released into the circulatory system during apoptosis or necrosis of tumor cells, and is a part of circulating free DNA (cfDNA). The ctDNA has a length of 132-145 bp and a short half-life (generally <2 h), and can reflect the dynamic change of tumor in real time; it carries genetic features derived from the association of tumor cells, such as gene mutation, methylation, amplification or rearrangement, etc., and can overcome the defects caused by tumor heterogeneity in tissue biopsies. With the development of liquid biopsy technology, the ctDNA-based high-throughput sequencing (next generation sequencing, NGS) technology is increasingly widely applied in clinic due to the advantages of non-invasive or minimally invasive, short detection time, capability of reflecting intratumoral and metastatic focus heterogeneity, capability of dynamically monitoring therapeutic effect and the like.
Factors such as tumor pathological tissue type, location, stage, tumor burden, drug treatment, etc. can influence ctDNA release. Relevant factors for the release of ctDNA in early Non-small-cell lung cancer (NSCLC) are Non-adenocarcinoma histology, necrosis, proliferation index increase and lymphovascular infiltration. Similar factors affecting ctDNA release were also observed in triple negative breast cancer (Tripple-Negative Breast Cancer, TNBC): necrosis, increased proliferation index and higher ctDNA levels of TNBC than other breast cancer subtypes. The molecular evolution of the tumor characteristics can not be dynamically reflected when the traditional methods such as tumor markers, imaging and the like are adopted to evaluate the therapeutic effect of the molecular targeting and immune checkpoint inhibitor, and ctDNANGS detection can monitor the abundance change of ctDNA related to tumor driving genes to judge the tumor therapeutic response. After the molecular targeting of advanced solid tumor or the treatment of immune checkpoint inhibitors is started, ctDNA level quantitative and dynamic change analysis based on NGS detection is expected to become an emerging curative effect assessment path.
ctDNA carries genetic features derived from the association of tumor cells, and the mutation abundance of tumor-derived mutations can intuitively represent ctDNA levels. The prior art quantifies ctDNA levels by monitoring the average mutation abundance of primary clone mutations, but does not take into account the effects of copy number variation and genotype on mutation abundance, and under the same ctDNA levels, mutation signals of homozygous variation are higher than heterozygous mutation, and mutation signals of increased copy number are amplified. Different patients monitored for differences in genotype and copy number variation of the mutation, which is detrimental to ctDNA level comparison between different patients.
Disclosure of Invention
The invention aims to provide a method for quantifying the plasma ctDNA level of a subject.
In one aspect of the present disclosure, there is provided a method of quantifying the plasma ctDNA level of a subject comprising:
(1) Detecting one or more somatic mutations in a tumor tissue of a subject, obtaining a read comprising the somatic mutationsTotal number of reads comprising the position of the somatic mutation>Sequencing error level e, frequency of detection of somatic mutations;
(2) Detecting an allele-specific copy number (ASCN) of tumor cells in the tumor tissue;
(3) Obtaining the tumor cell content t in the tumor tissue;
(4) According to the detection frequency of the somatic mutation, the ASCN and the tumor cell content in the tumor tissue, carrying out clone cluster analysis on the somatic mutation to obtain that the tumor cells carrying the somatic mutation occupy the tumor groupThe proportion of tumor cells in the tissue (CCF), the CCF of mutation i is denoted as f i ；
(5) Genotyping said somatic mutation based on the tumor cell content in said tumor tissue, the frequency of detection of said somatic mutation, said ASCN and said CCF to obtain the total copy number CN of tumor cells not carrying said somatic mutation t0 And a copy number x of the somatic mutation in a tumor cell carrying the somatic mutation;
(6) Detecting the frequency of detection of said one or more somatic mutations in plasma, according to which the frequency of detection of said one or more somatic mutations in plasma, said ASCN, said f, said CN t0 And said x, obtaining ctDNA content in plasma.
In some embodiments, the somatic mutation of step (1) is a single nucleotide base mutation (SNV). In some embodiments, the somatic mutation of step (1) is a short insertion/deletion mutation (Indel).
In some embodiments, the software for detecting the somatic mutation in step (1) is selected from one or more of mutct 2, TNScope, or VarScan 2. In some preferred embodiments, the somatic mutation detection software in step (1) is selected from mutec 2.
In some embodiments, the somatic mutation in step (1)
In some embodiments, the allele copy number comprises a major allele copy numberAnd minor genotype copy number +.>Wherein the major allele is the allele with the high copy number in the alleles, and the minor allele is the allele with the low copy number in the alleles.
In some embodiments, the software for detecting the allele-specific copy number in step (2) is selected from one or more of Facets, sequenza, pureCN or AllelicCNV (GATK). In some preferred embodiments, the detection software for detecting the allele-specific copy number in step (2) is selected from AllelicCNV (GATK).
In some embodiments, the step of obtaining the tumor cell content in the tumor tissue in step (3) further comprises: obtaining the tumor cell content in the tumor tissue according to the detection frequency of the somatic mutation and/or the ASCN, wherein the software for obtaining the tumor cell content in the tumor tissue is selected from one or more of ABSOLUTE, pureCN, facets or Sequenza. In some preferred embodiments, the step of obtaining the tumor cell content in the tumor tissue in step (3) further comprises: obtaining the tumor cell content in the tumor tissue according to the detection frequency of the somatic mutation and/or the ASCN, wherein the software for obtaining the tumor cell content in the tumor tissue is selected from ABSOLUTE.
In some embodiments, the method of obtaining the tumor cell content in the tumor tissue in step (3) further comprises: the tumor tissue was subjected to microscopic examination, and tumor cells and total cells in the tumor tissue were counted, the tumor cell content = tumor cell number/total cell number.
In some embodiments, the software for obtaining the CCF in step (4) is selected from PyCloneVI.
In some embodiments, the somatic mutation occurs in step (5) of CN prior to the copy number variation t0 Value of 2, x value range of [1, CN major ]. In some embodiments, the somatic mutation occurs after a copy number variation, CN t0 Take the value of CN total X takes on a value of 1.
In some embodiments, step (5) further comprises:
according to CN major 、CN minor 、t、f、e、CN t0 And x, obtaining the theoretical detection frequency af of somatic mutation theroy ，
Wherein CN total ＝CN major +CN minor ，CN n CN as copy number in normal cells n ＝2，
Actual detection of mutationThe probability of reads with a bar supporting mutation is p, which satisfies the binomial distribution:
according toCN minor ，CN major T and f infer genotypes at the locations where mutations occur, traversing all CNs t0 Taking the value of x and CN corresponding to the maximum p value t0 And x is the genotype related information of the mutation.
In some embodiments, step (6) further comprises:
(a) Extracting cfDNA in plasma;
(b) Sequencing the extracted cfDNA, detecting the one or more somatic mutations, and obtaining a read number containing mutation i detected by a plasma sampleTotal number of reads of position containing mutation i detected in plasma samples +.>Sequencing error level e of the mutant i position i ；
(c) Let ctDNA content in plasma sample be t p ；
(d) According tot p 、f i 、e i 、And x i Theoretical mutation frequency of the mutation i is obtained>
(e) Detection at mutation i positionIn the case of strip reads, the number of reads supporting mutation i actually detected by plasma +.>The binomial distribution is satisfied:
actually measured in plasmaProbability p of reads with bar support mutation i i The method comprises the following steps:
constructing ctDNA content t according to N monitoring mutation detection conditions in plasma p Maximum likelihood function L (t) p )：
(f) At (0, 1)]Within the range, traverse all possible t in steps of 0.00001 p And (5) taking a value. When L (t) p ) Maximum, corresponding t p Namely the ctDNA content of the blood plasma
In another aspect of the present disclosure, there is also provided a device for quantifying the level of ctDNA in plasma of a subject, comprising:
a somatic mutation detection module for obtaining tumor tissue sequencing data of a subject, and obtaining a read number comprising one or more somatic mutations, and a total read number comprising a location of the one or more somatic mutations using the sequencing data;
an allele-specific copy number detection module for obtaining an allele-specific copy number (ASCN) of tumor cells in tumor tissue;
the tumor cell content detection module is used for obtaining the tumor cell content in the tumor tissue;
the somatic mutation CCF detection module is used for carrying out clone cluster analysis on the somatic mutation according to the detection frequency of the somatic mutation, the ASCN and the tumor cell content in the tumor tissue to obtain the proportion (CCF) of the tumor cells carrying the somatic mutation to the tumor cells in the tumor tissue;
the genotype judgment module is used for carrying out genotype analysis on the somatic mutation according to the content of tumor cells in the tumor tissue, the detection frequency of the somatic mutation, the ASCN and the CCF to obtain the total copy number of the tumor cells which do not carry the somatic mutation and the copy number of the tumor cells carrying the somatic mutation;
and the ctDNA content prediction module is used for detecting the detection frequency of the one or more somatic mutations in the blood plasma, and obtaining the ctDNA content in the blood plasma according to the detection frequency of the one or more somatic mutations in the blood plasma, the ASCN, the CCF, the total copy number of the tumor cells which do not carry the somatic mutations and the copy number of the tumor cells carrying the somatic mutations.
In some embodiments, the somatic mutations include single nucleotide base mutations (SNV) and/or short insertion/deletion mutations (Indel).
In some embodiments, the somatic mutation detection module obtains the number of reads comprising one or more somatic mutations, and the total number of reads comprising the location of the one or more somatic mutations using one or more of mutct 2, TNScope, or VarScan2 software.
In some embodiments, the somatic mutation detection module uses mutec 2 software to obtain the number of reads comprising one or more somatic mutations, and the total number of reads comprising the location of the one or more somatic mutations.
In some embodiments, the frequency of detection of the somatic mutation = number of reads comprising the somatic mutation/total number of reads comprising the position of the somatic mutation.
In some embodiments, the allele-specific copy number comprises a major allele copy number and a minor allele type copy number, wherein the major allele is a high copy number allele of the alleles and the minor allele type is a low copy number allele of the alleles.
In some embodiments, the allele-specific copy number detection module obtains the ASCN using one or more of Facets, sequenza, pureCN or AllelicCNV (GATK) software.
In some embodiments, the allele-specific copy number detection module obtains the ASCN using AllelicCNV (GATK) software.
In some embodiments, the tumor cell content detection module obtains the tumor cell content in the tumor tissue based on the frequency of detection of the somatic mutation and/or the ASCN.
In some embodiments, the tumor cell content detection module obtains the tumor cell content in the tumor tissue using one or more of ABSOLUTE, pureCN, facets or Sequenza software.
In some embodiments, the tumor cell content detection module obtains the tumor cell content in the tumor tissue using ABSOLUTE software.
In some embodiments, the tumor cell content detection module obtains the tumor cell content in the tumor tissue by microscopic examination of the tumor tissue and counting tumor cells and total cells in the tumor tissue. In some embodiments, the tumor cell content = tumor cell number/total cell number.
In some embodiments, the somatic mutation CCF detection module obtains the CCF using PyCloneVI software.
In some embodiments, the ctDNA content prediction module obtains ctDNA content by:
(a) Extracting cfDNA in plasma;
(b) Sequencing the extracted cfDNA, detecting the one or more somatic mutations, and obtaining the number of reads containing the somatic mutations detected by the plasma sample and the total number of reads containing the positions of the somatic mutations detected by the plasma sample.
(c) Obtaining theoretical mutation frequency of the somatic mutation according to the total copy number of the ASCN, the CCF and the tumor cells which do not carry the somatic mutation, wherein the tumor cells carrying the somatic mutation carry the somatic mutation;
(d) Constructing a maximum likelihood function of the ctDNA content of the blood plasma according to the theoretical mutation frequency;
(e) Traversing all possible values of the plasma ctDNA content, and when the maximum likelihood function is maximum, obtaining the corresponding value of the plasma ctDNA content as the plasma ctDNA content.
In another aspect of the present disclosure, there is also provided a device for quantifying the level of ctDNA in plasma of a subject, comprising:
a memory for storing a program;
a processor for implementing the method as described in the present disclosure by executing the program stored in the memory.
In another aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program executable by a processor to implement a method as described in the present disclosure.
In another aspect of the present disclosure, there is also provided a method of the present disclosure, an apparatus of the present disclosure, or a storage medium of the present disclosure, for use in detecting a minimal residual lesion.
Compared with the prior art, the method has the following beneficial effects:
1. when the mutation frequency of somatic mutation is detected to obtain ctDNA content, the influence of copy number, CCF and genotype on the mutation frequency is fully considered, so that the ctDNA content can be obtained more accurately.
2. Under the application scene of detecting the somatic mutation of tumor tissue sources, the tissue can be ensured to have higher tumor cell content through the quality control of a sample, and the copy number of the position where the monitoring mutation is located and the CCF carrying the tumor cell for monitoring the mutation can be more accurately obtained through the tissue detection.
3. To accurately detect the copy number of the location of the monitored mutation and to obtain a CCF of the tumor cells carrying the mutation, it is often necessary to cover a sufficient number of genomic locations when the sample is tested, but plasma testing typically requires a higher depth of detection to achieve a lower mutation detection limit, and it is often necessary to have both of these two aspects at a high cost of detection in plasma testing. The ctDNA of the blood plasma is derived from tumor tissues, and the copy number of the genome position of the mutation is monitored through tissue detection, and CCF carrying the tumor cells with the monitored mutation is obtained, so that the detection cost of the blood plasma can be effectively reduced.
4. The method of obtaining ctDNA content by average mutation frequency can be better suited for inter-patient ctDNA content comparison.
Drawings
Fig. 1 shows a flow chart of a method of quantifying the plasma ctDNA level of a subject in accordance with one embodiment of the present disclosure.
Fig. 2 shows a flow chart of a method of quantifying the plasma ctDNA level of a subject in accordance with one embodiment of the present disclosure.
FIG. 3 shows a linear fit of predicted ctDNA content results to actual ctDNA content in accordance with one embodiment of the present disclosure.
FIG. 4 shows a linear fit R of predicted ctDNA content to actual ctDNA content in accordance with one embodiment of the present disclosure 2 Comparison of the values.
FIG. 5 shows a comparison of the slope of a linear fit of predicted ctDNA to actual ctDNA content in one embodiment according to the present disclosure.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. The specific embodiments described herein are for purposes of illustration only and are not to be construed as limiting the invention in any way. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure. Such structures and techniques are also described in a number of publications.
Fig. 1 illustrates a flow chart of a method of quantifying plasma ctDNA levels in a subject of the present disclosure.
As shown in fig. 1, a method 100 of quantifying the plasma ctDNA level of a subject comprises: step 102, detecting one or more somatic mutations in tumor tissue of the subject, and obtaining a somatic mutation detection frequency.
Step 104, detecting an allele-specific copy number (ASCN) of tumor cells in the tumor tissue.
Step 106, obtaining the tumor cell content in the tumor tissue.
And step 108, performing clone cluster analysis on the somatic mutation according to the detection frequency of the somatic mutation, the ASCN and the tumor cell content in the tumor tissue to obtain the proportion (CCF) of the tumor cells carrying the somatic mutation to the tumor cells in the tumor tissue.
Step 110, performing genotype analysis on the somatic mutation according to the tumor cell content in the tumor tissue, the detection frequency of the somatic mutation, the ASCN and the CCF, so as to obtain the total copy number CN of the tumor cells without the somatic mutation t0 And the copy number x of the somatic mutation in the tumor cell carrying the somatic mutation.
Step 112, detecting the frequency of detection of said one or more somatic mutations in plasma, based on the frequency of detection of said one or more somatic mutations in plasma, said ASCN, said CCF, said CN t0 And said x, obtaining ctDNA content in plasma.
Fig. 2 illustrates another flow chart of a method of quantifying plasma ctDNA levels in a subject of the present disclosure.
As shown in fig. 2, the method of quantifying the plasma ctDNA level of a subject comprises: detecting the plasma paired tissues, and selecting one or more monitoring mutations to obtain the detection frequency and copy number CCF of the monitoring mutations in the plasma paired tissues; detecting the blood plasma to obtain the detection frequency of the monitoring mutation in the blood plasma; obtaining the tumor cell content of the plasma paired tissues, and obtaining genotype information of the monitored mutation by using a genotype presumption algorithm according to the copy number CCF of the monitored mutation and the tumor cell content; correcting the frequency of the monitored mutation according to the copy number CCF of the monitored mutation, the genotype information of the monitored mutation, the detection frequency of the monitored mutation in the blood plasma and the detection frequency of the monitored mutation in the blood plasma paired tissues; the plasma ctDNA content was obtained.
Definition of the definition
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly used in the art to which this invention belongs. For the purposes of explaining the present specification, the following definitions will apply, and terms used in the singular will also include the plural and vice versa, as appropriate.
The terms "a" and "an" as used herein include plural referents unless the context clearly dictates otherwise. For example, reference to "a cell" includes a plurality of such cells, equivalents thereof known to those skilled in the art, and so forth.
The term "about" as used herein means a range of + -20% of the numerical values thereafter. In some embodiments, the term "about" means a range of ±10% of the numerical value following that. In some embodiments, the term "about" means a range of ±5% of the numerical value following that.
In the present disclosure, the term "subject" refers to any animal, mammal, or human. The subject has, may have, or is suspected of having, one or more diseases. The subject may have cancer, the subject may exhibit symptoms associated with cancer, the subject may not exhibit symptoms associated with cancer, or the subject may not be diagnosed with cancer. In some embodiments, the subject is a human.
In the present disclosure, the term "tumor" refers to a mass or neoplasm, which is itself defined as an abnormal growth of cells that generally grow faster than normal cells and will continue to grow if untreated, sometimes resulting in damage to adjacent structures. The tumor sizes may vary widely. The tumor may be solid or liquid filled. A tumor may refer to benign (non-malignant, typically harmless) or malignant (capable of metastasis) growth. Some tumors may contain benign neoplastic cells (e.g., carcinoma in situ) while also containing malignant cancer cells (e.g., adenocarcinoma). It should be understood to include neoplasms located in multiple locations throughout the body. Thus, for purposes of this disclosure, tumors include primary tumors, lymph nodes, lymphoid tissue, and metastatic tumors.
In the present disclosure, non-limiting examples of the cancer include biliary tract cancer, bladder cancer, transitional cell cancer, urothelial cancer, breast cancer, cervical squamous cell cancer, rectal cancer, colorectal cancer, colon cancer, hereditary non-polyposis colorectal cancer, colorectal adenocarcinoma, gastrointestinal stromal tumor, endometrial cancer, endometrial stromal sarcoma, esophageal cancer, esophageal squamous cell cancer, esophageal adenocarcinoma, ocular melanoma, uveal melanoma, gall bladder cancer, gall bladder adenocarcinoma, renal cell cancer, clear cell renal cell carcinoma, transitional cell carcinoma, urothelial cancer, renal cell carcinoma, liver cancer, hepatic epithelial cancer, hepatocellular carcinoma, cholangiocarcinoma, hepatoblastoma, lung cancer, non-small cell lung cancer, nasopharyngeal carcinoma, neuroblastoma, oral cancer, oral squamous cell carcinoma, ovarian cancer, pancreatic ductal adenocarcinoma, pseudopapillary tumor, acinar cell carcinoma, prostate cancer, skin cancer, melanoma, malignant melanoma, skin melanoma, small intestine cancer, stomach cancer, gastric epithelial cancer, uterine sarcoma, or uterine sarcoma.
In the present disclosure, a read refers to a nucleotide sequence produced by a sequencer. Sequence reads can be between tens to thousands of nucleotides in length.
In the present disclosure, mutation (mutation) refers to a change in the nucleotide sequence of the genome of an organism or of the extrachromosomal DNA genome. "mutation" and "variation" are used interchangeably.
In the present disclosure, the term "SNV" refers to a mutation or variation of a single nucleotide occurring at a specific position in the genome.
In the present disclosure, the term "Indel" refers to an insertion/deletion mutation that adds or reduces a small fragment of a gene sequence change in one chromosome.
In the present disclosure, the term "copy number variation" or "CNV" refers to a comparative numerical change in the presence or absence/acquisition or loss of gene segments having the same nucleotide sequence.
In the present disclosure, the term "allele" refers to one of two or more alternative forms of a gene and is found at the same locus on the genome.
In the present disclosure, the term "allele-specific copy number" or "ASCN" refers to the copy number of the maternal and/or paternal allele in the genome of a tumor cell. In some embodiments, detecting the allele-specific copy number (ASCN) of the tumor cells in the tumor tissue enables obtaining the copy number of the maternal and/or paternal allele in the tumor cell genome.
In the present disclosure, the term "tumor cell content" refers to the proportion of tumor cells in a tumor sample to all cells in the sample.
In the present disclosure, the term "major allele" refers to an allele with a high copy number among alleles. In the present disclosure, the term "minor allele" refers to an allele with a low copy number among alleles. In some embodiments, the master allele copy number is the allele copy number of the higher of the alleles from the maternal and paternal parent in the tumor cell. For example, if the number of copies of the allele from the female parent is higher than the number of copies of the allele from the male parent in the tumor cell, the number of copies of the allele from the female parent is the primary number of copies of the allele and the number of copies of the allele from the male parent is the secondary number of copies of the allele. Conversely, if the allele copy number from the male parent is higher than the allele copy number of the female parent in the tumor cell, the allele copy number from the male parent is the primary allele copy number and the allele copy number from the female parent is the secondary allele copy number.
In the present disclosure, the term "CCF" refers to the proportion of tumor cells carrying a mutation in the tumor cells to all tumor cells.
Examples and figures are provided below to aid in the understanding of the invention. It is to be understood that these examples and drawings are for illustrative purposes only and are not to be construed as limiting the invention in any way. The actual scope of the invention is set forth in the following claims. It will be understood that any modifications and variations may be made without departing from the spirit of the invention.
Example 1: preparation of simulated plasma samples by mixing SW480 (human colon cancer cells) and NA12878 (non-tumor) cell lines with different ctDNA contents
In this example, simulated plasma samples of different ctDNA content were prepared.
gDNA of SW480 and NA12878 cell lines were extracted separately, disrupted according to the mononuclear cell length, and SW480 cell line DNA was mixed with NA12878 cell line DNA to obtain a simulated plasma sample with 0.1% ctDNA content. WES sequencing was performed on SW480 and NA1287 cell lines, and 4 mutations were selected for quantification of 0.1% ctdna content to mimic plasma samples based on sequencing results. The selection conditions for the 4 mutations were: a) The genomic position at which the mutation was located was 2 copies in the SW480 cell line and the frequency of detection was 100%; 2) The genomic position at which the mutation was located was 2 copies in the NA12878 cell line and carried no mutation. The ddPCR quantitative results for the 4 mutations are shown in Table 1.
TABLE 1.4 ddPCR results for quantitation of mock plasma samples at 0.1% ctDNA content
Exogenous viral DNA was added to the simulated plasma at 0.1% ctdna content, and the disrupted NA12878 cell line DNA was used to further dilute the simulated plasma samples at 0.1% ctdna content to 0.05%, 0.02%, 0.01%, 0.005%, 0.003% and 0.001%. For mock plasma samples with ctDNA content below 0.1%, the change in the number of copies of exogenous viral DNA in the mock plasma relative to the change in mock plasma with ctDNA content of 0.1% was ensured, and the change in the number of copies of virus after dilution is shown in table 2.
TABLE 2 actual dilution of simulated plasma samples with ctDNA content below 0.1%
Theoretical ctDNA content% Virus copy number (copy/ng) Actual ctDNA content%
0.100 8079.63 -
0.050 4532.67 0.0561
0.020 1631.91 0.0202
0.010 732.46 0.0091
0.005 395.62 0.0049
0.003 286.32 0.0035
0.001 89.46 0.0011
Example 2: NGS sequencing to mimic plasma samples
In this example, NGS sequencing was performed on simulated plasma samples for each ctDNA content, as follows:
1) DNA inventory input: 60ng.
2) Library construction
a. Terminal repair and addition of "a": (1) Adding a tail end repair reaction solution and a tail end repair reaction enzyme into the fragmented product, oscillating, uniformly mixing and centrifuging; (2) incubation on a thermostatic mixer or PCR instrument: 20 ℃ for 30min;65 ℃ for 30min; (3) After incubation was completed, the temperature was lowered to room temperature and centrifuged briefly using a palm centrifuge.
b. And (3) joint connection: the ligase and the linker are removed. The linker was dissolved at room temperature and the ligase was placed on the ice bin. Before use, the reaction liquid of the linker and the ligase is fully and evenly mixed by shaking and centrifuged for a short time.
c. Purifying after joint connection: (1) Taking out the magnetic beads 30min in advance, placing at room temperature, and fully oscillating and uniformly mixing before use; (2) Sucking magnetic beads with corresponding volumes into a 1.5mL centrifuge tube, transferring the products into the magnetic beads, lightly blowing and mixing the products by a pipettor, incubating the mixture for 10 minutes at room temperature to fully combine the magnetic beads with DNA fragments, and preparing 80% ethanol during incubation; (4) After incubation, placing a 1.5mL centrifuge tube on a magnetic rack, standing for 10-20min (depending on the amount of magnetic beads) until the liquid is clear, and discarding the supernatant; (5) Keeping a 1.5mL centrifuge tube fixed on a magnetic rack, adding freshly prepared 80% ethanol, and discarding the supernatant, wherein the dosage is enough to submerge the magnetic beads (500 mu L is recommended); (5) Repeating the step (4) for one time, and sucking the liquid at the bottom of the pipe as much as possible; (6) Opening a cover of a 1.5mL centrifuge tube, heating and drying the centrifuge tube on a 37 ℃ metal bath until the surfaces of the magnetic beads are not reflective, and taking down the centrifuge tube; (7) Adding a dissolving solution into a 1.5mL centrifuge tube, blowing and mixing uniformly by a pipettor, and incubating for 5min at room temperature to fully dissolve the DNA fragments in the DNA dissolving solution; (8) Placing a 1.5mL centrifuge tube on a magnetic rack until the liquid is completely clarified; (9) The supernatant was pipetted into a new 1.5mL centrifuge tube and the 1.5mL centrifuge tube with magnetic beads was discarded.
d. Before hybridization capture PCR enrichment (Non-C-PCR), 1, taking out Index with corresponding number, dissolving at room temperature, fully oscillating, mixing and centrifuging; (2) Taking out the DNA polymerase reaction liquid from the refrigerator, placing the DNA polymerase reaction liquid on the refrigerator at the temperature of 4 ℃ for dissolution, gently oscillating, uniformly mixing, centrifuging and placing on an ice box; (3) Adding the reaction components into a PCR tube, mixing uniformly by shaking, and centrifuging. (4) the PCR tube was placed on a PCR apparatus for PCR.
Purification of non-C-PCR product: the sample after PCR was purified using magnetic beads.
Dna fragmentation followed by: and (3) a purification step after the ligation with the linker.
g. Library quality control: using Qubit fluorescent quantitative instrumentdsDNABRAssay Kit) for quantifying the productAnd quantifying the length distribution range of the product, wherein the total quantity is required to meet the requirement, and the product has no joint and large fragment pollution.
3) Hybrid capture
a. Hybridization capture: after quality control of the library is acceptable, probe capture is performed, and the probe region includes monitoring for mutations, hybridization capture is performed with reference to instructions provided by the probe manufacturer. Finally, the magnetic beads are eluted by eluting back 20 mu L ddH2O band hybridization.
b. Eluted product amplification enrichment (LM-PCR): (1) Taking out the DNA polymerase reaction solution and the primer from the refrigerator, placing the solution in room temperature for dissolution, fully oscillating, uniformly mixing and centrifuging; (2) Adding all elution products with magnetic beads B into the PCR reaction liquid according to the specification, and blowing and uniformly mixing; (3) Placing the PCR tube on a PCR instrument for amplification reaction; the sample after PCR of (4) was purified using magnetic beads.
c. Control of elution library quality: using Qubit fluorescent quantitative instrumentdsdnabasssay Kit) and the length distribution range of the product, the total amount is required to meet the requirements, and no linker and large fragment contamination.
4) Sequencing on a machine: and (3) performing on-machine PE100 sequencing by using a T7 sequencer and other sequencers with the same principle, wherein the data size of the on-machine is 20G. Sequencing experiment operations the on-machine sequencing operations were performed according to the instructions provided by the manufacturer.
5) Credit analysis
a. And (5) sequencing original off-machine data quality control. The UID was removed and the reads filtered using the information analysis procedure (RealSeq Pipeline) of the plasma ctDNA low frequency mutation Enrichment sequencing technique, ER-seq (engineering & Rarallele Sequence) (China patent publication No. CN 105063208A).
b. Sequence alignment. Sequence alignment was performed using GRCh37 as a reference sequence and bwa (version number 0.7.17-r 1188).
c. Remove PCR repeat reads: clustering error correction based on ER-seq analysis flow was performed on post-reads using realSeq2 comparison, extracting de-duplicated reads including natural repeated fragments.
Indel heavy alignment and base quality correction: local realignment of sequences near Indel was performed using a realignertargetceater of GATK, reducing the alignment error rate near Indel. And (3) re-correcting the base quality value of the read in the bam file by using the BaseRecalifier and the Print read of GATK, so that the quality value of the base in the read in the finally output bam file can be more close to the probability of mismatch between the true and reference genomes.
Mutations were monitored using the RealDcaler 2 (version: 1.1.3) software. The mutation detection conditions were determined as follows: (1) Monitoring SNV and Indel with mutation length less than 5bp, wherein DS is more than or equal to 1 or SS+single is more than or equal to 2; (2) Indels with mutation length larger than or equal to 5bp, DS not less than 1 or SS not less than 1 or Single not less than 1; (3) non-monitoring hotspot mutation AD is more than or equal to 4; (4) other mutations AD.gtoreq.8. Wherein DS (Double Strandreads) refers to a bidirectional error correction read, SS (Single Strandreads) is a unidirectional error correction read, and Single (Single reads) is an uncorrected read with cluster size 1. The number of reads supporting the mutation and the total sequencing reads of the genomic position where the mutation was located for each monitored mutation were counted, and when the mutation was not detected, the number of reads supporting the mutation was 0.
Example 3: ctDNA content estimation of mock plasma samples
The present example predicts ctDNA content in a simulated plasma sample for each ctDNA content by the following method:
1. genotype prediction for monitoring mutations:
1) Based on WES (whole exon) assay results of SW480 cell line and NA12878 cell line pairing, mutation detection was performed using mutec 2, tumor cell content analysis was performed using ABSOLUTE, ASCN was analyzed using AllelicCNV (GATK), mutated CCF was obtained using pycalonevi analysis; wherein the sequencing error level e=0.001; tumor cell content t=1, and mutation was detectedCN major 、CN minor And CCF are shown in table 3.
TABLE 3 results relating to mutation detection by WES paired SW480 and NA12878 cell lines
2) The genotype of the mutation is presumed and monitored by using a genotype presumption algorithm according to the tumor cell content of the tissue, ASCN of the position of the mutation and CCF of the mutation; af in which mutation is detected theroy 、CN t0 And x is shown in Table 4.
TABLE 4 results relating to detection of mutant genotypes by WES paired with SW480 and NA12878 cell lines
2.ctDNA content estimation: and according to the detection condition of the monitoring mutation in the NGS detection result of the plasma sample, the ctDNA content is estimated by using a ctDNA content estimation algorithm. First, from the plasma test results, the total number of reads including the mutation and the total number of reads including the mutation position of all the monitoring mutations were obtained, and the sequencing error level of all the monitoring mutation positions in the plasma was 0.001. Then at (0, 1)]Within the range, all possible ctDNA content values were traversed in steps of 0.00001, the number of reads comprising the mutation, the total number of reads comprising the mutation position and the sequencing error level of the mutation position were determined according to all detection of the monitoring mutation in plasma, and the allele copy number information CN of the monitoring mutation major And CN minor CCF and genotype information CN t0 And x, calculating likelihood function value under each ctDNA content value. After traversing all the ctDNA content values, the ctDNA content value with the maximum likelihood function value is the ctDNA content value of the plasma to be detected.
3. ctDNA content prediction results: NGS sequencing was performed on ctDNA content-simulated plasma samples of 0.1%, 0.05%, 0.02%, 0.01%, 0.005%, 0.003% and 0.001%, with each gradient sample being repeated at least 42 times. Detection of ctDNA content prediction results for mock plasma samples significantly correlated linearly with the actual ctDNA content of the samples (fig. 3), R 2 The value was 0.99.
Example 4: performance verification of ctDNA content estimation algorithm in simulated plasma samples
1. Table 5 shows the composition of 4 sets of monitoring mutations, wherein the total number of monitoring mutations is 20. Table 6 shows specific monitoring sites in the 4 monitoring sets.
TABLE 5.4 composition of monitoring mutation sets
Mutation type Type 1 Type 2 Type 3 Type 4
Copy number 2 Non-2 2 2
Genotype of the type Homozygote Homozygote Heterozygosity Heterozygosity
Cloning clusters Master cloning Master cloning Master cloning Subcloning
Monitoring site set 1 20 0 0 0
Monitoring bit Point set 2 7 0 13 0
Monitoring bit Point set 3 2 18 0 0
Monitoring bit Point set 4 18 0 0 2
TABLE 6.4 monitoring sites in monitoring mutation sets
2.ctDNA content predictions were made using ctDNA content prediction algorithms of the present disclosure and prediction algorithms that calculate average mutation frequencies, respectively, using ctDNA content simulation plasma samples of 0.1%, 0.05%, 0.02%, 0.01%, 0.005%, 0.003%, and 0.001%, with each gradient repeated at least 42 times. The predicted ctDNA content was linearly fitted to the actual ctDNA content in different monitoring site sets.
3. Algorithm for predicting ctDNA content by mean mutation frequency: assuming that N mutations are monitored, mutation i is detected with frequency AF in plasma i If mutation i is not detected (trusted) in plasma, AF i =0. Then ctDNA content calculation formula is:
4. at different monitored point sets, linear fitting R of the predicted ctDNA content to the actual ctDNA content is obtained by comparison using the method of the present disclosure and a ctDNA content prediction algorithm that calculates the average mutation frequency 2 Values (fig. 4) and slopes (fig. 5). The ctDNA content predicted by using the ctDNA prediction algorithm of the present disclosure and the ctDNA prediction algorithm for calculating the average mutation frequency has high linear correlation with the actual ctDNA content, R 2 All values are>0.9。
From the slope, the ctDNA content predicted by the ctDNA content prediction algorithm of the present disclosure is closer to the actual ctDNA content, i.e., the slope is closer to 1, than the algorithm that predicts ctDNA content by the average mutation frequency; and when the ctDNA content is the same and the monitoring site sets are different, the ctDNA content predicted by the algorithm for predicting the ctDNA content by the average mutation frequency has larger fluctuation compared with the ctDNA content prediction algorithm of the present disclosure, that is, when the ctDNA content is the same and the monitoring site sets are different, the slope difference of the linear fitting result of the ctDNA predicted by the same algorithm and the actual ctDNA content is larger.
The ctDNA content predicted by the ctDNA content prediction algorithm and the calculated average mutation frequency of the present disclosure both have a good correlation with the actual ctDNA content, but the ctDNA content predicted by the ctDNA content prediction algorithm of the present disclosure has better performance in terms of ctDNA content comparison among patients, because the difference between the cases of the monitored mutation sets is often meant among different patients.
The technical scheme of the invention is not limited to the specific embodiment, and all technical modifications made according to the technical scheme of the invention fall within the protection scope of the invention.

Claims (17)

1. A method of quantifying the level of ctDNA in the plasma of a subject, the method comprising:
(1) Detecting one or more somatic mutations in a tumor tissue of a subject, obtaining a read comprising the somatic mutationsTotal number of reads comprising the position of the somatic mutation>Sequencing error level e, frequency of detection of somatic mutations;
(2) Detecting an allele-specific copy number (ASCN) of tumor cells in the tumor tissue;
(3) Obtaining the tumor cell content t in the tumor tissue;
(4) According to the detection frequency of the somatic mutation, the ASCN and the tumor cell content in the tumor tissue, carrying out clone cluster analysis on the somatic mutation to obtain the proportion (CCF) f of the tumor cells carrying the somatic mutation to the tumor cells in the tumor tissue;
(5) Genotyping said somatic mutation based on the tumor cell content in said tumor tissue, the frequency of detection of said somatic mutation, said ASCN and said CCF to obtain the total copy number CN of tumor cells not carrying said somatic mutation t0 And a copy number x of the somatic mutation in a tumor cell carrying the somatic mutation;
(6) Detecting the frequency of detection of said one or more somatic mutations in plasma, according to which the frequency of detection of said one or more somatic mutations in plasma, said ASCN, said CCF, said CN t0 And said x, obtaining ctDNA content in plasma.
2. The method of claim 1, wherein in step (1), the somatic mutation comprises a single nucleotide base mutation (SNV) and/or a short insertion/deletion mutation (Indel).
3. The method according to claim 1, wherein in step (1) the software for detecting somatic mutations is selected from one or more of mutec 2, TNScope or VarScan2, preferably mutec 2.
4. The method according to claim 1, wherein in step (1), the
5. The method of claim 1, wherein in step (2), the allele-specific copy number comprises a major allele copy numberAnd minor genotype copy number +.>Wherein the major allele is the allele with high copy number in the alleles, and the minor allele is the allele with low copy number in the alleles.
6. The method of claim 1, wherein in step (2) the software for detecting the allele-specific copy number is selected from one or more of Facets, sequenza, pureCN or AllelicCNV (GATK), preferably AllelicCNV (GATK).
7. The method of claim 1, wherein in step (3), the step of obtaining the tumor cell content in the tumor tissue further comprises: obtaining the tumor cell content in the tumor tissue according to the detection frequency of the somatic mutation and/or the ASCN.
8. The method according to claim 7, wherein in step (3) the software for obtaining the tumor cell content in the tumor tissue is selected from one or more of ABSOLUTE, pureCN, facets or Sequenza, preferably ABSOLUTE.
9. The method of claim 1, wherein in step (3), the step of obtaining the tumor cell content in the tumor tissue further comprises: the tumor tissue was subjected to microscopic examination, and tumor cells and total cells in the tumor tissue were counted, the tumor cell content = tumor cell number/total cell number.
10. The method of claim 1, wherein in step (4), the software for obtaining the CCF is selected from pyclone vi.
11. The method of claim 1, wherein step (5) further comprises: according to the CN major 、CN minor 、t、f、e、CN t0 And x, obtaining the theoretical detection frequency af of somatic mutation theroy
Wherein CN total ＝CN major +CN minor ，CN n CN as copy number in normal cells n ＝2。
12. The method according to claim 11, characterized in thatCharacterized in that the step (5) further comprises: actual detection of mutationThe probability of reads with a bar supporting mutation is p, which satisfies the binomial distribution:
according toCN minor ，CN major T and f infer genotypes at the locations where mutations occur, traversing all CNs t0 Taking the value of x and CN corresponding to the maximum p value t0 And x is the genotype related information of the mutation.
13. The method of claim 1, wherein step (6) further comprises:
(a) Extracting cfDNA in plasma;
(b) Sequencing the extracted cfDNA, detecting the one or more somatic mutations, and obtaining a read number containing the somatic mutations detected by a plasma sample and a total read number containing the somatic mutations detected by the plasma sample;
(c) Let ctDNA content in plasma sample be t p ；
(d) According tot p 、f i 、e i 、And x i Theoretical mutation frequency of the mutation i is obtained>
(e) Detection at mutation i positionIn the case of strip reads, the number of reads supporting mutation i actually detected by plasma +.>The binomial distribution is satisfied:
actually measured in plasmaProbability p of reads with bar support mutation i i The method comprises the following steps:
constructing ctDNA content t according to N monitoring mutation detection conditions in plasma p Maximum likelihood function L (t) p )：
(f) Traversing all possible values of the plasma ctDNA content, and when the maximum likelihood function is maximum, obtaining the corresponding value of the plasma ctDNA content as the plasma ctDNA content.
14. A device for quantifying the level of ctDNA in plasma of a subject, comprising:
a somatic mutation detection module for obtaining sequencing data of tumor tissue of a subject, and obtaining a read number comprising one or more somatic mutations, a total read number comprising a location of the somatic mutations, and a detection frequency of the somatic mutations using the sequencing data;
an allele-specific copy number detection module for obtaining an allele-specific copy number (ASCN) of tumor cells in the tumor tissue;
the tumor cell content detection module is used for obtaining the tumor cell content in the tumor tissue;
the somatic mutation CCF detection module is used for carrying out clone cluster analysis on the somatic mutation according to the detection frequency of the somatic mutation, the ASCN and the tumor cell content in the tumor tissue to obtain the proportion (CCF) of the tumor cells carrying the somatic mutation to the tumor cells in the tumor tissue;
the genotype judgment module is used for carrying out genotype analysis on the somatic mutation according to the content of tumor cells in the tumor tissue, the detection frequency of the somatic mutation, the ASCN and the CCF to obtain the total copy number of tumor cells which do not carry the somatic mutation and the copy number of tumor cells carrying the somatic mutation;
and the ctDNA content prediction module is used for detecting the detection frequency of the one or more somatic mutations in the blood plasma, and obtaining the ctDNA content in the blood plasma according to the detection frequency of the one or more somatic mutations in the blood plasma, the ASCN, the CCF, the total copy number of the tumor cells which do not carry the somatic mutations and the copy number of the tumor cells carrying the somatic mutations.
15. A device for quantifying the level of ctDNA in plasma of a subject, comprising:
a memory for storing a program;
a processor for implementing the method of any one of claims 1-13 by executing a program stored in said memory.
16. A computer readable storage medium having stored thereon a program executable by a processor to implement the method of any of claims 1-13.
17. Use of the method of any one of claims 1-13, the device of claim 14 or 15, or the storage medium of claim 16 for detecting microscopic residual lesions.
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