TWI670495B - Method and system for identifying tumor burden in a sample - Google Patents

Abstract

The present invention provides a method and system for identifying tumor burden in a sample. Specifically, the present invention provides a method for non-diagnosticly identifying tumor burden in a sample, comprising the steps of: (i) providing a sample to be tested; (ii) Sequencing the test sample to obtain a genomic sequence of the sample; (iii) comparing the genomic sequence obtained in step (ii) with a reference genome to obtain position information of the genomic sequence on the reference genome; iv) the reference gene component is divided into M region fragments, where each region fragment is a window b, and the copy number of each window b is calculated; (v) a Z-test is performed on each window b in step (iv), Thus, the Z value of each window b is calculated; and (vi) the genomic disorder (GAS) is calculated according to the Z value obtained in step (v), and the tumor burden in the test sample is identified based on the value of the genomic disorder. The method and system of the invention can improve the sensitivity and versatility of tumor detection.

Description

Method and system for identifying tumor load in a sample

The field relates to the field of biotechnology, and in particular, to a method and system for identifying tumor burden in a sample.

In the fields of scientific research and clinical application of biomedicine, tumor cells of tumor patients often have a large number of genomic copy number variations. Copy number variations can exist in tumor tissues, body fluids (such as blood, interstitial fluid, lymph fluid, cerebrospinal fluid, urine, saliva, etc.), and body fluids specifically exist in free circulating tumor cells (CTC), extracellular free DNA (cfDNA), exosomes, etc. The variation of genomic copy number in body fluids is an important indicator for the identification of tumor burden. Identification of tumor burden can be used in early screening, diagnosis, patient monitoring, and prognosis treatment of tumors. At present, the main methods for detecting copy number variations of tumor genomes are: comparative genomic hybridization (CGH), real-time quantitative quantitative PCR (RTFQ PCR), and fluorescence in situ hybridization (FISH) , Multiplex ligation-dependent probe amplification (MLPA). However, comparative genomic hybridization has relatively low resolution, Mb level, low throughput, and high cost; fluorescent quantitative PCR also has low throughput and high cost, and can only measure one copy number variation at a time; fluorescent in situ hybridization is only for specific Location, low resolution, and unstable probe hybridization efficiency. Multiplexed probe amplification technology is complicated in operation, low in throughput, high in cost, small in coverage, and easy to cause PCR contamination. In addition to the above-mentioned technical defects, most of the above techniques detect only specific regions on the genome, and tumors are very heterogeneous, and specific one or more sites cannot effectively comprehensively evaluate the tumor load in body fluids. Therefore, there is an urgent need in the art to develop a method and device that can more effectively comprehensively evaluate the burden of tumors in body fluids, and improve the sensitivity and versatility of tumor detection.

The invention provides a method and a device that can more effectively comprehensively evaluate the load of tumors in body fluids and improve the sensitivity and versatility of tumor detection. A first aspect of the present invention provides a method for non-diagnosticly identifying a tumor burden in a sample, comprising the steps of: (i) providing a sample to be tested; (ii) sequencing the sample to be tested, thereby obtaining a sample of Genomic sequence; (iii) comparing the genomic sequence obtained in step (ii) with a reference genome to obtain position information of the genomic sequence on the reference genome; (iv) dividing the reference gene component into M region fragments, where Each region fragment is a window b, and the number of copies of each window b is calculated; (v) performs a Z-test on each window b of step (iv) to calculate the Z value of each window b; and (vi) according to The Z value obtained in step (v) is used to calculate the genomic disorder (GAS), and the tumor burden in the test sample is identified based on the value of the genomic disorder.

In another preferred example, the reference genome may be continuous or discontinuous.

In another preferred example, the reference genome includes a whole genome.

In another preferred example, the reference genome refers to the full length of all chromosomes of the species (such as a human), the full length of a single or multiple chromosomes, a portion of a single or multiple chromosomes, or a combination thereof.

In another preferred example, the coverage of the reference genome reaches more than 50% of the whole genome, preferably 60% or more, more preferably 70% or more, more preferably 80% or more, most preferably, above 95.

In another preferred example, the sample is from an individual to be tested.

In another preferred example, the individual to be detected is a human or a non-human mammal.

In another preferred example, the sample is a solid sample or a liquid sample.

In another preferred example, the sample includes a body fluid sample.

In another preferred example, the sample is selected from the group consisting of blood, plasma, interstitial fluid, lymph fluid, cerebrospinal fluid, urine, saliva, aqueous humor, semen, or a combination thereof.

In another preferred example, the sample is selected from the group consisting of free circulating tumor cells (CTC), extracellular free DNA (cfDNA), exosomes, or a combination thereof.

In another preferred example, the sequencing is selected from the group consisting of single-ended sequencing, double-ended sequencing, or a combination thereof.

In another preferred example, the step (iv) further includes the steps of correcting the copy number of each window b and calculating the corrected copy number of each window b.

In another preferred example, the correction method is selected from the following group: Loess correction, weighting method, residual method, or a combination thereof.

In another preferred example, according to the position information of the genomic sequence on the reference genome, the number of sequences falling into each window b, the distribution of the base groups, and the base distribution of the reference genome are counted.

In another preferred example, the copy number of each window b is corrected according to the sequence and the fluorene content of each window b.

In another preferred example, the Z value of each window b is calculated using the following formula: Where i is any positive integer from 1 to M; M is the total number of windows formed by the reference gene components, where M is A positive integer of 50, preferably 50 M 10 ⁵ , better yet, 100 M 10 ⁵ , best of 200 M 10 ⁵ ; x _i is the copy value detected by the sample under test in the i-th window b _i ; b _i is the i-th window; μ _i is the arithmetic mean of the copy number of the normal control sample in the window b _i . Calculated as follows: Where j is any positive integer from 1 to N; N is the total number of normal control samples, where N is A positive integer of 30, preferably 30 N 10 ⁸ , better yet, 50 N 10 ⁷ , optimally, 100 N 10 ⁴ ; X _j refers to the copy value detected by the j-th normal control sample in the window b _i ; σ _i is the standard deviation of the copy number of the normal control sample in the window b _i and is calculated by the following formula: In the formula, N, j, X _j and μ _i are defined as above.

In another preferred example, the normal control sample refers to a homogeneous sample of a normal person of the same species.

In another preferred example, the following formula is used to calculate the degree of genomic confusion: Among them, m _b is the window sorted at the m-th percentile, p _b is the window sorted at the p-th percentile, m is 30-98, preferably 40-97, more preferably 60-96, most preferably, 80-95, optimally, 95, p is 80-100, preferably, 85-100, more preferably, 90-100, optimally, 100, and pm 2 (preferably, 5. Better yet, 10, better yet, 15, optimally, 20).

In another preferred example, before calculating the degree of genomic confusion, the method includes the following steps: (a) removing centromeres, telomeres, satellites, heterochromatin and other high-throughput sequencing tests on the genome according to the characteristics of the reference genome sequence. To the region, remove the region of length L near the centromere, telomere, satellite, heterochromatin on the genome, where L is any length less than 3M; or (b) remove the centromere on the genome according to the copy number characteristics of the sample , Telomere, satellite, heterochromatin and other areas not detected by high-throughput sequencing.

In another preferred example, before step (v), the method further includes the following steps: (iv1) calculating the coefficient of variation CV _i of each window b in the normal control sample according to the copy number of each window b in step (iv) ; And (iv2) sort the CV _i from small to large, removing the largest first n% of the window, where n is any value greater than 0 and less than or equal to 5, preferably n = 1, 2, 2.5, 3, 3.1, 4, 4.2 or 5.

In another preferred example, the coefficient of variation CV _i is calculated using the following formula: Among them, μ _i is the arithmetic average of the copy number of the normal control sample, and is calculated by the following formula: σ _i is the standard deviation of the copy number of the normal control sample, and is calculated using the following formula: In the formula, N, j, X _j , μ _i and σ _i are defined as above.

A second aspect of the present invention provides a system (equipment) for identifying a tumor burden in a sample, including: a sequencing unit, the sequencing unit is configured to perform nucleic acid sequencing on a sample to be tested, thereby obtaining a genomic sequence of the sample; A unit, the comparison unit is connected to the sequencing unit, and is configured to compare the obtained genomic sequence of the sample with a reference genome, thereby obtaining position information of the genomic sequence on the reference genome; a calculation and inspection unit, all The calculation is connected with the inspection unit and the comparison unit, and is used for calculating the copy number of each window b of the reference genome, and performing a Z test on each window, thereby calculating the Z value of each window b; and identifying A unit, the identification unit and the calculation and inspection unit are connected to calculate a genomic disorder (GAS) based on the obtained value of Z, and identify a tumor burden in the sample based on the value of the genomic disorder. In another preferred example, the system further includes a correction unit, and the correction unit and the calculation and inspection unit are connected to correct a copy number of each window b of the reference genome, thereby calculating each window b Corrected copy number. In another preferred example, in the calculation and inspection unit, before performing the Z test on each window b, the coefficient of variation CV _{i of} each window b may be calculated according to the copy number of each window b, and The CV _{i is} sorted from small to large, removing the largest first n% of the window, where n is any value greater than 0 and less than or equal to 5, preferably, n = 1, 2, 2.5, 3, 3.1, 4, 4.2 or 5. It should be understood that, within the scope of the present invention, the above technical features of the present invention and the technical features specifically described in the following (such as the embodiments) may be combined with each other to form a new or preferred technical solution. Due to space limitations, I will not repeat them here.

Through extensive and in-depth research, the inventors have established for the first time an effective method for identifying tumor burden in samples that can improve the sensitivity and versatility of tumor detection. Specifically, by calculating the genomic disorder (GAS), Numeric values identify the tumor burden in the sample. In addition, the present invention also provides a system (equipment) for identifying tumor burden in a sample, the system (equipment) includes: a sequencing unit; a comparison unit; a calculation and inspection unit and an identification unit. In a preferred example of the present invention, a correction unit is further included. On this basis, the present inventors have completed the present invention. As used herein, the term "Copy Number Variations (CNV)" refers to the abnormal copy number of chromosomes or chromosome fragments of a sample genome, including but not limited to chromosome aneuploidies, deletions, and duplications, and microbes greater than 1000 bp. Missing, microduplicates. As used herein, the term “Genomic Abnormality Score (GAS)” is a score calculated based on the abnormal copy number of a chromosome or a segment of a chromosome of a sample. The detection range of the score includes, but is not limited to, the entire genome, specific chromosomes, Chromosome fragments, specific genes. As used herein, the term "Z-score" is also called a standard score, which is a process of dividing the difference between a numerical value and an average by the standard deviation. Formulated as: Z score = (x-μ) / σ where x is a specific value, μ is the arithmetic mean, and σ is the standard deviation; Z value represents the distance between the original value and the reference average, which is Calculated in standard deviation. As used herein, the term "partial response (PR, partial response)" refers to a reduction of the sum of the maximum diameters of target lesions by ≥30% for at least 4 weeks. As used herein, the term "progressive disease (PD)" means that the sum of the maximum diameters of target lesions increases by at least ≥20%, or new lesions appear. As used herein, the terms "system" and "device" have the same meaning. Reference genome In the present invention, taking a human as an example, the reference genome may be a whole genome or a partial genome. Moreover, the reference genome may be continuous or discontinuous. When the reference genome is a partial genome, the total coverage (F) of the reference genome is more than 50% of the whole genome, preferably, preferably, 60% or more, more preferably, 70% or more, more Preferably, it is above 80%, and most preferably above 95%, wherein the total coverage (F) refers to the percentage of the reference genome in the entire genome. In a preferred embodiment, the reference genome is a whole genome. In a preferred embodiment, the reference genome is the full length of all chromosomes of the species (such as a human), the full length of a single or multiple chromosomes, a portion of a single or multiple chromosomes, or a combination thereof. Tumor burden In the present invention, the "tumor burden" refers to the degree of harm the tumor has to the body, such as the size of the tumor, the degree of tumor activity, the metastasis of the tumor, and the degree of danger to the body from tumors in different parts. Some indicators for evaluating tumor burden include (but are not limited to): tumor size, tumor marker level, clinical symptoms (wheezing, pain, etc.), related complications (superior vena cava syndrome, etc.), and consumption (anemic, low Proteinemia, etc.). Sequencing In the present invention, sequencing can be performed using conventional sequencing techniques and platforms. The sequencing platform is not particularly limited. The second-generation sequencing platform includes (but is not limited to): Illumina's GA, GAII, GAIIx, HiSeq1000 / 2000/2500/3000/4000, X Ten, X Five, NextSeq500 / 550, MiSeq , MiSeqDx, MiSeq FGx, MiniSeq; SOLiD of Applied Biosystems; 454 FLX of Roche; Ion Torrent, Ion PGM, Ion Proton I / II of Thermo Fisher Scientific (Life Technologies); BGISEQ1000, BGISEQ500, BGISEQ100 of BGI BioelectronSeq 4000 of Biological Group; DA8600 of Daan Gene Co., Ltd. of Sun Yat-sen University; NextSeq CN500 of Berry Hekang; BIGIS of Zixin Pharmaceutical, a subsidiary of Zixin Pharmaceutical; HYK-PSTAR-IIA. The third-generation single molecule sequencing platform includes (but is not limited to): Helicos BioSciences 'HeliScope system, Pacific Bioscience's SMRT system, Oxford Nanopore Technologies' GridION, MinION. The sequencing type can be single-end sequencing or paired-end sequencing. The sequencing length can be any length greater than 30bp, such as 30bp, 40bp, 50bp, 100bp, 300bp, etc., and the sequencing depth can be 0.01, 0.02, 0.1, 1, 5, 10, 30 times, etc. Any multiple greater than 0.01. In the present invention, a HiSeq2500 high-throughput sequencing platform from Illumina is preferred. The sequencing type is Single End sequencing, the sequencing length is 41 bp, and the amount of sequencing data is 5M. Data processing In the present invention, data processing generally includes the following steps: (a) performing nucleic acid extraction and sequencing of the genome of a sample to be tested to obtain a genomic sequence; (b) comparing the genomic sequence of the sample to a reference genome to obtain The position of the sequence on the reference genome; (c) the reference gene component is formed into a window of a certain length, and the copy number of each window b is calculated; (d) a Z test is performed on each window b, and the Z value of each window is calculated; and (e) Calculate genomic disorder (GAS). Wherein, in step (a), the type of the sample to be tested is body fluid, which may be blood, interstitial fluid (interstitial fluid or intercellular fluid), lymph fluid, cerebrospinal fluid, urine, saliva, The detection target is DNA contained in body fluids. The DNA specifically exists in free circulating tumor cells (CTC), extracellular free DNA (cfDNA), and exosomes. The DNA extraction method of the test sample includes (but is not limited to): column extraction, magnetic bead extraction. Library samples were constructed using a high-throughput sequencing platform to sequence the samples. Wherein, in step (b), the method further includes: removing the linker and low-quality data from the sequencing result, and comparing the result to the reference genome. The reference genome can be a whole genome, any chromosome, or part of a chromosome. The reference genome usually selects a sequence that has been generally determined. For example, the human genome can be hg18 (GRCh18), hg19 (GRCh19), hg38 (GRCh38) of NCBI or UCSC, or any chromosome and a part of a chromosome. The comparison software can be any free or commercial software, such as BWA (Burrows-Wheeler Alignment tool), SOAPaligner / soap2 (Short Oligonucleotide Analysis Package), Bowtie / Bowtie2. The sequences are aligned to the reference genome to obtain the position of the sequence on the genome. You can select the uniquely aligned sequence on the genome, remove multiple aligned sequences on the genome, and eliminate the error caused by the repeated sequence on the copy number calculation. Wherein, in step (c), the method further specifically includes: forming the gene component into a window of a certain length, and the window length may also be the same or different integer in the range of 100bp-3,000,000bp (3M) according to the measured data amount. The number of windows can be any integer in the range of 1,000-30,000,000. According to the position of the measured sequence on the genome, the number of sequences falling into each window, the distribution of the bases, and the base distribution of the reference genome are counted. The copy number of each window is corrected according to the sequence of each window and the base GC content. The correction method includes, but is not limited to, Loess correction, and calculates the corrected copy number of each window. Among them, in step (d), it also specifically includes: taking a sample of N (N is a natural number of not less than 30) normal people, and extracting, building and sequencing the same conditions, and repeating the above steps (a)-( c), as a reference set. For each window b _i , there are N normal copy values. Control sample of normal copy number of the arithmetic mean μ _i, μ _i the arithmetic mean value is calculated as: ; Calculate the standard deviation σ _{i of the} normal control sample copy number, and the standard deviation calculation formula is: X X, X X, X X, ... X _j is the copy value of the normal sample. Calculate the Z value of each window b _i of the sample to be tested. The formula for calculating the Z value is: ; X _i is the copy value detected by window b _i . Wherein, in step (e), the method further includes: there is a highly repetitive region around the entire genome, a certain chromosome, a chromosome fragment, or a gene, such as a region near a centromere, a telomere, a satellite, a heterochromatin, and the like. First remove the highly repetitive regions to eliminate the impact on the confusion calculation. In a preferred embodiment, the method of removal includes (but is not limited to): a. Removal of centromeres, telomeres, satellites, heterochromatin, etc. that are not detected by high-throughput sequencing according to the characteristics of the reference genome sequence. Region, remove the region of length L near the centromere, telomere, satellite, heterochromatin on the genome, L can be any length less than 3M; or b. Remove for each window bi according to the copy number characteristics of normal samples, Calculate the coefficient of variation CV _i (Coefficient of Variation) of the normal control sample in this window. The formula for CV _i is: ; Μ _i is the arithmetic mean of the copy number of the normal control sample, and σ _i is the standard deviation of the copy number of the normal control sample. CV is sorted from small to large, removing the largest first n% of the window, n can be any value greater than 0 and less than or equal to 5. Wherein, in step (e), the calculation method of genomic disorder (GAS) is specifically included: firstly, the detection range of the disorder is determined, and the detection range includes, but is not limited to, the entire genome, a specific chromosome, a specific chromosome fragment, or a specific gene, etc. Any value ranging from 1M to the length of the genome (e.g., about 3G of the human genome). In the detection range of chaos, the Z value of the window excluding the influence of the repeated sequence is taken as an absolute value, the absolute value of the Z value is sorted from small to large, and the absolute value of the ordered Z value is evenly distributed within the range of 0% -100% The minimum value of the absolute value of Z is assigned to 0%, and the maximum value of the absolute value of Z is assigned to 100%. Calculate the cumulative value of the absolute value of the Z value corresponding to each window in the range from m% to p%, where m is 30-98, preferably 40-97, more preferably 60-96, and most preferably , 80-95, optimally, 95; p is 80-100, preferably, 85-100, more preferably, 90-100, optimally, 100, and pm 2 (preferably 5. Better 10, better 15, best 20), the cumulative value is the genomic disorder (GAS), and the calculation formula is: m _b is the window sorted at m%, and p _b is the window sorted at p%. Tumor burden in body fluids was identified using GAS values.

Method for identifying tumor burden in a sample

In the present invention, a method for identifying tumor burden in a sample that is effective and can improve the sensitivity and versatility of tumor detection, includes the steps of: (i) providing a sample to be tested; (ii) analyzing the sample to be tested Performing sequencing to obtain the genomic sequence of the sample; (iii) comparing the genomic sequence obtained in step (ii) with a reference genome to obtain position information of the genomic sequence on the reference genome; (iv) comparing the The reference gene component is divided into M region fragments, where each region fragment is a window b, and the copy number of each window b is calculated; (v) performing a Z test on each window b in step (iv) to calculate each window b Z value; and (vi) Calculate genomic disorder (GAS) based on the Z value obtained in step (v), and identify the tumor burden in the test sample based on the value of the genomic disorder.

In a preferred example of the present invention, the method includes the steps of: (a) performing nucleic acid extraction and sequencing on a sample genome to obtain a genomic sequence; (b) aligning the sequence to a reference genome to obtain the position of the sequence on the genome ; (C) divide the reference gene component into a certain length of window b, calculate the copy number of each window b; and (d) perform a Z test on each window b, calculate the Z value of each window b, and calculate the degree of genome confusion ( GAS) to identify tumor burden in a sample based on numerical values of genomic disruption.

System (equipment) to identify tumor burden in a sample

In the present invention, a system (equipment) for identifying tumor burden in a sample is also provided, including: a sequencing unit, the sequencing unit is configured to perform nucleic acid sequencing on a sample to be tested, thereby obtaining a genomic sequence of the sample; an alignment unit The comparison unit is connected to the sequencing unit, and is configured to compare the obtained genomic sequence of the sample with a reference genome, thereby obtaining position information of the genomic sequence on the reference genome; a calculation and inspection unit, said The calculation is connected to the inspection unit and the comparison unit, for calculating the copy number of each window b of the reference genome, and performing a Z test on each window, thereby calculating a Z value of each window b; and an identification unit; The identification unit and the calculation and inspection unit are connected to calculate a genomic disorder (GAS) based on the obtained value of Z, and identify a tumor burden in the sample based on the value of the genomic disorder. In a preferred embodiment, the system further includes a correction unit, the correction unit and the calculation and inspection unit are connected to correct a copy number of each window b of the reference genome, thereby calculating each window b Corrected copy number. The main advantages of the present invention include: (1) The present invention establishes a method and system for identifying tumor load in a sample for the first time. The method and system of the present invention can accurately and effectively identify tumor load in a sample. (2) The method and system of the present invention can improve the sensitivity and versatility of tumor detection. (3) The method and system of the present invention can reduce the pain caused by sampling during the detection of tumor patients and realize non-invasive detection. (4) The method and system of the present invention can effectively detect some patients who cannot be sampled by conventional tests; (5) The method and system of the present invention can detect tumor patients in real time, monitor the efficacy of medication, and make certain decisions for doctors' medication and treatment Guidance. The present invention is further described below in conjunction with specific embodiments. It should be understood that these examples are only used to illustrate the present invention and not to limit the scope of the present invention. The experimental methods without detailed conditions in the following examples are generally performed according to conventional conditions such as those described in Sambrook et al., Molecular Cloning: Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or according to the manufacturer Suggested conditions. Unless stated otherwise, percentages and parts are by weight. Unless otherwise specified, the materials used in the examples are all commercially available products. Example 1 The present invention has been applied to 15 examples and achieved good results. In order to make the usage and effect of the present invention easier to understand and master, an example will be further described below. A brief flow chart of the implementation is shown in Figure 1. The detailed implementation process is as follows: 1 ． Nucleic acid extraction and sequencing of the sample genome In this embodiment, the source of the test sample is blood from a gastric cancer patient, and free DNA (cfDNA) and white blood cells are extracted from the blood. Nucleic acid extraction uses Kangwei Century Biotechnology Co., Ltd.'s CW2603 nucleic acid extraction kit. The extraction method is based on the product instructions provided by Kangwei Century Biotechnology Co., Ltd. The library was constructed using Kangwei Century Biotechnology Co., Ltd.'s CW2185 library construction kit, and sequenced on the computer. HiSeq2500 high-throughput sequencing platform from Illumina was used for sequencing on the machine, and the instructions provided by Illumina were used. The sequencing type is Single End sequencing, the sequencing length is 41bp, and the amount of sequencing data is 5M.

2. Align the sequence to the reference genome to get the position of the sequence on the genome

The sequencing results were removed from the adapter and low-quality data, and compared to the reference genome. The reference genome is the human genome UCSC hg19 (GRCh19), and the alignment software is BWA (Burrows-Wheeler Alignment tool). Using default parameters, the sequences are aligned to the reference genome to obtain the position of the sequence on the genome and select the genome Unique aligned sequences.

3． Make reference gene components into windows of a certain length, and calculate the copy number of each window

The gene group is divided into 15489 windows b (areas), each window b is 200K in length. According to the position of the sequence in the genome, the number of sequences that fall into each window b, the distribution of base groups, and the base distribution of the reference genome are counted. According to the sequence of each window b and the base GC content, the copy number of each window b is corrected. The correction method is Loess, and the corrected copy number of each window b is calculated.

4． Calculate the CV value of each window

Take 100 normal human samples and repeat the above steps 1, 2, and 3 for the same extraction, database, and sequencing conditions to obtain normal control sample data. As a reference data set, calculate the CV value of each window b _i of the sample to be tested. .

For each window b _i , there are N (N = 100 in this embodiment) normal copy values.

Control sample of normal copy number of the arithmetic mean μ _i, μ _i the arithmetic mean value is calculated as: Calculate the standard deviation σ _{i of the} copy number of the normal control sample, and the formula for calculating the standard deviation is: X ₁ , X ₂ , X ₃ ,... X _j are copy values of normal samples.

Calculate the CV value of each window b _i of the sample to be tested. The calculation formula of the CV value is:

5． Perform a Z test on each window and calculate the Z value of each window

Calculate the Z value of each window b _i of the sample to be tested. The formula for calculating the Z value is: x _i is the copy value detected by window b _i , μ _i is the arithmetic mean of the copy number of the normal control sample, and σ _i is the standard deviation of the copy number of the normal control sample. The calculation formula is the same as step 4.

6. Calculating Genomic Chaos (GAS)

In this embodiment, the CV of each window is sorted from small to large, the largest top 5% window is removed, and it does not participate in the following confusion calculation. The detection range of the disorder is the entire genome; the absolute value of the Z value is sorted from small to large, and the cumulative value of the absolute value of the Z value from the m% to the p% window is calculated, and the cumulative value is the genome disorder (GAS). The calculation formula is: M _b is the window sorted at the m-th percentile, and p _b is the window sorted at the p-th percentile, where m is 95 and p is 100. Tumor burden in body fluids was identified using GAS values. 7. Test results Tested on a dozen samples. A typical pathological situation is shown below. The test results are shown in Table 1, Figure 2 and Figure 3. Table 1 Example 1 Results of tumor burden test on clinical drug effects of a patient with gastric cancer The results showed that the patient was diagnosed with gastric cancer before clinical medication. At this time, the cfDNA copy number was severely abnormal (Figure 3 S1), the whole genome disorder was 999.84, and the tumor burden in the blood was severe. With the medication, the cfDNA copy number was normal by the fourth cycle, and the genome disorder was 728.80, which was close to that of normal white blood cells 729.86. Using the same method of this embodiment, the whole genome disorder degree of the 100 normal persons is calculated, with a normal range of 722.87-739.89 and an arithmetic average of 733.22. The fourth medication cycle and the whole genome disorder value of the white blood cells in the present embodiment are in the normal range. It shows that the tumor load in the blood is small, which corresponds to its clinical evaluation result PR (partial response). With further medication, tumors develop resistance, cfDNA copy number abnormalities become serious, genome-wide disorder scores increase, and tumor burden in the blood becomes severe. By the seventh cycle of medication, the genome-wide disorder is the highest, and its clinical evaluation As a result, PD (progression of disease) corresponds. The results show that genomic disorder can effectively identify tumor burden in body fluids. All documents mentioned in the present invention are incorporated by reference in this application, as if each document was individually incorporated by reference. In addition, it should be understood that after reading the above teaching content of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the scope of the patents attached to this application.

Figure 1 shows a flow chart of an analytical method for identifying tumor burden in body fluids. Figure 2 shows the tumor load test results of patients in different clinical medication cycles. Figure 3 shows the genome-wide copy number variation of S1-7 and the corresponding GAS.

Claims (10)

A method for non-diagnosticly identifying tumor burden in a sample, comprising the steps of: (i) providing a sample to be tested; (ii) sequencing the sample to be tested to obtain a genomic sequence of the sample; (iii) aligning the genomic sequence obtained in step (ii) with a reference genome to obtain position information of the genomic sequence on the reference genome; (iv) dividing the reference gene component into M region fragments, each of which The fragment is a window b, and the copy number of each window b is calculated; (v) a Z-test is performed on each window b of step (iv) to calculate the Z value of each window b; and (vi) according to step (v ) To obtain the Z value, calculate the genomic disorder, and identify the tumor burden in the test sample based on the value of the genomic disorder, where the genomic disorder is calculated using the following formula: Among them, m _b is the window sorted at the m%, p _b is the window sorted at the p%, m is 30-98, p is 80-100, and pm 2. The method of claim 1, wherein the reference genome comprises a whole genome. The method according to claim 1 or 2, wherein the coverage of the reference genome reaches more than 50% of the entire genome. The method of claim 1, wherein the sample is selected from the group consisting of blood, plasma, interstitial fluid, lymph fluid, cerebrospinal fluid, urine, saliva, aqueous humor, semen, or a combination thereof. The method according to claim 1, wherein the step (iv) further comprises the steps of correcting the copy number of each window b and calculating the corrected copy number of each window b. The method of claim 1, wherein the Z value of each window b is calculated using the following formula: Where i is any positive integer from 1 to M; M is the total number of windows formed by the reference gene components, where M is A positive integer of 50; x _i is the copy value detected by the sample under test in the i-th window b _i ; b _i is the i-th window; μ _i is the arithmetic mean of the copy number of the normal control sample in window b _i , Calculated with the following formula: Where j is any positive integer from 1 to N; N is the total number of normal control samples, where N is A positive integer of 30; X _j refers to the copy value detected by the j-th normal control sample in the window b _i ; σ _i is the standard deviation of the copy number of the normal control sample in the window b _i , and is calculated using the following formula: In the formula, N, j, X _j and μ _i are defined as above. The method according to claim 1, wherein m is 40-97, p is 85-100, and pm 5. The method according to claim 1, wherein before step (v), the method further comprises the following steps: (iv1) Calculate the number of each window b in the normal control sample according to the copy number of each window b in step (iv). Coefficient of variation CV _i ; (iv2) Sort the CV _i from small to large, and remove the largest first n% of the window, where n is any value greater than 0 and less than or equal to 5. The method according to claim 8, wherein the coefficient of variation CV _i is calculated using the following formula: Among them, μ _i is the arithmetic average of the copy number of the normal control sample, and is calculated by the following formula: σ _i is the standard deviation of the copy number of the normal control sample, and is calculated using the following formula: In the formula, N, j, X _j , μ _i and σ _i are defined as above. The method according to claim 3, wherein the coverage of the reference genome reaches more than 60% of the whole genome.