A combination of microsatellite loci for detecting microsatellite instability based on single tumor samples and its application

Abstract

The present invention discloses a microsatellite locus combination for detecting microsatellite instability based on a single tumor sample, including 117 microsatellite loci, 80 microsatellite loci, 50 microsatellite loci, 44 microsatellite loci or 30 microsatellite loci. At the same time, the present invention also provides a system for detecting microsatellite instability based on the microsatellite locus combination, the system uses the microsatellite locus combination as a marker to detect microsatellite instability, and can perform microsatellite instability detection based on a single tumor sample, which not only avoids the acquisition of normal samples used for pairing / comparison in traditional microsatellite instability detection, greatly reduces the detection cost, but also ensures that the sensitivity, specificity and consistency of the detection results are all 100%, and has excellent economic value, social value and clinical practical value.

Description

Technical Field

[0001] The present invention relates to the technical field of gene sequencing, and in particular to a microsatellite locus combination for detecting microsatellite instability based on a single tumor sample and an application thereof. Background Art

[0002] Next-generation sequencing (NGS) has been applied in many clinical fields, and plays an increasingly important role in assisted reproduction, such as non-invasive prenatal testing (NIPT); genetic diseases, such as genetic pathogenic mutation screening; and tumor research, such as early tumor diagnosis, medication guidance, and prognosis. In recent years, many NGS-based tumor studies have been conducted to provide a comprehensive molecular landscape of cancer, identify genetic changes that contribute to tumor occurrence, development, and metastasis, and study the complexity, consistency, and evolution of tumors.

[0003] As a breakthrough technology, NGS has brought new opportunities to molecular diagnosis, but it also faces many challenges. Currently, some clinical laboratories have adopted NGS technology to identify variants and perform precise genetic profiling of oncology and cardiovascular diseases; NGS technology and bioinformatics tools will continue to develop and become the main diagnostic method and treatment standard for genomic analysis to meet the growing needs of precision medicine.

[0004] Microsatellite (MS) refers to a DNA sequence in the cell genome that is repeated in tandem in units of a few nucleotides (mostly 1 to 6); members of the mismatch repair (MMR) system include proteins such as MLH1, MSH2, MSH6 and PMS2. Occasionally, small DNA mismatches occur during DNA replication, and these mismatches can be recognized and sheared by members of the mismatch repair system, and then synthesized into new chains for repair. However, when the function of the DNA mismatch repair gene is abnormal (dMMR), the replication errors of microsatellites cannot be corrected and continue to accumulate, causing changes in the length or base composition of the microsatellite sequence, which is called microsatellite instability (MSI). MSI can be divided into three categories according to its degree: microsatellite high instability (MSI-high, MSI-H), microsatellite low instability (MSI-low, MSI-L) and microsatellite stability (MSS); it can also be divided into two categories: microsatellite high instability (MSI-high, MSI-H) and microsatellite stability (Microsatellite stability, MSS).

[0005] MMR gene mutations (systemic / germline / promoter methylation) can lead to abnormal expression of MMR proteins, which in turn affects microsatellite instability. When microsatellite high instability (MSI-H) occurs, it will cause high mutations in the genome, which will increase the risk of tumors. Microsatellite instability is generally considered to be one of the factors that affect the treatment of cancers such as colorectal cancer. Cancer patients with microsatellite instability usually have better results with immunotherapy because their tumors are more easily recognized and attacked by the immune system. In addition, microsatellite instability is also associated with patients who are more responsive to treatment with immune checkpoint inhibitors.

[0006] In 2017, the FDA approved the PD-1 inhibitor pembrolizumab (pembrolizumab, drug K) for the treatment of solid tumors with microsatellite high instability (MSI-H) / mismatch repair deficiency (dMMR). Drug K also became the first anti-tumor immune drug that "looks at markers instead of locations"; in the field of gynecological malignancies, the 2020 NCCN guidelines also pointed out that "pembrolizumab is recommended for the second-line treatment of advanced endometrial cancer with MSI-H / dMMR and advanced cervical cancer with positive PD-L1 expression or MSI-H / dMMR". Therefore, detecting and evaluating the microsatellite stability status of cancer patients will help to fine-tune the patient population, and then assist clinicians in choosing more accurate treatment plans, which is of great clinical significance.

[0007] The main method of MMR detection is to detect MLH1, MSH2, MSH6 and PMS2 through immunohistochemistry. If any one of the proteins is lost (negative expression), it is considered to be dMMR (equivalent to MSI-H), and if all four proteins are positive, it is pMMR (equivalent to MSS). Immunohistochemistry is currently a common method for detecting MMR in hospitals. It is simple, easy and inexpensive, but its interpretation and identification of dMMR requires high requirements for pathologists. The test results are largely affected by the subjective factors of the interpreters, and there is a certain probability of false negatives and false positives. On the other hand, the selection of primary antibodies for detecting MMR by immunohistochemistry is crucial to the final result. The operating procedures are difficult to standardize and normalize, and the repeatability of immunohistochemistry results cannot be guaranteed, which greatly affects the interpretation of the results. Therefore, the selection of high-quality antibodies (primary antibodies) is also crucial to the accuracy of the result interpretation.

[0008] MSI is mainly detected by multiplex fluorescence PCR combined with capillary electrophoresis. The genomic DNA of cancer tissue and control samples is extracted, and then amplified by multiplex fluorescence PCR. After amplification, it is detected by capillary electrophoresis. The electrophoresis test results of cancer tissue and control samples are judged by professionals for stability. The instability of 5 microsatellite sites on the genome is detected to determine the degree of microsatellite instability. This detection method requires not only DNA from tumors, but also DNA from normal cells of the body as a control group. The two DNAs are compared by PCR. In the comparison results, ≥2 sites are unstable, which is microsatellite high instability (MSI-H), 1 site is unstable, which is microsatellite low instability (MSI-L), and no site is unstable, which is microsatellite stable (MSS). The detection results of multiplex fluorescence PCR are also largely affected by the subjective factors of the interpreters, and there is a certain probability of false negatives and false positives.

[0009] However, with the in-depth development of next-generation sequencing (NGS) in the field of tumor biology, there are now some algorithms that can determine the status of MSI based on NGS data; there are also some algorithms, such as MSIsensor and MANTIS, which can analyze the proportion of unstable microsatellite sites through sequencing data of tumor samples and normal paired samples and then evaluate the microsatellite status.

[0010] MSI testing has gradually become a routine test in medical institutions. However, in actual clinical operations, it is not easy to obtain normal samples for pairing / comparison, and the analysis based on tumor-normal paired will significantly increase the cost of testing. Moreover, if the paired samples come from blood or saliva, the tumor / normal tissue analysis cannot capture the system noise of sample preservation and nucleic acid extraction, which will cause deviations in the test results.

[0011] Therefore, how to perform microsatellite instability analysis based on a single tumor sample has become an urgent issue, and microsatellite instability analysis based on a single tumor sample has high economic value, social value and clinical practical value. Summary of the invention

[0012] The purpose of the present invention is to overcome the above-mentioned deficiencies of the prior art and to provide a microsatellite locus combination for detecting microsatellite instability based on a single tumor sample and its application.

[0013] The first object of the present invention is to provide a microsatellite locus combination for detecting microsatellite locus instability based on a single tumor sample.

[0014] The second object of the present invention is to provide the use of the above-mentioned microsatellite locus combination as a marker in detecting microsatellite locus instability for non-diagnostic purposes.

[0015] The third object of the present invention is to provide the use of the above-mentioned microsatellite locus combination as a marker in the preparation of a system for detecting microsatellite instability.

[0016] The fourth object of the present invention is to provide the use of the above-mentioned microsatellite site combination as a marker in the preparation of a product for evaluating the efficacy of immunotherapy and / or prognosis analysis of patients with solid tumors.

[0017] The fifth object of the present invention is to provide the use of a detection reagent for detecting the above-mentioned microsatellite site combination in the preparation of a product for detecting microsatellite instability.

[0018] A sixth object of the present invention is to provide a system for detecting microsatellite instability.

[0019] A seventh object of the present invention is to provide the application of the above system in detecting microsatellite instability for non-diagnostic purposes.

[0020] In order to achieve the above object, the present invention is implemented by the following scheme:

[0021] A combination of microsatellite loci for detecting microsatellite instability based on a single tumor sample includes 117 microsatellite loci, 80 microsatellite loci, 50 microsatellite loci, 44 microsatellite loci or 30 microsatellite loci, and the hg19 gene with accession number GCF_000001405.13 is used as a reference genome. The information of the 117 microsatellite loci is shown in the following table.

[0022]

[0023]

[0024]

[0025]

[0026] Taking the hg19 gene with accession number GCF_000001405.13 as the reference genome, the information of the 80 microsatellite loci is shown in the following table.

[0027]

[0028]

[0029]

[0030] Taking the hg19 gene with accession number GCF_000001405.13 as the reference genome, the information of the 50 microsatellite loci is shown in the following table.

[0031]

[0032]

[0033] Taking the hg19 gene with accession number GCF_000001405.13 as the reference genome, the information of the 44 microsatellite loci is shown in the following table.

[0034]

[0035]

[0036] Taking the hg19 gene with accession number GCF_000001405.13 as the reference genome, the information of the 30 microsatellite loci is shown in the following table.

[0037]

[0038]

[0039] Preferably, 44 microsatellite loci are included.

[0040] The present invention uses stable samples (all of which are microsatellite stable samples) as a baseline database, screens out sites that can distinguish microsatellite unstable samples from microsatellite stable samples by comparing a training set (samples with known MSI status) with the baseline database, and performs a statistical test on each site to screen out a microsatellite site combination that can be used to detect microsatellite instability; based on the screened microsatellite site combination, a validation set (samples with known MSI status) is used for comparison with the baseline database, and the proportion of microsatellite unstable sites in the microsatellite stable samples and the microsatellite unstable samples is analyzed, and it is finally determined that: when the above-mentioned microsatellite site combination is detected, if the proportion of microsatellite unstable sites is ≥18%, then the sample to be tested is a microsatellite unstable sample.

[0041] Therefore, the present invention also seeks to protect the use of any of the above-mentioned microsatellite loci combinations as markers in detecting microsatellite instability samples for non-diagnostic purposes.

[0042] The present invention also seeks to protect the use of any of the above-mentioned microsatellite loci combinations as markers in the preparation of a system for detecting microsatellite instability.

[0043] The present invention also seeks to protect the use of any of the above-mentioned microsatellite site combinations as markers in the preparation of products for evaluating the efficacy of immunotherapy and / or prognosis analysis of patients with solid tumors.

[0044] Preferably, the solid cancer patient is a nasopharyngeal cancer patient, a lung cancer patient, a gastric cancer patient, a liver cancer patient, an esophageal cancer patient, a colorectal cancer patient, a bile duct cancer patient, a prostate cancer patient, an upper urinary tract urothelial carcinoma patient and / or a bladder urothelial carcinoma patient.

[0045] The present invention also seeks to protect the use of a detection reagent for detecting any of the above-mentioned microsatellite site combinations in the preparation of a product for detecting microsatellite instability.

[0046] The present invention also claims a system for detecting microsatellite instability, the system comprising a data acquisition module, a detection module and a result output module;

[0047] The data acquisition module is used to acquire sequencing data of the sample to be tested and sequencing data of the negative quality control sample;

[0048] The samples to be tested are derived from humans; the number of negative quality control samples is not less than 2, and all of them are microsatellite stable samples;

[0049] The detection module is used to detect microsatellite instability of the sample to be tested, and specifically includes the following steps:

[0050] S1. Using each microsatellite locus in any of the microsatellite locus combinations described above as a site to be detected, selecting a site to be detected with a sequencing depth of ≥50 in the sequencing data of the sample to be tested as an analysis site, and calculating the number of analysis sites;

[0051] Acquire the sequencing data of each analysis site in the sequencing data of the sample to be tested and the sequencing data of each negative quality control sample as the analysis data of the sample to be tested and the analysis data of each negative quality control sample;

[0052] Based on the analysis data of the sample to be tested and the analysis data of each negative quality control sample, the number of occurrences of the repeating unit type at each analysis site in each analysis data is obtained, and the frequency of occurrence of the repeating unit type at the analysis site in each analysis data is calculated, and the frequency of occurrence of the repeating unit type at each analysis site in the sample to be tested and the frequency of occurrence of the repeating unit type at each analysis site in the negative quality control sample are obtained respectively;

[0053] The repeating unit type of each analysis site is the same as the repeating unit type of any of the above analysis sites;

[0054] S2. For negative control samples, using the occurrence frequency of the repeating unit type at each analysis site in the negative control samples obtained in step S1, calculate the JS divergence of the analysis site between every two negative control samples, and calculate the average value to obtain the negative JS divergence mean of the analysis site;

[0055] For the sample to be tested, using the occurrence frequency of the repeating unit type at each analysis site in the sample to be tested obtained in step S1, the JS divergence of the analysis site between the sample to be tested and each negative quality control sample is calculated respectively, and the average value is calculated to obtain the mean JS divergence of the analysis site to be tested;

[0056] S3. Perform a T test on the negative JS divergence mean of the analysis site obtained in step S2 and the tested JS divergence mean of the analysis site to obtain a P value;

[0057] Calculate the difference between the negative JS divergence mean of the analysis site obtained in step S2 and the JS divergence mean to be tested of the analysis site, and take the absolute value as the JS divergence mean difference of the analysis site;

[0058] The analysis sites with P value < 0.01 and JS divergence mean difference ≥ 0.1 were recorded as unstable sites of the samples to be tested, and the number of unstable sites of the samples to be tested was calculated;

[0059] S4. Calculate the proportion of unstable sites in the sample to be tested. If the proportion of unstable sites is ≥18%, the test result is "microsatellite unstable"; if the proportion of unstable sites is <18%, the test result is "microsatellite stable";

[0060] The unstable site ratio = the number of unstable sites of the sample to be tested obtained in step S3 / the number of analysis sites obtained in step S1 × 100%;

[0061] The result output module is used to output the detection result obtained by the detection module in step S4.

[0062] Preferably, the sequencing data of the sample to be tested and the sequencing data of the negative quality control sample are: the sequencing data obtained by high-throughput sequencing is quality controlled and PCR duplicate sequences are removed, and the effective sequencing depth is ≥200; the sequencing depth of the high-throughput sequencing is not less than 1000×.

[0063] More preferably, the high-throughput sequencing is performed using an Illumina high-throughput sequencing platform.

[0064] More preferably, the quality control and removal of PCR duplicate sequences are specifically: the offline data obtained by high-throughput sequencing are quality controlled using Fastp, and compared with the human reference genome hg19 (GCF_000001405.13) using BWA, and the PCR duplicate sequences are removed using Gencore software.

[0065] Further preferably, the version number of Fastq is 0.23.0, and the quality control is performed based on the preset parameters of Fastp with the version number of 0.23.0.

[0066] Further preferably, the version number of the BWA is 0.7.15-r1140, and the comparison is performed based on the BWA with the version number of 0.7.15-r1140.

[0067] Further preferably, the version number of the Gencore software is 0.13.0, and the removal of PCR duplicate sequences is based on the removal of PCR duplicate sequences of the Gencore software with the version number of 0.13.0.

[0068] Preferably, the negative quality control sample is confirmed to have a microsatellite stable status by multiplex fluorescence PCR combined with capillary electrophoresis.

[0069] The present invention also claims the use of the above system in detecting microsatellite instability for non-diagnostic purposes.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] The present invention provides a combination of microsatellite loci for detecting microsatellite instability based on a single tumor sample, including 117 microsatellite loci, 80 microsatellite loci, 50 microsatellite loci, 44 microsatellite loci or 30 microsatellite loci. At the same time, the present invention also provides a system for detecting microsatellite instability based on the microsatellite loci combination, the system uses the microsatellite loci combination as a marker for microsatellite instability detection, and can perform microsatellite instability detection based on a single tumor sample, which not only avoids the acquisition of normal samples for pairing / comparison in traditional microsatellite instability detection, greatly reduces the detection cost, but also ensures that the sensitivity, specificity and consistency of the detection results are all 100%, and has excellent economic value, social value and clinical practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 Flow chart of the screening of microsatellite loci combinations for detecting microsatellite instability. DETAILED DESCRIPTION

[0073] The present invention is further described in detail below in conjunction with the accompanying drawings and specific examples of the specification. The examples are only used to explain the present invention and are not used to limit the scope of the present invention. The test methods used in the following examples are conventional methods unless otherwise specified; the materials and reagents used are reagents and materials that can be obtained from commercial channels unless otherwise specified.

[0074] Example 1 Screening of microsatellite loci combinations for detecting microsatellite instability

[0075] 1. Experimental methods

[0076] The screening flow chart of microsatellite loci combination for detecting microsatellite instability is shown in Figure 1 shown.

[0077] (1) The hg19 gene with accession number GCF_000001405.13 was used as the reference genome, and sites with single base repeats and repeat units ≥ 6 were screened to obtain a set S containing 6568 microsatellite sites and the repeat unit types of the 6568 microsatellite sites.

[0078] (2) Three cohorts were set up, namely, a baseline set, a training set, and a validation set. The samples in the three cohorts were completely independent and the MSI status results were determined by multiplex fluorescence PCR combined with capillary electrophoresis (refer to the prior art: Boland, CR, et al., A National Cancer Institute Workshop on Microsatellite Instability for Cancer Detection and Familial Predisposition: Development of International Criteria for the Determination of Microsatellite Instability in Colorectal Cancer. Cancer research, 1997. 58: p. 5248-5257.); the MSI status of each sample was recorded as “actual MSI status”.

[0079] The baseline set contained 100 samples whose MSI status was all microsatellite stable (MSS);

[0080] The training set contains 160 samples with known MSI status, including 16 samples with MSI status of microsatellite high instability (MSI-H) and 144 samples with MSI status of microsatellite stable (MSS);

[0081] The validation set contained 126 samples with known MSI status, including 13 samples with microsatellite high instability (MSI-H) and 113 samples with microsatellite stable (MSS).

[0082] (3) Sequencing data of each sample in the three cohorts were obtained separately (sequencing was performed based on the Illumina high-throughput sequencing platform). The sequencing data of each sample was as follows: the sample was sequenced using the Illumina high-throughput sequencing platform (sequencing depth was 1000×). The off-machine data was quality controlled using Fastp (version: 0.23.0) based on preset parameters, and the sequence alignment software BWA (version: 0.7.15-r1140) was used to align with the human reference genome hg19 (GCF_000001405.13) based on preset parameters. The PCR duplicate sequences were removed using Gencore software (version: 0.13.0) based on preset parameters, and the effective sequencing depth was ≥200.

[0083] The sites with sequencing depth less than 50 in set S are eliminated (i.e., the sites with less than 50 sequences in the sequencing data of each sample are eliminated), and the eliminated sites are represented by NA to obtain quality control sites;

[0084] The sequencing data of the quality control site in the sequencing data of each sample is obtained to obtain the analysis data of the baseline set, the analysis data of the training set and the analysis data of the validation set; the number of occurrences of the repeating unit type of the quality control site in each analysis data is obtained respectively, and the occurrence frequency of the repeating unit type in the quality control site of each analysis data is calculated; wherein the repeating unit type of the quality control site is the same as the repeating unit type of the same site in the 6568 microsatellite sites in step (1).

[0085] The frequency distribution information of the repeating unit type at the quality control site in each sample is obtained, and the quality control site information of each sample (the occurrence frequency of the repeating unit type at each quality control site in each sample) is recorded.

[0086] The microsatellite loci set of each sample in the baseline set, the microsatellite loci set of each sample in the training set and the microsatellite loci set of each sample in the validation set were obtained respectively. The number of quality control loci in each microsatellite loci set was 6568, and the occurrence frequency of each quality control locus was known.

[0087] (4) Calculate the JS divergence matrix 1 of the baseline set. The calculation method is as follows: take the site as the row and the combination of every two samples in the baseline set as the column, totaling (99+98+97+…+2+1)=4950 columns, and obtain a JS divergence matrix 1 of 6568×4950; take the site as the unit, calculate the JS divergence between the A site of every two samples in the baseline set, and input the obtained 4950 JS divergences into the row where the A site is located in the JS divergence matrix 1;

[0088] Each column of the JS divergence matrix 1 represents the JS divergence of 6568 sites between every two samples in the baseline set, and each row represents the JS divergence of site A between every two samples in the baseline set; where site A represents any site in the baseline set microsatellite site set obtained in step (3).

[0089] Calculate the JS divergence matrix 2 of each sample in the training set. Each training set sample has 1 JS divergence matrix 2, for a total of 160 JS divergence matrices 2;

[0090] The calculation method of the JS divergence matrix 2 of sample D in the training set is as follows: take the sites as rows and the samples in the baseline set as columns to obtain a JS divergence matrix 2 of 6568×100; take the sites as units, calculate the JS divergence of the B sites of sample D in the training set and the B sites of 100 samples in the baseline set, and enter the obtained 100 JS divergence values ​​into the row where the B site is located in the JS divergence matrix 2 of sample D;

[0091] Each column of the JS divergence matrix 2 represents the JS divergence of 6568 sites between sample D and each sample in the baseline set, and each row represents the JS divergence of site B between sample D and each sample in the baseline set; where site B represents any site in the training set microsatellite site set obtained in step (3), and sample D represents any sample in the training set.

[0092] The JS divergence formula is shown in Formula I;

[0093] Formula I:

[0094] Wherein, P and Q represent two different samples respectively, p(x) represents the frequency of occurrence of the repeating unit type at site A in sample P, and q(x) represents the frequency of occurrence of the repeating unit type at site A in sample Q.

[0095] (5) Taking the row where the B site is located as a unit, respectively calculate the JS divergence mean of the B site in the JS divergence matrix 2 of sample D obtained in step (4) and the JS divergence mean of the B site in the JS divergence matrix 1, and calculate the absolute value of the difference between the JS divergence mean of the B site in the JS divergence matrix 2 of sample D and the JS divergence mean of the B site in the JS divergence matrix 1 to obtain the mean difference of the B site, and then perform a T test on the JS divergence mean of the B site in the JS divergence matrix 1 and the JS divergence mean of the B site in the JS divergence matrix 2 and calculate the P value to obtain the mean difference and P value of the B site in the training set sample D, record the B site with a mean difference ≥ 0.1 and a P value < 0.01 as an unstable site, and obtain a preliminary screened unstable site set.

[0096] Then, microsatellite loci that existed in ≥50 samples in the baseline set and had a sequencing depth of ≥50 were screened out and recorded as microsatellite loci set 1. Microsatellite loci that existed in both microsatellite loci set 1 and the preliminary screening unstable loci set were screened to obtain a microsatellite locus combination for detecting microsatellite instability, which included 117 microsatellite loci for detecting microsatellite instability.

[0097] (6) Taking the JS divergence matrix 2 data of sample D in the training set as an example (i.e., the JS divergence matrix of 6568 sites in sample D in the training set), calculate the proportion of unstable sites in sample D:

[0098] According to the microsatellite loci combination for detecting microsatellite instability obtained in step (5), 117 microsatellite loci for detecting microsatellite instability of sample D are selected, and according to the sequencing data of sample D in the training set obtained in step (3), loci with a sequencing depth of less than 50 (i.e., loci covering less than 50 sequences) are eliminated to obtain microsatellite loci after quality control, and the number of microsatellite loci after quality control is recorded;

[0099] Based on the JS divergence matrix 2 of sample D obtained in step (4), the JS divergence corresponding to the quality-controlled microsatellite loci in sample D in the JS divergence matrix 2 of sample D is screened and the JS divergence mean of the quality-controlled microsatellite loci in sample D is calculated, a T test is performed on the JS divergence mean of the quality-controlled microsatellite loci in sample D and the JS divergence mean of the same loci in the JS divergence matrix 1 and the P value is calculated, and the absolute value of the difference between the JS divergence mean of the quality-controlled microsatellite loci in sample D and the JS divergence mean of the same loci in the JS divergence matrix 1 is calculated to obtain the mean difference of the quality-controlled microsatellite loci in sample D, the loci with a mean difference ≥ 0.1 and a P value < 0.01 are recorded as unstable microsatellite loci in sample D, the number of unstable microsatellite loci in sample D is obtained, and the proportion of unstable microsatellite loci in sample D is calculated;

[0100] The proportion of unstable microsatellite sites = (the number of unstable microsatellite sites in sample D / the number of microsatellite sites after quality control) × 100%.

[0101] Sample D was replaced with the remaining 159 samples in the training set to obtain the microsatellite instability ratio of each sample in the training set, and the lowest value of the microsatellite instability ratio of the 16 MSI-H samples and the highest value of the microsatellite instability ratio of the 144 MSS samples in the training set were recorded.

[0102] (7) Based on the lowest value of the microsatellite instability ratio of the 16 MSI-H samples in the training set and the highest value of the microsatellite instability ratio of the 144 MSS samples obtained in step (6), the judgment threshold for distinguishing between MSS samples and MSI-H samples in the training set is obtained, and the judgment threshold is 18%.

[0103] (8) Based on the microsatellite locus combination for detecting microsatellite instability obtained in step (5), the JS divergence matrix 3 of each sample in the validation set is calculated, and there are 126 JS divergence matrices 3 in total. The calculation method is as follows: the loci in the microsatellite locus combination for detecting microsatellite instability obtained in step (5) are used as rows, and the number of samples in the baseline set are used as columns to obtain a 117×100 JS divergence matrix 3; taking the loci as units, the JS divergence between the C loci of each sample in the validation set and the C loci of the 100 samples in the baseline set is calculated, and the 100 JS divergences obtained are input into the row where the C loci in the JS divergence matrix 3 is located;

[0104] Each column in the JS divergence matrix 3 represents the JS divergence of 117 microsatellite loci for detecting microsatellite instability between the samples in the validation set and each sample in the baseline set; each row represents the JS divergence of the C loci between the samples in the validation set and each sample in the baseline set; wherein the C loci represent any locus in the combination of microsatellite loci for detecting microsatellite instability obtained in step (5); the JS divergence calculation formula is shown in formula I in step (4).

[0105] As shown in step (6), the sample D in step (6) is replaced with the sample in the validation set, the proportion of microsatellite unstable sites of each sample in the validation set is calculated, and the proportion of microsatellite unstable sites of each sample in the validation set is compared with the judgment threshold obtained in step (7); if the proportion of microsatellite unstable sites of the sample is ≥ the judgment threshold, the sample exhibits microsatellite instability (MSI-H); if the proportion of microsatellite unstable sites of the sample is < the judgment threshold, the sample exhibits microsatellite stability (MSS), and the microsatellite instability status result of each sample in the validation set is obtained.

[0106] (9) Perform consistency analysis on the microsatellite instability status results (MSS sample number and MSI-H sample number) of each sample in the validation set obtained in step (8) and the actual MSI status of each sample in the validation set.

[0107] 2. Experimental results

[0108] The screened microsatellite loci combination for detecting microsatellite instability contained 117 microsatellite loci for detecting microsatellite instability. The information of the 117 microsatellite loci (based on the hg19 gene with accession number GCF_000001405.13) is shown in Table 1.

[0109] Table 1 Information of 117 microsatellite loci

[0110]

[0111]

[0112]

[0113] The microsatellite instability detection results of the training set (160 samples with known MSI status) combined with the microsatellite loci and judgment threshold (18%) shown in Table 1 are shown in Table 2; the microsatellite instability detection results of the validation set (126 samples with known MSI status) are shown in Table 3.

[0114] Table 2. Training set detection results

[0115]

[0116] Table 3 Validation set test results

[0117]

[0118] The results showed that when the detection results of the training set were compared with the actual MSI status, the sensitivity, specificity and consistency of the detection were all 100%; when the detection results of the validation set were compared with the actual MSI status, the sensitivity was 100%, the specificity was 99.1%, and the consistency was 99.2%, and the consistency was ≥95%.

[0119] The results show that the use of the 117 microsatellite loci shown in Table 1 combined with an 18% judgment threshold can accurately detect the microsatellite instability of the sample.

[0120] Example 2 Microsatellite locus combination and verification for detecting microsatellite instability

[0121] 1. Experimental methods

[0122] (1) Screening and determination of sites

[0123] Validation set 2 was used for microsatellite instability analysis; validation set 2 contained 41 samples with known MSI status, including 7 samples with microsatellite high instability (MSI-H) MSI status and 34 samples with microsatellite stable (MSS) MSI status.

[0124] Based on the sequencing data of each sample in validation set 2 (sequencing was performed on the Illumina high-throughput sequencing platform), 117 microsatellite loci as shown in Table 1 were screened in each sample in validation set 2, and the loci with a site depth of <50 (i.e., sites covering less than 50 sequences) were removed from the 117 microsatellite loci.

[0125] The sequencing data of each sample in validation set 2 are as follows: each sample in validation set 2 was sequenced by Illumina high-throughput sequencing platform (sequencing depth was 1000×), and the obtained offline data were quality controlled by Fastp (version: 0.23.0) based on preset parameters, and the sequence alignment software BWA (version: 0.7.15-r1140) was used to align with the human reference genome hg19 (GCF_000001405.13) based on preset parameters, and the Gencore software (version: 0.13.0) was used to remove PCR duplicate sequences based on preset parameters, and the effective sequencing depth was ≥200 sequencing data.

[0126] After removing the sites, it was shown that the 41 samples shown in validation set 2 only covered 44 microsatellite sites among the 117 microsatellite sites shown in Table 1, and the remaining 73 microsatellite sites were removed in each sample.

[0127] The information of 44 microsatellite loci (based on the hg19 gene with accession number GCF_000001405.13) is shown in Table 4.

[0128] Table 4 Information of 44 microsatellite loci

[0129]

[0130]

[0131] (2) Verification of the effect of detecting microsatellite instability

[0132] The validation set 2 (actual MSI status: 7 samples with MSI status of MSI-H and 34 samples with MSI status of MSS) was used to replace the validation set in step (8) of Example 1. Based on the 44 microsatellite loci shown in Table 4, microsatellite instability detection was performed according to the method shown in steps (8) to (9) of Example 1.

[0133] The test results were recorded and compared with the actual MSI status of validation set 2 to obtain the sensitivity, specificity and consistency of microsatellite instability detection based on a combination of 44 microsatellite loci.

[0134] 2. Experimental results

[0135] The results of microsatellite instability detection in validation set 2 (41 samples with known MSI status) combined with the 44 microsatellite loci shown in Table 4 are shown in Table 5.

[0136] Table 5 Detection results of validation set 2

[0137]

[0138]

[0139] The results showed that the sensitivity, specificity and consistency of the detection results of validation set 2 were all 100% compared with the actual MSI status.

[0140] Results show that the 44 microsatellite loci shown in Table 4 can accurately detect the microsatellite instability of the samples.

[0141] Example 3 A system for detecting microsatellite instability

[0142] The system for detecting microsatellite instability comprises a data acquisition module, a detection module and a result output module.

[0143] The data acquisition module is used to obtain sequencing data of the sample to be tested and the negative quality control sample;

[0144] The samples to be tested are derived from humans; the negative quality control samples are the baseline set shown in Example 1, including 100 samples whose MSI status is all microsatellite stable (MSS);

[0145] The sequencing data of the test samples and negative quality control samples are as follows: the test samples and negative quality control samples are sequenced using the Illumina high-throughput sequencing platform (sequencing depth is 1000×), and the obtained offline data are quality controlled using Fastp (version: 0.23.0) based on preset parameters, and the sequence alignment software BWA (version: 0.7.15-r1140) is used to align with the human reference genome hg19 (GCF_000001405.13) based on preset parameters, and the Gencore software (version: 0.13.0) is used to remove PCR duplicate sequences based on preset parameters, and the effective sequencing depth is ≥ 200 sequencing data;

[0146] The detection module is used to detect microsatellite instability of the sample to be tested, and performs the following steps:

[0147] S1. Using the 117 microsatellite loci shown in Table 1 of Example 1 or the 44 microsatellite loci shown in Table 4 of Example 2 as the sites to be detected, selecting the sites to be detected with a sequencing depth of ≥50 in the sequencing data of the sample to be tested as the analysis sites, and calculating the number of analysis sites;

[0148] Acquire the sequencing data of each analysis site in the sequencing data of the sample to be tested and the sequencing data of each negative quality control sample as the analysis data of the sample to be tested and the analysis data of each negative sample;

[0149] Based on the analysis data of the sample to be tested and the analysis data of each negative quality control sample, the number of occurrences of the repeating unit type at each analysis site in each analysis data is obtained, and the frequency of occurrence of the repeating unit type at the analysis site in each analysis data is calculated, and the frequency of occurrence of the repeating unit type at each analysis site in the sample to be tested and the frequency of occurrence of the repeating unit type at each analysis site in the negative quality control sample are obtained respectively;

[0150] wherein the repeating unit type of each analysis site is the same as the repeating unit type of the analysis site shown in Table 1 of Example 1 or Table 4 of Example 2;

[0151] S2. For negative control samples, the occurrence frequency of the repeating unit type at each analysis site in the negative control samples obtained in step S1 is used, combined with the formula shown in formula I in Example 1, to calculate the JS divergence of the analysis site between every two negative control samples, and calculate the average value to obtain the negative JS divergence mean of the analysis site;

[0152] For the sample to be tested, the occurrence frequency of the repeating unit type at each analysis site in the sample to be tested obtained in step S1 is used, combined with the formula shown in formula I in Example 1, to calculate the JS divergence of the analysis site between the sample to be tested and each negative quality control sample, and calculate the average value to obtain the JS divergence mean of the analysis site to be tested;

[0153] S3. Perform a T test on the negative JS divergence mean of the analysis site obtained in step S2 and the tested JS divergence mean of the analysis site to obtain a P value;

[0154] Calculate the difference between the negative JS divergence mean of the analysis site obtained in step S2 and the JS divergence mean to be tested of the analysis site, and take the absolute value as the JS divergence mean difference of the analysis site;

[0155] The analysis sites with P value < 0.01 and JS divergence mean difference ≥ 0.1 were recorded as unstable sites of the samples to be tested, and the number of unstable sites of the samples to be tested was calculated;

[0156] S4. Calculate the proportion of unstable sites in the sample to be tested. If the proportion of unstable sites is ≥18%, the test result is "microsatellite unstable"; if the proportion of unstable sites is <18%, the test result is "microsatellite stable";

[0157] The unstable site ratio = the number of unstable sites of the sample to be tested obtained in step S3 / the number of analysis sites obtained in step S1 × 100%;

[0158] The result output module is used to output the detection result obtained by the detection module in step S4.

[0159] Example 4 Microsatellite locus combination and verification for detecting microsatellite instability

[0160] 1. Experimental methods

[0161] Based on the 117 microsatellite loci for detecting microsatellite instability obtained in Example 1 (as shown in Table 1), the microsatellite loci with serial numbers 1 to 30 among the 117 microsatellite loci shown in Table 1 are selected as a locus combination 1 containing 30 microsatellite loci; the microsatellite loci with serial numbers 1 to 50 among the 117 microsatellite loci shown in Table 1 are selected as a locus combination 2 containing 50 microsatellite loci; and the microsatellite loci with serial numbers 1 to 80 among the 117 microsatellite loci shown in Table 1 are selected as a locus combination 3 containing 80 microsatellite loci.

[0162] The information of the 30 microsatellite loci contained in site combination 1 (based on the hg19 gene with accession number GCF_000001405.13) is shown in Table 6; the information of the 50 microsatellite loci contained in site combination 2 (based on the hg19 gene with accession number GCF_000001405.13) is shown in Table 7; the information of the 80 microsatellite loci contained in site combination 3 (based on the hg19 gene with accession number GCF_000001405.13) is shown in Table 8.

[0163] Table 6 Information of 30 microsatellite loci

[0164]

[0165] Table 7 Information of 50 microsatellite loci

[0166]

[0167]

[0168] Table 8 Information of 80 microsatellite loci

[0169]

[0170]

[0171]

[0172] 125 samples with known MSI status (actual MSI status: 13 samples of MSI-H, recorded as positive; 112 samples of MSS, recorded as negative) were used as the test validation set. According to the method shown in steps (8) to (9) of Example 1, 44 microsatellite loci shown in Table 4, 30 microsatellite loci shown in Table 6, 50 microsatellite loci shown in Table 7, and 80 microsatellite loci shown in Table 8 were used to perform microsatellite instability detection. The detection results were recorded and compared with the actual MSI status of the test validation set.

[0173] 2. Experimental results

[0174] The microsatellite instability detection results of the test validation set are shown in Table 9.

[0175] Table 9 Detection results of test validation set

[0176]

[0177]

[0178] The results showed that only by using the combination of the 44 microsatellite loci shown in Table 4 in combination with the method shown in steps (8) to (9) of Example 1 can the microsatellite instability of the sample be detected with 100% accuracy; and although false positive or false negative test results occurred when the 30 microsatellite loci shown in Table 6, the 50 microsatellite loci shown in Table 7, and the 80 microsatellite loci shown in Table 8 were used to detect microsatellite instability in the test validation set, the consistency was ≥95%.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above descriptions and ideas. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A system for detecting microsatellite instability, characterized in that: The system includes a data acquisition module, a detection module and a result output module; The data acquisition module is used to acquire sequencing data of the sample to be tested and sequencing data of the negative quality control sample; The samples to be tested are derived from humans; the number of negative quality control samples is not less than 2, and all of them are microsatellite stable samples; The detection module is used to detect microsatellite instability of the sample to be tested, and specifically includes the following steps: S1. Take microsatellite loci as the sites to be detected, select the sites to be detected with a sequencing depth ≥ 50 in the sequencing data of the sample to be tested as the analysis sites, and calculate the number of analysis sites; Acquire the sequencing data of each analysis site in the sequencing data of the sample to be tested and the sequencing data of each negative quality control sample as the analysis data of the sample to be tested and the analysis data of each negative quality control sample; Based on the analysis data of the sample to be tested and the analysis data of each negative quality control sample, the number of occurrences of the repeating unit type at each analysis site in each analysis data is obtained, and the frequency of occurrence of the repeating unit type at the analysis site in each analysis data is calculated, and the frequency of occurrence of the repeating unit type at each analysis site in the sample to be tested and the frequency of occurrence of the repeating unit type at each analysis site in the negative quality control sample are obtained respectively; The repeat unit type of each analysis site is the same as the repeat unit type of the microsatellite site; S2. For negative control samples, using the occurrence frequency of the repeating unit type at each analysis site in the negative control samples obtained in step S1, calculate the JS divergence of the analysis site between every two negative control samples, and calculate the average value to obtain the negative JS divergence mean of the analysis site; For the sample to be tested, using the occurrence frequency of the repeating unit type at each analysis site in the sample to be tested obtained in step S1, the JS divergence of the analysis site between the sample to be tested and each negative quality control sample is calculated respectively, and the average value is calculated to obtain the mean JS divergence of the analysis site to be tested; S3. Perform a T test on the negative JS divergence mean of the analysis site obtained in step S2 and the tested JS divergence mean of the analysis site to obtain a P value; Calculate the difference between the negative JS divergence mean of the analysis site obtained in step S2 and the JS divergence mean to be tested of the analysis site, and take the absolute value as the JS divergence mean difference of the analysis site; The analysis sites with P values ​​< 0.01 and JS divergence mean difference ≥ 0.1 were recorded as unstable sites of the samples to be tested, and the number of unstable sites of the samples to be tested was calculated; S4. Calculate the proportion of unstable sites in the sample to be tested. If the proportion of unstable sites is ≥18%, the test result is "microsatellite unstable"; if the proportion of unstable sites is <18%, the test result is "microsatellite stable"; The unstable site ratio = the number of unstable sites of the sample to be tested obtained in step S3 / the number of analysis sites obtained in step S1 × 100%; The result output module is used to output the detection result obtained by the detection module in step S4.

2. The system according to claim 1, characterized in that The microsatellite loci in step S1 are microsatellite loci in a microsatellite locus combination, wherein the microsatellite loci combination includes 117 microsatellite loci, 80 microsatellite loci, 50 microsatellite loci, 44 microsatellite loci or 30 microsatellite loci, and the hg19 gene with accession number GCF_000001405.13 is used as the reference genome. The information of the 117 microsatellite loci is shown in the following table. Taking the hg19 gene with accession number GCF_000001405.13 as the reference genome, the information of the 80 microsatellite loci is shown in the following table. Taking the hg19 gene with accession number GCF_000001405.13 as the reference genome, the information of the 50 microsatellite loci is shown in the following table. Taking the hg19 gene with accession number GCF_000001405.13 as the reference genome, the information of the 44 microsatellite loci is shown in the following table. Taking the hg19 gene with accession number GCF_000001405.13 as the reference genome, the information of the 30 microsatellite loci is shown in the following table.

3. The system according to claim 1, characterized in that The sequencing data of the sample to be tested and the sequencing data of the negative quality control sample are: the sequencing data obtained by high-throughput sequencing are quality controlled and PCR duplicate sequences are removed, and the effective sequencing depth is ≥200; the sequencing depth of the high-throughput sequencing is not less than 1000×.

4. The system according to claim 3, characterized in that The high-throughput sequencing is performed using the Illumina high-throughput sequencing platform.

5. Use of the system according to any one of claims 1 to 4 in detecting microsatellite instability for non-diagnostic purposes.

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