A method and device for detecting microsatellite instability based on second-generation sequencing technology

Abstract

The present invention discloses a method and device for detecting microsatellite instability based on second-generation sequencing technology, and belongs to the field of biological detection technology. The method includes: selecting MSI sites, designing capture probes and / or amplicon primers based on any combination of sites; selecting several samples, and calculating the ratio of missing fragments and normal fragments at each MSI site for each sample; selecting several samples judged to be in MSI-L or MSS state by PCR method as baseline samples, and setting different depth levels for each baseline sample to construct a baseline; performing MSI state detection; selecting samples with MSI state of MSI-H, and judging whether they are abnormal samples by Spearman rank correlation coefficient; if so, adjusting the baseline and re-judging the MSI state. The present invention has high stability and high accuracy, and does not require control samples, and can be simultaneously applied to microsatellite instability detection of hybridization capture and amplicon sequencing data.

Description

Technical Field

[0001] The present invention belongs to the field of biological detection technology, and in particular relates to a microsatellite instability detection method and device based on second-generation sequencing technology. Background Art

[0002] The existing technology for detecting microsatellite instability (MSI) mainly uses the PCR method to determine the MSI status of the sample, which is the current gold standard for MSI detection. This method uses fluorescently labeled primers and capillary electrophoresis to determine the fragment length polymorphism of five microsatellite loci: NR-21, NR-24, BAT-25, BAT-26, and MONO-27. By comparing the polymorphisms of tumor tissues and normal control samples at these five microsatellite loci, it can be confirmed whether the loci are unstable. If two or more of the five microsatellite loci show microsatellite instability, it is judged as microsatellite high instability (MSI-high, MSI-H), otherwise it is microsatellite low instability (MSI-low, MSI-L) or microsatellite stable (MSS).

[0003] In addition to PCR, methods for MSI detection based on hybrid capture and next-generation sequencing of amplicon have developed rapidly, and a variety of methods and tools have been published and applied in clinical testing and scientific research. However, they all have their own shortcomings and defects, specifically:

[0004] 1) PCR detection is the current gold standard for MSI detection, with high accuracy, but it has high requirements for samples. The use of PCR requires the simultaneous detection of tumor tissue samples and control samples, and has certain requirements for the purity of tumor tissue samples.

[0005] 2) MSI detection based on the second-generation sequencing technology has low requirements for tumor purity and does not require control samples. These methods can perform microsatellite instability detection and other tests at the same time, but the accuracy of the results still needs to be improved.

[0006] In addition, due to the differentiated data characteristics of hybridization capture and amplicon sequencing, there is currently no second-generation sequencing MSI detection algorithm that can be used for both hybridization capture and amplicon sequencing data. Summary of the invention

[0007] The present invention aims to solve one of the technical problems in the above-mentioned related art at least to a certain extent.

[0008] To this end, the purpose of the present invention is to provide a method and device for detecting microsatellite instability based on second-generation sequencing technology, which has high stability and accuracy, does not require a control sample, and can be simultaneously applied to microsatellite instability detection of hybridization capture and amplicon sequencing data.

[0009] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0010] The embodiment of the present invention provides a method for detecting microsatellite instability based on second-generation sequencing technology, the method comprising:

[0011] A group of MSI sites with a repeat sequence length of 10-15 bp are selected, and capture probes and / or amplicon primers are designed based on any combination of the MSI sites;

[0012] Select several samples, and for each sample, calculate the deletion ratio (ratio of deleted fragments) and reference ratio (ratio of normal fragments) at each MSI site;

[0013] Selecting a number of samples that are judged as MSI-L or MSS by PCR as baseline samples from the above samples, setting a different depth level for each baseline sample, and constructing a baseline based on each depth level;

[0014] Based on the constructed baseline, the MSI status of each selected sample was tested;

[0015] Samples with MSI status detection results of MSI-H were selected, and the Spearman rank correlation coefficient was calculated to determine whether they were abnormal samples; if they were determined to be abnormal samples, the baseline of the sample was adjusted and the MSI status was re-determined.

[0016] In addition, the microsatellite instability detection method based on the second generation sequencing technology according to the present invention may also have the following additional technical features:

[0017] In some embodiments, calculating the missing fragment ratio and the normal fragment ratio of the sample includes:

[0018] a) Align the sequenced base sequence to the human genome reference sequence, and extract the sequenced base sequence that completely covers the region where the MSI site is located and at least two bases in length on both sides;

[0019] b) filtering the sequencing data of the captured sequencing samples;

[0020] c) removing duplicates from the sequenced base sequences after filtering the captured sequencing samples;

[0021] d) For each remaining sequenced base sequence, the length of the repeated sequence at the current MSI site is counted, and the sequenced base sequences are classified according to the relative relationship between the length of the repeated sequence of the sequenced base sequence and the human genome reference sequence; the number of sequenced base sequences in each category and the total number of sequenced base sequences are counted, and the ratio of missing fragments and the ratio of normal fragments are calculated.

[0022] In some embodiments, filtering the sequencing data of the captured sequencing samples includes:

[0023] Extract the sequences of three bases on each side of the MSI site on each sequencing base sequence; if the two sides of the MSI site cover less than three bases at the read edge, only extract two bases; if the base sequence of the sequencing base sequence at these positions is not completely consistent with the human genome reference sequence, filter out the sequencing base sequence;

[0024] The sequence corresponding to the MSI site region on each sequencing base sequence is extracted; if there are more than or equal to two bases in the sequence that are inconsistent with the reference sequence, the sequencing base sequence is filtered; if there is a base in the sequence that is inconsistent with the human genome reference sequence and the sequence is exactly at the starting or ending position of the MSI site, the sequencing base sequence is filtered out.

[0025] In some embodiments, deduplication of the sequenced base sequence after filtering the captured sequencing sample includes:

[0026] For the captured sequencing samples, the source of the original DNA molecular fragments of the sequencing base sequence is determined according to the starting and ending sites of each sequencing base sequence; the sequencing base sequence is a single-end sequencing base sequence or a double-end sequencing base sequence;

[0027] For repeated sequencing base sequences originating from the same original DNA molecule fragment, one base sequence is randomly selected from the repeated sequencing base sequences for retention.

[0028] In some embodiments, the process of establishing a baseline includes:

[0029] a) Calculate the ratio of deleted fragments and normal fragments at the corresponding depth level of each MSI site for each baseline sample;

[0030] b) Calculate the baseline for each MSI site at its depth level; this includes:

[0031] The baseline mean and standard deviation of the proportion of deleted fragments in the baseline samples of each MSI site were calculated;

[0032] The baseline mean and standard deviation of the normal fragment ratio in the baseline samples of each MSI site were calculated.

[0033] In some implementations, based on the constructed baseline, the content of performing MSI status detection on each selected sample includes:

[0034] a) Calculate the ratio of deleted fragments and normal fragments at each MSI site for each sample;

[0035] b) calculating the depth of each MSI site and matching the depth to a baseline of the same depth level;

[0036] c) Calculate the Z_score of the sample's deletion fragment ratio and normal fragment ratio at each MSI site relative to the baseline;

[0037] d) According to the calculated depth and Z_score, the status of the sample at each MSI site is determined;

[0038] The judgment result is site quality control failure, site instability or site stability;

[0039] e) Comprehensively analyze the status of all MSI sites, calculate the MSI score, count the number of sites that pass quality control, and determine the MSI status of the sample based on the statistical results.

[0040] In some embodiments, the Spearman rank correlation coefficient is the Spearman rank correlation coefficient of the missing fragment ratio and the mean of the baseline missing fragment ratio;

[0041] Set the abnormal threshold of the Spearman rank correlation coefficient. When the Spearman rank correlation coefficient is greater than or equal to the abnormal threshold, the corresponding sample is judged to be an abnormal sample; otherwise, the corresponding sample is judged to be normal.

[0042] In some embodiments, the number of MSI loci with repeat sequence lengths between 10-15 bp is 74.

[0043] In some embodiments, adjusting the baseline of the abnormal sample and re-determining the MSI status includes: adjusting the baseline matched at each site of the sample, re-determining the site status according to the adjusted baseline, combining the determination results of all sites to obtain a new MSI status of the sample, and replacing the original MSI status with the MSI status;

[0044] The contents of adjusting the baseline include: adjusting the baseline mean of the missing fragment ratio to minimize the sum of squares of the stable site Z_score of the abnormal samples; recalculating the Z_score of all sites based on the adjusted baseline mean of the missing fragment ratio and updating the site status; repeating the above steps until the status of all sites no longer changes.

[0045] An embodiment of the present invention also provides a microsatellite instability detection device based on second-generation sequencing technology, comprising a processor and a memory, wherein a software program is stored on the memory, and when the processor runs the software program, the content of the microsatellite instability detection method based on second-generation sequencing technology as described in any one of the above can be implemented.

[0046] Compared with the prior art, the present invention has at least the following beneficial effects:

[0047] In an embodiment of the present invention, a method for detecting microsatellite instability based on the second generation sequencing technology is provided, which provides a group of 74 MSI sites, and the length of the repetitive sequences of these MSI sites is between 10-15 bp; compared with sites with a repetitive sequence length greater than 15 bp, these 74 sites selected by the present invention can obtain a higher alignment accuracy when aligned to a reference genome;

[0048] In the embodiment of the present invention, the provided method for detecting microsatellite instability based on the second generation sequencing technology uses any combination of 74 loci, and according to the distribution of the length of the repeated sequence at each locus of any sample, the deletion ratio (ratio of deleted fragments) and the reference ratio (ratio of normal fragments) of the sample at each locus can be calculated, which is used for the construction of MSI baseline and the MSI detection of the sample to be tested;

[0049] In the embodiment of the present invention, the microsatellite instability detection method based on the second-generation sequencing technology is provided. Before calculating the deletion ratio and the reference ratio, the present invention will filter the second-generation sequencing data according to specific rules to reduce the influence of the alignment error on the distribution of the length of the repetitive sequence of the microsatellite locus, so as to obtain a more accurate MSI detection result;

[0050] In the embodiment of the present invention, the microsatellite instability detection method based on the second-generation sequencing technology is provided, and the reference ratio is introduced as an auxiliary reference. Compared with the detection using the deletion ratio alone, it can identify some repeated sequence length distribution deviations caused by experimental and sequencing errors, and further improve the detection accuracy;

[0051] In an embodiment of the present invention, a method for detecting microsatellite instability based on the second generation sequencing technology is provided, using a group of MSS / MSI-L samples, and according to the deletion ratio and reference ratio of this group of samples at each MSI site, an MSI baseline can be constructed; after the baseline is constructed, the baseline can be used to perform MSI detection on any sample to be tested, without providing a corresponding control sample for each sample;

[0052] In the embodiment of the present invention, the provided method for detecting microsatellite instability based on the second generation sequencing technology determines whether the sample is abnormal or not. If the sample is abnormal, the MSI baseline is adjusted to adapt to the abnormal sample, which can reduce the false difference between the sample to be tested and the MSI baseline caused by improper sample storage, unstable library construction experimental process, and sequencing abnormality, and reduce the false positive rate of MSI detection (the proportion of samples detected as MSI-H that are actually MSI-L or MSS);

[0053] In the embodiment of the present invention, a method for detecting microsatellite instability based on the second-generation sequencing technology is provided. By comparing the differences in deletion ratio and reference ratio between the sample to be tested and the baseline at each MSI site, the stable state of each site is determined, and the stable states of multiple sites are comprehensively considered to determine the MSI state of the sample to be tested. The requirement for tumor purity is low. Under any data volume, the present invention can always maintain a stable high specificity in MSS / MSI-L samples. Even if the amount of sequencing data is reduced, a high sensitivity can still be obtained in samples with a tumor purity as low as 5%-10%.

[0054] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of the detection limit of a microsatellite instability detection method based on the second-generation sequencing technology in capturing sequencing data disclosed in one embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The embodiments of the present invention are described in detail below through specific embodiments and application scenarios in conjunction with the accompanying drawings.

[0058] See also Figure 1 As shown, in some embodiments of the present invention, a method for detecting microsatellite instability based on the second-generation sequencing technology is provided, which can be used for capture sequencing or amplicon sequencing. When the proportion of tumor cells is low and there is no control sample, the MSI status of tumor tissue samples or body fluid samples can be accurately detected, and the treatment options available to the patient are suggested; the following technical solutions are adopted:

[0059] Step 1. Select 74 MSI sites for designing capture probes or amplicon primers. When designing capture probes or amplicon primers, any combination of the 74 MSI sites can be used. The specific locations of the sites on the human reference genome hg19 are shown in Table 1.

[0060] Table 1. Repeat sequence length and detailed location of 74 MSI sites

[0061] Chromosome start position Gene length Chromosome start position Gene length Chromosome start position Gene length chr10:63760087-63760100 ARID5B 13 chr1:16245610-16245620 SPEN 10 chr3:78663958-78663972 ROBO1 14 chr10:89650474-89650489 PTEN 15 chr1:181690750-181690763 CACNA1E 13 chr4:142950186-142950196 INPP4B 10 chr10:89697373-89697388 PTEN 15 chr1:243667925-243667937 AKT3 12 chr4:187532981-187532995 FAT1 14 chr10:89725293-89725304 PTEN 11 chr1:46597496-46597507 PIK3R3 11 chr4:55130161-55130176 PDGFRA 15 chr11:108114661-108114676 ATM 15 chr22:29674983-29674997 EWSR1 14 chr5:44332084-44332094 FGF10 10 chr11:108141955-108141970 ATM 15 chr22:39637913-39637923 PDGFB 10 chr5:67584512-67584524 PIK3R1 12 chr11:3381798-3381813 ZNF195 15 chr2:209110006-209110016 IDH1 10 chr5:79951103-79951114 MSH3 11 chr11:94212930-94212941 MRE11A 11 chr2:223071111-223071123 PAX3 12 chr5:80088493-80088503 MSH3 10 chr12:11997693-11997708 ETV6 15 chr2:25505023-25505035 DNMT3A 12 chr5:86659147-86659158 RASA1 11 chr12:11999539-11999550 ETV6 11 chr2:42553086-42553096 EML4 10 chr6:106534484-106534496 PRDM1 12 chr12:124242458-124242471 ATP6V0A2 13 chr2:46809063-46809075 PIGF 12 chr7:116350757-116350767 MET 10 chr12:133237753-133237767 POLE 14 chr2:46825359-46825370 PIGF 11 chr7:140421095-140421107 NA 12 chr13:48954159-48954172 RB1 13 chr2:46839487-46839498 PIGF 11 chr7:140508253-140508266 BRAF 13 chr14:38062697-38062709 FOXA1 12 chr2:47600881-47600891 EPCAM 10 chr7:151884279-151884291 KMT2C 12 chr14:68934755-68934768 RAD51B 13 chr2:47635523-47635536 MSH2 13 chr7:50800100-50800115 GRB10 15 chr14:68944321-68944332 RAD51B 11 chr2:61308569-61308584 KIAA1841 15 chr8:117861112-117861123 RAD21 11 chr14:69149592-69149604 NA 12 chr3:10094206-10094219 FANCD2 13 chr8:117866426-117866436 RAD21 10 chr15:88574025-88574035 NTRK3 10 chr3:12411792-12411803 PPARG 11 chr8:38175279-38175290 WHSC1L1 11 chr15:88627317-88627332 NTRK3 15 chr3:138400782-138400795 PIK3CB 13 chr8:38298093-38298108 FGFR1 15 chr15:91303325-91303337 BLM 12 chr3:142274739-142274749 ATR 10 chr9:87357642-87357652 NTRK2 10 chr16:81954996-81955011 PLCG2 15 chr3:178937630-178937641 PIK3CA 11 chr9:87487518-87487529 NTRK2 11 chr17:40647390-40647404 ATP6V0A1 14 chr3:185010850-185010860 MAP3K13 10 chr9:87513596-87513607 NTRK2 11 chr17:66526937-66526947 PRKAR1A 10 chr3:30691871-30691881 TGFBR2 10 chr9:87641845-87641857 NTRK2 12 chr19:50911947-50911959 POLD1 12 chr3:33626186-33626201 CLASP2 15 chrX:44949951-44949962 KDM6A 11 chr1:161309335-161309346 SDHC 11 chr3:69988437-69988451 MITF 14

[0062] Step 2. Select S of the 74 MSI sites and calculate the deletion ratio and reference ratio of each sample at each MSI site.

[0063] a) Align the sequenced base sequence to the human genome reference sequence, and extract reads (sequencing base sequence) that completely cover the region where the MSI site is located and at least two bases long on both sides;

[0064] b) Filter the sequencing data of capture sequencing samples (skip this step for amplicon sequencing samples):

[0065] ① Extract the three-base sequences on each side of the corresponding MSI site on each read. If the two sides of the MSI site are at the edge of the read and cover less than three bases, only two bases are extracted; if the base sequence of the read at these positions is not completely consistent with the human genome reference sequence, the read is filtered.

[0066] ② Extract the sequence corresponding to the MSI site region on each read. If there are two or more bases in this sequence that are inconsistent with the reference sequence, filter the read; if there is one base in this sequence that is inconsistent with the human genome reference sequence, and the sequence is exactly at the start or end position of the MSI site, filter the read.

[0067] c) De-duplicate reads after filtering for capture sequencing samples (skip this step for amplicon sequencing samples):

[0068] For captured sequencing samples, determine the source of the original DNA molecular fragments according to the start and end sites of each sequencing read (single-end sequencing) or each pair of reads (double-end sequencing);

[0069] For duplicate reads originating from the same original molecular fragment, one read is randomly selected from the duplicate reads and retained.

[0070] d) For each remaining read, count the length of the repeat sequence at the current MSI site, and classify the reads according to the relative relationship between the length of the read repeat sequence and the human genome reference sequence: (1) the same length: normal (reference read); (2) shorter length relative to the reference sequence: deletion (deletion read); (3) longer length relative to the reference sequence: insertion (insertion read). Count the ratio of reference reads to all reads (reference ratio) and the ratio of deletion reads to all reads (deletion ratio) at the current site.

[0071] Step 3. Select N samples in MSI-L or MSS status as baseline samples, and set different depth levels for the selected S MSI sites. , construct baselines for each depth level d. The MSI status of the baseline sample selected here is the MSI status pre-determined by the PCR method.

[0072] a) For each site s among the S sites, filter and deduplicate the reads of each baseline sample n according to the method described in steps 2.a - 2.c (skip this step for amplicon sequencing), and randomly select d reads from the retained reads.

[0073] b) According to the method described in step 2.d, calculate the deletion ratio and reference ratio of each baseline sample n at the depth level d at the site s, which are respectively denoted as and .

[0074] c) Calculate the baseline at each site s at depth level d . It includes:

[0075] Mean and standard deviation of the deletion ratio of site s in the baseline sample: and ;

[0076] Baseline mean and standard deviation of the reference ratio of site s in the baseline sample: and .

[0077] Step 4: Detect the MSI status of the sample i:

[0078] a) According to the method described in 2, calculate the deletion ratio of the sample at each site s in the S sites: ; and reference ratio: ;

[0079] b) Calculate the depth at each site s ,according to Match the baseline of the same depth level for site s . The matching method is: select and d with the smallest difference;

[0080] c) Calculate the deletion ratio of the sample to be tested at each site ( ) and referenceratio( ) relative to the baseline ( )’s Z-score:

[0081]

[0082]

[0083] d) According to , and , the state of the sample to be tested at site s Make a judgment:

[0084] like : Site quality control failed;

[0085] like And at the same time satisfy and : Site instability;

[0086] like And the situation in the previous item is not met, the site is stable;

[0087] in, is the quality control threshold of the MSI site depth, is the Z-score threshold of the MSI locus.

[0088] e) After obtaining each site Finally, the status of all sites is comprehensively analyzed, the MSI score is calculated, and the number of sites that pass the quality control is counted to make the first judgment on the MSI status of the sample to be tested:

[0089] Counting the number of unstable sites and the number of stable sites ; The number of sites that passed quality control is ; ;

[0090] like and : The sample to be tested is MSS / MSI-L;

[0091] like and : The sample to be tested is MSI-H;

[0092] like and : The sample to be tested is MSI-H;

[0093] like and :The quality control of the sample to be tested is unqualified (quality not satisfied, QNS);

[0094] in, is the threshold of qualified sites for quality control; The number of quality control sites is greater than or equal to When msi score threshold; The number of quality control sites is less than The unstable site number threshold at .

[0095] Step 5. If the sample is judged as MSI-H in step 4, you can choose to calculate the sample to be tested , to determine whether the sample is an abnormal sample. If you choose to calculate, The calculation and use of is as follows:

[0096] a) Calculate the deletion ratio of the sample i to be tested ({mean_del1,mean_del2,…, mean_del S}) and the baseline deletion ratio mean ({del_ratio 1,i ,del_ratio 2,i ,…, del_ratio S,i}) The higher the value, the stronger the correlation between the deletion ratio of the tested sample and the baseline at S MSI sites.

[0097] b) for The abnormal threshold of and :

[0098] like Greater than or equal to , indicating that the deletion ratio of the tested sample and the baseline at S MSI sites is strongly correlated, and the sample may have an increase in deletion ratio at all MSI sites at the same magnitude; this situation is relatively rare in real MSI-H samples, and the sample is likely to have experimental or sequencing abnormalities; these samples are marked as abnormal samples;

[0099] like Less than , indicating that the correlation between the deletion ratio of the tested sample and the baseline at S MSI sites is relatively weak, which is consistent with the determination status of MSI-H.

[0100] Step 6. If the operation in step 5 is performed and the sample to be tested is marked as an abnormal sample, the baseline matched to the sample at each site s is Adjust and use the adjusted baseline Re-determine the site status. Finally, combine the new determination results of all sites to obtain the MSI status of the sample, replacing the results obtained in step 4:

[0101] a) Yes of After adjustment, the adjusted baseline is , the adjustment method is:

[0102] ① Obtain the set of MSI sites currently judged to be stable I ;

[0103] ②Yes I For each stable site i in ;in, is the deletion ratio of the sample to be tested at site i, , and is the mean and standard deviation of the baseline deletion ratio matched to site i, and a and b are the parameters to be estimated;

[0104] ③Calculation I All sites in Sum of squares: . Traverse different combinations of parameters a and b to find the one that minimizes The optimal solution of , and the estimated values ​​of a and b are obtained.

[0105] b) Using the estimated parameters a and b, calculate the ,use replace get ;

[0106] c) Repeat steps 4.c - 4.e, according to Calculate the msiscore of the sample to be tested after this round of correction: ;

[0107] d) Compare the changes of MSI score before and after this round of calibration:

[0108] like , Update the MSI stable site set I , continue to repeat steps 6.a - 6.c;

[0109] like , output the current .

[0110] e) Based on the criteria described in step 4.e, Determine the MSI status of the sample to be tested.

[0111] Example 1: Hybridization capture sequencing data is used to verify the performance of the MSI detection method described in steps 1 to 4 of the present invention.

[0112] 1. Sample preparation and sample information

[0113] a) Baseline clinical samples: A total of 67 MSS samples were obtained from patients with non-small cell lung cancer.

[0114] b) Cell line simulation validation samples:

[0115] Cell line samples 22RV1, RL95 and GM12878 were used for mixing. Among them, the MSI (Microsatellite Instability) status of GM12878 was MSS (microsatellite stable), and the MSI status of 22RV1 and RL95 was MSI-H (high microsatellite instability). 22RV1 or RL95 was added to the GM12878 sample in different proportions, and 5 mixing ratios were set. Finally, 5 mixed samples with 22RV1 mass proportions of 20%, 10%, 5%, 2.5% and 1.25% and 5 mixed samples with RL95 mass proportions of 20%, 10%, 5%, 2.5% and 1.25% were obtained. The mixed samples of each group of 5 mass proportions were used as MSI-H tissue simulation samples with tumor purity of 20%, 10%, 5%, 2.5% and 1.25%, respectively.

[0116] For the above MSI-H tissue simulation samples and the GM12878 samples that were not mixed, 10 repeated library construction experiments were performed to obtain a total of 110 libraries. The specific library information and quantity are shown in Table 2.

[0117] Table 2. Detailed information of capture sequencing cell line simulation validation samples

[0118]

[0119] c) Clinical validation samples: a total of 181 cases, including 148 MSS (microsatellite stable) or MSI-L (microsatellite instability-low) samples, and 33 MSI-H (microsatellite instability-high) samples.

[0120] 2. Obtain capture sequencing data and perform data preprocessing

[0121] Capture probes were designed for 74 MSI sites (Table 1). Library construction experiments were performed on 67 baseline clinical samples, 110 cell line simulation validation samples, and 181 clinical validation samples, and hybridization capture was performed using capture probes. The captured libraries were sequenced on the DNBSEQ-T7 sequencing platform to obtain the original offline data bcl files. The bcl files were split using bcl2fastq to obtain the double-end sequencing fastq data of each sample. The offline data volume of each sample was about 2G, and the depth before deduplication was about 4000x.

[0122] In this example, for each sample's fastq data, Trimmomatic was used to remove the adapter sequence and low sequencing quality sequence of each read, and the remaining sequence of the read was aligned to the human reference genome hg19 using bwa to obtain the original bam file. The reads aligned to the MSI site in the original bam file were extracted, and these reads were aligned again using GATKrealigner to obtain a more accurate alignment position, which was stored in the preprocessed bam.

[0123] 3. Constructing an MSI baseline using 67 baseline samples

[0124] a) For each MSI site, extract reads that completely cover the site region and at least two bases on each side from the preprocessed bam of each sample according to the method described in steps 2.a-2.c, and filter and remove duplicates.

[0125] b) Set different depth levels: 100x, 150x, 200x, 250x, 300x, 400x, 500x, 600x, 800x, 1000x, 1200x, 1500x, 2000x, 2500x, 3000x, 3500x, 4000x. For each depth level:

[0126] First, extract the corresponding number of reads from the deduplicated reads of each sample at site s, and calculate the deletion ratio and reference ratio of the 67 samples at site s at this depth level;

[0127] Then, the mean and standard deviation of the deletion ratio and the standard deviation of the reference ratio of the 67 samples at the corresponding depth level are calculated and recorded as the baseline at that depth level.

[0128] c) Merge the baselines at different depth levels and save them as one baseline file.

[0129] 4. Verify the MSI status of the sample using the MSI baseline test obtained in the previous step

[0130] a) Perform the following steps for each site s of each sample to be tested:

[0131] ①According to the method described in step 2.a, extract reads that completely cover the region where the site is located and at least two bases long on both sides from the pre-processed bam of each sample, and filter and remove duplicates;

[0132] ② According to the method described in step 4.a, calculate the deletion ratio and reference ratio of the sample to be tested at site s based on the deduplicated reads;

[0133] ③ According to the method described in step 4.b, calculate the depth of the sample to be tested at site s based on the extracted reads, and match the appropriate baseline for the sample according to the depth;

[0134] ④ Using the formula in step 4.c, calculate the deletion ratio and reference ratio of the sample to be tested and the matched baseline at site s. and ;

[0135] ⑤Settings is 100, is 4, and the state of the sample site s to be tested is determined according to the rule of step 4.d.

[0136] b) For each sample to be tested, count the number of stable sites and unstable sites at the 74 MSI sites and calculate the MSI score. For 40, is 0.1, If the value is 6, the MSI status of the sample is determined according to the rule in step 4.e.

[0137] c) Statistical analysis of MSI detection results in cell line simulation validation samples:

[0138] In this embodiment, the sensitivity in repeated samples of 22RV1 and RL95 with different mixing ratios and the specificity in repeated samples of GM12878 are shown in Table 3. The sensitivity refers to the correct ratio of true MSI-H samples; the specificity refers to the correct ratio of true MSS / MSI-L samples.

[0139]

[0140] In this embodiment, Probit regression is used to fit the sensitivity of the cell line simulation validation samples under different tumor cell proportions (eg Figure 1 As shown in FIG. 1 ), the limit of detection (LOD) of this embodiment in the captured sequencing data was obtained: when 95% sensitivity was required, the LOD of this embodiment was 2.61%, proving that this embodiment can be used for the detection of low tumor purity samples. Figure 1 In the figure, the x-axis represents the tumor cell ratio, and the y-axis represents the detection sensitivity of the present invention in the captured sequencing samples corresponding to the tumor cell ratio; the solid line in the figure represents the relationship between y and x fitted after Probit regression of the sensitivity in the cell line simulation samples with different tumor cell ratios; through the intersection of the dotted line y=0.95 and the solid line, it can be known that the detection limit of the present invention under the condition of 95% sensitivity is 2.61%, that is: when the tumor purity is 2.61%, the present invention can obtain a sensitivity of 95%.

[0141] In this embodiment, the detection results of MSI in clinical validation samples are statistically analyzed: the sensitivity of this embodiment in clinical validation samples is 100%, and the specificity is 100%. Among them, the sensitivity refers to the proportion of true MSI-H samples that are correctly detected; the specificity refers to the proportion of true MSS / MSI-L samples that are correctly detected. It is confirmed that this embodiment can achieve higher performance in clinical samples.

[0142] 5. Verify the detection performance under different data amounts

[0143] a) Reduce the data volume of cell line simulation validation samples and clinical validation samples to verify the performance of the present invention in capturing sequencing data with low data volume. Randomly extract 75%, 50%, 25%, 20%, and 10% of the reads from the double-end sequencing fastq data of each sample, and then use Trimmomatic to remove the adapter sequence and low sequencing quality sequence of each read, and use bwa to align the remaining sequence of the read to the human reference genome hg19 to obtain the original bam file. Extract the reads aligned at the MSI site in the original bam file, and use GATK realigner to align these reads again to obtain a more accurate alignment position, which is stored in the preprocessed bam.

[0144] b) According to the MSI detection method for validation samples with original data volume in this embodiment, the MSI status of validation samples obtained after randomly selecting different data volumes is detected. The performance of cell line simulation validation samples with different data volumes (Table 4) and clinical validation samples (Table 5) are statistically analyzed:

[0145] In this example, when the amount of sequencing data was reduced to 10%, only one case of QNS occurred, and the remaining verification samples were able to meet the quality control conditions and obtain test results, proving that this example has high stability;

[0146] At 10% data volume, this embodiment can maintain 100% specificity. Although the sensitivity is reduced, the detection limit can still reach 5.72%, which proves that this embodiment has high detection performance at low sequencing data volume.

[0147] Table 4. Detection limits of simulated validation samples of capture sequencing cell lines at different data volumes

[0148] Extraction ratio Data volume QNS quantity Detection limit (after deducting QNS, sensitivity 95%) 100% 2G 0 2.61% 75% 1.5G 0 2.83% 50% 1G 0 3.02% 25% 0.5G 0 5.12% 20% 0.4G 0 5.47% 10% 0.2G 0 5.72%

[0149] Table 5. Capture sequencing clinical validation sample detection performance under different data volumes

[0150] Extraction ratio Data volume QNS quantity Sensitivity (after deducting QNS) Specificity (after deducting QNS) 100% 2G 0 100% 100% 75% 1.5G 0 100% 100% 50% 1G 0 96.97% 100% 25% 0.5G 0 90.91% 100% 20% 0.4G 0 87.88% 100% 10% 0.2G 1 75.76% 100%

[0151] Example 2: Amplicon sequencing data used to verify the effect of the present invention in amplicon sequencing and the abnormal sample correction method described in steps 5-6

[0152] 1. Sample preparation and sample information

[0153] Baseline clinical samples: 35 MSS / MSI-L samples.

[0154] Clinical validation samples: 103 cases in total, including 84 MSS / MSI-L samples and 19 MSI-H samples.

[0155] 2. Obtain amplicon sequencing data and perform data preprocessing

[0156] Amplicon primers were designed for 68 MSI sites (Table 6). Library construction experiments were performed on 35 baseline clinical samples and 103 clinical validation samples, and amplicons were used for enrichment. The amplicon libraries were sequenced single-end using the miniseq sequencing platform to obtain the original offline data bcl file. The bcl file was split using bcl2fastq to obtain the double-end sequencing fastq data of each sample.

[0157] For each sample's fastq data, Trimmomatic was used to remove the adapter sequence of each read, and the remaining sequence of the read was aligned to the human reference genome hg19 using bwa to obtain the original bam file. The reads aligned to the MSI site in the original bam file were extracted and re-aligned with GATK realigner to obtain a more accurate alignment position, which was stored in the preprocessed bam.

[0158] Table 6. 68 MSI sites used to design amplicon primers

[0159] Chromosome start position Gene length Chromosome start position Gene length Chromosome start position Gene length chr1:161309335-161309346 SDHC 11 chr16:81954996-81955011 PLCG2 15 chr3:78663958-78663972 ROBO1 14 chr1:16245610-16245620 SPEN 10 chr17:40647390-40647404 ATP6V0A1 14 chr4:142950186-142950196 INPP4B 10 chr1:181690750-181690763 CACNA1E 13 chr17:66526937-66526947 PRKAR1A 10 chr4:55130161-55130176 PDGFRA 15 chr1:243667925-243667937 AKT3 12 chr2:209110006-209110016 IDH1 10 chr5:44332084-44332094 FGF10 10 chr1:46597496-46597507 PIK3R3 11 chr2:223071111-223071123 PAX3 12 chr5:67584512-67584524 PIK3R1 12 chr10:63760087-63760100 ARID5B 13 chr2:25505023-25505035 DNMT3A 12 chr5:79951103-79951114 MSH3 11 chr10:89650474-89650489 PTEN 15 chr2:42553086-42553096 EML4 10 chr5:80088493-80088503 MSH3 10 chr10:89697373-89697388 PTEN 15 chr2:46809063-46809075 PIGF 12 chr5:86659147-86659158 RASA1 11 chr10:89725293-89725304 PTEN 11 chr2:46825359-46825370 PIGF 11 chr6:106534484-106534496 PRDM1 12 chr11:108114661-108114676 ATM 15 chr2:46839487-46839498 PIGF 11 chr7:116350757-116350767 MET 10 chr11:108141955-108141970 ATM 15 chr2:47600881-47600891 EPCAM 10 chr7:140421095-140421107 NA 12 chr11:3381798-3381813 ZNF195 15 chr2:47635523-47635536 MSH2 13 chr7:140508253-140508266 BRAF 13 chr11:94212930-94212941 MRE11A 11 chr2:61308569-61308584 KIAA1841 15 chr7:151884279-151884291 KMT2C 12 chr12:11997693-11997708 ETV6 15 chr22:39637913-39637923 PDGFB 10 chr7:50800100-50800115 GRB10 15 chr12:11999539-11999550 ETV6 11 chr3:10094206-10094219 FANCD2 13 chr8:117861112-117861123 RAD21 11 chr12:124242458-124242471 ATP6V0A2 13 chr3:12411792-12411803 PPARG 11 chr8:117866426-117866436 RAD21 10 chr12:133237753-133237767 POLE 14 chr3:138400782-138400795 PIK3CB 13 chr8:38175279-38175290 WHSC1L1 11 chr14:38062697-38062709 FOXA1 12 chr3:142274739-142274749 ATR 10 chr8:38298093-38298108 FGFR1 15 chr14:68934755-68934768 RAD51B 13 chr3:178937630-178937641 PIK3CA 11 chr9:87357642-87357652 NTRK2 10 chr14:68944321-68944332 RAD51B 11 chr3:185010850-185010860 MAP3K13 10 chr9:87487518-87487529 NTRK2 11 chr14:69149592-69149604 NA 12 chr3:30691871-30691881 TGFBR2 10 chr9:87641845-87641857 NTRK2 12 chr15:88574025-88574035 NTRK3 10 chr3:33626186-33626201 CLASP2 15 chrX:44949951-44949962 KDM6A 11 chr15:88627317-88627332 NTRK3 15 chr3:69988437-69988451 MITF 14

[0160] 3. Constructing an MSI baseline using 35 baseline samples

[0161] a) For each MSI site, extract reads that completely cover the site region and at least two bases long on both sides from the pre-processed bam of each sample according to the method described in step 2.a.

[0162] b) Set different depth levels: 100x, 150x, 200x, 250x, 300x, 400x, 500x, 600x, 800x, 1000x, 1200x, 1500x, 2000x, 2250x, 2500x, 2750x, 3000x, 3250x, 3500x, 3750x, 4000x, 4500x, 5000x, 5500x, 6000x, 6500x, 7000x. For each depth level:

[0163] First, extract the corresponding number of reads from the reads of each sample at site s, and calculate the deletion ratio and reference ratio of the 35 samples at site s at this depth level;

[0164] Then, the mean and standard deviation of the deletion ratio and the standard deviation of the mean of the reference ratio of the 35 samples at the corresponding depth level are calculated and recorded as the baseline at that depth level.

[0165] c) Merge the baselines at different depth levels and save them as one baseline file.

[0166] 4. MSI status of 103 amplicon sequencing clinical validation samples using MSI baseline testing

[0167] a) Perform the following steps for each site s of each sample to be tested:

[0168] ①According to the method described in step 2.a, extract reads that completely cover the region where the site is located and at least two bases long on both sides from the pre-processed bam of each sample;

[0169] ② According to the method described in step 4.a, calculate the deletion ratio and reference ratio of the sample to be tested at site s based on the extracted reads;

[0170] ③ According to the method described in step 4.b, calculate the depth of the sample to be tested at site s based on the extracted reads, and match the appropriate baseline for the sample according to the depth;

[0171] ④ Using the formula in step 4.c, calculate the deletion ratio and reference ratio of the sample to be tested and the matched baseline at site s. and ;

[0172] ⑤Settings is 100, is 4, and the state of the sample site s to be tested is determined according to the rule of step 4.d.

[0173] b) For each sample to be tested, count the number of stable sites and unstable sites at the 68 MSI sites and calculate the MSI score. For 40, 0.15, =6, according to the rule of step 4.e, the first determination of the MSI status of the samples is performed. According to the first determination result, 80 samples are determined to be MSS / MSI-L, and 23 samples are determined to be MSI-H.

[0174] c) Calculate the MSI-H score for the 23 samples detected according to the method described in step 5. .set up is 0.95, and the Greater than or equal to 0.95.

[0175] d) According to the method described in step 6, adjust the baseline, re-determine the status of each site of the 4 abnormal samples, re-count the number of stable sites and unstable sites, and calculate the MSI score. For 40, 0.15, The MSI status of the four abnormal samples was 6. Based on this result, the final MSI status of the four abnormal samples was determined to be MSS / MSI-L.

[0176] e) Statistics of MSI test results in clinical validation samples: The sensitivity of this embodiment in 23 MSI-H clinical validation samples by amplicon sequencing was 100%, and the specificity in 80 MSS / MSI-L clinical validation samples by amplicon sequencing was 100%. Among them, sensitivity refers to the correct proportion of true MSI-H samples; specificity refers to the correct proportion of true MSS / MSI-L samples. Detailed MSI score and The values ​​are shown in Table 7.

[0177] Table 7. Amplicon sequencing clinical validation sample test results

[0178] Sample No. True MSI status Number of qualified quality control sites msi score ρ Test results Sample No. True MSI status Number of qualified quality control sites msi score ρ Test results lib0433s MSI-H 68 0.559 0.913 MSI-H lib0429s MSS / MSI-L 67 0 - MSS / MSI-L lib0437s MSI-H 68 0.485 0.911 MSI-H lib0431s MSS / MSI-L 67 0 - MSS / MSI-L lib0488S MSI-H 67 0.642 0.749 MSI-H lib0434s MSS / MSI-L 68 0.029 - MSS / MSI-L lib0495S MSI-H 68 0.824 0.815 MSI-H lib0439s MSS / MSI-L 65 0.015 - MSS / MSI-L lib0674S MSI-H 66 0.788 0.771 MSI-H lib0443s MSS / MSI-L 66 0 - MSS / MSI-L lib0676S MSI-H 67 0.836 0.774 MSI-H lib0445s MSS / MSI-L 68 0 - MSS / MSI-L lib0678S MSI-H 66 0.833 0.864 MSI-H lib0447s MSS / MSI-L 67 0.015 - MSS / MSI-L lib0680S MSI-H 66 0.788 0.603 MSI-H lib0449s MSS / MSI-L 67 0 - MSS / MSI-L lib0682S MSI-H 62 0.79 0.715 MSI-H lib0451s MSS / MSI-L 66 0 - MSS / MSI-L lib0455S MSS / MSI-L 68 0 - MSS / MSI-L lib0453S MSS / MSI-L 68 0 - MSS / MSI-L lib0457S MSS / MSI-L 68 0 - MSS / MSI-L lib0490S MSS / MSI-L 68 0 - MSS / MSI-L lib0459S MSS / MSI-L 67 0.03 - MSS / MSI-L lib0492S MSS / MSI-L 67 0 - MSS / MSI-L lib0461S MSS / MSI-L 66 0 - MSS / MSI-L lib0494S MSS / MSI-L 67 0 - MSS / MSI-L lib0463S MSS / MSI-L 67 0 - MSS / MSI-L lib0497S MSS / MSI-L 66 0 - MSS / MSI-L lib0465S MSS / MSI-L 66 0.015 - MSS / MSI-L lib0499S MSS / MSI-L 68 0.015 - MSS / MSI-L lib0467S MSS / MSI-L 67 0.03 - MSS / MSI-L lib0501S MSS / MSI-L 67 0 - MSS / MSI-L lib0469S MSS / MSI-L 66 0 - MSS / MSI-L lib0503S MSS / MSI-L 67 0 - MSS / MSI-L lib0471S MSS / MSI-L 68 0 - MSS / MSI-L lib0505S MSS / MSI-L 67 0 - MSS / MSI-L lib0473S MSS / MSI-L 66 0 - MSS / MSI-L lib0540S MSS / MSI-L 68 0 - MSS / MSI-L lib0476S MSS / MSI-L 68 0 - MSS / MSI-L lib0597S MSS / MSI-L 67 0 - MSS / MSI-L lib0478S MSS / MSI-L 67 0 - MSS / MSI-L lib0600S MSS / MSI-L 67 0 - MSS / MSI-L lib0480S MSS / MSI-L 66 0 - MSS / MSI-L lib0606S MSS / MSI-L 68 0 - MSS / MSI-L lib0482S MSS / MSI-L 68 0 - MSS / MSI-L N20240834 MSS / MSI-L 68 0.544->0.088 0.973 MSS / MSI-L lib0484S MSS / MSI-L 67 0 - MSS / MSI-L N20240867 MSS / MSI-L 68 0.5->0.059 0.974 MSS / MSI-L lib0423s MSS / MSI-L 66 0 - MSS / MSI-L N20240870 MSS / MSI-L 68 0.544->0.132 0.971 MSS / MSI-L lib0425s MSS / MSI-L 67 0 - MSS / MSI-L N20240875 MSS / MSI-L 68 0.529->0.103 0.975 MSS / MSI-L lib0427s MSS / MSI-L 67 0 - MSS / MSI-L

[0179] 5. Re-build the library for samples with abnormal amplicon sequencing and compare the consistency of MSI results of the two library construction data

[0180] right For the 4 clinical validation samples with amplicon sequencing score greater than or equal to 0.95, the library was rebuilt and amplicon captured, and the rebuilt library and sequencing data were tested again according to the method of the first MSI test. After the re-library was rebuilt and sequenced, the msi score calculated at 68 MSI sites for the 4 samples was lower than 0.15, and the sum of the number of stable sites and unstable sites was greater than or equal to 40. The results of this test were all MSS / MSI-L. The MSI test results of the sequencing data of the two library construction experiments of the 4 samples are summarized (Table 8). Although there were abnormalities in the samples of the first library construction, after calculation, After adjusting the baseline, these samples were finally judged as MSS / MSI-L, which was consistent with the results after re-experimental library construction. This result shows that the present invention can obtain relatively stable and accurate detection results in the case of experimental or sequencing abnormalities.

[0181]

[0182] The parts of the present invention that are not described in detail may refer to the prior art in the art or are known to those skilled in the art.

[0183] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for detecting microsatellite instability based on second-generation sequencing technology, characterized in that: The method comprises: A group of MSI sites with a repeat sequence length of 10-15 bp are selected, and capture probes and / or amplicon primers are designed based on any combination of the MSI sites; Select several samples, and for each sample, calculate the ratio of deleted fragments and normal fragments at each MSI site; Selecting a number of samples that are judged to be in the MSI-L or MSS state by the PCR method from the above samples as baseline samples, setting a different depth level for each of the baseline samples, and constructing a baseline based on each depth level; Based on the constructed baseline, the MSI status of each selected sample is tested; the content includes: a) Calculate the ratio of deleted fragments and normal fragments at each MSI site for each sample; b) calculating the depth of each MSI site and matching the depth to a baseline of the same depth level; c) Calculate the Z_score of the sample's deletion fragment ratio and normal fragment ratio at each MSI site relative to the baseline; d) According to the calculated depth and Z_score, the status of the sample at each MSI site is determined; The judgment result is site quality control failure, site instability or site stability; e) Comprehensively analyze the status of all MSI sites, calculate the MSI score, count the number of sites that pass quality control, and determine the MSI status of the sample based on the statistical results; Select samples whose MSI status detection results are MSI-H, and determine whether they are abnormal samples by calculating the Spearman rank correlation coefficient; when determined to be abnormal samples, adjust the baseline of the sample and re-determine the MSI status; the Spearman rank correlation coefficient is the Spearman rank correlation coefficient of the missing fragment ratio and the mean of the baseline missing fragment ratio; set the abnormal threshold of the Spearman rank correlation coefficient, and when the Spearman rank correlation coefficient is greater than or equal to the abnormal threshold, determine that the corresponding sample is an abnormal sample; otherwise, determine that the corresponding sample is normal.

2. The method for detecting microsatellite instability based on the second generation sequencing technology according to claim 1, characterized in that: The contents of calculating the missing fragment ratio and normal fragment ratio of the sample include: a) Align the sequenced base sequence to the human genome reference sequence, and extract the sequenced base sequence that completely covers the region where the MSI site is located and at least two bases in length on both sides; b) filtering the sequencing data of the captured sequencing samples; c) removing duplicates from the sequenced base sequences after filtering the captured sequencing samples; d) For each remaining sequenced base sequence, the length of the repeated sequence at the current MSI site is counted, and the sequenced base sequences are classified according to the relative relationship between the length of the repeated sequence of the sequenced base sequence and the human genome reference sequence; the number of sequenced base sequences in each category and the total number of sequenced base sequences are counted, and the ratio of missing fragments and the ratio of normal fragments are calculated.

3. The method for detecting microsatellite instability based on the second generation sequencing technology according to claim 2, characterized in that: The contents of filtering the sequencing data of the captured sequencing samples include: Extract the sequences of three bases on each side of the MSI site on each sequencing base sequence; if the two sides of the MSI site cover less than three bases at the read edge, only extract two bases; if the base sequence of the sequencing base sequence at these positions is not completely consistent with the human genome reference sequence, filter out the sequencing base sequence; The sequence corresponding to the MSI site region on each sequencing base sequence is extracted; if there are more than or equal to two bases in the sequence that are inconsistent with the reference sequence, the sequencing base sequence is filtered; if there is a base in the sequence that is inconsistent with the human genome reference sequence and the sequence is exactly at the starting or ending position of the MSI site, the sequencing base sequence is filtered out.

4. The method for detecting microsatellite instability based on the second generation sequencing technology according to claim 2, characterized in that: The content of deduplication of the sequencing base sequence after filtering the captured sequencing sample includes: For the captured sequencing samples, the source of the original DNA molecular fragments of the sequencing base sequence is determined according to the starting and ending sites of each sequencing base sequence; the sequencing base sequence is a single-end sequencing base sequence or a double-end sequencing base sequence; For repeated sequencing base sequences originating from the same original DNA molecule fragment, one base sequence is randomly selected from the repeated sequencing base sequences for retention.

5. The method for detecting microsatellite instability based on the second generation sequencing technology according to claim 1, characterized in that: The process of building a baseline includes: a) Calculate the ratio of deleted fragments and normal fragments at the corresponding depth level of each MSI site for each baseline sample; b) Calculate the baseline for each MSI site at its depth level; this includes: The baseline mean and standard deviation of the proportion of deleted fragments in the baseline samples of each MSI site were calculated; The baseline mean and standard deviation of the normal fragment ratio in the baseline samples of each MSI site were calculated.

6. The method for detecting microsatellite instability based on the second generation sequencing technology according to claim 1, characterized in that: The number of MSI sites with repeat sequence lengths between 10 and 15 bp was 74.

7. The method for detecting microsatellite instability based on the second generation sequencing technology according to claim 1, characterized in that: Adjusting the baseline of an abnormal sample and re-judging the MSI status includes: adjusting the baseline matched at each site of the sample, re-judging the site status based on the adjusted baseline, combining the judgment results of all sites to obtain a new MSI status of the sample, and replacing the original MSI status with the new MSI status.

8. A microsatellite instability detection device based on the second generation sequencing technology, comprising a processor and a memory, wherein a software program is stored in the memory, characterized in that: When the processor runs the software program, the content of the microsatellite instability detection method based on the second-generation sequencing technology as described in any one of claims 1 to 7 can be implemented.

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