All-site biological information analysis method for detecting ultralow-frequency mutation of ctDNA in blood

Abstract

The invention discloses a biological information analysis method for detecting ultralow-frequency mutation of ctDNA in blood of a tumor patient. The method comprises the following steps: data splitting, second-generation sequencing data quality control, comparison with a human reference genome, UMI deduplication correction of a comparison result, left alignment operation of the comparison result and correction of a base quality value, statistics of base composition of all sites in a detection area, and determination of a background value according to a negative sample. Clinical sample background polishing and variation result first-round filtering; variation result annotation and second-round filtering. Different from common mutation detection software in the market, the method provided by the invention can accurately detect ultralow-frequency ctDNA mutation in blood of a tumor patient by carrying out detailed statistics on basic group composition conditions of all sites in a tumor Panel detection area and formulating a background polishing algorithm and a mutation screening method, the lowest detection frequency can reach five ten thousandths, and the detection result is more accurate. The method has important clinical application prospects in the aspects of cancer early screening, disease monitoring, treatment effect evaluation, prognosis monitoring and the like.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics, and in particular relates to a full-site bioinformatics method for detecting ultra-low frequency mutations in ctDNA in the blood of tumor patients. Background Art

[0002] ctDNA (circulating tumor DNA) refers to tumor DNA that exists in body fluids such as blood and cerebrospinal fluid and is free from cells. It is about 170bp in length. When tumor cells grow, divide or die, the DNA fragments they release can enter the blood circulation and form circulating tumor DNA. As a potential biomarker, circulating tumor DNA (ctDNA) has attracted much attention in the field of tumor diagnosis and monitoring in recent years. With the rise of the concept of personalized treatment, liquid biopsy technology represented by ctDNA has gradually become a research hotspot as a non-invasive tumor diagnosis and monitoring method. ctDNA carries the genetic information of tumor cells, including tumor-specific mutations, gene rearrangements and other variation information. By detecting ctDNA in blood samples, non-invasive and real-time tumor genetic information can be obtained, providing a new means for early diagnosis of tumors, monitoring of treatment effects, and prognosis evaluation.

[0003] Traditional gene mutation detection methods mainly rely on PCR and its derivative technologies, such as digital PCR and digital titration PCR (ddPCR). However, these methods are usually used to detect known mutation sites, and are less efficient for multiple sites or large-scale screening. In contrast, capture-based second-generation sequencing technology enriches circulating tumor DNA (ctDNA) and uses high-throughput sequencing technology (such as Illumina sequencing) to sequence the entire exon or specific region, thereby comprehensively detecting mutations in multiple genes. This method has the characteristics of high throughput and high sensitivity, and plays an increasingly important role in personalized tumor treatment. However, the analysis of ctDNA high-throughput sequencing data faces certain challenges, because the frequency of tumor mutations is usually low, which requires high-throughput data support, but as the amount of data increases, background noise such as sequencing errors and false positives introduced by PCR will inevitably increase. In this case, how to ensure that the influence of background noise is avoided so as to accurately identify low-frequency mutation signals of tumors is crucial. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a full-site bioinformatics analysis method for detecting ultra-low frequency mutations of blood ctDNA. By detailed statistics of the base composition of all sites in the tumor panel sequencing area, and formulating a background polishing algorithm and a variation screening method, it can accurately detect ultra-low frequency ctDNA mutations in the blood of tumor patients. The minimum detection frequency can reach 0.5 per 10,000, which has important clinical application prospects in early cancer screening, disease monitoring, treatment effect evaluation and prognosis monitoring.

[0005] The present application provides a full-site bioinformatics analysis method for detecting ultra-low frequency mutations in blood ctDNA, comprising the following steps:

[0006] 1. After the sequencing data is downloaded, the data is split according to the label sequence (barcode) of each sample to obtain the second-generation sequencing data in Fastq format for each sample.

[0007] 2. Quality control of the second-generation sequencing data includes removing adapter sequences, removing sequences with base quality values ​​lower than 30, removing read sequences with 1 or more N bases, removing one base at the end of the read, removing low-complexity sequences, and removing read sequences with a length lower than 50bp after control.

[0008] 3. Compare the quality-controlled data to the human reference genome, store the comparison results as BAM format files, and sort and index the BAM files.

[0009] 4. Use UMI deduplication to correct the comparison results, including the following steps:

[0010] (1) Reads aligned to the reference genome are grouped according to chromosome, start and end positions, and reads with the same alignment position are grouped together.

[0011] (2) For each of the above groups, clustering is performed based on the UMI in the group. Each UMI cluster includes at least 2 read sequences. If the data has no UMI, this step is skipped.

[0012] (3) If the reads are paired, find the overlapping regions of each pair of reads and score the bases in the overlapping regions based on the consistency and quality of the bases.

[0013] (4) For each base position in each UMI cluster, calculate the score of each base A / T / C / G / N.

[0014] (5) Correct the site based on the frequency of occurrence. If there is a dominant base at a certain position, use this base as the corrected base for that position. If there are one or more reads that are consistent with a high-quality reference genome, or if all reads at that position are of low quality, use the corresponding base in the reference genome as the corrected base for that position.

[0015] 5. Before variant detection, it is necessary to left-align the insertions and deletions (indels) in the resulting BAM file and correct the base quality in the BAM file to eliminate the base quality deviation introduced during the sequencing process.

[0016] 6. Perform mutation detection by statistically analyzing the base composition of all sites in the detection region. The principles are as follows:

[0017] (1) Count the number of A / T / C / G / N and insertion / deletion numbers with base quality values ​​greater than 30 at each site in the tumor panel detection area;

[0018] (2) For sites without indels, the base with the largest number, excluding the bases identical to the reference genome, is the variant base at that position. The mutation frequency of the variant base is equal to the number of variant bases divided by the total depth of the site.

[0019] (3) For sites with indels, count the number of reads with indels at the site, and divide this number by the total depth of the site to get the frequency of the indel;

[0020] (4) If the remaining three bases and the number of insertions and deletions except the bases that are the same as the reference genome are all 0, it indicates that no base mutation has occurred at this site;

[0021] (5) If a site has a point mutation and the number of insertions and deletions is not unique, the site will not be counted.

[0022] 7. The present invention counts the base composition of all sites of 100 negative samples and further determines the background value of sequencing data, specifically comprising the following steps:

[0023] (1) Count the base composition of all sites in the panel detection area of ​​100 negative samples, and if there is a mutation, calculate the mutation frequency of the variant base;

[0024] (2) Count the number of reads at the variant sites of 100 negative samples, and calculate the 99% confidence interval of the number of reads at each variant site, taking the upper limit of the interval as the read background value;

[0025] (3) Count the mutation frequencies of the variant sites in 100 negative samples, and calculate the 99% confidence interval of the mutation frequency of each variant site, and take the upper limit of the interval as the background value of the mutation frequency;

[0026] (4) Variants with mutation frequencies above 30% are not included in the calculation of confidence intervals in (2) and (3).

[0027] 8. Clinical sample background polishing refers to the actual mutation read number of the site minus the read background value of the site, that is, the mutation read number of the clinical sample variant site after background polishing; similarly, the mutation frequency of the clinical sample variant site after background polishing is equal to the actual mutation frequency of the site minus the mutation frequency background value of the site;

[0028] 9. The first round of filtering of variant sites, the filtering principles are as follows:

[0029] (1) Remove mutations with strand preference, where strand preference refers to the percentage of mutation sites on a single strand (positive strand or negative strand) exceeding 90%;

[0030] (2) The mutation sites were distinguished based on whether they fell into the COSMIC database. As shown in Table 1, different thresholds were set based on the total site depth, number of reads, and mutation frequency to screen the mutation sites after background polishing.

[0031] Table 1. Variant site filtering conditions

[0032] Total Depth Number of reads Mutation frequency Non-cosmic >＝500 >＝5 >＝0.10％ cosmic >＝500 >＝2 >＝0.05％

[0033] (3) Backtracking of read number and mutation frequency: after filtering, the actual read number and mutation frequency before background polishing are output.

[0034] 10. Variant site annotation and second round of filtering, the principles are as follows:

[0035] (1) SnpEff, SnpSift and ANNOVAR were used to annotate variant sites;

[0036] (2) Remove mutations that fall in UTR, intron, and are annotated as synonymous_variant;

[0037] (3) Remove mutations with a population frequency above 5%. If one of the three databases, 1000Genomes Project, ExAC, and genomAD, has a population frequency greater than 5%, then remove it;

[0038] (4) Remove mutation sites annotated as benign by ClinVar or InterVar and with a mutation frequency of 45% < 55% or a mutation frequency of > 95%;

[0039] (5) Use the hotspot database built by this application to annotate the mutation sites after filtering.

[0040] Compared with the prior art, this application has the following beneficial effects:

[0041] 1. The UMI correction and background polishing algorithms provided by the present invention remove the impact of background noise such as sequencing errors and false positives introduced by PCR on variant detection, thereby reducing false positives.

[0042] 2. The variation detection method provided by the present invention is different from the common variation detection software on the market. This method can accurately detect ultra-low frequency ctDNA mutations in the blood of tumor patients by detailed statistics of the base composition of all sites in the tumor panel sequencing area and screen out reliable variation results. The minimum detection frequency can reach 0.5%, which improves the sensitivity of detection.

[0043] 3. The present invention provides detailed annotations of variant sites and filtering principles, which are helpful for gaining a deeper understanding of the pathogenesis of tumors, guiding individualized treatment, evaluating prognosis, discovering new drug targets, and determining drug sensitivity, which is of great significance for improving the treatment effect and quality of life of tumor patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention provides a flow chart of a bioinformatics analysis method for detecting ultra-low frequency mutations of blood ctDNA.

[0045] Figure 2 Flowchart for determining background read and mutation frequencies.

[0046] Figure 3 Flowchart of clinical sample background polishing and first round of filtration.

[0047] Figure 4 Variant site annotation and second round filtering flow chart. DETAILED DESCRIPTION

[0048] In order to more clearly illustrate the contents disclosed by the present invention, including the purpose, technical solutions and advantages, we will use drawings and detailed descriptions to illustrate. The purpose of providing these embodiments is to make the understanding of the present invention more thorough and comprehensive.

[0049] Unless otherwise defined, all technical and scientific terms used herein are generally understood by those skilled in the art in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0050] Example 1

[0051] like Figure 1 As shown, a bioinformatics analysis method for detecting ultra-low frequency mutations of blood ctDNA provided by a specific embodiment of the present invention comprises the following steps:

[0052] 1. Data splitting: In the present invention, bcl2fastq v1.2.6 software is used to split the next-generation sequencing data to obtain the sequencing data in fastq format for each sample. The --barcode-mismatches parameter is specified as 1, which means that the barcode sequence allows one base mismatch.

[0053] 2. Second-generation sequencing data quality control. In the present invention, fastp v0.23.2 is used to perform quality control on the data after splitting, including removing adapter sequences, removing sequences with base quality values ​​less than 30, removing read sequences with 1 or more N bases, removing 1 base at the end of the read, removing low-complexity sequences, and removing read sequences with a length less than 50bp after control. For the entire sample, the sample is qualified only if the Q30 base data volume is greater than or equal to 90%, the adapter contamination ratio is less than 1%, and the effective data volume after quality control must be greater than 80%. In addition, in this step, the UMI length is specified by the --umi_len parameter, and the --umi_loc parameter specifies the UMI position for subsequent analysis.

[0054] 3. In the present invention, bwa v0.7.17-r1188 was used to align the fastq sequence after quality control with the reference genome sequence, and samtools v1.6 was used to sort and index the alignment results in BAM format.

[0055] 4. Use UMI deduplication to correct the alignment results. In the present invention, gencore v0.17.2 is used to perform UMI deduplication correction on the alignment results, so as to more accurately identify and correct the deviations in PCR amplification and sequencing processes, and improve the quality and reliability of cfDNA sequencing data. The parameters are set as -s 2--umi_prefix=UMI--ratio_threshold=0.9.

[0056] 5. In the present invention, the BAM format alignment results were preprocessed by GATK 4.0.5.1 software before variation detection. First, the insertions and deletions (indels) in the BAM file were left-aligned by the LeftAlignIndels command. Then, the BaseRecalibrator command was used to correct the base quality deviation in the BAM file based on the known variation site information. By analyzing the known variation sites and the base quality scores in the sequencing data, BaseRecalibrator can calculate the correction value of each base quality. Finally, the base quality correction value generated by BaseRecalibrator was applied to the original BAM file by ApplyBQSR command to correct the base quality in the BAM file and eliminate the base quality deviation introduced during the sequencing process.

[0057] 6. The present invention uses bam-readcount v1.0.1 and combines it with a self-written Python script to count the number of A / T / C / G / N and indels with a base quality value greater than 30 at each site in the tumor panel detection area of ​​the BAM file; the bam-readcount parameter is set to -b 30-w 0; for sites without indels, the base with the largest number excluding the bases identical to the reference genome is the variant base at the position, and a variant base of 0 indicates that the site has not mutated, and the mutation frequency of the variant base is equal to the number of variant bases divided by the total depth of the site; for sites with indels, the number of reads with indels at the site is counted, and the number divided by the total depth of the site is the frequency of the indel; if the other three bases excluding the bases identical to the reference genome and the number of indels are all 0, it indicates that no base mutation has occurred at this site; if the number of point mutations and indels at a site is not unique, the site will not be counted.

[0058] 7. Such as Figure 2 As shown, the present invention counted the base composition of all sites of 100 negative samples by self-written Python script, and further determined the background value. First, the base composition of all sites of the 100 negative samples in the panel detection area was counted. If there was a mutation, the mutation frequency of the variant base was calculated, and the mutation with a frequency of more than 30% was removed; then the number of reads of the variant sites of the 100 negative samples was counted, and the 99% confidence interval of the number of reads of each variant site was calculated, and the upper limit of the interval was taken as the read background value; finally, the mutation frequency of the variant sites of the 100 negative samples was counted, and the 99% confidence interval of the mutation frequency of each variant site was calculated, and the upper limit of the interval was taken as the mutation frequency background value.

[0059] 8. As Figure 3As shown, in the present invention, by writing a self-written Python script, the actual mutation read number of all sites is subtracted from the read background value of the corresponding site, and the resulting value is the mutation read number of the clinical sample variant site after background polishing; similarly, the mutation frequency of the clinical sample variant site after background polishing is equal to the actual mutation frequency of the site minus the background value of the mutation frequency of the site.

[0060] 9. In the present invention, two rounds of filtering of mutations were performed by self-written Python scripts, such as Figure 3 As shown, in the first round of filtering, mutations with a single-strand ratio of more than 90% are first removed, and then the mutation sites are distinguished based on whether they fall into the COSMIC database. For mutations that fall into the COSMIC database, the threshold is set to total depth>=500, mutation read number>=2, and mutation frequency>=0.05%; for mutations outside the COSMIC database, the threshold is set to total depth>=500, mutation read number>=5, and mutation frequency>=0.1%. Finally, the mutation read number and frequency are backtracked to output the actual read number and mutation frequency before the variation background polishing.

[0061] 10. The present invention firstly uses SnpEff, SnpSift and ANNOVAR in combination with a self-written Python script to annotate the variant sites; then Figure 4 As shown, a second round of filtering is performed on the annotated sites, and the filtering principles are as follows:

[0062] (1) Remove mutations that fall in UTR, intron, and are annotated as synonymous_variant;

[0063] (2) Remove mutations with a population frequency above 5%. If one of the three databases, 1000Genomes Project, ExAC, and genomAD, has a population frequency greater than 5%, then remove it;

[0064] (3) Remove mutation sites annotated as benign by ClinVar or InterVar and with a mutation frequency of 45% < 55% or a mutation frequency of > 95%;

[0065] (4) Finally, the hotspot database built by the present invention is used to annotate the mutation sites after filtering.

[0066] Example 2

[0067] In order to evaluate the actual effect of the bioinformatics analysis method for detecting ultra-low frequency mutations in blood ctDNA provided in the above-mentioned Example 1, the present invention starts with the six genes of EGFR, KRAS, BRAF, PIK3CA, TP53, and BRCA1, and mixes DNA fragments containing mutations and wild-type sequences in a certain proportion to form standards with different mutation frequencies. In order to test the detection sensitivity, the standards are designed with five frequency gradients of 0.03%, 0.05%, 0.1%, 0.15%, and 0.2%, and a total of 10 groups of repetitions are performed. The following Table 2 shows the 13 known mutation sites used for the standards.

[0068] Table 2. 6 genes and 13 mutation sites of the standard

[0069]

[0070]

[0071] In the present invention, the variation detection method provided by the present invention is respectively used to perform parallel tests and comparisons with the variation detection software GATK, Vardict and VarScan2 commonly used in the market. The four methods perform variation detection on the same comparison results. Tables 3, 4, 5 and 6 show the detection of standards under different frequency gradients. It can be clearly seen that the variation detection method provided by the present invention is significantly higher in sensitivity than the other three software, especially in the detection of indel, the detection rate is significantly higher than the other three software.

[0072] Table 3. Detection of 0.03% standard products

[0073]

[0074] Table 4. Detection of 0.05% standard products

[0075]

[0076]

[0077] Table 5. Detection of 0.10% standard products

[0078]

[0079] Table 5. Detection of 0.15% standard products

[0080]

[0081]

[0082] Table 6. Detection of 0.2% standard products

[0083]

Claims

1. A bioinformatics analysis method for detecting ultra-low frequency mutations in ctDNA in the blood of tumor patients, characterized in that: The following steps are involved: S-1. Data splitting to obtain the second-generation sequencing data in Fastq format; S-2. Quality control of next-generation sequencing data; S-3. Compare with the human reference genome and store the results in a BAM format file; S-4. Use UMI (Unique Molecular Identifier) ​​to deduplicate and correct the comparison results; S-5. Left-align the comparison results and correct the base quality values; S-6. Perform mutation detection by statistically analyzing the base composition of all sites in the detection region; S-7. Determine the background value based on negative samples; S-8.Clinical sample background polishing; S-9. The first round of filtering of mutation results; S-10. Annotation of variant results and second round of filtering.

2. A bioinformatics analysis method for detecting ultra-low frequency mutations in ctDNA in the blood of tumor patients according to claim 1, characterized in that: The UMI deduplication correction comparison result described in step S-4 includes the following steps: (1) Group the reads mapped to the reference genome according to chromosome, start and end positions, and group the reads with the same mapping position into one group; (2) For each group in (1), cluster according to the UMI in the group. Each UMI cluster includes at least 2 read sequences. If the data has no UMI, skip this step. (3) If the reads are paired, find the overlapping regions of each pair of reads and score the bases in the overlapping regions based on the consistency and quality of the bases; (4) For each base position in each UMI cluster, calculate the score of each base A / T / C / G / N; (5) Correct the site based on the frequency of occurrence. If there is a dominant base at a certain position, use this base as the corrected base for that position. If there are one or more reads that are consistent with a high-quality reference genome, or if all reads at that position are of low quality, use the corresponding base in the reference genome as the corrected base for that position.

3. A bioinformatics analysis method for detecting ultra-low frequency mutations in ctDNA in the blood of tumor patients according to claim 1, characterized in that: The principle of performing variation detection by statistically analyzing the base composition of all sites in the detection region in step S-6 is as follows: (1) Count the number of A / T / C / G / N and indels with a base quality value greater than 30 at each site in the tumor panel detection area; (2) For sites without indels, the base with the largest number, excluding the bases identical to the reference genome, is the variant base at that position. The mutation frequency of the variant base is equal to the number of variant bases divided by the total depth of the site. (3) For sites with indels, count the number of reads with indels at the site, and divide this number by the total depth of the site to get the frequency of the indel; (4) If the remaining three bases and the number of insertions and deletions except the bases that are the same as the reference genome are all 0, it indicates that no base mutation has occurred at this site; (5) If a site has a point mutation and the number of insertions and deletions is not unique, the site will not be counted.

4. A bioinformatics analysis method for detecting ultra-low frequency mutations in ctDNA in the blood of tumor patients according to claim 1, characterized in that: Determining the background value according to the negative sample in step S-7 comprises the following steps: (1) Count the base composition of all sites in the panel detection area of ​​100 negative samples, and if there is a mutation, calculate the mutation frequency of the variant base; (2) Count the number of reads at the variant sites of 100 negative samples, and calculate the 99% confidence interval of the number of reads at each variant site, and take the upper limit of the interval as the read background value; (3) Count the mutation frequencies of the variant sites in 100 negative samples, and calculate the 99% confidence interval of the mutation frequency of each variant site, and take the upper limit of the interval as the background value of the mutation frequency; (4) Variants with mutation frequencies above 30% are not included in the calculation of confidence intervals in (2) and (3).

5. A bioinformatics analysis method for detecting ultra-low frequency mutations in ctDNA in the blood of tumor patients according to claim 1, characterized in that: The clinical sample background polishing described in step S-8 refers to the actual mutation read number of the site minus the read background value of the site, that is, the mutation read number of the clinical sample variant site after background polishing; similarly, the mutation frequency of the clinical sample variant site after background polishing is equal to the actual mutation frequency of the site minus the mutation frequency background value of the site.

6. A bioinformatics analysis method for detecting ultra-low frequency mutations in ctDNA in the blood of tumor patients according to claim 1, characterized in that: In the first round of filtering of variant sites described in step S-9, chain preference mutations are first removed. Chain preference refers to the proportion of mutation sites in a single chain (positive chain or negative chain) exceeding 90% or more. Then, the mutation sites are distinguished according to whether they fall in the COSMIC database. As shown in Table 1, different thresholds are set based on the total depth of the site, the number of reads, and the frequency to screen the mutation sites after background polishing. Finally, the number of reads and the mutation frequency are backtracked to output the actual number of reads and the mutation frequency before background polishing. Table 1. Variant site filtering conditions 7. A bioinformatics analysis method for detecting ultra-low frequency mutations in ctDNA in the blood of tumor patients according to claim 1, characterized in that: The variant site annotation and second round of filtering described in step S-10 are specifically performed as follows: (1) SnpEff, SnpSift and ANNOVAR were used to annotate variant sites; (2) Remove mutations that fall in UTR, intron, and are annotated as synonymous_variant; (3) Remove mutations with a population frequency above 5%. If one of the three databases, 1000Genomes Project, ExAC, and genomAD, has a population frequency greater than 5%, then remove it; (4) Remove mutation sites annotated as benign by ClinVar or InterVar and with a mutation frequency of 45% < 55% or a mutation frequency of > 95%; (5) Use the hotspot database built by this application to annotate the mutation sites after filtering.

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