Methods, apparatuses, media, and devices for ngs-based detection of chromosomal aneuploidy

Abstract

The application discloses a chromosome aneuploidy detection method, device, medium and equipment based on NGS, and relates to the technical field of biomedicine. The method comprises the following steps: receiving NGS sequencing data of tumor tissues and normal tissues; preprocessing the NGS sequencing data to obtain an intermediate data file; performing consistency evaluation on gender and germ line SNPs by using the intermediate data file; obtaining coverage depth information and SNP genotype information on a to-be-detected sample genome by using the intermediate data file; detecting tumor sample purity, ploidy and SCNV fragments; calculating SCNV at a chromosome arm level of each tumor sample according to a single tumor sample and a prepared SCNV database of a pan-cancer queue; and calculating a final chromosome aneuploidy score of each tumor sample based on SCNV at a sample chromosome arm level. The device, medium and equipment are all based on the method. The application improves the accuracy of tumor chromosome aneuploidy detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomedicine, and particularly relates to a chromosome aneuploidy detection method, device, medium and equipment based on NGS. BACKGROUND

[0002] Next-generation sequencing (NGS) is a high-throughput parallel sequencing technology, which is a DNA sequencing technology developed on the basis of PCR and gene chip. This technology can obtain the genetic mutation and chromosomal structural variation information of multiple target regions on the genome at one time with low cost.

[0003] Aneuploidy is an imbalance in the number of chromosomes in cells, and this important tumor characteristic is found in about 90% of solid tumors. Aneuploidy score is defined as the number of tumor samples with chromosomal arm level SCNV, which can be used as an immune biomarker to predict the efficacy of immunotherapy.

[0004] At present, there are some methods for calculating the aneuploidy score based on the SCNV data of NGS. However, these methods have some shortcomings, such as non-uniformity of SCNV-segment detection methods and filtering standards, no tumor purity and ploidy correction, and complicated operation steps, so that the aneuploidy cannot be accurately detected. SUMMARY

[0005] The present application provides a chromosome aneuploidy detection method and device based on NGS, which aims to improve the detection accuracy of tumor chromosome aneuploidy. Meanwhile, the present application also provides a corresponding computer readable storage medium and electronic device.

[0006] Technical scheme: In a first aspect, the present application provides a chromosome aneuploidy detection method based on NGS, which comprises the following steps:

[0007] receiving NGS sequencing data of tumor tissue and normal tissue;

[0008] preprocessing the NGS sequencing data to obtain an intermediate data file;

[0009] using the intermediate data file to evaluate the consistency of gender and germline SNP;

[0010] using the intermediate data file to obtain coverage depth information and SNP genotype information on the genome of the sample to be tested;

[0011] detecting tumor sample purity, ploidy and SCNV segment;

[0012] According to the single tumor sample and the prepared SCNV database of the pan-cancer cohort, the SCNV at the chromosome arm level of each tumor sample is calculated.

[0013] Based on the SCNV at the chromosome arm level of the sample, the final chromosomal aneuploidy score of each tumor sample is calculated.

[0014] Further, the NGS sequencing data of tumor tissue and normal tissue includes whole genome sequencing and whole exon capture sequencing.

[0015] Further, the consistency evaluation of gender and germline SNPs using the intermediate data file includes:

[0016] Based on the sequencing depth of the Y chromosome, the sample gender is evaluated for consistency, and the gender is evaluated for consistency.

[0017] The germline SNPs are evaluated for consistency using the Conpair software.

[0018] Further, the intermediate data file is used to obtain the coverage depth information and SNP genotype information on the genome of the sample to be tested.

[0019] Using the intermediate data file as input and using the SNP site database, the snp-pileup software is used to obtain the coverage depth information and SNP genotype information on the genome of the sample to be tested.

[0020] Further, FACETS or Sequenza software is used to detect tumor sample purity, ploidy and SCNV fragments.

[0021] Further, according to the single tumor sample and the prepared SCNV database of the pan-cancer cohort, the SCNV at the chromosome arm level of each tumor sample is calculated.

[0022] The single tumor sample and the prepared SCNV database of the pan-cancer cohort are used as input, and the GISTIC software is used for analysis.

[0023] The threshold parameter is set, and whether CNV occurs at the chromosome arm level is determined according to the set threshold parameter.

[0024] Further, based on the SCNV at the chromosome arm level of the sample, the final chromosomal aneuploidy score of each tumor sample is calculated.

[0025] The sum of the number of non-0 of all 22 autosomal long arms and short arms is calculated.

[0026] In a second aspect, the present application provides a device for detecting chromosomal aneuploidy based on NGS, which detects chromosomal aneuploidy of a tumor by using any of the methods for detecting chromosomal aneuploidy based on NGS, comprising:

[0027] a data receiving module configured to receive NGS sequencing data of tumor tissue and normal tissue;

[0028] a data preprocessing module configured to preprocess the NGS sequencing data to obtain an intermediate data file;

[0029] a consistency evaluation module configured to evaluate the consistency of gender and germline SNPs by using the intermediate data file;

[0030] an information obtaining module configured to obtain coverage depth information and SNP genotype information on a genome of a sample to be tested by using the intermediate data file;

[0031] an SCNV detection module configured to detect tumor sample purity, ploidy and SCNV fragments;

[0032] an SCNV calculation module configured to calculate SCNV at a chromosome arm level of each tumor sample according to a single tumor sample and a prepared SCNV database of a pan-cancer queue;

[0033] a score calculation module configured to calculate a final chromosomal aneuploidy score of each tumor sample based on SCNV at a chromosome arm level of the sample.

[0034] In a third aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and the computer instructions can be executed by a processor to implement any of the methods for detecting chromosomal aneuploidy based on NGS.

[0035] In a fourth aspect, the present application provides an electronic device, comprising the computer readable storage medium and a processor configured to execute the computer instructions in the computer readable storage medium.

[0036] English abbreviation explanation:

[0037] (1) NGS: Next generation, sequencing, second-generation sequencing technology;

[0038] (2) Aneuploidy: chromosomal aneuploidy;

[0039] (3) SCNV: somatic copy number variants, somatic copy number variation;

[0040] (4) SNP: Single Nucleotide Polymorphism, single nucleotide polymorphism.

[0041] Compared with the prior art, the purity and ploidy state of the tumor sample are considered when calculating the SCNV, the Aneuploidy score calculated finally has higher accuracy and robustness in combination with the SCNV results of the pan-cancer database, so that the tumor chromosomal aneuploidy can be detected more accurately. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A flowchart of the chromosomal aneuploidy detection method based on NGS in the embodiment of the present application is shown.

[0043] Figure 2 A box plot of the difference between the Aneuploidy scores of the lung cancer recurrence group and the non-recurrence group in the embodiment of the present application is shown.

[0044] Figure 3 A survival difference plot of the lung cancer recurrence-free survival time (RFS) when the cutoff = 18 in the embodiment of the present application is shown.

[0045] Figure 4 A forest plot of the lung cancer HR value when the cutoff = 18 in the embodiment of the present application is shown.

[0046] Figure 5 A block diagram of the chromosomal aneuploidy detection device based on NGS in the present application is shown. DETAILED DESCRIPTION

[0047] The present application will be further described below in conjunction with the embodiments and the accompanying drawings.

[0048] Figure 1 A flowchart of the chromosomal aneuploidy detection method based on NGS in the embodiment of the present application is shown. As shown in the figure, the method comprises steps S110-S170, which are specifically as follows: Figure 1

[0049] Step S110: receiving NGS sequencing data of tumor tissue and normal tissue (or blood cells). Specifically, as shown in the figure, the sequencing data of the tumor tissue and the normal tissue (or blood cells) are received. Figure 2 In the embodiment of the present application, the sequencing data can be obtained based on whole genome sequencing (WGS) or whole exome capture sequencing (WES) in second-generation sequencing. The sequencing data can be double-end sequencing or single-end sequencing, and the double-end sequencing is preferred in the embodiment of the present application, and the sequencing strategy is PE150 (Paired-end). The sequencing depth: the WGS sequencing is preferably >30X; the WES sequencing is preferably >100X.

[0050] ​Step S120: preprocessing the NGS sequencing data to obtain an intermediate data file.

[0051] Specifically, the tumor tissue and normal tissue sequencing raw data are quality controlled, mainly including removing sequencing adapter sequences and low-quality sequences using Trimmomatic software, and then using BWA software to align the filtered clean reads to the human reference genome hg19 to obtain a raw BAM file. The raw BAM also needs to go through the following steps to obtain the final BAM file: 1) Samtools software sorting; 2) Picard removing repetitive sequences; 3) GATK4 software local re-alignment; 4) GATK4 software base quality correction and index file generation. After the above operations, the intermediate data file is obtained, which is a BAM file.

[0052] Step S130: using the intermediate data file to evaluate the consistency of gender and germline SNPs. In the embodiments of the present application, the gender consistency is evaluated by the sequencing depth of the Y chromosome. The intermediate data file obtained in step S120 is used as input, and Samtools software is used to determine the sequencing depth of the Y chromosome to evaluate the gender consistency.

[0053] Further, the Conpair software is used to evaluate the consistency of the germline SNPs. Through ~7000 SNP sites in the Conpair software, the consistency of the germline SNPs is evaluated. By evaluating the consistency of the gender and the germline SNPs, the accuracy of the detection is improved.

[0054] Step S140: using the intermediate data file to obtain the coverage depth information and SNP genotype information on the genome of the sample to be tested.

[0055] Using the intermediate data file and the dbSNP138 database, the snp-pileup software is used to obtain the coverage depth information and SNP genotype information on the genome of the sample.

[0056] Step S150: detecting tumor sample purity, ploidy and SCNV fragment. Specifically, in the embodiments of the present application, FACETS or Sequenza software is used to detect tumor sample purity, ploidy and SCNV fragment based on coverage depth information and SNP genotype information. Preferably, FACETS software is used for SCNV detection, and the main parameters are: ndepth (WGS = 10; WES = 35); snp.nbhd (WGS = 500; WES = 250); cval (WGS = 600; WES = 250); min.nhet (WGS = 15; WES = 15). FACETS uses a binary genome segmentation method to analyze allele-specific copy number variation and simultaneously calculate sample purity, ploidy, LOH, CNV clonal structure information, which is fast in analysis speed and accurate in results.

[0057] Step S160: calculating the SCNV of each tumor sample at the chromosome arm level according to the single tumor sample and the prepared pan-cancer queue SCNV database.

[0058] Based on the SCNV results of the single tumor sample and the prepared pan-cancer queue SCNV database, GISTIC 2.0 software is used for analysis. The main parameters of the software are shown in Table 1. Among them, the pan-cancer queue SCNV database is a database constructed based on the SCNV result files obtained from the pan-cancer samples using the above method and meeting the above quality control conditions, which is used for queue sample chromosome arm level CNV analysis.

[0059] -rx -ta -td -js -qvt -cap -broad -brlen -maxseg -conf 1 0.1 0.1 4 0.25 1.5 1 0.8 8500 0.95

[0060] In this step, the CNV threshold parameter at the chromosome arm level is 0.8, that is, the interval of CNV occurring on each chromosome arm is greater than 80% of the length of the whole chromosome arm.

[0061] Step S170: calculating the final chromosome aneuploidy score of each tumor sample based on the sample chromosome arm level SCNV. Based on the sample chromosome arm level SCNV, the total number of chromosomes with copy number changes of each tumor sample is calculated, which is the final chromosome aneuploidy score (Aneuploidy score) of the sample. In the calculation, the sum of the number of non-0 (0 represents no CNV) of all 22 autosomal long arms and short arms is calculated. Since the short arms of chr13, chr14, chr15, chr21 and chr22 are too short, they are not included in the calculation. Therefore, the value range of Aneuploidy score is 0-39. According to Aneuploidy score, the chromosome aneuploidy state of the tumor sample can be evaluated, and the higher the Aneuploidy score, the more unstable the chromosomes of the tumor sample.

[0062] To verify the practicability of the method proposed in the present application, 81 pairs of early lung cancer tissue and normal tissue samples collected were subjected to WES sequencing (average sequencing depth, 257X for tumor samples and 219X for normal tissue samples) according to the above method steps, and 2 pairs were removed after quality control (gender or germ line consistency was not consistent), and finally the Aneuploidy score of 79 pairs of samples was calculated. As shown in Figure 2 , in combination with the clinical recurrence state of the patients, the Aneuploidy score of the recurrence group was significantly higher than that of the non-recurrence group (Wilcoxon rank-sum test, p = 0.012). Further, as shown in Figure 3 and 4 , in combination with the RFS time of the patients, a single factor Cox regression analysis was performed using R software, and a Kaplan-Meier survival curve and a forest plot were drawn. The results showed that when the median value of the Aneuploidy score (cutoff = 18) was used as the threshold, the recurrence prognosis of the low Aneuploidy score group was significantly better than that of the high Aneuploidy score group (p = 0.0383, HR = 2.3693). This result further confirmed the accuracy of the method proposed in the present application and the recurrence indication role of the Aneuploidy score in early lung cancer.

[0063] In another aspect, the present application provides a NGS-based chromosome aneuploidy detection device, which can utilize any of the NGS-based chromosome aneuploidy detection devices proposed in combination with Figure 5 , the device comprises a data receiving module 210, a data preprocessing module 220, a consistency evaluation module 230, an information acquisition module 240, an SCNV detection module 250, an SCNV calculation module 260 and a score calculation module 270. Among them, the data receiving module is configured to receive NGS sequencing data of tumor tissue and normal tissue; the data preprocessing module is configured to preprocess the NGS sequencing data to obtain an intermediate data file; the consistency evaluation module is configured to evaluate the consistency of gender and germ line SNPs using the intermediate data file; the information acquisition module is configured to acquire coverage depth information and SNP genotype information on the genome of the sample to be tested using the intermediate data file; the SCNV detection module is configured to detect tumor sample purity, ploidy and SCNV fragments; the SCNV calculation module is configured to calculate the SCNV of each tumor sample at the chromosome arm level according to the single tumor sample and the prepared pan-cancer queue SCNV database; and the score calculation module is configured to calculate the final chromosome aneuploidy score of each tumor sample based on the SCNV of the sample at the chromosome arm level.

[0064] The manner in which each module implements the corresponding function corresponds to the description of the above method, and will not be repeated here.

[0065] In a third aspect, the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, when the computer program is executed, can include the flow of the above-mentioned embodiments of each method. Wherein, any reference to memory, database or other medium used in each embodiment provided by the embodiments of the present disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limited, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0066] In a fourth aspect, the present application provides an electronic device, the electronic device includes any of the above computer readable storage medium and processor in the disclosed embodiments, the processor involved in each embodiment provided by the embodiments of the present disclosure can be a general processor, central processing unit, graphics processing unit, digital signal processor, programmable logic device, quantum computing-based data processing logic device, etc., not limited to. When the computer program stored in the computer readable storage medium is executed by the processor, the flow steps of the above-mentioned methods can be realized.

[0067] The above embodiments are only preferred embodiments of the present application, and it should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and equivalent replacements can be made, and these technical solutions improved and equivalent replaced by the claims of the present application fall within the protection scope of the present application.

Claims

1. A method for detecting chromosomal aneuploidy based on NGS, characterized in that, The method comprises the following steps: receiving NGS sequencing data of tumor tissue and normal tissue; preprocessing the NGS sequencing data to obtain an intermediate data file; specifically, performing quality control on tumor tissue and normal tissue sequencing raw data, including removing sequencing adapter sequences and low-quality sequences using Trimmomatic software, then aligning the filtered clean reads to the human reference genome hg19 using BWA software, sorting using Samtools software, removing duplicate sequences using Picard, local realignment using GATK4 software, base quality correction using GATK4 software, and generating an index file to obtain the intermediate data file; using the intermediate data file to evaluate the consistency of gender and germline SNPs; using the intermediate data file to obtain coverage depth information and SNP genotype information on the genome of the sample to be tested; based on the coverage depth information and SNP genotype information, detecting tumor sample purity, ploidy, and SCNV fragments; based on the SCNV at the chromosome arm level of each tumor sample, calculating the final chromosomal aneuploidy score of each tumor sample. The NGS sequencing data of tumor tissue and normal tissue includes whole genome sequencing and whole exon capture sequencing.

2. The method of claim 1, wherein, The step of using the intermediate data file to evaluate the consistency of gender and germline SNPs comprises:

3. The method of claim 1, wherein, evaluating the sample gender based on the sequencing depth of the Y chromosome to evaluate the consistency of gender; using Conpair software to evaluate the consistency of germline SNPs. The step of using the intermediate data file to obtain coverage depth information and SNP genotype information on the genome of the sample to be tested comprises:

4. The method according to any one of claims 1 to 3, characterized in that, using the intermediate data file as input and using a SNP site database to obtain coverage depth information and SNP genotype information on the genome of the sample to be tested using snp-pileup software. The step of detecting tumor sample purity, ploidy, and SCNV fragments using FACETS or Sequenza software.

5. The method of claim 4, wherein, The step of calculating the SCNV at the chromosome arm level of each tumor sample based on the single tumor sample and the prepared pan-cancer queue SCNV database comprises:

6. The method of claim 5, wherein, using the single tumor sample and the prepared pan-cancer queue SCNV database as input and using GISTIC software for analysis; setting threshold parameters to determine whether CNV occurs at the chromosome arm level according to the set threshold parameters. The step of calculating the final chromosomal aneuploidy score of each tumor sample based on the SCNV at the chromosome arm level of the sample comprises:

7. The method of claim 6, wherein, calculating the sum of the number of non-0 for all 22 autosomal long arms and short arms. The method comprises the following steps:

8. An NGS-based chromosomal aneuploidy detection device for detecting chromosomal aneuploidy in a tumor, using the NGS-based chromosomal aneuploidy detection method according to any one of claims 1 to 7. a data receiving module configured to receive NGS sequencing data of tumor tissue and normal tissue; a data preprocessing module configured to preprocess the NGS sequencing data to obtain an intermediate data file; a consistency evaluation module configured to use the intermediate data file to evaluate the consistency of gender and germline SNPs; ​ an information obtaining module configured to obtain coverage depth information and SNP genotype information on a genome of a sample to be tested using the intermediate data file; an SCNV detecting module configured to detect tumor sample purity, ploidy, and SCNV fragments; an SCNV calculating module configured to calculate SCNV at a chromosome arm level for each tumor sample according to a single tumor sample and a prepared pan-cancer queue SCNV database; a score calculating module configured to calculate a final chromosomal aneuploidy score for each tumor sample based on SCNV at a chromosome arm level for the sample.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium has stored therein computer instructions executable by a processor to implement the NGS-based chromosomal aneuploidy detection method of any one of claims 1-7.

10. An electronic device, comprising: comprising: The computer readable storage medium of claim 9, and a processor configured to be able to execute the computer instructions in the computer readable storage medium.

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