A method for detecting homologous recombination deficiency based on whole genome sequencing

Abstract

This invention discloses a method for detecting homologous recombination defects (HRDs) based on whole-genome sequencing, belonging to the field of gene detection technology. It includes the following steps: analyzing and comparing the sequencing data of the test sample aligned to a reference genome, performing data sorting and deduplication, and then assessing the HRD status. The HRD status assessment includes: importing the deduplicated data file into HRD status scoring software, setting the configure file to diploid parameters, and scoring the HRD status. This method has the technical advantages of low experimental complexity and low sample requirements, and maintains the same level of accuracy as similar products while requiring low sequencing volume (average sequencing depth ≤ 2×), significantly reducing detection costs by approximately one order of magnitude.

Description

Technical Field

[0001] This invention relates to the field of gene detection technology, and more specifically, to a method for detecting homologous recombination defects based on whole-genome sequencing. Background Technology

[0002] DNA is constantly damaged and repaired within the human body. There are multiple repair pathways for DNA damage. The preferred repair mechanism for DNA double-strand breaks (DSBs) is homologous recombination repair (HRR). Homologous recombination deficiency (HRD) refers to a state of HRR dysfunction at the cellular level. HRD has become a novel biomarker for the clinical application of poly(ADP-ribose) polymerase (PARP) inhibitors in patients with advanced ovarian cancer, and may also have guiding value for the clinical use of PARP inhibitors and platinum-based drugs in tumors such as breast cancer and prostate cancer.

[0003] Currently, several PARP inhibitors have been approved for marketing worldwide, such as olaparib, rucaparib, niraparib, and talazoparib approved by the FDA, as well as fluzoparib and pamiparib recently approved by the NMPA. These inhibitors have been approved for numerous indications in tumors such as ovarian cancer, prostate cancer, breast cancer, and pancreatic cancer. At the same time, HRD clinical testing, as an important predictive biomarker for the efficacy of PARP inhibitors, has also developed rapidly.

[0004] HRD is caused by a variety of factors, including germline or somatic mutations in the HRR gene and epigenetic alterations, and can produce quantifiable, specific, and stable genomic changes (which can be scored). In ovarian cancer, if only the HRR gene (including BRCA1 / 2) is tested, the proportion of the population that benefits is 31%, but if genomic HRD scoring (including BRCA1 / 2 testing) is performed, the proportion of the population that benefits can increase to 50%.

[0005] Currently, genomic HRD detection methods are broadly divided into two types: one requires a panel composed of a large number of non-contiguous SNP sites, typically ranging from 30,000 to 50,000 (e.g., patent CN112226495A), where a comprehensive score of LOH, LST, and TAI is calculated using the scarHRD method after capturing cancer tissue and peripheral blood samples from the patient; the other is a low-depth WGS method (e.g., patent CN113257346), but this method still requires 10 times the amount of data. Existing methods are characterized by experimental complexity, long experimental cycles, and high costs. Therefore, how to economically and accurately screen HRD-positive patients who can use PARP inhibitor drugs is an important problem that needs to be solved.

[0006] In view of this, the present invention is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a method for detecting homologous recombination defects based on whole-genome sequencing to solve the problems of high sequencing depth requirements, large number of SNP sites, high probe cost, complex experiments, long cycle, and low accuracy.

[0008] This invention is implemented as follows:

[0009] This invention provides a method for detecting homologous recombination defects based on low-coverage whole-genome sequencing, comprising the following steps:

[0010] The sequencing data of the test samples aligned to the reference genome were analyzed, sorted, deduplicated, and then HRD status was assessed. The HRD status assessment included: importing the deduplicated data file into the HRD status scoring software, setting the configure file to diploid parameters, and scoring the HRD status.

[0011] If the HRD score is greater than or equal to the preset positive threshold for homologous recombination defects, then it is predicted that there is a homologous recombination defect in the sample to be tested.

[0012] If the HRD score is less than the preset positive threshold for homologous recombination defects, it is predicted that there are no homologous recombination defects in the sample to be tested.

[0013] The average sequencing depth of whole-genome sequencing is ≤2×.

[0014] The method for obtaining the preset positive threshold for homologous recombination defect includes: scoring the HRD status of different positive and negative standards for homologous recombination defect using the HRD status assessment method, and obtaining the preset positive threshold for homologous recombination defect based on the relationship between the HRD score and the standard HRD values ​​of the positive and negative standards for homologous recombination defect.

[0015] The inventors discovered that by using the HRD status assessment method provided in this invention to score the HRD status of different homologous recombination defect positive and negative standards, and based on the relationship between the HRD score and the standard HRD values ​​of the homologous recombination defect positive and negative standards, the corresponding homologous recombination defect positive threshold is determined. The obtained threshold is then compared with the HRD score of the sample to be tested to predict whether homologous recombination defects exist in the sample. This method has low experimental complexity, short experimental cycle, and maintains the same level of accuracy as similar products while requiring low sequencing volume (average sequencing depth ≤ 2×). Material costs can be reduced from 1600 yuan to approximately 170 yuan, significantly reducing detection costs by about an order of magnitude.

[0016] Furthermore, the method provided by this invention does not require setting up a control group or control sample, and can reduce the cost of detecting a single sample from thousands of yuan to hundreds of yuan.

[0017] The HRD status scoring software is controlfreec, an R package that scores HRD status based on second-generation data.

[0018] In a preferred embodiment of the present invention, parallel sampling tests are performed on homologous recombination defect positive standards of different gradients to determine the corresponding homologous recombination defect positive threshold and data volume (i.e., sequencing depth).

[0019] In a preferred embodiment of this invention, the aforementioned HRD score refers to the copy number variation (CNV) assessed using the LGAs index. Specifically, Q1 and Q3 are defined as Z... i For quality values, large segments must satisfy Z. i ≥ (Q1 + Q3) / 2. CNV cut-off is optimized to: CNV cut-off = min(max(0.025, M), 0.45), where M is (Z i -Z i The minimum value of ), where i and j are large segments. LGAs are defined as regions within a chromosome ≥10Mb, i.e., Z. i Z i+1 ≥ 10 Mb, where S i and Z i These represent the median and size of chromosomal copy number variants, respectively. Variants include, but are not limited to, loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transition (LST).

[0020] In a preferred embodiment of the present invention, the preset positive threshold for homologous recombination defects is 5-8. For example, the positive threshold for homologous recombination defects is 5, 6, 7, or 8.

[0021] When the positive threshold for homologous recombination defects is 8, it has better detection accuracy for a variety of clinical samples.

[0022] In a preferred embodiment of the present invention, the tumor cell content in the sample to be tested is at least 20%. For example, the tumor cell content in the sample to be tested is 20%-80%.

[0023] The types of tumor cells in the sample to be tested include, but are not limited to, breast cancer, ovarian cancer, prostate cancer, or pancreatic cancer.

[0024] In a preferred embodiment of the present invention, the sample to be tested is selected from tumor tissue samples; the sample to be tested originates from the human body.

[0025] The sequencing data of the sample to be tested undergoes quality control and alignment with the reference genome to obtain sequencing data that can be used for subsequent analysis.

[0026] Data quality control includes the following steps: For the raw sequencing data, FASTP software is used for quality control, including primer identification and removal, and filtering of low-quality sequences. The sliding window filter value is set to 4, and the base quality value filter is set to Q20.

[0027] Data alignment: Clean data was aligned to the human genome (hg19) using BWA's mem mode with the default parameters.

[0028] Data sorting includes using the samtools sort command to sort the deduplicated sequences according to their alignment positions.

[0029] Data deduplication: To remove duplicates caused by PCR amplification, picard software was used to deduplicat the sorted results.

[0030] In a preferred embodiment of the present invention, the average sequencing depth of whole genome sequencing is 0.2×, 1×, 1.5× or 2×.

[0031] The present invention also provides a detection device for homologous recombination defects based on low-coverage whole-genome sequencing, comprising the following modules:

[0032] Data comparison, sorting, and deduplication modules;

[0033] HRD scoring calculation module: Calculates scores according to the LGA indicator definition, which are then used as HRD scores.

[0034] In an alternative implementation, the data comparison, sorting, and deduplication modules can also be configured as separate data comparison, data sorting, and data deduplication modules.

[0035] The detection method of the detection system includes: analyzing the sequencing data of the test sample aligned to the reference genome, sorting the data, removing duplicate data, and then assessing the HRD status;

[0036] HRD status assessment includes: importing the deduplicated data file into the HRD status scoring software, setting the configure file to diploid parameters, and scoring the HRD status;

[0037] If the HRD score is greater than or equal to the preset positive threshold for homologous recombination defects, then it is predicted that there is a homologous recombination defect in the sample to be tested.

[0038] If the HRD score is less than the preset positive threshold for homologous recombination defects, it is predicted that there are no homologous recombination defects in the sample to be tested.

[0039] The average sequencing depth of whole-genome sequencing is ≤2×.

[0040] Data quality control includes the following steps: For the raw sequencing data, FASTP software is used for quality control, including primer identification and removal, and filtering of low-quality sequences. The sliding window filter value is set to 4, and the base quality value filter is set to Q20.

[0041] The data alignment module uses the following alignment methods: using BWA's mem mode to align clean data to the human genome (hg19), with the default parameters.

[0042] The data sorting module uses the sort command of samtools to sort the deduplicated sequences according to their alignment positions.

[0043] The deduplication methods in the data deduplication module include: using picard software to deduplicate the sorted results in order to remove duplicates formed by PCR amplification.

[0044] The present invention also provides a device for detecting homologous recombination defects based on whole-genome sequencing, comprising:

[0045] Memory, used to store programs;

[0046] A processor is used to implement the above-described method by executing a program stored in memory.

[0047] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0048] A processor can be an integrated circuit chip with signal processing capabilities. This processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0049] In one alternative implementation, the device for detecting homologous recombination defects based on whole-genome sequencing is an electronic device, further comprising a bus and a communication interface. The memory, processor, and communication interface are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more buses or signal lines. The processor can process information and / or data related to target identification to perform one or more functions described in this application.

[0050] The present invention also provides a computer-readable storage medium having a program stored thereon, the program being executable by a processor to implement the above-described method.

[0051] The present invention has the following beneficial effects:

[0052] This invention scores the HRD status of different homologous recombination defect positive standards using the HRD status assessment method provided by this invention. Based on the relationship between the HRD score and the standard HRD value of the homologous recombination defect positive standards, a corresponding homologous recombination defect positive threshold is determined. The obtained threshold is then compared with the HRD score of the sample to be tested to predict whether homologous recombination defects exist in the sample. This method has the technical advantages of low experimental complexity and short experimental cycle. Furthermore, it maintains the same level of accuracy as similar products while requiring low sequencing volume (average sequencing depth ≤ 2×), significantly reducing detection costs by approximately one order of magnitude.

[0053] Furthermore, the method provided by this invention eliminates the need for a control group or control sample, further reducing detection costs. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A graph showing the correspondence between HRD values ​​and standard product values ​​using this method with 2× data volume;

[0056] Figure 2 A graph showing the correspondence between HRD values ​​of this method and standard product values ​​with a data volume of 1.5 times.

[0057] Figure 3 A graph showing the correspondence between HRD values ​​of this method and standard product values ​​under a data volume of 1×.

[0058] Figure 4 A graph showing the correspondence between HRD values ​​of this method and standard product values ​​at a data volume of 0.2.

[0059] Figure 5 The figure shows the HRD numerical results and threshold line division results of this method with a data volume of 1×. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0061] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0062] Example 1

[0063] This embodiment provides a method for detecting homologous recombination defects using low-coverage whole-genome sequencing.

[0064] Specifically, the steps include the following:

[0065] 1. Construction of DNA library.

[0066] (1) gDNA fragmentation.

[0067] Samples were selected from breast cancer cell lines. When using fragmentation tubes, label them with the corresponding DNA number. For detailed DNA fragmentation procedures, please refer to the "M220 Operating Procedures".

[0068]

[0069] (2) End repair

[0070] Transfer 37 μl of the fragmented gDNA to a 200 μl PCR tube pre-numbered with the corresponding sample number. Add 13 μl of Repair Mix as described below. This step should be performed on an ice box. The following are reference mixing systems for 1 sample and 4 samples.

[0071]

[0072] After gently blowing and mixing 10 times, briefly centrifuge to collect the reaction solution to the bottom of the tube, avoiding air bubbles, and then run the following procedure:

[0073]

[0074] Note: The buffer and enzymes can be used to prepare premixed solutions. Before use, mix the buffer thoroughly and briefly centrifuge the enzymes. Operate on an ice pack.

[0075] (3) Adaptor connection

[0076] i: Add the following reagents directly to the end-repair reaction mixture in sequence. Perform the procedure on an ice pack. Mix thoroughly by blowing up and down 10 times, then briefly centrifuge to collect the reaction solution to the bottom of the tube. The following are reference mixing systems for 1 sample and 4 samples.

[0077]

[0078] Note: Before use, mix the Ligase mix thoroughly by blowing and agitating. Ligase MM is a viscous liquid; when taking it, mix well and draw out a sufficient amount, then blow and agitate to mix thoroughly, ensuring no air bubbles are present during mixing. The working adapter must be added separately.

[0079] ii: Place the above system in a PCR instrument and run the program: 22℃, 15min, with the hot lid closed.

[0080] iii: Purification after ligation reaction:

[0081] a. Take out the AMPure XP magnetic beads in advance and let them stand at room temperature for at least 30 minutes. Mix them well before use.

[0082] b. Transfer approximately 72 μl of the ligation reaction solution to a correspondingly numbered 1.5 ml centrifuge tube and add 57.6 μl (0.8X) of resuspended AMPure XP.

[0083] c. Vortex the magnetic beads for 5 seconds to ensure even mixing, then briefly centrifuge to collect the liquid, keeping the beads suspended. Incubate at room temperature for 5 minutes.

[0084] c. Place the product on a magnetic rack, and discard the residual liquid after the solution has clarified.

[0085] d. Add 200 μL of freshly prepared 80% ethanol, invert the magnetic rack 5 times to wash the magnetic beads, and discard the residual liquid. Repeat once, for a total of two washes.

[0086] e. Remove the tube containing the magnetic beads from the magnetic rack, centrifuge briefly for 3 seconds, place the PCR tube back on the magnetic rack, and discard any remaining 80% ethanol using a pipette tip. Open the tube cap and allow it to dry at room temperature until the magnetic beads are matte.

[0087] Note: Do not over-dry the magnetic beads.

[0088] f. Remove the sample from the magnetic rack. Add 21 μl of LOW EDTA TE or nuclease-free water to the magnetic beads, gently shake, and incubate at room temperature for 5 min.

[0089] g. Place the PCR tube on a magnetic rack and let it stand for 5 minutes. After the supernatant separates from the magnetic beads, take 20 μl of the supernatant into a new PCR tube for the next amplification.

[0090] (4) PCR enrichment and purification

[0091] i: Add the corresponding reagents to the PCR tube according to the table below.

[0092]

[0093] Note: i7 is the index, and samples are added sequentially according to the index number.

[0094]

[0095] ii: PCR product purification:

[0096] a. Vortex oscillate the AMPure Xp magnetic beads to suspend them. Let stand at room temperature for 30 min.

[0097] b. Transfer the PCR product into a 1.5 ml centrifuge tube, add 50 μl (1X) XP beads, gently shake, and incubate at room temperature for 5 min.

[0098] c. Place the centrifuge tube on a magnetic rack and discard the supernatant after the solution has clarified.

[0099] d. Keep the product containing magnetic beads on a magnetic rack, add 200 μL of freshly prepared 80% ethanol, invert the magnetic rack 5 times, wash the magnetic beads, and discard the 80% ethanol. Repeat once, for a total of two washes.

[0100] e. Remove the tube containing the magnetic beads from the magnetic rack, centrifuge briefly for 3 seconds, place the tube back on the magnetic rack, and discard any remaining 80% ethanol using a pipette tip. Open the tube cap and allow the magnetic beads to air dry on the magnetic rack until they turn matte.

[0101] Note: Do not over-dry the magnetic beads.

[0102] f. Remove the sample from the magnetic rack. Add 31 μl of ddH2O to the magnetic beads, shake gently, and incubate at room temperature for 5 min.

[0103] g. Place the PCR tube on a magnetic rack and let it stand for 2 minutes. After the supernatant separates from the magnetic beads, take 30 μl of the supernatant into a new EP tube with the corresponding code. The captured library can be stored at -20℃.

[0104] (5) Quality control.

[0105] After the library is constructed, the Qubit concentration is determined.

[0106] After determining the concentration of the library, the distribution of Agilent 2100 fragments in the library was determined.

[0107] 2. Perform whole-genome sequencing on the constructed library.

[0108] The libraries were sequenced using an MGI-2000 sequencer, with each whole-genome library generating 9GB of data to facilitate random sampling in subsequent bioinformatics analysis.

[0109] 3. Bioinformatics Analysis

[0110] Data quality control: For the raw sequencing data, FASTP software was used for quality control, including primer identification and removal, and filtering of low-quality sequences. The sliding window filter value was set to 4, and the base quality value filter was set to Q20.

[0111] Data alignment: Clean data was aligned to the human genome (hg19) using BWA's mem mode with default parameters.

[0112] Data sorting: Use the samtools sort command to sort the duplicate sequences according to their alignment positions;

[0113] Data deduplication: To remove duplicates caused by PCR amplification, picard software was used to deduplicat the sorted results;

[0114] HRD Status Assessment: controlfreec is an R package that scores HRD status based on second-generation data. The deduplicated alignment files are imported into the controlfreec package, the configure file is set to diploid parameters, and the HRD status is scored.

[0115] The scoring criteria and scoring rules are as follows:

[0116] HRD score refers to the assessment of copy number variation (CNV) using the LGAs index. Specifically, Q1 and Q3 are defined as Z... i For quality values, large segments must satisfy Z. i ≥ (Q1 + Q3) / 2. The CNV cut-off is optimized to: CNV cut-off = min(max(0.025, M), 0.45), where M is (Z i -Z j The minimum value of ), where i and j are large segments. LGAs are defined as regions within a chromosome ≥10Mb, i.e., Z. i Z i+1 ≥ 10 Mb, where S i and Z i These represent the median and size of the chromosome copy number variation fragment, respectively.

[0117] 4. Establish standards for sequencing data volume.

[0118] Seven positive and negative standards with different HRD values ​​from the kit were used for 10X WGS sequencing. The scarHRD values ​​and sequencing volumes of the standards are shown in the table below. The standard samples were from Jingliang (patent number CN_112980834_A), and the method was based on the calculation of LOH+TAI+LST according to scarHRD. A standard HRD score exceeding 42 was considered HRD positive.

[0119]

[0120] To establish thresholds for the sequencing data volume provided by this invention, 2X, 1.5X, 1X, and 0.2X data points were extracted from each standard sample, repeated three times. HRD scoring was performed according to the HRD scoring method provided by this invention, and the results are referenced... Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown.

[0121] The results show that when the HRD score of the present invention is ≥8, that is, when the threshold is ≥8, all positive standards are positive; when the HRD score of the present invention is <8, that is, when the threshold is <8, all negative standards are negative.

[0122] The HRD results of 2X, 1.5X, 1X, and 0.2X data points and their three repetitions were 100% consistent with the HRD values ​​of the standard product.

[0123] Given the limited tumor content in real samples, this embodiment uses 1X data volume.

[0124] 5. Establishment of HRD interpretation criteria.

[0125] Using 10X WGS data from each of the above 7 standards, 1X data was randomly extracted from each sample, with 10 parallel samples each time, to determine the threshold line for HRD interpretation standards.

[0126] The results are as follows Figure 5 As shown, in 10 replicates of 7 samples, with 8 or more positive results and 8 or fewer negative results as the criteria, the interpretation accuracy was 100%.

[0127] Experimental Example 1

[0128] This experimental example performs a consistency test between the method provided in Example 1 and the existing scarHRD method.

[0129] Assessing the overall status of three “genomic scarring” phenomena (LOH, TAI, LST) at the whole genome level based on SNP genotyping and performing HRD scoring is the strategy chosen by most products on the market. For example, the approved products Myriad myChoiceCDx and FoundationFocusTMCDx BRCA LOH both use scarHRD for SNP panel scoring.

[0130] To verify the consistency between the detection method provided in this invention and mainstream methods, 12 ovarian cancer patients were used, including 6 negative and 6 positive cases, with sequencing depths of at least 20X. ScarHRD was calculated using WGS and HRD interpretation was performed using the method of this invention. The sample size, data volume, and scarHRD values ​​are shown in the table below. The method used was based on the calculation of LOH+TAI+LST using scarHRD.

[0131]

[0132] Data were sampled from tumor samples of 12 ovarian cancer patients, with 1X data points extracted each time, and the sampling was repeated three times. The method of this invention was then used for scoring and HRD interpretation. The results showed that the HRD interpretation was 100% consistent with the WGS (Wide Gauge Survey) scarHRD results in all three replicates, indicating that the detection method provided by this invention has high accuracy. The detection results are shown in the table below.

[0133]

[0134] Experimental Example 2

[0135] In this experimental example, the HRD detection was performed on 22 clinically relevant samples using the method provided in Example 1 of the present invention.

[0136] The Expert Consensus on Clinical Detection and Application of Homologous Recombination Repair Deficiency (2021 Edition) recommends that in clinical practice, the pathogenic mutation of BRCA1 / 2 gene and HRD score be used to comprehensively assess the HRD status of tumors. HRD positive is defined as the presence of pathogenic mutations in BRCA1 / 2 and / or a positive HRD score in the tumor, while HRD negative is defined as the absence of pathogenic mutations in BRCA1 / 2 and a negative HRD score in the tumor.

[0137] The HRD status of 22 clinically relevant patients was scored using the method of this invention, and the results were combined with the comprehensive interpretation of pathogenic mutations in the BRCA1 / 2 gene. The results were then compared with those of commercially available scarHRD detection products that combine BRCA1 / 2 gene pathogenic mutation detection.

[0138] The table below shows the results of tumor content detection and quality control data analysis of relevant samples from 22 clinical patients using the method provided in this invention (1X sequencing depth roughly corresponds to 3,000,000,000 bp of sequencing data).

[0139]

[0140] By analyzing the BRCA gene mutation status and genomic scarring characteristics of 22 clinically relevant patients, such as the impact and frequency of copy number variations in the genome, genomic instability was comprehensively assessed. The HRD of the subjects is shown in Table 5 below. Table 5 shows that, compared with commercially available kits and evaluation methods, the method provided by this invention achieved 100% consistency with the results in the 22 clinical samples. Furthermore, this method possesses outstanding advantages such as simplicity, short detection time, fast analysis speed, and significantly lower detection cost compared to approved commercial kits.

[0141]

[0142] The HRD results of this method and those of commercially available products refer to a comprehensive interpretation of BRCA and scoring.

[0143] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A device for detecting homologous recombination deficiency based on low coverage whole genome sequencing, characterized by, Includes the following modules: Data comparison, sorting, and deduplication modules; HRD scoring calculation module: Calculates scores according to the LGA indicator definition, which are used as HRD scores; The detection method of the detection device includes: analyzing the sequencing data of the test sample aligned to the reference genome, sorting the data, removing duplicate data, and then assessing the HRD status; The HRD status assessment includes: importing the deduplicated data file into the HRD status scoring software, setting the configure file to diploid parameters, and scoring the HRD status. If the HRD score is greater than or equal to the preset positive threshold for homologous recombination defects, then it is predicted that there is a homologous recombination defect in the sample to be tested. If the HRD score is less than the preset positive threshold for homologous recombination defects, it is predicted that there are no homologous recombination defects in the sample to be tested. The average sequencing depth of the whole genome sequencing is ≤2×; The method for obtaining the preset positive threshold for homologous recombination defects includes: scoring the HRD status of different positive and negative standards for homologous recombination defects using the HRD status assessment method, and obtaining the preset positive threshold for homologous recombination defects based on the relationship between the HRD score and the standard HRD values ​​of the positive and negative standards for homologous recombination defects. The HRD score refers to the copy number variation assessed by the LGA index; the preset positive threshold for homologous recombination defects is 8, and LGA is defined as the severity of genomic instability in regions ≥10Mb within a chromosome.

2. The detection device of claim 1, wherein, The tumor cell content in the sample to be tested is at least 20%.

3. The detection device of claim 2, wherein, The sample to be tested was selected from tumor tissue samples; the sample to be tested originated from the human body.

4. The detection device of claim 3, wherein, The tumor cells in the sample to be tested are of the following types: breast cancer, ovarian cancer, prostate cancer, or pancreatic cancer.

5. The detection device according to claim 3, characterized in that, The sequencing data of the sample to be tested are sequentially subjected to quality control and aligned to a reference genome to obtain sequencing data that can be used for subsequent analysis.

6. The detection device according to claim 1, characterized in that, The average sequencing depth of the whole genome sequencing is 0.2×, 1×, 1.5× or 2×.

7. A device for detecting homologous recombination defects based on whole-genome sequencing, characterized in that, include: Memory, used to store programs; A processor for executing a program stored in the memory to perform the detection of homologous recombination defects based on low-coverage whole-genome sequencing as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, It stores a program that can be executed by a processor to perform the detection of homologous recombination defects based on low-coverage whole-genome sequencing as described in any one of claims 1 to 6.

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