Single-sample HRD state evaluation method, device and system and storage medium

Abstract

The invention provides a single-sample HRD state evaluation method, device and system and a storage medium, and relates to the technical field of bioinformatics. The method comprises the following steps: constructing a control negative library according to a normal negative sample, and respectively calculating the coverage depth of each SNP site of a target sample to be detected and the control negative library; determining similar change region fragments and calculating to obtain a fragment average comparison rate; determining a total copy number and calculating to obtain an allele copy number; calculating to obtain an LOH characteristic value, a TAI characteristic value and an LST characteristic value; calculating to obtain a homologous recombination defect result; the BRCA1 gene variation and the BRCA2 gene variation are analyzed, and a BRCA mutation state result is obtained; and obtaining an HRD state according to a homologous recombination defect result and a BRCA mutation state result.

Description

Technical Field

[0001] The present application relates to the technical field of bioinformatics, and in particular to a single-sample HRD status assessment method, device, system and storage medium. Background Art

[0002] In modern medicine, the assessment of homologous recombination deficiency (HRD) status is of great significance for cancer treatment. HRD refers to the condition in which the homologous recombination repair mechanism in cells is impaired. This state is closely related to the sensitivity of certain cancers to specific drugs such as PARP inhibitors. Patients with HRD-positive are more likely to respond to PARP inhibitors. Therefore, accurate assessment of HRD status is crucial to guiding clinical treatment decisions.

[0003] Existing HRD assessment technologies are mainly based on high-density whole-genome SNP analysis or whole-genome sequencing (WGS). There are two approved HRD kits on the market, Myriad myChoice CDx and FoundationOne CDx (F1CDx), which evaluate genomic scars by analyzing SNPs to determine HRD status. Myriad myChoice CDx comprehensively calculates three indicators: LOH (loss of heterozygosity), TAI (telomeric allele imbalance), and LST (large-scale transition), while FoundationOneCDx mainly detects LOH to determine HRD status.

[0004] However, these existing technologies have some defects. First, they require tumor samples and corresponding paired samples (such as adjacent tumor samples or blood samples) for analysis, which is a challenge for patients with advanced tumors because these samples are often difficult to obtain. In addition, these methods may be complex in sample processing, data analysis, and result interpretation, requiring professional bioinformatics knowledge and technical support. Furthermore, existing technologies may have certain false positive or false negative results when evaluating HRD status, which limits their accuracy and reliability in clinical applications.

[0005] In summary, existing HRD assessment technologies have shortcomings in sample acquisition, analysis complexity, and result accuracy. These defects limit the widespread application of HRD assessment technologies and may affect the formulation of clinical treatment decisions. Therefore, developing new HRD assessment methods to improve the accuracy of assessments, simplify operational procedures, and reduce reliance on paired samples is an urgent issue to be addressed in the current field of bioinformatics. Summary of the invention

[0006] In a first aspect, the present invention provides a single-sample HRD status assessment method, comprising:

[0007] Constructing a control negative library according to the normal negative sample; and calculating the coverage depth of each SNP site of the target sample and the control negative library respectively;

[0008] Determine similar change region segments based on the coverage depth, and calculate the average segment alignment ratio corresponding to the similar change region segments;

[0009] Determine the total copy number according to the average alignment rate of the fragments, and calculate the allele copy number;

[0010] Calculating the LOH characteristic value, TAI characteristic value and LST characteristic value according to the total copy number and the allele copy number; and calculating the homologous recombination deficiency result according to the LOH characteristic value, TAI characteristic value and LST characteristic value;

[0011] Analyze the BRCA1 and BRCA2 gene mutations of the target sample to be tested to obtain BRCA mutation status results;

[0012] According to the homologous recombination deficiency result and the BRCA mutation status result, the HRD status corresponding to the target sample to be tested is obtained.

[0013] In an optional embodiment, constructing a control negative library based on normal negative samples comprises:

[0014] Calculate the coverage depth data of each SNP site of each normal negative sample;

[0015] The coverage depth data of all the normal negative samples corresponding to each SNP site are statistically analyzed, and the median of all the coverage depth data corresponding to each SNP site is calculated as the coverage depth of each SNP site in the control negative library.

[0016] In an optional embodiment, determining similar change region segments based on the coverage depth, and calculating the average segment alignment ratio corresponding to the similar change region segments, includes:

[0017] Compare the coverage depth of each SNP site in the target sample to be tested with the coverage depth of the corresponding SNP site in the control negative library to obtain the depth change rate of each SNP site;

[0018] Calculating the minor allele frequency of each heterozygous site in the SNP site in the target sample to be tested;

[0019] Merging the SNP regions composed of continuous SNP sites having similar depth change rates and minor allele frequencies in the target sample to be tested to obtain the similar change region fragments;

[0020] The average value of all the depth change rates in the similar change region segments is used as the segment average comparison rate corresponding to the similar change region segments.

[0021] In an optional embodiment, after calculating and obtaining the average segment comparison ratio corresponding to the similar change region segments, the method further includes:

[0022] Obtaining the tumor cell content and tumor cell ploidy corresponding to the target sample to be tested;

[0023] The average fragment alignment rate is corrected using the tumor cell content and the tumor cell ploidy to obtain a corrected average fragment alignment rate.

[0024] In an optional implementation, the expression of the correction process is:

[0025]

[0026] Wherein, R`(x) represents the average alignment ratio of the fragments after correction processing; R(x) represents the average alignment ratio of the fragments after correction processing; a represents the tumor cell content; and T represents the tumor cell ploidy.

[0027] In an optional embodiment, determining the total copy number according to the average alignment rate of the fragments comprises:

[0028] The total copy number was determined using the following expression:

[0029]

[0030] Among them, total cn represents the total copy number; R mean represents the average alignment rate of the fragments; T represents the ploidy of tumor cells.

[0031] In an optional embodiment, the calculation to obtain the allele copy number comprises:

[0032] Averaging the minor allele frequencies of the heterozygous SNP sites in the similar variation region fragments to obtain the fragment average BAF;

[0033] The allele copy number is calculated using the average BAF of the fragments; wherein the allele copy number includes the copy number of the A allele and the copy number of the B allele.

[0034] In an optional embodiment, the method for calculating the copy number of the A allele comprises:

[0035] Aallele cn =total cn ×BAF segmentmean ;

[0036] The method for calculating the copy number of the B allele includes:

[0037] Ballele cn =total cn ×(1-BAF segmentmean );

[0038] Among them, Aallele cn represents the copy number of the A allele; Ballele cn represents the copy number of the B allele; total cn represents the total copy number; BAF segment mean represents the average BAF of the fragments.

[0039] In an optional embodiment, the calculating of the LOH characteristic value, the TAI characteristic value and the LST characteristic value according to the total copy number and the allele copy number; and the calculating of the homologous recombination deficiency result according to the LOH characteristic value, the TAI characteristic value and the LST characteristic value comprises:

[0040] Obtaining the chromosome number of the chromosome where the similar change region fragment is located, as well as the starting position and the ending position of the similar change region fragment;

[0041] The chromosome number, starting position, ending position, total copy number, copy number of A allele, copy number of B allele and tumor cell ploidy of the similar change region fragments together constitute the cnv result;

[0042] Grouping the cnv results according to the chromosome numbers to obtain different chromosome groups;

[0043] Sorting the cnv results in each of the chromosome groups according to the starting position to obtain the cnv results after sorting;

[0044] The LOH eigenvalue, the TAI eigenvalue and the LST eigenvalue are calculated according to the cnv result after sorting;

[0045] The LOH characteristic value, the TAI characteristic value and the LST characteristic value are summed to obtain the homologous recombination deficiency result.

[0046] In an optional embodiment, the LOH characteristic value is the number of similar change region fragments that simultaneously meet all of the following conditions:

[0047] A. In the same chromosome, the fragment of the similar change region is larger than 15Mb;

[0048] B. In the same chromosome, the fragment of the similar variation region is smaller than the length of the entire chromosome;

[0049] The LST feature value is the number of similar change region segments that simultaneously meet all of the following conditions:

[0050] A. In the same chromosome, the lengths of adjacent similarly changed regions are all greater than 10 Mb;

[0051] B. In the same chromosome, the interval between adjacent similar variation regions is less than 3 Mb;

[0052] The LST feature value is the number of similar change region segments that simultaneously meet all of the following conditions:

[0053] A. In the same chromosome, the similar change region extends to the telomere but not beyond the centromere;

[0054] B. In the same chromosome, the similar change region fragment is larger than 10Mb.

[0055] In an optional embodiment, obtaining the HRD status corresponding to the target sample to be tested according to the homologous recombination deficiency result and the BRCA mutation status result includes:

[0056] Performing qualitative classification processing on the homologous recombination deficiency result and the BRCA mutation status result respectively to obtain a qualitative result corresponding to the homologous recombination deficiency result and a qualitative result corresponding to the BRCA mutation status result;

[0057] If the qualitative result corresponding to the homologous recombination deficiency result is positive, and the qualitative result corresponding to the BRCA mutation status result is positive, then the HRD status corresponding to the target sample to be tested is positive;

[0058] If the qualitative result corresponding to the homologous recombination deficiency result is negative, and the qualitative result corresponding to the BRCA mutation status result is positive, then the HRD status corresponding to the target sample to be tested is positive;

[0059] If the qualitative result corresponding to the homologous recombination deficiency result is positive, and the qualitative result corresponding to the BRCA mutation status result is negative, then the HRD status corresponding to the target sample to be tested is positive;

[0060] If the qualitative result corresponding to the homologous recombination deficiency result is negative, and the qualitative result corresponding to the BRCA mutation status result is negative, then the HRD status corresponding to the target sample to be tested is negative.

[0061] In an optional embodiment, the method for qualitative classification of homologous recombination deficiency results includes:

[0062] Comparing the homologous recombination deficiency result with a preset classification threshold;

[0063] If the homologous recombination deficiency result is not less than the preset classification threshold, determining that the qualitative result corresponding to the homologous recombination deficiency result is positive;

[0064] If the homologous recombination deficiency result is less than the preset classification threshold, determining that the qualitative result corresponding to the homologous recombination deficiency result is negative;

[0065] The method for qualitative classification processing of the BRCA mutation status result includes:

[0066] Determine whether there is a pathogenic variation in the BRCA mutation status result;

[0067] If yes, the qualitative result corresponding to the BRCA mutation status result is determined to be positive;

[0068] If not, the qualitative result corresponding to the BRCA mutation status result is determined to be negative.

[0069] In a second aspect, the present invention provides a single-sample HRD state evaluation device, comprising:

[0070] A control library construction module is used to construct a control negative library based on a normal negative sample; and to calculate the coverage depth of each SNP site of the target sample and the control negative library respectively;

[0071] A fragment analysis module, used to determine similar change region fragments based on the coverage depth, and calculate the average fragment alignment rate corresponding to the similar change region fragments;

[0072] A copy number calculation module, used to determine the total copy number according to the average alignment rate of the fragments, and calculate the allele copy number;

[0073] A characteristic value calculation module, used to calculate the LOH characteristic value, TAI characteristic value and LST characteristic value according to the total copy number and the allele copy number; and to calculate the homologous recombination defect result according to the LOH characteristic value, TAI characteristic value and LST characteristic value;

[0074] A mutation analysis module is used to analyze the BRCA1 and BRCA2 gene mutations of the target sample to be tested to obtain the BRCA mutation status result;

[0075] The status evaluation module is used to obtain the single sample HRD status evaluation status corresponding to the target sample to be tested according to the homologous recombination defect result and the BRCA mutation status result.

[0076] In a third aspect, the present invention provides a computer device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the single-sample HRD status assessment method described in any one of the aforementioned embodiments.

[0077] In a fourth aspect, the present invention provides a computer storage medium storing a computer program, wherein when the computer program is executed on a processor, the single sample HRD status assessment method according to any one of the aforementioned embodiments is implemented.

[0078] The single-sample HRD status assessment method provided by the present invention reduces the reliance on additional control samples by simplifying the detection process, thereby improving the accessibility and applicability of HRD detection, especially in clinical situations where paired samples are difficult to obtain. The method enhances the accuracy and reliability of the results by accurately calculating the coverage depth and average fragment alignment rate of SNP sites, providing more accurate quantification of copy number changes in the genome. In addition, the comprehensive assessment of HRD status, including the calculation of LOH, TAI, and LST characteristic values ​​and the analysis of BRCA1 and BRCA2 gene variations, provides more comprehensive genetic information for clinical decision-making, helps to more accurately predict the patient's response to a specific treatment, thereby improving the treatment effect and patient prognosis. These features make the present invention have significant advantages in improving the accuracy of HRD assessment, clinical relevance, and the implementation of personalized medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and should not be regarded as limiting the scope of protection of the present application. For ordinary technicians in this field, other related drawings can also be obtained based on these drawings without creative work.

[0080] Figure 1 It is a structural diagram of the hardware operating environment involved in the embodiment of the single-sample HRD status evaluation method of the present invention;

[0081] Figure 2 Schematic diagram of the process of Example 1 of the single sample HRD status assessment method of the present invention;

[0082] Figure 3 This is a schematic diagram of a detailed process of step S100 in Example 2 of the single-sample HRD status assessment method of the present invention;

[0083] Figure 4 This is a schematic diagram of a detailed flow chart of step S200 in Example 2 of the single-sample HRD status assessment method of the present invention;

[0084] Figure 5 Schematic diagram of the process of Example 3 of the single-sample HRD status assessment method of the present invention (including steps S700 and S800);

[0085] Figure 6 This is a schematic diagram of a detailed process of step S400 in Example 4 of the single-sample HRD status assessment method of the present invention;

[0086] Figure 7 This is a schematic diagram of the logic and correlation relationship of obtaining the HRD state corresponding to the target sample to be tested according to the analysis of the homologous recombination deficiency result and the BRCA mutation status result in Example 5 of the single sample HRD state evaluation method of the present invention;

[0087] Figure 8 Schematic diagram of module connection of a single-sample HRD state assessment device in an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0089] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0090] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present application, are intended only to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations of the foregoing items.

[0091] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0092] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.

[0093] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0094] like Figure 1 , which is a schematic diagram of the structure of the hardware operating environment of the terminal involved in the embodiment of the present invention.

[0095] The single-sample HRD state evaluation system provided in the embodiment of the present invention may be a PC, or a mobile terminal device such as a smart phone, a tablet computer or a portable computer. The single-sample HRD state evaluation system may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005 and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen, an input unit such as a keyboard, and a remote control. The user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory, such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001. Optionally, the single-sample HRD state evaluation system may also include an RF (Radio Frequency) circuit, an audio circuit, a WiFi module, and the like.

[0096] Those skilled in the art will understand that Figure 1 The single-sample HRD status assessment system shown in the figure does not constitute a limitation thereto, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a data interface control program, a network connection program, and a single-sample HRD state evaluation program.

[0097] Example 1

[0098] Reference Figure 2 Embodiment 1 of the present invention provides a single-sample HRD status evaluation method, comprising:

[0099] Step S100, constructing a control negative library according to the normal negative sample; and respectively calculating the coverage depth of each SNP site of the target sample and the control negative library;

[0100] The above steps involve using normal negative samples (non-tumor samples) to construct a control negative library. This library is used as a reference standard for subsequent comparison with the target sample to be tested (tumor sample). The constructed control negative library contains the genetic information of the normal sample, especially the coverage depth of each SNP site.

[0101] As mentioned above, coverage depth, in genome sequencing analysis, refers to the number of times a DNA sequence is covered by sequencing reads in a specific region. In other words, coverage depth describes how densely the sequencing data covers a certain region in the genome.

[0102] As mentioned above, SNP sites (Single Nucleotide Polymorphism) refer to variations occurring at specific single nucleotide positions in the genome. This variation usually occurs at a specific position in the DNA sequence, where one nucleotide is replaced by another nucleotide, such as adenine (A) being replaced by cytosine (C).

[0103] In the above steps, the control negative library provides a baseline, which helps to identify abnormal changes in the target test samples and enhances the comparative and accuracy of the detection.

[0104] Specifically, it can be achieved by collecting and analyzing a certain number of normal samples and using bioinformatics tools to calculate the coverage depth of each SNP site. In this embodiment, 50 normal negative samples that passed the quality control were selected to construct a control negative library.

[0105] Step S200, determining similar change region segments based on the coverage depth, and calculating the average segment alignment ratio corresponding to the similar change region segments;

[0106] As described above, based on the coverage depth of the SNP sites in the target sample and the control negative library, the regional fragments with similar changes in the genome (similar change regional fragments, segment) are determined, and the average segment alignment ratio of these fragments [segment.mean(log2ratio)] is calculated.

[0107] In this step, the fragments of similarly changed regions and their corresponding average alignment rates are identified. These data reflect the changes in copy number in the sample. This method can reveal structural variations in the genome and provide important information for further genetic analysis.

[0108] Specifically, by comparing the coverage depth of SNP sites in the test sample and the control library, statistical methods can be used to determine regions with similar changes.

[0109] Step S300, determining the total copy number according to the average alignment rate of the fragments, and calculating the allele copy number;

[0110] As mentioned above, the total copy number of each fragment is determined based on the average fragment alignment rate. cn ) and allele copy number (such as Ballele cn and Aallele cn ). The total copy number and allele copy number of each fragment are obtained, and these data can be used for subsequent calculation of LOH, TAI and LST characteristic values; thus providing quantitative information on the copy number status of the fragment, which helps to more accurately evaluate the HRD status.

[0111] The alignment ratio can be converted to copy number using a mathematical formula, for example, calculated using the log2ratio and the median depth of the fragments.

[0112] Step S400, calculating the LOH characteristic value, TAI characteristic value and LST characteristic value according to the total copy number and the allele copy number; and calculating the homologous recombination deficiency result according to the LOH characteristic value, TAI characteristic value and LST characteristic value;

[0113] As described above, LOH (loss of heterozygosity), TAI (telomeric allelic imbalance), and LST (large-scale state transition) feature values ​​were calculated based on the total copy number and allele copy number.

[0114] (1) LOH (Loss of Heterozygosity): LOH refers to a situation in which a heterozygous SNP site (i.e., an individual inherits different alleles from his or her parents) in a specific genomic region loses one allele, causing the region to become homozygous. This change may occur during tumor development, causing both copies of certain genes to mutate, thereby affecting the function of the gene.

[0115] In HRD assessment, LOH signature values ​​are usually expressed as the number of genomic regions that meet specific size and copy number change conditions.

[0116] (2) TAI (Telomeric Allele Imbalance): TAI refers to the imbalance in the copy number of alleles at the SNP site close to the telomere region of the chromosome. Telomeres are protective structures at the ends of chromosomes. TAI may be related to the instability and recombination events of chromosome ends.

[0117] In HRD assessment, the TAI feature value is usually expressed as the number of telomeric regions that meet specific size and copy number imbalance conditions.

[0118] (3) LST (Large-Scale State Transitions): LST refers to a dramatic change in the copy number state between two adjacent large fragments in the genome, which may involve large regions of chromosomes. LST may be related to chromosome structural rearrangement and large-scale genomic instability.

[0119] The calculation of these three feature values ​​is based on genome sequencing data and is identified by analyzing the coverage depth and allele frequency changes of SNP sites. The assessment of HRD status can involve adding these feature values ​​to obtain a comprehensive HRD score to determine whether the sample has homologous recombination deficiency. HRD-positive samples may be more sensitive to certain cancer treatment drugs (such as PARP inhibitors).

[0120] In HRD assessment, LST feature values ​​are usually expressed as the number of region pairs that meet specific size and copy number state transition conditions.

[0121] Through this step, LOH, TAI and LST characteristic values ​​can be obtained, which comprehensively reflect the HRD status of the sample. These characteristic values ​​are key indicators for evaluating HRD status and help identify homologous recombination repair defects.

[0122] As described above, these characteristic values ​​can be calculated by specific algorithms and formulas. For example, the LOH value can be determined by identifying the number of fragments with heterozygous loss.

[0123] Step S500, analyzing the BRCA1 and BRCA2 gene variations of the target sample to be tested to obtain a BRCA mutation status result;

[0124] As described above, the BRCA1 and BRCA2 gene mutations in the target sample to be tested are analyzed to determine the BRCA mutation status, thereby obtaining a BRCA mutation status result, indicating the mutation status of the BRCA1 and BRCA2 genes in the sample.

[0125] The mutation status of BRCA genes is closely related to HRD status and is of great significance for clinical treatment decisions. BRCA1 and BRCA2 are two genes closely related to the risk of hereditary breast cancer and ovarian cancer. They are tumor suppressor genes, and the proteins they encode play a vital role in the DNA damage repair process, especially the homologous recombination repair (HRR) pathway.

[0126] In this step, the mutations of BRCA1 and BRCA2 genes can be identified through sequencing data alignment and mutation detection algorithms.

[0127] Step S600, obtaining the HRD status corresponding to the target sample according to the homologous recombination deficiency result and the BRCA mutation status result.

[0128] As described above, the HRD status of the target sample to be tested is determined by combining the LOH, TAI, LST characteristic values ​​and BRCA mutation status results, and the HRD status of the target sample to be tested is obtained, indicating whether the sample has homologous recombination defect.

[0129] Comprehensive assessment provides more comprehensive information on HRD status and facilitates personalized treatment and prognostic assessment.

[0130] Specifically, a logical model or decision tree can be constructed to comprehensively evaluate the HRD status based on the feature value and mutation status.

[0131] In summary, the single-sample HRD status assessment method provided in this embodiment reduces the reliance on additional control samples by simplifying the detection process, thereby improving the accessibility and applicability of HRD detection, especially in clinical situations where paired samples are difficult to obtain. This method enhances the accuracy and reliability of the results by accurately calculating the coverage depth and average fragment alignment rate of SNP sites, providing more accurate quantification of copy number changes in the genome. In addition, the comprehensive assessment of HRD status, including the calculation of LOH, TAI, and LST characteristic values ​​and the analysis of BRCA1 and BRCA2 gene variations, provides more comprehensive genetic information for clinical decision-making, helps to more accurately predict the patient's response to a specific treatment, thereby improving the treatment effect and patient prognosis. These features make the present invention have significant advantages in improving the accuracy of HRD assessment, clinical relevance, and the implementation of personalized medicine.

[0132] In this embodiment, a comprehensive single-sample HRD status assessment method is provided. From construction of a control library to the final HRD status assessment, each step provides necessary information for the final result and can be implemented using a variety of bioinformatics tools and algorithms.

[0133] Example 2

[0134] Reference Figure 3 Embodiment 2 of the present invention provides a single sample HRD status assessment method based on the above embodiment 1. In which, the step S100, constructing a control negative library based on a normal negative sample, comprises:

[0135] Step S110, calculating the coverage depth data of each SNP site of each normal negative sample;

[0136] When the above steps involve the National Construction Control Negative Library, the genome of each normal negative sample (non-tumor sample) is sequenced and the coverage depth of all SNP sites in each sample is calculated. Coverage depth refers to the number of times the DNA sequence is covered by sequencing reads in a specific area, thereby obtaining coverage depth data for each SNP site in each normal negative sample.

[0137] The above steps provide basic data for constructing a control negative library, which is helpful for subsequent comparative analysis with target samples (tumor samples).

[0138] Data can be obtained through high-throughput sequencing technology (such as Next Generation Sequencing, NGS), and coverage depth can be calculated using bioinformatics tools.

[0139] Step S120, statistics are collected on the coverage depth data of all the normal negative samples corresponding to each SNP site, and the median of all the coverage depth data corresponding to each SNP site is calculated as the coverage depth of each SNP site in the control negative library.

[0140] The coverage depth data of each SNP site in all normal negative samples are summarized and counted to obtain the coverage depth data set of each SNP site in all normal negative samples. By counting the data of multiple samples, the influence of individual differences can be reduced and the representativeness and reliability of the control negative library can be improved.

[0141] The above steps involve calculating the median of the coverage depth data of all normal negative samples for each SNP site, thereby obtaining the median coverage depth of each SNP site, which will be used as the reference coverage depth of the SNP site in the control negative library. It should be noted that the median is a robust statistic that is not easily affected by extreme values ​​and can provide more accurate baseline data.

[0142] The constructed control negative library contains the median coverage depth of each SNP site and serves as a reference standard for subsequent analysis. The control negative library provides a standardized baseline to help identify and evaluate abnormal changes in the target sample.

[0143] In summary, in this embodiment, a specific method for constructing a control negative library is provided, which calculates the median by counting and analyzing the SNP site coverage depth data of normal negative samples, and constructs a control negative library. The advantage of this method is its robustness and representativeness, which can provide an accurate reference baseline for HRD status assessment. The implementation of this method requires the use of high-throughput sequencing technology and bioinformatics tools, including steps such as data collection, statistical analysis, and database management.

[0144] For further reference, Figure 4 The step S200, determining similar change region segments based on the coverage depth, and calculating the average segment comparison rate corresponding to the similar change region segments, includes:

[0145] Step S210, comparing the coverage depth of each SNP site in the target sample to be tested with the coverage depth of the corresponding SNP site in the control negative library to obtain the depth change rate of each SNP site;

[0146] The above steps involve comparing the coverage depth of each SNP site in the target test sample with the coverage depth of the corresponding SNP site in the control negative library, and calculating the depth change rate (usually expressed as a log2 ratio), so as to obtain the depth change rate of each SNP site in the target test sample.

[0147] Deep change rate helps to identify changes in copy number in the genome and is the basis for detecting characteristic values ​​such as LOH, TAI and LST.

[0148] Bioinformatics software packages can be used to calculate the coverage depth of each SNP site and then calculate its ratio to the coverage depth of the corresponding site in the control negative library.

[0149] Step S220, calculating the minor allele frequency of each heterozygous site in the SNP site in the target sample to be tested;

[0150] The above steps involve calculating the minor allele frequency (BAF) of each heterozygous SNP site in the target sample to be tested. The minor allele frequency provides information about the distribution of alleles at the heterozygous site and helps to identify unbalanced regions in the genome.

[0151] A genotype calling algorithm can be used to determine the minor allele frequency for each heterozygous site.

[0152] Step S230, merging the SNP regions consisting of continuous SNP sites having similar depth change rates and minor allele frequencies in the target sample to be tested to obtain the similar change region fragments;

[0153] The above steps involve merging continuous SNP sites with similar deep change rates and minor allele frequencies in the target sample into one segment to obtain similar change region segments.

[0154] Merging similar SNP sites helps identify structural variations in the genome, such as large copy number changes.

[0155] Clustering algorithms or sliding window methods can be used to identify and merge consecutive SNP sites with similar deep variation rates and minor allele frequencies.

[0156] Step S240: taking the average of all the depth change rates in the similar change region segments as the segment average comparison rate corresponding to the similar change region segments.

[0157] The above steps involve calculating the average of all depth change rates in the similar change region segment as the average comparison rate of the segment, thereby obtaining the segment average comparison rate of the similar change region segment.

[0158] The average fragment alignment rate provides basic data for further calculation of LOH, TAI and LST feature values.

[0159] In summary, the method provided in this embodiment determines similar variation region fragments based on coverage depth and calculates the average comparison rate of fragments. This method calculates the depth change rate and the minor allele frequency by comparing the coverage depth in the target sample to be tested with the control negative library, merges similar SNP sites, and calculates the average comparison rate of fragments. The advantage of this method is that it can accurately identify structural variations in the genome and provide important information for HRD status assessment. The implementation of this method requires the use of high-throughput sequencing technology and bioinformatics tools, including steps such as data collection, statistical analysis, and data merging.

[0160] Example 3

[0161] Reference Figure 5 Embodiment 3 of the present invention provides a single sample HRD state assessment method based on the above embodiment 1. Wherein, after calculating the average segment comparison rate corresponding to the similar change region segment in step S200, it also includes:

[0162] Step S700, obtaining the tumor cell content and tumor cell ploidy corresponding to the target sample to be tested;

[0163] The above steps involve using specific bioinformatics tools or experimental methods to determine the tumor cell content (the proportion of tumor cells in the sample) and tumor cell ploidy (the number of chromosome sets in tumor cells) in the target sample to be tested.

[0164] It should be noted that humans are diploid, and the ploidy in normal tissues is 2. However, in tumor samples, the ploidy of tumor cells may change and may be higher than 2 or lower than 2.

[0165] In this example, PureCN software was used to obtain the tumor cell content and ploidy of the target sample.

[0166] Through this step, the values ​​of tumor cell content (a) and tumor cell ploidy (T) can be obtained.

[0167] This information is critical to correct for variations in coverage depth due to changes in tumor cell content and ploidy, helping to improve the accuracy of HRD status assessment.

[0168] Step S800, using the tumor cell content and the tumor cell ploidy to correct the average fragment alignment rate, to obtain the corrected average fragment alignment rate.

[0169] The above steps involve using a formula to correct the average fragment alignment ratio to take into account the effects of tumor cell content and ploidy.

[0170] Furthermore, the expression of the correction process is:

[0171]

[0172] Wherein, R`(x) represents the average alignment ratio of the fragments after correction processing; R(x) represents the average alignment ratio of the fragments after correction processing; a represents the tumor cell content; and T represents the tumor cell ploidy.

[0173] Correction processing helps to eliminate the impact of tumor cell content and ploidy changes on coverage depth, making HRD status assessment more accurate and reliable.

[0174] In this embodiment, a method for correcting the average fragment alignment rate is provided to take into account the influence of tumor cell content and ploidy. This method obtains information on tumor cell content and ploidy and uses a specific formula to correct the average fragment alignment rate. The advantage of this method is that it can improve the accuracy and reliability of HRD status assessment, especially when the tumor cell content and ploidy vary greatly in tumor samples. To implement this method, bioinformatics tools are needed to predict tumor cell content and ploidy, and programming languages ​​are used to implement the correction formula.

[0175] Example 4

[0176] Embodiment 4 of the present invention provides a single sample HRD status assessment method, based on the above embodiment 1. In the step S300, the total copy number is determined according to the average alignment rate of the fragments, including:

[0177] Step S310, using the following expression to determine the total copy number:

[0178]

[0179] Among them, total cn represents the total copy number; R mean represents the average alignment rate of the fragments; T represents the ploidy of tumor cells.

[0180] In the above steps, the average segment alignment ratio used may be the average segment alignment ratio determined in Example 1, or the average segment alignment ratio after correction in Example 3.

[0181] The above steps involve using a specific formula to calculate the total copy number based on the average fragment alignment rate and the tumor cell ploidy (T). This formula takes into account the average fragment alignment rate and the ploidy of tumor cells, providing a method to accurately calculate the total copy number of a specific region in the genome. This is critical for assessing the HRD status of a sample.

[0182] Furthermore, in step S300, calculating the allele copy number includes:

[0183] Step S320, averaging the minor allele frequencies of the heterozygous SNP sites in the similar variation region segments to obtain a segment average BAF;

[0184] The above steps involve averaging the minor allele frequency (BAF) of all heterozygous SNP sites in the fragments of similar variation regions to obtain the average BAF of the fragments, which is an overall indicator reflecting the minor allele frequency of heterozygous SNP sites in the fragments.

[0185] The fragment-averaged BAF provides a comprehensive metric that helps assess the distribution of alleles within a fragment and is critical for subsequent allele copy number calculations.

[0186] Specifically, it can be achieved through programming (or statistical software) to average the BAF values ​​of all heterozygous SNP sites in the fragment.

[0187] Step S330, using the average BAF of the fragments, calculate the allele copy number; wherein the allele copy number includes the copy number of the A allele and the copy number of the B allele.

[0188] The above steps involve using the fragment average BAF to calculate the allele copy number, including the copy number of the A allele (Aallele_cn) and the copy number of the B allele (Ballele_cn). Obtaining the copy number of the A allele and the B allele is very important for evaluating the HRD status and the heterozygosity changes of the genome.

[0189] This approach can provide detailed information about the copy number of alleles in specific regions of the genome, helping to identify variants and recombination events in the genome.

[0190] Furthermore, the method for calculating the copy number of the A allele includes:

[0191] Aallele cn =total cn ×BAF segmentmean ;

[0192] The method for calculating the copy number of the B allele includes:

[0193] Ballele cn =total cn ×(1-BAF segmentmean );

[0194] Among them, Aallele cn represents the copy number of the A allele; Ballele cn represents the copy number of the B allele; total cn represents the total copy number; BAF segment mean represents the average BAF of the fragments.

[0195] In summary, in the present embodiment, a method for calculating allele copy number is provided, the method first calculates the mean value (fragment average BAF) of the secondary allele frequency of the heterozygous SNP site in the similar variation region fragment, and then uses this mean value to calculate the allele copy number. The advantage of this method is that it can provide detailed information about the specific region allele copy number in the genome, which is very important for assessing the heterozygous variation of HRD state and genome. Realizing this method requires using bioinformatics tools to calculate BAF, and using programming languages ​​to realize the calculation formula of allele copy number. The implementation of this method can improve the accuracy and reliability of HRD assessment, and provide more comprehensive information for clinical decision making.

[0196] For further reference, Figure 6, the step S400, calculating the LOH characteristic value, TAI characteristic value and LST characteristic value according to the total copy number and the allele copy number; and calculating the homologous recombination defect result according to the LOH characteristic value, TAI characteristic value and LST characteristic value includes:

[0197] Step S410, obtaining the chromosome number of the chromosome where the similar change region segment is located, as well as the starting position and the ending position of the similar change region segment;

[0198] The above steps involve determining the specific location of each similar variation region fragment in the genome, including the chromosome number where they are located and the starting and ending positions of the fragment. This enables the precise location information of each fragment to be obtained, which is crucial for subsequent analysis.

[0199] Precise position information helps to accurately understand the changes in genome structure and provides the necessary background for subsequent eigenvalue calculations.

[0200] Step S420, the chromosome number, starting position, ending position, total copy number, copy number of A allele, copy number of B allele and tumor cell ploidy of the similar change region fragment are combined to form a cnv result;

[0201] The above steps involve integrating the position information of similar change region fragments, total copy number, A allele copy number, B allele copy number and tumor cell ploidy to form a complete CNV (copy number variation) result. A CNV result containing multiple key parameters is obtained, which together describe the genetic characteristics of the fragment.

[0202] The integrated data provide a comprehensive view and facilitate a deep understanding of the genetic changes in the fragments.

[0203] Step S430, grouping the cnv results according to the chromosome numbers to obtain different chromosome groups;

[0204] The above steps involve grouping all cv results according to the chromosome number they are in, so that a set of cv results organized by chromosome number can be obtained, one group for each chromosome. Grouping helps to process the data on each chromosome separately and simplify the analysis process.

[0205] Step S440, sorting the cnv results in each of the chromosome groups according to the starting position to obtain the sorted cnv results;

[0206] The above steps involve sorting the CNV results according to the starting position of the fragment in each chromosome group. The sorted CNV results are obtained so that the data on each chromosome are arranged in the order of the physical position of the genome.

[0207] Sequencing helps identify adjacent fragments and large structural changes.

[0208] Step S450, calculating the LOH eigenvalue, the TAI eigenvalue and the LST eigenvalue according to the cnv result after sorting;

[0209] As mentioned above, the LOH (loss of heterozygosity), TAI (telomeric allelic imbalance) and LST (large-scale state transition) feature values ​​are calculated based on the CNV results after sequencing, and the LOH, TAI and LST feature values ​​of each fragment are obtained, which reflect the specific change patterns of the genome.

[0210] Step S460, summing the LOH characteristic value, the TAI characteristic value and the LST characteristic value to obtain the homologous recombination deficiency result.

[0211] This step involves summing the LOH, TAI, and LST eigenvalues ​​to obtain a comprehensive homologous recombination deficiency result (HRDscore). This results in a comprehensive index for evaluating the HRD status of the sample. The comprehensive index provides a concise evaluation result that helps to quickly determine the HRD status of the sample.

[0212] In this embodiment, a method for calculating LOH, TAI and LST characteristic values ​​based on the total copy number and allele copy number, and evaluating the HRD status accordingly is provided. This method calculates the key genetic characteristic values ​​by integrating the position information, copy number and ploidy information of the fragment, and finally obtains the HRD status evaluation result of the sample. The advantage of this method is that it can provide comprehensive and accurate genome change information, which helps to deeply understand the genetic characteristics and HRD status of the sample. To implement this method, it is necessary to use genome analysis tools to process and analyze a large amount of sequencing data, and use programming languages ​​to integrate, sort and calculate the data.

[0213] Furthermore, the LOH characteristic value is the number of similar change region fragments that simultaneously meet all of the following conditions:

[0214] A. In the same chromosome, the fragment of the similar change region is larger than 15Mb;

[0215] B. In the same chromosome, the fragment of the similar variation region is smaller than the length of the entire chromosome;

[0216] The LST feature value is the number of similar change region segments that simultaneously meet all of the following conditions:

[0217] A. In the same chromosome, the lengths of adjacent similarly changed regions are all greater than 10 Mb;

[0218] B. In the same chromosome, the interval between adjacent similar variation regions is less than 3 Mb;

[0219] The LST feature value is the number of similar change region segments that simultaneously meet all of the following conditions:

[0220] A. In the same chromosome, the similar change region extends to the telomere but not beyond the centromere;

[0221] B. In the same chromosome, the similar change region fragment is larger than 10Mb.

[0222] In the above method of the present embodiment, a method for calculating LOH, LST and TAI characteristic values ​​is provided, and these characteristic values ​​are determined based on specific conditions of similar change region fragments. The advantage of this method is that it can provide comprehensive and accurate genome change information, which helps to deeply understand the genetic characteristics and HRD status of the sample. To implement this method, it is necessary to use genome analysis tools to process and analyze a large amount of sequencing data, and use programming languages ​​to implement data screening, counting and calculation. The implementation of this method can improve the accuracy and reliability of HRD assessment and provide more comprehensive information for clinical decision-making.

[0223] Example 5

[0224] Reference Figure 7 Embodiment 5 of the present invention provides a single sample HRD status assessment method, based on the above embodiment 1. The step S600, according to the homologous recombination deficiency result and the BRCA mutation status result, obtains the HRD status corresponding to the target sample to be tested, including:

[0225] Step S610, performing qualitative classification processing on the homologous recombination deficiency result and the BRCA mutation status result respectively to obtain a qualitative result corresponding to the homologous recombination deficiency result and a qualitative result corresponding to the BRCA mutation status result;

[0226] In the above, the homologous recombination deficiency results (HRDscore) and the BRCA mutation status results (BRCAness) are qualitatively classified. This means that these results can be divided into two categories: "positive" or "negative". Through the above steps, two qualitative results can be obtained: the qualitative result of the homologous recombination deficiency result and the qualitative result of the BRCA mutation status result.

[0227] Qualitative classification provides a simple and clear way to interpret complex genetic data, making the results easier to understand and apply to clinical decision making.

[0228] The qualitative classification of each result can be determined by a preset threshold or standard. For example, if the HRD score exceeds a certain threshold, the qualitative result is positive; if a specific mutation is detected in the BRCA1 / 2 gene, the qualitative result is positive.

[0229] Step S620, if the qualitative result corresponding to the homologous recombination deficiency result is positive, and the qualitative result corresponding to the BRCA mutation status result is positive, then the HRD status corresponding to the target sample to be tested is positive;

[0230] Step S630, if the qualitative result corresponding to the homologous recombination deficiency result is negative, and the qualitative result corresponding to the BRCA mutation status result is positive, then the HRD status corresponding to the target sample to be tested is positive;

[0231] Step S640, if the qualitative result corresponding to the homologous recombination deficiency result is positive, and the qualitative result corresponding to the BRCA mutation status result is negative, then the HRD status corresponding to the target sample to be tested is positive;

[0232] Step S650: If the qualitative result corresponding to the homologous recombination deficiency result is negative, and the qualitative result corresponding to the BRCA mutation status result is negative, then the HRD status corresponding to the target sample to be tested is negative.

[0233] The above steps involve comprehensively evaluating the HRD status of the target sample to be tested based on the qualitative results of the homologous recombination deficiency result and the qualitative results of the BRCA mutation status result.

[0234] Specifically, the HRD status can be determined according to the following conditions, which may include the following four situations:

[0235] (1) If the homologous recombination deficiency result is positive and the BRCA mutation status is positive, the HRD status is positive.

[0236] (2) If the homologous recombination deficiency result is negative and the BRCA mutation status is positive, the HRD status is positive.

[0237] (3) If the homologous recombination deficiency result is positive and the BRCA mutation status is negative, the HRD status is positive.

[0238] (4) If the homologous recombination deficiency result is negative and the BRCA mutation status is negative, the HRD status is negative.

[0239] The above analysis method in this embodiment comprehensively considers two different genetic markers, providing a more comprehensive HRD status assessment, which helps to identify patients who may respond to specific treatments (such as PARP inhibitors). This embodiment provides a method for comprehensively assessing HRD status, which processes homologous recombination deficiency results and BRCA mutation status results by qualitative classification, and comprehensively assesses the HRD status of the target sample to be tested based on these results. The advantage of this method is that it can provide more comprehensive HRD status information, which is helpful for personalized medicine and precision treatment. To implement this method, bioinformatics tools are needed to process and analyze genetic data, and logical judgment or decision tree algorithms are used to comprehensively assess HRD status. The implementation of this method can improve the accuracy and reliability of HRD assessment and provide more comprehensive information for clinical decision-making.

[0240] Furthermore, in step S610, the method for qualitative classification of homologous recombination deficiency results includes:

[0241] Step S611, comparing the homologous recombination deficiency result with a preset classification threshold;

[0242] As described above, when the HRDscore is calculated by the three-indicator method and then the HRDscore is used for qualitative classification, the preset classification threshold can be used for evaluation and classification. For example, the preset classification threshold can be set to 42. In this embodiment, it is found through verification that there is an obvious dividing line around 38-40, so the preset classification threshold selected in this embodiment is 40.

[0243] Step S612, if the homologous recombination deficiency result is not less than the preset classification threshold, determining that the qualitative result corresponding to the homologous recombination deficiency result is positive;

[0244] Step S613, if the homologous recombination deficiency result is less than the preset classification threshold, determining that the qualitative result corresponding to the homologous recombination deficiency result is negative;

[0245] In the above, the homologous recombination deficiency result is compared with a preset classification threshold to determine its qualitative result (positive or negative). A qualitative classification of the homologous recombination deficiency result is obtained, i.e., positive or negative. By comparing with the preset threshold, the evaluation process can be standardized, making the results more objective and repeatable.

[0246] In step S610, the method for qualitative classification of the BRCA mutation status result includes:

[0247] Step S614, determining whether there is a pathogenic variation in the BRCA mutation status result;

[0248] Step S615: If yes, then determine that the qualitative result corresponding to the BRCA mutation status result is positive;

[0249] Step S616: If not, determine that the qualitative result corresponding to the BRCA mutation status result is negative.

[0250] As mentioned above, after obtaining the BRCA mutation status results, the pathogenicity of BRCA1 and BRCA2 gene mutations can be annotated for the BRCA mutation status results. Then, based on the annotation information, it is determined whether there are pathogenic mutations in the BRCA mutation status results to determine its qualitative results (positive or negative). Thus, a qualitative classification of the BRCA mutation status results, i.e., positive or negative, is obtained.

[0251] For example, in this embodiment, vardict software was used to analyze BRCA1 and BRCA2 gene variations, and annovar and VEP software were used to annotate the pathogenicity of BRCA1 and BRCA2 gene variations.

[0252] If a pathogenic variant is detected, the test is BRACness positive; otherwise, it is BRCAness negative.

[0253] This qualitative classification facilitates rapid identification of samples with pathogenic variants in BRCA genes, which is critical for clinical decision making and treatment selection.

[0254] In addition, reference Figure 8 In an embodiment of the present application, a single sample HRD state evaluation device is also provided, comprising:

[0255] A control library construction module 10 is used to construct a control negative library based on a normal negative sample; and to calculate the coverage depth of each SNP site of the target sample and the control negative library respectively;

[0256] A fragment analysis module 20, used to determine similar change region fragments based on the coverage depth, and calculate the average fragment alignment rate corresponding to the similar change region fragments;

[0257] A copy number calculation module 30, used to determine the total copy number according to the average alignment rate of the fragments, and calculate the allele copy number;

[0258] The characteristic value calculation module 40 is used to calculate the LOH characteristic value, the TAI characteristic value and the LST characteristic value according to the total copy number and the allele copy number; and to calculate the homologous recombination defect result according to the LOH characteristic value, the TAI characteristic value and the LST characteristic value;

[0259] A mutation analysis module 50 is used to analyze the BRCA1 and BRCA2 gene mutations of the target sample to be tested to obtain a BRCA mutation status result;

[0260] The status evaluation module 60 is used to obtain the single sample HRD status evaluation status corresponding to the target sample to be tested according to the homologous recombination deficiency result and the BRCA mutation status result.

[0261] In addition, in an embodiment of the present application, a computer device is also provided, the computer device includes a processor and a memory, the memory stores a computer program, and the processor is used to execute the computer program to implement the single-sample HRD state assessment method described above.

[0262] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU) and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or at least one of other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.

[0263] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory is used to store a computer program, and the processor may execute the computer program accordingly after receiving an execution instruction.

[0264] In addition, a computer storage medium is provided in an embodiment of the present application, which stores a computer program. When the computer program is executed on a processor, the single-sample HRD state assessment method described above is implemented.

[0265] For example, the computer-readable storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0266] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0267] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0268] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0269] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A single sample HRD status assessment method, characterized in that: include: A control negative library was constructed based on normal negative samples; And, respectively calculating the coverage depth of each SNP site of the target sample to be tested and the control negative library; Determine similar change region segments based on the coverage depth, and calculate the average segment alignment ratio corresponding to the similar change region segments; Determine the total copy number according to the average alignment rate of the fragments, and calculate the allele copy number; Calculate the LOH characteristic value, TAI characteristic value and LST characteristic value according to the total copy number and the allele copy number; And, the homologous recombination deficiency result is calculated based on the LOH characteristic value, the TAI characteristic value and the LST characteristic value; Analyze the BRCA1 and BRCA2 gene mutations of the target sample to be tested to obtain BRCA mutation status results; According to the homologous recombination deficiency result and the BRCA mutation status result, the HRD status corresponding to the target sample to be tested is obtained.

2. The single sample HRD status assessment method according to claim 1, characterized in that: The method of constructing a control negative library based on normal negative samples comprises: Calculate the coverage depth data of each SNP site of each normal negative sample; The coverage depth data of all the normal negative samples corresponding to each SNP site are statistically analyzed, and the median of all the coverage depth data corresponding to each SNP site is calculated as the coverage depth of each SNP site in the control negative library.

3. The single sample HRD status assessment method according to claim 1, characterized in that: The determining of similar change region segments based on the coverage depth, and calculating the average segment alignment ratio corresponding to the similar change region segments, includes: Compare the coverage depth of each SNP site of the target sample to be tested with the coverage depth of the corresponding SNP site in the control negative library to obtain the depth change rate of each SNP site; Calculating the minor allele frequency of each heterozygous site in the SNP site in the target sample to be tested; Merging the SNP regions composed of continuous SNP sites having similar depth change rates and minor allele frequencies in the target sample to be tested to obtain the similar change region fragments; The average value of all the depth change rates in the similar change region segments is used as the segment average comparison rate corresponding to the similar change region segments.

4. The single sample HRD status assessment method according to claim 1, characterized in that: After calculating and obtaining the average segment comparison rate corresponding to the similar change region segments, the method further includes: Obtaining the tumor cell content and tumor cell ploidy corresponding to the target sample to be tested; The average fragment alignment rate is corrected using the tumor cell content and the tumor cell ploidy to obtain a corrected average fragment alignment rate.

5. The single sample HRD status assessment method according to claim 4, characterized in that: The expression of the correction process is: Wherein, R`(x) represents the average alignment ratio of the fragments after correction processing; R(x) represents the average alignment ratio of the fragments after correction processing; a represents the tumor cell content; and T represents the tumor cell ploidy.

6. The single sample HRD status assessment method according to claim 1, characterized in that: Determining the total copy number according to the average alignment rate of the fragments comprises: The total copy number was determined using the following expression: total cn =2 Rmean ×T; Among them, total cn represents the total copy number; R mean represents the average alignment rate of the fragments; T represents the ploidy of tumor cells.

7. The single sample HRD status assessment method according to claim 3, characterized in that: The calculation to obtain the allele copy number includes: Averaging the minor allele frequencies of the heterozygous SNP sites in the similar variation region fragments to obtain the fragment average BAF; The allele copy number is calculated using the average BAF of the fragments; wherein the allele copy number includes the copy number of the A allele and the copy number of the B allele.

8. The single sample HRD status assessment method according to claim 7, characterized in that: The method for calculating the copy number of the A allele includes: Aallele cn =total cn ×BAF segmentmean ; The method for calculating the copy number of the B allele includes: Ballele cn =total cn ×(1-BAF segmentmean ); Among them, Aallele cn represents the copy number of the A allele; Ballele cn represents the copy number of the B allele; total cn represents the total copy number; BAF segmentmean represents the average BAF of the fragments.

9. The single sample HRD status assessment method according to claim 1, characterized in that: The LOH characteristic value, TAI characteristic value and LST characteristic value are calculated according to the total copy number and the allele copy number; And, the homologous recombination defect results calculated based on the LOH characteristic value, TAI characteristic value and LST characteristic value include: Obtaining the chromosome number of the chromosome where the similar change region fragment is located, as well as the starting position and the ending position of the similar change region fragment; The chromosome number, starting position, ending position, total copy number, copy number of A allele, copy number of B allele and tumor cell ploidy of the similar change region fragments together constitute the cnv result; Grouping the cnv results according to the chromosome numbers to obtain different chromosome groups; Sorting the cnv results in each of the chromosome groups according to the starting position to obtain the cnv results after sorting; The LOH eigenvalue, the TAI eigenvalue and the LST eigenvalue are calculated according to the cnv result after sorting; The LOH characteristic value, the TAI characteristic value and the LST characteristic value are summed to obtain the homologous recombination deficiency result.

10. The single sample HRD status assessment method according to claim 9, characterized in that: The LOH characteristic value is the number of similar change region fragments that simultaneously meet all of the following conditions: A. In the same chromosome, the fragment of the similar change region is larger than 15Mb; B. In the same chromosome, the fragment of the similar variation region is smaller than the length of the entire chromosome; The LST feature value is the number of similar change region segments that simultaneously meet all of the following conditions: A. In the same chromosome, the lengths of adjacent similarly changed regions are all greater than 10 Mb; B. In the same chromosome, the interval between adjacent similar variation regions is less than 3 Mb; The LST feature value is the number of similar change region segments that simultaneously meet all of the following conditions: A. In the same chromosome, the similar change region extends to the telomere but not beyond the centromere; B. In the same chromosome, the similar change region fragment is larger than 10Mb.

11. The single sample HRD status assessment method according to claim 1, characterized in that: The step of obtaining the HRD status corresponding to the target sample to be tested according to the homologous recombination deficiency result and the BRCA mutation status result includes: Performing qualitative classification processing on the homologous recombination deficiency result and the BRCA mutation status result respectively to obtain a qualitative result corresponding to the homologous recombination deficiency result and a qualitative result corresponding to the BRCA mutation status result; If the qualitative result corresponding to the homologous recombination deficiency result is positive, and the qualitative result corresponding to the BRCA mutation status result is positive, then the HRD status corresponding to the target sample to be tested is positive; If the qualitative result corresponding to the homologous recombination deficiency result is negative, and the qualitative result corresponding to the BRCA mutation status result is positive, then the HRD status corresponding to the target sample to be tested is positive; If the qualitative result corresponding to the homologous recombination deficiency result is positive, and the qualitative result corresponding to the BRCA mutation status result is negative, then the HRD status corresponding to the target sample to be tested is positive; If the qualitative result corresponding to the homologous recombination deficiency result is negative, and the qualitative result corresponding to the BRCA mutation status result is negative, then the HRD status corresponding to the target sample to be tested is negative.

12. The single sample HRD status assessment method according to claim 11, characterized in that: The method for qualitative classification of homologous recombination deficiency results comprises: Comparing the homologous recombination deficiency result with a preset classification threshold; If the homologous recombination deficiency result is not less than the preset classification threshold, determining that the qualitative result corresponding to the homologous recombination deficiency result is positive; If the homologous recombination deficiency result is less than the preset classification threshold, determining that the qualitative result corresponding to the homologous recombination deficiency result is negative; The method for qualitative classification processing of the BRCA mutation status result includes: Determine whether there is a pathogenic variation in the BRCA mutation status result; If yes, the qualitative result corresponding to the BRCA mutation status result is determined to be positive; If not, the qualitative result corresponding to the BRCA mutation status result is determined to be negative.

13. A single sample HRD status evaluation device, characterized in that: include: A control library construction module, used to construct a control negative library based on normal negative samples; And, respectively calculating the coverage depth of each SNP site of the target sample to be tested and the control negative library; A fragment analysis module, used to determine similar change region fragments based on the coverage depth, and calculate the average fragment alignment rate corresponding to the similar change region fragments; A copy number calculation module, used to determine the total copy number according to the average alignment rate of the fragments, and calculate the allele copy number; A characteristic value calculation module, used for calculating the LOH characteristic value, the TAI characteristic value and the LST characteristic value according to the total copy number and the allele copy number; And, the homologous recombination deficiency result is calculated based on the LOH characteristic value, the TAI characteristic value and the LST characteristic value; A mutation analysis module is used to analyze the BRCA1 and BRCA2 gene mutations of the target sample to be tested to obtain the BRCA mutation status result; The status evaluation module is used to obtain the single sample HRD status evaluation status corresponding to the target sample to be tested according to the homologous recombination defect result and the BRCA mutation status result.

14. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the single-sample HRD status assessment method according to any one of claims 1 to 12.

15. A computer storage medium, characterized in that The computer program is stored therein, and when the computer program is executed on a processor, the single-sample HRD state assessment method according to any one of claims 1-12 is implemented.

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