Evaluation method of tumor cell content based on single sample targeting gene sequencing

Abstract

The invention belongs to the field of intelligent medical treatment, and particularly relates to a tumor cell content evaluation method based on single-sample targeted gene sequencing. The method comprises the following steps: S1, acquiring gene sequencing data of candidate heterozygous sites of a tumor single sample; s2, according to the gene sequencing data, obtaining whether the frequency of a secondary allele shifts or not, and calculating to obtain a copy number; and S3, calculating the content of the tumor cells based on whether the frequency of the secondary allele shifts and the copy number: if the frequency of the secondary allele shifts and the copy number is greater than or equal to the upper bound of a normal interval of the copy number, determining the content of the tumor cells. And calculating the content of the tumor cells according to the secondary allele frequency mean value of the candidate heterozygosis sites higher than 0.5 and the secondary allele frequency mean value of the candidate heterozygosis sites lower than 0.5. The tumor cell content is based on the sequencing data of the target gene region obtained by using the probe, the cost is low, and the defects of the prior art in clinical application are overcome.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical treatment, and more specifically, to a method, device, medium and program product for evaluating the tumor cell content based on single-sample targeted gene sequencing. Background Art

[0002] The tumor cell content is an important quality control point for accurately detecting tumor-related mutations. The Expert Consensus on Next-Generation Sequencing Testing in Clinical Molecular Pathology Laboratories states that the tumor cell content in NGS sequencing tissue specimens is recommended to be above 20%. If it is lower than this standard, it can be enriched and tested. The tumor cell content will affect the abundance of mutations. If the tumor cell content is too low, it will cause missed detection and false negatives.

[0003] Clinically, tumor cell content is usually obtained through pathological staining, but the problem faced by this method is that since it uses cell staining to calculate the proportion of abnormal cells for evaluation, it is difficult to obtain sufficient tissue samples for evaluation and testing when it is difficult to obtain lesion tissue. Sometimes it is difficult to obtain too much patient lesion tissue for certain types of cancer or specific situations, or the abnormal area seen in imaging is very small, which is not conducive to puncture. For example, for some lung cancer patients, it is not easy to obtain sufficient tissue samples from patients even when doing bronchoscopy. The tumor cell content is difficult to evaluate through pathological detection methods.

[0004] At present, in addition to pathological staining, there are also software that evaluates the obtained sequencing data through sequencing methods, such as Absolute, Facets, PureCN and other software. These software require strict conditions. For example, Absolute and Facets tools are usually calculated based on control samples and large-scale panel sequencing, but the problem is that in some cases, control samples are difficult to obtain. For example, it is difficult to obtain adjacent cancer controls or blood controls as control samples for some cancers; in addition, Facets, Absolute, PureCN and other software based on sequencing data evaluate the tumor cell content of samples through high-throughput sequencing data of WES or WGS. These sequencing costs are very expensive and not suitable for widespread clinical application. Since the tumor cell content is an indicator that needs to be measured as recommended by guidelines or other reference materials, when it is actually measured in clinical practice, it is found that some cancer types or situations are not suitable or difficult to obtain tumor cell content, and the lack of tumor cell content may affect the detection of SNVs, indels, etc.

[0005] "CN 202311392297.6" discloses the steps for detecting heterozygous loss, including "If the minor allele frequency shifts and the copy number does not change, it is copy-neutral LOH, if the minor allele frequency shifts and the copy number is lost, it is heterozygous loss, if the minor allele frequency does not shift and the copy number is lost, it is homozygous loss." Summary of the invention

[0006] In view of the above problems, the present invention provides a method for evaluating the tumor cell content based on single-sample targeted gene sequencing. The inventors have thought of combining the previous methods for further creative thinking, using sequencing data to evaluate the tumor cell content according to different states, and using different formulas to calculate the tumor cell content in each different state.

[0007] The present application (first aspect) discloses a method for evaluating the tumor cell content based on single-sample targeted gene sequencing, comprising: S1: Obtain gene sequencing data of candidate heterozygous sites of a single tumor sample; S2: Determine whether the minor allele frequency is shifted according to the gene sequencing data, and calculate the copy number; S3: The tumor cell content is calculated based on whether the minor allele frequency is shifted and the copy number: If the minor allele frequency is shifted and the copy number is greater than or equal to 2, the tumor cell content is calculated based on the mean minor allele mutation frequency of the candidate heterozygous sites that is higher than 0.5 and the mean minor allele mutation frequency of the candidate heterozygous sites that is lower than 0.5.

[0008] Further, if the minor allele frequency is offset and the copy number is greater than or equal to 2, the tumor cell content is the difference between the mean minor allele mutation frequency of the candidate heterozygous site that is higher than 0.5 and the mean minor allele mutation frequency of the candidate heterozygous site that is lower than 0.5; Optionally, the calculation formula for the tumor cell content is expressed as:

[0009] Among them, Purity represents the tumor cell content, AFupper, is the average mutation frequency of the sites where the minor allele mutation frequency of the candidate heterozygous site is higher than 0.5, AFdownner, is the average mutation frequency of the sites where the minor allele mutation frequency of the candidate heterozygous site is lower than 0.5.

[0010] Further, S3 also includes: if the minor allele frequency is offset and the copy number is in the interval (1, 2), calculating the tumor cell content based on the copy number; Optional, the calculation formula is: ;Wherein, Purity represents the tumor cell content and CN represents the copy number.

[0011] Optionally, S3 further includes: if the minor allele frequency is not offset and the copy number is in the interval (0, 2), calculating the tumor cell content based on the copy number; Optional, the calculation formula is: ; Wherein, Purity represents the tumor cell content and CN represents the copy number.

[0012] Further, determining in S2 whether a minor allele frequency shift occurs according to the gene sequencing data includes: S21: Obtaining the number of heterozygous sites according to the gene sequencing data, S22: judging whether the minor allele is shifted according to the number of the heterozygous sites; Optionally, S21 includes: calculating the minor allele frequency of the candidate heterozygous site according to the gene sequencing data, retaining the candidate heterozygous site whose minor allele frequency is in the heterozygous interval as the heterozygous site, and counting the number of the heterozygous sites; Optionally, S22 includes: determining whether the BAF is offset according to whether the number of the heterozygous sites is greater than a offset threshold; Optionally, the heterozygous interval is calculated based on the gene sequencing data of the candidate heterozygous sites in the LOH-negative sample group verified by FISH; Optionally, obtain gene sequencing data of candidate heterozygous sites of the LOH-negative sample set verified by FISH, count the minor allele frequency of each candidate heterozygous site in the gene sequencing data of the candidate heterozygous sites in the LOH-negative sample set, calculate the absolute value of the difference between the minor allele frequency of each candidate heterozygous site and 0.5 to obtain the AbsBAF of each candidate heterozygous site, calculate the mean value MT and standard deviation SDT of the true positive heterozygous sites and the mean value MF and standard deviation SDF of the false positive heterozygous sites in the AbsBAF of each candidate heterozygous site; the minor allele frequency interval of the heterozygous site is the intersection of the complement of the true positive heterozygous site distribution interval and the complement of the false positive heterozygous site distribution interval; Optionally, the minor allele frequency interval of the heterozygous site is expressed as:

[0013]

[0014] Among them, max() means to find the larger value of the two, and min() means to find the smaller value of the two. MT represents the mean value of AbsBAF of true positive heterozygous sites, SDT represents the variance of AbsBAF of true positive heterozygous sites, MF represents the mean value of AbsBAF of false positive heterozygous sites, SDF represents the variance of AbsBAF of false positive heterozygous sites, and C represents the size of the confidence interval determined according to the confidence level; Optionally, if the number of the heterozygous sites is less than the offset threshold, it is determined that the BAF is offset, otherwise the BAF is not offset; Optionally, the deviation threshold is determined based on the number of heterozygous sites in a verified LOH-negative sample set; Optionally, the deviation threshold is greater than the number of heterozygous sites in all verified LOH-negative sample sets.

[0015] Further, obtaining gene sequencing data of candidate heterozygous sites of a single tumor sample includes: screening SNP sites with a minor allele site in the targeted gene region and a subpopulation frequency of 1000 genomes in the extended range of about 50% as candidate heterozygous sites according to information in the dbSNP database; obtaining gene sequencing data of candidate heterozygous sites of the targeted gene region and its flanking regions using probes; Optionally, the expansion interval near 50% is 50%±20%.

[0016] Further, the gene sequencing data of the candidate heterozygous site of the single tumor sample is used to calculate the average gene copy ratio of the heterozygous site according to the copy ratio and weight of each window of the sliding window using the sliding window method, and the copy number is calculated according to the average gene copy ratio; Optionally, the copy number is calculated using a CNVkit tool; Optionally, the baseline data for copy number calculation using the CNVkit tool is the baseline of tissue samples collected with no copy number changes verified by FISH or ddPCR or methylation.

[0017] Further, in a public tumor gene database, corresponding targeted gene regions are determined according to different tumors, the candidate heterozygous site is obtained based on sequence screening of the targeted gene region, a minor allele frequency shift detection probe is designed based on the candidate heterozygous site, and the candidate heterozygous site is targeted and captured based on the minor allele frequency shift detection probe, and then second-generation sequencing is performed to obtain gene sequencing data of the candidate heterozygous site; Optionally, the targeted gene region includes the region where the targeted gene is located and its flanking regions; Optionally, the flanking region is 300kb in size.

[0018] The second aspect of the present application discloses a tumor cell content evaluation system based on single sample targeted gene sequencing, comprising: Acquisition module: used to obtain gene sequencing data of candidate heterozygous sites of a single tumor sample; Feature extraction module: used to determine whether the minor allele frequency is offset according to the gene sequencing data, and calculate the copy number; Evaluation output module: used to calculate the tumor cell content based on whether the minor allele frequency is offset and the copy number: if the minor allele frequency is offset and the copy number is greater than or equal to 2, the tumor cell content is calculated based on the mean minor allele mutation frequency of the candidate heterozygous sites that is higher than 0.5 and the mean minor allele mutation frequency of the candidate heterozygous sites that is lower than 0.5.

[0019] The third aspect of the present application discloses a computer device, which includes: a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, it is used to execute the steps of the above method.

[0020] A fourth aspect of the present application discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0021] A fifth aspect of the present application discloses a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0022] This application has the following beneficial effects: (1) The present application uses different formulas to calculate the tumor content according to different deficiency states, which overcomes the problem in the prior art that it is difficult to obtain sufficient tissue test samples for tumors and difficult to obtain control samples for tumor cell content assessment when using pathological staining methods. It is also low-cost and has medical value that is widely used in clinical practice. (2) The importance of the copy number intervals in this application to different states; specifically, in the case of a shift, it is further divided into two different states of heterozygosity loss, one is a state of relatively reduced copy number, and the other is a state of relatively increased copy number. Different methods for calculating the tumor cell content are used for these two different states of heterozygosity loss; the calculation is more accurate; (3) When calculating the tumor cell content in this application, the copy-neutral LOH and the situation of CN>2 in the heterozygous loss in (2) are combined into one state to calculate the tumor cell content, making the calculation more convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a schematic diagram of a method flow chart provided by the first aspect of an embodiment of the present invention; Figure 2 is a schematic diagram of a program product provided by the second aspect of an embodiment of the present invention; Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present invention; Figure 4 is a schematic diagram of the architecture of an exemplary computing device provided by an embodiment of the present invention; Figure 5 It is a schematic diagram of a storage medium provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0026] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The sequence numbers of the operations, such as S101, S102, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0028] Figure 1 The present invention provides a flow chart of a method for evaluating the tumor cell content based on single-sample targeted gene sequencing, specifically, the method comprises the following steps: S101: Obtain the gene sequencing data of candidate heterozygous sites of a single tumor sample; S102: Determine whether the minor allele frequency has shifted based on the gene sequencing data, and calculate the copy number; S103: Calculate the tumor cell content based on whether the minor allele frequency has shifted and the copy number: If the minor allele frequency has shifted and the copy number is greater than or equal to 2, calculate the tumor cell content according to the mean minor allele mutation frequency of the candidate heterozygous sites higher than 0.5 and the mean minor allele mutation frequency of the candidate heterozygous sites lower than 0.5.

[0029] In some embodiments, the core design concept of the present application is to design probes covering the entire exon and flanking regions of a gene, and according to the information in the dbSNP database, screen out SNP sites with a minor population frequency of 30%-70% in the targeted gene region and its flanking regions. Perform high-throughput sequencing according to the probe coverage area, and calculate the minor allele frequency BAF (B-Allele Frequency) according to the screened SNP sites. Then, comprehensively consider the copy number change information in the targeted gene region and its flanking regions to obtain states such as loss of heterozygosity and homozygosity loss, and then calculate the tumor cell content according to different states.

[0030] Table 1 State Judgment

[0031] Table 1 shows that the present application obtains different states according to whether the BAF has shifted and the range of the copy number. The prior patent document "CN 202311392297.6" discloses that if the minor allele frequency has shifted and the copy number has not changed, it is copy-neutral LOH; if the minor allele frequency has shifted and the copy number is missing, it is loss of heterozygosity; if the minor allele frequency has not shifted and the copy number is missing, it is homozygous deletion. Specifically as shown in Table 2: Table 2 State Judgment in the Prior Art

[0032] The comparison between Table 1 and Table 2 shows that, compared with the prior application document, the present application includes the following differences: (1) The copy number threshold for determining the copy number in the present application is different from that in the prior patent document; (2) The loss of heterozygosity in case 2 in the prior patent document is further divided into loss of heterozygosity with 1 < CN < 2 and loss of heterozygosity with CN > 2 in the present application, but for these two types of loss of heterozygosity, different calculation methods for the tumor cell content are used; The loss of heterozygosity includes two cases: One of them is that AB changes to A or B, and the copy number is a missing state (case 1 in Table 1); The other is that AB becomes n A or n B, then the copy number increases (case 3 in Table 1); (3) In the present application, the tumor cell content is calculated by combining the copy-neutral LOH and the case of CN>2 in the heterozygous loss in (2) into one state to calculate the tumor cell content; this is because the neutral LOH can be regarded as a special case of the second heterozygous loss with n=2.

[0033] Based on the differences between the prior patent documents and the present application documents, the technical problem that the present application solves in the state division stage is actually: how to determine the importance of accurate interval division of copy number intervals to the calculation of tumor cell content under the premise of whether BAF deviation occurs.

[0034] In the patent application document published by the applicant, it is stated in paragraph

[0112] of the specification that "it indicates that the copy number is within the range of [1.5, 2.7] as a normal value, and the copy number does not change. The copy number value below 1.5 is a copy number deletion (Deletion), and the copy number value above 2.7 is a copy number duplication (Duplication). The judgment of the copy number in Table 1 is based on the theoretical value of 2. In actual sequencing, as shown in the prior patent document, the normal range of the copy number calculated based on the reference sample may be an interval near 2.

[0035] In some embodiments, a summary of accurate interval division of copy number intervals and calculation of tumor cell content is shown in Table 3: Table 3 Status judgment after accurate interval division

[0036] Table Note: Because the copy number of normal negative people is not always 2, and because sequencing is volatile, Indicates the fluctuation threshold.

[0037] Table Note: Because the copy number of normal negative people is not always 2, and because sequencing is volatile, Indicates the fluctuation threshold.

[0038] In some embodiments, .

[0039] In some embodiments, .

[0040] Table 1 special case.

[0041] In some embodiments, multiple probes covering the exon regions of CDKN2A and CDKN2B genes and partially covering 300 kb of the flanking regions are designed, and sequencing is performed using the probes.

[0042] In a specific embodiment, 46 SNP sites with minor allele frequencies higher than 20% in the 1000 Genome Subpopulation of CDKN2A and CDKN2B gene exon regions and the 200Kb flanking regions on both sides are selected as candidate heterozygous sites, and probes are designed based on the screened 46 SNP sites.

[0043] In some embodiments, SNP sites with a sub-allele frequency of 30-70% in the 1000 genome sub-population of CDKN2A and CDKN2B gene exon regions and the 200Kb flanking regions on both sides are selected as candidate heterozygous sites, and probes are designed based on the screened SNP sites. It is worth noting that 46 SNP sites with a sub-allele frequency of more than 20% in the 1000 genome sub-population of CDKN2A and CDKN2B genes were selected in the prior patent documents, and SNP sites with a sub-allele frequency of 30-70% in the 1000 genome sub-population of CDKN2A and CDKN2B genes were selected as candidate heterozygous sites in the present application. The reason why the sub-allele frequency of CDKN2A and CDKN2B genes is adjusted to 30%-70% in the present application is that the frequency distribution of sub-allele sites fluctuates around 50% in theory. The setting of the fluctuation of 20% in this application to screen SNP sites is more in line with the law of probability distribution, so that the efficiency of screening candidate SNP sites is higher.

[0044] (3) Determine whether BAF is offset: ① Calculate BAF: Perform capture sequencing based on the designed probes with a sequencing depth of more than 300X. Calculate the B-Allele Frequency (BAF) of each SNP site based on the screened SNP sites. ② Rules for filtering candidate heterozygous sites: In some embodiments, when testing a single sample, there may be homozygous sites in the 46 candidate heterozygous sites, which need to be filtered. Take 3 FISH-verified FFPE LOH-negative samples (FISH verification is tested by Anbiping's "CDKN2A (9p21) Gene Probe" product, item number F.01265-01), count the BAF of each site in the 3 LOH-negative samples, and calculate the absolute value of the difference between BAF and 0.5, which is AbsBAF.

[0045] There are two types of AbsBAF characteristics in negative samples. One type is true positive heterozygous site AB, and the distribution of AbsBAF is:

[0046] We selected 46 candidate sites from three FISH-verified FFPE LOH-negative samples (46*3 should be obtained), but since some sites could not detect BAF (or BAF=0 or 1), we filtered out such sites and obtained the characteristics of true positive heterozygous sites, which were AbsBAF_mean (average value) of 0.04 and AbsBAF_std (standard deviation) of 0.024. The mean 3 times the standard deviation was used as the threshold, AbsBAF_mean + AbsBAF_std*3 was 0.11. The true positive heterozygous sites were distributed in [0.39, 0.61]. False positive heterozygous sites (AA), AbsBAF distribution is:

[0047] Similarly, the characteristics of the false positive heterozygous sites after filtering out the 46*3 sites are AbsBAF_mean (average value) of 0.49 and AbsBAF_std (standard deviation) of 0.015. The mean 3 times the standard deviation is used as the threshold, AbsBAF_mean-AbsBAF_std*3 is 0.445. The false positive heterozygous sites are distributed in [0.945, 1] ​​and [0, 0.055].

[0048] Filter candidate heterozygous sites: The BAF of the 46 candidate sites of the sample was calculated, and the sites whose BAF fell outside the intervals of (0.61, 0.945) and (0.055, 0.39) were filtered out. The number of remaining sites was counted and recorded as BAFN. If BAFN>=10, it was determined that the BAF had shifted.

[0049] The reason for using 10 as the threshold is: Three more LOH-negative samples verified by FISH were taken and BAFN was calculated to be 9, 3, and 6. It is more reasonable to take BAFN>=10.

[0050] (4) Calculate the copy number: ① Collect normal tissue as baseline: Forty tissue samples with no copy number changes of CDKN2A and CDKN2B verified by FISH, ddPCR or methylation were collected as baseline, and then used as baseline by CNVKit.

[0051] ② Calculate the copy number: Using the CNVkit tool, calculate the copy number changes in the exon regions of the CDKN2A and CDKN2B genes and the 200 Kb flanking regions on both sides. The baseline data is the baseline obtained from the above 40 samples. The sliding window is set to 100 bp to obtain the copy ratio (log2) and weight of each window, and calculate the average copy ratio of the gene (log2P = , where n is the number of sliding windows), and the copy number (CN) is calculated as CN = .

[0052] (5) Perform calculations: In case 1: BAF offset, copy number 1 < CN < 2, in the case of loss of heterozygosity: In normal cells, the normal copy of AB is AB, and the BAF is 0.5; the copy number is 2; In tumor cells, the abnormal copy of AB is A or B. When the AB copy is A, the BAF is 0, and the copy number is 1. When the AB copy is B, the BAF is 1, and the copy number is 1; Since the copy number measured based on sequencing data is the result of the mixture of normal cells and tumor cells, assuming the tumor cell content is Purity, the copy number of tumor cells is ; the copy number of normal cells is ; the total cells are 1, then the copy number CN measured based on sequencing data can be obtained by mixing the copy numbers of tumor cells and normal cells, expressed as:

[0053] Among them, CN is the copy number of the target gene, After transforming the above expression, it is obtained:

[0054] In case 2 and 3: BAF offset, copy number CN >= 2, In normal cells, the normal copy of AB is AB, and the BAF is 0.5; the copy number is 2; In tumor cells, the abnormal copy of AB is multiple As such as AAA or n Bs such as BBBB. When the AB copy is multiple As, the minor allele mutation frequency BAF is 0, and the copy number is an uncertain n. When the AB copy is multiple Bs, the BAF is 1, and the copy number is n; At this time, since n is uncertain, the calculation method of case 1 can no longer be used to solve it; at this time, considering that due to the mixture of normal cells and tumor cells in the tissue, the part with BAF higher than 0.5 is the result of the mixture of tumor cells and normal cells when the abnormal copy of AB is multiple B, that is, the part of BAF higher than 0.5 extracted from the sequencing data is the tumor cells with BAF of 1 (the content is expressed as Purity1) and BAF is:

[0055] After transformation, we get:

[0056] The part with BAF lower than 0.5 is the result of a mixture of tumor cells with multiple copies of AB (the content is expressed as Purity2) and normal cells, which is expressed as:

[0057] After transformation, we get:

[0058] Among them, AFupper is the average mutation frequency of sites with candidate minor allele mutation frequency BAF higher than 0.5 in the targeted region, Afdownner is the average mutation frequency of sites with candidate minor allele mutation frequency BAF lower than 0.5 in the targeted region, and AF is the candidate minor allele mutation frequency in the targeted region.

[0059] Then the overall cell content is the average of the tumor cell content in the part with BAF higher than 0.5 and the tumor cell content in the part with BAF lower than 0.5, expressed as: Purity = (Purity1 + Purity2) / 2 Substituting the Purity1 and Purity2 after the above transformation, and further deducing, we get:

[0060] Among them, Purity represents the tumor cell content, AFupper, is the average mutation frequency of the sites where the minor allele mutation frequency of the candidate heterozygous site is higher than 0.5, AFdownner, is the average mutation frequency of the sites where the minor allele mutation frequency of the candidate heterozygous site is lower than 0.5.

[0061] Case 4: BAF not offset, copy number 0 <CN<2, At this point, it is judged to be a homozygous deletion; Assume that the tumor cell content is Purity and the copy number of tumor cells is , Normal cell content is 1-purity, and the copy number is , The copy number is expressed as:

[0062] So .

[0063] In some embodiments, δ=0 was selected, and the tumor cell content was obtained using the above method, which was similar to the gold standard of tumor cell content measured clinically (as shown in Table 4), demonstrating the application potential of the present method.

[0064] Table 4 Similarity between tumor cell content assessment results based on single-sample targeted gene sequencing and clinical measurement results

[0065] Based on the prior patent documents, in order to realize the calculation of tumor cell content, the present application first readjusts the threshold of copy loss and further divides heterozygosity loss into two cases, which require different calculation methods to calculate tumor cell content; The tumor cell content of the present application is based on sequencing data of the targeted gene region obtained using probes, which is low-cost and suitable for scenarios where it is inconvenient to obtain control samples and it is impossible to obtain sufficient tissue samples in clinical practice, thus making up for the shortcomings of existing technologies in clinical applications.

[0066] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present invention, such as Figure 3 As shown, the device may include: one or more processors, and one or more memories; wherein the memories store computer-readable codes, and when the computer-readable codes are run by the one or more processors, the method described above may be executed.

[0067] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, operations and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can be an X86 architecture or an ARM architecture.

[0068] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0069] For example, the method or device according to the embodiment of the present disclosure may also be implemented by Figure 4 The architecture of the computing device 3000 shown in FIG. Figure 4 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage device in the computing device 3000, such as ROM 3030 or hard disk 3070, may store various data or files used for processing and / or communication of the method provided by the present disclosure and program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 4 One or more components of a computing device are shown.

[0070] The embodiment of the present invention also provides a computer-readable storage medium, such as Figure 5As shown, it is a schematic diagram of a storage medium provided by an embodiment of the present invention, and a computer readable instruction 4010 is stored on the computer storage medium 4020. When the computer readable instruction 4010 is executed by the processor, the method according to the embodiment of the present disclosure described with reference to the above figures can be executed. The computer readable storage medium in the embodiment of the present disclosure can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be 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) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory. It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0071] The present disclosure also provides a computer program product or a computer program, which implements the steps of the above method when executed by a processor, such as Figure 2 As shown, the computer program product or computer program comprises: Acquisition module 201: used to acquire gene sequencing data of candidate heterozygous sites of a single tumor sample; Feature extraction module 202: used to determine whether the minor allele frequency is offset according to the gene sequencing data, and calculate the copy number; Evaluation output module 203: used to calculate the tumor cell content based on whether the minor allele frequency is offset and the copy number: if the minor allele frequency is offset and the copy number is greater than or equal to 2, the tumor cell content is calculated based on the mean minor allele mutation frequency of the candidate heterozygous sites that is higher than 0.5 and the mean minor allele mutation frequency of the candidate heterozygous sites that is lower than 0.5.

[0072] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. 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, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession 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 block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0073] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0074] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0075] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0076] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0078] The exemplary embodiments of the present disclosure described in detail above are merely illustrative and not restrictive. It should be understood by those skilled in the art that various modifications and combinations may be made to these embodiments or their features without departing from the principles and spirit of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A method for evaluating tumor cell content based on single-sample targeted gene sequencing, characterized in that: The method comprises: S1: Obtain gene sequencing data of candidate heterozygous sites of a single tumor sample; S2: Determine whether the minor allele frequency is shifted according to the gene sequencing data, and calculate the copy number; S3: The tumor cell content is calculated based on whether the minor allele frequency is offset and the copy number: If the minor allele frequency is offset and the copy number is greater than or equal to the upper limit of the normal copy number range, the tumor cell content is calculated based on the mean minor allele frequency of the candidate heterozygous sites that is higher than 0.5 and the mean minor allele frequency of the candidate heterozygous sites that is lower than 0.

5.

2. The method for evaluating tumor cell content based on single sample targeted gene sequencing according to claim 1, characterized in that: If the minor allele frequency is offset and the copy number is greater than or equal to the upper limit of the normal copy number interval, the tumor cell content is the difference between the mean minor allele frequency of the candidate heterozygous site that is higher than 0.5 and the mean minor allele frequency of the candidate heterozygous site that is lower than 0.5; Optionally, the calculation formula for the tumor cell content is expressed as: Among them, Purity represents the tumor cell content, is the average mutation frequency of the sites where the minor allele frequency of the candidate heterozygous sites is higher than 0.5, is the average mutation frequency of the candidate heterozygous sites where the minor allele frequency is lower than 0.5; Optionally, the normal copy number interval is expressed as an interval [ , the upper limit of the normal copy number interval is ; Wherein, δ represents the fluctuation threshold around 2 for normal negative tissue sequencing; Optional, is 0.

3. The method for evaluating tumor cell content based on single sample targeted gene sequencing according to claim 1, characterized in that: S3 further includes: if the minor allele frequency is offset and the copy number is in the heterozygous deletion interval to the left of the normal copy number interval, calculating the tumor cell content based on the copy number; Optionally, the heterozygous deletion interval on the left side of the normal copy number interval is (1-δ, 2-δ), where δ represents the fluctuation threshold around 2 of normal negative tissue sequencing; Optional, the calculation formula is: ;Wherein, Purity represents the tumor cell content, and CN represents the copy number; Optionally, S3 further includes: if the minor allele frequency is not offset and the copy number is in the homozygous deletion interval, calculating the tumor cell content based on the copy number; Optionally, the homozygous deletion interval is (0, 2-δ), where δ represents the fluctuation threshold around 2 of normal negative tissue sequencing; Optional, the calculation formula is: ; Wherein, Purity represents the tumor cell content and CN represents the copy number.

4. The method for evaluating tumor cell content based on single sample targeted gene sequencing according to claim 1, characterized in that: In S2, determining whether the minor allele frequency is offset according to the gene sequencing data includes: S21: Obtaining the number of heterozygous sites according to the gene sequencing data, S22: judging whether the minor allele is shifted according to the number of the heterozygous sites; Optionally, S21 includes: calculating the minor allele frequency of the candidate heterozygous site according to the gene sequencing data, retaining the candidate heterozygous site whose minor allele frequency is in the heterozygous interval as the heterozygous site, and counting the number of the heterozygous sites; Optionally, S22 includes: determining whether the BAF is offset according to whether the number of the heterozygous sites is greater than a offset threshold; Optionally, the heterozygous interval is calculated based on the gene sequencing data of the candidate heterozygous sites in the LOH-negative sample group verified by FISH; Optionally, obtain the gene sequencing data of the candidate heterozygous sites of the LOH-negative sample set verified by FISH, count the minor allele frequency of each candidate heterozygous site in the gene sequencing data of the candidate heterozygous sites in the LOH-negative sample set, calculate the absolute value of the difference between the minor allele frequency of each candidate heterozygous site and 0.5 to obtain the AbsBAF of each candidate heterozygous site, and calculate the average value M of the true positive heterozygous sites in the AbsBAF of each candidate heterozygous site. T , standard deviation SD T and the average value of false positive heterozygous sites M F , standard deviation SD F ; The minor allele frequency interval of the heterozygous site is the intersection of the complement of the true positive heterozygous site distribution interval and the complement of the false positive heterozygous site distribution interval; Optionally, the minor allele frequency interval of the heterozygous site is expressed as: Among them, max() means to find the larger value of the two, and min() means to find the smaller value of the two. M T The mean value of AbsBAF of true positive heterozygous sites, SD T represents the variance of AbsBAF of true positive heterozygous sites, M F The mean value of AbsBAF of false positive heterozygous sites, SD F represents the variance of AbsBAF of false-positive heterozygous sites, and C represents the size of the confidence interval determined according to the confidence level; Optionally, if the number of the heterozygous sites is less than the offset threshold, it is determined that the BAF is offset, otherwise the BAF is not offset; Optionally, the deviation threshold is determined based on the number of heterozygous sites in a verified LOH-negative sample set; Optionally, the deviation threshold is greater than the number of heterozygous sites in all verified LOH-negative sample sets.

5. The method for evaluating tumor cell content based on single sample targeted gene sequencing according to claim 1, characterized in that: Obtaining gene sequencing data of candidate heterozygous sites of a single tumor sample includes: screening, based on information from the dbSNP database, SNP sites with minor allele sites in the targeted gene region and a subpopulation frequency of 1000 genomes in an extended range of about 50% as candidate heterozygous sites; using probes to obtain gene sequencing data of candidate heterozygous sites in the targeted gene region and its flanking regions; Optionally, the expansion interval around 50% is 50%±20%.

6. The method for evaluating tumor cell content based on single sample targeted gene sequencing according to claim 1, characterized in that: The gene sequencing data of the candidate heterozygous site of the single tumor sample is used to calculate the average gene copy ratio of the heterozygous site according to the copy ratio and weight of each window of the sliding window using the sliding window method, and the copy number is calculated according to the average gene copy ratio; Optionally, the copy number is calculated using a CNVkit tool; Optionally, the baseline data for copy number calculation using the CNVkit tool is the baseline of tissue samples collected with no copy number changes verified by FISH or ddPCR or methylation.

7. The method for evaluating tumor cell content based on single sample targeted gene sequencing according to claim 1, characterized in that: Determining corresponding targeted gene regions according to different tumors in a public tumor gene database, obtaining the candidate heterozygous site based on sequence screening of the targeted gene region, designing a minor allele frequency shift detection probe based on the candidate heterozygous site, performing second-generation sequencing after targeted capture of the candidate heterozygous site based on the minor allele frequency shift detection probe, and obtaining gene sequencing data of the candidate heterozygous site; Optionally, the targeted gene region includes the region where the targeted gene is located and its flanking regions; Optionally, the flanking region is 300kb in size.

8. A computer device, characterized in that: The device comprises: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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