Screening method of gene region set for detecting small residual focus of ovarian cancer, gene region set and detection system thereof

Abstract

The invention discloses a screening method of a gene region set for detecting small residual focuses of ovarian cancer, the gene region set and a detection system of the gene region set, and belongs to the technical field of gene molecular diagnosis. According to the method, a preoperative blood sample, an intraoperative multipoint tumor tissue sample and postoperative daily peritoneal drainage liquid of an ovarian cancer patient on the first to sixth days are taken as detection samples, a novel ovarian cancer nuclear genome hot spot region screening method is developed, a gene region for ovarian cancer tumor DNA detection is constructed, and tumor tissue mutation information is taken as a tracing basis, so that an ovarian cancer tumor DNA detection result is obtained. The characteristics of tumor tissue specific mutation in the free DNA of the peritoneal drainage liquid are analyzed, and the purpose of detecting the free tumor DNA of the peritoneal drainage liquid is achieved. The method can be applied to the postoperative residual focus detection of the ovarian cancer patient, can sensitively and accurately evaluate the postoperative residual focus state of the patient at an early stage, and provides timely prognosis judgment basis for clinic, so as to support the adjustment of clinical decisions.

Description

Technical Field

[0001] The present invention belongs to the technical field of gene molecular diagnosis, and particularly relates to a screening method for a gene region set for detecting minimal residual lesions of ovarian cancer, the gene region set and a detection system thereof. Background Art

[0002] Ovarian cancer is the gynecological malignant tumor with the highest fatality rate. Peritoneal implantation metastasis is the most common metastasis route of ovarian cancer, and 80% of ovarian cancer patients have extensive peritoneal implantation metastasis at the initial treatment. Surgery is a necessary means for the treatment of ovarian cancer, and its purpose is to remove as many visible tumor lesions as possible to achieve a state of no visible residue (R0). However, only R0 can be achieved during the operation, and the invisible lesions cannot be removed, resulting in the residue of minimal lesions.

[0003] Minimal Residue Disease (MRD) refers to the microscopic tumor lesions that are macroscopically invisible after treatment, and its presence is a necessary condition for recurrence. Due to the existence of MRD, 70% of R0 ovarian cancer patients will still relapse within 2 to 3 years after treatment, and the recurrence site of the vast majority of patients is the abdominal cavity, indicating that the presence or absence of abdominal microscopic residual lesions determines the prognosis of ovarian cancer patients. MRD studies on various cancers such as lung cancer and colorectal cancer have also proven that there are significant differences in the prognosis between MRD-positive and MRD-negative patients. Therefore, accurately detecting microscopic residual lesions in the abdominal cavity after surgery helps to accurately evaluate the prognosis of ovarian cancer patients, timely adjust the treatment strategy, and improve the therapeutic effect of patients. Traditional detection methods such as pathology, tumor markers, and imaging are limited by problems such as difficult sampling, low sensitivity, and insufficient resolution, making it difficult to detect microscopic residual lesions after surgery. The emerging detection methods for microscopic residual lesions are mainly liquid biopsy. Liquid biopsy refers to the detection of a series of biomarkers shed from tumors in body fluids such as blood, urine, and cerebrospinal fluid, including tumor cells themselves, tumor DNA, tumor RNA, etc., so as to determine the presence of tumor lesions. Currently, most studies focus on exploring biomarkers shed from tumors in the blood, such as free tumor DNA, tumor cells, and extracellular vesicles of tumor cells in the blood. However, the separation and detection of free tumor cells in the blood are difficult, and the poor stability of extracellular vesicles of tumor cells results in extremely low detection sensitivity for both, so their application is less. In the body fluids of cancer patients, there are two types of cell-free DNA, one from tumor cells and the other from normal cells. The two together form cell-free DNA (cfDNA). Compared with the DNA released by normal cells, tumor DNA carries the genetic information of tumor cells, such as gene mutations, copy number variations, gene fusions, etc. Detecting tumor DNA by analyzing gene mutations in cfDNA, thereby reflecting the presence of tumor lesions, is a widely recognized method. However, due to the absolute predominance of the number of normal cells, the free DNA from normal cells also accounts for a relatively large proportion, becoming an interference in detecting mutations of free tumor DNA.

[0004] Ovarian cancer mainly metastasizes by peritoneal implantation. The peritoneal-plasma barrier restricts the release of tumor DNA into the blood, resulting in extremely low levels of free tumor DNA in the blood, which is not suitable as a specimen for detecting microscopic residual lesions of ovarian cancer. The strategy of high-depth sequencing combined with MRD analysis informed by tumor tissue is an effective method to improve the sensitivity of mutation detection. However, the increase in depth significantly increases the sequencing cost. Currently, some studies have adopted the strategy of sequencing only a small number of high-frequency mutation sites or genes in the nuclear genome of ovarian cancer. However, since not all patients carry these specific mutations, the universality of this method is limited. In summary, the current detection methods have deficiencies in both increasing the amount of free tumor DNA and improving the sensitivity of mutation detection methods. Summary of the Invention

[0005] In order to overcome the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a screening method for a gene region set for detecting minimal residual lesions of ovarian cancer, the gene region set and its detection system, so as to solve the technical problems of low sensitivity, low accuracy, high cost and lack of universality in the existing detection methods for minimal residual lesions after ovarian cancer surgery.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solutions to be realized:

[0007] In the first aspect of the present invention, a screening method for a gene region set for detecting minimal residual lesions of ovarian cancer is disclosed, including the following steps:

[0008] Step 1: Screen high-frequency mutated genes related to ovarian cancer from a public database to obtain gene region set Ⅰ;

[0009] Step 2: Screen out exons with mutations that are not included in gene region set Ⅰ and appear ≥ 2 times from the clinically self-tested ovarian cancer data, and incorporate them into gene region set Ⅰ to obtain gene region set Ⅱ;

[0010] Step 3: Screen out exons that are not included in gene region set Ⅱ and have only a single mutation occurring on gene region set Ⅱ from the clinically self-tested ovarian cancer data, and incorporate them into gene region set Ⅱ to obtain gene region set Ⅲ;

[0011] Step 4: Screen out exons with ovarian cancer gene mutations that are not included in gene region set Ⅲ and appear ≥ 5 times from a public database, and incorporate them into gene region set Ⅲ to obtain gene region set Ⅳ;

[0012] Step 5: Screen out exons that are not included in gene region set Ⅳ, have only a single ovarian cancer gene mutation occurring on gene region set Ⅳ, and the number of patients with ovarian cancer gene mutations on the exon / the length of the exon in Mbp ≥ 20 from a public database, and incorporate them into gene region set Ⅳ to obtain a gene region set for detecting minimal residual lesions of ovarian cancer.

[0013] In the second aspect of the present invention, a gene region set for detecting minimal residual lesions of ovarian cancer obtained by the above screening method is disclosed.

[0014] In the third aspect of the present invention, the application of the above gene region set for detecting minimal residual lesions of ovarian cancer in the preparation of a detection kit for minimal residual lesions in the abdominal cavity after ovarian cancer surgery is disclosed.

[0015] In the fourth aspect of the present invention, there is disclosed the use of the above gene region set for detecting minimal residual lesions of ovarian cancer in the preparation of a detection system for minimal residual lesions in the abdominal cavity after ovarian cancer surgery.

[0016] In the fifth aspect of the present invention, there is disclosed the above-mentioned detection kit for free tumor DNA in abdominal cavity drainage fluid after ovarian cancer surgery, which is characterized by comprising primers or capture probes for detecting the above gene region set for detecting minimal residual lesions of ovarian cancer.

[0017] In the sixth aspect of the present invention, there is disclosed a detection system for minimal residual lesions in the abdominal cavity after ovarian cancer surgery, comprising:

[0018] A data input module for inputting free DNA data of abdominal cavity drainage fluid, blood cell DNA data and tumor tissue DNA data of ovarian cancer patients;

[0019] A DNA library construction module for constructing a free DNA library of abdominal cavity drainage fluid according to the free DNA data of abdominal cavity drainage fluid, constructing a blood cell DNA library according to the blood cell DNA data, and constructing a tumor tissue DNA library according to the tumor tissue DNA data;

[0020] A judgment module for comparing the mutation data in the free DNA library of abdominal cavity drainage fluid, the blood cell DNA library, the tumor tissue DNA library and the above gene region set for detecting minimal residual lesions of ovarian cancer, and judging whether the mutations in the abdominal cavity drainage fluid contain tumor tissue-specific mutations;

[0021] A result output module for outputting the detection result of the residual lesions after ovarian cancer surgery of ovarian cancer patients according to the judgment result of the judgment module.

[0022] Preferably, in the data input module, the abdominal cavity drainage fluid of ovarian cancer patients is the abdominal cavity drainage fluid of ovarian cancer patients every day after surgery.

[0023] Preferably, in the judgment module, the recognition method of tumor tissue-specific mutations is: taking the sequencing depth of the mutation site being greater than the minimum sequencing depth required at the variant allele frequency as the true mutation recognition condition to obtain the true mutations of tumor tissue DNA; comparing the true mutations of tumor tissue DNA and the blood cell DNA mutations, and finding the tumor tissue-specific mutations by removing the mutations that also exist in the blood cell DNA from the true mutations of tumor tissue DNA.

[0024] Preferably, in the judgment module, the method for identifying mutations in peritoneal drainage fluid is as follows: taking the sequencing depth of the mutation site being greater than the minimum sequencing depth required at the variant allele frequency as the true mutation identification condition, and obtaining the true mutations in the cell-free DNA of peritoneal drainage fluid; among the true mutations in the cell-free DNA of peritoneal drainage fluid, the mutations that are the same as the tumor tissue-specific mutations are the mutations in peritoneal drainage fluid.

[0025] Preferably, in the judgment module, the method for judging whether the mutations in peritoneal drainage fluid contain tumor tissue-specific mutations is as follows: according to the results of each comparison of the mutation data in the cell-free DNA library of peritoneal drainage fluid, the blood cell DNA library, the tumor tissue DNA library, and the above gene region set for detecting minimal residual disease of ovarian cancer, judge whether the mutations in peritoneal drainage fluid contain tumor tissue-specific mutations. If the mutation is continuously undetectable or detected first and then disappears, it indicates a negative minimal residual disease after ovarian cancer surgery; if the mutation persists and the tumor burden is stable, it indicates a positive minimal residual disease after ovarian cancer surgery.

[0026] Among them, the calculation method of the tumor burden is: the average value of the variant allele frequencies of the tumor tissue-specific mutation sites in the peritoneal drainage fluid DNA / the sum of the variant allele frequencies of the same mutation sites in the tumor tissue.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The screening method of the gene region set for detecting minimal residual disease of ovarian cancer provided by the present invention, on the basis of the public database, by adding the exons with mutations that are not included in the gene region set Ⅰ and appear ≥2 times in the clinically self-tested ovarian cancer data to ensure that the gene region set can cover more patients; by adding the exons that are not included in the gene region set Ⅱ and have only a single mutation occurring in the gene region set Ⅱ in the clinically self-tested ovarian cancer data to increase the number of individual mutations in the gene region set; through multi-data source extension of the gene region, the universality of the gene region set can be enhanced. It is proved by experiments that the gene region set screened by this method is applicable to most ovarian cancer patients, and can accurately and sensitively reflect the state of minimal residual disease after ovarian cancer surgery, can be used for the accurate evaluation of the prognosis of ovarian cancer patients, so as to evaluate the curative effect of radical treatment, predict recurrence in advance, and has great significance for the postoperative clinical management of ovarian cancer patients.

[0029] The present invention provides a system for detecting micro-residual lesions in the abdominal cavity after ovarian cancer surgery, (1) using peritoneal drainage fluid instead of conventional plasma samples. This improvement has two advantages: first, it increases the amount of free tumor DNA. Compared with plasma, the anatomical position of peritoneal drainage fluid is closer to the micro-residual lesions in the abdominal cavity of ovarian cancer, and can more directly receive substances released by tumor lesions, thereby making it easier to detect DNA from tumor lesions; second, the acquisition of peritoneal drainage fluid has clinical operational advantages. Not only does the sample have a higher concentration of free DNA, only a small amount of sample is required to obtain an ideal detection effect, and the sampling process is simple and non-invasive. Every ovarian cancer patient will routinely place a peritoneal drainage tube and connect a drainage bag after surgery. The daily drainage volume usually exceeds 100 mL, so there is no need to worry about insufficient sample volume. This sampling method will not increase the trauma of the patient and is conducive to improving patient compliance. (2) Screening hot spots in the ovarian cancer nuclear genome and constructing a gene region set of residual lesions after ovarian cancer surgery. The present invention has developed a new method for screening hotspot regions of the nuclear genome of ovarian cancer. The method has two significant features: one is to ensure that the screened regions can cover multiple mutations of almost all ovarian cancer patients, and the other is to narrow the scope of the gene region as much as possible through precise screening. Compared with the traditional method of simply selecting a few high-frequency mutation genes, this precise screening strategy significantly improves the sensitivity of detection and makes the detection of ovarian cancer DNA more reliable. (3) Using tumor tissue as the source, the mutation information of tumor tissue is used to analyze the mutations in the peritoneal drainage fluid. The present invention uses tumor tissue as the reference benchmark for mutation analysis. By comparing and analyzing the mutation information of tumor tissue and normal DNA, the tumor tissue-specific mutation spectrum of each ovarian cancer patient is first determined, and then based on this, only the characteristics of tumor tissue-specific mutations in the peritoneal drainage fluid are analyzed. This method ensures that the detected mutations are completely derived from tumor cells, thereby significantly improving the credibility and accuracy of mutation analysis.

[0030] In summary, the method for detecting free tumor DNA in peritoneal drainage fluid after ovarian cancer surgery of the present invention is sensitive, non-invasive and rapid. The detection system generated based on this method not only significantly improves the detection sensitivity of tiny residual lesions in the abdominal cavity of ovarian cancer, but also ensures the reliability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a mutation result diagram of Chinese ovarian cancer patients in the self-test database that gene region B in Example 1 of the present invention can cover;

[0032] Figure 2 This is a mutation result diagram of ovarian cancer patients in the COMSIC public database that can be covered by gene region B in Example 1 of the present invention;

[0033] Figure 3This is a comparison result graph of the simulated detection limit and detection rate between gene region B and the whole exome in Example 1 of the present invention;

[0034] Figure 4 This is a comparison result graph of the simulated detection limit, detection rate and sequencing data volume between gene region B and the whole exome in Example 1 of the present invention; wherein, A is the sequencing of gene region B, and B is the whole genome sequencing;

[0035] Figure 5 This is a flow chart for detecting residual lesions after ovarian cancer surgery in Example 3 of the present invention;

[0036] Figure 6 This is a graph for determining the detectable mutation frequency at different sequencing depths of tumor tissue DNA in Example 3 of the present invention;

[0037] Figure 7 This is a graph for determining the detectable mutation frequency at different sequencing depths of cell-free DNA in the supernatant of peritoneal drainage fluid in Example 3 of the present invention;

[0038] Figure 8 This is a result graph of the change trend of the mutation frequency of cell-free tumor DNA in peritoneal drainage fluid of patients with macroscopic residual in the abdominal cavity in Example 3 of the present invention; wherein, A is the detection data of patient 1, B is the detection data of patient 2, C is the detection data of patient 3, and D is the detection data of patient 4;

[0039] Figure 9 This is a result graph of the change trend of the mutation frequency of cell-free tumor DNA in peritoneal drainage fluid of patients without macroscopic residual in the abdominal cavity and negative for MRD in Example 3 of the present invention; wherein, A is the detection data of negative patient 1, B is the detection data of negative patient 2, C is the detection data of negative patient 3, and D is the detection data of negative patient 4;

[0040] Figure 10 This is a result graph of the change trend of the mutation frequency of cell-free tumor DNA in peritoneal drainage fluid of patients without macroscopic residual in the abdominal cavity but positive for MRD in Example 3 of the present invention; wherein, A is the detection data of positive patient 1, B is the detection data of positive patient 2, C is the detection data of positive patient 3, and D is the detection data of positive patient 4;

[0041] Figure 11 This is a result graph of the comparison of the recurrence rates between the MRD positive group and the negative group in Example 3 of the present invention;

[0042] Figure 12 This is a result graph of the Kaplan-Meier survival analysis between the MRD positive group and the negative group in Example 3 of the present invention. Detailed implementation manners

[0043] To enable those skilled in the art to understand the features and effects of the present invention, the following provides a general description and definition of the terms and phrases mentioned in the specification and claims. Unless otherwise specified, all technical and scientific terms used herein shall have the ordinary meaning understood by those skilled in the art with respect to the present invention. In case of conflicts, the definitions in this specification shall prevail. The theories or mechanisms described and disclosed herein, whether correct or incorrect, shall in no way limit the scope of the present invention, that is, the content of the present invention can be implemented without being limited by any specific theory or mechanism.

[0044] In this article, the sample "peritoneal drainage fluid" used refers to the fluid collected from the peritoneal drainage bag of ovarian cancer patients, and its fluid components include a mixture of residual physiological saline in the abdominal cavity before closing the abdomen and exudate from the surgical wound surface. Since these fluids come into direct contact with the cells and their exfoliates in the abdominal cavity when they accumulate in the abdominal cavity, they contain a large number of cells and biomolecules such as DNA released from the cells.

[0045] The following further elaborates the present invention in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0046] The following embodiments use conventional instrument and equipment in the art. For the experimental methods without specific conditions noted in the following embodiments, they are generally carried out under conventional conditions or according to the conditions recommended by the manufacturer. In the following embodiments, the nuclease-free water used is purchased from Qiagen, product number 129117; the tissue DNA extraction kit is purchased from OMEGA, product number D3396-02; the blood cell DNA extraction kit is purchased from TIANGEN, product number DP319-02; the free DNA extraction reagent used is from Circulating Nucleic Acid Kit, purchased from Qiagen, product number 55114; the DNA library construction reagent is purchased from Vazyme, product number ND903; the adapter is purchased from BGI Sequencing, product number: G2412201101; the hybridization capture probe is ordered from Aegigen; the hybridization capture reagent is purchased from Aegigen, product number: 82124407; the hybridization amplification reagent is purchased from Aegigen, product number: 82128401; the vacuum filtration system is purchased from Qiagen; various types of samples come from clinical units. For other various raw materials used, unless otherwise stated, conventional commercially available products are used, and their specifications are conventional specifications in the art.

[0047] Example 1

[0048] A screening method for a gene region set for detecting minimal residual disease of ovarian cancer, comprising the following steps:

[0049] Step 1. Screening of frequently mutated genes

[0050] (1) Extract the top 30 genes with the highest mutation frequencies in ovarian cancer from the TCGA database, the Catalogue of Somatic Mutations in Cancer (COSMIC) database, the cBioPortal (Landscape of somatic alterations in large-scale solid tumors from an Asian population) database, and the Chinese ovarian cancer patient mutation literature (A comprehensive analysis of somatic alterations in Chinese ovarian cancer patients, PMID: 33432021);

[0051] (2) By counting the number of occurrences of the top 30 genes collected in step (1) in the four data sources, screen out the genes that appear more than 2 times in the four data sources to obtain the frequently mutated gene set shown in Table 1, which serves as the most common mutated genes in ovarian cancer;

[0052] Table 1 Frequently mutated gene set

[0053] TP53 NF1 KMT2D KMT2C LRP1B ARID1A BRCA1 PIK3CA KRAS SPEN PTEN MTOR CDK12 SMARCA4 BRCA2 ATM NOTCH2 FANCA ARID1B NOTCH1 CSMD3 RAD54L BARD1 CREBBP KMT2A MUC16 BCOR NOTCH3 FAM135B MED12

[0054] (3) Since most mutations occur in the exons of genes, further obtain the full exon gene coordinates of each gene in the frequently mutated gene set in Table 1 to form gene region B, which serves as the initial gene region.

[0055] Step 2. Optimize the frequently mutated gene set to cover more patients

[0056] Based on gene region B, and based on the gene mutation data of clinically self-tested ovarian cancer tumor samples (which can also be replaced with ovarian cancer mutation data in public databases), expand the scope of gene region B according to the following steps:

[0057] (1) From the 450-gene targeted sequencing information of 93 clinically self-tested ovarian cancer tumor patient samples, screen out the set of patients - ptB that have mutations in gene region B;

[0058] (2) From the self-tested data, remove the exons that are already included in gene region B, and screen out the exons with mutations in ≥2 patients from the remaining data;

[0059] (3) Count the number of new patients with mutations on each candidate exon selected in step (2) (i.e., the mutant patients are not in ptB), and calculate the RI value of each candidate exon (the calculation method of the RI value: the number of patients with mutations on a certain exon / the length of the exon in Mbp);

[0060] (4) According to the number of new patients with mutations on the candidate exons, preferentially incorporate the exon with the largest number into gene region B;

[0061] (5) If the numbers are the same, incorporate the exon with a larger RI value into gene region B;

[0062] (6) Iterate steps (1) to (5) until no exon meets the criteria (i.e., there are no new patient mutations on the exons not incorporated into gene region B).

[0063] Step 3. Expand the gene region to increase the number of individual mutations

[0064] Based on the gene mutation data of clinically self-tested ovarian cancer tumor samples, continue to expand gene region B on the basis of step 2, and the steps are as follows:

[0065] (1) Remove the exons already included in gene region B from the 450-gene targeted sequencing information of 93 clinically self-tested ovarian cancer tumor patient samples, and the remaining exons are candidate exons;

[0066] (2) Screen out the patients with only a single mutation occurring on gene region B;

[0067] (3) Count the number of patients in (2) with mutations on each candidate exon, and calculate the RI value of the candidate exon;

[0068] (4) According to the number of patients with a single mutation on the candidate exons, preferentially incorporate the exon with the largest number of patients with a single mutation into gene region B;

[0069] (5) If the numbers are the same, select the exon with a larger RI value into gene region B;

[0070] (6) Iterate steps (1) to (5) until no exon meets these criteria (there are no patients with single mutations on the candidate exons) or all patients have at least 2 mutations on gene region B.

[0071] Step 4. Expand the gene region with multiple data sources to enhance universality

[0072] On the basis of step 3, continue to expand gene region B using the 1780 cases of ovarian cancer mutation data downloaded from the COMSIC library, and the steps are as follows:

[0073] (1) Replace the "450-gene targeted sequencing information of 93 ovarian cancer tumor patient samples in the clinical self-test" in step 2(1) with "1780 ovarian cancer mutation data downloaded from the COMSIC database", and change the exon screening condition in step 2(2) to ≥ 5 patients, then repeat step 2;

[0074] (2) Replace the "450-gene targeted sequencing information of 93 ovarian cancer tumor patient samples in the clinical self-test" in step 3(1) with "1780 ovarian cancer mutation data downloaded from the COMSIC database", and replace the "Remove the exons already included in gene region B, and the remaining exons are candidate exons" in step 3(1) with "Remove the exons already included in gene region B, and the exons with an RI value ≥ 20 in the remaining exons are candidate exons", then repeat step 3; Obtain gene region B, and the results are shown in Table 2. Gene region B contains 736 genes, 1970 exon segments, with a total length of 692.591 kbp.

[0075] Table 2 Gene region set

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] Step 5. Simulation verification of gene region B

[0092] (1) Apply the gene region B screened in Step 4 to the gene mutation data of clinically self-tested ovarian cancer tumor samples and public databases respectively for data simulation, verify the proportion of patients covered by this gene region, and analyze the possible number of mutations that can be detected in different ovarian cancer samples by the obtained gene region B.

[0093] The data simulation results are as Figure 1 and Figure 2 shown. For Chinese ovarian cancer patients in the gene mutation data of clinically self-tested ovarian cancer tumor samples, 99% of the patients can be covered with at least 2 mutations, and 89% of the patients can be covered with at least 4 mutations ( Figure 1 ); for ovarian cancer patients in the COMSIC database, 85% of the patients can be covered with at least 1 mutation, and 43% of the patients can be covered with at least 2 mutations ( Figure 2 ). The above results indicate that almost all ovarian cancer patients have mutations in the screened gene region B, suggesting that this gene region is applicable to most ovarian cancer patients.

[0094] (2) Use the Poisson distribution to explore the relationship between the detection rate of gene region B and the detection limit (the minimum proportion of tumor DNA that can be detected in normal cell DNA), where: n = average sequencing depth; d = the proportion of tumor DNA in normal DNA, representing the detection limit; k = the number of detected mutations; λ = n × d. If ≥1 mutant read is detected, it is considered that the tumor DNA is detected. The probability that any one of the k independent mutations is detected (that is, the probability that the tumor DNA is detected) is: Assume that the sequencing data volume is 3G. Compared with the whole exome sequencing (WES) of cell-free DNA, at the same detection limit, the detection rate of the required region is higher, indicating higher sensitivity ( Figure 3 ). Use the above model to simulate the relationship between the detection rate, detection limit and sequencing data volume. The variable n = data volume / sequencing region length. At the same detection rate, the gene region requires less sequencing data volume and lower detection limit than WES, indicating higher sensitivity and lower cost ( Figure 4 ).

[0095] Example 2

[0096] A kit for detecting minimal residual lesions after ovarian cancer surgery, including a probe prepared based on the gene region B obtained in Example 1.

[0097] The gene region B obtained through Example 1 was used. By applying the design studio provided on the Illumina official website, the genes to be detected and the detection region were input for automated design (https: / / designstudio.illumina.com / ), and probes for each gene locus in the gene region B were synthesized. Then it was handed over to Beijing E-Genomics Co., Ltd. for preparation. The prepared probe combination was: One Hyb&Wash Kit v2.0, where each probe has a length of 100 bp and covers the selected gene region in a 3x tiling manner. One Hyb&Wash Kit v2.0 can efficiently capture DNA fragments in the gene region B, and the capture efficiency is ≥ 75%.

[0098] Example 3

[0099] A detection method for free tumor DNA in peritoneal drainage fluid after ovarian cancer surgery, as Figure 5 shown, includes the following steps:

[0100] Step 1. Sample collection

[0101] Collect the preoperative blood of ovarian cancer patients, multiple tumor tissues during the operation, and peritoneal drainage fluid every day from the 1st to the 6th / 7th day after the operation.

[0102] The collection and processing steps of peritoneal drainage fluid are as follows: Collect 15 mL of peritoneal drainage fluid stored in the drainage bag for 6 - 8 h with a 50 mL centrifuge tube. After standing at 4°C or on an ice box for 1 h, take the supernatant, centrifuge at 600 g for 10 min at 4°C, continue to take the supernatant and place it in a 15 mL centrifuge tube, centrifuge at 16000 g for 10 min at 4°C, and then take the supernatant again and place it in a 15 mL centrifuge tube for storage and standby.

[0103] Step 2. Sample DNA extraction

[0104] 1. Referring to Table 3, use Circulating Nucleic Acid Kit to extract the supernatant of peritoneal drainage fluid of ovarian cancer patients on each day from the 1st to the 6th day after the operation processed in Step 1 to obtain free DNA in peritoneal drainage fluid. The specific steps are as follows:

[0105] (1) Take the supernatant of peritoneal drainage fluid and place it in a 15 mL centrifuge tube, and then add 200 μL / mL of proteinase K to it; add 1.6 mL of Buffer ACL to each milliliter of the sample, vortex for 30 s, mix thoroughly, and immediately incubate in a water bath at 60°C for 30 min;

[0106] (2) Add an appropriate volume of Buffer ACB to the centrifuge tube in step (1) according to the ratio of 3.6 mL of Buffer ACB per 1 mL of sample. Vortex for 30 s. After thorough mixing, immediately incubate on ice for 5 min;

[0107] (3) Add the mixture obtained in step (2) to the filter column of the vacuum filtration system. Turn on the suction pump for suction, and open the valve to allow the mixture to completely pass through the filter membrane in the filter column; then close the valve and the suction pump. Add 600 μL of Buffer ACW1 to the filter column, turn on the suction pump and the valve for suction to allow Buffer ACW1 to completely pass through the filter membrane in the filter column; then close the valve and the suction pump again. Add 750 μL of Buffer ACW2 to the filter column, turn on the suction pump and the valve for suction to allow Buffer ACW2 to completely pass through the filter membrane in the filter column; finally, close the valve and the suction pump, add 750 μL of absolute ethanol to the filter column, turn on the suction pump and the valve for suction to allow the absolute ethanol to completely pass through the filter membrane in the filter column;

[0108] (4) Place the filter column in a 2 mL centrifuge tube and centrifuge at 17460 g for 4 min at room temperature; then discard the centrifuge tube. Place the filter column in a new 2 mL centrifuge tube, open the lid, and incubate at 56 °C for 10 min to completely dry the filter membrane;

[0109] (5) Add 200 μL of nuclease-free water to the center of the membrane, cover the lid, and incubate at room temperature for 3 min. Then centrifuge at 17460 g for 2 min at room temperature to elute the nucleic acid. Collect the cfDNA solution obtained after filtration into a centrifuge tube. After measuring 260 / 280 and 260 / 230 with Onedrop and measuring the concentration with Qubit 4.0, obtain the free DNA in the peritoneal drainage fluid and store it at -80 °C for later use.

[0110] Table 3 Corresponding volumes of Proteinase K, sample, Buffer ACL, and Buffer ACB

[0111] Abdominal drainage fluid (mL) 1 2 3 Proteinase K (μL) 200 400 600 Buffer ACL + carrierRNA (mL) 1.6 3.2 4.8 Buffer ACB (mL) 3.6 7.2 10.8

[0112] 2. Use a blood cell DNA extraction kit to extract the preoperative blood of ovarian cancer patients collected in step 1 to obtain blood cell DNA for later use.

[0113] 3. Use a tissue DNA extraction kit to extract multiple tumor tissues during the operation of ovarian cancer patients collected in step 1 to obtain tumor tissue DNA for later use.

[0114] Step 3: Construct a DNA library

[0115] 1. Construct a library for the free DNA in the peritoneal drainage fluid obtained in step 2. The specific steps are as follows:

[0116] (1) Fragmentation of cell-free DNA in peritoneal drainage fluid. Dilute the cell-free DNA in peritoneal drainage fluid extracted in Step 2 to 10 ng / μL with nuclease-free water in a 100 μL system. Use Scientz18-A ultrasonic to fragment the diluted cell-free DNA in peritoneal drainage fluid to 100 - 500 bp (fragmentation conditions: 100 times, interval 5 s, frequency 80). Detect the fragment size by agarose gel electrophoresis (140 V, electrophoresis for 20 min).

[0117] (2) Library construction of cell-free DNA in peritoneal drainage fluid. Take 30 μL of the fragmented cell-free DNA in peritoneal drainage fluid, that is, the DNA input amount for library construction is 300 ng, and it must be the fragmented cell-free DNA in peritoneal drainage fluid; ligate the adapter at the ends. The 300 ng input amount of the fragmented cell-free DNA in peritoneal drainage fluid corresponds to 5 μL of the adapter with a concentration of 10 μM (using a non-complete adapter, this part of the Adapter only contains the sample tag and does not contain the Illumina P5 and P7 adapters, prepared by BGI Sequencing Company); amplify the adapter with a concentration of 10 μM, the volume of P5 adapter is 2.5 μL, the volume of P7 adapter is 2.5 μL, and perform PCR amplification with 12 cycles; finally, elute the library with 80 μL of nuclease-free water, take 75 μL and put it into a 1.5 mL centrifuge tube to complete the library construction of cell-free DNA in peritoneal drainage fluid. The constructed library is measured for 260 / 280 and 260 / 230 using Onedrop to check if it is qualified, the concentration is measured using Qubit 4.0, and the fragment size is evaluated using Agilent Bioanalyzer2100 (Agilent, USA).

[0118] 2. Refer to Step 1 to construct a library for the blood cell DNA obtained in Step 2.

[0119] 3. Refer to Step 1 to construct a library for the tumor tissue DNA obtained in Step 2.

[0120] Step Four: Targeted capture of the target region

[0121] Use One Hyb&Wash Kit v2.0 (purchased from Aegtek) to perform targeted capture of the target gene region on the library obtained in Step 3. After capture, measure 260 / 280 and 260 / 230 using Onedrop to check if it is qualified, measure the concentration using Qubit4.0, and evaluate the fragment size using Agilent Bioanalyzer 2100 (Agilent, USA).

[0122] Step Five: Obtain DNA mutation data of multiple samples

[0123] After capture was completed, paired-end sequencing was performed on an Illumina X PLUS sequencer to obtain 10G of sequencing data. After the sequencing data was downloaded, first, software (v2.0) was used to remove adapter-contaminated, reads with an N ratio > 5%, and low-quality sequencing products (reads) to obtain clean data; then, the clean data was trimmed using fastp (v0.23.4) software to cut off adapters, low-quality bases, etc., to obtain good data; next, the good data was aligned to the human hg19 reference genome using the BWA aligner (v0.7.17) software; the Picard (v3.0) software was used to sort the aligned reads, and the samtools rmdup software was used to remove duplicates; the GATK (4.4.0.0) software was used for local realignment to reduce alignment errors caused by InDels. SNVs and InDels were identified and detected using the VarDict (v1.8.3) software. The reads of the major and minor alleles located on the sense and antisense strands at each base site of nuclear DNA were counted, and then the site mutation frequency was calculated. After filtering, the final nuclear DNA mutations were obtained. The filtering conditions were as follows: a. VAF (variant allele frequency) ≥ 1%; b. remove reads with a mismatch number > 3; c. site depth ≥ 100; d. mutant reads on both the sense and antisense strands ≥ 3. The ANNOVAR software was used for mtDNA mutation site annotation.

[0124] Step 6: Identification of free tumor DNA in the peritoneal drainage fluid of ovarian cancer

[0125] 1. Setting the threshold for true mutations: First, determine the lowest variant allele frequency that can be accurately detected in tumor tissue samples and peritoneal drainage fluid supernatant samples: Divide the same tumor tissue DNA and peritoneal drainage fluid DNA into three parts and perform three repeated sequencing experiments starting from fragmentation. Define mutations detected ≥ 2 times in the repeated sequencing as true mutations. Sort the mutation sites according to the sequencing depth. At four levels of variant allele frequency, namely 0.1%, 0.5%, 1%, and 2%, evaluate the relationship between the site sequencing depth and consistency (consistency = the number of all true mutations greater than the variant allele frequency with a sequencing depth less than a certain value / the total number of mutations). Use the sequencing depth with a consistency ≥ 90% as the minimum sequencing depth required for a certain variant allele frequency. The relationship between the sequencing depth and consistency of tumor tissue DNA and free DNA in peritoneal drainage fluid at the four variant allele frequency levels is as Figure 6 and Figure 7As shown, it can be found that for tumor tissue DNA, when the sequencing depth exceeds 10,000x, the mutation consistency of variant alleles with a frequency above 1% stabilizes at 90%; for free DNA in peritoneal drainage fluid, when the sequencing depth exceeds 14,000x, the mutation consistency of variant alleles with a frequency above 0.1% stabilizes at 90.

[0126] 2. True mutation identification: Taking the sequencing depth of the mutation site being greater than the minimum sequencing depth required at the variant allele frequency as the condition for true mutation identification, according to the condition for true mutation identification, the true mutations of tumor tissue DNA and the true mutations in peritoneal drainage fluid are obtained respectively.

[0127] 3. Identification of free tumor DNA in peritoneal drainage fluid of ovarian cancer:

[0128] (1) Identification of tumor tissue-specific mutations: By comparing the true mutations of tumor tissue DNA and the mutations of blood cell DNA, and removing the mutations in the true mutations of tumor tissue DNA that also exist in blood cell DNA, the tumor tissue-specific true mutations are found.

[0129] (2) Identification of tumor tissue-specific mutations in free DNA of peritoneal drainage fluid: First, among all the mutations in free DNA of peritoneal drainage fluid, the true mutations are screened out; further, among the true mutations, the mutations that are the same as the tumor tissue-specific true mutations are found. If found, it is positive for free tumor DNA in peritoneal drainage fluid, otherwise it is negative. When the free tumor DNA in peritoneal drainage fluid is positive, calculate the "average value of the variant allele frequency of the tumor tissue-specific mutation site in peritoneal drainage fluid DNA / the sum of the variant allele frequencies of the mutation site in tumor tissue" to represent the tumor burden in peritoneal drainage fluid.

[0130] 4. Judgment of peritoneal MRD status: Analyze the status of free tumor DNA in peritoneal drainage fluid every day and the tumor burden in the supernatant of peritoneal drainage fluid every day to judge whether the patient is positive for minimal residual disease. If the mutation cannot be detected continuously or is detected first and then disappears, it indicates negative for minimal residual disease; if the mutation persists and the tumor burden is stable, it indicates positive for minimal residual disease.

[0131] Step 7. Verification of the accuracy of the method for detecting free tumor DNA in peritoneal drainage fluid of ovarian cancer

[0132] 1. Using patients who did not achieve macroscopic residual disease during surgery as the positive cohort, verify whether the method for detecting cell-free DNA in peritoneal drainage fluid can detect tumor DNA in the peritoneal drainage fluid of patients who did not achieve macroscopic residual disease every day, and whether it can accurately judge the MRD status of these patients. Peripheral blood before surgery, intraoperative tumor tissue, and peritoneal drainage fluid from the 1st to the 6th day after surgery were collected from 4 patients who did not achieve macroscopic residual disease (Patients 1, 2, 3, and 4). The cell-free tumor DNA in the supernatant of peritoneal drainage fluid was detected and the peritoneal MRD status was judged according to the methods of Steps 1 to 6 above. The results are as Figure 8 shown. In the 4 patients, tumor DNA was present in the supernatant of peritoneal drainage fluid every day, and the variant allele frequency was relatively stable, indicating the presence of residual lesions in the abdominal cavity, which was consistent with the actual results. It was proved that the method for detecting cell-free tumor DNA in peritoneal drainage fluid of ovarian cancer was suitable for the detection of residual lesions in the abdominal cavity.

[0133] 2. Peripheral blood before surgery, intraoperative tumor tissue, and peritoneal drainage fluid from the 1st to the 6th day after surgery of ovarian cancer patients were collected from clinical units. Patients who achieved macroscopic residual disease-free during surgery and were subsequently pathologically confirmed to have ovarian cancer were enrolled. After enrollment, the cell-free tumor DNA in the supernatant of peritoneal drainage fluid was detected and the peritoneal MRD status was judged according to the above method. At the same time, the patients were followed up for 2 years. Tumor markers and imaging were rechecked every 3 months in the 1st to 2nd year. Abdominopelvic ultrasound was the preferred imaging examination. When the tumor markers were abnormal but the abdominopelvic ultrasound was normal, CT / MRI examination was performed. All patients received standard treatment regimens after surgery. Finally, a total of 29 patients completed the follow-up, and the follow-up duration was 24 months. According to the MRD test results, the patients were grouped. 11 patients were identified as MRD positive, and 18 patients were identified as MRD negative. Figure 9 showed the change of tumor DNA load in peritoneal drainage fluid over time in 4 patients identified as minimal residual disease negative (Negative Patients 1, 2, 3, and 4), Figure 10 showed the change of tumor DNA load in peritoneal drainage fluid over time in patients with minimal residual disease positive (Positive Patients 1, 2, 3, and 4). Among the 11 MRD-positive patients, 9 patients had peritoneal recurrence. Only 1 of the 18 patients in the MRD-negative group had peritoneal recurrence ( Figure 11 ); Kplan-Meier survival analysis showed that the survival of the MRD-negative group was better than that of the MRD-positive group, with statistical significance ( Figure 12 ). The above results indicated that the method for detecting cell-free tumor DNA in peritoneal drainage fluid after ovarian cancer surgery could accurately identify MRD-positive patients.

[0134] The above content is only for explaining the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any modification made on the basis of the technical solution in accordance with the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A screening method for a set of gene regions for detecting minimal residual lesions of ovarian cancer, characterized in that: The following steps are involved: Step 1: Screen the high-frequency mutation genes related to ovarian cancer from the public database to obtain gene region set I; Step 2: Screen out the exons with mutations that are not included in gene region set I and appear ≥2 times from the clinical self-tested ovarian cancer data, and include them in gene region set I to obtain gene region set II; Step 3: Screen out exons that are not included in gene region set II and have only a single mutation occurring in gene region set II from clinical self-tested ovarian cancer data, and include them in gene region set II to obtain gene region set III; Step 4: Screen out exons with ovarian cancer gene mutations that are not included in gene region set III and appear ≥5 times from the public database, and include them in gene region set III to obtain gene region set IV; Step 5. Screen out from the public database the exons that are not included in gene region set IV, have only a single ovarian cancer gene mutation occurring in gene region set IV, and the number of patients with ovarian cancer gene mutations in the exon / the length of the exon Mbp ≥ 20 exons, and include them in gene region set IV to obtain the gene region set for detecting minimal residual ovarian cancer lesions.

2. A set of gene regions for detecting minimal residual lesions of ovarian cancer obtained by the screening method of claim 1.

3. Use of the gene region set for detecting minimal residual lesions of ovarian cancer as claimed in claim 2 in the preparation of a kit for detecting minimal residual lesions in the abdominal cavity after ovarian cancer surgery.

4. Use of the gene region set for detecting minimal residual lesions of ovarian cancer as claimed in claim 2 in preparing a system for detecting minimal residual lesions in the abdominal cavity after ovarian cancer surgery.

5. A kit for detecting free tumor DNA in peritoneal drainage fluid after ovarian cancer surgery, characterized in that: It comprises primers or capture probes for detecting the gene region set for detecting minimal residual lesions of ovarian cancer as claimed in claim 2.

6. A system for detecting micro-residual lesions in the abdominal cavity after ovarian cancer surgery, characterized in that: include: A data input module, used to input peritoneal drainage fluid free DNA data, blood cell DNA data and tumor tissue DNA data of ovarian cancer patients; A DNA library construction module, used to construct a peritoneal drainage fluid free DNA library based on peritoneal drainage fluid free DNA data, to construct a blood cell DNA library based on blood cell DNA data, and to construct a tumor tissue DNA library based on tumor tissue DNA data; A judgment module, for comparing the mutation data in the peritoneal drainage fluid free DNA library, the blood cell DNA library, the tumor tissue DNA library and the gene region set for detecting minimal residual ovarian cancer lesions according to claim 2, to judge whether the peritoneal drainage fluid mutation contains tumor tissue-specific mutations; The result output module is used to output the detection results of residual lesions after surgery of ovarian cancer patients according to the judgment results of the judgment module.

7. The system for detecting micro-residual lesions in the abdominal cavity after ovarian cancer surgery according to claim 6, characterized in that: In the data input module, the peritoneal drainage fluid of the ovarian cancer patient is the peritoneal drainage fluid of the ovarian cancer patient every day after surgery.

8. The system for detecting micro-residual lesions in the abdominal cavity after ovarian cancer surgery according to claim 6, characterized in that: In the judgment module, the method for identifying tumor tissue-specific mutations is as follows: the sequencing depth of the mutation site is greater than the minimum sequencing depth required at the frequency of the variant allele as the true mutation identification condition, and the true mutation of tumor tissue DNA is obtained; the true mutation of tumor tissue DNA is compared with the mutation of blood cell DNA, and the tumor tissue-specific mutation is found by removing the mutations that also exist in blood cell DNA from the true mutation of tumor tissue DNA.

9. The system for detecting micro-residual lesions in the abdominal cavity after ovarian cancer surgery according to claim 6, characterized in that: In the judgment module, the method for identifying peritoneal drainage fluid mutations is: the sequencing depth of the mutation site is greater than the minimum sequencing depth required at the frequency of the variant allele as the true mutation identification condition, and the true mutation of the free DNA of the peritoneal drainage fluid is obtained; among the true mutations in the free DNA of the peritoneal drainage fluid, the mutation that is identical to the tumor tissue-specific mutation is the peritoneal drainage fluid mutation.

10. A system for detecting micro-residual lesions in the abdominal cavity after ovarian cancer surgery according to any one of claims 6 to 9, characterized in that: In the judgment module, the method for judging whether the peritoneal drainage fluid mutation contains tumor tissue-specific mutations is as follows: according to the result of each comparison of the mutation data in the peritoneal drainage fluid free DNA library, the blood cell DNA library, the tumor tissue DNA library and the above-mentioned gene region set for detecting minimal residual lesions of ovarian cancer, whether the peritoneal drainage fluid mutation contains tumor tissue-specific mutations, if the mutation is continuously undetectable or is first detected and then disappears, it indicates that the postoperative minimal residual lesions of ovarian cancer are negative; if the mutation persists and the tumor load is stable, it indicates that the postoperative minimal residual lesions of ovarian cancer are positive; The tumor load is calculated as follows: the average value of the variant allele frequency of the tumor tissue-specific mutation site in the peritoneal drainage fluid DNA / the sum of the variant allele frequencies of the mutation site in the tumor tissue.

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