A biomarker for detecting bile duct cancer and its application

Abstract

The present invention discloses a biomarker for the detection of bile duct cancer and its application. The biomarker combination for the detection of bile duct cancer of the present invention includes a DNA+RNA dual-level detection of tumor tissue samples, detecting a wide range of genetic variations, including single nucleotide variations (SNVs), insertions and deletions (indels), copy number variations (CNVs), gene fusions (Fusion) and microsatellite instability (MSI), with more comprehensive detection; at the same time, the biomarker combination also includes a site combination for identifying potential sample contamination or contamination, which can achieve matching and proofreading of DNA+RNA samples, making the detection more accurate; the biomarker combination of the present invention can be used for the diagnosis, treatment, prognosis and other stages of the occurrence and development of bile duct cancer, laying the foundation for comprehensive and systematic detection and research of bile duct cancer.

Description

Technical Field

[0001] The present invention relates to a biomarker for bile duct cancer detection and application thereof, belonging to the technical field of biomedical detection. Background Art

[0002] Biliary tract carcinoma (BTC) primarily includes gallbladder cancer (GBC) and intrahepatic and extrahepatic bile duct cancer (CC). The incidence of both intrahepatic and extrahepatic bile duct cancers (hereinafter referred to as cholangiocarcinomas) is increasing worldwide. According to statistics, cholangiocarcinoma currently accounts for 3% of all digestive tract malignancies and 2% of all cancer-related deaths worldwide each year. In China, more than 6 cases occur per 100,000 residents. Cholangiocarcinoma originates from the bile duct epithelium and is mostly adenocarcinoma. Depending on the location of the tumor, it is divided into intrahepatic bile duct carcinoma (iCCA) and extrahepatic bile duct carcinoma (eCCA). Extrahepatic bile duct carcinoma can be further divided into hilar bile duct carcinoma (pCCA) and distal bile duct carcinoma (dCCA). Biliary duct carcinomas in different locations have distinct clinical manifestations, resulting in different diagnosis and treatment. Because the clinical symptoms of cholangiocarcinoma are atypical, symptoms often appear in the late stage. Although there is a first-line chemotherapy regimen of gemcitabine combined with platinum for patients with advanced cholangiocarcinoma, the efficacy is only 15-26%, which is not ideal. Therefore, new strategies and plans are urgently needed for the diagnosis and treatment of cholangiocarcinoma.

[0003] With the application of next-generation sequencing technology, genetic testing and targeted drug combinations have opened up new avenues for cancer treatment. Genomic analysis has shown that nearly 40% of patients with cholangiocarcinoma harbor potentially targetable gene mutations, suggesting potential for targeted therapy. FGFR1-3 gene mutations are detected in 11% of ICC and 3% of gallbladder cancer. FGFR2 gene fusions, most commonly FGFR2-ZMYM4 and FGFR2-BICC1 fusions, are detected in 11% to 45% of CCA. A clinical trial (FIGHT-202) in second-line treatment for patients with advanced / metastatic or unresectable CCA demonstrated significant efficacy with pemigatinib (INCB54828) in patients with CCA harboring FGFR2 gene translocations. In HCC, the frequency of PIK3CA mutations is approximately 4%, and the frequency of PTEN loss mutations is approximately 7%. Clinical studies suggest that PI3K, AKT, and mTOR inhibitors may be potential therapeutic targets for patients with tumors harboring PIK3CA mutations. HER2 gene mutations occur in approximately 3.9% of CCA and approximately 11.0% of gallbladder cancer. Studies have shown that HER2 is associated with the prognosis of gallbladder cancer. MET mutations occur in approximately 1.89% of HCC and 1.44% of CCA. Studies have found that c-MET expression levels are high in tumor tissues of HCC patients and are associated with sorafenib resistance. In HCC cells with high c-MET expression, cabozantinib can effectively inhibit MET activity, inhibiting angiogenesis, cell proliferation, cell migration and invasion, and promoting cell apoptosis. Therefore, the identification of genetic or structural variations in cholangiocarcinoma is crucial for its diagnosis and treatment.

[0004] Molecular analysis using reverse transcription polymerase chain reaction (RT-PCR), fluorescence in situ hybridization (FISH), and more recently, next-generation sequencing (NGS) is a valuable diagnostic tool. FISH or RT-PCR are used to detect fusion events at the genomic or transcriptional levels, respectively. However, both methods have limitations. FISH can only identify the presence of a fusion mutation but cannot determine the fusion partner gene or the specific breakpoint location. RT-PCR, which requires the design of specific primers and probes, is limited to detecting known fusion mutations and cannot be used to detect unknown fusion mutations.

[0005] NGS is a high-throughput, low-cost tool that can sequence multiple chromosomal regions in parallel to detect a wide range of genetic variations, including single nucleotide variations (SNVs), insertions and deletions (indels), translocations, and copy number variations (CNVs). Whole-genome sequencing and exome sequencing can discover new genomic changes, while targeted NGS is a cost-effective technology for detecting known structural variations that can target specific regions of interest and perform more in-depth sequencing. Targeted NGS, in particular, can enrich and sequence a group of genes or regions in a single test, reducing costs, turnaround time, and the burden of data analysis. This makes it more suitable for sparse specimens such as FFPE or biopsies. Summary of the Invention

[0006] The purpose of the present invention is: to address the shortcomings of existing bile duct cancer detection technology, the present invention provides a biomarker combination for bile duct cancer detection and its application. The biomarker combination for bile duct cancer detection of the present invention includes DNA+RNA dual-level detection of tumor tissue samples, detecting a wide range of genetic variations, including single nucleotide variations (SNVs), insertions and deletions (Indels), copy number variations (CNVs), gene fusions (Fusion) and microsatellite instability (MSI), for more comprehensive detection; at the same time, the biomarker combination also includes a site combination for identifying potential sample contamination or contamination, which can achieve matching and proofreading of DNA+RNA samples, making the detection more accurate; the biomarker combination of the present invention can be used for the diagnosis of bile duct cancer and the monitoring of the disease at various stages of occurrence and development, such as treatment and prognosis, laying the foundation for comprehensive and systematic detection and research of bile duct cancer.

[0007] In order to achieve the purpose of solving the above problems, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a use of a biomarker in preparing a product for detecting gene mutations in bile duct cancer, wherein the biomarker comprises a biomarker A for detecting DNA mutations in bile duct cancer and a biomarker B for detecting RNA mutations in bile duct cancer;

[0009] Biomarker A consists of genes ALK, CDK12, FGFR1, MTOR, PIK3CA, AR, CDKN2A, FGFR3, NF1, PTCH1, ATM, CHEK1, FGFR4, NRAS, PTEN, BARD1, DPYD, FLI1, NRG1, RAD51C, BRAF, EGFR, IDH1, NTRK1, ROS1, BRCA1, ERBB2, IDH2, NTRK3, TSC1, BRCA2, ESR1, KDM6A, PALB2, TSC2, BRIP1, EZH2, KRAS, and PDGFRA;

[0010] The biomarker B consists of ALK, BRAF, ETV6, FGFR1, FGFR2, FGFR3, FGFR4, NRG1, NTRK1, NTRK2, NTRK3, RET, ROS1, FLI1, PDGFB and MET.

[0011] Preferably, the DNA variation includes gene fusion, single nucleotide variations (SNVs), insertions and deletions (Indels), copy number variation (CNV) and microsatellite instability (MSI); the RNA variation includes gene fusion.

[0012] Preferably, the application includes preparing a kit for detecting bile duct cancer gene mutations using a biomarker detection reagent.

[0013] Preferably, the kit at least includes a probe for detecting a biomarker.

[0014] Preferably, the application includes preparing a detection system or detection device for detecting bile duct cancer gene mutations using biomarkers as detection indicators.

[0015] In a second aspect, the present invention provides a kit for detecting gene mutations in bile duct cancer, wherein the kit comprises at least a probe for detecting a biomarker.

[0016] Preferably, the kit further comprises reagents for extracting DNA and / or RNA from a sample, and the sample is bile duct cancer tumor tissue.

[0017] A third aspect of the present invention provides a system or device for detecting gene mutations in bile duct cancer, the system or device comprising:

[0018] A data acquisition module is used to obtain sample sequencing data. The sequencing data is obtained by hybridizing and capturing the probes for detecting the cholangiocarcinoma biomarker combination with the pre-library, followed by purification with magnetic beads to obtain the sequencing library. The sequencing library is sequenced on a NextSeq 550Dx / Novaseq 6000 gene sequencer to obtain the sequencing data.

[0019] The data processing module is used to obtain analytical data that meets quality control requirements through data preprocessing, data comparison and quality control of sequencing data;

[0020] Data detection and analysis module, used for detection and analysis of sequencing data;

[0021] and a test result acquisition module;

[0022] The system or device is used to detect bile duct cancer gene mutations through the following steps:

[0023] Step 1: Obtaining biomarker combination expression profile data in a sample of a test subject, wherein the biomarker combination is derived from a tumor tissue sample of a cholangiocarcinoma patient;

[0024] Step 2: Data processing, which specifically includes:

[0025] Step 2.1, Data Preprocessing: Use software to analyze the sequencing quality parameter Q30 base ratio. If the Q30 ratio is ≥60%, the quality control passes; otherwise, the quality control fails. Then, use Illumina software to convert the BCL files generated by the NextSeq550Dx / Novaseq 6000 sequencing system into Fastq files. Then, use Trimmomatic-0.36 software to remove adapter sequences and low-quality base fragments introduced during library construction.

[0026] Step 2.2, data alignment: Remove low-quality bases and adapter sequences from the raw data, perform base quality statistics on the filtered data, map the sequenced fragment reads back to the reference genome, and calculate the read count, alignment rate, sequencing depth and coverage, and variant detection;

[0027] Step 2.3, Data Quality Control: Determine the quality of sample sequencing based on parameters such as the sample Q30 base ratio, the ratio of sequence alignment to the reference genome, and the average sequencing depth of the target region. If Q30 is ≥60%, the ratio of sequence alignment to the reference genome is ≥90%, and the average sequencing depth is ≥300X, the sample sequencing data quality control passes. Otherwise, it fails and is considered a test failure, requiring resequencing.

[0028] Step 3: Data analysis: SNV / Indel mutation detection was performed using Vardict V1.8.2-2 and MuTect2 V 4.0.12.0; MSI detection was performed using MSIsensor2; gene fusion detection was performed using Manta V1.6.0 and Delly2 V1.1.15; and CNV detection was performed using cnvkit V0.9.6. Analysis parameters required sequence alignment quality ≥ 20 and base quality ≥ 30; all other settings were default.

[0029] Step 4: Obtain the results. If the mutation abundance of SNV / Indels is ≥0.5%, it is judged as positive; otherwise, it is negative; if the mutation abundance of fusion gene is ≥0.5%, it is judged as positive; otherwise, it is negative; if the MSI score is ≥10%, it is judged as MSI-H; otherwise, it is MSS (microsatellite stable); if the CNV CN is ≥3.25, it is judged as positive, otherwise, it is negative.

[0030] According to a fourth aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory stores a program, and the processor implements the steps described in the above technical solution when executing the program.

[0031] A fifth aspect of the present invention is a computer-readable storage medium, the computer-readable storage medium comprising a stored computer program;

[0032] Wherein, when the computer program is running, the computer-readable storage medium is controlled to implement the steps described in the above technical solution.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] (1) The cholangiocarcinoma-related biomarkers provided by the present invention have wide coverage and high detection performance

[0035] DNA and RNA are extracted from the same specimen, undergo different pre-treatments, and then enter the same testing process for simultaneous sequencing and analysis. DNA can be used to detect single nucleotide variants (SNVs), insertions and deletions (Indels), copy number variations (CNVs), and microsatellite instability (MSI). RNA can be used to detect gene fusions. Both DNA structural variations and RNA splicing variations can produce fusion genes. DNA is less affected by sample quality, but probes are limited by unknown breakpoints and large intronic regions, which can easily lead to missed detections. RNA can make up for the shortcomings of DNA fusion detection, discover more new fusion forms, and detect splicing variations at the RNA level.

[0036] (2) Low testing cost and wider application range

[0037] The cholangiocarcinoma biomarker combination and detection system provided by the present invention can realize DNA+RNA dual-level detection of genetic variations at the DNA and RNA levels of tumor tissue samples from cholangiocarcinoma patients, including single nucleotide variations (SNVs), insertions and deletions (Indels), copy number variations (CNVs), and microsatellite instability (MSI). One detection and analysis process can detect multiple types, with lower costs and greater promotion value.

[0038] 3) Convenient analysis and strong operability

[0039] The system of the present invention can complete the process from offline data to various variation results in one step, and can simultaneously analyze multiple biomarkers for bile duct cancer detection, greatly shortening the analysis time and gaining valuable detection and analysis time for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Distribution of homozygotes at 442 germline mutation sites detected in 66 DNA sequencing samples;

[0041] Figure 2 Distribution of DNA and RNA sample matching scores. The gray box plot shows the score distribution when the DNA and RNA samples are not from the same patient (4290 scores in total); the red box plot shows the score distribution when the DNA and RNA samples are from the same patient (66 scores in total).

[0042] Figure 3 .SNV / INDEL distribution;

[0043] Figure 4 .Copy number variation results of 34 samples;

[0044] Figure 5 .Microsatellite instability test results of 226 clinical samples;

[0045] Figure 6 .Microsatellite instability test results of 226 clinical samples;

[0046] Figure 7 . Detected DNA and RNA FGFR fusions. DETAILED DESCRIPTION

[0047] To make the present invention more clearly understood, preferred embodiments are described in detail below with reference to the accompanying drawings.

[0048] Example 1 Screening of biomarkers

[0049] 1. Screening method

[0050] This example uses genomics and technology to perform high-throughput genetic analysis on cholangiocarcinoma (CCA) tissue and normal tissue to identify molecular differences between cholangiocarcinoma tissue and normal tissue, as well as candidate marker genes that are significantly upregulated or downregulated in cholangiocarcinoma. Through comparative analysis, we searched for cholangiocarcinoma-specific expression patterns and gene mutations, and screened a biomarker combination for cholangiocarcinoma detection, including 39 genes such as IDH1 / 2, FGFR2, PIK3CA, PTEN, and BRAF for DNA variation detection, and 16 genes such as FGFR1, FGFR2, FGFR3, and FGFR4 for RNA fusion variation detection, as shown in Tables 1 and 2 below, respectively.

[0051] Table 1. List of genes for DNA mutation detection

[0052] ALK CDK12 FGFR1 MTOR PIK3CA AR CDKN2A FGFR3 NF1 PTCH1 ATM CHEK1 FGFR4 NRAS PTEN BARD1 DPYD FLI1 NRG1 RAD51C BRAF EGFR IDH1 NTRK1 ROS1 BRCA1 ERBB2 IDH2 NTRK3 TSC1 BRCA2 ESR1 KDM6A PALB2 TSC2 BRIP1 EZH2 KRAS PDGFRA

[0053] Table 2. List of genes for RNA fusion variant detection

[0054] ALK BRAF ETV6 FGFR1 FGFR2 FGFR3 FGFR4 NRG1 NTRK1 NTRK2 NTRK3 RET ROS1 FLI1 PDGFB MET

[0055] The above biomarker combination of the present invention can be used to identify a combination of sites of potential sample confounding or contamination, match and proofread DNA+RNA samples, making the detection more accurate; detect gene mutations at the DNA and RNA levels of tumor tissue samples of cholangiocarcinoma patients at the DNA+RNA double level, and more comprehensively reflect the gene mutation situation of tumor samples. The relationship between samples is estimated by using the homozygous proportion at the high-frequency germline mutation sites of Chinese people at set positions to match and proofread DNA+RNA samples.

[0056] 2. Selection of germline mutation sites

[0057] Select high-frequency germline mutations in 442 populations from the public germline mutation database of the 1000Genome project, with the frequency (AF, alternative allele frequency) between 0.3 and 0.7 (0.3 < AF < 0.7). These mutations fall within the target range of the JUS70 panel. The coordinates of these 442 germline mutations on the hg19 reference genome and their frequencies in the 1000Genome populations are shown in Table 3 specifically.

[0058] Table 3. 442 high-frequency germline mutations in populations for calculating the matching of DNA and RNA sequencing data

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] 3. Method for judging the matching of DNA and RNA samples

[0071] 66 clinical samples were analyzed using JUS70 panel DNA and RNA sequencing. After performing mutation detection on DNA sequencing data and RNA sequencing data, the DNA and RNA genotype results at high-frequency germline sites in 442 populations were summarized. Taking the homozygous mutations at the 442 sites in the DNA sequencing data results as the benchmark (assuming there are n), the number of homozygous mutations at these 100 DNA homozygous mutation sites in the RNA sequencing data (k) was observed. The matching score of the DNA sample and the RNA sample is

[0072]

[0073] Theoretically, if a DNA sample is homozygous for a mutation at a locus in the same individual, then the RNA sample should also be homozygous for the mutation. However, due to random variations in sequencing depth at the target locus, there may be some errors in the homozygous determination. This error may result in the RNA observed value at the DNA homozygous locus not being 100% homozygous for the mutation. However, when such observation points are large (>>60, see Figure 1 ), the match score calculated by Formula 1 will tend to 1. However, when the DNA sample and RNA sample are not from the same person, this match score will be much less than 1 (see below). In this way, based on the match score, it is possible to statistically determine whether the DNA sample and RNA sample are from the same person.

[0074] 4. DNA sample detection of homozygous number

[0075] A major purpose of selecting high-frequency germline mutations in these populations is to ensure that any individual sample contains sufficient homozygous mutations for analysis in DNA and RNA sample matching. Let's first determine whether this requirement is met. Based on the mutation frequencies of germline mutations in Table 3, we can estimate the expected frequency of homozygous mutations.

[0076]

[0077] The expected value of the homozygous frequency calculated in this way is 0.2363. Therefore, the expected number of homozygous mutations detected in a DNA sample for 442 germline mutations is 442×0.2363≈104. This is very close to the observed value in 66 DNA samples. Figure 1 .

[0078] 5. DNA and RNA sample matching score discrimination threshold

[0079] To validate the theoretical analysis described above and identify a threshold for determining matching scores between DNA and RNA samples, we used DNA and RNA sequencing samples from 66 individuals, analyzed using the JUS70 sequencing kit. Different combinations of DNA and RNA samples were used to match and calculate matching scores. A total of 66 × 66 = 4,356 possible combinations were identified, of which 66 matched (DNA and RNA samples came from the same individual) and 4,290 mismatched (DNA and RNA samples came from different individuals). It is important to note that because matching analysis is based on homozygous DNA samples, if the DNA and RNA samples come from different individuals, calculating the matching score for person B's RNA sample using person A's DNA sample as the benchmark is different from calculating the matching score for person A's RNA sample using person B's DNA sample as the benchmark.

[0080] The analysis results show that the matching scores of DNA and RNA samples of the same person are distributed in the range of [0.75, 1] ​​(e.g. Figure 2 (as shown in red), while the matching scores for DNA and RNA samples from different individuals are distributed in the range [0.14, 0.5]. There is a large and distinct gap between the two distributions. This observation is consistent with the theoretical assumptions above, and it is easy to set a matching score threshold to distinguish between the two groups. Therefore, the matching score threshold for DNA and RNA samples is set to 0.6. When the matching score is ≥ 0.6, the DNA and RNA samples are considered to come from the same individual; otherwise, they are from different individuals.

[0081] Example 2 Application of biomarkers

[0082] This example provides a method for detecting bile duct cancer using the biomarkers screened in Example 1. The method and process are as follows:

[0083] 1. Sample DNA and RNA Extraction

[0084] DNA and RNA were extracted and purified from FFPE samples using a magnetic bead-based paraffin total nucleic acid extraction kit according to the manufacturer's instructions. The extracted DNA and RNA were accurately quantified (using a Qubit fluorescence quantifier, recommended). DNA was stored at -20°C, and RNA at -70°C.

[0085] 2. DNA Prelibrary Preparation

[0086] DNA libraries were prepared from FFPE samples using a library preparation kit (KAPA HyperPlus Kit, Roche, cat. no. 07962428001) according to the manufacturer's instructions. The specific steps are as follows:

[0087] 2.1 Fragmentation processing

[0088] 1) Based on the sample concentration, prepare 100 ng of gDNA in a 35 μL volume for fragmentation. If the sample volume is less than 35 μL, add 10 mM Tris-HCl, pH 8.0 to make up to 35 μL.

[0089] 2) The fragmentation reaction solution is prepared as follows for each test:

[0090] Component name Volume (μL) Shearing enzyme buffer (10X) 5 shearing enzyme 10 total 15

[0091] 2) Add 15 μL of the mixed fragmentation reaction solution to the sample to be tested, mix thoroughly, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube into the PCR instrument. Set the program as follows and run the cycle (heated lid temperature: 50°C).

[0092] step temperature time Precooling 4℃ 1min Fragmentation 37℃ 10min Hold 4℃ ∞

[0093] 2.2 End Repair and A Addition

[0094] 1) The end-repair reaction solution is prepared as follows for each test:

[0095] Component name Volume (μL) End repair buffer 7 End repair enzymes 3 total 10

[0096] 2) Add 10 μL of the mixed end-repair reaction solution to the fragmented PCR tube, mix thoroughly, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube into the thermal cycler. Set the program as follows and run the cycle (heated lid temperature: 85°C).

[0097] step temperature time End repair and A addition 65℃ 30min Hold 4℃ ∞

[0098] 3) After the reaction, centrifuge briefly and place on ice for later use.

[0099] 2.3 Connector connection

[0100] 1) Prepare the ligation reaction solution according to the following system for each test:

[0101] Component name Volume (μL) Ligase buffer 30 DNA ligase 10 Nuclease-free water 5 total 45

[0102] 2) Add 5 μL of the universal adapter to the end-repaired PCR tube, vortex to mix, and centrifuge briefly.

[0103] 3) Add 45 μL of the ligation reaction solution, mix thoroughly, and centrifuge briefly. Collect the reaction solution at the bottom of the tube and immediately place the PCR tube into the PCR machine. Set the program as follows and run the cycle (heated lid temperature: 50°C).

[0104] step temperature time Connector connection 20℃ 15min Hold 4℃ ∞

[0105] 4) After the reaction, centrifuge briefly and place on ice for later use.

[0106] 2.4 Magnetic bead purification of ligation products

[0107] 1) KAPA HyperPure Beads (Roche, Cat. No. 08963860001) are recommended. Add 88 μL of purified magnetic beads to the ligation product PCR tube and perform magnetic bead purification according to the purification kit instructions.

[0108] 2) Elute the purified product with 22 μL of 10 mM Tris-HCl, pH 8.0, equilibrated to room temperature. Pipette 20 μL of the supernatant into a new PCR tube, avoiding touching the magnetic beads. Place on ice until ready for use.

[0109] 2.5 Library Amplification

[0110] 1) Add 25 μL of PCR amplification reaction solution 1 and 5 μL of PCR amplification primer (including the sample tag sequence) to each sample, mix well, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube into the thermal cycler. Set the program as follows and run: (heated lid temperature: 105°C)

[0111]

[0112] 2) After the reaction is completed, place it on an ice box for later use.

[0113] 2.6 Magnetic bead purification of amplified products

[0114] 1) We recommend using KAPA HyperPure Beads. Add 45 μL of purified magnetic beads to a PCR tube and perform magnetic bead purification according to the purification kit instructions.

[0115] 2) Elute the purified product with 32 μL of 10 mM Tris-HCl, pH 8.0, equilibrated to room temperature. Pipette 30 μL of the supernatant into a new PCR tube without touching the magnetic beads. Place on ice until ready for use.

[0116] 2.7 Library Quality Control

[0117] 1) Using a nucleic acid quantification kit The dsDNA HS Assay Kit and its supporting instruments are used to determine the concentration of the sample DNA library.

[0118] 2) Prepare a DNA library sample and perform fragment quality control using capillary electrophoresis reagents High Sensitivity D5000 Reagents and accompanying instruments. The average fragment length of the library is between 200-600 bp, with no obvious small or large fragment peaks.

[0119] 3. RNA Preliminary Library Preparation

[0120] RNA libraries from FFPE samples were prepared using a library preparation kit (KAPA RNA HyperPrep Kit, Roche, Cat. No. 8105952001) according to the manufacturer's instructions. The specific steps are as follows:

[0121] 3.1 RNA fragmentation

[0122] 1) Take 200 ng of total RNA and fragment it in a total volume of 10 μL. If the sample volume is less than 10 μL, add 10 mM Tris-HCl, pH 8.0 solution to make up to 10 μL.

[0123] 2) Aliquot 10 μL of the shearing premix (2X) into each 10 μL RNA sample. Mix thoroughly, centrifuge briefly to collect the reaction mixture at the bottom of the tube, and immediately place the PCR tube into the thermal cycler. Set up the program and run the cycle as follows:

[0124] step temperature time Precooling 4℃ 1min Fragmentation 65℃ 1min Hold 4℃ ∞

[0125] 3) After the reaction, centrifuge briefly and place on ice for later use.

[0126] 3.2 Single-strand synthesis

[0127] 1) Prepare the following reverse transcription premix solution 1, vortex to mix, and briefly centrifuge to collect the reaction solution at the bottom of the tube.

[0128] Component name Volume (μL) Reverse transcription buffer 11 Reverse transcriptase 1 total 12

[0129] 2) Add 10 μL of the mixed reverse transcription premix 1 to the fragmented PCR tube, mix thoroughly, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube into the PCR machine. Set the program as follows and run the cycle (heated lid temperature: 85°C).

[0130] temperature time 25℃ 10min 42℃ 15min 70℃ 15min 4℃ ∞

[0131] 3) After the reaction, centrifuge briefly and place on ice for later use.

[0132] 3.3 Second-chain synthesis

[0133] 1) Prepare the reverse transcription premix 2 as follows, vortex to mix, and briefly centrifuge to collect the reaction mixture at the bottom of the tube.

[0134]

[0135]

[0136] 2) Add 30 μL of the mixed reverse transcription premix 2 to the PCR tube after single-strand synthesis. Mix thoroughly, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube in a thermal cycler. Set the program as follows and run the cycle (heated lid temperature: 85°C).

[0137] temperature time 16℃ ∞ 16℃ 30min 62℃ 10min 4℃ ∞

[0138] 3) After the reaction, centrifuge briefly and place on ice for later use.

[0139] 3.4 Connector connection

[0140] 1) The ligation reaction solution is prepared as follows for each test system, shaken to mix, and briefly centrifuged to collect the reaction solution at the bottom of the tube

[0141] Component name Volume (μL) Ligase buffer 40 DNA ligase 10 total 50

[0142] 2) Take 5 μL of the universal adapter and add it to the PCR tube after the second-strand synthesis, shake and mix thoroughly, and centrifuge briefly.

[0143] 3) Add 45 μL of ligation reaction solution, mix thoroughly, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube into the PCR machine. Set the program as follows and run the cycle (heated lid temperature: 50°C).

[0144] step temperature time Connector connection 20℃ 15min Hold 4℃ ∞

[0145] 4) After the reaction, centrifuge briefly and place on ice for later use.

[0146] 3.5 Magnetic bead purification of ligation products

[0147] 1) We recommend using KAPA HyperPure Beads (Roche). Add 70 μL of purified magnetic beads to the ligation product PCR tube. Vortex or pipette gently at least 10 times to mix thoroughly. Incubate at room temperature for 5 minutes.

[0148] 2) Place the PCR tube on a magnetic rack to adsorb the magnetic beads. After the solution is clear, carefully aspirate and discard the supernatant.

[0149] 3) Keep the PCR tube on the magnetic rack and add 200 μL of freshly prepared 80% ethanol. Time for 30 seconds, carefully aspirate and discard the supernatant.

[0150] 4) Add 200 μL of freshly prepared 80% ethanol again, time for 30 seconds, carefully aspirate and discard the supernatant.

[0151] 5) Keep the PCR tube on the magnetic rack at all times, open the lid and dry the magnetic beads at room temperature for 5-10 minutes until no ethanol residue remains.

[0152] 6) Add 50 μL of 10 mM Tris-HCl, pH 8.0 solution equilibrated to room temperature, vortex to mix, centrifuge briefly, and incubate at room temperature for 2 minutes to elute RNA from the magnetic beads.

[0153] 7) Add 35 μL of PEG / NaCl solution and mix thoroughly by vortexing or pipetting gently at least 10 times. Incubate at room temperature for 5 min.

[0154] 8) After incubation, briefly centrifuge the PCR tube and place it on a magnetic rack to absorb the magnetic beads. Once the solution is clear, carefully aspirate and discard the supernatant.

[0155] 9) Keep the PCR tube on the magnetic rack and add 200 μL of freshly prepared 80% ethanol. Time for 30 seconds, then carefully aspirate and discard the supernatant.

[0156] 10) Add 200 μL of freshly prepared 80% ethanol again, time for 30 seconds, carefully aspirate and discard the supernatant.

[0157] 11) Keep the PCR tube on the magnetic rack at all times, open the lid and dry the magnetic beads at room temperature for 5-10 minutes until no ethanol residue remains.

[0158] 12) Add 22 μL of 10 mM Tris-HCl, pH 8.0 solution equilibrated to room temperature, vortex to mix, and centrifuge briefly.

[0159] 13) Incubate at room temperature for 2 minutes, then place on a magnetic stand. Once the solution has clarified, pipette 20 μL of the supernatant into a new PCR tube, avoiding the magnetic beads. Place on ice until ready for use.

[0160] 3.6 Library Amplification

[0161] 1) Add 25 μL of PCR amplification reaction solution 1 and 5 μL of PCR amplification primer (including the sample tag sequence) to each sample, mix well, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube into the thermal cycler. Set the program as follows and run: (heated lid temperature: 105°C)

[0162]

[0163]

[0164] 2) After the reaction is completed, place it on an ice box for later use.

[0165] 3.7 Magnetic bead purification of amplified products

[0166] 1) We recommend using KAPA HyperPure Beads. Add 50 μL of purified magnetic beads to a PCR tube and perform magnetic bead purification according to the purification instructions.

[0167] 2) Elute the purified product with 22 μL of 10 mM Tris-HCl, pH 8.0, equilibrated to room temperature. Pipette 20 μL of the supernatant into a new PCR tube, avoiding touching the magnetic beads. Place on ice until ready for use.

[0168] 3.8 Library Quality Control

[0169] 1) Use nucleic acid quantification kit The dsDNA HS Assay Kit and its supporting instruments are used to determine the concentration of the sample RNA library.

[0170] 2) Prepare a sample RNA library and perform fragment quality control using capillary electrophoresis reagents High Sensitivity D5000 Reagents and supporting instruments. The average fragment length of the library is between 200-600 bp, with no obvious small or large fragment peaks.

[0171] 4. Library Capture

[0172] The library was captured by hybridization using a hybridization capture kit (KAPA HyperCapture Reagent kit and KAPA HyperCapture Bead kit, Roche, catalog numbers 09075828001 and 09075798001), and the capture probe used was the Roche customized JUS70 panel (KAPA HyperChoice, Roche, 9052771001).

[0173] 4.1 Library hybridization

[0174] 1) Sample preparation

[0175] ①Use a 1.5mL centrifuge tube and mix 2-8 libraries in equal amounts according to the sample DNA library concentration. The total volume of one pool is 1μg-4μg.

[0176] ②Use a 1.5mL centrifuge tube and mix 2-8 libraries in equal amounts according to the sample RNA library concentration. The total volume of one pool is 1μg-4μg.

[0177] ③ If the pool volume is greater than or equal to 45 μL, proceed directly to the experiment; if the pool volume is less than 45 μL, add 10 mM Tris-HCl, pH 8.0 solution to 45 μL and proceed with the experiment.

[0178] 2) Hybridization reaction

[0179] ① Add 20 μL DNA blocking buffer to a 1.5 mL centrifuge tube. Add 2 times the volume (pool volume + 20 μL DNA blocking buffer) of magnetic beads (KAPA HyperPure Beads (Roche) is recommended) that have been equilibrated to room temperature and mixed well. Mix well, centrifuge briefly, and incubate at room temperature for 10 minutes.

[0180] ②After the incubation is completed, place the 1.5mL centrifuge tube on a magnetic stand to adsorb the magnetic beads. After the solution is clarified, carefully aspirate and discard the supernatant.

[0181] ③ Place the 1.5 mL centrifuge tube on the magnetic stand and add 1000 μL of freshly prepared 80% ethanol. Let it stand at room temperature for 30 seconds. Carefully aspirate and discard the supernatant.

[0182] ④ Keep the 1.5 mL centrifuge tube on the magnetic rack at all times, open the lid and dry the magnetic beads at room temperature until no ethanol remains (make sure the magnetic beads do not dry out, otherwise it will affect the elution efficiency).

[0183] ⑤ Add 13.4 μL of blocking sequence that has been equilibrated to room temperature, vortex thoroughly to mix, and centrifuge briefly.

[0184] 4.2 Probe hybridization

[0185] 1) Add hybridization buffer and nuclease-free water to a centrifuge tube for hybridization reaction. 2) Vortex to mix, and briefly centrifuge to collect the reaction solution at the bottom of the tube.

[0186] 3) Take 43 μL of the mixed hybridization reaction solution and add it to the 1.5 mL centrifuge tube after the hybridization reaction, mix well, centrifuge briefly, and incubate at room temperature for 2 minutes.

[0187] 4) Place the 1.5 mL centrifuge tube on a magnetic stand to absorb the magnetic beads. After the solution is clarified, transfer 56.4 μL of the supernatant to a new PCR tube.

[0188] 5) Add 4 μL of hybridization probe to the PCR tube, vortex thoroughly to mix, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube into the thermal cycler. Set the program as follows and run the cycle (heated lid temperature: 105°C).

[0189] step temperature time transsexual 95℃ 5min hybridization 55℃ 16-20h

[0190] 4.3 Washing

[0191] 1) Prepare 1X Wash Buffer for each pool as follows:

[0192]

[0193]

[0194] 2) Aliquot 1X Wash Buffer 1 into PCR tubes, 200 μL per tube (two tubes are required for each pool), and preheat in a 55°C PCR instrument for at least 15 minutes.

[0195] 3) Aliquot 1X Wash Buffer 2 into PCR tubes, 100 μL per tube (one tube per pool), and preheat in a 55°C PCR instrument for at least 15 minutes.

[0196] 4) Thoroughly mix the capture magnetic beads that have been equilibrated to room temperature (Dynabeads is recommended). TM M-270 Streptavidin (Thermo Fisher)).

[0197] 5) Aliquot 50 μL of capture beads per pool into PCR tubes or 1.5 mL centrifuge tubes. Place on a magnetic rack. After the solution has clarified, carefully aspirate and discard the supernatant.

[0198] 6) Place the PCR tube or 1.5 mL centrifuge tube on the magnetic stand and add twice the volume of captured magnetic beads in 1X magnetic bead wash buffer and mix thoroughly.

[0199] 7) Briefly centrifuge the PCR tube or 1.5 mL centrifuge tube and return it to the magnetic rack. After the solution is clear, carefully aspirate and discard the supernatant. Add 1X magnetic bead wash buffer (twice the volume of capture magnetic beads) and mix thoroughly.

[0200] 8) Briefly centrifuge the PCR tube or 1.5 mL centrifuge tube and return it to the magnetic rack. Once the solution is clear, carefully aspirate and discard the supernatant.

[0201] 9) Add 1X magnetic bead wash buffer (one volume of capture magnetic beads), vortex thoroughly to mix, centrifuge briefly, and transfer 50 μL to a new PCR tube.

[0202] 10) Briefly centrifuge the PCR tube and return it to the magnetic rack. After the solution has clarified, carefully aspirate and discard the supernatant.

[0203] 11) Transfer the hybridized sample to a PCR tube containing capture magnetic beads, vortex thoroughly to mix, centrifuge briefly, and immediately place the PCR tube in a thermal cycler and incubate at 55°C for 15 minutes.

[0204] 12) Keep the PCR tube in the PCR instrument, add 100 μL of preheated 1X Wash Buffer 2, and mix thoroughly.

[0205] 13) After a brief centrifugation, place the PCR tube on a magnetic rack to absorb the magnetic beads. After the solution is clear, carefully aspirate and discard the supernatant.

[0206] 14) Keep the PCR tube in the thermal cycler and add 200 μL of preheated 1X Wash Buffer 1. Vortex thoroughly to mix. After a brief centrifugation, immediately place the PCR tube in the thermal cycler and incubate at 55°C for 5 minutes.

[0207] 15) After incubation, remove the PCR tube from the thermal cycler and place it on a magnetic stand to absorb the magnetic beads. After the solution has clarified, carefully aspirate and discard the supernatant. Add 200 μL of preheated 1X Wash Buffer 1, vortex thoroughly to mix, centrifuge briefly, and immediately place the PCR tube in the thermal cycler and incubate at 55°C for 5 minutes.

[0208] 16) After incubation, remove the PCR tube from the PCR instrument and place it on a magnetic rack to absorb the magnetic beads. After the solution has clarified, carefully aspirate and discard the supernatant.

[0209] 17) Add 200 μL of room temperature 1X Wash Buffer 2 to the PCR tube, mix thoroughly, and centrifuge briefly.

[0210] 18) After incubation at room temperature for 1 minute, place the PCR tube on a magnetic rack to absorb the magnetic beads. After the solution has clarified, carefully aspirate and discard the supernatant.

[0211] 19) Add 200 μL of room temperature 1X Wash Buffer 3 to the PCR tube, mix thoroughly, centrifuge briefly, and transfer to a new PCR tube.

[0212] 20) After incubation at room temperature for 1 minute, place the PCR tube on a magnetic rack to absorb the magnetic beads. After the solution has clarified, carefully aspirate and discard the supernatant.

[0213] 21) Add 200 μL of room temperature 1X Wash Buffer 4 to the PCR tube, mix thoroughly, and centrifuge briefly.

[0214] 22) After incubation at room temperature for 1 minute, place the PCR tube on a magnetic rack to absorb the magnetic beads. After the solution has clarified, carefully aspirate and discard the supernatant.

[0215] 23) Remove the PCR tube from the magnetic rack and add 20 μL of nuclease-free water to the PCR tube. Vortex thoroughly to mix, centrifuge briefly, and incubate at room temperature for 2 minutes before proceeding to the next step.

[0216] 4.4 Capture Library Amplification

[0217] 1) The PCR amplification reaction solution is prepared as follows for each pool:

[0218] Component name Volume (μL) PCR amplification reaction solution 2 25 PCR amplification primer 2 5 total 30

[0219] 2) Vortex to mix, and centrifuge briefly to collect the reaction solution at the bottom of the tube.

[0220] 3) Add 30 μL of the mixed PCR amplification reaction solution to the washed PCR tube, mix thoroughly, centrifuge briefly to collect the reaction solution at the bottom of the tube, and immediately place the PCR tube into the thermal cycler. Set the program as follows and run the cycle (heated lid temperature: 105°C).

[0221]

[0222] Note*: 12 cycles are recommended for a pool of 2-5 libraries and 11 cycles for a pool of 6-8 libraries.

[0223] 4) Proceed to the next step immediately after the program ends.

[0224] 4.5 Purification of captured products by magnetic beads

[0225] 1) Vortex the PCR tube containing the amplified library, centrifuge briefly, place on a magnetic rack until clear, and transfer all supernatant to a new PCR tube.

[0226] 2) We recommend using KAPA HyperPure Beads (Roche). Add 70 μL of purified magnetic beads to a PCR tube and perform magnetic bead purification according to the kit's purification instructions.

[0227] 3) Elute the purified product with 22 μL of 10 mM Tris-HCl, pH 8.0 solution that has been equilibrated to room temperature. Pipette 20 μL of the supernatant into a new PCR tube. Do not touch the magnetic beads and place on ice until ready for use.

[0228] 4.6 Capture Library Quality Control

[0229] 1) Use nucleic acid quantification kit The dsDNA HS Assay Kit and its accompanying instruments determine the concentration of the sample capture library.

[0230] 2) Take the captured library and perform library fragment quality control using capillary electrophoresis reagents High Sensitivity D5000 Reagents and supporting instruments. The average fragment length of the library is between 200-600bp, and there are no obvious small and large fragment peaks.

[0231] 5. Sequencing

[0232] The sequencing library was sequenced using the NextSeq 550Dx or Novasek 6000 gene sequencer and supporting sequencing reagents according to the manufacturer's instructions to generate sequencing data.

[0233] 6. Sample data analysis and detection methods

[0234] 6.1 DNA sample data analysis and processing process

[0235] The original offline data is analyzed using analysis software. The analysis process can be divided into the following steps:

[0236] (1) Data preprocessing: Software was used to analyze the sequencing quality parameter Q30 base ratio. If the Q30 ratio was ≥60%, the quality control passed; otherwise, the quality control failed. The BCL files generated by sequencing were then converted to Fastq files using Illumina software. Trimmomatic-0.36 software was then used to remove adapter sequences and low-quality base fragments introduced during library construction.

[0237] (2) Data alignment: Use bwa Version: 0.7.10-r806 to align the fragments to the hg19 reference genome;

[0238] (3) Data quality control: The sample sequencing quality is determined based on parameters such as the sample Q30 base ratio, the sequence alignment ratio to the reference genome, and the average sequencing depth of the target region. If Q30 is ≥60%, the sequence alignment ratio to the reference genome is ≥90%, and the average sequencing depth is ≥300X, the sample sequencing data quality control passes; otherwise, it fails and is considered a test failure, requiring resequencing.

[0239] (4) Mutation analysis: SNV / Indel mutation detection was performed using the combined detection software Vardict V1.8.2-2 and MuTect2 V4.0.12.0; MSI detection was performed using the software MSIsensor2; gene fusion detection was performed using the software Manta V1.6.0 and Delly2 V1.1.15; CNV detection was performed using the software cnvkit V0.9.6; and germline mutations in samples were detected using bcftools v1.7. Analysis parameters were set to require sequence alignment quality ≥ 20 and base quality ≥ 30; all other parameters were set to default.

[0240] (5) Mutation annotation: SNVs / Indels and gene fusions were annotated in HGVS format using the snpEff V5.0C software. The annotations were also performed using the COSMIC database (v91), ClinVar database (v20191112), dbSNP database (v145), 1000Genomes database (v201508), and ExAC database (v0.3).

[0241] (6) For mutations that meet the quality requirements after the above processing, if the mutation abundance of SNV / Indels is ≥0.5%, it is judged as positive; otherwise, it is negative; if the mutation abundance of fusion gene is ≥0.5%, it is judged as positive; otherwise, it is negative; if the MSI score is ≥10%, it is judged as MSI-H; otherwise, it is MSS (microsatellite stable); if the CNV CN is ≥3.25, it is judged as positive, otherwise, it is negative.

[0242] 6.2 RNA sample data analysis and processing process

[0243] (1) Data preprocessing: Software was used to analyze the sequencing quality parameter Q30 base ratio. If the Q30 ratio was ≥60%, the quality control passed; otherwise, the quality control failed. The BCL files generated by sequencing were then converted to Fastq files using Illumina software. Trimmomatic-0.36 software was then used to remove adapter sequences and low-quality base fragments introduced during library construction.

[0244] (2) Data alignment: The fragments were aligned to the hg19 reference genome using STAR v 2.7.9a alignment software with default parameters. The sample quality was considered acceptable only if 90% of the fragments were successfully aligned to the human reference genome.

[0245] (3) Gene fusions were detected using Arriba v2.1.0. The default analysis parameters were used.

[0246] (4) Positive judgment criteria: If the confidence of the detected fusion mutation is high or medium, it is positive; otherwise, it is negative.

[0247] 4. Test results of 226 clinical samples of intrahepatic cholangiocarcinoma

[0248] According to the above process, among the 226 samples, 211 samples were detected with SNV / INDEL, CNV, FUSION or MSI-H, with a total positive rate of 93.36%; 203 samples were detected with SNV / INDEL ( Figure 3 ), accounting for 89.82%; CNV was detected in 34 samples ( Figure 4 ); 5 samples tested positive for MSI-H, and 221 samples tested positive for MSS ( Figures 5-6 ); FGFR2 fusion was detected in 25 samples ( Figure 7 ), accounting for 11.06%, which is similar to the literature reports; a total of 18 FGFR2 fusion sites were detected in DNA and 21 FGFR2 fusion sites were detected in RNA ( Figure 7), DNA+RNA dual-level detection of tumor tissue samples can improve the detection rate of FGFR2 fusion.

[0249] The above embodiments are only preferred embodiments of the present invention and are not intended to limit the present invention in any form or substance. It should be noted that ordinary technicians in this technical field can make several improvements and supplements without departing from the present invention, and these improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. Use of a biomarker detection reagent in the preparation of a product for detecting bile duct cancer gene mutations, characterized in that: The biomarkers consist of biomarker A for detecting DNA variation in bile duct cancer and biomarker B for detecting RNA variation in bile duct cancer; Biomarker A consists of genes ALK, CDK12, FGFR1, MTOR, PIK3CA, AR, CDKN2A, FGFR3, NF1, PTCH1, ATM, CHEK1, FGFR4, NRAS, PTEN, BARD1, DPYD, FLI1, NRG1, RAD51C, BRAF, EGFR, IDH1, NTRK1, ROS1, BRCA1, ERBB2, IDH2, NTRK3, TSC1, BRCA2, ESR1, KDM6A, PALB2, TSC2, BRIP1, EZH2, KRAS, and PDGFRA; The biomarker B consists of ALK, BRAF, ETV6, FGFR1, FGFR2, FGFR3, FGFR4, NRG1, NTRK1, NTRK2, NTRK3, RET, ROS1, FLI1, PDGFB and MET; The DNA variations include gene fusion, single nucleotide variations (SNVs), insertions and deletions (Indels), copy number variations (CNVs) and microsatellite instability (MSI); the RNA variations include gene fusion.

2. The use according to claim 1, characterized in that The product for detecting bile duct carcinoma gene mutation is a kit for detecting bile duct carcinoma gene mutation.

3. The use according to claim 2, characterized in that The kit at least includes a probe for detecting the biomarker.

4. The use according to claim 1, wherein The product for detecting bile duct carcinoma gene mutation is a detection device for detecting bile duct carcinoma gene mutation.

5. A device for detecting gene mutations in bile duct cancer, characterized in that: The device comprises: A data acquisition module is used to obtain sample sequencing data. The sequencing data is obtained by hybridizing and capturing probes for detecting cholangiocarcinoma biomarkers with a pre-library, followed by purification with magnetic beads to obtain a sequencing library. The sequencing library is sequenced on a NextSeq 550Dx / Novaseq 6000 gene sequencer to obtain sequencing data. The data processing module is used to obtain analytical data that meets quality control requirements through data preprocessing, data comparison and quality control of sequencing data; Data detection and analysis module, used for detection and analysis of sequencing data; and a test result acquisition module; The device is used to detect bile duct cancer gene mutations through the following steps: Step 1: Obtaining biomarker expression profile data from a sample of a test subject, wherein the biomarker is derived from a tumor tissue sample of a cholangiocarcinoma patient; Step 2: Data processing, which specifically includes: Step 2.1, Data Preprocessing: Use software to analyze the sequencing quality parameter Q30 base ratio. If the Q30 ratio is ≥60%, the quality control passes; otherwise, the quality control fails. Then, convert the BCL file generated by NextSeq 550Dx / Novaseq 6000 sequencing into a Fastq file. Then, use Trimmomatic-0.36 software to remove adapter sequences and low-quality base fragments introduced during library construction. Step 2.2, data alignment: Remove low-quality bases and adapter sequences from the raw data, perform base quality statistics on the filtered data, map the sequenced fragment reads back to the reference genome, and calculate the read count, alignment rate, sequencing depth and coverage, and variant detection; Step 2.3, Data Quality Control: Determine the sample sequencing quality based on parameters such as the sample Q30 base ratio, the sequence alignment ratio to the reference genome, and the average sequencing depth of the target region. If Q30 is ≥60%, the sequence alignment ratio to the reference genome is ≥90%, and the average sequencing depth is ≥300X, the sample sequencing data quality control passes. Otherwise, the test is deemed to have failed and requires resequencing. Step 3: Data analysis: SNV / Indel mutation detection was performed using the combined detection software Vardict V1.8.2-2 and MuTect2 V4.0.12.0; MSI detection was performed using the software MSIsensor2; gene fusion detection was performed using the software Manta V1.6.0 and Delly2 V1.1.15; and CNV detection was performed using the software cnvkit V0.9.

6. Analysis parameters required sequence alignment quality ≥20 and base quality ≥30; all other settings were default. Step 4: Obtain the results. If the mutation abundance of SNV / Indels is ≥0.5%, it is considered positive; otherwise, it is negative. If the mutation abundance of fusion genes is ≥0.5%, it is considered positive; otherwise, it is negative. If the MSI score is ≥10%, it is considered MSI-H; otherwise, it is MSS (microsatellite stable). If the CNV score is ≥3.25, it is considered positive; otherwise, it is negative. The biomarker is the biomarker according to claim 1.

6. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a program, and the processor implements the steps of claim 5 for detecting bile duct cancer gene mutations when executing the program.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program; Wherein, when the computer program is running, the computer-readable storage medium is controlled to implement the steps of claim 5 for detecting bile duct cancer gene mutations.

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