Marker combination and its application in diagnosing pancreatobiliary cancer

Abstract

The present invention belongs to the field of biomedicine, and specifically relates to a biomarker combination and its application in the diagnosis of pancreatobiliary cancer. Specifically, the present invention provides a biomarker combination, which includes methylation biomarkers and gene mutations, and the methylation biomarkers include one or more of SOX17, 3-OST-2, NXPH1, SEPT9, and TERT.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to a biomarker combination and its application in the diagnosis of pancreatobiliary cancer. Background Art

[0002] Pancreatobiliary tract cancer includes bile tract cancer (BTC) and pancreatic cancer.

[0003] Bile tract cancer originates from cholangiocytes at different anatomical locations, such as intrahepatic, extrahepatic, and gallbladder, or may directly originate from hepatocytes. Bile tract cancer includes cholangiocarcinoma (CCA), gallbladder cancer (GBC), and ampullary cancer. Cholangiocarcinoma is the second most common primary liver cancer, accounting for about 3% of all gastrointestinal tumors. Cholangiocarcinoma is further divided into intrahepatic (iCCA), perihilar (pCCA), or distal (dCCA) cholangiocarcinoma. Although bile tract cancer is relatively uncommon globally, its global incidence has increased rapidly in recent years. Risk factors for bile tract cancer include primary sclerosing cholangitis (PSC), liver flukes, fibropolycystic liver diseases (such as bile duct adenoma and bile duct papillomatosis), biliary and gallbladder stones, viral hepatitis, and exposure to chemical carcinogens, etc. Pancreatic cancer is one of the cancers with the highest fatality rate globally and is the seventh leading cause of cancer death worldwide due to its poor prognosis, specifically including pancreatic head cancer, pancreatic tail cancer, diffuse cancer, etc.

[0004] The diagnosis of pancreatobiliary cancer is challenging. Due to the non-specific or even asymptomatic early symptoms, most patients are diagnosed at an advanced stage. Late diagnosis will at least lead to poor prognosis of BTC patients, and the 5-year overall survival rate is below 20%. Patients with biliary stricture and jaundice may have cholangiocarcinoma, gallbladder cancer, or pancreatic cancer, and it is very difficult to distinguish malignant tumors from benign strictures (iatrogenic bile duct injury, primary sclerosing cholangitis (PSC), and choledocholithiasis). Conventionally, pancreatobiliary cancer is diagnosed by a combination of multiple methods, including clinical examination, imaging, endoscopic procedures, pathological evaluation, and biochemical tests (such as CA19-9). However, these methods have some limitations. For example, CA19-9 is not applicable to patients negative for Lewis antigen (accounting for 7% of the general population), and at the same time, the sensitivity and specificity of the above methods are not satisfactory. It is reported that about 15-24% of patients undergoing surgery for malignant biliary stricture are finally diagnosed as benign.

[0005] Therefore, there is an urgent need to develop a better detection method for diagnosing pancreaticobiliary cancer with high sensitivity, high specificity, and high safety. Summary of the Invention

[0006] Patients with pancreaticobiliary cancer usually have a poor clinical prognosis, and the 5-year overall survival rate is less than 20%. This is mainly related to late diagnosis. In addition, accurately differentiating malignant cancer from benign diseases before surgery can avoid unnecessary trauma. Therefore, there is an urgent need to develop a detection method for diagnosing malignant pancreaticobiliary cancer with high sensitivity, strong specificity, and high safety.

[0007] To achieve the above technical objectives, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a marker combination, which includes methylation markers and gene mutations;

[0009] The methylation markers include one or more of SOX17, 3-OST-2, NXPH1, SEPT9, and TERT;

[0010] The other markers include at least one of the following gene mutations: AKT1, KRAS, APC, NRAS, ARID1A, PIK3CA, AXIN1, PPP2R1A, BAP1, PTEN, BRAF, SMAD4, CDKN2A, TERT, TP53, EGFR, FBXW7, FGFR2, HRAS, IDH1, IDH2.

[0011] Preferably, the methylation markers consist of SOX17, 3-OST-2, NXPH1, SEPT9, and TERT.

[0012] Preferably, the methylation markers may further include any one or more of EBF3, RASSF1, APC, EYA4, RUNX3, BNIP3, FHIT, SALL3, CCND2, FOXE1, CD1D, GSTP1, SFRP1, CDH1, hMLH1, SLIT2, CDH13, KCNK12, SLIT3, CDKN2A, MGMT, CDKN2B, NDRG4, CDO1, NPTX2, TFPI2, CLEC11, TIMP3, CNRIP1, PENK, TMEFF2 (HPP1), DAPK1, PRKCB, VIM, DCLK1, PTCHD2, ZSCAN18, DLC1, RARβ2 (RARB).

[0013] The term "methylation" used in this article refers to a natural modification method of DNA. In eukaryotes, it mainly refers to the process in which a methyl group is attached to the 5th carbon atom of cytosine at the 5' end of CpG dinucleotide to form 5-methylcytosine (5-mC) under the action of methyl CpG-binding domain (MBD) and DNA methyltransferase (DNMT). DNA methylation is a well-studied form in epigenetics and has various biological significances. DNA methylation is closely related to normal embryonic development, gene expression regulation, X chromosome inactivation in female individuals, suppression of parasitic DNA sequences, imprinted genes, and genomic structural stability, etc.

[0014] The term "gene mutation" used in this article generally refers to the variation of one or several nucleotides at a specific position in the coding sequence of a certain gene in the human body. Proto-oncogenes are highly stable and harmless to the human body without mutation. When proto-oncogenes mutate, they become oncogenes, causing cell carcinogenesis and being harmful to the human body. Cancer gene mutations can cause uncontrolled cell growth, leading to the occurrence of cancer. Therefore, in clinical practice, gene detection can be used to diagnose related cancers. In addition, gene detection can also be used to guide the use of targeted drugs, guide the use of immunotherapy drugs, and judge the prognosis of cancer patients, etc.

[0015] On the other hand, the present invention provides a BileScreen model composed of the above-mentioned biomarker combinations.

[0016] In the said model, it is judged whether the subject has pancreatobiliary cancer according to the detection results of methylation biomarkers and gene mutations.

[0017] Preferably, the detection of methylation biomarkers refers to detecting the methylation degree of genes, and the detection of gene mutations refers to detecting whether the subject has mutations in these genes.

[0018] Preferably, the subject includes a suspected pancreatobiliary cancer patient. If the methylation degree reaches the threshold or has any gene mutation provided by the present invention, the subject is judged to be a pancreatobiliary cancer patient; otherwise, the subject is judged to be a non-cancer patient. The non-cancer patients in the present invention are subjects who have not been diagnosed with cancer within at least 12 months. Optionally, the non-cancer patients may have the following symptoms of non-malignant tumors: gallstones, biliary obstruction, biliary stricture, pancreatic mass, pancreatic cyst, pancreatitis.

[0019] Preferably, the detection is performed on a sample derived from the subject.

[0020] Samples that can be selected in this field include bile, cells, tissues, peripheral blood, blood, serum, plasma, urine, saliva, tears, etc.

[0021] Preferably, the sample includes bile or cells and tissues taken from the biliary tract.

[0022] More specifically, the cell tissues taken from the biliary tract include samples obtained by endoscopic retrograde cholangiopancreatography (ERCP) biopsy / brushing. There are three methods for collecting bile, namely duodenal drainage, gallbladder puncture, and direct surgical collection. ① Duodenal drainage method: Duodenal fluid drainage includes duodenal fluid (D fluid, colorless or light yellow, transparent or slightly turbid), common bile duct fluid (A bile, golden yellow, transparent), gallbladder fluid (B bile, dark brown, transparent), and hepatobiliary duct fluid (C bile, transparent); actually, it is a mixture of duodenal fluid, pancreatic fluid, bile, and a small amount of gastric juice. Under aseptic conditions, duodenal drainage is collected using a catheter, and the collected bile is divided into three parts: A, B, and C. ② Gallbladder puncture method: When performing cholecystography, bile can be collected simultaneously. ③ Surgical collection method: When performing gallbladder and bile duct surgery, bile can be directly punctured and collected from the gallbladder.

[0023] The term "pancreatobiliary cancer" as used in the present invention can also be referred to as pancreatobiliary tract cancer, which includes bile duct cancer (BTC, also known as cholangiocarcinoma) and pancreatic cancer; the cholangiocarcinoma includes cholangiocarcinoma (CCA), gallbladder cancer (GBC), and ampullary cancer; the pancreatic cancer includes pancreatic head cancer, pancreatic tail cancer, and diffuse cancer.

[0024] On the other hand, the present invention provides the use of a reagent for detecting the above-mentioned marker combination in a product for diagnosing pancreatobiliary cancer.

[0025] Preferably, the methylation markers can be detected by methods well-known in the art, specifically, for example: whole genome bisulfite sequencing (WGBS), pyrosequencing, bisulfite sequencing, methylation-specific polymerase chain reaction (Methylation-Specific PCR, MS-PCR), bisulfite-specific polymerase chain reaction, methylation-sensitive restriction enzyme-PCR / Southern method, combined bisulfite restriction analysis (COBRA), digital polymerase chain reaction, restriction landmark genomic scanning, CpG island microarray, single nucleotide primer extension SNUPE, methylation profiling, one or more of methylation chips.

[0026] Preferably, the reagents for detecting gene mutations include the reagents used in any of the following methods: TaqMan probe method, sequencing method, chip method, matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOFMS) detection, polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP), allele-specific PCR (AS-PCR), SNaPshot method, SNPlex genotyping system, SNPStream analysis system, Sequenom genotyping system, denaturing high performance liquid chromatography (DHPLC), denaturing gradient gel electrophoresis (DGGE).

[0027] Preferably, to simultaneously detect methylation and gene mutations, the detection method used in the specific embodiments of the present invention is the Mutation Capsule technology.

[0028] The Mutation Capsule technology of the present invention has achieved a breakthrough in liquid biopsy technology, which can simultaneously capture and analyze gene mutation and methylation variation signals in a small amount of liquid samples, greatly improving the detection sensitivity.

[0029] More specifically, the diagnosis of pancreaticobiliary cancer refers to distinguishing patients with malignant tumors (pancreaticobiliary cancer) from patients without malignant tumors. The patients without malignant tumors may have benign diseases such as gallstones, biliary obstruction, biliary stricture, pancreatic mass, pancreatic cyst, pancreatitis, etc.

[0030] On the other hand, the present invention also provides a diagnostic system for diagnosing pancreaticobiliary cancer, and the system reports a computing device that obtains a diagnostic conclusion based on the detection results of the above-mentioned marker combinations of the subject's samples.

[0031] Preferably, the system includes:

[0032] (1) A sample collection and processing device for performing the following steps: collecting a sample from a subject and processing the sample;

[0033] (2) A methylation detection device;

[0034] (3) A sequence determination device (gene mutation detection device);

[0035] (4) A calculation device for obtaining a diagnostic conclusion based on the detection results of (3) and (4).

[0036] Preferably, the sample includes bile, exfoliated cells in the biliary tract, and tissue samples.

[0037] Preferably, the processing includes steps such as purification, quality inspection, and DNA extraction.

[0038] On the other hand, the present invention also provides a method for diagnosing pancreaticobiliary cancer, and the method determines whether the subject has a malignant disease (pancreaticobiliary cancer) based on the detection results of the above-mentioned marker combinations of the subject's sample.

[0039] The present invention has the following beneficial effects:

[0040] The technical solution provided by the present invention can obtain bile as a sample for detection in a non-invasive sampling manner, featuring high specificity and high sensitivity; it can accurately diagnose the disease at an earlier stage of the disease, enabling early targeted treatment of the patient, increasing the possibility of cure, and prolonging the survival period; at the same time, for non-cancer patients, unnecessary surgical trauma is avoided. Description of the Drawings

[0041] Figure 1 is the flowchart of the subject inclusion criteria and research involved in the present invention.

[0042] Figure 2 is the statistical graph of the basic information of the subjects involved in the present invention.

[0043] Figure 3 is the verification of the diagnostic efficacy of each diagnostic model in different datasets.

[0044] Figure 4 is the statistical graph of the detection results of the subjects in the training cohort and the validation cohort.

[0045] Figure 5 is the verification of the diagnostic efficacy of the methylation marker.

[0046] Figure 6It is the comparison result of the diagnostic efficacy of each diagnostic model in different datasets and the diagnostic efficacy of CA19-9.

[0047] Figure 7 It is the result statistics of the consistency of gene mutation detection results in brush biopsy samples and biopsy samples. Specific implementation manners

[0048] The present invention will be further described below in conjunction with embodiments. The following description is only for the preferred embodiments of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to change it into an equivalent embodiment with equivalent changes. Any simple modification or equivalent change made to the following embodiments based on the technical essence of the present invention without departing from the content of the present invention falls within the protection scope of the present invention.

[0049] Example 1, Screening, Identification and Verification of Mutated Genes and Methylated Genes

[0050] 1. Study Population and Experimental Design

[0051] The research population was selected from patients with pancreaticobiliary diseases admitted to the following five hospitals from November 2018 to October 2020, with a total of 338 cases. The five hospitals are: Cancer Hospital, Chinese Academy of Medical Sciences; Dazhou Central Hospital, Sichuan Province; Beijing Chaoyang Hospital, Capital Medical University; Dongfang Hospital, Beijing University of Chinese Medicine; and Gucheng County People's Hospital, Xiangyang City, Hebei Province. 79 cases were excluded due to various reasons such as non-biliary cancer or insufficient bile DNA, and finally 259 cases were selected for molecular detection.

[0052] Among these 259 patients, 209 patients diagnosed as malignant or benign were selected as the training group (n = 104) and the validation group (n = 105), including 116 cases of malignant tumors and 93 cases of benign diseases. Malignant tumors were confirmed as biliary malignancies (pancreaticobiliary cancers) after being diagnosed by ERCP (endoscopic retrograde cholangiopancreatography) biopsy / brush biopsy (ERCP-obtained biopsies / brushings). Benign disease patients included 78 patients with cholelithiasis. No malignant lesions were found in the specimens removed surgically from these 78 patients, but chronic cholangitis was found. Among them, 15 patients were followed up for more than 12 months, and no malignant lesions were found after taking gallstones by ERCP.

[0053] In addition, considering that some patients with bile tract cancer (BTC) have low sensitivity to ERCP pathology, 50 patients with negative or suspicious diagnostic results from ERCP biopsy / brushing were classified into an independent test cohort. In the test cohort, 40 patients with malignancies were confirmed during follow-up through pathological evaluation (biopsy / brushing obtained from surgically resected specimens, percutaneous needles, or ERCP, n = 21), radiological imaging (n = 2), or clinical criteria (n = 17), while 10 patients with benign diseases were confirmed through surgical pathology (n = 4), radiological imaging (n = 1), or no malignancies detected after at least 12 months of follow-up (n = 5).

[0054] Patient inclusion and study design were as Figure 1 shown. This study was approved by the ethics review committees of the above five hospitals (ID: NCC2018JJJ-001).

[0055] 2. Sample Preparation

[0056] Bile samples were obtained from all patients. Among them, 181 patients had bile samples obtained through ERCP before treatment, and 78 patients with cholelithiasis had bile samples obtained during cholecystectomy.

[0057] The obtained bile samples were centrifuged at 12,000 revolutions per minute for 10 minutes to separate the supernatant and particles. DNA was extracted from the bile samples using the TIANAMP Genomic DNA Kit (Tiangen Biotech, Beijing, China). The human GAPDH gene was detected by RQ-PCR using Taqman probes to determine the DNA quality.

[0058] In addition to bile samples, paired biopsy or brushing tissues obtained during ERCP from 34 and 9 patients were subjected to NGS (Next-generation sequencing technology) detection and sequencing, and genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, USA).

[0059] The serum of most patients was further detected for CA19-9 using the Roche E601 system (Roche Diagnostics, Switzerland) through electrochemiluminescence technology.

[0060] 3. Analysis of Gene Mutations and Gene Methylation Using BileScreen

[0061] Table 1. Mutated genes and genes with methylation modifications found by the sequencing of the present invention

[0062]

[0063] Take 400 ng of DNA, fragment it by sonication, and then perform end repair. Then, digest the DNA fragments with the methylation-sensitive restriction endonuclease HHAⅠ (R0139S, New England Biolabs, MA, USA), and perform targeted gene sequencing based on amplicon-targeted capture technology through the Mutation Capsule technology. The specific technical scheme can refer to the literature Qu, C. et al. Detection of early-stage hepatocellular carcinoma in asymptomatic HBsAg-seropositive individuals by liquid biopsy. Proceedings of the National Academy of Sciences of the United States of America. 116, 6308-6312 (2019).

[0064] Briefly, process the DNA fragments digested with the restriction endonuclease HHAⅠ using the KAPA Hyper Prep kit (Roche, Switzerland). Through a series of steps, including end repair, adenylation A tailing, ligation of custom adapters, and three rounds of PCR amplification (using common sequence primers in the first round and primers containing target specificity and common sequences in the last two rounds), a sequencing library is obtained. Sequence 23 mutant genes and 44 genes with methylation modifications (Table 1).

[0065] 4. Data Processing and Mutation / Methylation Detection

[0066] Gene sequencing was performed using the Digital (UID) high-throughput sequencing platform. Briefly, a unique UID tag was added to each fragment in the sample before PCR amplification, followed by library amplification. After sequencing, by aligning the sequences of the fragments, the repeated fragments with the same UID label were merged, while the natural repeats with different UID labels were retained. The end coordinates of the reads were calculated based on the start coordinates and cigar information. According to the start and end coordinates of the reads, the reference sequences corresponding to the reads were truncated from the reference genome. The reads were aligned again with the hg19 genome to obtain the start and end positions of the mutations. An effective UID (EUID, Effective UID) family was defined as a UID (Unique Identifier) family that contained at least two reads and at least 80% of the read types were the same. The frequency of each mutation was calculated by dividing the number of alternative EUID families by the sum of the alternative and reference families. We further manually inspected the mutations (in IGV) and used VEP (Ensembl Variant Effect Predictor) to annotate the candidate variant genes.

[0067] At least four EUID families were detected for the mutations. For oncogenes with hot-spot mutations including KRAS (G12, G13, Q61, and A146), the limit of detection (LOD) was set at 0.5%. For other mutant genes, including common tumor suppressor genes without hot-spots, such as TP53, Smad4, and rare mutations, in order to reduce the occurrence of false positives, the LOD was set at 1%. Since there were no matched white blood cells to exclude germline mutations, the mutations detected in the samples were screened using germline and somatic mutation databases to determine the highest likelihood of germline mutations. Mutations with a frequency of ≥0.1% found in the germline mutation databases (1000AF, ESP6500 AA / EA, Exac AF) were first excluded. The passed mutations were further screened. For those genes with a higher frequency (≥40%), if they appeared in less than 10 samples in the COSMIC database, they were likely to be germline mutations and were thus further excluded.

[0068] In terms of methylation analysis, clusters with an HHA I restriction site at the end were unmethylated sequences, and molecules with at least one HHA I restriction site and not a restriction site at the end were methylated sequences. The methylation ratio of each base was the ratio of the number of methylated molecules to the sum of the number of methylated and unmethylated molecules.

[0069] 5. Construction of the BileScreen Diagnostic Model

[0070] Malignant tumors occur when AKT1, KRAS, APC, NRAS, ARID1A, PIK3CA, AXIN1, PPP2R1A, BAP1, PTEN, BRAF, SMAD4, CDKN2A, TERT, TP53, EGFR, FBXW7, FGFR2, HRAS, IDH1, or IDH2 are mutated (as shown in Table 2 below).

[0071] Table 2. Detection Results of Mutations

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] Among the 44 methylated genes, using the training cohort (Training set), 5 methylated genes, SOX17, 3-OST-2, NXPH1, SEPT9, and TERT, were selected by stepwise penalized logistic regression for constructing a diagnostic model. The above 5 methylated gene markers were used for penalized logistic regression on the training cohort, and leave-one-out cross-validation was adopted. The performance of the model was evaluated by the area under the receiver operating characteristic curve (ROC curve), sensitivity, and specificity. The cut-off value of methylation was determined according to the Youden index of ROC analysis.

[0087] In the BileScreen model, when mutation and methylation are integrated, if either of them is positive, the result is positive. Next, the performance of the BileScreen model was further evaluated in separate validation and test cohorts.

[0088] In addition, in the training and validation cohorts, most of the selected benign cases were young women with gallstones, resulting in a certain bias in age and gender between malignant and benign patients. To exclude the influence of this artificial sample selection on the diagnostic prediction results, 52 malignant patients and 52 benign patients matched by age and gender were assigned to the training cohort. Therefore, the age and gender distributions in the validation cohort were uneven ( Figure 2 ). However, all mutations and methylations were not significantly correlated with age and gender (correlation coefficient < 0.5, or Wilcoxon test P > 0.05).

[0089] 6. Statistical Analysis

[0090] ROC analysis (pROC package) and Wilcoxon tests were used to evaluate the role of individual mutated genes or methylated modified genes in predicting disease status. The penalized Logistic regression method (glmnet package) was used to screen for gene markers for the diagnostic model. In the training cohort, the ROC curve took the raw scores as input, and the Youden index was used alone to determine the optimal cut-off point for methylation. In addition, ROC analysis was used to compare the performance of different methods, with the "0 or 1" values determined by the corresponding cut-off points as input. Sensitivity and specificity were calculated using standard 2×2 contingency tables. All R package-related analyses were based on R software (V.3.6.3).

[0091] 7. Results

[0092] 1) Establishment of the BileScreen Model Based on Gene Mutations and Methylation Modifications

[0093] Sequencing and data analysis were performed on 104 patients in the training cohort, including 52 patients with malignant tumors pathologically confirmed by ERCP and 52 patients with benign diseases. Among these 52 patients, some had no canceration in the tissue site determined by surgical pathology, and the other part were patients with bile duct stones and had been followed up for at least 12 months and were determined to have benign diseases. The clinical characteristics are summarized as Figure 2 .

[0094] Using the Mutation Capsule technology, DNA in bile samples was analyzed, and 23 mutated genes and 44 methylated genes were detected. The most common mutated genes in cancer were TP53 (50%) and KRAS (46%). CTNNB1 and GNAS mutations were detected in both cancer and benign disease patients, so there was no significant correlation between the genes and the malignant state, and CTNNB1 and GNAS mutations were excluded from the BileScreen model.

[0095] Table 3. Detection accuracy of each dataset

[0096]

[0097] Using gene mutations to distinguish patients with pancreaticobiliary cancer from non-cancer (benign disease) subjects:

[0098] Mutations in AKT1, KRAS, APC, NRAS, ARID1A, PIK3CA, AXIN1, PPP2R1A, BAP1, PTEN, BRAF, SMAD4, CDKN2A, TERT, TP53, EGFR, FBXW7, FGFR2, HRAS, IDH1, IDH2, where at least one detected mutation was considered positive; the sensitivity of distinguishing patients with pancreaticobiliary cancer from non-cancer subjects by mutations alone was 81%, the specificity was 100%, and the AUC was 0.90 (Table 3, Figure 3 A).

[0099] Using methylation markers to distinguish patients with pancreaticobiliary cancer from non-cancer (benign disease) subjects:

[0100] For methylation markers, through the stepwise penalized Logistic regression method, 5 markers, namely SOX17, 3-OST-2, NXPH1, SEPT9, and TERT, were selected to construct a diagnostic model ( Figure 3 , Table 4). By the leave-one-out method, only methylation markers could well identify patients with pancreaticobiliary cancer from non-cancer cases, with a sensitivity of 88%, a specificity of 98%, and an AUC of 0.93 (Table 3, Figure 3 ). The cut-off value of the methylation score was 0.422, which produced the maximum Youden index ( Figure 5 ).

[0101] Table 4. Construction of a diagnostic model based on 5 methylation markers

[0102]

[0103]

[0104] Finally, when the gene mutation and methylation markers are combined, i.e., BileScreen:

[0105] A positive result is defined as positive for either of them, and its performance is further improved, with a sensitivity of 94%, a specificity of 98%, and an AUC of 0.96 (Table 3, Figure 3 ). BileScreen was validated in another 105 cases (validation cohort), including 64 malignant cases and 41 benign cases ( Figure 4 ). BileScreen accurately predicted the disease status of 59 malignant cases and 40 non-cancer cases. BileScreen showed a sensitivity of 92% and a specificity of 98% in the validation cohort, with an AUC of 0.95 (Table 3, Figure 3 ). If only gene mutation is used, the sensitivity and specificity are 78% and 100% respectively. The sensitivity and specificity of only methylation marker analysis are 81% and 98% respectively (Table 3).

[0106] 2. The BileScreen Model for Detecting Suspected Malignant Tumors

[0107] We further validated BileScreen in 50 patients with indeterminate ERCP (test cohort) results because the ERCP diagnosis results of these 50 patients were "suspicious malignancy" or "cancer cannot be excluded". These cases were followed up for at least 12 months, and 40 out of 50 were found to be malignant. The remaining 10 did not develop cancer during the follow-up period and were diagnosed as benign. 36 out of 40 malignant lesions were positive, and 2 out of 10 benign lesions were positive, with a sensitivity of 90% and a specificity of 80% ( Figure 3 ). For those patients who could not be diagnosed by ERCP, the BileScreen results were significantly correlated with the clinical results (P < 0.001, continuity-corrected chi-square test).

[0108] Only mutation or methylation can distinguish cancer and benign patients, with sensitivities of 75% and 80% respectively, specificities of 90% and 80% respectively, and AUCs of 0.83 and 0.8 respectively (Table 3, Figure 3 ).

[0109] 3. Comparison of the Detection Results of CA19-9 and the BileScreen Model in Each Group

[0110] In the training cohort and validation cohort, we screened out 85 and 74 patients respectively, for whom serum CA19-9 data were available ( Figure 2, Table 5), a direct comparison of serum CA19-9 and BileScreen was performed on these patients. The AUCs of serum CA19-9 for differentiating benign and malignant in the two cohorts were 0.78 and 0.81 ( Figure 6 ), respectively. With a cut-off value of ≥27 U / mL, the sensitivities of serum CA19-9 were 88% and 91%, and the specificities were 67% and 70% (Table 5).

[0111] Table 5. Validation results of each model and CA19-9 in different datasets

[0112]

[0113]

[0114] In contrast, the sensitivities of BileScreen were 93% and 94%, and the specificities were 98% and 96%, respectively. In the entire training and validation cohorts, the sensitivities and specificities of CA19-9 were 90% and 68%, respectively, both lower than 93% and 97% of BileScreen. Therefore, BileScreen is superior to serum CA19-9 in detecting pancreatobiliary cancer, especially in terms of detection specificity.

[0115] In addition, the serum CA19-9 results of 38 patients in the test cohort were as Figure 6 shown in A. The specificity of serum CA19-9 was extremely low (14%), the AUC was 0.51, and the sensitivity was 84% (Table 4, Figure 6 ). Compared with the training and validation cohorts, the accuracy of CA19-9 in predicting patients with suspected malignancy diagnosed by ERCP was lower. In contrast, the sensitivities and specificities of BileScreen in this group were 87% and 86%, respectively.

[0116] 4. Comparison of Gene Mutation Results between Bile and ERCP Biopsy / Brush Specimens

[0117] In the test cohort, biopsy samples were obtained from 34 patients by ERCP and brush samples from 9 patients. We analyzed gene and tissue samples for a head-to-head comparison study. Among 43 cases, 70 mutations were present in both sample types, 5 mutations were detected only in bile (brush samples), and 9 were detected only in tissue (biopsy samples) ( Figure 7)). Therefore, 93% (70 / 75) of the mutations in bile could also be detected in tissue, some other mutations were only found in bile, and 89% (70 / 79) of the tissue-derived mutations could be detected in bile. In addition, mutations were detected in at least one type of sample in 36 out of 43 samples, and among them, 34 cases (94%) had at least one common mutation detected between the two sample types. Therefore, the coincidence rate of the mutation status detected by bile and that detected by tissue was 95% (41 / 43).

Claims

1. A biomarker combination, which includes methylation biomarkers and gene mutations; The methylation biomarkers are a combination of SOX17, 3-OST-2, NXPH1, SEPT9, and TERT; The gene mutations are a combination of AKT1, KRAS, APC, NRAS, ARID1A, PIK3CA, AXIN1, PPP2R1A, BAP1, PTEN, BRAF, SMAD4, CDKN2A, TERT, TP53, EGFR, FBXW7, FGFR2, HRAS, IDH1, and IDH2.

2. Use of a reagent for detecting the biomarker combination according to claim 1 in the preparation of a product for diagnosing pancreatobiliary cancer.

3. The use according to claim 2, wherein the pancreatobiliary cancer includes biliary cancer and pancreatic cancer.

4. The use according to claim 3, wherein the biliary cancer includes cholangiocarcinoma, gallbladder cancer, and ampullary cancer.

5. The use according to claim 3, wherein the pancreatic cancer includes pancreatic head cancer, pancreatic tail cancer, and diffuse cancer.

6. The use according to claim 2, wherein the detection is performed on a sample derived from a subject, and the sample is bile.

7. The use according to claim 6, wherein the sample includes tissue taken from the biliary tract and exfoliated cells in the biliary tract.

8. The use according to claim 7, wherein the exfoliated cells in the biliary tract and the tissue sample taken from the biliary tract are samples obtained by ERCP biopsy / brushing.

9. The use according to claim 2, wherein the reagent for detecting gene mutations includes reagents used in any one or more of the following methods: TaqMan probe method, sequencing method, chip method, MALDI-TOF mass spectrometry detection, restriction fragment length polymorphism method, single-strand conformation polymorphism method, allele-specific PCR, SNaPshot method, SNPlex genotyping system, SNPStream analysis system, Sequenom genotyping system, denaturing high performance liquid chromatography method, denaturing gradient gel electrophoresis method.

10. The use according to claim 2, wherein the reagent for detecting methylation biomarkers includes reagents used in any one or more of the following methods: whole genome bisulfite sequencing, pyrosequencing, bisulfite sequencing, methylation-specific polymerase chain reaction, bisulfite-specific polymerase chain reaction, methylation-sensitive restriction endonuclease-PCR / Southern method, bisulfite-conjugated restriction endonuclease method, digital polymerase chain reaction, restriction landmark genomic scanning, CpG island microarray, single nucleotide primer extension SNUPE, methylation profiling, methylation chip.

11. The use according to claim 2, wherein the reagent for detecting the biomarker combination according to claim 1 is a reagent used in the mutation capsule technology.

12. A diagnostic system for diagnosing pancreatobiliary cancer, the system includes a computing device for obtaining a diagnostic conclusion based on the detection result of the biomarker combination according to claim 1 of a subject sample; The system includes: (1) A sample collection and processing device for performing the following steps: collecting a sample from a subject and processing the sample; (2) Methylation detection device; (3) Gene mutation detection device; (4) A computing device that obtains a diagnostic conclusion based on the detection results of (2) and (3).

13. The system according to claim 12, wherein the pancreaticobiliary cancer includes cholangiocarcinoma and pancreatic cancer.

14. The system according to claim 13, wherein the cholangiocarcinoma includes cholangiocarcinoma, gallbladder cancer, and ampullary cancer.

15. The system according to claim 13, wherein the pancreatic cancer includes pancreatic head cancer, pancreatic tail cancer, and diffuse cancer.

16. The system according to claim 12, wherein the sample of the subject is bile.

17. The system according to claim 16, wherein the sample of the subject includes tissue taken from the biliary tract and exfoliated cells in the biliary tract.

18. The system according to claim 17, wherein the exfoliated cells in the biliary tract and the tissue sample taken from the biliary tract are samples obtained by ERCP biopsy / brushing.

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