A biomarker combination, a reagent containing the same and its application

Abstract

The present invention discloses a biomarker combination, a reagent containing the same and its application. The biomarkers contained in the biomarker combination are detailed in the present invention. The above-mentioned protein molecular markers provided by the present invention have been found to have significant changes in expression levels in clinical samples of patients with different treatment responses to colorectal cancer through experiments. Therefore, the protein molecular markers provided in the present invention can be used as a model for predicting different responses of colorectal cancer patients to conventional chemotherapy, with the advantages of high sensitivity and high specificity, and provide favorable technical support for predicting the acceptance of conventional chemotherapy by colorectal cancer patients.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology and diagnosis, and specifically relates to a biomarker combination, a reagent containing the biomarker and an application thereof. The application specifically relates to the preparation of a prognosis product for colorectal cancer patients receiving conventional chemotherapy combined with targeted therapy. Background Art

[0002] Colon cancer (CRC) is the third most common cancer in the world. Its morbidity and mortality rate rank third and second among various malignant tumors, respectively, and it poses a serious threat to human health. Surgery is the main treatment for early cases, but early colon cancer has no obvious clinical symptoms. When symptoms appear, it is often in the late stage and the opportunity for radical surgery has been lost. Patients can only rely on neoadjuvant drug therapy to improve their quality of life or even maintain survival. Neoadjuvant drugs for colorectal cancer mainly include oxaliplatin ( ), leucovorin calcium ), 5-Fluorouracil, 5- ), cetuximab ), Bevacizumab ). The targeted drug cetuximab is commonly used in patients with K-ras, N-ras, and BRAF wild-types. It binds to the epidermal growth factor (EGF) receptor and blocks the intracellular signal transduction pathway, thereby inhibiting the proliferation of cancer cells and inducing apoptosis of cancer cells. The targeted drug bevacizumab can bind to vascular endothelial growth factor (VEGF) to inhibit the proliferation of tumor vascular endothelium and the formation of new blood vessels, reduce tumor tissue nutrition, and thus inhibit tumor growth. In clinical practice, there are large individual differences in the treatment effect of tumors, but there is too little basis for personalized drug selection. Clinically, there is an urgent need for markers to guide personalized precision medicine to alleviate drug tolerance problems.

[0003] Therefore, in order to achieve personalized treatment of colorectal cancer, it is necessary to identify subclasses based on molecular genetics and pathological characteristics, and discover and apply corresponding target genes. In addition, in colorectal cancer research, it has been reported that the prognosis of colorectal cancer can be classified according to the subtype of colorectal cancer. Currently, many research patents have emerged to achieve diagnosis and prognosis prediction of colorectal cancer based on the gene expression levels of the genome and transcriptome, such as prognosis prediction of colorectal cancer (patent number CN101389957B), the application of colorectal cancer-related miRNA as a biomarker and a colorectal cancer detection kit (patent number CN107326092A). However, in clinical practice, conventional chemotherapy combined with targeted therapy still does not provide an effective basis for treatment selection for patients with colorectal cancer.

[0004] Proteomics plays a significant role in revealing the complex molecular events of tumorigenesis, such as tumorigenesis, invasion, metastasis, and resistance to treatment. Proteomic tumor diagnosis has the advantages of high sensitivity, strong specificity, and clear background mechanism, and has been increasingly used in tumor detection in recent years. Moreover, the research on these tumor markers is often based on a certain amount of experimental data, and the types of cancer and sample sizes involved are relatively limited. Therefore, by collecting proteomic data and using big data analysis methods to establish a model for predicting effective treatment, it is helpful to achieve personalized chemotherapy and has important clinical significance for recommending appropriate treatment plans for patients. Summary of the invention

[0005] To solve the above technical problems, the present invention provides a biomarker combination, a reagent containing the biomarker and its application, and a prediction system for conventional chemotherapy combined with targeted therapy for colorectal cancer. Specifically, the present invention solves the above technical problems through the following technical solutions.

[0006] One of the technical solutions of the present invention is: a biomarker combination, which includes the following biomarkers:

[0007] AAR2, ACYP1, AKT2, AMY2B, APOC2, ARL6IP1, ARMCX1, ASNA1, BAIAP2L1, C10orf54, C19orf66, C4BPA, C9, CCDC137, CCDC43, CCDC 47. CD55, CDK12, CDK4, CDX1, CFL2, CLEC11A, CNEP1R1, CPB2, CPPED1, CST1, CTR9, CYP51A1, D2HGDH, DCAKD, DHF, DHODH, DNM2, D OLPP1, EBNA1BP2, EGFR, ENTPD4, EPS8, EVPL, F10, FDX1, FERMT1, FGA, FRY, GNAI1, HIST1H1E, HLA-DPA1, HLA-DRB4, HOOK1, INPP 5F, ITIH4, JUND, KIF20A, LBP, MAD1L1, MAGED1, MAGI3, MAGT1, MAP3K3, MASTL, MEPCE, MPP5, MPP7, MRPL3, MRPS2, MRPS28, MSI2, MST1R, MTA3, MTFR1, NAE1, NCKAP1L, NOL3, OAF, PES1, PLEKHG3, PLVAP, POGLUT1, POLR2L, POLRMT, PPP1CA, PPP1R13L, PRIM1, PR TFDC1, PSIP1, PTGR2, QPCT, RAB4A, RBBP5, RDH10, RELB, RNF123, RNF220, ROCK1, RPL18A, SAA2, SDR16C5, SERPINA5, SH3BGRL2, SLC25A26, SLC35B1, SLC37A3, SMC2, SP3, SRGN, STON2, SUFU, SUPT20H, TAOK3, TBCB, TCEA3, THUMPD3, TLR8, TMEM55B, TMEM63B, TMEM68, TOR3A, TP53RK, TRMT10C, TXN2, UBR4, UHRF1, USP11, VNN2, VPS33A, VWA8, WDHD1, WDR18, WDR70, WNK2, XXYLT1 and ZNF830.

[0008] A second technical solution of the present invention is: a reagent for detecting the expression level of the biomarker combination as described in one of the technical solutions, wherein the reagent comprises a biological molecule that specifically hybridizes with the biomarker combination as described in one of the technical solutions;

[0009] Preferably, the biomolecules include one or more selected from primers, probes and antibodies.

[0010] The third technical solution of the present invention is: the use of the biomarker combination as described in the first technical solution, or the reagent as described in the second technical solution in the preparation of a product for the prognosis of colorectal cancer patients receiving conventional chemotherapy combined with targeted therapy.

[0011] A fourth technical solution of the present invention is: a kit comprising the biomarker combination as described in one of the technical solutions, or the reagent as described in the second technical solution.

[0012] The fifth technical solution of the present invention is: use of the kit described in the fourth technical solution in preparing a prognosis product for colorectal cancer patients receiving conventional chemotherapy combined with targeted therapy.

[0013] The sixth technical solution of the present invention is: a system for evaluating the prognosis of colorectal cancer patients receiving conventional chemotherapy combined with targeted therapy, the system comprising:

[0014] A data processing module, used to calculate the data of the biomarker combination of the patient as described in one of the technical solutions received or input, and obtain a calculation result; and

[0015] A judgment and output module is used to judge whether the calculation result meets the preset judgment condition and output the prediction result;

[0016] Optionally, in the judgment and output module, when the calculation result meets the judgment condition, the output prediction result is "good prognosis, the treatment plan is recommended"; when the calculation result does not meet the judgment condition, the output prediction result is "poor prognosis, the treatment plan is not recommended";

[0017] Optionally, in the data processing module, the data is the expression amount information of the biomarker combination of the patient as described in one of the technical solutions;

[0018] Optionally, the expression amount information of the biomarker is obtained by sequencing method;

[0019] Optionally, the judgment and output module preferably uses a GLM generalized linear model;

[0020] Optionally, in the judgment and output module, the judgment includes the performance of the expression information of the biomarker combination as described in one of the technical solutions in the model, that is, the sensitive prediction probability and the non-sensitive prediction probability;

[0021] Optionally, the judgment condition is a comparison of the sensitive prediction probability and the non-sensitive prediction probability: 1) if the sensitive prediction probability is greater than the non-sensitive prediction probability, and the sensitive prediction probability is greater than 0.8, the treatment regimen is recommended to the patient; 2) if the non-sensitive prediction probability is greater than the sensitive prediction probability, and the non-sensitive prediction probability is greater than 0.8, the treatment regimen is not recommended; 3) if the sensitive prediction probability or the non-sensitive prediction probability is less than or equal to 0.8, the treatment regimen is not recommended.

[0022] Preferably, the system further comprises a sample extraction module, which extracts sample data and transmits it to the biomarker combination verification module.

[0023] The seventh technical solution of the present invention is: a device including a machine learning model, which includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, it can realize the functions of the system described in the sixth technical solution.

[0024] The eighth technical solution of the present invention is: a computer-readable storage medium storing a computer program, which, when executed by a processor, can realize the functions of the system described in the sixth technical solution.

[0025] The ninth technical solution of the present invention is: use of the system described in the sixth technical solution in the preparation of a product for evaluating the prognosis of colorectal cancer patients receiving conventional chemotherapy combined with targeted therapy;

[0026] Preferably, the product is a chip, a kit, a test paper or a high-throughput sequencing platform.

[0027] It can be seen that the present invention mainly provides a group of protein molecules for predicting the response of conventional chemotherapy combined with targeted therapy for colorectal cancer, and establishes a prediction model to help achieve personalized treatment; based on this, a device and decision-making system for conventional chemotherapy combined with targeted therapy for colorectal cancer based on clinical sample detection are provided.

[0028] In addition, the present invention also provides a method for predicting treatment response based on clinical sample proteome, the method comprising the following steps:

[0029] (1) Collect clinical samples before treatment, such as paraffin-embedded tumor tissue sections, from patients with colorectal cancer who have different responses to conventional chemotherapy combined with targeted therapy;

[0030] (2) Protein preparation from clinical samples of colorectal cancer patients;

[0031] (3) Detecting the expression levels of protein molecules in samples from patients with colorectal cancer;

[0032] (4) Find the highly expressed proteomic molecules in patients with colorectal cancer with different treatment responses and construct a classifier to distinguish them.

[0033] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0034] The reagents and raw materials used in the present invention are commercially available.

[0035] The positive and progressive effects of the present invention are:

[0036] The above-mentioned protein molecular markers provided by the present invention were found through experiments to have significant changes in expression levels in clinical samples of colorectal cancer patients with different treatment responses. Therefore, the protein molecular markers provided by the present invention can be used as a model for predicting the different responses of colorectal cancer patients to conventional chemotherapy treatments, and have the advantages of high sensitivity and high specificity, providing favorable technical support for predicting the acceptance of conventional chemotherapy treatments by colorectal cancer patients.

[0037] The development of a corresponding prediction device based on protein molecular markers of clinical samples of colorectal cancer patients with different responses to conventional chemotherapy has broad scientific research value and can provide personalized treatment for colorectal cancer patients and recommend whether the treatment plan is suitable for the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Displays the prediction accuracy, sensitivity, and specificity of the training set.

[0039] Figure 2 Prediction accuracy, sensitivity, and specificity of the internal validation set are shown. DETAILED DESCRIPTION

[0040] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples. The experimental methods in the following examples without specifying specific conditions are carried out according to conventional methods and conditions, or selected according to the product specifications.

[0041] The clinical samples of 31 patients with colorectal cancer who received conventional chemotherapy combined with targeted therapy before treatment required in the embodiment were from Zhongshan Hospital of Fudan University. The design and implementation of this study were approved and supervised by the Medical Ethics Committee of Zhongshan Hospital of Fudan University through ethical voting. Written informed consent was obtained from all patients.

[0042] Example 1 Pretreatment of clinical samples before colorectal cancer treatment

[0043] Clinical samples were formalin-fixed paraffin-embedded tissues. Sample pretreatment: 3-10 μm thick slices were taken from paraffin blocks for macroscopic dissection, dewaxed with xylene, washed with ethanol, and air-dried to obtain white slices. At the same time, 3 μm thick slices were stained with hematoxylin-eosin as tumor microscopy to evaluate the tumor cell content in the slices and select the tumor area. The corresponding tumor samples were collected from 10 μm thick slices into centrifuge tubes and frozen at -80°C for later use.

[0044] Example 2 Protein and peptide extraction from clinical samples

[0045] Collect equal amounts of FFPE tissues in EP tubes, add lysis buffer (0.1M Tris-HCL pH8.0, 0.1M DTT, 1mM PMSF), and then grind with a grinding rod for 3 minutes; add sodium dodecyl sulfate (SDS) to a final concentration of 4%, and shake at 99°C and 1800rpm for 2-2.5 hours; centrifuge at 12,000g for 5 minutes, collect the supernatant in an EP tube, add 4 volumes of acetone, and place at -20°C for 4 hours or overnight; centrifuge at 12,000g at 4°C for 1 minute, discard the supernatant and retain the precipitate, wash the precipitate three times with cold acetone, and dry the protein precipitate with a super-clean typhoon; re-dissolve the protein precipitate with 8M Urea and 50mM NH4HCO3 and add it to a FASP tube, and repeatedly centrifuge with 50mM NH4HCO3 to remove Urea; add 50μL 50mM NH4HCO3 was added to the FASP tube and incubated at 37°C for 18-20 hours for enzymatic hydrolysis; the peptides were collected by centrifugation at 12,800g for 15 minutes. To increase the yield of peptides, they were washed twice with 200μL MS water and collected together; the tubes were vacuum dried at 60°C to obtain the peptides required for mass spectrometry detection.

[0046] Example 3 Mass spectrometry detection of clinical samples

[0047] The peptide samples were detected using a Q-Exactive HF-X hybrid quadrupole orbital trap mass spectrometer (Thermo Fisher Scientific, Rockford, IL, USA) and a high performance liquid chromatography system (EASY nLC 1200, Thermo Fisher) to obtain the mass spectrometry data corresponding to the peptide samples. The specific operations were as follows:

[0048] The dried peptide sample was redissolved in solvent A (0.1% formic acid in water) and loaded onto a trap column (100 μm × 2 cm; particle size, 3 μm; pore size, ), and then separated on an analytical column (150 μm × 12 cm, particle size, 1.9 μm; pore size, ), with a gradient of 5-35% mobile phase B (80% acetonitrile and 0.1% formic acid), a flow rate of 600 nL / min, and a total elution time of 75 minutes. MS analysis was performed on QE-HFX, with a full scan (300-1400 m / z, resolution = 12000), the maximum number of ions allowed to enter the ion trap (automatic gain control target, AGC target) was 3E+06 ions, followed by high-energy collision-induced dissociation (isolation window of 1.6 m / z, collision energy of 27%, AGCtarget of 5E+04 ions, maximum injection time of 30 ms, and dynamic exclusion set to 18 seconds). The liquid chromatography tandem mass spectrometry system was controlled by Xcalibur software (Thermo Scientific) for data acquisition.

[0049] Example 4 Data Analysis

[0050] All data were processed using Firmiana. Firmiana is a workflow based on the Galaxy system, which consists of multiple functional modules such as user login interface, raw data, identification and quantification, data analysis and knowledge mining. The raw files were searched against the Refseq protein database of the National Center for Biotechnology Information (NCBI) of human. Trypsin was selected as the proteolytic enzyme, with a maximum of two missed cleavage sites allowed, fixed modifications as carbamidomethyl (C), and dynamic modifications as protein acetyl (protein N-term), oxidation (M). The first search mass tolerance was 20 ppm, and the main search peptide tolerance was 0.5 Da. The false discovery rate (FDR) of peptide spectrum matches (PSMs) and proteins was less than 1%. The quantitative results of the identified peptides were recorded as the average of the peak areas of the chromatographic fragment ions in all reference libraries. The label-free intensity-based absolute quantification (iBAQ) method was used for protein quantification. We calculated the peak area values ​​as a fraction of the corresponding protein. The total fraction (FOT) was used to represent the normalized abundance of a specific protein in the sample. FOT was defined as the iBAQ of a protein divided by the total iBAQ of all identified proteins in the sample. Proteins with at least one unique peptide and 1% FDR were selected for further analysis.

[0051] The 132 protein molecular markers (AAR2, ACYP1, AKT2, AMY2B, APOC2, ARL6IP1, ARMCX1, ASNA1, BAIAP2L1, C10orf54, C19orf66, C4BPA, C9, CCDC137, CCDC43, CCDC47, CD55, CDK12, CDK4, CDX1, CFL2, CLEC11A, CNEP1R1, CPB2, CPPED1, CST1, CTR9, CYP51A1, D2HGDH, DCAKD, DHF, DH ODH, DNM2, DOLPP1, EBNA1BP2, EGFR, ENTPD4, EPS8, EVPL, F10, FDX1, FERMT1, FGA, FRY, GNAI1, HIST1H1E, HLA-DPA1, HLA-DRB4, HOOK1, INPP5F , ITIH4, JUND, KIF20A, LBP, MAD1L1, MAGED1, MAGI3, MAGT1, MAP3K3, MASTL, MEPCE, MPP5, MPP7, MRPL3, MRPS2, MRPS28, MSI2, MST1R, MTA3, MTFR 1. NAE1, NCKAP1L, NOL3, OAF, PES1, PLEKHG3, PLVAP, POGLUT1, POLR2L, POLRMT, PPP1CA, PPP1R13L, PRIM1, PRTFDC1, PSIP1, PTGR2, QPCT, RAB4 A. RBBP5, RDH10, RELB, RNF123, RNF220, ROCK1, RPL18A, SAA2, SDR16C5, SERPINA5, SH3BGRL2, SLC25A26, SLC35B1, SLC37A3, SMC2, SP3, SRGN, The relative expression levels of STON2, SUFU, SUPT20H, TAOK3, TBCB, TCEA3, THUMPD3, TLR8, TMEM55B, TMEM63B, TMEM68, TOR3A, TP53RK, TRMT10C, TXN2, UBR4, UHRF1, USP11, VNN2, VPS33A, VWA8, WDHD1, WDR18, WDR70, WNK2, XXYLT1, and ZNF830 were used to calculate the prediction accuracy, sensitivity, and specificity. A training set of 24 cases was included, with a prediction accuracy of 100%, a diagnostic sensitivity of 100%, and a specificity of 100% (see Figure 1 ), the remaining 7 cases were used as internal validation sets, with a prediction accuracy of 100%, a diagnostic sensitivity of 100%, and a specificity of 100% (see Figure 2). For patients with colorectal cancer to be treated, the output results of different responses of the patient to conventional chemotherapy combined with targeted therapy are obtained according to the expression levels of protein molecular markers, so as to recommend or not recommend the treatment plan to the patient (see Table 1).

[0052] The data is based on the GLM model. In the R language environment, the predict function is used, such as predict(glm.model,test_data,type="prob"). Here, glm.model is the marker prediction model of the treatment regimen, test_data is the marker combination expression information measured by the proteome of the patient to be treated, and type="prob" is the probability value of predicting sensitivity and insensitivity. As described above, the marker combination expression value is input into the model, and the predict function is used to output the probability of the patient being sensitive and insensitive to conventional chemotherapy combined with targeted therapy. The output result is the result in the table. Whether to use the treatment regimen is to refer to the sensitive prediction probability value and the insensitive treatment probability value of the treatment regimen. The reference value recommended by this patent: based on the sensitive prediction probability being greater than the insensitive prediction probability, and the sensitive prediction probability being greater than 0.8, the treatment regimen is recommended to the patient; based on the insensitive prediction probability being greater than the sensitive prediction probability, and the insensitive prediction probability being greater than 0.8, the treatment regimen is not recommended; the sensitive prediction probability or the insensitive prediction probability is less than or equal to 0.8, and the treatment regimen is not recommended.

[0053] Table 1

[0054]

[0055] From the above results, it can be seen that 132 protein molecular markers (AAR2, ACYP1, AKT2, AMY2B, APOC2, ARL6IP1, ARMCX1, ASNA1, BAIAP2L1, C10orf54, C19orf66, C4BPA, C9, CCDC137, CCDC43, CCDC47, CD55, CDK12, CDK4, CDX1, CFL2, CLEC11A, CNEP1R1, CPB2, CPPED1, CST1, CTR9, CYP51A1, D2HGDH, DCAKD, DHF, DHODH, DNM2, DOLPP1, EBNA1BP2, EGFR, ENTPD4, EPS8, EVPL, F10, FDX1, FERMT1, FGA, FRY, GNAI1, HIST1H1E, HLA-DPA1, HLA-DRB4, HOO K1, INPP5F, ITIH4, JUND, KIF20A, LBP, MAD1L1, MAGED1, MAGI3, MAGT1, MAP3K3, MASTL, MEPCE, MPP5, MPP7, MRPL3, MRPS2, MRPS28, MSI2, MS T1R, MTA3, MTFR1, NAE1, NCKAP1L, NOL3, OAF, PES1, PLEKHG3, PLVAP, POGLUT1, POLR2L, POLRMT, PPP1CA, PPP1R13L, PRIM1, PRTFDC1, PSIP 1. PTGR2, QPCT, RAB4A, RBBP5, RDH10, RELB, RNF123, RNF220, ROCK1, RPL18A, SAA2, SDR16C5, SERPINA5, SH3BGRL2, SLC25A26, SLC35B1, SL C37A3, SMC2, SP3, SRGN, STON2, SUFU, SUPT20H, TAOK3, TBCB, TCEA3, THUMPD3, TLR8, TMEM55B, TMEM63B, TMEM68, TOR3A, TP53RK, TRMT10C, TXN2, UBR4, UHRF1, USP11, VNN2, VPS33A, VWA8, WDHD1, WDR18, WDR70, WNK2, XXYLT1, ZNF830) or any combination of these molecules can be used to predict whether patients with colorectal cancer are suitable for conventional chemotherapy combined with targeted therapy.

[0056] Finally, the above specific implementation method is only used to illustrate the technical solution of the present invention, but not to limit it.

Claims

1. Use of a reagent for detecting the expression level of a combination of biomarkers in the preparation of a prognostic product for colorectal cancer patients receiving conventional chemotherapy combined with targeted therapy; The biomarker combination is AAR2, ACYP1, AKT2, AMY2B, APOC2, ARL6IP1, ARMCX1, ASNA1, BAIAP2L1, C10orf54, C19orf66, C4BPA, C9, CCDC137, CCDC43, CCDC47, CD55, CDK12, CDK4, CDX1, CFL2, CLEC11A, CNEP1R1, CPB2, CPPED1, CST1, CTR9, CYP51A1, D2HGDH, DCAKD, DHF, DHOD H, DNM2, DOLPP1, EBNA1BP2, EGFR, ENTPD4, EPS8, EVPL, F10, FDX1, FERMT1, FGA, FRY, GNAI1, HIST1H1E, HLA-DPA1, HLA-DRB4, HOO K1, INPP5F, ITIH4, JUND, KIF20A, LBP, MAD1L1, MAGED1, MAGI3, MAGT1, MAP3K3, MASTL, MEPCE, MPP5, MPP7, MRPL3, MRPS2, MRPS28, MSI2, MST1R, MTA3, MTFR1, NAE1, NCKAP1L, NOL3, OAF, PES1, PLEKHG3, PLVAP, POGLUT1, POLR2L, POLRMT, PPP1CA, PPP1R13L, PRIM 1. PRTFDC1, PSIP1, PTGR2, QPCT, RAB4A, RBBP5, RDH10, RELB, RNF123, RNF220, ROCK1, RPL18A, SAA2, SDR16C5, SERPINA5, SH3BGR L2, SLC25A26, SLC35B1, SLC37A3, SMC2, SP3, SRGN, STON2, SUFU, SUPT20H, TAOK3, TBCB, TCEA3, THUMPD3, TLR8, TMEM55B, TMEM63 B. TMEM68, TOR3A, TP53RK, TRMT10C, TXN2, UBR4, UHRF1, USP11, VNN2, VPS33A, VWA8, WDHD1, WDR18, WDR70, WNK2, XXYLT1 and ZNF830.

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