A method for constructing a cancer drug efficacy prediction model, a molecular marker for evaluating the prognosis of intrahepatic cholangiocarcinoma and application thereof

Abstract

The application belongs to the technical field of medical biological detection, and particularly relates to a construction method of a cancer drug efficacy prediction model, a molecular marker for evaluating the prognosis of intrahepatic cholangiocarcinoma obtained by the construction method, and application of the molecular marker in preparation of a reagent or kit for evaluating the prognosis of intrahepatic cholangiocarcinoma. The application provides a construction method of a cancer drug efficacy prediction model, and uses the construction method to evaluate the drug efficacy prediction of intrahepatic cholangiocarcinoma. The method can analyze the cell and transcriptome characteristic differences in the tumor environment of different efficacy patients in a high-throughput and high-resolution manner by capturing and transcriptome sequencing of a large number of cells in the tumor environment of different patients, so as to establish the correlation between the proportion of specific cell subgroups in the tumor before treatment and the efficacy of the patient.

Description

Technical Field

[0001] This invention belongs to the field of medical biological detection technology, specifically relating to a method for constructing a cancer drug efficacy prediction model, molecular markers for assessing the prognosis of intrahepatic cholangiocarcinoma obtained by this method, and their application in the preparation of reagents or kits for assessing the prognosis of intrahepatic cholangiocarcinoma. Background Technology

[0002] Intrahepatic cholangiocarcinoma (ICC) is a type of liver cancer that seriously endangers public health. In 2021, the treatment regimen of gemcitabine + oxaliplatin + lenvatinib + PD-1 monoclonal antibody was recommended by the Chinese Society of Clinical Oncology (CSCO) as a first-line treatment for advanced intrahepatic cholangiocarcinoma. However, there are still patients with poor treatment response to this regimen. Therefore, constructing a predictive model for the efficacy of this treatment regimen is of great clinical value for patients' treatment choices.

[0003] Single-cell transcriptome sequencing is a revolutionary technology that has been developed in recent years. This technology can capture single cells and accurately analyze the transcriptome expression characteristics of various cells in the tumor microenvironment, providing important technical support for high-throughput screening of tumor microenvironment cell subpopulations and functional states.

[0004] Immunohistochemistry is a commonly used technique in clinical pathological diagnosis. Through antigen-antibody reactions and colorimetric reactions, it can label the expression levels of specific antigens in tissue sections, enabling the quantification of specific antigens and specific cells. Currently, there is no protocol for using immunohistochemical staining to predict the efficacy of gemcitabine + oxaliplatin + lenvatinib + PD-1 monoclonal antibody treatment for intrahepatic cholangiocarcinoma. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a method for constructing a cancer drug efficacy prediction model, molecular markers for assessing the prognosis of intrahepatic cholangiocarcinoma obtained by this method, and their application in the preparation of reagents or kits for assessing the prognosis of intrahepatic cholangiocarcinoma.

[0006] To achieve the above objectives, the solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for constructing a cancer drug efficacy prediction model, comprising the following steps:

[0008] Collect tumor biopsy samples from patients before cancer drug treatment;

[0009] Obtain a single-cell suspension from a tumor biopsy sample and capture single cells;

[0010] Single-cell transcriptome sequencing and analysis were performed to obtain the single-cell transcriptome gene expression matrix;

[0011] screening out a cell subpopulation that can be used to predict the drug efficacy response;

[0012] The patient is stained by immunohistochemistry before and after cancer drug treatment, and the tumor reduction ratio before and after treatment is evaluated according to the RECIST 1.1 rating relationship;

[0013] The linear relationship between the proportion of each cell subpopulation screened out by single-cell transcriptome and the corresponding tumor reduction ratio before and after treatment is fitted.

[0014] Preferably, the tumor tissue sample is punctured to obtain a single-cell suspension, and then the cell suspension is captured by the oil droplet method (10X Genomics Company) for on-machine library construction sequencing and off-machine quality control to obtain a single-cell transcriptome gene expression matrix.

[0015] Preferably, the cancer is intrahepatic cholangiocarcinoma, and the tumor puncture sample of the patient is an in situ cancer tissue puncture sample of an intrahepatic cholangiocarcinoma patient.

[0016] Preferably, the cell subpopulation that can be used to predict the drug efficacy response includes a Macro_CD5L subpopulation extracted from myeloid cells (Myeloid) and a CD8_GZMB subpopulation extracted from lymphocytes (T lymphocytes, B lymphocytes and NK lymphocytes).

[0017] Preferably, the baseline proportion of the Macro_CD5L subpopulation is negatively correlated with the tumor reduction ratio before and after treatment, indicating that the higher the baseline proportion of the Macro_CD5L subpopulation of the patient, the worse the drug efficacy response of the patient can be predicted.

[0018] Preferably, the baseline proportion of the CD8_GZMB subpopulation is positively correlated with the tumor reduction ratio before and after treatment, indicating that the higher the baseline proportion of the CD8_GZMB subpopulation of the patient, the better the drug efficacy response of the patient can be predicted.

[0019] Preferably, the cancer drug treatment is 3 cycles of gemcitabine + oxaliplatin + lenvatinib + PD1 monoclonal antibody treatment.

[0020] In a second aspect, the present application also provides a molecular marker for evaluating the prognosis of intrahepatic cholangiocarcinoma, which is obtained by the construction method of a cancer drug efficacy prediction model as described above, and the molecular marker is a Macro_CD5L subpopulation extracted from myeloid cells (Myeloid) and a CD8_GZMB subpopulation extracted from lymphocytes (T lymphocytes, B lymphocytes and NK lymphocytes).

[0021] Preferably, the prognosis includes detection, efficacy evaluation and recurrence monitoring.

[0022] In a third aspect, the present application also provides a use of the molecular marker for evaluating prognosis of intrahepatic cholangiocarcinoma in preparation of a reagent or kit for evaluating prognosis of intrahepatic cholangiocarcinoma.

[0023] Preferably, the proportion of cells of the Macro_CD5L subpopulation and the proportion of cells of the CD8_GZMB subpopulation in the intrahepatic cholangiocarcinoma tissue are detected by immunohistochemistry.

[0024] Preferably, the proportion of cells of the Macro_CD5L subpopulation is negatively correlated with the tumor reduction ratio before and after treatment, indicating that the higher the proportion of cells of the Macro_CD5L subpopulation in the patient, the worse the drug efficacy response of the patient can be predicted; the proportion of cells of the CD8_GZMB subpopulation is positively correlated with the tumor reduction ratio before and after treatment, indicating that the higher the proportion of cells of the CD8_GZMB subpopulation in the patient, the better the drug efficacy response of the patient can be predicted.

[0025] Compared with the prior art, the present application has the following beneficial effects:

[0026] The present application provides a method for constructing a cancer drug efficacy prediction model, and uses the method for evaluating drug efficacy prediction of intrahepatic cholangiocarcinoma. The method can analyze the differences in cells and transcriptome characteristics in the tumor environment of patients with different efficacy in high throughput and high resolution by capturing and transcriptome sequencing of a large number of cells in the tumor environment of different patients, so as to establish the correlation between the proportion of specific cell subpopulation in the tumor before treatment and the efficacy of the patient. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The figure on the left is a cell subpopulation classification graph of intrahepatic cholangiocarcinoma in the embodiment of the present application, and the figure on the right is a key gene classification reference graph.

[0028] Figure 2 The figure on the left is a myeloid cell subpopulation classification graph, and the figure on the right is a classification marker gene graph.

[0029] Figure 3 The figure on the left is a myeloid cell subpopulation classification graph, and the figure on the right is a classification marker gene graph.

[0030] Figure 4 The figure on the left is a linear correlation analysis graph of baseline level Macro_CD5L subpopulation and CD8_GZMB subpopulation with tumor reduction degree of patients.

[0031] Figure 5 The figure on the left is a linear correlation analysis graph of baseline level Macro_CD5L subpopulation and CD8_GZMB subpopulation with tumor reduction degree of patients. DETAILED DESCRIPTION

[0032] The application provides a method for constructing a cancer drug efficacy prediction model, comprising the following steps:

[0033] (1) collecting tumor puncture samples of patients before cancer drug treatment;

[0034] (2) obtaining a single cell suspension of the tumor puncture sample and capturing single cells;

[0035] (3) performing single cell transcriptome sequencing and analysis to obtain a single cell transcriptome gene expression matrix;

[0036] (4) screening out a cell subpopulation that can be used to predict drug efficacy response;

[0037] (5) treating patients before and after cancer drug treatment by immunohistochemical staining, and evaluating the tumor reduction ratio before and after treatment according to the RECIST 1.1 rating relationship;

[0038] (6) fitting a linear relationship according to the proportion of each cell subpopulation screened out by the single cell transcriptome and the corresponding tumor reduction ratio before and after treatment.

[0039] The application takes intrahepatic cholangiocarcinoma as an example, and predicts the efficacy of the commonly used treatment method of gemcitabine + oxaliplatin + lenvatinib + PD1 monoclonal antibody for intrahepatic cholangiocarcinoma by the above method for constructing a cancer drug efficacy prediction model.

[0040] The technical solutions of the application will be further described in combination with specific embodiments, but the protection scope of the application is not limited to these embodiments. Any changes or equivalent replacements without departing from the concept of the application are included in the protection scope of the application.

[0041] Embodiment:

[0042] The embodiment provides a method for predicting the efficacy of the commonly used treatment method of gemcitabine + oxaliplatin + lenvatinib + PD1 monoclonal antibody for intrahepatic cholangiocarcinoma, and the specific steps comprise:

[0043] Step (1), collecting in-situ tumor puncture tissue samples of 8 patients with intrahepatic cholangiocarcinoma before drug treatment.

[0044] Step (2), obtaining a single cell suspension of the liver tumor puncture by sample dissociation from the collected tumor puncture tissue samples.

[0045] Step (3), after capturing the obtained single cell suspension by the oil droplet method (provided by 10X Genomics Company), library construction sequencing, and off-machine quality control, a single cell transcriptome gene expression matrix is obtained.

[0046] Step (4), performing dimension reduction and clustering of the obtained single cell transcriptome gene expression matrix, and according to theFigure 1 The key gene expression profile annotations shown are obtained for Epithelial, Hepatocyte, Myeloid, Neutrophil, T cell, NK cell, Plasma, Mast cell, Endothelial, pDC, Fibroblast, B cell, etc. major cell subpopulations.

[0047] Myeloid cell subpopulations are further extracted and divided into 14 sub-subpopulations (as shown in Figure 2 ) according to key gene expression profiles: Macro_GPNMB, Macro_CD5L, Macro_SPINK1, Macro_MRC1, Macro_IGLC2, Macro_IL32, Macro_FBP1, Mono_CCL3, Mono_CXCL10, Mono_CCL20, Mono_FCN1, DC_IDO1, DC_CD1C, DC_TYMS.

[0048] Lymphocyte subpopulations (including T lymphocytes, B lymphocytes and NK lymphocytes) are further extracted and divided into 12 sub-subpopulations (as shown in Figure 3 ) according to subpopulation marker gene expression profiles: NK_GNLY, NK_FCER1G, CD4 CD4_CXCL13, CD4_SOCS3, CD4 Treg, CD8 proliferating, CD8_GZMK, CD8_GZMB, CD8_KLRB1, CD8 Trm, γδT cell.

[0049] Step (5), the patient is treated with gemcitabine + oxaliplatin + lenvatinib + PD1 monoclonal antibody for 3 cycles, and the tumor reduction ratio before and after treatment is evaluated by MRI or CT imaging according to RECIST 1.1.

[0050] RECIST 1.1 is a standard method for measuring the response of solid tumors to treatment in clinical research, which is used to determine the efficacy of solid tumors after receiving treatment, including: complete remission (CR), partial remission (PR), stable disease (SD) and progressive disease (PD).

[0051] Step (6), linear fitting of the proportion of each cell subpopulation calculated according to the single cell transcriptome and the tumor reduction ratio before and after treatment, it is found that the cells of Macro CD5L and CD8 GZMB subpopulation have a significant linear correlation with the tumor reduction ratio. Among them, Macro CD5L is negatively correlated with tumor reduction, indicating that the higher the baseline proportion of Macro CD5L in patients, the worse the patient's response to the treatment plan; CD8 GZMB is positively correlated with tumor reduction, indicating that the higher the proportion of CD8 GZMB in patients, the better the patient's response to the treatment plan (as shown in Figure 4

[0052] Step (7), the puncture wax block sections of 55 patients with intrahepatic cholangiocarcinoma treated by gemcitabine+oxaliplatin+lenvatinib+PD1 monoclonal antibody after treatment are cut, and the baseline levels of Macro_CD5L subpopulation and CD8_GZMB subpopulation of patients evaluated as PR and SD by RECIST1.1 using immunohistochemical analysis can be seen. The baseline level of Macro_CD5L subpopulation of SD patients with poor efficacy is higher than that of PR patients with good efficacy, and the CD8_GZMB subpopulation is lower (as shown in Figure 5

[0053] Therefore, Macro_CD5L subpopulation and CD8_GZMB subpopulation can be used as molecular markers for evaluating the prognosis of intrahepatic cholangiocarcinoma, including detection, efficacy evaluation and recurrence monitoring.

[0054] The present application provides a method for predicting the efficacy of gemcitabine+oxaliplatin+lenvatinib+PD1 monoclonal antibody treatment for intrahepatic cholangiocarcinoma, by obtaining the liver puncture sample of the patient before treatment, and evaluating the cell proportion of Macro_CD5L and CD8_GZMB subpopulation by single cell transcriptome sequencing and immunohistochemical analysis. The cell proportion of Macro_CD5L subpopulation in patients is negatively correlated with the tumor reduction ratio before and after treatment, indicating that the higher the cell proportion of Macro_CD5L subpopulation in patients, the worse the patient's response to drug efficacy; the cell proportion of CD8_GZMB subpopulation in patients is positively correlated with the tumor reduction ratio before and after treatment, indicating that the higher the cell proportion of CD8_GZMB subpopulation in patients, the better the patient's response to drug efficacy.

[0055] The above only describes the preferred embodiments of the present application, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered within the scope of protection of the present application.​​

Claims

1. The use of a reagent for assessing the prognostic molecular markers of intrahepatic cholangiocarcinoma in the preparation of reagents or kits for assessing the prognosis of intrahepatic cholangiocarcinoma; The molecular markers are the Macro_CD5L subset extracted from myeloid cells and the CD8_GZMB subset extracted from lymphocytes. The lymphocytes include T lymphocytes, B lymphocytes, and NK lymphocytes; The prognosis of intrahepatic cholangiocarcinoma is to predict the efficacy of gemcitabine + oxaliplatin + lenvatinib + PD-1 monoclonal antibody treatment in patients with intrahepatic cholangiocarcinoma.

2. The application according to claim 1, characterized in that, Immunohistochemistry was used to detect the proportions of Macro_CD5L and CD8_GZMB subsets in intrahepatic cholangiocarcinoma tissues.

3. The application according to claim 1, characterized in that, The proportion of Macro_CD5L subpopulation cells was negatively correlated with the tumor reduction rate before and after treatment, indicating that the higher the proportion of Macro_CD5L subpopulation cells in the patient, the worse the patient's response to the drug. The proportion of CD8_GZMB subpopulation cells was positively correlated with the tumor reduction rate before and after treatment, indicating that the higher the proportion of CD8_GZMB subpopulation cells in the patient, the better the patient's response to the drug.

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