CN114184790A - Application of BGN gene in prediction of treatment effect of immune checkpoint inhibitor of colon cancer patient - Google Patents
Application of BGN gene in prediction of treatment effect of immune checkpoint inhibitor of colon cancer patient Download PDFInfo
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Abstract
The invention discloses application of a BGN gene in predicting treatment effect of an immune checkpoint inhibitor of a colon cancer patient, and the BGN gene is used as an effective biomarker for predicting treatment effect of the immune checkpoint inhibitor of the colon cancer patient, so that a patient most likely to benefit from the immune checkpoint inhibitor therapy can be identified. The invention provides a reliable and convenient immune checkpoint inhibitor treatment response rate prediction index, and has positive effects on clinical medication guidance and accurate treatment of colon cancer patients.
Description
Technical Field
The invention relates to the technical field of prediction of treatment effects of immune checkpoint inhibitors, in particular to application of a BGN gene in prediction of treatment effects of immune checkpoint inhibitors of colon cancer patients.
Background
Global cancer statistics in 2020 show that colorectal cancer (CRC) ranks third in the most common malignancies and second in tumor-related deaths. CRC has an annual incidence of 190 tens of thousands, and 935,000 deaths, accounting for 9.8% and 9.2% of the total incidence of cancer, respectively (Arnold, Sierra et al, 2017; Sung, Ferlay et al, 2021). Although the diagnostic rate of CRC has improved in recent years due to colonoscopic screening and the use of three-dimensional CT reconstruction or imaging techniques such as PET-CT, about 25% of patients have advanced to advanced stages of the disease (Ganesh, Stadler et al, 2019) and have limited benefit from traditional treatments such as surgery, chemotherapy and radiotherapy (Dekker, Tanis et al, 2019, Sveen, Kopetz et al, 2019, Siegel, Miller et al, 2021). Immunotherapy has great promise in cancer treatment, providing a new therapeutic strategy for advanced or drug-resistant colorectal cancer patients (Procaccio, schirropaetal.2017).
In view of the safety and efficacy of tumor immunotherapy, various immunodot point inhibitors are approved by the U.S. Food and Drug Administration (FDA) for the treatment of colorectal cancer. However, the process of mobilizing autoimmunity to achieve tumor eradication is delicate and complex, including antigen presentation, T cell activation, tumor targeting, overcoming local inhibition, and killing the tumor (joyean Fearon 2015). The completion of these critical steps determines the efficacy of immunotherapy. However, different types of colorectal cancer patients respond widely to immunotherapy (Barbee, ogunniyietal.2015, Garon, rizviet al 2015). Therefore, accurate indication of the immune status of a patient, prediction of the efficacy of immunotherapy (e.g., anti-PD-1 treatment) and search for biomarkers related to efficacy are of particular importance for the treatment of colorectal cancer (Overman, McDermott et al, 2017; Wu, Qu et al, 2020). Unfortunately, in current clinical practice, there is a lack of a molecular marker with good predictive efficacy to predict immune status and immune checkpoint inhibitor treatment response rate in colon cancer patients.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide the application of the BGN gene in predicting the treatment effect of the immune checkpoint inhibitor of a colon cancer patient. The BGN gene is used as an effective biomarker for predicting the treatment effect of the immune checkpoint inhibitor of the colon cancer patient, so that the patient most likely to benefit from the immune checkpoint inhibitor treatment can be identified, and the BGN gene is a new attempt for individualized and accurate treatment of the colon cancer patient.
The purpose of the invention is realized by the following technical scheme:
in a first aspect, the invention provides a BGN gene detection reagent for preparing a detection product for predicting treatment effect of an immune checkpoint inhibitor for a colon cancer patient.
Preferably, the immune effect comprises an immunotherapy response rate.
Preferably, the detection product comprises at least one of a detection test paper and a detection kit.
In a second aspect, the invention provides a test product for predicting the efficacy of a treatment with an immune checkpoint inhibitor in a patient with colon cancer.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a reliable and convenient immune checkpoint inhibitor treatment response rate prediction index, and has positive effects on clinical medication guidance and accurate treatment of colon cancer patients.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 shows the results of screening for different genes in example 1; wherein, FIG. 1A is the screening results of differentially expressed genes from three GEO data sets; FIG. 1B is the result of constructing a protein-protein interaction network; FIG. 1C is the rank score results for seven genes (the darker the color, the higher the rank score);
FIG. 2 is the results of a correlation analysis of expression of BGN in example 2 with the immune score, stromal score and ESTIMATE score of colon cancer;
FIG. 3 shows the correlation between BGN expression and Pearson analysis of 10 immune cells in example 3;
FIG. 4 shows the correlation between BGN expression and Pearson analysis of 22 immune cells in example 3;
FIG. 5 shows the expression results of M2 macrophages and Treg cell-specific marker genes in colon cancer samples with high expression of BGN and low expression of BGN in example 3; wherein, FIG. 5A is an immunohistochemistry chart with a scale of 50 μm; FIG. 5B is a quantification of mean optical density values for immunohistochemistry;
FIG. 6 shows the results of enrichment of the C7 gene set by the GESA analysis in example 4;
FIG. 7 shows the results of the enrichment of GSVA analysis in example 4 for immune response-related functions and pathways;
FIG. 8 is the results of the correlation of BGN expression in example 4 with immune response-related functions and pathways in a GSVA enrichment assay;
FIG. 9 is the expression levels of the immune checkpoint signature genes in the BGN high expression subgroup and the BGN low expression subgroup of example 5;
FIG. 10 is the relative ratios of 24 tumor immunoinfiltrating cell types in the BGN high expression subgroup and the BGN low expression subgroup of example 5;
figure 11 is a correlation analysis of the expression of BGN with the number of two subpopulations of Treg cells in example 5; among them, fig. 11A is a correlation analysis of BGN expression and numbers of iTreg (inducible Treg) cells; fig. 11B is a correlation analysis of BGN expression with the number of nTreg (natural Treg) cells;
FIG. 12 shows the results of scoring T cell inflammatory signals (TIS) for the high expression and low expression subgroups of BGN in example 5;
FIG. 13 is a graph showing the results of predicting the response rate of immune checkpoint inhibitor treatment for colon cancer patients in example 5; wherein, FIG. 13A is the predicted result according to ImmuCellAI; FIG. 13B shows the results obtained according to the TIDE algorithm.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1 screening of differential genes
This example analyzed three GEO datasets (GSE4107, GSE110224, GSE4183) including 44 colon cancer tissues and 35 normal tissues to determine differentially expressed genes. According to the established criteria, 1039, 396 and 256 differentially expressed genes were obtained, respectively. As shown in FIG. 1A, 14 differentially expressed genes whose expression overlaps were selected. Subsequently, a protein-protein interaction network (PPI network) was constructed by the STRING tool, and as a result, as shown in fig. 1B, it can be seen that ABCG2, CXCL8, CXCL12, BGN, SULF1, THBS2 and FAP have interactions. The PPI network was then uploaded to Cytoscape software, and seven genes ABCG2, CXCL8, CXCL12, BGN, SULF1, THBS2, and FAP were identified as potentially critical genes, as seen from the ratio of stromal-immune component in TME of each sample generated by the cytoshibba module in Cytoscape software as a rank score calculated using the ESTIMATE package in R (fig. 1C). The BGN with the highest grade score (darker color and higher grade) is determined as the target gene for subsequent functional analysis and verification.
Example 2 correlation of BGN expression levels with immune score, stromal score and ESTIMATE score in tumor immune microenvironment
In this example, the correlation analysis of expression of BGN and immune score, stroma score, and ESTIMATE score of colon cancer was performed by the following specific method: and calculating and analyzing the ratio of the matrix-immune components in the tumor immune microenvironment of each sample of the TCGA colon cancer through an ESTIMATE packet in R software. A higher immune or stromal score represents a greater proportion of immune or stromal components in the tumor immune microenvironment, while the estamate score represents their combined proportion. The results are shown in FIG. 2: pearson correlation analysis finds that expression of BGN is positively correlated with immune score, stroma score and ESTIMATE score in colon cancer, namely the higher expression level of BGN is, the higher immune score, stroma score and ESTIMATE score are in a tumor immune microenvironment. The change of tumor immune microenvironment can cause the expression quantity of BGN in colon cancer to be obviously changed.
Example 3 BGN Regulation of macrophage polarization in Colon cancer
To further investigate the relationship between BGN and immune cell subtypes in colon cancer tissues, this example analyzed the proportion of tumor infiltrating immune cells (tics) in the TCGA database using the quattiseq method. In these TIICs, macrophages (M1+ M2) account for approximately 38% of colon cancer tissue (M1 accounts for approximately 27%, M2 accounts for approximately 11%), neutrophils for approximately 32% of colon cancer tissue, CD4+ T cells for 10% of colon cancer tissue, and NK cells for 7% of colon cancer tissue. Subsequently, we investigated the relationship between BGN expression level and 10 immune cell infiltrates in colon cancer (fig. 3), and the results showed: expression of BGN was most clearly associated with a population of macrophages M2 (Macrophage M2) and a population of Regulatory T cells (Regulatory T cells). Next, we validated the correlation of BGN expression with immune components by constructing 22 immune cell profiles of colon cancer using the CIBERSORT method and analyzed the proportion of tics. The results show that the first three largest immune cell components are macrophage M2 (20%), CD4+ memory-quiescent T cells (14%) and macrophage M0 (14%), suggesting that macrophages may play a potential role in tumor immunity. The relationship between BGN expression level and infiltration of 22 immune cells is shown in fig. 4, and the results show that: a total of five types of tics are closely related to BGN expression. Two of the TIICs (M0 macrophage and M2 macrophage) were positively correlated with BGN expression; three TIICs (plasma B cells, follicular helper T cells and memory-quiescent CD4+ T cells) were negatively associated with BGN expression. In addition, protein expression levels of M2 macrophage marker (CD163) and Tregs marker (FOXP3) were further confirmed in patient-derived tissue samples. Colon cancer samples with High expression of BGN (BGN High in FIG. 5, defined as the ratio of BGN gene expression rate cancer/paracancer > 1) and Low expression of BGN (BGN Low in FIG. 5, defined as the ratio of BGN gene relative expression rate cancer/paracancer < 1) were used for immunohistochemical analysis. The results show that: both CD163 and FOXP3 were significantly upregulated in the BGN-highly expressed colon cancer samples compared to the BGN-low expression samples (fig. 5A and 5B). The above results indicate that high expression of BGN may promote polarization of M2 macrophages.
Example 4 potential interaction of BGN in colon cancer immune response
In this example, colon cancer patients were divided into BGN high expression group and BGN low expression group according to the median value of BGN expression in TCGA, and then differentially expressed genes between BGN high expression and BGN low expression subgroups were analyzed. According to the set standard, 1483 up-regulated genes and 50 down-regulated genes are obtained in total. In subsequent GO enrichment assays, the differential genes were mainly focused on immune-related functions, including regulation of leukocyte migration and regulation of T cell activation. The results of the KEGG analysis indicated that Cell Adhesion Molecules (CAMs), cytokine-cytokine receptor interactions, and PI3K-Akt signaling pathways were significantly enriched. As shown in fig. 6, the results of the C7 genome enrichment analysis showed that the differential genes of the BGN high expression group were enriched during the immune response, especially in the immunosuppression-related gene set. The above results suggest that BGN may be a potential indicator of the immune status of the tumor immune microenvironment. To further investigate this problem, we validated the potential impact of BGN on the tumor immune response process. The results show that with increased expression of BGN, a range of immune cell-mediated immune responses tend to "suppress", as shown in fig. 7-8, indicating that BGN is involved in negative regulation of immune responses to colon cancer.
Example 5 potential role of BGN expression in predicting response to treatment with colon cancer against PD-1
Compared with the BGN Low expression subgroup (BGN Low), the immune checkpoint signature genes (LAG3, SIGLEC15, CD274, PDCD1LG2, PDCD1, TIGIT, HAVCR2, CTLA4) all showed higher expression levels in the BGN High expression subgroup (BGN High) (fig. 9). Subsequently, we quantified the relative abundance of 24 TIICs in the tumor immune microenvironment using the ImmuCellAI approach. Notably, the proportion of tics differed significantly between the high and low expression subgroups of BGN (fig. 10). Among these, we focused on two subpopulations of Treg cells, namely, induced Treg cells (iTregs) and natural Treg cells (nTregs). nTreg cells can inhibit the progression of cancer by reducing inflammation, while iTreg cells are the main suppressor cell subset present at the tumor site and responsible for suppressing the anti-tumor immune response. In our results, expression of BGN was positively correlated with the number of iTreg cells (fig. 11A), and inversely with the number of nTreg cells (fig. 11B). To further investigate the potential contribution of BGN in predicting colon cancer immunotherapeutic responses, we calculated T cell inflammatory signal (TIS) scores for the BGN high expression and low expression subgroups. The results in fig. 12 show that BGN-highly expressed colon cancer patients exhibit higher TIS scores, indicating a higher response rate of BGN-highly expressed colon cancer patients to treatment with the immune checkpoint inhibitor pembrolizumab. In addition, we used the ImmuCellAI and TIDE algorithms to further predict the response rate of colon cancer patients to immune checkpoint inhibitor treatment. Based on the prediction by ImmuCellAI, patients with high expression of BGN (79.1%) were more likely to respond to immune checkpoint inhibitor treatment than patients with low expression of BGN (66.1%) (fig. 13A). The TIDE algorithm concluded that patients with high expression of BGN (87%) responded more frequently to treatment with immune checkpoint inhibitors than patients with low expression of BGN (79.1%) (FIG. 13B). In conclusion, BGN is a promising index for quantifying tumor immune microenvironment and predicting response rate of colon cancer anti-PD-1 therapy.
According to the effect verification of the foregoing embodiment, the following prediction criteria are obtained:
calculating the relative expression conditions of the BGN genes of the colon cancer tissues and the paracancer normal tissues, and if the expression rate of the BGN genes of the colon cancer tissues of the patient is greater than that of the BGN genes of the paracancer tissues, the fact that the proportion of inhibitory immune cells in the microenvironment in the tumor of the patient is higher and the response to the treatment of the immune checkpoint inhibitor is more likely to occur; if the expression rate of the BGN gene of the colon cancer tissue of the patient is less than or equal to that of the BGN gene of the paracarcinoma tissue, the proportion of the inhibitory immune cells in the microenvironment in the tumor of the patient is low, and the probability of response to the treatment of the immune checkpoint inhibitor is low.
Example 6
This example provides a test kit for predicting the therapeutic effect of an immune checkpoint inhibitor for colon cancer patients using BGN as a molecular marker, comprising a monoclonal primary antibody (anti-biglycan primary antibody, diluted 1: 2000, from Abcam, cat # ab209234), a secondary antibody (HRP-conjugated good anti-rabbitG, diluted 1: 200, from Servicobio, cat # GB23303) against BGN.
The detection method based on the kit comprises the following steps:
A. obtaining intestinal cancer tissues and tissues beside the cancer of a patient with definite diagnosis of the colon cancer by means of colonoscopy biopsy or surgical resection, immediately putting the intestinal cancer tissues and the tissues beside the cancer into paraformaldehyde fixing liquid, and preparing paraffin sections of the intestinal cancer tissues and the tissues beside the cancer of the patient through the steps of dehydration, embedding, sectioning and the like.
B. Paraffin section is dewaxed to water, the paraffin section is put into dimethylbenzene I for 15 minutes, dimethylbenzene II for 15 minutes, dimethylbenzene III for 15 minutes, absolute ethyl alcohol for 5 minutes, 85 percent alcohol for 5 minutes, 75 percent alcohol for 5 minutes and distilled water is washed.
② antigen retrieval, namely placing the tissue slices in a retrieval box filled with citric acid antigen retrieval buffer solution (PH is 6.0) to perform antigen retrieval in a microwave oven, wherein the medium fire lasts for 8 minutes until the tissue slices are boiled, stopping the fire for 8 minutes, keeping the temperature, and turning to the medium fire for 7 minutes, wherein the buffer solution is prevented from excessively evaporating, and the tissue slices are not cut into dry slices. After cooling naturally, the tissue sections were washed 3 times for 5 minutes in PBS (pH 7.4) with shaking on a destaining shaker.
And thirdly, blocking endogenous peroxidase, namely putting the tissue slices into a 3% hydrogen peroxide solution, incubating for 25 minutes at room temperature in a dark place, putting the tissue slices into PBS (PH 7.4), and washing for 5 minutes for 3 times by shaking on a decoloration shaking table.
And fourthly, sealing the serum, namely dripping 3 percent fetal calf serum into the organized circle of the tissue slice to uniformly cover the tissue, and sealing the tissue for 30 minutes at room temperature.
Fifthly, adding primary antibody, namely slightly throwing off the confining liquid, dripping PBS (phosphate buffer solution) on the tissue section to prepare a BGN primary antibody (ABCAM) according to a certain proportion, and flatly placing the tissue section in a wet box to incubate overnight at 4 ℃.
Sixthly, adding a secondary antibody, placing the tissue section in PBS (PH 7.4) and shaking and washing the tissue section on a decoloration shaking table for 3 times and 5 minutes each time. After the tissue sections were spun down slightly, rabbit-derived secondary antibody (HRP labeled) was added dropwise to the rings to cover the tissues and incubated at room temperature for 50 minutes.
And (seventhly) DAB color development, namely placing the tissue slices in PBS (PH 7.4) and shaking and washing the tissue slices on a decoloring shaking table for 3 times and 5 minutes each time. After the tissue slices are slightly dried, a DAB color developing solution which is prepared freshly is dripped into the ring, the color developing time is controlled to be 10 minutes under a microscope, the positive color is brown yellow, and the tissue slices are washed by tap water to stop color development.
And eighthly, counterstaining cell nuclei, namely counterstaining tissue sections with hematoxylin for 3 minutes, washing with tap water, differentiating hematoxylin differentiation solution for several seconds, washing with tap water, returning hematoxylin to blue by the blue solution, and washing with running water.
Ninthly, dehydrating and sealing, namely putting the tissue slices into 75% alcohol for 5 minutes, 85% alcohol for 5 minutes, absolute ethyl alcohol I for 5 minutes, absolute ethyl alcohol II for 5 minutes, n-butyl alcohol for 5 minutes and xylene I for 5 minutes in sequence for dehydrating and transparency, taking the tissue slices out of the xylene, slightly drying the tissue slices, and sealing the slices with neutral gum.
And performing microscopic examination on the red component and image acquisition and analysis on the expression condition of BGN in the colon cancer and the tissues beside the cancer of the patient.
C. Prediction of the therapeutic effect of immune checkpoint inhibitors and evaluation according to the results of the previous examples is as follows:
the BGN gene expression rate of the colon cancer tissue is greater than that of the adjacent cancer tissue, which shows that the patient has high response rate to the treatment of the immune checkpoint inhibitor and good immune treatment effect;
the BGN gene expression rate of the colon cancer tissue is less than or equal to that of the paracancerous tissue, which shows that the patient has low response rate to the treatment of the immune checkpoint inhibitor and poor immune treatment effect.
The invention has many applications, and the above description is only a preferred embodiment of the invention. It should be noted that the above examples are only for illustrating the present invention, and are not intended to limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications can be made without departing from the principles of the invention and these modifications are to be considered within the scope of the invention.
Claims (4)
1. Application of a reagent for detecting BGN genes in preparation of detection products for predicting treatment effect of immune checkpoint inhibitors of colon cancer patients.
2. The use of claim 1, wherein the immune effect comprises an immunotherapy response rate.
3. The use of claim 1, wherein the test product comprises at least one of a test strip and a test kit.
4. A test product for predicting the therapeutic effect of an immune checkpoint inhibitor in a patient with colon cancer, comprising a reagent for detecting the BGN gene.
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| CN113462776A (en) * | 2021-06-25 | 2021-10-01 | 复旦大学附属肿瘤医院 | m6Application of A modification-related combined genome in prediction of immunotherapy efficacy of renal clear cell carcinoma patient |
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| CN113462776A (en) * | 2021-06-25 | 2021-10-01 | 复旦大学附属肿瘤医院 | m6Application of A modification-related combined genome in prediction of immunotherapy efficacy of renal clear cell carcinoma patient |
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