A combination of cancer prognosis markers and use thereof

Abstract

The application belongs to the technical field of medical treatment and discloses a cancer prognosis marker combination and application thereof, which comprises the following markers: CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c and Collagen I. The cancer prognosis evaluation model trained by using the 7-marker combination exhibits superior prognosis accuracy compared to a model relying on high-dimensional multi-omics data. The application proposes a multiple immunohistochemistry (mIHC) cancer prognosis evaluation detection product based on the above-mentioned cancer prognosis marker combination. Satisfactory prognosis results are achieved by using only two five-marker combinations. The method exhibits high accuracy in predicting the survival of gastric cancer patients, so that it becomes a low-cost, high-benefit and scalable solution for other cancers.

Description

Technical Field

[0001] This invention relates to the field of medical technology, specifically to a combination of cancer prognostic biomarkers and their applications. Background Technology

[0002] Gastric cancer (GC) is a global health problem, ranking fifth in incidence and fifth in mortality worldwide in 2022. Currently, the main treatments for gastric cancer include surgery, chemotherapy, and radiotherapy; however, their effectiveness is limited for patients with advanced-stage cancer. Despite continuous advancements in clinical and basic research, the five-year survival rate for patients with advanced gastric cancer remains below 40%.

[0003] Given the poor prognosis of patients with advanced gastric cancer, the tumor microenvironment (TME) has become a key factor influencing treatment efficacy and the effectiveness of treatment regimens. The TME is a complex network composed of cellular and non-cellular components that plays a decisive role in cancer dynamics and treatment response. Macrophages play a crucial role within this network, significantly influencing tumor behavior and treatment response. 5 Recent studies have revealed the complex relationship between macrophages and gastric cancer, highlighting novel therapeutic targets and mechanisms driving tumor progression. For example, research has shown that under hypoxic conditions, macrophages secrete CXCL8, which activates gastric cancer invasion and proliferation through the CXCR1 / 2 and JAK / STAT1 signaling pathways, forming a feedback loop that promotes M2 macrophage polarization. This further promotes CXCL8 secretion, presenting a self-reinforcing tumor-promoting mechanism. Furthermore, a study using multiplex immunohistochemistry revealed the heterogeneity of tumor-associated macrophages (TAMs), discovering that their subsets are associated with immune signaling pathways and PD-L1 expression, further demonstrating the prognostic significance of TAMs in gastric cancer. Current research further reveals a novel mechanism by which TAMs mediate tumor metastasis: M2 TAMs can also deliver ApoE via exosomes, activating the PI3K-Akt signaling pathway in recipient tumor cells, leading to gastric cancer cell metastasis. In addition, SIGLEC10, as an immunosuppressive molecule on CD68⁺ macrophages, can inhibit T cell function, thus making it a potential immune checkpoint with prognostic and therapeutic significance.

[0004] The formation and function of tertiary lymphoid structures (TLS) in the tumor microenvironment have also attracted attention. These structures resemble secondary lymphoid organs but are located outside of traditional lymphoid organs, helping to activate naïve T / B cells and enable them to exert tumor immune effects. Studies in various malignant tumors, including colorectal cancer, lung cancer, breast cancer, and malignant melanoma, have shown that the presence of TLS is associated with a favorable prognosis. In gastric cancer (GC), two key studies further emphasized the important role of TLS and specific immune cell populations in enhancing the efficacy of cancer immunotherapy. The first study revealed the relationship between TLS and CXCL13. + CD103+ CD8 + Interactions between tissue-resident memory T cells (Trm) were investigated, and their co-existence was found to enhance the efficacy of anti-PD-1 therapy. The study also found that increased secretion of CXCL13 and granzyme B, via the TNFR2 axis and mTOR signaling pathway, significantly enhanced the efficacy of anti-PD-1 treatment under B cell activation. TLS was classified into three distinct states using machine learning. This classification revealed the significant distribution of TLS in gastric cancer and demonstrated that different patients' TLS scores could be grouped into different prognostic groups. Patients with higher TLS scores had significantly improved survival rates, and the TLS score remained an independent prognostic factor even after adjusting for other clinicopathological variables and tumor-infiltrating lymphocytes.

[0005] The immune microenvironment has a profound impact on the prognosis and treatment outcomes of gastric cancer. This understanding lays the foundation for developing more personalized and effective immunotherapies, potentially revolutionizing the management strategies for malignant tumors. However, how spatial characteristics (especially macrophage activity) affect immune cell function remains under-explored. To comprehensively elucidate the complex relationships between cells and spatial composition within the tumor microenvironment (TME), advanced analytical methods with spatial resolution are needed to effectively reveal spatial heterogeneity. Imaging mass cytometry (IMC) further combines histology and IMC, allowing for the simultaneous detection of 45 metal-labeled antibodies on a single slide, precisely revealing cell interactions and biomarker co-expression. Integrating multi-omics approaches can overcome the resolution limitations of single-cell transcriptomics and spatial transcriptomics without sacrificing spatial information, providing a more refined perspective on TME cell interactions, which is crucial for developing targeted therapies. Summary of the Invention

[0006] To overcome the problems of high cost, technical complexity, and difficulty in implementation in clinical settings of existing technologies, this invention proposes a combination of cancer prognostic biomarkers and their applications. The above objective is achieved through the following technical solutions:

[0007] A combination of cancer prognostic biomarkers, comprising the following biomarkers:

[0008] CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c and Collagen I.

[0009] The use of a product for detecting the above-mentioned combination of cancer prognostic biomarkers in the preparation of products for cancer prognostic assessment.

[0010] Optional products for detecting combinations of cancer prognostic biomarkers include reagents, test strips, kits, or instruments.

[0011] Optionally, the cancer includes stomach cancer.

[0012] Optionally, the product for detecting the combination of cancer prognostic biomarkers is a multiplex immunohistochemistry assay product; the multiplex immunohistochemistry assay product includes a first assay product and a second assay product;

[0013] The first detection product is used to detect markers in group A, and the second detection product is used to detect markers in group B;

[0014] The A group markers and the B group markers each contain 2 to 5 markers from the combination of cancer prognostic markers, and each marker in the combination of cancer prognostic markers is included in at least one of the A group markers or the B group markers.

[0015] Optionally, the first detection product is used to detect CD68, PD-L1, PAN-CK, and HLA-DR in the sample;

[0016] The second detection product is used to detect CD68, CD45, CD11c, and Collagen I in the sample;

[0017] The first tested product includes:

[0018] CD68 primary antibody that specifically binds to CD68, PD-L1 primary antibody that specifically binds to PD-L1, PAN-CK primary antibody that specifically binds to PAN-CK, HLA-DR primary antibody that specifically binds to HLA-DR, DAPI, and secondary antibodies containing horseradish peroxidase labeling.

[0019] The second testing product includes:

[0020] CD68 primary antibody that specifically binds to CD68, CD45 primary antibody that specifically binds to CD45, CD11c primary antibody that specifically binds to CD11c, Collagen I primary antibody that specifically binds to Collagen I, DAPI, and secondary antibodies containing horseradish peroxidase labeling.

[0021] Optionally, the multiplex immunohistochemistry assay product may also include eluent and fluorescent dye;

[0022] The fluorescent dye is used in conjunction with the horseradish peroxidase-labeled secondary antibody to stain the biomarker; the multiplex immunohistochemical assay product contains at least 7 fluorescent dyes with different wavelengths.

[0023] The eluent is used to elute fluorescent dyes bound to the sample after multiplex immunohistochemical detection of the sample using the first detection product.

[0024] The method for establishing a cancer prognostic risk assessment model using a combination of cancer prognostic biomarkers includes the following steps:

[0025] Step 1) Obtain the imaging mass cytometry analysis dataset, which includes the average expression intensity of each marker in the above-mentioned combination of cancer prognostic biomarkers, based on epithelial cells, HLA-DR... + Macrophages, CD11c + Macrophages and Collagen I + Spatial similarity characteristics and prognosis of fibroblasts;

[0026] Step 2) Analyze the data in the dataset using the imaging mass spectrometry flow cytometry technology, and obtain a cancer prognostic risk assessment model through deep neural network training.

[0027] Optionally, the method based on epithelial cells and HLA-DR... + Macrophages, CD11c + Macrophages and Collagen I + Spatial similarity characteristics of the four cell types of fibroblasts include structural similarity indices.

[0028] A cancer prognostic risk assessment system, comprising:

[0029] The detection module is used to perform multiplex immunohistochemical detection of CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c, and Collagen I in the test tissue sample, obtaining the multiplex immunohistochemical detection results of the test tissue sample; and to calibrate the epithelial cells and HLA-DR in the test tissue sample based on the multiplex immunohistochemical detection results. + Macrophages, CD11c + Macrophages and Collagen I + Fibroblast cell types were identified, and cell type labeling results were obtained.

[0030] The results evaluation module inputs the multiple immunohistochemical detection results and cell type labeling results of the tested tissue sample into the cancer prognostic risk assessment model obtained above to perform cancer prognostic risk assessment and obtain the cancer prognostic risk assessment result.

[0031] The present invention has the following beneficial effects:

[0032] This invention proposes a combination of cancer prognostic biomarkers and their applications. The combination includes the following biomarkers: CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c, and Collagen I. A cancer prognostic assessment model trained using this combination of seven biomarkers demonstrates superior prognostic accuracy compared to models relying on high-dimensional multi-omics data.

[0033] This invention proposes a multiplex immunohistochemical (mIHC) cancer prognostic assessment product based on the aforementioned combination of cancer prognostic biomarkers. Existing technologies employ highly complex multiplex marker methods to predict cancer, but these methods are costly, technically complex, and difficult to implement in clinical settings. In contrast, the model based on this invention achieves satisfactory prognostic results using only two five-marker combinations. This method demonstrates high accuracy in predicting the survival of gastric cancer patients, making it a cost-effective solution that can be extended to other cancers. Attached Figure Description

[0034] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0035] Figure 1 This refers to the model building process in the embodiments;

[0036] Figure 2 This describes the data acquisition process for multiplex immunohistochemistry (mIHC) detection in the embodiments;

[0037] Figure 3 This describes the data processing procedure for multiplex immunohistochemistry (mIHC) detection in the embodiments.

[0038] Figure 4 This is a graph showing the results of survival analysis during model validation.

[0039] Figure 5 This is a comparison chart of the evaluation results of the model in the embodiment and the traditional survival prediction model based on all IMC markers;

[0040] Figure 6 This is a comparison chart of the evaluation results of the model in the embodiment and the existing prognostic evaluation model. Detailed Implementation

[0041] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention. It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the present invention.

[0042] Furthermore, regarding the numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, are also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0043] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar to or equivalent to those described herein may be used in the implementation or testing of this invention.

[0044] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0045] To identify regional factors influencing the prognosis and clinical characteristics of gastric cancer (GC) patients, a detailed spatial multi-omics analysis was performed on 205 GC patients. Imaging mass cytometry (IMC) analysis was conducted on 205 tumors and 39 regions of interest (ROIs) surrounding the tumors from these 205 patients. This IMC-based single-cell spatial proteomics technique can identify different regions within the gastric cancer tumor microenvironment (TME). Furthermore, this technique helped reveal other spatial features, including cell-cell interactions, such as tertiary lymphoid structures (TLS). By integrating these spatial features with clinical data (e.g., prognosis), the most influential spatial determinants can be identified.

[0046] Imaging mass cytometry (IMC) single-cell segmentation:

[0047] To perform single-cell segmentation on IMC images, a pre-trained DeepCell model was used. The DeepCell model requires two imaging channels for segmentation: the first channel is the nuclear staining channel (typically using DAPI staining) to identify the location of the cell nucleus; the second channel is the cell membrane or cytoplasmic region to define the cell boundary. In this study, the membrane staining channel was selected as the second imaging channel for segmentation analysis.

[0048] IMC pretreatment and cell type identification:

[0049] First, the marker expression was transformed using a hyperbolic arcsine function, and the signal values ​​of each channel were limited to the 1% and 99% ranges to set minimum and maximum values. Min-Max Normalization was then applied to each channel individually to standardize the data. To address potential batch effects, alignment was performed using the R package Harmony (version 1.2.3). Cell clustering was then performed using FastPG (version 0.0.8) with a nearest neighbor count of 100 and two rounds of clustering. In the first round of clustering, the principal cell population was identified using the following lineage-specific markers: ECadherin, Pankeratin, CD3, CD4, CD8, CD20, CD7, CD57, CD45, CD68, CD15, CD14, CD16, CD31, αSMA, Collagen I, and Vimentin. In the second round of clustering, myeloid cell subsets were re-clustered using additional markers such as FOXP3, CD16, CD69, CD4, CD8, Caspase3, B7H4, VISTA, CD7, CD103, LAG3, CD20, Granzyme B, PD-1, KI67, GATA3, CD45RA, CD3, TNFα, TL1β, CD45RO, CD57, and CD25. Lymphocytes were re-clustered using markers such as IL-6, CD14, CD16, Caspase3, CD163, PD-L1, CD11B, CD11C, CD15, Ki-67, HLA-DR, TNFα, and TL1β. Epithelial cells were clustered using markers such as ECadherin, PDL1, KI67, and Pankeratin. Finally, the average expression level of markers in each cluster was visualized using heatmaps, and this information was used to annotate the corresponding cell types (CTs).

[0050] Identification of spatial regions / patches:

[0051] First, based on spatial coordinates, the k-nearest neighbor (KNN) algorithm is used to compute the 20 nearest neighbors for each cell, thus constructing a local spatial adjacency graph for each cell. Next, for each cell's neighborhood, various marker expression data from neighboring cells are aggregated and clustered according to cell type. Subsequently, the local subgraphs are filtered to ensure that the central node of each subgraph corresponds to a specific cell type (e.g., B cell). These subgraphs are then connected through shared nodes to form a global connectivity graph, thereby generating spatial regions / patches. Finally, the spatial extent of the constructed patches is expanded by adding n neighboring nodes to existing patches to extend their spatial range.

[0052] Downstream spatial analysis:

[0053] Based on the spatial connectivity graph, three main regions are defined:

[0054] Immune regions: including "lymphocytes" and "myeloid cells";

[0055] Fibroblast region: includes "myofibroblasts" and "Collagen I". + "Fibroblasts";

[0056] Epithelial region: includes "epithelial cells".

[0057] In addition, three overlapping regions were defined: the immune-fibroblast junction, the immune-epithelial junction, and the fibroblast-epithelial junction. The number of interactions between the central cell and its neighbors was counted by calculating the average number of surrounding cells within each region of interest (ROI). Subsequently, differential analyses were performed on cell frequencies, biomarker expression, and cell-cell interactions within the defined regions.

[0058] Multivariate survival analysis:

[0059] After spatial analysis, functional biomarker expression, cell type frequency, and intercellular interactions were extracted and integrated for multivariate survival analysis. Features with a proportion of zero values ​​below 50% were screened and cross-validated using LASSO regression to identify non-zero coefficients. The selected variables were then incorporated into a multivariate Cox regression model, and the proportional hazards hypothesis (PH hypothesis) was assessed using the cox.zph() function. Variables conforming to the PH hypothesis were retained, and their association with survival was evaluated. The results were visualized using the ggforest() function.

[0060] Based on the above analysis, this invention identified a core set of spatial determinants, including macrophage subsets and fibroblast interactions, which have been shown to play important roles in driving tumor progression and immune escape. The analysis results indicate that cellular spatial organization plays a crucial role in shaping tumor immune and therapeutic responses. Therefore, spatial features can be further incorporated into expression-based biomarkers to construct survival prediction models, thereby improving the accuracy of cancer prognostic assessment. Therefore, based on the biomarkers identified by the aforementioned imaging mass cytometry (IMC) technique, this invention proposes a more clinically promising method for predicting the survival of gastric cancer (GC) patients.

[0061] Based on the biomarkers identified by the imaging mass cytometry (IMC) technique described above, seven biomarkers were selected in this embodiment: CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c, and Collagen I.

[0062] The expression of these seven biomarkers reflects four key cell types: epithelial cells, HLA-DR... + Macrophages, CD11c + Macrophages and Collagen I + The interactions between fibroblasts were analyzed, and independent images were extracted from each region of interest (ROI) to generate masks corresponding to these cell types, thereby establishing a survival prediction model.

[0063] The methods for building the model are as follows Figure 1 As shown, it includes the following steps:

[0064] From imaging mass cytometry (IMC), two types of integrated image features are designed: marker intensity features and spatial similarity features.

[0065] The biomarker intensity characteristics are the average expression intensity measurements of seven biomarkers in the IMC image data: CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c, and Collagen I.

[0066] Spatial similarity features were calculated using the Structural Similarity Index (SSIM) to assess epithelial cell-based and HLA-DR-based similarity features. + Macrophages, CD11c + Macrophages and Collagen I + The paired spatial relationships of fibroblasts in IMC image data reflect the relationship between epithelial cells and HLA-DR. + Macrophages, CD11c + Macrophages and Collagen I + Interactions between fibroblasts. The Structural Similarity Index (SSIM) is a metric used to measure the similarity between two images. Unlike traditional pixel-based evaluation methods such as mean squared error (MSE) or peak signal-to-noise ratio (PSNR), SSIM considers more aspects of image brightness, contrast, and structural information, thus more accurately reflecting the human visual system's perception of image quality.

[0067] These features, along with the prognostic survival data of patients in the dataset, are input into a deep neural network. The trained model can then be used for clinical prognostic prediction of gastric cancer patients. Combining biomarker expression intensity features and spatial similarity features yields significant advantages. The validation data were analyzed using existing prognostic analysis models, including Random Forest (RF), Graph Convolutional Network (GCN), and Deep Survival Model. The results show that the model of this invention exhibits a significant performance improvement compared to existing studies. Figure 6 ).

[0068] Given that multiplex immunohistochemistry (mIHC) is a cost-effective and scalable method for predicting cancer survival, but its ability to simultaneously detect multiple biomarkers is limited, we further validated the generalization ability of the identified key cell types in a separate mIHC cohort. Figure 2 ).

[0069] This embodiment proposes a multiplex immunohistochemical assay product for detecting a combination of cancer prognostic biomarkers; the multiplex immunohistochemical assay product includes a first assay product and a second assay product.

[0070] The first detection product is used to detect markers in group A, and the second detection product is used to detect markers in group B;

[0071] The A group markers and the B group markers each contain 2 to 5 markers from the combination of cancer prognostic markers, and each marker in the combination of cancer prognostic markers is included in at least one of the A group markers or the B group markers.

[0072] In one specific embodiment, two sets of five-marker combinations were designed. Combination A includes CD68, PD-L1, PAN-CK, HLA-D, and DAPI, while combination B includes CD68, CD45, CD11c, Collagen I, and DAPI. These two combinations integrate key spatial features identified through multi-omics analysis.

[0073] The first detection product is used to detect CD68, PD-L1, PAN-CK, and HLA-DR in the sample; the second detection product is used to detect CD68, CD45, CD11c, and Collagen I in the sample.

[0074] In one specific embodiment: the first detection product includes: a CD68 primary antibody that specifically binds to CD68, a PD-L1 primary antibody that specifically binds to PD-L1, a PAN-CK primary antibody that specifically binds to PAN-CK, an HLA-DR primary antibody that specifically binds to HLA-DR, DAPI, and a secondary antibody containing horseradish peroxidase labeling.

[0075] The second detection product includes: CD68 primary antibody specifically binding to CD68, CD45 primary antibody specifically binding to CD45, CD11c primary antibody specifically binding to CD11c, Collagen I primary antibody specifically binding to Collagen I, DAPI, and secondary antibody containing horseradish peroxidase labeling.

[0076] The multiplex immunohistochemistry assay product also includes eluents and fluorescent dyes;

[0077] The fluorescent dye is used in conjunction with the horseradish peroxidase-labeled secondary antibody to stain the biomarker; the multiplex immunohistochemical assay product contains at least 7 fluorescent dyes with different wavelengths.

[0078] The eluent is used to elute fluorescent dyes bound to the sample after multiplex immunohistochemical detection of the sample using the first detection product.

[0079] To verify the accuracy of the model based on the above seven biomarkers, tissue samples from 27 patients were used to validate the biomarkers and the model.

[0080] First, imaging mass cytometry (IMC) images of tissue samples from the aforementioned 27 patients were obtained and input into both a traditional survival prediction model based on all IMC biomarkers and a cancer prognostic risk assessment model trained spatially based on seven key biomarkers in this invention. The experimental results ( Figure 4 and Figure 5 The results show that our method outperforms traditional survival prediction models based on all IMC markers in both C-index (CI) and stratification performance, demonstrating the effectiveness of the identified markers in predicting GC progression.

[0081] Then, model validation was performed based on the results of multiplex immunohistochemistry detection.

[0082] The experiment was performed using two multiplex immunohistochemical (mIHC) combinations:

[0083] Combination A: CD68, PD-L1, PAN-CK, HLA-D, and DAPI;

[0084] Combination B: CD68, CD45, CD11c, Collagen I, and DAPI.

[0085] Multiplex Immunohistochemistry (mIHC): In this example, multiplex immunohistochemistry (mIHC) was performed using the Opal Polaris™ 7-color manual immunohistochemistry kit. The simplified procedure is as follows: Tissue sections were blocked after dewaxing to reduce nonspecific binding. Subsequently, the sections were incubated with a specific primary antibody, followed by incubation with the corresponding secondary antibody and a fluorescently labeled secondary antibody reagent for signal amplification. DAPI staining was used to counterstain the tissue and visualize the labeled structures. Finally, the stained sections were analyzed using the Vectra Polaris Quantitative Pathology Imaging System (Akoya Biosciences, USA) to obtain high-resolution multiplex imaging data.

[0086] The primary antibodies used in this multicolor immunohistochemistry experiment included:

[0087] Pan-CK (Biolegend, catalog number: 914204)

[0088] HLA-DR (Abcam, catalog number: ab92511)

[0089] CD68 (Biolegend, catalog number: 916104)

[0090] PD-L1 (Proteintech, catalog number: 66248-1-Ig)

[0091] CD45 (CST, catalog number: 13917SF)

[0092] CD11c (Abcam, catalog number: ab52632)

[0093] Collagen I (Abcam, catalog number: ab138492)

[0094] Multiplex immunohistochemistry specifically includes the following steps: First, the four biomarkers in combination A are detected:

[0095] Step 1: Preparation of tissue slides

[0096] Prepare the tissues or cells for detection using the Opal kit, following standard fixation and embedding techniques. (It is recommended to run isotype control slides and replace the primary antibody with the appropriate isotype control for each experiment.)

[0097] Each slide was baked in an oven at 68°C for 1.5 hours.

[0098] Dewaxing: Dewax with xylene for 20 min → Rehydrate with a series of fractionated ethanol solutions: 95% ethanol for 10 min → 85% ethanol for 10 min → 75% ethanol for 10 min. (This step ensures that the tissue is completely immersed in xylene and ethanol; if ethanol is insufficient, prepare more in time).

[0099] Place the slide in a staining tank containing ultrapure water and wash it three times on a shaker, 5 minutes each time.

[0100] Place the tissue slide in a humidified chamber, wipe it dry, and add 4% paraformaldehyde fixative (stored at -20℃) to completely cover the tissue. Fix for 30 minutes. Place the slide in a staining jar containing ultrapure water and wash it three times with ultrapure water on a shaker, 2 minutes each time.

[0101] Step 2: Citric Acid Repair

[0102] Place the plastic staining jar containing a sufficient amount of 1X citric acid repair solution in a boiling heater and preheat for 5 minutes. Then, place the tissue slides into the preheated 1X citric acid repair solution.

[0103] Cover and boil for 20 minutes, keep warm for 10 minutes, and cool to room temperature for 30 minutes. Do not let the slide dry out.

[0104] Take out each slide and place it in a humidified chamber. Immediately use a pipette to apply 1X AR6 Buffer (citric acid repair solution) to the slide to prevent it from drying out. After the slide has cooled, rinse it with IXTBST. Rinse it once and then wash for 2 minutes.

[0105] Step 3: Seal

[0106] Wipe dry the 1X TBST wash buffer and circle the tissue sections on the slide using a histochemistry pen.

[0107] Apply blocking solution (Super blocking, stored at 4°C) to the tissue, ensuring complete coverage, and block at room temperature for 10 minutes. This protocol uses PerkinEimer Antibody Diluent / Block for blocking. Other options should be validated independently.

[0108] Step 4: Primary antibody incubation

[0109] Drain the blocking solution, add primary antibody (which needs to be prepared in advance, and note that the primary antibody is different for each round) to completely cover the tissue section (generally 50uL per slide), and incubate at room temperature in the dark for one hour or at 4°C overnight.

[0110] Rinse the slide with 1X TBST, wash 3 times with 1X TBST, 2 min each time.

[0111] Step 5: Secondary antibody incubation

[0112] Wipe dry the tissue section with 1X TBST wash buffer, add the secondary antibody (ready-made, no need to prepare, store at 4°C), completely cover the tissue section, and incubate at room temperature in the dark for 10 minutes.

[0113] Rinse the slide with 1X TBST, wash 3 times with 1X TBST, 2 min each time.

[0114] Note: Opal polymer HRP Ms + Rb is recommended for experiments using human tissues and mouse or rabbit primary antibodies. Other options should be validated independently.

[0115] Step 6: Opal signal generation (fluorescence incubation)

[0116] Wipe dry with 1X TBST washing buffer, add fluorescent dye (to be prepared and stored at -20°C; for the first round of fluorescence, use Opal Polaris 480), and incubate at room temperature for 10 minutes.

[0117] Rinse the slide with 1X TBST, 3 times with 1X TBST, 2 min each time.

[0118] Rinse the slide with 1X citric acid repair solution.

[0119] Step 7: Citric Acid Repair

[0120] Place the plastic staining jar containing a sufficient amount of 1X citric acid repair solution in a boiling electric heater to preheat for 5 minutes, and then place the tissue slide into the preheated 1X citric acid repair solution.

[0121] Cover and boil for 20 minutes, keep warm for 10 minutes, and cool to room temperature for 30 minutes. Do not let the slide dry out.

[0122] Remove each slide one by one and place it in a humidified chamber. Immediately use a pipette to apply 1XAR6 Buffer (citric acid repair solution) to the slide to prevent it from drying out. After the slide has cooled, rinse it with IXTBST (rinse briefly, then wash for 2 minutes).

[0123] This microwave step strips the primary antibody-secondary antibody-HRP complex, allowing the introduction of the next primary antibody.

[0124] Repeat steps 3-7:

[0125] This process continued until all four targets were tagged (first round of fluorescence with Opal Polaris480, second round with Opal520, third round with Opal570, and fourth round with Opal620).

[0126] Step 8: Nuclear staining and mounting

[0127] Drain excess washing buffer, add DAPI (1 ml 1xTBST, prepared with 3 drops of DAPI) onto the tissue slide, and incubate at room temperature for 5 min.

[0128] Wash in 1X TBST buffer for 2 min, then wash in water for 2 min.

[0129] Drain the water from the slides. Mount the slides with antifluorescent mounting solution, ensuring there are no air bubbles. After staining with combination A, analyze the stained slides using the Vectra Polaris quantitative pathological imaging system (Akoya Biosciences, USA) to obtain high-resolution multiplex imaging data of combination A.

[0130] After scanning, elution is performed: The slide is vertically immersed in PBS solution for 5-10 minutes, allowing the coverslip to detach naturally. Then, the slide is washed three times with PBS, 5 minutes each time. The dye elution buffer (Miledi MXT-5611 elution buffer) is poured into a transparent container, and the slide is placed inside, ensuring complete coverage. The container is placed between two LED light panels and incubated at room temperature for 1 hour under illumination for fluorescent dye elution. Then, the slide is washed four times with PBS, 5 minutes each time. After section processing, the four markers in combination B are analyzed using steps 2-8 of the above method. High-resolution multiplex imaging data of the detection results from combination B are obtained.

[0131] like Figure 3 As shown, to process mIHC biomarker data from two combinations, automated, non-rigid tissue image registration was applied to the data registration dataset. First, keypoints were detected in the detection result images of combinations A and B, and then the DAPI biomarker keypoints of combinations A and B were aligned. DAPI was used as the reference channel, and spatial registration was performed using Elastix, a robust image registration tool based on the ITK library. After registration, cell segmentation, dimensionality reduction, and clustering were performed. For single-cell segmentation in the DAPI channel, a deep learning-based segmentation method was used to quantify biomarker expression at the single-cell level and export the data in matrix format. Cell types were annotated, and the preprocessing and cell type identification annotation methods were consistent with those used for IMC image data processing. Relevant features were extracted for validation of the deep learning model.

[0132] After selecting regions of interest (ROIs) and performing batch correction, 262,114 cells were collected from 88 ROIs (500×500 pixels) of the 27 patients used for validation, and the same four key cell types were identified. After inputting the image results into the survival prediction model, prognostic results were observed on the mIHC dataset. Figure 4 and Figure 5 Notably, this method demonstrates high accuracy in predicting the survival of gastric cancer patients, making it a cost-effective and scalable solution. Experimental results demonstrate the potential of this method to enhance the accuracy of prognostic assessment for gastric cancer.

[0133] Based on the above method, the present invention also proposes a cancer prognostic risk assessment system, comprising:

[0134] The detection module is used to perform multiplex immunohistochemical detection of CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c, and Collagen I in the test tissue sample, obtaining the multiplex immunohistochemical detection results of the test tissue sample; and to calibrate the epithelial cells and HLA-DR in the test tissue sample based on the multiplex immunohistochemical detection results. + Macrophages, CD11c + Macrophages and Collagen I + Fibroblast cell types were identified, and cell type labeling results were obtained.

[0135] The results evaluation module inputs the multiple immunohistochemical detection results and cell type labeling results of the tested tissue sample into the cancer prognostic risk assessment model obtained above to perform cancer prognostic risk assessment and obtain the cancer prognostic risk assessment result.

[0136] The system described above can run on a computer device, which includes one or more processors, memory, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processor can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interface).

[0137] The processor can be a central processing unit, a network processor, or a combination thereof. The processor may further include hardware chips. These hardware chips can be application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or combinations thereof. The programmable logic devices can be complex programmable logic devices (CLPs), field-programmable gate arrays (FPGAs), general-purpose array logic (GDAs), or any combination thereof.

[0138] The memory stores instructions executable by at least one processor to cause the at least one processor to perform the method shown in the above embodiments.

[0139] The memory may include a stored program area and a stored data area. The stored program area may store the operating system and applications required for at least one function; the stored data area may store data created based on the use of the computer device. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory may include memory remotely located relative to the processor, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0140] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A combination of cancer prognostic biomarkers, characterized in that, It consists of the following markers: CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c and Collagen I; The cancer in question is stomach cancer.

2. The use of a product for detecting the combination of cancer prognostic biomarkers of claim 1 in the preparation of a product for cancer prognostic assessment; wherein the cancer is gastric cancer.

3. Use according to claim 2, characterized in that, Products that detect combinations of cancer prognostic biomarkers include reagents, test strips, kits, or instruments.

4. The application according to claim 3, characterized in that, The product for detecting a combination of cancer prognostic biomarkers is a multiplex immunohistochemistry assay; the multiplex immunohistochemistry assay includes a first assay and a second assay. The first detection product is used to detect markers in group A, and the second detection product is used to detect markers in group B; The A group markers and the B group markers each contain 2 to 5 markers from the combination of cancer prognostic markers, and each marker in the combination of cancer prognostic markers is included in at least one of the A group markers or the B group markers.

5. The application according to claim 4, characterized in that, The first detection product is used to detect CD68, PD-L1, PAN-CK, and HLA-DR in samples; The second detection product is used to detect CD68, CD45, CD11c, and Collagen I in the sample; The first tested product includes: CD68 primary antibody that specifically binds to CD68, PD-L1 primary antibody that specifically binds to PD-L1, PAN-CK primary antibody that specifically binds to PAN-CK, HLA-DR primary antibody that specifically binds to HLA-DR, DAPI, and secondary antibodies containing horseradish peroxidase labeling. The second testing product includes: CD68 primary antibody that specifically binds to CD68, CD45 primary antibody that specifically binds to CD45, CD11c primary antibody that specifically binds to CD11c, Collagen I primary antibody that specifically binds to Collagen I, DAPI, and secondary antibodies containing horseradish peroxidase labeling.

6. The application according to claim 5, characterized in that, The multiplex immunohistochemistry assay product also includes eluents and fluorescent dyes; The fluorescent dye is used in conjunction with the horseradish peroxidase-labeled secondary antibody to stain the biomarker; the multiplex immunohistochemical assay product contains at least 7 fluorescent dyes with different wavelengths. The eluent is used to elute fluorescent dyes bound to the sample after multiplex immunohistochemical detection of the sample using the first detection product.

7. The method for establishing a cancer prognostic risk assessment model using a combination of cancer prognostic biomarkers according to claim 1, characterized in that, The cancer in question is stomach cancer, and the procedure includes the following steps: Step 1) Obtain the imaging mass cytometry analysis dataset, which includes the average expression intensity of each biomarker in the cancer prognostic biomarker combination described in claim 1, based on epithelial cells, HLA-DR... + Macrophages, CD11c + Macrophages and Collagen I + Spatial similarity characteristics and prognosis of fibroblasts; Step 2) Analyze the data in the dataset using the imaging mass spectrometry flow cytometry technology, and obtain a cancer prognostic risk assessment model through deep neural network training.

8. The method for establishing a cancer prognostic risk assessment model according to claim 7, characterized in that, The epithelial cell-based, HLA-DR + Macrophages, CD11c + Macrophages and Collagen I + Spatial similarity characteristics of the four cell types of fibroblasts include structural similarity indices.

9. A cancer prognostic risk assessment system, characterized in that, The cancer in question is stomach cancer, including: The detection module is used to perform multiplex immunohistochemical detection of CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c, and Collagen I in the test tissue sample, obtaining the multiplex immunohistochemical detection results of the test tissue sample; and to calibrate the epithelial cells and HLA-DR in the test tissue sample based on the multiplex immunohistochemical detection results. + Macrophages, CD11c + Macrophages and Collagen I + Fibroblast cell types were identified, and cell type labeling results were obtained. The results evaluation module inputs the multiple immunohistochemical detection results and cell type labeling results of the tested tissue sample into the cancer prognostic risk assessment model obtained in claim 7 or 8 to perform cancer prognostic risk assessment and obtain the cancer prognostic risk assessment result.

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