Detection kit, system and method for predicting curative effect of cancer immunotherapy

Abstract

The invention belongs to the cross technical field of biomedical detection and information technology, and particularly relates to a detection kit, system and method for cancer immunotherapy curative effect prediction. The detection kit comprises a combination of a first detection reagent for specifically detecting the LAMC2 gene or protein and a second detection reagent for specifically detecting the SPP1 gene or protein. Research finds that in a complex tumor microenvironment, LAMC < 2 + > basal cells and SPP1 < + > macrophages, which are respectively derived from specific subgroups of tumor cells and immune cells, jointly form a key biological signal capable of remarkably influencing immunotherapy response according to the existence and the quantity of the two specific subgroups of the LAMC < 2 + > basal cells and the SPP1 < + > macrophages. By jointly detecting the pair of biomarkers with a synergistic effect, the problems of insufficient prediction accuracy and incapability of reflecting tumor microenvironment heterogeneity caused by dependence on PD-L1, TMB and other single markers in the prior art are solved, and the technical effect of remarkably improving the sensitivity and reliability of immunotherapy curative effect prediction is achieved.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of biomedical detection and information technology, specifically relating to detection kits, systems, and methods for predicting the efficacy of cancer immunotherapy. Background Technology

[0002] Lung cancer is one of the leading causes of cancer-related morbidity and mortality worldwide, with non-small cell lung cancer (NSCLC) being the most prevalent pathological type. In recent years, cancer immunotherapy, represented by immune checkpoint inhibitors (ICIs), has brought breakthrough treatment hope to patients with advanced NSCLC. However, clinical practice shows that the overall response rate of immunotherapy is limited, with only about 20%–30% of patients benefiting. The majority of the remaining patients face problems such as primary drug resistance, secondary drug resistance, and even hyperprogression. This significant difference in efficacy makes it difficult for clinicians to accurately screen for the most suitable patients before treatment, causing some patients to miss the optimal treatment window, suffer unnecessary drug side effects and financial burdens, and resulting in a huge waste of medical resources.

[0003] Currently, biomarkers used in clinical practice to predict the efficacy of immunotherapy mainly rely on the protein expression level of programmed death-ligand 1 (PD-L1) and tumor mutational burden (TMB). However, both of these biomarkers have significant limitations. PD-L1 expression exhibits high spatiotemporal heterogeneity, with expression levels potentially differing significantly between different regions of tumor tissue and between primary and metastatic lesions. Furthermore, its detection results are easily affected by antibody clone number, detection platform, and interpretation criteria, leading to unstable results. While TMB, as a quantitative indicator, reflects the immunogenicity of tumors to some extent, its detection cost is high, standardization is difficult, and its predictive value is limited in some cancer types (such as lung adenocarcinoma). More fundamentally, both PD-L1 and TMB are relatively isolated molecular-level indicators, unable to comprehensively and dynamically reflect the complex cellular composition, intercellular interactions, and active status of key signaling pathways within the tumor microenvironment (TME). The immunosuppressive state of the TME is considered a core cause of immunotherapy resistance. Furthermore, recent studies have shown that specific spatial interactions between tumor cells and immune cells are key to influencing immune responses, but there are currently no clinically available detection methods that can effectively capture and quantify such spatial interactions.

[0004] While cutting-edge technologies such as single-cell transcriptome sequencing (scRNA-seq) can reveal cellular heterogeneity in tumor microenvironments (TMEs), traditional methods often overlook spatial location information of cells. Tumor biological behavior, particularly the interaction between immune cells and tumor cells, is highly dependent on their specific spatial distribution; for example, the tumor invasion front (ITF) is a key region for immune editing and drug resistance development.

[0005] The development of a novel biomarker detection kit based on integrated cell type and spatial information is of great significance for solving the problem of insufficient accuracy of existing prediction technologies. Summary of the Invention

[0006] The technical problem this invention aims to solve is to overcome the shortcomings of existing methods for predicting the efficacy of cancer immunotherapy (such as PD-L1 expression and TMB) in terms of accuracy and inability to reflect the heterogeneity of the tumor microenvironment. This invention provides a detection kit, system, and method for predicting the efficacy of cancer immunotherapy.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A first aspect of the present invention provides a detection kit for predicting the efficacy of cancer immunotherapy, comprising:

[0009] The first detection reagent for the specific detection of the LAMC2 gene or protein;

[0010] A second detection reagent for the specific detection of the SPP1 gene or protein;

[0011] And instructions for interpreting the test results, the interpretation including assessing the efficacy of immunotherapy based on the spatial distribution characteristics of LAMC2 positive cells and SPP1 positive cells.

[0012] This invention provides a diagnostic kit for predicting the efficacy of cancer immunotherapy, comprising a combination of a first diagnostic reagent specifically detecting the LAMC2 gene or protein and a second diagnostic reagent specifically detecting the SPP1 gene or protein. Studies have found that in the complex tumor microenvironment, the presence and abundance of LAMC2+ basal cells and SPP1+ macrophages—two specific subpopulations derived from tumor cells and immune cells respectively—constitute a key biological signal that can significantly influence the immunotherapy response. By jointly detecting this pair of synergistic biomarkers, the invention overcomes the problems of insufficient predictive accuracy and inability to reflect the heterogeneity of the tumor microenvironment caused by relying on single biomarkers such as PD-L1 and TMB in existing technologies. This achieves a significant improvement in the sensitivity and reliability of immunotherapy efficacy prediction.

[0013] Furthermore, the first detection reagent and / or the second detection reagent includes at least one of specific primers, probes, or antibodies.

[0014] Furthermore, the kit is used to detect spatially adjacent LAMC2-positive basal cells and SPP1-positive macrophages in the tumor invasion front zone.

[0015] A second aspect of the present invention provides a predictive system for the efficacy of cancer immunotherapy, comprising:

[0016] The data acquisition module is configured to acquire single-cell transcriptome data and spatial transcriptome data from tumor tissue;

[0017] The feature extraction module is configured to extract feature information of LAMC2+ basal cells and SPP1+ macrophages from the data;

[0018] The predictive analysis module is configured to output therapeutic effect prediction results based on the aforementioned feature information through a predictive model.

[0019] Furthermore, the feature extraction module is specifically configured as follows: a) Based on single-cell transcriptome data, LAMC2+ basal cells and SPP1+ macrophages are identified by the expression of cell type marker genes; b) Based on spatial transcriptome data, the spatial coordinates of the cells are located; c) The spatial proximity index between LAMC2+ basal cells and SPP1+ macrophages is calculated; d) The spatial proximity index and cell abundance data are used together as the feature information.

[0020] Furthermore, the feature information includes at least one of the following:

[0021] Abundance data of LAMC2+ basal cells and SPP1+ macrophages;

[0022] Spatial distribution characteristics of LAMC2+ basal cells and SPP1+ macrophages;

[0023] Activity indicators of the LAMC2-SPP1 signaling pathway.

[0024] The LAMC2-SPP1 signaling pathway activity index reflects the strength of SPP1-mediated signaling communication between cells and neighboring cells via LAMC2 in the tissue microenvironment. This index comprehensively considers three factors: the LAMC2 expression level of the sending cell, the SPP1 expression level of the receiving cell, and the spatial proximity of the two cell types. Higher spatial proximity increases the likelihood of cell interaction and thus strengthens the signaling activity. This index can be used to quantify potential intercellular interactions and signal transduction intensity.

[0025] The formula is expressed as follows:

[0026] Pathway Activity i,j =Expression(LAMC2) i ×Expression(SPP1) j ×f(SpatialProximity i,j );

[0027] Expression(LAMC2)_i represents the LAMC2 expression level of the sending cell;

[0028] Expression(SPP1)_j represents the SPP1 expression level in the receiving cells;

[0029] f(SpatialProximity_i,j) is a spatial distance function (such as the inverse distance or proximity weight) used to reflect the physical proximity between cells.

[0030] Furthermore, the prediction model uses the LASSO algorithm for feature selection and the Cox regression model to construct the prediction model.

[0031] Further, the activity indicators are the mRNA expression ratio of SPP1 and LAMC2 or the immunofluorescence intensity.

[0032] A third aspect of the present invention is a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the functions of the system described above.

[0033] A fourth aspect of the present invention provides a method for constructing a predictive model for the efficacy of cancer immunotherapy, comprising:

[0034] Acquire single-cell transcriptome data and spatial transcriptome data of the training samples;

[0035] Characteristic information of LAMC2+ basal cells and SPP1+ macrophages was extracted from the data.

[0036] The feature information is correlated with clinical efficacy data, and a prediction model is obtained by training a machine learning algorithm.

[0037] A fifth aspect of the present invention provides a drug screening system, comprising:

[0038] The model building unit is configured to establish a detection system containing LAMC2+ basal cells and SPP1+ macrophages;

[0039] A drug processing unit is configured to apply a candidate drug to the detection system;

[0040] The efficacy evaluation unit is configured to detect the effect of the candidate drug on LAMC2+ basal cells and / or SPP1+ macrophages.

[0041] A sixth aspect of the present invention provides the application of the above-described reagent kit in the preparation of products for predicting the efficacy of cancer immunotherapy.

[0042] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0043] 1. This invention provides a diagnostic kit for predicting the efficacy of cancer immunotherapy, comprising a combination of a first diagnostic reagent specifically detecting the LAMC2 gene or protein and a second diagnostic reagent specifically detecting the SPP1 gene or protein. Studies have found that in the complex tumor microenvironment, the presence and abundance of LAMC2+ basal cells and SPP1+ macrophages—two specific subpopulations derived from tumor cells and immune cells respectively—constitute a key biological signal that significantly influences the immunotherapy response. By jointly detecting this pair of synergistic biomarkers, the invention overcomes the problems of insufficient predictive accuracy and inability to reflect the heterogeneity of the tumor microenvironment caused by relying on single biomarkers such as PD-L1 and TMB in existing technologies. This achieves a significant improvement in the sensitivity and reliability of immunotherapy efficacy prediction.

[0044] 2. This invention provides a predictive system for the efficacy of cancer immunotherapy, constructing an integrated analysis platform comprising a data acquisition module, a feature extraction module, and a predictive analysis module. The data acquisition module simultaneously collects single-cell transcriptome data (providing cell-resolution gene expression information) and spatial transcriptome data (preserving the in-situ spatial location information of cells) from the same tumor tissue. The feature extraction module accurately extracts feature information of LAMC2+ basal cells and SPP1+ macrophages closely related to immunotherapy efficacy from these two complementary data sets. Finally, the predictive analysis module uses a machine learning model to perform in-depth mining and correlation analysis on these multidimensional features. This system achieves high-precision, quantifiable prediction of immunotherapy efficacy from both molecular and spatial dimensions.

[0045] 3. This invention provides a method for constructing a predictive model for the efficacy of cancer immunotherapy. It establishes a reliable predictive tool through three key steps: First, single-cell transcriptome and spatial transcriptome data of training samples are acquired as the basic dataset. Next, feature information of LAMC2+ basal cells and SPP1+ macrophages is specifically extracted from this multi-dimensional data as model input features. Finally, the extracted feature information is correlated with clinical efficacy data, and a predictive model is obtained through machine learning algorithm training. Based on a technical path combining multi-omics data-driven approaches and machine learning, this method systematically correlates gene expression features at the single-cell level, the topological relationships of spatial distribution, and clinical endpoint events, thereby establishing a quantitative predictive bridge from molecular features to clinical efficacy. This method achieves the technical effect of constructing an immunotherapy efficacy prediction model with high accuracy and strong generalization ability. Attached Figure Description

[0046] Figure 1 Single-cell transcriptome identification and analysis of LAMC2+ basal cells.

[0047] Figure 2Single-cell transcriptome identification and analysis of SPP1+ macrophages.

[0048] Figure 3 Comparison of ROC curves for the predictive performance of LAMC2 and SPP1 individually and in combination. Figure 3 In this context, A represents multicolor immunofluorescence quantitative LAMC2. + Basal (CK5 / 6) + LAMC2 + Cell density and its predictive ability for immunotherapeutic efficacy; Figure 3 B in the text represents multicolor immunofluorescence quantitative SPP1. + Macrophage (CD68) + SPP1 + Cell density and its predictive ability for immunotherapeutic efficacy; Figure 3 C in the figure represents multicolor immunofluorescence quantitative LAMC2. + Basal (CK5 / 6) + LAMC2 + ) and SPP1 + Macrophage (CD68) + SPP1 + Mean cell density and its predictive ability for immunotherapeutic efficacy; Figure 3 D in the figure represents multicolor immunofluorescence quantitative LAMC2. + Basal (CK5 / 6) + LAMC2 + ) and SPP1 + Macrophage (CD68) + The shortest average distance of SPP1+ and its predictive ability for immunotherapy efficacy.

[0049] Figure 4 Validation of the SPP1 / LAMC2 biomarker in multiple independent cancer cohorts; Figure 4 In the figure, A and B represent the correlation between the expression levels of LAMC2 and SPP1 and the immunotherapy efficacy in the IMvigor210 immunotherapy cohort. Figure 4 In various pan-cancer immunotherapy cohorts, the expression levels of LAMC2 and SPP1 were correlated with immunotherapy efficacy.

[0050] Figure 5 To validate the co-localization of LAMC2 and SPP1 proteins in the tumor invasion front region (multiplex immunofluorescence). Detailed Implementation

[0051] The present invention will now be described in detail with reference to the accompanying drawings.

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0053] The first aspect of this embodiment provides a detection kit for predicting the efficacy of cancer immunotherapy, comprising:

[0054] The first detection reagent for the specific detection of the LAMC2 gene or protein;

[0055] A second detection reagent for the specific detection of the SPP1 gene or protein.

[0056] Research has revealed that in the complex tumor microenvironment, the presence and abundance of two specific subsets—LAMC2+ basal cells and SPP1+ macrophages, originating from tumor cells and immune cells respectively—constitute a key biological signal that significantly influences immunotherapy response. By jointly detecting this pair of synergistic biomarkers, the limitations of existing technologies that rely on single biomarkers such as PD-L1 and TMB, which suffer from insufficient predictive accuracy and fail to reflect the heterogeneity of the tumor microenvironment, are addressed. This achieves a significant improvement in the sensitivity and reliability of immunotherapy efficacy prediction.

[0057] In some embodiments, the first detection reagent and / or the second detection reagent includes at least one of a specific primer, a probe, or an antibody.

[0058] In some embodiments, the kit is used to detect spatially adjacent LAMC2-positive basal cells and SPP1-positive macrophages in the tumor invasion front zone.

[0059] A second aspect of this embodiment provides a system for predicting the efficacy of cancer immunotherapy, comprising:

[0060] The data acquisition module is configured to acquire single-cell transcriptome data and spatial transcriptome data from tumor tissue;

[0061] The feature extraction module is configured to extract feature information of LAMC2+ basal cells and SPP1+ macrophages from the data;

[0062] The predictive analysis module is configured to output therapeutic effect prediction results based on the aforementioned feature information through a predictive model.

[0063] The system simultaneously acquires single-cell transcriptome data (providing cell-resolution gene expression information) and spatial transcriptome data (preserving the in-situ spatial location information of cells) from the same tumor tissue using a data acquisition module. A feature extraction module then precisely extracts characteristic information of LAMC2+ basal cells and SPP1+ macrophages, which are closely related to immunotherapy efficacy, from these two complementary data sets. Finally, a machine learning model in the predictive analysis module performs in-depth mining and correlation analysis on these multidimensional features. This system achieves high-precision, quantifiable prediction of immunotherapy efficacy from both molecular and spatial dimensions.

[0064] In some embodiments, the feature information includes at least one of the following:

[0065] Abundance data of LAMC2+ basal cells and SPP1+ macrophages;

[0066] Spatial distribution characteristics of LAMC2+ basal cells and SPP1+ macrophages;

[0067] Activity indicators of the LAMC2-SPP1 signaling pathway.

[0068] In some embodiments, the prediction model uses the LASSO algorithm for feature selection and a Cox regression model to construct the prediction model.

[0069] A third aspect of this embodiment is a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the functions of the above-described system.

[0070] The fourth aspect of this embodiment provides a method for constructing a predictive model for the efficacy of cancer immunotherapy, including:

[0071] Acquire single-cell transcriptome data and spatial transcriptome data of the training samples;

[0072] Characteristic information of LAMC2+ basal cells and SPP1+ macrophages was extracted from the data.

[0073] The feature information is correlated with clinical efficacy data, and a prediction model is obtained by training a machine learning algorithm.

[0074] This approach, combining multi-omics data-driven methods with machine learning, systematically correlates gene expression characteristics and spatial distribution topology at the single-cell level with clinical endpoint events, thereby establishing a quantitative prediction bridge from molecular features to clinical efficacy. This method achieves the technical effect of constructing an immunotherapy efficacy prediction model with high accuracy and strong generalization ability.

[0075] The fifth aspect of this embodiment provides a drug screening system, including:

[0076] The model building unit is configured to establish a detection system containing LAMC2+ basal cells and SPP1+ macrophages;

[0077] A drug processing unit is configured to apply a candidate drug to the detection system;

[0078] The efficacy evaluation unit is configured to detect the effect of the candidate drug on LAMC2+ basal cells and / or SPP1+ macrophages.

[0079] The sixth aspect of this embodiment provides an application of the above-described reagent kit in the preparation of a product for predicting the efficacy of cancer immunotherapy.

[0080] To clearly understand the above technical solution, the following more detailed implementation methods are provided for further explanation.

[0081] In the following examples, the clone numbers of primers, probe sequences, or antibodies used to detect human LAMC2 and SPP1 are provided.

[0082] Quantitative real-time PCR (qRT-PCR)

[0083] Total RNA was extracted from cells using the RNeasy Mini Kit (Qiagen), and cDNA was synthesized via reverse transcription using the iScript cDNA Synthesis Kit (Bio-Rad). qRT-PCR was performed using the SYBR Green Supermix (Bio-Rad), with reaction system configuration and amplification program settings following the kit instructions. β-actin was used as an internal control gene, and the relative expression levels of the target gene were calculated using the 2^–ΔΔCT method. All primers were designed and synthesized by TSINGKE.

[0084] The primer sequences are shown in Table 1.

[0085] Table 1

[0086] Gene Forward(5'→3') Reverse(5'→3') SPP1 ACTGATTTTCCCACGGACCT CTCCTCGCTTTCCATGTGTG LAMC2 CATAACGGGTTCAGCTGCTC CACTGGGGTCCACATTGTTG mLAMC2 GTGATTCTGGCCGATGTGTC GACCAGTGGAGTTTTGCAGG

[0087] Immunohistochemical staining (IHC)

[0088] Immunohistochemical staining was used to detect the expression level of PD-L1 protein in paraffin-embedded tumor tissue, and the slides were independently reviewed and evaluated by two pathologists. LUSC clinical tissues were fixed in 10% formaldehyde and routinely dehydrated, with normal lung tissue serving as a negative control. 4μm paraffin sections were baked, dewaxed, and rehydrated before antigen retrieval and peroxidase blocking. Primary antibody was added and incubated overnight at 4°C. The next day, secondary antibody was added and incubated at room temperature for 50 minutes. DAB staining was performed for 3–5 minutes, followed by hematoxylin counterstaining, dehydration, clearing, and mounting.

[0089] Primary antibody: PD-L1 monoclonal antibody (Abcam), 1:100; Secondary antibody and DAB kit: Dako.

[0090] Opal multiplex immunofluorescence staining

[0091] Multiplex immunofluorescence staining of 4μm paraffin sections was performed using the Opal Polaris 7-color Manual IHC Kit. After dewaxing and rehydration of the sections with xylene and graded alcohols, only double-distilled water and freshly prepared washing buffer were used throughout the experiment to avoid contact with tap water.

[0092] The slides were placed in antigen retrieval solution and heated at maximum power for 8 minutes twice, followed by cooling for 15 minutes. After blocking at room temperature for 10 minutes, the primary antibody was added and incubated overnight at 4°C. The next day, the slides were brought back to room temperature for 40 minutes, washed, and HRP-labeled secondary antibody was added and incubated at 37°C for 15 minutes. TSA fluorescence enhancer was added and incubated for 10 minutes in the dark, followed by washing. Antigen retrieval was performed again to remove the antibody, and the antibody-secondary antibody-TSA process was repeated. Finally, the slides were stained with DAPI and mounted with Fluoromount-G.

[0093] Spectral imaging using PerkinElmer A multispectral tissue imaging system was used, and image analysis was performed using Phenochart 1.0 and inForm software.

[0094] The list of reagents and antibodies is shown in Table 2.

[0095] Table 2

[0096]

[0097]

[0098] Example 1

[0099] This embodiment first collects various cell types from the tumor microenvironment, especially cells closely related to immunotherapy, from tumor patient samples. Specific steps include:

[0100] Step 1: Single-cell data acquisition and transcriptome library construction

[0101] 1) Sample collection and cell isolation

[0102] Fresh samples were obtained from tumor tissue of lung cancer patients. The tumor tissue was digested into a single-cell suspension using mechanical or enzymatic methods. Cell sorting was performed using flow cytometry or microfluidic technology to identify tumor cells, immune cells, and other related cells.

[0103] 2) Single-cell RNA extraction

[0104] RNA was extracted from isolated single cells, and the RNA from the single cells was amplified using microfluidic chip technology (such as the 10X Genomics platform) or micro-RNA extraction technology to generate a cDNA library.

[0105] 3) Construction of single-cell transcriptome libraries

[0106] Using single-cell transcriptome library construction technologies such as SMART-Seq and 10X Genomics, RNA from single cells is converted into cDNA, and libraries are constructed for subsequent sequencing.

[0107] Step 2: High-throughput sequencing and data preprocessing

[0108] 4) High-throughput sequencing

[0109] The constructed single-cell transcriptome library was sequenced using Illumina NovaSeq or other high-throughput sequencing platforms. Deep sequencing was used to ensure high coverage and accurately capture the gene expression profile of each cell.

[0110] 5) Data preprocessing

[0111] Quality control is performed on the raw sequencing data to filter out low-quality cells and libraries. Commonly used quality control metrics include:

[0112] Cells with too few genes detected (possibly empty droplets)

[0113] 6) Cells with excessively high mitochondrial gene expression (may be dead cells)

[0114] Low-quality sequencing reads are removed using software such as CellRanger or Seurat for these preprocessing operations.

[0115] Step 3: Transcriptome Data Analysis and Single-Cell Atlas Construction

[0116] 7) Data standardization and dimensionality reduction

[0117] The preprocessed data is standardized to eliminate the effects of sequencing depth or batch effects. Then, dimensionality reduction techniques such as t-SNE and UMAP are used to reduce the dimensionality of single-cell data, making it easier to visualize the relationships between different cell populations.

[0118] 8) Cluster analysis

[0119] Using the Louvain algorithm for clustering, single-cell populations are clustered according to their gene expression characteristics to identify different cell subpopulations. These subpopulations may include epithelial cells, mesenchymal cells, immune cells, etc.

[0120] 9) Cell type annotation

[0121] like Figure 1 As shown, each cell subpopulation was annotated using known cell markers (such as epithelial cell marker EPCAM and macrophage marker CD68). Particular attention was paid to the characteristic expression profiles of LAMC2 cells and SPP1 macrophages.

[0122] Step 4: Identify key cell populations: LAMC2+ basal cells and SPP1+ macrophages

[0123] 10) LAMC2 gene expression statistics and LAMC+ basal cell recognition

[0124] The LAMC2 gene (Laminin subunit gamma-2) is believed to be associated with tumor invasion and metastasis in various cancers. By analyzing LAMC2 expression in tumor cell populations, we can identify cell populations with high LAMC2 expression. These cells may be closely related to epithelial cells or metastasis-associated cell populations.

[0125] 11) SPP1 gene expression statistics and SPP1+ macrophage recognition

[0126] like Figure 2 As shown, SPP1 (Osteopontin) is an important marker of tumor-associated macrophages. SPP1-expressing macrophages are associated with tumor immune escape, immunosuppression, and inflammatory responses. In this invention, macrophage populations with high SPP1 expression are identified through macrophage population clustering in single-cell data, and the roles of these cells in the tumor microenvironment are further analyzed.

[0127] Step 5: Clinical Data Integration and Immunotherapy Efficacy Prediction

[0128] 12) Integration of clinical data

[0129] We collected clinical data from lung cancer patients, including responses to immunotherapy (such as the efficacy of PD-1 / PD-L1 inhibitors), patient survival rates, and recurrence rates. We then integrated and analyzed single-cell transcriptome atlas data with this clinical data.

[0130] 13) Construction of a predictive model for immunotherapy efficacy

[0131] By combining the dynamic changes of LAMC2+ basal cells and SPP1+ macrophages in the patient's tumor microenvironment, a predictive model was constructed to assess the relationship between the expression levels of these cell populations and the efficacy of immunotherapy. Machine learning (LASSO analysis and Cox regression model) was used to validate the predictive value of these cell populations. The model's reliability and clinical application potential were ensured through cross-validation and further validation with clinical samples.

[0132] 14) Model Validation and Extended Applications

[0133] The model underwent external validation, with independent clinical cohorts used to further evaluate its accuracy and stability. Furthermore, the model can be extended to evaluate other types of cancer immunotherapy, enhancing its versatility.

[0134] Example 2

[0135] Example 2 provides the AUC using only LAMC2, the AUC using only SPP1, and the AUC using both LAMC2 and SPP1, as detailed below. Figure 3 As shown, the predictive AUC of LAMC2+Basal alone was 0.725, and that of SPP1+Macrophage alone was 0.783. The predictive AUC of the combined LAMC2+Basal and SPP1+Macrophage was 0.81, all of which were greater than the predictive AUC of either alone. Statistical analysis shows that the AUC of the combined use was significantly higher than that of any single indicator (p < 0.05).

[0136] like Figure 4 As shown, validation was performed using the existing immunotherapy cohort IMvigor210CoreBiologies (containing approximately 298 bladder cancer patients), revealing that high expression of SPP1 and LAMC2 was closely associated with poor patient prognosis and immunotherapy resistance. Subsequently, this conclusion was further validated in multiple independent cohorts, including datasets from non-small cell lung cancer such as LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), HNSC (head and neck squamous cell carcinoma), and Melanoma (melanoma), demonstrating that high expression of SPP1 / LAMC2 may serve as a potential biomarker for predicting immune tolerance and treatment efficacy. Figure 5 In the image, red LAMC2 indicates the location, green SPP1 indicates the location, and the arrows in the last image indicate the location.

[0137] Opal multiplex immunohistochemistry (mIHC) assay

[0138] The Opal Polaris 7-color manual IHC kit was used to stain 4 μm thick paraffin-embedded tissue sections. The experimental procedure is as follows:

[0139] Paraffin-embedded tissue sections (4 μm thick) were used for immunohistochemical experiments. Lung squamous cell carcinoma (LUSC) tissue was fixed in 10% formaldehyde and dehydrated, with normal lung tissue serving as a negative control. After baking, dewaxing, and rehydration, the sections underwent antigen retrieval and peroxidase blocking.

[0140] Slice pretreatment

[0141] The sections were dewaxed and rehydrated in xylene and gradient alcohol solutions.

[0142] Use double-distilled water and freshly prepared working buffer to wash tissues throughout the process; avoid using tap water.

[0143] Antigen repair

[0144] Immerse the slide in antigen retrieval solution and microwave or heat at full power for 8 minutes. Repeat twice.

[0145] After allowing it to cool naturally for 15 minutes, proceed to the next step.

[0146] Blocking and primary antibody incubation

[0147] Place the slices on a shaker and wash them three times in washing buffer for two minutes each time.

[0148] Add the blocking solution and incubate for 10 minutes under constant temperature conditions.

[0149] Add the primary antibody and incubate overnight at 4°C.

[0150] Secondary antibodies and fluorescence enhancement

[0151] The next day, the slices were brought to room temperature for 40 minutes.

[0152] Wash the slices three times on a shaker for two minutes each time.

[0153] Add HRP-labeled secondary antibody and incubate at 37°C for 15 minutes.

[0154] Wash three more times, for two minutes each time.

[0155] Add TSA (Tyramide Signal Amplification) fluorescence enhancement reagent and incubate at room temperature in the dark for 10 minutes.

[0156] Wash three more times, for two minutes each time.

[0157] Repeated cyclic staining

[0158] The slides were then subjected to antigen retrieval again (heated at full power for 8 minutes x 2, then cooled for 15 minutes).

[0159] Perform primary antibody incubation, secondary antibody incubation, and fluorescent labeling sequentially according to the above procedure until all target antibody staining is completed.

[0160] Covering and Imaging

[0161] Finally, DAPI nuclear staining was performed, and the slides were mounted using Fluoromount-G.

[0162] Using a multispectral tissue imaging system (PerkinElmer) (Image acquisition)

[0163] Pathological results were analyzed using Phenochart 1.0 and PerkinElmer software.

[0164] Experimental materials and antibodies

[0165] Washing buffer: Dako (K8000 / K8002 / K8007 / K8023).

[0166] Antibody list:

[0167] Anti-CK5 / 6 (Millipore, MAB1620).

[0168] Anti-CD68 (Abcam, ab955).

[0169] Anti-SPP1 (Abcam, ab214050).

[0170] Anti-LAMC2 (Abcam, ab210959).

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A diagnostic kit for predicting the efficacy of cancer immunotherapy, characterized in that, Include: The first detection reagent for the specific detection of the LAMC2 gene or protein; A second detection reagent for the specific detection of the SPP1 gene or protein.

2. The reagent kit according to claim 1, characterized in that, The first detection reagent and / or the second detection reagent includes at least one of specific primers, probes or antibodies.

3. The reagent kit according to claim 1, characterized in that, The kit is used to detect spatially adjacent LAMC2-positive basal cells and SPP1-positive macrophages in the tumor invasion front zone.

4. A predictive system for the efficacy of cancer immunotherapy, characterized in that, include: The data acquisition module is configured to acquire single-cell transcriptome data and spatial transcriptome data from tumor tissue; The feature extraction module is configured to extract feature information of LAMC2+ basal cells and SPP1+ macrophages from the data; the predictive analysis module is configured to output efficacy prediction results based on the feature information through a predictive model.

5. The system according to claim 4, characterized in that, The feature information includes at least one of the following: Abundance data of LAMC2+ basal cells and SPP1+ macrophages; Spatial distribution characteristics of LAMC2+ basal cells and SPP1+ macrophages; Activity indicators of the LAMC2-SPP1 signaling pathway.

6. The system according to claim 4, characterized in that, The prediction model uses the LASSO algorithm for feature selection and the Cox regression model to construct the prediction model.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it performs the functions of the system as described in any one of claims 4-6.

8. A method for constructing a predictive model for the efficacy of cancer immunotherapy, characterized in that, include: Acquire single-cell transcriptome data and spatial transcriptome data of the training samples; Characteristic information of LAMC2+ basal cells and SPP1+ macrophages was extracted from the data. The feature information is correlated with clinical efficacy data, and a prediction model is obtained by training a machine learning algorithm.

9. A drug screening system, characterized in that, include: The model building unit is configured to establish a detection system containing LAMC2+ basal cells and SPP1+ macrophages; A drug processing unit is configured to apply a candidate drug to the detection system; The efficacy evaluation unit is configured to detect the effect of the candidate drug on LAMC2+ basal cells and / or SPP1+ macrophages.

10. The use of the kit according to any one of claims 1-3 in the preparation of a product for predicting the efficacy of cancer immunotherapy.

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