Predictive model and construction method for predicting prognosis after neoadjuvant immunotherapy by metastatic lymph nodes

Abstract

The present invention belongs to the field of biological detection technology, and specifically relates to a prediction model for predicting the prognosis of non-small cell lung cancer (NSCLC) after neoadjuvant immunotherapy through metastatic lymph nodes (mLN) and a construction method thereof. The present invention uses a variety of spatial high-throughput sequencing bioinformatics technologies to analyze the key immune cell subsets, immune microenvironment topological characteristic structures and tertiary lymphoid body-related functional cell subsets of mLN after neoadjuvant immunotherapy. The proposed prediction model for predicting the prognosis of NSCLC after neoadjuvant immunotherapy through metastatic lymph nodes and its construction method are more in line with the changes in spatial histological characteristics of the immune microenvironment after treatment. Compared with the previous transcriptome sequencing method without spatial information, it more accurately characterizes the changes in the spatial tumor immune microenvironment after immunotherapy, and can more accurately predict the prognosis of NSCLC after neoadjuvant immunotherapy through cell targets that truly affect the efficacy of immunity.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological detection, and specifically relates to a prediction model for predicting the prognosis of non-small cell lung cancer (NSCLC) after neoadjuvant immunotherapy through metastatic lymph nodes (mLN) and a construction method thereof. Background Art

[0002] Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer, and its incidence is gradually increasing. Recently, a series of clinical trials of neoadjuvant immunotherapy have shown that patients with resectable non-small cell lung cancer showed significantly increased tumor regression rate and significantly reduced recurrence rate after neoadjuvant immunotherapy combined with chemotherapy, which has led to the gradual increase in the application of neoadjuvant immunotherapy in clinical practice and has become the standard treatment for some NSCLC.

[0003] At present, the focus of neoadjuvant immunotherapy research is mainly on the microenvironment of the primary tumor. The classical theory holds that immunotherapy mainly achieves anti-tumor effects by reactivating exhausted T cells in the tumor microenvironment. However, more and more evidence has found that newly activated and newly expanded effector T cells from the tumor-draining lymph nodes (TDLN) in the tumor area may play a more important role in anti-tumor immunity. On the one hand, TDLN has an important physiological function in tumor immunity. After antigen-presenting cells capture tumor antigens in the primary tumor tissue, they migrate to TDLN and initiate the activation of antigen-specific T cells. Subsequently, newly activated and newly expanded T cells re-infiltrate the primary tumor and play an anti-tumor role. On the other hand, various components in TDLN, including immune and non-immune cells, also play an important role in immunotherapy response. Studies have confirmed in animal models that surgical resection of TDLN greatly reduces the efficacy of immunotherapy.

[0004] Recent studies have found that the primary tumor and regional metastatic lymph nodes of the same lung cancer patient may have completely different responses to neoadjuvant immunotherapy. Metastatic lymph nodes may respond poorly and distant lymph nodes may respond even worse, suggesting that there may be many differences in the immune microenvironment between the primary lesion and the lymph nodes. Tumor cells in metastatic TDLN may represent a special subpopulation that can not only escape immune surveillance and resist immunotherapy, but also be a key source of tumor recurrence. However, which immune cell subpopulation or immune microenvironmental characteristic structure plays a key role in affecting the efficacy of mLN neoadjuvant immunotherapy has not been fully studied.

[0005] At present, the few studies exploring the characteristics of the immune microenvironment after mLN neoadjuvant immunization of NSCLC are limited to immunohistochemical staining techniques of a few indicators, which can only roughly depict the major cell categories of the immune microenvironment, and there is no detailed characterization of the mLN immune microenvironment at the single cell level. Imaging mass cytometry (IMC) is an emerging technology in spatial proteomics. It couples different target antibodies with rare earth metals and incubates them on the same tissue slide. The sample is scanned point by point using a high-resolution laser system and analyzed using a mass spectrometer. This technology can detect dozens of protein markers at the same time and can retain the spatial microinformation of cells, thereby realizing single-cell phenotypic analysis with spatial resolution.

[0006] The inventors have explored the key immune cell subsets, immune microenvironment topological characteristic structures and tertiary lymphoid body-related functional cell subsets of mLN after neoadjuvant immunotherapy, by performing imaging mass spectrometry (IMC), single-cell transcriptome sequencing, spatial transcriptome sequencing and other analyses on metastatic lymph nodes of NSCLC patients receiving neoadjuvant immunotherapy (PD-1 monoclonal antibody) combined with chemotherapy. On this basis, a new prediction model for neoadjuvant immunotherapy prognosis of NSCLC based on mLN immune microenvironment and its construction method have been developed.

[0007] There is no report on relevant research results in the prior art. Summary of the invention

[0008] In order to overcome the defects and shortcomings in the prior art, the present invention provides a novel prediction model for predicting the prognosis of non-small cell lung cancer (NSCLC) after neoadjuvant immunotherapy by metastatic lymph nodes (mLN) and a method for constructing the model.

[0009] In order to achieve the purpose of the present invention, the present invention adopts the following technical scheme:

[0010] In a first aspect, the present invention provides a method for constructing a prediction model for predicting the prognosis of NSCLC after neoadjuvant immunotherapy by mLN, wherein the prediction model uses imaging mass spectrometry (IMC) technology, single-cell transcriptome sequencing technology, and spatial transcriptome sequencing technology to predict the prognosis of NSCLC after neoadjuvant immunotherapy, and the prediction model construction method comprises the following steps:

[0011] Step 1: Use IMC technology to detect mLN samples after neoadjuvant therapy to obtain the cell cluster (CT) types associated with the efficacy of mLN and immunotherapy, so as to determine the differential characteristics of the CT components of the tumor microenvironment presented by mLN after neoadjuvant immunotherapy;

[0012] Step 2: Further analyze the cell neighborhood (CN) and cell interactions of mLNs to determine the differences in CN enrichment of mLNs with differential immunotherapy responses, so as to determine the differential characteristics of CN components of the tumor microenvironment presented by mLNs after neoadjuvant immunotherapy;

[0013] Step 3: Further analyze the spatial location characteristics of CN, define the area dominated by B cells as a "patch", and use this to demarcate the structure of the tertiary lymphoid corpuscle (TLS). Define the area around the "patch" as a complement, and find that the spatial location characteristics are closely related to the key immune microenvironment structure TLS that affects the immune response. By analyzing the differential distribution of CN related to the efficacy inside and outside TLS, it is determined that the key CN that determines the efficacy of immunotherapy is located inside TLS, and the predictive value of CN in TLS for patient prognosis is verified;

[0014] Step 4: Use single-cell transcriptome sequencing and spatial transcriptome sequencing to identify cells within TLS that are associated with the efficacy of neoadjuvant immunotherapy for mLN;

[0015] Step 5: Based on the differential characteristics of tumor microenvironment CT components between patients who respond to neoadjuvant immunotherapy and patients who resist treatment obtained in step 1, the differential characteristics of key CNs that determine the immune efficacy located in TLS obtained in step 2, and the differential characteristics of key cell subsets related to the neoadjuvant immune efficacy in TLS obtained in steps 3 and 4, the prediction model is constructed using the least absolute shrinkage and selection operator (LASSO) regression model.

[0016] As an optional method, in the above-mentioned prediction model construction method, in step 1, the marker set is identified by IMC technology, CT is defined by unsupervised clustering, and a factorial correlation analysis is performed between the CT distribution status and the mLN response status to neoadjuvant immunotherapy to determine the differential characteristics of tumor microenvironment components with statistical differences.

[0017] As an optional approach, in the above-mentioned prediction model construction method, in step 2, the identification of CN is based on the central cell and its 10 nearest neighbor cells, and different CNs are identified according to the abundance of the original cell clusters in each CN. The differences in the CN enrichment levels of mLNs showing differential immunotherapy responses are statistically analyzed using a paired Wilcox test.

[0018] As an optional method, in the above prediction model construction method, in step 3, imcRtools (version 1.5.4) was used to determine the "patch" according to the standard of the maximum distance between cells being 20 µm, requiring at least 10 cells to gather. Subsequently, the "patch" was enlarged by 20 µm to fully divide its environment, that is, the "complement", and by comparing the differential distribution of CN inside and outside the TLS, it was determined that the key CN that determines the immune efficacy is located within the TLS, and the Kaplan-Meier curve fitting was used to verify the accuracy of CN in the TLS in predicting prognosis.

[0019] As an optional method, in the above-mentioned prediction model construction method, in step 4, based on unsupervised clustering and spot features, the immune microenvironment is divided into 13 cell groups, namely Macrophage, T cell, B cell, NK cell, Epithelial cell, Neutrophils, Fibroblast, Endothelial cell, Plasma cell, LAMP3+ DC, Mast cell, Smooth muscle cell, and pDC. Subcluster dimensionality reduction and subcluster expression difference molecular interaction and localization analysis are mainly performed on Macrophage, T cell, and B cell, and the TCGA non-small cell lung cancer database and immunotherapy database are used to verify the relationship between the expression of various cell population subtypes and the prognosis after treatment.

[0020] As an optional method, in the above prediction model construction method, in step 5, the treatment-responsive patients are those who, after comprehensive pathological evaluation, have no residual tumor cells in the metastatic lymph nodes of lung cancer and achieve complete pathological remission (pathological completely response, pCR), that is, the lymph node treatment-responsive patients; the treatment-resistant patients are those who, after comprehensive pathological evaluation, have residual tumor cells in the metastatic lymph nodes of lung cancer and have not achieved complete pathological remission, that is, the lymph node treatment-resistant patients.

[0021] As an optional method, in the above prediction model construction method, in step 5, the LASSO regression model is used to screen variables of CT (including CT with good and poor efficacy), CN in TLS, and FLOR2 cell subpopulations identified by the single-cell idle algorithm, assign correlation coefficients, and construct a model.

[0022] In a second aspect, the present invention provides a prediction model system for predicting the prognosis after neoadjuvant immunotherapy for NSCLC through mLN, which is constructed by the prediction model construction method described in the first aspect.

[0023] As an optional method, in the above-mentioned prediction model system, the cell cluster types related to the efficacy of neoadjuvant immunotherapy of mLN obtained by IMC technology include Epithelial, CD4T&Fibroblast, Myofibroblast, Neutrophil, NK&Epithelial, Treg&InfMP, MatureB, Naïve CD8T, TRM&CD8+ T, ActivatedCD8T, B&FDC, CD4T&B, CD8T&B, and B.

[0024] Furthermore, the cell cluster types that were negatively correlated with the efficacy of neoadjuvant immunotherapy for mLN were Epithelial, CD4T&Fibroblast, Myofibroblast, Neutrophil, NK&Epithelial, and Treg&InfMP.

[0025] Furthermore, the cell cluster types positively correlated with the efficacy of neoadjuvant immunotherapy for mLN were MatureB, NaiveCD8T, TRM&CD8+T, Activated CD8T, B&FDC, CD4T&B, CD8T&B, and B.

[0026] As an optional approach, in the above-mentioned prediction model system, CN8: B enriched, CN9: TLS compartment, and CN11: B&T interaction within TLS significantly enhance the efficacy of neoadjuvant immunotherapy of mLN.

[0027] As an optional approach, in the above-mentioned efficacy prediction model system, cells associated with the efficacy of neoadjuvant immunotherapy for NSCLC determined by single-cell transcriptome sequencing technology include Exhausted CD8 Tem and B cells in mLN.

[0028] As an optional method, in the above prediction model system, based on the difference characteristics of tumor microenvironment CT components between patients who respond to treatment after neoadjuvant immunotherapy and patients who resist treatment, the difference characteristics of key CNs that determine the efficacy of immunotherapy located in TLS, and the difference characteristics of key cell subsets related to the efficacy of neoadjuvant immunotherapy in TLS, the least absolute shrinkage and selection operator (LASSO) regression model was used to construct the following model: M=0.21 B(CT)+0.03 CD8T&B (CT) +0.02 B&FDC (CT) -0.22 CD4T&Fibroblast (CT) - 0.40 Neutrophil+0.95 CN8+0.44 CN11, and verified that the prediction performance was good.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention uses a variety of spatial high-throughput sequencing bioinformatics technologies to analyze key immune cell subsets, immune microenvironment topological characteristic structures and tertiary lymphoid body-related functional cell subsets after neoadjuvant immunotherapy, and proposes a new prediction model for predicting the prognosis of NSCLC after neoadjuvant immunotherapy through mLN and its construction method. Previously, there were no reports in the field on fine cell subsets closely related to the efficacy of mLN neoadjuvant immunotherapy, nor were there any reports on prognosis prediction models after neoadjuvant immunotherapy based on key cell subsets in mLN. In addition, the present invention comprehensively utilizes spatial multi-omics analysis methods such as imaging mass spectrometry flow, single-cell transcriptome combined with spatial transcriptome, and the established prognosis prediction model is more in line with the changes in spatial histological characteristics of the immune microenvironment after treatment, more accurately characterizes the changes in the spatial tumor immune microenvironment after immunotherapy, and can more accurately predict the prognosis of NSCLC after neoadjuvant immunotherapy through cell targets that truly affect the efficacy of immunotherapy.

[0031] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a new or preferred technical solution. Due to space limitations, they will not be described one by one here. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 :Flowchart of the method for constructing a prediction model for predicting the prognosis of NSCLC after neoadjuvant immunotherapy through mLN of the present invention. Imaging mass spectrometry (IMC) detection was performed on mLN of all enrolled patients; single-cell transcriptome sequencing (scRNA sequencing) was performed on 5 metastatic lymph nodes of 4 enrolled patients. Spatial transcriptome sequencing was performed on 2 patients, and both had supporting single-cell transcriptome sequencing.

[0033] Figure 2 : Cell cluster (CT) heatmap. The heatmap shows the maximum-minimum normalized mean marker expression of the 22 CTs defined by unsupervised clustering.

[0034] Figure 3 : Heat map of T cell subclusters. T cells were further clustered into 9 subclusters.

[0035] Figure 4:Correlation analysis of mLN immune efficacy. Factorial correlation analysis revealed the cell cluster subtypes closely related to the mLN immune Hilton efficacy.

[0036] Figure 5 : Representative IMC staining images and comparisons of CT of interest.

[0037] Figure 6 : Cell neighborhood (CN) heatmap. 15 CNs were defined by unsupervised clustering based on the abundance of primitive cell clusters in each CN.

[0038] Figure 7 :Topological representation and comparison of CNs of interest. CN8: B enriched, CN9: TLS compartment, and CN11: B&T interaction in mLNs of responding patients were significantly higher than those of resistant patients, suggesting the key role of B cells.

[0039] Figure 8 :Schematic diagram of the Patch algorithm. The area dominated by B cells is defined as a "patch", which is used to demarcate the TLS structure, and the area around the patch is defined as the complement.

[0040] Fig. 9 :The relationship between the distribution of CN inside and outside the patch and the difference in efficacy. CN1 (B&TRM), CN8 (B enriched), CN9 (TLS compartment) and CN11 (B&T interaction) were mainly distributed inside the patch, and CN6 (TRM enriched) was mainly distributed outside the patch. Whether inside or outside the patch, CN8 (B enriched) and CN11 (B&T interaction) increased significantly in the mLN response group, but the internal CN played a major role. CN1 (B&TRM), CN6 (TRMenriched) and CN9 (TLS compartment) increased significantly in the mLN response group only when they were inside the patch.

[0041] Fig.10 :The predictive value of CN within TLS for prognosis.

[0042] Fig.11 :UMAP diagram of major cell categories. In the single-cell transcriptome data, based on unsupervised clustering and spot features, the microenvironment is divided into 13 cell groups, including epithelial cells, Macrophage (Mps), T cells, B cells, NK cells, Neutrophil and Fibroblast.

[0043] Fig.12 :UMAP diagram of cell subsets. In the single-cell transcriptome data, T cells, B cells and Mps were further subdivided and subpopulated, and 33 cell subsets were identified using molecular markers or functional states.

[0044] Fig.13 :Heat map of cell subpopulation characteristic gene expression. The heat map shows the maximum-minimum standardized expression levels of characteristic molecular markers of each cell subpopulation.

[0045] Fig.14 :Single-cell spatial mapping and TLS annotation. The cell subpopulations obtained from the single-cell data are mapped and labeled in their spatial positions, and the TLS positions are defined accordingly.

[0046] Fig.15 :Display of the number of cell subsets within TLS. mLN responders are mainly CRIP1+B and IGHD+ B, and T cells are mainly CD4Tem, CD4Tcm and CD8Tem.

[0047] Fig.16 : Characteristic gene expression of different B cell and T cell subsets.

[0048] Fig.17 : Based on the difference characteristics of tumor microenvironment CT between patients who respond to and resist mLN treatment, the difference characteristics of key CN within TLS, and the difference characteristics of key cell subpopulations within TLS, a Lasso regression model and its prediction efficiency were constructed.

[0049] Fig.18 :The predictive value of different T and B subtype expression levels for prognosis. The TCGA non-small cell lung cancer database verifies that the high expression of the Exhausted CD8 Tem subtype is a protective factor for prognosis. In two external immunotherapy datasets, it is verified that the high expression of B cells is a protective factor for prognosis. DETAILED DESCRIPTION

[0050] The present invention will be further described with reference to specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0051] If no specific techniques or conditions are specified in the examples, the techniques or conditions described in the literature in the field or the product instructions are used. If no manufacturer is specified for the reagents or instruments used, they are all conventional products that can be purchased through regular channels.

[0052] The experimental methods in the following examples are conventional methods unless otherwise specified. The experimental materials used in the following examples are commercially available products unless otherwise specified.

[0053] Example:

[0054] Eighteen patients with non-small cell lung cancer and lymph node metastasis were enrolled and received neoadjuvant immunotherapy combined with chemotherapy. The immunotherapy they received included PD-1 monoclonal antibodies such as tislelizumab, pembrolizumab, and sintilimab. This study has been approved by the Ethics Committee of Peking University People's Hospital (approval number: 2022PHB140-001).

[0055] Imaging mass spectrometry (IMC) detection was performed on mLN of all enrolled patients; single-cell transcriptome sequencing (scRNA sequencing) was performed on 5 metastatic lymph nodes of 4 enrolled patients. Spatial transcriptome sequencing was performed on 2 patients, and both patients had supporting single-cell transcriptome sequencing (see Figure 1 ).

[0056] Based on the identification results of 33 markers in IMC (see Table 1), these markers were selected based on their coverage of the lineage markers of major immune cells in the tumor immune microenvironment and functional indicators such as a variety of more important immune checkpoint inhibitor molecules. 22 cell clusters (CT, see Figure 2 ), the heat map shows the maximum-minimum normalized mean marker expression of the 22 clusters, and further clusters T cells into 9 subclusters (see Figure 3 Based on the IMC results, a factorial correlation analysis was performed between the CT distribution and the mLN response to neoadjuvant immunotherapy, and the statistically significant results are shown in Table 2.

[0057] Table 1: Imaging Mass Spectrometry Antibody Panel

[0058]

[0059]

[0060] Table 2: Factorial analysis of IMC cell clusters and mLN responses to neoadjuvant immunotherapy

[0061]

[0062] Among them, B cells, CD8T&B, CD4T&B, B&FDC and TRM&CD8T were positively correlated with the response of mLN to neoadjuvant immune therapy, while Epithelial, CD4T&Fibroblast, Myofibroblast and Neutrophil were positively correlated with the resistance to neoadjuvant immune therapy of mLN (see Figure 4), which shows that mLN presents different characteristics of tumor microenvironment components after neoadjuvant immunotherapy, suggesting the direction of further exploring the mechanism of differential response. The differences in cell cluster distribution between different mLN efficacy response groups are shown by scatter plots and IMC representative diagrams (see Figure 5 ).

[0063] To explore the potential mechanisms by which these specific cellular interactions contribute to treatment response, we performed cell neighborhood (CN) and cell interaction analysis using high-resolution spatial data. CNs were identified based on the central cell and its 10 nearest neighbors, and 15 different CNs were identified based on the abundance of the original cell clusters in each CN (see Figure 6 ), named after the name or function of the central cell cluster, and the maximum-minimum normalized level of the central cell cluster of each CN is displayed by a heat map.

[0064] The combination of box plots and dot plots showed the differences in CN enrichment of mLN with differential immunotherapy response (CN1: B&TRM, CN6: TRM enriched, CN8: B enriched, CN9: TLS compartment, CN11: B&T interaction), and paired Wilcox test was used for statistical analysis. Among them, CN8 (B enriched), CN9 (TLS compartment) and CN11 (B&T interaction) were significantly increased in the mLN response group, suggesting the core position of B cells in immunotherapy response (see Figure 7 More importantly, in the actual IMC images, it was found that several key CNs obtained from the differential efficacy analysis were able to focus on the structure closely related to the efficacy - the tertiary lymphoid structure (TLS).

[0065] Subsequently, in order to clarify how these cellular functional modules CN function inside and outside the TLS, the area dominated by B cells was defined as a "patch" to mark the TLS structure, and the area around the patch was defined as the complement (see Figure 8 Using imcRtools (version 1.5.4), “patches” were defined based on a criterion of a maximum intercellular distance of 20 µm, requiring at least 10 cells to be clustered. Subsequently, the “patches” were enlarged by 20 µm to fully partition their environment, i.e., “complementation”.

[0066] The relationship between the distribution of CNs closely related to efficacy inside and outside the patch and the difference in efficacy was analyzed. It was found that for mLN, CN1 (B&TRM), CN8 (B enriched), CN9 (TLS compartment) and CN11 (B&T interaction) were mainly distributed inside the patch, while CN6 (TRM enriched) was mainly distributed outside the patch. Regardless of whether inside or outside the patch, CN8 (B enriched) and CN11 (B&T interaction) increased significantly in the mLN response group, but the internal CN played a major role. CN1 (B&TRM), CN6 (TRM enriched) and CN9 (TLS compartment) increased significantly in the mLN response group only when they were inside the patch (see Fig. 9 ), and further verified the accuracy of CN in TLS in predicting prognosis by using Kaplan-Meier curve fitting (see Fig.10 ).

[0067] The IMC panel of the present invention is limited to 33 markers. In order to analyze the IMC-enriched cell clusters with higher resolution, single-cell transcriptome combined with spatial transcriptome sequencing was performed. Based on unsupervised clustering and spot features, the microenvironment was divided into 13 cell groups, including epithelial cells, macrophage (Mps), T cells, B cells, NK cells, neutrophil, and fibroblasts (see Fig.11 ), and mainly targets T cells, B cells and Mps (see Fig.12 ) performed subcluster dimensionality reduction and subcluster characteristic gene expression analysis (see Fig.13 ). We will further map the cell subsets obtained from the single-cell data to their spatial locations, and use this to define the locations of TLS formed by B cell aggregation (see Fig.14 ).

[0068] The distribution of different cell subsets within TLS was statistically analyzed and compared, and it was found that mLN responders were mainly CRIP1+B and IGHD+B, and T cells were mainly CD4Tem, CD4Tcm and CD8Tem, suggesting the key role of B cells and T cells (see Fig.15 ). The characteristic gene expression of B cells and T cells was further analyzed (see Fig.16 ).

[0069] The differential characteristics of tumor microenvironment CT components between patients who respond to neoadjuvant immunotherapy and those who resist treatment, the differential characteristics of key CNs that determine the efficacy of immunotherapy located in TLS, and the differential characteristics of key cell subsets related to the efficacy of neoadjuvant immunotherapy in TLS were included in the analysis. The LASSO regression model was used to select positive and negative factors that have an impact on prognosis, and the prediction model was constructed: M=0.21 B(CT)+0.03 CD8T&B (CT) +0.02 B&FDC (CT) -0.22 CD4T&Fibroblast (CT) - 0.40 Neutrophil+0.95 CN8+0.44 CN11, and the AUC curve was used to evaluate the predictive performance of the model (see Fig.17 ).

[0070] The prognostic role of B cell and T cell subtypes was verified using the TCGA non-small cell lung cancer database, among which Exhausted CD8 Tem was a protective factor for prognosis (see Fig.18 ). Furthermore, high B cell expression was verified as a protective factor for prognosis in two external datasets of immunotherapy (see Fig.18 ).

[0071] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for constructing a prediction model for predicting the prognosis of non-small cell lung cancer (NSCLC) after neoadjuvant immunotherapy by metastatic lymph node (mLN) for non-diagnostic purposes, characterized in that: The prediction model uses imaging mass spectrometry flow IMC technology, single-cell transcriptome sequencing technology and spatial transcriptome sequencing technology to predict the prognosis of NSCLC after neoadjuvant immunotherapy, wherein the neoadjuvant immunotherapy is PD-1 monoclonal antibody therapy, and the prediction model construction method comprises the following steps: Step 1: Use IMC technology to detect mLN samples after neoadjuvant therapy to obtain the CT type of cell clusters related to the efficacy of mLN and immunotherapy, so as to determine the differential characteristics of the CT components of the tumor microenvironment presented by mLN after neoadjuvant immunotherapy; Step 2: Further analyze the cell neighborhood CN and cell interactions of mLNs to determine the differences in CN enrichment of mLNs with differential immunotherapy responses, so as to determine the differential characteristics of CN components of the tumor microenvironment presented by mLNs after neoadjuvant immunotherapy; Step 3: Further analyze the spatial location characteristics of CN, define the area dominated by B cells as a "patch", and use this to demarcate the structure of the tertiary lymphoid body TLS. Define the area around the "patch" as a complement. It is found that the spatial location characteristics are closely related to the key immune microenvironment structure TLS that affects the immune response. By analyzing the differential distribution of CN related to the efficacy inside and outside TLS, it is determined that the key CN that determines the immune efficacy is located inside TLS, and the predictive value of CN in TLS for patient prognosis is verified; Step 4: Use single-cell transcriptome sequencing and spatial transcriptome sequencing to identify cells within TLS that are associated with the efficacy of neoadjuvant immunotherapy for NSCLC; Step 5: Based on the differential characteristics of tumor microenvironment CT components between patients who respond to neoadjuvant immunotherapy and patients who resist treatment obtained in step 1, the differential characteristics of key CNs that determine the immune efficacy located in TLS obtained in step 2, and the differential characteristics of key cell subpopulations related to the neoadjuvant immune efficacy in TLS obtained in steps 3 and 4, the prediction model is constructed using the minimum absolute shrinkage and selection operator LASSO regression model.

2. The prediction model construction method according to claim 1, characterized in that: In step 1, the marker set was identified using IMC technology, CT was defined by unsupervised clustering, and a factorial correlation analysis was performed between the CT distribution and the mLN response status to neoadjuvant immunotherapy to determine the differential characteristics of the tumor microenvironment components with statistical differences.

3. The prediction model construction method according to claim 1, characterized in that: In step 2, CNs were identified based on the central cell and its 10 nearest neighbor cells. Different CNs were identified based on the abundance of the original cell cluster in each CN. Differences in CN enrichment of mLNs showing differential immunotherapy responses were statistically analyzed using paired Wilcox test.

4. The prediction model construction method according to claim 1, characterized in that: In step 3, the "patch" was determined based on the criterion of a maximum intercellular distance of 20 µm, requiring the aggregation of at least 10 cells. Subsequently, the "patch" was enlarged by 20 µm to fully divide its environment, namely the "complement", and by comparing the differential distribution of CNs inside and outside the TLS, it was determined that the key CNs that determine the efficacy of the immune response are located within the TLS. The Kaplan-Meier curve fitting was used to verify the accuracy of CNs within the TLS in predicting prognosis.

5. The prediction model construction method according to claim 1, characterized in that: In step 4, based on unsupervised clustering and spot features, the immune microenvironment was divided into 13 cell populations, including Macrophage, T cell, B cell, NK cell, Epithelial cell, Neutrophil, Fibroblast, Endothelial cell, Plasma cell, LAMP3+DC, Mast cell, Smooth muscle cell, and pDC. Subcluster dimensionality reduction and subcluster expression differential molecular interaction and localization analysis were performed mainly on T cells and B cells. The TCGA non-small cell lung cancer database and immunotherapy database were used to verify the relationship between the expression of various cell population subtypes and the prognosis after treatment. 6 . A prediction model system for prognosis after neoadjuvant immunotherapy for NSCLC using mLN constructed using the prediction model construction method according to any one of claims 1 to 5 .

7. The prediction model system according to claim 6, characterized in that: The cell cluster types associated with the efficacy of neoadjuvant immunotherapy for mLN obtained using IMC technology include Epithelial, CD4T&Fibroblast, Myofibroblast, Neutrophil, NK&Epithelial, Treg&InfMP, MatureB, NaiveCD8T, TRM&CD8T, ActivatedCD8T, B&FDC, CD4T&B, CD8T&B, and B.

8. The prediction model system according to claim 6, characterized in that: CN8: B enriched, CN9: TLS compartment, and CN11: B&T interaction within TLS significantly enhance the efficacy of neoadjuvant immunotherapy of mLN.

9. The prediction model system according to claim 6, characterized in that: Single-cell transcriptome sequencing technology identified cells associated with the efficacy of neoadjuvant immunotherapy for NSCLC, including ExhaustedCD8 TEM and B cells in mLN.

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