A method for typing tumor immune microenvironment based on three-level lymph structure maturity and application

Abstract

The application belongs to the technical field of biological medicine, and specifically discloses a tumor immune microenvironment typing method based on three-level lymphatic structure maturity and application. Based on a multi-center large sample clinical cohort, the application systematically depicts a panoramic space-time atlas of the development of TLS from early aggregation to mature follicle, and creatively constructs a five-typing tumor immune microenvironment evaluation system integrating TLS maturity and its spatial positioning distribution characteristics of tumors. The system can accurately identify tumor subtypes with different immune microenvironment characteristics, and provides a solid theoretical model and technical tool for breaking through the existing diagnosis and treatment limitations and guiding the development of personalized combined treatment strategies.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to a tumor immune microenvironment typing method and its application based on the maturity of three-level lymphoid structures. Background Technology

[0002] Although immunotherapy, represented by Immune Checkpoint Blockade (ICB), has reshaped the clinical treatment standards for advanced malignancies, its efficacy exhibits significant heterogeneity among different patients, especially in "cold tumors" with poor immune cell infiltration or immune rejection phenotypes, where the clinical response rate is low due to the lack of sufficient effector immune cells.

[0003] In recent years, tertiary lymphoid structures (TLSs), as immune functional hubs newly formed from ectopic lymphoid tissue in non-lymphoid tissues, have attracted much attention due to their ability to mimic the complete structure and function of secondary lymphoid organs. They are not only highly efficient platforms for tumor-specific antigen presentation but also key effector sites for inducing adaptive anti-tumor immune responses. Studies have confirmed that TLSs are not in a uniform functional state; their maturity directly determines the anti-tumor activity of the local immune microenvironment. Therefore, in-depth analysis of the mechanisms of development and spatial heterogeneity of mature tertiary lymphoid structures (mTLSs) has become a cutting-edge strategy for overcoming immunotherapy resistance and reshaping the tumor immune microenvironment.

[0004] The evolution of follicular dendritic cells (TLS) is a spatiotemporally dynamic process precisely regulated by local chemokine and cytokine networks, and the integrity of its tissue structure is a core indicator for assessing antitumor immune potential. Pathologically, based on the degree of orderliness of cellular components and tissue architecture, its developmental trajectory is usually divided into three progressive stages: early TLS, which only exhibits loose aggregation of T cells and B cells; primary follicular-like TLS, which has formed a follicular dendritic cell network but lacks germinal centers; and TLS with functional germinal centers and CD21-mediated growth. + FDC Network and CD23 + Mature secondary follicular TLS with well-defined mantle regions. This structural difference in maturity profoundly reflects the functional state of immune cells in the tumor microenvironment (TME). Extensive clinical evidence indicates that only fully developed mTLS exhibiting germinal center responses can effectively support affinity maturation of B cells and plasma cell differentiation, thereby maintaining the durable killing activity of effector T cells. It is an independent and potent biomarker for predicting clinical benefit and survival prognosis in patients undergoing immunotherapy.

[0005] Existing tumor immune microenvironment assessment technologies still have significant limitations. Conventional transcriptome sequencing or single-indicator immunohistochemical staining can often only assess the abundance of TLS or the infiltration level of a single cell subpopulation, making it difficult to analyze the maturity heterogeneity and spatial distribution characteristics of TLS in a spatial dimension. Currently, there is a lack of a standardized quantitative typing system in clinical practice, which cannot integrate the dynamic developmental stages of TLS with their spatial topological relationships in a multidimensional way, making it difficult to accurately identify subtypes with different immune landscapes. Summary of the Invention

[0006] This invention provides a tumor immune microenvironment typing method and its application based on the maturity of three-level lymphoid structures. It can accurately identify tumor subtypes with different immune microenvironment characteristics, providing a solid theoretical model and technical tool for overcoming existing diagnostic and treatment limitations and guiding the development of personalized combination therapy strategies.

[0007] To achieve the above objectives, the first aspect of this application provides a tumor immune microenvironment typing method based on the maturity of three-tiered lymphoid structures, comprising the following steps: (1) The maturity of the three-level lymphoid structures in the tumor tissue sample was graded, and the TLS was divided into early TLS, primary follicular TLS and mature secondary follicular TLS; (2) Determine the spatial distribution of TLS in tumor tissue and distinguish between intratumoral TLS and peritumoral TLS; (3) Based on the maturity grading results and spatial distribution characteristics of TLS, the tumor immune microenvironment is divided into five subtypes: Type A global mature type, Type B peritumoral mature type, Type C immature intermediate type, Type D early aggregation type, and Type E immune desert type.

[0008] As an implementation scheme of this application, the TLS maturity grading standard is as follows: Early TLS: T cells and B cells loosely aggregate; Primary follicular-like TLS: expresses follicular dendritic cell markers; Mature secondary follicular TLS: possesses functional germinal centers and expresses CD21 and CD23 markers.

[0009] As an embodiment of this application, the follicular dendritic cell marker is CD21.

[0010] As an implementation scheme of this application, the criteria for determining the five subtypes are as follows: Type A: Mature secondary follicular TLS exists within the tumor and is also present around the tumor; Type B: Mature secondary follicle-like TLS is present only around the tumor, and no mature secondary follicle-like TLS is present around the tumor. Type C: Primary follicular TLS is present only around the tumor; Type D: Early TLS is present only around the tumor; Type E: No TLS distribution within the tumor tissue.

[0011] As an embodiment of this application, the tumor is bladder cancer.

[0012] The second aspect of this application provides an application of a tumor immune microenvironment typing method in an immunotherapy efficacy prediction product.

[0013] As an embodiment of this application, the immunotherapy is an immune checkpoint blockade therapy.

[0014] As an implementation of this application, the efficacy prediction includes distinguishing between individuals with complete remission, partial remission, stable disease, and disease progression.

[0015] A third aspect of this application provides the application of a typing method in the preparation of a prognostic assessment product for cancer patients.

[0016] The fourth aspect of this application provides an application of a typing method in guiding the development of personalized immunotherapy regimens for tumors.

[0017] The beneficial effects of this invention are as follows: (1) Based on a multi-center, large-sample clinical cohort, this invention systematically depicts a panoramic spatiotemporal map of TLS from early aggregation to mature follicle development, and creatively constructs a five-category tumor immune microenvironment assessment system that integrates TLS maturity and its spatial localization distribution characteristics of tumors. This system can accurately identify tumor subtypes with different immune microenvironment characteristics, providing a solid theoretical model and technical tool for overcoming existing diagnostic and treatment limitations and guiding the development of personalized combination therapy strategies.

[0018] (2) The tumor immune microenvironment typing method described in this application effectively overcomes the shortcomings of traditional methods that only focus on TLS abundance and lack assessment of its functional heterogeneity. It can accurately identify spatial phenotypes that lack intratumoral infiltration due to insufficient TLS maturity or TLS only existing in the peritumoral stroma. This tumor immune microenvironment typing method is significantly superior to traditional indicators such as PD-L1 expression or TMB in predicting immunotherapy response and patient prognosis, and successfully fills the technical gap in patient stratification based on fine features of TLS. Attached Figure Description

[0019] Figure 1 A correlation diagram to clearly define the dynamic developmental trajectory of TLS and establish maturity as a decisive factor in immunotherapy response.

[0020] Figure 2A correlation diagram was constructed to establish a five-part classification standard based on spatiotemporal developmental heterogeneity of TLS and its application in the stratification of immunotherapy efficacy prediction.

[0021] Figure 3 Correlation diagram for verifying the accuracy and reliability of the TLS pentagenization strategy in assessing TME immune activity using multi-omics mapping.

[0022] Figure 4 Correlation diagram to verify the predictive efficacy of the TLS five-classification strategy for ICB treatment response based on a multi-center, large-sample cohort. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In this invention, the technical features described in an open-ended manner include both closed-ended technical solutions composed of the listed features and open-ended technical solutions that include the listed features.

[0025] In this invention, numerical ranges are involved. Unless otherwise specified, the numerical ranges are considered continuous and include the minimum and maximum values ​​of the range, as well as every value between the minimum and maximum values. Furthermore, when the range refers to integers, it includes every integer between the minimum and maximum values ​​of the range. Additionally, when multiple ranges are provided to describe features or characteristics, the ranges may be merged. In other words, unless otherwise specified, all ranges disclosed herein should be understood to include any and all subranges to which they are included.

[0026] Unless otherwise specified, all reagents or instruments used in this invention are commercially available conventional products. Unless otherwise specified, the raw materials used in each comparative example and the parallel experiments of each embodiment are the same commercially available products.

[0027] The present application is further illustrated below with specific embodiments:

[0028] Example 1 Five immunotherapy tumor microenvironment subtyping strategies based on TLS maturity status and their clinical significance 1. Clearly define the dynamic developmental trajectory of TLS and establish TLS maturity as a decisive factor in immunotherapy response. This study constructed a prospective, multicenter clinical cohort of 183 patients with bladder cancer (BCa) who received chemotherapy combined with anti-PD-(L)1 immunotherapy. The study described in this invention has received formal approval from the ethics committees of all participating medical institutions, and the research process strictly adhered to relevant medical ethics guidelines, including the Declaration of Helsinki. Patient samples and data were obtained from five clinical centers in China: Sun Yat-sen Memorial Hospital of Sun Yat-sen University, the Second Hospital of Tianjin Medical University, Xiangya Hospital of Central South University, Shantou Central Hospital, and Fujian Provincial Hospital. Collected samples included prospectively obtained bladder cancer tissue, paired adjacent normal tissues (NATs), and peripheral blood samples. In addition, for independent validation of flow cytometry and fluorescence-activated cell sorting (FACS), an independent cohort of 50 fresh bladder cancer tissue specimens were obtained postoperatively at Sun Yat-sen Memorial Hospital of Sun Yat-sen University. The histopathological diagnosis of all tissues was confirmed by double-blind review by three qualified independent pathologists. All enrolled patients signed written informed consent forms before undergoing downstream single-cell RNA sequencing (scRNA-seq), spatial transcriptome sequencing (ST-seq), immunostaining, and primary cell isolation.

[0029] To quantitatively analyze the abundance and maturity status of TLS in tumor tissues in situ, this study performed multiplex immunofluorescence staining and whole-section scanning on formalin-fixed paraffin-embedded sections from the aforementioned clinical cohorts. Based on the above results, we clearly defined the dynamic developmental trajectory of TLS from its inception to maturity and established its grading criteria, specifically including: early TLS characterized by T-cell and B-cell aggregation (E-TLS); primary follicular-like TLS expressing the follicular dendritic cell marker CD21 (PFL-TLS); and mature secondary follicular-like TLS with GC structure and expressing CD23 (SFL-TLS). Simultaneously, we strictly stratified the above multicenter clinical cohorts clinically according to the RECIST v1.1 criteria based on the patients' clinical immunotherapy response: response group: complete remission (CR) or partial remission (PR); non-response group: stable disease (SD) or progressive disease (PD). We found a significant increase in total TLS abundance in tumor tissues of immunotherapy-responsive patients compared to those who did not respond to immunotherapy. However, total TLS abundance alone could not provide further significant differentiation within the immunotherapy-responsive group (CR vs. PR) or within the non-responsive group (SD vs. PD). Further combining TLS abundance with its maturity status, we found that the proportion of mTLS showed a significant gradient increase as the patient's clinical treatment response level progressed from disease progression, disease stability, partial remission to complete remission. This finding suggests that simply assessing the presence or absence of TLS is insufficient to accurately predict efficacy; the functional maturity status of TLS after formation is the key biological indicator determining whether a patient achieves the best immunotherapy response.

[0030] 2. Constructing a five-level TME classification standard based on TLS spatiotemporal developmental heterogeneity and its application in stratifying the efficacy of immunotherapy. To establish a systematic classification standard integrating the dynamic maturation stages and spatial distribution characteristics of tumor lesions (TLS), based on patient information from the aforementioned multicenter clinical cohort (n=183) and high-resolution images of multicolor immunofluorescence whole sections, we first defined the maturation stages of TLS according to the expression characteristics of the specific biomarkers proposed above: including E-TLS, PFL-TLS, and SFL-TLS. Secondly, by identifying the tumor parenchymal boundaries, we precisely located the spatial location of TLS, strictly distinguishing them into intratumoral (iTLS) and peritumoral stromal (pTLS). We found that only a small percentage of patients had iTLS, and most of these iTLS were mTLS. Simultaneously, iTLS-rich tumors were often accompanied by high abundance and diverse developmental stages of peritumoral TLS (pTLSs) in the stromal region, indicating that TLS maturation and its infiltration into the intratumoral region are typical TME characteristics of this subgroup of patients. In contrast, approximately 90% of patients showed pTLSs only in the stromal region, exhibiting three different developmental stages: SFL, PFL, and E-TLS; the remaining minority of patients showed complete absence of TLS. Figure 2 As shown, based on the above results, a systematic classification standard integrating the dynamic maturity stage of TLS and its spatial distribution characteristics in the "intratumoral-peritumoral" region was proposed and established for the first time. We redefined TLS as five subtypes with clear clinical significance: including the "fully mature" type A (SFL-iTLS), characterized by the simultaneous presence of mature iTLS and pTLS. + Type B "peritumoral mature" (SFL-pTLS) only has mature pTLS in the peritumoral region. + The "immature intermediate" type C (PFL-pTLS) only presents with immature primary follicle-like pTLS in the peritumoral region. + The "early aggregation type" D (E-pTLS) only exists in the early aggregation stage of pTLS. + ); and the "immune desert" E type (iTLS-pTLS) characterized by widespread TLS absence. - ).

[0031] Regarding the prediction of immunotherapy efficacy, efficacy stratification results based on the RECIST v1.1 clinical gold standard showed that the dominant mTLS subtypes (types A and B) were highly concentrated in the CR population; while the immature subtypes (types C and D) mostly corresponded to the PR population, effectively solving the problem that traditional methods could not distinguish patients with high-quality responses to immunotherapy. At the same time, the low immune infiltration subtypes (types D and E) accurately defined more than 80% of SD or PD patients, establishing the accurate early warning function of this subtype for identifying immunoresistant populations in bladder cancer.

[0032] For long-term prognostic assessment, Kaplan-Meier survival curves were used to analyze overall survival (OS) and progression-free survival (PFS), and statistical significance was assessed using the log-rank test. The results confirmed that patients defined as enriched in mTLS (types A and B) showed statistically significant prolonged survival compared to patients in the imTLS or TLS-negative subsets (types C, D, and E) (P < 0.05).

[0033] In summary, the five-classification system constructed in this invention successfully fills the technical gap in patient stratification based on TLS fine features. It can specifically identify the advantageous population with long-term survival benefits from complex clinical heterogeneity, providing a solid theoretical basis and tool for making individualized precision immunotherapy decisions for bladder cancer and other solid tumors.

[0034] 3. Multi-omics mapping validates the accuracy and reliability of the TLS pentagenization strategy in assessing TME immune activity. To verify the accuracy and reliability of the TLS maturity typing strategy in assessing the tumor immune microenvironment (TME), this study constructed a multi-omics joint analysis system combining single-cell transcriptomics (scRNA-seq) and spatial transcriptomics (ST-seq). Specifically, this study obtained fresh tissue samples (n=24) and quality-controlled pathological slides (n=48) from bladder cancer patients who received immunotherapy, and performed scRNA-seq (10x Genomics Chromium platform) and ST-seq (covering both Visium and Stereo-seq platforms) sequencing analyses, respectively. For ST-seq data, spatial TLS regions were precisely located using H&E pathological staining, and the gene set enrichment score (AddModuleScore) and receiver operating characteristic (AUC) curves were used for validation to extract and establish the spatial gene expression characteristics specific to mature / immature TLS. Building upon this foundation, to further explore the distribution patterns of T cell subsets within tumor microenvironments (TLS) at different maturity levels, this study employed the Seurat package to perform unsupervised clustering of scRNA-seq data, extracting differentially expressed gene features of various immune cells (especially targeting T cell subsets). Subsequently, deconvolution algorithms such as SPOTlight, RCTD, and Cell2location were used to precisely project the single-cell features identified by scRNA-seq onto spatial targets (Spots) of ST-seq. Finally, by calculating the cell type-specific weights and relative abundances at each spatial target, high-resolution spatial analysis of the distribution characteristics of T cell subsets in the tumor microenvironment was achieved.

[0035] We integrated and analyzed the spatial transcriptome and single-cell sequencing data of the above bladder cancer samples, such as... Figure 3 As shown, the accuracy and reliability of the TLS maturity-based subtyping strategy in evaluating TME were verified. The results showed that, compared to the immature and TLS-deficient subtypes, the mTLS-rich subgroups A and B had higher levels of CD4+ in the tumor microenvironment. + and CD8 + The overall infiltration level of T cells was significantly increased, and effector T cell clusters with strong anti-tumor functions were highly enriched, mainly including tissue-resident memory T cells (Trm), follicular helper T cells (Tfh), inflammatory T cells (T_infla), and effector T cells (Teff); conversely, the C and D subsets, which were dominated by imTLS, mainly showed the phenotypes of naive T cells and exhausted T cells.

[0036] In summary, these results establish the molecular biological basis for this grading system to assess the immune activity of the tumor microenvironment (TME) at the high-resolution single-cell transcriptome level. The grading criteria described in this study accurately reflect the deep immune function status of the tumor microenvironment, establishing it as an intuitive and reliable biological surrogate indicator for clinical use as an alternative to expensive sequencing technologies to rapidly assess the activation level of effector T cells and the response potential to immunotherapy.

[0037] 4. Validating the predictive efficacy of the TLS pentatonic strategy for ICB treatment response based on a multi-center, large-sample cohort study. This study, based on the aforementioned multicenter, large-sample cohort (n=183), utilized digital pathology technology to conduct quantitative analysis, further validating the clinical predictive value of the TLS five-type strategy. Specifically, HALO digital pathology image analysis software was used to systematically scan and analyze multicolor immunofluorescence slides. Its HighPlex FL module was used for precise single-cell segmentation, and immune cell phenotypic classification was performed based on multiple biomarker expression patterns. Subsequently, the exported single-cell spatial data were used to perform t-distributed random neighborhood embedding (t-SNE) dimensionality reduction analysis using R software. The final results are as follows: Figure 4 As shown, we further validated the clinical predictive value of the TLS five-category strategy. t-SNE analysis revealed that the mTLS subtypes (A / B types) were mainly characterized by T-bet. + CD4 + T cells and GZMB + CD8 + Predominantly T-cell infiltration, these lineages are known to be associated with strong responses to immune checkpoint inhibitors (ICBs), suggesting that this subset is ICB-sensitive. In contrast, the imTLS subtypes (C / D type) or TLS-deficient type (E type) are characteristically enriched for FOXP3. + CD4 + T cells and TOX+ CD8 + The presence of T cells suggests that this subtype exhibits immunosuppression and T cell exhaustion. More importantly, digital quantitative analysis revealed CD4... + The ratio of effector T cells to regulatory T cells, and CD8+ + The functional ratio of effector T cells to exhausted T cells showed a significant decreasing trend from type A to type E. This fully demonstrates that the typing strategy can accurately distinguish between immunodominant and immunosuppressive populations, providing strong pathological evidence for clinical prediction of the efficacy of immune checkpoint inhibitors.

[0038] In summary, the TME typing system based on TLS maturity constructed in this study is supported by a robust multicenter clinical cohort, high-throughput multi-omics atlas, and quantitative pathological evidence. It is an innovative tool that combines intuitiveness and quantitative evidence and can be used to accurately assess the immune status and treatment potential of patients with bladder cancer and other solid tumors.

[0039] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the technical spirit of the present invention. The technical scope of the present invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A tumor immune microenvironment typing method based on the maturity of three-tiered lymphoid structures, characterized in that, Includes the following steps: (1) The maturity of the three-level lymphoid structures in the tumor tissue sample was graded, and the TLS was divided into early TLS, primary follicular TLS and mature secondary follicular TLS; (2) Determine the spatial distribution of TLS in tumor tissue and distinguish between intratumoral TLS and peritumoral TLS; (3) Based on the maturity grading results and spatial distribution characteristics of TLS, the tumor immune microenvironment is divided into five subtypes: Type A global mature type, Type B peritumoral mature type, Type C immature intermediate type, Type D early aggregation type, and Type E immune desert type.

2. The tumor immune microenvironment typing method according to claim 1, characterized in that, The TLS maturity grading criteria are as follows: Early TLS: T cells and B cells loosely aggregate; Primary follicular-like TLS: expresses follicular dendritic cell markers; Mature secondary follicular TLS: possesses functional germinal centers and expresses CD21 and CD23 markers.

3. The tumor immune microenvironment typing method according to claim 1, characterized in that, The marker for follicular dendritic cells is CD21.

4. The tumor immune microenvironment typing method according to claim 1, characterized in that, The criteria for determining the five subtypes are as follows: Type A: Mature secondary follicular TLS exists within the tumor and is also present around the tumor; Type B: Mature secondary follicle-like TLS is present only around the tumor, and no mature secondary follicle-like TLS is present around the tumor. Type C: Primary follicular TLS is present only around the tumor; Type D: Early TLS is present only around the tumor; Type E: No TLS distribution within the tumor tissue.

5. The tumor immune microenvironment typing method according to claim 1, characterized in that, The tumor is bladder cancer.

6. The application of the tumor immune microenvironment typing method according to any one of claims 1 to 5 in immunotherapy efficacy prediction products.

7. The application according to claim 6, characterized in that, The immunotherapy mentioned is immune checkpoint blockade therapy.

8. The application according to claim 5, characterized in that, The efficacy prediction includes distinguishing between individuals with complete remission, partial remission, stable disease, and disease progression.

9. The application of any of the typing methods described in claims 1 to 5 in the preparation of prognostic assessment products for cancer patients.

10. The application of any of the typing methods described in claims 1 to 5 in guiding the development of personalized immunotherapy regimens for tumors.

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