Method for predicting curative effect of triple negative breast cancer immunotherapy by spatially quantizing CD39+CD103+CD8 + T cells

Abstract

The invention discloses a method for predicting the curative effect of triple negative breast cancer immunotherapy by spatially quantizing CD39 + CD103 + CD8 + T cells, and relates to the technical field of biology.The method comprises the following specific steps that S1, an FFPE tumor tissue sample of a TNBC subject before immunotherapy is obtained, and a continuous section with the thickness of 3-5 microns is prepared and attached to a positive charge glass slide; s2, staining the section by using a combination containing CD8 / CD103 / CD39 / Pan-CK antibodies through a TSA sequential staining method, and then re-staining cell nucleuses by using DAPI; according to the method, spatial quantitative analysis is performed on a specific CD39 + CD103 + CD8 + T cell subset, a percentage index of target cells in CD8 + T cells and an average distance index of the target cells and nearest Pan-CK positive cells in a cancer nest region are set, and the target cells are determined according to comparison between the two indexes and a preset threshold value. According to the method, whether a subject is a potential benefit crowd treated by an anti-PD-1 / PD-L1 immune checkpoint inhibitor or not is accurately judged, a standardized technical means is provided for clinically screening the potential benefit crowd for immunotherapy, the accuracy and effectiveness of triple-negative breast cancer immunotherapy are effectively improved, and invalid treatment is reduced.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, specifically to a method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer. Background Technology

[0002] Triple-negative breast cancer (TNBC), a special subtype of breast cancer, is negative for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). It accounts for approximately 10%-20% of all breast cancer cases. Due to the lack of a clear molecular target, patients with TNBC cannot benefit from endocrine therapy or anti-HER2 targeted therapy and have long relied mainly on cytotoxic chemotherapy. In recent years, with the advancement of medical research, immune checkpoint inhibitors targeting programmed death receptor-1 and its ligand PD-L1 have been gradually applied in the treatment of TNBC. However, in actual treatment, there are significant differences in the efficacy of immune checkpoint inhibitors among patients. Some patients do not respond to treatment and even develop secondary drug resistance. Therefore, how to accurately screen the patient population that may benefit from immunotherapy and optimize the treatment plan has become a key issue that urgently needs to be addressed in the current treatment of TNBC.

[0003] Currently, several biomarkers have been used to predict the efficacy of immunotherapy in triple-negative breast cancer, but all have limitations to varying degrees. PD-L1 expression is one of the more commonly used biomarkers; however, its detection process requires different antibody clones, staining platforms, and interpretation standards, which leads to a lack of uniformity between different test results, making accurate comparison and comprehensive evaluation difficult, thus limiting its reliability in widespread clinical application. Tumor mutational burden (TMB) is also a predictive indicator, but in triple-negative breast cancer, the overall tumor mutational burden is at a low to medium level, and its detection relies on high-throughput sequencing platforms and complex bioinformatics analysis processes. At the same time, the threshold definition for high TMB has not been standardized, which affects the stability and accuracy of TMB as a biomarker, making it difficult to meet the needs of precise clinical prediction. Conventional assessment of tumor-infiltrating lymphocytes (TILs) is mainly based on semi-quantitative counting of H&E-stained sections, which not only fails to distinguish different functional lymphocyte subsets but also does not include the spatial distribution information of lymphocytes in tumor tissue. However, the spatial distribution characteristics of lymphocytes may have an important impact on the efficacy of immunotherapy, so the accuracy of this assessment method in predicting the efficacy of immunotherapy is greatly limited. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer. This method involves obtaining paraffin-embedded FFPE tumor tissue samples from triple-negative breast cancer subjects before immunotherapy and preparing sections. Using an antibody combination containing specific binding agents to CD8, CD103, CD39, and pan-cytokeratin Pan-CK, the sections are stained with multiplex fluorescent immunohistochemistry (mIHC) using a tyramine signal amplification TSA sequential staining method. After staining, the sections are counterstained with nuclear dyes to ensure accurate identification of different cell types and cell structures. Then, a multi-channel... The imaging system acquires multispectral digital images under a 20x objective lens to obtain the spatial coordinate information of cells. Then, digital image analysis software is used to perform in-depth analysis of the images, segmenting tissue regions, identifying CD39+CD103+CD8+ T cells, and calculating the percentage of target cells among CD8+ T cells in the cancer nest and the average distance between the target cells and the nearest Pan-CK positive cells. Finally, these two indicators are compared with preset thresholds to accurately determine whether the subject is a potential beneficiary of immunotherapy. This provides a standardized technical means for predicting the efficacy of immunotherapy for triple-negative breast cancer, which helps to optimize treatment plans and improve patients' treatment outcomes and quality of life.

[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: a method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer, the method comprising the following specific steps: S1: Obtain FFPE tumor tissue samples from TNBC subjects before immunotherapy, prepare 3-5μm thick serial sections and attach them to positively charged glass slides; S2: The sections were stained with TSA sequential staining using a combination of CD8 / CD103 / CD39 / Pan-CK antibodies, and the cell nuclei were counterstained with DAPI. S3: A multi-channel imaging system with a 20x objective lens is used to acquire multispectral digital images of the entire slice area, preserving the spatial coordinates of cells; S4: The software segmented the cancer nest and the stroma region, and the individual cell nuclei were segmented based on the DAPI signal and the cell boundary was extended and defined. CD8+ T cells were identified by the fluorescence intensity threshold calibrated by the pre-experiment, and the percentage of target cells in the cancer nest and the average distance between the target cells and the nearest Pan-CK+ cells were calculated. S5: Based on the correlation between the efficacy of immunotherapy for triple-negative breast cancer and the characteristics of target cells, two thresholds are set for efficacy assessment. If both indicators are met, the subject is considered a potential beneficiary of immunotherapy.

[0006] Furthermore, in step S2, the specific procedure for staining using the TSA sequential staining method is as follows: The slides were sequentially immersed in xylene solution to remove paraffin components from the tissue, and then subjected to gradient treatment with anhydrous ethanol, 80% ethanol solution, and 70% ethanol solution to achieve tissue hydration. Immerse the glass slide in Tris-EDTA buffer at pH 9.0, heat to the preset temperature and maintain for the preset time to complete antigen thermal retrieval and expose the antigenic epitopes required for antibody binding; Primary antibody incubation solution for a single biomarker is added to the tissue section, and the section is placed in a humidified chamber and incubated at a preset temperature for a preset time. After removing the incubation solution and washing, HRP-labeled secondary antibody incubation solution is added and incubated at room temperature for a preset time. Then, a specific fluorescent dye solution conjugated with TSA is added and incubated at room temperature to achieve fluorescent labeling. After labeling, the slide is immersed again in Tris-EDTA buffer at pH 9.0 and heated to peel off the antibody complex of the current biomarker to avoid cross-interference with subsequent biomarker staining. The above cycle was repeated to stain CD8, CD103, CD39 and Pan-CK in sequence, with each marker using a TSA fluorescent dye with a different emission wavelength. After all markers were stained, DAPI staining solution was added to the slides and incubated at room temperature for a preset time to label the nuclei of all cells in the tissue.

[0007] Furthermore, in step S3, the acquisition channels include the DAPI corresponding channel, the 480nm channel, the 520nm channel, the 670nm channel, and the 780nm channel, generating a multispectral digital image that retains the spatial coordinate information of the cells.

[0008] Furthermore, in step S4, the specific steps for segmenting the cancer nests and stroma regions using software are as follows: create a tissue segmentation model in the software, manually label more than 5 typical Pan-CK positive regions and more than 5 Pan-CK negative regions in the image, start the model training function, and after the model training is completed, apply the model to automatically segment the entire image to generate labeled layers of Pan-CK positive cancer nest regions and Pan-CK negative stroma regions.

[0009] Furthermore, in step S4, each cell nucleus in the image is identified based on the fluorescence signal features of DAPI. The cell boundary expansion parameter is set according to the average cytoplasmic radius of different cell types. The corresponding range is expanded outward from the cell nucleus to define the complete boundary of a single cell. The negative control area in the image is selected, and the background fluorescence intensity of CD8, CD103, and CD39 in the area is measured. A value higher than the preset multiple of the background intensity is set as the fluorescence intensity threshold of each marker. The fluorescence intensity of the above markers in each segmented cell is automatically measured. Cells with fluorescence intensity reaching the threshold are identified as positive cells of the corresponding marker. Among them, CD8 positive cells are defined as CD8+ T cells, and cells that are positive for CD8, CD103, and CD39 are defined as target cells.

[0010] Furthermore, in step S4, the software counts the number of target cells within the cancer nest area and the total number of all CD8+ T cells within that area, calculates the percentage of target cells among CD8+ T cells, and simultaneously activates the nearest neighbor analysis algorithm to calculate the straight-line distance from each target cell to its nearest Pan-CK positive cell. The average of this distance for all target cells is then used to obtain the proximity index.

[0011] Furthermore, in step S5, two thresholds are set for determining the therapeutic effect. The first threshold is the percentage of target cells in the cancer nest area relative to CD8+ T cells, which is set to 8%. The second threshold is the average distance between the target cells and the nearest Pan-CK positive cells, which is set to 130 μm.

[0012] Furthermore, in step S5, for the triple-negative breast cancer patient samples to be predicted, the percentage index obtained through digital image analysis is compared with a first threshold, and the proximity index is compared with a second threshold; when the percentage index of the sample is ≥ the first threshold and the proximity index is ≤ the second threshold, the subject is determined to be a potential beneficiary of anti-PD-1 / PD-L1 immune checkpoint inhibitor therapy, and when the percentage index of the sample is < the first threshold or the proximity index is > the second threshold, the subject is determined to be a potential non-beneficiary of immunotherapy.

[0013] Compared with existing technologies, this method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer has the following advantages: I. This invention performs spatial quantitative analysis on a specific CD39+CD103+CD8+ T cell subset, sets the percentage of target cells among CD8+ T cells within the cancer nest region and the average distance between the target cells and the nearest Pan-CK positive cells, and accurately determines whether the subject is a potential beneficiary of anti-PD-1 / PD-L1 immune checkpoint inhibitor therapy based on the comparison of these two indicators with preset thresholds. This provides a standardized technical means for clinical screening of potential beneficiaries of immunotherapy, effectively improves the accuracy and effectiveness of immunotherapy for triple-negative breast cancer, reduces ineffective treatment, and lowers the economic burden and physical damage to patients.

[0014] Second, this invention, by employing multiplex fluorescence immunohistochemical staining combined with multispectral digital image acquisition and analysis technology, can not only accurately identify CD39+CD103+CD8+ T cells, but also obtain the spatial coordinate information of these cells in tumor tissue. Through digital image analysis software, it can calculate in detail the percentage of target cells in CD8+ T cells within the cancer nest area and the average distance between the target cells and the nearest Pan-CK positive cells, etc., which helps to explore the mechanism of triple-negative breast cancer immunotherapy in depth and promote the research progress in the field of triple-negative breast cancer immunotherapy.

[0015] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 Flowchart of a method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer; Figure 2 This is a schematic diagram of phenotypic cell fluorescence. Figure 3 This is a schematic diagram of the organization's regional segmentation; Figure 4 This is a schematic diagram for cell phenotype identification. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] This invention provides a method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer. The method involves obtaining paraffin-embedded FFPE tumor tissue samples from triple-negative breast cancer subjects before immunotherapy and preparing sections. A combination of antibodies specifically binding to CD8, CD103, CD39, and pan-cytokeratin Pan-CK is used. Multiplex immunohistochemical (mIHC) staining is performed on the sections using a tyramine signal amplification TSA sequential staining method. After staining, nuclear dyes are used for counterstaining to ensure accurate identification of different cell types and cell structures. Finally, a multi-channel imaging system is used under a 20x objective lens. Multispectral digital images are acquired to obtain the spatial coordinate information of cells. Then, digital image analysis software is used to perform in-depth analysis of the images, segment tissue regions, identify CD39+CD103+CD8+ T cells, and calculate the percentage of target cells among CD8+ T cells in the cancer nest and the average distance between the target cells and the nearest Pan-CK positive cells. Finally, these two indicators are compared with preset thresholds to accurately determine whether the subject is a potential beneficiary of immunotherapy. This provides a standardized technical means for predicting the efficacy of immunotherapy for triple-negative breast cancer, which helps to optimize treatment plans and improve patients' treatment outcomes and quality of life.

[0020] One patient with pathologically confirmed triple-negative breast cancer (TNBC), aged 45 years and clinically stage IIB, was selected. Immunohistochemical (IHC) testing confirmed that estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) were all negative.

[0021] Sample materials: Before receiving immunotherapy, subjects underwent ultrasound-guided biopsy to obtain paraffin-embedded (FFPE) tumor tissue samples. The tissue block volume was approximately 0.3cm × 0.2cm × 0.2cm. Pathological evaluation showed no obvious necrosis or fatty tissue contamination, ensuring that the sample quality met the requirements for subsequent testing.

[0022] Reagents and consumables: Antibody combination: Specific antibodies were used, including CD103 antibody, CD39 antibody, CD8 monoclonal antibody, pan-cytokeratin (Pan-CK) monoclonal antibody, and horseradish peroxidase (HRP)-labeled goat anti-rabbit secondary antibody; Staining reagents: analytical grade xylene, anhydrous ethanol, gradient concentrations of ethanol (80%, 70%), Tris-EDTA buffer at pH 9.0, TSA-coupled fluorescent dye kit (containing TSA-480, TSA-520, TSA-670, TSA-780), 1 mg / mL DAI staining solution, and PBS buffer at pH 7.4. Consumables: Positively charged anti-detachment glass slides, homemade sterile humidification chamber (containing absorbent cotton to maintain humidity).

[0023] Instruments and equipment: Pathological microtome (slice thickness accuracy ±0.1μm, ensuring uniform slice thickness); Automated staining platform (enables standardized staining processes and reduces operational variations); Multi-channel imaging system (supports multispectral whole-slice scanning, preserving cell spatial information); Digital image analysis software (with machine learning segmentation and spatial distance analysis functions).

[0024] like Figure 1 As shown, the FFPE tumor tissue block was fixed on the sample holder of the pathology microtome, the section thickness parameter was adjusted to 4μm, and the microtome was started to cut at a uniform speed to avoid tissue wrinkling, breakage or damage. A total of 3 consecutive tissue sections were obtained (1 for subsequent staining and detection, and 2 as spare samples). Use sterile forceps to pick up the slide and lay it flat in the center of a positively charged, anti-detachment slide. Gently press the slide to remove air bubbles between the slide and the slide, ensuring that the slide is completely attached to the surface of the slide. Place the slides in a well-ventilated environment at room temperature for 2.5 hours until the slides are completely dry. Then, transfer them to a 4°C refrigerator for short-term storage to prevent the slides from getting damp and affecting the subsequent staining results.

[0025] On the automated staining platform, the staining process is operated according to the set tyramine signal amplification (TSA) sequence. The specific steps are as follows: Dewaxing and hydration: The slides were sequentially immersed in three xylene solutions for 5 minutes each time to completely remove the paraffin components from the tissue; then transferred to two anhydrous ethanol solutions for 3 minutes each time for initial dehydration; then sequentially immersed in 80% ethanol solution for 3 minutes and 70% ethanol solution for 3 minutes each time to restore the tissue water content through gradient hydration and avoid tissue shrinkage; finally, the slides were washed three times with PBS buffer for 2 minutes each time to remove residual ethanol; Antigen heat retrieval: Immerse the slide in pH 9.0 Tris-EDTA buffer preheated to 95°C and keep it for 20 minutes. Heat retrieval of exposed antigen epitopes in the tissue enhances antibody binding efficiency. After heat retrieval, turn off the heating device and allow the buffer to cool to room temperature naturally. Then wash the slide three times with PBS buffer for 2 minutes each time. TSA sequential staining cycle: First round (CD103 staining): CD103 antibody incubation solution was added to the surface of the tissue section. The antibody was diluted 1:200 using a dedicated antibody diluent. The slide was placed in a humidified chamber and incubated at 4°C for 12 hours. After incubation, the primary antibody incubation solution was discarded, and the slide was washed three times with PBS buffer for 2 minutes each time. HRP-labeled secondary antibody incubation solution was added at a dilution of 1:500, and the slide was incubated at room temperature for 30 minutes. After washing three times again with PBS buffer, TSA-520 fluorescent dye solution was added at a dilution of 1:1000, and the slide was incubated at room temperature in the dark for 10 minutes to complete the CD103 fluorescent labeling. Antibody stripping: Immerse the slide again in 95°C pH 9.0 Tris-EDTA buffer and heat for 10 minutes to strip the antibody complex from the previous round, avoiding cross-reaction with subsequent marker staining; after stripping, wash the slide three times with PBS buffer for 2 minutes each time. Subsequent staining rounds: Following the above procedure, stain CD39 (using TSA-670 fluorescent dye), CD8 (using TSA-480 fluorescent dye), and Pan-CK (using TSA-780 fluorescent dye) in sequence. The antibody dilution ratios are CD39 (1:150), CD8 (1:250), and Pan-CK (1:300), respectively. The incubation time and temperature should be kept consistent to ensure uniform staining conditions. Nuclear counterstaining: After staining all markers, DAPI staining solution was added to the surface of the slide at a dilution of 1:5000 and incubated at room temperature in the dark for 5 minutes to mark the nuclei of all cells in the tissue, which facilitates subsequent cell segmentation. After incubation, the slides were washed three times with PBS buffer for 2 minutes each time, and finally mounted with anti-fluorescence quenching mounting medium to prevent fluorescence signal quenching.

[0026] After mounting the slide, fix it on the stage of the multi-channel imaging system, install a 20x objective lens, adjust the stage position so that the tissue section is in the center of the field of view, and fine-tune the focus to make the DAPI fluorescence signal clearly visible. Set the acquisition parameters in the imaging system control software: Acquisition channels: Covering 5 key channels, namely DAPI channel (excitation wavelength 405nm, emission wavelength 460nm), 480nm channel (corresponding to CD8-TSA-480 fluorescence signal), 520nm channel (corresponding to CD103-TSA-520 fluorescence signal), 670nm channel (corresponding to CD39-TSA-670 fluorescence signal), and 780nm channel (corresponding to Pan-CK-TSA-780 fluorescence signal); Exposure time: The exposure time of each channel was adjusted by pre-scanning and finally set to 200ms for DAPI channel, 300ms for 480nm channel, 250ms for 520nm channel, 350ms for 670nm channel, and 400ms for 780nm channel to avoid overexposure or underexposure of fluorescence signal and ensure stable signal intensity. Activate the full-slice scanning function, setting the scan range to cover the entire tissue section (approximately 1.5cm × 1.0cm). The imaging system automatically stitches together images from adjacent fields of view, generating a multispectral digital image that preserves cell spatial coordinate information. In the acquired images, the original multicolor merged image visually presents the colocalization relationship of various markers, while the separated single-channel signal image clearly distinguishes cell nuclei (DAPI, blue), cancer cells (Pan-CK, yellow), CD8+ T cells (CD8, purple), CD103-positive cells (CD103, green), and CD39-positive cells (CD39, turquoise). Figure 2 As shown; save the image for later analysis.

[0027] Open the digital image analysis software and perform the analysis according to the set tissue segmentation-cell typing-index calculation process: Organizational region segmentation: Training the machine learning model: Manually label 8 typical Pan-CK positive regions (cancer nests formed by cancer cell aggregation) and 8 Pan-CK negative regions (stromal tissue containing connective tissue, immune cells, etc.) in the image, start the model training function until the model's recognition accuracy for Pan-CK positive / negative regions is ≥95%; Automatic segmentation: The trained model is applied to the full-frame digital image, and the system automatically segments the cancer nest region (marked in red) and the stroma region (marked in blue). The segmentation results can clearly distinguish the cancer cell clusters from the surrounding stroma tissue, such as... Figure 3 As shown; the software also calculated the total area of ​​the cancer nest region to be approximately 2.8 mm². Cell segmentation and phenotypic identification: Cell nucleus segmentation: Based on the difference in gray values ​​of DAPI fluorescence signals, with a gray value threshold of 125, the software automatically identifies and segments all cell nuclei in the image, resulting in a total of 2863 cell nuclei. Cell boundary definition: The complete boundary of a single cell is defined by extending 5 μm outward from the center of each cell nucleus; Fluorescence threshold calibration: Cell-free blank areas in the image were selected as negative controls, and the background fluorescence intensity of CD8, CD103, and CD39 in these areas was measured (26, 31, and 29, respectively). Three times the background fluorescence intensity of each marker was set as the positive threshold (CD8: 78, CD103: 93, CD39: 87). Cell phenotype identification: The software automatically detects the fluorescence intensity of CD8, CD103, and CD39 in each segmented cell. Cells with only CD8 fluorescence intensity ≥78 are defined as CD8+ T cells, and cells that simultaneously meet the criteria of CD8 ≥78, CD103 ≥93, and CD39 ≥87 are defined as CD39+CD103+CD8+ T cells, i.e., target cells. After identification, the software generates a labeled map that clearly shows the distribution of CD8+ T cells (green) and target cells (yellow) in the cancer nest region, as shown in the image. Figure 4 As shown; a total of 412 CD8+ T cells and 57 target cells were identified. Indicator Calculation: Percentage indicator: The number of target cells within the cancer nest area was 57, and the total number of CD8+ T cells within the cancer nest area was 412. According to the formula Target Cell Percentage = (Number of target cells in cancer nest / Total number of CD8+ T cells in cancer nest) × 100%, the target cell percentage was calculated to be 13.8%. Proximity index: Activate the nearest neighbor analysis function of the software to calculate the straight-line distance (range 55-112μm) from each target cell to its nearest Pan-CK positive cell (cancer cell). Take the average distance of all target cells to obtain the proximity index of 79μm.

[0028] This invention constructs a training cohort of 10 TNBC patients who received immune checkpoint inhibitor treatment and were followed up for more than 14 months. Combined with receiver operating characteristic (ROC) curve analysis, the optimal threshold for predicting the efficacy of immunotherapy is determined: target cell percentage ≥8% and proximity index ≤130μm.

[0029] The indicators of the subjects in this embodiment were determined as follows: Target cell percentage (13.8%) ≥ 8%; Proximity index (79μm) ≤ 130μm; In summary, this subject was determined to be a potential beneficiary of pembrolizumab plus paclitaxel combined immunotherapy, and the risk of tumor recurrence within 12 months after receiving this treatment was predicted to be low. This immunotherapy regimen can be given priority in clinical practice.

[0030] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer, characterized in that... The method includes the following specific steps: S1: Obtain FFPE tumor tissue samples from TNBC subjects before immunotherapy, prepare 3-5μm thick serial sections and attach them to positively charged glass slides; S2: The sections were stained with TSA sequential staining using a combination of CD8 / CD103 / CD39 / Pan-CK antibodies, and the cell nuclei were counterstained with DAPI. S3: A multi-channel imaging system with a 20x objective lens is used to acquire multispectral digital images of the entire slice area, preserving the spatial coordinates of cells; S4: The software segmented the cancer nest and the stroma region, and the individual cell nuclei were segmented based on the DAPI signal and the cell boundary was extended and defined. CD8+ T cells were identified by the fluorescence intensity threshold calibrated by the pre-experiment, and the percentage of target cells in the cancer nest and the average distance between the target cells and the nearest Pan-CK+ cells were calculated. S5: Based on the correlation between the efficacy of immunotherapy for triple-negative breast cancer and the characteristics of target cells, two thresholds are set for efficacy assessment. If both indicators are met, the subject is considered a potential beneficiary of immunotherapy.

2. The method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer according to claim 1, characterized in that, In step S2, the specific procedure for staining using the TSA sequential staining method is as follows: The slides were sequentially immersed in xylene solution to remove paraffin components from the tissue, and then subjected to gradient treatment with anhydrous ethanol, 80% ethanol solution, and 70% ethanol solution to achieve tissue hydration. Immerse the glass slide in Tris-EDTA buffer at pH 9.0, heat to the preset temperature and maintain for the preset time to complete antigen thermal retrieval and expose the antigenic epitopes required for antibody binding; Primary antibody incubation solution for a single biomarker is added to the tissue section, and the section is placed in a humidified chamber and incubated at a preset temperature for a preset time. After removing the incubation solution and washing, HRP-labeled secondary antibody incubation solution is added and incubated at room temperature for a preset time. Then, a specific fluorescent dye solution conjugated with TSA is added and incubated at room temperature to achieve fluorescent labeling. After labeling, the slide is immersed again in Tris-EDTA buffer at pH 9.0 and heated to peel off the antibody complex of the current biomarker to avoid cross-interference with subsequent biomarker staining. The above cycle was repeated to stain CD8, CD103, CD39 and Pan-CK in sequence, with each marker using a TSA fluorescent dye with a different emission wavelength. After all markers were stained, DAPI staining solution was added to the slides and incubated at room temperature for a preset time to label the nuclei of all cells in the tissue.

3. The method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer according to claim 1, characterized in that... In step S3, the acquisition channels include the DAPI corresponding channel, the 480nm channel, the 520nm channel, the 670nm channel, and the 780nm channel, generating a multispectral digital image that retains the spatial coordinate information of the cells.

4. The method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer according to claim 1, characterized in that, In step S4, the specific steps for segmenting cancer nests and stroma regions using software are as follows: create a tissue segmentation model in the software, manually label more than 5 typical Pan-CK positive regions and more than 5 Pan-CK negative regions in the image, start the model training function, and after the model training is completed, apply the model to automatically segment the entire image to generate labeled layers of Pan-CK positive cancer nest regions and Pan-CK negative stroma regions.

5. The method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer according to claim 1, characterized in that, In step S4, each cell nucleus in the image is identified based on the fluorescence signal features of DAPI. The cell boundary expansion parameter is set according to the average cytoplasmic radius of different cell types. The corresponding range is expanded outward from the cell nucleus to define the complete boundary of a single cell. The negative control area in the image is selected, and the background fluorescence intensity of CD8, CD103, and CD39 in the area is measured. A value higher than the preset multiple of the background intensity is set as the fluorescence intensity threshold of each marker. The fluorescence intensity of the above markers in each segmented cell is automatically measured. Cells with fluorescence intensity reaching the threshold are identified as positive cells of the corresponding marker. Among them, CD8 positive cells are defined as CD8+ T cells, and cells that are positive for CD8, CD103, and CD39 are defined as target cells.

6. The method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer according to claim 1, characterized in that, In step S4, the software counts the number of target cells in the cancer nest area and the total number of CD8+ T cells in the area, calculates the percentage of target cells among CD8+ T cells, and simultaneously starts the nearest neighbor analysis algorithm to calculate the straight-line distance from each target cell to its nearest Pan-CK positive cell. The average of this distance for all target cells is then used to obtain the proximity index.

7. The method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer according to claim 1, characterized in that, In step S5, two thresholds are set for determining the efficacy. The first threshold is the percentage of target cells in the cancer nest area relative to CD8+ T cells, which is set to 8%. The second threshold is the average distance between the target cells and the nearest Pan-CK positive cells, which is set to 130 μm.

8. The method for spatially quantifying CD39+CD103+CD8+ T cells to predict the efficacy of immunotherapy for triple-negative breast cancer according to claim 1, characterized in that, In step S5, for the triple-negative breast cancer patient samples to be predicted, the percentage index obtained through digital image analysis is compared with a first threshold, and the proximity index is compared with a second threshold. When the percentage index of the sample is ≥ the first threshold and the proximity index is ≤ the second threshold, the subject is determined to be a potential beneficiary of anti-PD-1 / PD-L1 immune checkpoint inhibitor therapy. When the percentage index of the sample is < the first threshold or the proximity index is > the second threshold, the subject is determined to be a potential non-beneficiary of immunotherapy.

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