Method for constructing a marker and scoring system for predicting the efficacy of lung cancer immunotherapy

Abstract

The application provides a kind of marker for predicting the curative effect of lung cancer immunotherapy and the construction method of scoring system, and relates to the field of biotechnology.The inventors have found that the mRNA of CypB, the RNA transcribed by FAM83H-AS1 gene and LncRNA VPS9D1-AS1 are related to the prognosis of lung cancer, and the immune cell infiltration around lung cancer lesions is related to the treatment of lung cancer patients.Accordingly, the application takes the mRNA of CypB, the RNA transcribed by FAM83H-AS1 gene, LncRNA VPS9D1-AS1, panCK, CD3, CD4, CD8 and Foxp3 as markers, and provides a construction method of scoring system for predicting the curative effect of lung cancer immunotherapy.The scoring system constructed by the method can comprehensively evaluate the state of tumor and its microenvironment, accurately predict the progression risk and prognosis of lung cancer, and better guide clinical medication.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a method for constructing a biomarker and scoring system for predicting the efficacy of immunotherapy for lung cancer. Background Technology

[0002] Cancer is a major disease that seriously threatens human life and health. Among cancers, lung cancer has the highest incidence and mortality rate. For the treatment of cancer patients, targeted therapy against specific driver gene mutations and the emerging immunotherapy that targets the regulation of the body's immune system have both shown good results. However, these two treatment options have their indications. More than 60% of patients do not have a clear gene mutation as a drug target, nor do they have high expression of PD-L1. They can only try different chemotherapy drugs and observe the efficacy before deciding whether to continue treatment or change drugs.

[0003] However, currently, there is a lack of effective prognostic biomarkers for cancer patients in clinical practice. For patients in the intermediate stage of treatment, the main assessment is imaging to evaluate changes in tumor size; progression is defined as a change exceeding 15%. However, subtle changes in tumor volume and molecular alterations within the tumor cannot be adequately assessed through imaging.

[0004] Furthermore, a large portion of existing research on prognostic biomarkers for cancer patients focuses on serum biomarkers. However, serum samples are often sourced far from the tumor lesion, and some studies have reported significant differences between cytokine levels in the tumor site and in serum. Therefore, serum may not accurately reflect the in-situ state of the tumor. Moreover, assessment using a single biomarker is far inferior to a comprehensive assessment using multiple biomarkers and multiple dimensions.

[0005] Due to the reasons mentioned above, some patients with rapidly progressing lung cancer may experience delayed treatment because they do not receive timely and effective treatment, thus failing to stop the rapid progression of the tumor. Therefore, there is an urgent need for a molecular marker that can assess the prognosis of patients in the early stages of cancer, screen out patients with poor prognosis and rapid progression, detect minute tumor progression during treatment, and assist in the selection of treatment plans and timely intervention. This is of great significance in saving patients' lives.

[0006] In view of this, the present invention is hereby proposed. Summary of the Invention

[0007] The first objective of this invention is to provide a biomarker for predicting the efficacy of immunotherapy for lung cancer, thereby addressing at least one of the aforementioned problems.

[0008] A second objective of this invention is to provide the application of the above-mentioned biomarkers in the preparation of products for predicting the efficacy of lung cancer immunotherapy.

[0009] A third objective of this invention is to provide a kit for predicting the efficacy of immunotherapy for lung cancer.

[0010] The fourth objective of this invention is to provide a method for constructing a scoring system for predicting the efficacy of immunotherapy for lung cancer.

[0011] In a first aspect, the present invention provides a biomarker for predicting the efficacy of immunotherapy for lung cancer, the biomarker comprising RNA and cell surface marker proteins;

[0012] The RNAs include CypB mRNA, RNA transcribed from the FAM83H-AS1 gene, and LncRNA VPS9D1-AS1.

[0013] The cell surface marker proteins include panCK, CD3, CD4, CD8, and Foxp3.

[0014] Secondly, the present invention provides the application of the above-mentioned biomarkers in the preparation of products for predicting the efficacy of lung cancer immunotherapy.

[0015] Thirdly, the present invention provides a kit for predicting the efficacy of immunotherapy for lung cancer, the kit comprising probes for detecting RNA and LncRNA VPS9D1-AS1 transcribed from CypB and FAM83H-AS1 genes, and biomolecules for detecting panCK, CD3, CD4, CD8 and Foxp3.

[0016] As a further technical solution, the biomacromolecule includes an antibody or an antibody functional fragment.

[0017] Fourthly, this invention provides a method for constructing a scoring system to predict the efficacy of immunotherapy for lung cancer, comprising the following steps:

[0018] a. Using lung cancer tissue from patients as samples, we detected the expression levels of CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1 in the samples, and obtained expression scores for CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1.

[0019] b. Detect panCK, CD3, CD4, CD8 and Foxp3 in the sample from step a. Based on the detection results, distinguish tumor cells, cytotoxic T cells, helper T cells and regulatory T cells, and obtain the counts of cytotoxic T cells, helper T cells and regulatory T cells in the tumor cell region and the tumor stroma region.

[0020] c. Construct the scoring system formula: Immune score = a1 * Expression level of CypB mRNA + a2 * Expression level of FAM83H-AS1 gene transcribed RNA + a3 * Expression level of LncRNA VPS9D1-AS1 + b1 * Number of cytotoxic T cells in the tumor cell region / Number of helper T cells in the tumor cell region + b2 * Number of cytotoxic T cells in the tumor cell region / Number of regulatory T cells in the tumor cell region + b3 * Number of cytotoxic T cells in the tumor cell region / Number of cytotoxic T cells in the tumor stroma region + b4 * Number of helper T cells in the tumor cell region / Number of helper T cells in the tumor stroma region;

[0021] a1, a2, a3, b1, b2, b3, and b4 are coefficients;

[0022] The expression scores of CypB mRNA, FAM83H-AS1 gene transcribed RNA, LncRNA VPS9D1-AS1, the ratio of cytotoxic T cells to helper T cells within the tumor cell region, the ratio of cytotoxic T cells to regulatory T cells within the tumor cell region, the ratio of cytotoxic T cells to cytotoxic T cells within the tumor cell region, and the ratio of helper T cells to helper T cells within the tumor cell region and tumor stroma region were used as independent variables, and the patient's post-immunotherapy outcome data were used as dependent variables. Mathematical modeling methods were used to determine the values ​​of coefficients a1, a2, a3, b1, b2, b3, and b4.

[0023] As a further technical solution, in step a, the detection methods for CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1 in the sample include the RNA scope method.

[0024] As a further technical solution, in step b, the detection methods for panCK, CD3, CD4, CD8 and Foxp3 in the sample include the Opal multicolor fluorescent labeling method.

[0025] As a further technical solution, in step b, panCK positive cells are identified as tumor cells; CD3 and CD8 positive cells are identified as cytotoxic T cells; CD3 positive, CD4 positive, and Foxp3 negative cells are identified as helper T cells; and CD3 positive, CD4 positive, and Foxp3 positive cells are identified as regulatory T cells.

[0026] As a further technical solution, step b also includes a step of staining the cell nuclei of the sample to identify each individual cell.

[0027] As a further technical solution, the mathematical modeling method includes at least one of lasso regression, support vector machine, random forest or decision tree.

[0028] As a further technical solution, a1 = -0.202, a2 ​​= -0.734, a3 = -0.505, b1 = 2.331, b2 = 0.785, b3 = 1.732, b4 = -2.862.

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

[0030] The inventors discovered that CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1 are associated with the prognosis of lung cancer, and the infiltration of immune cells around lung cancer lesions is related to the treatment of lung cancer patients. Based on this, the present invention provides a method for constructing a scoring system to predict the efficacy of lung cancer immunotherapy, using CypB mRNA, FAM83H-AS1 gene transcribed RNA, LncRNA VPS9D1-AS1, panCK, CD3, CD4, CD8, and Foxp3 as markers. The scoring system constructed using this method can comprehensively assess the state of the tumor and its microenvironment, accurately predict the risk of lung cancer progression and prognosis, and better guide clinical medication. Attached Figure Description

[0031] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 The dyeing results of some of the dyes provided in Example 1;

[0033] Figure 2 This is a schematic diagram illustrating the calculation of Hscore based on multicolor fluorescence staining results provided in Example 1.

[0034] Figure 3 The results of staining panCK, DAPI, and CD8 with dyes provided in Example 1;

[0035] Figure 4 Examples of staining results with high and low immune scores provided in Example 1;

[0036] Figure 5 Survival analysis of the immune score provided in Example 1;

[0037] Figure 6 The predicted efficacy of immunotherapy provided in Example 2. Detailed Implementation

[0038] The embodiments and examples of the present invention will be described in detail below. However, those skilled in the art will understand that the following embodiments and examples are for illustrative purposes only and should not be considered as limiting the scope of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Unless otherwise specified, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all commercially available conventional products.

[0039] In a first aspect, the present invention provides a biomarker for predicting the efficacy of immunotherapy for lung cancer, the biomarker comprising RNA and cell surface marker proteins;

[0040] The RNAs include CypB mRNA, RNA transcribed from the FAM83H-AS1 gene, and LncRNA VPS9D1-AS1.

[0041] The cell surface marker proteins include panCK, CD3, CD4, CD8, and Foxp3.

[0042] Among the biomarkers provided by this invention, CypB mRNA, RNA transcribed from the FAM83H-AS1 gene, and LncRNA VPS9D1-AS1 are associated with the prognosis of lung cancer; panCK is a tumor cell surface marker protein used to distinguish between tumor cells and non-tumor cells; CD3, CD4, CD8, and Foxp3 are lymphocyte surface marker proteins, which, based on the status of cell surface marker proteins, can distinguish between cytotoxic T cells (Tc, CD3+CD8+), helper T cells (Th, CD3+CD4+Foxp3-), and regulatory T cells (Treg, CD3+CD4+Foxp3+); furthermore, based on the detection of panCK, CD3, CD4, CD8, and Foxp3, the counts of cytotoxic T cells, helper T cells, and regulatory T cells within the tumor cell region and the tumor stroma region can be obtained.

[0043] The inventors' research found that the infiltration of immune cells around lung cancer lesions is related to the treatment of lung cancer patients. Using CypB mRNA, RNA transcribed from the FAM83H-AS1 gene and LncRNA VPS9D1-AS1 in combination with panCK, CD3, CD4, CD8 and Foxp3 as biomarkers can be used to predict the efficacy of lung cancer immunotherapy.

[0044] Secondly, the present invention provides the application of the above-mentioned biomarkers in the preparation of products for predicting the efficacy of lung cancer immunotherapy.

[0045] Thirdly, the present invention provides a kit for predicting the efficacy of immunotherapy for lung cancer, the kit comprising probes for detecting RNA and LncRNA VPS9D1-AS1 transcribed from CypB and FAM83H-AS1 genes, and biomolecules for detecting panCK, CD3, CD4, CD8 and Foxp3.

[0046] In this invention, the biomacromolecules include antibodies or antibody functional fragments.

[0047] The kit provided by this invention includes probes for detecting the expression levels of RNA transcribed from CypB and FAM83H-AS1 genes and the lncRNA VPS9D1-AS1; and biomacromolecules for detecting panCK, CD3, CD4, CD8, and Foxp3. Based on the detection results, tumor cells, non-tumor cells, cytotoxic T cells, helper T cells, and regulatory T cells are identified. This kit predicts the efficacy of lung cancer immunotherapy by detecting the above RNAs and cell surface marker proteins.

[0048] Fourthly, this invention provides a method for constructing a scoring system to predict the efficacy of immunotherapy for lung cancer, comprising the following steps:

[0049] a. Using lung cancer tissue from patients as samples, we detected the expression levels of CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1 in the samples, and obtained expression scores for CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1.

[0050] b. Detect panCK, CD3, CD4, CD8 and Foxp3 in the sample from step a. Based on the detection results, distinguish tumor cells, cytotoxic T cells, helper T cells and regulatory T cells, and obtain the counts of cytotoxic T cells, helper T cells and regulatory T cells in the tumor cell region and the tumor stroma region.

[0051] c. Construct the scoring system formula: Immune score = a1 * Expression level of CypB mRNA + a2 * Expression level of FAM83H-AS1 gene transcribed RNA + a3 * Expression level of LncRNA VPS9D1-AS1 + b1 * Number of cytotoxic T cells in the tumor cell region / Number of helper T cells in the tumor cell region + b2 * Number of cytotoxic T cells in the tumor cell region / Number of regulatory T cells in the tumor cell region + b3 * Number of cytotoxic T cells in the tumor cell region / Number of cytotoxic T cells in the tumor stroma region + b4 * Number of helper T cells in the tumor cell region / Number of helper T cells in the tumor stroma region;

[0052] a1, a2, a3, b1, b2, b3, and b4 are coefficients;

[0053] The expression scores of CypB mRNA, FAM83H-AS1 gene transcribed RNA, LncRNA VPS9D1-AS1, the ratio of cytotoxic T cells to helper T cells within the tumor cell region, the ratio of cytotoxic T cells to regulatory T cells within the tumor cell region, the ratio of cytotoxic T cells to cytotoxic T cells within the tumor cell region, and the ratio of helper T cells to helper T cells within the tumor cell region and tumor stroma region were used as independent variables, and the patient's post-immunotherapy outcome data were used as dependent variables. Mathematical modeling methods were used to determine the values ​​of coefficients a1, a2, a3, b1, b2, b3, and b4.

[0054] The scoring system constructed using the method provided in this invention can comprehensively assess the state of tumors and their microenvironment, accurately predict the progression risk and prognosis of lung cancer, and better guide clinical medication.

[0055] It should be noted that tumor tissue includes tumor cells and tumor stroma. In this invention, the tumor stroma region refers to the region composed of tumor stroma, and the tumor cell region refers to the region other than the tumor stroma region or the region composed of tumor cells. Lymphocytes can infiltrate into the interior of tumor tissue, including both the tumor cell region and the tumor stroma region.

[0056] In some preferred embodiments, in step a, the detection methods for CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1 in the sample include, but are not limited to, the RNA scope method, or other detection methods well known to those skilled in the art.

[0057] This invention does not impose specific limitations on the methods for scoring the expression levels of CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1. For example, H-scores can be obtained using software scoring methods or scores can be obtained according to the 0-4 scoring method recommended in the RNAscope reagent instructions. The latter scoring method is as follows:

[0058] The staining results were scored based on the number of staining signal points under a bright-field microscope: 0, no staining or <1 staining signal point per 10 cells; 1+, 1-3 signal points per cell; 2+, 4-10 signal points per cell with no or very few signal point clusters; 3+, 10-15 signal points per cell with <10% signal point clusters; 4+, >15 signal points per cell with >10% signal point clusters.

[0059] In some preferred embodiments, in step b, the detection methods for panCK, CD3, CD4, CD8 and Foxp3 in the sample include, but are not limited to, Opal multicolor fluorescent labeling, or other detection methods well known to those skilled in the art.

[0060] In step b of this invention, panCK positive cells are identified as tumor cells; CD3 and CD8 positive cells are identified as cytotoxic T cells; CD3, CD4, and Foxp3 negative cells are identified as helper T cells; and CD3, CD4, and Foxp3 positive cells are identified as regulatory T cells.

[0061] In some preferred embodiments, step b further includes a step of staining the sample for cell nuclei to identify each individual cell.

[0062] This invention does not impose specific limitations on the method of cell staining; for example, DAPI can be used for cell nuclear staining.

[0063] In some preferred embodiments, after processing samples using the RNA scope method, Opal multicolor fluorescent labeling method, and DAPI staining, the staining results are analyzed using PerkinElmer inForm™ software.

[0064] Using PerkinElmer inForm TM The software intelligently identifies tumor tissue and stromal tissue by utilizing fluorescence staining and differences in tissue morphology. It identifies each individual cell based on the nuclear marker DAPI and cell morphology. It uses a combination of lymphocyte surface markers to identify lymphocytes infiltrating within and around the tumor. Based on staining intensity and results, it obtains the H-score of the RNA probe and the count of each lymphocyte.

[0065] In some preferred embodiments, the mathematical modeling method includes, but is not limited to, at least one of lasso regression, support vector machine, random forest, or decision tree.

[0066] In a preferred embodiment, a1 = -0.202, a2 ​​= -0.734, a3 = -0.505, b1 = 2.331, b2 = 0.785, b3 = 1.732, and b4 = -2.862.

[0067] The inventors used tumor tissues from 184 lung cancer patients (90 cases of squamous cell carcinoma and 94 cases of adenocarcinoma) as samples to construct a scoring system formula as follows: Immune Score = -0.202 * Expression level of CypB mRNA - 0.734 * Expression level of FAM83H-AS1 gene transcribed RNA - 0.505 * Expression level of LncRNA VPS9D1-AS1 + 2.331 * Number of cytotoxic T cells in the tumor cell region / Number of helper T cells in the tumor cell region + 0.785 * Number of cytotoxic T cells in the tumor cell region / Number of regulatory T cells in the tumor cell region + 1.732 * Number of cytotoxic T cells in the tumor cell region / Number of cytotoxic T cells in the tumor stroma region - 2.862 * Number of helper T cells in the tumor cell region / Number of helper T cells in the tumor stroma region

[0068] The present invention will be further illustrated below with specific embodiments. However, it should be understood that these embodiments are merely for the purpose of more detailed illustration and should not be construed as limiting the present invention in any way.

[0069] It should be noted that, in the following examples, the design principles of the probes used to detect CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1 are shown in the table below:

[0070]

[0071] Example 1

[0072] 1. Experimental Methods

[0073] 1.1 RNA scope staining method

[0074] (1) Baking the tablets at 60℃ for 1 hour, dewaxing them twice with xylene for 5 minutes each time, and soaking them twice with 100% alcohol for 1 minute each time.

[0075] (2) Let the slices air dry at room temperature for 5 minutes, while preheating Pretreat2 (1×) to 100°C (for at least 30 minutes) on a hot platform.

[0076] (3) Add Pretreat1 dropwise to cover the tissue, react at room temperature for 10 min, and wash twice with distilled water.

[0077] (4) Place the slices in preheated Pretreat2 and react for 15 min, then wash twice with distilled water.

[0078] (5) Place the slice in 100% alcohol for 5 minutes to dry, and circle the tissue with a crayon.

[0079] (6) Pre-adjust the temperature of the hybridization furnace to 40℃, place the slices into the hybridization furnace, add Pretreat3, act for 30 min, wash twice with distilled water, and at the same time preheat the probe in a 40℃ water bath for 10 min, and 1×washbuffer for 15 min.

[0080] (7) Add the probe that recognizes the target RNA directly to the slice and react at 40°C for 2 hours.

[0081] (8) Wash the Washbuffer twice, 2 minutes each time.

[0082] (9) Add AMP1 and apply for 30 minutes, then wash twice with the wash buffer; add AMP2 and apply for 30 minutes, then wash twice with the wash buffer; add AMP3 and apply for 30 minutes, then wash twice with the wash buffer; add AMP4 and apply for 30 minutes, then wash twice with the wash buffer; add AMP5 and apply for 30 minutes, then wash twice with the wash buffer; add AMP6 and apply for 30 minutes, then wash twice with the wash buffer. (The washing process can be repeated by lifting and pulling).

[0083] (10) Add 3-5 drops of DAB colorimetric solution to develop color, then rinse with tap water.

[0084] (11) Counterstain with hematoxylin for 1 min, rinse with tap water, differentiate with hydrochloric acid and alcohol, and return to blue with warm water. (12) 70% alcohol, 95% alcohol, and 100% alcohol for 2 min each, xylene X2 for 2 min each, and mount with neutral resin.

[0085] (12) Then take pictures under a microscope (Nikon ECLIPSE55i) and save the images. (Pretreat 1, 2, 3 and AMP 1, 2, 3, 4, 5, 6 and Washbuffer are all reagents in the RNAscope 2.0 HDReagentkit BROWN kit).

[0086] 1.2 Multicolor Fluorescent Staining Method

[0087] (1) Sectioning: Place the selected tissue wax block on the freezing table and freeze for half an hour. After trimming, cut it into 3-5μm paraffin strips using a microtome.

[0088] (2) Slide preparation: Spread the paraffin tissue strip in warm water at 50°C, and then use a clean glass slide to retrieve the slide. The tissue strip will then adhere to the glass slide.

[0089] (3) Baking the slides: Place the glued slides on a slide baking machine at about 80°C and bake for 2 hours. Cut 5 white slides from each tissue block, one of which will be stained later, and the rest will be reserved for later use.

[0090] (4) Dewaxing and hydration of paraffin sections: The baked sections were immersed in 100% xylene solution for dewaxing three times consecutively, the first time for 30 minutes and the next two times for 5 minutes each. Then the dewaxed tissue sections were successively immersed in a gradient of 100%, 95%, and 75% alcohol for hydration, each gradient was immersed three times, each time for 5 minutes. After hydration, the sections were removed and immersed in deionized water three times, each time for 5 minutes; then immersed in phosphate buffered saline (PBS) three times, each time for 5 minutes.

[0091] (5) Antigen retrieval: Dilute 20ml of EDTA (Ethylene Diamine Tetraacetic Acid) antigen retrieval solution with deionized water at a ratio of 1:50 to 1000ml (20ml EDTA antigen retrieval solution + 980ml deionized water). Pour the prepared antigen retrieval solution into a pressure cooker and boil it. Arrange the slides on a heat-resistant plastic pathological slide rack and immerse them in the above antigen retrieval solution. Continue to boil under high pressure for 5 minutes. After the slides have cooled to room temperature, immerse them in deionized water and PBS buffer once each for 5 minutes.

[0092] (6) Antigen blocking: Immerse the tissue sections in 3% hydrogen peroxide (H2O2) solution to block endogenous peroxidase, and incubate at room temperature for 30 min.

[0093] (7) Immerse the tissue sections in PBS buffer three times for 5 minutes each time. Lay each section flat and add 3% bovine serum albumin (BSA) to the surface of the section, ensuring that the BSA solution completely covers the tissue. Place the sections flat in an incubator with water and incubate at 37°C for 1 hour to block non-specific binding sites.

[0094] (8) Antibody Incubation and Staining: Remove the blocking solution from the slides, add diluted primary antibody A, and incubate at room temperature for 1–2 hours. After rinsing three times with PBST, incubate with HRP-labeled secondary antibody (1 μg mL⁻¹) at room temperature for 30 minutes. Rinse three more times with PBST, and incubate with tyramine-iFluor488 tyramine reaction solution at room temperature for 5–15 minutes. After the reaction, rinse once with PBS, place the slides in citric acid (pH = 6.0) solution, and microwave on a sub-boiling state for 10 minutes to wash away the primary and secondary antibodies. After heating, cool the container in tap water to room temperature, remove the slides, rinse three times with PBST, and repeat the above steps three times. Label with primary antibodies B, C, D, E and tyramine-Cy3, tyramine-AzidoCy5, tyramine-Pacificblue, and Opal-690. Specifically, in this experiment, primary antibodies ABCD are CD3, CD4, CD8, Foxp3, and panCK, respectively.

[0095] (9) After all antibody staining is completed, incubate with DAPI staining solution (5 μg·mL-1) for 3 to 5 min, then add 0.3% Sudan Black B solution and incubate at room temperature for 3 min. After rinsing with TBS, mount with anti-fluorescence quenching mounting medium.

[0096] (10) Add anti-fluorescence quenching mounting solution to the surface of the stained tissue sections and seal them with coverslips.

[0097] (11) All fluorescence channels were photographed using a Leica DMI4000 fluorescence microscope or a Nikon A1 laser confocal microscope to obtain high-quality fluorescence images. The scanning was performed under a laser confocal fluorescence microscope, with blue, green, red and brick-red fluorescence excited by lasers with wavelengths of 405, 488 and 546 nm, respectively. The scanning resolution was 1024@1024 pixels, and the images were acquired by computer and digitally imaged.

[0098] 1.3 Multicolor Fluorescence Scoring Method

[0099] (1) Import the image file using Perkin Elmer's inform 2.4 or later software, use the spectral library to perform spectral splitting, and identify the staining results of each antibody.

[0100] (2) Machine learning is performed by combining tissue morphology recognition with staining results. AI image recognition is used to classify tissue types, such as tumor nest tissue and tumor surrounding stroma tissue.

[0101] (3) Based on cell morphology, combined with staining that specifically identifies cell nucleus, cytoplasm, and cell membrane components, such as DAPI staining of cell nucleus, machine learning methods are used to identify individual cells.

[0102] (4) Based on cell morphology and cell surface markers, AI identifies and labels specific lymphocytes using machine recognition methods. For example, double positive staining for CD3 and CD8 identifies cytotoxic T cells.

[0103] (5) Select target lymphocytes and staining areas, and obtain expression score H-score using the 0-3 4-bin method.

[0104] 2. Experimental Results

[0105] The study used tumor tissue samples from 184 lung cancer patients before immunotherapy, including 90 cases of squamous cell carcinoma and 94 cases of adenocarcinoma.

[0106] The expression of three RNAs (CypB mRNA, FAM83H-AS1, and VPS9D1-AS1) was detected using the RNA scope staining method, and scores were obtained according to the 0-4 scoring method recommended in the RNAscope reagent instructions.

[0107] Multicolor fluorescence staining was used to detect tumor surface marker proteins (panCK) and lymphocyte surface marker proteins (CD3, CD4, CD8, Foxp3). The counts of cytotoxic T cells, helper T cells and regulatory T cells within the tumor cell region and the tumor stroma region were obtained according to the multicolor fluorescence scoring method.

[0108] The expression levels of CypB mRNA, FAM83H-AS1, and VPS9D1-AS1, the ratio of cytotoxic T cells to helper T cells within the tumor cell region, the ratio of cytotoxic T cells to regulatory T cells within the tumor cell region, the ratio of cytotoxic T cells to cytotoxic T cells within the tumor stroma, and the ratio of helper T cells to helper T cells within the tumor cell region were used as independent variables, and the patient's immunotherapy outcome data (i.e., whether the patient's immunotherapy outcome was progression or no progression) was used as the dependent variable. Lasso regression model coefficients were used.

[0109] The constructed scoring system formula is: Immune score = -0.202 * CypB mRNA expression level score - 0.734 * FAM83H-AS1 expression level score - 0.505 * VPS9D1-AS1 expression level score + 2.331 * Number of cytotoxic T cells in the tumor cell region / Number of helper T cells in the tumor cell region + 0.785 * Number of cytotoxic T cells in the tumor cell region / Number of regulatory T cells in the tumor cell region + 1.732 * Number of cytotoxic T cells in the tumor cell region / Number of cytotoxic T cells in the tumor stroma region - 2.862 * Number of helper T cells in the tumor cell region / Number of helper T cells in the tumor stroma region.

[0110] An immune score cutoff of 0.545 was established. An immune score less than the cutoff value indicates a poor predicted treatment outcome, suggesting that PD-L1 inhibitor treatment may not be beneficial. An immune score greater than the cutoff value indicates a good predicted treatment outcome, suggesting that PD-L1 inhibitor treatment may be beneficial.

[0111] The above formula was used to perform immune scoring on 184 lung cancer patients and to conduct survival analysis. The results are as follows: Figure 5 As shown, patients with high immune scores had longer overall survival (OS), longer progression-free survival (PFS), slower tumor progression, and their treatment regimens effectively prevented tumor recurrence, resulting in better patient prognosis. Conversely, patients with low immune scores had shorter overall survival (OS), shorter PFS, and their treatment regimens failed to prevent tumor progression, leading to poorer patient prognosis.

[0112] Figure 1 The first row shows the staining results using DAPI, Opal520, and Opal570+Opal650, respectively. The second row, from left to right, shows the staining results using DAPI+Opal520, DAPI+Opal570, and DAPI+Opal650, respectively. Figure 1 In the middle blue, DAPI is used to label the cell nucleus; in the green, Opal520 is used to label panCK; in the red, Opal-570 is used to label CD4; and in the yellow, Opal-650 is used to label CD8.

[0113] Figure 2This diagram illustrates the calculation of H-score based on multicolor fluorescence staining results. The first row (left) shows the staining results using the multicolor fluorescence staining method; the first row (right) shows the single-color fluorescence image of Opal 570; the second row (left) shows the tumor cell region and tumor stroma region divided by panCK staining and AI, with red representing the tumor cell region and green representing the tumor stroma region; the second row (right) shows individual cells separated by nuclear and cell membrane staining, each cell marked with a green shape; the third row shows the staining calculation method, categorizing fluorescence intensity into four levels: 0-1, 1-2, 2-3, and above 3. The score is calculated by combining the percentage of each intensity level.

[0114] Figure 3 The results of staining panCK, DAPI, and CD8 with dyes are shown (green represents fluorescently labeled panCK, blue represents fluorescently labeled DAPI, and red represents fluorescently labeled CD8). The left image shows a case of high CD8 expression staining, and the right image shows a case of low CD8 expression staining.

[0115] Figure 4 The results of the multicolor fluorescent staining method are shown. The left column shows a case with a high immune score, and the right column shows a case with a low immune score. The first row shows the observation results under 40x magnification, and the second row shows the observation results under 200x magnification.

[0116] Example 2

[0117] Using tumor tissue samples from 19 lung cancer patients before immunotherapy, the PD-L1 and scoring systems from Example 1 of this invention were used to predict the patients' benefit from immunotherapy, and the results were compared with those of patients who actually received immunotherapy. The results are as follows: Figure 6 As shown, the sensitivity and specificity of the scoring system of this invention were found to be 78% and 80%, respectively, while the sensitivity and specificity predicted using PD-L1 greater than 50% as an indicator were 50% and 40%, respectively. The results show that the immune scoring method is superior to PD-L1 immunohistochemistry. Furthermore, it can be seen from... Figure 6 The study observed that there was both coverage and complementarity between the immune score and the PD-L1 detection results.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a scoring system to predict the efficacy of immunotherapy for lung cancer, characterized in that, Includes the following steps: a. Using lung cancer tissue from patients as samples, we detected CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1 in the samples, and obtained expression scores for CypB mRNA, FAM83H-AS1 gene transcribed RNA, and VPS9D1-AS1. b. Detect panCK, CD3, CD4, CD8 and Foxp3 in the sample from step a. Based on the detection results, distinguish tumor cells, cytotoxic T cells, helper T cells and regulatory T cells, and obtain the counts of cytotoxic T cells, helper T cells and regulatory T cells in the tumor cell region and the tumor stroma region. c. Constructing the scoring system formula: Immune score = a1 CypB mRNA expression level score + a2 FAM83H-AS1 gene transcribed RNA expression level score + a3 LncRNA VPS9D1-AS1 expression level score + b1 Number of cytotoxic T cells within the tumor cell region / Number of helper T cells within the tumor cell region + b2 Number of cytotoxic T cells within the tumor cell region / Number of regulatory T cells within the tumor cell region + b3 Number of cytotoxic T cells in the tumor cell region / Number of cytotoxic T cells in the tumor stroma region + b4 Number of helper T cells in the tumor cell region / Number of helper T cells in the tumor stroma region; a1, a2, a3, b1, b2, b3, and b4 are coefficients; The expression scores of CypB mRNA, FAM83H-AS1 gene transcribed RNA, LncRNA VPS9D1-AS1, the ratio of cytotoxic T cells to helper T cells within the tumor cell region, the ratio of cytotoxic T cells to regulatory T cells within the tumor cell region, the ratio of cytotoxic T cells to cytotoxic T cells within the tumor cell region, and the ratio of helper T cells to helper T cells within the tumor cell region and tumor stroma region were used as independent variables, and the patient's post-immunotherapy outcome data were used as dependent variables. Mathematical modeling methods were used to determine the values ​​of coefficients a1, a2, a3, b1, b2, b3, and b4.

2. The construction method according to claim 1, characterized in that, In step a, the detection methods for CypB mRNA, FAM83H-AS1 gene transcribed RNA, and LncRNA VPS9D1-AS1 in the sample include the RNA scope method.

3. The construction method according to claim 1, characterized in that, In step b, the detection methods for panCK, CD3, CD4, CD8 and Foxp3 in the sample include the Opal multicolor fluorescent labeling method.

4. The construction method according to claim 1, characterized in that, Step b also includes staining the sample for cell nuclei to identify each individual cell.

5. The construction method according to claim 1, characterized in that, The mathematical modeling method includes at least one of lasso regression, support vector machine, random forest or decision tree.

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