Application of immune index as a detection target in the preparation of a kit for predicting the prognosis of cancer immunotherapy

Abstract

The present invention discloses the application of an immune index as a detection target in the preparation of a kit for predicting the prognosis of cancer immunotherapy, wherein the immune index = T density + αAPC density . The present invention can accurately predict the prognosis of patients receiving immune checkpoint inhibitor therapy, providing an important reference for the selection of treatment regimens for patients with malignant tumors.

Description

Technical Field

[0001] The present invention belongs to the technical field of cancer treatment prognosis prediction, and specifically relates to the application of an immune index as a detection target in the preparation of a kit for predicting the prognosis of cancer immunotherapy. Background Art

[0002] Malignant melanoma (Malignant melanoma) is a highly invasive skin cancer with a high degree of malignancy and is one of the main causes of death from skin cancer. Its incidence has been steadily increasing in recent years. The treatment of Malignant melanoma depends on the stage of disease development. The main treatment methods are surgical resection of the tumor and postoperative adjuvant therapy to reduce the risk of recurrence. Because the improvement under radiotherapy and chemotherapy is very limited, the current treatment options for metastatic and high-risk malignant melanoma also include targeted and immunotherapy.

[0003] Tumor markers are antigens and other biologically active substances that are generated or reduced by tumor cells during the process of carcinogenesis due to changes in gene expression levels. They can be used for early diagnosis and staging of tumors, monitoring tumor progression, and evaluating the therapeutic effects of drugs. When tumor markers can be detected before clinical symptoms appear or can be used for real-time monitoring of treatment effects, they can have a huge impact on the clinical treatment of tumors.

[0004] At present, in order to meet the needs of clinical diagnosis and treatment of tumors, the development of markers for predicting tumor immunotherapy is urgent. Cancer immunotherapy has fewer side effects and shows better effects, which has attracted people's attention in recent years. In cancer immunotherapy, anti-PD-1 immune checkpoint inhibition is currently the most important immunotherapy. The effects of anti-PD-1 antibodies have been confirmed in renal cancer, head and neck cancer, gastrointestinal cancer, gynecological cancer, malignant lymphoma and breast cancer. Although anti-PD-1 antibodies seem to have achieved significant clinical success, anti-PD-1 antibodies actually have some obvious problems. In almost all anti-PD-1 antibody clinical trials, an "ineffective group" in which the disease worsens within three months can be found from the progression-free survival (PFS) data. At the same time, in the group where the anti-PD-1 antibody is effective for 1 year or more, almost no deterioration of the disease is observed thereafter, revealing a state close to cure. This shows that there are three different subgroups, namely, the "ineffective group", "highly effective group" and "intermediate group" according to the clinical effect, but the biomarkers used for their prediction are unknown. The administration of anti-PD-1 antibodies, which are expected to become standard therapy in almost all cancers and tumors, to the ineffective group accounts for about 40%, which is not only a medical problem but also a problem of medical economics.

[0005] There are some literatures that use immunohistochemistry (IHC) to obtain immune scores (immunoscore) and predict the efficacy of tumor immune checkpoint inhibitors through the immune scores. At the same time, such methods can be applied to different types of cancer, including non-small cell lung cancer, colorectal cancer, breast cancer, etc. However, the methods for calculating immune scores are different for different types of cancer.

[0006] "The immune contexture and immunoscore in cancer prognosis and therapeutic efficacy" (Nature Reviews Cancer, 2020) is a comprehensive evaluation article on biomarkers for predicting immune efficacy. It mentions that immunoscore uses immunohistochemical staining to quantify CD3+ and CD8+ lymphocytes in the tumor center and infiltration edge, and calculates an immune cell infiltration score on a 5-point scale from 0 to 4 in the tumor center and infiltration edge respectively.

[0007] The technical solution disclosed in "Immunoscore encompassing CD3+and CD8+T cell densities in distant metastasis is a robust prognostic marker for advanced colorectal cancer" (Oncotarget, 2016) targets advanced colorectal cancer. It uses immunohistochemical staining to obtain the expression of CD3, CD4, FOXP3, CD68, and CD163 in the tumor center and invasive edge areas, and proposes three immune scoring methods: Immunoscore (IS), IS-metastatic, and IS-macrophage, all of which are significantly correlated with prognosis, among which IS-metastasis can be used as an independent prognostic marker.

[0008] "Assessing PDL-1and PD-1in non-small cell lung cancer: a novel immunoscore approach" (Clin Lung Cancer, 2017), the public technical solution uses immunohistochemical staining to quantify the expression of PD-L1 and PD-1 in the primary lesions and metastatic lymph node tissues of non-small cell lung cancer, and calculates the immune score based on the value. Summary of the invention

[0009] The purpose of the present invention is to provide an application of an immune index as a detection target in the preparation of a kit for predicting the prognosis of cancer immunotherapy.

[0010] Another object of the present invention is to provide a kit for predicting the prognosis of cancer immunotherapy, which is used to detect the immune index in tumor samples.

[0011] The technical solution of the present invention is as follows:

[0012] Application of immune index as a detection target in the preparation of a kit for predicting the prognosis of cancer immunotherapy, wherein the immune index = T density +aAPC density ,in,

[0013] T density =(#CD3 + T / #Cells)×100%,

[0014] APC density =(#HLA-DR + / #Cells)×100%,

[0015] #CD3 + T is the number of all CD3-positive T cells in the cancer area ROI of the tumor sample,

[0016] #HLA-DR + is the number of all HLA-DR positive antigen presenting cells in the cancer area ROI of the tumor sample,

[0017] #Cells is the number of all cells in the cancerous area ROI of the tumor sample.

[0018] α is a constant greater than or equal to 0 and less than or equal to 100.

[0019] In a preferred embodiment of the present invention, the method of cancer immunotherapy is immune checkpoint inhibitor therapy.

[0020] Further preferably, the cancer is malignant melanoma.

[0021] In a preferred embodiment of the present invention, the tumor sample is a paraffin-embedded section sample, a frozen section sample or a floating section sample.

[0022] In a preferred embodiment of the present invention, it also includes a device capable of identifying and counting CD3-positive T cells and HLA-DR-positive antigen-presenting cells in the cancerous region ROI of the tumor sample.

[0023] Another technical solution of the present invention is as follows:

[0024] A kit for predicting the prognosis of cancer immunotherapy, comprising a reagent for detecting an immune index, wherein the immune index = T density +αAPC density ,

[0025] T density =(#CD3 + T / #Cells)×100%,

[0026] APC density =(#HLA-DR + / #Cells)×100%,

[0027] #CD3 + T is the number of all CD3-positive T cells in the cancer area ROI of the tumor sample,

[0028] #HLA-DR + is the number of all HLA-DR positive antigen presenting cells in the cancer area ROI of the tumor sample,

[0029] #Cells is the number of all cells in the cancerous area ROI of the tumor sample.

[0030] α is a constant greater than or equal to 0 and less than or equal to 100.

[0031] In a preferred embodiment of the present invention, the method of cancer immunotherapy is immune checkpoint inhibitor therapy.

[0032] Further preferably, the cancer is malignant melanoma.

[0033] In a preferred embodiment of the present invention, the tumor sample is a paraffin-embedded section sample, a frozen section sample or a floating section sample.

[0034] More preferably, the reagent is a reagent capable of simultaneously labeling CD3 protein and HLA-DR protein.

[0035] The above-mentioned immune checkpoint inhibitor therapies include PD-1 inhibitors, PD-L1 inhibitors, and their combination therapy with other drugs such as chemotherapy or anti-angiogenic drugs.

[0036] The beneficial effect of the present invention is that the present invention can accurately predict the prognosis of patients receiving immune checkpoint inhibitor therapy, and provide an important reference for the selection of treatment plans for patients with malignant tumors. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is embodiment 1 of the present invention. DETAILED DESCRIPTION

[0038] The technical solution of the present invention is further illustrated and described below through specific implementation modes in combination with the accompanying drawings.

[0039] Example 1

[0040] The method of the present invention is applied to the prognosis prediction of malignant melanoma patients receiving immune checkpoint inhibitor therapy, and the patients are followed up after anti-PD-1 inhibitor immunotherapy, and the prognosis of the patients' treatment is evaluated according to the efficacy evaluation criteria for solid tumors (RECIST 1.1). In the cohort of melanoma patients, there were 4 non-responders and 6 responders.

[0041] (1) The patient's tumor sample (tumor resection surgical sample, which includes tumor cells and adjacent tissues) is processed, fixed, and serially sliced ​​(two tissue sample slices are required later). After collecting the sample, fix or freeze the sample quickly and thoroughly. The most common method is paraffin embedding (FFPE), and frozen sections and floating sections can also be selected.

[0042] Paraffin-embedded sections were prepared using the following procedure.

[0043] A. Fix the tissue in 10% formaldehyde (or other fixative) for 12-48 hours. The fixation time depends on the thickness of the tissue, the temperature and the nature of the antigen to be detected.

[0044] B. Paraffin embedding.

[0045] C. Cut sections 3-5 μm thick.

[0046] D. Place the sections on SIH4 / poly-L-lysine-coated slides using a non-adhesive water bath.

[0047] E. Absorb water along the edge of the slice.

[0048] F. Place the slices in a 60°C oven for 30-60 minutes.

[0049] G. After dewaxing, stain the sections.

[0050] It should be noted that: for fat-rich tissues (such as brain and breast), extending the air-drying time (such as overnight at room temperature) may enhance the adhesion of tissue sections; drying temperatures exceeding 60°C may have an adverse effect on the detection of certain antigens or increase background staining; regardless of the storage conditions, extending the storage time of paraffin sections may reduce the immunoreactivity of certain antigens. According to the author's experience, sections of paraffin blocks are not suitable for long-term storage; if the staining of archived paraffin sections is inconsistent, the experiment should be repeated with fresh sections.

[0051] Cryosectioning was performed using the following steps:

[0052] A. Cut 7 μm thick unfixed frozen sections.

[0053] B. The sections were placed on SIH4 / poly-L-lysine-coated slides.

[0054] C. Air-dry the sections for 30 min.

[0055] D. Place in fresh acetone precooled to 4°C and fix for 20 minutes.

[0056] E. Air-dry the slices at room temperature for at least 15 minutes.

[0057] F. Stain the sections.

[0058] It should be noted that: 100% ethanol can be used instead of fresh acetone; the choice of fixative depends on the target antigen to be stained in the tissue; unstained slides can be wrapped in aluminum foil and stored at -20°C. When preparing for staining: open the aluminum foil, remove the slides and return to room temperature, post-fix with the same fixative as before storage for at least 30 seconds, air-dry the sections, rehydrate in buffer for 5 minutes, and then stain; the stability of unstained slides depends on the nature of the antigen, some antigens are very unstable and must be stained immediately after smearing

[0059] (2) Immunohistochemical staining (IHC) was performed on frozen section FFPE samples to label CD3 and HLA-DR proteins. The specific staining process is as follows:

[0060] A. Dewax and hydrate the slices so that the antibodies and other reagents can fully react with the antigens in the tissue. For dewaxing, bake the sample in a 60-degree constant temperature oven for 20 minutes, then immediately soak the slices in xylene for 10 minutes, then replace the xylene and soak for another 10 minutes. For hydration, soak in gradient ethanol (from high to low 100%, 95%, 70% for 5 minutes each).

[0061] B. Use 3% hydrogen peroxide to inactivate endogenous POD (about 10 minutes).

[0062] C. Perform antigen retrieval using conventional high pressure retrieval, microwave retrieval or enzyme digestion methods.

[0063] D. Add normal goat serum, BSA or calf serum blocking solution for serum blocking (20 minutes at room temperature) to prevent nonspecific antibody binding and reduce background and potential false positive results.

[0064] E. Add 50 μl of primary antibody mixture (CD3: CD3 recombinant rabbit / mouse monoclonal antibody reagent, HLA-DR: HLA-DR recombinant rabbit / mouse monoclonal antibody reagent) and let stand at room temperature for 1 hour or at 4°C overnight.

[0065] F. Use enzyme-labeled secondary antibody HRP / DAB to develop the color of the sections. Add 40-50 μl of secondary antibody and let stand at room temperature or 37°C for 1 hour, then monitor the degree of staining under a microscope.

[0066] G. Counterstain with hematoxylin for 2 minutes to form cell outlines for better localization of the target protein.

[0067] H. Use neutral gum to seal the stained sections for better preservation.

[0068] Note: For laboratories with the necessary conditions, this step can use multiplex IHC technology to stain multiple proteins on one slice at the same time, saving sample usage.

[0069] (3) Based on the prognostic signature molecular markers CD3 and HLA-DR, the CD3+T cells and HLA-DR+ antigen presenting cells were counted in the cancer area ROI.

[0070] In traditional pathology diagnosis, pathologists manually interpret IHC images based on their knowledge of pathology diagnosis and clinical experience. This method requires high diagnostic experience from doctors, and the diagnostic results are easily affected by the characteristics of the image itself, with large errors, strong subjectivity, and high time consumption. Therefore, computer technology and image processing algorithms can be introduced to perform quantitative analysis of immunohistochemistry images.

[0071] This embodiment uses a computer image processing algorithm to process the IHC image according to the following steps:

[0072] A. Image preprocessing: The image preprocessing steps include denoising, removing the background area, and obtaining the tissue area. First, remove the noise by eliminating hot pixels. Then divide the image into pixel blocks of fixed size, calculate the mean and variance of the pixel values ​​for each pixel block, and divide the pixel blocks into background area "0" or tissue area "1" according to the statistical values ​​of all pixel blocks on the image and the adaptive threshold algorithm. Reduce the original image in proportion to the pixel block size so that each pixel block is represented by a pixel on the reduced image. Perform dilation and erosion operations on the reduced image, and then enlarge it to the original image size; after removing the background area, a binary image of the same size as the original image is obtained, and its pixel value "1" represents the tissue area, and "0" represents the background area. This embodiment uses the image processing software ImageJ to complete the image preprocessing operation of IHC.

[0073] B. Cancer area segmentation: Perform image segmentation in the tissue area to obtain cancer area and normal tissue area. This embodiment uses a pre-trained cancer area segmentation model based on the deep learning network U-Net to perform cancer area segmentation. After segmentation, a three-value image of the same size as the original image is obtained, and its pixel value is 2 for cancer area, 1 for normal tissue area, and 0 for background area.

[0074] C. Selection of ROI target area in the cancerous area of ​​the tumor sample: This embodiment only counts positive cells in a specific area of ​​the sample rather than the entire cancerous area, thereby improving the ability of the test to predict the results of the immune response. In particular, this embodiment only uses the infiltration edge area of ​​a specific tumor, that is, the tumor area between the boundary line between tumor cells and normal tissues and the distance extending from the boundary line to the center of the tumor by about 1-2 mm as the ROI for cell counting, and counts positive cells in this area.

[0075] D. Cell segmentation: Perform cell segmentation in the cancerous area. This embodiment uses a pre-trained cell segmentation model based on a deep learning network U-Net to perform cell segmentation.

[0076] E. Quantification and positive cell counting: For the IHC image, the two protein markers are respectively calculated by color deconvolution. In this embodiment, color deconvolution is performed by the color deconvolution function of ImageJ (Image->Color->ColorDeconvolution).

[0077] F. According to the result of cell segmentation, for each cell in the cancer area, the average value of all pixel points covered on the staining image of a certain protein marker is used as the expression level of the protein in the cell. In this embodiment, the IHC Profiler plug-in of ImageJ is used to count positive cells.

[0078] (5) Interpret the results obtained in step (4) according to the immune index (IS):

[0079] Immune index = T density +αAPC density ,in,

[0080] T density =(#CD3 + T / #Cells)×100%,

[0081] APC density =(#HLA-DR+ / #Cells)×100%,

[0082] #CD3 + T is the number of all CD3-positive T cells in the ROI of the cancerous area of ​​the tumor sample,

[0083] #HLA-DR + The number of all HLA-DR positive antigen presenting cells in the ROI in the cancerous area of ​​the tumor sample,

[0084] #Cells is the number of all cells in the cancer area ROI of the tumor sample.

[0085] When interpreting, the pathologist should first determine the approximate number of viable tumor cells in the entire section, then calculate the approximate number of various cells that survive in the tumor cell area in the section, and finally calculate the IS value. If the tissue area is too large to fully calculate the number of two positive cells at one time, the tissue can be artificially divided into several areas with equal amounts of viable tumor cells (the area contains roughly the same number of viable tumor cells), and the IS value of each area is calculated separately, and finally the IS value of the entire tissue is determined by calculating the average.

[0086] The IS value is used as the predictive value. The larger the IS value, the better the prognosis (such as Figure 1 As shown). Finally, the IS is binarized using a specified threshold value, thereby providing a method for grouping patients. α can be selected based on experience or other data sets with known efficacy data as a reference. Patients are grouped according to the immune index IS. In this example, this embodiment only adopts the threshold comparison method, and it can be expanded to adopt other mathematical models in the future, such as logistic regression, using the IS value as a feature to predict the patient's response to immunotherapy. In this embodiment, this embodiment selects parameter α=7, and sets the IS threshold for grouping patients to 50 based on the IS value calculated from the discovery set data. That is, when distinguishing the responding group and the non-responding group of the discovery set samples, the sensitivity and specificity can reach 100% (see Figure 1 a). The patients were divided into two groups according to the median IS value: high IS and low IS. The overall survival of the two groups was (see Figure 1 b) and progression-free survival (see Figure 1 c) There is a significant difference.

[0087] The above description is only a preferred embodiment of the present invention, and therefore cannot be used to limit the scope of the present invention. That is, equivalent changes and modifications made according to the patent scope of the present invention and the contents of the specification should still fall within the scope of the present invention.

Claims

1. Use of a reagent for detecting an immune index in the preparation of a kit for predicting the prognosis of cancer immunotherapy, characterized in that: The immune index = T density + αAPC density ,in, T density =(#CD3 + T / #Cells)×100%, APC density =(#HLA-DR + / #Cells)×100%, #CD3 + T is the number of all CD3-positive T cells in the cancer area ROI of the tumor sample, #HLA-DR + is the number of all HLA-DR positive antigen presenting cells in the cancer area ROI of the tumor sample, #Cells is the number of all cells in the cancerous area ROI of the tumor sample. α is a constant greater than or equal to 0 and less than or equal to 100, The above reagents are CD3 antibody and HLA-DR antibody. The method of cancer immunotherapy is anti-PD-1 inhibitor immunotherapy. The cancer is malignant melanoma. The above tumor samples are tissue samples, and the infiltrated edge area of ​​the tumor is used as the cancer area ROI for cell counting.

2. The use according to claim 1, characterized in that: The tumor sample is a paraffin-embedded section sample, a frozen section sample or a floating section sample.

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