CN115602313A - Biomarker for disease curative effect and survival prognosis prediction and application thereof - Google Patents
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Abstract
A biomarker for predicting the curative effect and survival prognosis of diseases and its application are disclosed, wherein the biomarker is PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average cell number. The invention firstly proposes to use PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average finenessThe cell number is used as a biomarker to predict the treatment efficacy and/or survival prognosis of patients with diseases such as breast cancer.
Description
Technical Field
The invention relates to the field of digital pathological image processing, in particular to a biomarker for predicting disease curative effect and survival prognosis and application thereof.
Background
The breast cancer is a common malignant tumor in clinic, the morbidity and the mortality of the breast cancer are in the front of the malignant tumor, and the life health and the life quality of a patient are seriously threatened. Due to the low rate of early diagnosis, most patients are already late in diagnosis. At present, the 5-year survival rates of patients with locally advanced stage or distant metastasis are 99% and 27%, respectively, and it can be seen that although patients with locally advanced stage can be treated by surgical resection, the life quality is seriously affected, and meanwhile, the survival rate of patients with distant metastasis is low, and the treatment difficulty is high. In recent years, immune Checkpoint Inhibitors (ICIs), in particular, inhibitors of programmed cell death receptor 1 (pd-1) and its ligand (pd-L1), have attracted wide attention because of their universality, significant antitumor activity and good safety, to improve the prognosis of patients with advanced non-small cell breast cancer (breast cancer). However, the efficacy of PD-1/PD-L1 inhibitors is not ideal in all patients and may be associated with serious immune-related adverse events (irAEs) and even life threatening. The existing biomarkers have certain value on prognosis and curative effect prediction of breast cancer patients, but have limitations and disadvantages, and more effective biomarkers need to be developed to optimize the benefits of the patients and guide treatment.
Many newly discovered biomarkers, particularly biomarkers for tumor immunotherapy, are associated with the Tumor Immune Microenvironment (TIME). TIME is the result of a complex dynamic cross-interaction between the tumor and the immune system, and TIME for solid tumors includes, inter alia, the density, location, and composition of immune cells within the tumor. Knowledge of the expression pattern and function of immune and tumor-associated molecules under TIME is crucial for the selection of the patient population most likely to benefit from immunotherapy. Traditional immunohistochemistry staining/immunofluorescence staining (IHC/IF) is the most common detection method in TIME research at present, plays a crucial role in the assessment of pathological types and biomarkers of breast cancer, can assist clinicians in timely and accurately making treatment decisions, but still has many limitations, and a more reliable and efficient immunohistochemical system must be developed.
Conventional IHC/IF detection is performed by staining formalin-fixed and paraffin-embedded (FFPE) samples with enzyme or fluorescently labeled antibodies, and shows the expression and localization distribution of specific target antigens in tissues, which is a histopathological diagnostic technique widely used in TIME research at present. The biggest limitation of traditional IHC/IF detection is that only 1 to 3 targets can be stained on one FFPF section, and for tumor assessment for accurate treatment, detection of multiple protein targets is required, which requires sufficient histological specimens. In most cases, biopsy samples from patients fail to satisfy additional tests beyond tumor histopathological typing, which results in missing opportunities to obtain important diagnostic and prognostic information from patient samples. Furthermore, even IF there are enough samples to perform a series of serial conventional IHC/IF staining of tissue sections, the correlation between proteins in the study of multiple cell populations cannot be accurately assessed. Therefore, although IHC/IF is a practical and cost-effective detection method, this method cannot account for all cases of complex TIME. Another limitation of conventional IHC/IF is high inter-observer variability, with the interpretation of the results being largely qualitative or semi-quantitative by human, and somewhat subjective. For example, ki-67 is a prognostic biomarker for a variety of malignancies. However, in the 2017 san gallon international conference of expertise, experts raised the problem of reproducibility of IHC for Ki-67 detection and its impact on clinical decision making. In order to reduce the influence of subjectivity, at present, there is a consensus internationally, and a laboratory is required to have experienced pathology experts. In addition, studies have shown that scoring Ki-67 using reproducible and quantitative numerical analysis can eliminate inter-observer variation.
A recent new multi-marker immunohistochemistry staining/immunofluorescent staining (mIHC/miif) technique enables to obtain multiple biomarkers on one tissue section, simultaneously to obtain multi-channel information on cell composition and spatial arrangement, for high dimensional analysis of TIME. mIHC/mIF detection realizes the detection of a plurality of biomarkers on FFPE (Formalin-Fixed and partial-Embedded, formalin Fixed paraffin Embedded) tissue slices, can automatically distinguish tumor tissues from non-tumor tissues by matching with quantitative analysis software, can objectively analyze a plurality of biomarkers, cell composition, functional state and cell-cell interaction, and has the advantages of high repeatability, high efficiency and high cost benefit.
Triple Negative Breast Cancer (TNBC) is known for its early onset, poor prognosis and short median Overall Survival (OS) after metastasis. First-line immune checkpoint inhibitors in combination with chemotherapy improve survival in advanced TNBC patients positive for programmed cell death ligand 1 (PD-L1). However, immunotherapy-based regimens have not been found to be effective in the latter-line scenario or in PD-L1 negative tumor patients. According to the National Comprehensive Cancer Network (NCCN) and the european medical oncology institute (ESMO) clinical practice guidelines, single or dual dose chemotherapy and sacituzumab-govitecan (a Trop-2 directed antibody-drug conjugate) are currently recommended treatment regimens as second or second line post-treatment for advanced TNBC patients. However, the objective effective rate (ORR) of single-drug chemotherapy is between 5 and 26.6%, the ORR of double-drug chemotherapy is between 22.2 and 31.6%, and the ORR of the Saxituzumab-Govier kang is about 31.0%. Survival results for patients treated with these drugs are also unsatisfactory, as median progression-free survival (PFS) is between 1.7 and 5.6 months. Thus, the need to develop new antineoplastic agents or therapeutic combinations for these life-threatening, advanced TNBC patients has not been met.
In recent years, breast cancer treatment has entered the era of precision medicine, where effective biomarker detection is a key link for accurate selection of benefitting populations. It has been found that the high dimensional characteristics of TIME before and after ICIs treatment are correlated with treatment response in breast cancer patients. The interaction between the Tertiary Lymphoid Structure (TLS) in TIME and tumor cells is close, the growth and invasion of tumors can be inhibited or prevented, and the prognosis and curative effect prediction value are realized in breast cancer. Since TLS is a population of cells with large heterogeneity, further refinement and quantification of specific indices is required. The mIHC/mIF assay enables simultaneous analysis of multiple biomarkers, objectively assessing TLS location, number and area. Currently, there have been several studies exploring TLS in cancer patients using mhic/mIF assays with different biomarker combinations, finding that high levels of TLS indicators correlate with longer Overall Survival (OS) in breast cancer patients.
Tertiary Lymphoid Structures (TLS) are organized aggregates of immune cells that form in non-lymphoid tissues at a later date, and more mature TLS are generally composed of CD4 positive T cells, CD8 positive T cells, and CD20 positive memory B cells. TLS is not found under physiological conditions, but is produced in the context of chronic inflammation, such as autoimmune diseases, chronic infections and cancer. With few exceptions, the presence of TLS in tumors correlated with better prognosis and clinical outcome after immunotherapy. The mIHC/mIF detection not only can analyze a specific cell type, but also can integrate the relation between immune cells at different parts of a tumor, and TLS in-depth analysis of breast cancer patients through mIHC/mIF detection can obtain more prognosis and prediction data, thereby being beneficial to accurately selecting the breast cancer patients who can benefit from immunotherapy. Particularly, under the condition that histological specimens are rare, mIHC/mIF detection can be selected as a new detection method, and is expected to become a clinically powerful tool.
At present, although the conventional TLS related mIHC/mIF detection technology can obtain indexes for predicting prognosis, the conventional TLS related mIHC/mIF detection technology cannot achieve the functions of simultaneously predicting curative effect and prognosis, and the TLS flow is identified to have the disadvantages of high complexity, long time consumption, strong subjectivity and the like. The traditional TLS identification protocol involves manual individual identification and selection of marker (e.g. CD3 and CD 20) positive brightfield Immunohistochemistry (IHC) areas for each tissue section. The technology has high requirements on pathological experiences of operators, the subjectivity of circle selection personnel is strong, and the consistency and repeatability of the circle selection result of the same tissue slice at different time are poor. Meanwhile, since this technique involves the operator to perform the selection and counting of all TLS structures in one tissue slice, it takes a long time (30 minutes to 1 hour per tissue slice).
Disclosure of Invention
According to a first aspect, in an embodiment, there is provided a biomarker which is PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average cell number.
According to a second aspect, in one embodiment, panCK is provided + TLS in the cell-centered 1000 micron range + CD20 + Use of the mean cell number as a biomarker in the construction of a model for the prediction of efficacy and/or a model for the prediction of prognosis of survival.
According to a third aspect, in one embodiment, a method of operating a semiconductor device is provided with PanCK + TLS in the cell-centered 1000 micron range + CD20 + Use of the average cell number as a biomarker in the preparation or screening of a medicament.
According to a fourth aspect, in an embodiment, there is provided a method of analyzing tertiary lymphoid structure regions and statistics of cells, comprising:
a step of circle selection, which comprises the steps of obtaining an immunohistochemical microscopic panorama of a sample to be detected and circle selecting a target area;
a phenotype area identification step, which comprises identifying the phenotype area of the cells in the image according to the expression marker to obtain a phenotype area according with the pathophysiology characteristics;
a cell nucleus morphological identification step, which comprises identifying a cell nucleus in the image;
a cell phenotype data acquisition step, which comprises performing phenotype identification on cells in the image according to the expression marker, judging the cells meeting the judgment condition as positive cells, and acquiring corresponding cell phenotype data;
and a biomarker quantitative value calculation step, which comprises the step of carrying out biomarker analysis calculation on the fluorescence expression data of the image target area of each positive cell to obtain the biomarker quantitative value of each positive cell.
According to a fifth aspect, in an embodiment, there is provided an apparatus for analyzing tertiary lymphoid structure regions and statistical cells, comprising:
the selection module is used for acquiring an immunohistochemical microscopic panorama of a sample to be detected and selecting a target area;
the phenotype area identification module is used for carrying out phenotype area identification on the cells in the image according to the expression markers to obtain a phenotype area which accords with the pathophysiology characteristics;
the cell nucleus morphological recognition module is used for recognizing the cell nucleus in the image;
the cell phenotype data acquisition module is used for carrying out phenotype identification on the cells in the image according to the expression markers, judging the cells meeting the judgment condition as positive cells and acquiring corresponding cell phenotype data;
and the biomarker quantitative numerical value calculation module is used for carrying out biomarker analysis calculation on the fluorescence expression data of the image target area of each positive cell to obtain the biomarker quantitative numerical value of each positive cell.
According to a sixth aspect, in an embodiment, there is provided an apparatus for analyzing tertiary lymphoid structure regions and statistical cells, comprising:
a memory for storing a program;
a processor configured to implement the method of any of the fourth aspects by executing the program stored in the memory.
According to a seventh aspect, in an embodiment, there is provided a computer readable storage medium having a program stored thereon, the program being executable by a processor to implement the method of any of the fourth aspects.
The biomarker for predicting the disease curative effect and survival prognosis and the application thereof are provided according to the embodiments. The invention firstly proposes to use PanCK + TLS in the cell-centered 1000 micron range + CD20 + The average cell number is used as a biomarker, and the prediction of the treatment curative effect and/or survival prognosis of patients with diseases such as breast cancer and the like is realized.
In one embodiment, panCK is used + TLS in the cell-centered 1000 micron range + CD20 + Average number of cells asThe biomarker can also be used for predicting remission of patients with breast cancer and the like after treatment.
Drawings
FIG. 1 is a schematic diagram of a flow chart of an exemplary analysis of tertiary lymphoid structure of breast cancer;
FIG. 2 shows a panoramic analysis region selection map, a panoramic picture manual selection TLS result and a panoramic picture software identification region result of a sample 21R5317SLZA (A: the analysis region selection map is circled with a circled border of cyan; B: the panoramic TLS result map is manually circled with a circled border of white; C: the software identification panoramic region result, the yellow region of TLS, the gray region of other);
FIG. 3 shows the results of software-identified regions of sample 21R5317SLZA (A: the result of software-identified panoramic region, white region TLS, gray region other, and border of analysis region cyan; B: the enlarged result of software-identified region, yellow region TLS, gray region other; C: the same view field full channel map as B, DAPI blue, CD20 yellow, CD8 cyan, CD4 orange, panCK red; D: DAPI single channel map, blue; E: CD 20) + Single channel plot, yellow; f: panCK + Single channel plot, red; g: CD8 + Single channel plot, cyan; h: CD4 + Single channel plot, orange);
FIG. 4 is a diagram showing the analysis of the spatial distribution of sample 21R5317SLZA (A: panoramas in PanCK) + Cell centered 1000 micron TLS + CD20 + Cell distribution map, panCK + Cells are red in color, panCK + Cell centered 1000 micron TLS + CD20 + The cells are green; b-enlargement of one field of view in Panel A, panCK + Cells are red in color, panCK + Cell centered 1000 micron TLS + CD20 + Cells are green, with connecting lines representing TLS + CD20 + Cell to PanCK + Cells in the 1000 micron range);
FIG. 5 is a TLS artificial circle selection chart of sample 21R5317SLZA (A: the circle selection result of panoramic TLS, the circle selection border of the analysis area of panoramic picture is cyan, the circle selection border of TLS is white; B: the enlarged image of the TLS full channel in the same visual field as that in FIGS. 3B-3H; C: DAPI single-channel picture, blue; D: CD20 single-channel picture, yellow; E: CD8 single-channel picture, cyan; F: CD4 single-channel picture, orange);
FIG. 6 shows the distribution of mIHC/mIF TLS indexes in different treatment effect groups of breast cancer patients;
FIG. 7 is a graph of median predictive efficacy of mIHC/mIF TLS in breast cancer patients;
FIG. 8 is a ROC graph showing median predictive efficacy of each index of mIHC/mIF TLS in breast cancer patients;
FIG. 9 is a graph showing the relationship between median and survival in mIHC/mIF TLS indices of breast cancer patients.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, one skilled in the art would readily recognize that some of the features may be omitted in different instances or may be replaced by other materials, methods. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of clearly describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where a certain sequence must be followed.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning.
In one embodiment, the present invention provides a simple, efficient, objective and highly repeatable TLS identification method, and based thereon provides an index that can predict both efficacy and prognosis in a small sample cohort. The invention relates to the identification and extraction of features with enriched regions of multiple TLS marker positive cells using image recognition technology, and the identification of TLS regions in one tissue section is applied to other tissue sections stained in the same batch of experiments. The invention has the characteristics of high accuracy (multiple immunofluorescence allows 3 or more markers to be detected simultaneously, and the co-localization TLS structure is more accurate than the localization of 1-2 markers), simplicity, high efficiency (about 1 hour/30 tissue slices), strong repeatability (the recognition result can be stored as a file, the consistency of the recognition result aiming at the same slice reaches 100%), strong objectivity (the tissue slices adopt a fixed algorithm and the characteristic extraction of a sampling area, and the recognition result is strong in interpretability), and the like.
In one embodiment, the invention relates to the calculation and statistics of the distance between cells, and can be used to derive the distance between specific cells and tumor cells in the TLS region (co-staining of 3 or more TLS markers in tumor markers) in a small sample array and to predict therapeutic efficacy and prognosis of production. In one embodiment, the present invention provides an innovative biomarker to quantify the number of TLS-critical cells and tumor cells within a certain range, and to guide clinical treatment for patients with higher levels of this biomarker, and to provide less treatment for the first line of the future (patients with advanced disease after treatment with an immunopharmaceutical) or advanced cancer patients.
According to a first aspect, in an embodiment, there is provided a biomarker which is PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average cell number.
With PanCK + TLS in the cell-centered 1000 micron range + CD20 + The average cell number refers to: with panCK + Cell-centered mean cell positive for tertiary lymphoid structures and positive for CD20 in the 1000 micron rangeAnd (4) counting.
In one embodiment, the biomarkers are used for prognosis of survival of all cancer patients, including breast cancer, who have a good inhibitory response to Tertiary Lymphoid Structures (TLS).
In one embodiment, the prognosis comprises a natural prognosis or an intervention prognosis.
In one embodiment, intervention prognosis refers to that a doctor acquires disease condition information of a patient according to clinical symptoms or images, tests and other ways, grasps information of etiology, pathology, disease condition degree and the like, predicts and judges the near and far term curative effect, outcome or progress recovery degree of a disease according to treatment time and method, combines new conditions found in treatment operation, and combines current clinical medical intervention level and clinical experience, and then needs treatment time and procedures. Including recovery, and the occurrence or disappearance and death of other abnormalities such as symptoms, signs and complications left after interventional therapy.
In one embodiment, the biomarkers are used to predict the probability of survival of a patient with a disease after a certain time.
In one embodiment, the certain period of time is greater than or equal to 6 months.
In one embodiment, the biomarkers are used to predict the survival probability of a disease patient after 1 year, 3 years, or 5 years.
In one embodiment, the biomarker is used to assess the therapeutic efficacy of a drug.
In one embodiment, the drug includes, but is not limited to, at least one of an immune checkpoint inhibitor, programmed cell death receptor 1, or a ligand thereof.
In one embodiment, the disease includes, but is not limited to, cancer.
In one embodiment, the cancer comprises a cancer that has a suppressive response to Tertiary Lymphoid Structures (TLS).
In one embodiment, the cancer includes, but is not limited to, breast cancer.
In one embodiment, the biomarkers are used for prediction of remission in all cancer patients, including breast cancer, who have a good inhibitory response to TLS. The biomarker can be used to differentiate remission after treatment of a breast cancer patient, i.e., the biomarker can be used to differentiate breast cancer patients into treatment remission (ORR), treatment non-remission (NOR) groups.
In one embodiment, the biomarkers are used to predict the presence or absence of remission in a breast cancer patient.
According to a second aspect, in one embodiment, panCK is provided + TLS in the cell-centered 1000 micron range + CD20 + The use of the average cell number as a biomarker in the construction of a prognosis prediction model for the survival of a patient with a disease.
In one embodiment, the disease includes, but is not limited to, cancer.
In one embodiment, the cancer comprises a cancer that has a suppressive response to Tertiary Lymphoid Structures (TLS).
In one embodiment, the cancer includes, but is not limited to, breast cancer.
According to a third aspect, in one embodiment, a method is provided for operating a semiconductor device with PanCK + TLS in the cell-centered 1000 micron range + CD20 + Use of the mean cell number as a biomarker in the construction of a model for predicting remission in a patient with a disease.
In one embodiment, the disease includes, but is not limited to, cancer.
In one embodiment, the cancer comprises a cancer that has a tumor suppressor response to a Tertiary Lymphoid Structure (TLS).
In one embodiment, the cancer includes, but is not limited to, breast cancer.
According to a fourth aspect, in an embodiment, panCK is provided + TLS in the cell-centered 1000 micron range + CD20 + Use of the average cell number as a biomarker in the preparation or screening of a medicament. In particular, the average cell number can be used as a biomarker for screening clinical treatment means. For example, immunotherapy single or combination therapy may be preferred for patients with higher levels of biomarkers.
In one embodiment, the biomarker is used to guide medication.
In one embodiment, the biomarkers are used for prognosis of survival of a patient with a disease.
In one embodiment, the biomarkers are used to assess survival of a disease patient.
In one embodiment, the medicament comprises a drug having therapeutic relief for a disease.
In one embodiment, the disease includes, but is not limited to, cancer.
In one embodiment, the cancer comprises a cancer that has a suppressive response to Tertiary Lymphoid Structures (TLS).
In one embodiment, the cancer includes, but is not limited to, breast cancer.
In one embodiment, the drug includes, but is not limited to, at least one of an immune checkpoint inhibitor, programmed cell death receptor 1, or a ligand thereof.
According to a fifth aspect, in an embodiment, provision is made for PanCK + TLS in the cell-centered 1000 micron range + CD20 + The use of the average cell number as a biomarker in the construction of a model for predicting the efficacy of a therapy and/or a model for predicting the prognosis of survival. The biomarkers can be used for predicting the curative effect and predicting the survival prognosis.
In one embodiment, the use includes use in constructing an efficacy prediction model and a survival prognosis prediction model.
In one embodiment, the efficacy prediction model is used to predict the efficacy of a drug in the treatment of a disease patient.
In one embodiment, the prognosis of survival prediction model is used for predicting the prognosis of survival of a drug for a disease patient.
In one embodiment, the use comprises constructing an assessment model for assessing the severity of a disease.
In one embodiment, the disease includes, but is not limited to, cancer.
In one embodiment, the cancer comprises a cancer that has a suppressive response to Tertiary Lymphoid Structures (TLS).
In one embodiment, the cancer includes, but is not limited to, breast cancer.
According to a sixth aspect, in an embodiment, there is provided a biomarker for preparing or screening a drug, the biomarker being PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average cell number.
In one embodiment, the disease treated by the drug includes, but is not limited to, cancer.
In one embodiment, the cancer comprises a cancer that has a suppressive response to Tertiary Lymphoid Structures (TLS).
In one embodiment, the cancer includes, but is not limited to, breast cancer.
According to a seventh aspect, in an embodiment, there is provided a method of analyzing tertiary lymphoid structure regions and cell statistics, comprising:
a step of selection, which comprises the steps of obtaining an immunohistochemical microscopic panorama of a sample to be detected, and selecting a target area and all cell areas of a three-level lymph structure;
a phenotype area identification step, which comprises identifying phenotype areas of the cells in the image according to the expression markers, and obtaining a phenotype area which is consistent with the pathophysiology characteristics, other phenotype areas which are not consistent with the pathophysiology characteristics and a general area;
a cell nucleus morphological identification step, which comprises identifying a cell nucleus in the image;
the cell phenotype data acquisition step comprises the following steps of performing phenotype identification on cells in the image according to the expression markers, judging the cells meeting judgment conditions as positive cells, and acquiring corresponding cell phenotype data;
and a biomarker quantitative value calculation step, which comprises carrying out biomarker analysis calculation on fluorescence expression data of the image target area (conforming to and not conforming to pathophysiological characteristics and the total area) of each positive cell to obtain a biomarker quantitative value of each positive cell.
In one embodiment, in the biomarker quantitative value calculating step, the biomarker quantitative value includes a tertiary lymphoid structure density. The density of the tertiary lymph structure is called TLS density for short, and the TLS density = the number of the tertiary lymph structures in the whole tissue slice after quality control/the area of the whole tissue slice after quality control.
In one embodiment, the immunohistochemical microscopy panoramas are scanned from stained specimens.
In one embodiment, the staining process uses reagents comprising a nuclear staining agent and an antibody.
In one embodiment, the nuclear stain comprises DAPI.
In one embodiment, the antibody comprises a monoclonal antibody.
In one embodiment, the monoclonal antibodies comprise an anti-CD 4 monoclonal antibody, an anti-CD 8 monoclonal antibody, and an anti-PanCK monoclonal antibody.
In one embodiment, the sample includes, but is not limited to, a tissue section.
In one embodiment, the sample is derived from a human or animal, preferably a human.
In one embodiment, the sample is derived from a breast cancer patient.
In one embodiment, the step of circle-selecting includes circle-selecting the target region within the boundary of the region composed of the infiltrative tumor and the stroma in the vicinity thereof.
In one embodiment, in the cell phenotype data acquiring step, the cells meeting the judgment condition refer to the cells with the fluorescence intensity of the target being greater than the predetermined value.
In one embodiment, the method further comprises a step of acquiring cell coordinate position data, which includes calculating the spatial distance and distribution between positive cells of each two specific phenotypes according to the biomarker quantitative value of each positive cell acquired by the biomarker quantitative value calculating step.
In one embodiment, the method further comprises a spatial distance quantitative value calculation step, which includes calculating the spatial distance of the biomarkers in the image target area of each test organism to obtain a spatial distance quantitative value between positive cells of each two specific phenotypes of each test organism.
In one embodimentIn the step of calculating a quantitative value of spatial distance, the quantitative value of spatial distance is PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average cell number.
In one embodiment, the method further comprises a predicting step, which comprises predicting whether the sample is derived from the test organism with therapeutic remission or predicting the probability that the sample is derived from the test organism with therapeutic remission according to the quantitative value of the biomarker;
in an embodiment, the predicting step further includes performing efficacy prediction and/or survival prognosis prediction according to the quantitative value of the spatial distance.
In one embodiment, the tertiary lymphoid structure is derived from a patient with a disease.
In one embodiment, the disease includes, but is not limited to, cancer.
In one embodiment, the cancer comprises a cancer that has a tumor suppressor response to a Tertiary Lymphoid Structure (TLS).
In one embodiment, the cancer includes, but is not limited to, breast cancer.
According to an eighth aspect, in one embodiment, there is provided an apparatus for analyzing tertiary lymphoid structure region and statistics of cells, comprising:
the selection module is used for acquiring an immunohistochemical microscopic panorama of a sample to be detected and selecting a target area;
the phenotype area identification module is used for carrying out phenotype area identification on the cells in the image according to the expression markers to obtain a phenotype area which accords with the pathophysiology characteristics;
the cell nucleus morphological recognition module is used for recognizing cell nuclei in the image;
the cell phenotype data acquisition module is used for carrying out phenotype identification on the cells in the image according to the expression markers, judging the cells meeting the judgment conditions as positive cells and acquiring corresponding cell phenotype data;
and the biomarker quantitative numerical value calculation module is used for carrying out biomarker analysis calculation on the fluorescence expression data of the image target area of each positive cell to obtain the biomarker quantitative numerical value of each positive cell.
In one embodiment, the biomarker quantitation value in the biomarker quantitation calculation module comprises a PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average cell number.
In one embodiment, the immunohistochemical microscopy panoramas are scanned from stained specimens.
In one embodiment, the reagent used in the staining process comprises a nuclear staining reagent and an antibody.
In one embodiment, the nuclear stain comprises DAPI.
In one embodiment, the antibody comprises a monoclonal antibody.
In one embodiment, the monoclonal antibodies comprise an anti-CD 4 monoclonal antibody, an anti-CD 8 monoclonal antibody, and an anti-PanCK monoclonal antibody.
In one embodiment, the sample includes, but is not limited to, a tissue section.
In one embodiment, the sample is derived from a human or animal, preferably a human.
In one embodiment, the sample is derived from a breast cancer patient.
In an embodiment, the method further includes a cell coordinate position data obtaining module, configured to calculate spatial distances and distributions between positive cells of each two specific phenotypes according to the biomarker quantitative value of each positive cell obtained in the biomarker quantitative value calculating step.
In an embodiment, the quantitative spatial distance calculation module is further included, and is configured to calculate spatial distances of the biomarkers in the image target region of each test organism, so as to obtain quantitative spatial distance values between positive cells of each two specific phenotypes of each test organism.
In one embodiment, the quantitative value of spatial distance comprises PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average cell number.
In one embodiment, the method further comprises a predicting module for predicting whether the sample is derived from the test organism in therapeutic remission or predicting the probability that the sample is derived from the test organism in therapeutic remission based on the quantitative value of the biomarker.
In one embodiment, the prediction module is further configured to perform efficacy prediction and/or survival prognosis prediction according to the spatial distance quantitative value.
According to a ninth aspect, in an embodiment, there is provided the use of tertiary lymphoid structure density (TLS density) as a biomarker in the manufacture or screening of a medicament. Comprising the application of the biomarker in the clinical treatment means screening. For example, immunotherapy single or combination therapy may be preferred for patients with higher levels of biomarkers. Also, for patients with advanced cancer or in the latter line (patients with disease still progressing after treatment with an immunotherapeutic), if the biomarker levels are high, a multi-drug combination therapy may be considered to be preferred.
In one embodiment, the medicament comprises a drug having therapeutic relief for a disease.
In one embodiment, the disease includes, but is not limited to, cancer.
In one embodiment, the cancer includes, but is not limited to, breast cancer.
In one embodiment, the drug includes, but is not limited to, at least one of an immune checkpoint inhibitor, programmed cell death receptor 1, or a ligand thereof.
According to a tenth aspect, in an embodiment, there is provided an apparatus for analyzing spatial distribution of tertiary lymphoid structures, comprising:
a memory for storing a program;
a processor configured to implement the method of any of the seventh aspects by executing a program stored by the memory.
According to an eleventh aspect, in an embodiment, there is provided a computer readable storage medium having a program stored thereon, the program being executable by a processor to implement the method of any one of the seventh aspects.
In one embodiment, a method and system for analyzing immunohistochemical or immunofluorescent multiple labeling of tertiary lymphoid structures and their associated markers in microscopic images is provided. The method and the system are characterized in that fluorescent pigments or enzymatic products with different colors are adopted on the same FFPE tissue section to mark the in-situ positions and distribution of different antigen macromolecular substances in tissues or cells, and panoramic tissue and cell phenotype information obtained by an image recognition system (such as HALO v3.3.2541.323, quPath v0.2.3 and the like) and corresponding spatial coordinate information and the like are utilized to identify and analyze the tumor immune microenvironment biomarkers.
In one embodiment, the invention provides a breast cancer tertiary lymph structure spatial distribution analysis method and system based on multiple immunohistochemistry or multiple immunofluorescence technology, and the TLS of a breast cancer immune microenvironment is semi-automatically analyzed by utilizing a panoramic image recognition system (such as HALO v3.3.2541.323, quPath v0.2.3 and the like) to obtain panoramic tissue and cell phenotype information, fluorescence intensity information of corresponding targets and the like through the multiple immunohistochemistry or multiple immunofluorescence technology. The invention mainly solves the technical problems that enough biomarkers are lacked in tumor immunotherapy curative effect evaluation, and how to enable indexes related to tumor immune microenvironment to evaluate the immunotherapy curative effect in a larger scale and a larger range, so that the requirement of tumor immune microenvironment analysis through a multiple immunohistochemical technology in clinical pathological work and scientific research can be well solved, the complicated work of manual calculation and analysis by medical staff and scientific research personnel is omitted, the number and the difficulty of samples required by TLS identification are reduced, and the analysis of various immunohistochemical indexes after immunohistochemistry or immunofluorescence multiple marking is efficiently assisted by the doctors and the scientific research personnel.
Example 1
The embodiment of the invention provides a breast cancer tertiary lymphatic structure analysis method and system based on multiple immunohistochemistry or multiple immunofluorescence technology. As shown in fig. 1, the method comprises the steps of:
1. a breast cancer tissue sample from each subject is obtained, prepared into a tissue section, subjected to multiple immunohistochemical staining treatment (such as a Bond RX automated staining instrument of Leica company), wherein staining markers at least comprise DAPI dyes (DAPI is a non-specific dye and stains all cell nuclei) and CD4, CD8, CD20 and PanCK antibodies, and then an imaging scanning instrument (such as a Vectra Polaris spectral quantitative pathology analysis system of Akoya company) is used for obtaining a corresponding immunohistochemical microscopic panorama.
2. Each immunohistochemical microscopy panorama is subjected to analysis area selection through an image recognition analysis software system (such as HALO, quPath and the like, specifically, the HALO system in the embodiment) of the mhhc. The circle selection target area meets the following conditions: (1) Selecting the area boundary composed of the infiltrative tumor and the matrix nearby the infiltrative tumor; (2) Excluding normal tissues outside the invasive tumor and stroma and near the invasive tumor; (3) Excluding abnormal areas in the tumor region caused by autolysis, carcinoma in situ, DAPI color abnormalities, carbonization, wrinkles, scratches, bubbles, compression artifacts, necrosis, clearing and other artifacts; (4) And adjusting the intensity of the fluorescence signal of the picture, removing the background signal and enhancing the true positive signal.
3. Phenotypic region identification of cells from mhhc pathology images using a classifier (classifier) based on expression markers (marker) was set as follows: (1) building a classfier, and setting an algorithm as Random Forest; (2) adding categories TLS and Other; (3) Selecting different phenotypes and collecting areas enriched with corresponding phenotypes on a picture, TLS selecting areas enriched with more positive DAPI, CD4, CD8 and CD20, other selecting areas enriched with single positive or negative DAPI, CD4, CD8 and CD 20; (4) Selecting a plurality of visual fields containing all phenotypes to browse the classification results in real time, if the classification results are not ideal, adding more acquisition areas to train a classifier until the classification results accord with the pathophysiology characteristics, and finishing the debugging of the classifier; (5) The classfier analysis settings are saved for bulk application to other pictures.
4. In the step of Nuclear morphology identification (Nuclear Detection), the parameters are set as follows (parameters not mentioned are set as default): analysis Settings selects the index Labs HighPlex FL v4.1.3, the number Contrast Threshold parameter is set to 0.497, the minimum number Intensity parameter is set to 0.075, the maximum Image Brightness parameter is set to 1, the number Segmentation availability parameter is set to 0.5, the fill number hierarchy parameter is set to false, the number Size parameter is set to 5 to 549.576, the minimum number round nodes parameter is set to 0, the number of number colours parameter is set to 1, the number colour 1 parameter is set to DAPI, and the number colour 1Weight parameter is set to 1. And identifying cell nucleuses of the mIHC pathological image based on the parameters.
5. Phenotypic identification of cells of the mhhc pathology image according to the expression marker, the parameters were set as follows (parameters not mentioned set as default): (1) setting a Maximum cytoplasms Radius parameter to be 2; (2) The mean Segmentation acquisition parameter is set to 0.9; (3) Cell Size parameter set to 5 to 600; (4) the Number of Membrane Dyes parameter is set to 0; (4) The DAPI Nucleus Positive Threshold Weak parameter is set to 3; (5) CD20 + The cytoplasma Positive Threshold week parameter is set to be 30-55; (6) CD8 + The cytoplasma Positive Threshold week parameter is set to 40-80; (7) CD4 + The parameter of the cytoplasma Positive Threshold week is set to be 25-30; (8) PanCK + The cytoplasma Positive Threshold week parameter is set to 17.65; (9) All markers, as not specifically mentioned, default Positive Threshold Weak, default Positive Threshold Mod, default Positive Threshold Strong, cytoplasma Positive Threshold Weak, cytoplasma Positive Threshold Mod and cytoplasma Positive Threshold Strong parameter settings default to 255; (10) All markers, as not specifically mentioned, default to 0 for the Nucleus% Completeness Threshold and the cytoplasms% Completeness Threshold parameter settings; (11) A Class List parameter in Advanced settings is set to TLS | Other, a Classifier parameter is set to the classfier setting saved in the last step, and a Classifier Output Type is set to Mask. Cells in which the fluorescence intensity of each target is larger than the cutoff value mentioned in (4) to (8) are judged as positive cells, and data of the corresponding cell phenotype are obtained.
6. Performing biomarker analysis calculation on fluorescence expression data of a target area of the mIHC pathological image of each subject to obtain a biomarker quantitative value of each subject, and comprising the following steps (parameters are not mentioned to be set as defaults):
(1) Addition of PanCK-positive cell data to the spacial analysis Module designated PanCK + (ii) a (2) Addition of TLS-positive and CD 20-positive cell data to the spaciai analysis Module, named TLS + CD20 + (ii) a (3) proximity analysis centered on PanCK cells; measure parameter set to TLS + CD20 + With the Within parameter set to 1000 μm and the Proximaty of parameter set to PanCK + The Number of Bands parameter is set to 20; storing the analysis parameters; (4) Selecting all cell identification data analysis results, and performing batch proximity analysis by using the analysis parameters obtained in the previous step; (5) Results of proximity analysis were derived and obtained as PanCK + Cell centered 1000 micron TLS + CD20 + Average cell number.
The samples used in this example were tissue section samples from 24 clinical breast cancer patients examined in this laboratory. Multiple immunohistochemical staining of patient tumor tissue specimens (7 colors in 6: DAPI, CD20, panCK, CD8, CD 4) was done using Bond RX automated stainer and immunohistochemical micrographs were obtained by a full scan through VECTRA POLARIS system. The HALO v3.3.2541.323 system is introduced, and each immunohistochemical microscopy panorama is subjected to analysis area selection. Fig. 2A is a selection of panoramic analysis areas (DAPI blue, CD20 yellow, CD8 cyan, CD4 orange, panCK red) for sample 21R5317SLZA, and table 2 contains the analysis areas obtained in this step.
Fig. 2 shows a panoramic analysis area selection map, a panoramic picture manual selection TLS result, and a panoramic picture software identification area result of a sample 21R5317SLZA (a: the analysis area selection map, the selection border of which is cyan; B: the manual selection panoramic TLS result map, the selection border of which is white; C: the software identification panoramic area result, the yellow area of which is TLS, and the gray area of which is other).
Phenotypic region identification using classifier was performed on cells of the mhhc pathology image based on the expression marker, set as follows: (1) building a classfier, and setting an algorithm as Random Forest; (2) adding classes TLS and Other; (3) Selecting different phenotypes and collecting corresponding tables on picturesTLS selects DAPI, CD4, CD8 and CD20 multiple positive enrichment areas, other selects DAPI, CD4, CD8 and CD20 single positive or negative enrichment areas; (4) Selecting a plurality of visual fields containing all phenotypes to browse the classification results in real time, if the classification results are not ideal, adding more acquisition areas to train a classifier, and completing classifier debugging until the classification results accord with pathophysiological characteristics; (5) Saving classifier analysis settings for batch application to other pictures; (6) Apply classifier to other pictures to derive TLS area and calculate TLS average area (see table 1.). FIG. 2C is a software identification panoramic area map of sample 21R5317SLZA (TLS for color area, other for gray area), FIG. 3 is a software identification area result of sample 21R5317SLZA (A: TLS for white area, other for gray area, cyan for border of analysis area, B: enlarged image of software identification area result, TLS for yellow area, other for gray area, C: same visual field full channel map as B, DAPI blue, CD20 yellow, CD8 cyan, CD4 orange, panCK red, D: DAPI single channel map, blue, E: CD20 SLZA + Single channel plot, yellow; f: panCK + Single channel plot, red; g: CD8 + Single channel plot, cyan; h: CD4 + Single channel plot, orange).
Phenotypical identification of cells from mIHC pathology images based on expression marker, parameters were set as follows (parameters not mentioned set as default): (1) setting a Maximum cytoplasms Radius parameter to be 2; (2) The Membrane Segmentation activation parameter is set to 0.9; (3) Cell Size parameter set to 5 to 600; (4) the Number of Membrane Dyes parameter is set to 0; (4) The DAPI Nucleus Positive Threshold Weak parameter is set to 3; (5) CD20 + The cytoplasma Positive Threshold week parameter is set to be 30-55; (6) CD8 + The cytoplasma Positive Threshold week parameter is set to 40-80; (7) CD4 + The parameter of the cytoplasma Positive Threshold week is set to be 25-30; (8) The PanCK + cytoplasma Positive Threshold Weak parameter is set to 17.65; (9) All markers, as not specifically mentioned, include Nucleus Positive Threshold Weak, nucleus Positive Threshold Mod, nucleus Positive Threshold Strong, cytoplasma Positive Threshold Weak, cytoplasma Positive Threshold,The cytoplasma Positive Threshold Mod and the cytoplasma Positive Threshold Strong parameter settings default to 255; (10) All markers, as not specifically mentioned, default to 0 for the Nucleus% complete Threshold and Cytoplasma% complete Threshold parameter settings; (11) The Class List parameter in the Advanced settings is set to TLS | Other, the Classifier parameter is set to the classsifier setting saved in the previous step, and the Classifier Output Type is set to Mask. The cells with fluorescence intensity of each target greater than the cutoff value mentioned in (4) to (8) were judged as positive cells, and data of the corresponding cell phenotype were obtained, table 1 is a specific setting parameter, and table 2 contains the number of TLS cells obtained in this step.
TABLE 1 pathological image cell phenotype identification analysis parameters
Performing biomarker analysis calculation on fluorescence expression data of a target region of the mIHC pathological image of each subject to obtain a biomarker quantitative value of each subject, and comprising the following steps (parameters are not mentioned to be set as defaults): (1) Addition of PanCK-positive cell data to the spacial analysis Module designated PanCK + (ii) a (2) Add TLS-positive and CD 20-positive cell data to the spacial analysis Module, named TLS + CD20 + (ii) a (3) proximity analysis centered on PanCK cells; measure parameter set to TLS + CD20 + The Within parameter is set to 1000 μm, and the maximum of parameter is set to PanCK + The Number of Bands parameter is set to 20; storing the analysis parameters; (4) Selecting all cell identification data analysis results, and performing batch proximity analysis by using the analysis parameters obtained in the previous step; (5) Results of proximity analysis were derived and obtained as PanCK + Cell centered 1000 micron TLS + CD20 + Average cell number. FIG. 4 shows a sample 21R5317SLZA space distribution analysis chart (A: panorama in PanCK) + Cell centered 1000 micron TLS + CD20 + Cell profiling, panCK + Cells are red in color, panCK + Cell centered 1000 micron TLS + CD20 + The cells are green; b an enlarged view of a field of view in FIG. A, panCK + Cells are red in color, panCK + Cell centered 1000 micron TLS + CD20 + Cells are green, and cells with connecting lines represent TLS + CD20 + Cell to PanCK + Cells in the 1000 micron range). Table 3 contains the PanCK obtained in this step + Cell centered 1000 micron TLS + CD20 + Average cell number.
The TLS of each immunohistochemical micrograph was manually circled by the HALO system. The circled target area meets the following conditions: (1) Regions significantly enriched in DAPI, CD4, CD8 and CD20 positive cells were selected as much as possible for each specimen, with the minimum area of each target region selected at 0.0001mm 2 (0.01mm) or more. FIG. 5 shows a TLS artificial circle selection chart of sample 21R5317SLZA (A: the circle selection result of the panoramic TLS, the circle selection border of the analysis area of the panoramic image is cyan, the circle selection border of the TLS is white; B: the enlarged view of the TLS full channel in the same visual field as that in FIGS. 3B-3H; C: DAPI single channel chart, blue; D: CD20 single channel chart, yellow; E: CD8 single channel chart, cyan; F: CD4 single channel chart, orange).
The results are shown in the following table, where table 2 shows the results of the manual analysis and software analysis of tissue samples from breast cancer patients, and table 3 shows the manual TLS density, software TLS density and PanCK for tissue samples from breast cancer patients + Cell centered 1000 micron TLS + CD20 + Average cell number.
TABLE 2 Manual analysis of tissue samples from breast cancer patients and results of software analysis
In table 2, the number of TLS is the manual analysis result, and the analysis area and the number of TLS cells are the software analysis result.
TABLE 3 breast cancer patient tissue samples Artificial TLS Density, software TLS Density and in PanCK + Cell centered 1000 micron TLS + CD20 + Average cell number
Clinical information of a tested breast cancer patient is collected, and clinical data are collected aiming at an advanced triple negative breast cancer patient, in the embodiment, the patient adopts triple drug combination therapy (using Camrelizumab, apatinib and Eribulin), and single drug therapy or chemotherapy for the advanced triple negative breast cancer has poor effect generally, and the treatment means is very limited, so that the triple drug combination therapy is adopted. It is demonstrated that the markers of the present invention can predict the therapeutic effect and prognosis even under such rare treatment conditions and under such severe clinical conditions. As shown in table 4 (ORR is treatment remission group, NOR is treatment remission group, PFS is progression-free survival in months, 0 in Stat is progression-free in follow-up period, and 1 is progression in follow-up period), patients were grouped according to treatment efficacy, and TLS-related indices of each group were counted. Both the artificial TLS density (TLS intensity) and the software identified TLS density (TLS intensity auto) were found to be significantly higher in post-treatment remission patients, as shown in figure 6 for the distribution of mhhc/mIF TLS indices in different efficacy groups in breast cancer patients. The results demonstrate that the software identification technique for statistics and analysis of tertiary lymphoid structures using mIHC/mIF technique in this example is accurate and that TLS density can be used as a biomarker for breast cancer patients after treatmentAre distinguished. Meanwhile, FIG. 6 also shows the spatial distribution index proposed in this embodiment as PanCK + Cell centered 1000 micron TLS + CD20 + Average cell number (Average number of TLS) + CD20 + within 1000μm of PanCK + ) Can also be used as a biomarker to distinguish remission after treatment of breast cancer patients.
TABLE 4 clinical information of breast cancer patients
Furthermore, the prediction analysis of the therapeutic effect correlation was performed on these indices, and the therapeutic effect was predicted using the median of each index, with High (High) being assigned to the median or higher and Low (Low) being assigned to the median or lower, and the ORR ratio was predicted and counted (see the median High/Low group information of each index of TLS correlation for breast cancer patients shown in table 5, and the results calculated based on tables 2 and 3 in table 5) to find that the artificial TLS density, the software-identified TLS density, and the PanCK are used as the artificial TLS density + Cell centered 1000 micron TLS + CD20 + The average cell number was significantly different. Using the AUC measure of the ROC curve, the median predicted efficacy performance was found to be the highest performance (0.84) for TLS density, artificial TLS density and as PanCK + Cell centered 1000 micron TLS + CD20 + The average cell number was the same (0.76).
TABLE 5 median high-low grouping information of TLS-related indexes of breast cancer patients
Finally, median analysis of each index was used to determine the relationship between survival time, FIG. 7 shows the mIHC/mIF TLS of breast cancer patientsMedian predictive efficacy profile of the index. FIG. 8 is a ROC graph showing median predictive efficacy of each index of mIHC/mIF TLS in breast cancer patients. FIG. 9 is a graph showing the relationship between median and survival in mIHC/mIF TLS indices of breast cancer patients. It can be found that only PanCK + Cell centered 1000 micron TLS + CD20 + The average cell number (rightmost survival chart, abscissa is month) can distinguish survival remarkably, the median of PFS is 10.251 months in a High group, the median of PFS is 4.862 months in a Low group, and the difference is about 5.389 months, which indicates that the difference of progression-free survival periods in two groups of patients predicted by using the space distance index is at least 5 months, and only the median of the index can distinguish the patients to have remarkable survival difference, and the artificial TLS density and the software are used for recognizing the TLS density to distinguish survival without remarkable difference. These analysis results prove that the breast cancer tertiary lymph structure spatial distribution analysis technology based on multiple immunohistochemistry or multiple immunofluorescence provided by the embodiment can be used as a biomarker to predict the treatment efficacy and survival prognosis of breast cancer patients, and further prove that the biomarker provided by the invention is superior to the traditional TLS density index in the aspect of prognosis prediction and is equal to the traditional TLS density index in the aspect of efficacy prediction. Meanwhile, the TLS density based on software identification provided by the invention is superior to the artificial TLS density index obtained by the traditional method in the aspect of curative effect prediction. In one embodiment, the invention can realize semi-automatic fluorescent target biomarker analysis on multiple immunohistochemistry or multiple immunofluorescence microscopic panoramagrams, identify tumor cells and three-level lymph structure space distribution information, classify tumor immune microenvironment, well meet the requirement of reliable quantitative analysis on immunohistochemistry multiple markers in clinical pathological work and scientific research, reduce the complex work of manual calculation and analysis of pathologists and scientific researchers, and efficiently assist the doctors and the scientific researchers in completing analysis of various tumor microenvironment indexes after immunohistochemistry or immunofluorescence multiple marking.
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (10)
1. A biomarker, wherein the biomarker is PanCK + TLS in the cell-centered 1000 micron range + CD20 + Average cell number.
2. The biomarker of claim 1, wherein the biomarker is used for prognosis of survival of a patient with a disease;
preferably, the prognosis comprises a natural prognosis or an intervention prognosis;
preferably, the biomarkers are used to predict the survival probability of a disease patient after a certain time;
preferably, the certain time is more than or equal to 6 months;
preferably, the biomarkers are used to predict the survival probability of a disease patient after 1 year, 3 years or 5 years;
preferably, the biomarker is used to assess the therapeutic efficacy of a drug;
preferably, the biomarkers are used for disease patient remission prediction;
preferably, the biomarkers are used to predict remission or lack of remission in a patient with the disease;
preferably, the disease comprises cancer;
preferably, the cancer comprises a cancer that has a suppressive response to tertiary lymphoid structures;
preferably, the cancer comprises breast cancer.
3. With PanCK + TLS in the cell-centered 1000 micron range + CD20 + The use of the average cell number as a biomarker in the construction of a model for predicting the efficacy of a therapy and/or a model for predicting the prognosis of survival.
4. The use according to claim 3, wherein the use comprises use in constructing a model for the prediction of efficacy and a model for the prognosis of survival;
preferably, the therapeutic effect prediction model is used for predicting therapeutic effect of the drug on the disease patient;
preferably, the survival prognosis prediction model is used for predicting the survival prognosis of the disease patient by the medicine;
preferably, the use comprises constructing an assessment model for assessing the severity of a disease;
preferably, the disease comprises cancer;
preferably, the cancer comprises a cancer that has a suppressive response to tertiary lymphoid structures;
preferably, the cancer comprises breast cancer.
5. With PanCK + TLS in the cell-centered 1000 micron range + CD20 + Use of the average cell number as a biomarker in the preparation or screening of a medicament.
6. A method for analyzing tertiary lymphoid structure regions and for counting cells, comprising:
a step of circle selection, which comprises the steps of obtaining an immunohistochemical microscopic panorama of a sample to be detected and circle selecting a target area;
a phenotype area identification step, which comprises the step of carrying out phenotype area identification on the cells in the image according to the expression marker to obtain a phenotype area according with the pathophysiology characteristics;
a cell nucleus morphological identification step, which comprises identifying a cell nucleus in the image;
the cell phenotype data acquisition step comprises the following steps of performing phenotype identification on cells in the image according to the expression markers, judging the cells meeting judgment conditions as positive cells, and acquiring corresponding cell phenotype data;
and a biomarker quantitative value calculation step, which comprises the step of carrying out biomarker analysis calculation on the fluorescence expression data of the image target area of each positive cell to obtain the biomarker quantitative value of each positive cell.
7. The method of claim 6, wherein in the biomarker quantitation value calculation step, the biomarker quantitation value comprises a tertiary lymphoid structure density;
preferably, the method further comprises a cell coordinate position data acquisition step, wherein the cell coordinate position data acquisition step comprises the steps of calculating the spatial distance and distribution between positive cells of each two specific phenotypes according to the biomarker quantitative value of each positive cell acquired in the biomarker quantitative value calculation step;
preferably, the method further comprises a spatial distance quantitative value calculation step, which comprises calculating the spatial distance of the biomarkers in the image target area of each test organism to obtain a spatial distance quantitative value between positive cells of each two specific phenotypes of each test organism;
preferably, the quantitative value of spatial distance comprises PanCK + TLS in the cell-centered 1000 micron range + CD20 + The average cell number;
preferably, the method further comprises a predicting step, which comprises predicting whether the sample is derived from the test organism with remission treatment or predicting the probability that the sample is derived from the test organism with remission treatment according to the quantitative value of the biomarker;
preferably, the predicting step further comprises the step of predicting the curative effect and/or the survival prognosis according to the quantitative value of the spatial distance.
8. An apparatus for analyzing tertiary lymphoid structure regions and for counting cells, comprising:
the selection module is used for acquiring an immunohistochemical microscopic panorama of a sample to be detected and selecting a target area;
the phenotype area identification module is used for carrying out phenotype area identification on the cells in the image according to the expression markers to obtain a phenotype area which accords with the pathophysiology characteristics;
the cell nucleus morphological recognition module is used for recognizing the cell nucleus in the image;
the cell phenotype data acquisition module is used for carrying out phenotype identification on the cells in the image according to the expression markers, judging the cells meeting the judgment condition as positive cells and acquiring corresponding cell phenotype data;
and the biomarker quantitative numerical value calculation module is used for carrying out biomarker analysis calculation on the fluorescence expression data of the image target area of each positive cell to obtain a biomarker quantitative value of each positive cell.
9. An apparatus for analyzing tertiary lymphoid structure regions and for counting cells, comprising:
a memory for storing a program;
a processor for implementing the method of any one of claims 6 to 7 by executing the program stored in the memory.
10. A computer-readable storage medium having stored thereon a program executable by a processor to implement the method of any one of claims 6 to 7.
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