CN115602313B

CN115602313B - Biomarker for disease curative effect and survival prognosis prediction and application thereof

Info

Publication number: CN115602313B
Application number: CN202211183523.5A
Authority: CN
Inventors: 范师恒
Original assignee: Shenzhen Yuce Biotechnology Co ltd
Current assignee: Shenzhen Yuce Biotechnology Co ltd
Priority date: 2022-09-27
Filing date: 2022-09-27
Publication date: 2023-08-22
Anticipated expiration: 2042-09-27
Also published as: CN115602313A

Abstract

Biomarker for disease curative effect and survival prognosis prediction and application thereof ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Average cell number. The invention firstly proposes that panCK is adopted ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ The average cell number is used as a biomarker to predict the curative effect and/or survival prognosis of patients suffering from diseases such as breast cancer.

Description

Biomarker for disease curative effect and survival prognosis prediction and application thereof

Technical Field

The invention relates to the field of digital pathological image processing, in particular to a biomarker for disease curative effect and survival prognosis prediction and application thereof.

Background

Breast cancer is a clinically common malignant tumor, and the morbidity and mortality of the breast cancer are all in front of the malignant tumor, thereby seriously threatening the life health and quality of life of patients. Most patients are already late at the time of diagnosis due to low early diagnosis rate. The survival rate of patients with local advanced stage or distant metastasis is 99% and 27% respectively in 5 years at present, and it can be seen that although patients with local advanced stage can be treated by surgical excision, 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 (immune checkpoint inhibitors, ICIs), particularly inhibitors of the programmed cell death receptor 1 (programmed cell death receptor 1, pd-1) and its ligands (programmed cell death ligand 1, pd-L1), have received a great deal of attention because of their versatility, remarkable antitumor activity and good safety, and have improved prognosis in patients with advanced non-small cell breast cancer (non-small cell lung cancer, breast cancer). However, the efficacy of PD-1/PD-L1 inhibitors is not ideal in all patients and may be accompanied by serious immune-related adverse events (immune-related adverse events, irAEs) or even life threatening. The existing biomarkers have certain value for prognosis and efficacy prediction of breast cancer patients, but have limitations and disadvantages, and more effective biomarkers need to be developed to optimize patient benefits and guide treatment.

Many newly discovered biomarkers, particularly biomarkers for tumor immunotherapy, are associated with the tumor immune microenvironment (tumor immune microenvironment, TIME). TIME is the result of complex dynamic cross-actions between tumor and immune system, and TIME for solid tumors includes density, localization and composition of immune cells within the tumor, etc. Knowledge of the expression pattern and function of immune and tumor-associated molecules at TIME is critical to the selection of the patient population most likely to benefit from immunotherapy. Traditional immunohistochemical staining/immunofluorescence staining (IHC/IF) is the most commonly used detection method for TIME studies at present, plays a vital role in the evaluation of pathological types and biomarkers of breast cancer, can assist clinicians in making therapeutic decisions timely and accurately, but still has many limitations, and a more reliable and efficient immunohistochemical system must be developed.

Traditional IHC/IF detection is to dye formalin-fixed and paraffin-embedded (formalin fixation and paraffin embedding, FFPE) samples with enzyme or fluorescent-labeled antibodies, and shows the expression and localization distribution of specific target antigens in tissues, which is a histopathological diagnosis technology widely applied to 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 slice, whereas accurate tumor assessment requires detection of multiple protein targets, requiring sufficient histological specimens. In most cases, a patient's biopsy sample fails to meet additional tests beyond tumor histopathological typing, which results in missing the opportunity to obtain important diagnostic and prognostic information from the patient sample. Furthermore, even with sufficient samples to perform a series of consecutive tissue sections with conventional IHC/IF staining, correlation between proteins in a multicellular population study could not be accurately assessed. Thus, while IHC/IF is a practical and cost-effective detection method, this method does not account for all cases of complex TIME. Another major limitation of conventional IHC/IF is the high variability between observers, with the interpretation of the results being largely by human nature or semi-quantitative, with some subjectivity. For example, ki-67 is a prognostic biomarker for a variety of malignant tumors. However, at 2017 the holy gallon international expert consensus conference, experts presented the problem of repeatability of IHC for Ki-67 detection and its impact on clinical decisions. To reduce the subjective impact, there is currently an international consensus that requires a laboratory with a high level of experience of pathologists. Furthermore, studies have shown that the use of reproducible and quantitative numerical analysis to score Ki-67 can eliminate the difference between observers.

A recent technique of multi-labeled immunohistochemical staining/immunofluorescent staining (multiplex immunohistochemistry/multiplex immunofluorescence, mhic/mIF) has enabled the acquisition of multiple biomarkers on one tissue section, while obtaining multi-channel information on cell composition and spatial arrangement for high-dimensional analysis of TIME. The mIHC/mIF detection realizes detection of a plurality of biomarkers on FFPE (Formalin-Fixed and Parrffin-Embedded in Formalin fixed paraffin) tissue sections, can automatically distinguish tumor and non-tumor tissues by matching quantitative analysis software, objectively analyzes a plurality of biomarkers and cell composition, functional state and cell-cell interaction, and has the advantages of high repeatability, high efficiency and high cost efficiency.

Triple Negative Breast Cancer (TNBC) is known for its early onset, poor prognosis, and short overall median survival (OS) after metastasis. First-line immune checkpoint inhibitors combined with chemotherapy improved survival in patients with advanced TNBC positive for programmed cell death ligand 1 (PD-L1). However, in the latter line scenario or in PD-L1 negative tumor patients, immunotherapy-based regimens have not been found to be effective. Single or dual dose chemotherapy and sacituzumab-govitean (a Trop-2 directed antibody-drug conjugate) are currently recommended treatment regimens as two-line or two-line post-treatment for advanced TNBC patients, according to national integrated cancer network (NCCN) and european medical society of oncology (ESMO) clinical practice guidelines. However, objective effective rate (ORR) of single-drug chemotherapy is between 5 and 26.6%, ORR of dual-drug chemotherapy is between 22.2 and 31.6%, and ORR of saxicatheter-goltecan is about 31.0%. The survival results of patients treated with these drugs are also unsatisfactory because the median Progression Free Survival (PFS) is between 1.7 and 5.6 months. Thus, the need for developing new antitumor drugs 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 accurate medicine, where effective biomarker detection is a key element in accurately selecting beneficiary populations. It was found that in breast cancer patients, the high dimensional characteristics of TIME before and after ICIs treatment are correlated with treatment response. The tertiary lymphoid structure (Tertiary Lymphoid Structure, TLS) in TIME has close interaction with tumor cells, can inhibit or prevent tumor growth and invasion, and has prognostic and curative effect prediction value in breast cancer. Since TLS is a population of cells with a large heterogeneity, further refinement and quantification of specific indicators is required. The mhc/mIF assay is capable of simultaneously analyzing multiple biomarkers, objectively assessing TLS location, number and area. Currently, there have been several studies exploring TLS in cancer patients using mhic/mIF detection with different biomarker combinations, and high levels of TLS indicators were found to correlate with longer Overall Survival (OS) in breast cancer patients.

Tertiary Lymphoid Structures (TLS) are organized aggregates of immune cells formed in non-lymphoid tissues from the acquired day, with more mature TLS generally consisting 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 cancers. With few exceptions, the presence of TLS in tumors correlates with better prognosis and clinical outcome after immunotherapy. The mhc/mIF assay allows us to analyze not only a specific cell type, but also integrate the relationship between immune cells at different locations of the tumor, and by further analysis of TLS in breast cancer patients with the mhc/mIF assay, more prognostic and predictive data can be obtained, helping to accurately select breast cancer patients who would benefit from immunotherapy. Particularly in the case of rare histological specimens, the mIHC/mIF detection can be selected as a novel detection method, and is expected to be a powerful tool in clinic.

The existing TLS related mIHC/mIF detection technology can obtain indexes for predicting prognosis, but cannot achieve the functions of simultaneously predicting curative effect and prognosis, and the TLS identification process has the disadvantages of high complexity, long time consumption, high subjectivity and the like. Traditional TLS identification procedures involve manually identifying and circling marker (e.g., CD3 and CD 20) positive bright field Immunohistochemical (IHC) regions one by one for each tissue section. The technology has high requirements on pathological experience of operators, and the circling operators have strong subjectivity, and the consistency and the repeatability of the circling results of the same tissue slice at different times are poor. Also, because this technique involves the operator performing the gating and statistics on all TLS structures in one tissue section, it takes a long time (30 minutes to 1 hour per tissue section).

Disclosure of Invention

According to a first aspect, in an embodiment, there is provided a biomarker, said biomarker being in PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Average cell number.

According to a second aspect, in an embodiment, provided with PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Use of an average cell number as a biomarker in the construction of a efficacy prediction model and/or a survival prognosis prediction model.

According to a third aspect, in an embodiment, there is provided a PanCK ⁺ The cells are 1000 micro-meters at the centerTLS in meter range ⁺ CD20 ⁺ Use of an average cell number as a biomarker in the manufacture or screening of a medicament.

According to a fourth aspect, in one embodiment, there is provided a method of analyzing tertiary lymphoid structure regions and counting cells, comprising:

the method comprises the steps of selecting, namely obtaining an immunohistochemical microscopic panorama of a sample to be detected, and selecting a target area in a ring mode;

a phenotypic region identification step, comprising performing phenotypic region identification on cells in the image according to the expression markers, to obtain a phenotypic region conforming to the pathophysiological characteristics;

a cell nucleus morphology identification step including identifying a cell nucleus in the image;

the cell phenotype data acquisition step comprises the steps of carrying out phenotype identification on cells in the image according to the expression markers, judging the cells meeting the judgment conditions as positive cells, and obtaining corresponding cell phenotype data;

and calculating the biomarker quantitative value, namely performing biomarker analysis calculation on 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 one embodiment, there is provided an apparatus for analyzing tertiary lymphoid structure areas and for counting cells, comprising:

the circle selection module is used for acquiring an immunohistochemical microscopic panorama of the sample to be detected and circling a target area;

the phenotype area identification module is used for identifying the phenotype area of the cells in the image according to the expression markers to obtain the phenotype area conforming to the pathophysiological characteristics;

the cell nucleus morphology identification module is used for identifying the cell nucleus in the image;

the cell phenotype data acquisition module is used for carrying out phenotype identification on cells in the image according to the expression markers, judging the cells meeting the judgment conditions as positive cells, and obtaining corresponding cell phenotype data;

and the biomarker quantitative 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 value of each positive cell.

According to a sixth aspect, in one embodiment, there is provided an apparatus for analyzing tertiary lymphoid structure areas and for counting cells, comprising:

a memory for storing a program;

a processor configured to implement the method of any one of the fourth aspects by executing a program stored in the memory.

According to a seventh aspect, in an embodiment, a computer readable storage medium is provided, the medium having stored thereon a program executable by a processor to implement the method of any of the fourth aspects.

A biomarker for disease efficacy and survival prognosis prediction according to the above embodiments and uses thereof. The invention firstly proposes that panCK is adopted ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ The average cell number is used as a biomarker to realize the prediction of the curative effect and/or survival prognosis of patients suffering from diseases such as breast cancer.

In one embodiment, with PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ The average cell number is used as a biomarker, and can also predict the remission condition of patients with breast cancer and the like after treatment.

Drawings

FIG. 1 is a schematic diagram of a three-stage lymphatic structure analysis of breast cancer according to an embodiment;

FIG. 2 is a panoramic analysis area circling chart, a panoramic picture manual circling TLS result and a panoramic picture software identification area result of a sample 21R5317SLZA (A: analysis area circling chart, circling frame is cyan, B: manual circling panoramic TLS result chart, circling frame is white, C: software identification panoramic area result, yellow area is TLS, gray area is other);

FIG. 3 is a software identification area result of sample 21R5317SLZA (A: software identification panoramic area junction)If the white area is TLS, the gray area is other, and the surrounding frame of the analysis area is cyan; b: software recognizes the result enlarged graph of the area, the yellow area is TLS, and the gray area is other; c: the same view full channel diagram as B, DAPI blue, CD20 yellow, CD8 cyan, CD4 orange, panCK red; d: DAPI single channel plot, blue; e: CD20 ⁺ Single channel plot, yellow; f: panCK ⁺ Single-channel diagram, red; g: CD8 ⁺ Single channel diagram, cyan; h: CD4 ⁺ Single channel plot, orange);

FIG. 4 is a graph of the spatial distribution analysis of sample 21R5317SLZA (A: panorama in PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ Cell distribution map, panCK ⁺ Cells are red, with PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ The cells are green; b an enlarged view of one field of view in FIG. A, panCK ⁺ Cells are red, with PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ The cells are green, and cells with connecting lines represent TLS ⁺ CD20 ⁺ Cell to PanCK ⁺ Cells in the 1000 micron range);

FIG. 5 is a TLS manual circled view of sample 21R5317SLZA (A: panorama TLS circled result, panorama analysis area circled border is cyan, TLS circled border is white; B: TLS full channel magnified view of the same view as FIGS. 3B-3H; C: DAPI single channel view, blue; D: CD20 single channel view, yellow; E: CD8 single channel view, cyan; F: CD4 single channel view, orange);

FIG. 6 shows the distribution of the various indicators of mIHC/mIF TLS in different treatment efficacy groups for breast cancer patients;

FIG. 7 is a graph showing the median predictive efficacy of each index of mIHC/mIF TLS for breast cancer patients;

FIG. 8 is a graph of the median predicted efficacy ROC for each index of mIHC/mIF TLS for breast cancer patients;

FIG. 9 is a graph showing the median and survival relationship of each index of mIHC/mIF TLS for breast cancer patients.

Detailed Description

The application will be described in further detail below with reference to the drawings by means of specific embodiments. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted in various situations, or replaced by other materials, methods. In some instances, related operations of the present application have not been shown or described in the specification in order to avoid obscuring the core portions of the present application, and may be unnecessary to persons skilled in the art from a detailed description of the related operations, which may be presented in the description and general knowledge of one skilled in the art.

Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.

The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning.

In one embodiment, the present invention provides a simple, efficient, objective and highly reproducible TLS identification method, and on that basis provides an indicator that can predict both efficacy and prognosis in a small sample queue. The invention relates to an image recognition technology, which is used for recognizing and extracting the characteristics of a plurality of TLS marker positive cell enrichment areas and applying TLS area recognition in one tissue slice to other tissue slices dyed in the same batch of experiments. The method has the characteristics of high accuracy (multiple immunofluorescence allows simultaneous detection of 3 or more markers, the co-localization TLS structure is more accurate than 1-2 markers localization), simplicity and high efficiency (about 1 hour/30 tissue sections), strong repeatability (the identification result can be stored as a file, the identification result for the same section is 100 percent consistent), strong objectivity (the tissue sections adopt a fixed algorithm and the feature extraction of a sampling area, and the identification result has strong interpretability) and the like.

In one embodiment, the invention relates to the calculation and statistics of intercellular distances, which can be used to derive the distances of specific cells from tumor cells in the TLS region (3 or more TLS markers co-stained with tumor markers) in a small sample queue, and to predict efficacy and prognosis of production. In one embodiment, the invention provides an innovative biomarker for quantifying the number of cells in a range of TLS-critical cells and tumor cells, and for patients with higher levels of the biomarker, the clinical treatment can be directed to provide a few treatments for the latter line (patients with advanced disease after treatment with an immune drug) or for patients with advanced cancer.

By PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Average cell number refers to: by PanCK ⁺ The cells were the average number of cells positive for tertiary lymphoid structure in the central 1000 micron range and positive for CD 20.

In one embodiment, the biomarker is used for prognosis of survival for all cancer patients with good cancer suppressing response to Tertiary Lymphoid Structures (TLS), including breast cancer.

In one embodiment, the prognosis includes a natural prognosis or an intervention prognosis.

In one embodiment, the prognosis of intervention refers to the steps that a doctor obtains patient disease information according to clinical symptoms or images, assays and other ways, grasps the information of etiology, pathology, disease degree and the like, combines new conditions found in treatment operation according to treatment time and methods, and predicts and judges the degree of recent and distant curative effects, prognosis or progress recovery of diseases according to the current clinical medical intervention level and clinical experience, and the treatment time and procedure required in the follow-up. Including how to recover, and the occurrence or disappearance and death of symptoms, signs, complications and other abnormalities left after intervention treatment.

In one embodiment, the biomarker is used to predict the probability of survival of a patient with a disease after a period of time.

In one embodiment, the certain time is not less than 6 months.

In one embodiment, the biomarker is used to predict the probability of survival of a patient with a disease 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 agent includes, but is not limited to, at least one of an immune checkpoint inhibitor, a 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 is cancer-inhibiting responsive to Tertiary Lymphoid Structures (TLS).

In one embodiment, the cancer includes, but is not limited to, breast cancer.

In one embodiment, the biomarker is used for all cancer patient remission predictions including breast cancer for all cancer patients with good cancer suppression response to TLS. The biomarker can be used to distinguish between remission after treatment of a breast cancer patient, i.e., the biomarker can be used to distinguish breast cancer patients into treatment-buffered groups (ORR), treatment-non-buffered groups (NOR).

In one embodiment, the biomarker is used to predict the presence or absence of remission in a breast cancer patient.

According to a second aspect, in an embodiment, provided with PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Use of average cell number as biomarker in constructing a prognosis prediction model for survival of a patient with 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.

According to a third aspect, in an embodiment, there is provided a PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Use of average cell number as biomarker in constructing a predictive model of remission in a patient suffering from 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.

According to a fourth aspect, in an embodiment, provided with PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Use of an average cell number as a biomarker in the manufacture or screening of a medicament. Specifically, the method can be the application of the average cell number as a biomarker in screening clinical treatment means. For example, immunotherapy single or multi-drug combination therapy may be preferred for patients with higher biomarker levels.

In one embodiment, the biomarker is used to guide administration.

In one embodiment, the biomarker is used for prognosis evaluation of survival of a patient with a disease.

In one embodiment, the biomarker is used to assess survival of a patient with a disease.

In one embodiment, the medicament comprises a medicament having a therapeutic ameliorating effect on the 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.

According to a fifth aspect, in an embodiment, provided with PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Use of an average cell number as a biomarker in the construction of a efficacy prediction model and/or a survival prognosis prediction model. The biomarker can be used for predicting the curative effect and predicting the survival prognosis.

In one embodiment, the use includes use in constructing a 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 treating a patient with a disease.

In one embodiment, the survival prognosis prediction model is used to predict the survival prognosis of a drug for a patient with a disease.

In an embodiment, the use comprises constructing an assessment model for assessing the severity of the 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.

According to a sixth aspect, in one embodiment, there is provided a biomarker for use in the preparation or screening of a medicament, the biomarker being as PanCK ⁺ TLS in the 1000 micron cell-centered 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 includes, but is not limited to, breast cancer.

According to a seventh aspect, in one embodiment, there is provided a method of analyzing tertiary lymphoid structure area and cell statistics, comprising:

the method comprises the steps of selecting, namely obtaining an immunohistochemical microscopic panorama of a sample to be detected, and selecting a tertiary lymphatic structure to be a target area and all cell areas;

a phenotypic region identification step comprising performing phenotypic region identification on cells in the image based on the expressed markers, obtaining a phenotypic region comprising areas that meet the pathophysiological characteristics, other phenotypic regions that do not meet the pathophysiological characteristics, and an overall region;

and calculating the biomarker quantitative value, namely performing biomarker analysis calculation on fluorescence expression data of image target areas (meeting and not meeting pathophysiological characteristics and overall areas) of each positive cell to obtain the biomarker quantitative value of each positive cell.

In one embodiment, in the biomarker quantitative value calculation step, the biomarker quantitative value comprises tertiary lymphoid structure density. Tertiary lymphoid structure density abbreviated TLS density, TLS density = number of tertiary lymphoid structures in mass-controlled whole tissue section/mass-controlled whole tissue section area.

In one embodiment, the immunohistochemical micro-panorama is scanned from a stained sample.

In one embodiment, the reagent used in the staining process comprises a nuclear stain 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 anti-CD 4 monoclonal antibodies, anti-CD 8 monoclonal antibodies, and anti-PanCK monoclonal antibodies.

In one embodiment, the sample includes, but is not limited to, a tissue slice.

In an embodiment, the sample is derived from a human or an animal, preferably a human.

In one embodiment, the sample is derived from a breast cancer patient.

In one embodiment, the targeting step is performed by targeting the target region within the boundary of the region of the invasive tumor and the stroma in the vicinity of the invasive tumor.

In one embodiment, in the step of obtaining the cell phenotype data, the cell meeting the judgment condition is a cell whose target fluorescence intensity is larger than a preset value.

In one embodiment, the method further comprises a step of obtaining cell coordinate position data, wherein the step 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 obtained in the step of calculating the biomarker quantitative value.

In one embodiment, the method further comprises a spatial distance quantitative value calculation step, which comprises calculating the spatial distance of the biomarker in the image target area of each tested organism to obtain the spatial distance quantitative value between positive cells of each two specific phenotypes of each tested organism.

In one embodiment, in the step of calculating the spatial distance quantitative value, the spatial distance quantitative value includes the value obtained by using PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Average cell number.

In one embodiment, the method further comprises a predicting step comprising predicting whether the sample is derived from a therapeutic-remitting test organism or predicting the probability that the sample is derived from a therapeutic-remitting test organism based on the biomarker quantitative value;

in one embodiment, the predicting step further comprises predicting a therapeutic effect and/or a prognosis for survival based on the spatial distance quantitative value.

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 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 areas and counting cells, comprising:

In one embodiment, the biomarker quantitative value calculation module comprises the steps of ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Average cell number.

In one embodiment, the nuclear stain comprises DAPI.

In one embodiment, the antibody comprises a monoclonal antibody.

In one embodiment, the sample includes, but is not limited to, a tissue slice.

In one embodiment, the sample is derived from a breast cancer patient.

In one embodiment, the method further comprises a cell coordinate position data acquisition module for calculating the spatial distance and distribution between positive cells of each two specific phenotypes according to the biomarker quantitative value of each positive cell obtained in the biomarker quantitative value calculation step.

In one embodiment, the method further comprises a spatial distance quantitative value calculation module for calculating the spatial distance of the biomarker in the image target area of each tested organism to obtain the spatial distance quantitative value between positive cells of each two specific phenotypes of each tested organism.

In one embodiment, the spatial distance quantitative value comprises a value calculated by PanCK ⁺ TLS in the 1000 micron cell-centered range ⁺ CD20 ⁺ Average cell number.

In an embodiment, the method further comprises predicting whether the sample originates from a therapeutic-remitting test organism or predicting a probability that the sample originates from a therapeutic-remitting test organism based on the biomarker quantitative value.

In an embodiment, the prediction module is further configured to predict a therapeutic effect and/or a prognosis of survival based on 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. Including the use of biomarkers in clinical therapeutic screening. For example, immunotherapy single or multi-drug combination therapy may be preferred for patients with higher biomarker levels. Meanwhile, for the latter line (patients who still have advanced disease after treatment with an immune drug) or patients with advanced cancer, if the biomarker level is high, the preference for multi-drug combination therapy may be considered.

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.

According to a tenth aspect, in one 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 one of the seventh aspect by executing a program stored in the memory.

According to an eleventh aspect, in an embodiment, there is provided a computer-readable storage medium having stored thereon a program executable by a processor to implement the method of any of the seventh aspects.

In one embodiment, an analytical method and system for immunohistochemical or immunofluorescence multiplex labeling of tertiary lymphoid structures and analysis of their associated indices is provided. The method and the system for identifying and analyzing tumor immune micro-environment biomarkers are carried out by marking the positions and the distributions of different antigen macromolecular substances in tissues or cells in situ by adopting fluorescent pigments or enzymatic products with different colors on the same FFPE tissue slice, and then utilizing panoramic tissue and cell phenotype information and corresponding space coordinate information and the like obtained by an image recognition system (such as HALO v3.3.2541.323, quPath v0.2.3 and the like).

In one embodiment, a method and a system for analyzing spatial distribution of tertiary lymph structure of breast cancer based on multiple immunohistochemistry or multiple immunofluorescence technology are provided, and the TLS of immune microenvironment of the breast cancer 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. The invention mainly solves the technical problems that the tumor immune treatment efficacy evaluation lacks enough biomarkers and how to enable related indexes of tumor immune microenvironment to evaluate the immune treatment efficacy in a larger scale, can well solve the requirements of tumor immune microenvironment analysis through multiple immune hybridization technology in clinical pathology work and scientific research, omits the complicated work of manual calculation and analysis by medical staff and scientific researchers, reduces the number of samples and difficulty required by TLS identification, and efficiently assists doctors and scientific researchers to complete the analysis of various immune hybridization indexes after immune hybridization or immune fluorescence multiple marking.

Example 1

The embodiment of the invention provides a breast cancer tertiary lymphoid 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. obtaining breast cancer tissue samples from each subject, preparing into tissue sections, performing multiple immunohistochemical staining treatment (such as a Bond RX automatic staining instrument of Leica company) on the breast cancer tissue samples, and obtaining corresponding immunohistochemical microscopic panorama by an imaging scanning instrument (such as a Vectra polar is spectral quantitative pathological analysis system of Akoya company) after the stained markers at least comprise DAPI dye (DAPI is nonspecific dye and stains all cell nuclei) and CD4, CD8, CD20 and PanCK antibodies.

2. Analysis region-circling is performed on each immunohistochemical microscopic panorama by an image recognition analysis software system (such as HALO, quPath, etc., in this embodiment, the HALO system). The circled target area meets the following conditions: (1) Selecting in the boundary of the region formed by the invasive tumor and the matrix nearby the invasive tumor; (2) Excluding invasive tumors and their nearby extra-stromal and paracancerous normal tissues; (3) Excluding areas of tumor with autolysis, carcinoma in situ, abnormal DAPI coloration, carbonization, wrinkling, scoring, air bubbles, compression artifacts, necrosis, resolution of transparence, and other artifacts; (4) And adjusting the fluorescent signal intensity of the picture, removing the background signal and enhancing the true positive signal.

3. Phenotypic region identification of cells from the mhic pathology image using a classifier (classifier) based on expression markers (markers) was set as follows: (1) newly creating a classifier, and setting an algorithm as Random Forest; (2) adding a category TLS and Other; (3) Selecting different phenotypes and collecting corresponding phenotypically enriched areas on a picture, wherein TLS selects more positive enriched areas of DAPI, CD4, CD8 and CD20, and Other selects single positive or negative enriched areas of DAPI, CD4, CD8 and CD 20; (4) Selecting a plurality of visual fields containing all phenotypes to browse the classification results in real time, and adding more acquisition areas to train the classifier if the classification results are not ideal until the classification results accord with pathophysiological characteristics, so that the classifier debugging can be completed; (5) The classifer analysis settings are saved for batch application to other pictures.

4. In the step of nuclear morphology identification (Nuclear Detection), the parameters are set as follows (no mention is made of parameter setting as default): analysis Settings the Indica Labs HighPlex FL v4.1.3, nuclear Contrast Threshold parameter setting 0.497,Minimum Nuclear Intensity parameter setting 0.075,Maximum Image Brightness parameter setting 1,Nuclear Segmentation Aggressiveness parameter setting 0.5,Fill Nuclear Holes parameter setting false, nuclear Size parameter setting 5 to 549.576,Minimum Nuclear Roundness parameter setting 0,Number of Nuclear Dyes parameter setting 1, nuclear Dye 1 parameter setting DAPI, nuclear Dye 1Weight parameter setting 1. The nuclei of the mhic pathology image are identified based on the above parameters.

5. Phenotype identification of cells from the mhic pathology image was performed according to expression markers, with the following parameters set (parameters not mentioned as default): (1) Maximum Cytoplasm Radius parameter is set to 2; (2) Membrane Segmentation Agressiveness parameter set to 0.9; (3) Cell Size parameter set to 5 to 600;(4) Number of Membrane Dyes parameter is set to 0; (4) DAPI Nucleus Positive Threshold Weak parameter is set to 3; (5) CD20 ⁺ Cytoplasm Positive Threshold Weak parameters are set to 30-55; (6) CD8 ⁺ Cytoplasm Positive Threshold Weak parameters are set to 40-80; (7) CD4 ⁺ Cytoplasm Positive Threshold Weak parameters are set to 25-30; (8) PanCK ⁺ Cytoplasm Positive Threshold Weak parameter set to 17.65; (9) All markers as not specifically mentioned, nucleus Positive Threshold Weak, nucleus Positive Threshold Mod, nucleus Positive Threshold Strong, cytoplasm Positive Threshold Weak, cytoplasm Positive Threshold Mod and Cytoplasm Positive Threshold Strong parameter settings default to 255; (10) All markers as not specifically mentioned, nucleolus% Completeness Threshold and cytoplasms% Completeness Threshold parameter settings default to 0; (11) Advanced settings the Class List parameter is set to TLS|other, the Class identifier parameter is set to the Classifier setting saved in the previous step, and Classifier Output Type is set to Mask. Cells whose fluorescence intensities of the respective targets were higher than the cutoff values mentioned in (4) to (8) were judged as positive cells, and data of the corresponding cell phenotypes were obtained.

6. Biomarker analysis calculations were performed on the fluorescence expression data of the target region of the mhic pathology image of each subject to obtain a biomarker quantitative value for each subject, comprising the following steps (parameter set as default is not mentioned):

(1) Addition of PanCK positive cell data to the spatial analysis Module was named PanCK ⁺ The method comprises the steps of carrying out a first treatment on the surface of the (2) TLS-positive and CD 20-positive cell data were added to the spatial analysis module, named TLS ⁺ CD20 ⁺ The method comprises the steps of carrying out a first treatment on the surface of the (3) performing proximity analysis centering on PanCK cells; the measurement parameter is set to TLS ⁺ CD20 ⁺ The Within parameter is set to 1000 μm and the Proximity of parameter is set to PanCK ⁺ The Number of Bands parameter is set to 20; storing analysis parameters; (4) Selecting all cell identification data analysis results, and performing batch proximity analysis analysis by using the analysis parameters obtained in the previous step; (5) Deriving proximity analysis analysis results and obtaining the same as PanCK ⁺ Cell is the center 10TLS in the 00 micron range ⁺ CD20 ⁺ Average cell number.

The samples used in this example were tissue slice samples of 24 clinical breast cancer patients tested in this laboratory. Multiple immunohistochemical staining of patient tumor tissue samples (6:7:DAPI, CD20, panCK, CD8, CD 4) was done using Bond RX automated staining instrument, and full-film scanning was performed through VECTRA POLARIS system to obtain immunohistochemical microscopic panoramic images. The HALO v3.3.2541.323 system was introduced and the analysis area was circled for each immunohistochemical microscopic panorama. Fig. 2A is a panoramic analysis area circled plot (DAPI blue, CD20 yellow, CD8 cyan, CD4 orange, panCK red) of sample 21R5317SLZA, and table 2 contains the analysis areas obtained in this step.

Fig. 2 shows a panoramic analysis area circling diagram, a panoramic picture manual circling TLS result, and a panoramic picture software identification area result (a: analysis area circling diagram, circling border is cyan, B: manual circling panoramic TLS result diagram, circling border is white, C: software identification panoramic area result, yellow area is TLS, and gray area is other) of sample 21R5317 SLZA.

Phenotypic region identification was performed on cells of the mhic pathology image using classifier according to expression marker, set as follows: (1) newly creating a classifier, and setting an algorithm as Random Forest; (2) adding a category TLS and Other; (3) Selecting different phenotypes and collecting corresponding phenotypically enriched areas on a picture, wherein TLS selects more positive enriched areas of DAPI, CD4, CD8 and CD20, and Other selects single positive or negative enriched areas of DAPI, CD4, CD8 and CD 20; (4) Selecting a plurality of visual fields containing all phenotypes to browse the classification results in real time, and adding more acquisition areas to train the classifier if the classification results are not ideal until the classification results accord with pathophysiological characteristics, so that the classifier debugging can be completed; (5) Saving the classifer analysis settings for batch application to other pictures; (6) The TLS area was derived and the TLS average area calculated by applying classifier to the other pictures (see table 1.). FIG. 2C is a view of the software identified panoramic area of sample 21R5317SLZA (TLS for area, other gray areas), FIG. 3 is a result of the software identified panoramic area of sample 21R5317SLZA (A: result of software identified panoramic area, white) The areas are TLS, the gray areas are other, and the surrounding frame of the analysis area is cyan; b: software recognizes the result enlarged graph of the area, the yellow area is TLS, and the gray area is other; c: the same view full channel diagram as B, DAPI blue, CD20 yellow, CD8 cyan, CD4 orange, panCK red; d: DAPI single channel plot, blue; e: CD20 ⁺ Single channel plot, yellow; f: panCK ⁺ Single-channel diagram, red; g: CD8 ⁺ Single channel diagram, cyan; h: CD4 ⁺ Single channel plot, orange).

Phenotype identification of cells from the mhic pathology image was performed according to expression markers, with the following parameters set (parameters not mentioned as default): (1) Maximum Cytoplasm Radius parameter is set to 2; (2) Membrane Segmentation Agressiveness parameter set to 0.9; (3) Cell Size parameter set to 5 to 600; (4) Number of Membrane Dyes parameter is set to 0; (4) DAPI Nucleus Positive Threshold Weak parameter is set to 3; (5) CD20 ⁺ Cytoplasm Positive Threshold Weak parameters are set to 30-55; (6) CD8 ⁺ Cytoplasm Positive Threshold Weak parameters are set to 40-80; (7) CD4 ⁺ Cytoplasm Positive Threshold Weak parameters are set to 25-30; (8) The panck+ Cytoplasm Positive Threshold Weak parameter was set to 17.65; (9) All markers as not specifically mentioned, nucleus Positive Threshold Weak, nucleus Positive Threshold Mod, nucleus Positive Threshold Strong, cytoplasm Positive Threshold Weak, cytoplasm Positive Threshold Mod and Cytoplasm Positive Threshold Strong parameter settings default to 255; (10) All markers as not specifically mentioned, nucleolus% Completeness Threshold and cytoplasms% Completeness Threshold parameter settings default to 0; (11) Advanced settings the Class List parameter is set to TLS|other, the Class identifier parameter is set to the Classifier setting saved in the previous step, and Classifier Output Type is set to Mask. Cells with fluorescence intensities of each target greater than the cutoff values mentioned in (4) to (8) were judged as positive cells, data of the corresponding cell phenotype were obtained, table 1 is a specific set parameter, and table 2 contains the numbers of TLS cells obtained in this step.

TABLE 1 pathological image cell phenotype identification analysis parameters

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Biomarker analysis calculations were performed on the fluorescence expression data of the target region of the mhic pathology image of each subject to obtain a biomarker quantitative value for each subject, comprising the following steps (parameter set as default is not mentioned): (1) Addition of PanCK positive cell data to the spatial analysis Module was named PanCK ⁺ The method comprises the steps of carrying out a first treatment on the surface of the (2) TLS-positive and CD 20-positive cell data were added to the spatial analysis module, named TLS ⁺ CD20 ⁺ The method comprises the steps of carrying out a first treatment on the surface of the (3) performing proximity analysis centering on PanCK cells; the measurement parameter is set to TLS ⁺ CD20 ⁺ The Within parameter is set to 1000 μm and the Proximity of parameter is set to PanCK ⁺ The Number of Bands parameter is set to 20; storing analysis parameters; (4) Selecting all cell identification data analysis results, and performing batch proximity analysis analysis by using the analysis parameters obtained in the previous step; (5) Deriving proximity analysis analysis results and obtaining the same as PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ Average cell number. FIG. 4 is a graph of the spatial distribution analysis of sample 21R5317SLZA (A: panorama in PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ Cell distribution map, panCK ⁺ Cells are red, with PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ The cells are green; b an enlarged view of one field of view in FIG. A, panCK ⁺ Cells are red, with PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ The cells are green, and cells with connecting lines represent TLS ⁺ CD20 ⁺ Cell to PanCK ⁺ Cells in the 1000 micron range). Table 3 contains the results obtained in this step in PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ Average cell number.

The TLS for each immunohistochemical microscopic panorama was manually circled by the HALO system. The circled target area meets the following conditions: (1) For each specimen, regions significantly enriched in DAPI, CD4, CD8 and CD20 positive cells were circled as much as possible, with a minimum area of 0.0001mm for each target region ² (0.01 mm. Times.0.01 mm) or more. Fig. 5 is a TLS manual circling of sample 21R5317SLZA (a: panorama TLS circling result, panorama analysis area circling border is cyan, TLS circling border is white, B: TLS full channel enlarged view of the same view as fig. 3B-3H, C: DAPI single channel view, blue, D: CD20 single channel view, yellow, E: CD8 single channel view, cyan, F: CD4 single channel view, orange).

The results are shown in the table below, where Table 2 shows the results of manual analysis and software analysis of the tissue samples from a breast cancer patient, and Table 3 shows the manual TLS density, software TLS density and the results of analysis with PanCK for the tissue samples from a breast cancer patient ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ Average cell number.

TABLE 2 results of Manual analysis and software analysis of tissue samples from breast cancer patients

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In Table 2, the TLS numbers are manual analysis results, and the analysis areas and TLS cell numbers are software analysis results.

TABLE 3 tissue samples of breast cancer patients Manual TLS Density, software TLS Density and PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ Average cell number

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Clinical information of the breast cancer patients is collected, clinical data is collected for the breast cancer patients with advanced triple negative, and in the embodiment, the patients are treated by three-drug combination therapy (combined use of Camrelizumab, apatinib and Eribulin), and the treatment effect of single drug therapy or chemotherapy of the breast cancer with advanced triple negative is generally poor, so that the treatment means are very limited, and the three-drug combination therapy is adopted. The markers provided by the invention can be used for proving that the curative effect and prognosis can be still predicted under the condition of such rare treatment means and the condition of such serious clinical conditions. As shown in table 4 (ORR is treatment-buffered group, NOR is treatment-non-buffered group, PFS is progression-free survival in months, 0 in Stat is progression-free in follow-up period, 1 in follow-up period), patients are grouped according to treatment efficacy, and TLS-related indicators for each group are counted. The artificial TLS density (TLS density) and the software identification TLS density (TLS density auto) were found to be significantly higher in post-treatment remission patients, as shown in fig. 6, which illustrates the distribution of the individual indicators of the breast cancer patients mIHC/mIF TLS in different efficacy groupings. This result demonstrates that the software identification technique of the mhic/mIF technique for statistics and analysis of tertiary lymphoid structure in this example is accurate, and that TLS density can be used as a biomarker to distinguish between remission after breast cancer patient treatment. At the same time, FIG. 6 also shows the spatial distribution index for PanCK proposed in the present embodiment ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ Average cell number (Average number of TLS) ⁺ CD20 ⁺ within 1000μm of PanCK ⁺ ) Can also be used as a biomarker to distinguish the remission condition of breast cancer patients after treatment.

Table 4 clinical information of breast cancer patients

Further, the efficacy-related prediction analysis was performed on these indices, the efficacy was predicted using the median of each index, the efficacy was determined to be High (High) at the median or higher, the efficacy was determined to be Low (Low) at the median or lower, the ORR ratio was predicted and counted (see median-level group information of each index related to TLS of a breast cancer patient shown in table 5, table 5 is the result calculated based on tables 2 and 3), and it was found that the artificial TLS density, the software-identified TLS density, and the efficacy was determined as PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ There were significant differences in average cell numbers. Using ROC curve AUC metrics to measure median predicted efficacy performance, software was found to identify TLS density with highest performance (0.84), artificial TLS density, and PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ The average cell number was the same (0.76).

TABLE 5 median height grouping information for each index related to TLS of breast cancer patients

Finally, the relationship between the median of each index and the survival time is analyzed, and FIG. 7 is a graph of the predicted therapeutic effect of the median of each index of the breast cancer patients mIHC/mIF TLS. FIG. 8 is a graph of the median predicted efficacy ROC for each index of mIHC/mIF TLS in breast cancer patients. FIG. 9 is a graph showing the median and survival relationship of each index of mIHC/mIF TLS for breast cancer patients. It can be found that only with PanCK ⁺ TLS in the 1000 micron cell centered range ⁺ CD20 ⁺ The average cell number (the right-most survival chart, the abscissa is month) can significantly differentiate survival, the median of PFS is 10.251 months in the High group, the median of PFS is 4.862 months in the Low group, and the difference is about 5.389 months, which indicates that the median progression-free survival time difference between two groups of patients predicted by using the spatial distance index is at least 5 months, and the survival difference can not be significantly differentiated by using the artificial TLS density and the software identification TLS density only when the index median discriminates the patientsDifferences. The analysis results prove that the breast cancer tertiary lymphoid 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 curative effect and survival prognosis of a breast cancer patient, and further prove that the biomarker provided by the invention is superior to the traditional TLS density index in terms of prognosis prediction, and is equivalent to the traditional TLS density index in terms of curative effect 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 mode in the aspect of efficacy prediction. In one embodiment, the invention can realize semi-automatic fluorescent target biomarker analysis on the multi-immunohistochemical or multi-immunofluorescence microscopic panorama, identify tumor cells and three-level lymphoid structure space distribution information, classify tumor immune microenvironment, well solve the requirement of reliable quantitative analysis on the immunohistochemical multi-marker in clinical pathology work and scientific research, reduce the complicated work of manual calculation analysis of pathologists and researchers, and efficiently assist doctors and researchers to complete analysis of various tumor microenvironment indexes after the immunohistochemical or immunofluorescence multi-marker.

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 a computer program. When all or part of the functions in the above embodiments are implemented by means of a computer program, the program may be stored in a computer readable storage medium, and the storage medium may include: read-only memory, random access memory, magnetic disk, optical disk, hard disk, etc., and the program is executed by a computer to realize the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above can be realized. In addition, when all or part of the functions in the above embodiments are implemented by means of 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 the program in the above embodiments may be implemented by downloading or copying the program into a memory of a local device or updating a version of a system of the local device, and when the program in the memory is executed by a processor.

The foregoing description of the invention has been presented for purposes of illustration and description, and is not intended to be limiting. Several simple deductions, modifications or substitutions may also be made by a person skilled in the art to which the invention pertains, based on the idea of the invention.

Claims

1. A biomarker, wherein the biomarker is PanCK in an image target region of an immunohistochemical microscopic panorama ⁺ The cells were the average number of cells positive for tertiary lymphoid structure in the central 1000 micron range and positive for CD 20.

2. A method of analyzing tertiary lymphoid structure regions and counting cells, comprising:

A biomarker quantitative value calculation step, which comprises the steps of carrying out biomarker analysis calculation on fluorescence expression data of an image target area of each positive cell to obtain a biomarker quantitative value of each positive cell;

a cell coordinate position data acquisition step, which comprises the step 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 obtained in the biomarker quantitative value calculation step;

calculating the spatial distance quantitative value, namely calculating the spatial distance of the biomarker in the image target area of each tested organism to obtain the spatial distance quantitative value between positive cells of each two specific phenotypes of each tested organism;

the spatial distance quantitative value comprises PanCK of the target area of the image ⁺ The cells were the average number of cells positive for tertiary lymphoid structure in the central 1000 micron range and positive for CD 20.

3. The method of claim 2, wherein in the biomarker quantification value calculation step, the biomarker quantification value comprises tertiary lymphoid structure density.

4. The method of claim 3, further comprising a predicting step comprising predicting whether the sample is derived from a therapeutic-remitted test organism or predicting the probability that the sample is derived from a therapeutic-remitted test organism based on the biomarker quantification value.

5. The method of claim 4, wherein the predicting step further comprises predicting efficacy and/or prognosis of survival based on the spatial distance quantitative value.

6. An apparatus for analyzing tertiary lymphoid structure areas and for counting cells, comprising:

the biomarker quantitative value calculation module is used for carrying out biomarker analysis calculation on fluorescence expression data of the image target area of each positive cell to obtain a biomarker quantitative value of each positive cell;

the cell coordinate position data acquisition module is used for calculating the space distance and distribution between the positive cells of each two specific phenotypes according to the biomarker quantitative value of each positive cell obtained by the biomarker quantitative value calculation module;

The spatial distance quantitative value calculation module is used for calculating the spatial distance of the biomarker in the image target area of each tested organism to obtain the spatial distance quantitative value between positive cells of each two specific phenotypes of each tested organism;

7. The apparatus of claim 6, further comprising a prediction module that predicts whether the sample is derived from a therapeutic-remitted test organism or predicts a probability that the sample is derived from a therapeutic-remitted test organism based on the biomarker quantitative value.

8. The apparatus of claim 6, wherein the prediction module further comprises a treatment effect prediction and/or a survival prognosis prediction based on the spatial distance quantitative value.

9. An apparatus for analyzing tertiary lymphoid structure areas and for counting cells, comprising:

a memory for storing a program;

a processor for implementing the method of any one of claims 2 to 5 by executing a program stored in said memory.

10. A computer readable storage medium having stored thereon a program executable by a processor to implement the method of any of claims 2 to 5.