A new model for accurate prediction of the efficacy of breast cancer immunotherapy and its application

Abstract

The present invention discloses a novel model for accurately predicting the efficacy of immunotherapy for breast cancer and its application. The model contains IFN-γ, aSMA, collagen-I and Vimentin. According to the model, the prognosis and therapeutic effect of immunotherapy for breast cancer patients can be accurately predicted. A high score indicates that the breast cancer patients can benefit from the immunotherapy strategy. The model provided by the present invention has guiding significance for predicting the long-term efficacy of breast cancer immunotherapy patients and has wide clinical applicability.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology and relates to a model for accurately predicting the efficacy of disease immunotherapy. Specifically, it relates to a new model for accurately predicting the efficacy of breast cancer immunotherapy and its application. Background Art

[0002] Breast cancer is a global disease, and its incidence ranks first among female malignant tumors worldwide. According to the data of the Global Cancer Survey, new cases of breast cancer account for 24.2% of all new malignant tumors worldwide, and the mortality rate of breast cancer accounts for 15% of the mortality rate of malignant tumors, and it shows a trend of continuous growth (Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA: a cancer journal for clinicians, 2018, 68 (6): 394-424.). Although in the past 30 years, with the continuous discovery of new anticancer drugs and the continuous development of diagnosis and treatment methods, the mortality rate of breast cancer has been reduced by 39%, but due to the high variability of breast cancer genes and the special biological microenvironment, as well as the difficulty of patients to tolerate the side effects of traditional treatment methods (including surgery, radiotherapy, chemotherapy, endocrine therapy, etc.), breast cancer is still the second leading cause of cancer death in women. Therefore, there is an urgent need in the art for effective treatment methods that can improve the survival rate of breast cancer patients.

[0003] With the continuous development of immunology and biology, immunotherapy has long become another important treatment for breast cancer after traditional chemotherapy, radiotherapy and surgery. In the tumor microenvironment, the presence of multiple immune checkpoints during the activation of T lymphocytes stimulated by antigens leads to the inability of T cells to be effectively activated. Immune checkpoints play a key role in the occurrence and development of tumors. The identification of immune checkpoint inhibition pathways has led to significant improvements in immunotherapy. At present, most tumor treatments are mainly targeted at related immune checkpoint molecules such as lymphocyte activation gene-3 (LAG-3), cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) and programmed death receptor-1 (PD-1 / PD-L1). Although immune checkpoint inhibitors (ICI) (e.g., anti-PD-1 / PD-L1 antibodies) have achieved remarkable results in the treatment of breast cancer, they are limited to a small number of tumor patients, and a considerable proportion of tumor patients do not respond to anti-PD1 / PD-L1 antibodies. The main challenge facing this field is to identify and develop biomarkers that can predict the efficacy of ICI in order to accurately identify tumor patients who benefit from immunotherapy.

[0004] Immunohistochemistry is currently the most commonly used screening and evaluation method to evaluate the PD-L1 expression level in tumors. Many clinical studies have shown that patients with elevated PD-L1 expression benefit more from ICI treatment. Therefore, some studies have considered PD-L1 expression as the main evaluation target, and based on the PD-L1 expression level, it has a good distinguishing effect on the prediction of ICI efficacy in some tumors. However, for PD-L1 immunohistochemistry detection, current studies have found that there is no correlation between PD-L1 expression and ICI treatment response or overall survival (OS) in some patients, and even many patients with PD-L1 expression as low as undetectable have obtained lasting clinical benefits from ICI treatment. In addition, using PD-L1 expression as a predictor of immune efficacy has the following defects: PD-L1 expression is heterogeneous in different stages and regions of tumor development, and its detection results may be affected by sampling time and sampling site, and affected by previous or concurrent chemoradiotherapy. There is a lack of recognized standards for PD-L1 detection and evaluation methods, resulting in inconsistent results in different studies. Therefore, the current screening methods based on PD-L1 immunohistochemistry need to be further improved. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a new model for accurately predicting the efficacy of breast cancer immunotherapy and its application, so as to overcome the above-mentioned technical problems currently existing in the field.

[0006] The above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0007] The first aspect of the present invention provides a combination of markers for predicting the efficacy of immunotherapy for breast cancer.

[0008] Furthermore, the marker combination includes IFN-γ, aSMA, collagen-I, and Vimentin.

[0009] In a specific embodiment of the present invention, the present invention collected actual clinical samples for analysis and verification and found that the combination of IFN-γ, aSMA, collagen-I, and Vimentin can be accurately used to predict the efficacy of breast cancer immunotherapy in breast cancer patients, and is significantly better than other marker combinations, has good diagnostic predictive efficacy, sensitivity and specificity, and can be used to accurately identify breast cancer patients who benefit from immunotherapy in clinical practice, and guide the clinical application of immunotherapy.

[0010] The second aspect of the present invention provides the use of a reagent for detecting the expression level of the marker combination described in the first aspect of the present invention in a sample in the preparation of a product for predicting the efficacy of immunotherapy for breast cancer.

[0011] Further, the reagents include reagents for detecting the expression level of the marker combination in a sample by tissue mass spectrometry imaging technology, digital imaging technology, protein immunoassay technology, dye technology, nucleic acid sequencing technology, nucleic acid hybridization technology, chromatography technology, and / or next-generation sequencing technology;

[0012] Preferably, the reagent includes a reagent for detecting the mRNA expression level of the marker combination;

[0013] More preferably, the reagent is a reagent for detecting the level of cDNA complementary to mRNA transcribed in combination with the marker;

[0014] Most preferably, the reagent is a primer or a probe;

[0015] Preferably, the reagent includes a reagent for detecting the expression level of the marker combination protein;

[0016] More preferably, the reagent is a reagent for detecting the level of a polypeptide or protein encoded by the marker combination;

[0017] Most preferably, the agent is an antibody, antibody fragment or affinity protein.

[0018] Furthermore, the sample is derived from a tissue sample, a peripheral blood sample, a serum sample, a plasma sample, a urine sample, or a saliva sample of a subject;

[0019] Preferably, the sample is derived from a tissue sample of a subject. In a specific embodiment of the present invention, the subject is preferably a breast cancer patient, and the tissue sample is preferably a breast cancer tumor tissue sample.

[0020] In a specific embodiment of the present invention, the immunotherapy is preferably PD-L1 monoclonal antibody immunotherapy, and the breast cancer is preferably HER2-negative breast cancer.

[0021] In addition, the present invention also provides a product for predicting the efficacy of immunotherapy for breast cancer.

[0022] Further, the product is an in vitro diagnostic product;

[0023] Preferably, the in vitro diagnostic product is an in vitro diagnostic kit.

[0024] Further, the product comprises a reagent for detecting the expression level of the marker combination described in the first aspect of the present invention in a sample;

[0025] Preferably, the reagent comprises a reagent for detecting the mRNA expression level of the marker combination, and / or a reagent for detecting the protein expression level of the marker combination;

[0026] More preferably, the reagents include reagents for detecting the level of cDNA complementary to the mRNA transcribed by the marker combination, and reagents for detecting the level of a polypeptide or protein encoded by the marker combination;

[0027] Most preferably, the reagent is a primer, a probe, an antibody, an antibody fragment and / or an affinity protein.

[0028] Furthermore, the product also includes total RNA extraction reagents, reverse transcription reagents, and / or second-generation sequencing reagents.

[0029] The expression level of the marker combination in the present invention can be detected by a determination method known in the art, including but not limited to a method for detecting the amount of RNA transcripts of the genes in the marker combination or the amount of polypeptides encoded by the genes in the marker combination;

[0030] Preferably, the RNA transcript of the gene can be converted into a cDNA complementary thereto by a method known in the art, and the amount of the RNA transcript can be obtained by measuring the amount of the complementary cDNA. The amount of the RNA transcript of the gene or the cDNA complementary thereto can be standardized to the amount of total RNA or total cDNA in the sample or to the amount of RNA transcripts of a group of housekeeping genes or the cDNA complementary thereto;

[0031] Preferably, the RNA transcript can be detected and quantified by methods such as hybridization, amplification or sequencing, including but not limited to methods of hybridizing RNA transcripts with probes or primers, and methods of detecting the amount of RNA transcripts or their corresponding cDNA products by various quantitative PCR techniques or sequencing techniques based on polymerase chain reaction (PCR). The quantitative PCR technique includes but is not limited to fluorescent quantitative PCR, real-time PCR or semi-quantitative PCR techniques. The sequencing technique includes but is not limited to Sanger sequencing, second-generation sequencing, third-generation sequencing and single-cell sequencing, etc. Preferably, the sequencing technique is second-generation sequencing, and more preferably, a transcriptome sequencing technique targeting RNA-seq.

[0032] In a specific embodiment of the present invention, the immunotherapy is preferably PD-L1 monoclonal antibody immunotherapy, and the breast cancer is preferably HER2-negative breast cancer.

[0033] A third aspect of the present invention provides a risk prediction model for predicting the efficacy of immunotherapy for breast cancer.

[0034] Furthermore, the risk prediction model includes the marker combination described in the first aspect of the present invention.

[0035] Furthermore, the risk prediction model uses the following regression equation to calculate the risk prediction score 4RF-panel score :

[0036] Risk Prediction Score 4RF-panel score =IFN-γ*(-62.5851559)+aSMA*(-0.3814794)+colla gen-I*(28.1856122)+Vimentin*(8.9254198)-0.4248442;

[0037] Among them, IFN-γ, aSMA, collagen-I, and Vimentin are relative intensity values ​​compared to the DNA antibody channel;

[0038] Preferably, the risk prediction score 4RF-panel score When >0.400, the subject has a good prognosis after immunotherapy;

[0039] Preferably, the risk prediction score 4RF-panel score When <0.400, the subject has a poor prognosis after immunotherapy.

[0040] In a specific embodiment of the present invention, the immunotherapy is preferably PD-L1 monoclonal antibody immunotherapy, and the breast cancer is preferably HER2-negative breast cancer.

[0041] The present invention verified the constructed risk prediction model in the collected training set and validation set and found that it has high diagnostic prediction efficiency, with an AUC value of up to 0.843 in the training set and an AUC of up to 0.830 in the validation set, which are significantly higher than the AUC values ​​of other comparison models (AUC=0.674-0.789), indicating that the four-risk factor (4RF-panel) model constructed by the present invention has a good ability to predict the efficacy of immunotherapy for breast cancer patients.

[0042] A fourth aspect of the present invention provides an evaluation method for predicting the efficacy of immunotherapy for breast cancer.

[0043] Further, the method comprises the following steps:

[0044] (1) obtaining clinical data of the subject, wherein the clinical data includes relative intensity values ​​of IFN-γ, aSMA, collagen-I, and Vimentin compared with DNA antibody channels;

[0045] (2) Inputting the clinical data described in step (1) into the risk prediction model described in the third aspect of the present invention to obtain the risk prediction score of the subject 4RF-panel score ;

[0046] (3) Risk prediction score 4RF-panel obtained according to step (2) score Predictive evaluation of the efficacy of immunotherapy on subjects:

[0047] Preferably, the risk prediction score 4RF-panel score When >0.400, the subject has a good prognosis after immunotherapy;

[0048] Preferably, the risk prediction score 4RF-panel score When <0.400, the subject has a poor prognosis after immunotherapy.

[0049] Furthermore, the relative intensity values ​​of the IFN-γ, aSMA, collagen-I, and Vimentin to the DNA antibody channel are test results obtained by performing 28-channel tissue mass spectrometry imaging on tumor tissues of breast cancer patients.

[0050] Furthermore, the subject is preferably a human.

[0051] Furthermore, the subject is preferably a breast cancer patient.

[0052] Furthermore, the clinical data of the subject is obtained by detecting the subject's sample through 28-channel tissue mass spectrometry imaging, and the sample is preferably a tumor tissue sample of a breast cancer subject.

[0053] In a specific embodiment of the present invention, the immunotherapy is preferably PD-L1 monoclonal antibody immunotherapy, and the breast cancer is preferably HER2-negative breast cancer.

[0054] A fifth aspect of the present invention provides a device or system for predicting the efficacy of immunotherapy for breast cancer.

[0055] Further, the device or system comprises:

[0056] (1) a data acquisition module, used to obtain the relative intensity value of the marker combination in the breast cancer patient sample to be tested compared with the DNA antibody channel, wherein the marker combination is IFN-γ, aSMA, collagen-I, and Vimentin;

[0057] (2) a prediction module, which is used to provide the relative intensity value data of the marker combination compared with the DNA antibody channel obtained by the data acquisition module as input data to a risk prediction model, wherein the risk prediction model performs a prognosis prediction on the immunotherapy efficacy of the breast cancer patient based on the relative intensity value of the marker combination compared with the DNA antibody channel in the breast cancer patient sample;

[0058] (3) a prediction result acquisition module, used to obtain the output result of the risk prediction model in the prediction module to obtain the prognosis prediction result of the breast cancer patient to be tested;

[0059] Preferably, the risk prediction model described in step (2) is the risk prediction model described in the third aspect of the present invention;

[0060] Preferably, the risk prediction score 4RF-panel score When >0.400, the breast cancer patient has a good prognosis after immunotherapy;

[0061] Preferably, the risk prediction score 4RF-panel score When <0.400, the breast cancer patient has a poor prognosis after immunotherapy.

[0062] Furthermore, the sample is derived from a tissue sample, a peripheral blood sample, a serum sample, a plasma sample, a urine sample, or a saliva sample of a subject;

[0063] Preferably, the sample is derived from a tissue sample of a breast cancer patient of the subject. In a specific embodiment of the present invention, the tissue sample is preferably a breast cancer tumor tissue sample.

[0064] In a specific embodiment of the present invention, the immunotherapy is preferably PD-L1 monoclonal antibody immunotherapy, and the breast cancer is preferably HER2-negative breast cancer.

[0065] A sixth aspect of the present invention provides a computer device.

[0066] Furthermore, the computer device includes a memory and a processor, the memory stores a program, and the processor implements the following method when executing the program:

[0067] Obtaining relative intensity value data of a marker combination in a sample of a breast cancer patient to be tested compared to a DNA antibody channel, wherein the marker combination is IFN-γ, aSMA, collagen-I, and Vimentin;

[0068] Providing the relative intensity value data of the marker combination compared to the DNA antibody channel as input data to a risk prediction model;

[0069] Output the prognosis prediction results of the breast cancer patients to be tested;

[0070] Preferably, the risk prediction model is the risk prediction model described in the third aspect of the present invention;

[0071] Preferably, the risk prediction score 4RF-panel score When >0.400, the breast cancer patient has a good prognosis after immunotherapy;

[0072] Preferably, the risk prediction score 4RF-panel score When <0.400, the breast cancer patient has a poor prognosis after immunotherapy.

[0073] Furthermore, the sample is derived from a tissue sample, a peripheral blood sample, a serum sample, a plasma sample, a urine sample, or a saliva sample of a subject;

[0074] Preferably, the sample is derived from a tissue sample of a breast cancer patient of the subject. In a specific embodiment of the present invention, the tissue sample is preferably a breast cancer tumor tissue sample.

[0075] In a specific embodiment of the present invention, the immunotherapy is preferably PD-L1 monoclonal antibody immunotherapy, and the breast cancer is preferably HER2-negative breast cancer.

[0076] A seventh aspect of the present invention provides a computer-readable storage medium.

[0077] Furthermore, the computer-readable storage medium stores a program, and when the program is executed, the following method is implemented:

[0078] Obtaining relative intensity value data of a marker combination in a sample of a breast cancer patient to be tested compared to a DNA antibody channel, wherein the marker combination is IFN-γ, aSMA, collagen-I, and Vimentin;

[0079] Providing the relative intensity value data of the marker combination compared to the DNA antibody channel as input data to a risk prediction model;

[0080] Output the prognosis prediction results of the breast cancer patients to be tested;

[0081] Preferably, the risk prediction model is the risk prediction model described in claim 4 or 5;

[0082] Preferably, the risk prediction score 4RF-panel score When >0.400, the breast cancer patient has a good prognosis after immunotherapy;

[0083] Preferably, the risk prediction score 4RF-panel score When <0.400, the breast cancer patient has a poor prognosis after immunotherapy.

[0084] Furthermore, the sample is derived from a tissue sample, a peripheral blood sample, a serum sample, a plasma sample, a urine sample, or a saliva sample of a subject;

[0085] Preferably, the sample is derived from a tissue sample of a breast cancer patient of the subject. In a specific embodiment of the present invention, the tissue sample is preferably a breast cancer tumor tissue sample.

[0086] In a specific embodiment of the present invention, the immunotherapy is preferably PD-L1 monoclonal antibody immunotherapy, and the breast cancer is preferably HER2-negative breast cancer.

[0087] An eighth aspect of the present invention provides the application of any of the following aspects:

[0088] (1) Use of the marker combination described in the first aspect of the present invention in the preparation of a product for predicting the efficacy of immunotherapy for breast cancer;

[0089] (2) Use of the marker combination described in the first aspect of the present invention in constructing a risk prediction model for predicting the efficacy of immunotherapy for breast cancer;

[0090] (3) Application of the risk prediction model described in the third aspect of the present invention in constructing a device or system for predicting the efficacy of immunotherapy for breast cancer;

[0091] (4) Application of the risk prediction model described in the third aspect of the present invention in constructing a computer device or a computer-readable storage medium.

[0092] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0093] The present invention provides a novel model for accurately predicting the efficacy of immunotherapy for breast cancer. The model can realize accurate prediction of the efficacy of immunotherapy for breast cancer patients. The results calculated according to the model can very clearly distinguish between responders and non-responders to breast cancer immunotherapy. It can effectively help clinicians to accurately individualize the effectiveness of PD-L1 monoclonal antibody immunotherapy for breast cancer patients before receiving immunotherapy, guide doctors to perform accurate immunotherapy for breast cancer patients, thereby bringing better survival benefits to breast cancer patients, and has important clinical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, wherein:

[0095] Figure 1 The result diagram is a graph showing the differential expression of IFN-γ between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0096] Figure 2 The result diagram is a result of differential expression of HLA-DR between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0097] Figure 3 The result diagram of the differential expression of PD1 between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0098] Figure 4 The result diagram is a graph showing the differential expression of PDL1 between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0099] Figure 5 The result diagram is a graph showing the differential expression of PDL2 between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0100] Figure 6 The result diagram is a diagram showing the differential expression of TGF-β between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0101] Figure 7 The result diagram is a graph showing the differential expression of CKAE1 / AE3 between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0102] Figure 8 The result diagram is a graph showing the differential expression of TNF-α between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0103] Fig. 9 The result diagram is a result of differential expression of aSMA between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0104] Fig.10The result diagram is a graph showing the differential expression of collagen-I between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0105] Fig.11 The result diagram is a graph showing the differential expression of E-cadherin between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0106] Fig.12 This is a result diagram showing the differential expression of Vimentin between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0107] Fig.13 The figure is a result of differential expression of Ki-67 between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0108] Fig.14 This is a result diagram showing the differential expression of β-catenin between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0109] Fig.15 The result diagram is a diagram showing the differential expression of FoxP3 between tumor cells of immunotherapy responders and immunotherapy non-responders;

[0110] Fig.16 Heat map of the correlation between markers CD68, CD45, TGF-β, Vimentin, CD8, CD3, CD11b, Ki-67, β-catenin, CD33, αSMA, PDL2, HLA-DR, CD4, FoxP3, TNF-α, collagen-I, CD15, E-cadherin, CD14, CD16, CD19, PDL1, CD11c, CKAE1 / AE3, IFN-γ, PD1, and CD56 and genes for breast cancer immunotherapy;

[0111] Fig.17 A diagram showing the diagnostic efficacy of the four-risk factor (4RF-panel) model (including four markers: IFN-γ, aSMA, collagen-I, and Vimentin) constructed by the present invention for predicting the efficacy of immunotherapy for breast cancer patients in the training set;

[0112] Fig.18 A graph showing the diagnostic efficacy of the four-risk factor (4RF-panel) model (comprising four markers: IFN-γ, aSMA, collagen-I, and Vimentin) constructed for the present invention for predicting the efficacy of immunotherapy in breast cancer patients in the validation set. DETAILED DESCRIPTION

[0113] The present invention is further described below in conjunction with specific embodiments, which are only used to explain the present invention and cannot be understood as limiting the present invention. It can be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and purpose of the present invention, and the scope of the present invention is defined by the claims and their equivalents. The experimental methods used in the following embodiments are conventional methods unless otherwise specified; the reagents, biological materials, etc. used in the following embodiments, unless otherwise specified, can all be obtained from commercial sources.

[0114] In order to better explain the present invention, the professional terms involved in the present invention are explained as follows.

[0115] In the context of the present invention, the term "PD-L1 monoclonal antibody immunotherapy" refers to a treatment method for breast cancer treatment, and the PD-L1 monoclonal antibody drugs include atezolizumab, avelumab, durvalunab, etc. The mechanism of PD-L1 monoclonal antibody immunotherapy is as follows: in the body's immune process against tumor cells, T lymphocytes infiltrating in the tumor tissue overexpress the negative regulatory molecule PD-1, or tumor cells overexpress their ligand PD-L1. As an inhibitory second signal molecule, the interaction between the two inhibits the body's cellular immune response to a certain extent, thereby allowing the tumor to escape the surveillance and clearance of the immune system (partial mechanism of tumor immune escape). The mechanism of action of PD-1 antibody immunotherapy is to design specific protein antibodies against PD-1 or PD-L1. These antibodies can bind to PD-1 or PD-L1, thereby preventing the interaction between PD-1 on the surface of T cells and PD-L1 on tumor cells, thereby partially restoring T cell function, thereby enhancing the effect of T cells in killing tumor cells. PD-L1 monoclonal antibody immunotherapy can benefit patients with HER2-negative breast cancer in terms of survival, but some patients still do not respond well to PD-L1 monoclonal antibody immunotherapy. Therefore, the present invention provides a novel model for accurately predicting the efficacy of immunotherapy for breast cancer, so as to achieve accurate prediction of the efficacy of immunotherapy for breast cancer patients.

[0116] In the context of the present invention, the terms "comprises", "comprising", "having", "containing" or "involving" are used to mean inclusive or open-ended, and do not exclude other unlisted elements or method steps. The term "consisting of" is considered to be a preferred embodiment of the term "comprising". If a group is defined herein as comprising at least a certain number of embodiments, this should also be understood to disclose a group that preferably consists only of these embodiments.

[0117] In the context of the present invention, the term "sample" used refers to a composition obtained from or derived from a subject (e.g., an individual of interest) that contains cells and / or other molecular entities to be characterized and / or identified based on, for example, physical, biochemical, chemical and / or physiological characteristics. For example, a sample refers to any sample derived from a subject of interest that is expected or known to contain cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples (e.g., tumor tissue samples), primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous humor, lymphatic fluid, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tumor lysates, tissue culture fluid, tissue extracts, homogenized tissues, tumor tissues, cell extracts, and combinations thereof. As a preferred embodiment, the sample is selected from a tissue sample of a subject.

[0118] In the context of the present invention, the term "AUC (area under the receiver operating characteristic curve)" is used as an indicator of the performance or accuracy of a diagnostic procedure. The accuracy of a diagnostic method is best described by its receiver operating characteristic (ROC). A ROC plot is a line graph of all sensitivity / specificity pairs derived from continuously varying the decision threshold over the entire range of data observed. The clinical performance of a laboratory test depends on its diagnostic accuracy, or the ability to correctly classify a subject into clinically relevant subgroups. Diagnostic accuracy measures the ability of a test to correctly discriminate between two different conditions in the subject under investigation.

[0119] In the context of the present invention, the term "expression level" used refers to the amount of polynucleotides or amino acid products or proteins in a biological sample. "Expression" generally refers to the process in which the information encoded by a gene is converted into a structure that exists and operates in a cell. Therefore, the "expression" of a marker used herein refers to transcription into polynucleotides, translation into proteins or even post-translational modifications of proteins. Fragments of transcribed polynucleotides, translated proteins or post-translationally modified proteins are also considered to be expressed, whether they are derived from transcripts produced by alternative splicing or degraded transcripts, or from post-translational processing of proteins (e.g., by proteolysis). "Expressed genes" include those that are transcribed into polynucleotides (such as mRNA) and then translated into proteins, and those that are transcribed into RNA but not translated into proteins (e.g., transfer RNA and ribosomal RNA).

[0120] In the context of the present invention, the term "computer device" used includes but is not limited to any terminal such as a personal computer, server, etc. that can interact with a user through a keyboard, touchpad, or voice control device. The computing device herein may also include a mobile terminal, which includes but is not limited to any electronic device that can interact with a user through a keyboard, touchpad, or voice control device, such as a tablet computer, a smart phone, a personal digital assistant (PDA), a smart wearable device, and other terminals. The network in which the computing device is located includes (but is not limited to) the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0121] In the context of the present invention, the term "memory" used includes non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. The code of the operating system is stored thereon. For example, the memory also stores codes or instructions, and by running these codes or instructions, the risk assessment model for colorectal cancer prognosis prediction and diagnosis provided by the embodiments disclosed herein can be implemented. Volatile memory may include random access memory (RAM) or external cache memory.

[0122] In the context of the present invention, the term "processor" used may include one or more microprocessors and digital processors. The processor may call program codes stored in a memory to execute related functions. The processor is also called a central processing unit (CPU), which may be a very large scale integrated circuit, a computing core (Core) and a control core (Control Unit).

[0123] Example 1 Screening of markers associated with breast cancer patients' responsiveness to immunotherapy (PD-L1 monoclonal antibody immunotherapy)

[0124] 1. Study population

[0125] This invention retrospectively collected 26 patients diagnosed with HER2-negative breast cancer according to the AJCC 8th edition criteria at the Cancer Hospital of the Chinese Academy of Medical Sciences from January 2020 to October 2021, collected tumor tissue samples from the patients, explained the experimental conditions to the patients and their families, signed an informed consent form with them, and obtained the approval of the organization's ethics committee.

[0126] Inclusion criteria:

[0127] 1) Male or female who is 18 years of age or older on the day of signing the informed consent form.

[0128] 2) The subject has localized invasive ductal breast carcinoma, staged T1c-T2 and clinical lymph node stage cN1-cN3, or T3-T4 / cN0-cN3, or M1, confirmed by a local pathologist.

[0129] 3) Patients with HER2- ductal breast carcinoma according to the most recent American Society of Clinical Oncology / College of American Pathologists guidelines.

[0130] 4) Female subjects are not pregnant or breastfeeding, and have used contraception for at least 12 months after the last dose.

[0131] 5) Have proper organ function.

[0132] Exclusion criteria:

[0133] 1) A history of pneumonia (non-infectious) requiring steroid treatment or current pneumonia.

[0134] 2) Active infection requiring systemic treatment.

[0135] 3) Positive urine pregnancy test within 72 hours before the first dose of study treatment.

[0136] 4) Patients who have received previous treatment with anti-PD-1, anti-PD-L1, or anti-PD-L2 drugs or targeted drugs that act on another stimulatory or synergistic inhibitory T cell receptor.

[0137] 5) Diagnosed with immunodeficiency or receiving long-term systemic steroid therapy (daily dose exceeding 10 mg prednisone equivalents) or any form of immunosuppressive therapy within 7 days before the first dose of trial treatment.

[0138] 6) Active autoimmune disease requiring systemic treatment (e.g., use of disease-modifying drugs, corticosteroids, or immunosuppressive drugs) within the past 2 years.

[0139] 7) Known history of active tuberculosis.

[0140] 8) Echocardiography was performed at screening to confirm a left ventricular ejection fraction (LVEF) <50% or below the institutional normal limit.

[0141] 9) Known history of human immunodeficiency virus (HIV) infection.

[0142] 10) Known history of hepatitis B or known active hepatitis C virus infection.

[0143] 2. Perform 28-channel tissue mass spectrometry imaging on tumor tissues of breast cancer patients

[0144] The breast cancer patients were all treated with PD-L1 monoclonal antibody immunotherapy. First, this example performs 28-channel tissue mass spectrometry imaging on the tumor tissue of patients with immunotherapy (PD-L1 monoclonal antibody immunotherapy) and draws a single-cell resolution map. According to the single-cell sequencing research results previously published by the inventor research group of the present invention (PMID: 34653365), tumor microenvironment markers commonly used in multiple labeling imaging were screened. Mainly including cell identity proteins, cell function markers, immunomodulatory proteins, etc. Finally, an IMC panel of 28 markers including an immune microenvironment antibody marker combination was formed.

[0145] Tissue mass spectrometry imaging was performed on the tumor tissues of the patients included in the study. The study first distinguished the pathological microanatomical areas through pathological morphological analysis, and repeated scanning of two ROIs (Regions of interest) in each of the three structural areas of the tumor tissue was performed for each patient's tumor tissue. The imaging data was stored in MCD files, and the Cytomapper program package could be used to draw channel-synthesized pseudo-color photos.

[0146] Cell identification and clustering were performed on breast cancer tissue mass spectrometry imaging data. CellProfiler's built-in machine learning algorithm was used for image single-cell segmentation. The unsupervised learning algorithm Phenograph was then used to cluster based on single-cell morphology and shared protein markers, resulting in a total of 25 cell subpopulations, including epithelial tumor cells, immune cells, and stromal cells. After marker visualization, the same subpopulations were merged, and finally 23 biologically meaningful cell subpopulation annotations were obtained.

[0147] 3. Experimental results

[0148] The results showed that the cell identity protein markers screened included: CKAE1 / AE3, β-catenin, E-cadherin, CD45, CD3, CD8, CD4, FoxP3, CD11b, CD11c, CD14, CD15, CD16, CD19, CD33, CD56, CD68, Vimentin, αSMA, collagen-I; the cell function protein markers included: dsDNA, Ki-67, HLA-DR, IFN-γ; the immunomodulatory protein markers included: PD1, PDL1, PDL2, TGF-β, TNF-α; that is, the markers related to the responsiveness of breast cancer patients to PD-L1 monoclonal antibody immunotherapy obtained by screening and identification of the present invention include: CKAE1 / AE3, β-cateni n, E-cadherin, CD45, CD3, CD8, CD4, FoxP3, CD11b, CD11c, CD14, CD15, CD16, CD19, CD33, CD56, CD68, Vimentin, αSMA, collagen-I, dsDNA, Ki-67, HLA-DR, IFN-γ, PD1, PDL1, PDL2, TGF-β, TNF-α; among them, 15 markers related to breast cancer tumor cells include: IFN-γ, HLA-DR, PD1, PDL1, PDL2, TGF-β, CKAE1 / AE3, TNF-α, aSMA, collagen-I, E-cadherin, Vimentin, Ki-67, β-catenin, FoxP3 (see Figure 1-Figure 15 ); the results of the gene correlation heat map are shown in Fig.16 The results showed that the expression intensity of the above markers in breast cancer tumor cells was significantly correlated with the response to immunotherapy.

[0149] The results showed the characteristics of tissue mass spectrometry imaging of breast cancer patients with different immunotherapy responses: in metastatic TNBC patients whose lesions were significantly reduced after receiving PD-L1 monoclonal antibody treatment, their baseline tumor tissue microenvironment was rich in CD8+T cells and mononuclear macrophages, and B cells also showed a different trend; in patients with poor immunotherapy response and disease progression, more myeloid cells were seen in their baseline tumor tissue microenvironment. There was no significant difference in Treg cell subsets between the groups. In the single-cell expression profile analysis, we found that tumor cells in the effective group (immunotherapy response group) expressed more TGF-β and Ki-67, while tumor cells in the ineffective group (immunotherapy non-response group) were more commonly highly expressed in CKAE1 / AE3, aSMA and other markers, and it was speculated that the high degree of differentiation may be related to lower immunogenicity. A subset of immune cells was identified by unbiased analysis technology, which co-expressed TGF-β in multiple samples and was enriched in patients with better immunotherapy effects.

[0150] Example 2 Establishment and verification of a model for predicting the efficacy of immunotherapy (PD-L1 monoclonal antibody immunotherapy) in breast cancer patients

[0151] 1. Establishment of the Four Risk Factor (4RF-panel) Model

[0152] The breast cancer patients collected in Example 1 were randomly and evenly distributed into a training cohort (n=13) and a validation cohort (n=13). The 4RF-panel model was constructed in the training cohort using a Logistic regression analysis method of four clinical risk factors associated with the efficacy of immunotherapy. The predictive scoring model for evaluating the efficacy of immunotherapy in subjects is as follows:

[0153] Prediction score 4RF-panel score =IFN-γ*(-62.5851559)+aSMA*(-0.3814794)+collagen-I*(28.1856122)+Vimentin*(8.9254198)-0.4248442

[0154] Wherein, * represents a multiplication sign, the unit of IFN-γ expression amount is the relative intensity value of DNA antibody channel, the unit of aSMA expression amount is the relative intensity value of DNA antibody channel, the unit of collagen-I expression amount is the relative intensity value of DNA antibody channel, and the unit of Vimentin expression amount is the relative intensity value of DNA antibody channel.

[0155] In the training cohort, the prediction score at the point of 0% false positive rate and 100% true positive rate was used as the optimal cut-off value (Cut-off), and it was directly applied to the training cohort and the validation cohort to divide the patients into low-score group and high-score group.

[0156] 2. Use the four-risk factor (4RF-panel) model to predict the efficacy of immunotherapy and evaluate its predictive diagnostic efficacy

[0157] In order to determine the effectiveness of the four risk factors (4RF-panel) model constructed above in clinically predicting the efficacy of PD-L1 monoclonal antibody immunotherapy in breast cancer patients, this example uses the 4RF-panel model to score each breast cancer patient in the training cohort to obtain the prediction score 4RF-panelscore. In order to further verify the predictive ability of the 4RF-panel model, this example analyzes its diagnostic efficacy (ROC curve).

[0158] 3. Statistical analysis

[0159] The data were expressed as mean ± standard deviation (SD) when normally distributed, as median and interquartile range (IQR) when non-normally distributed, and as percentage when categorized. The Wilcoxon signed-rank test (non-normally distributed variables), Student's t-test (normally distributed variables), and chi-square test (categorical or binary variables) were used to compare the differences in baseline characteristics between the two groups. All baseline characteristics were stratified according to the number of relapses. The area under the receiver operating characteristic curve (AUROC) analysis was used to determine the predictive performance of the efficacy of each variable and the identified risk factor group, and the Delong test was used for comparison. P < 0.05 was considered statistically significant. All statistical analyses were performed using R language software (v4.0.3) and Bioconductor software (v3.13).

[0160] 4. Experimental results

[0161] The AUC value of the four risk factor (4RF-panel) model constructed in the present invention for predicting the efficacy of immunotherapy in the training set was as high as 0.843 (see Fig.17 ), the AUC value for predicting the efficacy of immunotherapy in the validation set was as high as 0.830 (see Fig.18 ), where the optimal cutoff value in the training set is 0.5 and the optimal cutoff value in the validation set is 0.4. A score greater than the cutoff value indicates that the subject can benefit from immunotherapy.

[0162] Comparative Example 1 Performance comparison of the model for predicting the efficacy of immunotherapy (PD-L1 monoclonal antibody immunotherapy) for breast cancer patients established by the present invention and other models

[0163] 1. Experimental methods

[0164] The model (target model) established by the present invention for predicting the efficacy of immunotherapy (PD-L1 monoclonal antibody immunotherapy) for breast cancer patients, namely the four-risk factor (4RF-panel) model, includes four markers: IFN-γ, aSMA, collagen-I, and Vimentin.

[0165] In addition, in this embodiment, several other models (i.e., comparison models) for predicting the efficacy of immunotherapy (PD-L1 monoclonal antibody immunotherapy) for breast cancer patients were constructed for comparison with the diagnostic efficacy of the target model. Comparison model 1 contains 4 markers: IFN-γ, aSMA, collagen-I, E-cadherin, comparison model 2 contains 4 markers: IFN-γ, aSMA, collagen-I, β-catenin, comparison model 3 contains 4 markers: IFN-γ, aSMA, E-cadherin, Ki-67, comparison model 4 contains 4 markers: IFN-γ, aSMA, E-cadherin, β-catenin, comparison model 5 contains 4 markers : IFN-γ, collagen-I, E-cadherin, Vimentin, comparison model 6 contains 4 markers: IFN-γ, collagen-I, E-cadherin, Ki-67, comparison model 7 contains 5 markers: IFN-γ, aSMA, collagen-I, E-cadherin, Ki-67, comparison model 8 contains 5 markers: IFN-γ, aSMA, collagen-I, E-cadherin, β-catenin n, comparison model 9 includes 5 markers: IFN-γ, aSMA, collagen-I, Ki-67, β-catenin, comparison model 10 includes 5 markers: IFN-γ, aSMA, E-cadherin, Ki-67, β-catenin, comparison model 11 includes 6 markers: IFN-γ, TNF-α, aSMA, collagen-I, E-cadherin, Ki-67, comparison model 12 includes 6 markers: IFN-γ, TNF-α, aSMA, E-cadherin, Ki-67, β-catenin, comparison model 13 includes 6 markers: IFN-γ, TNF-α, collagen-I, E-cadherin, Ki-67, β-catenin, and comparison model 14 includes 6 markers: IFN-γ, aSMA, collagen-I, E-cadherin, Ki-67, β-catenin. This embodiment uses the above models to analyze the ROC curves of all breast cancer patients.

[0166] 2. Experimental results

[0167] The results showed that the AUC values ​​of the comparison models containing 4-6 other markers related to the efficacy of breast cancer immunotherapy were all lower than 0.800 (see Table 1), while the AUC value of the four-risk factor (4RF-panel) model constructed by the present invention for predicting the efficacy of immunotherapy was as high as 0.843 (see Table 1 and Fig.17 and Fig.18 ), wherein AUC in the training set was 0.843, and AUC in the validation set was 0.830, which were significantly higher than the AUC values ​​of the comparison model containing 4-6 markers (AUC = 0.674-0.789), indicating that the four-risk factor (4RF-panel) model established in the present invention has a good ability to predict the efficacy of immunotherapy for breast cancer patients and has good diagnostic predictive efficacy.

[0168] Table 1 Comparison of prediction performance of different prediction models

[0169]

[0170] The description of the above embodiments is only used to understand the method and core idea of ​​the present invention. It should be pointed out that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications will also fall within the scope of protection of the claims of the present invention.

Claims

1. A device or system for predicting the efficacy of breast cancer immunotherapy, characterized in that: The device or system comprises: (1) a data acquisition module, used to obtain the relative intensity value of the marker combination in the breast cancer patient sample to be tested compared with the DNA antibody channel, wherein the marker combination is IFN-γ, aSMA, collagen-I and Vimentin; (2) a prediction module, configured to provide the relative intensity value data of the marker combination compared to the DNA antibody channel obtained by the data acquisition module as input data to a risk prediction model, wherein the risk prediction model performs a prognosis prediction on the immunotherapy efficacy of the breast cancer patient based on the relative intensity value of the marker combination compared to the DNA antibody channel in the breast cancer patient sample; (3) a prediction result acquisition module, used to obtain the output result of the risk prediction model in the prediction module to obtain the prognosis prediction result of the breast cancer patient to be tested; The risk prediction model uses the following regression equation to calculate the risk prediction score 4RF-panel score : Risk Prediction Score 4RF-panel score =IFN-γ*(-62.5851559)+aSMA*(-0.3814794)+collagen-I*(28.1856122)+Vimentin*(8.9254198)-0.4248442; Among them, IFN-γ, aSMA, collagen-I, and Vimentin are relative intensity values ​​compared to the DNA antibody channel; The risk prediction score 4RF-panel score When >0.400, the breast cancer patient has a good prognosis after immunotherapy; The risk prediction score 4RF-panel score When <0.400, the breast cancer patient has a poor prognosis after immunotherapy.

2. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a program, and the processor implements the following method when executing the program: Obtaining relative intensity value data of a marker combination in a sample of a breast cancer patient to be tested compared to a DNA antibody channel, wherein the marker combination is IFN-γ, aSMA, collagen-I, and Vimentin; Providing the relative intensity value data of the marker combination compared to the DNA antibody channel as input data to a risk prediction model; Output the prognosis prediction results of the breast cancer patients to be tested; The risk prediction model uses the following regression equation to calculate the risk prediction score 4RF-panel score : Risk Prediction Score 4RF-panel score =IFN-γ*(-62.5851559)+aSMA*(-0.3814794)+collagen-I*(28.1856122)+Vimentin*(8.9254198)-0.4248442; Among them, IFN-γ, aSMA, collagen-I, and Vimentin are relative intensity values ​​compared to the DNA antibody channel; The risk prediction score 4RF-panel score When >0.400, the breast cancer patient has a good prognosis after immunotherapy; The risk prediction score 4RF-panel score When <0.400, the breast cancer patient has a poor prognosis after immunotherapy.

3. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is executed, the following method is implemented: Obtaining relative intensity value data of a marker combination in a sample of a breast cancer patient to be tested compared to a DNA antibody channel, wherein the marker combination is IFN-γ, aSMA, collagen-I, and Vimentin; Providing the relative intensity value data of the marker combination compared to the DNA antibody channel as input data to a risk prediction model; Output the prognosis prediction results of the breast cancer patients to be tested; The risk prediction model uses the following regression equation to calculate the risk prediction score 4RF-panel score : Risk Prediction Score 4RF-panel score =IFN-γ*(-62.5851559)+aSMA*(-0.3814794)+collagen-I*(28.1856122)+Vimentin*(8.9254198)-0.4248442; Among them, IFN-γ, aSMA, collagen-I, and Vimentin are relative intensity values ​​compared to the DNA antibody channel; The risk prediction score 4RF-panel score When >0.400, the breast cancer patient has a good prognosis after immunotherapy; The risk prediction score 4RF-panel score When <0.400, the breast cancer patient has a poor prognosis after immunotherapy.

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