A method, apparatus, and medium to assist in predicting immune-related adverse reactions

Abstract

The application provides a method, device, medium and program product for assisting in predicting immune-related adverse reactions, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring cell data of a subject; calculating the proportion of target cell subgroups including a CXCR3+CCR6+CD8+T cell subgroup and an NK cell subgroup in single nucleus cells based on the cell data; calculating a prediction score based on the proportion of the target cell subgroups; and outputting an auxiliary prediction result of the probability of the subject suffering from an immune-related adverse reaction according to the prediction score. It is explored and verified in the application that a higher baseline level of the CXCR3+CCR6+CD8+T cell subgroup and the NK cell subgroup in immunotherapy is closely related to the occurrence of irAE.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent medical treatment, and more particularly, to a method, device, medium and program product for assisting in predicting immune-related adverse reactions. BACKGROUND

[0002] Immunotherapy represented by PD-1 / PD-L1 inhibitors has brought revolutionary progress to the treatment of tumors, greatly improving the prognosis of tumor patients and prolonging the survival of patients. However, it has also led to overactivation of the immune system, causing tissue immune inflammatory damage, i.e., immune-related adverse reactions (irAEs). The incidence of irAEs in PD-1 / PD-L1 is as high as 74%, the incidence in CTLA4 is 89%, and the incidence in combined immunotherapy is more than 90%, and the incidence of severe irAEs can reach 50%. irAEs not only affect the quality of life of patients, but also seriously affect the efficacy of patients. 30% of patients have treatment interruption due to irAEs, and 13% of patients have hospitalization due to irAEs.

[0003] How to reduce the incidence of irAEs and the resulting serious consequences has become an important clinical problem that needs to be urgently solved. If a means of predicting irAEs can be found and intervention can be made in advance, it will become an important method to solve this clinical problem. A small number of studies have reported that some markers are related to the occurrence of AE, some studies suggest that patients with a neutrophil:lymphocyte ratio less than 3 are prone to irAEs, and some suggest that the level of IL-17 is related to irAEs, but these studies are small sample studies and have not been verified by external data, and the reliability is poor, which manifests that these indicators are only applicable in a certain tumor, and negative results will be obtained after changing the tumor. There is an urgent need for a marker with higher predictive value, higher sensitivity and higher reliability to predict the occurrence of irAEs. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a method, device, medium and program product for assisting in predicting immune-related adverse reactions; the present application constructs an auxiliary prediction model based on a target cell subpopulation obtained by processing and screening a cell data set of a training set sample, to improve the prediction performance of immune therapy adverse reactions and solve related life science problems.

[0005] The first aspect of the present application discloses a method for assisting in predicting immune-related adverse reactions, the method comprising:

[0006] 101, obtaining cell data of a subject;

[0007] 102, calculate the proportion of a target cell subpopulation in the single nucleus cells based on the cell data; the target cell subpopulation includes a CXCR3+CCR6+CD8+T cell subpopulation, an NK cell subpopulation;

[0008] 103, calculate a prediction score based on the proportion of the target cell subpopulation;

[0009] 104, output an auxiliary prediction result of the probability of the subject developing an immune-related adverse reaction according to the prediction score; when the prediction score is greater than a first threshold value, output an auxiliary prediction result of a high probability of the subject developing an immune-related adverse reaction; when the prediction score is less than the first threshold value, output an auxiliary prediction result of a low probability of the subject developing an immune-related adverse reaction.

[0010] In some embodiments, the prediction score calculated in 103 is calculated by inputting the proportion of the target cell subpopulation into an auxiliary prediction model;

[0011] Optionally, the method for constructing the auxiliary prediction model comprises:

[0012] obtaining a cell data set of a training set sample; processing the cell data set to obtain the proportion of each immune cell subpopulation of each sample; selecting a target cell subpopulation from each immune cell subpopulation; inputting the proportion of the target cell subpopulation and whether the corresponding sample develops an immune adverse reaction as a classification label into a machine learning model to obtain a prediction classification result; comparing the classification label, and optimizing the model according to the comparison result to obtain the auxiliary prediction model;

[0013] Optionally, the method for processing the cell data set comprises quality control, clustering, subpopulation annotation, and calculating the proportion of each immune cell subpopulation of each sample.

[0014] In some embodiments, the method for selecting the target cell subpopulation comprises: analyzing the correlation between each immune cell subpopulation, and selecting an immune cell subpopulation with a collinearity greater than a second threshold value as the target cell subpopulation;

[0015] Optionally, the collinearity is a spearman correlation coefficient.

[0016] In some embodiments, the target cell subpopulation includes: a CXCR3+CCR6+CD8+T cell subpopulation, an NK cell subpopulation, and a CXCR3+CCR6+CD4+T cell subpopulation.

[0017] In some embodiments, the method for selecting the target cell subpopulation further comprises: removing a cell subpopulation with an AUC value less than a third threshold value from the immune cell subpopulation with the collinearity greater than the second threshold value by a single factor selection method as the target cell subpopulation.

[0018] In some embodiments, the method of calculating the prediction score in 103 comprises that the auxiliary prediction model comprises: logit(P) = 0.758 * C14-0.119 * C20- 0.715; wherein, C14 is the proportion of CXCR3+CCR6+CD8+T cell subpopulation, C20 is the proportion of CD56dimNK cell subpopulation, and logit(P) is the prediction score.

[0019] In some embodiments, the method of calculating the proportion in 102 comprises: the cell subpopulation of interest / all the total number of cells;

[0020] Optionally, the cell data is from a peripheral blood sample of a subject.

[0021] The second aspect of the present application discloses a computer device, comprising: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to realize the steps of the above method.

[0022] The third aspect of the present application discloses a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the above method.

[0023] The fourth aspect of the present application discloses a computer program product, comprising a computer program, which is executed by a processor to realize the steps of the above method.

[0024] The present application has the following beneficial effects:

[0025] 1. The present application innovatively constructs an irAE prediction model based on the largest sample size of the current immune therapy adverse reaction single cell data set, processes and screens the cell data set of the training set sample, finally obtains the target cell subpopulation, and improves the prediction performance of the immune therapy adverse reaction, which makes up for the small sample size of the current detection index; and the present scheme is constructed by discovering set data and verified by external data, which is more reliable and makes up for the poor reliability of the existing index.

[0026] 2. The application discloses a method for assisting in predicting immune-related adverse reactions, explores the relationship between CXCR3+CCR6+CD8+T cell subgroups, NK cell subgroups, CXCR3+CCR6+CD4+T cell subgroups and irAE, and proves that higher baseline levels of the two cell subtypes before immunotherapy are closely related to the occurrence of irAE. Further, the exact relationship between the proportion of two or three or more cell subgroups and the occurrence of immune-related adverse reactions is studied, and two cell subgroups with high performance and more simple are screened out, and the proportion of the two cell subgroups is used to calculate a prediction score, and the prediction score is used to predict the probability of occurrence of immune-related adverse reactions. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0028] Figure 1 is a method flowchart provided by the first aspect of the embodiment of the present application;

[0029] Figure 2 is a system schematic diagram for assisting in predicting immune-related adverse reactions provided by the second aspect of the embodiment of the present application;

[0030] Figure 3 is a schematic diagram of a computer device provided by the embodiment of the present application;

[0031] Figure 4 is a schematic diagram of the architecture of an exemplary computing device provided by the embodiment of the present application;

[0032] Figure 5 is a schematic diagram of a storage medium provided by the embodiment of the present application;

[0033] Figure 6 is a flowchart of constructing an auxiliary prediction model provided by the embodiment of the present application;

[0034] Figure 7 is a result diagram of cell subgroups related to immune therapy adverse reactions obtained by single factor analysis provided by the embodiment of the present application;

[0035] Figure 8 is a correlation analysis result diagram of immune cell subgroups provided by the embodiment of the present application;

[0036] Figure 9 is a schematic diagram of evaluating the prediction performance of C14 and C04 on adverse reactions by AUROC provided by the embodiment of the present application; wherein,Figure 9 A is the AUROC display diagram of C14, Figure 9 B is the AUROC display diagram of C04;

[0037] Figure 10 is a schematic diagram provided by an embodiment of the present application for evaluating the prediction performance of C20 cell subgroups on adverse reactions;

[0038] Figure 11 is a schematic diagram provided by an embodiment of the present application for evaluating the prediction performance of the auxiliary prediction model in the training set samples; wherein, Figure 11 A is the AUROC display diagram of the model in the training set samples, Figure 11 B is a comparison diagram of the scores of patients with and without adverse reactions calculated by using the model;

[0039] Figure 12 is a result diagram provided by an embodiment of the present application for annotating each immune cell subgroup in the validation set samples;

[0040] Figure 13 is a schematic diagram provided by an embodiment of the present application for clustering cells in the validation set samples;

[0041] Figure 14 is a schematic diagram provided by an embodiment of the present application for Figure 13 comparing immune cell subgroups in the validation set samples and the training set samples;

[0042] Figure 15 is a schematic diagram provided by an embodiment of the present application for evaluating the prediction performance of the auxiliary prediction model in the validation set samples; Figure 15 A is the AUROC display diagram of the model in the validation set samples, Figure 15 B is a comparison diagram of the scores of patients with and without adverse reactions calculated by using the model;

[0043] Figure 16 is an AUROC display diagram provided by an embodiment of the present application for constructing a prediction model by using C14, C04 and C20 in the training set samples. DETAILED DESCRIPTION

[0044] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings.

[0045] In some of the flowcharts described in the specification and claims of the present application and in the above description of the drawings, a plurality of operations are included which occur in a particular order, but it should be clearly understood that the operations can be performed in an order other than that in which they appear herein or in parallel, and the serial numbers of the operations, such as 101, 102, etc., are merely used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, the flowcharts can include more or fewer operations, and the operations can be performed in sequence or in parallel. It should be noted that the descriptions herein, such as "first", "second", etc., are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do "first" and "second" represent different types.

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely in the specification of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] Figure 1 is a method flowchart for assisting in predicting immune-related adverse reactions provided by an embodiment of the present application. Specifically, the method comprises the following steps:

[0048] 101: Obtain cell data of a subject;

[0049] In some embodiments, the term "subject" or "testee" or "test sample" used herein refers to any animal (for example, a mammal), including but not limited to a human, a non-human primate, a rodent, etc., who will be the recipient of a particular treatment. Generally, the terms "subject" and "patient" are used interchangeably herein when referring to a human subject.

[0050] Preferably, the subject is a human.

[0051] In some embodiments, the test sample is a patient clinically used for prognosis evaluation.

[0052] In some embodiments, the cell data is from a peripheral blood sample of the subject.

[0053] 102: Calculate the proportion of a target cell subpopulation in mononuclear cells based on the cell data; the target cell subpopulation includes a CXCR3+CCR6+CD8+T cell subpopulation, an NK cell subpopulation;

[0054] In some embodiments, the calculation method of the proportion in 102 includes: the subpopulation of cells of interest / all total cells.

[0055] 103: calculating a prediction score based on the proportion of the target cell subpopulation;

[0056] In some embodiments, the prediction score calculated in 103 is calculated by inputting the proportion of the target cell subpopulation into an auxiliary prediction model.

[0057] Optionally, the method for constructing the auxiliary prediction model comprises:

[0058] obtaining a cell dataset of training set samples; processing the cell dataset to obtain the proportion of each immune cell subpopulation of each sample; selecting a target cell subpopulation from each immune cell subpopulation; inputting the proportion of the target cell subpopulation and whether the corresponding sample has an adverse immune reaction as a classification label into a machine learning model to obtain a prediction classification result; comparing the classification label, and optimizing the model according to the comparison result to obtain the auxiliary prediction model.

[0059] Optionally, the method for processing the cell dataset comprises quality control, clustering, subpopulation annotation, and calculating the proportion of each immune cell subpopulation of each sample.

[0060] In some embodiments, the method for selecting the target cell subpopulation comprises: performing correlation analysis on the correlation between each immune cell subpopulation, and selecting an immune cell subpopulation with a collinearity greater than a second threshold value as the target cell subpopulation; in some embodiments, the target cell subpopulation comprises: a CXCR3+CCR6+CD8+T cell subpopulation, an NK cell subpopulation, and a CXCR3+CCR6+CD4+T cell subpopulation.

[0061] Optionally, the collinearity is a spearman correlation coefficient; and the second threshold value is 0.8.

[0062] In some embodiments, the method for selecting the target cell subpopulation further comprises: removing a cell subpopulation with an AUC value less than a third threshold value from the immune cell subpopulation with a collinearity greater than the second threshold value by a single factor screening method, as the target cell subpopulation.

[0063] In some embodiments, the method for calculating the prediction score in 103 comprises: the auxiliary prediction model comprises: logit(P) = 0.758 * C14-0.119 * C20- 0.715; wherein C14 is the proportion of the CXCR3+CCR6+CD8+T cell subpopulation, C20 is the proportion of the CD56dimNK cell subpopulation, and logit(P) is the prediction score.

[0064] 104: outputting an auxiliary prediction result of the probability of the subject suffering from the immune-related adverse reaction according to the prediction score; when the prediction score is greater than the first threshold, outputting the auxiliary prediction result of the subject suffering from the immune-related adverse reaction with a high probability; and when the prediction score is less than the first threshold, outputting the auxiliary prediction result of the subject suffering from the immune-related adverse reaction with a low probability.

[0065] In some embodiments, the first threshold is trained by the training set samples, which can be a specific threshold or an interval range, and the specific form is not limited in the embodiment.

[0066] In some embodiments, the auxiliary prediction result output according to the prediction score includes but is not limited to a paper or electronic report form, which is only analyzed by the intelligent machine based on the relevant data of the subject and is only used as a reference for medical personnel, and is not used as a diagnosis result of the subject.

[0067] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present application, as shown in Figure 3 The device can include one or more processors and one or more memories, wherein the memory stores computer readable code, and the computer readable code can execute the method described above when run by the one or more processors.

[0068] The processor in the embodiment can be an integrated circuit chip with signal processing capability. The processor can be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The methods, operations and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general purpose processor can be a microprocessor or the processor can be any conventional processor or the like, which can be X86 architecture or ARM architecture.

[0069] Generally, various example embodiments of the present disclosure can be implemented in hardware or special-purpose circuitry, software, firmware, logic, or any combination thereof. Certain aspects can be implemented in hardware, while other aspects can be implemented in firmware or software which can be executed by a controller, microprocessor or other computing device. When aspects of the present disclosure are illustrated or described as a block diagram, flow chart or using some other pictorial representation, it will be understood that the blocks, devices, systems, techniques or methods described herein can be implemented in hardware, software, firmware, special-purpose circuitry or logic, general purpose hardware or controller or other computing device, or some combination thereof.

[0070] For example, the method or device according to the embodiments of the present disclosure can also be implemented by means of Figure 4The architecture of the computing device 3000 shown is used for implementation. For example... Figure 4 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage devices in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for processing and / or communication of the methods provided in this disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 4 One or more components in the computing device shown.

[0071] This invention also includes a computer-readable storage medium, such as... Figure 5 The diagram illustrates a storage medium provided in an embodiment of the present invention. The computer storage medium 4020 stores computer-readable instructions 4010. When the computer-readable instructions 4010 are executed by a processor, the method described above according to embodiments of the present disclosure can be performed. The computer-readable storage medium in the embodiments of the present disclosure can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM), and Direct Memory Bus Random Access Memory (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0072] This disclosure also provides a computer program product or system, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0073] In some embodiments, the present embodiments also disclose a system for assisting in predicting immune-related adverse reactions, as shown in Figure 2 The system comprises:

[0074] The acquisition module 201 is configured to acquire cell data of a subject.

[0075] The cell subpopulation proportion calculation module 202 is configured to calculate the proportion of a target cell subpopulation in mononuclear cells based on the cell data; the target cell subpopulation comprises a CXCR3+CCR6+CD8+T cell subpopulation and an NK cell subpopulation.

[0076] The prediction score calculation module 203 is configured to calculate a prediction score based on the proportion of the target cell subpopulation.

[0077] The assistance result output module 204 is configured to output an assistance prediction result of the probability of the subject having an immune-related adverse reaction according to the prediction score; when the prediction score is greater than a first threshold value, the assistance prediction result of the subject having a high probability of having an immune-related adverse reaction is output; and when the prediction score is less than the first threshold value, the assistance prediction result of the subject having a low probability of having an immune-related adverse reaction is output. Specific embodiments

[0078] The training set sample in the present scheme has 68 names, the validation set sample has 16 names, and the construction process of the model is specifically as shown in Figure 6 First, the mass cytometry or single-cell data is subjected to quality control, clustering, subpopulation annotation, and the proportion of each immune cell subpopulation of each patient is calculated, and the immune cell subpopulation proportion is taken as the patient feature and the input of the model. Correlation analysis obtains the correlation between the characteristics, and the subpopulation with high collinearity (spearman correlation coefficient > 0.8) is removed, and the final model in the figure is the assistance prediction model described in the present embodiments. Further single factor analysis suggests that the number of CXCR3+CCR6+ T cells, NK cells, and classical monocytes is related to the immune therapy adverse reaction alone (as shown in Figure 7 Therefore, we integrated each cell subpopulation positive for CXCR3+CCR6+ to model to improve the prediction performance of immune therapy adverse reactions.

[0079] Further, several machine learning techniques including the use of random forests, support vector machines, and other nonlinear modeling methods are evaluated, but due to the relative simplicity and robustness of the generalized linear model, the logistic regression (glm in R language) method is finally selected for modeling. Based on the immune cell subpopulation before immunotherapy and the immune-related adverse reactions (such as Figure 7Based on the relationship between the C14 and C04 subsets, we identified three cell subpopulations closely associated with immune-related adverse reactions: C04 (CXCR3+CCR6+CD4+T), C14 (CXCR3+CCR6+CD8+T), and C20 (CD56dim NK). Correlation analysis showed a high correlation between the C14 and C04 subsets (r = 0.835, p < 0.001). Figure 8 As shown in the figure), the correlation with C20 was relatively low (p < 0.5). We then evaluated the predictive performance of C14 and C04 for adverse reactions by calculating the area under the receiver operating characteristic curve (AUROC). The results showed that C14 had a higher AUROC value than C04 (0.76 vs. 0.73, as shown in the figure). Figure 9 As shown, Figure 9 A is the AUROC display diagram for C14. Figure 9 B is the AUROC plot for C04. Simultaneously, the predictive performance of the C20 cell subset for adverse reactions was evaluated using AUROC, with results indicating an AUROC value of 0.74 for C20 (e.g., ...). Figure 10 (As shown). Ultimately, we chose C14 and C20 for further modeling.

[0080] We constructed a logistic regression model with the formula: logit(P) = 0.758 * C14 - 0.119 * C20 - 0.715, where C14 and C20 represent the proportions of these subsets in peripheral blood mononuclear cells. This model achieved an AUROC of 0.79 in the mass cytometry training cohort. The model score for patients with adverse events was significantly higher than that for patients without adverse events (e.g., ...). Figure 11 As shown, Figure 11 A is the AUROC graph of the model in the training set samples. Figure 11 B is a graph comparing the scores of patients with and without adverse reactions calculated using this model. This model outperforms any single cell subset, such as C04, C14, or C20, in predicting immune-related adverse reactions. In some embodiments, Figure 16 This is an AUROC chart showing the AUROC values ​​of the prediction model constructed using three cell types (C14, C04, and C20) in the training set samples, as provided in this embodiment of the invention. Figure 11 The AUROC values ​​in A are the same, meaning that the 2-cell model achieved the same predictive value as the 3-cell model, indicating that the 2-cell model in this case is simpler without sacrificing performance.

[0081] To validate the model's performance, we selected a single-cell public dataset for validation. We extensively annotated various immune cell subsets, annotating eight major classes (e.g., ...). Figure 12As shown), the cells were further clustered into 34 clusters (e.g. Figure 13 (As shown). Analysis of the expression characteristics of these clusters revealed that cluster 10 corresponds to CD8+CXCR3+ T cells, cluster 26 corresponds to CD4+CXCR3+ T cells, and cluster 2 corresponds to NK cells (e.g., ...). Figure 14 (As shown). Therefore, we designated cluster 10 of the single-cell subsets as the equivalent C14 cell subset (CXCR3+CD8+T) in mass cytometry, and cluster 2 as the equivalent C20 subset (CD56dimNK). Using the proportions of these two subsets as input, we predicted the occurrence of sAEs (9 sAEs out of 16 samples), achieving an AUROC of 0.75 in the single-cell validation cohort (P=0.06) (as shown). Figure 15 As shown, Figure 15 A is the AUROC graph of the model in the validation set samples. Figure 15 B is a comparison chart of scores for patients with and without adverse reactions calculated using this model.

[0082] Ultimately, it was confirmed that the model has good predictive value for the occurrence of immune-related adverse reactions, and its predictive performance is better than that of any single subgroup.

[0083] CD8+ T cells and CD4+ T cells are two major types of cells in the immune system, each playing a crucial role in the anti-tumor immune response. CD8+ T cells, also known as cytotoxic T cells, are immune cells capable of directly recognizing and killing tumor cells. They initiate cytotoxicity by expressing T cell receptors (TCRs) and binding to antigenic peptides on tumor cells. The main function of CD8+ T cells is to recognize and eliminate infected and tumor cells, making them a major killing force of the immune system.

[0084] CD4+ T cells, also known as helper T cells, are primarily responsible for regulating and coordinating the immune response. CD4+ T cells present antigens to CD8+ T cells, activating the latter and enhancing their anti-tumor capabilities. Simultaneously, CD4+ T cells can secrete various cytokines, such as IL-2, IFN-γ, and TNF-α, which directly kill tumor cells or enhance the function of other immune cells. CD4+ T cells play a helper and regulatory role in the immune response; they can promote the proliferation and differentiation of B cells, enhance the activity of other immune cells, and regulate the type and intensity of the immune response.

[0085] These two immune cells are closely related to the efficacy and adverse reactions of immunotherapy represented by PD-1 / PD-L1. Among these two cell types, there are two special subtypes of cells, namely cells that highly express both CXCR3 and CCR6 molecules. CXCR3 is a G protein-coupled receptor, which belongs to one of the CXC chemokine receptor family members. It is mainly expressed on various immune cells, including T cells, natural killer cells (NK cells), and dendritic cells, etc. CXCR3 binds to specific CXC chemokines, which can attract immune cells to migrate to the site of inflammation or infection.

[0086] CXCR3 plays an important role in immune response, especially in fighting viral infections and certain types of tumor cells. It is involved in the recruitment and activation of immune cells, as well as the regulation of immune response. For example, CXCR3 is crucial in regulating the migration and activation of T cells, which is essential for clearing infections and controlling tumor growth.

[0087] Studies have found that CXCR3 plays a role in various diseases, including autoimmune diseases, viral infections, and tumors. In the tumor environment, the expression and function of CXCR3 may change, affecting the distribution and activity of immune cells, thereby affecting the development of tumors and treatment response.

[0088] In summary, CXCR3 is a key immune regulator, and its role in immune response, disease development, and treatment is increasingly being recognized. Further research will help better understand the specific mechanisms of CXCR3 in healthy and disease states, and develop new treatment methods.

[0089] CCR6 is a member of the CC chemokine receptor family, which is a G protein-coupled receptor responsible for binding C-C chemokines. These chemokines include CCL19, CCL20, and CCL21, which play a key role in the migration and localization of immune cells. The receptor encoded by the CCR6 gene is mainly expressed on various immune cells, including T cells, B cells, macrophages, and dendritic cells, etc. It plays a role in various physiological and pathological processes, including:

[0090] Immune cell homing: CCR6 is involved in regulating the migration of immune cells from blood to tissues, especially in primary and memory immune responses. For example, CCR6 plays an important role in the migration of T cells to lymph nodes, which is a key site for antigen presentation and immune response.

[0091] Inflammation and autoimmune diseases: CCR6 may also play a role in the pathogenesis of inflammation and autoimmune diseases. The binding of chemokines to CCR6 can attract immune cells to the site of inflammation, thereby exacerbating the inflammatory response.

[0092] Overall, CCR6 is an important immune regulator, whose roles in immune responses, disease development, and treatment are being discovered and understood. Further research will help to better utilize the knowledge of CCR6 to improve the treatment and management of diseases.

[0093] It should be noted that the flow diagrams and block diagrams in the drawings are illustrations of the possible architectures, functional processes, and operations for implementations of the systems, methods, and computer program products in accordance with various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and

[0094] In general, the various example embodiments of the present disclosure can be implemented in hardware or special-purpose circuits, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software which can be executed by a controller, microprocessor or other computing device, Although the various aspects of the disclosure can be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that the blocks, apparatus, systems, techniques or methods described herein can be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof.

[0095] The specific process of the described system, device and unit can be clearly understood by those skilled in the art, and the corresponding process in the foregoing method embodiments can be referred to for description and simplification, which will not be described here.

[0096] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the embodiments of the device described above are merely schematic; for example, the division of the units is only a logical function division; there can be another division manner for the actual implementation; for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electric, mechanical or other forms.

[0097] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0098] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0099] The example embodiments of the present disclosure described in detail above are merely illustrative, rather than limiting. Those skilled in the art should understand that various modifications and combinations can be made to the embodiments or features thereof without departing from the principles and spirits of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A method of aiding in the prediction of immune-related adverse reactions, characterized in that, The method comprises: 101, obtaining cell data of a subject; 102, calculating the proportion of a target cell subpopulation in mononuclear cells based on the cell data; the target cell subpopulation comprises a CXCR3+CCR6+CD8+T cell subpopulation and an NK cell subpopulation; 103, calculating a prediction score by inputting the proportion of the target cell subpopulation into an auxiliary prediction model; the construction method of the auxiliary prediction model comprises: obtaining a cell data set of a training set sample; processing the cell data set to obtain the proportion of each immune cell subpopulation of a single sample; screening a target cell subpopulation from each immune cell subpopulation; inputting the proportion of the target cell subpopulation and whether the corresponding sample has an adverse immune reaction as a classification label into a machine learning model to obtain a prediction classification result; comparing with the classification label, and optimizing the model according to the comparison result to obtain the auxiliary prediction model; the screening method of the target cell subpopulation comprises: analyzing the correlation between each immune cell subpopulation to screen immune cell subpopulations with a collinearity greater than a second threshold; removing immune cell subpopulations with an AUC value less than a third threshold from the immune cell subpopulations with a collinearity greater than the second threshold by a single factor screening method as the target cell subpopulation; the auxiliary prediction model is: logit(P) = 0.758 * C14-0.119 * C20- 0.715; wherein C14 is the proportion of the CXCR3+CCR6+CD8+T cell subpopulation, C20 is the proportion of the CD56dimNK cell subpopulation, and logit(P) is the prediction score; 104, outputting an auxiliary prediction result of the probability of the subject having an immune-related adverse reaction according to the prediction score; when the prediction score is greater than a first threshold, outputting an auxiliary prediction result of the subject having a high probability of having an immune-related adverse reaction; when the prediction score is less than the first threshold, outputting an auxiliary prediction result of the subject having a low probability of having an immune-related adverse reaction.

2. The method of claim 1, wherein the immune-related adverse effect is selected from the group consisting of colitis, hepatitis, pancreatitis, pneumonitis, nephritis, myocarditis, and endocrinopathy. The method for processing the cell data set comprises quality control, clustering, subpopulation annotation, and calculating the proportion of each immune cell subpopulation of each sample.

3. The method of claim 1, wherein the immune-related adverse effect is selected from the group consisting of colitis, hepatitis, pancreatitis, pneumonitis, nephritis, myocarditis, and endocrinopathy. The collinearity is a spearman correlation coefficient.

4. The method of claim 3, wherein the immune-related adverse effect is selected from the group consisting of colitis, hepatitis, pancreatitis, pneumonitis, nephritis, myocarditis, and endocrinopathy. The target cell subpopulation comprises a CXCR3+CCR6+CD8+T cell subpopulation, an NK cell subpopulation, and a CXCR3+CCR6+CD4+T cell subpopulation.

5. The method of claim 1, wherein the immune-related adverse effect is selected from the group consisting of colitis, hepatitis, pancreatitis, pneumonitis, nephritis, myocarditis, and endocrinopathy. The calculation method of the proportion in 102 comprises: the proportion of the cell subpopulation of interest / all cell total number; The cell data is from a peripheral blood sample of a subject.

6. A computer device, comprising: The device comprises a memory and a processor; the memory is used to store a computer program; and the processor executes the computer program to realize the steps of the method of any one of claims 1-5.

7. A computer readable storage medium characterized in that, A computer program is stored thereon, and the computer program is executed by a processor to realize the steps of the method of any one of claims 1-5.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by a processor to realize the steps of the method of any one of claims 1-5.

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