Markers for predicting ICI treatment related adverse events

Abstract

The invention discloses a marker for predicting ICI treatment related adverse events. According to the application, a disease-specific influencing cell is determined through multi-omics combined analysis, the immune checkpoint inhibitor-related autoimmune encephalopathy can be predicted, and the immune checkpoint inhibitor-related autoimmune encephalopathy can be predicted by evaluating the number of specific immune cells in cerebrospinal fluid of a related patient in the later period.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and particularly relates to a biomarker for predicting immune checkpoint inhibitor (ICI)-treatment related adverse events. Background Art

[0002] Immune checkpoint inhibitor (ICI) therapy significantly prolongs the survival prognosis of almost all cancer types by activating the tumor immune microenvironment. However, it simultaneously unleashes autoimmune diseases in almost all organs, leading to immune-related adverse events (irAEs), with an estimated incidence of 10% to 90%. Most irAEs occur in barrier sites such as the skin, colon, and lung, but rarely in solid organs. Approximately 1% - 3% of irAEs affect the central nervous system (CNS), including checkpoint blockade-related autoimmune encephalopathy (irAE-CNS). irAE-CNS is clinically variable, with onset unpredictable since the start of ICIs. Most case reports have symptoms of headache, altered mental status, and psychological and behavioral changes. Although the incidence is rare, anecdotal case series suggest that irAE-CNS is characterized by intracranial inflammation, poor response to anti-inflammatory steroid treatment, and a high incidence of functional sequelae after encephalopathy (over 40%). To date, few studies have investigated the pattern of intracranial inflammatory status reflected by the local immunopathological microenvironment. Since the literature hypothesizes that irAE-CNS parthenogenesis has an autoimmune background based on its similar clinical manifestations to paraneoplastic encephalopathy, further identification of antigen stimulation patterns in the intracranial region is needed to establish a bridge between neuroantigen responses and immune cells affected. Summary of the Invention

[0003] To make up for the deficiencies of the prior art, the present invention provides a biomarker for predicting ICI-treatment related adverse events.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] The first aspect of the present invention provides a method for screening cells for predicting irAE-CNS, the method comprising:

[0006] 1) Obtaining genes / proteins differentially expressed in the irAE-CNS group;

[0007] 2) Performing a correlation analysis between the metabolic characteristics of the lesion area and the differentially expressed genes / proteins in 1);

[0008] 3) Performing a clustering analysis on the cells to obtain different cell clusters;

[0009] 4) Selecting characteristic genes / proteins for the cell clusters in 3) to obtain cells for predicting irAE-CNS.

[0010] Furthermore, the differentially expressed genes / proteins in 1) include the differentially expressed genes / proteins between the irAE-CNS group and the control group and / or the differentially expressed genes / proteins between the irAE-CNS group and the treatment group.

[0011] Furthermore, the prediction of irAE-CNS is to distinguish patients with irAE-CNS from CNSI patients treated with ICIs, AME patients not treated with ICIs, and patients without intracranial inflammation treated with ICIs.

[0012] Furthermore, the value of the metabolic feature in 2) is SUV.

[0013] Furthermore, the SUV is obtained by using 18-F FDG PET / CT.

[0014] Furthermore, the clustering analysis in 3) includes preprocessing, dimensionality reduction, and clustering.

[0015] Furthermore, the clustering includes PCA and UMAP.

[0016] Furthermore, the differentially expressed genes / proteins include CCL11, MCP4, MCP1, TNFRSF9, and IL17A.

[0017] Furthermore, the characteristic genes / proteins in the cell clusters include TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

[0018] The second aspect of the present invention provides a screening system / device for cells predicting irAE-CNS, and the system / device includes:

[0019] Data acquisition module: acquiring the genes / proteins differentially expressed in the irAE-CNS group;

[0020] Analysis module: performing a correlation analysis on the metabolic features of the lesion area and the genes / proteins differentially expressed in the data acquisition module; performing a clustering analysis on the cells to obtain different cell clusters;

[0021] Feature selection module: selecting characteristic genes / proteins for the cell clusters in the analysis module to obtain cells predicting irAE-CNS.

[0022] Furthermore, the genes / proteins differentially expressed in the data acquisition module include the genes / proteins differentially expressed between the irAE-CNS group and the control group and / or the genes / proteins differentially expressed between the irAE-CNS group and the treatment group.

[0023] Further, the predicted irAE-CNS is used to distinguish irAE-CNS patients from CNSI patients treated with ICIs, AME patients not treated with ICIs, and patients without intracranial inflammation treated with ICIs.

[0024] Further, the value of the metabolic feature in the analysis module is SUV.

[0025] Further, the SUV is obtained by using 18-F FDG PET / CT.

[0026] Further, the clustering analysis in the analysis module includes preprocessing, dimensionality reduction, and clustering.

[0027] Further, the clustering includes PCA and UMAP.

[0028] Further, the differentially expressed genes / proteins include CCL11, MCP4, MCP1, TNFRSF9, and IL17A.

[0029] Further, the characteristic genes / proteins in the cell clusters include TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

[0030] The third aspect of the present invention provides an electronic device, which includes:

[0031] A memory: for storing program instructions;

[0032] A processor: for calling program instructions, and when the program instructions are executed, implementing the screening method described in the first aspect of the present invention.

[0033] The fourth aspect of the present invention provides a computer-readable storage medium, on which executable instructions are stored. When the executable instructions are executed by a processor, the screening method described in the first aspect of the present invention is implemented.

[0034] The fifth aspect of the present invention provides the use of a reagent for detecting cells obtained by the screening method described in the first aspect of the present invention in the preparation of a product for diagnosing irAE-CNS.

[0035] Further, the reagent includes antibodies that specifically bind to TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, primers that specifically amplify TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, or probes that specifically recognize TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

[0036] Further, the product further includes reagents used in other immunoassays, in situ hybridization, PCR detection, immunoblotting, and DNA sequence analysis.

[0037] The sixth aspect of the present invention provides a product for diagnosing irAE-CNS, and the product includes a reagent for detecting the cells obtained by the screening method described in the first aspect of the present invention.

[0038] Furthermore, the reagent includes antibodies that specifically bind to TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, primers that specifically amplify TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, or probes that specifically recognize TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

[0039] Furthermore, the reagent further includes a detectable label.

[0040] Furthermore, the product includes a kit.

[0041] Furthermore, the kit further includes a buffer.

[0042] Furthermore, the kit further includes an instruction manual.

[0043] The seventh aspect of the present invention provides the use of the cells obtained by the screening method described in the first aspect of the present invention in constructing a system / apparatus for diagnosing irAE-CNS.

[0044] The eighth aspect of the present invention provides a system / apparatus for diagnosing irAE-CNS, and the system includes:

[0045] A data acquisition module: used to acquire the level of the cells obtained by the screening method described in the first aspect of the present invention in a sample to be tested;

[0046] A classification and prediction module: classifies and predicts according to the level of the cells to obtain a prediction result of irAE-CNS.

[0047] Furthermore, the detection reagent for the level of the cells includes antibodies that specifically bind to TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, primers that specifically amplify TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, or probes that specifically recognize TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

[0048] Advantages and beneficial effects of the present invention:

[0049] Through multi-omics joint analysis, this application has identified a disease-specific cell that can predict immune checkpoint inhibitor-related autoimmune encephalopathy. In the later stage, it can evaluate the number of specific immune cells in the cerebrospinal fluid of relevant patients, and accordingly predict immune checkpoint inhibitor-related autoimmune encephalopathy. Description of the Drawings

[0050] Figure 1 It is a flowchart of the screening method for cells predicting irAE-CNS;

[0051] Figure 2 It is a schematic diagram of the screening system / device for cells predicting irAE-CNS;

[0052] Figure 3 It is a schematic diagram of an electronic device;

[0053] Figure 4 It is a schematic diagram of the system / device for diagnosing irAE-CNS;

[0054] Figure 5 It is a differential expression protein map;

[0055] Figure 6 It is a differential expression protein map in the comparison of cerebrospinal fluid proteomics between the irAE-CNS group and after 3 months of treatment;

[0056] Figure 7 It is a correlation analysis map of the 18F FDG PET / CT SUVmean value and the expression levels of 43 proteins;

[0057] Figure 8 It is a distribution map of cell clusters in the patient group;

[0058] Figure 9 It is a distribution percentage map of cell clusters in the patient group;

[0059] Figure 10 It is a multi-secretory T cell expression map;

[0060] Figure 11 It is a correlation map of the SUVmean value of the patient's marginal zone PET / CT imaging and the proportion of multi-secretory T cells;

[0061] Figure 12 It is a UMAP clustering map of irAE-CNS samples;

[0062] Figure 13 It is an ROC curve graph of the training set;

[0063] Figure 14 It is a graph of the number of multi-secretory T cells in irAE-CNS in the validation set;

[0064] Figure 15It is the ROC curve graph of the validation set. Detailed implementation manners

[0065] The following provides definitions of some terms used in this specification. Unless otherwise specified, all technical and scientific terms used herein generally have the same meaning as commonly understood by those of ordinary skill in the art to which this invention pertains.

[0066] In some processes described in the specification, claims and above-mentioned drawings of the present invention, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. Additionally, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0067] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0068] Figure 1 It is a flow chart of a method for screening cells for predicting irAE-CNS provided by an embodiment of the present application. The method includes the following steps:

[0069] 101: Obtain genes / proteins differentially expressed in the irAE-CNS group.

[0070] In some implementation manners, the differentially expressed genes / proteins include genes / proteins differentially expressed between the irAE-CNS group and the control group and / or genes / proteins differentially expressed between the irAE-CNS group and the treatment group. Among them, the control group includes CNSI patients (cancer patients with central nervous system infection (CNSI) treated with ICIs), AME patients (patients with autoimmune paraneoplastic encephalopathy (AME) among cancer patients not treated with immune checkpoint inhibitors (ICIs)), and negative controls (patients without intracranial inflammation treated with ICIs, NC). The treatment group is patients 3 months after steroid treatment.

[0071] In some implementation manners, the differentially expressed genes / proteins include CCL11, MCP4, MCP1, TNFRSF9, and IL17A.

[0072] 102: Perform a correlation analysis on the metabolic characteristics of the lesion area and the differentially expressed genes / proteins in 101.

[0073] In some embodiments, the value indicating the metabolic characteristics of the lesion area is the SUV, and the SUV includes SUVmax and SUVmean.

[0074] In a specific embodiment, the SUV is SUVmean.

[0075] In some embodiments, the SUV is obtained by analyzing a radiological image, and the radiological image can be obtained by any suitable radiological imaging method, for example, by positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and / or single photon emission computed tomography (SPECT). Generally, the image is a PET image.

[0076] In a preferred embodiment, the PET image uses the tracer fluorine-18 (18-F) fluorodeoxyglucose (FDG) for PET scanning (referred to as FDG-PET), for example, the radiological image is obtained by PET imaging using 18F-FDG.

[0077] In a specific embodiment, the metabolic characteristics of the lesion area are obtained by regional 18-F FDG PET / CT metabolic SUVmean.

[0078] 103: Perform a clustering analysis on the cells to obtain different cell clusters.

[0079] In some embodiments, the clustering analysis includes preprocessing, dimensionality reduction, and clustering. Among them, preprocessing is to exclude cells without any features and cells with less than 50 cells. Dimensionality reduction refers to performing dimensionality reduction through principal component analysis (PCA) and then through uniform manifold approximation and projection (UMAP) embedding.

[0080] In some embodiments, the UMAP map maps all cells into 7 clusters through a dimensionality reduction method. Six cytokines, including TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, are almost only found in cluster 6, indicating that cluster 6 has multifunctional characteristics, including pro-inflammatory (CMCP1, MCP4, IL17A), regulatory (TNFRSF9), pro-chemotherapeutic (CCL11), and effector (GZMB). Temporarily name this group of cells in cluster 6 as multi-secretory T cells.

[0081] 104: Select characteristic genes / proteins for the cell clusters in 103 to obtain cells that predict irAE-CNS.

[0082] In an embodiment of the present application, the identified cells that predict irAE-CNS express TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, and are named multi-secretory T cells.

[0083] Figure 2 It is a schematic diagram of a screening system / device for cells that predict irAE-CNS provided by the present application, specifically as follows:

[0084] 201 Data acquisition module: Acquire genes / proteins that are differentially expressed in the irAE-CNS group.

[0085] 202 Analysis module: Perform a correlation analysis on the metabolic characteristics of the lesion area and the differentially expressed genes / proteins in the data acquisition module; perform a clustering analysis on the cells to obtain different cell clusters.

[0086] 203 Feature selection module: Select feature genes / proteins for the cell clusters in the analysis module to obtain cells that predict irAE-CNS.

[0087] Figure 3 It is a schematic diagram of an electronic device provided by the present application. The electronic device includes:

[0088] Memory: Used to store program instructions;

[0089] Processor: Used to call program instructions. When the program instructions are executed, the above screening method is executed.

[0090] Figure 4 It is a schematic diagram of a system / device for diagnosing irAE-CNS provided by the present application, specifically including:

[0091] 301 Data acquisition module: Used to acquire the levels of cells obtained by the above screening method for the test sample.

[0092] 302 Classification prediction module: Perform classification prediction according to the levels of the cells, and then obtain the prediction result of irAE-CNS.

[0093] In some embodiments, if the levels of the cells obtained by the above screening (i.e., multi-secretory T cells) are high, the result of irAE-CNS is obtained.

[0094] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0095] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0096] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of this implementation plan.

[0097] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0098] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. The storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk, or optical disc, etc.

[0099] The present invention provides the use of a reagent for detecting cells obtained by the above screening method in the preparation of a product for diagnosing irAE-CNS.

[0100] The reagent includes antibodies that specifically bind to TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, primers that specifically amplify TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, or probes that specifically recognize TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

[0101] In some embodiments, primers and amplification primers can be used interchangeably. A primer refers to an oligonucleotide that hybridizes to a target nucleic acid or its complement and participates in a nucleic acid amplification reaction. An amplification primer hybridizes to a template nucleic acid and has a 3'-OH (3'-hydroxyl) group that can be extended by a polymerization reaction.

[0102] In some embodiments, a probe refers to an oligonucleotide capable of hybridizing to a target nucleic acid of interest. As will be understood by one of ordinary skill in the art, depending on the stringency of the hybridization conditions, the probe will typically substantially form chemical bonds with target sequences that lack complete complementarity to the probe sequence. The probe can be associated with an appropriate label or reporter moiety so that the probe (and thus its target) can be detected, visualized, measured, and / or assayed.

[0103] In some embodiments, the cells obtained by the above screening method can be detected by any technique known in the art, such as immunoassays, in situ hybridization, PCR detection, immunoblotting, DNA sequence analysis, etc., and these methods can be used in combination. Preferably, it is an immunoassay, such as a detection method based on flow cytometry technology. Although in the examples of this application, the detection is mainly implemented by flow cytometry. However, it should be understood that according to the instructions of this application, after identifying the target cells to be detected, those skilled in the art can prepare a variety of suitable reagents and kits and implement the detection by a variety of methods, and these should also be covered within the protection scope of this application.

[0104] The reagent also includes a detectable label.

[0105] In some embodiments, a label refers to a composition capable of generating a detectable signal indicating the presence of target cells in a sample being assayed. Suitable labels include, but are not limited to, radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent moieties, magnetic particles, bioluminescent moieties. Thus, a label is any composition capable of being detected by an apparatus or method, including but not limited to spectroscopic, photochemical, biochemical, immunochemical, electrical, optical, chemical detection apparatuses, or any other suitable apparatus. In some embodiments, the label can be visually detected without the aid of an apparatus. A label is used to refer to any chemical group or moiety having a detectable physical property or any compound capable of causing a chemical group or moiety to exhibit a detectable physical property, such as an enzyme that catalyzes the conversion of a substrate into a detectable product. A label also encompasses a compound that inhibits the manifestation of a particular physical property. A label can also be a compound that is a member of a binding pair, the other member of which has a detectable physical property.

[0106] Among them, radioisotopes include but are not limited to 3 H, 14 C, 35 S,125 I、 131 I。

[0107] The enzymes include, but are not limited to, horseradish peroxidase, β-galactosidase, luciferase, alkaline phosphatase, and acetylcholinesterase.

[0108] The fluorescent molecules include, but are not limited to, FITC, rhodamine, and lanthanide phosphors.

[0109] The products include kits, chips, test strips, and nucleic acid membrane strips.

[0110] The product includes a kit.

[0111] In some embodiments, a kit refers to any delivery system for delivering materials. In the context of nucleic acid purification systems and reaction assays, such delivery systems include systems that allow for the storage, transportation, or delivery of reagents and devices (e.g., chaotropic salts, particles, buffers, denaturants, oligonucleotides, filters, etc. in appropriate containers) and / or supporting materials (e.g., sample processing or sample storage containers, written instructions for performing procedures, etc.) from one location to another. For example, a kit includes one or more outer shells (e.g., boxes) containing the relevant reaction reagents and / or supporting materials.

[0112] In some embodiments, the kit includes a fragmentation kit and a combination kit. A fragmentation kit refers to a delivery system that includes two or more separate containers, each containing a sub - part of all the kit components. The containers can be delivered together or separately to the intended recipient. For example, the first container may contain materials and buffers for sample collection, while the second container contains capture oligonucleotides and denaturants. A combination kit refers to a delivery system that contains all the components of a reaction assay in a single container (e.g., in a single box that houses each required component).

[0113] The present invention will be further illustrated below with specific examples. It should be understood that the specific embodiments described herein are presented by way of example and are not intended to limit the present invention. Without departing from the scope of the present invention, the main features of the present invention can be used in various embodiments.

[0114] Examples

[0115] 1. Experimental Materials and Methods

[0116] 1) Included Population

[0117] irAE-CNS is defined as an intracranial inflammatory adverse event that occurs in cancer patients after ICI treatment, involving the meninges and parenchyma, with or without involvement of the optic tract. The diagnostic criteria for irAE-CNS follow the "Consensus Statement on Adverse Events of the Nervous System". Briefly, irAE-CNS is defined as new-onset encephalitis, meningitis, demyelination, and cerebral vasculitis that occur in cancer patients after the start of ICI. Brain inflammation imaging is diagnosed by radiologists using 18-FDG PET / CT or MRI tools. Neurologists and oncologists conduct consultations to determine the onset or rule out the symptoms of irAE-CNS diagnosis, and all described signs meet the Common Terminology Criteria for Adverse Events (version 5.0). The diagnosis date of irAE-CNS is recorded by reviewing the outpatient and inpatient medical records of the oncology department. The key exclusion criteria are: 1) metastatic or thrombotic brain lesions masking imaging analysis; 2) imaging or cerebrospinal fluid specimens that fail quality control analysis; 3) active viral or bacterial infections, brain metastases, or severe brain injuries found at the time of recruitment or follow-up; 4) history of brain radiotherapy; 5) 18-F FDG PET / CT images that cannot be read due to imaging artifacts or position changes during the scanning process.

[0118] At least two independent oncologists unrelated to this study consulted on the formal diagnosis of irAE-CNSs at the study site. In addition to sampling cerebrospinal fluid samples from cancer patients treated with ICI who had never been diagnosed with irAE-CNS, samples were also taken from patients with autoimmune paraneoplastic encephalopathy (AME) and central nervous system infection (CNSI). AME is defined as a paraneoplastic autoimmune encephalopathy that occurs in cancer patients without a history of immunotherapy (including ICI). The pathology of AME is considered similar to that of irAE-CNS, both having an autoimmune background, although there are differences in demographics and epidemiology. The diagnostic criteria for AME and CNSI were proposed by Graus et al. IrAE-CNS patients who develop active viral or bacterial infections, brain metastases, or severe brain injuries during recruitment or follow-up will be excluded from the study.

[0119] 2) 18F-FDG PET / CT Scanning and Image Analysis

[0120] During the follow-up of irAE-CNS patients, consecutive sampling was performed by independent clinical coordination technicians, including 18-F FDG PET / CT, BFTs, and CSF biomarker studies in an independent cohort. The patients participating in the study were followed up by experienced neurologists and oncologists in outpatient and inpatient settings, with routine physical examinations conducted every 2-5 months at the cancer screening clinic of the study site. 18-F FDG PET / CT and BFTs are clinical tools for evaluating disease progression after the onset of irAE-CNS. The rationale for follow-up sampling was designed to coincide with medical examinations for the progression of irAE-CNSs. In addition to 18-F FDG PET / CT and BFT tests, all irAE-CNS patients underwent at least one lumbar puncture during follow-up to evaluate the level of intracranial inflammation. Lumbar puncture samples were obtained after careful examination of overall function, including motor and cancer-related assessments. The side effects of lumbar puncture and any accompanying adverse events were medically managed at the oncology inpatient clinic of the study site. In this study, cerebrospinal fluid samples obtained from all patients were discarded samples in the neuro laboratory setting.

[0121] 3) 18F-FDG PET / CT Scanning and Image Analysis Methods

[0122] The 18F-FDG PET / CT brain imaging method and image segmentation protocol have been published previously. Briefly, patients fasted for 6 hours before PET / CT scanning and were scanned using an integrated PET / CT scanner (Discovery ST 16, GE Healthcare) with parameters of 3 minutes / bed position, matrix 200*200, field of view, or FOV, 740 mm, slice thickness 3 mm, and full width at half maximum, or FWHM, of 5 mm. After injection of 5 MBq / kg 18F-FDG, a head-to-thigh PET / CT scan was performed for 45-60 min, and reconstruction was performed using the ordered subset expectation maximization iterative algorithm. The 18-F FDG PET / CT images were obtained with the imaging head in the axilla position for the head and neck region, and the axilla superior position was used for patients without head and neck lesions to avoid artifacts. In all cohorts, brain imaging was obtained with the arms down. Unreadable artifacts or position changes in the 18-F FDG PET / CT images were excluded.

[0123] The OSEM PET / CT images were automatically segmented into brain lobes using Scenium software (Siemens Healthcare). Using a brain template included in Scenium that contains regions for three-dimensional structure fitting, the uptake values of each cortical and subcortical region were calculated as the mean standardized uptake value (SUVmean). The SUV was normalized by weight and injection dose using the following formula:

[0124]

[0125] A c : Activity concentration (Bq / ml); D: Injection dose (Bq); W: Body weight (g); ∆t: Delay between injection time and start time of scanning (s); T 1 / 2 : Half-life of the radionuclide (s)

[0126] The template automatically segmented the regions of interest according to the Automated Anatomical Labeling (AAL) standard, with permission from CEA / Groupe d'Imagerie fonctionnelle. In this study, the mean SUV values of the following bilateral brain regions were monitored: temporal lobe (TEM), cerebellum (CER), frontal lobe (FRONT), occipital lobe (OCC), parietal lobe (PAR), limbic region (LIM), brainstem (stem), and whole brain (WB).

[0127] 4) Brain function tests

[0128] At each time point, a neurologist used clinical rating scales and cognitive tests to evaluate the brain function status of irAE-CNS patients, which were collectively referred to as brain function tests (BFTs). BFT aimed to monitor the overall function of patients and the key cognitive function levels of patients with brain lesions. Collectively, these tests were considered to be consistent with the multi-level prognosis of brain pathology during medium- and long-term follow-up in previous studies. It included four results from three tests. The modified Rankin scale (mRS) aimed to evaluate the overall functional status related to neurological diseases. The Clinical Assessment Scale for Autoimmune Encephalitis (CASE) evaluated the overall function of autoimmune-related central nervous system diseases and had high consistency in evaluating autoimmune brain pathology. The Hopkins Verbal - Learning Test-Revised (HVLT, divided into two parts: HVLTi and HVLTd) was an objective psychological cognitive test that specifically evaluated short-term and long-term memory status.

[0129] 5) Determination of acellular CSF protein levels

[0130] The processing of cerebrospinal fluid (CSF) samples has been reported previously, where CSF specimens were centrifuged within 4 - 9 hours (4°C, 1900g for 10 minutes, then 16000g for 10 minutes), and the supernatant was stored at -80°C for further analysis. Proteins were measured using the proximity extension assay (PEA, Olink custom panel) according to the manufacturer's instructions. Briefly, oligonucleotide-labeled antibody probe pairs bind to the target proteins. If the probe pairs indicate proximity, the oligonucleotides will hybridize in a pairwise manner. The addition of DNA polymerase results in a proximity-dependent polymerization event, generating unique PCR sequences. Subsequently, the DNA sequences were quantified by microfluidic real-time PCR. The data were quality controlled and normalized by extension controls and inter-plate controls to adjust for within-group and between-group variations. The final readings were expressed as normalized protein expression (NPX) values, which are arbitrary units from 0 to 100 on a normalized and log-transformed scale, where higher values correspond to higher protein expression. The assay validation data are available on the manufacturer's website (www.olink.com).

[0131] 6) Single-cell secretory proteomics

[0132] Mononuclear cells in fresh (within <24 h after procurement) CSF were obtained by density centrifugation with Ficoll-Paque (GE healthcare) and cryopreserved in Cryostor 10 (BioLife) with liquid nitrogen for further experiments. After the experiment, the cells were thawed and cultured in RPMI medium (Fisher Scientific) supplemented with 10 ng / mL IL2 (Biolegend) to 1×10 5 cells / mL (37°C, 5% CO2, overnight). For CD137+ samples, CD137+ MicroBead-activated cell (CD137+) sorting kits (TNFRSF9, Miltenyi Biotec) were used to perform positive sorting of CD137+ cells among all mononuclear cells according to the production manual. The cells were purified from dead cells using Ficoll-Paque Plus medium (GE Healthcare). CD137+ microbeads were used to enrich CD137+ cells in cohort 2 and cohort 3. At 1×10 6After resuspending at a density of / mL in complete RPMI medium, 100 - 500 µL of the cell suspension was seeded onto a 96-well plate pre-coated with anti-human CD3 (OKT3, Thermo Fisher / Invitrogen, 10 µg / mL in PBS, 4°C, O / N) at a concentration of 5 µg / mL. The cells were loaded onto IsoCode chips and incubated at 37°C, 5% CO2 for 16 hours. After incubation, the 32-plex antibody barcode chips captured the secreted proteins of 100 - 2000 cells and were analyzed by a backend fluorescence ELISA method. Poly-secretory T cells were defined here as cells co-secreting 6 cytokines.

[0133] 7) Single-cell data analysis

[0134] Preprocessing and clustering analysis were performed using the "Seurat" package on R Statistics. Cells without any features and cells with less than 50 cells were excluded from the matrix. First, dimensionality reduction was performed by principal component analysis (PCA), and then by uniform manifold approximation and projection (UMAP) embedding to observe the unsupervised clustering of single cells. PCA analysis was performed on all features. The elbow plot was used to identify significant components, and UMAP plots with 4 components were used for each analysis with a resolution set to 0.1. The "FindAllMarkers" function with default parameters was used to label specific clusters with a 25% difference from all other clusters. For secreted and surface proteomics, global scaling was performed before log transformation and centering from surface barcode sequencing or luminescent secretion index counting. Secondary filtering was performed on the membrane proteomics to remove cells related to mitochondrial and ribosomal expression.

[0135] 2. Experimental results

[0136] Paired cerebrospinal fluid samples were obtained from 30 irAE-CNS patients (onset group) and the 3-month follow-up time point (3-month steroid treatment group). The control groups included 24 cancer patients with central nervous system infection (CNSI) treated with ICIs, 35 patients with autoimmune paraneoplastic encephalopathy (AME), and 24 patients without intracranial inflammation treated with ICIs (negative control, NC), all at the disease onset stage (CNSI and AME).

[0137] First, the landscape of inflammatory protein levels in these cerebrospinal fluid samples was explored, and targeted analysis was performed using the OLINK proteomics panel (406 proteins). 335 proteins were identified and quantified in the cerebrospinal fluid samples of irAE-CNS and the 3 control groups. After comparing the protein levels between the irAE-CNS samples and the control groups, 53 proteins were found to be significantly upregulated in irAE-CNS patients ( Figure 5 )

[0138] Then, the cerebrospinal fluid protein levels were compared between irAE-CNS patients (onset group) and the 3-month follow-up time point (3-month group). At this time, the levels of 43 proteins were stably expressed, or even slightly increased ( Figure 6 ). Functional annotation of these proteins indicated that 32 proteins were extracellular secreted cytokines, and notably, 10 proteins with the largest log2FC values were specialized extracellular cytokines. These proteins included classical inflammatory biomarkers (TNFA, TNFB, and IFNG), as well as multifunctional biomarkers (CCL11, MCP4, MCP1, TNFRSF9, and IL17A). These results suggested a potentially pathological role of inflammation-related cytokines in irAE-CNS.

[0139] Correlation analysis between the metabolic SUVmean of regional 18-F FDG PET / CT and the protein levels at the onset stage showed that the mean standardized uptake values (SUVmean) in multiple brain regions, especially the limbic region, temporal lobe, and parietal lobe, were significantly correlated with these screened proteins (CCL11, MCP4, MCP1, TNFRSF9, IL17A, Figure 7 ). These findings indicated that these proteins were inflammation-related extracellular cytokines and might serve as specific biomarkers for irAE-CNS.

[0140] A total of 18,831 cells were obtained from 8 irAE-CNS patients (8 pairs of patients, onset and 3 months after onset), 6 CNSI patients (6 cases, 8,687 cells), 4 AME patients (4 cases, 7,222 cells), and 7 NC patients (7 cases, 10,728 cells). The UMAP plot mapped all cells into 7 clusters by dimensionality reduction. Six cytokines ( Figure 8 ), including TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, were found almost exclusively in cluster 6, as shown in the UMAP plot. This finding indicated that cluster 6 had multifunctional characteristics, including pro-inflammatory (CMCP1, MCP4, IL17A), regulatory (TNFRSF9), pro-chemotherapeutic (CCL11), and effector (GZMB), and tentatively named this group of cells in cluster 6 as polysecretory T cells (poly6 + or poly6).

[0141] Polysecretory T cells were only (100%) distributed in irAE-CNS patients at onset and 3 months, and not in AME, NC, or CNSI patient samples, indicating that polysecretory T cells were specific T cells for irAE-CNS ( Figure 9 ).

[0142] In irAE-CNS patients, during the onset period and the 3-month period, polysecretory T cells showed significantly higher and stable secretory capacity, with signals expressed in all other cells ( Figure 10 ).

[0143] Regarding imaging correlation, it was found that there was a strong correlation only between the proportion of polysecretory T cells and the 18-F FDG PET / CT metabolic level in the marginal region, which confirmed the most prominent and common marginal region of intracranial inflammation irAE-CNS found previously. In addition, in the results of the 12-month follow-up brain function test (BFT), patients with a higher proportion of polysecretory T cells showed poorer performance ( Figure 11 ).

[0144] Single-cell transcriptomic studies were performed on cerebrospinal fluid-derived cells from these 8 irAE-CNS donors to further characterize the phenotypic features of polysecretory T cells. The UMAP plot mapped all cells into 8 clusters. All 6 cytokine genes from polysecretory T cells were highly expressed in cluster 2 ( Figure 12 ). In addition, polysecretory T cells were only distributed in cluster 2. Other highly expressed genes included CD3, CD4, and PDCD1, indicating that helper T cells had an exhausted phenotype in polysecretory T cells. Further annotation of cluster 2 showed that these cells were of the effector memory (PTPRC-CCR7-) type ( Figure 12 ), confirming previous evidence that irAE-CNS occurring in the nervous system is characterized by effector memory T cells during acute episodes. These findings suggest that polysecretory T cells are characterized by exhausted T helper effector memory (exThem) cells, suggesting activation and multifunctional T cells in iatrogenic intracranial inflammation. These findings highlight the multifunctional helper T cell-mediated immunity in irAE-CNS.

[0145] The training set used cerebrospinal fluid samples from 8 irAE-CNS patients (onset group), 6 cancer patients with central nervous system infection (CNSI) treated with ICIs, 4 patients with autoimmune paraneoplastic encephalopathy (AME) occurring in cancer patients not treated with ICIs, and 7 patients without intracranial inflammation treated with ICIs (negative control, NC), all at the disease onset stage (CNSI and AME). Single-cell sequencing analysis found that polysecretory T cells were only (100%) distributed in the cerebrospinal fluid of 8 irAE-CNS patients (the average number of polysecretory T cells in irAE-CNS was 9515, and that in the control group was 0). The ROC curve graph showed AUC = 1 and the p value was less than 0.0001 ( Figure 13 ).

[0146] The validation set used cerebrospinal fluid samples from 20 irAE-CNS patients (onset group), 16 cancer patients with central nervous system infection (CNSI) treated with ICIs, 13 patients with autoimmune paraneoplastic encephalopathy (AME) occurring in cancer patients not treated with ICIs, and 9 patients without intracranial inflammation treated with ICIs (negative control, NC), all during the disease onset period (CNSI and AME). Single-cell sequencing analysis found that polysecretory T cells were only distributed (100%) in the cerebrospinal fluid of 20 irAE-CNS patients. The average number of polysecretory T cells in irAE-CNS was 15,013, and in the control group was 318 ( Figure 14 ). The ROC curve showed that AUC = 1 and the p-value was less than 0.0001 ( Figure 15 ).

[0147] The description of the above embodiments is only for understanding the method of the present invention and its core idea. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. A screening method for cells predicting irAE-CNS, characterized in that, The method includes: 1) Obtaining genes / proteins with differential expression in the irAE-CNS group; 2) Performing correlation analysis on the metabolic characteristics of the lesion area and the genes / proteins with differential expression in 1); 3) Performing clustering analysis on the cells to obtain different cell clusters; 4) Selecting characteristic genes / proteins for the cell clusters in 3) to obtain cells for predicting irAE-CNS.

2. The screening method according to claim 1, wherein The genes / proteins with differential expression in 1) include the genes / proteins with differential expression between the irAE-CNS group and the control group and / or the genes / proteins with differential expression between the irAE-CNS group and the treatment group; Preferably, the prediction of irAE-CNS is to distinguish irAE-CNS patients from CNSI patients treated with ICIs, AME patients not treated with ICIs, and patients without intracranial inflammation treated with ICIs; Preferably, the value of the metabolic characteristic in 2) is SUV; Preferably, the SUV is obtained by using 18-F FDG PET / CT; Preferably, the clustering analysis in 3) includes preprocessing, dimensionality reduction, and clustering; Preferably, the clustering includes PCA and UMAP.

3. The screening method according to claim 1, wherein The genes / proteins with differential expression include CCL11, MCP4, MCP1, TNFRSF9, and IL17A; Preferably, the characteristic genes / proteins in the cell clusters include TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

4. A screening system / device for cells predicting irAE-CNS, characterized in that, The system / apparatus includes: Data acquisition module: Obtaining genes / proteins with differential expression in the irAE-CNS group; Analysis module: Performing correlation analysis on the metabolic characteristics of the lesion area and the genes / proteins with differential expression in the data acquisition module; Performing clustering analysis on the cells to obtain different cell clusters; Feature selection module: Selecting characteristic genes / proteins for the cell clusters in the analysis module to obtain cells for predicting irAE-CNS; Preferably, the genes / proteins with differential expression in the data acquisition module include the genes / proteins with differential expression between the irAE-CNS group and the control group and / or the genes / proteins with differential expression between the irAE-CNS group and the treatment group; Preferably, the prediction of irAE-CNS is to distinguish irAE-CNS patients from CNSI patients treated with ICIs, AME patients not treated with ICIs, and patients without intracranial inflammation treated with ICIs; Preferably, the value of the metabolic characteristic in the analysis module is SUV; Preferably, the SUV is obtained by using 18-F FDG PET / CT; Preferably, the clustering analysis in the analysis module includes preprocessing, dimensionality reduction, and clustering; Preferably, the clustering includes PCA and UMAP; Preferably, the genes / proteins with differential expression include CCL11, MCP4, MCP1, TNFRSF9, and IL17A; Preferably, the characteristic genes / proteins in the cell clusters include TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

5. An electronic device, characterized in that, The electronic device includes: Memory: Used to store program instructions; Processor: used to call program instructions, and when the program instructions are executed, execute the screening method according to any one of claims 1-3.

6. A computer-readable storage medium having executable instructions stored thereon, characterized in that, When the executable instructions are executed by the processor, the screening method according to any one of claims 1-3 is implemented.

7. Use of a reagent for detecting cells obtained by the screening method according to any one of claims 1-3 in the preparation of a product for diagnosing irAE-CNS; Preferably, the reagent includes antibodies specifically binding to TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, primers specifically amplifying TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, or probes specifically recognizing TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB; Preferably, the product further includes reagents used in other immunoassays, in situ hybridization, PCR detection, immunoblotting, and DNA sequence analysis.

8. A product for diagnosing irAE-CNS, characterized in that, The product includes a reagent for detecting cells obtained by the screening method according to any one of claims 1-3; Preferably, the reagent includes antibodies specifically binding to TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, primers specifically amplifying TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, or probes specifically recognizing TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB; Preferably, the reagent further includes a detectable label; Preferably, the product includes a kit; Preferably, the kit further includes a buffer; Preferably, the kit further includes an instruction manual.

9. Use of cells obtained by the screening method according to any one of claims 1-3 in constructing a system / device for diagnosing irAE-CNS.

10. A system / apparatus for diagnosing irAE-CNS, characterized in that, The system includes: Data acquisition module: used to acquire the level of cells obtained by the screening method according to any one of claims 1-3 in a test sample; Classification and prediction module: perform classification and prediction based on the level of cells to obtain a prediction result of irAE-CNS; Preferably, the detection reagent for the level of the cells includes antibodies specifically binding to TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, primers specifically amplifying TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB, or probes specifically recognizing TNFRSF9, CCL11, MCP1, MCP4, IL17A, and GZMB.

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