Application of IL1RL1 in prediction of irAEs

Abstract

The invention discloses an application of IL1RL1 in prediction of irAEs (irregularized alphabetic alphabets). According to the application, the change of plasma proteomics related to irAEs with different severity degrees in patients with different cancer types receiving anti-PD-1 / anti-PD-L1 immunotherapy is comprehensively analyzed, it is found that IL1RL1 has high diagnostic value in prediction of irAEs and irAEs with different severity degrees, and a new thought is provided for early prediction and personalized treatment of irAEs.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to the application of IL1RL1 in predicting irAEs. Background Art

[0002] In recent years, immune checkpoint inhibitors (ICIs), represented by PD-1 and PD-L1 monoclonal antibodies, have been rapidly applied to the treatment of various malignant tumors. However, the toxic side effects they cause, called immune-related adverse events (irAEs), can affect various organs and are difficult to predict. Among them, the incidence of severe irAEs is about 10%, which can be clinically manifested as severe myocarditis, severe hepatitis, severe enteritis, etc., which are difficult to treat, with long hospital stays and high mortality. Glucocorticoids are the main treatment. Even for hormone-sensitive patients, the hormone dose is large and the use time is long (at least 4-6 weeks, or even more than half a year), and the side effects of hormones such as infection and tumor progression cannot be ignored. For hormone-insensitive patients, treatment is even more difficult. Major guidelines recommend trying immunosuppressants such as infliximab and mycophenolate mofetil, but these drugs have no evidence-based medical evidence and limited clinical effects. The prognosis of such patients is extremely poor. Therefore, screening biomarkers of irAEs is of great significance for the early diagnosis of irAEs and the effective treatment of patients. Summary of the invention

[0003] To make up for the deficiencies of the prior art, the present invention provides the application of IL1RL1 in predicting irAEs.

[0004] To achieve the above purpose, the present invention adopts the following technical solution.

[0005] A first aspect of the present invention provides the use of a reagent for detecting the expression level of IL1RL1 in the preparation of a product for predicting the risk of irAEs / predicting the severity of irAEs.

[0006] Furthermore, the product also includes a reagent for detecting the expression level of FABP3.

[0007] Furthermore, the reagents include primers that specifically amplify IL1RL1, probes that specifically recognize IL1RL1, or binding agents that specifically bind to the protein encoded by the IL1RL1 gene.

[0008] Furthermore, the reagent also includes a detectable label.

[0009] Furthermore, the detectable labels include radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent moieties, magnetic particles, and bioluminescent moieties.

[0010] A second aspect of the present invention provides a product for predicting the risk of irAEs / predicting the severity of irAEs, wherein the product comprises a reagent for detecting the expression level of IL1RL1.

[0011] Furthermore, the product includes a chip, a test paper, a test kit or a nucleic acid membrane strip.

[0012] Furthermore, the kit also includes reagents for detecting the expression level of IL1RL1 gene or protein by RT-PCR, biochip detection, Southern blotting, in situ hybridization, immunoblotting, ELISA, or mass spectrometry.

[0013] Furthermore, the kit also includes instructions.

[0014] Furthermore, the kit also includes a buffer solution.

[0015] Furthermore, the chip includes a gene chip and a protein chip.

[0016] A third aspect of the present invention provides a model for predicting the risk of irAEs / predicting the severity of irAEs, wherein the model includes IL1RL1.

[0017] Furthermore, the model also includes FABP3.

[0018] A fourth aspect of the present invention provides the use of IL1RL1 in preparing a model for predicting the risk of irAEs / predicting the severity of irAEs.

[0019] The fifth aspect of the present invention provides the use of IL1RL1 in preparing a system / device for predicting the risk / predicting the severity of irAEs.

[0020] Advantages and beneficial effects of the present invention:

[0021] This application comprehensively analyzed the changes in plasma proteomics associated with irAEs of different severity in patients with different cancer types receiving anti-PD-1 / anti-PD-L1 immunotherapy, and found that IL1RL1 has a high diagnostic value in predicting irAEs and irAEs of different severity, providing new ideas for the early prediction and personalized treatment of irAEs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1is a graph of proteomics data quality and clinical relevance, where 1a is a flowchart of plasma protein analysis, 1b is a graph of the number of proteins identified during the quality control process, 1c is a graph of the number of comparable proteins in different irAEs groups, 1d is a graph of the functional relationship between the mean value of protein intensity and the abundance level in the plasma proteome, 1e is a graph of the coefficient of variation (CV) of each protein in different irAEs groups, 1f and 1g are graphs of scores of dimensions 1 and 2 based on PCA and PLS-DA of comparable proteins, 1h is a graph of the correlation between clinical information and proteomic analysis of irAEs severity assessed by PCA / PLS-DA scores, 1i and 1j are graphs of secretion and tissue-specific proteins identified in samples based on HPA annotations;

[0023] Figure 2 2a and 2b are Venn diagrams of proteins with significant trend differences with irAEs severity determined by two-sided Jonckheere-Terpstra trend test and Kruskal-Wallis test, 2c is a clustering heat map of proteins with trend differences between different irAEs groups, 2d is a fold change scatter plot of proteins with strong or weak trend differences, 2e is a fold change scatter plot of proteins with significant differences between any two irAEs groups or between the control group and the mild group, 2f is a bar chart of KEGG and Reactome terms that are significantly reduced (blue) or increased (red) in trend proteins associated with irAEs severity (FDR P < 0.05 and gene ratio > 0.1) compared with various subgroups;

[0024] Figure 3 Figure 3a and 3b are the relationship diagrams of irAEs network proteins detected together with trend differences in the consensus protein co-expression network analysis, 3c is the distribution diagram of proteins detected together with trend differences in the irAEs network modules, 3d is the clustering heat map of important proteins in the co-expression module (c), and 3e is the significant correlation diagram of KEGG and Reactome terms with proteins detected together in M1 and M4 with strong trends in irAEs severity (terms marked as decreased (blue) or increased (red), FDR P < 0.05 and gene ratio > 0.1).

[0025] Figure 4 4a and 4c are the comparison diagrams of the model performance of ProIRAE and the single protein model, and 4b and 4d are the distribution diagrams of the predicted risk scores of the selected irAEs severity monitoring models in the discovery cohort and the validation cohort;

[0026] Figure 5 It is a correlation analysis diagram between proteomic profiles and clinical characteristics, among which 5a is a similarity diagram between patient protein profile samples, 5b is a correlation difference distribution diagram between protein profiles and different clinical characteristics under different irAEs groups based on PCA score evaluation, 5c and 5d are correlation diagrams between secretory proteomics and organ-specific proteomics and irAEs clinical characteristics evaluated based on PCA / PLS-DA scores;

[0027] Figure 6 Bar chart of GO enrichment analysis terms that were significantly decreased (blue) or increased (red) in trend proteins (FDR P < 0.05 and gene ratio > 0.1) associated with the severity of irAEs compared with various subgroup comparisons;

[0028] Figure 7 is a consensus protein co-expression network analysis, wherein 7a is a determination diagram of the soft threshold power (β) in the consensus protein co-expression network analysis, 7b is a protein dendrogram based on dissimilarity measurement, 7c is an analysis diagram of the impact of missing values ​​(threshold 10-40% missing) on ​​the evaluation of irAEs network modules (dashed blue (1.96) and red (10) indicate weak statistical significance and high statistical significance, respectively), 7d is a box plot of the differences between the original module characteristic proteins (the first principal component of module protein expression) in the irAEs-related modules between different irAEs groups, 7e is a correlation analysis diagram between the original module characteristic proteins and the module characteristic proteins generated by the proteins selected from the hypergeometric test (3c), and 7f is a correlation analysis diagram between potential biomarkers and consensus network module characteristic proteins;

[0029] Figure 8 This is a bar chart of GO enrichment analysis terms that are significantly reduced (blue) or increased (red) in proteins enriched in the irAEs-related module (FDR P < 0.05 and gene ratio > 0.1);

[0030] Fig. 9 It is a graph of related proteins in KEGG and Reactome entries that are significantly enriched in M1 and M4 modules with high stability;

[0031] Fig.10 This is a model hyperparameter diagram based on five-fold cross-validation of the discovery set data;

[0032] Fig.11 It is an evaluation diagram of the efficacy of using IL1RL1 protein to predict the severity of irAEs, where 11a is a correlation diagram and a corresponding scatter plot between the results of quantification of IL1RL1 protein abundance using mass spectrometry (MS), and 11b is an evaluation diagram of the efficacy of the ProIRAE model in monitoring the severity of irAEs in the discovery cohort and validation cohort. DETAILED DESCRIPTION

[0033] The definitions of some terms used in this specification are provided below. Unless otherwise defined, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0034] The present invention provides the use of a reagent for detecting the expression level of IL1RL1 in the preparation of a product for predicting the risk of irAEs / predicting the severity of irAEs.

[0035] In some embodiments, IL1RL1 includes wild-type, mutant, or fragments thereof. The term encompasses full-length, unprocessed IL1RL1, as well as any form of IL1RL1 derived from processing in cells. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of IL1RL1. The term encompasses, for example, human and any other vertebrate-derived IL1RL1, including mammals, such as primates and rodents (e.g., mice and rats), Gene ID: 9173.

[0036] The reagents include primers that specifically amplify IL1RL1, probes that specifically recognize IL1RL1, or binding agents that specifically bind to the protein encoded by the IL1RL1 gene.

[0037] In some embodiments, primer refers to a short nucleic acid molecule, such as a DNA oligonucleotide, which can anneal with a complementary target nucleic acid molecule by nucleic acid hybridization to form a hybrid between the primer and the target nucleic acid chain. The primer can be extended along the target nucleic acid molecule by a polymerase. Therefore, the primer can be used to amplify the target nucleic acid molecule, wherein the sequence of the primer is specific for the target nucleic acid molecule, such as the primer will hybridize with the target nucleic acid molecule under very high stringency hybridization conditions.

[0038] In some embodiments, the probe can be DNA, RNA, DNA-RNA chimera, PNA or other derivatives. The length of the probe is not limited, as long as it completes specific hybridization and specifically binds to the target nucleotide sequence, any length is fine. The length of the probe can be as short as 25, 20, 15, 13 or 10 bases. Similarly, the length of the probe can be as long as 60, 80, 100, 150, 300 bases or longer, even the entire gene.

[0039] In some embodiments, primers or probes can be chemically synthesized using phosphoramidite solid phase support method or other well-known methods. Many means known in the art can also be used to modify. The limiting examples of these modifications include methylation, capping, displacement with one or more analogs of natural nucleotides and modification between nucleotides. For example, modify uncharged linkers (for example, methyl phosphate, phosphotriester, phosphoramidite, carbamate, etc.), or modify charged linkers (for example, phosphorothioate, phosphorodithioate, etc.).

[0040] The reagent also includes a detectable label.

[0041] In some embodiments, detectable label refers to the composition that can produce the detectable signal that shows that there is target polynucleotide in the assay sample.Suitable detectable label includes but is not limited to radioisotope, nucleotide chromophore, enzyme, substrate, fluorescent molecule, chemiluminescent part, magnetic particle, bioluminescent part.Therefore, label is any composition that can be detected by device or method, includes but is not limited to spectrum, photochemistry, biochemistry, immunochemistry, electricity, optics, chemical detection device or any other suitable device.In some embodiments, label can be detected visually without the aid of device.

[0042] Among them, radioactive isotopes include but are not limited to 3 H. 14 C. 35 S. 125 I. 131 I.

[0043] Enzymes include, but are not limited to, horseradish peroxidase, beta-galactosidase, luciferase, alkaline phosphatase, acetylcholinesterase.

[0044] Fluorescent molecules include, but are not limited to, FITC, rhodamine, and lanthanide phosphors.

[0045] The products include chips, test strips, test kits or nucleic acid membrane strips.

[0046] In some embodiments, the chip is also referred to as an array, which refers to a solid support comprising a connected nucleic acid or peptide probe. The array typically comprises a plurality of different nucleic acid or peptide probes connected to the substrate surface according to different known positions. These arrays, also referred to as "microarrays", can typically be produced using mechanical synthesis methods or light-guided synthesis methods that incorporate a combination of photolithography and solid phase synthesis methods. The array can comprise a flat surface, or can be a nucleic acid or peptide on a bead, gel, polymer surface, fiber such as an optical fiber, glass, or any other suitable substrate. The array can be packaged in a certain manner to allow for diagnosis of a fully functional device or manipulation of other methods.

[0047] The chips include gene chips and protein chips.

[0048] In some embodiments, the gene chip includes a solid phase carrier and a probe fixed on the solid phase carrier, the probe including an oligonucleotide probe for the IL1RL1 gene for detecting the transcription level of the IL1RL1 gene; the protein chip includes a solid phase carrier and a specific antibody for the IL1RL1 protein fixed on the solid phase carrier; the gene chip can be used to detect the expression level of multiple genes including the human IL1RL1 gene (e.g., multiple genes associated with irAEs). The protein chip can be used to detect the expression level of multiple proteins including the human IL1RL1 protein (e.g., multiple proteins associated with irAEs). By detecting multiple markers related to irAEs simultaneously, the accuracy of diagnosing irAEs can be greatly improved.

[0049] In some embodiments, the kit may further include a fluorescent pigment, and a variety of known fluorescent pigments may be used. For example, a method using an intercalator with a labeling function, a method using a probe that binds a fluorescent substance to a nucleotide that is specifically hybridized to a relatively amplified DNA sequence, etc. may be cited. As an intercalator, ethidium bromide and SYBR Green I as unsaturated fluorescent pigments, and Resolight and EvaGreen as saturated fluorescent pigments may be cited. For the amount used, the manufacturer's recommendation of the fluorescent pigment used may be followed.

[0050] The kit also includes instructions and buffer. The instructions may include instructions for obtaining and processing samples. In addition, the kit may include genomic DNA of bacteria used as a positive control for PCR and sterile water used as a negative control.

[0051] In some embodiments, the components of the test kit can be packaged in the form of an aqueous medium or in a lyophilized form. Suitable containers in the test kit usually include at least a vial, test tube, flask, PET bottle, syringe or other container, in which a component can be placed, and preferably, can be appropriately divided. When there is more than one component in the test kit, the test kit will usually also include a second, third or other additional container, in which additional components are placed separately. However, the components of different combinations can be contained in a vial. The test kit of the present invention will also usually include a container for accommodating reactants, sealed for commercial sale. This container can include a plastic container of injection molding or blow molding, in which the required vial can be retained.

[0052] The solid support of the kit can be, for example, plastic, silicon wafer, metal, resin, glass, membrane, particle, precipitate, gel, polymer, sheet, ball, polysaccharide, capillary, film, plate or slide. The biological sample can be, for example, cell culture, cell line, tissue, oral tissue, gastrointestinal tissue, organ, organelle, biological fluid, plasma sample, urine sample or skin.

[0053] In some embodiments, the nucleic acid membrane strip includes a substrate and a probe fixed on the substrate; the substrate can be any substrate suitable for fixing the probe, including but not limited to nylon membrane, nitrocellulose membrane, polypropylene membrane, glass sheet, silica gel wafer, micro-magnetic beads.

[0054] The invention will be further described below in conjunction 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 invention. The main features of the invention may be used in a variety of embodiments without departing from the scope of the invention.

[0055] Example

[0056] 1. Experimental methods

[0057] Patient cohort and sample collection

[0058] This study is a prospective discovery-validation cohort study conducted at the Affiliated Cancer Hospital of Harbin Medical University from August 2020 to April 2024. This study has been approved by the Ethics Committee of the Affiliated Cancer Hospital of Harbin Medical University (approval number: kyk2020003) and follows the guidelines of the Declaration of Helsinki. Informed consent was obtained from all participants before enrollment.

[0059] A total of 65 patients who received anti-PD-1 / anti-PD-L1 blockade therapy were recruited and divided into two cohorts: a discovery cohort and a validation cohort. The discovery cohort included 47 patients recruited between August 2020 and February 2023, while the validation cohort included 18 patients recruited between April 2023 and April 2024.

[0060] Eligible patients were aged between 30 and 75 years and had received anti-PD-1 or anti-PD-L1 therapy (monotherapy or combination) for at least one week. Patients with a history of immune system diseases (including AIDS, systemic lupus erythematosus, hyperthyroidism, or Cushing's syndrome) or who had undergone an organ transplant in the past six months were not included in the study. All adverse reactions were evaluated according to the National Comprehensive Cancer Network (NCCN) guidelines version 1.2020. These adverse events affected different organ systems, including but not limited to the skin, liver, lungs, kidneys, nervous system, and cardiovascular system. The demographic and clinical data of participants collected included sex, age, body mass index (BMI), treatment duration, cancer type, and stage. According to the severity of irAEs, patients were divided into three groups: 1) control group: patients who did not experience any irAEs; 2) mild group: patients with irAEs graded less than grade 3; 3) severe group: patients with irAEs graded 3 or above. Plasma samples were obtained from all participants. In the mild and severe groups, plasma samples were collected on the first day of irAE diagnosis.

[0061] Plasma protein extraction and trypsin digestion

[0062] Use EDTA tube (BD Beckton Dickinson, Franklin Lakes, NJ, US) and centrifuged at 1000 × g for 10 min at 4°C within 2 h of collection. Plasma was separated at -80°C using a standardized protocol and then centrifuged at 12,000 × g for 10 min at 4°C to remove cellular debris. The supernatant was transferred to a new centrifuge tube. The supernatant was centrifuged using Pierce TM The top 14 abundant proteins were removed using the Top 14 Abundant Protein Depletion Spin Columns Kit (Thermo Fisher Scientific). Protein concentration was determined using the BCA assay kit according to the manufacturer's instructions.

[0063] An equal amount of protein was used for digestion in each sample. Sample amounts were adjusted with lysis buffer to ensure consistency. The protein solution was reduced with 5 mM dithiothreitol at 56 °C for 30 min and then alkylated with 11 mM iodoacetamide at room temperature in the dark for 15 min. The alkylated sample was transferred to an ultrafiltration device and centrifuged at 12,000 × g for 20 min at room temperature. The sample was washed three times with 8 M urea and then three times with buffer solution. Trypsin was added at a 1:50 enzyme-protein ratio and digested overnight. The resulting peptides were collected by centrifugation at 12,000 × g for 10 min at room temperature and then washed once more with double distilled water. The combined peptide solution was used for liquid chromatography-tandem mass spectrometry (LC-MS / MS) analysis.

[0064] LC-MS / MS analysis

[0065] The peptides were dissolved in solvent A (0.1% formic acid and 2% acetonitrile in water) and then separated using a Vanquish ultra-high performance liquid chromatography system. The peptides were separated using a gradient method, with the concentration of solvent B (0.1% formic acid and 90% acetonitrile) increasing from 7% to 20% over 16 minutes; 20% to 32% over 8 minutes; then to 80% over 3 minutes and maintained at 80% for the last 3 minutes. The flow rate was maintained at 500 nL / min. After separation, the peptides were ionized using a nanospray ionization (NSI) source and analyzed using an Orbitrap Exploris 480 mass spectrometer. The ion source voltage was set to 2.3 kV, and the FAIMS compensation voltage was set to -45 V and -70 V. High-resolution detection and analysis of precursor and fragment ions were performed using the Orbitrap. The full MS scan resolution was set to 30,000 with a scan range of 390–810 m / z; the MS / MS scan resolution was set to 30,000 with a fixed start point of 200 m / z.

[0066] Data acquisition was performed in data independent acquisition (DIA) mode. After each full mass spectrometry scan, multiple consecutive m / z peptide ion windows were selected and fragmented in the high energy collision dissociation (HCD) collision cell. Fragmentation was performed at normalized collision energies of 25%, 30%, and 35%, followed by MS / MS analysis. To optimize the efficiency of mass spectrometry analysis, the automatic gain control (AGC) target was set to 3E6 and the maximum injection time was set to automatic.

[0067] Peptide identification and protein quantification

[0068] The resulting DIA data were searched against the UniProt human protein database (updated 2023-01-03, 20,389 entries) using Spectronaut (v17) and the Pulsar search engine (default settings). A reverse decoy database was included in the search to calculate the false discovery rate (FDR) and to account for random hits. Trypsin / P was specified as the cleavage enzyme, and a maximum of two missed cleavages were allowed. Cysteine ​​aminomethylation was specified as a fixed modification, while acetylation at the protein N-terminus and oxidation of methionine residues were specified as variable modifications. The FDR threshold for peptide spectrum matches (PSMs), peptide and protein identification and quantification was strictly set to 1%.

[0069] Proteomics data quality control and preprocessing

[0070] Protein abundance values ​​less than 10 were marked as missing values ​​(NA). Proteins identified in less than 50% of the samples were not further analyzed, and the remaining missing value proteins were estimated using the R software package "DreamAI" with a parameter setting of k = 10 and the k-nearest neighbor (k-NN) method. After estimation, protein intensities were log-2 transformed for subsequent statistical and bioinformatics analysis.

[0071] Differential protein analysis

[0072] Differential analysis of protein expression was performed to identify proteins with significant trends between the control group, mild and severe irAEs groups. Proteins that were consistently up- or down-regulated were identified using the Kruskal-Wallis test and the Jonckheere-Terpstra test to assess monotonic trends in protein expression. The direction of these trends was determined by the median protein intensity of each group. The Wilcoxon rank sum test was used to compare protein expression between the control group and the mild group, the mild group and the severe group, the control group and the severe group, and the protein expression with strong trend changes between irAEs and no irAEs, and severe irAEs and non-severe irAEs. If the FDR < 0.05, the protein was considered to have a significant abundance difference under a given condition.

[0073] Functional annotation and enrichment analysis

[0074] Secretory proteins and tissue-specific proteins were annotated according to the Human Protein Atlas (HPA). Gene Ontology (GO), KEGG pathway, and Reactome enrichment analysis of differentially expressed proteins were performed using the R packages “clusterProfiler” and “ReactomePA” with an FDR < 0.05.

[0075] Weighted Correlation Network Analysis (WGCNA)

[0076] Protein co-expression modules were identified by unsupervised clustering using the blockwiseModules subroutine in the R package “WGCNA” with the following settings: soft power = 6, correlations were calculated using bidweight mid-correlations (bicor), deepSplit = 4, minimum module size was 14, merged cut height was 0.07, average TOM denominator, signed network, minimum kME of 0.4 for retaining proteins in a module, and redistribution threshold was P < 0.05.

[0077] To reduce the number of modules with highly similar module signature proteins (representative abundance values ​​of each module), the WGCNA::mergeCloseModules() function was used. Proteins were reassigned based on Spearman correlation with a maximum similarity threshold of 0.25. Module preservation at different levels of proteome loss was assessed using the WGCNA::modulePreservation() function, and unassigned proteins were marked as M0 and annotated in gray.

[0078] Enzyme-linked immunosorbent assay (ELISA)

[0079] Plasma protein levels were quantified using commercially available ELISA kits according to the manufacturer's instructions. The specific kits used in this study were as follows: IL1RL1 (CSB-E13789h), ADM (CSB-E09146h), FABP3 (CSB-E09185h), TNC (CSB-E13125h), TNFIP6 (CSB-EL023959HU), VSIG4 (CSB-EL025928HU), PLIN3 (MM-63554H2), and VWCE (MM-51800H2). All experimental procedures were performed according to the protocols provided by the kit manufacturers.

[0080] Machine learning-based protein model for irAEs risk prediction

[0081] Feature importance analysis was performed using the variable importance in projection (VIP) method based on protein abundance data derived from mass spectrometry (MS) and enzyme-linked immunosorbent assay (ELISA) to estimate the contribution of each variable in predicting the risk of irAEs. To build a predictive model, an ordinal logistic regression model was trained in the discovery cohort using the best combination of variables. Hyperparameters were optimized using grid search and five-fold cross validation.

[0082] Statistical analysis

[0083] All statistical analyses were performed using R (v4.2.0) and related software packages. Intergroup comparisons of continuous variables were performed using the Wilcoxon rank sum test or the Kruskal Wallis test, as appropriate. Categorical variables were analyzed using the Fisher exact test. For model evaluation, the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPR) were calculated to evaluate the performance of the machine learning model.

[0084] Experimental Results

[0085] Study design and clinical characteristics of the irAEs cohort

[0086] To investigate plasma proteomic patterns and identify potential non-invasive biomarkers of immune checkpoint blockade-related irAEs, comprehensive proteomic analysis of plasma samples was performed. A total of 65 patients treated with anti-PD-1 / PD-L1 therapy were included in this study and divided into three groups according to the severity of irAEs: control group (no irAEs), mild group (irAEs grade <3), and severe group (irAEs grade ≥3). Figure 1 a), patients were further divided into a discovery cohort consisting of 47 patients (11 controls, 14 mild groups, and 22 severe groups) and a validation cohort consisting of 18 patients (7 controls, 5 mild groups, and 6 severe groups). The discovery cohort was used for proteomic analysis, biomarker identification, and model construction, while the validation cohort was used to evaluate the generalizability of the model.

[0087] The average age of the patients ranged from 56.6 to 65.4 years, and the body mass index ranged from 20.9 to 25.7. The study adopted a pan-cancer approach because all participants had irAEs due to disruption of the immune signaling pathways targeted by anti-PD-1 / PD-L1 therapy. The most common cancer types among the participants were lung cancer and liver cancer, as well as 13 other less common cancers.

[0088] Plasma proteome analysis of irAEs

[0089] Using the Blood+DIA technique, a total of 15,745 peptides and 2,304 proteins were identified in 47 plasma samples from the discovery cohort ( Figure 1 b). After quality control, 1,459 proteins were retained for further analysis. Comparative proteomic analysis showed that there was no significant difference in proteome coverage between the control group (median, 1,316 proteins) and the mild irAEs group (median, 1,311 proteins) (Wilcoxon rank sum test, P = 0.956) ( Figure 1 c). However, there was a significant difference between the severe irAEs group (median, 1,339 proteins) and the other two groups (Wilcoxon rank-sum test, P = 0.027). Protein intensities spanned six orders of magnitude, with the ten most abundant proteins accounting for 39% to 45% of the total abundance of plasma proteins ( Figure 1 d). Notably, the variability of protein expression increased with increasing severity of irAEs, with the median coefficient of variation increasing from 6.5% in the control group to 9.9% in the severe irAEs group, indicating increased proteomic heterogeneity ( Figure 1 e and Figure 5 a).

[0090] Unsupervised principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) were used to compare and observe the plasma proteome profiles, and the results showed that there were significant differences among the three irAEs groups ( Figure 1 fh and Figure 5 b). Further annotation of the HPA identified 657 secreted proteins (45.0% of the analyzed proteins), of which 300 (45.7%) were actively secreted into the blood and 141 (21.5%) were transmembrane proteins ( Figure 1 i and Figure 5 c). In addition, 269 tissue-specific proteins were found (18.4% of the analyzed proteins), most of which were derived from the liver (124 proteins, 46.1%), implying a significant contribution of the liver to the plasma proteome ( Figure 1 j and Figure 5 d). In different irAEs groups, both secreted proteins and tissue-specific proteins showed consistent patterns and had no significant correlation with other clinical characteristics. In conclusion, these findings suggest that plasma proteomics can effectively reflect the onset and severity of irAEs.

[0091] Identification of potential plasma protein biomarkers for irAEs

[0092] To further investigate the changes in plasma proteome associated with the onset and severity of irAEs, a differential trend analysis was performed, and it was found that the expression of 217 proteins changed significantly in different irAEs severity. Among them, the expression of 184 proteins increased significantly, and the expression of 33 proteins decreased significantly. In addition, the abundance of 106 proteins changed weakly, of which the abundance of 85 proteins increased with the severity of irAEs, and the abundance of 21 proteins decreased with the severity of irAEs ( Figure 2 a). Proteins with a clear strong trend showed significant differences in distinguishing irAEs samples (mild group + severe group) from severe irAEs samples, and the changes between the mild irAEs group and the severe irAEs group were more obvious than those between the control group and the mild irAEs group, indicating that the changes in the proteome became more obvious with the escalation of irAEs severity ( Figure 2 b).

[0093] Enrichment analysis of these dysregulated proteins showed that, although the clustering of samples was relatively consistent, proteins with stronger trends were more strongly regulated in each irAEs group compared with proteins with weaker trends ( Figure 2 c, 2f, Figure 6). Functional annotation using Reactome and KEGG showed that proteins with strong trends were mainly involved in inflammation and chemotaxis (such as neutrophil degranulation, neutrophil interstitial, neutrophil apoptosis, neutrophil degranulation, interleukin signaling, and interleukin-1 family signaling), metabolism and cellular responses (such as amino acid and carbohydrate metabolism, extracellular matrix organization, and cellular response to chemical stress), and immunity and antigen processing (such as class I MHC-mediated antigen processing and presentation and antigen processing: ubiquitination and proteasomal degradation). In contrast, pathways related to complement and coagulation functions and humoral immune functions (such as antigen binding and immunoglobulin secretion) were continuously downregulated, indicating that with the increase in the severity of irAEs, humoral immunity (antibody-mediated) has shifted to cellular immunity (T cell-mediated) ( Figure 2 f and Figure 6 ).

[0094] Several previously identified irAEs biomarkers were also differentially expressed in plasma, including C-reactive protein (CRP, associated with systemic inflammatory response; Kruskal-Wallis FDR P = 0.028), cluster of differentiation 177 (CD177, involved in immune-mediated intestinal diseases; Kruskal-Wallis FDR P = 0.009), and lymphocyte cytoplasmic protein 1 (LCP1; Kruskal-Wallis FDR P = 0.009), although their expression was stable in the control and mild groups ( Figure 2 d). By comparing different irAEs groups, it was found that 8 proteins continued to increase with the increase in the severity of irAEs ( Figure 2 b, 2e). These proteins highlight their potential as non-invasive biomarkers for predicting and monitoring the risk and progression of irAEs in patients receiving anti-PD-1 / PD-L1 therapy.

[0095] Clinically relevant functional protein modules

[0096] To identify co-regulated proteins and their association with irAEs, WGCNA analysis of the proteomic data of plasma samples revealed 17 different protein modules, ranging in size from 255 proteins in M0 to 15 proteins in M16 ( Figure 7 a, 7b). Robustness analysis shows that these modules remain highly stable at different data integrity thresholds (10% to 50%) ( Figure 7 c). In addition, five modules were significantly associated with the risk and severity of irAEs, indicating that they were involved in the progression of irAEs regardless of other clinical characteristics ( Figure 3 a, 3b and Figure 7d). Unsupervised hierarchical clustering of 704 proteins in four key protein modules (M1, M2, M4, and M16) revealed that the irAEs in the control, mild, and severe groups were significantly different ( Figure 3 d), indicating that these modules are closely related to the clinical manifestations of irAEs.

[0097] Then, 217 proteins with strong differential trends (identified in the previous analysis) were mapped to these functional protein modules using the hypergeometric test. The results showed that three modules (M1, M2, and M16) were significantly enriched for proteins with an up-regulated trend, with M1 enriched 2.34 times, M2 enriched 4.27 times, and M16 enriched 3.96 times. In contrast, module M4 was enriched for down-regulated proteins by as much as 13.96 times ( Figure 3 c). These findings highlight the close association between module composition and irAEs progression, as the recalculated signature proteins showed high correlations with the original module signature proteins, ranging from 0.93 to 0.99 ( Figure 7 e). Functional enrichment analysis was performed based on the KEGG and Reactome databases to identify the main biological processes of each protein module. It was found that M1 was significantly involved in inflammatory and chemotaxis pathways and matrix organization, indicating increased T cell activity; M2 was mainly related to antigen-antibody interactions and disease-related pathways; while M4 showed a downward trend in complement and coagulation-related functions ( Figure 3 d, Figure 8 , Fig. 9 ).

[0098] Development and validation of a plasma protein-based prediction model for irAEs

[0099] To evaluate the clinical applicability and reproducibility of the eight potential protein biomarkers identified, plasma protein levels were measured in previous patients using enzyme-linked immunosorbent assay. Figure 4 As shown in a, the plasma protein levels of most candidate biomarkers measured by ELISA were well correlated with those measured by MS. Then, feature selection was performed using the VIP method to identify the most informative protein biomarkers to establish a predictive model. The proteomic data (MS and ELISA) of the discovery cohort showed two highly informative biomarkers: interleukin 1 receptor-like 1 (IL1RL1) and fatty acid binding protein 3 (FABP3), which were selected into the plasma protein-based irAEs risk prediction (PROIRAE) model ( Figure 4 b). The PROIRAE model was constructed using the ordinal regression method, and hyperparameters were optimized through five-fold cross validation and grid search ( Fig.10). The PROIRAE model showed strong predictive performance. For the discovery cohort, PROIRAE achieved an AUROC of 0.932 and an AUPR of 0.980 in identifying patients at risk of developing irAEs, and an AUROC of 0.980 and an AUPR of 0.982 in identifying patients at risk of developing severe irAEs ( Figure 4 c). The risk score predicted by the PROIRAE model showed a clear stratification of the severity of irAEs ( Figure 4 d).

[0100] To verify the universality of PROIRAE, it was applied to an independent cohort of 18 patients. The PROIRAE model maintained strong predictive performance, with an AUROC of 0.727 and an AUPR of 0.880 in distinguishing non-irAEs from irAEs patients; and an AUROC of 0.972 and an AUPR of 0.979 in predicting severe irAEs ( Figure 4 e). The distribution of predicted risk scores in the control group, mild group, and severe group in the validation cohort also showed obvious stratification, further validating the ability of the model to reliably distinguish patients with different degrees of irAEs ( Figure 4 f). In addition, the combination of IL1RL1 and FABP3 in the PROIRAE model outperformed the predictive power of either protein biomarker alone ( Figure 4 c, 4e), which highlights the synergistic role of these two protein biomarkers in predicting the risk and severity of irAEs.

[0101] The description of the above embodiments is only used to understand the method and core idea of ​​the present invention. It should be pointed out that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications will also fall within the scope of protection of the claims of the present invention.

Claims

1. The use of reagents for detecting IL1RL1 expression levels in the preparation of products for predicting the risk of irAEs / predicting the severity of irAEs; Preferably, the product further comprises a reagent for detecting the expression level of FABP3.

2. The use according to claim 1, characterized in that: The reagents include primers that specifically amplify IL1RL1, probes that specifically recognize IL1RL1, or binding agents that specifically bind to the protein encoded by the IL1RL1 gene.

3. The use according to claim 2, characterized in that: The reagent also includes a detectable label; Preferably, the detectable label comprises a radioisotope, a nucleotide chromophore, an enzyme, a substrate, a fluorescent molecule, a chemiluminescent moiety, a magnetic particle, or a bioluminescent moiety.

4. A product for predicting the risk of irAEs / predicting the severity of irAEs, characterized in that: The product includes a reagent for detecting the expression level of IL1RL1.

5. The product according to claim 4, characterized in that The products include chips, test strips, test kits or nucleic acid membrane strips.

6. The product according to claim 5, characterized in that The kit also includes reagents for detecting the expression level of IL1RL1 gene or protein by RT-PCR method, biochip detection method, Southern blotting method, in situ hybridization method, immunoblotting method, ELISA method, and mass spectrometry; Preferably, the kit further comprises instructions; Preferably, the kit further comprises a buffer.

7. The product according to claim 5, characterized in that The chips include gene chips and protein chips.

8. A model for predicting the risk of irAEs / predicting the severity of irAEs, characterized in that: The model includes IL1RL1; Preferably, the model further comprises FABP3.

9. Application of IL1RL1 in preparing models for predicting the risk / severity of irAEs.

10. Application of IL1RL1 in the preparation of a system / device for predicting the risk / predicting the severity of irAEs.

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