Use of reagent for detecting ILA concentration in preparation of product for predicting curative effect of esophageal squamous cell carcinoma immunotherapy, prediction method and equipment

Abstract

The application discloses application of a reagent for detecting ILA concentration in preparation of a product for predicting the curative effect of esophageal squamous cell carcinoma immunotherapy, a prediction method and equipment, and relates to the technical field of immunotherapy curative effect prediction. The reagent for detecting indole-3-lactic acid concentration is aimed at samples selected from serum, plasma, blood, tumor tissue or fecal samples. By establishing a prediction model for the curative effect of esophageal squamous cell carcinoma immunotherapy, the curative effect of immunotherapy on esophageal squamous cell carcinoma patients can be accurately predicted, valuable reference bases can be provided for clinicians, thus helping to formulate individualized immunotherapy strategies and improving the curative effect of immunotherapy.

Description

Technical Field

[0001] This invention relates to the field of immunotherapy efficacy prediction technology, and more specifically, to the application, prediction method and equipment of reagents for detecting ILA concentration in the preparation of products for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma. Background Technology

[0002] Esophageal squamous cell carcinoma (ESCC) is a common and highly malignant digestive system tumor. Currently, ESCC diagnosis and treatment have formed a complete technical system centered on surgical treatment, integrating neoadjuvant therapy, adjuvant therapy, systemic therapy, and cutting-edge research. In recent years, immune checkpoint inhibitors (such as anti-PD-1 antibodies) have made significant progress in cancer immunotherapy; however, in clinical application, the efficacy of immunotherapy varies significantly among different patients. Immunotherapy response rates are low, and patients often develop drug resistance during treatment. Therefore, how to effectively predict the efficacy of immunotherapy in ESCC patients and provide a basis for individualized immunotherapy is a significant challenge in the field of tumor immunotherapy.

[0003] Currently, PD-L1 is one of the biomarkers used to predict immunotherapy responses in cancer. While PD-L1 testing is effective in some cancer types, its accuracy in predicting immunotherapy responses needs improvement, and it fails to fully account for immune escape mechanisms within the tumor microenvironment.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide the application, prediction method, and equipment of reagents for detecting ILA concentration in the preparation of products for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma, thereby accurately predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma and improving the efficacy and precision of immunotherapy.

[0006] This invention is implemented as follows:

[0007] In a first aspect, the present invention provides the application of a reagent for detecting indole-3-lactic acid concentration in the preparation of products for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma.

[0008] Secondly, this invention provides a method for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma, the steps of which are performed by a computer; the method includes the following steps:

[0009] Based on the characteristic data of indole-3-lactic acid contained in the test samples of subjects with esophageal squamous cell carcinoma, predictive results of the efficacy of immunotherapy for the subjects are generated.

[0010] In a third aspect, the present application provides an esophageal squamous cell carcinoma immunotherapy efficacy prediction device, the device comprising a processor and a memory, the memory storing a set of executable program instructions, the set of executable program instructions being loaded and executed by the processor to implement the esophageal squamous cell carcinoma immunotherapy efficacy prediction method described above.

[0011] The present application has the following beneficial effects:

[0012] The present application found that Lactobacillus salivarius was significantly enriched in the fecal samples of patients with esophageal squamous cell carcinoma (ESCC) who were ineffective after anti-PD-1 immunotherapy, and belonged to the main dominant species. L. salivarius Indole-3-lactic acid (ILA) secreted by Lactobacillus salivarius was significantly increased in patients who were ineffective after immunotherapy, and further correlation analysis of fecal microbiome and metabolome showed that there was a significant positive correlation between Lactobacillus salivarius and ILA, suggesting that ILA may serve as a biomarker for predicting the effect of immunotherapy. Through the establishment of an esophageal squamous cell carcinoma immunotherapy efficacy prediction model, the effect of immunotherapy on esophageal squamous cell carcinoma patients can be accurately predicted, and patients with lower ILA levels are more likely to respond to immunotherapy.

[0013] After comparative analysis, ILA as a biomarker for immunotherapy effect can more accurately reflect the immune escape mechanism in the tumor microenvironment, and has higher predictive value than PD-L1 expression.

[0014] The present application detects the level of ILA in serum, tumor tissue or feces non-invasively, avoiding the invasive operation in traditional biomarker detection and improving the comfort of patients.

[0015] The present application provides a technical basis for personalized immunotherapy, which can determine whether a patient is suitable for immunotherapy according to the level of ILA, avoiding unnecessary side effects and treatment costs. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, and it should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0017] Figure 1 Research cohort and sample collection schematic diagram;

[0018] Figure 2LEfSe (a) and LDA plots (b) for genus-level differential enrichment analysis of collected samples;

[0019] Figure 3 Figures showing the abundance comparison (a) and qPCR (b) results of the collected samples;

[0020] Figure 4 Figure (a) shows the cluster analysis results of metabolites of Lactobacillus salivarius, and Figure (b) shows the comparison results of metabolic patterns between the NR group (drug-resistant group) and the R group (sensitive group).

[0021] Figure 5 Box plot of fecal microbiome and metabolome;

[0022] Figure 6 A graph showing the statistical results of ILA level detection in 32 collected serum samples;

[0023] Figure 7 ROC curves were plotted for the training set composed of samples from Example 1 based on the two markers PD-L1 (a) and ILA (b). Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0025] Definition of noun

[0026] The term "marker" broadly refers to any detectable compound or cell present in or derived from a sample, such as a protein, peptide, proteoglycan, glycoprotein, lipoprotein, cell, or any of the foregoing substances, that is differentiating molecule or differentiating fragment. For example, the detection of or binding to a specific antibody can indicate the presence of a specific antigen (e.g., a protein) in a sample. Here, a differentiating molecule or fragment is a molecule or fragment that, upon detection, indicates the presence or abundance of the aforementioned identified compound or cell. Markers can, for example, be isolated from the sample, measured directly in the sample, or detected or determined in the sample. Markers can, for example, be functional, partially functional, or non-functional. Markers may also be synonymous with "biomarker."

[0027] As used in this article, the term "treatment" refers to a series of medical actions that involve interventions in an existing disease through medical means, techniques, or methods, with the aim of eliminating the cause, relieving symptoms, controlling disease progression, promoting tissue repair, restoring function, or alleviating pain.

[0028] The term "sample" refers to a biological specimen obtained from or derived from an individual for a purpose. The source of the biological specimen can be a fresh, frozen, and / or preserved organ or tissue sample or solid tissue derived from a biopsy or primer; blood or any blood component. The term "sample" includes biological samples that have been manipulated in any way after acquisition, such as by reagent treatment, stabilization, enrichment for certain components (e.g., proteins or polynucleotides), or embedding in a semi-solid or solid matrix for sectioning purposes. In this invention, the sample is particularly a tumor tissue sample, serum sample, or fecal sample.

[0029] The term "area under the curve" or "AUC" refers to the area under the receiver operating characteristic (ROC) curve, both of which are well-known in the field. The AUC measurement is useful for comparing the accuracy of classifiers across the entire data range. A classifier with a higher AUC has a greater ability to correctly classify unknowns between two target groups (e.g., esophageal squamous cell carcinoma immunotherapy-responsive samples and esophageal squamous cell carcinoma immunotherapy-ineffective samples). The ROC curve is useful for depicting the performance of a specific feature (e.g., any biomarkers and / or any entries of additional biomedical information described in this invention) when distinguishing between two populations (e.g., individuals with esophageal squamous cell carcinoma immunotherapy-responsive samples and individuals with esophageal squamous cell carcinoma immunotherapy-ineffective samples). Typically, feature data are selected across the entire population (e.g., cases and controls) in ascending order based on the value of a single feature. Then, for each value of that feature, the true positive and false positive rates of the data are calculated. The true positive rate is determined by counting the number of cases with values ​​higher than that feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls with values ​​higher than the characteristic and dividing by the total number of controls. While this definition refers to cases where the characteristic is higher than the control, it also applies to cases where the characteristic is lower than the control (in which case samples with values ​​lower than the characteristic are counted). ROC curves can be generated with respect to individual characteristics and other individual outputs. For example, combinations of two or more characteristics can be mathematically combined (e.g., addition, subtraction, multiplication, etc.) to provide individual sum values ​​that can be plotted on the ROC curve. Furthermore, any combination of multiple characteristics derived from individual output values ​​can be plotted on the ROC curve.

[0030] The term "subject" as used in this article can be understood as anyone involved in esophageal squamous cell carcinoma. A subject can be a patient in a clinical setting.

[0031] Indole-3-lactic acid, also known as ILA or Indole-3-lactate, is mainly produced by specific bacteria in the gut through the metabolism of tryptophan in the diet.

[0032] In a first aspect, the present invention provides the application of a reagent for detecting indole-3-lactic acid concentration in the preparation of products for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma.

[0033] Reagents for detecting indole-3-lactic acid concentration include, but are not limited to, reagents for liquid chromatography-tandem mass spectrometry. Any reagent capable of detecting indole-3-lactic acid levels in subject samples is within the scope of protection of this invention.

[0034] Reagents for detecting indole-3-lactic acid concentration include, but are not limited to, reagents related to methods such as GC-MS, LC-MS, triple quadrupole mass spectrometry, Q-TOF, MALDI-TOF, and Orbitrap.

[0035] In a preferred embodiment of the present invention, based on the separation by liquid chromatography, ILA molecules in the sample are detected by mass spectrometry, and an appropriate internal standard (such as 2-chlorophenylpropionic acid) is used to quantify ILA, ensuring the accuracy of the results. In one embodiment, a standard curve is established using ILA standards of known concentrations, and the ILA concentration in the sample is calculated using the standard curve.

[0036] Comparative analysis showed that ILA, as a biomarker of immunotherapy efficacy, can more accurately reflect the immune escape mechanism in the tumor microenvironment and has higher predictive value than PD-L1 expression.

[0037] In a preferred embodiment of the present invention, the reagent for detecting the concentration of indole-3-lactic acid is selected from serum, plasma, blood, tumor tissue, or fecal samples.

[0038] In a preferred embodiment of the present invention, immunotherapy is immune checkpoint inhibitor therapy.

[0039] In a preferred embodiment of the invention, the immune checkpoint inhibitor treatment is selected from treatment with at least one of the following inhibitors: PD-1 inhibitors, PD-L1 inhibitors, CTLA-4 inhibitors, and BTLA inhibitors. In particular, PD-1 inhibitor treatment is preferred.

[0040] PD-1 inhibitors include, but are not limited to: pembrolizumab, nivolumab, sintilimab, tislelizumab, camrelizumab, and cimiprimab.

[0041] PD-L1 inhibitors include, but are not limited to: atezolizumab, durvalumab, and avelumab.

[0042] CTLA-4 inhibitors include, but are not limited to, ipilimumab and trimemumab.

[0043] BTLA inhibitors include InVivoMAbanti-mouseBTLA, preferably clone6A6.

[0044] In a preferred embodiment of the present invention, the immunotherapy is selected from bispecific antibody or multispecific antibody therapy.

[0045] Bispecific antibodies such as cantulimumab (AK104) and Iza-bren (BL-B01D1) can be used.

[0046] In a preferred embodiment of the present invention, the product is selected from reagent kits, chips, or devices.

[0047] Furthermore, the kit may also include at least one of the following: a buffer solution for detecting indole-3-lactic acid, a detection reagent, a diluent, and a washing solution, and is not limited thereto.

[0048] The chip can also be called a suspension array or a liquid array.

[0049] The microfluidic chip is selected from T-type chip, flow focusing chip or coaxial flow chip PDMS chip or metal droplet generator or PMMA microfluidic chip.

[0050] Secondly, this invention provides a method for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma, the steps of which are performed by a computer; the method includes the following steps:

[0051] Based on the characteristic data of indole-3-lactic acid contained in the test samples of subjects with esophageal squamous cell carcinoma, predictive results of the efficacy of immunotherapy for the subjects are generated.

[0052] In a preferred embodiment of the present invention, the characteristic data of the marker are selected from the concentration of indole-3-lactic acid or the standardized data of the concentration of indole-3-lactic acid.

[0053] In a preferred embodiment of the present invention, generating a prediction result of the immunotherapy efficacy of the subject based on the characteristic data of indole-3-lactic acid contained in the test sample of the subject with esophageal squamous cell carcinoma includes: comparing the concentration of indole-3-lactic acid contained in the test sample of the subject with a predetermined threshold, and generating a prediction result of the immunotherapy efficacy of the subject based on the comparison result; the predetermined threshold is a threshold obtained based on the ROC curve generated by logistic regression.

[0054] The term "predetermined threshold" refers to the threshold used to compare the characteristic data of biomarkers in a subject's sample with the predicted efficacy of immunotherapy for esophageal squamous cell carcinoma. The efficacy of immunotherapy for esophageal squamous cell carcinoma in the subject is then output based on the comparison results.

[0055] Specifically, the predetermined thresholds include, but are not limited to, positive judgment values, prediction probability thresholds, and other critical values ​​that can be used to divide results and clarify judgment boundaries. The setting of these thresholds should be based on the performance requirements of the target detection scenario (such as sensitivity, specificity, accuracy, etc.) and determined through clinical data validation, statistical model analysis (such as ROC curve analysis combined with Youden index calculation, etc.) or industry standard calibration, so as to ensure their validity and reliability in the model for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma.

[0056] Specifically, the predetermined threshold in this paper is the positive cutoff value, which refers to the critical single concentration value of the biomarker determined based on clinical sensitivity and specificity requirements when using ROC curves or models for data fitting. In one specific implementation, the detection concentration value of the biomarker indole-3-lactic acid is directly compared with the positive cutoff value, and the efficacy result of immunotherapy for esophageal squamous cell carcinoma in the subject is output based on the comparison result.

[0057] Thirdly, the present invention provides a device for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma. The device includes a processor and a memory. The memory stores a set of executable program instructions, which are loaded and executed by the processor to achieve the above-mentioned method for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma.

[0058] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0059] A processor can be an integrated circuit chip with signal processing capabilities. This processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0060] Fourthly, the present invention also provides a device for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma, comprising: an input module, a control module, and an output module;

[0061] The input module is configured to: acquire characteristic data of indole-3-lactic acid in biological samples from subjects with esophageal squamous cell carcinoma;

[0062] The control module includes an analysis module configured to generate a prediction of the immunotherapy efficacy of the subject based on characteristic data of indole-3-lactic acid contained in the subject's test sample; the characteristic data is selected from the concentration of indole-3-lactic acid in the subject's test sample or the standardized data of the concentration of indole-3-lactic acid.

[0063] The output module is configured to output the predicted results of the subject's immunotherapy efficacy.

[0064] Standardization can eliminate the influence of units of measurement, transforming data of different specifications and magnitudes into pure numerical values ​​of a uniform scale, making different indicators comparable.

[0065] The standardization is selected from at least one of Z-score standardization, Min-Max normalization, robust scaling, and logarithmic transformation.

[0066] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0067] Example 1

[0068] This embodiment provides the process for strain screening.

[0069] 1. Study Cohort and Sample Collection: In this study, 122 stool samples were collected from ESCC patients receiving neoadjuvant immunotherapy. A total of 43 patients with esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant anti-PD-1 immunotherapy were included in the study (the included patients received a combination of multiple anti-PD-1 immunotherapies). Patients receiving neoadjuvant immunotherapy received two cycles of intravenous immunotherapy with the following drugs: pembrolizumab (200 mg, D1), sintilimab (200 mg, D1), toripalumab (200 mg, D1), camrelizumab (200 mg, D1), or adenomyumab (anti-PD-L1, 1200 mg, D1). All patients also received two cycles of intravenous cisplatin (75 mg / m² body surface area, D1) and paclitaxel (175 mg / m² body surface area, D1). Fecal samples were collected at baseline (43 samples before the first treatment), before the second treatment cycle (39 samples), and before surgery (40 samples), for a total of 122 samples. Genome extraction was performed on each sample, and 16S rDNA microbial detection was conducted.

[0070] Figure 1 This is a schematic diagram of the research cohort and sample collection.

[0071] 2. Differential enrichment analysis of genus-level flora was performed on the collected samples. After quality control of the 16S rDNA sequencing data of fecal samples, a genus-level abundance table was generated using QIIME2, and the samples were divided into R and NR groups according to their immunotherapy response. Subsequently, LEfSe differential enrichment analysis was performed: first, the Kruskal-Wallis test was used to screen for differentially expressed genera, then the Wilcoxon test was used to confirm significance, and finally, LDA was used to assess the effect size. The analytical parameters were set as P < 0.05 and LDA > 2.0. The LEfSe and LDA results are shown below. Figure 2 As shown in a and b, Ligilactobacillus was significantly enriched in the NR (ineffective treatment) group, among which L. salivarius Lactobacillus salivarius is the dominant bacterial genus.

[0072] 3. Abundance comparison and qPCR detection were performed on the collected samples.

[0073] (1) Abundance comparison steps:

[0074] After quality control and dechimeric extraction, 16S rDNA sequencing data from the samples were used to construct an OTU table. Species annotation was performed using the Silva database to obtain a genus / species level abundance matrix, followed by relative abundance standardization. Samples were divided into R and NR groups based on treatment outcomes, and the Mann-Whitney U test was used for comparison. L. salivariusAbundance differences were considered statistically significant with P < 0.05. Results are as follows: Figure 3 As shown in Figure a.

[0075] (2) qPCR detection steps:

[0076] Fecal DNA was extracted using a commercial kit and analyzed using targeted methods. L. salivarius qPCR primers specific to the 16S fragment were used for detection. A 20 μL mixture (SYBR Mix + primers + DNA template) was used, and the cycling conditions were 95℃ for 3 min; 95℃ for 10 s, 60℃ for 30 s × 40 cycles. Relative expression levels were calculated using ΔCt, and the differences between the R group and the NR group were compared. P < 0.05 was considered significant. Results are shown below. Figure 3 As shown in Figure b.

[0077] The detection primers are as follows:

[0078]

[0079] Figure 3 The results showed that in the NR group (ineffective treatment) Ligilactobacillus The abundance was significantly higher in group R (effective treatment) than in group R. P <0.05); further validation was achieved by qPCR. L. salivarius Enrichment of (Lactobacillus salivarius) in the NR group.

[0080] Example 2

[0081] This embodiment analyzes the metabolites of Lactobacillus salivarius.

[0082] The experimental steps are as follows:

[0083] (1) Sample processing: The collected serum, tumor tissue and fecal samples were first processed. Serum and fecal samples were purified by liquid extraction and filtration to remove impurities, while tumor tissue required appropriate tissue lysis and metabolite extraction.

[0084] (2) LC-MS / MS analysis: Based on the separation by liquid chromatography, ILA molecules in the sample are detected by mass spectrometry. An appropriate internal standard (such as 2-chlorophenylpropionic acid) is used to quantify ILA to ensure the accuracy of the results.

[0085] The liquid chromatography analysis method is as follows:

[0086] Column type:

[0087] Chromatographic column: Acquity BEH Amide column (2.1 × 100 mm, 1.7 μm, Waters, USA); Guard column: VanGuard guard column (2.1 × 5 mm, Waters, USA); Mobile phase: Mobile phase A: 5% (v / v) acetonitrile, containing 5 mM ammonium acetate and 0.1% (v / v) acetic acid; Mobile phase B: 95% (v / v) acetonitrile, containing 5 mM ammonium acetate and 0.1% (v / v) acetic acid.

[0088] Gradient elution procedure:

[0089] 0–1 min: 94% B; 1–7.5 min: 94–78% B; 7.5–12 min: 78–39% B; 12–17 min: 39% B; 17–19 min: 39–94% B; 19–30 min: 94% B. Sample loading volume: 5 μL (positive ion mode) or 10 μL (negative ion mode). Flow rate: 0.3 mL / min; Column temperature: 35°C;

[0090] Mass spectrometry analysis method

[0091] Mass spectrometer: Instrument model: Q-Exactive Plus Orbitrap high-resolution mass spectrometer (ThermoScientific, USA); Ion mode: positive ion mode and negative ion mode: switch as needed.

[0092] Data acquisition steps: Full MS scan: m / z 70–1000 Da, resolution 70,000; dd-MS2 (tandem mass spectrometry): resolution 17,500, normalized collision energies 20, 40 and 60 V; spray voltage: 3.0 kV (positive and negative ion modes).

[0093] Capillary temperature: 320°C; Carrier gas conditions: Sheath gas (N2): 35 arbitrary units; Auxiliary gas (N2): 15 arbitrary units.

[0094] Sampling volume: Positive ion mode injection volume: 5 μL; Negative ion mode injection volume: 10 μL.

[0095] Data acquisition software: Control and data acquisition software: Xcalibur software (version 4.2, Thermo Fisher Scientific, USA).

[0096] (3) Establishment of standard curve: A standard curve was established using ILA standards of known concentrations, and the ILA concentration in the samples was calculated using the standard curve. The detection accuracy of ILA concentration in fecal samples was 93.7%, the detection accuracy of ILA concentration in serum samples was 93.7%, and the detection accuracy of ILA concentration in tissue samples was 92.9%.

[0097] After the ILA level was measured, the data were used for subsequent immunotherapy response analysis.

[0098] This study investigated microbial-derived metabolites downstream of the tryptophan metabolic pathway. Untargeted metabolomics analysis of L. salivarius culture supernatant revealed indole-3-lactic acid (ILA) as the major metabolite, which was almost undetectable in control medium but significantly accumulated after bacterial culture. Based on these observations, targeted metabolomics analysis targeting the tryptophan metabolic pathway was subsequently performed. Among all quantified metabolites, ILA showed the most significant and consistent difference, with significantly higher levels of ILA in fecal samples from the NR group (non-responder group) than in the R group (responder group), suggesting that ILA may play a role in mediating resistance to anti-PD-1 immunotherapy.

[0099] Cluster analysis of identified metabolites showed that ( Figure 4 As shown in Figure a), there are significantly different metabolic patterns between the NR group (i.e., the drug-resistant group) and the R group (i.e., the drug-sensitive group). Figure 4 (See Figure b in the table). The results showed that 14 metabolites were significantly elevated in the NR group, including ILA.

[0100] Box plots of fecal microbiome and metabolome further demonstrate ( Figure 5 ), L. salivarius A significant positive correlation was found between *Lactobacillus salivarius* and ILA (P<0.05). LC-MS results showed that, compared to empty culture medium and *E. coli*, L. salivarius The ILA level in the culture supernatant was significantly elevated.

[0101] L. salivarius Strain (CICC 23 179) was provided by the China Industrial Microbial Culture Collection Center (CICC). *L. salivarius* was cultured in sterile De Man, Rogosa, and Sharpe (MRS) liquid medium (#288130, BDBiosciences) under anaerobic conditions at 37°C. The anaerobic environment was achieved using an anaerobic incubator (Oxoid) and anaerobic packaging (#A-06, MGC).

[0102] Example 3

[0103] This embodiment constructs a predictive model for the efficacy of immunotherapy in esophageal squamous cell carcinoma. By collecting and analyzing ILA level data from patients with different immunotherapy responses, a correlation model between ILA and immunotherapy efficacy is established. Specific steps include:

[0104] (1) First, a statistical analysis was performed on the immunotherapy response (response group (R) and non-response group (NR)) of 32 different patients. The sample came from patients with locally advanced esophageal squamous cell carcinoma (ESCC) at West China Hospital of Sichuan University, who were enrolled between 2023 and 2024. Patient grouping was based on postoperative pathological tumor regression grade (TRG): TRG 0 and TRG 1 were the response group (R), and TRG 2 and TRG 3 were the non-response group (NR). The detection method was the same as in Example 2, comparing the differences in ILA levels between the two groups. The statistical significance of ILA levels in immunotherapy response was determined by t-test or other suitable statistical methods.

[0105] Figure 6 The experimental results showed that the level of ILA in the serum samples of the response group (R, i.e., the sensitive group) was significantly lower than that in the serum samples of the non-responder group (NR, i.e., the drug-resistant group).

[0106] (2) Establishment of immunotherapy response model: Based on the statistical analysis results, a regression analysis prediction model was established to analyze the relationship between ILA level and patient immunotherapy response. The model was trained and optimized using a training set.

[0107] Based on the two biomarkers PD-L1 and ILA, the training set samples were the same as in Example 1. Specifically, 122 stool samples were collected from ESCC patients receiving neoadjuvant immunotherapy. A total of 43 esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant anti-PD-1 immunotherapy were included in the study (the included patients received a combination of multiple anti-PD-1 immunotherapies). The 32 samples from step (1) were used as the validation set.

[0108] Figure 7 ROC curves were plotted using the training sets mentioned above for the two biomarkers, PD-L1 (a) and ILA (b). The results showed that ILA had a higher AUC value in the training set, indicating that it was more accurate in predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma.

[0109] Based on the established predictive model, it is possible to predict whether a patient will develop resistance to immunotherapy by inputting their ILA level. Patients with high ILA levels may indicate the presence of immune escape mechanisms, suggesting poor or ineffective immunotherapy; while patients with low ILA levels may be more likely to respond well to immunotherapy.

[0110] The predictive model established in this invention can provide valuable reference for clinicians, thereby helping to develop personalized immunotherapy strategies and improve the effectiveness of immunotherapy.

[0111] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. The application of a reagent for detecting indole-3-lactic acid concentration in the preparation of products for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma, characterized in that, The reagent for detecting indole-3-lactic acid concentration is used on samples selected from serum, plasma, blood, tumor tissue, or fecal samples. The immunotherapy efficacy is defined as effective or ineffective treatment. The immunotherapy is an immune checkpoint inhibitor therapy, which is selected from PD-1 inhibitor therapy.

2. The application according to claim 1, characterized in that, The immunotherapy is selected from multispecific antibody therapy.

3. The application according to claim 1, characterized in that, The product is selected from the device.

4. The application according to claim 1, characterized in that, The product is selected from reagent kits or chips.

5. A method for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma, characterized in that, The steps of the method are performed by a computer; the method includes the following steps: Based on the characteristic data of indole-3-lactic acid contained in the test samples of subjects with esophageal squamous cell carcinoma, predictive results of the efficacy of immunotherapy for the subjects are generated, wherein the immunotherapy is immune checkpoint inhibitor therapy, and the immune checkpoint inhibitor therapy is selected from PD-1 inhibitor therapy.

6. The method for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma according to claim 5, characterized in that, The characteristic data of indole-3-lactic acid are selected from the concentration of indole-3-lactic acid or the standardized data of the concentration of indole-3-lactic acid.

7. The method for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma according to claim 6, characterized in that, The method of generating a prediction result of the immunotherapy efficacy of the subject based on the characteristic data of indole-3-lactic acid contained in the test sample of the subject with esophageal squamous cell carcinoma includes: comparing the concentration of indole-3-lactic acid contained in the test sample of the subject with a predetermined threshold, and generating a prediction result of the immunotherapy efficacy of the subject based on the comparison result; the predetermined threshold is a threshold obtained based on the ROC curve generated by logistic regression.

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