A device for predicting the efficacy of immunotherapy for lung squamous cell carcinoma patients

Abstract

The application provides a device for predicting the curative effect of immunotherapy for lung squamous carcinoma patients, which comprises a detection unit and a data analysis unit, wherein the detection unit extracts 9 intratumor bacterial genera as characteristic intratumor bacterial genera based on 16S rRNA sequencing data of a tumor puncture sample of a lung squamous carcinoma patient; the data analysis unit constructs a data model for predicting the curative effect of immunotherapy based on the characteristic intratumor bacterial genera and the 16S rRNA sequencing data of the tumor puncture sample of the lung squamous carcinoma patient; and the characteristic intratumor bacterial genera comprise Actinoplanes, Anaerobisporus, Cato, Fingoldia, Koch, Limnohabitans, Pararhodobacter, Achromobacter and Proteus. The prediction device can predict whether the immunotherapy for lung squamous carcinoma patients is effective, and can be used for screening and stratified management of patients before treatment, so as to guide individualized medication and improve the curative effect.

Description

Technical Field

[0001] This invention belongs to the field of gene detection technology, specifically relating to a device for predicting the efficacy of immunotherapy in patients with squamous cell carcinoma of the lung. Background Technology

[0002] Lung squamous cell carcinoma (LUSC) is the second most common type of lung cancer. Due to the lack of specific symptoms in early-stage LUSC, most LUSC patients are diagnosed at an intermediate or advanced stage, resulting in poor treatment outcomes (Lau SCM, Pan Y, Velcheti V, et al. Squamous cell lung cancer: Current landscape and future therapeutic options[J]. Cancer Cell, 2022, 40(11):1279-1293). In recent years, immunotherapy has shown significant efficacy in the treatment of lung squamous cell carcinoma patients. For patients with inoperable advanced lung squamous cell carcinoma, immunotherapy is considered a first-line treatment option (Xu Y, Li H, Huang Z, et al. Predictive values ​​of genomic variation, tumor mutational burden, and PD-L1 expression in advanced lung squamous cell carcinoma treated with immunotherapy[J]. Transl Lung Cancer Res, 2020, 9(6):2367-2379). However, due to tumor heterogeneity, the efficacy of immunotherapy varies greatly among patients with squamous cell lung cancer, with only a portion of patients benefiting from it clinically. Therefore, identifying biomarkers that can predict the efficacy of immunotherapy in patients with squamous cell lung cancer and classifying them into immunotherapy-sensitive and immunotherapy-insensitive patients is of great significance for clinical treatment selection.

[0003] CN118995934A discloses an oral microbiome related to the efficacy of immunotherapy in lung cancer patients. Based on Metagenomic Next-Generation Sequencing (mNGS) and non-targeted metabolomics liquid chromatography-mass spectrometry (LC / MS) analysis of salivary microbiota and metabolite data, it was found that the distribution concentration of 12 specific oral bacterial genera can predict the efficacy of lung cancer immunotherapy. The areas under the ROC curves for the 12 oral bacterial species were 0.85, 0.82, 0.79, 0.83, 0.80, 0.84, 0.78, 0.80, 0.79, 0.77, 0.85, and 0.79, respectively. CN118345143A discloses a method for predicting the efficacy of neoadjuvant therapy combining immunotherapy and chemotherapy in patients with locally advanced lung cancer using five enteric bacterial genera. Compared with the local tumor microenvironment, oral and gut microbiota, while having some value in predicting the efficacy of immunotherapy for lung cancer, have a weaker direct correlation with intratumoral immune status. They are easily affected by multiple external factors such as diet, medication, lifestyle, and environment, resulting in insufficient stability, poor reproducibility, and relatively limited explanatory power for the mechanism of immunotherapy response.

[0004] Recent studies have shown that intratumoral microbiota plays a crucial role in tumorigenesis and development by regulating the immune microenvironment (IME) and influencing the efficacy of immune checkpoint inhibitors (ICIs). On one hand, some microbiota can act as antigens, inducing inflammatory responses and activating anti-tumor immunity. For example, Lachnoclostridium, enriched in melanoma, is associated with cytotoxic CD8+. + T cell infiltration was positively correlated (Sfanos, KS, Cancer Res., 2023); while in esophageal squamous cell carcinoma, increased Streptococcus abundance was also associated with GrzB. + CD8 + T cell enrichment is closely associated with tumor progression (Zhu, G. et al., Eur. J. Cancer, 2021). On the other hand, certain bacterial flora can drive immunosuppression, thereby promoting tumor progression. For example, Methanobrevibacter can reduce CD8... + The number of T cells and memory T cells is a key indicator of poor prognosis in gastric cancer patients (Wu, H. et al., Cancer Res., 2023); while Escherichia coli inhibits CD8+ in colorectal cancer by creating a glycerophospholipid-rich microenvironment. +T lymphocyte recruitment (Peng, R. et al., Cancer Immunol. Res., 2022).

[0005] Overall, these findings reveal a complex and dynamic interaction between the intratumoral microbiota and the immune microenvironment, which not only deeply participates in tumorigenesis and development but also has a decisive impact on the efficacy of immunotherapy. Therefore, the intratumoral microbiota holds promise as an important biomarker for assessing immunotherapy response and for providing new intervention targets for precision oncology. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a device for predicting the efficacy of immunotherapy in patients with squamous cell carcinoma of the lung. The purpose is to provide a characteristic intratumoral bacteria model for predicting the efficacy of immunotherapy in patients with squamous cell carcinoma of the lung. This model can predict whether immunotherapy will be effective in patients with squamous cell carcinoma of the lung. It can also be used to screen and stratify patients before treatment, thereby guiding individualized medication and improving efficacy.

[0007] This invention utilizes 16S rRNA gene sequencing of biopsy specimens from patients with pre-treatment squamous cell lung cancer. Following sequencing, bacterial seronormalization analysis was performed, and the differential bacterial genera between the effective and ineffective immunotherapy groups were analyzed using MaAsLin2 (Microbiome Multivariate Association with LinearModels, v1.12.0) software. After correcting for clinical parameters such as tumor stage, patient age, sex, and smoking history, 22 enriched drug-resistant bacterial genera were identified in the ineffective immunotherapy group compared to the effective group. Based on the minimum lambda (λ) value, Lasso regression was used to select 9 characteristic intratumoral bacterial genera, constructing a characteristic intratumoral bacterial genera model to predict the effectiveness of immunotherapy in patients with squamous cell lung cancer. This model can be used for patient screening and stratification management before treatment, thereby guiding individualized medication, improving efficacy, and reducing unnecessary side effects and economic burden.

[0008] In a first aspect, the present invention provides the application of reagents for detecting the level of characteristic intratumoral bacteria in the preparation of products for predicting the efficacy of immunotherapy for lung squamous cell carcinoma, wherein the characteristic intratumoral bacteria include Actinoplanes, Anoxybacillus, Catonella, Finegoldia, Kocuria, Limnohabitans, Microbacterium, Achromobacter, and Proteus.

[0009] In this invention, the characteristic intratumoral bacterial genera refers to bacterial genera that, in tumor tissue microbiome studies, show significant differences in abundance, frequency of occurrence, or functional potential compared to control tissues (such as adjacent normal or healthy tissues), and may be associated with tumor development, immune microenvironment, or treatment response.

[0010] According to a specific embodiment of the present invention, the characteristic intratumoral bacteria genus is extracted based on differential bacterial genus analysis and Lasso regression of tumor puncture samples from patients with squamous cell carcinoma of the lung.

[0011] According to a specific embodiment of the present invention, the present invention performs bacterial genus standardization analysis on 16S rRNA gene sequencing data, analyzes the differential bacterial genera between the immunotherapy effective group and the treatment ineffective group using MaAsLin2 (Microbiome Multivariate Association with Linear Models, v1.12.0) software, selects characteristic intratumoral bacterial genera based on the minimum lambda value using Lasso regression, and constructs a characteristic intratumoral bacterial genera model to predict whether immunotherapy is effective in patients with squamous cell carcinoma of the lung.

[0012] In one preferred embodiment, the characteristic intratumoral bacteria are Actinobacteria, Bacillus, Catorium, Fingoldii, Cochlea, freshwater bacteria, Microbacterium, Achromobacterium, and Proteus.

[0013] According to a specific embodiment of the present invention, the efficacy of lung cancer immunotherapy is predicted by detecting the abundance value of the characteristic intratumoral bacteria in a lung squamous cell carcinoma biopsy sample before individual immunotherapy; wherein, the immunotherapy is tumor immunotherapy, which refers to a treatment method that controls and eliminates tumors by restarting and maintaining the tumor-immune cycle and restoring the body's normal anti-tumor immune response.

[0014] According to a specific embodiment of the present invention, the detection reagent for the predictive indicator includes a reagent for detecting the DNA level of characteristic intratumoral bacteria.

[0015] According to a specific embodiment of the present invention, the reagent for detecting the DNA level of the characteristic intratumoral bacteria includes a reagent for detecting the DNA level of the characteristic intratumoral bacteria using 16S rRNA gene sequencing.

[0016] According to a specific embodiment of the present invention, the reagent for detecting the level of characteristic intratumoral bacteria includes primers, probes, or antibodies.

[0017] According to a specific embodiment of the present invention, the reagent for detecting the level of characteristic intratumoral bacteria includes reagents used in high-throughput sequencing.

[0018] As a preferred embodiment, the high-throughput sequencing is 16S rRNA gene sequencing.

[0019] According to a specific embodiment of the present invention, the product includes a chip, a reagent kit, a test strip, a high-throughput sequencing platform, or a prediction device.

[0020] Secondly, the present invention provides a device for predicting the efficacy of immunotherapy in patients with squamous cell carcinoma of the lung, comprising a detection unit and a data analysis unit, wherein:

[0021] The detection unit detects characteristic intratumoral bacteria from the individual being tested and obtains the detection results;

[0022] The data analysis unit is used to analyze and process the detection results of the detection unit.

[0023] The characteristic intratumoral bacteria mentioned herein are the same as those described in the first aspect of the present invention.

[0024] According to a specific embodiment of the present invention, the detection unit extracts characteristic intratumoral bacteria genus of the individual to be tested based on 16S rRNA gene sequencing, and obtains the detection results including the abundance value of characteristic intratumoral bacteria genus.

[0025] According to a specific embodiment of the present invention, the abundance value of the characteristic intratumoral bacteria is calculated by multiplying the relative abundance value of the characteristic intratumoral bacteria in the individual sample by 1,000,000.

[0026] According to a specific embodiment of the present invention, the detection results include characteristic intratumoral bacteria abundance values.

[0027] In this invention, the data analysis unit further substitutes the characteristic intratumoral bacteria abundance value into the following detection model to calculate RAMS_score:

[0028] RAMS_score = -1.38738 + 0.00097 × abundance value of *Actinomyces* + 0.00578 × abundance value of *Bacillus* + 0.00879 × abundance value of *Catalystia* + 0.00659 × abundance value of *Fingoldii* + 0.00066 × abundance value of *Coxella* + 0.01982 × abundance value of *Freshwater Bacteria* + 0.00032 × abundance value of *Microbacteria* + 0.00436 × abundance value of *Achromobacter* + 0.00022 × abundance value of *Proteus*.

[0029] RAMS_score represents the abundance score of characteristic intratumoral bacteria serotypes. A higher abundance score indicates a higher degree of infiltration by drug-resistant bacteria serotypes and less sensitivity to immunotherapy.

[0030] In this invention, the data analysis unit further predicts the probability of effectiveness after immunotherapy based on the following formula:

[0031]

[0032] The range of P values ​​is 0.199827 ≤ P < 1. The larger the P value, the lower the probability of effective immunotherapy.

[0033] A P-value ≤ 0.5 is defined as effective treatment, and a P-value > 0.5 is defined as ineffective treatment.

[0034] According to a specific embodiment of the present invention, the characteristic intratumoral bacteria genera and their weight coefficients identified based on 16S rRNA data included in the prediction device are: Actinoplanes (0.00097), Anoxybacillus (0.00578), Catonella (0.00879), Finegoldia (0.00659), Kocuria (0.00066), Limnohabitans (0.01982), Microbacterium (0.00032), Achromobacter (0.00436), and Proteus (0.00022).

[0035] In this invention, the characteristic fragment sequences of bacterial genera are extracted from the 16S rRNA gene sequencing results. The obtained raw data is first subjected to quality control processing (removing low-quality sequences, adapters, and primers), and then a standard analysis of the bacterial community is performed after sequencing. After classification and annotation are completed by comparison with a reference database, the abundance value of each genera in the characteristic intratumoral bacteria genera is calculated. The abundance value of each genera in the characteristic intratumoral bacteria genera is the relative abundance (the abundance value of a certain genera divided by the abundance values ​​of all genera measured in the sample) × 1,000,000.

[0036] Bacterial abundance value: In this study, the bacterial abundance value refers to the ratio of the number of sequences of different bacterial genera in each lung squamous cell carcinoma puncture sample obtained by high-throughput sequencing of 16S rRNA genes to the total number of sequences in that sample (i.e., relative abundance), multiplied by 1,000,000. The relative abundance index reflects the relative dominance of a certain bacterial genus in the sample, rather than its absolute number. That is, the proportion of the number of sequences of a certain bacterial genus to the total number of sequences of all bacterial genera measured in that sample. To avoid incomplete display in Excel, all calculated relative abundance values ​​were multiplied by 1,000,000, and a standardized bacterial abundance value data table was generated accordingly, which served as the basis for subsequent statistical analysis.

[0037] Thirdly, the present invention provides a computer storage medium storing computer program instructions, which, when executed, achieve the following: obtaining a predicted result of the efficacy of immunotherapy for squamous cell carcinoma of the lung of the individual to be tested based on the characteristic intratumoral bacteria genus of the individual to be tested; wherein the characteristic intratumoral bacteria genus is the same as the characteristic intratumoral bacteria genus described in the first aspect of the present invention.

[0038] Fourthly, the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to achieve: obtaining a predictive result of the efficacy of immunotherapy for squamous cell carcinoma of the test individual based on the characteristic intratumoral bacteria genus of the test individual; wherein the characteristic intratumoral bacteria genus is the same as the characteristic intratumoral bacteria genus described in the first aspect of the present invention.

[0039] Beneficial effects:

[0040] This invention provides a predictive device for the efficacy of immunotherapy in patients with squamous cell lung cancer, capable of determining whether immunotherapy is effective. Based on 16S rRNA gene sequencing and Lasso regression analysis of pre-treatment biopsy specimens from squamous cell lung cancer patients, this invention calculates a score for characteristic intratumoral bacteria in the tested individual using 16S rRNA gene sequencing data from clinical samples. Lasso regression analysis is then used to analyze the correlation between characteristic intratumoral bacteria and immunotherapy efficacy, constructing a predictive model. In clinical practice, this predictive model can predict whether a patient will benefit from immunotherapy based on 16S rRNA gene sequencing data from a squamous cell lung cancer patient's biopsy specimen, thereby guiding the patient's treatment decision. It has the advantages of high accuracy, universality, and high efficiency.

[0041] In existing technologies, the prediction of tumor immunotherapy efficacy largely relies on indicators such as tumor mutational burden, PD-L1 expression levels, peripheral blood immune factors, or gut microbiota. However, these methods generally suffer from insufficient correlation with the tumor immune microenvironment and limited detection specificity. This invention uses intratumoral microbiota as a novel predictive entry point, providing a device for predicting the efficacy of immunotherapy in patients with squamous cell carcinoma of the lung. The predictive indicators are directly derived from within the tumor tissue, objectively reflecting the true state of the tumor microenvironment, and achieving stable and repeatable detection and analysis of intratumoral microbiota. Therefore, this invention not only significantly improves the accuracy and stability of immunotherapy efficacy prediction but also overcomes the limitations of existing technologies, demonstrating outstanding technical effects in personalized immunotherapy guidance, and possessing substantial characteristics and significant progress. Attached Figure Description

[0042] Figure 1 The differential bacterial genus is the one found in the immunotherapy effective group and ineffective group of 40 patients in Example 1 of this invention.

[0043] Figure 2 To screen for characteristic intratumoral bacterial genus combinations for predicting the efficacy of immunotherapy using Lasso regression.

[0044] Figure 3 This invention serves as an evaluation of the predictive effectiveness of the model for immunotherapy efficacy.

[0045] Figure 4 This is an example of the predictive indicators of the present invention being used in clinical practice. Detailed Implementation

[0046] Before further describing specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terminology used in the embodiments of the present invention is for describing specific embodiments and not for limiting the scope of protection of the present invention.

[0047] When numerical ranges are given in the embodiments, it should be understood that, unless otherwise stated in the present invention, both endpoints of each numerical range and any value between the two endpoints may be selected. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. In addition to the specific methods, apparatus, and materials used in the embodiments, based on the knowledge of the prior art possessed by one of ordinary skill in the art and the description of this invention, any prior art methods, apparatus, and materials similar to or equivalent to those described, apparatus, and materials in the embodiments of this invention may be used to implement the present invention.

[0048] Unless otherwise stated, the experimental methods, detection methods and preparation methods disclosed in this invention all adopt conventional techniques in this technical field.

[0049] In this invention, lung CT scans are performed after two cycles of immunotherapy to assess efficacy. Complete response (CR) or partial response (PR) is defined as effective immunotherapy, while stable disease (SD) or progressive disease (PD) is defined as ineffective immunotherapy.

[0050] Example 1

[0051] 1. Characteristic intratumoral bacteria were extracted from the 16S rRNA gene sequence of lung squamous cell carcinoma patients.

[0052] (1) Forty patients with inoperable stage III-IV squamous cell carcinoma of the lung were included, of whom 20 responded to immunotherapy and 20 did not. The basic information and clinical characteristics of these patients are shown in Table 1. 16S rRNA gene sequencing was performed on the lung squamous cell carcinoma tumor biopsy specimens before immunotherapy;

[0053] DNA extraction from samples

[0054] A biopsy specimen of squamous cell carcinoma of the lung was obtained before immunotherapy. Total genomic DNA of the microbial community was extracted according to the instructions of the FastPure Stool DNA Isolation Kit (MJYH, Shanghai, China). The integrity of the extracted genomic DNA was detected by 1% agarose gel electrophoresis, and the DNA concentration and purity were determined by NanoDrop2000 (Thermo Scientific, USA).

[0055] PCR amplification and sequencing library construction

[0056] Using the extracted DNA as a template, specific primers with barcodes were synthesized according to the specified sequencing regions (five regions of 16S: V2, V3, V5, V6, and V8) for multiplex PCR amplification. The primer sequence information is as follows:

[0057] V2:

[0058] F1-TGGCGAACGGGTGAGTAA (SEQ ID NO:1)

[0059] R1-CCGTGTCTCAGTCCCARTG (SEQ ID NO:2)

[0060] V3:

[0061] F2-ACTCCTACGGGAGGCAGC (SEQ ID NO:3)

[0062] R2-GTATTACCGCGGCTGCTG (SEQ ID NO:4)

[0063] V5:

[0064] F3-GTGTAGCGGTGRAATGCG (SEQ ID NO:5)

[0065] R3-CCCGTCAATTCMTTTGAGTT (SEQ ID NO:6)

[0066] V6:

[0067] F4-GGAGCATGTGGWTTAATTCGA (SEQ ID NO:7)

[0068] R4-CGTTGCGGGACTTAACCC (SEQ ID NO:8)

[0069] V8:

[0070] F5-GGAGGAAGGTGGGGATGAC (SEQ ID NO:9)

[0071] R5-AAGGCCCGGGAACGTATT (SEQ ID NO:10)

[0072] The amplification procedure is as follows:

[0073] Pre-denaturation at 95℃ for 3 minutes

[0074] 29 cycles (95℃ denaturation for 30s, 53℃ annealing for 30s, 72℃ extension for 45s)

[0075] Stable extension at 72℃ for 10 min

[0076] Store at 4℃ (PCR instrument: T100 Thermal Cycler)

[0077] The PCR reaction system is as follows:

[0078] 4 μL of 5X TransStart FastPfu buffer.

[0079] 2.5mM dNTPS 2μL

[0080] Upstream primer (5 μM) 0.8 μL

[0081] Downstream primer (5uM) 0.8 μL

[0082] TransStart FastPfu DNA polymerase 0.4 μL

[0083] Template DNA 10 ng

[0084] Make up to 20 μL

[0085] Negative control samples were also included (sampling negative control, DNA extraction negative control, PCR amplification negative control). Each sample was replicated in triplicate. PCR products from the same sample were mixed and recovered using a 2% agarose gel electrophoresis system. The products were purified, and the band size was detected by 2% agarose gel electrophoresis. The recovered products were then analyzed using Synergy HTX (Biotek, USA).

[0086] Library construction was performed on the purified PCR products using the NEXTFLEX® Rapid DNA-Seq Kit (Bioo Scientific, Austin, Texas, USA).

[0087] 1) Connector connection;

[0088] 2) Use magnetic beads to screen and remove self-connected segments at the connector;

[0089] 3) Enrichment of library templates using PCR amplification;

[0090] 4) PCR products were recovered using magnetic beads to obtain the final library.

[0091] Sequencing was performed using the NextSeq 2000 platform from Lumina (Shanghai Meiji Biopharmaceutical Technology Co., Ltd.).

[0092] After sequencing, bacterial genus normalization analysis was performed. The differentially expressed bacterial genera between the effective and ineffective treatment groups were analyzed using MaAsLin2 (Microbiome Multivariate Association with Linear Models, v1.12.0) software. After correcting for clinical parameters such as tumor stage, patient age, sex, and smoking history, 22 enriched drug-resistant bacterial genera were identified in the ineffective immunotherapy group compared to the effective group, as well as Neisseria genus enrichment in the effective group compared to the ineffective group. Figure 1 ).like Figure 1As shown, 22 differentially resistant bacterial genera were identified between the immunotherapy-ineffective and immunotherapy-effective groups: Anoxybacillus, Actinoplanes, Tepidimonas, Clostridium XIVa, Lactococcus, Microbacterium, Finegoldia, Kocuria, Achromobacter, and Microbisporus. The genera include *Aerococcus*, *Blauia*, *Flavobacterium*, *Mycobacterium*, *Proteus*, *Geobacillus*, *Campylobacter*, *Aurantimonas*, *Planifilum*, *Limnohabitans*, *Adhaeribacter*, and *Catonella*.

[0093] (2) The abundance values ​​of 22 differentially resistant bacterial genera were used as input variables for Lasso regression analysis. Based on the minimum lambda value of 0.03318063, 9 key characteristic intratumoral bacterial genera were screened out. The characteristic intratumoral bacterial genera include Actinobacterium spp., Bacillus anaerobicus, Catorium spp., Fingoldii spp., Cochlea spp., freshwater bacterium spp., Microbacterium spp., Achromobacterium spp., and Proteus spp.

[0094] 2. Constructing a model to predict the efficacy of immunotherapy using characteristic intratumoral bacteria in an immunotherapy cohort.

[0095] (1) Forty patients with squamous cell carcinoma of the lung who received immunotherapy were included (training set), of which 20 patients responded to immunotherapy and 20 patients did not respond to immunotherapy. Before immunotherapy, lung tissue was punctured and 16S rRNA gene sequencing was performed on the squamous cell carcinoma tumor samples obtained from the puncture.

[0096] (2) Based on the aforementioned established characteristic intratumoral bacteria genera and 16S rRNA gene sequencing data, the abundance values ​​of various characteristic intratumoral bacteria genera for each sample in the training set are calculated. In this invention, by extracting the characteristic fragment sequences of bacterial genera from the 16S rRNA gene sequencing results, the obtained raw data is first subjected to quality control processing (removing low-quality sequences, adapters, and primers), and then a standard analysis of the bacterial community is performed after sequencing. After completing the classification annotation by comparing with the reference database, the abundance value of each bacterial genera is calculated. The abundance value of the characteristic intratumoral bacteria genera is relative abundance (the abundance value of a certain bacterial genera divided by the abundance values ​​of all bacterial genera measured in the sample) × 1,000,000, to avoid incomplete data display in the table, and the final generated abundance table of each bacterial genera serves as the basis for subsequent statistical analysis.

[0097] (3) Using the abundance of various characteristic intratumoral bacteria as the independent variable X and the immunotherapy status as the dependent variable Y, with effective immunotherapy defined as 1 and ineffective immunotherapy defined as 0, the optimal combination of independent variables for predicting the immunotherapy status was screened using the Lasso regression method. The results showed that *Actinomyces*, *Bacillus*, *Catalystia*, *Fingoldii*, *Cocheria*, *Fishbacteria*, *Microbacteria*, *Achromobacterium*, and *Proteus* were the optimal combination of independent variables. Figure 2 As shown, where, Figure 2 In the equation, A represents the coefficients of 22 intratumoral bacteria genera used in Lasso regression to predict immunotherapy status. Figure 2 In this context, B represents the optimal number of independent variables in the Lasso regression output. When there are 9 independent variables, the predictive effect of immunotherapy is optimal.

[0098] (4) Using the abundance values ​​of the above 9 characteristic intratumoral bacteria as independent variables and the immunotherapy status as dependent variable, the effective immunotherapy was defined as 1 and the ineffective immunotherapy was defined as 0. A prediction model was constructed using Lasso regression. The regression coefficients of the independent variables in the model were: Actinoplanes (0.00097), Anoxybacillus (0.00578), Catonella (0.00879), Finegoldia (0.00659), Kocuria (0.00066), Limnohabitans (0.01982), Microbacterium (0.00032), Achromobacter (0.00436), and Proteus (0.00022). RAMS_score = -1.38738 + 0.00097 × abundance value of *Actinomyces* + 0.00578 × abundance value of *Bacillus* + 0.00879 × abundance value of *Catalystia* + 0.00659 × abundance value of *Fingoldii* + 0.00066 × abundance value of *Coxella* + 0.01982 × abundance value of *Freshwater Bacteria* + 0.00032 × abundance value of *Microbacteria* + 0.00436 × abundance value of *Achromobacter* + 0.00022 × abundance value of *Proteus*.

[0099] Model predicts the probability of immunotherapy efficacy:

[0100] The prediction model constructed in this embodiment includes a detection unit and a data analysis unit. The workflow of the model is as follows:

[0101] (1) The detection unit is used to detect characteristic intratumoral bacteria from the individual to be tested and obtain the detection results;

[0102] The detection unit includes a detection and data collection module, a data processing module, and a data output module;

[0103] The detection and data collection module performs 16S rRNA gene sequencing on the collected puncture lung squamous cell carcinoma tumor samples and collects raw sequencing data.

[0104] The data processing module extracts genus-specific fragment sequences from the 16S rRNA gene sequencing data, performs quality control processing on the obtained raw sequencing data (removing low-quality sequences, adapters, and primers), and performs standard microbial community analysis after sequencing. After classification and annotation are completed by comparison with a reference database, the abundance value of each characteristic intratumoral genus is calculated. The abundance value of the characteristic intratumoral genus is the relative abundance (the abundance value of a certain genus divided by the abundance values ​​of all genera measured in the sample) × 1,000,000.

[0105] The data output module is used to output the characteristic intratumoral bacteria abundance values ​​obtained by the data processing module.

[0106] (2) The data analysis unit is used to process the characteristic intratumoral bacteria abundance data obtained by the detection unit.

[0107] The data analysis unit includes a data input module, a RAMS_score calculation module, a P-value calculation module, and a result output module.

[0108] The RAMS_score calculation module calculates the RAMS_score based on the abundance values ​​of characteristic intratumoral bacteria in the data input module, using the following formula: RAMS_score = -1.38738 + 0.00097 × abundance value of *Actinomyces* + 0.00578 × abundance value of *Bacillus* + 0.00879 × abundance value of *Catalystia* + 0.00659 × abundance value of *Fingoldii* + 0.00066 × abundance value of *Coxella* + 0.01982 × abundance value of *Freshwateria* + 0.00032 × abundance value of *Microbacterium* + 0.00436 × abundance value of *Achromobacterium* + 0.00022 × abundance value of *Proteus*.

[0109] The P-value calculation module calculates the P-value based on RAMS_score:

[0110]

[0111] The result output module outputs a P-value and predicts the probability of immunotherapy efficacy based on the P-value. When the P-value is ≤0.5, the output result is that the treatment is effective; when the P-value is >0.5, the output result is that the treatment is ineffective.

[0112] 3. Verify the model's predictive performance.

[0113] (1) In the above cohort of 40 patients with squamous cell carcinoma of the lung who received immunotherapy (training set), 20 patients responded to immunotherapy and 20 patients did not. The abundance values ​​of 9 key characteristic intratumoral bacteria in each sample in the training set and the data analysis results are shown in Table 2. Among them, non_ORR represents treatment ineffectiveness and ORR represents treatment effectiveness.

[0114] The model calculates the predictive probability of whether a patient will respond effectively to immunotherapy. This probability is then compared to the patient's actual immunotherapy status. The area under the ROC curve (AUC) is used to evaluate the model's predictive performance on the training set. The AUC value is 0.935 (95% CI 0.86-1). See [link to relevant documentation]. Figure 3 In step A, the optimal cutoff value is determined based on the best combination of sensitivity and specificity, i.e., cut-off value = 0.5. At the optimal cutoff value, the model predicts a sensitivity of 0.95 and a specificity of 0.75 for immunotherapy.

[0115] (2) 16S rRNA gene sequencing data from a cohort of 60 patients with squamous cell lung cancer undergoing immunotherapy were used as an independent external validation set. The abundance values ​​of nine key characteristic intratumoral bacteria species in each sample within the independent external validation set, along with the data analysis results, are shown in Table 3. Using the same method, the area under the ROC curve for predicting immunotherapy status in the validation set was calculated to be 0.852 (95% CI 0.73-0.97). (See Table 3 for details.) Figure 3 In section B, at the same optimal cutoff value of 0.5, the model has a specificity of 0.84 and a sensitivity of 0.80 for predicting immunotherapy status.

[0116] Figure 3 A and Figure 3 In the figure, B represents the effect evaluation of the model in predicting pCR in the training set and the external independent validation set, respectively. The curve is the ROC curve, and the area under the curve is the AUC value. The AUC value and 95% confidence interval of the model in this invention in predicting the efficacy of immunotherapy are indicated in the lower right corner.

[0117] Table 1. Basic patient information and clinical characteristics

[0118] sample gender age smoking history diagnosis Installments Immunotherapy regimen Therapeutic effect evaluation G154785 male 83 Smoking Left lung squamous cell carcinoma Phase IIIA Pembrolizumab monotherapy invalid G159387 female 79 non-smoking Poorly differentiated squamous cells in the left lung Phase IV Pembrolizumab monotherapy invalid G180554 male 58 Smoking squamous cell carcinoma of the right lung Phase IV Pembrolizumab + Gemcitabine + Cisplatin invalid G185426 male 65 Smoking Central squamous cell carcinoma of the lower lobe of the right lung Phase IIIB M7824 / Placebo + Cisplatin + Etoposide + Concurrent Radiotherapy invalid G207109 male 68 Smoking Left lung squamous cell carcinoma Phase IV Sintilimab + Gemcitabine + Cisplatin invalid G190642 male 69 Smoking (quit smoking for more than 10 years) squamous cell carcinoma of the right lower lung Phase IV Gemcitabine + Cisplatin + Tislelizumab invalid G192868 female 79 non-smoking squamous cell carcinoma of the right lung Phase IV Gemcitabine + Nedaplatin + Sintilimab invalid G205422 male 62 Smoking Central squamous cell carcinoma of the right lung Phase IIIB Pembrolizumab + Gemcitabine + Cisplatin + Concurrent Radiotherapy invalid G209829 male 53 Smoking squamous cell carcinoma of the upper lobe of the left lung Phase IIIB Sintilimab + Gemcitabine + Cisplatin invalid G211760 male 59 Smoking (quit smoking for more than 10 years) squamous cell carcinoma of the upper lobe of the left lung Phase IIIC Atezolizumab + tislelizumab / placebo efficient G212843 male 65 Smoking squamous cell carcinoma of the upper lobe of the right lung Phase IV Tislelizumab + BGBA1217 / placebo + albumin-bound paclitaxel + carboplatin invalid G213315 male 60 Smoking squamous cell carcinoma Phase IIIc Atezolizumab + tislelizumab / placebo efficient G213634 male 72 Smoking squamous cell carcinoma of the right lung Phase IV Sugermab + Gemcitabine + Cisplatin invalid G216043 male 78 Smoking squamous cell carcinoma Phase III Pembrolizumab invalid G219738 female 39 non-smoking Poorly differentiated squamous cell carcinoma of the left lung Phase IV Pembrolizumab (MK3475) + MK7684 / placebo invalid G223218 female 68 non-smoking squamous cell carcinoma of the right lung Phase IV Atezolizumab + tislelizumab / placebo invalid G230098 male 56 Smoking Lung squamous cell carcinoma Phase IV Gemcitabine + Cisplatin + Tislelizumab invalid G233160 male 64 Smoking squamous cell carcinoma of the right lung Phase IV Gemcitabine + Nedaplatin + Sugemalmab efficient G234283 male 61 Smoking Lung squamous cell carcinoma Phase IV Tislelizumab + Gemcitabine + Cisplatin efficient G189340 male 68 Smoking Right lung intermediate shaft squamous cell carcinoma Phase IV Pembrolizumab + Gemcitabine + Cisplatin invalid G143321 male 74 Smoking Peripheral squamous cell carcinoma of the right lung Phase IV Enrolled in durvalumab monotherapy efficient G160896 male 67 Smoking Poorly differentiated squamous cell carcinoma of the right lung Phase IIIB Gemcitabine + cisplatin + radiotherapy + enrolled in CS1001 consolidation and maintenance therapy efficient G161481 male 44 Smoking squamous cell carcinoma of the lower lobe of the left lung Phase IV Enrolled in camrelizumab + paclitaxel + carboplatin efficient G163100 male 64 Smoking Moderately or poorly differentiated squamous cell carcinoma of the right lung Phase IIIC Gemcitabine + cisplatin + radiotherapy + enrolled in CS1001 consolidation and maintenance therapy invalid G166928 male 65 Smoking Lung squamous cell carcinoma Phase IIIc Gemcitabine + Cisplatin + Pembrolizumab + Radiation Therapy invalid G166931 male 61 Smoking Lung squamous cell carcinoma Phase IV Camrelizumab + Paclitaxel + Carboplatin efficient G171942 male 65 Smoking squamous cell carcinoma of the right lung Phase IIIB Pembrolizumab + Gemcitabine + Cisplatin efficient G178614 male 60 Smoking squamous cell carcinoma of the right lung Phase IIIA Gemcitabine + cisplatin + radiotherapy + enrolled in CS1001 consolidation and maintenance therapy efficient G182841 male 76 Smoking Left lung squamous cell carcinoma Phase IV Camrelizumab + Gemcitabine + Cisplatin efficient G185005 male 70 Smoking squamous cell carcinoma of the upper lobe of the left lung Phase IIIA M7824 / Placebo + Cisplatin + Etoposide + Concurrent Radiotherapy efficient G186674 male 56 Smoking Central moderately differentiated squamous cell carcinoma of the left lung Phase IIIC Pembrolizumab + cisplatin + etoposide + concurrent irradiation invalid G190590 male 55 Smoking squamous cell carcinoma of the right lung Phase IIIB Pembrolizumab + Gemcitabine + Cisplatin + Concurrent Radiotherapy invalid G193329 male 63 Smoking Poorly differentiated squamous cell carcinoma of the right lung Phase IIIA Tislelizumab + Gemcitabine + Cisplatin + Radiotherapy efficient G194252 male 80 Smoking Left lung squamous cell carcinoma Phase IIIB White purpura + pembrolizumab + radiotherapy efficient G194532 female 65 non-smoking Poorly differentiated squamous cell carcinoma of the lower lobe of the left lung Phase IIIC Pembrolizumab + Gemcitabine + Cisplatin + Radiation Therapy efficient G194927 male 64 Smoking Moderately differentiated squamous cell carcinoma of the right lung Phase IV Teximumab + durvalumab + gemcitabine + cisplatin efficient G195542 male 66 Smoking squamous cell carcinoma of the right lung Phase IIIB Sintilimab + Gemcitabine + Cisplatin efficient G195806 male 83 Smoking (quit smoking for more than 10 years) squamous cell carcinoma of the right lung Phase IIIC Sintilimab + purpura + radiotherapy efficient G196219 male 62 Smoking Left lung squamous cell carcinoma Phase IIIA Sintilimab + Gemcitabine + Cisplatin + Radiation Therapy efficient G239616 male 62 Smoking squamous cell carcinoma of the right lung Phase IV SKB274(TROP2 ADC)+KL-A167(PD-L1) efficient

[0119] Table 2. Abundance values ​​of nine key characteristic intratumoral bacteria species in each sample of the training set and data analysis results.

[0120] Group RAMS score P_value Actinoplanes Anoxybacillus Catonella Finegoldia Kocuria Limnohabitans Microbacterium Achrobactrum Proteus non_ORR 4.486560843 0.988866 0 96.23718 0 97.28813 5444.232 0 1031.284 0 3392.623 non_ORR 4.568288296 0.989731 201.6298 0 0 168.4417 4725.208 0 513.3304 212.9704 1959.428 non_ORR 4.663121663 0.990651 0 32.3082 0 0 6749.021 0 721.9267 266.3811 0 non_ORR 1.645909135 0.838337 0 37.48335 0 77.32073 764.3345 27.52401 1311.555 0 3840.867 non_ORR 1.433125133 0.807388 0 0 0 0 2628.072 0 3382.977 0 0 non_ORR 1.89057724 0.868821 0 152.9657 96.67686 0 565.1144 0 1174.433 106.2173 1528.385 non_ORR 1.994653434 0.880235 0 100.7015 0 0 476.9619 70.38827 755.7748 178.6252 324.1971 non_ORR 1.830468963 0.861818 0 0 0 168.3806 2707.641 0 476.3096 0 741.5668 non_ORR 1.615378271 0.834157 147.1454 0 0 356.3295 107.8251 0 406.1353 0 1427.24 ORR 0.5843824 0.642075 0 0 0 0 1973.429 0 643.064 64.90468 807.5013 non_ORR 1.498437694 0.817341 232.0194 0 55.76776 29.08233 747.991 38.35032 332.5292 104.0252 755.3415 ORR -0.245145558 0.439019 0 0 0 79.0563 629.5404 0 0 0 935.8229 non_ORR 2.83179548 0.94437 0 165.6182 0 0 0 0 0 749.0798 0 non_ORR 0.885900171 0.708043 0 102.7757 0 0 2533.895 0 0 0 0 non_ORR 3.508941494 0.970941 0 65.1172 0 636.99 486.3815 0 0 0 0 non_ORR -0.646347693 0.343813 0 42.42077 0 0 387.6262 0 305.8761 0 649.3641 non_ORR -0.626592794 0.348284 0 0 0 0 842.4663 0 0 0 928.4685 ORR -0.941111189 0.280676 0 0 0 0 445.0426 0 135.9775 0 495.1687 ORR -1.011337243 0.266718 0 0 0 0 162.9732 0 700.5219 0 205.0308 non_ORR -0.681242792 0.335984 0 0 0 0 942.748 0 168.9925 0 125.5543 ORR -1.10603505 0.248611 0 0 0 0 98.9526 0 529.3964 0 215.5517 ORR -1.117662494 0.246445 0 0 0 0 136.4522 0 100.8422 0 675.0127 ORR -0.94074626 0.28075 0 14.5851 0 0 0 9.451796 137.5399 21.91839 164.2657 non_ORR 0.588768913 0.643083 0 0 0 0 0 0 235.5057 436.497 0 non_ORR -0.837963582 0.301964 0 0 0 0 309.408 0 109.1366 61.8816 183.6196 ORR -1.011869843 0.266614 0 0 0 0 181.4511 0 172.1459 36.38992 192.0856 ORR -1.32417785 0.210124 0 0 0 0 0 0 113.3663 0 124.1492 ORR -0.898432063 0.289373 0 0 0 0 0 0 348.2011 86.77705 0 ORR -1.102787667 0.249218 0 0 0 0 316.6334 0 42.48436 0 280.6467 ORR -1.112511432 0.247403 0 0 0 0 0 0 170.8175 38.67962 238.3501 non_ORR 1.072230771 0.745021 0 0 0 303.665 0 0 0 105.2645 0 non_ORR -1.235012138 0.225305 0 0 0 0 156.2189 0 71.45509 0 119.4615 ORR -0.546317689 0.366719 0 64.557 0 0 0 0 0 107.487 0 ORR -1.321394614 0.210586 0 0 0 0 99.54549 0 0 0 0 ORR -0.942084945 0.280479 0 77.08478 0 0 0 0 0 0 0 ORR -1.387377762 0.199827 0 0 0 0 0 0 0 0 0 ORR -1.377465294 0.201416 0 0 0 0 0 0 31.09285 0 0 ORR -1.213065077 0.229159 0 4.305492 0 0 0 0 0 34.31267 0 ORR -1.3812528 0.200808 0 0 0 0 0 0 19.21242 0 0 ORR -1.387377762 0.199827 0 0 0 0 0 0 0 0 0

[0121] Table 3. Abundance values ​​of nine key characteristic intratumoral bacteria species in each sample from the independent validation set and data analysis results.

[0122] Group RAMS_score P_value Actinoplanes Anoxybacillus Catonella Finegoldia Kocuria Limnohabitans Microbacterium Achrobactrum Proteus ORR 5.842806 0.997108 0 0 0 421.9876 0 0 2304.069 692.6191 3203.432 non_ORR 2.831795 0.94437 0 165.6182 0 0 0 0 0 749.0798 0 non_ORR 0.588769 0.643083 0 0 0 0 0 0 235.5057 436.497 0 ORR -0.68124 0.335984 0 0 0 0 942.748 0 168.9925 0 125.5543 ORR 1.830469 0.861818 0 0 0 168.3806 2707.641 0 476.3096 0 741.5668 ORR -1.10604 0.248611 0 0 0 0 98.9526 0 529.3964 0 215.5517 ORR -1.10279 0.249218 0 0 0 0 316.6334 0 42.48436 0 280.6467 ORR -1.32418 0.210124 0 0 0 0 0 0 113.3663 0 124.1492 ORR -0.94111 0.280676 0 0 0 0 445.0426 0 135.9775 0 495.1687 ORR -1.01187 0.266614 0 0 0 0 181.4511 0 172.1459 36.38992 192.0856 non_ORR 3.473654 0.969929 0 63.31084 0 509.997 0 40.86997 828.0556 0 278.4423 ORR -0.54632 0.366719 0 64.557 0 0 0 0 0 107.487 0 ORR -0.95001 0.278882 0 0 0 0 541.737 0 59.2266 0 272.5252 non_ORR 4.568288 0.989731 201.6298 0 0 168.4417 4725.208 0 513.3304 212.9704 1959.428 ORR -0.64635 0.343813 0 42.42077 0 0 387.6262 0 305.8761 0 649.3641 non_ORR 0.306506 0.576032 395.8826 0 0 0 410.956 0 212.3981 201.0245 434.9364 ORR -0.80854 0.308202 0 0 0 53.89589 251.2646 0 179.1539 0 0 non_ORR -0.37522 0.407281 0 0 0 0 74.5074 0 231.5676 204.1083 0 non_ORR 1.433125 0.807388 0 0 0 0 2628.072 0 3382.977 0 0 ORR 0.574123 0.639714 0 0 0 53.11254 1660.554 0 620.1362 0 1436.558 ORR -1.38738 0.199827 0 0 0 0 0 0 0 0 0 ORR -1.21307 0.229159 0 4.305492 0 0 0 0 0 34.31267 0 ORR -1.37747 0.201416 0 0 0 0 0 0 31.09285 0 0 ORR 0.584382 0.642075 0 0 0 0 1973.429 0 643.064 64.90468 807.5013 ORR -1.38125 0.200808 0 0 0 0 0 0 19.21242 0 0 ORR -1.38738 0.199827 0 0 0 0 0 0 0 0 0 ORR -0.94208 0.280479 0 77.08478 0 0 0 0 0 0 0 ORR -1.32139 0.210586 0 0 0 0 99.54549 0 0 0 0 non_ORR 1.072231 0.745021 0 0 0 303.665 0 0 0 105.2645 0 ORR -0.60876 0.352341 0 37.47198 0 0 187.5024 15.95765 0 0 558.3752 ORR -0.62659 0.348284 0 0 0 0 842.4663 0 0 0 928.4685 ORR 0.331574 0.582142 23.71593 0 0 0 1081.37 0 23.33342 199.0353 481.3314 ORR -0.24515 0.439019 0 0 0 79.0563 629.5404 0 0 0 935.8229 ORR -0.89843 0.289373 0 0 0 0 0 0 348.2011 86.77705 0 non_ORR 4.663122 0.990651 0 32.3082 0 0 6749.021 0 721.9267 266.3811 0 non_ORR 1.994653 0.880235 0 100.7015 0 0 476.9619 70.38827 755.7748 178.6252 324.1971 non_ORR 1.890577 0.868821 0 152.9657 96.67686 0 565.1144 0 1174.433 106.2173 1528.385 non_ORR 1.645909 0.838337 0 37.48335 0 77.32073 764.3345 27.52401 1311.555 0 3840.867 non_ORR 4.999604 0.993305 0 121.3132 0 0 1880.984 45.88532 3034.717 0 11758.58 ORR 0.8859 0.708043 0 102.7757 0 0 2533.895 0 0 0 0 non_ORR 1.498438 0.817341 232.0194 0 55.76776 29.08233 747.991 38.35032 332.5292 104.0252 755.3415 ORR 2.130193 0.893803 0 0 0 246.3339 2257.952 0 206.0395 0 1522.702 ORR -1.11251 0.247403 0 0 0 0 0 0 170.8175 38.67962 238.3501 ORR -0.22073 0.445041 0 0 0 0 188.2217 0 158.9237 166.3554 1223.655 non_ORR -1.30152 0.213909 0 0 0 0 101.3504 0 58.57866 0 0 ORR -0.94075 0.28075 0 14.5851 0 0 0 9.451796 137.5399 21.91839 164.2657 ORR -1.01134 0.266718 0 0 0 0 162.9732 0 700.5219 0 205.0308 non_ORR -1.23501 0.225305 0 0 0 0 156.2189 0 71.45509 0 119.4615 ORR -0.83796 0.301964 0 0 0 0 309.408 0 109.1366 61.8816 183.6196 ORR 0.514328 0.625821 0 244.0489 0 0 517.0267 0 0 0 684.5988 ORR -0.7664 0.317257 0 0 0 0 936.8316 0 0 0 0 non_ORR 3.508941 0.970941 0 65.1172 0 636.99 486.3815 0 0 0 0 ORR -1.38738 0.199827 0 0 0 0 0 0 0 0 0 ORR -0.96154 0.27657 0 0 0 0 615.0192 0 0 0 83.40456 non_ORR 1.615378 0.834157 147.1454 0 0 356.3295 107.8251 0 406.1353 0 1427.24 non_ORR 4.486561 0.988866 0 96.23718 0 97.28813 5444.232 0 1031.284 0 3392.623 ORR -0.29826 0.425983 63.13258 115.6419 0 0 0 0 216.5629 46.13534 412.7899 ORR -0.64583 0.34393 0 0 0 0 794.8475 0 343.1753 0 483.0582 ORR -1.3474 0.206296 0 0 0 0 0 0 125.3975 0 0 ORR -1.11766 0.246445 0 0 0 0 136.4522 0 100.8422 0 675.0127

[0123] Example 2

[0124] The predictive indicators established in this invention are used to predict the efficacy of immunotherapy for Zhang XX, a clinical patient with squamous cell carcinoma of the lung.

[0125] Patient Zhang XX, male, 64 years old, with a smoking history of over 30 years, presented with a cough and sputum production for over a month. A biopsy confirmed squamous cell carcinoma of the lung. The patient wishes to assess his sensitivity to immunotherapy to determine whether or not to undergo the treatment.

[0126] The operation method is as follows:

[0127] 1. Perform 16S rRNA gene sequencing on lung squamous cell carcinoma biopsy samples from patients;

[0128] 2. Calculate the abundance values ​​of nine characteristic intratumoral bacterial genera based on 16S rRNA gene sequencing data;

[0129] 3. The abundance values ​​of characteristic intratumoral bacteria were substituted into the prediction model. The abundance values ​​of characteristic intratumoral bacteria of the subjects and the data analysis results are shown in Table 4. The probability value P of the patient's immunotherapy failure predicted by the model was about 0.28 (P value ≤ 0.5), that is, the model predicted that the patient had a 72% chance of being effective after immunotherapy. Therefore, the patient decided to receive immunotherapy.

[0130] 4. After the patient received 4 cycles of chemotherapy combined with immunotherapy (pembrolizumab + gemcitabine + cisplatin), a follow-up lung CT scan was performed. Figure 4 The image shows a CT scan comparison of patient Zhang XX before and after receiving immunotherapy. The examination results indicate that the tumor has shrunk significantly, consistent with the predicted results of the indicators.

[0131] Table 4. Abundance values ​​of characteristic intratumoral bacteria species in the subjects and data analysis results.

[0132]

[0133] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention.

Claims

1. A device for predicting the efficacy of immunotherapy in patients with squamous cell carcinoma of the lung, comprising a detection unit and a data analysis unit, characterized in that: The detection unit detects characteristic intratumoral bacteria from the individual being tested and obtains the detection results; The data analysis unit is used to analyze and process the detection results of the detection unit; The characteristic intratumoral bacteria mentioned above are: Actinoplanes, Anoxybacillus, Catonella, Finegoldia, Kocuria, Limnohabitans, Microbacterium, Achromobacter, and Proteus. The detection unit extracts characteristic intratumoral bacteria genera of the individual to be tested based on 16S rRNA gene sequencing, and obtains the detection results including the abundance value of characteristic intratumoral bacteria genera. The abundance value of the characteristic intratumoral bacteria genus is calculated by multiplying the relative abundance value of the characteristic intratumoral bacteria genus in the individual sample by 1,000,000. The data analysis unit substitutes the characteristic intratumoral bacteria abundance values ​​into the following detection model to calculate the RAMS_score: RAMS_score = -1.38738 + 0.00097 × abundance of *Actinomyces* + 0.00578 × abundance of *Bacillus* + 0.00879 × abundance of *Catarrhizium* + 0.00659 × abundance of *Fingoldii* + 0.00066 × abundance of *Coxella* + 0.01982 × abundance of *Freshwater Bacteria* + 0.00032 × abundance of *Microbacteria* + 0.00436 × abundance of *Achromobacter* + 0.00022 × abundance of *Proteus*; Among them, RAMS_score represents the abundance score of characteristic intratumoral bacteria. The higher the abundance score, the higher the degree of infiltration of drug-resistant bacteria and the less sensitive they are to immunotherapy. The data analysis unit further predicts the probability of effectiveness after immunotherapy based on the following formula: A larger P-value indicates a lower probability that immunotherapy is effective; A P-value ≤ 0.5 is defined as effective treatment, and a P-value > 0.5 is defined as ineffective treatment.

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