Application of exosome miRNA markers in evaluation of treatment efficacy of small cell lung cancer
By detecting exosomal miRNA markers in the plasma of small cell lung cancer patients and constructing an efficacy evaluation model in conjunction with TNM staging, the problem of insufficient sensitivity and specificity in the existing technology for evaluating the efficacy of immunotherapy combined with chemotherapy in small cell lung cancer patients has been solved, and the effect of early and accurate screening of the beneficiary population has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI CHEST HOSPITAL
- Filing Date
- 2026-03-02
- Publication Date
- 2026-06-09
Smart Images
Figure CN122168754A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of biological detection technology, specifically relating to the application of exosomal miRNA biomarkers in the evaluation of the efficacy of combined immunotherapy and chemotherapy in patients with small cell lung cancer. Background Technology
[0002] Lung cancer has the highest incidence and mortality rate in my country. Small cell lung cancer (SCLC), originating from neuroendocrine cell precursors, is an aggressive disease with distinct pathological, clinical, and molecular characteristics compared to non-small cell lung cancer. It accounts for approximately 15% of all newly diagnosed lung cancers and is most common in patients with a history of heavy smoking. Its rapid growth and high invasiveness make it a highly malignant subtype of lung cancer. About two-thirds of patients initially diagnosed with SCLC develop extensive-stage SCLC (ES-SCLC). Etoposide combined with platinum-based drugs is the standard first-line treatment for ES-SCLC, but this treatment has limited overall efficacy and a poor prognosis, with a median overall survival of 7-12 months and a 5-year survival rate of less than 2%. Therefore, ES-SCLC has become a major medical challenge posing a serious threat to human health.
[0003] For the past 30 years, etoposide combined with platinum-based chemotherapy has been the standard first-line treatment for ES-SCLC patients. With the advent of the immunotherapy era, tumor immunotherapy or combined chemotherapy regimens have become an important clinical approach for ES-SCLC treatment. Based on the groundbreaking progress achieved in the IMpower133 trial (PMID: 30280641) and the CASPIAN trial (PMID: 31590988) in immunotherapy combined with chemotherapy for ES-SCLC patients, the U.S. Food and Drug Administration (FDA) and the National Medical Products Administration (NMPA) have successively approved the PD-L1 monoclonal antibodies atezolizumab and durvalumab in combination with carboplatin and etoposide for first-line treatment of ES-SCLC patients. Based on the efficacy of immunotherapy combination therapy, patients are generally classified as having progressive disease (PD), stable disease (SD), partial remission (PR), and complete remission (CR). In this context, PD (progression-delayed disease), also known as the non-responsive group, refers to patients whose disease progresses after receiving combined immunotherapy; while SD (slow-progression), PR (partial response), and CR (complete remission) are classified as the responsive group, referring to patients whose disease does not progress after receiving combined immunotherapy. Patients typically see significant benefit from combined immunotherapy and chemotherapy regimens 3-6 months after treatment. Clinically, the evaluation of ES-SCLC treatment efficacy is mainly based on auxiliary imaging examinations (such as enhanced chest CT, chest X-ray, MRI, and ultrasound) and blood tumor markers (such as neuron-specific enolase (NSE) and pro-gastrin-releasing peptide (ProGRP)). However, current technologies for monitoring ES-SCLC efficacy have low sensitivity and specificity and are prone to false positives. Therefore, finding reliable predictive indicators of efficacy to screen the beneficiary population for combined immunotherapy and chemotherapy earlier and more accurately is an urgent clinical challenge that needs to be addressed.
[0004] Extracellular vesicles (EVs), also known as exosomes, are vesicles with a diameter of approximately 30-150 nm enclosed in a lipid bilayer. Exosomes carry bioactive substances such as nucleic acids (DNA, mRNA, and miRNAs), lipids, and proteins from their source cells. Differences in their molecular composition can reflect physiological or pathological changes in their cellular or tissue origin, showing great potential in the development of biomarkers for tumor diagnosis, immunotherapy, and prognostic monitoring. Researchers such as Chang Liu et al. constructed a predictive model by differentially expressing exosome transcriptome data from healthy individuals and SCLC patients. The model achieved a sensitivity and specificity of 91.23% and 98.30% for diagnosing SCLC, respectively. This predictive score can also be used to predict the sensitivity of SCLC patients to chemotherapy regimens (PMID: 36428585). However, research on the predictive efficacy of immunotherapy combined with chemotherapy in SCLC patients, especially ES-SCLC, is lacking. Summary of the Invention
[0005] The purpose of this invention is to provide the application of exosomal miRNA biomarkers in evaluating the efficacy of immunotherapy combined with chemotherapy in patients with small cell lung cancer. This invention studies plasma exosomal miRNAs in patients in response and non-respondership groups of patients receiving atezolizumab combined with carboplatin and etoposide in a prospective clinical cohort for extensive-stage small cell lung cancer. The study identifies plasma exosomal miRNA biomarkers that can be used to evaluate the efficacy of atezolizumab combined with chemotherapy and to screen patients in extensive-stage small cell lung cancer who benefit from immunotherapy combined with chemotherapy. This provides a non-invasive detection solution for earlier and more accurately distinguishing between patients who benefit from immunotherapy combined with chemotherapy and those whose disease progresses after treatment in patients with extensive-stage small cell lung cancer.
[0006] The technical solution adopted by the present invention to achieve the above objectives is as follows: This invention provides an application of exosomal miRNA markers in the preparation of products for evaluating the efficacy of immunotherapy combined with chemotherapy in patients with small cell lung cancer. The exosomal miRNA markers are a combination of miR-338-5p, miR-345-5p, miR-150-5p, miR-7706, and miR-328-3p.
[0007] As a preferred embodiment, the immunotherapy combined with chemotherapy is atezolizumab combined with chemotherapy.
[0008] As a preferred embodiment, the immunotherapy combined with chemotherapy is treatment with atezolizumab in combination with carboplatin and etoposide.
[0009] The present invention also provides a kit for evaluating the efficacy of immunotherapy combined with chemotherapy in patients with small cell lung cancer. The kit includes reagents for detecting the expression levels of biomarkers miR-338-5p, miR-345-5p, miR-150-5p, miR-7706, and miR-328-3p.
[0010] As a preferred embodiment, the method for detecting the expression level of exosomal miRNA markers is as follows: the expression level of exosomal miRNA markers in the plasma of small cell lung cancer patients is detected by next-generation sequencing technology.
[0011] As a preferred embodiment, the kit further includes a efficacy assessment model, the formula of which is as follows: , X i This indicates that the model calculates the numerical result based on the biomarker expression of sample i, P(X). i The probability value of sample i predicted by the evaluation model to respond to treatment is denoted as 1. Samples with a probability value greater than the reference value are considered to be likely to respond to treatment. Samples with a probability value less than the reference value may not respond to treatment and are denoted as 0. This is the final prediction result.
[0012] As a preferred embodiment, the efficacy evaluation model uses the extreme random tree algorithm.
[0013] The present invention also provides a system for evaluating the efficacy of immunotherapy combined with chemotherapy in patients with small cell lung cancer, the system comprising a detection unit and an evaluation unit; The detection unit is used to detect the expression level of the biomarker described in claim 1 in the subject and to determine the TNM stage value of the subject; The assessment unit is used to assess the efficacy of the subject's treatment based on the expression levels of the subject's biomarkers and TNM stage values obtained from the detection unit, using an efficacy assessment model.
[0014] The specific process of using the system for evaluating the efficacy of combined immunotherapy and chemotherapy is as follows: 1) Peripheral blood was collected from patients with suspected SCLC based on clinical diagnosis. Peripheral blood exosomes were obtained, and smallRNA sequencing was used to obtain the expression of exosomal miRNA biomarkers. 2) Using an immunotherapy combined with chemotherapy efficacy evaluation model, the expression values and TNM stage values of plasma exosomes miR-338-5p, miR-345-5p, miR-150-5p, miR-7706 and miR-328-3p were obtained. The values were then substituted into the trained model formula to calculate the probability of each patient being predicted as a response group or a non-response group. 3) Select the output result with the highest probability and give the predicted result of the combined immunotherapy and chemotherapy for each SCLC patient.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention is the first to discover that the combination of plasma exosomes miR-338-5p, miR-345-5p, miR-150-5p, miR-7706, miR-328-3p, and TNM staging can serve as biomarkers to effectively predict the population that will benefit from immunotherapy combined with chemotherapy. Due to the unique structure of exosomes and the advantages of liquid biopsy technology, the combination of plasma-derived exosomes miR-338-5p, miR-345-5p, miR-150-5p, miR-7706, miR-328-3p, and TNM staging from ES-SCLC patients in this invention has high sensitivity and convenience in screening the population that will benefit from immunotherapy combined with chemotherapy.
[0016] 2. The technical solution for evaluating the efficacy of combined immunotherapy and chemotherapy in ES-SCLC patients and identifying the beneficiary population provided by this invention is a detection method based on liquid biopsy, which has the advantages of being minimally invasive, sensitive and convenient, and has a very wide range of applications. Attached Figure Description
[0017] Figure 1 This is a transmission electron microscope image of the plasma exosome detection results of this invention.
[0018] Figure 2 This is a graph showing the plasma exosome particle size distribution results of this invention.
[0019] Figure 3 This is the ROC curve of the training queue for the optimal prediction model of this invention.
[0020] Figure 4 This is a graph showing the predicted results of the immunotherapy combined with chemotherapy efficacy evaluation model of the present invention. Detailed Implementation
[0021] The technical solution of the present invention will be described in detail below with reference to the embodiments. Unless otherwise specified, all reagents and biological materials used below are commercial products.
[0022] Example 1: Screening of miRNA biomarkers (1) Study cohort and clinical information A total of 38 patients suspected of having small cell lung cancer by existing clinical testing methods were included. Blood samples were collected from these patients before surgery or before drug treatment. Enrollment criteria included: 1) Pathological diagnosis of ES-SCLC; 2) Eastern Cooperative Oncology Group (ECOG) performance status (PS) score of 0 or 1; 3) First-time recipients of atezolizumab combined with carboplatin and etoposide, with no prior chemotherapy history. At the initial efficacy evaluation following atezolizumab combined with carboplatin and etoposide treatment, 14 ES-SCLC patients experienced disease progression (non-response group), and 24 patients achieved stable disease or partial remission (response group). Thirty-three patients were randomly selected as the training cohort, and the remaining 5 patients were selected as the test cohort.
[0023] (2) Extraction and characterization of plasma exosomes 1) Collection of plasma and extraction of exosomes Patient blood samples were collected before surgery or drug treatment and stored in 10 ml vacuum blood collection tubes (REF367525, BD, USA). The tubes were slowly inverted a few times and then placed upright. The blood samples were centrifuged twice to obtain plasma. The first centrifugation was performed at 1600 g for 10 min (4℃). After centrifugation, the supernatant was transferred to a new 1.5 ml centrifuge tube. The hemolysis grade of the blood samples was determined and recorded. Samples with a hemolysis grade ≤4 were included in subsequent studies. The second centrifugation was performed at 16000 g for 15 min (4℃). After centrifugation, 1 ml of supernatant was transferred to each tube and stored at -80℃ for later use.
[0024] Plasma samples from isolated and preserved ES-SCLC patients were used to extract plasma exosomes using exosome separation reagents. The exosome extraction process was as follows: the frozen plasma samples were thawed in a 37°C water bath, centrifuged at 12000 g for 10 min (4°C), and 500 µL of supernatant was transferred to a 0.45 µm filter column (Costar, CLS8163-100EA, Corning, USA), centrifuged at 12000 g for 5 min (4°C), and then the filtrate was transferred to a 0.22 µm filter column (Costar, CLS8161-100EA, USA), centrifuged at 12000 g for 5 min (4°C), and the filtrate was collected into a 1.5 ml EP tube. Measure the volume of the filtrate, add 1 / 4 volume of exosome separation reagent (L3525, 3DMed, Shanghai, China), mix thoroughly, incubate at 4℃ for 30 min, centrifuge at 4700 g for 30 min (4℃), discard the supernatant, and resuspend the exosomes in 200 µL PBS (Phosphate Buffer Saline).
[0025] 2) Characterization of plasma exosomes To detect the characteristics of exosomes in the plasma of patients with ES-SCLC, transmission electron microscopy (TEM) was used to examine the morphology of exosomes, and nanoparticle tracking analysis (NTA) was used to detect the particle size distribution of plasma exosomes. Exosome morphological identification: Exosome particles were resuspended in PBS, fixed with 4% paraformaldehyde, and 10 µL of the suspension was dropped onto a carbon-coated copper grid. The copper grid was washed twice with PBS, then washed for 3 min with PBS containing 50 mM glycine, and then washed for 10 min with PBS containing 0.5% BSA. Finally, the copper grid was stained with 2% uranyl acetate, and observed and photographed using a transmission electron microscope (H7650, HITACHI, Japan). TEM results showed that the plasma exosomes exhibited a typical "horseshoe" morphology (see...). Figure 1 ).
[0026] Plasma exosome particle size distribution assay: First, the exosomes were diluted with PBS to a concentration of 1*10⁻⁶. ^ 7 -1*10 ^ 9 / ml, mix thoroughly by pipetting. Then, turn on the NTA (NanoSight NS300, Malvern, UK) instrument and inject the diluted sample into the sample chamber. Use the 488 nm excitation module, set the camera lens parameters to 890 shutter speed, 146 gain, and 7 detection threshold. Analyze at least 200 complete tracks per video. Finally, analyze the plasma exosome nanoparticle tracking data using NTA analysis software (version 2.3). NTA results showed that the average particle size of plasma exosomes was 100.2 nm, consistent with the exosome particle size distribution (see...). Figure 2 ).
[0027] (3) Extraction and expression level detection of plasma exosome miRNA 1) Extraction of exosomal miRNA from plasma Plasma exosomal miRNA extraction was performed according to the product instructions. Total RNA was extracted from the obtained plasma exosomal tissue using the miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai, China), and finally, RNA was eluted with 15 µl of RNase-free water. Subsequently, the concentration and fragment distribution of miRNA were detected using an Agilent 2100 Bioanalyzer and the corresponding small RNA analysis kit (5067-1548, Agilent, USA).
[0028] 2) Detection of plasma exosomal miRNA expression Small RNA sequencing was used to detect the expression levels of miRNAs in plasma exosomes of ES-SCLC patients. Library preparation was performed using the NEBNext Multiplex Small RNA Library Prep Set for Illumina (E7300L, NEB, USA), with detailed instructions provided in the product manual. Each RNA sample was loaded with 100 ng (≤6 µL), followed by 3' adapter ligation, reverse transcription primer hybridization, 5' adapter ligation, reverse transcription, and PCR amplification (typically 18 cycles). The PCR enrichment products were purified using the NucleoSpin Gel and PCR Clean-up kit (740609.250, MN, Germany), and the DNA (prepared library) was washed away with 30 µL of nuclease-free water. DNA concentration was quantified using an Invitrogen Qubit 4.0 fluorometer and the accompanying Qubit dsDNAHS Assay Kit (Q32854, Thermofisher, USA). DNA fragment distribution was detected using an Agilent 2100 bioanalyzer and the accompanying chips and reagents, the Agilent High Sensitivity DNA Kit & Reagents (5067-4626, Agilent, USA). Sequencing was performed using the Illumina NovaSeq platform (sequencing strategy PE150).
[0029] (4) Sequencing data analysis workflow The expression levels of miRNAs in exosomes from the plasma of ES-SCLC patients were obtained using small RNA sequencing technology. The analysis workflow for the sequencing data is as follows: 1) Sequencing data alignment. After removing the sequencing adapters from the small RNA sequencing data, the sequencing data was aligned to the human reference genome hg19 using BWA software (version: 0.7.12-r1039) (genome download link: http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / ), and the number of reads aligned to miRNAs was counted.
[0030] 2) miRNA annotation. miRNAs were annotated using the Gencode v25 and miRBase v21 databases, retaining those annotated as known mature miRNAs for subsequent analysis.
[0031] 3) miRNA filtering. Mature miRNAs with a length of 30 nt or less and covering at least 2 reads per sample are retained for subsequent analysis.
[0032] 4) miRNA expression level standardization. The M-value weighted truncated mean (TMM) method in the edgeR analysis package of R language was used to standardize the miRNA expression level of the samples.
[0033] (5) Discovery of biomarkers Based on the expression levels of miRNAs in plasma exosomals of ES-SCLC patients, samples were grouped according to the evaluation results of the first treatment efficacy after immunotherapy combined with chemotherapy. Statistical tests were used to identify plasma exosomal miRNAs that can be used as biomarkers to predict the efficacy and benefit of immunotherapy combined with chemotherapy in ES-SCLC patients. The process is as follows: 1) Sample grouping. Based on the initial assessment of the treatment effect after immunotherapy combined with chemotherapy, patients were divided into two groups: a non-response group and a response group.
[0034] 2) Candidate Molecular Biomarkers. The differences in miRNA expression levels between the non-responder and responder groups were compared using the U-test and T-test statistical analyses. The results from both analyses were combined, and miRNAs with expression levels greater than 10 counts per million mapped reads (CPM), a difference of more than 0.5-fold between the two groups, and a p-value less than or equal to 0.2 were selected as candidate molecular biomarkers, totaling 23, for subsequent analysis. Five factors related to efficacy—PD-L1 expression, smoking, age, ECOG score, and TNM stage—were also included in the subsequent analysis. Five machine learning algorithms—random forest, extremely randomized trees, neural network, linear model, and XGBoost—were then used to calculate the feature weights of each biomarker in different algorithms. Biomarkers with negative feature weights were removed, and TNM staging and 11 miRNAs (miR-338-5p, miR-345-5p, miR-150-5p, miR-215-5p, miR-484, miR-125a-5p, miR-589-5p, miR-99a-5p, miR-143-3p, miR-7706, and miR-328-3p) were retained for subsequent construction of an immunotherapy combined with chemotherapy efficacy evaluation model.
[0035] (6) Construction of an evaluation model for the efficacy of immunotherapy combined with chemotherapy in ES-SCLC patients Using the 12 biomarkers discovered in (5), five machine learning algorithms were employed: random forest, extremely randomized trees, neural network, linear model, and gradient boosting machine (XGBoost). Different hyperparameters were preset for each algorithm, and multiple efficacy evaluation models using different machine learning algorithms were trained using the training set sample data. The model formulas are as follows: , X i This indicates that the model calculates the numerical result based on the biomarker expression of sample i, P(X). i The probability value of sample i predicted by the model to respond to treatment is used to evaluate the model's prediction. Samples with a probability value greater than the reference value are considered to be likely to respond to treatment and are represented by 1; samples with a probability value less than the reference value may not respond to treatment and are represented by 0. This is the final prediction result. The trained model parameters and weights are saved on the hard drive as files. When calling the model, the model prediction result can be obtained by inputting the sample marker expression value.
[0036] Model evaluation metrics included the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1), specificity (ranging from 0 to 1), and sensitivity (ranging from 0 to 1). Higher values indicated better model predictive performance. Using a reference value of 0.483, the training cohort samples were predicted as non-responders and responders. The evaluation results are shown in Table 1.
[0037] Among the 11 miRNAs selected using a combination of statistical methods and various machine learning algorithms, different machine learning algorithms selected different biomarkers, as shown in Table 1, which illustrates the biomarker combinations corresponding to different model algorithms. These combinations were selected by different model algorithms during model training based on the calculated miRNA feature weights. Each time, only miRNAs with weights > 0 were retained, and the AUC was re-evaluated. This process was iterated until the weights of all retained miRNAs in each model were > 0. As miRNAs with weights <= 0 were continuously removed, the model's AUC continuously improved. Therefore, the AUC corresponding to each model + biomarker combination in Table 1 represents the optimal performance of that model; adding or removing biomarkers would decrease the AUC. Comparing the AUCs, the ExtraTreesGini model was found to have the highest AUC; therefore, this model was selected as the final prediction model, and its five corresponding miRNAs and TNM staging were chosen as the final biomarkers.
[0038]
[0039] The results show that the optimal prediction model includes six biomarkers: miR-338-5p, miR-345-5p, miR-150-5p, miR-7706, miR-328-3p, and TNM staging. See also... Figure 3 The figure shows the ROC curve of the training cohort for this optimal prediction model. The AUC, specificity, and sensitivity of the training cohort for this prediction model are 0.855, 80%, and 95%, respectively.
[0040] Example 2: Predictive model for evaluating the efficacy of immunotherapy combined with chemotherapy in ES-SCLC patients In the test set, the model with the best predictive performance in the training set was selected for predicting the efficacy of immunotherapy combined with chemotherapy. This model includes six biomarkers: miR-338-5p, miR-345-5p, miR-150-5p, miR-7706, miR-328-3p, and TNM staging. Using a reference value of 0.483, the test set samples were divided into non-responder and responder groups. The predictive efficacy of the immunotherapy combined with chemotherapy efficacy assessment model was evaluated using imaging efficacy assessment results as the true value; higher accuracy indicates better predictive performance. See also... Figure 4 The results show the predictions of the immunotherapy combined with chemotherapy efficacy evaluation model. The results indicate that in a test cohort of 5 patients, the immunotherapy combined with chemotherapy efficacy evaluation model accurately predicted that 4 patients would respond to immunotherapy combined with chemotherapy, with an accuracy rate of 80%, demonstrating superior predictive efficacy. The above are merely some preferred embodiments of the present invention, and the present invention is not limited to the contents of these embodiments. For those skilled in the art, various changes and modifications can be made within the scope of the present invention's technical solutions, and any changes and modifications made are within the protection scope of the present invention.
Claims
1. The application of exosomal miRNA markers in the preparation of products for evaluating the efficacy of combined immunotherapy and chemotherapy in patients with small cell lung cancer, characterized by: The exosomal miRNA markers are a combination of miR-338-5p, miR-345-5p, miR-150-5p, miR-7706, and miR-328-3p.
2. The application according to claim 1, characterized in that: The immunotherapy combined with chemotherapy refers to treatment with atezolizumab combined with chemotherapy.
3. The application according to claim 2, characterized in that: The immunotherapy combined with chemotherapy refers to treatment with atezolizumab in combination with carboplatin and etoposide.
4. A kit for evaluating the efficacy of immunotherapy combined with chemotherapy in patients with small cell lung cancer, characterized in that: The kit includes reagents for detecting the expression level of the exosomal miRNA markers as described in claim 1.
5. The reagent kit according to claim 4, characterized in that, The method for detecting the expression level of exosomal miRNA markers is as follows: the expression level of exosomal miRNA markers in the plasma of small cell lung cancer patients is detected by next-generation sequencing technology.
6. The reagent kit according to claim 4, characterized in that: The kit also includes a efficacy assessment model, the formula of which is as follows: ， X i This indicates that the model calculates the numerical result based on the biomarker expression of sample i, P(X). i The probability value of sample i predicted by the evaluation model to respond to treatment is denoted as 1. Samples with a probability value greater than the reference value are considered to be likely to respond to treatment. Samples with a probability value less than the reference value may not respond to treatment and are denoted as 0. This is the final prediction result.
7. The reagent kit according to claim 6, characterized in that: The efficacy evaluation model uses the extreme random tree algorithm.
8. A system for evaluating the efficacy of immunotherapy combined with chemotherapy in patients with small cell lung cancer, characterized in that: Includes a detection unit and an evaluation unit; The detection unit is used to detect the expression level of the biomarker described in claim 1 in the subject and to determine the TNM stage value of the subject; The assessment unit is used to assess the efficacy of the subject's treatment based on the expression levels of the subject's biomarkers and TNM stage values obtained from the detection unit, using an efficacy assessment model.