Use of exosome protein marker combination in ovarian cancer recurrence monitoring

Abstract

This invention discloses the application of a combination of exosomal protein biomarkers in ovarian cancer recurrence monitoring. The invention provides a biomarker combination for ovarian cancer recurrence monitoring, comprising CA125, HE4, and C5a proteins from exosomals. Based on blood exosomal proteins as biomarkers, this invention establishes a high-performance recurrence monitoring scoring model using statistical and machine learning methods, which is applied to ovarian cancer recurrence monitoring. Its accuracy and sensitivity are significantly superior to traditional serum CA125 detection. This invention provides ovarian cancer patients, especially those with platinum-sensitive recurrence, with a new, efficient, accurate, and minimally invasive recurrence monitoring protocol, which helps guide subsequent treatment and improve patient prognosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection technology, specifically relating to a combination of exosomal protein biomarkers for monitoring ovarian cancer recurrence, a recurrence monitoring risk scoring model constructed based on the combination of biomarkers, and its application. Background Technology

[0002] Ovarian cancer is one of the deadliest gynecological cancers, posing a serious threat to women's health. According to global cancer statistics for 2020 and 2022, it is estimated that more than 300,000 people are diagnosed with ovarian cancer each year. Over 90% of ovarian cancers are epithelial, with high-grade serous carcinoma (HGSC) being the most common and deadliest. Due to the often subtle clinical symptoms in the early stages, most patients are diagnosed at an advanced stage, missing the optimal window for radical surgical treatment, resulting in a 5-year survival rate of less than 30%. Surgery and systemic chemotherapy are the cornerstones of first-line treatment for epithelial ovarian cancer. The efficacy rate of first-line platinum-based chemotherapy combined with taxane chemotherapy exceeds 80%, with more than half achieving complete remission (CR). However, even in advanced cases, 50%-70% of patients experience recurrence, often without typical symptoms. Ovarian cancer recurrence refers to the recurrence of cancer after initial treatment, when symptoms disappear and the patient achieves complete remission, but after a period of time following treatment cessation. This recurrence can occur anywhere in the pelvic and abdominal cavity, most commonly in the intestines, peritoneum, and retroperitoneal lymph nodes, and can even metastasize to more distant locations such as the liver, lungs, and cervical lymph nodes. The median time to recurrence for epithelial ovarian cancer is approximately 16 months. This high recurrence rate is a very challenging and extremely important clinical problem in gynecological oncology. Therefore, close follow-up to identify reliable recurrence monitoring biomarkers is of great clinical significance in guiding timely subsequent treatment.

[0003] Currently, clinical methods for monitoring and assessing ovarian cancer recurrence mainly include imaging examinations and serum tumor marker CA125 testing. Imaging examinations such as CT, PET / CT, and MRI have low sensitivity and specificity for detecting recurrent lesions in ovarian cancer, cannot detect small lesions, and pose radiation risks, limiting frequent monitoring. While serum CA125 testing is more convenient, its sensitivity in predicting recurrence varies considerably (between 56% and 74%). For second-line treatment of platinum-sensitive recurrence patients, the sensitivity of serum CA125 in predicting recurrence is even lower, only 28%-45%. Human epididymis protein 4 (HE4) has also been approved by the US Food and Drug Administration (FDA) for ovarian cancer recurrence monitoring; its combination with CA125 improves recurrence monitoring performance, but its sensitivity remains limited (approximately 76%). Therefore, there is an urgent need to develop highly sensitive and specific biomarkers for ovarian cancer recurrence monitoring.

[0004] Exosomes, also known as small extracellular vesicles (sEVs), are membrane-bound vesicles with a lipid bilayer secreted and released by cells. sEVs are rich in contents, highly stable, and have advantages in liquid biopsies. Increasing research is exploring the application value of exosome technology in the diagnosis and treatment of ovarian cancer, including auxiliary diagnosis, prognosis, and monitoring. Existing studies have shown that exosome-based biomarkers CA125, HE4, and C5a have demonstrated excellent performance in the auxiliary diagnosis of ovarian cancer. However, the application value of these biomarkers in monitoring ovarian cancer recurrence has not yet been fully explored and validated. Summary of the Invention

[0005] The purpose of this invention is to provide a combination of exosomal protein biomarkers for monitoring ovarian cancer recurrence, a recurrence monitoring risk scoring model constructed based on the combination of biomarkers, and its application.

[0006] The technical solution adopted by the present invention to achieve the above objectives is as follows: A combination of protein biomarkers for monitoring ovarian cancer recurrence, the combination of protein biomarkers comprising: CA125 protein, HE4 protein and C5a protein in exosomes.

[0007] As a preferred embodiment, the exosomes are exosomes in the blood.

[0008] As a preferred embodiment, the protein biomarker combination is used for monitoring the risk of recurrence in second-line and subsequent-line treatment of platinum-sensitive recurrent ovarian cancer.

[0009] The present invention also provides the application of the aforementioned protein biomarker combination in the preparation of an ovarian cancer recurrence monitoring kit.

[0010] This invention also provides a kit for monitoring ovarian cancer recurrence, the kit comprising reagents for detecting the expression levels of biomarkers CA125, HE4, and C5a proteins in biological samples. Specifically, the concentrations of CA125, HE4, and C5a proteins in the blood of ovarian cancer patients are detected using a human exosome CA125, HE4, and C5a detection kit (chemiluminescence method) (3103010001, 3DMed, Shanghai, China) in conjunction with a SMART 6500 fully automated chemiluminescence analyzer.

[0011] The present invention also provides a system for monitoring ovarian cancer recurrence, the system comprising: The data acquisition module is used to acquire the expression level data of CA125 protein, HE4 protein and C5a protein in exosomes of subject biological samples; The risk scoring module is used to input the obtained expression level data of CA125 protein, HE4 protein and C5a protein into the constructed relapse monitoring risk scoring model to calculate the risk probability value. The result determination module is used to compare the calculated risk probability value with a preset reference value and output the recurrence risk prediction result of the subject.

[0012] As a preferred embodiment, the formula for the risk scoring model 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 for treatment recurrence predicted by the recurrence monitoring risk scoring model is denoted as 1. Samples with a probability value greater than the reference value are considered to be ovarian cancer patients with recurrence. Samples with a probability value less than the reference value are considered to be ovarian cancer patients without recurrence. This is the final prediction result.

[0013] As a preferred embodiment, the risk scoring model is constructed using machine learning algorithms such as random forest or gradient boosting machine.

[0014] As a preferred embodiment, the reference value in the result determination module is 0.5.

[0015] This invention also provides a method for monitoring ovarian cancer recurrence, the method comprising the following steps: (1) Obtain blood samples from the subjects and isolate exosomes from the blood samples; (2) Detect the expression abundance values ​​of CA125, HE4 and C5a proteins in exosomes; (3) Substitute the expression abundance value detected in step (2) into the relapse monitoring risk scoring model to calculate the risk value; (4) Compare the risk value calculated in step (3) with the preset reference value, and determine the recurrence risk of the subject based on the comparison results.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention establishes a high-performance recurrence monitoring scoring model based on blood exosome proteins as biomarkers, using statistical and machine learning methods, for the recurrence monitoring of ovarian cancer. It provides each ovarian cancer patient with a prediction of the risk of recurrence after treatment, improves the accuracy of ovarian cancer recurrence monitoring in my country, guides subsequent treatment earlier and more timely, and improves patient prognosis.

[0017] 2. This invention, through research and exploration, has discovered that the appropriate combination of biomarkers varies for risk assessment at different stages of ovarian cancer. For the early diagnosis of ovarian cancer, the exosome combination with better detection results is CA125 protein and HE4 protein. For monitoring the risk of recurrence after second-line and later-line treatment of ovarian cancer, the exosome biomarker combination with better monitoring results is CA125 protein, HE4 protein, and C5a protein.

[0018] 3. Clinically, there is a lack of limited liquid biopsy methods for monitoring ovarian cancer recurrence. This invention has discovered and verified a combination of blood exosome protein biomarkers that can be used for monitoring ovarian cancer recurrence. The constructed recurrence monitoring risk scoring model has better sensitivity, specificity and accuracy than existing clinical imaging and CA125 tumor marker detection and other recurrence monitoring and assessment methods.

[0019] 4. The ovarian cancer recurrence monitoring technology provided by this invention is a detection method based on liquid biopsy. It causes less damage to patients and can be repeated multiple times. It has the characteristics of being minimally invasive, sensitive and convenient. Compared with CT, which has the advantages of being more expensive, having radiation risks and low sensitivity of CA125 monitoring, it is more economical, safe and efficient, and has a very wide range of applications. Attached Figure Description

[0020] Figure 1 This is a transmission electron microscope (TEM) image of the blood exosomes detected according to the present invention.

[0021] Figure 2 This is a graph showing the particle size distribution of blood exosomes according to the present invention.

[0022] Figure 3 This is the ROC curve of the risk scoring model constructed using Random Forest with the combination of the markers CA125, HE4, and C5a of this invention in the training queue.

[0023] Figure 4 This is the ROC curve of the risk scoring model constructed using Random Forest with the combination of the markers CA125, HE4, and C5a of this invention in the validation queue.

[0024] Figure 5 This is the ROC curve of the risk scoring model constructed using the combination of the markers CA125, HE4, and C5a of this invention with a gradient boosting machine (XGBoost) in the training queue.

[0025] Figure 6 This is the ROC curve of the risk scoring model constructed using the combination of the present invention's markers CA125, HE4, and C5a with a gradient boosting machine (XGBoost) in the validation queue. Detailed Implementation

[0026] 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.

[0027] Example 1: Screening of biomarker combinations and construction of risk scoring models (1) Study cohort and clinical information This study included 119 patients clinically diagnosed with ovarian cancer who had previously received treatment for platinum-sensitive recurrence of epithelial ovarian cancer. Blood samples were collected from patients during follow-up after previous treatment. Enrolled patients met the following criteria: (1) females aged 18 years or older; (2) pathologically confirmed stage I-IV epithelial ovarian cancer, with no limit on the number of lines of treatment (maintenance therapy was not counted), platinum-sensitive recurrence (last platinum-based chemotherapy achieved CR / PR, and the recurrence interval was >6 months), and radiographically confirmed tumor recurrence; (3) Eastern Cooperative Oncology Group (ECOG) performance status (PS) score ≤ 2; (4) expected survival of more than 6 months. Enrolled patients were divided into a training cohort and a validation cohort. The training cohort consisted of 90 patients, including 79 patients with high-grade serous carcinoma and 11 patients with non-high-grade serous carcinoma. The validation cohort consisted of 29 patients, including 23 patients with high-grade serous carcinoma and 6 patients with non-high-grade serous carcinoma. Table 1 presents the clinical information of the enrolled samples, showing no significant difference in the proportion of samples with different classifications, stages, and relapse states between the training and validation cohorts. Here, relapse state refers to whether a relapse occurs again after second-line and subsequent-line treatment following first-line relapse. In this study, the second-line and subsequent-line treatment methods for the patient samples were consistent with the first-line treatment, all employing platinum-based chemotherapy.

[0028]

[0029] (2) Extraction and characterization of blood exosomes 1) Blood collection and serum separation After blood samples from ovarian cancer patients were collected, they were stored in 10 ml serum vacuum blood collection tubes (REF367820, BD, USA). After being gently inverted and mixed, the samples were left to stand upright at room temperature for 1-2 hours until the blood clots coagulated and shrank. Then, the samples were centrifuged at 4000 g for 10 min at 4°C to remove residual cell debris. Finally, the supernatant was aliquoted into 1.5 ml EP tubes at 1 ml per tube and stored at -80°C for later use.

[0030] 2) Extraction of exosomes Exosomes were extracted from the blood of ovarian cancer patients using exosome separation reagent (N3525, 3DMed, Shanghai, China): After the frozen serum samples were taken out, they were first placed in a 37°C water bath to thaw. After the samples were completely thawed, they were centrifuged at 12000g for 10 min at 4°C. The supernatant was then filtered sequentially through a 0.45 µm filter column (CLS8163-100EA, Corning, USA) and a 0.22 µm filter column (CLS8161-100EA, Corning, USA). The centrifugation conditions were 12000g for 5 min at 4°C. The volume of the filtrate was collected and measured. 0.25 times the volume of exosome separation reagent N3525 was added, and the mixture was thoroughly mixed. The mixture was then incubated at 4°C for 30 min. After incubation, the mixture was centrifuged at 4700g for 30 min at 4°C. Discard the supernatant and resuspend the exosomes in 200 µL of phosphate-buffered saline (PBS, pH 7.4), or directly lyse the exosomes with an equal volume of exosome lysis buffer for subsequent protein detection.

[0031] 3) Characteristic identification of blood exosomes The morphology of exosomes resuspended in PBS was examined using transmission electron microscopy (TEM). Blood exosomes were first fixed in 4% paraformaldehyde and then transferred to a carbon-coated copper grid for an electron microscope. The copper grid was washed twice with PBS, then once each with PBS containing glycine (50 mM) and 0.5% BSA. The grid was then stained with 2% uranyl acetate. Finally, the morphology of the blood exosomes was characterized using a transmission electron microscope (H-7650, Hitachi High-Technologies, Japan). TEM results showed that the extracted exosomes exhibited a typical "horseshoe" morphology (see [link to TEM]). Figure 1 ).

[0032] The particle size distribution of blood exosomes was detected using a nanoparticle tracking analysis (NTA) system: Resuspended blood exosomes were diluted with PBS (1 × 10⁷ - 1 × 10⁹ / ml) and mixed thoroughly by pipetting. The sample was added to the sample chamber of the NTA instrument (NanoSight NS300, Malvern, UK). A 488 nm excitation module was used, and the camera lens parameters were set (shutter speed 890, gain 146, detection threshold 7). At least 200 complete tracks were analyzed for each video. The nanoparticle tracking data of the blood exosomes were analyzed using NTA software (version 2.3). The NTA results showed that the main particle size peak was 83 nm, consistent with the particle size distribution of exosomes (see [link to NTA software]). Figure 2 ).

[0033] (3) Detection of CA125, HE4 and C5a protein expression in blood exosomes The concentrations of CA125, HE4, and C5a proteins in the blood exosomes of ovarian cancer patients were detected using a human exosome CA125, HE4, and C5a assay kit (chemiluminescence method) (3103010001, 3DMed, Shanghai, China) in conjunction with a SMART 6500 fully automated chemiluminescence analyzer. Detailed experimental procedures can be found in the product manual.

[0034] (4) Serum CA125 detection The concentration of serum CA125 in ovarian cancer patients was detected using the Roche Elecsys CA125 II kit (11776223190, Roche, Shanghai, China) in conjunction with a cobas e 411 immunoassay analyzer. Detailed experimental procedures can be found in the product manual.

[0035] (5) Discovery of biomarkers Four features—patient age, exosome CA125 abundance, exosome HE4 abundance, and exosome C5a abundance—were used as candidate biomarkers for ovarian cancer. Four machine learning algorithms—Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost—were employed, and 5-fold cross-validation was used to train and evaluate the classification performance of all biomarker combinations on the training cohort data. Logistic Regression and SVM performed poorly. Therefore, Random Forest and XGBoost were used, and the best-performing combination of exosome CA125, exosome HE4, and exosome C5a was selected as the final biomarker for constructing an ovarian cancer recurrence monitoring risk scoring model.

[0036] (6) Construction of a risk scoring model for monitoring ovarian cancer recurrence 1) Recurrence monitoring risk scoring model. Using the recurrence and non-recurrence of ovarian cancer patients after second-line and subsequent treatments as the classification prediction target, the biomarker combination discovered in (5) was used. Machine learning algorithms of random forest and gradient boosting machine (XGBoost) were employed, with different hyperparameters preset for each algorithm. The abundance values ​​of exosomes CA125, HE4, and C5a in the training cohort were used as variables, and imaging results were used as the ground truth to construct multiple ovarian cancer recurrence monitoring risk scoring models using different machine learning algorithms. The model formula 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 (1) represents the predicted recurrence rate of sample i for treatment by the recurrence monitoring risk scoring model. Samples with a probability value greater than the reference value are considered to be patients with recurrent ovarian cancer and are represented by 1; samples with a probability value less than the reference value are considered to be patients with non-recurrent ovarian cancer and are represented by 0. This is the final prediction result. The trained model is saved on the hard drive as a file. When calling the model, inputting the sample marker expression value will yield the model prediction result.

[0037] 2) Reference value. When the hazard value is less than the reference value, the sample is predicted as non-relapsed; otherwise, it is predicted as relapsed. Receiver operating characteristic (ROC) curves are plotted for each patient in the training cohort based on their hazard value and imaging results. The reference value is determined based on the ROC curve results.

[0038] 3) Performance evaluation of the recurrence monitoring risk scoring model. Using a reference value of 0.5, the training cohort samples were divided into a non-recurrence group and a recurrence group. The predictive efficacy of the ovarian cancer recurrence monitoring risk scoring model was evaluated using imaging results as the true values. The model's predictive efficacy was evaluated using methods including accuracy (range 0–1), sensitivity (range 0–1), and specificity (range 0–1), with higher values ​​indicating better model classification performance. The evaluation results of different biomarker combinations in the training cohort are shown in Table 2.

[0039]

[0040] The results in Table 2 indicate that, in the training cohort, the ovarian cancer recurrence monitoring risk scoring model constructed from the combination of exosomal protein markers CA125, HE4, and C5a exhibited the highest accuracy, sensitivity, and specificity, demonstrating the best predictive efficacy. See also Figure 3The figure shows the ROC curve of a risk scoring model constructed using a random forest (RandomForest) based on the biomarkers exosomes CA125, HE4, and C5a in the training cohort. See also... Figure 5 The figure shows the ROC curve of a risk scoring model constructed using a gradient boosting machine (XGBoost) based on the biomarkers exosomes CA125, HE4, and C5a in the training queue.

[0041] Example 2: Validation of the predictive efficacy of the ovarian cancer recurrence monitoring risk scoring model To validate the predictive efficacy of the ovarian cancer recurrence monitoring risk scoring model, an independent cohort was selected as the validation cohort. Based on the risk scoring model and reference value determined in the training cohort, the model's efficacy in predicting recurrence and non-recurrence was validated. Using a reference value of 0.5, the validation cohort samples were divided into a non-recurrence group and a recurrence group. The evaluation results of different biomarker combinations in the validation cohort are shown in Table 3.

[0042]

[0043] Table 3 shows that, in the validation cohort, the combination of exosomal protein markers CA125, HE4, and C5a had better predictive power than the combination of exosomal markers CA125 and exosomal HE4. See also Figure 4 The figure shows the ROC curve of a risk scoring model constructed using a random forest (RandomForest) based on the biomarkers exosomes CA125, HE4, and C5a in the validation cohort. See also... Figure 6 The figure shows the ROC curve of a risk scoring model constructed using a gradient boosting machine (XGBoost) based on the biomarkers exosomes CA125, HE4, and C5a in the validation queue. 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. A combination of protein biomarkers for monitoring ovarian cancer recurrence, characterized in that, The protein biomarker combination includes CA125 protein, HE4 protein, and C5a protein in exosomes.

2. The protein biomarker combination according to claim 1, characterized in that: The exosomes are exosomes found in the blood.

3. The protein biomarker combination according to claim 1, characterized in that: The protein biomarker combination is used for monitoring the risk of recurrence in second-line and subsequent-line treatment of platinum-sensitive recurrent ovarian cancer.

4. The use of the protein biomarker combination according to any one of claims 1-3 in the preparation of an ovarian cancer recurrence monitoring kit.

5. A kit for monitoring ovarian cancer recurrence, characterized in that, The kit includes: a reagent for detecting the expression level of the combination of biomarkers of claim 1 in a biological sample.

6. A system for monitoring ovarian cancer recurrence, characterized in that, The system includes: The data acquisition module is used to acquire the expression level data of CA125 protein, HE4 protein and C5a protein in the exosomes of claim 1 in the biological samples of the subjects; The risk scoring module is used to input the obtained expression level data of CA125 protein, HE4 protein and C5a protein into the constructed relapse monitoring risk scoring model to calculate the risk probability value. The result determination module is used to compare the calculated risk probability value with a preset reference value and output the recurrence risk prediction result of the subject.

7. The system for monitoring ovarian cancer recurrence according to claim 6, characterized in that, The formula for the risk scoring model 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 for treatment recurrence predicted by the recurrence monitoring risk scoring model is denoted as 1. Samples with a probability value greater than the reference value are considered to be ovarian cancer patients with recurrence. Samples with a probability value less than the reference value are considered to be ovarian cancer patients without recurrence. This is the final prediction result.

8. The system for monitoring ovarian cancer recurrence according to claim 6 or 7, characterized in that: The risk scoring model is constructed using machine learning algorithms such as random forest or gradient boosting machine.

9. The system for monitoring ovarian cancer recurrence according to claim 6, characterized in that: The reference value in the result determination module is 0.

5.

10. A method for monitoring ovarian cancer recurrence, characterized in that, The method includes the following steps: (1) Obtain blood samples from the subjects and isolate exosomes from the blood samples; (2) Detect the expression abundance values ​​of CA125, HE4 and C5a proteins in exosomes; (3) Substitute the expression abundance value detected in step (2) into the relapse monitoring risk scoring model described in claim 7 to calculate the risk value; (4) Compare the risk value calculated in step (3) with the preset reference value, and determine the recurrence risk of the subject based on the comparison results.

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