Prediction model for evaluating immune checkpoint treatment benefit of ovarian cancer patient and application thereof

Abstract

The invention relates to the field of medical detection, in particular to a diagnostic model for evaluating immune checkpoint treatment benefit of an ovarian cancer patient and application of the diagnostic model. According to the invention, the correlation between the expression quantity of the PD-L1 in the ascites exosome of the ovarian cancer patient and the immune checkpoint treatment benefit of the patient is found for the first time, and the ratio of the expression level of the PD-L1 in a tumor tissue sample to the expression level of the PD-L1 in an ascites sample before the patient receives the immune checkpoint treatment is found; the method can effectively predict the benefit effect of the patient after receiving the immune checkpoint treatment. The beneficial crowd is accurately screened and risk stratification is realized before treatment, treatment selection is guided, and the treatment effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical detection. Specifically, the present invention relates to a prediction model for evaluating the benefit of immune checkpoint therapy in ovarian cancer patients and its application. Background Art

[0002] Immune checkpoint blockade (ICB) such as PD-1 and PD-L1 is a new generation of immunotherapy strategy after surgery, radiotherapy and chemotherapy, bringing good news to patients with recurrent, metastatic and refractory cancers. Among them, PD-L1, tumor mutation burden (TMB), and microsatellite instability (MSI) in tumor patients have important guiding values in predicting immune checkpoint therapy. Currently, the approved PD-L1 diagnostic kits detect PD-L1 at the tissue level (tumor cell membrane PD-L1, tumor-PD-L1) by immunohistochemistry.

[0003] However, according to the research report in the New England Journal of Medicine, only 8% of ovarian cancer patients respond to PD-1 inhibitors alone. It is suggested that the existing tumor-PD-L1 companion diagnosis alone cannot effectively screen patients suitable for ICB treatment, and more new companion diagnosis markers are urgently needed to accurately screen patients and improve the benefit rate of ICB treatment. Recently, Nature, Cell, etc. have successively reported that the level of exosome PD-L1 (Exosome PD-L1, exo-PD-L1) in tumor patients is inversely proportional to the benefit of immune checkpoint therapy. The lower the exo-PD-L1 level, the more suitable for immune checkpoint inhibitor treatment. Therefore, to evaluate whether a patient is suitable for immune checkpoint antibody treatment, not only the level of tumor-PD-L1 should be detected, but also the level of exo-PD-L1 should be detected. Based on this, this project intends to use the method developed by our laboratory for directly detecting exo-PD-L1 in plasma and ascites without relying on exosome extraction to study the relationship between the exo-PD-L1 level in the ascites of ovarian cancer patients and the benefit of immunotherapy. At the same time, a ratio assignment model of tumor-PD-L1 / exo-PD-L1 was proposed and established to assist in screening whether patients are suitable for immune checkpoint inhibitor treatment. The higher the ratio, the more beneficial; patients with a lower ratio are determined to be not suitable for immune checkpoint blockade treatment. At the same time, according to the change of exo-PD-L1 level before and after immune checkpoint therapy, the prognosis is judged.

[0004] In summary, a new immune therapy companion diagnosis method established based on the tumor-PD-L1 / ascites exo-PD-L1 ratio model is more conducive to accurately screening patients suitable for ovarian cancer immune checkpoint therapy. This model is expected to realize the detection of exo-PD-L1 in the LDT laboratory test items and assist in the selection of drug treatment plans for postoperative ovarian cancer patients, with good clinical transformation value and prospects.

[0005] Although there are detection kits for PD-L1 on the market, this detection method cannot distinguish the ratio of exosomal PD-L1 and soluble PD-L1 expressed therein, and can only detect the total expression of PD-L1 in both forms. If you want to separately detect the expression level of PD-L1 in exosomes, you need to perform an exosome isolation process (ultra-high speed centrifugation method at 10,000 g or kit method of polymer precipitation), which takes a long time, requires a large amount of samples, and has a complex process and high cost. Summary of the Invention

[0006] In order to overcome the defects existing in the prior art, the present invention established a tumor-PD-L1 / exo-PD-L1 = H-score / PHR-Score prediction model by screening biomarkers. By the value obtained from tumor-PD-L1 / exo-PD-L1, the immune checkpoint therapy benefit of the ovarian cancer patient is comprehensively evaluated, improving the accuracy of using immunohistochemistry to detect the tumor-PD-L1 value alone in the concomitant diagnosis of ovarian cancer immune checkpoint applicable patients. The technical solution provided by the present invention does not require the extraction of exosomes, and can quickly and quantitatively detect exo-PD-L1 with only 20 μL of sample.

[0007] In order to achieve the above technical effects, the present invention provides the following technical solutions:

[0008] First, in the first aspect, the present invention provides a method for constructing a prediction model for evaluating the immune checkpoint therapy benefit of ovarian cancer patients, wherein the construction method includes the following steps:

[0009] 1) Collect sufficient tumor tissues and ascites samples of ovarian cancer patients as the development cohort group;

[0010] 3) Follow up the patients in the development cohort group for more than 180 days, and evaluate whether the patients are treatment responders or non-responders through imaging;

[0011] 3) Detect tumor tissue sections by mIHC and exosomes in ascites samples by ELISA, and calculate the calculation of Tumor PD-L1 score (H-score) and Exo-PD-L1 entity expression score (exo-PD-L1-Score);

[0012] 4) Prediction model for the immune checkpoint therapy benefit of ovarian cancer patients:

[0013] tumor-PD-L1 / exo-PD-L1 = tumor PD-L1 H-score / exo-PD-L1-Score.

[0014] In one embodiment, the immune checkpoint therapy refers to the administration of monoclonal antibodies against the programmed death (PD-1) receptor.

[0015] In one embodiment, at 6 months, based on the imaging results of radiological examinations, when medical experts determine that there is no disease progression or tumor reduction at 6 months, it is determined as a treatment response.

[0016] In one embodiment, the calculation method of Tumor PD-L1 score (H-score) is: H-score = ∑Pi(i + 1), where Pi is the percentage of PD-L1 positive cells under different staining intensities (i); the calculation method of Exo-PD-L1 entity expression score (exo-PD-L1-Score) is: detecting the expression level of PD-L1 in exosomes in ascites by ELISA method, measuring the OD value with an enzyme-labeled instrument, and this OD value is counted as the entity expression score of exo-PD-L1.

[0017] In one embodiment, the model threshold / cut-off value is 1.514. When the tumor-PD-L1 / exo-PD-L1 ratio is greater than the threshold, it is determined that the immune checkpoint therapy is responsive; conversely, when the tumor-PD-L1 / exo-PD-L1 ratio is less than the threshold, it is determined that there is no response to the immune checkpoint therapy.

[0018] The second aspect of the present invention provides a prediction model for evaluating the benefit of immune checkpoint therapy in ovarian cancer patients, characterized in that the model is:

[0019] tumor-PD-L1 / exo-PD-L1 = tumor PD-L1 H-score / exo-PD-L1-Score;

[0020] Among them, the calculation method of Tumor PD-L1 score (H-score) is: H-score = ∑Pi(i + 1), where Pi is the percentage of PD-L1 positive cells under different staining intensities (i); the calculation method of Exo-PD-L1 entity expression score (exo-PD-L1-Score) is: detecting the expression level of PD-L1 in exosomes in ascites by ELISA method, measuring the OD value with an enzyme-labeled instrument, and this OD value is counted as the entity expression score of exo-PD-L1.

[0021] In one embodiment, the optimal Cut off value of this model is 1.514; when the tumor-PD-L1 / exo-PD-L1 ratio is greater than the threshold, it is determined that the immune checkpoint therapy is responsive; conversely, when the tumor-PD-L1 / exo-PD-L1 ratio is less than the threshold, it is determined that there is no response to the immune checkpoint therapy.

[0022] The third aspect of the present invention provides the use of a prediction model constructed according to the above construction method for evaluating whether ovarian cancer patients benefit from immune checkpoint therapy.

[0023] The fourth aspect of the present invention provides the use of the above model for evaluating whether ovarian cancer patients benefit from immune checkpoint therapy.

[0024] Compared with the prior art, the present invention has the following remarkable advantages:

[0025] 1) Improve the detection specificity of exo-PD-L1: A specially designed plate with specific grooves and ridges is used to facilitate the enrichment of exosomes, and the exosomes will not be lost even after washing.

[0026] 2) Shorten the detection time and cost: In this method, exosomes are directly enriched on the ELISA plate without ultracentrifugation, density gradient centrifugation or nanoparticle enrichment. Compared with the existing methods (>24 hours), the detection time is reduced by more than 50%, meeting the clinical need for rapid detection.

[0027] 3) Require less sample and have a high detection throughput: For this detection, only 20 μL of sample is required to meet the detection requirements; a 96-well detection plate can meet the detection of 5-10 parallel plates, and the sample volume covered in a single time can reach 500-1000 samples for rapid screening;

[0028] 4) By rapidly diagnosing and quantifying PD-L1 in the form of exosomes in body fluid samples, the level of PD-L1 in exosomes in the body fluid of tumor patients inhibiting T cell immunity can be quickly and accurately predicted, providing a new reliable strategy for predicting the benefit of immunotherapy for patients. Description of the Drawings

[0029] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0030] Figure 1 is a schematic structural diagram of a customized ELISA plate;

[0031] Figure 2 is a ROC curve diagram for predicting the immune checkpoint therapy response rate of (A) single tumor-PD-L1 and (B) single exo-PD-L1.

[0032] Figure 3 is a training set ROC curve for predicting the immune checkpoint therapy response by the Tumor-PD-L1 / exo-PD-L1 ratio;

[0033] Figure 4ROC curve of the validation set for predicting the response to immune checkpoint therapy using the Tumor-PD-L1 / exo-PD-L1 ratio. Detailed implementation manners

[0034] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0035] The reagents used in the examples are as follows:

[0036]

[0037] Example 1 Prospective study and establishment of a prognostic prediction model

[0038] In the prospective study, through the detection of malignant ascites of high-grade serous ovarian cancer that had been collected (ascites before treatment was collected from ovarian cancer patients in the Department of Obstetrics and Gynecology, Peking University Third Hospital. Ascites of patients after ICB treatment was collected from the Department of Oncology Chemotherapy and Radiation Sickness. Inclusion criteria: 1) Age: ≥25 years old and ≤65 years old; 2) Voluntarily participated in this trial and had signed the informed consent form; 3) High-grade serous ovarian cancer; Exclusion criteria: 1) Patients who had been diagnosed with other tumors; 2) Pregnant or lactating women), it was found that before ICB (immune checkpoint blockade) treatment, the exo-PD-L1 level in the tumor ascites group was 2-3 times that of the non-tumor group (confirmed non-ovarian cancer patients) body fluid, and there was a significant difference between the two groups. However, the total protein level of PD-L1 was not significantly different, suggesting that exosomal PD-L1 may play a more prominent role in tumor immunity than soluble PD-L1.

[0039] In the detection of plasma exosomes of 2 patients receiving ICB treatment, it was found that compared before and after ICB treatment, the higher the exo-PD-L1, the more the secretion of IFN-γ increased with the blockade of immune checkpoint braking molecules (PD-1 / PD-L1) and T cell activation, which might induce tumor cells to produce more exo-PD-L1. This increase might be one of the indicators of the response to ICB immunotherapy. However, the current results are only the plasma detection changes of two patients, and more cases are still needed to assist in judging the companion diagnostic value of exo-PD-L1 in immunotherapy response and benefit.

[0040] Furthermore, we enrolled >60 ovarian cancer patients receiving immune checkpoint therapy (collected from the Department of Oncology Chemotherapy and Radiation, Peking University Third Hospital, all of whom had signed the informed consent form) to verify the above results.

[0041] Inclusion criteria: ovarian cancer patients, and ≤65 years old; no other tumors or complications; high-grade serous ovarian cancer; patients who had previously undergone tumor debulking surgery or had tissue wax blocks or immunohistochemical staining electronic results.

[0042] All patients received ICB immunotherapy (Nivolumab monotherapy; 240 mg / 2-week infusion, treatment cycle 2 - 3 months). Before receiving immune checkpoint blockade therapy, tumor tissue sections and ascites specimens were collected from each enrolled patient. Immunohistochemical staining (IHC) was performed on the tumor tissue sections, and the staining results were panoramically scanned and scored. The Tumor PD-L1 score (H-score) and Exo-PD-L1 entity expression score (exo-PD-L1-Score) were calculated for each enrolled patient.

[0043] The tumor imaging results of the patients were followed up at 6 months and 12 months after treatment. At 6 months, the imaging results of the radiological examination were used, and 2 - 3 experts determined whether there was progression of the tumor signal. If there was no progression or the tumor decreased at 6 months, it was determined as a treatment response. According to the clinical outcome grouping, 20 treatment-responsive patients and 20 treatment-non-responsive patients were randomly selected from the enrolled patients as the training set, and another 15 treatment-responsive patients and 15 treatment-non-responsive patients were selected as the validation set.

[0044] Criterion for judging treatment response: After immune checkpoint blockade therapy, at 6 months, tumor tissue sections and ascites specimens were collected from the patients. Immunohistochemical staining (IHC) was performed on the tumor tissue sections of each enrolled patient, and the molecular expression level was semi-quantitatively evaluated by panoramic scanning and scoring of the staining results:

[0045] Calculation of Tumor PD-L1 score (H-score): The staining intensity and the proportion of positive cells (tumor cells expressing PD-L1) were comprehensively considered. The staining intensity could be divided into 0 (no staining), 1 (weak staining), 2 (medium staining), 3 (strong staining), and the proportion of positive cells was expressed as a percentage. The calculation formula was H-score = ∑Pi(i + 1), where Pi was the percentage of positive cells at different staining intensities (i). For example, if the weakly stained cells accounted for 30%, the medium-stained cells accounted for 20%, and the strongly stained cells accounted for 10%, then H-score = 10%×1 + 20%×2 + 10%×3 = 0.6.

[0046] Calculation of Exo-PD-L1 entity expression score (exo-PD-L1-Score): Exosomes were collected using a specially customized grooved ELISA plate. The customized ELISA plate had 200 nm grooves in the capture wells on the upper surface of the capture plate, as Figure 1As shown, the diameter of the capture hole 6 is approximately 0.69 cm, and the height is 1.4 cm. The spacing of the grooves 7 inside the hole is approximately 200 nm. Through the design of the nano-scale hole grooves 7, exosomes are more likely to enter and stably bind to these grooves. Since soluble proteins are smaller in size, they are not easily specifically bound to the grooves, thereby reducing the occupation and consumption of the capture antibodies on the plate by soluble proteins; enabling the characteristic maker antibodies for capturing exosomes to be more used for binding exosomes, which can significantly improve the enrichment and capture efficiency of exosomes. This structure effectively avoids the occupation and consumption of the capture antibodies on the plate by soluble proteins, and at the same time reduces the loss of exosomes caused by the washing process, provides a more suitable binding site for exosomes, thus greatly improving the exosome capture efficiency and reducing the detection error caused by low capture efficiency, meeting the requirements of high-sensitivity detection.

[0047] Coat the anti-CD9 antibody on a customized gyrus ELISA plate to specifically capture exosomes expressing CD9. The specific steps are as follows:

[0048] (1) Coating of the customized ELISA plate: Dilute the mouse anti-human CD9 antibody with R&D coating buffer at a ratio of 1:200, add 100 μL per well to the ELISA 96-well plate, and incubate overnight in a 4°C refrigerator;

[0049] (2) Blocking of the ELISA plate: Remove the coated antibody, add 300 μL of washing solution per well, wash 3 times, 1 minute each time; then add 300 μL of washing solution / well and incubate in a 37°C incubator for one hour;

[0050] (3) Washing: Discard the blocking solution, add 300 μL of washing solution per well, wash 3 times, 1 minute each time;

[0051] (4) Add the ascites or plasma sample to be tested: 50 μL / well, incubate in a 37°C incubator for 2 hours. The sample is pre-centrifuged to remove cells and cell debris;

[0052] (5) Washing: Discard the test solution, add 300 μL of washing solution per well, wash 3 times, 1 minute each time;

[0053] (6) Add antibody: Dilute the rabbit anti-human PD-L1 antibody with 1X blocking solution at a ratio of 1:1000, 100 μL / well, incubate in a 37°C incubator for 2 hours;

[0054] (7) Washing: Discard the test solution, add 300 μL of washing solution per well, wash 3 times, 1 minute each time;

[0055] (8) Add biotin-labeled IgG: Dilute the biotin-labeled goat anti-rabbit IgG antibody with 1X blocking solution at a ratio of 1:1000, 100 μL / well, incubate in a 37°C incubator for 1 hour;

[0056] (9) Washing: Discard the test solution, add 300 μL of washing solution to each well, for 1 minute each time, and wash 3 times;

[0057] (10) Add streptavidin-conjugated horseradish peroxidase (Streptavidin-HRP): Dilute Streptavidin-HRP 1:1000 with 1X blocking solution, 100 μL / well, and wash; Incubate in a 37 °C incubator for 30 minutes;

[0058] (11) Add substrate solution: Mix substrate solution A and substrate solution B at a ratio of 1:1, 100 μL / well, and HRP catalyzes the reaction;

[0059] (12) Stop color development; Add 50 μL / well of stop solution;

[0060] (13) Measure with an enzyme-linked immunosorbent assay (ELISA) reader; Use a Dynex ELISA reader to measure OD450.

[0061] (14) Plot the standard curve: Based on the OD values of the serially diluted body fluid samples, establish a standard curve;

[0062] (15) Quantify the test sample according to the OD value of the test sample.

[0063] This OD value is the entity expression score of exo-PD-L1.

[0064] The detection results of the training set are shown in Table 1-2:

[0065] Table 1 PD-L1 scores of tumor tissues of 40 patients (training set)

[0066]

[0067]

[0068] Table 2 exo-PD-L1 scores of ascites of 40 patients (training set)

[0069]

[0070] As can be seen from Table 1-2, exo-PD-L1 was significantly lower in patients who responded to immune checkpoint therapy compared to those who did not. The AUCs of tumor-PD-L1 alone and exo-PD-L1 alone in predicting immune checkpoint therapy response were 0.8346 and 0.8450 ( Figure 2 ).

[0071] During the study, we unexpectedly found that the tumor-PD-L1 / exo-PD-L1 ratio could more effectively predict the prognosis of immune checkpoint therapy than the original tumor-PD-L1 score or exo-PD-L1 score.

[0072] Based on the above findings, further, we calculated the tumor-PD-L1 / exo-PD-L1 ratio of 40 patients in the training set. The prognostic prediction model was calculated by the following formula:

[0073] tumor-PD-L1 / exo-PD-L1 = tumor PD-L1 H-score / exo-PD-L1-Score;

[0074] The results are shown in Table 3:

[0075] Table 3 Ratio of tumor-PD-L1 / exo-PD-L1 of 40 tumor patients (training set)

[0076]

[0077] The AUC of this model for predicting the response to immune checkpoint therapy was 0.9230, and the optimal Cut off value was 1.514; at the optimal Cut off value, the sensitivity and specificity of this model for predicting the response to immune checkpoint therapy in ovarian cancer patients were 90.0% and 96.5%, respectively.

[0078] Example 3 Validation of the model's efficacy (validation set)

[0079] According to the same method described in Example 2, 30 patients in the validation set were used to validate the prediction model for the response of ovarian cancer patients to immune checkpoint therapy. The test results are shown in Tables 4-6:

[0080] Table 4 PD-L1 scores of tumor tissues of 30 patients (validation set)

[0081]

[0082]

[0083] Table 5 exo-PD-L1 scores of ascites of 30 patients (validation set)

[0084]

[0085] Table 6 Ratio of tumor-PD-L1 / exo-PD-L1 of 30 tumor patients (validation set data)

[0086]

[0087]

[0088] The results showed that the AUCs of single tumor-PD-L1 and single exo-PD-L1 in predicting immune checkpoint therapy response were 0.8311 and 0.8434, respectively( Figure 2 ).

[0089] The AUC of the Tumor-PD-L1 / exo-PD-L1 ratio in predicting immune checkpoint therapy response was 0.9143 in the validation set, and the sensitivity and specificity were 88.8% and 94.5%, respectively, indicating that the Tumor-PD-L1 / exo-PD-L1 ratio is a more accurate prognostic marker for predicting the benefit of immune checkpoint therapy in cancer patients.

[0090] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for constructing a prediction model for evaluating the benefit of immune checkpoint therapy in patients with ovarian cancer, characterized in that: The construction method includes the following steps: 1) Collect sufficient tumor tissue and ascites samples from ovarian cancer patients as a development cohort group before immune checkpoint therapy; 2) Follow up patients in the development cohort for more than 180 days and evaluate whether the patients are responders or non-responders through imaging; 3) Tumor tissue sections were detected by mIHC and exosomes in ascites samples were detected by ELISA, and the TumorPD-L1 score (H-score) and Exo-PD-L1 entity expression score (exo-PD-L1-Score) were calculated; 4) Prediction model for benefit of immune checkpoint therapy in patients with ovarian cancer: tumor-PD-L1 / exo-PD-L1=tumor PD-L1 H-score / exo-PD-L1-Score.

2. The construction method according to claim 1, characterized in that: The immune checkpoint therapy refers to the administration of monoclonal antibodies targeting the programmed death (PD-1) receptor.

3. The construction method according to claim 1, characterized in that At 6 months, based on the imaging results of the radiology examination, medical experts determined that there was no disease progression or tumor reduction for 6 months, which was considered a treatment response.

4. The construction method according to claim 1, characterized in that: The Tumor PD-L1 score (H-score) was calculated as follows: H-score = ∑Pi(i+1), where Pi is the percentage of PD-L1-positive cells at different staining intensities (i); The calculation method of the Exo-PD-L1 entity expression score (exo-PD-L1-Score) is as follows: the expression level of exosome PD-L1 in ascites is detected by ELISA, and the OD value is measured by a microplate reader, and the OD value is calculated as the exo-PD-L1 entity expression score.

5. The construction method according to claim 1, characterized in that: The model threshold / cutoff value is 1.

514. When the tumor-PD-L1 / exo-PD-L1 ratio is greater than the threshold, it is judged to be responsive to immune checkpoint therapy; conversely, when the tumor-PD-L1 / exo-PD-L1 ratio is less than the threshold, it is judged to be unresponsive to immune checkpoint therapy.

6. A prediction model for evaluating the benefit of immune checkpoint therapy in patients with ovarian cancer, characterized in that: The model is: tumor-PD-L1 / exo-PD-L1=tumor PD-L1 H-score / exo-PD-L1-Score; Among them, the calculation method of Tumor PD-L1 score (H-score) is: H-score = ∑Pi(i+1), where Pi is the percentage of PD-L1 positive cells under different staining intensities (i); the calculation method of Exo-PD-L1 entity expression score (exo-PD-L1-Score) is: the expression level of exosome PD-L1 in ascites is detected by ELISA, and the OD value is measured by microplate reader, and the OD value is calculated as the entity expression score of exo-PD-L1.

7. The model according to claim 6, characterized in that The optimal cut off value of the model is 1.514; when the tumor-PD-L1 / exo-PD-L1 ratio is greater than the threshold, it is judged to be responsive to immune checkpoint therapy; conversely, when the tumor-PD-L1 / exo-PD-L1 ratio is less than the threshold, it is judged to be unresponsive to immune checkpoint therapy.

8. Use of the prediction model constructed according to any one of the construction methods according to claims 1-5 for evaluating whether ovarian cancer patients benefit from immune checkpoint therapy.

9. Use of the model according to claim 6 or 7 for evaluating whether ovarian cancer patients benefit from immune checkpoint therapy.

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