Method for predicting curative effect of immunotherapy accepted by hepatocellular carcinoma patient

Abstract

The invention relates to a method for predicting the treatment effect of a hepatocellular carcinoma patient receiving immunotherapy or immunotherapy combined with an anti-angiogenesis drug by using a peripheral blood protein spectrum, and belongs to the technical field of biological medicines. The method comprises the following steps: collecting a peripheral blood sample of a detected patient before treatment, and detecting the content of main protein in plasma; according to different treatment effect results of patients, screening out differential proteins related to the treatment effect; the differential protein comprises at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1, and the differential protein comprises at least one of SYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1; establishing a curative effect prediction model by using the screened differential proteins; and obtaining the detection kit based on the prediction model.

Description

Technical Field

[0001] The present invention relates to a method for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenesis drugs in patients with hepatocellular carcinoma by using peripheral blood protein spectrum, and belongs to the technical field of biomedicine. Background Art

[0002] The incidence of primary liver cancer ranks seventh among malignant tumors worldwide, and its mortality rate ranks second among malignant tumors. (1) More than two-thirds of liver cancer patients are already in the middle or advanced stages when they seek medical treatment and are unable to receive radical treatments such as surgical resection. Anti-angiogenic drugs combined with immune checkpoint inhibitors are important treatments for these patients. Among them, PD-1 / PD-L1 inhibitors combined with bevacizumab or biosimilars (IMbrave150 and ORIENT32) have been approved for first-line treatment of liver cancer. (2,3) PD-1 / PD-L1 inhibitors combined with tyrosine kinase inhibitors (TKIs) also showed a high objective response rate. (4) However, there are currently no effective efficacy prediction markers used in clinical practice.

[0003] Previous studies have shown that the PD-L1 status of tumor cells and tumor-associated immune cells, tumor mutation burden (TMB), microsatellite DNA instability, and tumor immune cell infiltration can be used to predict the efficacy of immunotherapy. (5) However, the detection of the above biomarkers requires tumor biopsy, which is rarely performed in clinical diagnosis and treatment due to the high risk of bleeding during liver cancer puncture and the easy formation of tumor implantation and metastasis in the puncture needle tract. The value of the above markers in liver cancer has not yet been established.

[0004] Peripheral blood samples are easy to obtain, minimally invasive, and can be dynamically monitored, making them more suitable for liver cancer than histological markers. Proteins are important carriers for executing biological processes. Inflammation- and immune-related proteins in plasma (such as cytokines, chemokines, etc.) have corresponding regulatory functions on the immune and inflammatory responses during immunotherapy. Compared with the expression levels in local tissues, plasma proteins can better reflect the body's overall immune response level, and the non-invasive nature of the detection is more suitable for dynamic monitoring. Therefore, the method of the present invention intends to use peripheral blood samples collected from patients during the screening period of targeted immunotherapy and tumor evaluation for proteomic testing, predict the therapeutic efficacy based on the test results, and dynamically monitor tumor progression and drug resistance.

[0005] References:

[0006] 1.Sung H,Ferlay J,Siegel RL,Laversanne M,Soerjomataram I,Jemal A,etal.Global Cancer Statistics 2020:GLOBOCAN Estimates of Incidence andMortality Worldwide for 36Cancers in 185Countries.CA Cancer J Clin.2021;71(3):209-49.

[0007] 2.Finn RS,Qin S,Ikeda M,Galle PR,Ducreux M,Kim T-Y,et al.Atezolizumabplus Bevacizumab in Unresectable Hepatocellular Carcinoma.The New EnglandJournal of Medicine.2020;382(20):1894-905.

[0008] 3.Ren Z,Xu J,Bai Y,Xu A,Cang S,Du C,et al.Sintilimab plusabevacizumab biosimilar(IBI305)versus sorafenib in unresectablehepatocellular carcinoma(ORIENT-32):a randomised,open-label,phase2-3study.Lancet Oncol.2021;22(7):977-90.

[0009] 4.Qin S,Chan SL,Gu S,Bai Y,Ren Z,Lin X,et al.Camrelizumab plusrivoceranib versus sorafenib as first-line therapy for unresectablehepatocellular carcinoma(CARES-310):a randomised,open-label,internationalphase 3study.Lancet(London,England).2023.

[0010] 5. Greten TF, Villanueva A, Korangy F, Ruf B, Yarchoan M, Ma L, et al. Biomarkers for immunotherapy of hepatocellular carcinoma. Nature Reviews Clinical Oncology. 2023; 20(11):780-98.

[0011] 6.Hu X,Chen R,Wei Q,Xu X.The Landscape Of Alpha Fetoprotein InHepatocellular Carcinoma:Where Are We? International Journal of BiologicalSciences.2022;18(2):536-51. Summary of the invention

[0012] The purpose of the present invention is to solve the technical problem of how to use peripheral blood protein spectrum to predict the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma.

[0013] In order to achieve the purpose of solving the above problems, the technical solution adopted by the present invention is to provide a method for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma using peripheral blood protein spectrum, and the method comprises the following steps:

[0014] Step 1: Collect peripheral blood samples before the patient receives treatment;

[0015] Step 2: Centrifuge the peripheral blood sample to collect plasma, and use proximity extension assay or mass spectrometry LC-MS to detect the content of major proteins in the plasma;

[0016] Step 3: Screening out differential proteins related to therapeutic efficacy according to different therapeutic efficacy results of the patients; the differential proteins include at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1;

[0017] Step 4: Use the screened differential proteins to establish a therapeutic efficacy prediction model;

[0018] Step 5: Establish a visual prediction model or develop a detection kit based on the prediction model.

[0019] Preferably, the pre-treatment period in the above step 1 refers to the screening period before the patient receives treatment.

[0020] Preferably, the method for establishing the efficacy prediction model in the above step 4 includes using random forest, logistic regression, CatBoost, naive Bayes, MLP, LightGBM, GBDT, XGBoost, ETC, linear discriminant analysis, support vector machine, k-nearest neighbor, decision tree, ADABoost machine learning or deep learning algorithm.

[0021] Preferably, the above method also includes establishing a fusion prediction model after the plasma proteomics prediction model is established, combining the basic information of the patient before receiving combined treatment, baseline tumor information, and baseline laboratory test results; visualizing the final established prediction model, or developing a rapid detection kit based on plasma proteins.

[0022] The present invention provides a method for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma by using peripheral blood protein spectrum, and its application in non-diagnostic methods and non-therapeutic methods.

[0023] The present invention provides a prediction model for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs on patients with hepatocellular carcinoma. The prediction model is constructed according to the above-mentioned method of predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs on patients with hepatocellular carcinoma using peripheral blood protein spectrum.

[0024] The present invention provides a biomarker for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma, wherein the biomarker comprises at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1 in a peripheral blood protein spectrum.

[0025] The present invention provides an application of a protein marker combination in the preparation of a detection reagent for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma, wherein the protein marker combination comprises at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1 in a peripheral blood protein spectrum.

[0026] The present invention provides an application of a protein marker combination in preparing a detection chip for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma, wherein the protein marker combination comprises at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1 in a peripheral blood protein spectrum.

[0027] The present invention provides a system, which comprises a data processing device and a substance for detecting differential protein expression; the differential protein comprises at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1; the system comprises a data processing device, which comprises a data input module, a data recording module, a data comparison module and a conclusion output module; the data input module is configured to input relative expression value values ​​of differential proteins in peripheral blood of patients; the data recording module is configured to store relative expression value values ​​and judgment threshold values ​​of differential proteins of patients; the data comparison module is configured to receive the relative expression value values ​​of differential proteins of patients sent by the data input module, call the judgment threshold value from the data recording module and compare it with the relative expression value values ​​of differential proteins of patients; the conclusion output module is configured to receive the comparison result sent by the data comparison module, and judge the comparison result according to a predetermined judgment condition; and predict the efficacy of immunotherapy or immunotherapy combined with anti-angiogenic drug treatment for patients with hepatocellular carcinoma.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1) The present invention does not require changes to routine clinical operations, but utilizes routine test results, and has high clinical usability and strong promotion potential;

[0030] 2) The detection kit obtained by the present invention has little trauma to the patient, does not require puncture biopsy, and can continuously observe the changes in indicators;

[0031] 3) The prediction model provided by the present invention has parameters that are consistent with the biological characteristics of tumors and has high prediction potential;

[0032] 4) The prediction method provided by the present invention has high repeatability and can flexibly adjust prediction parameters according to data characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a calorimetric diagram of protein expression in the detected samples.

[0034] Figure 2 This is a graph of differential plasma proteins in patients with different treatment responses in baseline samples before treatment.

[0035] Figure 3 Construct a BPS graph of the efficacy prediction model for the machine learning algorithm. DETAILED DESCRIPTION

[0036] In order to make the present invention more clearly understood, a preferred embodiment is described in detail with reference to the accompanying drawings as follows:

[0037] In targeted immunotherapy for hepatocellular carcinoma, patients need to collect peripheral blood for blood routine, biochemistry, tumor markers, myocardial enzyme spectrum and other indicators during the targeted immunotherapy screening period and tumor evaluation. Tumor markers such as AFP and PIVKA-II have a suggestive effect on the diagnosis of liver cancer and tumor progression or recurrence. Related studies have also shown that they are correlated with the patient's survival prognosis. (6) However, the ability of tumor markers to predict the efficacy of targeted immunotherapy for liver cancer is still lacking.

[0038] There are tens of thousands of circulating proteins in peripheral blood samples. The liver is the largest solid organ in the human body and plays an important role in the synthesis of secretory proteins. Patients with advanced liver cancer have a large tumor burden, and the proteins synthesized and secreted by tumor tissues are more easily released into the circulatory system. Therefore, there is a potential correlation between the expression of plasma proteins and proteins in tumor tissues. In addition, inflammatory and immune-related proteins in plasma (such as cytokines, chemokines, etc.) have corresponding regulatory functions on immune and inflammatory responses during immunotherapy. Compared with the expression level of local tissues, plasma proteins can better reflect the overall immune response level of the body. In the case of a large sample cohort, fully exploring the differential plasma proteins related to the efficacy of targeted immunotherapy for liver cancer can establish an efficacy prediction model with good predictive ability and a tumor progression and recurrence detection model.

[0039] Therefore, the method of the present invention intends to use peripheral blood samples collected from patients during the targeted immunotherapy screening period and tumor evaluation for proteomic testing, predict the treatment efficacy based on the test results, and dynamically monitor tumor progression and drug resistance.

[0040] The present invention provides a method for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma by using peripheral blood protein spectrum; the method comprises the following steps:

[0041] Step 1: Collect peripheral blood samples before the patient receives treatment;

[0042] Step 2: Centrifuge the peripheral blood to collect plasma, and use proximity extension assay (PEA) or mass spectrometry (LC-MS) to detect the content of major proteins in plasma;

[0043] Step 3: Screen out differentially expressed proteins related to therapeutic efficacy based on the different therapeutic efficacy results of the patients;

[0044] Step 4: Use the screened differential proteins to establish and validate the prediction model;

[0045] Step 5: Establish a visual prediction model or develop a detection kit based on the prediction model.

[0046] Example

[0047] The present invention provides a method for predicting the therapeutic effect of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma by using peripheral blood protein spectrum; the method comprises the following steps:

[0048] Step 1: Collect peripheral blood samples at different time periods of patient treatment;

[0049] A total of 114 patients with liver cancer who received first-line antiangiogenic drugs combined with immune checkpoint inhibitors were enrolled to prepare their efficacy evaluation data. Only the tumor lesions with the longest diameter in the liver were evaluated using the RECIST v1.1 standard or mRECIST. The best of all evaluation results was used as the final efficacy evaluation result of the patient. The patient's identification code, basic information before combined treatment, baseline tumor information, baseline laboratory test results, and efficacy evaluation results were saved as a text file (e.g., .txt, .csv, .xlsx);

[0050] Step 2: During the screening period before treatment, peripheral blood samples were collected from patients, and plasma was separated by centrifugation. The expression levels of 368 tumor inflammation and immune-related proteins were detected using proximity extension assay (PEA). The list of plasma protein panels for targeted detection is shown in Table 1, and the heat map of protein expression levels of all samples is shown in Figure 1 ;

[0051] Table 1

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] Step 3: Screen out differentially expressed proteins related to efficacy based on the different treatment responses and tumor progression and recurrence of the patients. The volcano plot of differentially expressed plasma proteins is shown in Figure 2 ;

[0063] Step 4: For the differential proteins related to efficacy, machine learning or deep learning algorithms such as random forest, logistic regression, CatBoost, naive Bayes, MLP, LightGBM, GBDT, XGBoost, ETC, linear discriminant analysis, support vector machine, k nearest neighbor, decision tree, ADABoost, etc. are used to construct the efficacy prediction model, and the model is verified and evaluated from the aspects of discrimination and calibration. The training results show that the prediction model constructed by the random forest algorithm has the best performance. The model includes 9 immune and inflammation-related characteristic proteins (SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK, TPP1) that are closely related to the treatment efficacy, and uses the plasma expression levels of 9 proteins to construct the risk prediction score Blood Proteomic Score (BPS). The AUC for predicting the efficacy of immunotherapy in liver cancer patients reaches 0.86. The model's predictive ability for efficacy and prognosis is shown in Figure 3 ;

[0064] Step 5: After the plasma proteomics prediction model is established, a fusion prediction model can be established by combining the patient's basic information before combined treatment, baseline tumor information, baseline laboratory test results and other indicators, and the fusion prediction model can be verified and evaluated;

[0065] Furthermore, external independent data is introduced to externally validate the model to evaluate the generalizability of the prediction model;

[0066] Furthermore, the final prediction model can be visualized using risk scoring, nomograms, etc., or a rapid detection kit based on plasma proteins can be developed to facilitate the application of the prediction model in actual clinical work.

[0068] like Figure 1 Shown is a calorimetric diagram of protein expression in peripheral blood samples collected from patients during the screening period before treatment.

[0069] like Figure 2 Shown are the plasma differential protein profiles of patients with different treatment responses in baseline samples before treatment.

[0070] like Figure 3 As shown, the BPS diagram of the efficacy prediction model constructed by the machine learning algorithm.

[0071] Figure A shows the correlation between BPS score and treatment response. DCB refers to patients with sustained clinical benefit (remission or stable period of 6 months); NDB refers to patients without sustained clinical benefit;

[0072] Figure B is a risk prediction model constructed by the present invention using the plasma expression levels of 9 proteins, and the ROC curve of BPS predicting the therapeutic response of immunotherapy efficacy in liver cancer patients.

[0073] Figure C shows the distribution ratio of patients with different treatment responses in the BPS high and low level groups.

[0074] Figure D is the DCA (Decision Curve Analysis, DCA) curve of the BPS score.

[0075] Figure E shows the overall survival (OS) curves of patients with high and low BPS levels.

[0076] Figure F shows the event-free survival (EFS) curves of patients with high and low BPS levels.

[0077] The above is only a preferred embodiment of the present invention, and is not any formal or substantial limitation of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the present invention, and these improvements and supplements should also be regarded as the protection scope of the present invention. Any technician familiar with this profession, without departing from the spirit and scope of the present invention, can make some changes, modifications and equivalent changes made by using the technical content disclosed above, which are equivalent embodiments of the present invention; at the same time, any changes, modifications and evolutions of any equivalent changes made to the above embodiments based on the essential technology of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for predicting the efficacy of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma using peripheral blood protein profiles, characterized in that: The method comprises the following steps: Step 1: Collect peripheral blood samples before the patient receives treatment; Step 2: Centrifuge the peripheral blood sample to collect plasma, and use proximity extension assay or mass spectrometry LC-MS to detect the content of major proteins in the plasma; Step 3: Screening out differential proteins related to therapeutic efficacy according to different therapeutic efficacy results of the patients; the differential proteins include at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1; Step 4: Use the screened differential proteins to establish a therapeutic efficacy prediction model; Step 5: Establish a visual prediction model or develop a detection kit based on the prediction model.

2. The method according to claim 1, characterized in that The pre-treatment period in step 1 refers to the screening period before the patient receives treatment.

3. The method according to claim 2, characterized in that The method for establishing the efficacy prediction model in step 4 includes using random forest, logistic regression, CatBoost, naive Bayes, MLP, LightGBM, GBDT, XGBoost, ETC, linear discriminant analysis, support vector machine, k nearest neighbor, decision tree, ADABoost machine learning or deep learning algorithm.

4. The method according to claim 3, characterized in that: The method also includes establishing a fusion prediction model by combining the patient's basic information before receiving combined treatment, baseline tumor information, and baseline laboratory test results after the plasma proteomics prediction model is established; visualizing the final prediction model, or developing a rapid detection kit based on plasma proteins.

5. Use of a method according to any one of claims 1 to 4 for predicting the efficacy of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma using peripheral blood protein profiles in non-diagnostic and non-therapeutic methods.

6. A prediction model for predicting the efficacy of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma, characterized in that: The prediction model is constructed according to the method according to any one of claims 1-4.

7. A biomarker for predicting the efficacy of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma, characterized in that: The biomarkers include at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1 in the peripheral blood protein profile.

8. Use of a protein marker combination in the preparation of a detection reagent for predicting the efficacy of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma, characterized in that: The protein marker combination includes at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1 in the peripheral blood protein spectrum.

9. Use of a protein marker combination in the preparation of a detection chip for predicting the efficacy of immunotherapy or immunotherapy combined with anti-angiogenic drugs in patients with hepatocellular carcinoma, characterized in that: The protein marker combination includes at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1 in the peripheral blood protein spectrum.

10. A system, characterized in that: The system comprises a data processing device and a substance for detecting differential protein expression; the differential protein comprises at least one of SULT2A1, KYNU, MMP1, SHMT1, FGF19, LAMA4, AMBN, MVK and TPP1; the system comprises a data processing device, and the data processing device comprises a data input module, a data recording module, a data comparison module and a conclusion output module; the data input module is configured to input the relative expression value of the differential protein in the peripheral blood of the patient; the data recording module is configured to store the relative expression value and judgment threshold of the differential protein of the patient; the data comparison module is configured to receive the relative expression value of the differential protein of the patient sent by the data input module, and call the judgment threshold from the data recording module and compare it with the relative expression value of the differential protein of the patient; The conclusion output module is configured to receive the comparison result sent by the data comparison module, and judge the comparison result according to a predetermined judgment condition; and predict the efficacy of immunotherapy or immunotherapy combined with anti-angiogenic drug treatment for patients with hepatocellular carcinoma.

Images