CN114778417B - Model building method for immune checkpoint inhibitor related adverse reaction risk prediction - Google Patents
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Abstract
The invention discloses a biomarker for risk prediction of immune checkpoint inhibitor related adverse reaction, a kit, a model building method and application, and belongs to the technical field of tumors. The biomarkers include peripheral blood platelets, lymphocytes, serum albumin, and serum soluble tumor suppressor (sST 2). The biomarker can well predict the risk of immune checkpoint inhibitor-related adverse reactions.
Description
Technical Field
The invention belongs to the technical field of tumor diagnosis, and particularly relates to a biomarker for predicting the risk of adverse reaction related to an immune checkpoint inhibitor, a kit, a model building method and application.
Background
Immune checkpoint inhibitors (Immune checkpoint inhibitors, ici) have been widely used in cancer patients who fail a first-line therapy regimen for decades, which release their inhibition by specifically binding to Immune checkpoints on the cell surface, so that Immune cells of the body are activated to exert an anti-tumor effect, but in contrast, the use of ici destroys the homeostasis of the Immune system, causes Immune-related adverse reactions (Immune-related adverse events, irAEs), and can involve various systemic organs. Previous studies have shown that common toxicities of ICIs treatment are represented by pulmonary toxicity, hepatotoxicity, endocrine toxicity and gastrointestinal toxicity, the overall occurrence rate is about 66%, and as a plurality of ICIs are brought into the medical insurance catalogue of China, the number of patients suffering from ICIs-related adverse reactions can be expected to rise significantly. However, there is no simple and reliable risk prediction model for predicting the risk of irAEs after ICIs treatment of tumor patients.
Disclosure of Invention
We have tried to explore the closely related indicators of ICIs occurrence in tumor patients and construct a method to effectively evaluate the risk of occurrence of the IRAEs, thereby guiding the clinic to better diagnose and treat the patients.
The inventors found from analysis of the results of hematological examination of patients with irAEs before and after ici treatment that tumor patients with irAEs had higher nutritional index before and after treatment (prognostic nutritional index, PNI), platelet to lymphocyte ratio (neutrophil to lymphocyte ratio, PLR) than patients without irAEs, and confirmed by statistical analysis that the nutritional index before and after treatment (PNI), platelet to lymphocyte ratio (PLR) and sST2 levels were independent risk factors for irAEs after ici treatment. Through further research, the inventor constructs a risk prediction model of immune checkpoint inhibitor-related adverse reactions based on novel index sST2 levels, in combination with two indexes of a Prognosis Nutrition Index (PNI) and a platelet to lymphocyte ratio (PLR).
In a first aspect of the invention, a biomarker for immune checkpoint inhibitor-related adverse reaction risk prediction is disclosed, comprising a serum-soluble tumor suppressor (sST 2).
In some preferred embodiments of the invention, peripheral blood platelets, lymphocytes and serum albumin are also included.
In some preferred embodiments of the invention, the peripheral blood platelets, lymphocytes and serum albumin are baseline peripheral blood platelets, lymphocytes and serum albumin of a patient prior to receiving ICIs treatment.
In some preferred embodiments of the invention, the serum soluble tumor suppressor (sST 2) is sST2 after the patient has been subjected to ICIs treatment, preferably sST2 at a peak after the patient has been subjected to ICIs treatment, and more preferably sST2 21 days after the patient has been subjected to ICIs treatment.
In a second aspect, the invention provides a kit for predicting the risk of an immune checkpoint inhibitor-related adverse reaction, comprising reagents for obtaining the amount and/or level of a biomarker according to the first aspect in a patient.
A third aspect of the present invention is to disclose a method for establishing an immune checkpoint inhibitor-related adverse reaction risk prediction model of a biomarker according to the first aspect, comprising the steps of:
s01, determining parameters of the immune checkpoint inhibitor related adverse reaction risk prediction model;
s02, establishing a regression equation between parameters and the adverse reaction risks related to the immune checkpoint inhibitor;
s03, calculating the probability of the immune checkpoint inhibitor related adverse reaction risk by using the regression equation;
s04, evaluating the immune checkpoint inhibitor related adverse reaction risk prediction model;
wherein in S01, the parameters include a prognosis nutritional index, a peripheral blood platelet to lymphocyte ratio, and post-treatment sST2 levels.
In some preferred embodiments of the present invention, in S01, the method for calculating the prognostic nutrition index is as follows:
prognosis index = serum albumin level +5 x total lymphocyte count, where serum albumin level is in g/L, total lymphocyte count is in 10 9 And (3) a meter.
In some preferred embodiments of the present invention, in S01, the method for calculating the ratio of peripheral blood platelets to lymphocytes is as follows:
peripheral platelet to lymphocyte ratio = peripheral platelet number/peripheral blood lymphocyte number, wherein both peripheral platelet number and peripheral blood lymphocyte number are 10 9 And (3) a meter.
In some preferred embodiments of the present invention, in S02, the regression equation is:
risk score logic (P) =a+b×prognostic nutrition index+c×peripheral platelet to lymphocyte ratio+d×sst2 peak level after treatment;
preferably, a is 9.041, b is-0.224, c is-0.003, and d is 0.013.
In some preferred embodiments of the invention, in S04, the predictive power of the immune checkpoint inhibitor-related adverse reaction risk predictive model is assessed according to the area under the patient' S working characteristics.
The fourth aspect of the invention is to disclose an application of the biomarker for predicting the risk of the immune checkpoint inhibitor related adverse reaction in preparation of an agent and a device for predicting the risk of the immune checkpoint inhibitor related adverse reaction.
The fifth aspect of the invention discloses an application of the kit for predicting the risk of the immune checkpoint inhibitor related adverse reaction in preparation of an agent for predicting the risk of the immune checkpoint inhibitor related adverse reaction and a device.
The beneficial technical effects of the invention are as follows:
the invention provides a risk prediction model of Immune checkpoint inhibitor-related adverse reactions (Immune-related adverse events, irAEs). Immune checkpoint inhibitors (Immune checkpoint inhibitors, ICIs) have revolutionized the paradigm of anti-tumor therapy over the past 10 years and are increasingly being used in first-line treatment regimens for a variety of malignancies, but as they are widely used in the clinic, there are increasing reports of immune-related adverse effects resulting from the therapy, the occurrence of irAEs will affect the progress of subsequent anti-tumor therapy, some of the serious irAEs will directly lead to death of the patient, and the treatment costs are high. Thus, how to predict the occurrence risk of irAEs early is particularly important for saving the life of tumor patients. The invention constructs a novel risk prediction model of irAEs based on three indexes of serum soluble tumor suppressor factor 2 (soluble suppression of tumorigenicity, sST 2) level, prognosis nutrition index (prognostic nutritional index, PNI) and platelet to lymphocyte ratio (neutrophil to lymphocyte ratio, PLR), and aims to early predict the risk of the generation of the irAEs of a tumor patient after receiving ICIS treatment, thereby helping a clinician to make the most reasonable diagnosis and treatment plan for the tumor patient and carrying out timely and effective doctor-patient communication.
Drawings
FIG. 1 is a comparison of the ROC curve of the Logistic regression model combining PNI, PLR and sST2 in example 1 with that of the regression model using PNI, PLR alone
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention.
Example 1: screening of differential biological markers:
1. study object and sample collection:
malignant tumor patient hospitalized in auxiliary Zhongshan hospital of double denier university between 10 months in 2018 and 12 months in 2020
1.1 inclusion and exclusion criteria:
inclusion criteria:
(1) 18 years or more in age (2) malignant patients receiving treatment with PD-1/PD-L1/CTLA-4 immune checkpoint inhibitor at the study center (3) have an expected survival period of greater than 1 year (4) KPS score of greater than 60;
exclusion criteria:
(1) patients older than 18 years (2) who had been treated with immune checkpoint inhibitors (3) had serum sST2 levels higher than normal (35 ng/mL) before treatment (4) patients with a history of autoimmune disease (5) combined with patients with definite cardiac insufficiency (left ventricular ejection fraction less than 40%) (6) expected survival period less than 1 year (7) KPS score less than 60. All enrolled patients signed informed consent.
1.2 group patient detection index
Serum sST2 levels, peripheral blood lymphocyte counts, peripheral blood neutrophil counts, peripheral blood platelet counts, peripheral blood cd4+/cd8+ T cell ratios, B cell counts, albumin levels, D-dimer levels, and post-treatment serum sST2 levels, cardiac troponin T levels, B type natriuretic peptide levels at baseline prior to ICI treatment were collected and neutrophil to lymphocyte ratios (neutrophil to lymphocyte ratio, NLR), prognosis nutritional index (prognostic nutritional index, PNI), platelet to lymphocyte ratios (neutrophil to lymphocyte ratio, PLR) were calculated, where PNI = serum albumin levels (g/L) +5 x total lymphocyte counts (×10) 9 And (c) a).
Patients were classified into irAEs and non-irAEs according to the occurrence or non-occurrence of immune-related adverse reactions, and definition of immune-related adverse events was performed by 2 oncologists with abundant clinical experience, and the diagnosis standard was in accordance with the immune checkpoint inhibitor-related toxicity management guidelines 2019 published by the chinese clinical oncology society.
2 screening biological markers for predicting iras occurrence
2.1 biological markers that determine significant differences in patients in the irAEs group
91 malignant tumor patients were selected, 53 men (58.2%), 38 women (41.8%), and the average age (57.5+ -12.4) were aged, and the patients were classified into an irAEs group and a non-irAEs group according to whether or not immune-related adverse reactions occurred, and definition of immune-related adverse events was performed by 2 oncologists with abundant clinical experience, and the diagnosis standard was in accordance with 2019 edition of toxicity management guidelines related to immune checkpoint inhibitors issued by the chinese clinical oncology society. Hepatotoxic events are defined as: 1. after treatment, the glutamic-oxaloacetic transaminase or glutamic-pyruvic transaminase level of the patient is increased to be more than 3 times of the upper limit of the normal value; 2. bilirubin levels rise above the upper limit of 3 times normal after treatment (both meeting one point). Endocrine toxicity is defined as: after treatment of new hypothyroidism, hyperthyroidism, pituitary, primary adrenal insufficiency and diabetes. The pulmonary toxicity event is defined as: the new respiratory shortness, cough, chest pain, fever and other respiratory symptoms after treatment, and the doctor of oncology and imaging department after chest CT jointly determines that the lung inflammation is caused by ICIs treatment. Skeletal muscle system toxic events are defined as: the new onset of muscle pain or muscle strength decline following treatment is accompanied by an increase in creatine kinase. Immune myocarditis is defined as: 1. symptoms such as chest pain, palpitation and cardiac insufficiency are not accompanied, the new myocardial injury markers are continuously increased by 3 after treatment, and the myocardial nuclear magnetic resonance clearly accords with the imaging performance of myocarditis (the three are required to be simultaneously satisfied).
In a total of 13 of 91 patients, irAEs was diagnosed with an incidence of 14.3%, and 21 immune-related adverse events were co-recorded, with 7 immune myocarditis, 6 endocrine toxic events, 5 hepatotoxic events, and 3 skeletal muscle system toxic events.
The baseline platelet count, B cell count, peripheral blood cd4+/cd8+ T cell ratio, D-dimer, NLR and levels and post-treatment cardiac troponin T and B-type natriuretic peptides were not significantly different from the non-irAEs group (P > 0.05), whereas the baseline PNI and PLR levels and post-treatment sST2 levels were significantly different for both groups (P < 0.05), as seen in table 1.
Table 1 comparison of the levels of biological markers in patients of the irAEs group with those of the patients of the irAEs group (mean.+ -. SD)
NLR: neutrophil to lymphocyte ratio; PNI: a prognosis nutritional index; PLR: platelet to lymphocyte ratio;
*：P＜0.05；
the statistical analysis method comprises the following steps: the study uses a taimei EDC system for collecting and managing clinical data, and SPSS 23.0 and GraphPad Prism8 statistical software for statistical analysis and drawing. The metering data are expressed as mean.+ -. Standard deviation (mean.+ -. SD) or percent (%). Analyzing the predictive value of the related index by adopting an ROC curve and searching an optimal cut-off value; the Student's t-test was used for comparison of data between groups that fit normal distribution and variance alignment, and the separate variance estimation t-test was used for comparison of data between groups that fit normal distribution but variance was unequal. The difference of P <0.05 is statistically significant.
The serum sST2 level detection method comprises the following steps:
sample collection: 5ml of venous blood of a patient in a fasting state is collected, placed in a vacuum blood collection tube containing an anticoagulant, and centrifuged at 3000rpm for 10min, and the supernatant is taken.
The detection method comprises the following steps: the plasma soluble ST2 level was measured by a double antibody sandwich ELISA method, and the kit was a persage ST2 kit (us Critical Diagnostics).
The detection step comprises:
1. centrifuging the standard product 1000 Xg for 1 min, adding 1ml of the standard product diluent into the freeze-dried standard product, screwing a tube cover, standing for about 10min, reversing and mixing uniformly, and carrying out the double-ratio dilution after the mixture is fully dissolved.
2. 100ul of blood supernatant, quality control, standards were removed from 50-fold diluted 96-well plates and added to the corresponding labeling areas. After the sample addition was completed, the reaction wells were sealed with a sealing plate membrane, and then the reaction plates were placed on a shaker and incubated at 20℃for 60min.
3. The reaction Kong Nawen incubation liquid was discarded and dried by rinsing with wash buffer. After 100ul of anti-ST 2 antibody was added to each reaction well, the reaction well was blocked and incubated at 20℃for 60 minutes.
4. And discarding the liquid in the hole, and then cleaning and airing again. Strepitavidin-HRP working solution 100ul was added to each well and incubated at 20℃for 30 min on a shaker.
5. And discarding the liquid in the hole, and then cleaning and airing again. 100ul of TMB reagent was added to each well, placed on a shaker and incubated at 20℃in the absence of light. 100ul of stop solution was added to each well, mixed for 30 seconds, and colorized at a wavelength of 450nm by a full-automatic Modular D microplate reader (Roche Co., USA) to measure the Optical Density (OD) of each well. And calculating the concentration of the plasma ST2 of the sample to be measured according to a curve equation and the measured OD value by taking the concentration of the standard substance as an ordinate and the corresponding OD value as an abscissa. The other indexes all adopt common detection methods.
3. Establishing a risk prediction model for predicting the occurrence of irAEs based on differential biological indexes
We selected 3 biological markers (prognosis nutritional index (prognostic nutritional index, PNI), platelet to lymphocyte ratio (neutrophil to lymphocyte ratio, PLR) and post-treatment serum sST2 levels) in table 1 that expressed significant differences between patients in the irase and the irise groups, and included a logistic regression analysis to obtain a logistic regression model, and analysis results showed that the overall test P of the model was less than 0.000, indicating that the model was overall significant, hosmer and lemeshow Test (model goodness of fit) p=0.754 was greater than 0.05, indicating that the model repeatedly extracted information in the data, and that the degree of fit was excellent. Table 2 shows the biological markers incorporated into the model and their parameters.
TABLE 2 multifactor Logistic regression model for risk of irAEs after patients received ICIS treatment
From the above regression results, a risk score logic (P) can be written for each subject to predict whether irAEs occurs based on three biological markers:
logit (P) =9.041-0.224×baseline PNI level-0.003×baseline PLR level+0.013×post-treatment sST2 peak;
and the prediction probability of the irAEs of each subject can be obtained according to the following calculation formula:
the predictive ability of these three biological markers for irAEs development was further judged by the area under the curve (AUC) of the subject's working characteristics (ROC), and analysis showed that the model built by the three indices of the combined prognosis nutritional index (prognostic nutritional index, PNI), platelet to lymphocyte ratio (neutrophil to lymphocyte ratio, PLR) and post-treatment serum sST2 level had an area under the curve of 0.857,
the sensitivity and specificity of the model were 84.6% and 84.9%, respectively, and compared with the predictive model combining PNI and PLR, the AUC curve was higher (0.857 > 0.786), indicating that the addition of the sST2 index further improved the predictive ability of the model, see fig. 1.
Example 2
Such as patients 2020-2-16 for diagnosis, age 78 years, men. 2020-2-14 gastroscope diagnosis of esophageal squamous carcinoma, pathological prompt: tumors invade the intrinsic muscle layer. ICIs treatment at 2020-2-27, pre-treatment baseline peripheral blood platelet count: 352×10 9 Number of lymphocytes 5.6X10 9 Serum albumin levels: 33g/L, sST2 level was 10ng/mL, PNI level was 61, PLR level was 63 at 21 days post ICIS treatment.
According to this Logistic regression model, the Logistic (P) =9.041-0.224×baseline PNI level-0.003×baseline PLR level+0.013×post-treatment sST2 peak
logit（P）=9.041-13.664-0.189+0.013=-4.799
P= 0.008238/1+0.008238=0.008, i.e. the probability of occurrence of irAEs after the patient receives ici s treatment is 0.8%, the patient receives 8 cycles of ici s treatment subsequently, the tumor control is completely relieved, and no adverse reaction related to ici s treatment occurs during the treatment.
While the preferred embodiments and examples of the present invention have been described in detail, the present invention is not limited to the above-described embodiments and examples, and various changes may be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (6)
1. A method for establishing an immune checkpoint inhibitor-related adverse reaction risk prediction model of a biomarker, comprising the steps of:
s01, determining parameters of the immune checkpoint inhibitor related adverse reaction risk prediction model;
s02, establishing a regression equation between parameters and the adverse reaction risks related to the immune checkpoint inhibitor;
s03, calculating the probability of the immune checkpoint inhibitor related adverse reaction risk by using the regression equation;
s04, evaluating the immune checkpoint inhibitor related adverse reaction risk prediction model;
wherein in S01, the parameters include a prognosis nutritional index, a peripheral blood platelet to lymphocyte ratio, and post-treatment sST2 levels;
in S02, the regression equation is:
risk score logic (P) =a+b×prognostic nutrition index+c×peripheral platelet to lymphocyte ratio+d×sst2 peak level after treatment;
wherein a is 9.041, b is-0.224, c is-0.003, and d is 0.013;
the prognostic nutrition index = serum albumin level +5×total lymphocyte count, wherein serum albumin level is in g/L and total lymphocyte count is in 10 9 A meter;
the biomarker comprises a serum soluble tumor suppressor (sST 2);
also includes peripheral blood platelets, lymphocytes and serum albumin;
the peripheral blood platelets, lymphocytes and serum albumin are baseline peripheral blood platelets, lymphocytes and serum albumin of the patient prior to receiving ICIs treatment.
2. The method of claim 1, wherein the serum-soluble tumor suppressor (sST 2) is sST2 after the patient has been subjected to ICIs treatment.
3. The method of claim 1, wherein the serum-soluble tumor suppressor (sST 2) is sST2 at a peak after the patient has been subjected to ICIs treatment.
4. The method of claim 1, wherein the serum-soluble tumor suppressor (sST 2) is sST2 21 days after the patient has received ICIs treatment.
5. The method of claim 1, wherein in S01, the peripheral blood platelet to lymphocyte ratio is calculated by:
peripheral platelet to lymphocyte ratio = peripheral platelet number/peripheral blood lymphocyte number, wherein both peripheral platelet number and peripheral blood lymphocyte number are 10 9 And (3) a meter.
6. The method according to claim 4 or 5, wherein in S04 the predictive power of an immune checkpoint inhibitor-related adverse reaction risk prediction model is assessed from the area under the working characteristic curve of the patient.
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