CN113764100B

CN113764100B - Prediction model for immunotherapy super-progression risk of liver cancer patient, construction method and application thereof

Info

Publication number: CN113764100B
Application number: CN202111112767.XA
Authority: CN
Inventors: 刘莉; 李绮美; 彭杰; 肖芦山
Original assignee: Southern Hospital Southern Medical University
Current assignee: Southern Hospital Southern Medical University
Priority date: 2021-09-18
Filing date: 2021-09-18
Publication date: 2022-09-20
Anticipated expiration: 2041-09-18
Also published as: CN113764100A

Abstract

The invention discloses a prediction model of the risk of the over-progress of immunotherapy of a liver cancer patient, a construction method and application thereof, and finds that a prediction model Logit (p) ═ 3.208+1.450 multiplied by lymph node metastasis +1.043 multiplied by lung metastasis +0.610 multiplied by NLR + -0.848 multiplied by albumin +0.323 multiplied by PS score is established by lymph node metastasis, lung metastasis, NLR, albumin and PS score. The model can accurately predict the risk of the over-progress of immunotherapy of liver cancer patients, the AUC reaches 0.801, and the model has diagnostic significance and certain guiding significance in clinical work.

Description

Prediction model for immunotherapy super-progression risk of liver cancer patient, construction method and application thereof

Technical Field

The invention belongs to the field of bioinformatics analysis, and particularly relates to a prediction model of the risk of the over-progress of immunotherapy of liver cancer patients, a construction method and application thereof.

Background

Primary Liver Cancer (PLC) is a common malignancy of the digestive system worldwide. The number of new annual liver cancer cases is 7 th in malignant tumors and the number of deaths is 3 rd in malignant tumors. 70 to 80 percent of liver cancer patients are already in the advanced stage when being diagnosed, lose the chance of radical surgical treatment, can only receive palliative treatment modes such as systemic treatment and the like, and have extremely poor prognosis. Treatment options for these patients are limited. Since 2017, first-line treatment of liver cancer by using Ranuncunib, second-line treatment of liver cancer by using the regorafenib, cabozantinib and ramoru monoclonal antibodies have been approved for clinical application, and the targeted treatment of liver cancer is marked to enter a new era. Although the objective remission rate of the middle and late stage liver cancer patients is remarkably improved, the overall survival time of the patients cannot be effectively prolonged. In recent years, immunotherapy, as typified by Immune Checkpoint Inhibitors (ICIs), has led to unprecedented improvements in the prognosis for many types of tumor patients. With the widespread clinical use of ICIs, there is increasing evidence that the tumor burden increases rapidly in some patients shortly after receiving ICIs, resulting in a significant reduction in survival, a phenomenon known as hyper-progressive disease (HPD).

There are several studies that use different approaches to assess and predict the hyper-progression of immunotherapy in tumor patients. In the Kim et al study, tumor growth kinetics were assessed by both TGK (tumor growth kinetics) which measures monthly diameter changes in the longest diameter of the tumor target lesion based on RECIST and TGR (tumor growth rate) which measures the monthly log of the change in the volume of the target lesion based on RECIST. Tumor response, i.e., tumor growth kinetics, were assessed based on continuous CT or MRI scans. The time interval between the evaluation time and the baseline measurement was 12 weeks. Baseline was defined as imaging immediately prior to treatment and trial cycle was defined as treatment until treatment was completed for 12 weeks. The fold change of TGK and TGR and the absolute value of TGR (Δ TGR) were used to express HPD. Their definition of hyper-progression is: TGK and TGR increase by more than 4-fold, and Δ TGR exceeds 40%. And a high NLR can predict a cutoff value of 4.125 for NLR.

There are many methods currently used to assess hyper-progression without a uniform standard. In actual clinical work, there are many limitations to assessing the hyper-progression of liver cancer patients. The Tumor Growth Rate (TGR) is currently used as a relatively extensive method, which has some important clinical limitations, such as the need for prior Computed Tomography (CT), but often lacks the necessary pre-treatment imaging in the real world; and the evaluation of new lesions and unmeasured disease is not included in the definition of TGR.

Disclosure of Invention

The invention aims to provide a prediction model capable of accurately evaluating the risk of the over-progress of immunotherapy of a liver cancer patient, a construction method and application thereof.

The technical scheme adopted by the invention is as follows:

the invention provides a method for constructing an immunotherapy super-progression risk assessment model for a liver cancer patient, which comprises the following steps:

s1: acquiring clinical data of HPD patients and non-HPD patients;

s2: detecting a variable in HPD patients and non-HPD patients using differential analysis;

s3: determining clinical variables related to HPD by using univariate and multivariate logistic regression analysis, and establishing a risk model;

the clinical variables include lymph node metastasis, lung metastasis, NLR (neutrophil to lymphocyte ratio), albumin and PS (physical activity status) scores.

In some embodiments of the invention, the differential analysis comprises an independent sample t-test, a chi-square test, or a Mann-Whitney U-test.

In some embodiments of the present invention, the construction method further comprises step S4 of evaluating the efficacy of the model.

In some embodiments of the present invention, the step S4 is to use ROC analysis to verify the accuracy of the risk model in evaluating the risk of the immunotherapy of the liver cancer patient for hyper-progression.

In some embodiments of the invention, the risk model is calculated as logit (p) ═ 3.208+1.450 × lymph node metastasis +1.043 × lung metastasis +0.610 × NLR + -0.848 × albumin +0.323 × PS score;

in some embodiments of the invention, the variables included in the model are assigned in the model formula as: (ii) having said lymph node metastasis with a variable assigned to 1, otherwise 0; (ii) having said lung metastasis, assigning a variable of 1, otherwise 0; if the NLR is more than or equal to 3, the variable is assigned to be 1, otherwise, the variable is assigned to be 0; if the albumin is more than or equal to 35g/L, the variable is assigned to be 1, otherwise, the variable is assigned to be 0; if the PS score is more than or equal to 2, the variable is assigned to be 0, otherwise, the variable is assigned to be 1;

wherein, if the Logit (p) is more than or equal to-2.60, the HPD is predicted to be a high-risk patient; if Logit (p) < -2.60, a low risk HPD patient is predicted.

In some embodiments of the invention, the HPD is defined as:

(1) measurable lesion increases by more than or equal to 10mm for PDX within about 2 months after treatment initiation; and

(2) the total diameter of the target focus is increased by more than or equal to 40 percent compared with the baseline and/or the total diameter of the target focus is more than or equal to 20 percent compared with the baseline, and new focuses appear in two different organs.

In some embodiments of the invention, the hyper-progression may also express HPD by a fold change in TGK and TGR and the TGR absolute value (Δ TGR).

In a second aspect of the present invention, there is provided a model for assessing risk of hyper-progression of immunotherapy for a liver cancer patient, the model comprising lymph node metastasis, lung metastasis, NLR, albumin, and PS score, the formula of the model is logit (p) ═ 3.208+1.450 × lymph node metastasis +1.043 × lung metastasis +0.610 × NLR + -0.848 × albumin +0.323 × PS score;

in some embodiments of the invention, the variables in the model formula that are included in the model are assigned: (ii) having said lymph node metastasis with a variable assigned to 1, otherwise 0; (ii) having said lung metastasis, assigning a variable of 1, otherwise 0; if the NLR is more than or equal to 3, the variable is assigned to be 1, otherwise, the variable is assigned to be 0; if the albumin is more than or equal to 35g/L, the variable is assigned to be 1, otherwise, the variable is assigned to be 0; if the PS score is more than or equal to 2, the variable is assigned to be 0, otherwise, the variable is assigned to be 1;

if the Logit (p) is more than or equal to-2.60, the HPD high risk patient is predicted; if Logit (p) < -2.60, a low risk HPD patient is predicted.

In a third aspect of the present invention, there is provided a system for evaluating risk of hyper-progression of immunotherapy for a liver cancer patient, comprising:

an input unit at least for inputting data to be evaluated;

an analysis unit: the risk assessment model based on the logistic algorithm, which is constructed by the method of the first aspect of the invention, is at least used for analyzing the data to be assessed;

an evaluation unit: at least for displaying the model score;

the score is more than or equal to-2.60, and the HPD high-risk patient is predicted;

the score is < -2.60 and is predicted to be a low risk patient for HPD.

In a fourth aspect of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the model building method according to the first aspect of the present invention when executing the computer program.

In a fifth aspect of the present invention, there is provided a storage medium having stored therein processor-executable instructions, wherein the processor-executable instructions, when executed by a processor, are configured to perform the model building method according to the first aspect of the present invention.

According to a sixth aspect of the present invention, there is provided a use of a substance for detecting a clinical variable according to the first aspect of the present invention in the preparation of a product for assessing risk of hyper-progression of immunotherapy for a liver cancer patient.

In some embodiments of the invention, risk assessment is performed by modeling by logistic regression analysis, where the model formula is logit (p) — 3.208+1.450 × lymph node metastasis +1.043 × lung metastasis +0.610 × NLR + -0.848 × albumin +0.323 × PS score.

In some embodiments of the invention, the variables included in the model are assigned in the model formula as: if the lymph node metastasis occurs, the variable is assigned to 1, otherwise, the variable is assigned to 0; (ii) having said lung metastasis, assigning a variable of 1, otherwise 0; if the NLR is more than or equal to 3, the variable is assigned to be 1, otherwise, the variable is assigned to be 0; if the albumin is more than or equal to 35g/L, the variable is assigned to be 1, otherwise, the variable is assigned to be 0; if the PS score is more than or equal to 2, the variable is assigned to be 0, otherwise, the variable is assigned to be 1;

In some embodiments of the invention, the substance is a reagent or an instrument.

In a seventh aspect of the present invention, there is provided a product comprising the substance according to the sixth aspect of the present invention.

In an eighth aspect of the present invention, there is provided a method according to the first aspect of the present invention, or a model according to the second aspect of the present invention, or a system according to the third aspect of the present invention, or an electronic device according to the fourth aspect of the present invention, or a storage medium according to the fifth aspect of the present invention, or a product according to the seventh aspect of the present invention, for use in assessing risk of hyper-progression of immunotherapy for a patient with liver cancer.

The invention has the beneficial effects that:

the present invention uses a research-proven effective method for assessing hyper-progression: within 2 months of immunotherapy, the increase in the target lesion was 40% on a basis of a measurable lesion increase of > 10mm from baseline, or 20% while new lesions appeared on at least 2 different organs. This method overcomes the limitations of imaging and the limitations of the assessment of new lesions and unmeasured diseases that exist with previous methods and is more intuitive and convenient to use. And evaluating the morbidity of the primary liver cancer patient using the ICI and the factor for predicting the HPD by using the method, and establishing a prediction model to evaluate the model.

The invention finds that a prediction model logit (p) ═ 3.208+1.450 x lymph node metastasis +1.043 x lung metastasis +0.610 x NLR + -0.848 x albumin +0.323 x PS score is established through lymph node metastasis, lung metastasis, NLR, albumin and PS score. The model can accurately predict the risk of the over-progress of the immunotherapy of the liver cancer patient, the AUC reaches 0.801, and the model has diagnostic significance and certain guiding significance in clinical work.

Drawings

Figure 1 is a flow chart of patients enrolled in the study.

FIG. 2 is a characteristic curve of the work of the subjects in HPD risk model of the primary liver cancer patients treated by PD-1 inhibitor.

Detailed Description

The concept and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments to fully understand the objects, features and effects of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and other embodiments obtained by those skilled in the art without inventive efforts are within the protection scope of the present invention based on the embodiments of the present invention.

HPD, hyper-progressive disease;

ICI, immune checkpoint inhibitors;

PD-1, programmed death receptor-1;

PD-L1, programmed death ligand-1;

PD, progression;

PFS, progression free survival;

PR, partial reaction;

PS, physical activity status;

SD, stable disease condition.

Materials and methods

This study was a retrospective study of patients receiving PD-1 inhibitor treatment after histological or clinical diagnosis of primary liver cancer in southern hospital, from month 8 in 2018 to month 10 in 2020, inclusion criteria as follows:

(1) PD-1 inhibitor infusion is recorded in electronic medical records and orders;

(2) the performance state (ECOG PS) of the eastern tumor cooperative group is less than or equal to 2 points;

(3) Child-Pugh scores stage A/B.

Exclusion criteria were as follows:

(1) a baseline data loss;

(2) no measurable target lesions;

(3) lack of necessary imaging examinations before and after treatment;

(4) in addition to primary liver cancer, there are other tumors.

A patient selection flow chart is shown in figure 1.

Applicants have retrospectively collected the following data: age, sex, alcohol consumption, smoking history, hepatitis c virus (HBV) infection, Hepatitis C Virus (HCV) infection, ECOG PS, Child-Pugh score, blood routine (neutrophil and lymphocyte counts) and blood biochemistry (alanine aminotransferase, aspartate aminotransferase, total bilirubin and albumin). Organs that metastasize prior to treatment with the PD-1 inhibitor, number of organs that metastasize, portal vein cancer emboli (PVTT), type of PVTT, type of treatment prior to treatment with the PD-1 inhibitor.

As mentioned in previous studies, PVTT is classified into four types. All patients received enhanced Computed Tomography (CT) or magnetic resonance imaging before and after immunotherapy and all target lesions received baseline and post-immunotherapy imaging for evaluation. Pre-baseline scans were performed between 3 months and baseline prior to treatment. The first evaluation scan was performed approximately 2 months after the initial immunotherapy. CT scans were used to assess treatment response based on the solid tumor response assessment criteria (RECIST) 1.1. The primary endpoint was the occurrence of HPD, which was defined as the HPD from the day of the first PD-1 inhibitor treatment to the first imaging assessment according to RECIST 1.1. The secondary endpoint was Progression Free Survival (PFS). PFS is defined as the time between the first immunosuppressive treatment and progression. RECIST1.1 was used to evaluate the efficacy of the patient at the time of first imaging evaluation and to calculate partial remission/stable disease (PR/SD), progressive disease without HPD (PD) and HPD rate at the time of first imaging evaluation.

Definition of HPD: according to applicants' earlier studies, applicants defined HPD according to RECIST1.1 as:

(1) the measurable lesion increase of PDX within 2 months after the treatment is started is more than or equal to 10 mm; and

(2) the total diameter of the target focus is increased by more than or equal to 40 percent compared with the baseline and/or the total diameter of the target focus is more than or equal to 20 percent compared with the baseline, and at the same time, new focuses appear in at least two different organs.

Applicants classified patients into non-HPD (PR, SD and PD without HPD) and HPD (PD with HPD) groups according to treatment response. Independent sample t-test, chi-square test or Mann-Whitney U-test were used to assess the correlation (as the case may be) between HPD and either categorical variables or continuous variables. RECIST1.1 was used to assess the efficacy of the treatment.

Applicants used univariate and multivariate logistic regression analysis to determine clinical variables associated with HPD. Risk models were built using Logistic regression based on clinical variables with predictive significance for HPD.

Applicants calculated the area under the curve (AUC) to evaluate the predictive power of the model. All tests were two-sided, and p-values <0.05 were considered statistically significant. All statistical analyses were performed using SPSS version 26.0 software (IBM corp., Armonk, NY, USA).

Statistical analysis

1. Patient characteristics

A total of 129 patients receiving PD-1 inhibitor treatment were included in the analysis. The two groups had significant differences in organ metastasis number, lung metastasis number, lymph node metastasis number, liver resection number before immunotherapy (p < 0.05); most patients are under 65 years of age (n 108, 83.7%), male (n 107, 82.9%), Child-Pugh grade a (n 105, 81.4%), Barcelona Clinical Liver Cancer (BCLC)) stage C (n 96, 74.4%), ECOG PS is good (0 or 1: 119% of n, 92.2%), HBV infection (114% of n, 88.4%). There were extrahepatic metastases in 49 patients. Lung metastasis in 35 cases and lymph node metastasis in 54 cases. The 58 patients had PVTT. Applicants' relevant data are shown in table 1.

Applicants used RECIST1.1 to evaluate 129 patients. According to RECIST1.1, 84 (65.1%) patients had PR/SD, 32 (24.8%) had PD without HPD, and 13 (10.1%) had HPD.

Table 1 baseline clinical characteristics of HPD and non-HPD patients.

2. Evaluation of HPD

Applicants evaluated HPD according to RECIST 1.1. Applicants first evaluated the percentage of tumor growth in patients receiving PD-1 inhibitor treatment. The baseline tumor diameter for each patient was assessed from images within 3 months prior to the first use of PD-1, and the percent tumor growth was assessed from images of the patient at about 2 months after the first immunotherapy. The average tumor growth rate was 4.98% in all patients. The percentage of tumor growth in 17 patients (13.1%) was not less than 20%, and the percentage of tumor growth in 6 patients (4.7%) was not less than 40%. Second, applicants evaluated the status of new metastases within about 2 months after initiation of immunotherapy. 31 (24.0%) had new metastases from two or more different organs, and 50 patients had no new metastases. Regarding metastasis, 48 people had 1 new metastasis.

Finally, according to RECIST1.1, the applicant identified 13 (13/129, 10.1%) patients with HPD. Wherein, the growth percentage of the tumors in 5 cases (38.5%) is more than or equal to 40%, the growth percentage of the tumors in 8 cases (61.5%) is more than or equal to 20%, and new metastasis occurs in more than two different organs. According to RECIST1.1, 84 PR/SD patients account for 65.1% of the total population, and 32 PD non-HPD patients account for 24.8% of the total population.

3. Clinical variable screening and model construction

Univariate and multivariate analyses were used to study clinical variables associated with HPD. The results of the univariate analysis are shown in Table 2, and the results of the multivariate analysis are shown in Table 3.

TABLE 2 univariate analysis results

TABLE 3 results of multivariate analysis

Univariate analysis showed significant differences in lung metastasis, lymph node metastasis and liver resection. Variables with p <0.2 (including lung metastasis, lymph node metastasis, hepatectomy, neutrophil-lymphocyte ratio [ NLR ], total bilirubin, PVTT and extrahepatic metastatic organ count) and ECOG PS, albumin and BCLC inclusion were subjected to multivariate analysis.

Among them, lymph node metastasis and lung metastasis were found to be significantly associated with HPD.

Criteria for lymph node metastasis: lymph node metastasis is determined from the patient MRI or CT results.

Judgment standard of lung metastasis: lung metastasis is determined from the patient MRI or CT results.

PS scoring criteria: if the tumor patient is able to move normally, score 0; if the tumor patients have mild symptoms, can take care of themselves in life and can engage in light physical activities, the score is 1; if the tumor patient has obvious symptoms, but the life can be self-care, the time spent in bed in the daytime is not more than 50 percent, and the score is 2; if the tumor patient has obvious symptoms, the time spent in bed exceeds 50% in the day, but the patient still can get up and stand, and the life can be partially self-care, and the score is 3; if the tumor patients have serious symptoms and are bedridden, the evaluation is 4; if the tumor patient died, 5 points were scored.

In multivariate analysis, lymph node metastasis and lung metastasis were found to be still significant. The NLR, albumin and PS scores are important clinical variables for assessing patient prognosis. Applicants included lymph node metastasis, lung metastasis, NLR, albumin and PS in logistic regression analysis to build a risk model for assessing HPD risk, with the formula logit (p) ═ 3.208+1.450 x lymph node metastasis +1.043 x lung metastasis +0.610 x NLR + -0.848 x albumin +0.323 x PS score.

The variable assignments are: lymph node metastasis 0 ═ none, 1 ═ present; lung metastasis is 0 ═ none, 1 ═ present; NLR0 is equal to <3, 1 is equal to or more than 3; albumin 0 ═ 35g/L, 1 ═ 35 g/L; the PS score 0 ═ 2, 1 ≧ 2.

When p is 0.06897 and the cumulative score reaches logit (p) -2.60, the jotan index is 0.55 at the maximum. When the Logit (p) is more than or equal to-2.60, the HPD high-risk patient is predicted, and when the Logit (p) is less than-2.60, the HPD low-risk patient is predicted.

4. Model accuracy assessment

The applicants next used ROC analysis to verify the accuracy of this risk model in assessing the risk of hyper-progression of immunotherapy in liver cancer patients.

The ROC analysis is shown in figure 2, the AUC is 0.801(p is less than 0.001), and it can be seen that the prediction model of the embodiment has higher accuracy and can be well used for predicting the risk of the over-progress of the immunotherapy of the liver cancer patient.

In summary, it can be seen that the HPD risk model of the present invention can be used to predict HPD risk in primary liver cancer patients treated with ICI.

The present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art. Furthermore, the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.

Claims

1. A method for constructing an immunotherapy super-progression risk assessment model for a liver cancer patient is characterized by comprising the following steps:

s1: acquiring clinical data of HPD patients and non-HPD patients;

s3: determining clinical variables related to HPD by using univariate and multivariate logistic regression analysis, and establishing a risk model; the clinical variables comprise lymph node metastasis, lung metastasis, NLR, albumin, and PS scores;

the formula for the risk model is logit (p) ═ 3.208+1.450 × lymph node metastasis +1.043 × lung metastasis +0.610 × NLR + -0.848 × albumin +0.323 × PS score;

the model formula assigns values to the variables included in the model as: (ii) having said lymph node metastasis with a variable assigned to 1, otherwise 0; (ii) having said lung metastasis, assigning a variable of 1, otherwise 0; if the NLR is more than or equal to 3, the variable is assigned to be 1, otherwise, the variable is assigned to be 0; if the albumin is more than or equal to 35g/L, the variable is assigned to be 1, otherwise, the variable is assigned to be 0; if the PS score is more than or equal to 2, the variable is assigned to be 0, otherwise, the variable is assigned to be 1;

2. The method of claim 1, wherein the collecting of clinical data comprises: patient age, sex, alcohol consumption, smoking history, hepatitis C virus (HBV) infection, Hepatitis C Virus (HCV) infection, ECOGPS, Child-Pugh score, neutrophil and lymphocyte counts in blood routine and alanine aminotransferase, aspartate aminotransferase, total bilirubin and albumin in blood biochemistry, organs that metastasize prior to treatment with PD-1 inhibitors, number of organs that metastasize, portal vein cancer embolus (PVTT), type of PVTT, and type of treatment prior to treatment with PD-1 inhibitors.

3. The construction method according to any one of claims 1-2, wherein the HPD is defined as:

(2) the total diameter of the target focus is increased by more than or equal to 40 percent compared with the baseline and/or the total diameter of the target focus is increased by more than or equal to 20 percent compared with the baseline, and simultaneously two different organs present new focuses.

4. A system for assessing risk of hyper-progression of immunotherapy for a liver cancer patient, comprising:

an input unit at least for inputting data to be evaluated;

an analysis unit: a risk assessment model based on a logistic algorithm and constructed by the method of any one of claims 1-3, and at least used for analyzing the data to be assessed;

an evaluation unit: at least for displaying the model score;

the score is < -2.60 and is predicted to be a low risk patient for HPD.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of constructing a risk assessment model according to any one of claims 1 to 3 when executing the computer program.

6. A storage medium having stored therein processor-executable instructions, which when executed by a processor, perform a method of constructing a risk assessment model according to any one of claims 1 to 3.