CN111748626A

CN111748626A - System for predicting treatment effect and prognosis of neoadjuvant radiotherapy and chemotherapy of esophageal squamous carcinoma patient and application of system

Info

Publication number: CN111748626A
Application number: CN202010625545.7A
Authority: CN
Inventors: 赫捷; 孙楠; 张超奇; 张国超; 薛奇; 张震; 王雪霏; 张志慧; 骆玥君; 王乐; 车云; 王亚龙
Original assignee: Cancer Hospital and Institute of CAMS and PUMC
Current assignee: Cancer Hospital and Institute of CAMS and PUMC
Priority date: 2020-07-01
Filing date: 2020-07-01
Publication date: 2020-10-09
Anticipated expiration: 2040-07-01
Also published as: CN111748626B

Abstract

The invention discloses a system for predicting the curative effect and/or prognosis of neoadjuvant radiotherapy and chemotherapy of an esophageal squamous carcinoma patient. The system for predicting the curative effect and/or prognosis of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient comprises a system for detecting the expression levels of four genes, namely SERPINE1, MMP12, PLAUR and EPS 8. The system can predict the curative effect of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient and also can predict the prognosis of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient, such as the total survival rate after the prognosis and the survival rate without relapse after the prognosis. The invention has important application value.

Description

System for predicting treatment effect and prognosis of neoadjuvant radiotherapy and chemotherapy of esophageal squamous carcinoma patient and application of system

Technical Field

The invention belongs to the field of biomedicine, and particularly relates to a system for predicting the curative effect and prognosis of neoadjuvant chemoradiotherapy of an esophageal squamous carcinoma patient and application thereof.

Background

Esophageal cancer is one of the most common malignancies, and up to 90% of patients with esophageal cancer are Esophageal Squamous Cell Carcinoma (ESCC). Currently, surgical resection is the main treatment means of ESCC, but the 5-year survival rate of patients with locally advanced ESCC is only about 20.64% -34.00%, and most patients have local recurrence or distant metastasis within 3 years after surgery. For patients with locally advanced ESCC, scholars at home and abroad make many attempts at comprehensive treatment, including neoadjuvant chemotherapy, neoadjuvant chemoradiotherapy (nCRT), adjuvant chemotherapy, and adjuvant chemoradiotherapy. In recent years, large-scale clinical trial research at home and abroad shows that the 5-year survival rate of patients with local advanced ESCC can be remarkably improved by the combination of new auxiliary radiotherapy and chemotherapy and operation treatment. In particular, the research of NEOCRTEC5010, which is conducted by scholars in China, shows the results of phase III clinical trials of nCRT combined operation for treating local advanced ESCC: the combined operation of the preoperative radiotherapy and chemotherapy can improve the median survival period (100.1 months vs 66.5 months, P is 0.025), the resection rate of R0 (98.4% vs 91.2%, P is 0.002) and the disease-free survival period (100.1 months vs 41.7 months, P is less than 0.001) of a patient with locally advanced ESCC, and further defines the important function of the treatment mode of the combined operation of the preoperative neoadjuvant radiotherapy and chemotherapy in the locally advanced ESCC. Thus, this protocol has been incorporated into the standardized treatment guidelines for the treatment of locally advanced ESCC at home and abroad.

Clinical application of nrts brings great benefit to locally advanced ESCC patients, but also presents new problems to clinical researchers. Complete remission of pathology (pCR) is an important indicator of sensitivity after ESCC receives nrt, and patients who reach pCR upon treatment are considered to be effective beneficiaries of nrt. In fact, however, pCR rates for nrct are only around 35%, and around 65% are < pCR (non-pCR) patients who not only do not significantly benefit from treatment, but also suffer from the toxic side effects of chemotherapy and radiotherapy, and may also risk mistaking the best surgical opportunity. Clinical studies have shown that nrct, while improving the survival benefit of a subset of patients, may also increase the risk of subsequent surgery in a subset of patients, leading to the development of post-operative complications and even death. Therefore, whether the curative effect of nCRT can be predicted or not, and sensitive and tolerant groups can be screened out, is the key for realizing the individual treatment of the esophageal cancer, ensuring that patients obtain the maximum benefit with the minimum toxicity and improving the long-term survival rate of the total population of the esophageal cancer.

There have been studies to indirectly evaluate pCR after NCRT treatment in ESCC patients by using examination methods such as CT, Endochoscope and 18FDG-PET, but recent meta-analyses have shown that the reliability of these clinical examinations for evaluating the sensitivity of NCRT treatment is still insufficient. The molecular marker predicts the new auxiliary treatment effect and survival condition of the tumor, and has good effect in various tumors including breast cancer, colorectal cancer and the like, and particularly, a prediction model consisting of a plurality of molecules shows strong prediction capability. The method provides a good idea for research in ESCC, and prompts that a molecular model has a broad prospect in predicting the neoadjuvant chemoradiotherapy effect and prognosis of esophageal cancer.

The expression mode of the local microenvironment immune molecules of the tissues before treatment plays a decisive role in the therapeutic effect of nCRT. The research result indicates that various immune molecules including PD-L1, indoleamine 2, 3-dioxygenase 1 (indolamine 2, 3-dioxygenase 1, IDO1) and various immune cells such as CD8+ T cells can predict the curative effect and long-term survival of ESCC receiving nCRT.

Therefore, the revealing of the immune molecule expression profile of tissues before treatment is significant for improving the treatment effect of nCRT and realizing accurate screening of patients. However, the immune molecule expression profile of ESCC endoscopic samples prior to receiving nCRT treatment is not yet revealed. Therefore, a new auxiliary chemoradiotherapy curative effect and prognosis prediction model based on an endoscope immune molecular map is urgently needed to be established in esophageal squamous carcinoma.

Disclosure of Invention

The invention aims to predict the curative effect and/or prognosis of neoadjuvant radiotherapy and chemotherapy for patients with esophageal squamous carcinoma.

The invention firstly protects a system which can comprise a system for detecting the expression quantity of four genes, namely SERPINE1, MMP12, PLAUR and EPS 8; the system is used for predicting the curative effect of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient or predicting the prognosis of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient.

The system can be composed of a system for detecting the expression quantity of four genes of SERPINE1, MMP12, PLAUR and EPS 8.

In any of the above systems, the system for detecting the expression levels of the four genes SERPINE1, MMP12, PLAUR and EPS8 can comprise reagents and/or instruments required for detecting the relative expression levels of the four genes by a fluorescence quantitative PCR method.

Further, the reagent and/or the instrument required for detecting the relative expression quantity of the four genes by the fluorescent quantitative PCR method comprise a primer pair for detecting the relative expression quantity of the four genes of SERPINE1, MMP12, PLAUR and EPS 8.

Furthermore, the reagent and/or the instrument for detecting the relative expression quantity of the four genes by the fluorescent quantitative PCR method also comprise a primer pair for detecting the internal reference gene. Namely, the relative expression quantity of the four genes can be the expression quantity of the four genes relative to the reference gene.

The reference gene is GAPDH gene.

Any of the above systems further comprises a data processing device; the data processing device is internally provided with a module; the module has the following functions (a1) and/or (a 2):

(a1) taking an isolated esophageal squamous carcinoma tissue of a population to be detected consisting of esophageal squamous carcinoma patients as a specimen, determining the relative expression quantity of the four genes in each specimen, and then calculating the discriminant score according to the relative expression quantity of the four genes according to the following formula: the discriminant score is-2.794 + (0.606 × SERPINE1 gene relative expression) + (0.614 × MMP12 gene relative expression) + (0.682 × PLAUR gene relative expression) - (1.751 × EPS8 gene relative expression), and the population to be detected is divided into a low-score group and a high-score group according to the discriminant score;

(a2) determining the curative effect, the pathological complete remission rate, the prognosis recurrence-free survival rate and/or the prognosis overall survival rate of the patient to be tested from the population to be tested according to the following criteria:

"from patients to be tested in the high score cohort" is more effective or candidate higher than "from patients to be tested in the low score cohort";

a higher or more candidate rate of complete remission of pathology "from patients to be tested in the high scoring cohort" than "from patients to be tested in the low scoring cohort";

the overall survival prognosis for "patients tested from the low scoring cohort" is higher or is more candidate than for "patients tested from the high scoring cohort";

the prognostic recurrence-free survival "is higher or more candidate than" test patient from the high scoring cohort ".

The specific method for dividing the population to be detected into a low-score group and a high-score group according to the discriminant score is as follows: determining a threshold value through a 'pROC' software package of R language software, comparing the discriminant score of the patient to be predicted with the esophageal squamous carcinoma patient who receives the new auxiliary chemoradiotherapy to obtain pCR with the size of the threshold value, wherein the patient with the score larger than the threshold value is listed in a high-score group, and the patient with the score smaller than or equal to the threshold value is listed in a low-score group.

The method for determining the threshold value through the 'pROC' software package of the R language software is specifically as follows: and (3) inputting the score of the patient with esophageal squamous carcinoma to be predicted and the matched new auxiliary chemoradiotherapy curative effect information into R language software, and under the algorithm of a 'pROC' software package, automatically calculating a division point with the minimum P value by the software, wherein the division point is a threshold value of a high-score group and a low-score group.

The invention also protects the application of any one of the systems described above, which can be any one of (b1) - (b 8):

(b1) preparing a product for evaluating the curative effect of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b2) evaluating the curative effect of the new adjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient;

(b3) preparing a product for evaluating the complete remission rate of the pathology of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b4) evaluating the complete pathological remission rate of the new adjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient;

(b5) preparing a product for evaluating the prognosis relapse-free survival rate of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b6) evaluating the prognosis recurrence-free survival rate of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b7) preparing a product for evaluating the total survival rate of new auxiliary chemoradiotherapy of an esophageal squamous carcinoma patient;

(b8) and (3) evaluating the overall survival rate of the prognosis of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient.

The invention also protects the application of four genes of SERPINE1, MMP12, PLAUR and EPS8 as markers, which can be any one of (b1) to (b 10):

(b8) evaluating the overall survival rate of the prognosis of the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient;

(b9) preparing a product for the prognosis of new adjuvant radiotherapy and chemotherapy of patients with esophageal squamous carcinoma;

(b10) prognosis is carried out on the neoadjuvant chemoradiotherapy of the esophageal squamous carcinoma patient.

The invention also protects the application of the substances for detecting the expression levels of the four genes of SERPINE1, MMP12, PLAUR and EPS8, and the substances can be any one of (b1) to (b 10):

The invention also protects a substance for detecting the expression levels of four genes of SERPINE1, MMP12, PLAUR and EPS8 and the application of any one of the data processing devices, which can be any one of (b1) to (b 10):

In any of the above applications, the substance for detecting the expression levels of the four genes SERPINE1, MMP12, PLAUR and EPS8 can be a reagent and/or an instrument required for detecting the relative expression levels of the four genes by a fluorescent quantitative PCR method.

The reference gene is GAPDH gene.

The primer sequences for detecting the five genes SERPINE1, MMP12, PLAUR, EPS8 and GAPDH by any one of the above-mentioned assays are specifically shown in Table 2.

The present invention also protects the application of any of the above data processing devices, which may be any of (b1) - (b 10):

Any one of the patients with esophageal squamous carcinoma can be patients with local middle and late esophageal squamous carcinoma.

Any one of the patients with locally advanced esophageal squamous carcinoma can be patients with esophageal squamous carcinoma with stage II or stage III of TNM.

Any one of the above new auxiliary chemoradiotherapy can be synchronous new auxiliary chemoradiotherapy (preoperative synchronous chemoradiotherapy) or sequential new auxiliary chemoradiotherapy (preoperative sequential chemoradiotherapy).

Any one of the isolated esophageal squamous carcinoma tissues can be a sample prepared by embedding the separated esophageal squamous carcinoma tissues of the patient to be predicted with esophageal squamous carcinoma through formalin fixation and paraffin or a frozen section of the separated esophageal squamous carcinoma tissues of the patient to be predicted with esophageal squamous carcinoma.

GenBank number of any SERPINE1 is NM-000602.5. GenBank number of any MMP12 is NM-002426.6. GenBank number of any of the PLAURs is NM-002659.4. The GenBank number of any EPS8 is NM-004447.6.

Based on 28 cases of gene chip results of ESCC endoscope samples before new adjuvant therapy is received in Guangzhou queue, 14 immune molecules with differential expression more than 2 times are screened out through bioinformatics analysis. Next, qPCR validation was performed on these 14 immune molecules in the beijing cohort discovery group, confirming 10 stably differentially expressed immune molecules. Further, the 10 immune molecules were subjected to qPCR detection using 71 pre-treatment endoscopic samples from the beijing cohort training group, and a personalized immunodiagnosis model based on 4 immune molecules (SERPINE1, MMP12, PLAUR and EPS8) was successfully constructed, which achieved a predicted AUC for pCR of 0.970, a sensitivity of 87.0%, and a specificity of 93.8% in the training group. Meanwhile, the model was successfully validated in ESCC queues from multiple centers in beijing, zheng, angyang, with predicted AUCs for pCR in the internal and external validation queues (zheng and angyang queues) being 0.890 and 0.859, respectively. Multifactorial analysis shows that the immune molecule model is the only independent predictor of pCR. In addition, at the same time, the model can effectively predict the overall survival and disease-progression-free survival of the patients receiving nCRT treatment, and the overall survival and relapse-free survival of the patients in the low discriminant score group are remarkably prolonged (P < 0.05). This is the first individualized immune molecular model established and confirmed by multicenter verification to be useful for clinical prediction of neoadjuvant chemoradiotherapy pCR and prognosis of esophageal squamous carcinoma. The successful construction of the model can greatly improve the diagnosis and treatment level of ESCC and practically and powerfully promote the implementation of ESCC accurate medical treatment. The invention has important application value.

Drawings

FIG. 1 is a flow chart for constructing a model of the efficacy and prognosis of new adjuvant radiotherapy and chemotherapy for esophageal squamous carcinoma. The study is a national multi-center study, including Guangzhou (Zhongshan university tumor center), Beijing (national cancer center), Zhengzhou (affiliated tumor hospital of Zhengzhou university), and Anyang (tumor hospital of Anyang city). pCR represents complete remission of pathology, < pCR represents complete remission of nonpathology, qPCR represents real-time fluorescent quantitative PCR, and FLDA represents Fisher's linear discriminant analysis for step-by-step variable screening.

Figure 2 is a graph of immune molecules differentially expressed from endoscopic samples prior to guangzhou cohort treatment. a is the volcano plot of differentially expressed molecules, all qPCR results were processed with log2(X + 1); b is a heatmap of the 14 immune-related genes differentially expressed in pCR and < pCR patients.

FIG. 3 shows the distribution of MMP1(a), INHBA (b), SERPINE1(c), KLK5(d), DSG1(e), MMP12(f), MMP9(g), FST (h), LGALS1(i), AIM2(j), PLAUR (k), CTSV (l), PTN (m), and EPS8(n) in the Beijing cohort discovery group. Distributions represent P <0.05, P <0.01, P <0.001 and P < 0.0001.

FIG. 4 is the establishment of a personalized immune molecule model for pCR prediction of patients with esophageal squamous carcinoma neoadjuvant radiotherapy and chemotherapy. a is a heat map based on the profile of 4 differentially expressed immune molecules; b is characteristic operating curve analysis (ROC) of the testees of the immune molecule expression profile in the training set; c is the distribution of the discriminant scores for pCR and < pCR patients in the training set. Represents P < 0.0001.

Fig. 5 is a graph of the diagnostic value of 4 immune molecules to predict pCR alone in the beijing cohort training set. a is SERPINE14 diagnosis AUC, b is MMP12 diagnosis AUC, c is PLAUR diagnosis AUC, and d is EPS8 diagnosis AUC.

Fig. 6 is a graph of the predicted effect of a personalized immune molecule prediction model consisting of 4 immune molecules on pCR in different populations. a is ROC curve of prediction model of immune molecules in 28 frozen tissue samples to predict pCR; b is an ROC curve of immune molecule prediction model prediction pCR in 30 FFPE samples in a Beijing queue discovery group; c is the discriminant score for pCR patients and < pCR patients in 28 frozen tissue samples; d is the discriminant score for pCR patients and < pCR patients in 30 FFPE samples of the beijing cohort discovery group. Represents P < 0.001.

Figure 7 is a graph of the predicted potency of the immune molecule model evaluated in the internal validation cohort, the entire beijing cohort, and the external validation cohort. In the internal validation cohort (a), the whole Beijing cohort (b) and the external validation cohort (c), heatmaps of the immune molecular profiles established from the four genes screened according to discriminant scores (left) and receiver characteristic operating curve analysis (ROC) (right). Discriminant score distributions between pCRs and < pCRs in the internal validation queue (d), the entire beijing queue (e), and the external validation queue (f). Represents P < 0.0001.

Fig. 8 is a graph of the predicted effect of a personalized immune molecule prediction model consisting of 4 immune molecules on pCR in different populations. A is an ROC curve of an immune molecule prediction model prediction pCR in Zhengzhou queue FFPE samples; b is an ROC curve of an immune molecule prediction model prediction pCR in an Anyang queue FFPE sample; c is the discriminant score for pCR patients and < pCR patients in zheng state cohort FFPE samples; d is the discriminant score for pCR patients and < pCR patients in the angyang cohort FFPE samples. Represents P < 0.001.

FIG. 9 is an OS analysis of immune molecule model for predicting prognosis of neoadjuvant chemoradiotherapy in patients with esophageal squamous carcinoma. a is the beijing queue discovery group (n-30); b is the Beijing cohort training set (n-71); c is the whole queue (n-172); d is the external authentication group (n-52).

FIG. 10 is an RFS analysis of an immune molecule prediction model for predicting prognosis of neoadjuvant chemoradiotherapy of esophageal squamous carcinoma patients. a is the Beijing cohort training set (n-71); b is the inside validation group of the Beijing queue (n is 71); c is the entire beijing queue (n-172).

Figure 11 is a respective cohort pCR and < pCR patient OS analysis. a is the beijing queue discovery group (n-30); b is the Beijing cohort training set (n-71); c is the inside validation group of the Beijing queue (n is 71); d is the whole queue (n-172); e is the external authentication group (n-52).

Figure 12 is RFS analysis for each cohort pCR and < pCR patients. a is the beijing queue discovery group (n-30); b is the Beijing cohort training set (n-71); c is the queue validation group of Beijing (n-71); d is the entire beijing queue (n-172).

Detailed Description

The present invention is described in further detail below with reference to specific embodiments, which are given for the purpose of illustration only and are not intended to limit the scope of the invention. The examples provided below serve as a guide for further modifications by a person skilled in the art and do not constitute a limitation of the invention in any way.

The experimental procedures in the following examples, unless otherwise indicated, are conventional and are carried out according to the techniques or conditions described in the literature in the field or according to the instructions of the products. Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.

The quantitative tests in the following examples, all set up three replicates and the results averaged.

Complete remission of pathology (pCR) was defined as the primary lesion of esophageal squamous carcinoma and no invasive tumor cell residue in the pathological examination of lymph node surgical specimens.

Overall Survival (OS) is defined as the time from enrollment to death or last follow-up due to any cause.

Overall survival is defined as the probability that a patient will survive from a particular time point to a particular time.

Recurrence Free Survival (RFS) is defined as the time from entry to local recurrence, or distant metastasis, or death from any cause, or last follow-up.

Recurrence-free survival is defined as the probability that a patient will have not had a local recurrence or metastasis by a certain time since the patient's follow-up from a certain time point.

The middle-stage and late-stage esophageal squamous carcinoma patients in the following examples refer to TNM staged II-III esophageal squamous carcinoma patients.

In the examples below, the clinical pathology of each cohort of patients is shown in table 1.

TABLE 1 clinical pathological characteristics of the cohorts of patients

Examples of the following,

Firstly, constructing new adjuvant radiotherapy and chemotherapy curative effect and prognosis model of esophageal squamous carcinoma

The flow chart for constructing the new adjuvant chemoradiotherapy curative effect and prognosis model of esophageal squamous carcinoma is shown in figure 1. The method comprises the following specific steps:

1. discovery phase-screening of immune molecule expression profiles of endoscopic samples of patients with different therapeutic effects

Dividing patients into patients with response treatment (namely pCR patients) and patients without response treatment (namely < pCR patients) according to the outcome of nCRT treatment, and then screening and verifying differential expression immune molecules by analyzing the immune molecule expression profile panorama of endoscope samples of the pCR patients and the < pCR patients before treatment, thereby providing a basis for further constructing immune molecule models.

(1) Screening of immune molecule expression profiles of endoscopic samples of patients with different curative effects

The inventors of the present invention first downloaded 3193 immune-related genes from AmiGO2 and obtained 2695 matching genes in the guangzhou cohort. After log2 transformation, the average expression level of 2695 immune-related genes in 28 pre-treated samples was 8.473. To better apply to clinical practice, the inventors of the present invention focused on highly expressed mRNAs, removing 1313mRNAs with an average value below 8.473, and 1382 highly expressed mRNAs were used for further analysis. Finally, 14 differentially expressed immune molecules between pCRs and pCRs were determined, with 12 mrnas (MMP1, INHBA, SERPINE1, KLK5, DSG1, MMP12, MMP9, FST, LGALS1, AIM2, PLAUR and CTSV) up-regulated and 2 mrnas (PTN and EPS8) down-regulated (as shown in fig. 2).

(2) Validation of differentially expressed immune molecules

To validate the immune-related molecules screened as a result of the preliminary sequencing, the inventors retrospectively collected pre-treatment endoscopic FFPE samples (11 pCR patients and 19 < pCR patients) of 30 ESCC patients receiving ncr treatment from 2007 to 2018 in the national cancer center/national medical academy of sciences oncology hospital (beijing cohort discovery group),

the FFPE sample RNA extraction technology is utilized to separate and prepare total RNA for paraffin coils with the diameter of 10 mu m of each sample, the qPCR technology is further adopted to detect the relative expression quantity of 14 genes in esophageal squamous cell carcinoma tissues of patients with esophageal squamous cell carcinoma, and the total verification shows that 10 target genes are in pCRs and pCRs<There were significant differences in expression between pCRs (P <0.05), including 9 immune molecules upregulated in pCR (MMP1, INHBA, SERPINE1, KLK5, DSG1, MMP12, MMP9, FST, LGALS1, AIM2, PLAUR, and LGALS 1)CTSV) and 1 immune molecule EPS8 down-regulated in pCR (as shown in figure 3). The specific detection method comprises the following steps: performing RNA extraction on the obtained FFPE sample; reverse transcribing the extracted RNA into corresponding cDNA; and (3) performing fluorescence quantitative PCR by using the reverse transcribed cDNA as a template. Taking GAPDH as an internal reference gene, recording the Ct value of each reaction, and expressing the detection result as delta Ct, wherein the delta Ct is Ct_Gene-Ct_GAPDH. The primer sequences for detecting each of the target gene and GAPDH gene are shown in Table 2.

TABLE 2 primer sequences

2. Construction of immune molecule model for training stage-esophagus squamous carcinoma new auxiliary radiotherapy and chemotherapy curative effect and prognosis prediction

In order to establish an immune molecule model for predicting pCRs, the inventor of the invention detects the expression conditions of the 10 genes in 71 samples of a Beijing cohort training set by using a qPCR technology. Based on the qPCR result of the verified differential expression immune molecules, a prediction model based on SERPINE1, MMP12, PLAUR and EPS8 is constructed by a Fisher's linear discriminant analysis method of step-by-step variable screening, and the specific formula of the model discriminant is as follows: discriminant score (Discriminant score) — 2.794+ (0.606 × SERPINE1 gene relative expression) + (0.614 × MMP12 gene relative expression) + (0.682 × PLAUR gene relative expression) - (1.751 × EPS8 gene relative expression), and the population to be tested is classified into a low-score group and a high-score group according to the Discriminant score.

The relationship between the expression profiles of the four immune-related genes included in the model and the discriminant scores based on this equation is shown as a in FIG. 4.

Patients with esophageal squamous carcinoma who received new adjuvant radiotherapy and chemotherapy were classified into high-score group (n ═ 23) and low-score group (n ═ 48) according to discriminant score of each patient in the Beijing cohort training group. The specific method comprises the following steps: determining a threshold value through a 'pROC' software package of R language software, comparing the discriminant score of the patient to be predicted with the esophageal squamous carcinoma patient who receives the new auxiliary chemoradiotherapy to obtain pCR with the size of the threshold value, wherein the patient with the score larger than the threshold value is listed in a high-score group, and the patient with the score smaller than or equal to the threshold value is listed in a low-score group.

The above-mentioned threshold value was 0.694, and patients with esophageal squamous carcinoma who received neoadjuvant chemotherapy and had a score of more than 0.694 were ranked in the high-score group, and patients with esophageal squamous carcinoma who received neoadjuvant chemotherapy and had a score of less than or equal to 0.8694 were ranked in the low-score group.

As a result, 20 of 23 pCR patients were found to be successfully predicted as pCRs with a sensitivity of 87.0%. Of the 48 < pCR patients 45 were successfully predicted to be < pCRs with 93.8% specificity. The overall accuracy of the model established in the present invention was 91.5% (65 cases/71 cases) and the area under the subject's working characteristic curve (AUC) was 0.970[ P <0.001, 95% Confidence Interval (CI) 0.937-1.000] (as shown in b and c in fig. 4).

To confirm that the diagnostic effect of this model is indeed superior to the independent diagnostic effect incorporating 4 molecules, the present invention analyzed the independent diagnostic value of SERPINE1, MMP12, PLAUR and EPS8 for pCR in the beijing cohort training set. In order to compare the prediction accuracy of single molecules in the model, the prediction capabilities of SERPINE1, MMP12, PLAUR and EPS8 in the Beijing cohort training set were evaluated. The results show that the AUC for SERPINE14 diagnosis is 0.892, the AUC for MMP12 diagnosis is 0.741, the AUC for the PLAUR diagnosis is 0.835, and the AUC for EPS8 diagnosis is 0.709, all lower than the AUC 0.970 for the model consisting of 4 molecules (as shown in fig. 5).

To preliminarily evaluate the predictive power of the immune molecule model, the present inventors tested the model in the Guangzhou cohort and Beijing cohort discovery groups (as shown in FIG. 6). In the guangzhou cohort, the model successfully predicted 24 of 28 patients with a total accuracy of 85.7% and an AUC of 0.866(P ═ 0.001, 95% confidence interval of 0.727-1.000). Likewise, in the beijing cohort discovery group, our model showed an overall accuracy of 90.0% with an AUC of 0.928(P <0.001, 95% confidence interval 0.837-1.000). These results preliminarily confirm that the new immune molecular model is reliable.

3. Verification stage-effectiveness verification of esophageal cancer neoadjuvant chemoradiotherapy curative effect and prognosis immune molecule prediction model

(1) Validation of pCR predictive Capacity in Beijing cohort validation group

In order to verify the curative effect of the novel adjuvant chemoradiotherapy of esophageal squamous carcinoma and the prediction performance of a prognostic immune molecule model, the inventor retrospectively collects 71 pretreatment endoscope FFPE samples (47 pCR patients and 24 pCR patients) of ESCC patients receiving nCRT treatment in the national cancer center/tumor hospital of Chinese medical academy of sciences (Beijing cohort verification group) from 2007 to 2018, also separates and prepares total RNA by using an RNA extraction technology of the FFPE samples, and further detects the target differential immune molecules by adopting a qPCR technology. The discriminant score for each sample was calculated based on the model discriminants, the predicted value of the model was evaluated with the therapeutic effect pCR as the end event and the discriminant scores for pCR patients and < pCR patients were compared.

The results show that the model can still stably predict the neoadjuvant chemoradiotherapy curative effect of the patients, the sensitivity is 83.3%, the specificity is 87.2% (as shown in a in figure 7), the total accuracy rate predicted by the model in 71 patients is 86.0%, and the AUC is 0.890(P <0.001, and 95% CI is 0.808-0.972).

In the Beijing cohort validation group, the discriminant score distribution between pCRs and < pCRs is shown as d in FIG. 7 (P < 0.05).

(2) Validation of pCR prediction capability across the entire Beijing cohort

To further validate the model across the entire Beijing cohort, the inventors of the present invention conducted a unified analysis of the Beijing cohort discovery, training and validation groups.

The results showed that the model performed well in 172 patients across the Beijing cohort with an overall accuracy of 76.2% and an AUC of 0.862(P <0.001, 95% CI of 0.810-0.915) (as shown in FIG. 7 b).

The discriminant score distribution between pCRs and < pCRs across the beijing cohort is shown as e in fig. 7 (P < 0.05).

(3) Validation of pCR predictive Capacity in external validation set

To determine whether immune-related features could be replicated in the chinese population, the inventors of the present invention integrated 52 independent institutions, the zheng state cohort and the angyang cohort from the high incidence of esophageal squamous carcinoma (south china heonan), as an external multicenter cohort validation set. Similarly, total RNA was prepared by FFPE sample RNA extraction techniques and qPCR techniques were used to detect gene expression incorporated into the model, and the score for each sample was calculated using model discriminants to further validate the predictive power of the model for pCR.

The results show that the model successfully identified 13 of 17 pCR patients with a sensitivity of 76.5%. Furthermore, 35 < 31 of the pCRs were successfully identified with a specificity of 88.6% (as shown in c in FIG. 7). The overall accuracy was 84.6% (44 out of 52 cases) and AUC 0.859(P <0.001, 95% CI 0.747-0.970) (as shown by f in fig. 7). In the outer multicenter cohort validation group, there was a significant difference in discriminant scores between pCRs and < pCRs (P < 0.0001).

The inventors of the present invention also verified model efficacy in zheng state cohort and angyang cohort, respectively. In the independent cohort, the results showed AUC diagnostic values of 0.833 and 0.925, respectively (as in fig. 8). These data indicate that the immune-related molecular model can stably predict pCR of esophageal squamous carcinoma patients who receive new adjuvant radiotherapy and chemotherapy in different medical institutions in China.

Secondly, verifying the evaluation capability of the model constructed in the first step on long-term prognosis

To explore the long-term prognostic assessment ability of the model for patients, the present invention will further assess the predictive ability of the model for patients' Overall Survival (OS) and Recurrence Free Survival (RFS).

Collecting age, sex, tumor part, tumor differentiation degree, clinical TNM stage, chemotherapy scheme and immunity related molecules of a patient, and analyzing influence factors of pCR after neoadjuvant radiotherapy and chemotherapy of esophageal squamous carcinoma by applying single-factor logistic regression.

The invention finds that a prediction model established based on 4 immune-related molecules is the only factor (P <0.05, as shown in Table 3) which is obviously related to pCR in a Beijing queue training group, a Beijing queue verification group, a whole Beijing queue and an external multi-center queue verification group. In addition, multifactorial logistic regression analysis showed that the predictive model built based on 4 immune-related molecules was the only independent factor significantly associated with pCR in the multicenter cohort after correction of other parameters (P <0.05, as shown in table 3).

TABLE 3 analysis of single and multifactorial components of patients with esophageal squamous cell carcinoma who have completely relieved their pathology after new adjuvant radiotherapy and chemotherapy in different cohorts

Note:^achi-square test or Fisher's exact test,^blogistic stepwise regression analysis, c 1, platinum drug in combination with paclitaxel; 2, platinum drug combined with fluorouracil; and 3, combining the platinum medicine with other medicines.

It is considered that patients who obtain pCR after neoadjuvant chemoradiotherapy have a significant survival advantage compared to < pCR patients. Hypothetical models can be used to predict survival of esophageal squamous carcinoma patients receiving nct treatment. To validate the hypothesis, the present invention first evaluated the relationship between immune-related molecular model discriminant scores and OS in the beijing cohort training set. Kaplan-Meier survival analysis showed a significant prolongation of survival in the high scoring group (P ═ 0.0190, HR0.3035, 95% confidence interval 0.1372-0.6716 as shown in a in figure 9).

To further confirm the study results, the prognostic predictive value of the model was analyzed in the validation group within the Beijing cohort. Detecting the relative expression quantity of four genes of each patient in the Beijing cohort verification group and the external multicenter cohort verification group according to the method in the step 1 (2), calculating discriminant scores, and dividing the patients into high-score groups and low-score groups (the threshold value is-0.048). The results showed that the mortality risk was significantly higher in patients in the low-score group than in patients in the high-score group (P ═ 0.0317, HR 0.3545, 95% confidence interval 0.1504-0.8353, as shown in b in fig. 9).

In addition, the predictive power of the model was further verified using the same scoring formula and OS data throughout the beijing queue. The total lifetime of the high score packets is significantly longer than the low score packets (P ═ 0.0136, HR 0.5128, 95% CI 0.2991-0.8793 as shown in c in fig. 9).

Finally, survival analysis in the outer multicenter cohort validation group also demonstrated that the OS was significantly longer in patients in the high scoring group than in patients in the low scoring group (P ═ 0.0030, HR 0.1994, 95% confidence intervals 0.0811-0.4903, as shown in d in fig. 9).

The invention also analyzes the relationship between the model and the RFS. The results of the Kaplan-Meier analysis showed that patients with high discriminant scores RFS were significantly longer than those with low scores in the different cohorts (P <0.05 as shown in figure 10).

Overall Survival (OS) data was collected for all patients to assess prognostic differences between pCRs and < pCRs patients. Kaplan-Meier analysis showed that in each cohort, patients with pCRs showed a better trend of OS (as shown in FIG. 11). In addition, the present invention also collects recurrence-free survival Rate (RFS) data from the entire beijing cohort. Also, patients with < pCR group had worse RFS (as shown in fig. 12) regardless of the beijing cohort training set, validation set, or the entire beijing cohort.

The present invention has been described in detail above. It will be apparent to those skilled in the art that the invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with reference to specific embodiments, it will be appreciated that the invention can be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The use of some of the essential features is possible within the scope of the claims attached below.

Claims

1. A system comprises a system for detecting the expression levels of four genes, namely SERPINE1, MMP12, PLAUR and EPS 8; the system is used for predicting the curative effect of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient or predicting the prognosis of neoadjuvant radiotherapy and chemotherapy of the esophageal squamous carcinoma patient.

2. The system of claim 1, wherein: the system for detecting the expression quantity of the four genes including SERPINE1, MMP12, PLAUR and EPS8 comprises reagents and/or instruments required for detecting the relative expression quantity of the four genes by a fluorescent quantitative PCR method.

3. The system of claim 1 or 2, wherein: the system also includes a data processing device; the data processing device is internally provided with a module; the module has the following functions (a1) and/or (a 2):

4. Use of the system of any one of claims 1 to 3, being any one of (b1) - (b 8):

5. The system of any one of claims 1 to 3 or the use of claim 4, wherein: the esophageal squamous carcinoma patient is a local middle and late esophageal squamous carcinoma patient.

6. The system or use of claim 5, wherein: the locally advanced esophageal squamous carcinoma patient is an esophageal squamous carcinoma patient with TNM stage II or stage III.

The application of four genes of SERPINE1, MMP12, PLAUR and EPS8 as markers is any one of (b1) - (b 10):

8. The application of the substances for detecting the expression quantity of four genes of SERPINE1, MMP12, PLAUR and EPS8 is any one of (b1) to (b 10):

9. The substance for detecting the expression levels of four genes of SERPINE1, MMP12, PLAUR and EPS8 and the use of the data processing device as claimed in claim 3 are any one of (b1) to (b 10):

10. The use of the data processing apparatus as claimed in claim 3, being any one of (b1) - (b 10):