Application of residual tumor cell distribution pattern in construction of grading model for predicting prognosis of gastric cancer patient radically treated by operation after neoadjuvant chemotherapy

Abstract

The invention relates to the technical field of biology, in particular to application of a residual tumor cell distribution mode to construction of a grading model for predicting prognosis of a gastric cancer patient subjected to operation radical treatment after neoadjuvant chemotherapy. The typing result of the distribution mode of the residual tumor cells is calculated through specific numerical values, the specific numerical values can be obtained only by accurately recognizing the tumor bed and the tumor cells on the basis of standard material taking, and calculation typing is carried out. According to the method, the prognosis scoring model of the gastric cancer patient subjected to the operation radical treatment after the neoadjuvant chemotherapy is further constructed through the residual tumor cell distribution mode, and the condition of the gastric cancer patient subjected to the operation radical treatment after the neoadjuvant chemotherapy can be accurately predicted.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to the application of residual tumor cell distribution patterns in constructing a scoring model for predicting the prognosis of gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy. Background Art

[0002] Gastric cancer is one of the common malignant tumors that threaten human health. The NCCN and CSCO diagnosis and treatment guidelines recommend that locally advanced gastric cancer can be treated with the "surgery + adjuvant chemotherapy" or "neoadjuvant chemotherapy + surgery + adjuvant chemotherapy" treatment strategy. Since neoadjuvant chemotherapy can "shrink tumors and reduce stage", which is conducive to radical surgical resection, more and more clinicians and patients are willing to adopt the "neoadjuvant chemotherapy + surgery + adjuvant chemotherapy" treatment strategy. The current clinical practice to evaluate the efficacy of neoadjuvant chemotherapy includes imaging standards (RECIST v1.1) and pathological standards (tumor regression grade, TRG). In imaging, the changes in tumor size and number before and after neoadjuvant chemotherapy are compared, but the number of residual tumor cells cannot be accurately evaluated, and it is impossible to distinguish whether the change in tumor size is caused by changes in the number of tumor cells or secondary changes, which is defective. In pathology, the evaluation of the proportion of tumor cells in the tumor bed range of the surgical resection specimen for grading can only accurately reflect the amount of tumor cells at a time point after neoadjuvant chemotherapy, and lacks the comparison of the baseline level of tumor cells before neoadjuvant chemotherapy, which is also defective. Summary of the invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide an application of the residual tumor cell distribution pattern in constructing a scoring model for predicting the prognosis of gastric cancer patients who have undergone radical surgery after neoadjuvant chemotherapy.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] The first objective of the present invention is to provide an application of a residual tumor cell distribution pattern in constructing a scoring model for predicting the prognosis of gastric cancer patients who have undergone radical surgery after neoadjuvant chemotherapy, wherein the residual tumor cell distribution pattern includes a diffuse residual type and a localized residual type.

[0006] In one embodiment of the present invention, when the horizontal width of the distribution of residual tumor cells in the tumor bed / the horizontal width of the tumor bed×100%>60%, it is a diffuse residual type.

[0007] In one embodiment of the present invention, when the horizontal width of the distribution of residual tumor cells in the tumor bed / the horizontal width of the tumor bed×100%≤60%, it is a localized residual type.

[0008] The second object of the present invention is to provide a prognostic scoring model for predicting gastric cancer patients who undergo radical surgery after neoadjuvant chemotherapy, wherein the variables in the prognostic scoring model include the above-mentioned residual tumor cell distribution pattern, lymph node metastasis, distant metastasis and vascular invasion;

[0009] The prognostic scoring model is as follows:

[0010] Risk score = score corresponding to the distribution pattern of residual tumor cells + score corresponding to lymph node metastasis + score corresponding to distant metastasis + score corresponding to vascular invasion;

[0011] Probability of death within 3 years = 1 / (1+2.71^(-x));

[0012] Among them, x = (risk score-97.06) / 38.73.

[0013] In one embodiment of the present invention, when the distribution pattern of the residual tumor cells is a localized residual type, the corresponding score of the distribution pattern of the residual tumor cells is 0;

[0014] When the distribution pattern of residual tumor cells is diffuse residual type, the corresponding score of the distribution pattern of residual tumor cells is 72.

[0015] In one embodiment of the present invention, when there is no lymph node metastasis, the corresponding score of lymph node metastasis is 0;

[0016] When the number of lymph node metastases is 1 to 6, the corresponding score for lymph node metastasis is 23;

[0017] When the number of lymph node metastases is 7 to 15, the corresponding score for lymph node metastasis is 65;

[0018] When the number of lymph node metastases is ≥16, the corresponding score of lymph node metastasis is 100.

[0019] In one embodiment of the present invention, when there is no distant metastasis, the score corresponding to the distant metastasis is 0;

[0020] When distant metastasis exists, the corresponding score is 55.

[0021] In one embodiment of the present invention, when there is no vascular invasion, the score corresponding to the vascular invasion is 0;

[0022] When vascular invasion is present, the corresponding score for vascular invasion is 19.

[0023] The third object of the present invention is to provide a system for predicting the prognosis of gastric cancer patients who have undergone radical surgery after neoadjuvant chemotherapy, the system comprising a data processing module, and the data processing module operates the above-mentioned scoring model.

[0024] In one embodiment of the present invention, the system includes a risk prediction module, which analyzes the prognosis of gastric cancer patients after neoadjuvant chemotherapy based on the calculation results of the data processing module.

[0025] In one embodiment of the present invention, the following formula is used to analyze the prognosis of gastric cancer patients after neoadjuvant chemotherapy:

[0026] Probability of death within 3 years = 1 / (1+2.71^(-x));

[0027] Where x = (risk score - 97.06) / 38.73;

[0028] Risk score = score corresponding to the distribution pattern of residual tumor cells + score corresponding to lymph node metastasis + score corresponding to distant metastasis + score corresponding to vascular invasion.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] (1) Strong repeatability: The classification standard for the distribution pattern of residual tumor cells is calculated based on specific data. As long as the tumor bed sampling is completed in accordance with the pathological operation specifications and the pathologist accurately identifies the tumor bed and tumor cells, the specific values ​​can be obtained and the classification can be calculated.

[0031] (2) To a certain extent, it integrates the results of imaging efficacy judgment and traditional pathological efficacy judgment: the change of tumor cell distribution width ratio reflects the nature of tumor size change more intuitively than imaging, and can also reflect the amount of residual tumor cells in the tumor bed to a certain extent.

[0032] (3) To a certain extent, it reflects the changes in the biological behavior of gastric cancer: the peripheral tumor cells are more invasive, and the smaller the width occupied by the tumor cells in the tumor bed, the more inert the remaining tumor cells are. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 These are full slide scans of pathological sections of tumor beds of two patients with gastric cancer who underwent radical surgery after neoadjuvant chemotherapy.

[0034] Figure 2 Schematic diagram of specimen collection for radical resection of gastric cancer after neoadjuvant chemotherapy;

[0035] Figure 3 A simplified schematic diagram of the distribution pattern of residual tumor cells (I);

[0036] Figure 4 A simplified schematic diagram of the distribution pattern of residual tumor cells (II);

[0037] Figure 5ROC curve diagram for the best Cutoff value of the training set;

[0038] Figure 6 A nomogram for a scoring model to predict the prognosis of gastric cancer patients after neoadjuvant chemotherapy;

[0039] Figure 7 It is a schematic diagram of the calibration curve of the training set based on the nomogram;

[0040] Figure 8 Schematic diagram of the calibration curve of the validation set based on the nomogram. DETAILED DESCRIPTION

[0041] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] In the following examples, unless otherwise specified, all reagents used are commercially available reagents; and all detection means and methods used are conventional detection means and methods in the art.

[0043] In the following examples, ex vivo gastric cancer radical specimens were used to determine the location and extent of the tumor bed, the gastric cavity was opened along the opposite side of the tumor, and the tumor bed was cut at intervals of 5 mm in the direction parallel to the oral side to the anal side. The tumor bed tissue and the gastric wall tissue within 2 cm of the edge of the tumor bed were all collected, placed in an embedding frame in sequence and numbered, and HE sections were prepared according to the routine pathology process.

[0044] Example 1

[0045] This embodiment provides a distribution pattern of residual tumor cells.

[0046] like Figures 1-2 As shown in the figure, the specimens of patients with gastric cancer who underwent neoadjuvant chemotherapy followed by radical surgery ( Figure 2 The middle gray shaded area is the tumor bed range, and the black parallel line is the incision direction). There are two distribution characteristics of residual tumor cells in the tumor bed. In some cases, the residual tumor cells in the tumor bed are concentrated in a small area; in some cases, the residual tumor cells in the tumor bed are diffusely distributed in the tumor bed. Based on this, it is speculated that the different distribution characteristics of residual tumor cells in the tumor bed of gastric cancer patients who have undergone radical surgery after neoadjuvant chemotherapy may be related to the patient's prognosis. This characteristic is called the distribution pattern of residual tumor cells (DPRTC) and is divided into two types, diffuse residual type (Diffuse Type, D_Type) and confined residual type (Concentrative Type, C_Type) (such as Figure 3 and Figure 4 As shown, in the stomach wall 1, the distribution width of the tumor cells 3 is recorded as L1; the width of the tumor bed 2 is recorded as L2);

[0047] Radical specimens of gastric cancer after neoadjuvant chemotherapy diagnosed by the Department of Pathology of Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from 2012 to 2019 were collected. A total of 381 cases met the criteria, and the proportion of the horizontal distribution width of residual tumor cells to the horizontal width of the tumor bed, survival status and time were evaluated. All cases were randomly divided into a training set (305 cases) and a validation set (76 cases) at a ratio of 4:1.

[0048] ROC curve analysis based on training set cases ( Figure 5 ), the optimal Cutoff value was obtained. The residual tumor cell distribution horizontal width / tumor bed horizontal width ≤ 60% was defined as limited residual type; the residual tumor cell distribution horizontal width / tumor bed horizontal width > 60% was defined as diffuse residual type.

[0049] Example 2

[0050] This embodiment provides a scoring model for predicting the prognosis of gastric cancer patients after neoadjuvant chemotherapy.

[0051] Methods A total of 381 specimens of radically resected gastric cancer after neoadjuvant chemotherapy diagnosed by the Department of Pathology of Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from 2012 to 2019 were collected, and the following information was collected: gender, age, Lauren classification, depth of invasion (ypT), lymph node metastasis (ypN), distant metastasis (ypM), residual extent of tumor after surgery (ypStage), perineural invasion (PNI), vascular invasion (LVI), imaging efficacy results (RECIST v1.1 standard), Becker standard pathological tumor regression grade (TRG), and distribution pattern of residual tumor cells (DPRTC).

[0052] First, sex (male vs. female), age (≤60 years vs. >60 years), ypT (T0-2 vs. T3 vs. T4), ypN (N0 vs. N1-2 vs. N3a vs. N3b), ypM (M0 (no distant metastasis) vs. M1 (distant metastasis)), PNI (N (no neural invasion) vs. P (neurological invasion)), LVI (N (no vascular invasion) vs. P (vascular invasion)), ypStage (0 vs. 1 vs. 2 vs. 3 vs. 4), RECIST v1.1 (Responder vs. Non-responder), TRG (Well-responder) were evaluated. Kaplan-Merier univariate survival analysis was performed based on the classification of C-type vs. Poor-responder, Lauren classification (intestinal type vs. diffuse type vs. no residual), and DPRTC (C_Type vs. D_Type). The results showed that except for gender, age, and imaging efficacy results (RECIST v1.1 standard), the rest were related to the patients' 3-year overall survival rate (P<0.05) (Table 1).

[0053] Table 1 Summary of Kaplan-Merier univariate survival analysis

[0054]

[0055]

[0056] Among them, in Table 1, Responder in RECIST v1.1 includes CR and PR, and Non-responder includes SD and PD; Well-responder in TRG includes G1a and G1b, and Poor-responder includes G2 and G3; T0-2 in ypT includes T0, T1, and T2 (there is no difference in the survival curves of T0, T1, and T2, so they are merged into T0-2); N1-2 in ypN includes N1 and N2 (there is no difference in the survival curves of N1 and N2, so they are merged into N1-2); ypStage is the result of comprehensive ypT, ypN, and ypM, and is not included in the COX regression analysis.

[0057] Based on the above univariate analysis results, all indicators with statistical significance in univariate analysis were included in the COX regression analysis, as shown in Table 2.

[0058] Table 2 Results of COX regression analysis of the indicators with statistical significance in univariate survival analysis

[0059]

[0060] The results showed that ypN, ypM, LVI and DPRTC were independent prognostic factors, p < 0.05, while Lauren classification, TRG, ypT and PNI were not independent prognostic factors (Table 2). The results of COX regression analysis were plotted into a nomogram. Figure 6 .

[0061] Specifically, the variables in the prognostic scoring model include the above-mentioned residual tumor cell distribution pattern, lymph node metastasis, distant metastasis, and vascular invasion;

[0062] The prognostic scoring model is as follows:

[0063] Risk score = score corresponding to the distribution pattern of residual tumor cells + score corresponding to lymph node metastasis + score corresponding to distant metastasis + score corresponding to vascular invasion;

[0064] Probability of death within 3 years = 1 / (1+2.71^(-x));

[0065] Where x = (risk score - 97.06) / 38.73;

[0066] When the distribution pattern of residual tumor cells is limited residual type (C_Type), the corresponding score of the residual tumor cell distribution pattern is 0; when the distribution pattern of residual tumor cells is diffuse residual type (D_Type), the corresponding score of the residual tumor cell distribution pattern is 72; when there is no lymph node metastasis (N0), the corresponding score of lymph node metastasis is 0; when the number of lymph node metastasis is 1 to 6 (N1-2), the corresponding score of lymph node metastasis is 23; when the number of lymph node metastasis is 7 When the number of lymph node metastases is ≤15 (N3a), the corresponding score is 65; when the number of lymph node metastases is ≥16 (N3b), the corresponding score is 100; when there is no distant metastasis (M0), the corresponding score is 0; when there is distant metastasis (M1), the corresponding score is 55; when there is no vascular invasion (N), the corresponding score is 0; when there is vascular invasion (P), the corresponding score is 19.

[0067] Furthermore, the above prognostic scoring model was verified by Figure 7 It can be found that the C index of the training set model is 0.791 (95% CI: 0.784-0.798). Figure 8 It can be found that the C index of the validation set model is 0.711 (95% CI: 0.660-0.763), and the predicted value of the 3-year OS of the calibration curve of the training set and the validation set is highly consistent with the actual result, indicating that the model has a good fit.

[0068] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the explanation of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. Application of the residual tumor cell distribution pattern in constructing a scoring model for predicting the prognosis of gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy, characterized in that: The distribution patterns of the residual tumor cells include diffuse residual type and localized residual type.

2. The use of the residual tumor cell distribution pattern according to claim 1 in constructing a scoring model for predicting the prognosis of gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy, characterized in that: When the horizontal width of the residual tumor cells in the tumor bed / the horizontal width of the tumor bed×100%>60%, it is the diffuse residual type.

3. The use of the residual tumor cell distribution pattern according to claim 2 in constructing a scoring model for predicting the prognosis of gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy, characterized in that: When the horizontal width of the residual tumor cells in the tumor bed / the horizontal width of the tumor bed × 100% ≤ 60%, it is a localized residual type.

4. A prognostic scoring model for predicting gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy, characterized in that: The variables in the prognostic scoring model include the residual tumor cell distribution pattern, lymph node metastasis, distant metastasis and vascular invasion as described in any one of claims 1 to 3; The prognostic scoring model is as follows: Risk score = score corresponding to the distribution pattern of residual tumor cells + score corresponding to lymph node metastasis + score corresponding to distant metastasis + score corresponding to vascular invasion; Probability of death within 3 years = 1 / (1+2.71^(-x)); Among them, x = (risk score-97.06) / 38.

73.

5. The prognostic scoring model for predicting gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy according to claim 4, characterized in that: When the distribution pattern of residual tumor cells is limited residual type, the corresponding score of the distribution pattern of residual tumor cells is 0; When the distribution pattern of residual tumor cells is diffuse residual type, the corresponding score of the distribution pattern of residual tumor cells is 72.

6. The prognostic scoring model for predicting gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy according to claim 4, characterized in that: When there is no lymph node metastasis, the corresponding score for lymph node metastasis is 0; When the number of lymph node metastases is 1 to 6, the corresponding score for lymph node metastasis is 23; When the number of lymph node metastases is 7 to 15, the corresponding score for lymph node metastasis is 65; When the number of lymph node metastases is ≥16, the corresponding score of lymph node metastasis is 100.

7. The prognostic scoring model for predicting gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy according to claim 4, characterized in that: When there is no distant metastasis, the corresponding score of distant metastasis is 0; When distant metastasis exists, the corresponding score is 55.

8. The prognostic scoring model for predicting gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy according to claim 4, characterized in that: When there is no vascular invasion, the corresponding score for vascular invasion is 0; When vascular invasion is present, the corresponding score for vascular invasion is 19.

9. A system for predicting the prognosis of patients with gastric cancer who underwent radical surgery after neoadjuvant chemotherapy, characterized in that: The system comprises a data processing module, which operates the scoring model described in claim 4.

10. The system for predicting the prognosis of gastric cancer patients undergoing radical surgery after neoadjuvant chemotherapy according to claim 9, characterized in that: The system comprises a risk prediction module, which analyzes the prognosis of gastric cancer patients after neoadjuvant chemotherapy according to the calculation results of the data processing module.

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