CN112687394A - Prognostic prediction model of metastatic castration resistant prostate cancer patient in abiraterone treatment and establishment method and application thereof - Google Patents
Prognostic prediction model of metastatic castration resistant prostate cancer patient in abiraterone treatment and establishment method and application thereof Download PDFInfo
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- 206010060862 Prostate cancer Diseases 0.000 title claims abstract description 44
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- GZOSMCIZMLWJML-VJLLXTKPSA-N abiraterone Chemical compound C([C@H]1[C@H]2[C@@H]([C@]3(CC[C@H](O)CC3=CC2)C)CC[C@@]11C)C=C1C1=CC=CN=C1 GZOSMCIZMLWJML-VJLLXTKPSA-N 0.000 title claims abstract description 44
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Abstract
The invention belongs to the field of prognosis evaluation of patients with metastatic castration-resistant prostate cancer, and particularly relates to a prognosis prediction model of patients with metastatic castration-resistant prostate cancer in abiraterone treatment, and an establishment method and application thereof. For mCRPC patients receiving abiraterone first-line therapy, scoring can be carried out through the nomogram disclosed by the invention, and the predicted probability that the patient does not have PSA development within the 6 th month and the 12 th month and the PSA development time of the patient in the 6 th month and the 12 th month are obtained according to the corresponding positions of the total nomogram, so that the use of a clinician in daily work is more convenient.
Description
Technical Field
The invention belongs to the field of prognosis evaluation of patients with metastatic castration-resistant prostate cancer, and particularly relates to a prognosis prediction model of patients with metastatic castration-resistant prostate cancer in abiraterone treatment, and an establishment method and application thereof.
Background
For patients with metastatic castration resistant prostate cancer (mCRPC), abiraterone is one of the standard first-line treatments recommended by the guidelines at home and abroad. The therapeutic effect of abiraterone is different for different patients with mCRPC, some patients can achieve longer-term tumor control in abiraterone treatment, and some patients can rapidly progress the disease in the treatment.
At present, no prognosis model capable of effectively predicting the curative effect of mCRPC patients in abiraterone treatment exists.
Disclosure of Invention
The invention aims to provide a prognostic prediction model of a metastatic castration-resistant prostate cancer patient in abiraterone treatment.
It is still another object of the present invention to provide a method for building the above prediction model.
It is still another object of the present invention to provide clinical applications of the above prediction model.
A prognostic prediction model for metastatic castration-resistant prostate cancer patients in abiraterone treatment according to a specific embodiment of the present invention is a histogram model made of the cancer status in the prostate duct, serum alkaline phosphatase level, serum hemoglobin level, prostate cancer Gleason score collected from metastatic castration-resistant prostate cancer patients to predict when the patients do not develop PSA progression in abiraterone treatment.
Prostate Specific Antigen (PSA) is an antigen associated with the prostate gland. PSA is a single chain glycoprotein with a molecular weight of 32kD, which is secreted by prostate epithelial cells. PSA normally excretes into the semen and plays a physiological role in the disruption of the seminal vesicle and the liquefaction of the semen. Only very low levels of PSA are present in the blood at normal times, and an increase in the concentration of PSA in the serum is indicative of pathological changes in the prostate or trauma. PSA levels are elevated in the vast majority of prostate cancer patients.
In a nomogram without PSA progression time, a first line is a score scale, and the score range is 0-100;
the second behavior is the prostate ductal carcinoma state, which corresponds to a score scale of 0 points if the prostate ductal carcinoma state is negative/IDC-PII type, and corresponds to a score scale of 88 points if the prostate ductal carcinoma state is IDC-PII type;
the third line shows the serum alkaline phosphatase level, if the serum alkaline phosphatase level is less than or equal to 160IU/L, the score scale is 0, and if the serum alkaline phosphatase level is more than 160IU/L, the score scale is 53;
the fourth line shows serum hemoglobin level, which corresponds to score scale 0 if not less than 120g/L, and corresponds to score scale 46 if <120 g/L;
the fifth element is the prostate cancer Gleason score, if 6-7 points, the corresponding score scale is 0 points, if 8-10 points, the corresponding score scale is 100 points;
the sixth action total score is 0-350;
the seventh behavior is that the probability of no PSA development appears within 6 months, and the range is 0.2-0.9;
the probability that the PSA does not progress within 12 months of the eighth action is within the range of 0.1-0.8;
the ninth behavior has median PSA-free progression time in the range of 6 to 24.
For any mCRPC patient receiving abiraterone first-line therapy, the actual conditions of all the influencing variables in the 2 nd to 5 th columns of the nomogram can be scored, and the specific score scales of all the variables are the scores of the positions corresponding to the 1 st column. The total score for the patient can be determined by summing the scores for the 4 variables and finding the corresponding score position in column 6, and then the predicted probability that the patient will not develop PSA progression at month 6, 12, and the time at which the position will not develop PSA progression can be derived from the corresponding positions of the total score in columns 7-9.
PSA progress is an important index of response treatment curative effect in abiraterone treatment, the method can effectively predict the time that the mCRPC patient does not have PSA progress in the abiraterone treatment, and is favorable for prognosis evaluation and treatment scheme selection of the mCRPC patient.
A method for establishing a prognostic predictive model for metastatic castration resistant prostate cancer patients in abiraterone treatment according to an embodiment of the invention, the method comprising the steps of:
(1) collecting clinical and pathological parameters of the metastatic castration resistant prostate cancer patient;
(2) analyzing the prediction capability of clinical and pathological parameters of a patient on the PSA-free progress time through a single-factor COX risk proportion model, and screening out risk factors which obviously influence the PSA-free progress time;
(3) bringing the risk factors screened in the step (2) into multi-factor analysis, analyzing the prediction capability of the risk factors on the PSA-free progress time through a multi-factor COX risk proportion model, and screening influence variables which obviously influence the PSA-free progress time;
(4) and (4) drawing a nomogram according to the influence variables screened in the step (3) to obtain a prediction model.
According to the method for establishing a prognostic prediction model of metastatic castration resistant prostate cancer patients in abiraterone treatment, in step (1), clinical and pathological parameters collected from the patients include age, prostate cancer Gleason score of prostate puncture specimens, status of cancer in prostatic ducts, ECOG score, presence or absence of visceral metastasis, time not to progress to mCRPC stage, baseline serum PSA level, baseline serum alkaline phosphatase level, baseline serum lactate dehydrogenase level, baseline hemoglobin level.
According to the method for establishing the prognosis prediction model of the metastatic castration resistant prostate cancer patient in the abiraterone treatment, in the step (2), the clinical and pathological parameters with the analysis result p less than 0.05 are selected as risk factors to be included in the multi-factor analysis.
According to the method for establishing a prognosis prediction model of metastatic castration-resistant prostate cancer patients in abiraterone treatment, in the step (3), risk factors of which the analysis result p is less than 0.05 are used as influence variables which can obviously influence the result events.
According to the method for establishing the prognosis prediction model of the metastatic castration resistant prostate cancer patient in the abiraterone treatment, in the step (3), the screened influencing variables comprise the cancer state in the prostatic duct, the serum alkaline phosphatase level, the serum hemoglobin level and the prostate cancer Gleason score.
According to the method for establishing the prognosis prediction model of the metastatic castration resistant prostate cancer patient in the abiraterone treatment, in the step (4), a nomogram is drawn by using a survivval package of R software.
The method for establishing a prognostic predictive model of a metastatic castration-resistant prostate cancer patient in abiraterone treatment according to an embodiment of the invention, further comprises the step of validating the established predictive model using influencing variables collected from the patient.
According to the method for establishing the prognosis prediction model of the metastatic castration resistant prostate cancer patient in the abiraterone treatment, the established prediction model is verified through the C-index and the consistency curve.
Preferably, the prognosis model without time to progression of PSA is tested by validating 37 patient data of the group in the following way:
1) the discrimination of the model or the accuracy of the prediction is evaluated by the C-index:
2) the consistency of the patient prognosis predicted by the model with the actual prognosis of the patients in the validation set was verified by a consistency curve (calistringcurve).
The invention relates to application of a prognostic prediction model of metastatic castration-resistant prostate cancer patients in abiraterone treatment in predicting the time when the patients do not have PSA progression in abiraterone treatment.
The invention has the beneficial effects that:
the invention provides a prognostic prediction model of a metastatic castration resistant prostate cancer patient in abiraterone treatment and an establishment method thereof. The prognosis prediction model is established by adopting a multi-factor COX risk ratio model, and is visualized in a nomogram form, wherein the C-index of the prognosis model is 0.767, and the prognosis prediction model has good prediction accuracy and discrimination.
For mCRPC patients receiving abiraterone first-line therapy, the actual conditions of the influencing variables in the nomogram can be scored, the predicted probability that the patient does not have PSA development within the 6 th month and the 12 th month and the PSA development time of the patient with no PSA development can be obtained according to the corresponding positions of the total nomogram, and therefore the use of a clinician in daily work is more convenient.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Figure 1 is a model nomogram of prediction of the time to no PSA progression in abiraterone treatment for mCRPC patients;
FIG. 2 is a graph of the consistency curve validation of the model to predict the absence of PSA progression in mCRPC patients within 6 months;
figure 3 is a graph of the consistency curve validation of the model to predict the absence of PSA progression in mCRPC patients within 12 months.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
The definition of mCRPC refers to the 2020 edition EAU prostate tumor guideline, namely: at castrate levels of serum testosterone (<50ng/dL or 1.7nmol/L), any of the following occurs: a. every at least 1 week, PSA rises 3 times continuously, all rise more than 50% than the lowest value, and final PSA >2 ng/mL; b. tumor progression confirmed by imaging.
PSA progression was defined as a 25% rise in serum PSA following abiraterone treatment, and finally 2ng/mL or more.
Example 1 establishment of a nomogram model predicting prognosis in first-line abiraterone treatment of metastatic castration resistant prostate cancer patients
The nomogram for predicting the time to no PSA progression in a first-line abiraterone treatment of a metastatic castration resistant prostate cancer patient according to the present invention is established by a method comprising the steps of:
(1) 122 patients who were diagnosed with metastatic prostate cancer and progressed to the metastatic castration-resistant prostate cancer stage (mCRPC) were collected during 2014-. All patients received first-line abiraterone treatment (1000 mg/day, combined prednisone 10 mg/day) after confirmation of mCRPC. After a follow-up visit with median time of 27.9 months, 100 (82.0%) patients eventually developed PSA progression;
(2) the clinical and pathological data of the patients are collected as follows: age, prostate cancer Gleason score of prostate puncture specimens, status of prostate intraductal carcinoma (IDC-P), ECOG score, presence or absence of visceral metastasis, time to not progress to mCRPC stage, baseline serum PSA level, baseline serum alkaline phosphatase (ALP) level, baseline serum Lactate Dehydrogenase (LDH) level, baseline Hemoglobin (HGB) level;
(3)122 patients were randomly assigned to the building block (85, 70%) and validation block (37, 30%); the patient of the establishing group is used for establishing a prognosis model, and the patient of the verifying group is used for verifying and evaluating the prognosis model;
(4) using SPSS software (V21.0), the predictive power of all clinical, pathological variables on PSA-free progression time was first analyzed by a one-way COX risk ratio model, with parameters whose analysis results p-value was less than 0.05 considered significantly correlated with PSA-free progression time and further included in the multi-factor analysis;
(5) analyzing the predictive power of risk factors for PSA-free progression time by a multifactorial COX risk ratio model, selecting variables with p-values less than 0.05 for inclusion in the final model, the selected influencing variables including prostate intraductal carcinoma status, serum alkaline phosphatase level, serum hemoglobin level, prostate cancer Gleason score, and the results are given in table 1;
(6) nomograms for predicting time to no PSA progression are plotted using the survivval package of the R software (V3.2.4) based on the beta values of the variables in table 1, see fig. 1.
TABLE 1 beta values and Scoring for the influencing variables
As shown in figure 1, for any mCRPC patient receiving abiraterone first-line therapy, the actual conditions of clinical pathological variables in the 2 nd to 5 th columns of a nomogram can be scored, and the specific score scales of the variables are the scores of the positions corresponding to the 1 st column. The total score for the patient can be determined by summing the scores for the 4 variables and finding the corresponding score position in column 6, and then the predicted probability that the patient will not develop PSA progression at month 6, 12, and the time at which the position will not develop PSA progression can be derived from the corresponding positions of the total score in columns 7-9.
Example 2 validation of the established prognostic model
After the prognosis prediction model of PSA-free progression time in abiraterone treatment was established for mCRPC patients, the model was validated using 37 patients in the validation group. The model was verified by C-index and consistency curve analysis. The method comprises the following specific steps:
(1) the C-index can reflect the model's prediction accuracy or discriminatory ability for prognosis when predicting time to progression without PSA. C-index was calculated by R software.
The C-index of the prognosis model for predicting the time without PSA progress is 0.767, and the model shows good prediction accuracy and discrimination.
(2) The consistency curve is used to reflect the consistency between the predicted probability of PSA progression occurring at month 6, month 12 and the proportion of actual PSA progression occurring in the validation group of patients predicted by the model.
The abscissa of the consistency curve shows the predicted probability of PSA progression occurring at the predicted 6 th and 12 th months, and the ordinate shows the actual probability of PSA progression occurring at the 6 th and 12 th months.
As shown in fig. 2 and 3, the consistency curve is close to 45 °, which indicates that the accuracy of model prediction is good, and the probability of model prediction and the actual probability have high consistency.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A prognostic predictive model for metastatic castration resistant prostate cancer patients in abiraterone treatment, wherein the predictive model is a histogram model of prostate intraductal carcinoma status of the prostate, serum alkaline phosphatase level, serum hemoglobin level, prostate cancer Gleason score taken from metastatic castration resistant prostate cancer patients to predict when the patients do not develop PSA progression in abiraterone treatment.
2. A method for establishing a prognostic predictive model in abiraterone treatment for a metastatic castration resistant prostate cancer patient, said method comprising the steps of:
(1) collecting clinical and pathological parameters of the metastatic castration resistant prostate cancer patient;
(2) analyzing the prediction capability of clinical and pathological parameters of a patient on the PSA-free progress time through a single-factor COX risk proportion model, and screening out risk factors which obviously influence the PSA-free progress time;
(3) bringing the risk factors screened in the step (2) into multi-factor analysis, analyzing the prediction capability of the risk factors on the PSA-free progress time through a multi-factor COX risk proportion model, and screening influence variables which obviously influence the PSA-free progress time;
(4) and (4) drawing a nomogram according to the influence variables screened in the step (3) to obtain a prediction model.
3. The method of claim 2, wherein in step (1), the clinical and pathological parameters collected from the patient include age, Gleason score of prostate cancer from prostate puncture specimens, status of cancer in prostate ducts, ECOG score, presence or absence of visceral metastasis, time to not progress to mCRPC stage, baseline serum PSA level, baseline serum alkaline phosphatase level, baseline serum lactate dehydrogenase level, baseline hemoglobin level.
4. The method for establishing a prognostic prediction model for metastatic castration-resistant prostate cancer patients under abiraterone treatment according to claim 2, wherein in step (2), clinical and pathological parameters with analysis result p less than 0.05 are selected as risk factors for multi-factor analysis.
5. The method for establishing a prognostic predictive model for metastatic castration-resistant prostate cancer patients under abiraterone treatment according to claim 2, wherein in step (3), risk factors whose analysis result p is less than 0.05 are used as influencing variables that will significantly influence the outcome events.
6. The method for establishing a prognostic prediction model for metastatic castration-resistant prostate cancer patients in abiraterone treatment according to any one of claims 2 to 5, wherein the influencing variables selected in step (3) include prostate ductal carcinoma status, serum alkaline phosphatase level, serum hemoglobin level, prostate cancer Gleason score.
7. The method for establishing a prognostic prediction model for metastatic castration-resistant prostate cancer patients during abiraterone treatment according to claim 2, wherein in step (4), a survivval package of R software is used to plot a nomogram.
8. The method of establishing a prognostic predictive model for metastatic castration-resistant prostate cancer patients during abiraterone treatment according to claim 2, further comprising the step of validating the established predictive model using influencing variables collected from the patient.
9. The method of claim 8, wherein the established predictive model is validated by C-index and consistency curves.
10. Use of the prognostic predictive model for metastatic castration resistant prostate cancer patients in abiraterone treatment according to claim 1 for predicting the time at which the patient does not show PSA progression in abiraterone treatment.
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