CN114807377A - Application of bladder cancer prognosis survival time marker, evaluation device and computer readable medium - Google Patents
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Abstract
The invention relates to application of a bladder cancer prognosis survival time marker, an evaluation device and a computer readable medium, belonging to the technical field of tumor medicine. According to the invention, single-factor and multi-factor analysis is carried out on genome sequencing data of a large number of bladder cancer patient tumor samples, the correlation between each factor and the overall survival period is evaluated, and a 5-feature prognosis model including lymph node state, TOP1 amplification, MYC amplification, CREBP mutation and CDKN2A deletion state is established, so that the model can effectively carry out prognosis stratification on bladder cancer patients, and provides a reference basis for clinical diagnosis and treatment.
Description
Technical Field
The invention relates to application of a bladder cancer prognosis survival time marker, an evaluation device and a computer readable medium, belonging to the technical field of tumor medicine.
Background
Bladder cancer is the most common malignancy of the urinary tract worldwide, with approximately 430,000 new cases and 165,000 deaths per year. Bladder Cancer (BLCA) is a highly prevalent Cancer caused by the urothelium, and 95% of Bladder cancers are urothelial cancers, also known as transitional cell carcinomas. Currently, bladder cancer can be divided into two major categories according to the stage of the tumor: non-muscle invasive bladder cancer (NMIBC) and Muscle Invasive Bladder Cancer (MIBC). Features of NMIBC include co-activation of the FGFR3 mutation, higher relapse rate (50-70%) and higher 5-year survival (> 90%). However, MIBC is characterized by frequent mutations in TP53, high metastasis rates and a 5-year survival rate of less than 50%. About 70-80% of patients with bladder cancer have NMIBC, of which 20-30% will progress to MIBC, and the prognosis of the patient will be significantly reduced once bladder cancer progression is detected. Currently, in clinical practice, radical cystectomy combined with new adjuvant therapies such as cisplatin is the main therapeutic approach for MIBC. However, some patients have a response rate to cisplatin treatment of less than 50%, 63% of patients are forced to adjust the dose due to the toxicity of chemotherapy, and the survival rate of MIBC has not improved over the last 30 years. Thus, bladder cancer remains a challenging disease.
Bladder cancer is a highly molecularly heterogeneous disease, with the phenotype of each cancer cell being influenced by a variety of cancer cell endogenous and exogenous characteristics that may drive disease development and treatment resistance. Currently, clinical features such as tumor TNM staging and lymph node status are most commonly used in clinical practice to assess the outcome of patients with bladder cancer. Previous studies reported poor Overall Survival (OS) in patients with high grade or positive lymph nodes. However, due to the biological heterogeneity of bladder cancer, it is difficult to predict disease progression if prognosis is assessed based only on clinical characteristics such as TNM staging, lymph node status, and the like. Therefore, given the highly invasive and genetic variability of bladder cancer, the determination of important risk markers associated with bladder cancer prognosis at the molecular level is of great value for the exploration of new individualized therapies and for improving survival rates. The invention tries to explore biomarkers related to bladder cancer prognosis from clinical and genomic characteristics, and provides reference for clinical diagnosis and treatment management.
Disclosure of Invention
The invention aims to solve the problem that a better marker is lacked for the bladder cancer prognosis effect, and provides a bladder cancer prognosis gene marker with a better prediction effect.
The training set of the invention is obtained from 185 patients with bladder cancer screened from MSK-IMPACT targeted sequencing data (PMID: 25092538 and MSK, 2020), and features which are significantly related to Overall Survival (OS) are screened by performing single-factor and multi-factor analysis on clinical features and genome sequencing data, and the prognosis of the patients with bladder cancer can be effectively layered by utilizing the features.
Based on one-factor Log-rank analysis and multifactor Cox regression analysis, the clinical characteristics of bladder cancer patients and the influence of genetic variation with the occurrence frequency of more than 5 percent on prognosis are evaluated, and 5 characteristics which are obviously related to OS (P < 0.050) are identified in total and respectively comprise the following components: lymph node status, TOP1 gene amplification, MYC gene amplification, CDKN2A gene deletion, and CREBBP gene mutation are independent predictors of bladder cancer prognosis.
The invention also constructs a combined discrimination model with higher classification accuracy based on the 5 characteristics, carries out prognosis stratification by calculating the risk score of the patient, and has better prediction performance than that of a single gene model.
The validation set of the present invention was from 265 bladder cancer patients screened in TCGA WES sequencing data (PMID: 28988769). According to a risk score formula obtained by a training set, calculating the risk score of each patient in a verification set, dividing the training set patients into a high risk group and a low risk group according to a risk score threshold value (the risk score is more than or equal to 1.07, and the high risk group), wherein Kaplan-Meier survival analysis shows that the low risk group of the high risk group OS is obviously worse, and the model can still distinguish some patients with poor OS.
The technical scheme is as follows:
use of a reagent or device for detecting a marker for the preparation of a reagent for assessing the prognostic survival of bladder cancer, said marker comprising:
lymph node status, TOP1 gene, MYC gene, CREBP gene or CDKN2A gene or a combination of several of them.
The lymph node state refers to positive or negative lymph node metastasis.
The TOP1 gene detection refers to the detection of the copy number of the TOP1 gene;
detecting the MYC gene refers to detecting the copy number of the MYC gene;
the MYC gene detection means that whether the CREBP gene is mutated or not is detected;
detecting the MYC gene refers to detecting the copy number of the CDKN2A gene.
In the application, the method further comprises the step of calculating the score:
score = (α × LS) + (β × MYC A ）+（γ×TOP1 A ）+（δ×CREBBP T ）+（ε×CDKN2A D ）；
LS means whether or not lymph nodes are metastasized; MYC (myoelectric MYC) A Refers to the presence or absence of amplification of the MYC group; TOP1 A Is the presence or absence of amplification of TOP1 gene; CREBBP T Refers to whether the CREBP gene has mutation; CDKN2A D Is whether CDKN2A has deletion, alpha, beta, gamma, delta and epsilon are parameters.
If the above-mentioned determination condition is "yes", the value is 1, and if "no", the value is 0.
1.0<α<1.1，1.3<β<1.4，1.1<γ<1.2，-1.2<δ<-1.3，0.2<ε<0.3。
A reagent or device for detecting lymph node metastasis comprising: imaging examination devices such as color doppler ultrasound, CT, PET-CT, and magnetic resonance imaging, and cytological examination reagents such as lymph node puncture biopsy and lymph node resection biopsy.
A device for prognostic evaluation of bladder cancer, comprising:
a lymph node state detection device for judging whether lymph node is transferred;
the sequencing module is used for sequencing the sample to obtain data information of TOP1 gene, MYC gene, CDKN2A gene and CREBP gene;
a determination module for calculating a score = (α × LS) + (β × MYC) from the data obtained by the lymph node status detection apparatus and the sequencing module A ）+（γ×TOP1 A ）+（δ×CREBBP T ）+（ε×CDKN2A D ）；
LS means whether or not lymph nodes are metastasized; MYC (myoelectric MYC) A Refers to the presence or absence of amplification of the MYC group; TOP1 A Is the presence or absence of amplification of TOP1 gene; CREBBP T Refers to whether the CREBP gene has mutation; CDKN2A D Whether CDKN2A has deletion or not is determined, and alpha, beta, gamma, delta and epsilon are parameters;
when the score is greater than the threshold, the sample bladder cancer is deemed to have a poorer prognosis.
The lymph node state detection device is selected from imaging examination devices such as color Doppler ultrasound, CT, PET-CT, magnetic resonance imaging and the like, and cytological examination instruments such as lymph node puncture biopsy, lymph node resection biopsy and the like.
A computer-readable medium bearing a computer program operable to perform the method of:
score = (α × LS) + (β × MYC A ）+（γ×TOP1 A ）+（δ×CREBBP T ）+（ε×CDKN2A D ）；
LS means whether or not lymph nodes are metastasized; MYC (myoelectric MYC) A Refers to the presence or absence of amplification of the MYC group; TOP1 A Is the presence or absence of amplification of TOP1 gene; CREBBP T Refers to whether the CREBP gene has mutation; CDKN2A D Whether CDKN2A has deletion or not is determined, and alpha, beta, gamma, delta and epsilon are parameters;
and 3, layering the prognosis of the bladder cancer patient according to the calculated score, and judging that the prognosis is poor if the score is larger than or equal to a threshold value.
The threshold is 1.0-1.1.
Drawings
FIG. 1 is a gene variation profile of bladder cancer patients in a training and validation set;
FIG. 2-1 is a Kaplan-Meier survival graph of the overall survival time of lymph node status in training set;
FIG. 2-2 is a Kaplan-Meier survival graph of the total survival time of TOP1 gene in training set;
FIG. 2-3 is a Kaplan-Meier survival plot of the overall survival time of MYC genes in a training set;
FIGS. 2-4 are Kaplan-Meier survival graphs of the overall survival time of the CREBP gene in the training set;
FIGS. 2-5 are Kaplan-Meier survival plots of the overall survival time of the CDKN2A gene in the training set;
FIG. 3 is a Kaplan-Meier survival curve of the overall survival time of the high-risk and low-risk group of the training centralized model;
FIG. 4 is a 1-year, 3-year, 5-year time-dependent ROC curve for a 5-feature prognosis model constructed based on a training set;
FIG. 5 is a Kaplan-Meier survival curve for verifying the overall survival time of the high-risk and low-risk groups of the centralized model;
FIG. 6 is a histogram of the frequency of mutations of TOP1, MYC, CREBP and CDKN2A genes in different cohorts.
Detailed Description
In the present invention, the training set was derived from 185 bladder cancer patients screened in MSK-IMPACT target sequencing data (PMID: 25092538 and MSK, 2020), and the validation set was derived from 265 bladder cancer patients screened in TCGA WES sequencing data (PMID: 28988769). By carrying out single-factor Log-rank analysis and multi-factor Cox regression analysis on clinical characteristics (age, sex and lymph node state) and genome characteristics (genetic variation with occurrence frequency of more than 5%) of patients in a training set, 5 characteristics are proved to be independent prediction markers of the total life cycle of patients with bladder cancer, a combined discriminant model with higher classification accuracy is constructed, and the prediction capability of the model is further verified by using a verification set.
The definition of "gene mutation" in the present invention is: including single nucleotide variations, indel mutations, missense mutations, nonsense mutations, splice mutations, frameshift mutations, non-frameshift mutations, and promoter mutations.
The definition of "amplification/deletion" in the present invention is: resulting in gene Copy Number Variation (CNV) in chromosomal localities with overexpression or reduced expression of the encoded protein, such as ERBB2 gene amplification.
The term "lymph node metastasis" as used herein refers to metastasis of a malignant tumor to the side of the malignant tumor or to the lymph nodes associated with the malignant tumor through lymphatic vessels, which results in the tumor cells being nourished and propagated in the lymph nodes to cause cancerous enlargement of the lymph nodes. The detection can be performed by imaging examination devices such as color Doppler ultrasound, CT, PET-CT, and magnetic resonance imaging, and cytological examinations such as lymph node puncture biopsy and lymph node excision biopsy.
The Overall Survival (OS) of the patient was calculated from the date of pathological diagnosis of bladder cancer to the date of death or last follow-up.
Acquisition of training and validation sets
The training set was derived from 185 bladder cancer patients screened in the MSK-IMPACT target sequencing data (PMID: 25092538 and MSK, 2020), and the validation set was derived from 265 bladder cancer patients screened in the TCGA WES sequencing data (PMID: 28988769), all with complete clinical and survival information.
Training and validation set patient basic information
The median age of patients in the training set was 66 years (range 31-88 years), and 51.9% of patients were over 66 years of age with a more balanced distribution. The proportion of male patients is 82.7%, the proportion of female patients is 17.3%, and the proportion of male patients is higher. The proportion of lymph node positive patients is 49.7%, and the proportion of negative patients is 50.3%, which is equivalent.
Meanwhile, median age of patients in the validation set was 68 years (range 41-90 years), 61.9% of patients were over 66 years of age, and the elderly population was higher than the training set. 72.5% of patients were male, the remaining 27.5% were female, the proportion of males being higher. In addition, 36.6% of patients were positive for lymph node status, slightly below the training set.
Specifically, as shown in table 1:
TABLE 1 training and validation sets patient basic information
We evaluated the prognostic relationship between different clinical features such as age, sex and lymph node status of bladder cancer patients and OS by single factor Log-rank analysis in the training set. As shown in Table 2, elderly (. gtoreq.66 years) patients tend to have a poorer prognosis (S) ((S))P= 0.041), the prognosis is worse in patients with positive lymph node status than in negative patients (P <0.001) while the influence of gender on prognosis is not significant (P= 0.117), may not be a predictor of the OS.
TABLE 2 Log-rank screening the clinical characteristics associated with prognosis of bladder cancer in the training set: (N = 3）
Identifying genetic variations that are prognostic of bladder cancer by:
first, we mapped the genetic variation of bladder cancer patients in the training set and compared the genetic variation of patients in the validation set. As shown in fig. 1, the high-frequency mutant genes in the training set include TP53 (61.6%), TERT (40.5%), KDM6A (33.5%), ARID1A (28.1%), and RB1 (25.4%), the high-frequency gene copy number changes include CDKN2A (28.6%), CDKN2B (26.5%), MCL1 (26.5%), MYC (24.3%), etc., the high-frequency variation of the patients in the validation set is more consistent with the training set, but the TERT gene mutation detection rate in the validation set is lower because all samples in the validation set are exon detection and cannot cover TERT gene promoters.
Then, based on single causeThe Log-rank analysis of the element assessed the impact of genetic variation occurring more than 5% in bladder cancer patients on prognosis. As shown in table 3, a total of 23 genetic variants were identified as being significantly associated with OS (a)P < 0.050）。
TABLE 3 Log-rank screening of bladder cancer prognosis-related genetic variations in training set: (N = 23）
Note: mut represents a gene mutation; amp represents gene copy number amplification; del represents a gene copy number deletion.
Identifying an independent predictor of bladder cancer prognosis by:
next, we used multifactorial Cox regression analysis to screen independent predictors of relevance for bladder cancer prognosis. We included 25 factors, including 2 clinical features (age, lymph node status) significantly associated with patient OS screened by one-way Log-rank analysis and 23 gene variants (TOP 1, MYC, BCL2L1, SRC, CDK4, FLT1, FLT3, CCNE1, PIK3CA, MCL1, RICTOR, IL7R, DDR2, BCL6, SOX2, CDK6 gene amplification, SMAD4, TP53, TSC2, CDKN2A gene deletion and BBP, ERCC2, PBRM1 gene mutation) into multifactor Cox regression analysis, as shown in Table 4, co-screening 5 OS-independent factors (5) (age, lymph node status)P <0.050), including lymph node status, TOP1 amplification, MYC amplification, CDKN2A deletion, and CREBBP mutation. Kaplan-Meier survival analysis of these 5 independent predictors showed (FIG. 2-1, FIG. 2-2, FIG. 2-3, FIG. 2-4, FIG. 2-5) significantly worse prognosis for bladder cancer patients with lymph node positivity, TOP1 amplification, MYC amplification, CDKN2A depletion (HR 2-A)> 1.00, P <0.050), whereas patients carrying a CREBBP mutation have a better prognosis (HR = 0.36,P = 0.037）。
TABLE 4 Multi-factor Cox regression analysis screening of independent predictors of bladder cancer prognosis in training set: (N = 5）
A prognosis model is constructed based on 5 features, specifically as follows:
based on the 5 independent predictors obtained by the multi-factor Cox regression analysis screening, we tried various combinations to construct risk models to evaluate the prognostic risk of bladder cancer patients, and based on the multi-factor regression analysis, AIC and C-index of each model were calculated, with the results shown in table 5. Among them, the risk scores of 5 features and 4 features (without CDKN2A Del) have lower AIC and higher C-index, and considering that the clinical application focuses more on the prediction accuracy, while the C-index belongs to the prediction accuracy in the model evaluation index, the prognosis model constructed based on 5 features with larger C-index is selected, and the risk score = (1.0723 × lymph node status) + (1.3738 × MYC) A ）+（1.1620×TOP1 A ）+（-1.2486×CREBBP T ）+（0.2154×CDKN2A D ) A refers to amplification, T refers to mutation, and D refers to deletion. Wherein, the lymph node status is a binary variable, if the lymph node status is positive, the lymph node status is assigned with 1, and if the lymph node status is negative, the lymph node status is assigned with 0. The 4 gene variables are also classified variables, and are assigned with 1 if the genes have corresponding variation, and are assigned with 0 if the genes have corresponding variation.
TABLE 5 prognostic model effect comparison based on various combinations
The five-feature prognosis model performance verification process is as follows:
to evaluate the predictive power of the prognostic model, we calculated a risk score for each patient in the training set and validation set. In order to stratify the death risk of the patient at high/low risk, the invention divides the patients in the training set into a high risk group (the risk score is more than or equal to 1.07) and a low risk group (the risk score is more than or equal to 1.07) according to the median of the risk scores<1.07), Kaplan-Meier survival analysis showed that the higher risk group had a significantly worse OS than the lower risk group (median OS: 2.61 months vs. 36.43 months,P <0.001) (fig. 3). Next, we performed a time-dependent ROC analysis using R software to obtain AUC values for the prognostic modelIn particular, ROC analysis was performed at three time points of 1 year, 3 years and 5 years by using corresponding R software packages according to the follow-up time and risk score of patients, and the results show that the AUC of the ROC curve of 1 year, 3 years and 5 years is 0.84, 0.83 and 0.85 respectively (fig. 4), which indicates that the prognosis model has good accuracy in predicting the survival rate of bladder cancer patients. The same threshold was used in the validation set to further validate the model, which still allowed discrimination between some patients with poor OS (median OS: NA vs. 44.32 months,P <0.001) (fig. 5).
The Japanese and Japanese samples were used for the detection of genetic variation in the prognosis model of bladder cancer, as follows:
the four kinds of gene variation related to the model can be detected by adopting Nanjing and Panel No. I of GeneBiotechnology corporation, the sequencing Panel covers all exons and important intron regions of the 4 genes, and a specific gene list is shown in Table 6. FIG. 6 shows the mutation frequency of 4 genes, TOP1 amplification, MYC amplification, CDKN2A deletion and CREBP mutation, in 141 bladder cancer patient tissue samples tested in panel and No. one. The detection frequencies of CREBP mutation in the training set, the verification set and the genetic data set are similar, and the detection frequencies of three kinds of copy number variation genes are different because the threshold values of all detection platforms for the gene copy numbers are different. In general, the world and the first panel have comprehensive coverage of the genes related to bladder cancer prognosis and can be used for detecting bladder cancer prognosis genes.
TABLE 6425 panel Gene List
The above examples are only for explanation and illustration of the present patent, and do not limit the scope of protection of the present patent.
Claims (9)
1. Use of a reagent or device for detecting a marker for the preparation of a reagent for assessing the prognostic survival of bladder cancer, said marker comprising:
lymph node status, TOP1 gene, MYC gene, CREBP gene or CDKN2A gene or a combination of several of them.
2. The use of claim 1, wherein the lymph node status is lymph node metastasis positive or negative; the TOP1 gene detection refers to the detection of the copy number of the TOP1 gene; detecting the MYC gene refers to detecting the copy number of the MYC gene; the MYC gene detection means that whether the CREBP gene is mutated or not is detected; detecting the MYC gene refers to detecting the copy number of the CDKN2A gene.
3. The use of claim 2, further comprising the step of calculating a score: score = (α × LS) + (β × MYC A ）+（γ×TOP1 A ）+（δ×CREBBP T ）+（ε×CDKN2A D ）；
LS means whether or not lymph nodes are metastasized; MYC (MYC) A Refers to the presence or absence of amplification of the MYC group; TOP1 A Is the presence or absence of amplification of TOP1 gene; CREBBP T Refers to whether the CREBP gene has mutation; CDKN2A D Whether CDKN2A has deletion or not is determined, and alpha, beta, gamma, delta and epsilon are parameters; when the judgment condition is yes, the marker takes a value of 1, and when the judgment condition is no, the marker takes a value of 0.
4. Use according to claim 3, characterized in that 1.0< α <1.1, 1.3< β <1.4, 1.1< γ <1.2, -1.2< δ < -1.3, 0.2< ε < 0.3.
5. Use according to claim 2, wherein the reagent or device for detecting lymph node metastasis comprises: one or more of color Doppler ultrasound, CT, magnetic resonance imaging, a reagent for lymph node biopsy and a reagent for lymph node excision biopsy.
6. A bladder cancer prognosis evaluation device, comprising:
a lymph node state detection device for judging whether lymph node is transferred;
the sequencing module is used for sequencing the sample to obtain data information of TOP1 gene, MYC gene, CDKN2A gene and CREBP gene;
a determination module for calculating a score according to the data obtained by the lymph node status detection apparatus and the sequencing module, the score = (α × LS) + (β × MYC) A ）+（γ×TOP1 A ）+（δ×CREBBP T ）+（ε×CDKN2A D ）；
LS means whether or not lymph nodes are metastasized; MYC (myoelectric MYC) A Refers to the presence or absence of amplification of the MYC group; TOP1 A Is the presence or absence of amplification of TOP1 gene; CREBBP T Refers to whether the CREBP gene has mutation; CDKN2A D Whether CDKN2A has deletion or not is determined, and alpha, beta, gamma, delta and epsilon are parameters; when the judgment condition is yes, the marker takes a value of 1, and when the judgment condition is no, the marker takes a value of 0;
when the score is greater than the threshold, the sample bladder cancer is deemed to have a poorer prognosis.
7. The apparatus of claim 6, wherein the threshold range is 1.0-1.1.
8. The apparatus according to claim 6, wherein the lymph node status detecting means is selected from one or more of color Doppler ultrasound, CT, and magnetic resonance imaging.
9. A computer-readable medium having a computer program recorded thereon, wherein the computer program is operable to:
step 1, obtaining information of whether the lymph node of a patient is metastasized and variation information of TOP1, MYC, CREBP and CDKN2A genes;
step 2, calculating the risk score by the following formula:
score = (α × LS) + (β × MYC A ）+（γ×TOP1 A ）+（δ×CREBBP T ）+（ε×CDKN2A D ）；
LS means whether or not lymph nodes are metastasized; MYC (myoelectric MYC) A Refers to the presence or absence of amplification of the MYC group; TOP1 A Is the presence or absence of amplification of TOP1 gene; CREBBP T Refers to whether the CREBP gene has mutation; CDKN2A D Whether CDKN2A has deletion or not is determined, and alpha, beta, gamma, delta and epsilon are parameters; when the judgment condition is yes, the marker takes a value of 1, and when the judgment condition is no, the marker takes a value of 0;
and 3, layering the prognosis of the bladder cancer patient according to the calculated score, and judging that the prognosis is poor if the score is larger than or equal to a threshold value.
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| CN116987787A (en) * | 2023-06-09 | 2023-11-03 | 北京泛生子基因科技有限公司 | Apparatus for detecting recurrence of bladder cancer and computer readable storage medium |
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| CN116013528B (en) * | 2023-01-10 | 2023-11-24 | 中山大学孙逸仙纪念医院 | Bladder cancer postoperative recurrence risk prediction method, device and medium combining with FISH detection |
| CN116987787A (en) * | 2023-06-09 | 2023-11-03 | 北京泛生子基因科技有限公司 | Apparatus for detecting recurrence of bladder cancer and computer readable storage medium |
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