CN114999653B - Training method and prediction device of prediction model of non-small cell lung cancer immunotherapy curative effect - Google Patents

Training method and prediction device of prediction model of non-small cell lung cancer immunotherapy curative effect Download PDF

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CN114999653B
CN114999653B CN202210693286.0A CN202210693286A CN114999653B CN 114999653 B CN114999653 B CN 114999653B CN 202210693286 A CN202210693286 A CN 202210693286A CN 114999653 B CN114999653 B CN 114999653B
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应建明
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宋朋
吴小璇
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Abstract

The invention discloses a training method and a prediction device of a prediction model of the curative effect of non-small cell lung cancer immunotherapy, and relates to the technical field of biomedical science. According to the invention, the risk coefficient of the patient on immunotherapy is predicted by detecting the KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutation of tumor cells of the patient, so that non-small cell lung cancer patients possibly benefiting from immunotherapy can be effectively screened, and a more suitable and timely treatment scheme can be obtained for the patients.

Description

Training method and prediction device of prediction model of non-small cell lung cancer immunotherapy curative effect
Technical Field
The invention relates to the technical field of biomedical science, in particular to a training method and a prediction device of a prediction model of the curative effect of non-small cell lung cancer immunotherapy.
Background
Lung cancer is one of the most common malignant tumors in the world, with non-small cell lung cancer accounting for about 80% of all lung cancers. Most non-small cell lung cancer patients are found to be in middle and late stages, the survival rate of 5 years is very low, the related treatment difficulty is high, the prognosis result is poor, and the disease is a moderate and large disease which afflicts the patients. Smoking, past chronic lung infection, long-term exposure, neutralization of polluted air, and other factors that are major inducers of non-small cell lung cancer. Clinically, the common treatment methods for non-small cell lung cancer are as follows: radiation therapy, chemotherapy, surgical treatment, and immunotherapy, among others.
Immune checkpoint inhibitors have completely altered the therapeutic promise of a variety of cancers, including non-small cell lung cancer. However, clinical application has found that only a fraction of patients respond to immunotherapy, resulting in a better therapeutic effect. Thus, finding potential methods to screen patients who may benefit from immunotherapy is currently an important task.
In view of this, the present invention has been made.
Disclosure of Invention
The invention aims to provide a training method and a prediction device for a prediction model of the curative effect of non-small cell lung cancer immunotherapy.
The invention is realized in the following way:
in a first aspect, an embodiment of the present invention provides a training method for a prediction model of therapeutic efficacy of immunotherapy of non-small cell lung cancer, including: obtaining detection results of the training samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutation and corresponding labeling results; inputting the detection result of the gene mutation into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is used for predicting the immunotherapy efficacy of the sample non-small cell lung cancer according to the number of KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutations; and updating parameters of the prediction model based on the labeling result and the prediction result.
In a second aspect, an embodiment of the present invention provides a device for predicting an efficacy of immunotherapy for non-small cell lung cancer, which includes an acquisition module and a prediction module. The acquisition module is used for acquiring detection results of the gene mutation of the samples to be detected KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM 1; and the prediction module is used for inputting the detection result of the gene mutation into the prediction model trained by the training method in the embodiment to obtain the prediction result of the sample to be detected.
In a third aspect, an embodiment of the present invention provides a training apparatus for treating an immune therapy of non-small cell lung cancer, which includes an acquisition module, a prediction module, and a parameter update module. The acquisition module is used for acquiring detection results of the gene mutation of the training samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 and corresponding labeling results; the prediction module is used for inputting the detection result of the gene mutation into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is used for predicting the immunotherapy efficacy of the sample non-small cell lung cancer according to the number of KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutations; and the parameter updating module is used for updating parameters of the prediction model according to the labeling result and the prediction result.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the electronic device includes a processor and a memory; the memory is configured to store a program that, when executed by the processor, causes the processor to implement a training method of a predictive model of efficacy of immunotherapy of non-small cell lung cancer or a prediction of efficacy of immunotherapy of non-small cell lung cancer as described in the previous embodiments; the prediction of the curative effect of the non-small cell lung cancer immunotherapy comprises the following steps: and inputting the results of the mutation of the genes of the samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 into a prediction model trained by the training method in the previous embodiment to obtain the prediction result of the sample to be detected.
In a fifth aspect, embodiments of the present invention provide a computer readable medium, on which a computer program is stored, which when executed by a processor, implements a training method of a prediction model of the efficacy of immunotherapy of non-small cell lung cancer or prediction of the efficacy of immunotherapy of non-small cell lung cancer as described in the previous embodiments; the prediction of the curative effect of the non-small cell lung cancer immunotherapy comprises the following steps: and inputting the results of the mutation of the genes of the samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 into a prediction model trained by the training method in the previous embodiment to obtain the prediction result of the sample to be detected.
In a sixth aspect, embodiments of the present invention provide an agent for predicting the efficacy of immunotherapy of non-small cell lung cancer, comprising an agent for detecting KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutations.
In a seventh aspect, embodiments of the present invention provide the use of a reagent as described in the previous embodiments for the preparation of a kit for predicting the efficacy of immunotherapy for non-small cell lung cancer.
The invention has the following beneficial effects:
according to the invention, the risk coefficient of the patient on immunotherapy is predicted by detecting the KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutation of tumor cells of the patient, so that non-small cell lung cancer patients possibly benefiting from immunotherapy can be effectively screened, and a more suitable and timely treatment scheme can be obtained for the patients.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a LASSO regression analysis;
FIG. 2 is a graph of candidate gene LASSO coefficient profiles;
FIG. 3 is a correlation of hotspot genes with prognosis;
FIG. 4 is a graph of the survival time of a high risk group and a low risk group of modeled samples;
FIG. 5 is a graph of validation cohort patient survival time;
FIG. 6 is an analysis of the sustained benefit of immunotherapy for a validated cohort patient;
FIG. 7 is a validation cohort tumor mutational load;
FIG. 8 is a graph of example patient survival time;
figure 9 illustrates an example patient immunotherapy sustained benefit analysis.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below. The specific conditions are not noted in the examples and are carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or apparatus used were conventional products commercially available without the manufacturer's attention.
The embodiment of the invention provides a training method of a prediction model of the curative effect of non-small cell lung cancer immunotherapy, which comprises the following steps: obtaining detection results of the training samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutation and corresponding labeling results;
inputting the detection result of the gene mutation into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is used for predicting the immunotherapy efficacy of the sample non-small cell lung cancer according to the number of KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutations;
and updating parameters of the prediction model based on the labeling result and the prediction result.
The gene is extracted from mutation data of treatment queues of 330 non-small cell lung cancer patients by the method of biological information. Firstly screening candidate genes based on mutation data, setting screening parameters through Univariate Cox regression analysis, further screening the candidate genes, and finally carrying out LASSO regression analysis based on the genes to obtain a calculation formula of a prediction model.
The LASSO regression analysis chart is shown in figure 1, the candidate gene LASSO coefficient distribution chart is shown in figure 2, and the correlation between the hot spot gene and prognosis is shown in figure 3.
In some embodiments, the genetic mutation comprises a mutation in the coding region. Mutations in the coding region may include mutations in one or more CDS or ORFs, or mutations in the entire coding region. In other embodiments, the gene mutation is a mutation that results in an amino acid sequence change of the encoded protein. Types of gene mutations include, but are not limited to, single base mutations, multiple base mutations, base deletions, base insertions, and the like, which have an effect on gene translation and protein expression.
The manner of obtaining the gene mutation is not limited at all as long as the gene mutation can be detected, for example, gene sequencing.
In some embodiments, the labeling result may be an immunotherapeutic effect corresponding to the training sample, such as: whether to have a therapeutic effect, and/or the degree of the therapeutic effect.
The embodiment of the invention also provides a device for predicting the curative effect of the non-small cell lung cancer immunotherapy, which comprises an acquisition module and a prediction module.
The acquisition module is used for acquiring detection results of the gene mutation of the samples to be detected KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM 1;
and the prediction module is used for inputting the detection result of the gene mutation into the prediction model trained by the training method according to any embodiment to obtain the prediction result of the sample to be detected.
In some embodiments, the genetic mutation comprises a mutation in the coding region. Mutations in the coding region may include mutations in one or more CDS or ORFs, or mutations in the entire coding region. In some embodiments, the gene mutation is a mutation that results in an amino acid sequence change of the encoded protein.
In some embodiments, the predictive model is calculated and a predictive result is obtained by the following formula;
Risk Score＝(a×KEAP1)+(b×PTPRD)+(c×EPHA3)+(d×EPHA5)+(e×ZFHX3)+(f×MGA)+(g×NTRK3)+(h×PBRM1)；
wherein a is 0.2-0.6, b is-0.1 to-0.6, c is-0.2 to-0.6, d is-0.6 to-1.0, e is-1 to-1.4, f is-0.4 to-0.8, g is-0.5 to-0.9, and h is 0.8-1.2;
KEAP1 is a mutation parameter of the KEAP1 gene, if the coding region of the KEAP1 gene has gene mutation, the KEAP1 is marked as 1, otherwise, the KEAP1 is marked as 0; the other genes are analogized in turn. The analogy specifically refers to: PTPRD is a mutation parameter of PTPRD gene, if there is gene mutation in coding region of PTPRD gene, PTPRD is marked as 1, otherwise it is marked as 0; EPHA3 is a mutation parameter of the EPHA3 gene, if the coding region of the EPHA3 gene has a gene mutation, the EPHA3 is marked as 1, otherwise, the EPHA3 is marked as 0; EPHA5 is a mutation parameter of the EPHA5 gene, if there is a mutation in the coding region of the EPHA5 gene, EPHA5 is marked as 1, otherwise it is marked as 0; ZFHX3 is the mutation parameter of the ZFHX3 gene, if the coding region of the ZFHX3 gene has gene mutation, the ZFHX3 is marked as 1, otherwise, the ZFHX3 is marked as 0; MGA is a mutation parameter of the MGA gene, if the coding region of the MGA gene has gene mutation, the MGA is marked as 1, otherwise, the MGA is marked as 0; NTRK3 is a mutation parameter of the NTRK3 gene, if the coding region of the NTRK3 gene has gene mutation, the NTRK3 is marked as 1, otherwise, the NTRK3 is marked as 0; PBRM1 is a mutation parameter of the PBRM1 gene, if the coding region of the PBRM1 gene has gene mutation, the PBRM1 is marked as 1, otherwise, the PBRM1 is marked as 0.
In some embodiments, in the Risk Score formula, a is 0.395, b is-0.384, c is-0.433, d is-0.827, e is-1.253, f is-0.584, g is-0.703, and h is 1.023.
In some embodiments, the predictive model further includes determining a sample non-small cell lung cancer immunotherapy efficacy based on a Score of the Risk Score: if the Score value of the Risk Score is more than or equal to a set threshold value, judging that the sample immunotherapy has poor curative effect and is a high Risk group; if the Score value of the Risk Score is less than the set threshold value, judging that the sample immunotherapy has good curative effect and is a low Risk group;
in some embodiments, the set threshold is between 0.5 and 0.7, and may specifically be any one or any range between two of 0.5, 0.55, 0.6, 0.6496 and 0.7. In some embodiments, the sample to be tested comprises a cancer cell sample of a non-small cell lung cancer patient, or a biological tissue sample of a healthy person, which may be fresh or frozen, and may be obtained by surgical or puncture means.
Preferably, the formula of the prediction model is: risk score= (0.395×keap 1) +(-0.384×ptprd) +(-0.433×epha3) +(-0.827 ×epha5) +(-1.253 ×zfhx3) +(-0.584×mga) +(-0.703×ntrk 3) + (1.023×pbrm 1). The threshold was set to 0.6496. The model predictive effect was verified by ROC curves, aucat 1 yes= 0.653,AUC at 3years =0.662 for the present model. Meanwhile, other prediction models are constructed based on the same non-small cell lung cancer patient treatment queue, but when the types of the gene mutations contained in the prediction models are changed, the optimal prediction effect cannot be achieved. For example, the predictive model constructed after deleting the PBRM1 mutation has reduced predictive effect on the therapeutic effect of immunotherapy at different periods (aucat 2 yes= 0.621,AUC at 3years =0.643).
The embodiment of the invention also provides a training device for the curative effect of the non-small cell lung cancer immunotherapy, which comprises an acquisition module, a prediction module and a parameter updating module.
The acquisition module is used for acquiring detection results of the gene mutation of the training samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 and corresponding labeling results;
the prediction module is used for inputting the detection result of the gene mutation into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is used for predicting the immunotherapy efficacy of the sample non-small cell lung cancer according to the number of KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutations;
and the parameter updating module is used for updating parameters of the prediction model according to the labeling result and the prediction result.
It will be appreciated that the training device corresponds to a training method according to any of the foregoing embodiments, where specific technical means may be described with reference to any of the foregoing embodiments.
Alternatively, the module described in any of the foregoing embodiments may be stored in a memory in the form of software or Firmware (Firmware) or cured in an Operating System (OS) of an electronic device provided in the present application, and may be executed by a processor in the electronic device. Meanwhile, data, codes of programs, and the like required to execute the above-described modules may be stored in the memory.
The embodiment of the invention also provides electronic equipment, which comprises: a processor and a memory; the electronic device includes a processor and a memory; the memory is configured to store a program that, when executed by the processor, causes the processor to implement a training method of a predictive model of efficacy of non-small cell lung cancer immunotherapy or prediction of efficacy of non-small cell lung cancer immunotherapy as described in the previous embodiments. The prediction of the curative effect of the non-small cell lung cancer immunotherapy comprises the following steps: and inputting the results of the mutation of the genes of the samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 into a prediction model trained by the training method in any embodiment to obtain the prediction result of the sample to be tested.
The electronic device may include a memory, a processor, a bus, and a communication interface that are electrically connected directly or indirectly to each other to enable transmission or interaction of data. For example, the elements may be electrically connected to each other via one or more buses or signal lines.
The Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor may be an integrated circuit chip having signal processing capabilities. The processor 120 may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
The electronic device may be a server, a cloud platform, a mobile phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a personal digital assistant (personal digital assistant, PDA), a wearable electronic device, a virtual reality device, or the like, so the embodiments of the present application do not limit the types of electronic devices.
The embodiment of the invention also provides a computer readable medium, wherein the computer readable medium is stored with a computer program, and the computer program realizes the training method of the prediction model of the curative effect of the non-small cell lung cancer immunotherapy or the prediction of the curative effect of the non-small cell lung cancer immunotherapy when being executed by a processor; the prediction of the curative effect of the non-small cell lung cancer immunotherapy comprises the following steps: and inputting the results of the mutation of the genes of the samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 into a prediction model trained by the training method in the previous embodiment to obtain the prediction result of the sample to be detected.
It will be appreciated that the predictive model in the electronic device and the computer readable medium may be the same as that described in any of the foregoing embodiments, and will not be repeated.
The computer readable medium may be a general purpose storage medium such as a removable disk, hard disk, or the like.
The embodiment of the invention also provides a reagent for predicting the curative effect of the immunotherapy of the non-small cell lung cancer, which comprises a reagent for detecting the mutation of the KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 genes.
In some embodiments, the genetic mutation includes a mutation in the coding region, which may specifically be a mutation that results in an amino acid sequence change of the encoded protein.
In some embodiments, the reagents include primers and/or probes.
In addition, the embodiment of the invention also provides application of the reagent in the preparation of a kit for predicting the curative effect of the immunotherapy of the non-small cell lung cancer.
The features and capabilities of the present invention are described in further detail below in connection with the examples.
Example 1
A method of predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising:
the method comprises the steps of (1) sequencing genes of samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 to obtain a detection result of gene mutation; mutations of these genes include single base mutations, multiple base mutations, base deletions, base insertions, and the like, which have an influence on gene translation and protein expression.
Inputting the detection result into a prediction model to obtain a risk score; the calculation formula of the prediction model is as follows:
Risk Score＝(0.395×KEAP1)+(-0.384×PTPRD)+(-0.433×EPHA3)+(-0.827×EPHA5)+(-1.253×ZFHX3)+(-0.584×MGA)+(-0.703×NTRK3)+(1.023×PBRM1)；
wherein, KEAP1 is a mutation parameter of the KEAP1 gene, if the coding region of the KEAP1 gene has gene mutation, the KEAP1 is marked as 1, otherwise, the KEAP1 is marked as 0; the other genes are analogized in turn.
The predictive model also includes determining a sample non-small cell lung cancer immunotherapy efficacy based on the Score of the Risk Score: if the Score value of the Risk Score is more than or equal to a set threshold value, judging that the sample immunotherapy has poor curative effect and is a high Risk group; if the Score value of the Risk Score is less than the set threshold value, judging that the sample immunotherapy has good curative effect and is a low Risk group; the set threshold is 0.6496.
Example 2
The efficacy prediction was performed on 330 non-small cell lung cancer patients using the prediction model in example 1, resulting in 53 high risk patients and 277 low risk patients. Samples were subjected to survival analysis and found to have significant differences in the two groups, with the high risk group having significantly lower survival times than the low risk group (see fig. 4), P <0.0001.
Clinical data of 240 patients receiving immunotherapy in the sample are used as verification queue 1, and are analyzed by using a prediction model to obtain 162 patients in a high risk group and 78 patients in a low risk group. The time to live analysis of the two groups had significant differences (see fig. 5), P <0.0001. The proportion of patients with persistent clinical benefit (DCB) in the high risk group was 21.6% and the proportion of patients without persistent clinical benefit (NDB) was 78.4%; the proportion of patients with persistent clinical benefit in the low risk group was 48.6% and the proportion of patients without persistent clinical benefit was 51.4% (see fig. 6). There was a significant difference in the proportion of patients who benefited clinically from immunotherapy in the high-risk group versus the low-risk group, P <0.05. The predictive model proves effective in identifying patients who continue to benefit from immunotherapy.
Furthermore, tumor Mutation Burden (TMB) analysis showed that the tumor mutation burden was significantly lower in the high risk group than in the low risk group, P<1.5e -15 (see FIG. 7). According to the prior literature, the higher the tumor mutation load, the stronger the patient's continued benefit in immunotherapy [ Jiao, X., et al (2021): NPJ Precis Oncol 5 (1): 36.]Is consistent with the conclusions of the examples.
Example 3
91 clinical samples were predicted using the prediction method provided in example 1.
91 non-small cell lung cancer patients were subjected to steps such as surgical sampling, cancer cell isolation, gene sequencing, etc., to obtain the gene sequencing results of KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 genes, and the parameters of mutation of these genes were analyzed to calculate Risk Score, and the patients were classified into 64 high Risk groups and 27 low Risk groups (see fig. 8) using Risk score= 0.6496 as a threshold.
There were significant differences in the time-to-live analysis of the two groups (fig. 8), p= 0.00091. Analyzing the immunotherapy benefit of both groups, the proportion of patients with persistent clinical benefit (DCB) in the high risk group was 39.1% and the proportion of patients without persistent clinical benefit (NDB) was 60.9%; the proportion of patients with persistent clinical benefit in the low risk group was 70.4% and the proportion of patients without persistent clinical benefit was 29.6% (see fig. 9). There was a significant difference in the proportion of patients who benefited clinically from immunotherapy in the high-risk group versus the low-risk group, P <0.05. The model described in this application has proven effective in identifying patients who continue to benefit from immunotherapy.
Example 4
Non-small cell lung cancer-clinical immunotherapy has sustained benefit (DCB) cases.
The samples used in example 3 were from 65 year old male patients with prior history of smoking, tumor samples from which RNA sequencing of KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 genes were obtained, and parameters of mutations of these genes were analyzed based on the predictive method of example 1, and Risk Score was calculated to be-1.242, a low Risk patient with continued benefit from immunotherapy, a survival time of 9.37 years, significantly higher than the average survival time of 5.59 years.
Example 5
Non-small cell lung cancer-clinical immunotherapy has no persistent benefit (NDB) cases.
Samples used in example 4 were obtained from 71 year old male patients with prior history of smoking, tumor samples from the patients were sequenced by gene RNA to obtain RNA sequencing results for KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 genes, parameters of mutations of these genes were analyzed based on the predictive method of example 1, risk Score was calculated to be 1.418, high Risk patients, immunotherapy did not continue to benefit, and survival time of the patients was 1.87 years, significantly lower than the average survival time of 5.59 years.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (23)

1. A method for training a predictive model of the efficacy of immunotherapy of non-small cell lung cancer, comprising:
obtaining detection results of the training samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutation and corresponding labeling results;
inputting the detection result of the gene mutation into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is used for predicting the immunotherapy efficacy of the sample non-small cell lung cancer according to the number of KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutations;
updating parameters of the prediction model based on the labeling result and the prediction result;
the prediction model is calculated through the following formula and a prediction result is obtained;
Risk Score＝(a×KEAP1)+(b×PTPRD)+(c×EPHA3)+(d×EPHA5)+(e×ZFHX3)+(f×MGA)+(g×NTRK3)+(h×PBRM1)；
wherein a is 0.2-0.6, b is-0.1 to-0.6, c is-0.2 to-0.6, d is-0.6 to-1.0, e is-1 to-1.4, f is-0.4 to-0.8, g is-0.5 to-0.9, and h is 0.8-1.2;
KEAP1 is a mutation parameter of the KEAP1 gene, if the coding region of the KEAP1 gene has gene mutation, the KEAP1 is marked as 1, otherwise, the KEAP1 is marked as 0; PTPRD is a mutation parameter of PTPRD gene, if there is gene mutation in coding region of PTPRD gene, PTPRD is marked as 1, otherwise it is marked as 0; EPHA3 is a mutation parameter of the EPHA3 gene, if the coding region of the EPHA3 gene has a gene mutation, the EPHA3 is marked as 1, otherwise, the EPHA3 is marked as 0; EPHA5 is a mutation parameter of the EPHA5 gene, if there is a mutation in the coding region of the EPHA5 gene, EPHA5 is marked as 1, otherwise it is marked as 0; ZFHX3 is the mutation parameter of the ZFHX3 gene, if the coding region of the ZFHX3 gene has gene mutation, the ZFHX3 is marked as 1, otherwise, the ZFHX3 is marked as 0; MGA is a mutation parameter of the MGA gene, if the coding region of the MGA gene has gene mutation, the MGA is marked as 1, otherwise, the MGA is marked as 0; NTRK3 is a mutation parameter of the NTRK3 gene, if the coding region of the NTRK3 gene has gene mutation, the NTRK3 is marked as 1, otherwise, the NTRK3 is marked as 0; PBRM1 is a mutation parameter of the PBRM1 gene, if the coding region of the PBRM1 gene has gene mutation, the PBRM1 is marked as 1, otherwise, the PBRM1 is marked as 0.
2. The training method of claim 1, wherein the genetic mutation comprises a mutation in a coding region.
3. Training method according to claim 1, characterized in that the gene mutation is a mutation resulting in an amino acid sequence change of the encoded protein.
4. A device for predicting the efficacy of immunotherapy of non-small cell lung cancer, comprising:
the acquisition module is used for acquiring detection results of the gene mutation of the samples to be detected KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM 1;
the prediction module is used for inputting the detection result of the gene mutation into a prediction model trained by the training method according to any one of claims 1 to 3 to obtain a prediction result of a sample to be detected;
the prediction model is calculated through the following formula and a prediction result is obtained;
Risk Score＝(a×KEAP1)+(b×PTPRD)+(c×EPHA3)+(d×EPHA5)+(e×ZFHX3)+(f×MGA)+(g×NTRK3)+(h×PBRM1)；
wherein a is 0.2-0.6, b is-0.1 to-0.6, c is-0.2 to-0.6, d is-0.6 to-1.0, e is-1 to-1.4, f is-0.4 to-0.8, g is-0.5 to-0.9, and h is 0.8-1.2;
KEAP1 is a mutation parameter of the KEAP1 gene, if the coding region of the KEAP1 gene has gene mutation, the KEAP1 is marked as 1, otherwise, the KEAP1 is marked as 0; PTPRD is a mutation parameter of PTPRD gene, if there is gene mutation in coding region of PTPRD gene, PTPRD is marked as 1, otherwise it is marked as 0; EPHA3 is a mutation parameter of the EPHA3 gene, if the coding region of the EPHA3 gene has a gene mutation, the EPHA3 is marked as 1, otherwise, the EPHA3 is marked as 0; EPHA5 is a mutation parameter of the EPHA5 gene, if there is a mutation in the coding region of the EPHA5 gene, EPHA5 is marked as 1, otherwise it is marked as 0; ZFHX3 is the mutation parameter of the ZFHX3 gene, if the coding region of the ZFHX3 gene has gene mutation, the ZFHX3 is marked as 1, otherwise, the ZFHX3 is marked as 0; MGA is a mutation parameter of the MGA gene, if the coding region of the MGA gene has gene mutation, the MGA is marked as 1, otherwise, the MGA is marked as 0; NTRK3 is a mutation parameter of the NTRK3 gene, if the coding region of the NTRK3 gene has gene mutation, the NTRK3 is marked as 1, otherwise, the NTRK3 is marked as 0; PBRM1 is a mutation parameter of the PBRM1 gene, if the coding region of the PBRM1 gene has gene mutation, the PBRM1 is marked as 1, otherwise, the PBRM1 is marked as 0.
5. The device for predicting the efficacy of immunotherapy for non-small cell lung cancer according to claim 4, wherein the genetic mutation comprises a mutation in the coding region.
6. The apparatus for predicting the therapeutic effect of immunotherapy of non-small cell lung cancer according to claim 5, wherein the mutation of the gene is a mutation that causes an amino acid sequence of the encoded protein to be changed.
7. The device for predicting the therapeutic efficacy of immunotherapy for non-small cell lung cancer according to claim 4, wherein in the Risk Score formula, a is 0.395, b is-0.384, c is-0.433, d is-0.827, e is-1.253, f is-0.584, g is-0.703, and h is 1.023.
8. The apparatus for predicting the efficacy of immunotherapy for non-small cell lung cancer according to claim 4, wherein the prediction model further comprises determining the efficacy of immunotherapy for sample non-small cell lung cancer based on the Score of Risk Score: if the Score value of the Risk Score is more than or equal to a set threshold value, judging that the sample immunotherapy has poor curative effect and is a high Risk group; if the Score value of the Risk Score is less than the set threshold value, the sample is judged to have good immune treatment effect and is a low Risk group.
9. The apparatus for predicting the efficacy of immunotherapy for non-small cell lung cancer according to claim 8, wherein the set threshold is 0.5 to 0.7.
10. The apparatus for predicting the efficacy of immunotherapy for non-small cell lung cancer according to claim 9, wherein the set threshold is 0.6496.
11. A training device for the therapeutic effect of immunotherapy of non-small cell lung cancer, comprising:
the acquisition module is used for acquiring detection results of the gene mutation of the training samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 and corresponding labeling results;
the prediction module is used for inputting the detection result of the gene mutation into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is used for predicting the immunotherapy efficacy of the sample non-small cell lung cancer according to the number of KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 gene mutations;
the parameter updating module is used for updating parameters of the prediction model according to the labeling result and the prediction result;
the prediction model is calculated through the following formula and a prediction result is obtained;
Risk Score＝(a×KEAP1)+(b×PTPRD)+(c×EPHA3)+(d×EPHA5)+(e×ZFHX3)+(f×MGA)+(g×NTRK3)+(h×PBRM1)；
wherein a is 0.2-0.6, b is-0.1 to-0.6, c is-0.2 to-0.6, d is-0.6 to-1.0, e is-1 to-1.4, f is-0.4 to-0.8, g is-0.5 to-0.9, and h is 0.8-1.2;
KEAP1 is a mutation parameter of the KEAP1 gene, if the coding region of the KEAP1 gene has gene mutation, the KEAP1 is marked as 1, otherwise, the KEAP1 is marked as 0; PTPRD is a mutation parameter of PTPRD gene, if there is gene mutation in coding region of PTPRD gene, PTPRD is marked as 1, otherwise it is marked as 0; EPHA3 is a mutation parameter of the EPHA3 gene, if the coding region of the EPHA3 gene has a gene mutation, the EPHA3 is marked as 1, otherwise, the EPHA3 is marked as 0; EPHA5 is a mutation parameter of the EPHA5 gene, if there is a mutation in the coding region of the EPHA5 gene, EPHA5 is marked as 1, otherwise it is marked as 0; ZFHX3 is the mutation parameter of the ZFHX3 gene, if the coding region of the ZFHX3 gene has gene mutation, the ZFHX3 is marked as 1, otherwise, the ZFHX3 is marked as 0; MGA is a mutation parameter of the MGA gene, if the coding region of the MGA gene has gene mutation, the MGA is marked as 1, otherwise, the MGA is marked as 0; NTRK3 is a mutation parameter of the NTRK3 gene, if the coding region of the NTRK3 gene has gene mutation, the NTRK3 is marked as 1, otherwise, the NTRK3 is marked as 0; PBRM1 is a mutation parameter of the PBRM1 gene, if the coding region of the PBRM1 gene has gene mutation, the PBRM1 is marked as 1, otherwise, the PBRM1 is marked as 0.
12. The training device of claim 11, wherein the genetic mutation comprises a mutation in a coding region.
13. Training device according to claim 11 or 12, characterized in that the gene mutation is a mutation resulting in an amino acid sequence change of the encoded protein.
14. An electronic device, comprising: a processor and a memory; the electronic device includes a processor and a memory; the memory is used for storing a program, which when executed by the processor, causes the processor to realize a training method of the prediction model of the efficacy of the immunotherapy of non-small cell lung cancer or the prediction of the efficacy of the immunotherapy of non-small cell lung cancer according to any one of claims 1 to 3;
the prediction of the curative effect of the non-small cell lung cancer immunotherapy comprises the following steps: inputting the results of the mutation of the genes of the samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 into a prediction model trained by the training method according to claim 1 to obtain a prediction result of the sample to be tested;
the formula of the prediction model is as follows: risk score= (a×keap 1) + (b×ptprd) + (c×epha3) + (d×epha5) + (e×zfhx3) + (f×mga) + (g×ntrk3) + (h×pbrm1);
wherein a is 0.2-0.6, b is-0.1 to-0.6, c is-0.2 to-0.6, d is-0.6 to-1.0, e is-1 to-1.4, f is-0.4 to-0.8, g is-0.5 to-0.9, and h is 0.8-1.2;
KEAP1 is a mutation parameter of the KEAP1 gene, if the coding region of the KEAP1 gene has gene mutation, the KEAP1 is marked as 1, otherwise, the KEAP1 is marked as 0; PTPRD is a mutation parameter of PTPRD gene, if there is gene mutation in coding region of PTPRD gene, PTPRD is marked as 1, otherwise it is marked as 0; EPHA3 is a mutation parameter of the EPHA3 gene, if the coding region of the EPHA3 gene has a gene mutation, the EPHA3 is marked as 1, otherwise, the EPHA3 is marked as 0; EPHA5 is a mutation parameter of the EPHA5 gene, if there is a mutation in the coding region of the EPHA5 gene, EPHA5 is marked as 1, otherwise it is marked as 0; ZFHX3 is the mutation parameter of the ZFHX3 gene, if the coding region of the ZFHX3 gene has gene mutation, the ZFHX3 is marked as 1, otherwise, the ZFHX3 is marked as 0; MGA is a mutation parameter of the MGA gene, if the coding region of the MGA gene has gene mutation, the MGA is marked as 1, otherwise, the MGA is marked as 0; NTRK3 is a mutation parameter of the NTRK3 gene, if the coding region of the NTRK3 gene has gene mutation, the NTRK3 is marked as 1, otherwise, the NTRK3 is marked as 0; PBRM1 is a mutation parameter of the PBRM1 gene, if the coding region of the PBRM1 gene has gene mutation, the PBRM1 is marked as 1, otherwise, the PBRM1 is marked as 0.
15. The electronic device of claim 14, wherein in the Risk Score formula, a is 0.395, b is-0.384, c is-0.433, d is-0.827, e is-1.253, f is-0.584, g is-0.703, and h is 1.023.
16. The electronic device of claim 14, wherein the predictive model further comprises determining a sample non-small cell lung cancer immunotherapy efficacy based on a Score of Risk Score: if the Score value of the Risk Score is more than or equal to a set threshold value, judging that the sample immunotherapy has poor curative effect and is a high Risk group; if the Score value of the Risk Score is less than the set threshold value, the sample is judged to have good immune treatment effect and is a low Risk group.
17. The electronic device of claim 16, wherein the set threshold is 0.5-0.7.
18. The electronic device of claim 17, wherein the set threshold is 0.6496.
19. A computer readable medium, wherein a computer program is stored on the computer readable medium, and when executed by a processor, the computer program realizes the training method of the prediction model of the curative effect of the non-small cell lung cancer immunotherapy or the prediction of the curative effect of the non-small cell lung cancer immunotherapy according to any one of claims 1 to 3;
the prediction of the curative effect of the non-small cell lung cancer immunotherapy comprises the following steps: inputting the results of the mutation of the genes of samples KEAP1, PTPRD, EPHA3, EPHA5, ZFHX3, MGA, NTRK3 and PBRM1 into a prediction model trained by the training method according to any one of claims 1 to 3 to obtain a prediction result of the sample to be tested;
the formula of the prediction model is as follows: risk score= (a×keap 1) + (b×ptprd) + (c×epha3) + (d×epha5) + (e×zfhx3) + (f×mga) + (g×ntrk3) + (h×pbrm1);
wherein a is 0.2-0.6, b is-0.1 to-0.6, c is-0.2 to-0.6, d is-0.6 to-1.0, e is-1 to-1.4, f is-0.4 to-0.8, g is-0.5 to-0.9, and h is 0.8-1.2;
KEAP1 is a mutation parameter of the KEAP1 gene, if the coding region of the KEAP1 gene has gene mutation, the KEAP1 is marked as 1, otherwise, the KEAP1 is marked as 0; PTPRD is a mutation parameter of PTPRD gene, if there is gene mutation in coding region of PTPRD gene, PTPRD is marked as 1, otherwise it is marked as 0; EPHA3 is a mutation parameter of the EPHA3 gene, if the coding region of the EPHA3 gene has a gene mutation, the EPHA3 is marked as 1, otherwise, the EPHA3 is marked as 0; EPHA5 is a mutation parameter of the EPHA5 gene, if there is a mutation in the coding region of the EPHA5 gene, EPHA5 is marked as 1, otherwise it is marked as 0; ZFHX3 is the mutation parameter of the ZFHX3 gene, if the coding region of the ZFHX3 gene has gene mutation, the ZFHX3 is marked as 1, otherwise, the ZFHX3 is marked as 0; MGA is a mutation parameter of the MGA gene, if the coding region of the MGA gene has gene mutation, the MGA is marked as 1, otherwise, the MGA is marked as 0; NTRK3 is a mutation parameter of the NTRK3 gene, if the coding region of the NTRK3 gene has gene mutation, the NTRK3 is marked as 1, otherwise, the NTRK3 is marked as 0; PBRM1 is a mutation parameter of the PBRM1 gene, if the coding region of the PBRM1 gene has gene mutation, the PBRM1 is marked as 1, otherwise, the PBRM1 is marked as 0.
20. The computer readable medium of claim 19, wherein in the Risk Score formula, a is 0.395, b is-0.384, c is-0.433, d is-0.827, e is-1.253, f is-0.584, g is-0.703, and h is 1.023.
21. The computer readable medium of claim 19, wherein the predictive model further comprises determining a sample non-small cell lung cancer immunotherapy efficacy based on a Score of the Risk Score: if the Score value of the Risk Score is more than or equal to a set threshold value, judging that the sample immunotherapy has poor curative effect and is a high Risk group; if the Score value of the Risk Score is less than the set threshold value, the sample is judged to have good immune treatment effect and is a low Risk group.
22. The computer readable medium of claim 21, wherein the set threshold is 0.5 to 0.7.
23. The computer readable medium of claim 22, wherein the set threshold is 0.6496.
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