Construction method and equipment for model reflecting immunotherapy reaction degree, medium and program product

Abstract

The invention provides a construction method and equipment for a model reflecting immunotherapy reaction degree, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring a transcriptome data set and clinical classification information of a training set sample; the clinical classification information comprises sensitivity and drug resistance; performing differential expression analysis on the transcriptome data set to obtain N differential expression genes; identifying M remarkably enriched Hallmark gene sets on the basis of the differential expression genes; respectively taking the intersection of the single Hallmark gene set and the differential expression gene to obtain M refined Hallmark gene sets; respectively calculating an activity value of a single refined hallmark gene set; and inputting the activity value into a machine learning model to train and construct a model reflecting immunotherapy reaction degree (HAPIR), wherein the HAPIR is an effective tool for predicting ICI reaction and guiding development of a new immunotherapy strategy.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical treatment, and more specifically, to a method, device, medium and program product for constructing a model reflecting the degree of immunotherapy response. Background Art

[0002] In recent years, immune checkpoint inhibitors (ICIs) have significantly improved the survival rate of cancer patients. ICIs have been used to treat a variety of cancers, such as melanoma, breast cancer, and esophageal squamous cell. These inhibitors can enhance the patient's immune system, enabling it to effectively recognize and attack cancer cells. However, 60% to 80% of treated patients do not show a good response to immunotherapy. This makes it urgent to discover highly sensitive and specific biomarkers that can predict response before treatment or promote the development of combination therapies to overcome resistance to ICIs.

[0003] Current predictive biomarkers for ICI response, such as programmed cell death ligand 1 (PD-L1) expression, have been clinically validated but are not robust enough. Due to this limitation, researchers have increasingly focused on gene sets that show greater robustness and accuracy in predicting ICI response. For example, pan-cancer stemness signatures have been successfully used to predict the response of multiple cancer types to immunotherapy; there are also studies that use the expression levels of multiple biological pathways as biomarkers combined with logistic regression models to predict ICI response. Similarly, researchers have demonstrated the strong predictive power of 14 signaling pathway activities for ICI. However, the predictive performance is still limited, and further exploration of other gene sets is needed to enhance the prediction of ICI response. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a method, device, medium and program product for constructing a model reflecting the degree of immunotherapy response; the method of the present invention proposes a method for predicting immunotherapy response based on Hallmark gene set refinement, namely HAPIR, and uses multiple cohorts for verification to conclude that HAPIR is an effective tool for predicting ICI response and guiding the development of new immunotherapy strategies.

[0005] The first aspect of the present application discloses a method for constructing a model reflecting the degree of immunotherapy response, the method comprising: S101, obtaining the transcriptome data set and clinical classification information of the training set samples; the clinical classification information includes sensitivity and resistance; S102, performing differential expression analysis on the transcriptome data set to obtain N differentially expressed genes; S103, performing gene set enrichment analysis based on the differentially expressed genes, and identifying M significantly enriched Hallmark gene sets; S104, taking the intersection of a single Hallmark gene set and the differentially expressed genes respectively to obtain M refined Hallmark gene sets; and calculating the activity values ​​of the single refined Hallmark gene sets respectively; S105, inputting the activity value into a machine learning model to obtain a predicted classification result, comparing it with the clinical classification information, optimizing the model according to the comparison result, and constructing a model reflecting the degree of immunotherapy response.

[0006] In some embodiments, the activity value calculation method includes: calculating the expression level of each gene in the refined Hallmark gene set and weighting it according to its importance in the gene set; summing the weighted expression levels of all genes in the gene set to obtain the activity value of the corresponding refined Hallmark gene set.

[0007] In some embodiments, the training set samples include immunotherapy sensitive samples and drug-resistant samples; the differentially expressed genes include significantly up-regulated and down-regulated differentially expressed genes.

[0008] In some embodiments, the method used by the machine learning model includes any one or more of the following: glm function, lm function, nls function, glmnet function, coxph function, mgcv function, randomForest function, gbm function, xgboost function, neuralnet function.

[0009] The second aspect of the present application discloses a method for predicting the probability of drug resistance based on HAPIR, the method comprising: S201, obtaining transcriptome data of the subject; S202, inputting the transcriptome data into the model reflecting the degree of immunotherapy response described in the first aspect of the present application, and outputting a result indicating whether the probability of drug resistance of the subject is high or low.

[0010] In some embodiments, the method further includes: if a result with a high probability of drug resistance is output, an auxiliary prediction result with a shorter survival period of the subject is obtained; if a result with a low probability of drug resistance is output, an auxiliary prediction result with a longer survival period of the subject is obtained.

[0011] The third aspect of the present application discloses a method for predicting the probability of drug resistance based on HAPIR, the method comprising: S301, obtaining transcriptome data of the subject; the transcriptome data includes any one or more of the following genes: S100A1, TYR, SERPINE2, SGCD, PHLDA1; S302, inputting the transcriptome data into the model reflecting the degree of immunotherapy response described in the first aspect of the present application, and outputting a result indicating whether the probability of drug resistance of the subject is high or low.

[0012] The fourth aspect of the present application discloses a computer device, which includes: a memory and a processor; the memory is used to store a computer program; and the processor executes the computer program to implement the steps of the above method.

[0013] A fifth aspect of the present application discloses a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0014] A sixth aspect of the present application discloses a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0015] This application has the following beneficial effects: 1. This application innovatively proposes an improved method for predicting immunotherapy response (HAPIR) based on the Hallmark gene set. We first refined seven Hallmark gene sets, which are enriched with genes that are differentially expressed between responders and non-responders. Then, the machine learning model is trained based on the activity of the seven gene sets. As a result, HAPIR outperforms 13 existing ICI response prediction biomarkers, including PD-1 and PD-L1-based biomarkers. In addition, HAPIR also shows higher accuracy compared with gene-based models and other gene set-based models. Validation of multiple cancer type cohorts confirmed its robustness and significant correlation with patient survival.

[0016] 2. The present application innovatively discloses a method for constructing a model that reflects the degree of immunotherapy response, and a method for predicting the probability of drug resistance using the model. During the model construction process, the transcriptome data of samples sensitive to immunotherapy and samples resistant to immunotherapy are obtained, and the differentially expressed genes are first analyzed. Then, by introducing the Hallmark gene set, 7 refined Hallmark gene sets containing differentially expressed genes are extracted, and the activity values ​​of the 7 refined Hallmark gene sets are calculated. The algorithm is used to mechanically process the activity values ​​and the clinical classification information of sensitivity or resistance corresponding to the samples to construct the HAPIR model.

[0017] 3. This application has discovered a gene set that can accurately predict the probability of drug resistance. Based on this gene set, the probability of drug resistance in patients can be predicted more accurately and efficiently, and it has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a schematic diagram of a method flow chart provided by the first aspect of an embodiment of the present invention; Figure 2 is a schematic diagram of a method flow chart provided by the second aspect of an embodiment of the present invention; Figure 3 is a schematic diagram of a method flow chart provided by the third aspect of an embodiment of the present invention; Figure 4 is a schematic diagram of a computer device provided by an embodiment of the present invention; Figure 5 is a schematic diagram of the architecture of an exemplary computing device provided by an embodiment of the present invention; Figure 6 is a schematic diagram of a storage medium provided by an embodiment of the present invention; Figure 7 is a schematic diagram of the identification process of the refined Hallmark gene set provided by an embodiment of the present invention; wherein, Figure 7 A is the identification of genes related to immunotherapy response. Orange represents significantly upregulated differentially expressed genes; blue represents significantly downregulated differentially expressed genes; and gray represents insignificant genes. Figure 7 B is the result of functional enrichment analysis of genes related to immunotherapy response. Figure 7 C is the refined Hallmark gene set and the genes it contains; Figure 8 is a comparison result diagram of HAPIR and different parameters provided by an embodiment of the present invention; Fig. 9 is a comparison result diagram of HAPIR provided by an embodiment of the present invention and a model based on genes and gene sets; wherein, Fig. 9 A is a comparison between HAPIR and the gene-based model. The heat map below shows the AUROC value of each data set, and the bar chart above shows the average AUROC value of the six data sets. Fig. 9 B is a comparison of HAPIR and other gene set-based models. The radar chart shows the AUROC value of each set of data; Fig.10 is the comparison result of HAPIR provided by the embodiment of the present invention and the characteristics of the immunotherapy effects that have been reported. Among them, Fig.10 A is the comparison result of AUROC between HAPIR and other methods in the training set. Fig.10B is the comparison result of the accuracy of HAPIR and other methods in the training set. Fig.10 C is the comparison result of AUROC between HAPIR and other methods in 5 validation sets. * indicates p < 0.05; ** indicates p < 0.01; ns indicates no significant difference; Fig.11 is the survival analysis of immunotherapy patients provided by the embodiment of the present invention. Fig.11 A is the Kaplan-Meier survival curve of HAPIR in the Riaz et al. dataset. Fig.11 B is the Kaplan-Meier survival curve of HAPIR in the Gide et al. dataset. Fig.11 C is the Kaplan-Meier survival curve of HAPIR in the Lauss et al. dataset. Patients were divided into high and low resistance probability groups according to the predicted probability of immunotherapy resistance. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0021] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] Figure 1 1 is a flow chart of a method for constructing a model reflecting the degree of immunotherapy response provided by an embodiment of the present invention. Specifically, the method comprises the following steps: S101, obtaining the transcriptome data set and clinical classification information of the training set samples; the clinical classification information includes sensitivity and resistance; In some embodiments, the training set samples include immunotherapy sensitive samples and drug-resistant samples; In one embodiment, the transcriptome dataset is from TIGER; S102, performing differential expression analysis on the transcriptome data set to obtain N differentially expressed genes; In some embodiments, the differentially expressed genes include significantly up-regulated and down-regulated differentially expressed genes. N is a natural number greater than 1.

[0024] S103, performing gene set enrichment analysis based on the differentially expressed genes, and identifying M significantly enriched Hallmark gene sets; In some embodiments, M is a natural number greater than 1. In this case, all Hallmark gene sets (50) are directly obtained from the database, and then the differentially expressed genes are used for enrichment analysis.

[0025] S104, taking the intersection of a single Hallmark gene set and the differentially expressed genes respectively to obtain M refined Hallmark gene sets; and calculating the activity values ​​of the single refined Hallmark gene sets respectively; In some embodiments, the activity value calculation method includes: for each sample, calculating the expression of each gene in the refined Hallmark gene set, and weighting it according to its importance in the gene set; for each sample, summing the weighted expression of all genes in the gene set to obtain the activity value of the refined Hallmark gene set corresponding to the sample. The weight can be the frequency of occurrence of the gene in the gene set or other indicators, which are not limited here.

[0026] S105, inputting the activity value into a machine learning model to obtain a predicted classification result, comparing it with the clinical classification information, optimizing the model according to the comparison result, and constructing a model reflecting the degree of immunotherapy response.

[0027] In some embodiments, the method used by the machine learning model includes any one or more of the following: glm function, lm function, nls function, glmnet function, coxph function, mgcv function, randomForest function, gbm function, xgboost function, neuralnet function. Preferably, the glm function is used.

[0028] The second aspect of the present application discloses a method for predicting the probability of drug resistance based on HAPIR, the method comprising: S201, obtaining transcriptome data of the subject; S202, inputting the transcriptome data into the model reflecting the degree of immunotherapy response described in the first aspect of the present application, and outputting a result indicating whether the probability of drug resistance of the subject is high or low.

[0029] In some embodiments, the method further includes: if a result with a high probability of drug resistance is output, an auxiliary prediction result with a shorter survival period of the subject is obtained; if a result with a low probability of drug resistance is output, an auxiliary prediction result with a longer survival period of the subject is obtained.

[0030] The third aspect of the present application discloses a method for predicting the probability of drug resistance based on HAPIR, the method comprising: S301, obtaining test transcriptome data; the transcriptome data includes any one or more of the following genes: S100A1, TYR, SERPINE2, SGCD, PHLDA1; In some embodiments, the transcriptome data further includes any one or more of the following genes: CLU, CD79A, C3, CXCL13, SERPINA1; In some embodiments, the transcriptome data also includes any one or more other genes in the refined Hallmark gene set, and the specific gene names are shown in Table 1.

[0031] S302, inputting the transcriptome data into the model reflecting the degree of immunotherapy response described in the first aspect of the present application, and outputting a result indicating whether the probability of drug resistance of the subject is high or low.

[0032] In some embodiments, the method further includes: if a result with a high probability of drug resistance is output, an auxiliary prediction result with a shorter survival period of the subject is obtained; if a result with a low probability of drug resistance is output, an auxiliary prediction result with a longer survival period of the subject is obtained.

[0033] In some embodiments, the auxiliary prediction results include but are not limited to paper or electronic report forms. The results are only obtained by the intelligent machine based on the analysis of relevant data of the subject, and are only used as a reference for medical staff, and are not used as the final diagnosis result of the subject.

[0034] In some embodiments, the term "subject" or "subject to be tested" or "sample to be tested" as used herein refers to any animal (e.g., mammal), including but not limited to humans, non-human primates, rodents, etc., which will be the recipient of a specific treatment. Generally, the terms "subject" and "patient" are used interchangeably herein when referring to human subjects. Preferably, the subject is a human. In some embodiments, the subject is a patient clinically used to receive a prognostic assessment.

[0035] Figure 4 is a schematic diagram of a computer device provided by an embodiment of the present invention, such as Figure 4As shown, the device 2000 may include: one or more processors 2010, and one or more memories 2020; wherein the memories store computer-readable codes, and when the computer-readable codes are run by the one or more processors, the method described above may be executed.

[0036] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, operations and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can be an X86 architecture or an ARM architecture.

[0037] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0038] For example, the method or device according to the embodiment of the present disclosure may also be implemented by Figure 5 The architecture of the computing device 3000 shown in FIG. Figure 5 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage device in the computing device 3000, such as ROM 3030 or hard disk 3070, may store various data or files used for processing and / or communication of the method provided by the present disclosure and program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 5 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 5 One or more components of a computing device are shown.

[0039] The embodiment of the present invention also provides a computer-readable storage medium, such as Figure 6As shown, it is a schematic diagram of a storage medium 4000 provided in an embodiment of the present invention, and a computer readable instruction 4010 is stored on the computer storage medium 4020. When the computer readable instruction 4010 is executed by a processor, the method according to the embodiment of the present disclosure described with reference to the above figures can be executed. The computer readable storage medium in the embodiment of the present disclosure can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory. It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0040] The embodiments of the present disclosure also provide a computer program product or system, including a computer program, which implements the steps of the above method when executed by a processor.

[0041] In some embodiments, this embodiment further discloses a system for constructing a model reflecting the degree of immunotherapy response, the system comprising: A first acquisition module is used or configured to acquire a transcriptome data set and clinical classification information of a training set sample; the clinical classification information includes sensitivity and resistance; A differentially expressed gene screening module, used for or configured to perform differential expression analysis on the transcriptome data set to obtain N differentially expressed genes; A gene set identification module, used for or configured to perform gene set enrichment analysis based on the differentially expressed genes, and identify M significantly enriched Hallmark gene sets; An activity value calculation module, used for or configured to take the intersection of a single Hallmark gene set and the differentially expressed genes to obtain M refined Hallmark gene sets; and calculate the activity value of each refined Hallmark gene set; The model training module is used for or configured to input the activity value into the machine learning model to obtain the predicted classification result, compare it with the clinical classification information, optimize the model according to the comparison result, and construct a model reflecting the degree of immunotherapy response.

[0042] In some embodiments, this embodiment further discloses a system for predicting drug resistance probability based on HAPIR, the system comprising: A second acquisition module is used or configured to acquire transcriptome data of the subject; The first drug resistance probability output module is used or configured to input the transcriptome data into the model reflecting the degree of immunotherapy response described in the first aspect of the present application, and output a result of whether the probability of drug resistance of the subject is high or low.

[0043] In some embodiments, the system also includes: a first survival prediction module, which is used or configured to obtain an auxiliary prediction result of a shorter subject survival if a result with a high probability of drug resistance is output; and to obtain an auxiliary prediction result of a longer subject survival if a result with a low probability of drug resistance is output.

[0044] In some embodiments, this embodiment further discloses a system for predicting drug resistance probability based on HAPIR, the system comprising: A third acquisition module is used or configured to acquire transcriptome data of the subject; the transcriptome data includes any one or more of the following genes: S100A1, TYR, SERPINE2, SGCD, PHLDA1; The second drug resistance probability output module is used for or configured to input the transcriptome data into the model reflecting the degree of immunotherapy response described in the first aspect of the present application, and output a result of whether the probability of drug resistance of the subject is high or low.

[0045] In some embodiments, the system also includes: a second survival prediction module, which is used or configured to obtain an auxiliary prediction result of a shorter subject survival if a result with a high probability of drug resistance is output; and to obtain an auxiliary prediction result of a longer subject survival if a result with a low probability of drug resistance is output.

[0046] Example 1 Construction of a model for predicting the degree of immunotherapy response 1.1 Experimental Materials Next-generation sequencing data (RNA-seq) of immunotherapy-sensitive and -resistant patients.

[0047] 1.2 Experimental methods (1) The Limma method was used to perform differential expression (DE) analysis on immunotherapy-sensitive and -resistant samples, and 200 significant (p < 0.05) most upregulated and most downregulated differentially expressed genes (DEGs) were obtained, respectively.

[0048] (2) The clusterProfiler R package was used to perform gene set enrichment analysis on these 400 differentially expressed genes, and 7 significantly enriched Hallmark gene sets were identified (FDR < 0.05).

[0049] (3) For each significantly enriched Hallmark gene set, only 400 genes in the differentially expressed gene set were retained. These enriched Hallmark gene sets were refined to obtain 7 “refined Hallmark gene sets”.

[0050] (4) Use the AUCell algorithm to score each refined Hallmark gene set to obtain the refined Hallmark gene set activity.

[0051] (5) A logistic regression model was constructed using the glm function to predict the response to immunotherapy, and the model was named HAPIR.

[0052] 1.3 Experimental Results Genes associated with immunotherapy response such as Figure 7 A. The results of functional enrichment analysis of genes related to immunotherapy response are shown in Figure 7 As shown in B, there are 7 significantly enriched Hallmark gene sets. The 7 refined Hallmark gene sets contain genes such as Figure 7 C and Table 1.

[0053] Table 1 Name of the refined Hallmark gene set, number of genes included and gene names

[0054] Example 2 Validation of the HAPIR model 1.1 Experimental Materials Six sets of data were downloaded from the TIGER database (http: / / tiger.canceromics.org / # / download) to verify the accuracy of HAPIR. As shown in Table 2, each set of data contains drug response signatures and gene expression information for immunotherapy, including the following data: Table 2. Summary of data for HAPIR model validation

[0055] 1.2 Experimental methods (1) First, we compared HAPIR with other models with different parameters, using the Riaz et al. dataset as the training set and the remaining datasets as validation sets. For the training set, the ten-fold cross-validation method was used for validation. The accuracy and area under the receiver operating characteristic curve (AUROC) of different models were compared as evaluation indicators. The specific parameters are as follows: number of differentially expressed genes (100, 200, 300, 400, 500); Hallmark gene set scoring method (AUCell, ssGSEA, GSVA, Addmodulescore, gene expression average); machine learning model (logistic regression, elastic net regression, ridge regression, XGboost, support vector machine, random forest, K nearest neighbor algorithm).

[0056] (2) Secondly, we also compared HAPIR with gene-based models. For genes, HAPIR involved 77 refined genes and 400 differentially expressed genes for predicting immunotherapy response, and combined them with seven machine learning models (logistic regression, elastic net regression, ridge regression, XGboost, support vector machine, random forest, K nearest neighbor algorithm) for prediction. We used AUROC values ​​to compare the prediction performance of different methods on six data sets.

[0057] (3) Again, we compared HAPIR with models based on other gene sets. For gene sets, the complete 50 Hallmark gene sets (50_Hallmark) and the original 7 Hallmark gene sets (7_Hallmark) were involved. In addition, we collected 386 tumor microenvironment-related gene sets (TME_Bio) and identified 32 refined gene sets using the same workflow as HAPIR. We also combined the Hallmark and tumor microenvironment-related gene sets (TME_Bio+50_Hallmark) and identified 40 refined gene sets using the same method. The AUCell scoring method and logistic regression model were used to predict immunotherapy response for these gene sets. We used the AUROC value to compare the prediction performance of different methods on the six data sets.

[0058] (4) Finally, to further evaluate the predictive performance, we compared HAPIR with reported immunotherapy response signatures, including four pan-cancer signatures (PD-1, PD-L1, C_ECM, and ADO) and nine melanoma-specific signatures (KDM5A, CD8_SF, B_cell_Helmink, ImmuneCells, CRMA, T_cell_exclusion, IMPRES, MPS, and Myeloid_DC). The detailed information of these signatures and their corresponding algorithms is shown in Table 3. For HAPIR, 0.5 was used as the cutoff value to distinguish between sensitive and resistant patients. For each compared method, the average value was used to distinguish between sensitive and resistant patients. We used accuracy and AUROC values ​​to compare the predictive performance of different methods on the six datasets.

[0059] (4) Survival analysis was performed on patients receiving immunotherapy using the survival R package, and Kaplan-Meier curves were drawn using the survminer R package. The pROC R package was used to draw receiver operating characteristic (ROC) curves and calculate AUROC values.

[0060] Table 3. Summary of reported imaging features of immunotherapy

[0061] 1.3 Experimental Results To demonstrate the superiority of the HAPIR parameter, we conducted a comparative analysis with other parameters. The average AUROC value of the prediction results of HAPIR in the six data sets was about 0.8, which was better than the results predicted by other parameters ( Figure 8 ).

[0062] Then, to demonstrate the superiority of the refined Hallmark gene set, we compared HAPIR with models based on genes and other functional gene sets. For the gene-based models, HAPIR showed the highest average AUROC values ​​in the six datasets ( Fig. 9 A), indicating that the use of refined Hallmark gene set activity scores provides superior predictive power compared to single gene expression. For gene set comparison, we first compared the performance of the refined Hallmark gene set with the 7 original Hallmark gene sets (7_Hallmark) and the 50 Hallmark gene sets (50_Hallmark). To reduce the complexity of the comparison, we used the same parameters "AUCell" and "Logistic Regression" as HAPIR to predict the degree of response to immunotherapy. The results showed that HAPIR showed the highest AUROC value in all six datasets ( Fig. 9B). This highlights the effectiveness of gene set refinement in improving model performance. In addition, we also compared with a model based on the TME-related gene set (TME_Bio) and a model combining TME_Bio with the 50 Hallmark gene sets (TME_Bio+50_Hallmark), and HAPIR achieved the highest AUROC ( Fig. 9 B). Taken together, these results suggest that HAPIR provides superior prediction performance compared with models based on genes, Hallmark gene sets, and TME-associated gene sets, enhancing its utility in immunotherapy response prediction.

[0063] Next, we compared HAPIR with other previously reported signature biomarkers of immunotherapy response, including melanoma-specific markers such as KDM5A and CD8_SF, and pan-cancer biomarkers such as C_ECM and ADO. In the training set, HAPIR achieved the highest AUROC and accuracy ( Fig.10 A and Fig.10 B). In the external validation set, HAPIR still achieved the best prediction performance ( Fig.10 C). These results suggest that HAPIR outperforms previously reported immunotherapy response-related biomarkers in predicting immunotherapy response and patient survival.

[0064] Finally, we performed a survival analysis, and the results showed that among the three data sets with survival information, the survival time of patients with a high probability of drug resistance was significantly (p < 0.05) lower than that of patients with a low probability ( Fig.11 ). These results further illustrate the effectiveness of the HAPIR model.

[0065] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0066] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0068] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0069] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0071] The exemplary embodiments of the present disclosure described in detail above are merely illustrative and not restrictive. It should be understood by those skilled in the art that various modifications and combinations may be made to these embodiments or their features without departing from the principles and spirit of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A method for constructing a model reflecting the degree of immunotherapy response, characterized in that: The method comprises: S101, obtaining the transcriptome data set and clinical classification information of the training set samples; the clinical classification information includes sensitivity and resistance; S102, performing differential expression analysis on the transcriptome data set to obtain N differentially expressed genes; S103, performing gene set enrichment analysis based on the differentially expressed genes, and identifying M significantly enriched Hallmark gene sets; S104, taking the intersection of a single Hallmark gene set and the differentially expressed genes respectively to obtain M refined Hallmark gene sets; and calculating the activity values ​​of the single refined Hallmark gene sets respectively; S105, inputting the activity value into a machine learning model to obtain a predicted classification result, comparing it with the clinical classification information, optimizing the model according to the comparison result, and constructing a model reflecting the degree of immunotherapy response.

2. The method for constructing a model reflecting the degree of immunotherapy response according to claim 1, characterized in that: The activity value calculation method includes: calculating the expression level of each gene in the refined Hallmark gene set, and weighting it according to its importance in the gene set; summing the weighted expression levels of all genes in the gene set to obtain the activity value of the corresponding refined Hallmark gene set.

3. The method for constructing a model reflecting the degree of immunotherapy response according to claim 1, characterized in that: The training set samples include immunotherapy sensitive samples and drug-resistant samples; the differentially expressed genes include significantly upregulated and downregulated differentially expressed genes.

4. The method for constructing a model reflecting the degree of immunotherapy response according to claim 1, characterized in that: The method used by the machine learning model includes any one or more of the following: glm function, lm function, nls function, glmnet function, coxph function, mgcv function, randomForest function, gbm function, xgboost function, neuralnet function.

5. A method for predicting drug resistance probability based on HAPIR, characterized in that: The method comprises: S201, obtaining transcriptome data of the subject; S202, inputting the transcriptome data into the model reflecting the degree of immunotherapy response as described in any one of claims 1 to 4, and outputting a result indicating whether the probability of drug resistance of the subject is high or low.

6. The method for predicting drug resistance probability based on HAPIR according to claim 5, characterized in that: The method further comprises: if a result with a high probability of drug resistance is output, an auxiliary prediction result with a shorter survival period of the subject is obtained; if a result with a low probability of drug resistance is output, an auxiliary prediction result with a longer survival period of the subject is obtained.

7. A method for predicting drug resistance probability based on HAPIR, characterized in that: The method comprises: S301, obtaining transcriptome data of the subject; the transcriptome data includes any one or more of the following genes: S100A1, TYR, SERPINE2, SGCD, PHLDA1; S302, inputting the transcriptome data into the model reflecting the degree of immunotherapy response as described in any one of claims 1 to 4, and outputting a result indicating whether the probability of drug resistance of the subject is high or low.

8. A computer device, characterized in that: The device comprises: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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