CN114627962A

CN114627962A - Method and device for predicting sensitivity of tumor patient to immunotherapy

Info

Publication number: CN114627962A
Application number: CN202210213254.6A
Authority: CN
Inventors: 王维锋; 虞韩川枝; 姚继成; 郑新
Original assignee: Shanghai Zhiben Medical Laboratory Co ltd; Origimed Technology Shanghai Co ltd
Current assignee: Shanghai Zhiben Medical Laboratory Co ltd; Origimed Technology Shanghai Co ltd
Priority date: 2022-03-04
Filing date: 2022-03-04
Publication date: 2022-06-14
Anticipated expiration: 2042-03-04
Also published as: CN114627962B

Abstract

The present invention relates to a method, apparatus, computer device, computer storage medium and computer program product for predicting the sensitivity of a tumor patient to immunotherapy, which propose the concept of deltaHED values by analyzing the leucocyte antigen evolutionary variability of HLA-class I genes of tumor tissue in combination with the leucocyte antigen heterozygous deletion status. Changes of HLA diversity and the degree of HLA LOH are quantified through the deltaHED value, the state of the immune environment in the body of the patient is reflected more accurately, the higher the deltaHED value is, the more HLA diversity loss of the patient is shown, namely, the more HLA loss capable of presenting new antigen is shown, and the tumor is more likely to realize immune escape. Therefore, the method can accurately predict the sensitivity of the tumor patient to the immunotherapy. In addition, deltaHED values can also be used in combination with other immunotherapeutic markers, further improving the accuracy of the prediction.

Description

Method and device for predicting sensitivity of tumor patient to immunotherapy

Technical Field

The present invention relates to the field of biological information, and in particular to a method and apparatus for predicting the sensitivity of a tumor patient to immunotherapy.

Background

Immunotherapy has shown promise in various types of malignancies, but to date, the proportion of patients who can benefit from immunotherapy is relatively small and immune-related adverse events and high costs are inevitable problems. Therefore, it is of great interest to find methods that are most likely to accurately predict whether a patient will benefit from immunotherapy.

Prediction of the efficacy of current immunotherapy focuses primarily on TMB levels, PD-L1 expression levels, and MSI status, but the role of antigen presentation in activating anti-tumor immune responses is not trivial. It has been found that the Major Histocompatibility Complex (MHC), which is a Human Leukocyte Antigen (HLA) in humans, is a very polymorphic genetic complex consisting of more than 200 genes. HLA class I genes encode cell surface molecules, distinguish self from non-self peptides, present antigenic peptides derived from pathogens or tumor cells to the cell surface, and are recognized by the T Cell Receptor (TCR), thereby generating an immune response. Studies have shown that HLA-heterozygous individuals present a greater variety of antigenic peptides than HLA-homozygous individuals, i.e., individuals with a greater HLA diversity present a greater variety of antigenic peptides, and thus generate an immune response to a wider range of antigens. It is therefore speculated that tumor cells deficient in antigen presentation may escape anti-tumor immune clearance and survive by interrupting tumor antigen recognition, thereby affecting the effectiveness of immunotherapy. However, researchers have found that the HLA diversity of tumor tissues is not an accurate predictor of patient sensitivity to immunotherapy.

Disclosure of Invention

Based on this, there is a need for a method for predicting the sensitivity of tumor patients to immunotherapy with high accuracy.

Furthermore, an apparatus, a computer device, a storage medium and a computer program product for predicting the sensitivity of a tumor patient to immunotherapy are provided.

A method of predicting the sensitivity of a patient with a tumor to immunotherapy, comprising the steps of:

obtaining the leucocyte antigen heterozygosity loss state and the leucocyte antigen evolution difference of the HLA-I class gene of the tumor tissue of a patient respectively, and recording the result as an HLA LOH (level of elevation) result and an HED (high-level differential expression) value respectively;

removing the HLA with heterozygosity loss according to the HLA LOH result, and calculating to obtain the leukocyte antigen evolution difference of the HLA-I genes with the heterozygosity loss removed, and recording the leukocyte antigen evolution difference as an adjHED value;

calculating the difference between the HED value and the adjHED value and recording the difference as a deltaHED value; and

the sensitivity of the patient to immunotherapy is predicted from the comparison of a given threshold value to the deltaHED value.

In one embodiment, the steps of obtaining the leukocyte antigen heterozygosity loss status and the leukocyte antigen evolutionary difference of the HLA-class I gene of the tumor tissue of the patient and recording the result as HLA LOH and HED values respectively comprise: and obtaining the leucocyte antigen heterozygosity loss state of the HLA-I gene of the tumor tissue of the patient according to the HLA gene sequence, the tumor content, the ploidy and the HLA typing of the patient, and recording the result as an HLA LOH result.

In one embodiment, the steps of obtaining the leukocyte antigen heterozygosity loss status and the leukocyte antigen evolutionary difference of the HLA-class I gene of the tumor tissue of the patient and recording the result as the HLA LOH and HED values respectively further comprise: according to the HLA type of the patient, the average value of the Greener distances of the peptide binding region sequences of three groups of alleles of HLA-A, HLA-B and HLA-C was obtained and recorded as HED value.

In one embodiment, the method further comprises obtaining the HLA gene sequence, the tumor content and ploidy, and the HLA typing from the sequencing data of the tumor tissue and the normal tissue of the patient.

In one embodiment, the method further includes: obtaining at least one of TMB level, PD-L1 expression level, and MSI status of the patient from the sequencing data of the tumor tissue and normal tissue of the patient; and using the comparison in combination with at least one of the above-described level of TMB, the above-described level of PD-L1 expression, and the above-described MSI status to predict the patient's sensitivity to immunotherapy.

In one embodiment, the HLA with heterozygous loss is removed according to the HLA LOH result, and the leukocyte antigen evolutionary difference of the HLA-class I gene after the HLA with heterozygous loss is removed is calculated and recorded as the adjHED value, including: and removing the HLA with heterozygosity loss according to the HLA LOH result, analyzing to obtain the HLA typing after removing the HLA with heterozygosity loss, and calculating the average value of the Greenseum distance of the peptide binding region sequences of three groups of alleles of HLA-A, HLA-B and HLA-C according to the HLA typing after removing the HLA with heterozygosity loss and recording the average value as an adjHED value.

In one embodiment, the HLA with heterozygous loss comprises HLA with copy number less than 0.5 and p value less than 0.01.

In one embodiment, the step of predicting the patient's sensitivity to immunotherapy based on the comparison of a given threshold to the deltaHED value comprises: predicting that the patient is susceptible to immunotherapy if the deltaHED value is less than the threshold; otherwise, the patient is predicted to be insensitive to immunotherapy.

In one embodiment, the given threshold comprises 4.8.

An apparatus for predicting the sensitivity of a tumor patient to immunotherapy, comprising the following modules:

the data acquisition module is used for respectively acquiring the leucocyte antigen heterozygosity loss state and the leucocyte antigen evolution difference of the HLA-I gene of the tumor tissue of the patient and respectively recording the leucocyte antigen heterozygosity loss state and the leucocyte antigen evolution difference as an HLA LOH result and an HED value;

the adjHED value calculation module is used for removing the HLA with heterozygosity loss according to the HLA LOH result, calculating and obtaining the leukocyte antigen evolution difference of the HLA-I genes after the HLA with heterozygosity loss is removed, and recording the leukocyte antigen evolution difference as an adjHED value;

the deltaHED value calculating module is used for calculating the difference value between the HED value and the adjHED value and recording the difference value as a deltaHED value; and

a prediction module for predicting the patient's sensitivity to immunotherapy based on a comparison of a given threshold value to a deltaHED value.

In one embodiment, the data acquisition module comprises a module for acquiring the leukocyte antigen heterozygosity loss state of the HLA-I gene of the tumor tissue of the patient according to the HLA gene sequence, the tumor content and ploidy of the patient and HLA typing, and recording the result as an HLA LOH result.

In one embodiment, the data acquisition module further comprises a module for obtaining the average of the Greenstein distance of the peptide binding region sequences of three sets of HLA-A, HLA-B and HLA-C alleles according to the HLA typing of the patient, and recording the average as the HED value.

In one embodiment, the apparatus further comprises a means for obtaining HLA gene sequence, tumor content and ploidy, and HLA typing from sequencing data of tumor tissue and normal tissue of the patient.

In one embodiment, the apparatus further comprises a processor configured to obtain at least one of TMB level, PD-L1 expression level, and MSI status of the patient based on sequencing data of tumor tissue and normal tissue of the patient; and using the comparison in combination with at least one of the above-described level of TMB, the above-described level of PD-L1 expression, and the above-described MSI status to predict the patient's sensitivity to immunotherapy.

In one embodiment, the adjHED value calculation module further comprises a module for removing HLA with heterozygosity loss according to the HLA LOH result, analyzing to obtain HLA typing after removing HLA with heterozygosity loss, calculating the glathermer distance of the peptide binding region sequences of three groups of alleles of HLA-A, HLA-B and HLA-C according to the HLA typing after removing HLA with heterozygosity loss, and calculating the average value of the glathermer distance as the adjHED value.

In one embodiment, the prediction module further comprises: predicting that the patient is susceptible to immunotherapy if the deltaHED value is less than the threshold; otherwise, the patient is predicted to be insensitive to immunotherapy.

In one embodiment, the given threshold comprises 4.8.

A computer device having a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, implements the steps of the method of any of the embodiments described above.

A computer storage medium having stored thereon a computer program which, when executed, carries out the steps of the method of any of the above embodiments.

A computer program product comprising a computer program which, when executed by a processor, carries out the steps of the method of any of the above embodiments.

The above-described methods, apparatus, computer devices, computer storage media and computer program products for predicting the sensitivity of a tumor patient to immunotherapy present the concept of deltaHED values by analyzing the leukocyte antigen evolutionary diversity of HLA-class I genes of tumor tissue in combination with the state of loss of heterozygosity for leukocyte antigens. Changes of HLA diversity and the degree of HLA LOH are quantified through the deltaHED value, the state of the immune environment in a patient body is reflected more accurately, the higher the deltaHED value is, the more HLA diversity loss of the patient is shown, namely, the more HLA loss capable of presenting a new antigen is, and the more possible immune escape of the tumor is realized. Therefore, the method can accurately predict the sensitivity of the tumor patient to the immunotherapy. In addition, deltaHED values can also be used in combination with at least one immunotherapeutic marker of TMB levels, PD-L1 expression levels, and MSI status, together to predict a patient's sensitivity to immunotherapy, further improving the accuracy of the prediction.

Drawings

FIG. 1 is a chart of TMB value statistics for different HLA LOH status of different types of tumor samples;

FIG. 2 is a statistical plot of HED values for different HLA LOH status for different types of tumor samples;

FIG. 3 is a statistical plot of the number of samples ranked according to HED value for different types of tumor samples that have lost one HLA heterozygosity;

FIG. 4 shows the results of survival analysis of 61 melanoma patients receiving immunotherapy in a public database divided into two groups, deltaHED ≧ 4.8 and deltaHED < 4.8.

Detailed Description

The present invention will be described in detail with reference to the following embodiments in order to make the aforementioned objects, features and advantages of the invention more comprehensible. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, as those skilled in the art will recognize without departing from the spirit and scope of the present invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

The "immunotherapy" described herein is a method of using the immune system to treat tumors, including but not limited to immune checkpoint inhibitor therapy, immune checkpoints including but not limited to PD-1, PD-L1, or CTLA 4. The HLA-I gene comprises at least three types of HLA-A, HLA-B and HLA-C, and is distributed on the surface of almost all nucleated cells, and the surface density of lymphocytes is the maximum. The 'leucocyte antigen Heterozygosity Loss state' refers to 'Loss of Heterozygosity in HLA', and is called HLA LOH for short; HLA is alloantigen with high polymorphism on human leukocyte surface, and human cell contains two sets of HLA molecule encoding genes, one set of gene is inherited from mother, and the other set of gene is inherited from father; a change in a gene may result in the loss of all or part of a set of genes, a condition known as heterozygous deletion. The "leukocyte antigen evolution difference" refers to "HLA evolution diversity", HED for short, and is an index for quantifying the breadth of immune peptide groups that can be presented by individual HLA allotypes.

"TMB" as used herein refers to the Tumor Mutation Burden (Tumor Mutation Burden), and generally refers to the number of somatic non-synonymous mutations or all mutations occurring per megabase in the region of the gene detected by whole exon sequencing or targeted sequencing in a Tumor sample (the calculation formula: number of mutations/length of exon Mb detected). The 'PD-L1' is short for Programmed Cell Death-Ligand 1, is a first type transmembrane protein with the size of 40kDa, normally reacts to foreign antigens gathered in lymph nodes or spleen to promote T Cell proliferation with antigen specificity, and the Cell Programmed Death receptor-1 (PD-1) is combined with PD-L1 to transmit inhibitory signals to reduce T Cell proliferation. The MSI refers to Microsatellite Instability (Microsatellite Instability), wherein microsatellites are short tandem repeats distributed in a human genome and are repeated by mononucleotide, dinucleotides or high-position nucleotides for 10-50 times, and the length of the Microsatellite is changed due to insertion or deletion of the repeat unit in a tumor cell compared with that in a normal cell, so that the Microsatellite Instability is called.

One embodiment of the present invention provides a method for predicting the sensitivity of a tumor patient to immunotherapy, comprising steps S10, S20, S30 and S40, specifically:

step S10: and respectively obtaining the leucocyte antigen heterozygosity loss state and the leucocyte antigen evolutionary difference of the HLA-I genes of the tumor tissues of the patient, and respectively recording the leucocyte antigen heterozygosity loss state and the leucocyte antigen evolutionary difference as an HLA LOH result and an HED value.

In one embodiment, step S10 includes: obtaining the leucocyte antigen heterozygosity loss state of HLA-I class gene of tumor tissue of the patient according to the HLA gene sequence, the tumor content, the ploidy and the HLA typing of the patient, and recording as HLA LOH result.

In one embodiment, the specific steps for determining the HLA LOH outcome are: taking the HLA gene sequence, the tumor content and the ploidy of the patient and HLA typing as input files, and locally comparing each group of homologous HLA alleles to determine the coverage rate of mismatch positions among the homologous HLA alleles; determining the copy number of each HLA haplotype and the p value related to allele imbalance; if the copy number of a certain HLA in a sample is less than 0.5 and the p-value is less than 0.01, the HLA is classified as a loss-prone HLA, and the sample is identified as having HLALOH. In an alternative specific example, the LOHHLA software may be used, but is not limited to, to perform the above steps to obtain HLA LOH results for the patient.

HLA LOH is present in a variety of cancers, and the present investigators have explored the association of TMB with HLALOH in 16 high-grade cancer species. As shown in fig. 1, exploratory analysis showed that TMB values were higher in patients with HLA LOH (dark bars in fig. 1, legend "Loss") compared to patients without HLA LOH (blank bars in fig. 1, legend "no Loss") in multiple cancer species. High TMB values generally indicate higher neoantigen levels, resulting in increased immune pressure on the tumor, and thus the results in fig. 1 indicate that tumors with high TMB values may use HLA LOH as an immune escape mechanism to lose HLA heterozygosity, thereby preventing a portion of the neoantigen from binding to HLA, and reducing the likelihood of immune response. Therefore, patients with high TMB are not necessarily more sensitive to immunotherapy and the results of binding HLA LOH may improve the accuracy of predicting the efficacy of immunotherapy. However, the result of HLA LOH shows only whether or not the loss of heterozygosity of HLA occurs, and does not show the degree of loss of heterozygosity of HLA, so deltaHED is required to quantify the degree of loss of heterozygosity of HLA.

In one embodiment, step S10 further includes: according to the HLA type of the patient, the average value of the Greener distances of the peptide binding region sequences of three groups of alleles of HLA-A, HLA-B and HLA-C was obtained and recorded as HED value.

Specifically, the glatherum distance is a classical metric that quantifies physiochemical differences between protein amino acid sequences, taking into account amino acid composition, polarity, and volume. The HED value is a quantitative index of HLA heterozygosity, and can be obtained through a normal sample, such as a control blood sample, by utilizing the difference between HLA allele sequences of a patient, but because the HED cannot reflect the difference between tumor tissues and normal tissues, the change of the HLA heterozygosity of a tumor patient cannot be reflected.

Further, the present investigators found that in some cancer species, the HED values of patients with HLA LOH (in FIG. 2, the dark bars are indicated by "Loss") were significantly higher than those without HLA LOH (in FIG. 2, the blank bars are indicated by "nolloss"), as shown in FIG. 2, indicating that high or low HED values may be associated with changes in HLA heterozygosity, as indicated by HLALOH. Still further, as shown in FIG. 3, the present inventors screened samples with loss of heterozygosity for 1 HLA, counted HED values of HLA-A, HLA-B and HLA-C for each sample, ranked the three HLA's from high to low according to their respective HED averages, with "1" in the legend representing the HLA with the highest HED value, and "2" and "3" being synonymous; the ordinate is the number of samples in which the corresponding HLA had been heterozygosity lost on the abscissa, and therefore, it is found from fig. 3 that HLA having a higher HED value has a higher frequency of heterozygosity loss, indicating that HLA having a higher HED value may be more likely to be lost, i.e., the probability that HLA having a higher HED value will be present in a patient may be higher. High HED values represent high HLA heterozygosity, which can present more neoantigens, and thus tumors may achieve immune escape by losing heterozygosity for HLA with high HED values.

In one embodiment, HLA gene sequence, tumor content and ploidy, and HLA typing are obtained from sequencing data of tumor and normal tissues of a patient.

In particular, sequencing data may be, but is not limited to, the results of Next Generation Sequencing (NGS). In other embodiments, the sequencing data can also be the result of first generation sequencing, third generation sequencing, or single molecule sequencing. It is understood that sequencing means capable of obtaining the gene sequences of both tumor and normal tissues of a patient may be employed.

In one example, BAM files of tumor and normal tissues were obtained from the sequencing data of the patient's tumor and normal tissues, and tumor content and ploidy were calculated using Sequenza software, while HLA typing was analyzed using Polysolver software. It will be appreciated that in other embodiments, other software may be used to analyze the patient's sequencing data for tumor content and ploidy and HLA typing.

In one example, a FASTQ file of whole exon sequencing of tumor and normal tissues was first obtained, quality controlled, disarmed, resulting in a high quality clean FASTQ file, and aligned to the human reference genome hg19 using bwa mem software to result in a BAM file.

Step S20: and removing the HLA with heterozygosity loss according to the HLA LOH result, and calculating to obtain the leukocyte antigen evolution difference of the HLA-I genes with the heterozygosity loss removed, and recording the leukocyte antigen evolution difference as an adjHED value.

In one embodiment, the HLA with heterozygous loss is removed according to the HLALOH result, and the leukocyte antigen evolutionary difference of the HLA-class I gene after the HLA with heterozygous loss is removed is calculated and recorded as the adjHED value, including: and (3) removing the HLA with heterozygosity loss according to the HLA LOH result, analyzing to obtain the HLA type after removing the HLA with heterozygosity loss, and calculating the average value of the Greenseum distance of the peptide binding region sequences of three groups of alleles of HLA-A, HLA-B and HLA-C according to the HLA type after removing the HLA with heterozygosity loss, and recording the average value as an adjHED value.

Step S30: the difference between the HED value and the adjHED value is calculated and recorded as deltaHED value.

In particular, according to the foregoing analysis, since tumors may achieve immune escape by losing heterozygosity for HLA whose HED value is high, there may be little correlation between how high and low the HED value is and whether a patient will be susceptible to immunotherapy. The deltaHED value is analyzed by combining the HLALOH result with the HED value, and can reflect the change situation of the HED value and quantify the loss degree of the heterozygosity of the HLA.

Step S40: the sensitivity of the patient to immunotherapy is predicted from the comparison of a given threshold value to the deltaHED value.

Specifically, a larger deltaHED value indicates a larger change in HED values in the tumor, and a greater loss of HLA makes it more likely that immune escape will be achieved, and is not sensitive to immunotherapy, i.e., may not respond well to immunotherapy.

In one embodiment, the threshold is given by calculating deltaHED values of a plurality of tumor samples and then using the number of the deltaHED values as the threshold. In an alternative specific example, the deltaHED values of 3215 tumor samples were calculated, with the median value of 4.8 as the threshold. It can be understood that the specific size of the threshold is directly related to the number of samples and the types of the samples, and the number and the types of the samples can be changed and the threshold can be changed correspondingly under the condition that the number of the samples to be selected is reasonable and the diversity of the types of the samples is ensured. The threshold of 4.8 in this embodiment enables a higher prediction accuracy.

In one example, the present inventors collected 91 melanoma patients who received immunotherapy and had a complete total survival time (OS) from a public database (DOI: 10.1016/j.cell.2016.02.065; DOI:10.1016/j.cell.2017.09.028), obtained HLA gene sequences, tumor contents, and ploidy and HLA typing of these patients according to the above method, further obtained HLA LOH results and HED values, and calculated deltaHED values. Since the data of patients with low tumor content is not representative of tumor, the data of patients with low tumor content (tumor content less than 20%) are screened, and the data of the rest 61 patients are obtained. Survival analysis was performed by dividing 61 patients into two groups of deltaHED high level (deltaHED.gtoreq.4.8) and deltaHED low level (deltaHED <4.8) at a threshold of 4.8. As shown in FIG. 4, patients with low levels of deltaHED survived better and were more susceptible to immunotherapy than patients with high levels of deltaHED.

In some embodiments, at least one of TMB level, PD-L1 expression level, and MSI status of the patient is also obtained from sequencing data of tumor tissue and normal tissue of the patient; and using the comparison in combination with at least one of the above-described level of TMB, the above-described level of PD-L1 expression, and the above-described MSI status to predict the patient's sensitivity to immunotherapy. It will be appreciated that the above comparison results may also be used in combination with other methods of predicting a patient's sensitivity to immunotherapy.

Specifically, given that TMB levels, PD-L1 expression levels, or MSI status, respectively, are correlated with a patient's sensitivity to immunotherapy, when TMB levels above a corresponding threshold, PD-L1 expression levels above a corresponding threshold, or MSI status is MSI-H (microsatellite high instability), it is predicted that the patient is likely to be sensitive to immunotherapy. The comparison between the deltaHED value and the threshold value, used in combination with at least one of the TMB level, PD-L1 expression level, and MSI status, respectively, can further improve the accuracy of the prediction. If the deltaHED value is above the threshold and at least one of the TMB level, PD-L1 expression level, and MSI status used in combination therewith predicts that the patient is likely to be sensitive to immunotherapy, the patient is more likely to be sensitive to immunotherapy and the accuracy of predicting that the patient is sensitive to immunotherapy is higher.

In some embodiments, the prediction methods of the present invention can be widely applied to a variety of cancer species, including but not limited to lung, liver, colorectal, pancreatic, gastric, breast, kidney, esophageal, gall bladder, soft tissue, uterine, extrahepatic bile duct, ovarian, hepatic portal, head and neck, or urinary tract tumors (renal pelvis, ureter, or bladder).

Based on the same inventive concept as the above method, an embodiment of the present invention further provides a device for predicting the sensitivity of a tumor patient to immunotherapy, which provides a solution similar to the solution described in the above method, so specific limitations in the device embodiment provided below can be referred to the limitations of the above corresponding method, and are not repeated herein.

In one embodiment, an apparatus for predicting the sensitivity of a tumor patient to immunotherapy is provided, the apparatus comprising a data acquisition module 10, an adjHED value calculation module 20, a deltaHED value calculation module 30, and a prediction module 40, in particular:

the data acquisition module 10 is used for respectively acquiring the leukocyte antigen heterozygosity loss state and the leukocyte antigen evolution difference of the HLA-I genes of the tumor tissues of the patient, and respectively recording the leukocyte antigen heterozygosity loss state and the leukocyte antigen evolution difference as an HLA LOH result and an HED value.

In one embodiment, the data obtaining module 10 includes a module for obtaining the leukocyte antigen heterozygosity loss status of the HLA-class I gene of the tumor tissue of the patient according to the HLA gene sequence, the tumor content and ploidy of the patient and the HLA typing, and recording the result as HLALOH.

In one embodiment, the data acquisition module 10 further comprises a module for obtaining the average of the Greenstein distance of the peptide binding region sequences of the three sets of HLA-A, HLA-B and HLA-C alleles, noted as HED value, based on the HLA typing of the patient.

and the adjHED value calculating module 20 is used for removing the HLA with heterozygosity loss according to the HLA LOH result, calculating and obtaining the leukocyte antigen evolution difference of the HLA-I genes with the heterozygosity loss removed, and recording the leukocyte antigen evolution difference as an adjHED value.

In one embodiment, the adjHED value calculating module 20 further comprises a module for removing HLA with heterozygosity loss according to the HLA LOH result, analyzing to obtain HLA type after removing HLA with heterozygosity loss, calculating the greige distance of the peptide binding domain sequences of three groups of HLA-A, HLA-B and HLA-C alleles according to the HLA type after removing HLA with heterozygosity loss, and calculating the average value of the greige distance as adjHED value.

And the deltaHED value calculating module 30 is used for calculating the difference value between the HED value and the adjHED value and recording the difference value as the deltaHED value.

A prediction module 40 for predicting the patient's sensitivity to immunotherapy based on a comparison of a given threshold value to the deltaHED value.

In one embodiment, the prediction module 40 further comprises: predicting that the patient is susceptible to immunotherapy if the deltaHED value is less than the threshold; otherwise, the patient is predicted to be insensitive to immunotherapy.

In one embodiment, the data acquisition module 10 of the above apparatus further comprises a processor for acquiring at least one of TMB level, PD-L1 expression level, and MSI status of the patient based on the sequencing data of the tumor tissue and normal tissue of the patient; and predicting module 40 is further configured to predict a patient's sensitivity to immunotherapy using the comparison in combination with at least one of the TMB level, the PD-L1 expression level, and the MSI status.

The various modules in the above-described apparatus for predicting the sensitivity of a tumor patient to immunotherapy may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

An embodiment of the present invention further provides a computer device, which has a processor and a memory, where the memory stores a computer program, and the processor implements the steps of the method in any of the above embodiments when executing the computer program.

An embodiment of the present invention also provides a computer storage medium having a computer program stored thereon, which when executed, performs the steps of the method in any of the above embodiments.

An embodiment of the present invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method in any of the above embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

The above-described methods, apparatus, computer devices, computer storage media and computer program products for predicting the sensitivity of a tumor patient to immunotherapy present the concept of deltaHED values by analyzing the leukocyte antigen evolutionary diversity of HLA-class I genes of tumor tissue in combination with the state of loss of heterozygosity for leukocyte antigens. Changes of HLA diversity and the degree of HLA LOH are quantified through the deltaHED value, the state of the immune environment in the body of the patient is reflected more accurately, the higher the deltaHED value is, the more HLA diversity loss of the patient is shown, namely, the more HLA loss capable of presenting new antigen is shown, and the tumor is more likely to realize immune escape. Therefore, the method can accurately predict the sensitivity of the tumor patient to the immunotherapy. In addition, deltaHED values can also be used in combination with existing immunotherapeutic markers such as at least one of TMB levels, PD-L1 expression levels, and MSI status, to jointly predict a patient's sensitivity to immunotherapy, further improving the accuracy of prediction.

All possible combinations of the technical features of the above embodiments may not be described for the sake of brevity, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. It should be understood that the technical solutions obtained by logical analysis, reasoning or limited experiments based on the technical solutions provided by the present invention are all within the protection scope of the appended claims of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims, and the description and the drawings can be used for explaining the contents of the claims.

Claims

1. A method of predicting the sensitivity of a patient having a tumor to immunotherapy, comprising the steps of:

respectively obtaining the leucocyte antigen heterozygosity deletion state and the leucocyte antigen evolution difference of the HLA-I class gene of the tumor tissue of a patient, and respectively recording the result as an HLA LOH result and an HED value;

predicting the patient's sensitivity to immunotherapy based on the comparison of a given threshold value to the deltaHED value.

2. The method of claim 1, wherein the steps of obtaining the status of loss of leukocyte antigen heterozygosity and leukocyte antigen evolutionary variability of HLA class I genes from tumor tissue of the patient and recording them as HLA LOH result and HED value, respectively, comprise:

obtaining the leucocyte antigen heterozygosity loss state of the HLA-I class gene of the tumor tissue of the patient according to the HLA gene sequence, the tumor content, the ploidy and the HLA typing of the patient, and recording the state as the HLA LOH result;

according to the HLA typing of the patients, the average value of the Greener distance of the peptide binding region sequences of three groups of alleles of HLA-A, HLA-B and HLA-C is obtained and recorded as the HED value.

3. The method of claim 2, further comprising obtaining the HLA gene sequence, the tumor content and ploidy, and the HLA typing from sequencing data of tumor and normal tissues of the patient.

4. The method of claim 3, further comprising:

obtaining at least one of TMB level, PD-L1 expression level, and MSI status of the patient from sequencing data of tumor tissue and normal tissue of the patient; and

using the comparison in combination with at least one of the TMB level, the PD-L1 expression level, and the MSI status predicts the patient's sensitivity to immunotherapy.

5. The method according to any one of claims 1 to 4, wherein the HLA with loss of heterozygosity is removed according to the HLA LOH result, and the leukocyte antigen evolution variability of the HLA-class I gene after the HLA with loss of heterozygosity is calculated and recorded as an adjHED value comprises the following steps:

and removing the HLA with heterozygosity loss according to the HLA LOH result, analyzing to obtain the HLA type after removing the HLA with heterozygosity loss, and calculating the average value of the Greenseum distance of the peptide binding region sequences of three groups of alleles of HLA-A, HLA-B and HLA-C according to the HLA type after removing the HLA with heterozygosity loss and recording the average value as the adjHED value.

6. The method of any one of claims 1 to 4, wherein said HLA with loss of heterozygosity comprises HLA with a copy number of less than 0.5 and a p-value of less than 0.01.

7. The method according to any one of claims 1 to 4, wherein the step of predicting the patient's sensitivity to immunotherapy based on the comparison of a given threshold value to the deltaHED value comprises:

predicting that the patient is susceptible to immunotherapy if the deltaHED value is less than a threshold value; otherwise, the patient is predicted to be insensitive to immunotherapy.

8. The method of claim 7, wherein the given threshold comprises 4.8.

9. An apparatus for predicting the sensitivity of a patient with a tumor to immunotherapy, comprising the following modules:

the adjHED value calculating module is used for removing the HLA with heterozygosity loss according to the HLA LOH result, calculating and obtaining the leukocyte antigen evolution difference of the HLA-I genes with the heterozygosity loss removed, and recording the leukocyte antigen evolution difference as an adjHED value;

a deltaHED value calculating module, configured to calculate a difference between the HED value and the adjHED value, and record the difference as a deltaHED value; and

a prediction module to predict the patient's sensitivity to immunotherapy based on a comparison of a given threshold value to the deltaHED value.

10. A computer device having a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 6.

11. A computer storage medium on which a computer program is stored, characterized in that the computer program, when executed, implements the steps of a method according to any one of claims 1 to 6.

12. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method according to any one of claims 1 to 6 when executed by a processor.