CN109448790B - Storage medium, equipment and device for determining tumor immunotherapy marker - Google Patents

Abstract

The invention relates to the field of biology, in particular to a marker for tumor immunotherapy. The present invention provides a computer readable storage medium storing a computer program which when executed performs method steps comprising: obtaining mRNA expression data of an individual; obtaining an antigen presentation index (APS) based on a GSVA algorithm according to mRNA expression data of an individual; calculating to obtain APS according to formula I_normalized(ii) a Tumor immunogenicity index (TIGS) was calculated according to formula II. The TIGS provided by the invention can predict the objective response rate of pan-cancer Immune Checkpoint Inhibitor (ICI) and the clinical effect of cancer patients after ICI treatment.

Description

Storage medium, equipment and device for determining tumor immunotherapy marker

Technical Field

The invention relates to the field of biology, in particular to a marker for tumor immunotherapy.

Background

The development of immunotherapy, represented by Immune Checkpoint Inhibitors (ICI), including anti-PD-1 antibodies, anti-PD-L1 antibodies, anti-CTLA-4 antibodies or combinations thereof, is a revolutionary breakthrough in the area of cancer therapy. Conventional cancer treatment methods (such as radiotherapy and chemotherapy) are often ineligible for the advanced metastatic cancers, and immunotherapy can exert very significant treatment effects on partial advanced metastatic cancers. However, most unselected patients do not respond to ICI. The response rate to PD- (L)1 inhibition was less than 40% for most tumor types, and the response rate reported for each tumor type was highly correlated with Tumor Mutational Burden (TMB) for each tumor type. Various factors have been reported to affect ICI effectiveness, including: PD-L1 expression, tumor mutation burden, DNA mismatch repair deficiency, degree of cytotoxic T cell infiltration, mutation profile, antigen presentation deficiency, interferon signaling, tumor aneuploidy, and T cell gene expression profile. However, none of these factors is sufficient to achieve an accurate prediction of the effectiveness of immunotherapy.

Disclosure of Invention

In view of the above-mentioned drawbacks of the prior art, it is an object of the present invention to provide a marker for tumor immunotherapy, which solves the problems of the prior art.

To achieve the above and other related objects, an aspect of the present invention provides a computer-readable storage medium storing a computer program which, when executed, can implement method steps, the method steps comprising:

acquiring APS based on GSVA algorithm according to individual mRNA expression data;

calculating to obtain APS according to formula I_normalized

Calculating to obtain TIGS according to formula II

TIGS＝APS_normalizedXln (TMB +1) (formula II).

In some embodiments of the invention, the individual is selected from an animal receiving tumor immunotherapy.

In some embodiments of the invention, the subject is selected from a human.

In some embodiments of the invention, the tumor immunotherapy comprises immune checkpoint inhibitor therapy.

In some embodiments of the invention, the tumor immunotherapy comprises a method of treatment of an individual by administering one or more of an anti-PD-1 antibody, a PD-L1 antibody, a CTLA-4 antibody.

In some embodiments of the invention, the mRNA expression data and/or TMB of the individual is derived from a tumor sample.

In some embodiments of the invention, the tumor is selected from melanoma, lung cancer, urothelial cancer, head and neck cancer, kidney cancer, lymphoma, prostate cancer, breast cancer, glioma, cervical cancer, endometrial cancer, esophageal cancer, liver cancer, mesothelioma, ovarian cancer, pancreatic cancer, paraganglioma, colon cancer, sarcoma, gastric cancer, testicular germ cell tumor, thyroid cancer, or thymoma.

In some embodiments of the invention, the genes used to calculate APS include at least 14 genes of PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, TAP1, TAP2, ERAP1, ERAP2, CANX, CALR, PDIA3, TAPBP, B2M, HLA-A, HLA-B, and HLA-C.

In some embodiments of the invention, APS_{pancan_min}＝-1～-0.86。

In some embodiments of the invention, APS_{pancan_max}＝0.88～1。

In another aspect, the invention provides an apparatus comprising: a processor and a memory;

the memory for storing a computer program, the processor for executing the computer program stored by the memory to cause the terminal to perform method steps comprising:

obtaining mRNA expression data of an individual;

acquiring APS based on GSVA algorithm according to individual mRNA expression data;

calculating to obtain APS according to formula I_normalized

Calculating to obtain TIGS according to formula II

TIGS＝APS_normalizedXln (TMB +1) (formula II).

Another aspect of the present invention provides an apparatus, which may include:

the mRNA expression data acquisition module is used for acquiring mRNA expression data of individuals;

the APS processing module is used for acquiring APS based on a GSVA algorithm according to individual mRNA expression data;

APS_normalizeda processing module for calculating APS according to formula I_normalized

And the TIGS processing module is used for calculating and obtaining TIGS according to the formula II.

TIGS＝APS_normalizedXln (TMB +1) (formula II).

Drawings

Figure 1 shows tumor immunogenicity index (TIGS) analysis for 32 cancer types.

FIG. 2 shows the Objective Response Rate (ORR) of TIGS in predicting PD-1 inhibition for different types of tumors.

FIG. 3 shows a comparison of the performance of TIGS and TMB in predicting the clinical effect of ICI in tumor patients.

Detailed Description

The inventor finds that the basic determinants of the tumor immunogenicity comprise tumor antigenicity and antigen processing and presenting efficiency, the antigen processing and presenting efficiency is calculated by applying a GSVA method, and the calculation result is expressed as: the present invention has been completed based on the fact that an antigen presentation index (APM Score, APS) and then a tumor immunogenicity index (TIGS) were calculated by combining APM Score and TMB, TIGS predicts the objective response rate of pan-cancer ICI and ICI clinical response is consistently superior to TMB, indicating that TIGS is a novel and effective tumor intrinsic biomarker for predicting ICI response.

In a first aspect, the present invention provides a diagnostic method comprising:

obtaining mRNA expression data of an individual;

acquiring APS (Antigen presentation score) based on GSVA algorithm according to mRNA expression data of an individual;

calculating to obtain APS according to formula I_normalized(standardized APS)

Calculating according to formula II to obtain TIGS (tumor immunogenicity index)

TIGS＝APS_normalizedXln (TMB +1) (formula II).

In the diagnostic method provided by the present invention, the subject is usually an animal (including human) that can be treated with tumor immunotherapy, and the subject usually includes human, non-human primate, and can be mammal, dog, cat, horse, sheep, pig, cow, etc. The mRNA expression data can generally be genome-wide mRNA expression data, and is typically derived from a tumor sample of the individual, and methods for obtaining mRNA expression data for the individual will be known to those skilled in the art, e.g., the sample can be examined by means including, but not limited to, gene chips, RNA-seq, etc., to obtain genome-wide mRNA expression data for the individual.

In the diagnostic method provided by the present invention, the method of acquiring the mRNA expression data of the individual may be inputting the mRNA expression data of the individual, or the like. Genes used for calculating APS comprise at least 14 or more genes of PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, TAP1, TAP2, ERAP1, ERAP2, CANX, CALR, PDIA3, TAPBP, B2M, HLA-A, HLA-B and HLA-C, the number of the included genes can be 14, 15, 16, 17 or 18, and when APS is actually calculated, expression data of no more than 4 genes are randomly deleted from 18 genes, so that the calculation result is not greatly influenced. GSVA algorithms should be known to the skilled person, for example, reference may be made to the literature (Hanzelmann S, Castelo R, Guinney J.GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14: 7).

In the diagnostic method provided by the present invention, APS_{pancan_min}Generally refers to the lowest value of APS in a population of tumor patients corresponding to an individual. In one embodiment of the present invention, APS_{pancan_min}Statistics of the minimum of the original APS of the 9613 TCGA 32 tumor samples from the public database of tumor genomes, the APS minimum is usually-0.86, APS_{pancan_min}The range may be-1 to-0.86. In one embodiment of the present invention, APS_{pancan_min}The value is typically-0.86, and in rare cases, if the original APS value of a sample is less than-0.86, the value can be taken to replace APS_{pancan_min}Final APS of the sample_normalized＝0。

In the diagnostic method provided by the present invention, APS_{pancan_max}Generally refers to the maximum value of APS in a population of tumor patients corresponding to an individual. In one embodiment of the present invention, APS_{pancan_max}Statistics of the maximum value of the original APS of 9613 TCGA 32 tumors from the public database of tumor genomes, the APS maximum value is usually 0.88, APS_{pancan_max}The value range may be 0.88-1. In one embodiment of the present invention, APS_{pancan_max}The value is generally 0.88, and in a rare case, if the original APS value of a sample is greater than 0.88, the value can be taken to replace APS_{pancan_max}Final APS of the sample_normalized＝1。

In the diagnostic method provided by the present invention, the total number of somatic gene coding errors, base substitutions, gene insertions or deletion errors detected in TMB (Tumor mutation load) usually per million bases, i.e. the number of nonsynonymous mutations per MB (10^6 base pairs), the corresponding test sample usually comes from the Tumor sample of the individual, and the method for obtaining the TMB of the individual should be known to those skilled in the art, for example, the sample can be detected by means including but not limited to WES, targeted sequencing, etc. to obtain the TMB of the individual.

In the diagnosis method provided by the present invention, the prognosis of the tumor immunotherapy effect of an individual can be determined by the TIGS obtained by calculation. For example, individuals with higher TIGS scores typically have a better prognosis (e.g., survival time, etc.) for tumor immunotherapy, and individuals with lower TIGS scores typically have a poorer prognosis for tumor immunotherapy. As another example, individuals with higher TIGS scores are generally considered to have a higher probability of responding well to, or to be better suited for, tumor immunotherapy (e.g., the rate of objective response of a tumor to ICI, ORR, etc.), and individuals with lower TIGS scores are generally considered to have a lower probability of responding well to, or to be less suited for, tumor immunotherapy.

In the diagnosis method provided by the present invention, whether an individual is susceptible to a tumor suitable for tumor immunotherapy can be determined by the TIGS obtained by calculation. For example, individuals with higher TIGS scores typically have a better prognosis (e.g., survival time, etc.) for tumor immunotherapy when developing a tumor, and individuals with lower TIGS scores typically have a poorer prognosis for tumor immunotherapy when developing a tumor. For another example, individuals with higher TIGS scores are generally considered to have a higher probability of responding well to, or to be better suited for, tumor immunotherapy (e.g., the objective response rate ORR of tumors to ICI, etc.) when developing tumors, and individuals with lower TIGS scores are generally considered to have a lower probability of responding well, or to be less suitable for, tumor immunotherapy when developing tumors.

In the diagnostic method provided by the present invention, whether or not a tumor suffered by an individual is suitable for tumor immunotherapy can be diagnosed by using the TIGS obtained by calculation. For example, individuals with tumors with higher TIGS scores are generally considered to have a better prognosis (e.g., survival time, etc.) for tumor immunotherapy, and individuals with tumors with lower TIGS scores are generally considered to have a poorer prognosis for tumor immunotherapy. For another example, individuals with tumors with higher TIGS scores are generally considered to have a higher probability of responding well to tumor immunotherapy or to be more suitable for tumor immunotherapy (e.g., the objective response rate ORR of tumors to ICI, etc.), and individuals with tumors with lower TIGS scores are generally considered to have a lower probability of responding well to tumor immunotherapy or to be less suitable for tumor immunotherapy.

In the diagnosis method provided by the present invention, the tumor to which the diagnosis method is applied may include, but is not limited to, melanoma, lung cancer, urothelial cancer, head and neck cancer, kidney cancer, lymphoma, prostate cancer, breast cancer, glioma, cervical cancer, endometrial cancer, esophageal cancer, liver cancer, mesothelioma, ovarian cancer, pancreatic cancer, paraganglioma, colon cancer, sarcoma, gastric cancer, testicular germ cell tumor, thyroid cancer, thymoma, etc.

In the diagnostic method provided by the present invention, the tumor immunotherapy effect can be any immunotherapy method suitable for tumor patients in the field, and specifically can be a therapy method including but not limited to immune checkpoint inhibitor therapy, and the like, and more specifically can be a therapy method including but not limited to administering one or more of anti-PD-1 antibody, PD-L1 antibody, CTLA-4 antibody, and the like to an individual, and these therapy methods can be administered alone or in combination with other therapies.

A second aspect of the invention provides a computer-readable storage medium storing a computer program which, when executed, may implement method steps including those provided by the diagnostic method as described above.

The third aspect of the present invention provides an apparatus which can be used for screening whether an individual is susceptible to develop a tumor suitable for tumor immunotherapy, diagnosing whether an individual has a tumor suitable for tumor immunotherapy, making a prognosis on the effect of tumor immunotherapy on the individual, and the like, comprising: a processor and a memory;

the memory is used for storing computer programs, and the processor is used for executing the computer programs stored by the memory so as to enable the terminal to execute the diagnosis method.

In a fourth aspect, the present invention provides a device, which can be used for screening whether an individual is susceptible to a tumor suitable for tumor immunotherapy, diagnosing whether an individual has a tumor suitable for tumor immunotherapy, performing prognosis on the effect of tumor immunotherapy on the individual, and the like, and the device can include:

an mRNA expression data acquisition module for acquiring mRNA expression data of an individual, for example, mRNA expression data of an input individual, or the like;

the APS processing module is used for acquiring APS based on a GSVA algorithm according to individual mRNA expression data;

APS_normalizeda processing module for calculating APS according to formula I_normalized；

And the TIGS processing module is used for calculating and obtaining TIGS according to the formula II.

In the present invention, the operation principle of each module in the above apparatus may refer to the diagnosis method described above, and is not described herein again.

ICI helps the patient's immune system to recognize and attack cancer cells, whose immunogenicity is the fundamental determinant of the ICI response. In theory, tumors with very low or no immunogenicity do not respond to therapeutic strategies that enhance the immune response, whereas ICI is generally applicable to tumors with sufficient immunogenicity. In addition, enhancing tumor immunogenicity may potentially convert immunotherapy non-responsive tumors to immunotherapy responsive tumors. Therefore, the actual immunogenicity of a tumor is not easily measurable, theoretically the tumor immunogenicity is determined by the tumor cells themselves, and is also influenced by the tumor microenvironment etc., e.g. the function of professional antigen presenting cells such as dendritic cells. The inventor provides a novel molecular marker for predicting whether the immunotherapy is effective, namely tumor immunogenicity index (TIGS). TIGS can efficiently distinguish immunotherapy non-responsive patients from immunotherapy responsive patients, comparing the predictive power of TIGS with TMB in the prediction of Objective Response Rate (ORR) for pan-cancerous ICI and prediction of clinical response to ICI in individual patients. The result shows that TIGS predicts the objective response rate of pan-cancer ICI, the ICI clinical response is always superior to TMB, the TIGS is a novel and effective tumor inherent biomarker for predicting the ICI response, and the prediction effect of the marker is better than that of the marker 'tumor mutation load' in the current clinical application, so that the marker has important clinical application value of immunotherapy.

The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.

Before the present embodiments are further described, it is to be understood that the scope of the invention is not limited to the particular embodiments described below; it is also to be understood that the terminology used in the examples is for the purpose of describing particular embodiments, and is not intended to limit the scope of the present invention; in the description and claims of the present application, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise.

When numerical ranges are given in the examples, it is understood that both endpoints of each of the numerical ranges and any value therebetween can be selected unless the invention otherwise indicated. 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. In addition to the specific methods, devices, and materials used in the examples, any methods, devices, and materials similar or equivalent to those described in the examples may be used in the practice of the invention in addition to the specific methods, devices, and materials used in the examples, in keeping with the knowledge of one skilled in the art and with the description of the invention.

Unless otherwise indicated, the experimental methods, detection methods, and preparation methods disclosed herein all employ techniques conventional in the art of molecular biology, biochemistry, chromatin structure and analysis, analytical chemistry, cell culture, recombinant DNA technology, and related arts. These techniques are well described in the literature, and may be found in particular in the study of the MOLECULAR CLONING, Sambrook et al: a LABORATORY MANUAL, Second edition, Cold Spring Harbor LABORATORY Press, 1989and Third edition, 2001; ausubel et al, Current PROTOCOLS IN MOLECULAR BIOLOGY, John Wiley & Sons, New York, 1987and periodic updates; the series METHODS IN ENZYMOLOGY, Academic Press, San Diego; wolffe, CHROMATIN STRUCTURE AND FUNCTION, Third edition, Academic Press, San Diego, 1998; (iii) METHODS IN ENZYMOLOGY, Vol.304, Chromatin (P.M.Wassarman and A.P.Wolffe, eds.), Academic Press, San Diego, 1999; and METHODS IN MOLECULAR BIOLOGY, Vol.119, chromatography Protocols (P.B.Becker, ed.) Humana Press, Totowa, 1999, etc.

Example 1

Based on the GSVA algorithm, the Antigen Presentation Score (APS) of the tumor sample was calculated. And acquiring the gene mRNA expression data of the tumor samples by using a gene chip or RNA-seq mode, and calculating the APS of each sample by using a GSVA algorithm on the basis. Genes used for calculating APS include (PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, TAP1, TAP2, ERAP1, ERAP2, CANX, CALR, PDIA3, TAPBP, B2M, HLA-A, HLA-B, HLA-C). The values of the original APS range from-1 to 1. To multiply with tumor antigenicity, a standardized APS was used, ranging from 0 to 1, as an indication of "antigen processing and presentation capacity".

Wherein, APS_{pancan_min}And APS_{pancan_max}The values of (a) are-0.86 and 0.88, respectively.

Tumor immunogenicity index (TIGS) was calculated using the following formula:

TIGS＝APS_normalized×In(TMB+1)。

the distribution of TIGS in different types of tumors is shown in fig. 1, where TIGS analysis of a, 32 cancer types. B, results of Cox proportional hazards regression analysis using TIGS on all solid cancers. Forest plots show loge risk ratios (95% confidence intervals). Cancer types with high TIGS include: cutaneous Melanoma (SKCM), diffuse large B-cell lymphoma (DLBC), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC). Univariate Cox regression analysis showed that TIGS scores correlated with survival rates of cancer patients, some were significant

Example 2

The predictive effect of TIGS on Objective Response Rate (ORR) of different tumor types against immune checkpoint inhibitors was further examined. Data were determined from literature searches for ORR of 26 tumor types or subtypes corresponding to immune checkpoint inhibitors. For each tumor type, response data from the largest published study that evaluated ORR was summarized (see table 1, table 1 showing median TMB, APS and clinical Objective Response Rate (ORR) to ICI for 26 tumors). Only anti-PD-1 or anti-PD-L1 monotherapy studies were included in which at least 10 patients not selected for PD-L1 tumor expression were enrolled. The median Tumor Mutational Burden (TMB) for each tumor type is derived from that provided by the literature (Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, et al. analysis of 100,000human cancer genes novales the landscapes of tumor tissue, genome Med 2017; 9: 34). Based on the public database TCGA for tumor genome and the public database GEO for gene expression, we calculated APS information for 26 tumor types, from which TIGS values for each tumor type were calculated, with the calculation method referred to example 1.

Objective Response Rate (ORR) for TIGS in predicting different types of tumors to PD-1 inhibition is shown in figure 2, where the correlation between aps (a), tmb (b), TIGS (c) and objective response rate to anti-PD-1 or anti-PD-L1 treatment is shown for 26 cancer types. Median normalized APS, log-scale median TMB median (non-synonymous mutations/MB) and TIGS among 26 tumor types or subtypes receiving PD-1 or PD-L1 inhibitors are shown. The number of patients evaluating the objective response rate was shown for each tumor type (size of circle), and the number of tumor samples analyzed to calculate the tumor mutation load (degree of shading of circle). Significant correlations were observed between APS, TMB, TIGS and ORR, with correlation coefficients of 0.529 and 0.667 between APS and ORR, TMB and ORR, respectively, indicating that 28% and 44% of the ORR differences for the cancer types were provided by the variation in APS and TMB, respectively. The correlation coefficient between TIGS and ORR is 0.814, suggesting that 66% of ORR difference can be provided by TIGS. This pan-cancerous ORR analysis indicates that TIGS performs significantly better than the known molecular marker TMB in predicting the response rate of immunotherapy.

TABLE 1

Cancer_Type	Pooled_ORR	Median_TMB	Median_APS
				Adrenocortical Carcinoma	6	2.7	0.4436434
Breast Carcinoma	6.18	3.6	0.3211102
				Cervical Cancer	18.28	4.7	0.7340055
Cutaneous Squamous Cell Carcinoma	47.3	45.2	0.5061751
				Endometrial Cancer	13	3.9	0.6345594
Esophagogastric Carcinoma	10.69	5	0.3533898
				Germ Cell Tumors	0	2.7	0.2472031
Glioblastoma	9.43	2.7	0.2699075
				Head and Neck	15.39	5	0.7336607
Hepatocellular Carcinoma	18.07	3.6	0.7651938
				Melanoma	36.62	13.3	0.6719016
Merkel Cell Carcinoma	43.12	4.3	0.5347724
				Mesothelioma	13.08	1.8	0.7745981
NSCLC	22.41	6.8	0.5265
				NSCLC-Nonsquamous	17.66	6.4	0.6018597
NSCLC-Squamous	19.77	9	0.4493587
				Ovarian Cancer	9.29	3	0.3160356
Pancreatic Cancer	0	2	0.488062
				Prostate Cancer	6.82	2.8	0.3467583
Renal Cell Carcinoma	23.69	2.7	0.6606
				Sarcoma	10.91	2.3	0.4649772
Small-Cell Lung Cancer	14.51	9.9	0.4545876
				Urothelial Carcinoma	19.03	6.8	0.5882142
Uveal Melanoma	36	13	04498145
				Lymphoma	69	10	08156333
Thymic Carcinoma	21.9	2.5	0.5681527

Example 3

It is now known that TMB can be used to predict the effect of a tumor patient on treatment with an immune checkpoint inhibitor. The performance of TIGS in predicting ICI clinical response in individual patients was further evaluated in this example. To assess the predictive power of TIGS in ICI clinical response, an ICI dataset with both TMB and transcriptome data of individual patients was selected. A total of two melanoma and one urothelial cancer data sets were available for this analysis, the data sources being specifically as follows: the Van Allen 2015 dataset was downloaded from a supplement to the literature reference (Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al, Genomic reactors of stress to CTLA-4block in metallic media. science 2015; 350: 207-11). This data set investigated the clinical effect of CTLA-4 antibodies in metastatic melanoma. The Hugo 2016 dataset was downloaded from the reference (Hugo W, Zarretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, et al, genomic and Transcriptomic Features of Response to Anti-PD-1Therapy in Metastatic Melanoma. cell 2016; 165: 35-44). This data set investigated the therapeutic effect of PD-1 antibody in metastatic melanoma. The Snyder 2017 dataset (Snyder A, Nathanson T, Funt SA, Ahuja A, Buros Novik J, Hellmann MD, et al. content of system and systematic factors to clinical response and resistance to PD-L1blockade in a clinical analyzer. plos Med 2017; 14: e1002309) was downloaded from https:// githu. com/hammerlab/multi-organic-nuclear-polar-interpol 1. This data set investigated the therapeutic effect of PD-L1 antibody in urothelial cancer.

To assess the predictive performance of ICI clinical responses, Receiver Operating Characteristic (ROC) curves were used to measure false and true positive rates at various thresholds of TIGS or TMB values, which consistently better performance was obtained in all three ICI datasets compared to the widely used ICI response biomarker TMB, with specific results as shown in fig. 3, where a, ROC curves show the performance of TIGS and TMB in predicting anti-CTLA 4 immunotherapy responses in 35 melanoma patients (Van Allen 2015 dataset); b, ROC curves for TIGS and TMB predicting the performance of anti-PD-1 immunotherapy responses in 27 melanoma patients (Hugo 2016 dataset); c, ROC curves show the performance of TIGS and TMB in predicting anti-PD-L1 immunotherapy responses in 22 patients with urothelial cancer (Snyder 2017 dataset); d, grouping the patients according to the TMB or TIGS state, comparing the TMB-High with the TMB-Low (left panel, 100 patients) or the Kaplan-Meier (KM) total survival curve between the TIGS-High and TIGS-Low (right panel, 35 patients), Van Ellen 2015 data set; e, grouping patients according to TMB or TIGS state, comparing KM overall survival curve between TMB-High and TMB-Low (left panel, 37 patients) or TIGS-High and TIGS-Low (right panel, 26 patients), Hugo 2016 dataset; f, grouping patients according to TMB or TIGS state, comparing KM overall survival curve between TMB-High and TMB-Low (left panel, 22 patients) or TIGS-High and TIGS-Low (right panel, 22 patients), and Snyder 2017 data set. In all three available data sets, the Kaplan-Meier overall survival curves were further compared in patients with high/low TIGS or TMB levels. Patients with TIGS above the median were defined as "TIGS-high" and the rest as "TIGS-low". Similar definitions of "TMB-high" and "TMB-low". TMB-high patients showed better survival curves than TMB-low in all three ICI data sets, even though the difference did not reach significance in all three data sets, probably due to sample size. However, TIGS-high patients showed significantly better survival curves compared to TIGS-low in all three ICI data sets (FIG. 3). These analyses indicate that, in all three available data sets, TIGS consistently outperforms TMB in prediction of ICI clinical response.

In conclusion, the present invention effectively overcomes various disadvantages of the prior art and has high industrial utilization value.

The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (12)

1. A computer-readable storage medium storing a computer program that when executed performs method steps comprising:

obtaining an antigen presentation index based on a GSVA algorithm according to mRNA expression data of an individual;

calculating to obtain APS according to formula I_normalized

In formula I:

APS_normalizeda normalized antigen presentation index;

APS refers to antigen presentation index;

APS_{pancan_min}refers to the lowest value of APS in the population of tumor patients corresponding to the individual;

APS_{pancan_max}refers to the maximum value of APS in a population of tumor patients corresponding to the individual;

calculating to obtain tumor immunogenicity index according to formula II

TIGS＝APS_normalizedX ln (TMB +1) (formula II);

in formula II:

TIGS refers to tumor immunogenicity index;

TMB refers to the tumor mutation burden, i.e., the total number of somatic gene coding errors, base substitutions, gene insertion or deletion errors detected per million bases.

2. The computer-readable storage medium of claim 1, wherein the individual is selected from an animal receiving tumor immunotherapy.

3. The computer-readable storage medium of claim 2, wherein the animal receiving tumor immunotherapy is a human.

4. The computer-readable storage medium of claim 2, wherein the tumor immunotherapy comprises an immune checkpoint inhibitor therapy.

5. The computer-readable storage medium of claim 2, wherein the tumor immunotherapy comprises a method of treatment of an individual by administering one or more of an anti-PD-1 antibody, a PD-L1 antibody, a CTLA-4 antibody.

6. The computer-readable storage medium of claim 1, wherein the mRNA expression data and/or TMB of the individual is derived from a tumor sample.

7. The computer-readable storage medium of claim 4, wherein the tumor is selected from melanoma, lung cancer, urothelial cancer, head and neck cancer, kidney cancer, lymphoma, prostate cancer, breast cancer, glioma, cervical cancer, endometrial cancer, esophageal cancer, liver cancer, mesothelioma, ovarian cancer, pancreatic cancer, paraganglioma, colon cancer, sarcoma, gastric cancer, testicular germ cell tumor, thyroid cancer, or thymoma.

8. The computer-readable storage medium of claim 1, wherein the genes used to calculate APS comprise at least 14 genes of PSMB5, PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, TAP1, TAP2, ERAP1, ERAP2, CANX, CALR, PDIA3, TAPBP, B2M, HLA-A, HLA-B, and HLA-C.

9. The computer-readable storage medium of claim 1, wherein APS_{pancan_min}＝-1～-0.86。

10. The computer-readable storage medium of claim 1, wherein APS_{pancan_max}＝0.88～1。

11. An apparatus, comprising: a processor and a memory;

the memory for storing a computer program, the processor for executing the computer program stored by the memory to cause the terminal to perform method steps comprising:

obtaining mRNA expression data of an individual;

acquiring APS based on GSVA algorithm according to individual mRNA expression data;

calculating to obtain APS according to formula I_normalized

In formula I:

APS_normalizeda normalized antigen presentation index;

APS refers to antigen presentation index;

APS_{pancan_min}refers to the lowest value of APS in the population of tumor patients corresponding to the individual;

APS_{pancan_max}refers to the maximum value of APS in a population of tumor patients corresponding to the individual;

calculating to obtain TIGS according to formula II

TIGS＝APS_normalizedX ln (TMB +1) (formula II);

in formula II:

TIGS refers to tumor immunogenicity index;

TMB refers to the tumor mutation burden, i.e., the total number of somatic gene coding errors, base substitutions, gene insertion or deletion errors detected per million bases.

12. An apparatus, the apparatus comprising:

the mRNA expression data acquisition module is used for acquiring mRNA expression data of individuals;

the APS processing module is used for acquiring APS based on a GSVA algorithm according to individual mRNA expression data;

APS_normalizeda processing module for calculating APS according to formula I_normalized

In formula I:

APS_normalizeda normalized antigen presentation index;

APS refers to antigen presentation index;

APS_{pancan_min}refers to the lowest value of APS in the population of tumor patients corresponding to the individual;

APS_{pancan_max}refers to the maximum value of APS in a population of tumor patients corresponding to the individual;

TIGS processing module for obtaining TIGS according to formula II calculation

TIGS＝APS_normalizedX ln (TMB +1) (formula II);

in formula II:

TIGS refers to tumor immunogenicity index;

TMB refers to the tumor mutation burden, i.e., the total number of somatic gene coding errors, base substitutions, gene insertion or deletion errors detected per million bases.

Images