Prediction of outcomes for patients with glioma

Abstract

The present invention relates to a method for predicting the outcome of a subject having a glioma, comprising the steps of determining a gene expression profile or receiving the result of determining a gene expression profile, wherein the gene expression profile is determined in a biological sample obtained from the subject, determining a prediction of the outcome based on the gene expression profile, and optionally providing the prediction to a healthcare provider or the subject.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the outcome of a subject with glioma, and a device for predicting the outcome of a subject with glioma. Furthermore, the present invention relates to a diagnostic kit, the use of the kit, the use of the kit in the method for predicting the outcome of a subject with glioma, the use of first, second, and / or third gene expression profiles in the method for predicting the outcome of a subject with glioma, and a corresponding computer program. [Background technology]

[0002] Cancer is a class of diseases characterized by uncontrolled proliferation, invasion, and occasional metastasis of cells. These three malignant characteristics of cancer distinguish it from benign tumors, which are self-limiting and do not invade or metastasize. Brain tumors, though relatively rare, can be life-threatening even if benign. They are grouped into cancers of the brain and other nervous systems. In 2020, approximately 24,000 new cases were diagnosed (1.3% of all new cancer cases), and it is estimated that over 18,000 people died from the disease, accounting for 3% of all cancer deaths. The median age at diagnosis was 59 years, with a considerable age range, and the 5-year relative survival rate was 32.6%. Generally, the earlier cancer is diagnosed, the higher the survival rate beyond 5 years after diagnosis. In brain and other nervous system cancers, 77% are diagnosed at a local stage, with a 5-year relative survival rate of 35.3%, slightly higher than for distant (32.7%) and localized (20.3%) locations at diagnosis. Men are slightly more affected than women and are more common in individuals with certain genetic syndromes. The causes of most adult brain and spinal cord tumors are unknown, but there are several risk factors for certain types of brain tumors. For example, exposure to vinyl chloride increases the risk of glioma, and infection with the Epstein-Barr virus, having AIDS, or undergoing organ transplantation may increase the risk of primary CNS lymphoma. In addition, having certain genetic syndromes may increase the risk of brain tumors, such as neurofibromatosis type 1 or 2, von Hippel-Lindau disease, tuberous sclerosis, Lie-Fraumeni syndrome, Turcott syndrome type 1 or 2, and nevus basal cell carcinoma syndrome.

[0003] Brain tumors can be classified into glial tumors and non-glial tumors. Most adult brain tumors begin in glial cells. These tumors are called gliomas. Non-glial tumors usually begin in areas outside brain tissue, such as nerves, covering the brain (meninges) or nearby glands, such as the pituitary gland or pineal gland.

[0004] Gliomas, or glial brain tumors, can spread throughout brain tissue, though they rarely spread to other parts of the body. Even benign forms can be harmful, as they tend to compress and destroy normal brain tissue, sometimes resulting in life-threatening damage. Because they are localized in the brain, the main concerns are whether they will spread to the rest of the brain where they are located, and how quickly they grow, if they can be removed. Brain tumors and spinal cord tumors differ between adults and children in terms of where they form, the types of cells they contain, and differences in prognosis and treatment options.

[0005] In adults, secondary brain tumors (tumors that originate in another part of the body and spread to the brain: non-glial tumors) are more common than primary brain tumors. Conversely, primary brain tumors rarely spread to other organs, but they can cause significant brain damage. The brain is composed of different types of tissue with different functions. Thus, depending on where a tumor originates, it will have different characteristics, and consequently, different treatments may be considered.

[0006] Gliomas originate in glial cells and can be astrocytomas (including gliablastomas), oligodendrogliomas, and ependymal cell tumors. Low-grade (I+II) astrocytomas tend to grow slowly, while high-grade (II+IV) ones grow rapidly and tend to spread into surrounding brain tissue. Gliablastomas are grade IV astrocytomas, the fastest-growing, accounting for more than 50% of all gliomas, and are the most common malignant brain tumors in adults. Oligodendrogliomas originate in glial cells of the brain and tend to grow slowly (grade II), but most spread into surrounding tissue, making their surgical removal difficult. Ependymal cell tumors originate in ependymal cells and typically grow in the ventricles or spinal cord of adults. These tumors tend to spread along the cerebrospinal fluid more easily than other gliomas, but do not spread outside the brain or spinal cord.

[0007] Meningiomas originate in the meninges, the layers surrounding the outer part of the brain, and account for about 30% of brain tumors, making them the most common primary brain tumor in adults. Medulloblastomas arise from neuroectoderm cells in the cerebellum. They grow rapidly and are more common in children than in adults. These tumors can be treated with surgery, radiation therapy, and chemotherapy. A less common type of brain tumor is the ganglioglioma, a slow-growing tumor containing nerve cells and glial cells. Eight percent of all CNS tumors are schwannomas, which are almost always grade I tumors. Finally, craniopharyngiomas are slow-growing tumors that, due to their location, often cause hormonal and vision problems.

[0008] There is no standard classification system for this type of tumor. Treatment is based on the type of cells from which the tumor originated, the location of the tumor, the amount of cancer remaining after surgery if possible, and the grade of the tumor. Repeated imaging is used to plan further treatment. Currently, there are five types of standard treatments used: Active monitoring: Used for slowly growing tumors to avoid or delay the need for radiation therapy or surgery. Surgical intervention: Diagnoses and relieves pressure on brain tumors. Surgical intervention may be combined with adjuvant chemotherapy or radiotherapy. RT: Kills cancer cells or slows their growth. Different forms of RT are used to prevent radiation damage to surrounding brain tissue. Chemotherapy: While it can be applied systemically, many drugs cannot cross the blood-brain barrier. Therefore, intrathecal chemotherapy can be used to deliver the drugs directly into the cerebrospinal fluid. Targeted therapy: This therapy specifically attacks tumor cells with particular characteristics and may have fewer side effects than chemotherapy and radiotherapy. Examples include monoclonal antibodies, tyrosine receptor kinase inhibitors, and VEGF inhibitors. Novel treatments being tested in clinical trials include different forms of immunotherapy and proton beam therapy.

[0009] Attempts to characterize gliomas aim to provide better insights into tumor biology / physiology for better treatment decisions. A few examples are summarized below. The diagnosis and treatment of gliomas are significantly limited by their molecular and cellular complexity, as well as intratumoral and intertumoral heterogeneity.

[0010] (1) The metabolic profile produced by H MRS can be used to distinguish between two distinct gliablastoma phenotypes: low-generation (LG) and high-generation (HG) tumors. LG tumors exhibit more pronounced anaerobic metabolism and a more malignant phenotype [Thorsen et al. NMR Biomed. 2008 Oct;21(8):830-8].

[0011] Molecular and cellular complexity, as well as heterogeneity factors, complicate the development of effective and reliable biomarkers. The most common glioma-related molecular abnormalities include IDH mutations, EGFR amplification, P53 and RB mutations, and abnormalities in pathways including PTK, Akt, PI3K, and Ras [Kan et al. BMJ Neurol Open. 2020 Aug 24;2(2):e000069].

[0012] In addition to molecular abnormalities, RNA expression profiling studies have found correlations with patient survival. In one study, using multiple datasets, unsupervised hierarchical clustering for subclassifications based on RNA expression profiles identified molecular subgroups distinct from histological subgroups that correlated more strongly with patient survival [Gravendeel et al. Cancer Res. 2009 DEC 1;69(23):9065-72]. These data also provided evidence of treatment response. Furthermore, specific genetic alterations (EGFR amplification, IDH1 mutations, and 1p19qLOH) were found to separate into different molecular subgroups. However, limitations of using unsupervised hierarchical clustering include insufficient sample size for rare tumors, failure to include all tumor types, and potential for inaccurate / inaccurate classification. Nevertheless, the authors concluded that the identified subtypes improve the histological classification of gliomas and are accurate predictors of diagnosis. Therefore, molecular classification can serve as a basis for clinical decision-making and decisions regarding novel targeted therapies.

[0013] Standard treatment for gliablastoma remains similar to that for other cancers, but it does not always yield strong results. Immunotherapy offers a more specific and effective approach that can extend patients' lifespan, but it faces several challenges. GBM is considered a “cold” tumor, and therefore immunotherapy often fails. Understanding the underlying mechanisms could improve treatment decisions, and recent research has turned its attention to proteomics, which can reveal the true state of tumor cells by quantifying thousands of functional proteins. This could complement “conventional” molecular subtyping, which reveals information about tumor origin.

[0014] While genetic indicators predicting outcomes in patients with gliomas have been described, for example, in US2015 / 038357 A1 and Freije et al. Cancer Research, vol. 64, No. 18, pages 6503-6510, there remains a need to provide improved or alternative genetic indicators for patient stratification and direct treatment decisions. [Overview of the project] [Problems that the invention aims to solve]

[0015] In conclusion, improved understanding of the (molecular) mechanisms of disease and the availability of new treatments highlight the strong need for better prediction of treatment responses. As many studies have concluded, there is a strong need for biomarkers that can aid in diagnosis and treatment, identify recurrent disease, and indicate treatment responses. [Means for solving the problem]

[0016] In a first aspect, the present invention relates to a method for predicting the outcome of a subject having a glioma, comprising the steps of determining the expression levels of six or more genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2, or receiving the results of the determination of the gene expression levels, wherein the gene expression levels are determined in a biological sample obtained from the subject, and determining a prediction of the outcome based on the six or more gene expression levels.

[0017] In a second aspect, the present invention relates to an apparatus for predicting the outcome of a subject having glioma, comprising a suitable input for receiving data indicating the expression levels of six or more genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2, the gene expression levels determined in a biological sample obtained from the subject, a suitable processor for determining a prediction of the outcome based on the six or more gene expression levels, and optionally, a suitable providing unit for providing the prediction to a healthcare provider or the subject.

[0018] In a third aspect, the present invention relates to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to perform the steps of receiving data indicating the expression levels of six or more genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2, wherein the gene expression levels are determined in a biological sample obtained from a subject, determining a prediction of the outcome of the subject based on the six or more gene expression levels, and optionally, providing the prediction to a healthcare provider or the subject, for a method of predicting the outcome of a subject having glioma.

[0019] In a fourth aspect, the present invention is a use of a kit, the use comprising determining the expression levels of six or more genes in a sample obtained from a subject having glioma, and providing an outcome of the subject based on the expression levels of the six or more genes, the kit comprising means for determining the expression levels of six or more genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0020] In a fifth aspect, the present invention is a treatment for use in the treatment or alleviation of a subject having glioma, the use comprising determining the outcome of a subject having glioma using the method defined in the first aspect of the present invention, and administering a treatment based on the outcome to the subject, wherein if the predicted outcome is favorable, the treatment is selected from surgery, radiotherapy, chemotherapy, or targeted therapy, or if the predicted outcome is unfavorable, the treatment is selected from two or more combinations selected from surgery, radiotherapy, chemotherapy, and targeted therapy, or immunotherapy, or an experimental drug or treatment (clinical trial), or alternatively, an adjuvant treatment selected from radiotherapy, chemotherapy with a high effective dose, and long-term CRT (chemoradiotherapy), and one or more treatments selected from immunotherapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] [Figure 1]Figure 1 provides a schematic diagram of how different gene sign-based models were trained in a training cohort and validated in a validation cohort. Briefly, cohorts of patient gene expression data from patients with different types of glioma were randomly divided into a validation cohort and a training cohort. The figure shows the development of a complete “GLCAI score” sign, but the same principle was applied to individual signs (TCR, IFR, RDE4D7-related genes) or genes individually selected from the different gene signs described herein. Using Cox regression analysis, the signs were trained in predicting overall survival. Validation of the different models was performed in the validation cohort. [Figure 2] Figure 2 schematically illustrates how different individual gene signs are generated and how they combine with the GLCAI score. [Figure 3] Figure 3 shows the Kaplan-Meier curves of the IDR_14 model for a cohort of 413 patients where all patients had gliomas (the training set used to develop the IDR_14 model; upper graph) or a cohort of 212 patients (the validation set to confirm the IDR_14 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the IDR_14 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 4]Figure 4 shows the Kaplan-Meier curves of the IDR_14 model for a cohort of 413 patients where all patients had gliomas (the training set used to develop the IDR_14 model; upper graph) or a cohort of 212 patients (the validation set to confirm the IDR_14 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the IDR_14 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 5] Figure 5 shows the Kaplan-Meier curves of the TCR_17 model for a cohort of 413 patients, all of whom had gliomas (the training set used to develop the TCR_17 model; upper graph) or a cohort of 212 patients (the validation set to confirm the TCR_17 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the TCR_17 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 6]Figure 6 shows the Kaplan-Meier curves of the TCR_17 model for a cohort of 413 patients, all of whom had gliomas (the training set used to develop the TCR_17 model; upper graph) or a cohort of 212 patients (the validation set to confirm the TCR_17 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the TCR_17 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 7] Figure 7 shows the Kaplan-Meier curves of the PDE4D7_CORR model for a cohort of 413 patients where all patients had gliomas (the training set used to develop the PDE4D7_CORR model; upper graph) or a cohort of 212 patients (the validation set to confirm the PDE4D7_CORR model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the PDE4D7_CORR model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 8]Figure 8 shows the Kaplan-Meier curves of the PDE4D7_CORR model for a cohort of 413 patients where all patients had gliomas (the training set used to develop the PDE4D7_CORR model; upper graph) or a cohort of 212 patients (the validation set to confirm the PDE4D7_CORR model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the PDE4D7_CORR model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 9] Figure 9 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 413 patients where all patients had gliomas (the training set used to develop the GLCAI model; upper graph) or a cohort of 212 patients (the validation set to confirm the GLCAI model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 10]Figure 10 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 413 patients where all patients had gliomas (the training set used to develop the GLCAI model; upper graph) or a cohort of 212 patients (the validation set to confirm the GLCAI model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 11] Figure 11 shows Kaplan-Meier curves for the GLCAI radiotherapy model for a cohort of 402 patients with gliomas (GLCAI low-risk group; upper graph) and a cohort of 201 patients (GLCAI high-risk group; lower graph). The clinical endpoint examined was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. In the low-risk group (GLCAI<0), patients who did not receive RT had a 40% lower chance of death, which tended to become more pronounced over time. In the high-risk group (GLCAI>0), patients who did not receive RT had a 2.3-fold increased chance of death. Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold=0, low risk (≦0), high risk (>0)). [Figure 12]Figure 12 shows Kaplan-Meier curves of the GLCAI radiotherapy model for a cohort of 402 patients with gliomas (GLCAI low-risk group; upper graph) and a cohort of 201 patients (GLCAI high-risk group; lower graph). The clinical endpoint examined was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. In the low-risk group (GLCAI<0), patients who did not receive RT had a 40% lower chance of death, which tended to become more pronounced over time. In the high-risk group (GLCAI>0), patients who did not receive RT had a 2.3-fold increased chance of death. Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold=0, low risk (≦0), high risk (>0)). [Figure 13] Figure 13 shows the Kaplan-Meier curves of the IDR_14 model for a cohort of 180 patients where all patients had gliomas (the training set used to develop the IDR_14 model; upper graph) or a cohort of 92 patients (the validation set to confirm the IDR_14 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the IDR_14 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 14]Figure 14 shows the Kaplan-Meier curves of the IDR_14 model for a cohort of 180 patients where all patients had gliomas (the training set used to develop the IDR_14 model; upper graph) or a cohort of 92 patients (the validation set to confirm the IDR_14 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the IDR_14 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 15] Figure 15 shows the Kaplan-Meier curves of the TCR_17 model for a cohort of 180 patients where all patients had gliomas (the training set used to develop the TCR_17 model; upper graph) or a cohort of 92 patients (the validation set to confirm the TCR_17 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for TCR_17 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 16]Figure 16 shows the Kaplan-Meier curves of the TCR_17 model for a cohort of 180 patients, all of whom had gliomas (the training set used to develop the TCR_17 model; upper graph) or a cohort of 92 patients (the validation set to confirm the TCR_17 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the TCR_17 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 17] Figure 17 shows the Kaplan-Meier curves of the PDE4D7_CORR model for a cohort of 180 patients where all patients had gliomas (the training set used to develop the PDE4D7_CORR model; upper graph) or a cohort of 92 patients (the validation set to confirm the PDE4D7_CORR model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the PDE4D7_CORR model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 18]Figure 18 shows the Kaplan-Meier curves of the PDE4D7_CORR model for a cohort of 180 patients where all patients had gliomas (the training set used to develop the PDE4D7_CORR model; upper graph) or a cohort of 92 patients (the validation set to confirm the PDE4D7_CORR model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the PDE4D7_CORR model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 19] Figure 19 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 180 patients where all patients had gliomas (the training set used to develop the GLCAI model; upper graph) or a cohort of 92 patients (the validation set to confirm the GLCAI model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 20]Figure 20 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 180 patients where all patients had gliomas (the training set used to develop the GLCAI model; upper graph) or a cohort of 92 patients (the validation set to confirm the GLCAI model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 21] Figure 21 shows Kaplan-Meier curves of the GLCAI radiotherapy model for a cohort of 167 patients with gliomas (GLCAI low-risk group; upper graph) or a cohort of 102 patients (GLCAI high-risk group; lower graph). The clinical endpoint examined was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. In the low-risk group (GLCAI<0), patients who did not receive RT had a 40% lower chance of death, which tended to become more pronounced over time. In the high-risk group (GLCAI>0), patients who did not receive RT had a 2.3-fold increased chance of death. Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold=0, low risk (≦0), high risk (>0)). [Figure 22]Figure 22 shows Kaplan-Meier curves of the GLCAI radiotherapy model for a cohort of 167 patients with gliomas (GLCAI low-risk group; upper graph) or a cohort of 102 patients (GLCAI high-risk group; lower graph). The clinical endpoint examined was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. In the low-risk group (GLCAI<0), patients who did not receive RT had a 40% lower chance of death, which tended to become more pronounced over time. In the high-risk group (GLCAI>0), patients who did not receive RT had a 2.3-fold increased chance of death. Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold=0, low risk (≦0), high risk (>0)). [Figure 23] Figure 23 shows the Kaplan-Meier curves of the IDR_14 model for a cohort of 281 patients where all patients had gliomas (the training set used to develop the IDR_14 model; upper graph) or a cohort of 147 patients (the validation set to confirm the IDR_14 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the IDR_14 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 24]Figure 24 shows the Kaplan-Meier curves of the IDR_14 model for a cohort of 281 patients where all patients had gliomas (the training set used to develop the IDR_14 model; upper graph) or a cohort of 147 patients (the validation set to confirm the IDR_14 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the IDR_14 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 25] Figure 25 shows the Kaplan-Meier curves of the TCR_17 model for a cohort of 281 patients where all patients had gliomas (the training set used to develop the TCR_17 model; upper graph) or a cohort of 147 patients (the validation set to confirm the TCR_17 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the TCR_17 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 26]Figure 26 shows the Kaplan-Meier curves of the TCR_17 model for a cohort of 281 patients, all of whom had gliomas (the training set used to develop the TCR_17 model; upper graph) or a cohort of 147 patients (the validation set to confirm the TCR_17 model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the TCR_17 model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 27] Figure 27 shows the Kaplan-Meier curves of the PDE4D7_CORR model for a cohort of 281 patients, all of whom had gliomas (the training set used to develop the PDE4D7_CORR model; upper graph) or a cohort of 147 patients (the validation set to confirm the PDE4D7_CORR model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the PDE4D7_CORR model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 28]Figure 28 shows the Kaplan-Meier curves of the PDE4D7_CORR model for a cohort of 281 patients, all of whom had gliomas (the training set used to develop the PDE4D7_CORR model; upper graph) or a cohort of 147 patients (the validation set to confirm the PDE4D7_CORR model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the PDE4D7_CORR model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 29] Figure 29 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 281 patients where all patients had gliomas (the training set used to develop the GLCAI model; upper graph) or a cohort of 147 patients (the validation set to confirm the GLCAI model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 30]Figure 30 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 281 patients, all of whom had gliomas (the training set used to develop the GLCAI model; upper graph) or a cohort of 147 patients (the validation set to confirm the GLCAI model; lower graph). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 31] Figure 31 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 203 patients per disease type with glioma (GLCAI low-risk group; upper graph) and a cohort of 216 patients (GLCAI high-risk group; lower graph). The clinical endpoint examined was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for the GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 32]Figure 32 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 203 patients per disease type with glioma (GLCAI low-risk group; upper graph) and a cohort of 216 patients (GLCAI high-risk group; lower graph). The clinical endpoint examined was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 33] Figure 33 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 203 patients per Karnovsky Performance Score category with glioma (GLCAI low-risk group; upper graph) and a cohort of 216 patients per Karnovsky Performance Score category with glioma (GLCAI high-risk group; lower graph). The clinical endpoint examined was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 34]Figure 34 shows the Kaplan-Meier curves of the GLCAI model for a cohort of 203 patients per Karnovsky Performance Score category with glioma (GLCAI low-risk group; upper graph) and a cohort of 216 patients per Karnovsky Performance Score category with glioma (GLCAI high-risk group; lower graph). The clinical endpoint examined was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≦0), high risk (>0)). [Figure 35] Figure 35 shows the Kaplan-Meier curves of the GLCAI6.1 model for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 36]Figure 36 shows the Kaplan-Meier curves of the GLCAI6.2 model for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 37] Figure 37 shows the Kaplan-Meier curves of the GLCAI6.3 model for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 38]Figure 38 shows the Kaplan-Meier curves for IDR14 6.1 for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for IDR14 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 39] Figure 39 shows the Kaplan-Meier curves for IDR14 6.2 in a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for IDR14 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 40]Figure 40 shows the Kaplan-Meier curves for IDR14 6.3 in a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for IDR14 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 41] Figure 41 shows the Kaplan-Meier curves for PDE4D7_R2 6.1 for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for PDE4D7_R2 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 42]Figure 42 shows the Kaplan-Meier curves for PDE4D7_R2 6.2 for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for PDE4D7_R2 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 43] Figure 43 shows the Kaplan-Meier curves for PDE4D7_R2 6.3 for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for PDE4D7_R2 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 44]Figure 44 shows the Kaplan-Meier curves for TCR17 6.1 for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for TCR17 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 45] Figure 45 shows the Kaplan-Meier curves for TCR17 6.2 for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for TCR17 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 46]Figure 46 shows the Kaplan-Meier curves for TCR17 6.3 for a cohort of 625 patients, all of whom had gliomas. The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by their respective Cox regression models. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1 and 2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included appendix list shows the number of patients at risk for TCR17 model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 47] Figure 47 shows Kaplan-Meier curves and GLCAI models for different prior art gene signs (22 gene signs and 44 gene signs) to compare the different gene signs. Each gene sign was trained on a cohort of 413 patients, all of whom had gliomas (training data is not shown as it was necessary to retrain the GLCAI model), and then validated on a cohort of 212 patients (shown in the graph in Figure 47). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included addendum list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 48]Figure 48 shows Kaplan-Meier curves and GLCAI models for different prior art gene signs (22 gene signs and 44 gene signs) to compare the different gene signs. Each gene sign was trained on a cohort of 413 patients, all of whom had gliomas (training data is not shown as it was necessary to retrain the GLCAI model), and then validated on a cohort of 212 patients (shown in the graph in Figure 48). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included addendum list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Figure 49] Figure 49 shows Kaplan-Meier curves and the GLCAI model for different prior art gene signs (22 gene signs and 44 gene signs) to compare the different gene signs. Each gene sign was trained on a cohort of 413 patients in which all patients had gliomas (training data is not shown as it was necessary to retrain the GLCAI model), and then validated on a cohort of 212 patients (shown in the graph in Figure 49). The validated clinical endpoint was time to death in months. Patients were stratified into two groups based on their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean of the predicted risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (groups 1+2 of the entire cohort) and fixed for confirmation in the validation cohort (group 3 of the entire cohort). Log-rank, HR, and confidence intervals are included in the figure. The included addendum list shows the number of patients at risk for GLCAI model class analysis (threshold = 0, low risk (≤0), high risk (>0)). [Modes for carrying out the invention]

[0022] definition Although the present invention is described in relation to specific embodiments, this description should not be construed as limiting.

[0023] Before describing exemplary embodiments of the present invention in detail, we will provide some important definitions for understanding the invention.

[0024] As used herein and in the appended claims, singular elements also include plural forms unless the context explicitly specifies otherwise.

[0025] In the context of the present invention, the terms “about” and “approximately” indicate intervals of accuracy that a person skilled in the art would understand to ensure the technical effect of the feature in question. The terms typically indicate a deviation of ±20%, preferably ±15%, more preferably ±10%, and even more preferably ±5% from a specified number.

[0026] "Approximately" and "about": When these terms refer to measurable values ​​such as quantity or temporary period, they encompass variations of ±20% or ±10%, more preferably ±5%, even more preferably ±1%, and even more preferably ±0.1% from a given value, and therefore mean that the variation is appropriate for carrying out the disclosed method.

[0027] "Antagonist" and "Inhibitor": These terms are used interchangeably and refer to compounds or agents that have the ability to reduce or inhibit the biological function of a target protein or polypeptide, for example, by reducing or inhibiting the activity or expression of the target protein or polypeptide. Therefore, the terms "antagonist" and "inhibitor" are defined in the context of the biological role of the target protein or polypeptide. Inhibitors do not need to completely suppress the biological function of the target protein or polypeptide, and in some embodiments, they reduce the activity by at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99%. Some antagonists herein interact specifically with (e.g., bind to) the target, but compounds that inhibit the biological activity of the target protein or polypeptide by interacting with other members of the signaling pathway of the target protein or polypeptide are also specifically included in this definition. Non-limiting examples of biological activity inhibited by antagonists include those associated with tumor development, growth, or expansion, or unwanted immune responses seen in autoimmune diseases.

[0028] "Anti-cancer effect": This refers to the effect a therapeutic agent has on cancer, such as reducing the proliferation, survival, or both of cancer cells. The IC50 of cancer cells can be used as a measure of anti-cancer effect. The IC50 refers to a measure of the effectiveness of a therapeutic agent in inhibiting cancer cells by up to 50%.

[0029] "To mitigate cancer": This term refers to breaking down a tumor, for example, by destroying its structural integrity or connective tissue, in the context of a particular cancer and / or its pathology, so that the tumor size is reduced compared to the tumor size before treatment. "To mitigate cancer metastasis" includes reducing the rate at which cancer spreads to other organs.

[0030] "Composition," "Product," or "Combination": These encompass, but are not limited to, compositions suitable for administration by various routes, including intravenous, subcutaneous, intradermal, intranodal, intratumoral, intramuscular, intraperitoneal, oral, nasal, topical (including buccal and sublingual), rectal, vaginal, aerosol, and / or parenteral or mucosal application. The compositions, formulations, and products described in the inventions of this disclosure typically comprise a drug / compound / inhibitor (alone or in combination) and one or more suitable pharmaceutically acceptable excipients or carriers.

[0031] "Combination therapy" or "in combination with": These terms refer to the use of one or more compounds or agents to treat a particular disorder or condition. For example, compound 1 may be administered in combination with at least one further therapeutic agent. The term "in combination with" is not intended to indicate that the other therapies and compound 1 must be administered simultaneously and / or formulated to be delivered together, but these methods of delivery are within the scope of this disclosure. Compound 1 may be administered simultaneously with, or before (e.g., 5 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, 24 hours, 48 ​​hours, 72 hours, 96 hours, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 8 weeks, 12 weeks, or 16 weeks prior to) or after (e.g., 5 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, 24 hours, 48 ​​hours, 72 hours, 96 hours, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 8 weeks, 12 weeks, or 16 weeks later) one or more other further agents. Generally, each therapeutic agent is administered in a dose and / or time schedule determined for that particular agent. Other therapeutic agents may be administered with the compounds herein, either in a single composition or separately in different compositions. Higher combinations, such as triplicates, are also considered herein.

[0032] The term "including" is understood to be non-limiting. For the purposes of this invention, the term "consisting of" is considered a preferred embodiment of the term "including." Hereafter, when a group is defined as containing at least a certain number of embodiments, it also means that a group consisting of only these embodiments is preferably included.

[0033] Furthermore, terms such as “First,” “Second,” “Third,” or “(a),” “(b),” “(c),” “(d),” etc., in the specification and claims are used to distinguish between similar elements and not necessarily to indicate order or chronology. Terms used in this manner are interchangeable under appropriate circumstances, and it is understood that embodiments of the present invention described herein may be operated in a different order than those described or illustrated herein.

[0034] Where the terms “First,” “Second,” “Third,” or “(a),” “(b),” “(c),” “(d),” etc. relate to steps or uses of a method, there is no consistency in time or time intervals between steps; that is, the steps may be performed simultaneously, or there may be time intervals of seconds, minutes, hours, days, weeks, months, or years between such steps, unless otherwise specified in this Spec.

[0035] As used herein, the term “at least” means a specific value or greater than or equal to that value. For example, “at least 2” is understood to be the same as “2 or greater,” i.e., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, etc. As used herein, the term “maximum” means a specific value or less than or equal to that value. For example, “maximum 5” is understood to be the same as “5 or less,” i.e., 5, 4, 3, ... -10, -11, etc.

[0036] As used herein, the term "and / or" indicates that one or more of the examples mentioned may occur alone or in combination with all of the examples mentioned, at least one of the examples mentioned.

[0037] As used herein, the word “contains” or its variations, such as “contains,” is understood to include the element, integer, or step, or group of elements, integers, or steps, mentioned, but not to exclude any other element, integer, or step, or group of elements, integers, or steps. The verb “contains” includes the verbs “to be basically from” and “to consist of.”

[0038] As used herein, the term “prior art” means a situation in which the methods of performing the prior art used in the methods of the present invention are apparent to those skilled in the art. Practices of the prior art in molecular biology, biochemistry, computer science, cell culture, recombinant DNA, bioinformatics, genomics, sequencing, and related fields are well known to those skilled in the art and are discussed, for example, in the following references: Sambrook et al., Molecular Cloning. A Laboratory Manual, 2nd edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 1989; Ausubel et al., Current Protocols in Molecular Biology, John Wiley & Sons, New York, 1987 and periodic updates; and the series Methods in Enzymology, Academic Press, San Diego.

[0039] As used herein, the term “identity” refers to a measure of the identity of a nucleotide or amino acid sequence. Generally, sequences are aligned to obtain the highest level of match. “Identity” itself has an accepted meaning in the art and can be calculated using published techniques. See, for example, (Computational Molecular Biology, Lesk, AM, ED., Oxford University Press, New York, 1988; Biocomputing: Informatics And Genome Projects, Smith, DW, ED., Academic Press, New York, 1993; Computer Analysis Of Sequence Data, Part I, Griffin, AM, And Griffin, HG, EDS., Humana Press, New Jersey, 1994; Sequence Analysis In Molecular Biology, Von Heinje, G., Academic Press, 1987; and Sequence Analysis Primer; Gribskov, M. and Devereux, J., eds., M Stockton Press, New York, 1991). Many methods exist for determining the identity between two nucleotide or amino acid sequences, but the term “identity” is well known to those skilled in the art (Carillo, H., and Lipton, D., SIAM J. Applied Math (1988) 48:1073). Methods commonly used to determine the identity or similarity between two sequences include, but are not limited to, those disclosed in Guide To Huge Computers, Martin J. Bishop, ed., Academic Press, San Diego, 1994, and Carillo, H., and Lipton, D., Siam J. Applied Math (1988) 48:1073. Methods for determining identity and similarity are systematized in computer programs.Preferred computer programming methods for determining identity and similarity between two sequences include, but are not limited to, the GCG program package (Devereux, J., et al., Nucleic Acids Research (1984) 12(1):387), BLASTP, BLASTN, and FASTA (Atschul, SF et al., J. Molec. Biol. (1990) 215:403).

[0040] For example, a polynucleotide having a nucleotide sequence that is at least, for example, 95% "identical" to a reference nucleotide sequence encoding a particular polypeptide sequence is intended to be identical to the reference sequence, except that the polynucleotide sequence may contain up to five point mutations per 100 nucleotides of the reference amino acid sequence. In other words, to obtain a polynucleotide having a nucleotide sequence that is at least 95% identical to the reference nucleotide sequence, up to 5% of the nucleotides in the reference sequence may be deleted and / or replaced by other nucleotides, and / or up to 5% of the total nucleotides in the reference sequence may be inserted into the reference sequence. These mutations in the reference sequence occur at either the 5' or 3' terminal position of the reference nucleotide sequence, or between these terminal positions, and are individually scattered among the nucleotides of the reference sequence or one or more contiguous groups within the reference sequence.

[0041] Similarly, with a polypeptide having an amino acid sequence that is at least, for example, 95% "identical" to the reference amino acid sequence of SEQ ID NO: X, the amino acid sequence of the polypeptide is intended to be identical to the reference sequence, except that the amino acid sequence may contain up to 5 amino acid changes per 100 amino acids of the reference amino acid sequence of SEQ ID NO: X. In other words, to obtain a polypeptide having an amino acid sequence that is at least 95% identical to the reference amino acid sequence, up to 5% of the amino acid residues of the reference sequence may be deleted or substituted with other amino acids, or up to 5% of the total number of amino acid residues of the reference sequence may be inserted into the reference sequence. These changes to the reference sequence occur at either the amino-terminal or carboxyl-terminal position of the reference amino acid sequence, or between these terminal positions, and are individually scattered among the residues of the reference sequence or one or more contiguous groups within the reference sequence.

[0042] As used herein, the term "in vitro" refers to an experiment or measurement performed using components of organisms isolated from their natural state.

[0043] As used herein, the term "ex vivo" refers to an experiment or measurement performed in or on a biological tissue in an external environment with minimal alteration to its natural state.

[0044] As used herein, the terms “nucleic acid,” “nucleic acid molecule,” and “polynucleotide” are intended to include DNA molecules and RNA molecules. Nucleic acid (molecule) may be single-stranded or double-stranded, but is preferably double-stranded DNA.

[0045] In this specification, when referring to a nucleotide, the terms “sequence,” “nucleic acid sequence,” “nucleotide sequence,” or “polynucleotide sequence” refer to the order of nucleotides in a nucleic acid and / or polynucleotide, or within a nucleic acid and / or polynucleotide. In the context of the present invention, the first nucleic acid sequence may be contained within a further nucleic acid sequence or overlap with a further nucleic acid sequence.

[0046] As used herein, the terms “subject,” “individual,” “animal,” “patient,” or “mammal” are interchangeable and refer to any subject, in particular mammalian subject, for whom diagnosis, prediction, or treatment is desired. Mammalian subjects include humans, domesticated animals, livestock animals, and zoo, sport, or pet animals, such as dogs, cats, guinea pigs, rabbits, rats, mice, horses, cattle, cows, bears, etc. Subjects may be alive or dead, as defined herein. Samples may be taken from the autopsy of a subject, i.e., postmortem, or samples may be taken from a living subject.

[0047] As used herein, the terms “treatment,” “to treat,” “to alleviate,” “to mitigate,” or “to improve” are interchangeable and refer to, but not limited to, approaches to obtain beneficial or desired outcomes, including therapeutic effects. Therapeutic effect means the elimination, improvement, reduction (or delay) of the progression of the underlying disease being treated. Therapeutic effect is also achieved by the elimination, improvement, reduction (or delay) of the progression of one or more physiological conditions associated with the underlying disease, such that, despite the patient still suffering from the underlying disease, an improvement, slowing, or reduction of decline is observed in the patient.

[0048] Here, the term “outcome” refers to a specific result or effect that can be measured. Examples of outcomes for subjects with glioma include glioma outcomes, pathological outcomes, outcomes of treatments addressing the glioma, e.g., surgical outcomes, radiotherapy outcomes, chemotherapy outcomes, and immunotherapy outcomes, as well as other outcomes, e.g., biomarker-related outcomes (e.g., CEA (carcinoembryonic antigen)), genome profile-related outcomes, imaging-related outcomes (e.g., changes in tumor morphology or texture), biological outcomes (e.g., inflammation or immune response), alternative marker-related outcomes, tumor size outcomes, treatment site effect outcomes, treatment toxicity outcomes, disease pain outcomes, quality of life outcomes, cancer-specific survival, and overall survival.

[0049] The term "clinical recurrence" refers to the presence of clinical signs indicating the presence of tumor cells, for example, measured using in vivo imaging.

[0050] As used herein, the term “immunoprotective response gene” is interchangeable with “IDR gene” or “immunoprotective gene” and refers to one or more genes selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1.

[0051] The term "metastasis" refers to the presence of metastatic disease in organs other than the bladder tissue.

[0052] As used herein, the term “PDE4D7-related gene” is interchangeable with “PDE4D7 gene” and refers to one or more genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0053] As used herein, the term “T cell receptor signaling gene” is interchangeable with “TCR signaling gene” or “TCR gene” and refers to one or more genes selected from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70.

[0054] The section headings used herein are for organizational purposes only and should not be construed as limiting the subjects described.

[0055] Parts of the present invention include materials that are protected by copyright (e.g., diagrams, device photographs, or any other aspects of this application that are protected by copyright or may be available under any authority). The copyright holder has no objection to copies made in either the Patent Office file or the Patent Record if they are found therein, but otherwise retains all copyrights.

[0056] Various terms relating to methods, compositions, uses, and other aspects of the present invention are used throughout the specification and claims. Unless otherwise indicated, such terms give their common meanings in the art to which the present invention relates. Other specifically defined terms are to be interpreted in a manner consistent with the definitions provided herein. While preferred materials and methods are described herein, any similar or equivalent methods and materials may be used in carrying out the tests of the present invention.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art.

[0058] This invention describes three identified genetic signs that are actually applicable in predicting patient outcomes. The identification and selection of these genetic signs are described in more detail below. Although the genetic signs were initially described for use in prostate cancer, they surprisingly also had a predictive effect on subjects with gliomas, as shown herein. This is surprising for at least the following reasons:

[0059] PDE4D7 is a splice variant of the PDE4D gene and has been identified as an important biomarker for prostate cancer. Deficiency of PDE4D7 expression in prostate cancer has been demonstrated by poor disease outcomes, such as androgen-independent growth and treatment resistance (Gulliver et al. Loss of PDE4D7 expression promotes androgen independence, neuroendocrine differentiation and alterations in DNA repair: applications for therapeutic strategies. Br J Cancer. 2023 Oct;129(9):1462-1476).

[0060] This realization led to the development of PDE4D7-related gene signs, including genes that appear to be co-regulated with PDE4D7, which can be used in place of or in addition to PDE4D7 expression and may provide alternative or further improved prediction of prostate cancer outcomes. Furthermore, two additional signs, immune response genes and T cell receptor signaling genes, were developed, representing genes from the immune response and T cell receptor signaling pathways, respectively, and all individually show significant correlations with disease outcomes. These gene signs were validated and evaluated individually and in combination. Furthermore, individual selections of genes from gene signs selected from individual gene signs or combinations of signs (e.g., 3, 4, 5, 6, 7, 8, 9, or 10 genes) have also been demonstrated to be predictive in prostate cancer, see, for example, WO2010131195A1, WO2010131194A1, WO2019122037A1, WO2022043299A1, WO2022043120A1, WO2021175986A1 and WO2021175973A1.

[0061] Here, surprisingly, we found that these signs also make it possible to predict the outcome of subjects with gliomas. To demonstrate this, we analyzed RNA sequencing data from glioma biopsies obtained during pretreatment and available follow-up data, such as correlated gene expression data for gene signs by treatment type and survival. First, the immune response defense gene sign (IDR14), the T cell receptor signaling sign (TCR17), and the PDE4D7 correlated sign (PDE4D7_R2) each showed to be individually predictive of outcomes, as demonstrated in Figures 3–8, 13–18, and 23–28. Furthermore, the prediction can be further improved by combining gene signs (called GLCAI signs), see Figures 9, 10, 19, 20, 29, and 30. To further support individual genes from each genetic sign sufficient to predict outcomes, random selections of six genes are then generated from either the IDR14, TCR17, or PDE4D7_R2 sign, or six genes are selected from those of the IDR14, TCR17, and PDE4D7_R2 signs. For each possibility, three random selections were made, considered representative of the selection of all six genes from each sign IDR14, TCR17, PDE4D7_R2, or CLCAI. These data are included in the patent application as Figures 35–46 and Example 3. Interestingly, each random selection of the six genes showed excellent predictive power, as demonstrated by Kaplan-Meier curve analysis, and the patient group could be stratified with high significance (all P-values ​​<0.0001). Each selection of six genes from individual or combined genetic signs may predict outcomes for subjects with glioma.

[0062] Therefore, in a first aspect, the present invention relates to a method for predicting the outcome of a subject having a glioma, comprising six or more selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2 (for example, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, The present invention relates to a method comprising the steps of determining the gene expression levels of all 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 genes, or receiving the results of the gene expression level determination, wherein the gene expression levels are determined in a biological sample obtained from a subject; and determining a prediction of an outcome based on the gene expression levels of six or more genes (for example, all 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 genes).

[0063] As shown herein, the provided genetic signatures stratify patients with gliomas by mean survival time. Furthermore, Figures 11, 12, 21, and 22 show that patient stratification predicts the success of radiotherapy treatment. These figures demonstrate that subjects with low scores in the GLCAI score model (=low risk, preferred) do not benefit from radiotherapy and, in fact, have a longer expected survival time even without radiotherapy (approximately twice the mean survival time); subjects with high scores in the GLCAI score model (=high risk, unpreferred) benefit from radiotherapy and increase their expected mean survival time by approximately 10 months. Thus, in one embodiment, the method further includes a step of providing a prediction to a healthcare provider or subject. In one embodiment, the prediction is mean survival time, mean survival time after radiotherapy, or mean survival time without radiotherapy. In one embodiment, the biological sample is obtained from the subject before the initiation of treatment.

[0064] Therefore, in one embodiment, the method is used to provide a treatment recommendation, predict the success of providing radiotherapy to a subject, or predict the optimal treatment strategy for a subject, where radiotherapy is not recommended if the predicted outcome is favorable (low GLCAI score), and radiotherapy is recommended if the predicted outcome is unfavorable (high GLCAI score). If the predicted outcome is favorable (low GLCAI score), alternative treatments such as surgery, chemotherapy, or targeted therapy may be recommended instead of radiotherapy, for example, but not limited to surgery.

[0065] In one embodiment, six or more gene expression levels may be selected from a single gene signature. Thus, in one embodiment, six or more gene expression levels may be selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0066] As described above, the examples and data included herein ensure that any combination of six genes from those provided herein can be used to predict the outcome of subjects with glioma. Predictive models were analyzed to identify the most contributing genes in each of the IDR14, TCR17, and PDE4D7_R2 models. While the inclusion of at least one preferred gene is not required for accurate prediction, it is assumed that doing so may increase the accuracy of the model. Therefore, in one embodiment, six or more gene expression levels are selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, CD2, CSK, PAG1, PRKACA, and PRKACB, preferably ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, and PDE4D. The expression levels include at least one, preferably two, three, four, five, or six gene expression levels, selected from SLC39A11, IFIT3, MYD88, CSK, and PRKACB, more preferably selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, and PDE4D, and most preferably selected from ABCC5, CUX2, IFIH1, OAS1, and ZAP70. Alternatively, the six or more gene expression levels include at least one, preferably two, three, four, five, or six gene expression levels, selected from CUX2, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, OAS1, CD2, CSK, PAG1, PRKACA, PRKACB, and ZAP70, preferably selected from CUX2, KIAA1549, PDE4D, SLC39A11, IFIT3, MYD88, OAS1, CSK, PRKACB, and ZAP70, and more preferably selected from CUX2, KIAA1549, PDE4D, OAS1, and ZAP70.

[0067] In one embodiment, six or more gene expression levels are: AIM2, CIAO1, DHX9, IFI16, LRRFIP1, and OAS1 (IDR14_6.1 model genes); APOBEC3A, DDX58, IFIT1, MYD88, TLR8, and ZBP1 (IDR14_6.2 model genes); DHX9, IFI16, IFIH1, IFIT3, MYD88, and OAS1 (IDR14_6.3 model genes); CD247, CD3E, EZR, LAT, PDE4D, and PRKACA (TCR17_6.1 model genes); CD28, CD4, FYN, PAG1, PRKACB, and ZAP70 (TCR17_6.2 model genes); CD2, CD3G, CSK, EZR, LCK, and PTPRC (TCR17_6.3 model genes); ABCC5, KIAA1549, PDE4D, SLC39A11, TDRD1, and VWA2 (PDE4D7_R2_6.1 model genes); CUX2, KIAA1549, PDE4D, RAP1GAP2, TDRD1, and VWA2 (PDE4D7_R2_6.1 model genes); ABCC5, CUX2, PDE4D, RAP1GAP2, SLC39A11, and VWA2 (PDE4D7_R2_6.1 model gene); AIM2, IFIH1, CD3E, PDE4D, ABCC5, and RAP1GAP2 (GLCAI_6.1 model genes); CIAO1, DHX9, CD4, FYN, KIAA1549 and VWA2 (GLCAI_6.2 model genes); or DDX58, IFIT3, CD28, ZAP70, CUX2, and TDRD1 (GLCAI_6.3 model genes) It comprises at least one, preferably two, three, four, five, or six gene expression levels selected from the following.

[0068] In one embodiment, the determination of the predicted outcome further includes the step of combining six or more gene expression levels with a regression function derived from a population of subjects having gliomas. In one embodiment, the determination of the outcome is further based on one or more clinical parameters obtained from the subjects, preferably the clinical parameters include one or more of (i) Karnovsky performance score; (ii) tumor malignancy; and (iii) presence and amount of lesions. In one embodiment, the biological sample is a biopsy obtained from the subject, preferably a biopsy from a glioma or metastasis. In one embodiment, the glioma is an astrocytoma, pilocytic astrocytoma, mixed oligoastrocytic neoplasm, gliablastoma (GBM), treated GBM, untreated (primary) GBM, oligoastrocytic neoplasm, oligodendroglioma, or oligodendroglioma.

[0069] In a second aspect, the present invention relates to a device for predicting the outcome of a subject having a glioma, comprising six selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2. The present invention relates to an apparatus comprising: an appropriate input for receiving data indicating one or more gene expression levels (for example, all 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38); an appropriate processor for determining predictions of outcomes based on six or more gene expression levels; and, optionally, a providing unit suitable for providing predictions to healthcare providers or subjects.

[0070] In one embodiment, six or more gene expression levels may be selected from a single gene signature. Thus, in one embodiment, six or more gene expression levels may be selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; or CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; or ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0071] In one embodiment, six or more gene expression levels are selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, CD2, CSK, PAG1, PRKACA, and PRKACB, preferably ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SL The expression levels include at least one, preferably two, three, four, five, or six gene expression levels, selected from C39A11, IFIT3, MYD88, CSK, and PRKACB, more preferably selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, and PDE4D, and most preferably selected from ABCC5, CUX2, IFIH1, OAS1, and ZAP70. Alternatively, the six or more gene expression levels include at least one, preferably two, three, four, five, or six gene expression levels, selected from CUX2, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, CD2, CSK, PAG1, PRKACA, PRKACB, and ZAP70, preferably selected from CUX2, KIAA1549, PDE4D, SLC39A11, IFIT3, MYD88, OAS1, CSK, PRKACB, and ZAP70, and more preferably selected from CUX2, KIAA1549, PDE4D, OAS1, and ZAP70.

[0072] In one embodiment, six or more gene expression levels are: AIM2, CIAO1, DHX9, IFI16, LRRFIP1, and OAS1 (IDR14_6.1 model genes); APOBEC3A, DDX58, IFIT1, MYD88, TLR8, and ZBP1 (IDR14_6.2 model genes); DHX9, IFI16, IFIH1, IFIT3, MYD88, and OAS1 (IDR14_6.3 model genes); CD247, CD3E, EZR, LAT, PDE4D, and PRKACA (TCR17_6.1 model genes); CD28, CD4, FYN, PAG1, PRKACB, and ZAP70 (TCR17_6.2 model genes); CD2, CD3G, CSK, EZR, LCK, and PTPRC (TCR17_6.3 model genes); ABCC5, KIAA1549, PDE4D, SLC39A11, TDRD1, and VWA2 (PDE4D7_R2_6.1 model genes); CUX2, KIAA1549, PDE4D, RAP1GAP2, TDRD1, and VWA2 (PDE4D7_R2_6.1 model genes); ABCC5, CUX2, PDE4D, RAP1GAP2, SLC39A11, and VWA2 (PDE4D7_R2_6.1 model gene); AIM2, IFIH1, CD3E, PDE4D, ABCC5, and RAP1GAP2 (GLCAI_6.1 model genes); CIAO1, DHX9, CD4, FYN, KIAA1549 and VWA2 (GLCAI_6.2 model genes); or DDX58, IFIT3, CD28, ZAP70, CUX2, and TDRD1 (GLCAI_6.3 model genes) It comprises at least one, preferably two, three, four, five, or six gene expression levels selected from the following.

[0073] In one embodiment, the determination of the predicted outcome further includes the step of combining six or more gene expression levels with a regression function derived from a population of subjects having gliomas. In one embodiment, the determination of the outcome is further based on one or more clinical parameters obtained from the subjects, preferably the clinical parameters include one or more of (i) Karnovsky performance score; (ii) tumor malignancy; and (iii) presence and amount of lesions. In one embodiment, the biological sample is a biopsy obtained from the subject, preferably a biopsy from a glioma or metastasis. In one embodiment, the glioma is an astrocytoma, pilocytic astrocytoma, mixed oligoastrocytic neoplasm, gliablastoma (GBM), treated GBM, untreated (primary) GBM, oligoastrocytic neoplasm, oligodendroglioma, or oligodendroglioma.

[0074] In a third aspect, the present invention relates to a computer program including instructions, wherein when the program is executed by a computer, six or more selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2 (e.g.) The present invention relates to a computer program that causes a computer to perform a method for predicting the outcome of a subject with a glioma, comprising the steps of: receiving data indicating the gene expression levels of all of the following (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38), wherein the gene expression levels are determined in a biological sample obtained from the subject; determining a prediction of the subject's outcome based on the six or more gene expression levels; and optionally providing the prediction to a healthcare provider or the subject.

[0075] In one embodiment, six or more gene expression levels may be selected from a single gene signature. Thus, in one embodiment, six or more gene expression levels may be selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; or CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; or ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0076] In one embodiment, six or more gene expression levels are selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, CD2, CSK, PAG1, PRKACA, and PRKACB, preferably ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SL The expression levels include at least one, preferably two, three, four, five, or six gene expression levels, selected from C39A11, IFIT3, MYD88, CSK, and PRKACB, more preferably selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, and PDE4D, and most preferably selected from ABCC5, CUX2, IFIH1, OAS1, and ZAP70. Alternatively, the six or more gene expression levels include at least one, preferably two, three, four, five, or six gene expression levels, selected from CUX2, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, OAS1, CD2, CSK, PAG1, PRKACA, PRKACB, and ZAP70, preferably selected from CUX2, KIAA1549, PDE4D, SLC39A11, IFIT3, MYD88, OAS1, CSK, PRKACB, and ZAP70, and more preferably selected from CUX2, KIAA1549, PDE4D, OAS1, and ZAP70.

[0077] In one embodiment, six or more gene expression levels are: AIM2, CIAO1, DHX9, IFI16, LRRFIP1, and OAS1 (IDR14_6.1 model genes); APOBEC3A, DDX58, IFIT1, MYD88, TLR8, and ZBP1 (IDR14_6.2 model genes); DHX9, IFI16, IFIH1, IFIT3, MYD88, and OAS1 (IDR14_6.3 model genes); CD247, CD3E, EZR, LAT, PDE4D, and PRKACA (TCR17_6.1 model genes); CD28, CD4, FYN, PAG1, PRKACB, and ZAP70 (TCR17_6.2 model genes); CD2, CD3G, CSK, EZR, LCK, and PTPRC (TCR17_6.3 model genes); ABCC5, KIAA1549, PDE4D, SLC39A11, TDRD1, and VWA2 (PDE4D7_R2_6.1 model genes); CUX2, KIAA1549, PDE4D, RAP1GAP2, TDRD1, and VWA2 (PDE4D7_R2_6.1 model genes); ABCC5, CUX2, PDE4D, RAP1GAP2, SLC39A11, and VWA2 (PDE4D7_R2_6.1 model gene); AIM2, IFIH1, CD3E, PDE4D, ABCC5, and RAP1GAP2 (GLCAI_6.1 model genes); CIAO1, DHX9, CD4, FYN, KIAA1549 and VWA2 (GLCAI_6.2 model genes); or DDX58, IFIT3, CD28, ZAP70, CUX2, and TDRD1 (GLCAI_6.3 model genes) It comprises at least one, preferably two, three, four, five, or six gene expression levels selected from the following.

[0078] In one embodiment, the determination of the predicted outcome further includes the step of combining six or more gene expression levels with a regression function derived from a population of subjects having gliomas. In one embodiment, the determination of the outcome is further based on one or more clinical parameters obtained from the subjects, preferably the clinical parameters include one or more of (i) Karnovsky performance score; (ii) tumor malignancy; and (iii) presence and amount of lesions. In one embodiment, the biological sample is a biopsy obtained from the subject, preferably a biopsy from a glioma or metastasis. In one embodiment, the glioma is an astrocytoma, pilocytic astrocytoma, mixed oligoastrocytic neoplasm, gliablastoma (GBM), treated GBM, untreated (primary) GBM, oligoastrocytic neoplasm, oligodendroglioma, or oligodendroglioma.

[0079] In a fourth aspect, the present invention relates to the use of a kit, the use comprising the steps of determining the expression levels of six or more genes in a sample obtained from a subject having a glioma, and providing an outcome for the subject based on the six or more gene expression levels, wherein the kit comprises ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD The present invention relates to a use that includes means for determining the expression levels of six or more genes selected from 3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2 (for example, all of 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38).

[0080] In one embodiment, six or more gene expression levels may be selected from a single gene signature. Thus, in one embodiment, six or more gene expression levels may be selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; or CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; or ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0081] In one embodiment, six or more gene expression levels are selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, CD2, CSK, PAG1, PRKACA, and PRKACB, preferably ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SL The expression levels include at least one, preferably two, three, four, five, or six gene expression levels, selected from C39A11, IFIT3, MYD88, CSK, and PRKACB, more preferably selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, and PDE4D, and most preferably selected from ABCC5, CUX2, IFIH1, OAS1, and ZAP70. Alternatively, the six or more gene expression levels include at least one, preferably two, three, four, five, or six gene expression levels, selected from CUX2, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, OAS1, CD2, CSK, PAG1, PRKACA, PRKACB, and ZAP70, preferably selected from CUX2, KIAA1549, PDE4D, SLC39A11, IFIT3, MYD88, OAS1, CSK, PRKACB, and ZAP70, and more preferably selected from CUX2, KIAA1549, PDE4D, OAS1, and ZAP70.

[0082] In one embodiment, six or more gene expression levels are: AIM2, CIAO1, DHX9, IFI16, LRRFIP1, and OAS1 (IDR14_6.1 model genes); APOBEC3A, DDX58, IFIT1, MYD88, TLR8, and ZBP1 (IDR14_6.2 model genes); DHX9, IFI16, IFIH1, IFIT3, MYD88, and OAS1 (IDR14_6.3 model genes); CD247, CD3E, EZR, LAT, PDE4D, and PRKACA (TCR17_6.1 model genes); CD28, CD4, FYN, PAG1, PRKACB, and ZAP70 (TCR17_6.2 model genes); CD2, CD3G, CSK, EZR, LCK, and PTPRC (TCR17_6.3 model genes); ABCC5, KIAA1549, PDE4D, SLC39A11, TDRD1, and VWA2 (PDE4D7_R2_6.1 model genes); CUX2, KIAA1549, PDE4D, RAP1GAP2, TDRD1, and VWA2 (PDE4D7_R2_6.1 model genes); ABCC5, CUX2, PDE4D, RAP1GAP2, SLC39A11, and VWA2 (PDE4D7_R2_6.1 model gene); AIM2, IFIH1, CD3E, PDE4D, ABCC5, and RAP1GAP2 (GLCAI_6.1 model genes); CIAO1, DHX9, CD4, FYN, KIAA1549 and VWA2 (GLCAI_6.2 model genes); or DDX58, IFIT3, CD28, ZAP70, CUX2, and TDRD1 (GLCAI_6.3 model genes) It comprises at least one, preferably two, three, four, five, or six gene expression levels selected from the following.

[0083] In one embodiment, the determination of the predicted outcome further includes the step of combining six or more gene expression levels with a regression function derived from a population of subjects having gliomas. In one embodiment, the determination of the outcome is further based on one or more clinical parameters obtained from the subjects, preferably the clinical parameters include one or more of (i) Karnovsky performance score; (ii) tumor malignancy; and (iii) presence and amount of lesions. In one embodiment, the biological sample is a biopsy obtained from the subject, preferably a biopsy from a glioma or metastasis. In one embodiment, the glioma is an astrocytoma, pilocytic astrocytoma, mixed oligoastrocytic neoplasm, gliablastoma (GBM), treated GBM, untreated (primary) GBM, oligoastrocytic neoplasm, oligodendroglioma, or oligodendroglioma.

[0084] In one embodiment, use includes the step of carrying out the method according to the first aspect of the present invention.

[0085] In a fifth aspect, the present invention relates to a treatment for use in the treatment or improvement of a subject having a glioma, wherein the use comprises the steps of determining an outcome for the subject having a glioma using the method defined in the first aspect of the present invention, and administering an outcome-based treatment to the subject, wherein if the predicted outcome is favorable, the treatment is selected from surgery, radiotherapy, chemotherapy, or targeted therapy, or if the predicted outcome is unfavorable, the treatment is selected from two or more combinations of surgery, radiotherapy, chemotherapy, and targeted therapy, or immunotherapy, or experimental drugs or treatments (clinical trials), or one or more treatments selected from adjuvant therapy, which is selected from high-dose radiotherapy, chemotherapy, and long-term CRT (chemoradiotherapy), and immunotherapy.

[0086] Immune response defense genes The integrity and stability of genomic DNA are permanently maintained under oxidative and replication stresses, as well as stresses induced by various intracellular and extracellular factors such as radiation exposure and viral or bacterial infections (see Gasser S. et al., “Sensing of dangerous DNA”, Mechanisms of Aging and Development, Vol. 165, pages 33-46, 2017). To maintain DNA structure and stability, cells must be able to recognize all types of DNA damage, such as single-strand or double-strand breaks induced by various factors. This process involves the involvement of numerous specific proteins, depending on the type of damage, as part of the DNA recognition pathway.

[0087] Recent evidence indicates that mislocalized DNA (e.g., DNA abnormally appearing in the cytoplasmic fraction of a cell, in contrast to the nucleus) and damaged DNA (e.g., due to mutations occurring in cancer development) are used by the immune system to identify infected or otherwise diseased cells, while genomic and mitochondrial DNA present in normal cells are ignored by the DNA recognition pathway. In diseased cells, cytoplasmic DNA sensor proteins have been shown to be involved in detecting DNA abnormally present in the cell's cytoplasm. Detection of such DNA by different nucleic acid sensors triggers similar responses resulting in nuclear factor kappa-B (NF-κB) and type I interferon (type I IFN) signaling, followed by activation of innate immune system components. While recognition of viral DNA is known to induce a type I IFN response, evidence that sensing DNA damage can initiate an immune response has only recently accumulated.

[0088] Endosome-localized TLR9 (Toll-like receptor 9) was identified as one of the first DNA sensor molecules involved in the immune recognition of DNA via downstream signaling mediated by the adapter protein, myeloid differentiation primary response protein 88 (MYD88). This interaction subsequently activates mitogenic factor-activated protein kinase (MAPK) and NF-κB. TLR9 also induces the production of type I interferon through the activation of IRF7 via IκB kinase alpha (IKK alpha) in plasmacytoid dendritic cells (pDCs). Various other DNA immune receptors, including IFI16 (IFN-gamma-inducing protein 16), cGAS (cyclic DMP-AMP synthase), DDX41 (DEAD-box helicase 41), and ZBP1 (Z-DNA binding protein 1), interact with STING (IFN gene stimulant) and activate the IKK complex and IRF3 via TBK1 (TANK-binding kinase 1). ZBP1 also activates NF-κB via the recruitment of RIP1 and RIP3 (receptor interacting proteins 1 and 3, respectively). Helicase DHX36 (DEAH-box helicase 36) interacts complexly with TRID and induces NF-κB and IRF-3 / 7, while DHX9 helicase is a phenotypic helicase. This stimulates MYD88-dependent signaling in cytoid dendritic cells. The DNA sensor, LRRFIP1 (leucine-rich repeat flightless interaction protein), complexes with beta-catenin to activate IRF3 transcription, while AIM2 (melanoma deficiency factor 2) recruits the adapter protein ASC (apoptosis-associated speck-like protein) to induce a caspase-1 activated inflammasome complex, leading to the secretion of interleukin-1 beta (IL-1 beta) and IL-18 (see Figure 1 of Gasser S. et al., 2017 above, which provides a schematic overview of the DNA damage and DNA sensor pathways that lead to the production of inflammatory cytokines and the expression of ligands for activating innate immune receptors. Members of the non-homologous end joining pathway (orange), homologous recombination (red), inflammasome (dark green), NF-κB, and interferon response (light green) are shown).

[0089] The factors and mechanisms responsible for the activation of DNA sensor pathways in cancer are not yet fully understood. Identifying tumor DNA species, sensors, and pathways involved in IFN expression across different cancer types at all stages of the disease is crucial. In addition to therapeutic targets in cancer, such factors may also have prognostic and predictive properties. Novel DNA sensor pathway agonists and antagonists are currently under development and being tested in preclinical trials. Such compounds are useful in characterizing the role of DNA sensor pathways in the pathogenesis of cancer, autoimmune diseases, and other potential diseases.

[0090] T cell receptor signaling genes The immune response to pathogens can be triggered at various levels: there are physical barriers, such as the skin, that prevent invaders from entering. If these are breached, innate immunity; the first, rapid, nonspecific response begins to work. If this is insufficient, the adaptive immune response is triggered. This is far more specific, but it takes time to manifest when encountering a pathogen for the first time. Lymphocytes are activated by interacting with activated antigen-presenting cells derived from the innate immune system and are also responsible for maintaining memory to respond more quickly on subsequent encounters with the same pathogen.

[0091] When activated, lymphocytes are highly specific and effective, and therefore undergo negative selection of their ability to recognize themselves, a process known as central tolerance. Since not all autoantigens are expressed at the selected sites, mechanisms of peripheral tolerance have also developed, such as ligation of TCRs in the absence of co-stimulation, expression of inhibitory coreceptors, and inhibition by Tregs. An imbalance between activation and inhibition can lead to autoimmune disorders, or immunodeficiency and cancer, respectively.

[0092] T cell activation can have various functional consequences depending on the location and type of T cells involved. CD8+ T cells differentiate into cytotoxic effector cells, while CD4+ T cells can differentiate into Th1 (secreting IFNγ and promoting cellular immunity) or Th2 (secreting IL4 / 5 / 13 and promoting B cell and humoral immunity). It is also possible to differentiate into other T cell subsets that have been recently identified, such as Tregs, which have an inhibitory effect on immune activation (see Mosenden R. and Tasken K., “Cyclic AMP-mediated immune regulation - Overview of mechanisms of action in T-cells”, Cell Signal, Vol. 23, No. 6, pages 1009-1016, 2011, specifically T cell activation and its modulation by PKA in Figure 4, and Tasken K. and Ruppelt A., “Negative regulation of T-cell receptor activation by the cAMP-PKA-Csk signaling pathway in T-cell lipid rafts”, Front Biosci, Vol. 11, pages 2929-2939, 2006).

[0093] Both signaling pathways regulated by PKA and PDE4 interact with TCR-induced T cell activation, fine-tuning its regulation through opposing effects (Abrahamsen H. et al., “TCR- and CD28-mediated recruitment of phosphodiesterase 4 to lipid rafts potentiates TCR signaling”, J Immunol, Vol. 173, pages 4847-4848, 2004; specifically, see Figure 6 illustrating the opposing effects of PKA and PDE4 on TCR activation). The molecule that links these effectors is cyclic AMP (cAMP), an intracellular second messenger of the effects of extracellular ligands. Within T cells, cAMP mediates the effects of prostaglandins, adenosine, histamine, beta-adrenergic agonists, neuropeptide hormones, and beta-endorphins. The binding of these extracellular molecules to GPCRs results in structural changes of the GPCR, the release of stimulant subunits, and subsequent activation of adenylyl cyclase (AC), which hydrolyzes ATP to cAMP (see Figure 6 in Abrahamsen H. et al., 2004 above). PKA is a major effector of cAMP signaling, though not the only one (see Mosenden R. and Tasken K., 2011 and Tasken K. and Ruppelt A., 2006 above). At a functional level, increased levels of cAMP lead to decreased production of IFNγ and IL-2 in T cells (see Abrahamsen H. et al., 2004 above). In addition to interfering with TCR activation, PKA has even more effectors (see Figure 15 in Torheim EA, “Immunity Leashed-Mechanisms of Regulation in the Human Immune System”, Thesis for the degree of Philosophiae Doctor (PhD), the Biotechnology Centre of Ola, University of Oslo, Norway, 2009).

[0094] In naive T cells, highly phosphorylated PAG targets Csk to lipid rafts. PKA targets Csk via the ezrin-EBP50-PAG scaffold complex. Specific phosphorylation by PKA allows Csk to negatively regulate Lck and Fyn, weakening their activity and downregulating T cell activation (see Figure 6 in Abrahamsen H. et al. above). Upon TCR activation, PAG is dephosphorylated, and Csk is released from the raft. Csk dissociation is necessary for T cell activation to proceed. Within the same time course, a Csk-G3BP complex appears to form, thereby sequestering Csk outside the lipid raft (see Mosenden R. and Tasken K., 2011, ibid, and Tasken K. and Ruppelt A., 2006 above).

[0095] Conversely, combined stimulation of the TCR and CD28 mediates the recruitment of the cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, which enhances cAMP degradation (see Figure 6 in Abrahamsen H. et al., 2004 above). This inhibits TCR-induced cAMP production and enhances the T cell immune response. When the TCR is stimulated alone, PDE4 recruitment is insufficient to sufficiently reduce cAMP levels, and therefore maximal T cell activation cannot occur (see Abrahamsen H. et al., 2004 above).

[0096] Therefore, by actively suppressing proximal TCR signaling, cAMP-PKA-Csk-mediated signaling is thought to establish a threshold for T cell activation. PDE recruitment can block this suppression. Tissue or cell type-specific regulation is achieved through the expression of multiple isoforms of AC, PKA, and PDE. As mentioned above, a tight balance between activation and suppression is necessary to prevent the development of autoimmune disorders, immunodeficiency, and cancer.

[0097] PDE4D7-related genes Phosphodiesterases (PDEs) provide the sole means of degrading the second messenger 3'-5'-cyclic AMP. Therefore, they can play a crucial regulatory role. Consequently, abnormal changes in their expression, activity, and intracellular location can all contribute to the molecular pathology underlying specific disease states. Indeed, recent studies have shown that mutations in the PDE gene are more common in prostate cancer patients, leading to increased cAMP signaling and potentially predisposing them to prostate cancer. However, diverse expression profiles across different cell types, combined with the complex and diverse isoform variants within each PDE family, make it difficult to understand the relationship between abnormal PDE expression and its functionality during disease progression. Several studies have attempted to document the total amount of PDE in the prostate, all of which have identified significant levels of PDE4 expression alongside other PDEs, leading to the development of the PDE4D7 biomarker (see Alves de Inda M. et al., “Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes”, Eur Urol Focus, Vol.4, No.3, pages 376-384, 2018). Since the PDE4D7 biomarker has been shown to be a good predictor, the ability to identify markers that correlate highly with the PDE47 biomarker is likely to be useful in predicting outcomes for specific cancer patients.

[0098] Genetic selection The list of genes was originally selected for prediction in prostate cancer patients. This specification also indicates that they are predictive of outcomes in patients with gliomas.

[0099] The identified immune defense response genes ZBP1, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 were identified as follows: A group of 538 prostate cancer patients were treated with RP, and prostate cancer tissue was stored along with clinical parameters (e.g., pathological Gleason grade group (pGGG), pathological status (pT stage)) and related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiotherapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes (see Alves de Inda M. et al., 2018 above). PDE4D7 score class 1 represents patient samples with the lowest PDE4D7 expression levels, while PDE4D7 score class 4 represents patient samples with the highest PDE4D7 expression levels. Next, RNASeq expression data (TPM - parts per million transcription) from 538 prostate cancer subjects was examined for the different gene expression levels between PDE4D7 score classes 1 and 4. Specifically, for approximately 20,000 protein-coding transcripts, it was determined whether the mean expression level of patients with PDE4D7 score class 1 was more than twice the mean expression level of patients with PDE4D7 score class 4. This analysis yielded 637 genes where the PDE4D7 score class 1 / PDE4D7 score class 4 ratio was greater than 2 at the minimum mean expression of one TPM in each of the four PDE4D7 score classes. These 637 genes were then subjected to further molecular pathway analysis (www.david.ncifcrf.gov) to obtain various enriched annotation clusters. Annotation cluster #2 showed enrichment (enrichment score: 10.8) in 30 genes that function in defense responses against viruses, negative regulation of viral genome replication, and type I interferon signaling.Further heatmap analysis confirmed that these immune defense response genes were generally more highly expressed in samples from patients with a PDE4D7 score of class 1 than in samples from patients with a PDE4D7 score of class 4. A literature search to identify additional genes with the same molecular function expanded the class of genes with functions in defense responses against viruses, negative regulation of viral genome replication, and type I interferon signaling to 61 genes. Further selection from the 61 genes based on combinatorial power was performed to separate patients who died from prostate cancer from those who did not, resulting in a preferred set of 14 genes. The number of events (metastasis, prostate cancer-specific death) was found to be increased in the subcohort with low expression of these genes compared to the total patient cohort (#538) and a subcohort of 151 patients receiving salvage RT (SRT) after postoperative stage recurrence.

[0100] The identified T cell receptor signaling genes, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 were identified as follows: A group of 538 prostate cancer patients were treated with RP, and prostate cancer tissue was stored along with clinical parameters (e.g., pathological Gleason grade group (pGGG), pathological status (pT stage)) and related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiotherapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes (see Alves de Inda M. et al., 2018 above). PDE4D7 score class 1 represents patient samples with the lowest PDE4D7 expression levels, while PDE4D7 score class 4 represents patient samples with the highest PDE4D7 expression levels. Next, RNASeq expression data (TPM - parts per million transcription) from 538 prostate cancer subjects was examined for the different gene expression levels between PDE4D7 score classes 1 and 4. Specifically, for approximately 20,000 protein-coding transcripts, it was determined whether the mean expression level of patients with PDE4D7 score class 1 was more than twice the mean expression level of patients with PDE4D7 score class 4. This analysis yielded 637 genes where the PDE4D7 score class 1 / PDE4D7 score class 4 ratio was greater than 2 at the minimum mean expression of one TPM in each of the four PDE4D7 score classes. These 637 genes were then subjected to further molecular pathway analysis (www.david.ncifcrf.gov) to obtain various enriched annotation clusters. Annotation cluster #6 showed enrichment (enrichment score: 5.9) in 17 genes that function in primary immunodeficiency and activation of T cell receptor signaling.Further heatmap analysis confirmed that these T cell receptor signaling genes are generally more highly expressed in samples from patients with a PDE4D7 score of class 1 than in samples from patients with a PDE4D7 score of class 4.

[0101] The identified PDE4D7-related genes, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, were identified as follows: RNA-seq data generated from 571 prostate cancer patients were identified for various genes, which comprise approximately 60,000 transcripts correlated with the expression of the known biomarker PDE4D7 in this data. The correlation between the expression of any of these genes and PDE4D7 in the 571 samples was calculated using Pearson correlation, with a value between 0 and 1 for positive correlation and a value between -1 and 0 for negative correlation. The input data for calculating the correlation coefficients were the PDE4D7 score (see Alves de Inda M. et al., 2018 above) and the RNA-seq determined TPM gene expression values ​​for each gene of interest (see below).

[0102] The largest negative correlation coefficient identified between the expression of any of the approximately 60,000 transcripts and the expression of PDE4D7 was -0.38, while the largest positive correlation coefficient identified between the expression of any of the approximately 60,000 transcripts and the expression of PDE4D7 was +0.56. Genes were selected within the correlation ranges of -0.31 to -0.38 and +0.41 to +0.56. A total of 77 transcripts were identified that fit these characteristics. From these 77 transcripts, eight PDE4D7-related genes—ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2—were selected by testing a repeated Cox regression join model in a subcohort of 186 patients receiving salvage radiotherapy (SRT) for postoperative biochemical recurrence. The clinical endpoint tested was prostate cancer-specific mortality after initiation of SRT. The boundary condition for selecting the eight genes was given by the restriction that the p-value in multivariate Cox regression must be <0.1 for all genes retained in the model.

[0103] Use of genetic signs in gliomas This literature has shown that these genes are also predictors of outcomes in individuals with glioma. Here, data obtained from different glioma patients are described, and the gene signs "immune defense response genes," "T cell receptor signaling genes," and "PDE4D7-related genes" can be used to stratify patients based on overall mortality (survival time in months), either independently or in combination (GLCAI score), as an endpoint.

[0104] The data described below in the Examples describe the validation and evaluation of individual or combined (GLCAI score), IDR, TCR, and PDE4D7 correlated gene signs in three different patient cohorts: TCGA, GSE16011, and GSE108476. The TCGA cohort includes gene expression data from patients with astrocytoma, oligodendrocele, oligodendroglioma, treated pleoplastic gliablastoma, and untreated (primary) pleoplastic gliablastoma. The GSE16011 cohort includes gene expression data from patients with astrocytoma (including pilocytic astrocytoma (PA), astrocytoma, and gliablastoma (GBM)) and oligodendroglioma (OD) (including pure OD and mixed oligodendrocele (MOA) tumors). The GSE108476 cohort includes gene expression data from patients with astrocytoma, GBM, mixed, non-tumor (control sample), oligodendroglioma, or unknown glioma type. In each cohort, models (IDR, TCR, PDE4D7-associated, and GLCAI) were trained using a training set, and the trained models were then validated on a validation dataset. The endpoint used to train the models was survival status based on survival time in months. Genetic signs allow for differentiation between poor survival and improved survival.

[0105] Further disclosed herein is a method for predicting the outcome of a subject having a glioma, the method being: A step of determining six or more gene expression levels selected from a first gene expression profile, a second gene expression profile and / or a third gene expression profile, or a step of receiving the results of the determination, - The first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - The second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; - The third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. The steps include determining the gene expression level in a biological sample obtained from the subject, and A step in which the prediction of the outcome is determined based on the expression levels of six or more genes. This is a method that has [something].

[0106] In one embodiment, the present invention is a computer-implemented method for predicting the outcome of a subject having a glioma, wherein the method is: A step of receiving the results of determining six or more gene expression levels selected from a first gene expression profile, a second gene expression profile and / or a third gene expression profile, - The first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - The second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; - The third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. The steps include determining the gene expression level in a biological sample obtained from the subject, and A step in which the prediction of the outcome is determined based on the expression levels of six or more genes. A method for holding is provided.

[0107] This specification states that the genetic signs described herein, or selections of individual genes thereof, may be used to predict the survival of patients diagnosed with glioma. Accordingly, in one embodiment, the present invention is a method for predicting the outcome of a subject having a glioma, the method comprising the steps of determining a gene expression profile including six or more gene expression levels, for example, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38, or receiving the result of determining the gene expression profile, wherein the six or more gene expression levels are from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 The present invention provides a method comprising the steps of: selecting an immune defense response gene selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; and / or selecting a PDE4D7-related gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, wherein the gene expression profile is determined in a biological sample obtained from the subject; and determining a prediction of an outcome based on the gene expression profile, wherein the prediction is survival. Survival may refer to overall survival or cancer-specific death.

[0108] Patients diagnosed with gliomas generally have a poor prognosis, but the distinction can be made between favorable and unfavorable outcomes. In this context, a favorable outcome is understood as a longer predicted survival time, and an unfavorable outcome as a shorter predicted survival time, for example, compared to the mean or median survival time of patients diagnosed with gliomas. This method demonstrates that patients with favorable or unfavorable outcomes can be stratified. This can be applied based on the predicted outcome (favorable or unfavorable), for example, in relation to treatment recommendations. For example, in patients with a predicted favorable outcome, one of the following treatment options may be considered: Surgical treatment: Surgical removal of the tumor is often the first line of treatment when possible. However, in cases of gliomas with a poor prognosis, complete removal may not be possible due to the location or invasiveness of the tumor. Radiation therapy: Radiation therapy uses high-energy beams to target and kill cancer cells. It is commonly used in the treatment of gliomas, including those with a poor prognosis. Different techniques, such as stationary radiotherapy or fractionated radiotherapy, may be used depending on the characteristics of the tumor. Chemotherapy: Chemotherapy involves the use of drugs that destroy cancer cells. Temozolomide is a commonly used chemotherapy drug for gliomas and may be administered in combination with radiation therapy, but other chemotherapy drugs are also known and may be used instead. Targeted therapy: Some gliomas may have specific gene mutations that can be targeted by certain drugs. For example, certain gliomas with mutations in the IDH1 or IDH2 genes may respond to targeted therapies such as ivosidenib or enasidenib. Other targeted therapies are also known and may be used instead.

[0109] For example, in patients with a predicted undesirable outcome, one of the following treatment options may be considered: Combination therapy: A combination of two or more treatments selected from surgery, chemotherapy, radiation therapy, and targeted therapy. Immunotherapy: Immunotherapy aims to enhance the body's immune response to fight cancer. While it has shown promising results in other types of cancer, its effectiveness in gliomas is still under investigation, and its use is limited in cases with a poor prognosis. Clinical Trials: Clinical trials may provide access to novel treatments or experimental therapies that are not yet widely available. Participation in a clinical trial should be considered after carefully evaluating the potential risks and benefits.

[0110] It is further stated that the treatment response of subjects with gliomas can be predicted using the genetic signs described herein or individual genes (e.g., six or more genes) selected from them. Accordingly, in one embodiment, the present invention is a method for predicting the outcome of a subject having a glioma, the method comprising the steps of determining a gene expression profile including gene expression levels of three or more, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38, or receiving the result of determining the gene expression profile, wherein three or more gene expression levels are AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 The present invention provides a method comprising the steps of: selecting an immune defense response gene selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; and / or selecting a PDE4D7-related gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, wherein the gene expression profile is determined in a biological sample obtained from the subject; and determining a prediction of an outcome based on the gene expression profile, wherein the prediction is a treatment response. The treatment may be, for example, radiotherapy, surgery, chemotherapy, targeted therapy, immunotherapy, or a combination thereof.

[0111] The present invention describes the use of a gene sign to predict the outcome of subjects with glioma. The gene sign comprises a gene expression profile including the expression levels of six or more genes, for example, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38, where the six or more gene expression levels are selected from: immune defense response genes and / or T cell receptor signaling genes and / or PDE4D7-related genes. Thus, in one embodiment, the six or more genes may be selected from immune defense response genes. In one embodiment, the six or more genes may be selected from T cell receptor signaling genes. In one embodiment, the six or more genes may be selected from PDE4D7-related genes. In one embodiment, the six or more genes include one or more immune defense response genes and one or more T cell receptor signaling genes. In one embodiment, the six or more genes include one or more immune defense response genes and one or more PDE4D7-related genes. In one embodiment, the six or more genes include one or more PDE4D7-related genes and one or more T cell receptor signaling genes. In one embodiment, the six or more genes include one or more immune defense response genes and one or more T cell receptor signaling genes and one or more PDE4D7-related genes.

[0112] Regarding the biological processes described above, three immune system-related gene signs were selected, including the genes listed in Tables 1-3. The association of these signs with prostate cancer survival prediction has been previously demonstrated.

[0113] [Table 1]

[0114] [Table 2]

[0115] [Table 3]

[0116] The genes AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, ZAP70, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 were found to predict outcomes in subjects with gliomas, either individually or in combination with other genes per gene panel, or in combination with other genes per gene panel.

[0117] The term "ABCC5" refers to the sequence defined in the human ATP binding cassette subfamily C member 5 gene (Ensembl:ENSG00000114770), for example, the NCBI reference sequence NM_001023587.2 or NCBI reference sequence NM_005688.3, ​​specifically the nucleotide sequence described in SEQ ID NO: 1 or SEQ ID NO: 2, which corresponds to the NCBI reference sequence of the ABCC5 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 3 or SEQ ID NO: 4, which corresponds to the protein sequence defined in the NCBI protein accession reference sequences NP_001018881.1 and NCBI protein accession reference sequences NP_005679 that encode the ABCC5 polypeptide.

[0118] The term "ABCC5" also refers to nucleotide sequences that show high homology to ABCC5, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 1 or SEQ ID NO: 2, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 3 or SEQ ID NO: 4. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence described in SEQ ID NO: 3 or SEQ ID NO: 4, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 1 or SEQ ID NO: 2.

[0119] The term "AIM2" refers to the sequence defined in the Absent in Melanoma 2 gene (Ensembl:ENSG00000163568), for example, the NCBI reference sequence NM_004833, specifically the nucleotide sequence described in Sequence ID No. 5, which corresponds to the NCBI reference sequence of the AIM2 transcript shown above. It also refers to the corresponding amino acid sequence described in Sequence ID No. 6, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_004824 that encodes the AIM2 polypeptide.

[0120] The term "AIM2" also refers to nucleotide sequences that show high homology to AIM2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 5, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 6. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence described in Sequence ID No. 6, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 5.

[0121] The term "APOBEC3A" refers to the Apolipoprotein B mRNA Editing Enzyme Catalytic Subunit 3A gene (Ensembl:ENSG00000128383), specifically the nucleotide sequence described in SEQ ID NO: 7, which corresponds to the NCBI reference sequence NM_145699, and also to the corresponding amino acid sequence described in SEQ ID NO: 8, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_663745 that encodes the APOBEC3A polypeptide.

[0122] The term "APOBEC3A" also refers to nucleotide sequences that show high homology to APOBEC3A, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 7, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 8. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a certain amino acid sequence or the sequence described in Sequence ID No. 8, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 7.

[0123] The term "CD2" refers to the gene of differentiation antigen group 2 (Ensembl:ENSG00000116824), specifically the sequence defined in the NCBI reference sequence NM_001767, and more specifically, the nucleotide sequence described in Sequence ID No. 9, which corresponds to the sequence of the NCBI reference sequence of the CD2 transcript shown above. It also refers to the corresponding amino acid sequence described in Sequence ID No. 10, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001758 that encodes the CD2 polypeptide.

[0124] The term "CD2" also refers to nucleotide sequences that show high homology to CD2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 9, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 10. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 10, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 9.

[0125] The term "CD247" refers to the gene for differentiation antigen group 247 (Ensembl:ENSG00000198821), specifically the sequence defined in NCBI reference sequence NM_000734 or NCBI reference sequence NM_198053, and more specifically, the nucleotide sequence described in SEQ ID NO: 11 or SEQ ID NO: 12, which corresponds to the sequence of the NCBI reference sequence of the CD247 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 13 or SEQ ID NO: 14, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_000725 and NCBI protein accession reference sequences NP_932170, which encode the CD247 polypeptide.

[0126] The term "CD247" also refers to nucleotide sequences that show high homology to CD247, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 11 or SEQ ID NO: 12, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 13 or SEQ ID NO: 14. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence described in SEQ ID NO 13 or SEQ ID NO 14, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO 11 or SEQ ID NO 12.

[0127] The term "CD28" refers to the sequence defined in the gene for differentiation antigen group 28 (Ensembl:ENSG00000178562), for example, the NCBI reference sequence NM_006139 or NCBI reference sequence NM_001243078, specifically the nucleotide sequence described in SEQ ID NO: 15 or SEQ ID NO: 16, which corresponds to the sequence of the NCBI reference sequence of the CD28 transcript shown above, and also refers to the corresponding amino acid sequence described in SEQ ID NO: 17 or SEQ ID NO: 18, which corresponds to the protein sequence defined in the NCBI protein accession reference sequences NP_006130 and NCBI protein accession reference sequences NP_001230007 that encode the CD28 polypeptide.

[0128] The term "CD28" also refers to nucleotide sequences that show high homology to CD28, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 15 or SEQ ID NO: 16, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 17 or SEQ ID NO: 18. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 17 or SEQ ID NO: 18, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 15 or SEQ ID NO: 16.

[0129] The term "CD3E" refers to the gene for differentiation antigen group 3E (Ensembl:ENSG00000198851), specifically the sequence defined in the NCBI reference sequence NM_000733, and more specifically, the nucleotide sequence described in SEQ ID NO: 19, which corresponds to the sequence of the NCBI reference sequence of the CD3E transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 20, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000724 that encodes the CD3E polypeptide.

[0130] The term "CD3E" also refers to nucleotide sequences that show high homology to CD3E, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 19, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 20. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 20, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 19.

[0131] The term "CD3G" refers to the gene for differentiation antigen group 3G (Ensembl:ENSG00000160654), specifically the sequence defined in the NCBI reference sequence NM_000073, and more specifically, the nucleotide sequence described in SEQ ID NO: 21, which corresponds to the sequence of the NCBI reference sequence of the CD3G transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 22, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000064 that encodes the CD3G polypeptide.

[0132] The term "CD3G" also refers to nucleotide sequences that show high homology to CD3G, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 21, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 22. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 22, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 21.

[0133] The term "CD4" refers to the gene for differentiation antigen group 4 (Ensembl:ENSG00000010610), specifically the sequence defined in the NCBI reference sequence NM_000616, and more specifically, the nucleotide sequence described in SEQ ID NO: 23, which corresponds to the sequence of the NCBI reference sequence of the CD4 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 24, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000607 that encodes the CD4 polypeptide.

[0134] The term "CD4" also refers to nucleotide sequences that show high homology to CD4, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 23, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 24. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 24, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 23.

[0135] The term "CIAO1" refers to the nucleotide sequence described in SEQ ID NO: 25, which corresponds to the sequence defined in the Cytosolic Iron-Sulfer Assembly Component 1 gene (Ensembl: ENSG00000144021), for example, the NCBI reference sequence NM_004804, specifically the sequence of the NCBI reference sequence of the CIAO1 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 26, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_663745 that encodes the CIAO1 polypeptide.

[0136] The term "CIAO1" also refers to nucleotide sequences that show high homology to CAIO1, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 25, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 26. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence described in SEQ ID NO 26, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO 25.

[0137] The term "CSK" refers to the C-terminal Src kinase gene (Ensembl:ENSG00000103653), specifically the sequence defined in the NCBI reference sequence NM_004383, and more specifically, the nucleotide sequence described in SEQ ID NO: 27, which corresponds to the sequence of the NCBI reference sequence of the CSK transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 28, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_004374 that encodes the CSK polypeptide.

[0138] The term "CSK" also refers to nucleotide sequences that show high homology to CSK, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 27, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 28. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 28, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 27.

[0139] The term "CUX2" refers to the sequence defined in the human Cut Like Homeobox 2 gene (Ensembl:ENSG00000111249), for example, the NCBI reference sequence NM_015267.3, specifically the nucleotide sequence described in SEQ ID NO: 29, which corresponds to the NCBI reference sequence of the CUX2 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 30, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_056082.2 that encodes the CUX2 polypeptide.

[0140] The term "CUX2" also refers to nucleotide sequences that show high homology to CUX2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 29, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 30. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 30, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 29.

[0141] The term "DDX58" refers to the DExD / H-box Helicase 58 gene (Ensembl:ENSG00000107201), specifically the nucleotide sequence described in SEQ ID NO: 31, which corresponds to the NCBI reference sequence NM_014314, and also to the corresponding amino acid sequence described in SEQ ID NO: 32, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_055129 that encodes the DDX58 polypeptide.

[0142] The term "DDX58" also refers to nucleotide sequences that show high homology to DDX58, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 31, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 32. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence described in SEQ ID NO. 32, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO. 31.

[0143] The term "DHX9" refers to the DExD / H-box Helicase 9 gene (Ensembl:ENSG00000135829), specifically the sequence defined in the NCBI reference sequence NM_001357, and more precisely, the nucleotide sequence described in SEQ ID NO: 33, which corresponds to the sequence of the NCBI reference sequence of the DHX9 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 34, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001348 that encodes the DHX9 polypeptide.

[0144] The term "DHX9" also refers to nucleotide sequences that show high homology to DHX9, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 33, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 34. This also includes nucleic acid sequences encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 34, or amino acid sequences encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 33.

[0145] The term "EZR" refers to the Ezrin gene (Ensembl:ENSG00000092820), specifically the sequence defined in the NCBI reference sequence NM_003379, and more precisely, the nucleotide sequence described in SEQ ID NO: 35, which corresponds to the sequence of the NCBI reference sequence of the EZR transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 36, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_003370 that encodes the EZR polypeptide.

[0146] The term "EZR" also refers to nucleotide sequences that show high homology to EZR, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 35, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 36 The sequence includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in sequence number 36, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in sequence number 35.

[0147] The term "FYN" refers to the sequence defined in the FYN Proto-Oncogene gene (Ensembl:ENSG00000010810), for example, NCBI reference sequence NM_002037, NCBI reference sequence NM_153047, or NCBI reference sequence NM_153048, specifically the nucleotide sequence described in SEQ ID NO: 37, SEQ ID NO: 38, or SEQ ID NO: 39, which corresponds to the sequence of the NCBI reference sequence of the FYN transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 40, SEQ ID NO: 41, or SEQ ID NO: 42, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_002028, NCBI protein accession reference sequences NP_694592, and NCBI protein accession reference sequences XP_005266949, which encode the FYN polypeptide.

[0148] The term "FYN" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to FYN, such as the sequences described in SEQ ID NO: 37, SEQ ID NO: 38, or SEQ ID NO: 39, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences described in SEQ ID NO: 40, SEQ ID NO: 41, or SEQ ID NO: 42 The sequence includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 40, SEQ ID NO: 41, or SEQ ID NO: 42, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 37, SEQ ID NO: 38, or SEQ ID NO: 39.

[0149] The term "IFI16" refers to the gene for Interferon Gamma Inducible protein 16 (Ensembl:ENSG00000163565), specifically the sequence defined in the NCBI reference sequence NM_005531, and more precisely, the nucleotide sequence described in SEQ ID NO: 43, which corresponds to the NCBI reference sequence of the IFI16 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 44, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_005522 that encodes the IFI16 polypeptide.

[0150] The term "IFI16" also refers to nucleotide sequences that show high homology to IFI16, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 43, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 44. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 44, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 43.

[0151] The term "IFIH1" refers to the Interferon Inducible With Helicase C Domain 1 gene (Ensembl:ENSG00000115267), specifically the nucleotide sequence described in SEQ ID NO: 45, which corresponds to the NCBI reference sequence NM_022168, and also to the corresponding amino acid sequence described in SEQ ID NO: 46, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_071451 that encodes the IFIH1 polypeptide.

[0152] The term "IFIH1" also refers to nucleotide sequences that show high homology to IFIH1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 45, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 46. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 46, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 45.

[0153] The term "IFIT1" refers to the sequence defined in the Interferon Induced Protein With Tetratricopeptide Repeats 1 gene (Ensembl:ENSG00000185745), for example, NCBI reference sequence NM_001270929 or NCBI reference sequence NM_001548.5, specifically the nucleotide sequence described in SEQ ID NO: 47 or SEQ ID NO: 48, which corresponds to the sequence of the NCBI reference sequence of the IFIT1 transcript shown above, and also refers to the corresponding amino acid sequence described in SEQ ID NO: 49 or SEQ ID NO: 50, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_001257858 and NCBI protein accession reference sequences NP_001539 that encode the IFIT1 polypeptide.

[0154] The term "IFIT1" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to IFIT1, such as the sequence described in SEQ ID NO: 47 or SEQ ID NO: 48, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 49 or SEQ ID NO: 50 This also includes nucleic acid sequences encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 49 or SEQ ID NO: 50, or amino acid sequences encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 47 or SEQ ID NO: 48.

[0155] The term "IFIT3" refers to the sequence defined in the Interferon Induced Protein With Tetratricopeptide Repeats 3 gene (Ensembl:ENSG00000119917), for example, the NCBI reference sequence NM_001031683, specifically the nucleotide sequence described in SEQ ID NO: 51, which corresponds to the NCBI reference sequence of the IFIT3 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 52, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001026853 that encodes the IFIT3 polypeptide.

[0156] The term "IFIT3" refers to a nucleotide sequence that shows high homology to IFIT3, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 51, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 52. This also includes nucleic acid sequences encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 52, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 51.

[0157] The term "KIAA1549" refers to the sequence defined in the human KIAA1549 gene (Ensembl:ENSG00000122778), for example, the NCBI reference sequence NM_020910 or NCBI reference sequence NM_00116466, specifically the nucleotide sequence described in SEQ ID NO: 53 or SEQ ID NO: 54, which corresponds to the NCBI reference sequence of the KIAA1549 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 55 or SEQ ID NO: 56, which corresponds to the protein sequence defined in the NCBI protein accession reference sequences NP_065961 and NCBI protein accession reference sequences NP_001158137 that encode the KIAA1549 polypeptide.

[0158] The term "KIAA1549" also refers to nucleotide sequences that show high homology to KIAA1549, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 53 or SEQ ID NO: 54, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 55 or SEQ ID NO: 56. The present invention includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoding an amino acid sequence, or a nucleic acid sequence encoding an amino

[0159] The term "LAT" refers to the gene (Ensembl:ENSG00000213658) for the Linker For Activation Of T-Cells, for example, the sequence defined in NCBI reference sequence NM_001014987 or NCBI reference sequence NM_014387, specifically the nucleotide sequence described in SEQ ID NO: 57 or SEQ ID NO: 58, which corresponds to the sequence of the NCBI reference sequence of the LAT transcript shown above, and also refers to the corresponding amino acid sequence described in SEQ ID NO: 59 or SEQ ID NO: 60, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_001014987 and NCBI protein accession reference sequences NP_055202 that encode the LAT polypeptide.

[0160] The term "LAT" also refers to a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a nucleotide sequence that shows high homology to LAT, for example, the sequence described in SEQ ID NO: 57 or SEQ ID NO: 58, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 59 or SEQ ID NO: 60. The sequence includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in sequence number 59 or sequence number 60, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in sequence number 57 or sequence number 58.

[0161] The term "LCK" refers to the LCK Proto-Oncogene gene (Ensembl:ENSG00000182866), specifically the sequence defined in the NCBI reference sequence NM_005356, and more specifically, the nucleotide sequence described in SEQ ID NO: 61, which corresponds to the sequence of the NCBI reference sequence of the LCK transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 62, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_005347 that encodes the LCK polypeptide.

[0162] The term "LCK" also refers to a nucleotide sequence that shows high homology to LCK, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 61, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 62. The sequence includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in sequence number 62, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in sequence number 61.

[0163] The term "LRRFIP1" stands for LRR Binding FLII Interacting Protein. This refers to the sequence defined in one gene (Ensembl:ENSG00000124831), for example, NCBI reference sequence NM_004735, NCBI reference sequence NM_001137550, NCBI reference sequence NM_001137553, or NCBI reference sequence NM_001137552, specifically the nucleotide sequence described in SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, or SEQ ID NO: 66, which corresponds to the sequence of the NCBI reference sequence of the LRRFIP1 transcript shown above, and also to the corresponding amino acid sequence described in SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, or SEQ ID NO: 70, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_004726, NCBI protein accession reference sequences NP_001131022, NCBI protein accession reference sequences NP_001131025, and NCBI protein accession reference sequences NP_001131024 that encode the LRRFIP1 polypeptide.

[0164] The term "LRRFIP1" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to LRRFIP1, such as the sequences described in SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, or SEQ ID NO: 66, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences described in SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, or SEQ ID NO: 70. The present invention includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, or SEQ ID NO: 70, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, or SEQ ID NO: 66.

[0165] The term "MYD88" refers to the nucleotide sequence described in SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, or SEQ ID NO: 75, which corresponds to the sequence of the NCBI reference sequence of the MYD88 transcript shown above, specifically the nucleotide sequence described in SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, or SEQ ID NO: 75, which corresponds to the sequence of the NCBI reference sequence of the MYD88 transcript shown above. This also relates to the corresponding amino acid sequences described in, for example, SEQ ID NO: 76, SEQ ID NO: 77, SEQ ID NO: 78, SEQ ID NO: 79, or SEQ ID NO: 80, which correspond to the protein sequences defined in the NCBI protein accession reference sequences NP_001166038, NP_001166039, NP_001166040, NP_001166037, and NP_002459 that encode ptido.

[0166] The term "MYD88" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to MYD88, such as the sequences described in SEQ ID NOs. 71, 72, 73, 74, or 75, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences described in SEQ ID NOs. 76, 77, 78, 79, or 80. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 76, SEQ ID NO: 77, SEQ ID NO: 78, SEQ ID NO: 79, or SEQ ID NO: 80, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, or SEQ ID NO: 75.

[0167] The term "OAS1" refers to 2'-5'-Oligoadenylate Synthetase This refers to the sequence defined in one gene (Ensembl:ENSG00000089127), for example, NCBI reference sequence NM_001320151, NCBI reference sequence NM_002534, NCBI reference sequence NM_001032409, or NCBI reference sequence NM_016816, specifically the nucleotide sequence described in SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, or SEQ ID NO: 84, which corresponds to the sequence of the NCBI reference sequence of the OAS1 transcript shown above, and also to the corresponding amino acid sequence described in SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, or SEQ ID NO: 88, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_001307080, NCBI protein accession reference sequences NP_002525, NCBI protein accession reference sequences NP_001027581, and NCBI protein accession reference sequences NP_058132, which encode the OAS1 polypeptide.

[0168] The term "OAS1" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to OAS1, such as the sequences described in SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, or SEQ ID NO: 84, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences described in SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, or SEQ ID NO: 88. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, or SEQ ID NO: 88, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, or SEQ ID NO: 84.

[0169] The term "PAG1" refers to the gene for Phosphoprotein Membrane Anchor With Glycosphingolipid Microdomains 1 (Ensembl:ENSG00000076641), specifically the sequence defined in the NCBI reference sequence NM_018440, and more precisely, the nucleotide sequence described in SEQ ID NO: 89, which corresponds to the sequence of the NCBI reference sequence of the PAG1 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 90, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_060910 that encodes the PAG1 polypeptide.

[0170] The term "PAG1" refers to a nucleic acid sequence that shows high homology to PAG1, for example, a sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 89, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 90. The sequence includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in sequence number 90, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in sequence number 89.

[0171] The term "PDE4D" refers to human phosphodiesterase 4D gene (Ensembl:ENSG00000113448), e.g. NCBI reference sequence NM_001104631, NCBI reference sequence NM_001349242, NCBI reference sequence NM_00119721 8, NCBI reference sequence NM_006203, NCBI reference sequence NM_001197221, NCBI reference sequence NM_001197220, NCBI reference sequence NM_001197223, NCBI reference sequence NM_00 NP refers to the nucleotide sequence defined in 1165899 or NCBI reference sequence NM_001165899, specifically the nucleotide sequence described in SEQ ID NOs. 91, 92, 93, 94, 95, 96, 97, 98, or 99, which corresponds to the NCBI reference sequence of the PDE4D transcript shown above, and is the NCBI protein accession reference sequence encoding the PDE4D polypeptide. This also relates to the corresponding amino acid sequences described in, for example, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, or SEQ ID NO: 108, corresponding to the protein sequences defined in _001098101, NCBI protein accession reference sequence NP_001336171, NCBI protein accession reference sequence NP_001184147, NCBI protein accession reference sequence NP_006194, NCBI protein accession reference sequence NP_001184150, NCBI protein accession reference sequence NP_001184149, NCBI protein accession reference sequence NP_001184152, NCBI protein accession reference sequence NP_001159371, and NCBI protein accession reference sequence NP_001184148.

[0172] The term "PDE4D" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to PDE4D, such as the sequences described in SEQ ID NOs. 91, 92, 93, 94, 95, 96, 97, 98, and 99, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences described in SEQ ID NOs. 100, 101, 102, 103, 104, 105, 106, 107, and 108. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, or SEQ ID NO: 108, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, or SEQ ID NO: 99.

[0173] The term "PRKACA" refers to the sequence defined in the Protein Kinase cAMP-Activated Catalytic Subunit Alpha gene (Ensembl:ENSG00000072062), for example, the NCBI reference sequence NM_002730 or NM_207518, specifically the nucleotide sequence described in SEQ ID NO: 109 or SEQ ID NO: 110, which corresponds to the NCBI reference sequence of the PRKACA transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 111 or SEQ ID NO: 112, which corresponds to the protein sequence defined in the NCBI protein accession reference sequences NP_002721 and NP_997401 that encode the PRKACA polypeptide.

[0174] The term "PRKACA" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to PRKACA, for example, the sequence described in SEQ ID NO: 109 or SEQ ID NO: 110, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 111 or SEQ ID NO: 112. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence described in SEQ ID NO 111 or SEQ ID NO 112, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO 109 or SEQ ID NO 110.

[0175] The term "PRKACB" refers to the gene for Protein Kinase cAMP-Activated Catalytic Subunit Beta (Ensembl:ENSG00000142875), such as NCBI reference sequences NM_002731, NM_182948, NM_001242860, NM_001242859, NM_001242858, NM_001242862, NM_001242861, NM_001300915, NM_207578, and NM_001242862, NM_001242861, NM_001300915, NM_207578, and NM_001242861. The sequence defined in M_001242857 or NCBI reference sequence NM_001300917, specifically the nucleotide sequence described in SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, SEQ ID NO: 118, SEQ ID NO: 119, SEQ ID NO: 120, SEQ ID NO: 121, SEQ ID NO: 122, or SEQ ID NO: 123, which corresponds to the sequence of the NCBI reference sequence of the PRKACB transcript shown above, and the NCBI protein accession reference sequence NP_002 which encodes the PRKACB polypeptide. 722, NCBI protein accession reference sequence NP_891993, NCBI protein accession reference sequence NP_001229789, NCBI protein accession reference sequence NP_001229788, NCBI protein accession reference sequence NP_001229787, NCBI protein accession reference sequence NP_001229791, NCBI protein accession reference sequence NP_001229790, NCBI protein accession reference sequence NP_00128 This relates to the corresponding amino acid sequences described in, for example, SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126, SEQ ID NO: 127, SEQ ID NO: 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131, SEQ ID NO: 132, SEQ ID NO: 133, or SEQ ID NO: 134, corresponding to the protein sequences defined in 7844, NCBI protein accession reference sequence NP_997461, NCBI protein accession reference sequence NP_001229786, and NCBI protein accession reference sequence NP_001287846.

[0176] The term "PRKACB" also refers to nucleotide sequences that show high homology to PRKACB, for example, sequences described in SEQ ID NOs. 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, or 123, with at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, and 96% homology. Nucleic acid sequences that are 97%, 98%, or 99% identical, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences described in SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126, SEQ ID NO: 127, SEQ ID NO: 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131, SEQ ID NO: 132, SEQ ID NO: 133, or SEQ ID NO: 134 A nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126, SEQ ID NO: 127, SEQ ID NO: 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131, SEQ ID NO: 132, SEQ ID NO: 133, or SEQ ID NO: 134, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126, SEQ ID NO: 127, SEQ ID NO: 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131, SEQ ID NO: 132, SEQ ID NO: 133, or SEQ ID NO: 134, or i includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, SEQ ID NO: 118, SEQ ID NO: 119, SEQ ID NO: 120, SEQ ID NO: 121, SEQ ID NO: 122, or SEQ ID NO: 123.

[0177] The term "PTPRC" refers to the gene for protein tyrosine phosphatase receptor type C (Ensembl:ENSG00000081237), specifically the sequence defined in NCBI reference sequence NM_002838 or NCBI reference sequence NM_080921, and more specifically, the nucleotide sequence described in SEQ ID NO: 135 or SEQ ID NO: 136, which corresponds to the sequence of the NCBI reference sequence of the PTPRC transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 137 or SEQ ID NO: 138, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_002829 and NCBI protein accession reference sequences NP_563578, which encode the PTPRC polypeptide.

[0178] The term "PTPRC" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to PTPRC, for example, the sequence described in SEQ ID NO: 135 or SEQ ID NO: 136, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 137 or SEQ ID NO: 138. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 137 or SEQ ID NO: 138, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 135 or SEQ ID NO: 136.

[0179] The term "RAP1GAP2" refers to the sequence defined in the human RAP1 GTPase Activating Protein 2 gene (ENSG00000132359), for example, NCBI reference sequence NM_015085, NCBI reference sequence NM_001100398, or NCBI reference sequence NM_001330058, specifically the nucleotide sequence described in SEQ ID NO: 139, SEQ ID NO: 140, or SEQ ID NO: 141, which corresponds to the sequence of the NCBI reference sequence of the RAP1GAP2 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 142, SEQ ID NO: 143, or SEQ ID NO: 144, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_055900, NCBI protein accession reference sequences NP_001093868, and NCBI protein accession reference sequences NP_001316987 that encode the RAP1GAP2 polypeptide.

[0180] The term "RAP1GAP2" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to RAP1GAP2, such as the sequences described in SEQ ID NO: 139, SEQ ID NO: 140, or SEQ ID NO: 141, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences described in SEQ ID NO: 142, SEQ ID NO: 143, or SEQ ID NO: 144. The present invention includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence described in SEQ ID NO: 142, SEQ ID NO: 143, or SEQ ID NO: 144, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence described in SEQ ID NO: 139, SEQ ID NO: 140, or SEQ ID NO: 141.

[0181] The term "SLC39A11" refers to the sequence defined in the human Solute Carrier Family 39Member 11 gene (Ensembl:ENSG00000133195), for example, NCBI reference sequence NM_139177 or NCBI reference sequence NM_001352692, specifically the nucleotide sequence described in SEQ ID NO: 145 or SEQ ID NO: 146, which corresponds to the NCBI reference sequence of the SLC39A11 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 147 or SEQ ID NO: 148, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_631916 and NCBI protein accession reference sequences NP_001339621 that encode the SLC39A11 polypeptide.

[0182] The term "SLC39A11" also refers to a nucleotide sequence that shows high homology to SLC39A11, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 145 or SEQ ID NO: 146, or a sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 147 or SEQ ID NO: 148. The present invention includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoding an amino acid sequence, or a nucleic acid sequence encoding an amino.

[0183] The term "TDRD1" refers to the sequence defined in the human Tudor Domain Containing 1 gene (Ensembl:ENSG00000095627), for example, the NCBI reference sequence NM_198795, specifically the nucleotide sequence described in SEQ ID NO: 149, which corresponds to the NCBI reference sequence of the TDRD1 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 150, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_942090 that encodes the TDRD1 polypeptide.

[0184] The term "TDRD1" also refers to a nucleotide sequence that shows high homology to TDRD1, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 149, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 150. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 150, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 149.

[0185] The term "TLR8" refers to the sequence defined in the Toll Like Receptor 8 gene (Ensembl:ENSG00000101916), for example, the NCBI reference sequence NM_138636 or NM_016610, specifically the nucleotide sequence described in SEQ ID NO: 151 or SEQ ID NO: 152, which corresponds to the NCBI reference sequence of the TLR8 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 153 or SEQ ID NO: 154, which corresponds to the protein sequence defined in the NCBI protein accession reference sequences NP_619542 and NPBI protein accession reference sequences NP_057694 that encode the TLR8 polypeptide.

[0186] The term "TLR8" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to TLR8, for example, the sequence described in SEQ ID NO: 151 or SEQ ID NO: 152, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 153 or SEQ ID NO: 154. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 153 or SEQ ID NO: 154, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 151 or SEQ ID NO: 152.

[0187] The term "VWA2" refers to the sequence defined in the human Von Willebrand Factor A Domain Containing 2 gene (Ensembl:ENSG00000165816), for example, the NCBI reference sequence NM_001320804, specifically the nucleotide sequence described in SEQ ID NO: 155, which corresponds to the NCBI reference sequence of the VWA2 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 156, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001307733 that encodes the VWA2 polypeptide.

[0188] The term "VWA2" also refers to nucleotide sequences that show high homology to VWA2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 155, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 156. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 156, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in Sequence ID No. 155.

[0189] The term "ZAP70" refers to the gene (Ensembl:ENSG00000115085) of the zeta chain of T-cell receptor-associated protein kinase 70, specifically the sequence defined in NCBI reference sequence NM_001079 or NM_207519, and more specifically, the nucleotide sequence described in SEQ ID NO: 157 or SEQ ID NO: 158, which corresponds to the sequence of the NCBI reference sequence of the ZAP70 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 159 or SEQ ID NO: 160, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_001070 and NP_997402, which encode the ZAP70 polypeptide.

[0190] The term "ZAP70" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to ZAP70, such as the sequence described in SEQ ID NO: 157 or SEQ ID NO: 158, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 159 or SEQ ID NO: 160. The present invention includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence described in SEQ ID NO: 159 or SEQ ID NO: 160, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 157 or SEQ ID NO: 158.

[0191] The term "ZBP1" refers to the sequence defined in the Z-DNA Binding Protein 1 gene (Ensembl:ENSG00000124256), for example, NCBI reference sequence NM_030776, NCBI reference sequence NM_001160418, or NCBI reference sequence NM_001160419, specifically the nucleotide sequence described in SEQ ID NO: 161, SEQ ID NO: 162, or SEQ ID NO: 163, which corresponds to the sequence of the NCBI reference sequence of the ZBP1 transcript shown above. It also refers to the corresponding amino acid sequence described in SEQ ID NO: 164, SEQ ID NO: 165, or SEQ ID NO: 166, which corresponds to the protein sequence defined in the NCBI protein accession reference sequences NP_110403, NCBI protein accession reference sequences NP_001153890, and NCBI protein accession reference sequences NP_001153891 that encode the ZBP1 polypeptide.

[0192] The term "ZBP1" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to ZBP1, such as the sequences described in SEQ ID NO: 161, SEQ ID NO: 162, or SEQ ID NO: 163, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences described in SEQ ID NO: 164, SEQ ID NO: 165, or SEQ ID NO: 166. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 164, SEQ ID NO: 165, or SEQ ID NO: 166, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence described in SEQ ID NO: 161, SEQ ID NO: 162, or SEQ ID NO: 163.

[0193] The term “biological sample” or “sample obtained from a subject” refers to any biological material obtained from a subject, for example, a patient with a glioma, by a preferred method known to those skilled in the art. The biological sample used may be collected in a clinically acceptable manner, for example, in a manner that preserves nucleic acids (especially RNA) or proteins.

[0194] Biological samples may include body tissues and / or body fluids, for example, blood (or blood-derived substances, such as serum, plasma, or PBMCs (peripheral blood mononuclear cells)), sweat, saliva, urine, and needle biopsies or excision biopsies. Furthermore, biological samples may contain cells derived from cancerous cells or tissues suspected of being cancerous, such as cell extracts or cell populations derived from glioma cells. In addition, if necessary, cells may be purified from the obtained body tissues and body fluids and then used as biological samples. In some realizations, samples may be tissue samples, urine samples, urine sediment samples, blood samples, saliva samples, semen samples, samples containing circulating tumor cells, extracellular vesicles, samples containing exosomes secreted by the prostate, or cell lines or cancer cell lines.

[0195] In a particular implementation, a biopsy or excision specimen may be obtained and / or used. Such a specimen may contain cells or cell lysates.

[0196] It is also conceivable to subject the contents of a biological sample to a concentration step. For example, the sample may be contacted with ligands specific to a particular cell type, such as the cell membrane or organelles of glioma cells, which are functionalized, for example, with magnetic particles. The material concentrated with magnetic particles may then be used for the detection and analysis steps described above or below in this specification.

[0197] Furthermore, cells, such as tumor cells, can be concentrated through a filtration process of body fluids or liquid samples, such as blood or urine. Such a filtration process can also be combined with the concentration step based on ligand-specific interactions described above.

[0198] In one embodiment, six or more gene expression levels are: - One or more immune defense response genes, preferably two or more, more preferably three or more, most preferably all immune defense genes, - One or more T cell receptor signaling genes, preferably two or more, more preferably three or more, most preferably all T cell receptor signaling genes, and - One or more PDE4D7-related genes, preferably two or more, more preferably three or more, most preferably all PDE4D7-related genes Includes.

[0199] In one embodiment: - One or more immune defense response genes include three or more, preferably six or more, more preferably nine or more, and most preferably all immune defense genes. - One or more T cell receptor signaling genes include three or more, preferably six or more, more preferably nine or more, most preferably all T cell receptor signaling genes, and / or - One or more PDE4D7-related genes include three or more, preferably six or more, and most preferably all PDE4D7-related genes.

[0200] The steps to determine the outcome are: - A step of combining the first gene expression profiles of two or more, for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all immune defense response genes with a regression function derived from a population of subjects with gliomas. - A step of combining a second gene expression profile of two or more T cell receptor signaling genes, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, with a regression function derived from a population of subjects with gliomas. - A step of combining a third gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7 or all PDE4D7-related genes, with a regression function derived from a population of subjects with glioma, and / or - A step of combining six or more gene expression levels, e.g., 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more, or all gene expression levels, with a regression function derived from a population of subjects with gliomas. It is preferable that it has

[0201] Cox proportional hazards regression allows for real-time analysis of the impact of multiple risk factors on an event under study, such as survival. The risk factors can be binary or discrete variables, such as risk scores or clinical stage, but they can also be continuous variables, such as biomarker measurements or gene expression values. The probability of an endpoint (e.g., death or disease recurrence) is called the hazard. In regression analysis, the time to the endpoint is considered, in addition to information about whether a subject in a patient cohort reached the endpoint (e.g., whether the patient died or not). The hazard is modeled as H(t) = H0(t)·exp(w1·V1+w2·V2+w3·V3+...), where V1, V2, V3... are predictors, H0(t) is the baseline hazard, and H(t) is the hazard at any given time t. The hazard rate (HR) (or the risk of reaching an event) is expressed as Ln[H(t) / H0(t)]=w1·V1+w2·V2+w3·V3+..., where the coefficients or weights w1, w2, w3... are estimated by Cox regression analysis and can be interpreted similarly to logistic regression analysis.

[0202] In a particular realization, the combination of the first gene expression profiles of six or more, e.g., 6, 7, 8, 9, 10, 11, 12, 13, or all immune defense response genes, and the regression function is determined as follows:

[0203]

number

[0204] In a particular realization, the combination of the second gene expression profiles of six or more T cell receptor signaling genes, e.g., 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all T cell receptor signaling genes, and the regression function is determined as follows:

[0205]

number

[0206] In a particular realization, the combination of the third gene expression profile of six or more, e.g., 6, 7, or all PDE4D7-related genes, and the regression function is determined as follows:

[0207]

number

[0208] The step of determining the prognosis prediction further preferably includes combining a first combination of gene expression profiles, a second combination of gene expression profiles, and a third combination of gene expression profiles with a regression function derived from a population of glioma subjects.

[0209] In a specific implementation, the prognosis prediction is determined as follows:

[0210]

Number

[0211] In a specific implementation, the combination of the expression levels of six or more genes is calculated as follows: GLCAI_6model: (w a · [Gene A]) + (w b · [Gene B]) + (w C · [Gene C]) + (w d · [Gene D]) + (w e · [Gene E]) + (w F · [Gene F]) Here, w xThe values ​​represent the weights of each gene X, and gene X is selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2, and gene A, gene B, It is understood that genes C, D, E, and F are different genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2. Generating combinations of six or more genes as specified herein is within the capabilities of those skilled in the art, and the weights of each of such models have been calculated. The predictive model can be supplemented by further genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2, or other genes, thus it is further understood that predictive models based on more than six genes can be produced. The predicted outcomes can also be classified or categorized into one of at least two risk groups based on the predicted outcome values.For example, there can be two risk groups, three risk groups, four risk groups, or more than four predefined risk groups. Each risk group covers (non-overlapping) values for an individual range of outcome prediction. For example, the risk groups can represent the probability of occurrence of a specific clinical event such as 0 to <0.1, 0.1 to <0.25, 0.25 to <0.5, or 0.5 to 1.0.

[0212] It is even more preferable that the determination of outcome prediction is further based on one or more clinical parameters obtained from the subject.

[0213] As described above, various scales based on clinical parameters have been investigated. By further basing on the prediction of outcome with such clinical parameters, it may be possible to further improve the prediction.

[0214] The clinical parameters preferably include (i) Karnofsky Performance Score; (ii) tumor malignancy; (iii) one or more of the presence and amount of lesions. Further or alternatively, the clinical parameters include one or more other clinical parameters related to the diagnosis and / or prognosis of glioma.

[0215] The Karnofsky Performance Score (KPS) ranking is performed from 100 to 0, where 100 is "completely" healthy and 0 is death. The scores are typically assigned at standard intervals of 10. The main purpose of its development is to enable physicians to evaluate the ability of cancer patients to survive chemotherapy. 100 - Normal; no symptoms; no evidence of disease 90 - Able to perform normal activities; slight signs or symptoms of disease 80 - Normal activities with effort; some signs or symptoms of disease 70 - Self-care; unable to perform normal activities or active work 60 - Occasionally requires assistance but can care for most personal needs 50 - Considerable assistance and frequent medical treatment required 40. Disability; requiring special care and support. 30 - Severe disability; hospitalization is recommended, but death is not imminent. 20 - Severe; hospitalization required; active supportive care required 10 - Near death; the progression of a fatal process is rapid. 0-death

[0216] For example, the Karnovsky Performance Score can be used to group patients as follows: 0-20; 20-40; 40-60; 60-80; and 80-100.

[0217] The steps for determining the outcome more preferably include: (i) a first gene expression profile of one or more immune defense response genes; (ii) a second gene expression profile of one or more T cell receptor signaling genes; (iii) a third gene expression profile of one or more PDE4D7-related genes; and (iv) a combination of the first gene expression profiles, the second gene expression profiles, and the third gene expression profiles, as well as one or more clinical parameters obtained from the subjects, and a regression function derived from a population of subjects with gliomas.

[0218] Biological samples are preferably obtained from the subject before the initiation of treatment. Gene expression profiles can be determined in the form of mRNA or protein in glioma tissue. Alternatively, if the gene is present in a soluble form, the gene expression profile can be determined in blood, for example, in a blood sample containing circulating tumor cells.

[0219] Treatment is more preferably surgery, radiation therapy, cytotoxic chemotherapy (CTX), short- or long-term courses of chemoradiotherapy (CRT), immunotherapy, targeted therapy, or any combination thereof.

[0220] The prediction of treatment response is preferably that the treatment is ineffective or effective, and the treatment is a recommendation based on the prediction, and if the prediction is negative, the recommended treatment includes (i) treatment provided earlier than standard; (ii) high effective dose radiotherapy; (iii) adjuvant treatment such as chemotherapy; (iv) a long course of CRT (chemoradiotherapy); and (iv) one or more alternative treatments such as immunotherapy.

[0221] In a further aspect of the present invention, a device for predicting the outcome of a subject having a glioma is: - Appropriate input to receive data showing six or more gene expression levels selected from the first gene expression profile, the second gene expression profile and / or the third gene expression profile, e.g., 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 gene expression levels; - A first gene expression profile consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - A second gene expression profile consisting of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; - A third gene expression profile consisting of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; The gene expression level determined in the biological sample obtained from the subject; and A suitable processor to determine outcome predictions based on the expression levels of six or more genes. This includes, in one embodiment, the device further includes the step of providing a unit suitable for providing predictions to a healthcare provider or subject.

[0222] In a further aspect of the present invention, a computer program including instructions, when the program is executed by a computer, - Step 1: Receiving data showing the gene expression levels of six or more genes selected from a first gene expression profile, a second gene expression profile and / or a third gene expression profile, for example, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38. - A first gene expression profile consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - A second gene expression profile consisting of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; - A third gene expression profile consisting of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; The gene expression level determined in the biological sample obtained from the subject; and A step in which the outcome of a subject is predicted based on the expression levels of six or more genes. A computer is used to predict the outcome of a subject having a glioma. In one embodiment, the computer program further includes instructions to provide the prediction to a healthcare provider or the subject.

[0223] In a further aspect of the present invention, the diagnostic kit is - A means for determining six or more gene expression levels selected from a first gene expression profile, a second gene expression profile, and a third gene expression profile in a biological sample obtained from a subject, wherein the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile, and at least one or more gene expression levels selected from the first and / or second gene expression profiles. - A first gene expression profile consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - A second gene expression profile consisting of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; - A third gene expression profile consisting of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. This includes, in some cases, the kit further includes the apparatus and / or the computer program as defined herein.

[0224] Or, - A means for determining the first gene expression profile of each of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, in a biological sample obtained from a subject. - Means for determining the respective second gene expression profiles of one or more genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 in a biological sample obtained from a subject, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of the T cell receptor signaling genes. - Means for determining the respective gene expression profiles of one or more genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 in a biological sample obtained from a subject, for example, 1, 2, 3, 4, 5, 6, 7, or all of the PDE4D7-related genes. - Optionally, an apparatus as defined herein and / or a computer program as defined herein A diagnostic kit comprising the above is disclosed.

[0225] The means for determining the expression level of a target gene can be primers and / or probes suitable for any one of PCR, quantitative PCR, digital PCR, RNA sequencing, or target RNA sequencing. Thus, for example, the means can be PCR primers, quantitative PCR primers and probes, digital PCR primers and probes, or capture probes for target mRNA sequencing. Preferably, the means is a PCR amplification primer, optionally a probe or an mRNA target sequencing capture probe.

[0226] In a further aspect of the invention, there is provided the use of a kit, said use comprising: - Determining the expression levels of six or more genes in a sample obtained from a subject having glioma; - Providing an outcome of the subject based on the expression levels of six or more genes comprising The kit comprises means for determining six or more gene expression levels selected from a first gene expression profile, a second gene expression profile, and / or a third gene expression profile; - A first gene expression profile consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - A second gene expression profile consisting of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; - A third gene expression profile consisting of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. This relates to uses including, in one embodiment, the use includes the step of carrying out a method for predicting the outcome of a subject having a glioma, as described more broadly herein.

[0227] In a further aspect of the present invention, in a method for predicting the outcome of a subject having a glioma, the use of six or more gene expression levels selected from a first gene expression profile, a second gene expression profile and a third gene expression profile is such that the six or more gene expression levels include at least one or more gene expression levels selected from the third gene expression profile, and at least one or more gene expression levels selected from the first and / or second gene expression profile, wherein the first gene expression profile includes AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, The first gene expression profile consists of immune defense response genes selected from the group consisting of IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; and the third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. - A step of determining the prediction of the outcome based on the expression levels of six or more genes. - Depending on the circumstances, a step to provide predictions to healthcare providers or the person being treated. Includes.

[0228] Alternatively, the method predicts the outcome of subjects with gliomas by using the first gene expression profiles of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, respectively, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, P The use of one or more selected from the group consisting of RKACA, PRKACB, PTPRC, and ZAP70, for example, a second gene expression profile for each of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all T cell receptor signaling genes, and / or the use of one or more selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, for example, a third gene expression profile for each of 1, 2, 3, 4, 5, 6, 7, or all PDE4D7-related genes, - A step of determining a first gene expression profile, a second gene expression profile, a third gene expression profile, or a prediction of the outcome based on the first, second, and third gene expression profiles. - Depending on the circumstances, a step of providing predictions, personalization, or treatment recommendations based on predictions or personalization to healthcare providers or patients. Including, regarding use.

[0229] In a further embodiment, the present invention relates to a product for use in the treatment or improvement of a subject having a glioma, wherein the product comprises a chemotherapy compound, a targeted cancer treatment compound, or an immunotherapy compound. The use is a step of determining six or more gene expression levels selected from a first gene expression profile, a second gene expression profile and / or a third gene expression profile, or a step of receiving the results of determining gene expression levels. - The first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - The second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; - The third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; The gene expression level is determined in a biological sample obtained from the subject, in the step, - A step of determining the prediction of the outcome based on the expression levels of six or more genes; and Steps to administer the product to the target Regarding products, including those mentioned above.

[0230] Therefore, the product contains chemotherapy compounds, targeted cancer treatment compounds, or immunotherapy compounds, or combinations thereof. Non-exclusive examples of chemotherapy compounds include Abraxane, Actinomycin, Alitretinoin, All-Trans Retinoic Acid, Altretamine, Azacitidine, Azathioprine, Belotecan, Bendamustine, Bexarotene, Bleomycin, Bortezomib, Busulfan, Cabazitaxel, Camptothecin, Carboplatin, Carbocon, Carmustine, Capecitabine, Cisplatin, Chlorambucil, Chlormethine, Chlorozotocin, Cyclophosphamide, Cytarabine, Dacarbazine, Daunorubicin, Docetaxel, Doxifluridine, Doxorubicin, Epirubicin, Epothiron, Erlotinib, Etoposide, Exatecan, Fluorouracil, Fotemustine, Gefitinib, Gemcitabine, Jaimatecan, Hydroxyurea, Idarubicin These include ifosfamide, imatinib, irinotecan, ixabepirone, larotaxel, lomustine, melphalan, melphalan, flufenamide, mercaptopurine, methotrexate, mitobronitol, mitomycin C, mitoxantrone, nimustine, nitrosourea, oxaliplatin, paclitaxel, pemetrexed, pipobromane, ranimustine, romidepsin, semustine, streptozotocin, tafluposide, taxotere, temozolomide, tesetaxel, teniposide, thiotepa, thioguanine, topotecan, treosulfan, tretinoin, triadicone, triethylenemelamine, barrubicin, vemurafenib, vinblastine, vincristine, vindesine, vinorelbine, bismodegib, and vorinostat. Therefore, in one embodiment, the product contains the chemotherapy compounds listed above.

[0231] Non-exclusive examples of immunotherapy compounds include lipilimumab (Yervoy), tremelimumab, nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (Tecentriq), avelumab (Bavencio), durvalumab (Imfinzi), semiplimab (Ributayo), dostallimab (Jemperli), JTX-4014, spartalizumab (PDR001), camrelizumab (SHR1210), cintilimab (IBI308), tislerizumab (BGB-A317), and tripalimab (JS 001), relatrimab (BMS-986016), INCMGA00012 (MGA012), AMP-224, AMP-514 (MEDI0680), KN035, CK-301, AUNP12, CA-170, BCD-100, and BMS-986189, and more preferably lipilimumab (Yervoy), tremelimumab, nivolumab (Opdivo), pembrol Zumab (Keytruda), Atezolizumab (Tecentriq), Avelumab (Bavencio), Durvalumab (Imfinzi), Semiprimab (Ributayo), Dostallimab (Jemperli), JTX-4014, Spartalizumab (PDR001), Camrelizumab (SHR1210), Syntilimab (IBI308), Tithrelizumab (BGB-A317), Tripalimab (JS The antibody treatment is selected from 001), INCMGA00012 (MGA012), AMP-224, AMP-514 (MEDI0680), KN035, CK-301, AUNP12, CA-170, LY3300054, INCAGN02385, Sym023, Cobolimab (TSR-022), RO7121661, AZD7789, LY3321367, LB1410, INCAGN02390, and BMS-986189. Therefore, in one embodiment, the product comprises the immunotherapy compounds listed above.

[0232] Non-limiting examples of targeted therapeutic compounds include verzutifan (Wellig), bevacizumab (Avastin), debrafenib (Tafinlar), everolimus (Afinitor), and trametinib (Mekinist). Therefore, in one embodiment, the product includes the targeted therapeutic compounds listed above.

[0233] In one embodiment, the product comprises a combination of two or more of the chemotherapeutic compounds, immunotherapy compounds, and / or targeted therapeutic compounds listed above.

[0234] When a favorable outcome is expected, the following treatment options are expected to be ideally recommended: surgery, radiotherapy, chemotherapy, or targeted therapy. When an unfavorable outcome is expected, the following treatment options are expected to be ideally recommended: a combination of two or more selected from surgery, radiotherapy, chemotherapy, and targeted therapy; or immunotherapy; or experimental drugs or treatments (clinical trials). Therefore, in one embodiment, when a favorable outcome is expected for the subject, the product contains a chemotherapy compound or a targeted therapy compound. In one embodiment, when an unfavorable outcome is expected for the subject, the product contains an immunotherapy compound or a combination of chemotherapy, or contains a chemotherapy compound or a targeted therapy compound.

[0235] In an alternative embodiment, the present invention relates to a method for treating a subject having a glioma, comprising the steps of determining six or more gene expression levels selected from a first gene expression profile, a second gene expression profile and / or a third gene expression profile, or receiving the results of such determination. - The first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - The second gene expression profile consists of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; - The third gene expression profile consists of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. The steps include determining the gene expression level in a biological sample obtained from the subject, and - A step of determining the prediction of the outcome based on the expression levels of six or more genes, and Steps to administer treatment to the subject based on the determined outcome. Regarding methods including

[0236] In one embodiment, a favorable outcome is anticipated, and a procedure selected from surgery, radiotherapy, chemotherapy, or targeted therapy, preferably surgery, chemotherapy, or targeted therapy, is administered or performed. In another embodiment, an unfavorable outcome is anticipated, and a combination of two or more procedures selected from surgery, radiotherapy, chemotherapy, and targeted therapy, or a procedure selected from immunotherapy, or an experimental drug or procedure (clinical trial), is administered or performed.

[0237] In alternative embodiments, a favorable outcome is anticipated, and a procedure is administered or performed that does not include radiotherapy, preferably selected from surgery, chemotherapy, or targeted therapy. In one embodiment, an unfavorable outcome is anticipated, and a procedure is administered or performed that includes radiotherapy, and optionally one or more selected from surgery, chemotherapy, targeted therapy, immunotherapy, or experimental drugs or procedures (clinical trials).

[0238] The methods for predicting outcomes in subjects with gliomas as broadly described herein, the apparatus as broadly described herein, the computer programs as broadly described herein, the diagnostic kits as broadly described herein, the use of the diagnostic kits as broadly described herein, the use of the first, second, and / or third gene expression profiles as broadly described herein, and the products for use as broadly described herein are understood to have, specifically, similar and / or identical preferred embodiments as defined in the dependent claims.

[0239] Preferred embodiments of the present invention may be any combination of the dependent or above embodiments and their respective independent embodiments.

[0240] These and other aspects of the present invention will become apparent from the embodiments described below and will be explained with reference thereto.

[0241] Figure 1 schematically and illustratively shows a flowchart of an embodiment of a method for predicting the outcome of a subject with a glioma.

[0242] Biological samples were obtained from each of the first set of patients (subjects) diagnosed with gliomas. Preferably, glioma monitoring was performed in these patients for a period of at least one year, or at least two years, or about five years, after obtaining the biological samples.

[0243] In the next step, one or more genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, the first gene expression profile of each of the immune defense response genes, and one or more genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 1, 2, 3 Second gene expression profiles for each of 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, and / or third gene expression profiles for each of two or more genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, e.g., 2, 3, 4, 5, 6, 7, or all PDE4D7-related genes, are obtained in each biological sample obtained from the first set of patients, for example by performing RT-qPCR (real-time quantitative PCR) on RNA extracted from each biological sample. Exemplary gene expression profiles include the expression levels (e.g., values) of each of two or more genes that can be normalized using the values ​​of each set of reference genes such as B2M, HPRT1, POLR2A, and / or PUM1. In one implementation, the gene expression levels of two or more genes in the first gene expression profile, the second gene expression profile, and / or the third gene expression profile are normalized with respect to one or more reference genes selected from the group consisting of ACTB, ALAS1, B2M, HPRT1, POLR2A, PUM1, RPLP0, TBP, TUBA1B, and / or YWHAZ, for example, with respect to at least one, at least two, at least three, or preferably all of these reference genes.

[0244] In the next step, the regression function that assigns the prediction of the outcome is derived from the first gene expression profiles of two or more immune defense response genes, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, obtained from the respective results obtained from at least some biological samples and monitoring obtained from the first set of patients, and two or more T cell receptor sigma The regression is determined based on the second gene expression profiles of the primary transmission genes CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and / or the third gene expression profiles of two or more PDE4D7-related genes ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. In a particular realization, the regression function is determined as described above.

[0245] In the next step, the biological sample is obtained from a patient (subject or individual). The patient may be a new patient or one of the first set.

[0246] In the next step, we will obtain the first gene expression profiles for each of the following immune defense response genes, selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG 1. A second gene expression profile for each of one or more T cell receptor signaling genes selected from the group consisting of PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them, and / or a third gene expression profile for each of two or more PDE4D7-related genes, e.g., 2, 3, 4, 5, 6, 7, or all of them, was obtained, for example, by performing PCR on a biological sample. In one implementation, the gene expression levels of two or more genes in the first gene expression profile, the second gene expression profile, and / or the third gene expression profile are normalized with respect to one or more reference genes selected from the group consisting of ACTB, ALAS1, B2M, HPRT1, POLR2A, PUM1, RPLP0, TBP, TUBA1B, and / or YWHAZ, for example, with respect to at least one, at least two, at least three, or preferably all of these reference genes.

[0247] In the next step, predictions of outcomes based on the first, second, and third gene expression profiles are determined for each patient using a regression function. This is described in more detail later in this specification.

[0248] In the next step, treatment recommendations may be provided, for example, to the patient or their monitor, physician, or other healthcare provider, based on prediction or personalization. For this purpose, prediction or personalization may be categorized into one of a predefined set of risk groups based on the value of the prediction or personalization. In a particular realization, the treatment may be radiotherapy, and the prediction of the treatment response may be that the treatment is ineffective or effective. If the prediction is ineffective, the recommended treatment may include (i) treatment delivered earlier than standard; (ii) radiotherapy with a high effective dose; (iii) adjuvant treatment such as chemotherapy; (iv) a long course of CRT (chemoradiotherapy); and (iv) one or more alternative treatments such as immunotherapy.

[0249] In one embodiment, the gene expression profile is determined by detecting mRNA expression using two or more primers and / or probes and / or two or more sets thereof.

[0250] In one embodiment, the step further comprises obtaining a first set of patients and clinical parameters from the patients. The clinical parameters may include one or more of (i) Karnovsky performance scores; (ii) tumor malignancy; and (iii) the presence and amount of lesions. Furthermore, the clinical parameters may include one or more other clinical parameters related to the diagnosis and / or prognosis of glioma. The regression function that assigns the prediction of the outcome determined above is then further based on one or more clinical parameters obtained from at least a portion of the first set of patients. In the step, the prediction of the outcome is then further based on one or more clinical parameters obtained from the patients and determined in the patients using the regression function, e.g., (i) Karnovsky performance scores; (ii) tumor malignancy; and (iii) the presence and amount of lesions. In a particular realization, the regression function is determined as described above.

[0251] Based on a significant correlation with post-treatment survival outcomes, we predicted that the identified molecules would provide predictive values ​​regarding the effectiveness of glioma treatment. Therefore, in one embodiment, the method is based on, for example, the expression levels of three or more genes selected from immune defense response genes, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or T cell receptor signaling genes selected from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and / or PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, and TDRD1, preferably 4, 5, 6, 7, 8, 9, or 10.

[0252] In a fifth aspect, the present invention relates to a product for use in the treatment or improvement of a subject having a glioma, wherein the product comprises a chemotherapy compound, a targeted cancer treatment compound or an immunotherapy compound, and the use comprises the step of determining six or more gene expression levels selected from a first gene expression profile, a second gene expression profile and / or a third gene expression profile, or the step of receiving the results of the determination of gene expression levels, wherein the first gene expression profile consists of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; the second gene The present invention relates to a product comprising the steps of: a first gene expression profile consisting of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; a third gene expression profile consisting of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; determining the gene expression levels in a biological sample obtained from a subject; determining the prediction of an outcome based on six or more gene expression levels; and administering the product to the subject. [Examples]

[0253] dataset The following dataset was used: The TGCA cohort includes data from patients with astrocytoma (29%), pleoplastic gliablastoma (GBM, 0.1%), oligodendronoma (19%), oligodendronoma (29%), treated primary GBM (0.1%), untreated primary (de novo) GBM (22%), or NA (0.3%). The median follow-up period for patients was 20 months, and the maximum follow-up period was approximately 17.5 years. Of the patients in the cohort, 31% received radiotherapy, 62% did not, and it was unknown whether 6.4% received radiotherapy. The cohort includes 669 patients, and combined RNA sequencing data and clinical metadata are available, with survival data available for 625 of these samples. The cohort was randomly divided into a training cohort of 413 patients and a validation cohort of 212 patients. The data obtained from this cohort are shown in Figures 3–12 and in Example 2 below. The GSE16011 cohort includes data from patients with astrocytoma (including pilocytic astrocytoma (PA; 2.8%), astrocytoma, and glial blastoma (GBM; 56%)) and oligodendroglioma (OD; 18%) (including pure OD tumors and mixed oligodendroglioma (MOA) tumors). The median follow-up period for patients was 14.5 months, with a maximum follow-up period of 20 years. Of the patients in the cohort, 70% received radiotherapy, 26% did not, and it is unknown whether 4% received radiotherapy. The cohort includes 284 patients, and combined RNA sequencing data and clinical metadata are available, with survival data available for 272 of these samples. The cohort was randomly divided into a training cohort of 180 patients and a validation cohort of 92 patients. The data obtained from this cohort are shown in Figures 13–22 and in Example 2 below. The GSE108476 cohort includes data from patients with astrocytoma (29%), pleoplastic gliablastoma (GBM, 39%), mixed (2%), non-tumor (4%), oligodendroglioma (12%), and unknown (14%). The median follow-up period for patients was 22 months, and the maximum follow-up period was approximately 21 years. The cohort includes 493 patients, and their combined RNA sequencing data and clinical metadata are available, with survival data available for 428 of these samples. The cohort was randomly divided into a training cohort of 281 patients and a validation cohort of 147 patients. The data obtained from this cohort are shown in Figures 23–34 and in Example 2 below.

[0254] Gene signature-based model creation and validation For each gene, log2 expression values ​​were obtained during the download from the TCGA database (TCGA Glioma).

[0255] The log2 expression levels of each gene are:

number

[0256] This process distributes the converted log2_gene values ​​with a standard deviation of 1, resulting in a mean of approximately 0.

[0257] For multivariate analysis of the target gene, the log2_gene converted z-score values ​​of each gene were used as input.

[0258] Cox regression analysis Next, we described testing combinations of 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-related genes, and whether these combinations predict glioma. Cox regression was used to model the expression levels of 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-related genes, respectively, on overall survival in a TCGA cohort of 377 colorectal cancer patients.

[0259] The Cox regression function was derived as follows: IDR_model:

number

[0260]

number

number

[0261] Weight w1 to w 39 Details are shown in Table 4 below.

[0262] [Table 4] JPEG2026528885000014.jpg250129

[0263] Based on three individual Cox regression models (IDE_model, TCR_SIGNALING_model, PDE4D7_CORR_model), and then again using Cox regression, we modeled the combinations of these to overall survival, depending on whether the clinical variable (N-stage characteristic) is present (GLCAI&Clinical_model) or not (GLCAI_model) in each cohort of glioma patients.

[0264] The Cox regression function was derived as follows: GLCAI_model

number

[0265] weight w 40 From lol 44 Details are shown in Table 5 below.

[0266] [Table 5]

[0267] [Table 6] JPEG2026528885000018.jpg249128

[0268] Based on three individual Cox regression models (IDE_model, TCR_SIGNALING_model, PDE4D7_CORR_model), and then again using Cox regression, we modeled the combinations of these to overall survival, depending on whether the clinical variable (N-stage characteristic) is present (GLCAI&Clinical_model) or not (GLCAI_model) in each cohort of glioma patients.

[0269] The Cox regression function was derived as follows: GLCAI_model

number

[0270] weight w 40 From lol 44 Details are shown in Table 7 below.

[0271] [Table 7]

[0272] [Table 8] JPEG2026528885000022.jpg250129

[0273] Based on three individual Cox regression models (IDE_model, TCR_SIGNALING_model, PDE4D7_CORR_model), and then again using Cox regression, we modeled the combinations of these to overall survival, depending on whether the clinical variable (N-stage characteristic) was present (GLCAI&Clinical_model) or absent (GLCAI_model) in each cohort of glioma patients. Two models were validated in Kaplan-Meier survival analysis.

[0274] The Cox regression function was derived as follows: GLCAI_model

number

[0275] weight w 40 From lol 44 Details are shown in Table 9 below.

[0276] [Table 9] [Examples]

[0277] Kaplan-Meier survival analysis For Kaplan-Meier survival curve analysis, the Cox functions of the risk models (IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model, and GLCAI_model) were categorized into two subcohorts based on cutoffs. The thresholds for separating into low-risk and high-risk groups were based on the risk of experiencing the clinical endpoint (outcome) predicted by each Cox regression model.

[0278] The Kaplan-Meier survival curve analyses shown in Figures 4-34 demonstrate the existence of different patient risk groups. Patient risk groups are determined by the likelihood of suffering each clinical endpoint (overall mortality) calculated by the respective risk models shown in the figures. Different types of interventions are indicated depending on the predicted risk of death from glioma (i.e., the risk group to which the patient may belong). In the low-risk group (probability < 0.5), standard care (SOC) delivers acceptable long-term oncological control. This is not the case at all for patients with risk > 0.5, who do not experience any associated outcomes. This patient group requires intervention or increased application of alternative therapies. Alternative options for increased treatment include adjuvant therapy with alternative therapies such as radiotherapy or cytotoxic agents or chemotherapy (e.g., atezolizumab; pembrolizumab; nivolumab; avelumab; durvalumab) or other experimental therapies.

[0279] conclusion The effectiveness of glioma treatment is limited, leading to disease progression and eventual death, particularly high-risk disease recurrence after primary intervention. Predicting treatment outcomes is highly complex due to the numerous factors involved in treatment effectiveness and disease recurrence. Important factors may still be unidentified, and the influence of other factors cannot be precisely determined. Multiple clinicopathological measures are currently being investigated and applied in clinical practice to improve response prediction and treatment selection, yielding some improvements. Nevertheless, better prediction of treatment response remains strongly needed to increase the success rate of glioma treatment.

[0280] We identified a molecule whose expression is significantly associated with survival rates after first-line treatment for gliomas, and therefore, is thought to improve the prediction of the effectiveness of second-line treatment. This can be achieved by guiding patients with low-risk or high-risk progressive disease who subsequently die from cancer to the most appropriate and potentially more effective form of treatment compared to currently applied standard treatments. This reduces the number of patients who spend time on ineffective treatments, improves opportunities for patients who need more aggressive treatment, and reduces the costs spent on ineffective treatments. [Examples]

[0281] 6 gene models Next, it was hypothesized that a small subset of genes still possessed predictive power. Models of six genes selected from the immune defense response signs, T cell signaling signs, and PDE4D7-related signs described herein were considered sufficient for prediction.

[0282] To validate this randomized model of six genes, separate gene signs (IDR14, TCR17, and PDE4D7_Correlated) or the gene signs described below were selected. Three randomly selected six-gene models were generated for each gene sign and for combination sets, and are considered representative of all signs, covering each gene of the sign at least once.

[0283] The predictive model was generated by assigning weights to each of the six gene signs shown below.

[0284] [Table 10] TIFF2026528885000026.tif25595TIFF2026528885000027.tif187108 [Examples]

[0285] Comparison with known glioma gene signs Comparative experiments were conducted to establish whether the presented methods and uses benefit the previously described genetic signs used to predict outcomes in glioma patients. For this purpose, US 2015 / 038357 A1 and Freije et al. Cancer Research, vol. 64, No. 18, pages 6503-6510 were selected to describe the genetic signs used to predict glioma outcomes.

[0286] US 2015 / 038357 A1 discloses genetic signatures for 22 genes. US 2015 / 038357 A1 presents data for the complete set of 22 genes, and for fewer subsets, with the fewest being 3 genes (Figure 5F). Freije et al. disclose 44 genetic signatures. This invention describes 38 different genetic signatures from which at least genes are selected. This application ensures that any such selection of six genes results in significant stratification of glioma patients based on predicted survival time.

[0287] Using 22-gene and 44-gene signatures, models based on these genes were trained in the same manner as the GLCAI model (which uses all 38 genes) on the same dataset. For comparison, the GLCAI model was also retrained. Each trained model was then validated on the same validation dataset. The results are shown in Figures 47, 48, and 49.

[0288] Therefore, we constructed models based on the 22 genes of US 2015 / 038357 A1 and the 44 genes of Freije et al., and trained them on the same TCGA glioma patient cohort used above. For control and comparison, the GLCAI gene set was also retrained in exactly the same manner. Two-thirds of the TCGA patient cohort were used as the training dataset, and then validation was performed on one-third of independent patients. The results are shown in Figure 47 (22-gene set), Figure 48 (44-gene set), and Figure 49 (GLCAI model, 38 genes).

[0289] Both literature-derived gene signatures resulted in significant model stratification and significantly lower hazard ratios compared to the models of the present invention. Compared to the hazard ratio of 15.3 for the GLCAI model, the model based on 22 genes (US 2015 / 038357 A1) had a hazard ratio of only 2.4, and the model based on 44 genes (Freije et al.) had a hazard ratio of 5.7.

[0290] These data are based on the total signs of 22, 44, and 38 genes, respectively. If the number of genes from these models is reduced to, for example, six, the hazard ratio is expected to decrease even further. In contrast, the hazard ratios for the six-gene models presented herein range from approximately 3.6 to 12.3. These comparative data suggest that the selection of six genes from the presented gene signs is an improvement over the selection of six genes from the literature.

[0291] Other variations of the disclosed realization can be understood and implemented in the claimed invention by those skilled in the art from the drawings, disclosures, and accompanying studies of the claims.

[0292] In the claims, the term “including” does not exclude other elements or steps, and the singular term “element” does not exclude plural elements.

[0293] One or more steps of the method shown in Figure 1 may be implemented in a computer program that can be run on a computer. The computer program may include a non-transient, computer-readable recording medium on which the control program is recorded (stored), such as a disk, hard drive, etc. Common forms of non-transient, computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes or any other magnetic storage media, CD-ROMs, DVDs or any other optical media, RAM, PROMs, EPROMs, FLASH-EPROMs or other memory chips or cartridges, or any other non-transient media from which a computer can read and use.

[0294] Alternatively, one or more steps of the method may be implemented in a transient medium such as a transmissible carrier wave in which the control program is embodied as a data signal, using a transmission medium such as sound waves or light waves, which are generated between radio and infrared data communications, etc.

[0295] Exemplary methods may be implemented on one or more general-purpose computers, dedicated computers, programmed microprocessors or microcontrollers and peripheral integrated circuit elements, ASICs or other integrated circuits, hardwired electronic or logic circuits such as digital signal processors, discrete element circuits, programmable logic devices such as PLDs, PLAs, FPGAs, graphics card CPUs (GPUs), or PALs. In general, any device capable of implementing a finite machine that can sequentially implement the flowchart shown in Figure 1 may be used to implement one or more steps of a risk stratification method for treatment selection in patients with prostate cancer. As will be understood, all steps of the method can be implemented on a computer, but in some embodiments, one or more steps may be performed at least partially manually.

[0296] Computer program instructions can also be mounted on a computer, other programmable data processing device, or other device, and can generate computer implementation processes that cause a series of work steps to be executed on the computer, other programmable device, or other device, providing a process for instructions executed on the computer or other programmable device to perform the functions / operations specified herein.

[0297] Any reference sign in the claims shall not be construed as limiting the scope.

[0298] The attached sequence list, named 2023PF00423 SEQ LST, is incorporated herein by reference in its entirety.

Claims

1. A method for predicting the outcome of a subject with a glioma, wherein the method is: A step of determining the expression levels of six or more genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2, or a step of receiving the results of determining the gene expression levels, wherein the gene expression levels are determined in a biological sample obtained from a subject. A step of determining the prediction of the outcome based on the expression levels of six or more genes. A method having

2. The method according to claim 1, further comprising the step of providing a prediction to a healthcare provider or subject.

3. The method according to claim 1 or 2, wherein the prediction is mean survival time, mean survival time after radiotherapy, or mean survival time without radiotherapy.

4. The method according to any one of claims 1 to 3, wherein the biological sample is obtained from the subject before the start of treatment.

5. Six or more gene expression levels, IFIH1, OAS1, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, and ZBP1, or ZAP70, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, and PTPRC, or ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 The method according to any one of claims 1 to 4, selected from the following.

6. Six or more gene expression levels are selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SLC39A11, IFIH1, IFIT3, MYD88, CD2, CSK, PAG1, PRKACA, and PRKACB, preferably ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, PDE4D, SLC39A11, IFIT3, MY The method according to any one of claims 1 to 5, comprising at least one, preferably two, three, four, five, or six gene expression levels, selected from D88, CSK, and PRKACB, more preferably selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, KIAA1549, and PDE4D, and most preferably selected from ABCC5, CUX2, IFIH1, OAS1, and ZAP70.

7. The method according to any one of claims 1 to 6, wherein the step of determining the prediction of the outcome further comprises the step of combining six or more gene expression levels with a regression function derived from a population of subjects having gliomas.

8. The method according to any one of claims 1 to 7, wherein the step of determining the outcome is further based on one or more clinical parameters obtained from the subject, preferably the clinical parameters being one or more of (i) Karnovsky performance score; (ii) tumor malignancy grade; and (iii) presence and amount of lesion.

9. The method according to any one of claims 1 to 8, wherein the biological sample is a biopsy obtained from a subject, preferably a biopsy from a glioma or metastasis.

10. The method according to any one of claims 1 to 9, wherein the glioma is an astrocytoma, a pilocytic astrocytoma, a mixed oligoastrocytic tumor, a gliablastoma (GBM), a treated GBM, an untreated (primary) GBM, an oligoastrocytic tumor, an oligodendroglioma, or an oligodendroglioma.

11. A device for predicting the outcome of a subject with a glioma, Appropriate input to receive data showing the expression levels of six or more genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2, The gene expression level determined in the biological sample obtained from the subject, A suitable processor for determining outcome predictions based on the expression levels of six or more genes, Depending on the circumstances, a suitable delivery unit may be provided to medical care providers or target individuals to deliver predictions. A device including a device.

12. A computer program that includes instructions, and when the program is executed by a computer, A step of receiving data indicating the expression levels of six or more genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2, wherein the gene expression levels are determined in a biological sample obtained from a subject. A step of determining the outcome of a subject based on the expression levels of six or more genes, and Depending on the circumstances, a step to provide predictions to healthcare providers or the subjects. A computer program that uses a computer to predict the outcomes of subjects with gliomas, including [specific example of a specific condition].

13. The use of the kit, and the said use is A step of determining the expression levels of six or more genes in a sample obtained from a subject with a glioma, A step that provides subject outcomes based on the expression levels of six or more genes. Includes, The kit includes means for determining the expression levels of six or more genes selected from ABCC5, CUX2, IFIH1, OAS1, ZAP70, AIM2, ABOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIT1, IFIT3, LRRFIP1, MYD88, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, KIAA1549, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

14. The use according to claim 13, comprising the step of carrying out the method according to any one of claims 1 to 10.

15. A treatment for use in the management or improvement of a subject with a glioma, The use is a step of determining the outcome of a subject having a glioma using the method described in any one of claims 1 to 10, and Steps to administer outcome-based treatment to the target patient. Includes, If the predicted outcome is favorable, the treatment is selected from surgery, radiotherapy, chemotherapy, or targeted therapy, or If the predicted outcome is unfavorable, the treatment is selected from two or more combinations of surgical procedures, radiotherapy, chemotherapy, and targeted therapies, or immunotherapy, or experimental drugs or procedures (clinical trials), or one or more treatments selected from high-dose radiotherapy, chemotherapy, and long-term CRT (chemoradiotherapy), and immunotherapy. Treatment.

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