Prediction of outcomes in bladder cancer subjects

Abstract

The present invention relates to a method of predicting the outcome of a bladder cancer subject, comprising determining a gene expression profile comprising gene expression levels, or receiving the outcome of determining a gene expression profile comprising gene expression levels, where the gene expression levels comprise a gene expression level selected from the group consisting of: a T-cell receptor signaling gene, a T-cell receptor signaling gene, and a T-cell receptor signaling gene; the present invention relates to a method for detecting a biological sample obtained from a subject, said method comprising the following steps: a gene expression profile 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 an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, AOCI1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, said gene expression profile being determined in the biological sample obtained from said subject, a prediction of said result

Description

Technical Field

[0001] The present invention relates to a method for predicting the outcome of a bladder cancer subject, and a computer program product for predicting the outcome of a bladder cancer subject. In addition, the present invention relates to a diagnostic kit, the use of a kit, the use of a kit in a method for predicting the outcome of a bladder cancer subject, the use of a gene expression profile of one or more immune defense response genes, one or more T-cell receptor signaling genes and / or one or more PDE4D7-related genes in a method for predicting the outcome of a bladder cancer subject, and corresponding computer program products. Background Art

[0002] Cancer is a class of diseases in which a group of cells show uncontrolled growth, invasion, and sometimes metastasis. These three malignant properties of cancer distinguish them from benign tumors, which are self-limited and do not invade or metastasize. Bladder cancer is a malignant tumor that is prevalent mainly in developed countries and accounts for 3% of cancer diagnoses worldwide. In the United States, bladder cancer is the sixth most prevalent cancer, accounting for 4.4% of all new cancer cases [seer.cancer.gov], and the disease is four times more common in men than in women. The overall prognosis of this disease is good, with an average 5-year survival of 77% in the United States. However, patients with metastatic disease have a very low 5% annual survival [Saginala et al. 2020]. Bladder cancer is divided into two main categories based on the degree of invasive nature of the tumor in the bladder wall: non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC).

[0003] NMIBC comprises almost 75-85% of cases and includes tumors that are limited to the mucosa and submucosa of the bladder without further myometrial invasion. To determine the clinical stage, testing involves physical examination, radiographic imaging, and histological evaluation of an initial transurethral resection of the bladder tumor (TURBT). Pathological staging is concluded after radical cystectomy and pelvic lymph node dissection. NMIBC includes the following 3 tumor stages: Ta, a papillary tumor limited to the epithelial mucosa; Tas, carcinoma in situ (CIS), a flat or sessile epithelial cancer that is difficult to detect; and T1, a papillary tumor that shows subepithelial connective tissue infiltration [Slovacek et al. 2021].

[0004] Guidelines from the American Urological Association (AUA), the European Association of Urology (EAU), and the United Kingdom's National Institute for Health and Service Excellence (NICE) classify NMIBC into low-risk, intermediate-risk, and high-risk groups based on tumor grade, size, and number and recurrence rate. The prognosis is generally favorable for low-grade and high-grade lesions, and even for high-grade tumors, the 10-year cancer-specific survival rate is approximately 70-85%. 50% of patients with low-grade Ta NMIBC tumors develop a recurrence, but only 6% of these show disease progression. In the case of patients with high-grade T1 tumors, recurrence occurs in approximately 45% and the risk of cancer progression is approximately 15-40% [Slovacek et al. 2021].

[0005] All NMIBC are usually removed by transurethral resection. In some resections, cystoscopy plus biopsy procedures can be used. The main line of treatment for metastatic disease is platinum chemotherapy, although new immunotherapies (such as checkpoint inhibitors) are used more frequently (even as the first line of treatment). In order to reduce the occurrence of recurrence and progression, guidelines recommend a continuous risk stratification approach to select adjuvant intravesical treatment. Treatment guidelines for low-risk and intermediate-risk NMIBC recommend immediate postoperative intravesical chemotherapy and / or immunotherapy. Intravesical chemotherapeutic agents include mitomycin, gemcitabine, pan-embrycin, and doxorubicin. Immunotherapy typically includes intravesical administration of Bacillus Calmette-Guerin (BCG), a live attenuated bacterium. Patients who experience recurrence after initial intravesical chemotherapy have been shown to benefit from intravesical BCG therapy, and we focus on this line of treatment to further emphasize its role.

[0006] Since the 1970s, Mycobacterium bovis (BCG) has been used as an immunotherapeutic treatment for bladder cancer patients to avoid recurrence and progression of the disease. The immune-mediated mechanism of action of BCG has been extensively studied. Studies have shown that BCG selectively binds to cells and is internalized, leading to a nonspecific immune response and subsequent tumor clearance through the activation of natural killer (NK) cells and CD8+ T cells [Ressel et al. 2021, Guallar-Garrido et al. 2020].

[0007] Only a single postoperative instillation is recommended for low-risk NMIBC lesions. In the case of intermediate-risk NMIBC (multifocal low-grade or small-volume high-grade Ta), induction chemotherapy or immunotherapy with Bacillus Calmette Guerin (BCG) is recommended, followed by maintenance therapy for 1 year in the case of response to induction. High-risk NMIBC (multifocal Ta, any T1, any CIS) is primarily treated with induction BCG, followed by maintenance therapy for up to 3 years [Slovacek et al. 2021]. Due to recent BCG supply shortages in the United States, BCG therapy is prioritized for patients with high risk, and NMIBC who have not yet received BCG treatment are offered an induction course, if available, followed by up to 1 year of BCG maintenance therapy at a reduced dose, while alternative intravesical chemotherapy is listed as the mainstay of treatment for intermediate-risk disease.

[0008] Even after adequate BCG treatment, relapse occurs in approximately 20-50% of patients. This failure to respond arises from two possible scenarios: (1) the prevalence of BCG intolerance in patients who cannot tolerate adequate levels of BCG due to associated toxicities. (2) the existence of a subgroup of BCG-unresponsive patients. A portion of the BCG-unresponsive group showed refractory disease, with persistent high-grade NMIBC present within 3 months of the first BCG treatment and within 6 months of progression after treatment. In the remainder of the unresponsive group, relapse occurred after a brief disease-free interval after adequate BCG administration. [Slovacek et al. 2021]. Cancers that are unresponsive to adequate BCG are extremely unlikely to respond to additional BCG cycles. In such patients, radical cystectomy is recommended, with the option of salvage intravesical therapy or enrollment in a clinical trial. Cystectomy in patients with high-grade NMIBC is associated with excellent oncological outcomes, but also with high morbidity and reduced quality of life. In addition, delays in cystectomy caused by serial ineffective BCG re-challenges negatively affect patient outcomes over time. Unfortunately, there are no tools available to predict which high-grade NMIBC patients will benefit from treatment, and therefore, all patients are treated with a "one-size-fits-all" BCG, leading to overtreatment and exposure to unnecessary toxicity. It would be very beneficial to be able to stratify patients who are predicted to be more suitable for BCG therapy based on additional clinical parameters of these patients, such as the presence of biomarkers or genetic features of the primary tumor.

[0009] 10-15% of patients diagnosed with bladder cancer develop metastases [Rosenberg et al. 2005]. More than half of patients who present with MIBC eventually die from metastatic disease. The most common sites of metastasis are lymph nodes (69%), bones (47%), lungs (37%), liver (26%), and peritoneum (16%) [Shinagare et al. 2011]. More common clinical manifestations of metastatic disease include: development of obstructive uropathy and ureteral obstruction due to lymphadenopathy; lymphatic obstruction; bone involvement; pulmonary involvement (hemostasis, dyspnea with pleural effusion, cough), elevated liver enzymes and dysfunction; and intestinal obstruction. These patients have a poor prognosis, with a 5-year survival rate of 62-68% after radical surgery. Neoadjuvant chemotherapy can reduce the risk of associated mortality by 33%, but the accompanying toxicity also needs to be considered [Chin et al. 2017]. Currently, limited imaging modalities and poor biomarkers make it difficult to have reliable prognostic predictions.

[0010] WO2022 / 069201A1 relates to a method for predicting the outcome of a bladder cancer subject or a kidney cancer subject, comprising: determining a gene expression profile or receiving the result of determining a gene expression profile.

[0011] The present invention aims to overcome these problems by means of the method and use defined in the appended claims. Summary of the invention

[0012] Patients with non-muscle-invasive bladder cancer have a favorable prognosis, but a subgroup of NMIBC patients have a high risk of developing progression to muscle-invasive or metastatic disease. Patients with high-risk NMIBC (HR-NMIBC) undergo transurethral resection of the bladder tumor followed by adjuvant intravesical Bacillus-Calmette Guérin (BCG) stimulation therapy for one to three years to reduce the likelihood of recurrence and progression. BCG exerts its beneficial effects by inducing a local immune response that promotes the removal of residual tumor cells. Despite the high initial response rate of BCG, 17-45% of HR-NMIBC patients develop progression, resulting in death in approximately 70% of cases within 5 years of diagnosis of progression. Unfortunately, no tools are available to predict which HR-NMIBC patients will benefit from treatment, and therefore, all patients are treated with BCG, resulting in overtreatment and exposure to unnecessary toxicity. Similarly, treatment of patients with metastatic bladder cancer with immune checkpoint inhibitors results in a binary response, in which approximately 22% of patients have an excellent response to treatment, while the remaining patients have an extremely poor prognosis and do not appear to respond to treatment at all. Currently, non-responders cannot be predicted or identified prior to treatment, however, alternative treatment strategies for this group would ideally be explored. We have come to recognize that gene signatures related to the immune system can predict response and survival in this group of patients.

[0013] In summary, the inventors postulated a strong need for better prediction of outcome of bladder cancer, and thereby of the outcome of subjects with bladder cancer, and provided herein are methods and means to achieve improved prediction of outcome.

[0014] In a first aspect, the invention relates to a method for predicting an outcome in a subject with bladder cancer, the method comprising: determining a gene expression profile comprising gene expression levels or receiving the results of determining a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group 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; and / or, immune defense response genes, It is selected from the following group: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and the gene expression profile is determined in a biological sample obtained from the subject, and the prediction of the outcome is determined based on the gene expression profile, wherein the prediction is an outcome for the subject, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

[0015] In a second aspect, the present invention relates to a computer program product comprising instructions which, when executed by a computer, cause the computer to perform a method comprising: - receiving data indicative of a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group 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; and / or Or, an immune defense response gene selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, wherein the gene expression profile is determined in a biological sample obtained from a bladder cancer subject, and a prediction of an outcome for the subject is determined based on the gene expression profile, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

[0016] In a third aspect, the present invention relates to the use of a diagnostic kit, the kit comprising: at least one of a polymerase chain reaction primer or a probe for determining a gene expression profile in a biological sample and / or in a sample obtained from a bladder cancer subject, the gene expression profile comprising expression levels, wherein the expression levels comprise gene expression levels selected from the group 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; and / or, an immune defense response gene selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, the uses comprising: predicting the outcome of a bladder cancer subject, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

[0017] In a fourth aspect, the present invention provides a Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy for use in treating bladder cancer in a subject, wherein the use comprises performing the method described in the first aspect of the present invention, and if a favorable outcome of the treatment is predicted, administering the Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy to the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Depicted is a schematic method for creating an immune scoring model for predicting outcomes in patients with bladder cancer. For the TCGA BLCA cohort consisting of 412 patients, processed RNAseq data were downloaded along with clinical and demographic data from TCGA. The main steps in the analysis pipeline are shown in the figure.

[0019] Figure 2 A schematic overview of the analytical method for validating the immune scoring model is depicted. A super dataset of bladder cancer consisting of multiple individual datasets is created. All BCAI-related expression values ​​for each dataset are converted to z scores to concentrate the average expression of all samples in each collection to mean_expression=0 and SD_expression=1; after this normalization step, the expression data for BCAI-related genes are combined to create a super dataset including >1,300 patients. Note that not all demographic or survival data for all patients are available. Therefore, the number of patients in the downstream data analysis can be less than the total number of patients in the super dataset. The number of patients in each analysis is indicated for each analysis performed.

[0020] Figure 3 A schematic diagram of the analytical method for creating and testing the metastatic immune score models (mBCAI and mBCAI_clinical) is depicted. To test the ability of the immune score in the setting of immunotherapy (anti-PD-L1; atezolizumab) treatment in patients with metastatic bladder cancer (IMVigor210 trial; see references below). To adapt to the metastatic setting, the metastatic model was retrained on a portion of the data (training data), while the model was tested on a validation set not used for model training (mBCAI and mBCAI_clinical).

[0021] Figure 4Describe the Kaplan-Meier survival curve analysis of the PDE4D7_R2 model. The clinical endpoint of the test is the time of all-cause death in months. According to the risk of clinical endpoints predicted by the corresponding Cox regression model, the patients were divided into 2 groups. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. Model parameters were established in the training set (group 1-2 of the total group, top figure), and the model parameters were fixed for verification in the test set (group 3-4-bottom figure of the total group). In the verification (test) group, the PDE4D7_R2 score predicted overall survival; the risk of death of patients classified into the high-risk group (>threshold 0.3) increased by 2.4 times.

[0022] Figure 5 Describe the Kaplan-Meier survival curve analysis of IDR_14 model.The clinical endpoint of the test is the time of death in months.According to the risk of the clinical endpoint predicted by the corresponding Cox regression model, the patient is divided into 2 groups.The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point for grouping.Model parameters are established in the training set (group 1-2-top figure of the total group), and the model parameters are fixed for the verification in the test set (group 3-4-bottom figure of the total group).In the verification (test) group, IDR_14 score predicts overall survival; The risk of death of patients classified into the high-risk group (>threshold 0) increases by 2.3 times.

[0023] Figure 6 Depict the Kaplan-Meier survival curve analysis of the TCR_17 model.The clinical endpoint of the test is the time of death in months.According to the risk of clinical endpoints predicted by the corresponding Cox regression model, the patients were divided into 2 groups.The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point for grouping.Model parameters are established in the training set (group 1-2 of the total group, top figure), and the model parameters are fixed for the verification in the test set (group 3-4 of the total group, bottom figure).In the verification (test) group, TCR_14 score predicts overall survival; The risk of death of patients classified into the high-risk group (>threshold 0) increases by 2 times.

[0024] Figure 7Describe the Kaplan-Meier survival curve analysis of BCAI score.The clinical endpoint of the test is the time of death in months.According to the risk of the clinical endpoint predicted by the corresponding Cox regression model of the patient experience, the patient is divided into 2 groups.The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point of grouping.Model parameters are established in training set (group 1-2 of total group, top figure), and model parameters are fixed for the verification in test set (group 3-4 of total group, bottom figure).In verification (test) group, BCAI score predicts overall survival; The risk of death of the patient classified into high-risk group (>threshold 0) increases by 3.2 times.

[0025] Figure 8 Describe the Kaplan-Meier survival curve analysis of BCAI clinical score.The clinical endpoint of the test is the time of death in months.According to the risk of the clinical endpoint predicted by the corresponding Cox regression model of the patient experience, the patient is divided into 2 groups.The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point of grouping.Model parameters are established in training set (group 1-2 of total group, top figure), and model parameters are fixed for the verification in test set (group 3-4 of total group, bottom figure).In verification (test) group, BCAI clinical score predicts overall survival; The risk of death of the patient classified into high-risk group (>threshold 0) increases 5.4 times.

[0026] Fig. 9 Describe the Kaplan-Meier survival curve analysis of BCAI score.The clinical endpoint of the test is the bladder cancer-specific death in months.According to the risk of the clinical endpoint predicted by the corresponding Cox regression model, the patient is divided into 2 groups.The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point of grouping.Model parameters are established in the training set (group 1-2 of the total group, top figure), and the model parameters are fixed for the verification in the test set (group 3-4 of the total group, bottom figure).In the verification (test) group, the BCAI score predicts overall survival; The risk of death of the patient classified into the high-risk group (>threshold 0) increases by 5.0 times.

[0027] Fig.10The survival endpoint (overall survival) in the whole bladder cancer group is tested using the BCAI (top figure) and BCAI_clinical (bottom figure) Cox regression models trained on 95TCGA data (see previous figure). The same endpoint is tested under the background of the different treatments of NMIBC (BCG) and MIBC (cystectomy). The corresponding TCGA samples for model training are excluded from the test analysis. In the validation (test) group, BCAI and BCAI clinical scores predict overall survival; The risk of death of patients classified into high-risk groups (respectively>threshold value 0 and 0.2) increases by 2.3 times or 2.6 times respectively.

[0028] Fig.11 The survival endpoint (cancer-specific survival) in the whole bladder cancer group is tested using the BCAI (top figure) and BCAI_clinical (bottom figure) Cox regression models trained on 95TCGA data (see previous figure). The corresponding TCGA samples for model training are excluded from the test analysis. In the validation (test) group, BCAI and BCAI clinical score predict cancer-specific survival; The risk of death of the patient classified into the high-risk group (respectively>threshold value 0 and 0.2) increases by 3.3 times or 4.2 times respectively.

[0029] Fig.12 Survival endpoints (overall survival) in NMIBC subtypes were tested using BCAI (top graph) and BCAI_clinical (bottom graph) Cox regression models trained on 95TCGA data (see previous figure). The corresponding TCGA samples for model training were excluded from the test analysis. In the validation (test) group, BCAI and BCAI clinical scores predicted cancer-specific survival; The risk of death of patients classified into high-risk groups (respectively > threshold 0 and 0.2) increased by 3.5 times or 3.0 times, respectively.

[0030] Fig.13 The survival endpoint (overall survival) in NMIBC after BCG treatment subtype is tested using BCAI (top figure) and BCAI_clinical (bottom figure) Cox regression models trained on 95TCGA data (see previous figure). The corresponding TCGA samples for model training are excluded from the test analysis. In the validation (test) group, BCAI and BCAI clinical scores predict cancer-specific survival; The risk of death of patients classified into high-risk groups (respectively>threshold 0 and 0.2) increases by 3.9 times or 3.7 times, respectively.

[0031] Fig.14Survival endpoints (overall survival) in MIBC subtypes were tested using BCAI (top graph) and BCAI_clinical (bottom graph) Cox regression models trained on 95TCGA data (see previous figure). The corresponding TCGA samples for model training were excluded from the test analysis. In the validation (test) group, BCAI and BCAI clinical scores predicted cancer-specific survival; The risk of death of patients classified into high-risk groups (respectively > threshold 0 and 0.2) increased by 1.9 times or 2.2 times, respectively.

[0032] Fig.15 Survival endpoints (overall survival) in MIBC after surgery (cystectomy) subtypes were tested using BCAI (top graph) and BCAI_clinical (bottom graph) Cox regression models trained on 95TCGA data (see previous figure). The same endpoints were tested in the context of different treatments for NMIBC (BCG) and MIBC (cystectomy). The corresponding TCGA samples for model training were excluded from the test analysis. In the validation (test) group, BCAI and BCAI clinical scores predicted cancer-specific survival; The risk of death of patients classified into high-risk groups (respectively>threshold 0 and 0.2) increased by 2.3 times or 2.2 times, respectively.

[0033] Fig.16 Based on BCAI immune score model, TCGA samples are divided into high risk and low risk.Then based on main treatment results (complete complete response (CR) / partial response (PR) or progressive disease (PD) / stable disease (SD)), high-risk group and low-risk group are plotted in Kaplan-Meier figure (top low-risk group and bottom high-risk group based on BCAI stratification).For low risk, the median survival (24 months vs 22 months) of increase can be observed in SD / PR groups.For low risk group, the median survival of CR / PR can not be calculated, but visually, for low risk group, the median survival in CR / PR group significantly increases.

[0034] Fig.17 Based on BCAI clinical immune score model, TCGA samples are divided into high risk and low risk.Then based on main treatment results (complete response (CR) / partial response (PR) or progressive disease (PD) / stable disease (SD)), high-risk group and low-risk group are plotted in Kaplan-Meier figure (top low-risk group and bottom high-risk group based on BCAI clinical classification).For low risk, the median survival (17 months vs 24 months) of increase can be observed in SD / PR groups.For low risk group, the median survival of CR / PR can not be calculated, but visually, for low risk group, the median survival in CR / PR group significantly increases.

[0035] Fig.18 Describe the ROC curve of 5-year cancer-specific survival (upper figure) and 5-year overall survival (lower figure). Draw the BCAI and BCAI clinical model trained on 95TCGA data. Indicate the area under the curve (AUC) for each drawing in the small figure.

[0036] Fig.19 Depict the Kaplan-Meier survival curve analysis of metastatic BCAI (mBCAI or met BCAI) scores of patients with metastatic bladder cancer treated with anti-PD-L1 therapy (atezolizumab). The clinical endpoint of the test is the time of death in months. According to the risk of clinical endpoints predicted by the corresponding Cox regression model, the patients were divided into 2 groups. The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point for grouping. Model parameters are established in the training set (groups 1-3 of the total group, top figure), and the model parameters are fixed for verification in the test group (group 4 of the total group, bottom figure). In the verification (test) group, the mBCAI score predicts overall survival; the risk of death of patients classified into the high-risk group (>threshold 0) increases by 2.2 times.

[0037] Fig. 20 Depict the Kaplan-Meier survival curve analysis of metastatic BCAI clinical (mBCAI clinical or met BCAI clinical) scores of patients with metastatic bladder cancer treated with anti-PD-L1 therapy (atezolizumab). The clinical endpoint of the test is the time of death in months. According to the risk of clinical endpoints predicted by the corresponding Cox regression model, the patients are divided into 2 groups. The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point for grouping. Model parameters are established in the training set (groups 1-3 of the total group, top figure), and the model parameters are fixed for the verification in the test group (group 4 of the total group, bottom figure). In the verification (test) group, the mBCAI clinical score predicts overall survival; the risk of death of patients classified into the high-risk group (>threshold 0) increases by 4.0 times.

[0038] Fig.21 Kaplan-Meier survival curve analysis of TCGA subtypes (top graph) or Lund2 classification (bottom graph) for patients with metastatic bladder cancer treated with anti-PD-L1 therapy (atezolizumab) is depicted. No statistically significant stratification was observed between groups based on TCGA or Lund2 subtypes.

[0039] Fig. 22Depicted is the classification of patients into anti-PD-L1 responders (upper panel) and non-responders (lower panel). Kaplan-Meier plots were generated based on the mBCAI clinical model. In the non-responder group, the mBCAI clinical score predicted overall survival; patients classified into the high-risk group (>threshold 0) had a 2.9-fold increased risk of death.

[0040] Fig.23 An overall binary response to anti-PD-L1 (atezolizumab) treatment in patients with metastatic bladder cancer was shown. The treatment had a response rate of 22.8%, with responders having an excellent median survival, while non-responders had a median survival of 7.7 months.

[0041] Fig.24 Depicts the use of mBCAI score based on low risk (upper figure) or high risk (lower figure) to treat patients with metastatic bladder cancer and anti-PD-L1 (atezolizumab) therapy divided into two groups. For each group, a Kaplan-Meier graph was drafted based on clinical outcomes (complete response (CR) / partial response (PR) or progressive disease (PD) / stable disease (SD)). The low-risk group showed a response rate of 41.3% to anti-PD-L1 treatment, and a median survival time of 16.3 months in non-responders. The high-risk group showed a response rate of 10.7% to anti-PD-L1 treatment, and a median survival time of months in non-responders.

[0042] Fig.25 Describes the use of mBCAI score based on low risk (upper figure) or high risk (lower figure) patients with metastatic bladder cancer and treated with anti-PD-L1 (atezolizumab) therapy divided into two groups. For each group, based on clinical outcome (complete response (CR), partial response (PR), progressive disease (PD) or stable disease (SD)) to draft Kaplan-Meier diagram.

[0043] Fig.26 ROC curves are depicted. The AUROC training group of 161 patients is used as a training set (top figure), and the AUROC test group of 49 patients is used to verify the model (bottom figure). The clinical endpoint used is the binary response (responder / non-responder) of anti-PD-L1 treatment. The models drawn are mBCAI, mBCAI clinical and mBCAI clinical 2. The area under the curve (AUC) for each curve graph is indicated in the small figure.

[0044] Fig. 27 ROC curve is depicted. The AUROC training group of 161 patients is used as the training group (top figure), and the AUROC test group of 49 patients is used to verify the model (bottom figure). The clinical endpoint used is overall death. The model drawn is mBCAI, mBCAI clinical.

[0045] Fig.28 Depict the Kaplan-Meier survival curve analysis of the IDR_14 model (top figure) or TCR_17 model (bottom figure) of high-grade NMIBC patients (165). The clinical endpoint of the test is progression-free survival in months. According to the risk of clinical endpoints predicted by the corresponding Cox regression model, the patients were divided into 2 groups. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, the IDR_14 score or TCR_17 score predicted progression-free survival; the risk of death of patients classified into the high-risk group (for IDR14> threshold 1 or for TCR17> threshold 2.89) increased by 2.0 times (IDR14) or 1.7 times (TCR17).

[0046] Fig.29 Kaplan-Meier survival curve analysis of the met IDR_14 model (upper figure) or met TCR_17 model (lower figure) for patients with metastatic bladder cancer (348) is depicted. The clinical endpoint of the test is the survival time in months after immune checkpoint inhibitor treatment (ICI). Patients were divided into 2 groups according to the risk of experiencing clinical endpoints predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, the IDR_14 score or the met TCR_17 score predicted survival after ICI; patients classified as high-risk groups (for both metIDR14 and met TCR17> threshold 0) had a 2.9-fold (met IDR14) or 2.0-fold (metTCR17) increase in the risk of death.

[0047] Fig.30 Kaplan-Meier survival curves for each Erasmus bladder cancer response type (BRS) 1, 2, and 3 are depicted (see De Jong et al., Sci. Transl. Med. 15, eabn4118 (2023)). The clinical endpoint tested was post-BCG treatment progression in cohort B patients.

[0048] Fig.31Depicts the Kaplan-Meier survival curve analysis of the immune score (combined IDR_14 and TCR_17 model) in group A bladder cancer patients (130). The clinical endpoint of the test is post-BCG progression in months. According to the risk of the patient experiencing the clinical endpoint predicted by the corresponding Cox regression model, the patients were divided into 2 groups. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, the immune score predicted post-BCR progression; the risk of progression of patients classified into the high-risk group (> threshold 4.185) increased by 2.5 times.

[0049] Fig.32 Kaplan-Meier survival curve analysis of the immune score (combined IDR_14 and TCR_17 model) in group B bladder cancer patients (135) is described. The clinical endpoint of the test is post-BCG progression in months. Patients were divided into 2 groups according to the risk of the patient experiencing the clinical endpoint predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, the immune score predicted post-BCR progression; the risk of progression of patients classified into the high-risk group (> threshold 4.185) increased by 4.6 times.

[0050] Fig.33 Kaplan-Meier survival curve analysis of the immune score_clinical (combined IDR_14 and TCR_17 model with clinical parameters) in cohort A and B bladder cancer patients (265). The clinical endpoint of the test was post-BCG progression in months. Patients were divided into 2 groups according to the risk of the clinical endpoints predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) cohort, immune score_clinical predicted post-BCR progression; the risk of progression of patients classified into the high-risk group (> threshold 4.025) increased by 2.5 times.

[0051] Fig.34 Depicted is a Kaplan-Meier survival curve analyzing the immune score clinical (IDR_14 and TCR_17 model with a combination of clinical parameters) in cohort A and B bladder cancer patients with BRS=1 (42). The clinical endpoint of the test was post-BCG progression in months. Patients were divided into 2 groups according to their risk of experiencing clinical endpoints predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) cohort, immune score_clinical predicted post-BCR progression; the risk of progression of patients classified into the high-risk group (>threshold 4.025) increased by 10.7 times.

[0052] Fig.35 Depicted is a Kaplan-Meier survival curve analysis of the immune score clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) in cohort A and B bladder cancer patients with BRS=2 (56). The clinical endpoint of the test is post-BCG progression in months. Patients were divided into 2 groups according to the risk of the patient experiencing a clinical endpoint predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) cohort, immune score_clinical predicted post-BCR progression; the risk of progression of patients classified into the high-risk group (>threshold 4.025) increased by 2.9 times.

[0053] Fig.36 Kaplan-Meier survival curve analysis of the immune score clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) in group B bladder cancer patients (135) is depicted. The clinical endpoint of the test is post-BCG disease-specific death in months. Patients were divided into 2 groups according to the risk of the patient experiencing the clinical endpoint predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, immune score_clinical predicted post-BCR disease-specific death; patients classified into the high-risk group (> threshold 4.025) had a 3.3-fold increased risk of disease-specific death.

[0054] Fig.37 Kaplan-Meier survival curve analysis of the immune score clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) in group B bladder cancer patients (135) is depicted. The clinical endpoint of the test is post-BCG death in months. Patients were divided into 2 groups according to the risk of the clinical endpoints predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, immune score_clinical predicted post-BCR death; the risk of death of patients classified into the high-risk group (> threshold 4.025) increased by 3.0 times.

[0055] Fig.38Kaplan-Meier survival curve analysis of the immune score clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) in cohort B bladder cancer patients with smoking status "none" (36) is depicted. The clinical endpoint tested was post-BCG death in months. Patients were divided into 2 groups based on the risk of the clinical endpoint predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) cohort, the immune score_clinical prediction did not predict post-BCG death in non-smokers.

[0056] Fig.39 Kaplan-Meier survival curve analysis of Immune Score Clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) in Cohort B bladder cancer patients with smoking status "yes" (91) is depicted. The clinical endpoint of the test was post-BCG death in months. Patients were divided into 2 groups according to their risk of experiencing clinical endpoints predicted by the corresponding Cox regression model. The mean of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) cohort, Immune Score_Clinical predicted post-BCR death in smokers; patients with smoking status "yes" who were classified into the high-risk group (>threshold 4.025) had a 3.9-fold increased risk of death.

[0057] Fig.40 Depicts the Kaplan-Meier survival curve analysis of the immune score clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) in group B bladder cancer patients (127). The clinical endpoint of the test is post-BCG progression in months. According to the risk of the patient experiencing the clinical endpoint predicted by the corresponding Cox regression model, the patients were divided into 2 groups. The high and low risk groups include both non-smokers and smokers, and the high risk group includes only smokers. The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point for grouping. In the validation (test) group, immune score_clinical predicted post-BCR progression; the risk of progression of patients classified into the high risk group (> threshold 4.025) increased by 7.4 times.

[0058] Fig.40Depicts the Kaplan-Meier survival curve analysis of immune score clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) of group B bladder cancer patients (127). The clinical endpoint of the test is post-BCG progression in months. According to the risk of the patient experiencing the clinical endpoint predicted by the corresponding Cox regression model, the patients were divided into 2 groups. The high and low risk groups include both non-smokers and smokers, and the high risk group includes only smokers. The mean value of the prognostic risk score derived from Cox regression is used as the cutoff point for grouping. In the validation (test) group, immune score_clinical predicted post-BCR progression; the risk of progression of patients classified into the high risk group (> threshold 4.025) increased by 7.4 times.

[0059] Fig.41 Kaplan-Meier survival curve analysis of immune score clinical (IDR_14 and TCR_17 models with combination of clinical parameters) of group B bladder cancer patients (127) is described. The clinical endpoint of the test is post-BCG death in months. According to their smoking status ("yes" or "no"), and for smokers, the patients were divided into 3 groups according to their risk of experiencing clinical endpoints predicted by the corresponding Cox regression model. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, immune score_clinical predicted post-BCR death.

[0060] Fig.42 Kaplan-Meier survival curve analysis of the immune score clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) in group B bladder cancer patients (127) is described. The clinical endpoint of the test is post-BCG death in months. Patients were divided into 2 groups according to the risk of the clinical endpoint predicted by the corresponding Cox regression model. The high and low risk groups included both non-smokers and smokers, and the high risk group included only smokers. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, immune score_clinical predicted post-BCR death; the risk of death of patients classified into the high risk group (> threshold 4.025) increased by 7.4 times.

[0061] Fig.43Kaplan-Meier survival curve analysis of the immune score clinical (IDR_14 and TCR_17 models with a combination of clinical parameters) in group B bladder cancer patients (127) is depicted. The clinical endpoint of the test is post-BCG cancer-specific death in months. Patients were divided into 2 groups according to the risk of the clinical endpoint predicted by the corresponding Cox regression model. The high and low risk groups included both non-smokers and smokers, and the high risk group included only smokers. The mean value of the prognostic risk score derived from Cox regression was used as the cutoff point for grouping. In the validation (test) group, immune score_clinical predicted post-BCR cancer-specific death; patients classified into the high risk group (>threshold 4.025) had a 7.4-fold increased risk of death from cancer.

[0062] definition

[0063] As used herein, the indefinite terms "a" or "an" do not exclude a plurality.

[0064] The term "biological sample" or "sample obtained from a subject" refers to any biological material obtained from a subject (eg, a bladder cancer subject) via suitable methods known to those skilled in the art.

[0065] As used herein, the term "and / or" means that one or more of the stated conditions may occur alone or in combination with at least one to all of the stated conditions.

[0066] As used herein, the term "at least" a particular value means the particular value or more. For example, "at least 2" is understood to be the same as "2 or more", i.e., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ... etc.

[0067] The term "bladder cancer" refers to cancer of the bladder tissue, which occurs when cells in the bladder mutate and begin to grow out of control.

[0068] The term "bladder cancer-specific death or disease-specific death" refers to the death of a patient due to bladder cancer.

[0069] The term "clinical recurrence" refers to the presence of clinical signs indicative of the presence of tumor cells as measured, for example, using in vivo imaging.

[0070] As used herein, the word "comprise" or variations such as "include" or "comprising" will be understood to include the stated elements, integers or steps, or groups of elements, integers or steps, but not to exclude any other elements, integers or steps, or groups of elements, integers or steps. The verb "comprise" includes the verbs "consist essentially of" and "consist of."

[0071] When used herein, the term "immune defense response gene" is used interchangeably with "IDR gene" or "immune defense gene" and refers to 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.

[0072] The term "metastasis" refers to the presence of metastatic disease in organs other than bladder tissue.

[0073] As used herein, the term "PDE4D7-related gene" is used interchangeably with "PDE4D7 gene" and refers to one or more genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0074] When used herein, the term "T cell receptor signaling gene" is used interchangeably with "TCR signaling gene" or "TCR gene" and refers to 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. DETAILED DESCRIPTION

[0075] The immune system in cancer

[0076] In recent years, the importance of the immune system in cancer suppression as well as cancer induction, promotion and metastasis has become very apparent (Mantovani et al., Nature. 454(7203): 436-44 (2008); Giraldo et al., Br J Cancer. 120(1): 45-53 (2019)). Immune cells and the molecules they secrete form a key part of the tumor microenvironment, and most immune cells can infiltrate tumor tissue. The immune system and tumors influence and shape each other. Therefore, anti-tumor immunity can prevent tumor formation, while the inflammatory tumor environment can promote the induction and proliferation of cancer. At the same time, tumor cells that may be generated in an immune system-independent manner will shape the immune microenvironment by recruiting immune cells, and can have a pro-inflammatory effect while also suppressing anti-cancer immunity.

[0077] Some immune cells in the tumor microenvironment will have a general tumor-promoting or general tumor-suppressing effect, while other immune cells exhibit plasticity and show the potential for both tumor promotion and tumor suppression. Therefore, the overall immune microenvironment of a tumor is a mixture of the various immune cells present, the cytokines they produce, and their interactions with tumor cells and other cells in the tumor microenvironment (Giraldo Br J Cancer. 120(1): 45-53(2019)).

[0078] Although therapy will be influenced by the immune components of the tumor microenvironment, RT itself extensively affects the composition of these components. Since suppressive cell types are rather insensitive to radiation, their relative numbers will increase. In contrast, radiation damage activates cell survival pathways and stimulates the immune system, triggering an inflammatory response and immune cell recruitment. Whether the net effect is tumor-promoting or tumor-suppressive is uncertain, but its potential to enhance cancer immunotherapy is being investigated.

[0079] In summary, the status of the immune system and immune microenvironment has an impact on the effectiveness of treatment.

[0080] The inventors have identified genetic signatures and combinations of these signatures with clinical parameters, wherein the resulting model shows a significant relationship with mortality and is therefore expected to improve the prediction of the effectiveness of these treatments.

[0081] Immune response defense genes

[0082] The integrity and stability of genomic DNA are permanently under stress induced by various internal and external factors of the cell (such as exposure to radiation, viral or bacterial infection), and are also under oxidative and replication stress (see Gasser S. et al. "Sensing of dangerous DNA", Mechanisms of Aging and Development, Vol. 165, pages 33-46, 2017). In order to maintain DNA structure and stability, cells must be able to recognize all types of DNA damage induced by various factors, such as single-strand or double-strand breaks. Depending on the type of damage, this process involves the participation of a large number of specific proteins as part of the DNA recognition pathway.

[0083] Recent evidence suggests that the immune system uses mislocalized DNA (e.g., DNA that occurs non-naturally in the cytoplasmic portion of a cell compared to the nucleus) and damaged DNA (e.g., through mutations that occur in cancer development) to recognize infected or otherwise diseased cells, while DNA recognition pathways ignore genomic and mitochondrial DNA present in healthy cells. In diseased cells, cytoplasmic DNA sensor proteins have been shown to be involved in detecting non-naturally occurring DNA in the cytoplasm of cells. Detection of such DNA by different nucleic acid sensors translates into similar responses, leading to nuclear factor kappa-B (NF-kB) and interferon type I (IFN type I) signaling, followed by activation of innate immune system components. Although recognition of viral DNA is known to induce IFN type I responses, evidence that sensing of DNA damage can trigger an immune response has only recently accumulated.

[0084] TLR9 (Toll-like receptor 9), located in endosomes, is one of the first DNA sensor molecules identified as involved in the immune recognition of DNA by signaling downstream via the adaptor protein myeloid differentiation primary response protein 88 (MYD88). This interaction in turn activates mitogen-activated protein kinases (MAPK) and NF-kB. TLR9 also induces the production of type I interferons by activating IRF7 via IkB kinase α (IKKα) in plasmacytoid dendritic cells (pDCs). Various other DNA immune receptors including IFI16 (IFN-γ-inducible protein 16), cGAS (cyclic DMP-AMP synthase), DDX41 (DEAD box helicase 41), and ZBP1 (Z-DNA-binding protein 1) interact with STING (stimulator of IFN genes), which activates the IKK complex and IRF3 to TBK1 (TANK binding kinase 1). ZBP1 also activates NF-kB by recruiting RIP1 and RIP3 (receptor-interacting proteins 1 and 3, respectively). While the helicase DHX36 (DEAH-box helicase 36) interacts with TRID in a complex to induce NF-kB and IRF-3 / 7, the DHX9 helicase stimulates MYD88-dependent signaling in plasmacytoid dendritic cells. The DNA sensor LRRFIP1 (leucine-rich repeat non-fluorescent interacting protein) complexes with β-catenin to activate transcription of IRF3, while AIM2 (absent in melanoma 2) recruits the adaptor protein ASC (apoptotic speck-like protein) to induce the caspase-1-activated inflammasome complex, leading to the secretion of interleukin-1β (IL-1β) and IL-18 (see Gasser S. et al., 2017, supra, which provides a schematic overview of DNA damage and DNA sensor pathways, leading to the production of inflammatory cytokines and the expression of ligands for activating innate immune receptors. It shows the non-homologous end joining pathway (orange), homologous recombination (red), inflammasome (dark green), NF-kB and interferon response (light green)).

[0085] The factors and mechanisms responsible for activating DNA sensor pathways in cancer are not well elucidated at present. It is important to identify intratumoral DNA species, sensors and pathways involved in IFN expression in different cancer types at all stages of the disease. In addition to therapeutic targets in cancer, these factors may also have prognostic and predictive value. Novel DNA sensing pathway agonists and antagonists are currently being developed and tested in preclinical trials. These compounds will be useful in characterizing the role of DNA sensor pathways in the pathogenesis of cancer, autoimmunity and potentially other diseases.

[0086] T cell receptor signaling genes

[0087] The immune response to pathogens can be triggered at different levels: There is a physical barrier, such as the skin, to prevent invasion of invaders. If this is breached, innate immunity comes into play; a first and rapid non-specific response. If this is not sufficient, an adaptive immune response is triggered. This is more specific and takes time to develop when encountering a pathogen for the first time. Lymphocytes are activated by interaction with activated antigen presenting cells from the innate immune system and are also responsible for maintaining memory to respond faster the next time the same pathogen is encountered.

[0088] Since lymphocytes are highly specific and efficient when activated, they are negatively selected due to their self-recognition ability, a process called central tolerance. Since not all self-antigens are expressed at select sites, peripheral tolerance mechanisms have also evolved, such as ligation of TCRs in the absence of co-stimulation, expression of inhibitory co-receptors, and suppression by Tregs. A disturbed balance between activation and suppression may lead to autoimmune diseases or immunodeficiencies and cancer, respectively.

[0089] T cell activation can have different functional consequences, depending on the location of the T cell type involved. CD8+ T cells differentiate into cytotoxic effector cells, while CD4+ T cells can differentiate into Th1 (IFNγ secretion and promotion of cell-mediated immunity) or Th2 (IL4 / 5 / 13 secretion and promotion of B cells and humoral immunity). Differentiation into other recently identified T cell subsets is also possible, such as Treg, which has 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), in particular Figure 4 , the T cell activation and its regulation by PKA, and Tasken K. and Ruppelt A., "Negative regulation of T-cell receptor activation by the cAMP-PKA-Csk signaling pathways in T-cell lipidrafts", Front Biosci, Vol.11, pages2929-2939 (2006)).

[0090] T cell activation can occur in naive T cells and differentiated T cells. At the molecular level, the events after TCR is connected to the cognate antigen and the crosstalk with the signal transduction induced by costimulatory and co-inhibitory receptors determine whether the T cell will be activated or whether it will become incompetent. It is not enough to trigger TCR itself, which leads to T cell incompetence. The B7:CD28 family of costimulatory molecules plays a central role in controlling the activation state of T cells after antigenic stimulation (Torheim EA, "Immity 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).

[0091] Activation occurs when the TCR on the surface of the T cell interacts with the MHC-peptide complex on the APC or target cell. Figure 2 ). An immune synapse is formed, lipid rafts in the T cell membrane merge, and Lck and Fyn are activated. These molecules phosphorylate ITAMs in the CD3 subunit of the TCR, which promotes the recruitment of Zap-70 close to Lck. Lck phosphorylates and activates Zap-70, which then phosphorylates LAT, SLP76, and PLCγ1. LAT is a docking site for other signaling molecules and is essential for downstream TCR signaling. Grb2, Gads, PI3K, and NCK are recruited to LAT, and propagation of signaling involves activation of RAS, PKC, mobilization of Ca2+, calcineurin, and polymerization of the actin cytoskeleton (Tasken et al., Front in Bioscience 11: 2929-2939 (2006)). This ultimately leads to the activation of transcription factors of the NFkB, NFAT, AP1 and ATF families, resulting in the transcription of genes for immune activation (Mosenden et al., Cell Signalling 23: 1009-1016 (2011); Tasken et al., Front in Bioscience 11: 2929-2939 (2006)).

[0092] Both PKA and PDE4 regulated signaling intersect with TCR-induced T cell activation to fine-tune its regulation, with opposing effects (see Abrahamsen H. et al., "TCR-and CD28-mediated recruitment of phosphodiesterase4 to lipid rafts potentiates TCR signaling", J Immunol, Vol. 173, pages 4847-4848 (2004), in particular Figure 6 , which shows the opposing effects of PKA and PDE4 on TCR activation). The molecule that connects these effectors is cyclic AMP (cAMP), an intracellular second messenger for the action of extracellular ligands. In T cells, it mediates the effects of prostaglandins, adenosine, histamine, beta-adrenergic agonists, neuropeptide hormones and beta-endorphins. The binding of these extracellular molecules to GPCRs leads to their conformational changes, the release of stimulatory subunits and the subsequent activation of adenylate cyclase (AC), which hydrolyzes ATP to cAMP (see Abrahamsen H. et al., 2004, in Figure 6 , supra). Although not the only one, PKA is the main effector of cAMP signaling (see Mosenden R. and Tasken K., 2011, supra, and Tasken K. and Ruppelt A., 2006, supra). At the functional level, increased cAMP levels lead to reduced IFNγ and IL-2 production in T cells (see Abrahamsen H. et al., 2004, supra). In addition to interfering with TCR activation, PKA has more effectors (see Torheim EA, 2009, supra). Fig.15 , as described above).

[0093] In naive T cells, hyperphosphorylated PAG targets Csk to lipid rafts. Through the Ezrin-EBP50-PAG scaffold, the complex PKA targets Csk. Through the specific phosphorylation of PKA, Csk can negatively regulate Lck and Fyn to inhibit their activity and downregulate T cell activation (see Abrahamsen H. et al., 2004, Figure 6 , supra). After TCR activation, PAG is dephosphorylated and Csk is released from rafts. Csk needs to be isolated for T cell activation. During the same time course, the Csk-G3BP complex is formed and appears to sequester Csk outside of lipid rafts (see Mosenden R. and Tasken K., 2011, supra, and Tasken K. and Ruppelt A., 2006, supra).

[0094] In contrast, combined TCR and CD28 stimulation mediates the recruitment of the cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, which enhances cAMP degradation (see Abrahamsen H. et al., Figure 6 , 2004, supra). Thus, TCR-induced cAMP production is counteracted and the T cell immune response is enhanced. Following TCR stimulation alone, PDE4 recruitment may be too low to fully reduce cAMP levels, and thus maximal T cell activation cannot occur (see Abrahamsen H. et al., 2004, supra).

[0095] Therefore, by actively inhibiting proximal TCR signal transduction, the signal transduction via cAMP-PKA-Csk is considered to set the threshold of T cell activation. The recruitment of PDE can offset this inhibition. Tissue or cell type specific regulation is achieved by the expression of multiple isoforms of AC, PKA and PDE. As mentioned above, the balance between activation and inhibition needs to be strictly regulated to prevent the development of autoimmune diseases, immunodeficiency and cancer.

[0096] PDE4D7-related genes

[0097] Phosphodiesterases (PDEs) provide the only means of degrading the second messenger 3'-5'-cyclic AMP. Therefore, they are prepared to provide a key regulatory role. Therefore, abnormal changes in their expression, activity and intracellular location may contribute to the underlying molecular pathology of specific disease states. In fact, it has been recently shown that mutations in PDE genes are enriched in prostate cancer patients, leading to elevated cAMP signaling and potential susceptibility to prostate cancer. However, the different expression profiles in different cell types combined with the complex array of isoform variants within each PDE family make it challenging to understand the connection between abnormal changes in PDE expression and function during disease progression. Several studies have endeavored to characterize the complement of PDEs in the prostate, all of which recognized the significant levels of PDE4 expression along with other PDEs, leading to the development of the PDE4D7 biomarker (see Alvesde 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, it was hypothesized that the ability to identify markers that are highly correlated with the PDE47 biomarker may also help predict outcomes in certain cancer subjects.

[0098] Based on the correlation between PDE4D7 expression and the pathological features of the disease, the purpose of the definition is to identify the prognostic association between PDE4D7 expression in patient prostate tissue collected by biopsy or surgery and clinically useful information related to the outcome of individual patients. Clinically relevant endpoints or surrogate endpoints significantly associated with the development of metastasis, cancer-specific or overall mortality are generally evaluated as prognostic cancer biomarkers. The most relevant rationale for using surrogate endpoints involves situations where data on established clinical endpoints are not available or the number of events in the data cohort is too limited for statistical data analysis. For the development of the PDE4D7 prognostic biomarker, assessment of BCR (biochemical recurrence) progression-free survival or the start of postoperative secondary treatment was assessed as a surrogate endpoint for metastasis and prostate cancer death. Using these specific endpoints, the number of relevant events in the selected clinical cohort is identified (e.g., for BCR>30%), which is particularly relevant to multivariate data analysis.

[0099] In the evaluation performed, standard methods of multivariate analysis, such as Cox regression and Kaplan-Meier survival analysis, were chosen in order to study the additive and independent value of the continuous and / or categorical "PDE4D7 score" compared to established prognostic clinical variables such as PSA and Gleason score (Alves de Inda, 2018). A risk model was established in which the "PDE4D7 score" was combined with preoperative or postoperative clinical predictors of postoperative progression using logistic regression. The final model was subsequently tested on multiple independent patient populations in Kaplan-Meier survival and ROC curve analysis to predict progression-free survival after treatment (Alves de Inda, 2018).

[0100] Using such a strategy, the prognostic value of the PDE4D7 score of retrospectively collected biopsies of excised prostate tissue from a consecutively managed patient cohort at a single surgical center in a postoperative setting was tested (Alves de Inda, 2018). The patient population included some 500 individuals, in which longitudinal follow-up of pathological and biological outcomes was performed. These clinical data were available for all patients and collected during a median follow-up of 120 months after treatment. The "PDE4D7 score" was determined as described above and then tested in univariate and multivariate analyses using available postoperative covariates (i.e., pathological Gleason score, pT stage, surgical margin status, seminal vesicle invasion status, and lymph node invasion status) in order to adjust the multivariate setting. In this case, biochemical progression-free survival after primary intervention was set as the clinical endpoint of the evaluation. Univariate analysis of these clinical samples (Alves de Inda, 2018), showed an inverse association between PDE4D7 expression (in terms of the "PDE4D7 score") and postoperative biological recurrence (HR = 0.53 per unit change; 95% CI 0.41-0.67; p < 0.0001), robustly confirming previous data (Boettcher 2015; Boettcher, 2016). In a multivariate analysis with such clinical variables, the "PDE4D7 score" remained an independent and valid means for predicting clinical outcome (HR = 0.56 per unit change; 95% CI 0.43-0.73; p < 0.0001). In addition, very similar results were obtained when evaluating the "PDE4D7 score" in a multivariate analysis (HR = 0.5495% CI 0.42-0.69; p < 0.0001) with the validated and clinically used risk model CAPRA-S. The CAPRA-S score, based on preoperative PSA and pathological parameters determined at the time of surgery, was developed to provide clinicians with information designed to help predict disease recurrence (including BCR, systemic progression, and PCSM) and has been validated in the United States and other populations.

[0101] Interestingly, when evaluating the hazard ratio (HR) compared to the continuous "PDE4D7 score", it was found that for score values ​​between 2 and 5, the risk increased linearly with decreasing "PDE4D7 score". However, at PDE4D7 scores less than 2, the risk of progression after surgery increased dramatically (Alves de Inda, 2018). This was also confirmed in the Kaplan-Meier survival curves, where patients grouped in the lowest "PDE4D7 score" category showed the highest risk of disease recurrence. Using logistic regression analysis, the CAPRA-S score was combined with the continuous "PDE4D7 score". The model was tested using ROC curve analysis, and a significant improvement of 4-6% in the AUC was noted for the 2-year and 5-year prediction of progression to BCR after treatment when compared to CAPRA-S alone. Therefore, the combined CAPRA-S & "PDE4D7 score" Cox regression combination model was evaluated in the Kaplan-Meier survival analysis and compared with the CAPRA-S score categories alone. Understanding this, the added value in risk prediction when using the model (combined “PDE4D7 & CAPRA-S” score) was confirmed compared to the clinical measure of using the CAPRA-S score alone (Alves de Inda, 2018).

[0102] After the diagnosis of prostate cancer, an accurate risk assessment is needed before stratification to a defined primary treatment. With this in mind, it was tested whether it was possible to translate the prognostic utility of the "PDE4D7 score" in the preoperative setting of testing tumor tissue obtained from diagnostic needle biopsy samples (van Strijp, 2018). In this case, needle biopsies were performed on 168 patients from a single diagnostic clinical center who had already undergone surgery as primary treatment. After this intervention, the minimum follow-up period for each patient was 60 months. The clinical covariates used to adjust the "PDE4D7 score" in multivariate analysis were age at surgery, preoperative PSA, PSA density, biopsy Gleason score, percentage of tumor-positive biopsy cores, percentage of tumor in biopsy, and clinical cT stage. Here, the utility of the "PDE4D7 score" and the combined "PDE4D7&CAPRA" score compared with the preoperative CAPRA score in Cox regression analysis for biochemical recurrence was evaluated (van Strijp2018).

[0103] This patient group was evaluated and found (van Strijp 2018) that the "PDE4D7 score" was inversely associated with BCR in multivariate analysis when adjusting for clinical variables (HR = 0.43; 95% CI 0.29-0.63; p < 0.0001) as well as the clinical CAPRA score (HR = 0.53; 95% CI 0.38-0.74; p = 0.0001). Kaplan-Meier analysis showed that, as previously mentioned, in the postoperative setting, the "PDE4D7 score" category was significantly associated with BCR progression-free survival (logrank p < 0.0001) and secondary treatment-free survival (logrank p = 0.01). Next, a combined logistic regression model developed on a previous group was used (van Strijp, 2018). This consists of the combined "CAPRA & PDE4D7 score", indicating that patients within the highest combined "CAPRA & PDE4D7" combined score category have little risk of biochemical progression or transfer to any secondary treatment after surgery. ROC curve analysis was also used to evaluate the logistic regression model to predict 5-year BCR after surgery. This showed that the AUC was increased by 5% over the CAPRA score alone (AUC = 0.82 vs 0.77, respectively; p = 0.004). The decision curve analysis of the combined "CAPRA & PDE4D7" scoring model confirmed the excellent net benefit of using the combined score compared to any score alone at all decision thresholds, so as to decide whether to intervene (e.g., surgery) based on the risk threshold of disease progression after surgery experienced by individual patients (van Strijp, 2018).

[0104] The prediction of treatment outcome is very complex because many factors play a role in treatment effectiveness and disease recurrence. Important factors may not yet be identified, and the influence of other factors cannot be accurately determined. A variety of clinical pathological measures are currently being studied and applied in clinical settings to improve response prediction and treatment selection, thereby providing a degree of improvement. However, there is still a strong need to better predict treatment responses in order to improve the success rate of these therapies.

[0105] Gene selection

[0106] Gene signatures and combinations of these signatures with clinical parameters have been identified, with the final model showing a significant relationship with mortality and thus expected to improve the prediction of the effectiveness of these treatments.

[0107] The identified immune defense response genes 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 tissues were stored together with clinical (e.g., pathological Gleason grade group (pGGG), pathological status (pT stage)) and relevant outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiation therapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, the PDE4D7 score was calculated and classified into four PDE4D7 score categories (see Alves de Inda M. et al., 2018, supra). PDE4D7 score category 1 represents patient samples with the lowest PDE4D7 expression level, while PDE4D7 score category 4 represents patient samples with the highest PDE4D7 expression level. Then, RNASeq expression data (TPM-transcripts per million) of 538 prostate cancer subjects were studied for differential gene expression between PDE4D7 score categories 1 and 4. In particular, for approximately 20,000 protein-coding transcripts, it was determined whether the average expression level of patients with PDE4D7 score category 1 was twice higher than the average expression level of patients with PDE4D7 score category 4. This analysis produced 637 genes, wherein the ratio of PDE4D7 score category 1 / PDE4D7 score category 4 was >2, with a minimum average expression of 1 TPM in each of the four PDE4D7 score categories. These 637 genes were then further subjected to molecular pathway analysis, which produced a series of enriched annotation clusters. Annotation cluster #2 demonstrated the enrichment of 30 genes (enrichment score: 10.8), which have the functions of defense response to virus, negative regulation of viral genome replication and type I interferon signaling. Further heat map analysis confirmed that the expression of these immune defense response genes in samples from patients with PDE4D7 score category 1 is usually higher than that from patients with PDE4D7 score category 4. The gene categories with the functions of defense response to virus, negative regulation of viral genome replication and type I interferon signaling were further enriched to 61 genes by literature search to identify other genes with the same molecular function. Further selection was made from 61 genes based on the combined ability to separate patients who died of prostate cancer from patients who did not die of prostate cancer, and a preferred set of 14 genes was obtained. It was found that the number of events (metastasis, prostate cancer-specific death) was enriched in the subgroup with low expression of these genes compared with the total patient group (#538) and the subgroup of 151 patients who experienced salvage RT (SRT) after disease recurrence after surgery.

[0108] 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 (e.g., pathological Gleason grade group (pGGG), pathological status (pT stage)) and relevant outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiation therapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, the PDE4D7 score was calculated and classified into four PDE4D7 score categories (see Alves de Inda M. et al., 2018, supra). PDE4D7 score category 1 represents patient samples with the lowest PDE4D7 expression level, while PDE4D7 score category 4 represents patient samples with the highest PDE4D7 expression level. Then, RNASeq expression data (TPM-transcripts per million) of 538 prostate cancer subjects were studied for differential gene expression between PDE4D7 score categories 1 and 4. In particular, for approximately 20,000 protein-coding transcripts, it was determined whether the average expression level of patients with PDE4D7 score category 1 was twice that of patients with PDE4D7 score category 4. This analysis produced 637 genes, wherein the ratio of PDE4D7 score category 1 / PDE4D7 score category 4 was >2, with a minimum average expression of 1TPM in each of the four PDE4D7 score categories. These 637 genes were then further subjected to molecular pathway analysis, which produced a series of enriched annotation clusters. Annotation cluster #6 demonstrated enrichment of 17 genes (enrichment score: 5.9), which have the functions of primary immune deficiency and activated T cell receptor signaling. Further heat map analysis confirmed that the expression of these immune defense response genes was generally higher in samples from patients in PDE4D7 score category 1 than in patients in PDE4D7 score category 4.

[0109] The identified PDE4D7-associated genes ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2 were identified as follows: In RNAseq data of nearly 60,000 transcripts generated from 571 prostate cancer patients, a series of genes were identified that 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 performed by Pearson correlation and expressed as a value between 0 and 1 in case of a positive correlation, or as a value between -1 and 0 in case of a negative correlation. To calculate the correlation coefficient, the PDE4D7 score (see Alves de Inda M. et al., 2018, supra) and the TPM gene expression values ​​for each gene of interest determined by RNAseq (see below) were used as input data.

[0110] The maximum negative correlation coefficient identified between the expression of any approximately 60,000 transcripts and the expression of PDE4D7 is -0.38, and the maximum positive correlation coefficient identified between the expression of any approximately 60,000 transcripts and the expression of PDE4D7 is +0.56. Select genes within the range of correlation -0.31 to -0.38 and +0.41 to +0.56. In a total of 77 transcript matches, these features are identified. From those 77 transcripts, 8 PDE4D7-related genes ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2 are selected by iteratively testing the Cox regression combination model in a subgroup of 186 patients who undergo salvage radiotherapy (SRT) due to postoperative biochemical recurrence. The clinical endpoint of the test is prostate cancer-specific death after the start of SRT. The boundary condition for the selection of 8 genes was given by the restriction that for all genes retained in the model, the p-value in the multivariate Cox-regression was < 0.1.

[0111] In the present document, it is shown that these genes also have prognostic value for the outcome of bladder cancer subjects.

[0112] Thus, in a first embodiment, the invention provides a method of predicting an outcome in a bladder cancer subject comprising determining a gene expression profile comprising gene expression levels or receiving the results of determining a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group consisting of: 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 / or, 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 gene expression profile being 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 an outcome for the subject, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

[0113] In an embodiment, the present invention provides a computer program product comprising instructions, which, when executed by a computer, cause the computer to implement a method, the method comprising receiving data indicative of a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group 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; and / or, 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 gene expression profile is determined in a biological sample obtained from a bladder cancer subject, and a prediction of an outcome for the subject is determined based on the gene expression profile, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

[0114] In an embodiment, the method further comprises providing a prediction of the result to a medical caregiver or subject. In an embodiment, determining the prediction of the result comprises combining the gene expression level with a regression function derived from a population of bladder cancer subjects. In an embodiment, determining the prediction of the result is also based on one or more clinical parameters obtained from the subject. In an embodiment, the determination of the result comprises combining the gene expression profile and one or more clinical parameters obtained from the subject with a regression function derived from a population of bladder cancer subjects. In one embodiment, the one or more clinical parameters comprise one or more of the following: EORTC score, the number of tumor-positive regional lymph nodes, metastatic disease status, ECOG score, and FoundationOne mutation load per MB DNA, preferably wherein one or more clinical parameters consist of EORTC score, the number of tumor-positive regional lymph nodes, or consist of metastatic disease status, ECOG score, and optionally FoundationOne mutation load per MB DNA. In an embodiment, a biological sample is obtained from the subject before treatment begins, preferably wherein the biological sample is a bladder sample or a bladder cancer sample. In an embodiment, therapy is recommended based on prediction. In an embodiment, the subject suffers from non-muscle invasive bladder cancer (NMIBC), preferably high-grade NMIBC or metastatic bladder cancer (mUC).

[0115] In an embodiment, the present invention is directed to a method of predicting an outcome in a bladder cancer subject, comprising determining a gene expression profile comprising gene expression levels or receiving a result of determining a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group 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, the gene expression profile being determined in a biological sample obtained from the subject, determining a prediction of an outcome based on the gene expression profile, wherein the prediction is an outcome for the subject, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy. In embodiments, the methods broadly described herein are further based on determining or receiving the results of determining at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, or all 23) gene expression level selected from immune defense response genes. and PDE4D7-related genes, the immune defense response gene being selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and the PDE4D7-related gene being selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0116] In an embodiment, the present invention relates to a method for predicting an outcome of a bladder cancer subject, comprising determining a gene expression profile comprising gene expression levels or receiving a result of determining a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group consisting 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, the gene expression profile being determined in a biological sample obtained from the subject, determining a prediction of an outcome based on the gene expression profile, wherein the prediction is an outcome for the subject, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy. In embodiments, the methods broadly described herein are further based on determining the expression level of at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or all 26) gene or receiving the results of determining the expression level of at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or all 26) gene The expression level is selected from the following: a T-cell receptor signaling 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 a PDE4D7-related gene selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0117] Also disclosed herein is a method of predicting an outcome in a subject with bladder cancer, comprising: determining a gene expression profile comprising expression levels of three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 or even 39) genes or receiving a gene expression profile determined to comprise expression levels of three or more genes. (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 or even 39) gene expression levels, wherein the three or more gene expression levels are selected from: immune defense response genes, which are selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1; and / or, a T-cell receptor signaling 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, 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 a prediction of an outcome is determined based on the gene expression profile, wherein the prediction is a favorable outcome or an unfavorable outcome for the subject. Optionally, the method further comprises the step of providing a prediction of outcome to a medical caregiver or the subject.

[0118] Also disclosed herein is a computer-implemented method for predicting an outcome of a bladder cancer subject, comprising: receiving a result of determining a gene expression profile comprising three or more gene expression levels, wherein the three or more gene expression levels are selected from: an immune defense response gene selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; and / or a T-cell receptor signaling 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, PDE4D7-related genes, which are selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, the gene expression profile being 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 for a favorable outcome or an unfavorable outcome for the subject, and optionally providing the prediction of the outcome to a medical caregiver or the subject.

[0119] The inventors describe herein that gene signatures can be used to predict survival of patients diagnosed with bladder cancer. Thus, in an embodiment, the invention provides a method of predicting an outcome in a subject with bladder cancer, comprising: determining a gene expression profile comprising expression levels of three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 or even 39) genes or receiving a confirmed The results of a gene expression profile comprising three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 or even 39) gene expression levels, wherein the three or more gene expression levels are selected from: immune defense response genes, which are selected from the following The group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1; and / or, a T-cell receptor signaling gene selected from the group consisting of: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG 1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70; and / or, PDE4D7-related genes 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 the prediction of the outcome is determined based on the gene expression profile, wherein the prediction is survival or progression. Survival can refer to overall survival or cancer-specific death. When used herein, disease progression refers to at least 20% growth in tumor size or tumor spread since the start of treatment.

[0120] In an embodiment, prediction is based on the combined gene signature of immune defense response gene and T-cell receptor signaling gene, the immune defense response gene is selected from the group consisting of: AIM2, APOBEC3A, CIA01, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, the T-cell receptor signaling gene is selected from the group consisting of: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70. This combination is also referred to as immune score in this article. In further implementation, immune score is further combined with clinical parameters (preferably EORTC score).

[0121] It should be understood that when predicting based on one or more gene signatures as described herein, slight modifications can be made to gene signatures without affecting prediction potential or even improving prediction potential. Therefore, it should be understood that any gene signature as described herein can be modified to retain at least 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% in the gene. The remaining gene can be omitted or replaced by alternative genes.

[0122] The inventors have also described that gene signatures can be used to predict treatment responses in bladder cancer patients. Thus, in an embodiment, the invention provides a method of predicting the outcome of a bladder cancer subject, comprising: determining a gene expression profile comprising expression levels of three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 or even 39) genes or receiving a confirmed The results of determining a gene expression profile comprising three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 or even 39) gene expression levels, wherein the three or more gene expression levels are selected from: immune defense response genes, which are selected from the group consisting of: The invention relates to a T-cell receptor signaling gene selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1; and / or a T-cell receptor signaling gene selected from the group consisting of: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PA G1, PDE4D, PRKACA, PRKACB, PTPRC and ZAP70; and / or, PDE4D7-related genes 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 the prediction of the result is determined based on the gene expression profile, wherein the prediction is a treatment response. The treatment can be surgery or therapy, such as immunotherapy or treatment with immune checkpoint inhibitors. The immunotherapy can be BCG. The immune checkpoint inhibitor can be anti-PD-L1, such as atezolizumab. The patient may have NMIBC, MIBC or metastatic bladder cancer.

[0123] The present invention describes the use of gene signatures to predict the outcome of subjects with bladder cancer. The gene signature comprises a gene expression profile comprising three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 or even 39) gene expression levels, wherein the three 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 an embodiment, three or more genes may be selected from immune defense response genes. In an embodiment, the three or more genes may be selected from T cell receptor signaling genes. In an embodiment, three or more genes may be selected from PDE4D7 related genes. In an embodiment, the three or more genes include one or more immune defense response genes and one or more T cell receptor signaling genes. In an embodiment, the three or more genes include one or more immune defense response genes and one or more PDE4D7-related genes. In an embodiment, the three or more genes include one or more PDE4D7-related genes and one or more T cell receptor signaling genes. In an embodiment, the three 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.

[0124] With respect to the above biological processes, three immune system-related gene signatures were selected, including the genes listed in Tables 1 to 3. Previously, the relevance of these signatures to the prediction of prostate cancer survival was shown.

[0125] Table 1: Immune defense response (IDR) characteristics.

[0126]

[0127]

[0128] Table 2: T cell receptor (TCR) characteristics.

[0129] Gene symbol Ensembl_ID CD2 ENSG00000116824 CD247 ENSG00000198821 CD28 ENSG00000178562 CD3E ENSG00000198851 CD3G ENSG00000160654 CD4 ENSG00000010610 CSK ENSG00000103653 EZR ENSG00000092820 FYN ENSG00000010810 LAT ENSG00000213658 LCK ENSG00000182866 PAG1 ENSG00000076641 PDE4D ENSG00000113448 PRKACA ENSG00000072062 PRKACB ENSG00000142875 PTPRC ENSG00000081237 ZAP70 ENSG00000115085

[0130] Table 3: PDE4D7-related (PDE4D7_R2) features.

[0131] Gene symbol Ensembl_ID ABCC5 ENSG00000114770 CUX2 ENSG00000111249 KIAA1549 ENSG00000122778 PDE4D ENSG00000113448 RAP1GAP2 ENSG00000132359 SLC39A11 ENSG00000133195 TDRD1 ENSG00000095627 VWA2 ENSG00000165816

[0132] 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 be predictive of the outcome of bladder cancer subjects each individually, one or more per gene set, in combination of one or more per gene set, or when all combined.

[0133] The term "ABCC5" relates to the human ATP-binding cassette subfamily C member 5 gene (Ensembl: ENSG00000114770), for example, a sequence as defined in the NCBI reference sequence NM_001023587.2 or the NCBI reference sequence NM_005688.3, ​​specifically, a nucleotide sequence as shown in SEQ ID NO: 1 or SEQ ID NO: 2, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the ABCC5 transcript, and also to a corresponding amino acid sequence, for example, as shown in SEQ ID NO: 3 or SEQ ID NO: 4, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001018881.1 and the NCBI protein identification reference sequence NP_005679 encoding the ABCC5 polypeptide.

[0134] The term "ABCC5" also includes nucleotide sequences that show a high degree of homology to ABCC5, 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 shown in SEQ ID NO:1 or SEQ ID NO:2, 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 shown in SEQ ID NO:3 or SEQ ID NO:4, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:3 or SEQ ID NO:4, or 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 shown in SEQ ID NO:3 or SEQ ID NO:4. 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%, 97%, 98% or 99% identical to the sequence shown in NO:1 or SEQ ID NO:2.

[0135] The term "AIM2" relates to a deletion in the melanoma 2 gene (Ensembl: ENSG00000163568), for example, a sequence defined in the NCBI reference sequence NM_004833, specifically, the nucleotide sequence shown in SEQ ID NO:5, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the AIM2 transcript, and also to a corresponding amino acid sequence, for example, as shown in SEQ ID NO:6, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_004824 encoding the AIM2 polypeptide.

[0136] The term "AIM2" also includes nucleotide sequences that show a high degree of homology to AIM2, 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 shown in SEQ ID NO:5, 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 shown in SEQ ID NO:6, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:6, or 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 shown in SEQ ID NO:6. 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 shown in NO:5.

[0137] The term "APOBEC3A" relates to the apolipoprotein B mRNA editing enzyme catalytic subunit 3A gene (Ensembl: ENSG00000128383), for example, the sequence defined in the NCBI reference sequence NM_145699, specifically, the nucleotide sequence as shown in SEQ ID NO:7, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the APOBEC3A transcript, and also to the corresponding amino acid sequence, for example, the amino acid sequence shown in SEQ ID NO:8, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_663745 encoding the APOBEC3A polypeptide.

[0138] The term "APOBEC3A" also includes nucleotide sequences that show a high degree of homology to APOBEC3A, 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 shown in SEQ ID NO:7, 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 shown in SEQ ID NO:8, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:8, or 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 shown in SEQ ID NO:8. 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 shown in NO:7.

[0139] The term "CD2" relates to the cluster of differentiation 2 gene (Ensembl: ENSG00000116824), for example, the sequence defined in the NCBI reference sequence NM_001767, specifically, to the nucleotide sequence shown in SEQ ID NO:9, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CD2 transcript, and also to the corresponding amino acid sequence, for example, the amino acid sequence shown in SEQ ID NO:10, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001758 encoding the CD2 polypeptide.

[0140] The term "CD2" also includes nucleotide sequences that show a high degree of homology to CD2, such as 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 shown in SEQ ID NO:9, 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 shown in SEQ ID NO:10, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:10, or 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 shown in SEQ ID NO:10. 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 shown in NO:9.

[0141] The term "CD247" relates to the cluster of differentiation 247 gene (Ensembl: ENSG00000198821), for example, to the sequence defined in the NCBI reference sequence NM_000734 or the NCBI reference sequence NM_198053, specifically, to the nucleotide sequence shown in SEQ ID NO: 11 or SEQ ID NO: 12, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CD247 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO: 13 or SEQ ID NO: 14, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_000725 and the NCBI protein identification reference sequence NP_932170 encoding the CD247 polypeptide.

[0142] The term "CD247" also includes nucleotide sequences that show a high degree of homology to CD247, 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 shown in SEQ ID NO: 11 or SEQ ID NO: 12, 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 shown in SEQ ID NO: 13 or SEQ ID NO: 14, or encodes 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 shown in SEQ ID NO: 13 or SEQ ID NO: 14. 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 shown in SEQ ID NO:11 or SEQ ID NO:12.

[0143] The term "CD28" relates to the cluster of differentiation 28 gene (Ensembl: ENSG00000178562), for example, to the sequence defined in the NCBI reference sequence NM_006139 or the NCBI reference sequence NM_001243078, specifically, to the nucleotide sequence shown in SEQ ID NO: 15 or SEQ ID NO: 16, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CD28 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO: 17 or SEQ ID NO: 18, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_006130 and the NCBI protein identification reference sequence NP_001230007 encoding the CD28 polypeptide.

[0144] The term "CD28" also includes nucleotide sequences that show a high degree of homology to CD28, 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 shown in SEQ ID NO: 15 or SEQ ID NO: 16, 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 shown in SEQ ID NO: 17 or SEQ ID NO: 18, or encodes 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 shown in SEQ ID NO: 17 or SEQ ID NO: 18. 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 shown in SEQ ID NO:15 or SEQ ID NO:16.

[0145] The term "CD3E" relates to the cluster of differentiation 3E gene (Ensembl: ENSG00000198851), for example, to the sequence defined in the NCBI reference sequence NM_000733, specifically, to the nucleotide sequence shown in SEQ ID NO: 19, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CD3E transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO: 20, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_000724 encoding the CD3E polypeptide.

[0146] The term "CD3E" also includes nucleotide sequences that show a high degree of homology to CD3E, 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 shown in SEQ ID NO:19, 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 shown in SEQ ID NO:20, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:20, or 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 shown in SEQ ID NO:20. 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 shown in NO:19.

[0147] The term "CD3G" relates to the cluster of differentiation 3G gene (Ensembl: ENSG00000160654), for example, to the sequence defined in the NCBI reference sequence NM_000073, specifically, to the nucleotide sequence shown in SEQ ID NO:21, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CD3G transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:22, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_000064 encoding the CD3G polypeptide.

[0148] The term "CD3ζ" also includes nucleotide sequences that show a high degree of homology to CD3ζ, 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 shown in SEQ ID NO:21, 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 shown in SEQ ID NO:22, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:22, or 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 shown in SEQ ID NO:22. 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 shown in NO:21.

[0149] The term "CD4" relates to the cluster of differentiation 4 gene (Ensembl: ENSG00000010610), for example, to the sequence defined in the NCBI reference sequence NM_000616, specifically, to the nucleotide sequence shown in SEQ ID NO:23, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CD4 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:24, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_000607 encoding the CD4 polypeptide.

[0150] The term "CD4" also includes nucleotide sequences that show a high degree of homology to CD4, 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 shown in SEQ ID NO:23, 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 shown in SEQ ID NO:24, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:24, or 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 shown in SEQ ID NO:24. 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 shown in NO:23.

[0151] The term "CIAO1" relates to the cytoplasmic iron-sulfur assembly component 1 gene (Ensembl: ENSG00000144021), for example, to the sequence defined in the NCBI reference sequence NM_004804, specifically, to the nucleotide sequence shown in SEQ ID NO:25, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CIAO1 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:26, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_663745 encoding the CIAO1 polypeptide.

[0152] The term "CIAO1" also includes nucleotide sequences that show a high degree of homology to CIAO1, 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 shown in SEQ ID NO:25, 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 shown in SEQ ID NO:26, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:26, or 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 shown in SEQ ID NO:26. 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 shown in NO:25.

[0153] The term "CSK" relates to the C-terminal Src kinase gene (Ensembl: ENSG00000103653), for example, to the sequence defined in the NCBI reference sequence NM_004383, specifically, to the nucleotide sequence shown in SEQ ID NO: 27, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CSK transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO: 28, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_004374 encoding the CSK polypeptide.

[0154] The term "CSK" also includes nucleotide sequences that show a high degree of homology to CSK, 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 shown in SEQ ID NO:27, 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 shown in SEQ ID NO:28, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:28, or 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 shown in SEQ ID NO:28. 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 shown in NO:27.

[0155] The term "CUX2" relates to the human Cut-like homeobox 2 gene (Ensembl: ENSG00000111249), for example, to the sequence defined in the NCBI reference sequence NM_015267.3, specifically, to the nucleotide sequence shown in SEQ ID NO:29, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the CUX2 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:30, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_056082.2 encoding the CUX2 polypeptide.

[0156] The term "CUX2" also includes nucleotide sequences that show a high degree of homology to CUX2, 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 shown in SEQ ID NO:29, 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 shown in SEQ ID NO:30, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:30, or 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 shown in SEQ ID NO:30. 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 shown in NO:29.

[0157] The term "DDX58" relates to the DExD / H box helicase 58 gene (Ensembl: ENSG00000107201), for example, to the sequence defined in the NCBI reference sequence NM_014314, specifically, to the nucleotide sequence shown in SEQ ID NO:31, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the DDX58 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:32, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_055129 encoding the DDX58 polypeptide.

[0158] The term "DDX58" also includes nucleotide sequences that show a high degree of homology to DDX58, 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 shown in SEQ ID NO:31, 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 shown in SEQ ID NO:32, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:32, or 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 shown in SEQ ID NO:32. 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 shown in NO:31.

[0159] The term "DHX9" relates to the DExD / H box helicase 9 gene (Ensembl: ENSG00000135829), for example, to the sequence defined in the NCBI reference sequence NM_001357, specifically, to the nucleotide sequence shown in SEQ ID NO:33, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the DHX9 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:34, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001348 encoding the DHX9 polypeptide.

[0160] The term "DHX9" also includes nucleotide sequences that show a high degree of homology to DHX9, 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 shown in SEQ ID NO:33, 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 shown in SEQ ID NO:34, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:34, or 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 shown in SEQ ID NO:34. 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 shown in NO:33.

[0161] The term "EZR" relates to the EZR gene (Ensembl: ENSG00000092820), for example, to the sequence defined in the NCBI reference sequence NM_003379, specifically, to the nucleotide sequence shown in SEQ ID NO:35, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the EZR transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:36, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_003370 encoding the EZR polypeptide.

[0162] The term "EZR" also includes nucleotide sequences that show a high degree of homology to EZR, 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 shown in SEQ ID NO:35, 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 shown in SEQ ID NO:36, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:36, or 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 shown in SEQ ID NO:36. 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 shown in NO:35.

[0163] The term "FYN" relates to the FYN pro-oncogene (Ensembl: ENSG00000010810), for example, to the sequence defined in the NCBI reference sequence NM_002037 or the NCBI reference sequence NM_153047 or the NCBI reference sequence NM_153048, specifically, to the nucleotide sequence shown in SEQ ID NO:37 or SEQ ID NO:38 or SEQ ID NO:39, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the FYN transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:40 or SEQ ID NO:41 or SEQ ID NO:42, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_002028, the NCBI protein identification reference sequence NP_694592 and the NCBI protein identification reference sequence XP_005266949 encoding the FYN polypeptide.

[0164] The term "FYN" also includes nucleotide sequences that show a high degree of homology to FYN, 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 shown in SEQ ID NO:37 or SEQ ID NO:38 or SEQ ID NO:39, 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 shown in SEQ ID NO:40 or SEQ ID NO:41 or SEQ ID NO:42, or encodes 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 shown in SEQ ID NO:40 or SEQ ID NO:41 or SEQ ID NO:42. 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 shown in SEQ ID NO:37, SEQ ID NO:38 or SEQ ID NO:39.

[0165] The term "IFI16" relates to the interferon gamma-inducible protein 16 gene (Ensembl: ENSG00000163565), for example, to the sequence defined in the NCBI reference sequence NM_005531, specifically, to the nucleotide sequence shown in SEQ ID NO:43, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the IFI16 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:44, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_005522 encoding the IFI16 polypeptide.

[0166] The term "IFI16" also includes nucleotide sequences that show a high degree of homology to IFI16, 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 shown in SEQ ID NO:43, 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 shown in SEQ ID NO:44, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:44, or 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 shown in SEQ ID NO:44. 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 shown in NO:43.

[0167] The term "IFIH1" relates to the interferon-induced helicase C domain 1 gene (Ensembl: ENSG00000115267), for example, to the sequence defined in the NCBI reference sequence NM_022168, specifically, to the nucleotide sequence shown in SEQ ID NO:45, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the IFIH1 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:46, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_071451 encoding the IFIH1 polypeptide.

[0168] The term "IFIH1" also includes nucleotide sequences that show a high degree of homology to IFIH1, 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 shown in SEQ ID NO:45, 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 shown in SEQ ID NO:46, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:46, or 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 shown in SEQ ID NO:46. 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 shown in NO:45.

[0169] The term "IFIH1" relates to the protein of the interferon-induced tetratricopeptide repeat 1 gene (Ensembl: ENSG00000185745), for example, to the sequence defined in the NCBI reference sequence NM_001270929 or the NCBI reference sequence NM_001548.5, specifically, to the nucleotide sequence shown in SEQ ID NO:47 or SEQ ID NO:48, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the IFIT1 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:49 or SEQ ID NO:50, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001257858 and the NCBI protein identification reference sequence NP_001539 encoding the IFIT1 polypeptide.

[0170] The term "IFIH1" also includes nucleotide sequences that show a high degree of homology to IFIT1, 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 shown in SEQ ID NO:47 or SEQ ID NO:48, 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 shown in SEQ ID NO:49 or SEQ ID NO:50, or encodes 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 shown in SEQ ID NO:49 or SEQ ID NO:50. NO:50, 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 shown in SEQ ID NO:47 or SEQ ID NO:48.

[0171] The term "IFIT3" relates to the interferon-induced protein with tetratricopeptide repeat 3 gene (Ensembl: ENSG00000119917), for example, to the sequence defined in the NCBI reference sequence NM_001031683, specifically, to the nucleotide sequence shown in SEQ ID NO:51, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the IFIT3 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:52, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001026853 encoding the IFIT3 polypeptide.

[0172] The term "IFIT3" also includes nucleotide sequences that show a high degree of 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 shown 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 shown in SEQ ID NO:52, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:52, or 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 shown in SEQ ID NO:52. 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 shown in NO:51.

[0173] The term "KIAA1549" relates to the human KIAA1549 gene (Ensembl: ENSG00000122778), for example, to the sequence defined in the NCBI reference sequence NM_020910 or the NCBI reference sequence NM_001164665, specifically, to the nucleotide sequence shown in SEQ ID NO:53 or SEQ ID NO:54, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the KIAA1549 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:55 or SEQ ID NO:56, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_065961 and the NCBI protein identification reference sequence NP_001158137 encoding the KIAA1549 polypeptide.

[0174] The term "KIAA1549" also includes nucleotide sequences that show a high degree of homology to KIAA1549, 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 shown in SEQ ID NO:53 or SEQ ID NO:54, 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 shown in SEQ ID NO:55 or SEQ ID NO:56, or encodes 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 shown in SEQ ID NO:55 or SEQ ID NO:56. NO:56, 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 shown in SEQ ID NO:53 or SEQ ID NO:54.

[0175] The term "LAT" relates to the T cell activation junction protein gene (Ensembl: ENSG00000213658), for example, to the sequence defined in the NCBI reference sequence NM_001014987 or the NCBI reference sequence NM_014387, specifically, to the nucleotide sequence shown in SEQ ID NO: 57 or SEQ ID NO: 58, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the LAT transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO: 59 or SEQ ID NO: 60, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001014987 and the NCBI protein identification reference sequence NP_055202 encoding the LAT polypeptide.

[0176] The term "LAT" also includes nucleotide sequences that show a high degree of homology to LAT, 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 shown 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 shown in SEQ ID NO:59 or SEQ ID NO:60, or encodes 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 shown in SEQ ID NO:59 or SEQ ID NO:60. NO: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 shown in SEQ ID NO:57 or SEQ ID NO:58.

[0177] The term "LCK" relates to the LCK pro-oncogene (Ensembl: ENSG00000182866), for example, to the sequence defined in the NCBI reference sequence NM_005356, specifically, to the nucleotide sequence shown in SEQ ID NO:61, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the LCK transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:62, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_005347 encoding the LCK polypeptide.

[0178] The term "LCK" also includes nucleotide sequences that show a high degree of 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 shown 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 shown in SEQ ID NO:62, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:62, or 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 shown in SEQ ID NO:62. 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 shown in NO:61.

[0179] The term "LRRFIP1" relates to the LRR-binding FLII interacting protein 1 gene (Ensembl: ENSG00000124831), for example, to the sequence defined in the NCBI reference sequence NM_004735 or the NCBI reference sequence NM_001137550 or the NCBI reference sequence NM_001137553 or the NCBI reference sequence NM_001137552, specifically, to the nucleotide sequence shown in SEQ ID NO:63 or SEQ ID NO:64 or SEQ ID NO:65 or SEQ ID NO:66, which corresponds to the sequence of the above-mentioned NCBI reference sequences of the LRRFIP1 transcript, and also to the corresponding amino acid sequence, for example SEQ ID NO:67 or SEQ ID NO:68 or SEQ ID NO:69 or SEQ ID NO:70. As shown in NO:70, it corresponds to the protein sequence defined in NCBI identified protein reference sequence NP_004726, NCBI identified protein reference sequence NP_001131022, NCBI identified protein reference sequence NP_001131025 and NCBI identified protein reference sequence NP_001131024 encoding LRRFIP1 polypeptide.

[0180] The term "LRRFIP1" also includes nucleotide sequences that show a high degree of homology to LRRFIP1, 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 shown in SEQ ID NO:63 or SEQ ID NO:64 or SEQ ID NO:65 or SEQ ID NO:66, 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 shown in SEQ ID NO:67 or SEQ ID NO:68 or SEQ ID NO:69 or SEQ ID NO:70, or encodes 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 shown in SEQ ID NO:67 or SEQ ID NO:68 or SEQ ID NO:69 or SEQ ID NO:70. 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 shown in SEQ ID NO:63 or SEQ ID NO:64 or SEQ ID NO:65 or SEQ ID NO:66.

[0181] The term "MYD88" relates to the MYD88 innate immune signaling adaptor gene (Ensembl: ENSG00000172936), for example, to the sequence defined in the NCBI reference sequence NM_001172567 or the NCBI reference sequence NM_001172568 or the NCBI reference sequence NM_001172569 or the NCBI reference sequence NM_001172566 or the NCBI reference sequence NM_002468, specifically, to the nucleotide sequence described in SEQ ID NO:71 or SEQ ID NO:72 or SEQ ID NO:73 or SEQ ID NO:74 or SEQ ID NO:75, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the MYD88 transcript, and also to the corresponding amino acid sequence, for example SEQ ID NO:76 or SEQ ID NO:77 or SEQ ID NO:78 or SEQ ID NO:79 or SEQ ID NO:80 NO: 80, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001166038, NCBI protein identification reference sequence NP_001166039, NCBI protein identification reference sequence NP_001166040, NCBI protein identification reference sequence NP_001166037 and NCBI protein identification reference sequence NP_002459 encoding the MYD88 polypeptide.

[0182] The term "MYD88" also includes nucleotide sequences that show a high degree of homology to MYD88, 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 shown in SEQ ID NO:71 or SEQ ID NO:72 or SEQ ID NO:73 or SEQ ID NO:74 or SEQ ID NO:75; 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 shown in SEQ ID NO:76 or SEQ ID NO:77 or SEQ ID NO:78 or SEQ ID NO:79 or SEQ ID NO:80, or encodes 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 shown in SEQ ID NO:76 or SEQ ID NO:77 or SEQ ID NO:78 or SEQ ID NO:79 or SEQ ID NO:80. 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 shown in SEQ ID NO:71 or SEQ ID NO:72 or SEQ ID NO:73 or SEQ ID NO:74 or SEQ ID NO:75.

[0183] The term "OAS1" relates to the 2'-5'-oligoadenylate synthetase 1 gene (Ensembl: ENSG00000089127), for example, to the sequence defined in the NCBI reference sequence NM_001320151 or the NCBI reference sequence NM_002534 or the NCBI reference sequence NM_001032409 or the NCBI reference sequence NM_016816, in particular, to the nucleotide sequence shown in SEQ ID NO:81 or SEQ ID NO:82 or SEQ ID NO:83 or SEQ ID NO:84, which corresponds to the sequence of the above-mentioned NCBI reference sequences of the OAS1 transcript, and also to the corresponding amino acid sequence, for example SEQ ID NO:85 or SEQ ID NO:86 or SEQ ID NO:87 or SEQ ID NO:88. As shown in NO:88, it corresponds to the protein sequence defined in NCBI protein identification reference sequence NP_001307080, NCBI protein identification reference sequence NP_002525, NCBI protein identification reference sequence NP_001027581 and NCBI protein identification reference sequence NP_058132 encoding OAS1 polypeptide.

[0184] The term "OAS1" also includes nucleotide sequences that show a high degree of homology to OAS1, 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 shown in SEQ ID NO:81 or SEQ ID NO:82 or SEQ ID NO:83 or SEQ ID NO:84, 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 shown in SEQ ID NO:85 or SEQ ID NO:86 or SEQ ID NO:87 or SEQ ID NO:88, or encodes 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 shown in SEQ ID NO:85 or SEQ ID NO:86 or SEQ ID NO:87 or SEQ ID NO:88. 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 shown in SEQ ID NO:81 or SEQ ID NO:82 or SEQ ID NO:83 or SEQ ID NO:84.

[0185] The term "PAG1" relates to the phosphoprotein membrane anchor 1 gene with glycosphingolipid microdomains (Ensembl: ENSG00000076641), for example, to the sequence defined in the NCBI reference sequence NM_018440, specifically, the nucleotide sequence as shown in SEQ ID NO:89, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the PAG1 transcript, and also to the corresponding amino acid sequence, for example, as shown in SEQ ID NO:90, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_060910 encoding the PAG1 polypeptide.

[0186] The term "PAG1" also includes nucleotide sequences that show a high degree of homology to PAG1, 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 shown in SEQ 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 shown in SEQ ID NO:90, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:90, or 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 shown in SEQ ID NO:90. 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 shown in NO:89.

[0187] The term "PDE4D" refers to the human phosphodiesterase 4D gene (Ensembl: ENSG00000113448), for example, to the sequence defined in NCBI reference sequence NM_001104631 or NCBI reference sequence NM_001349242 or NCBI reference sequence NM_001197218 or NCBI reference sequence NM_006203 or NCBI reference sequence NM_001197221 or NCBI reference sequence NM_001197220 or NCBI reference sequence NM_001197223 or NCBI reference sequence NM_001165899 or NCBI reference sequence NM_001165899, in particular, to SEQ ID NO: 91 or SEQ ID NO: 92 or SEQ ID NO: 93 or SEQ ID NO: 94 or SEQ ID NO: 95 or SEQ ID NO: 96 or SEQ ID The nucleotide sequence described in SEQ ID NO:97 or SEQ ID NO:98 or SEQ ID NO:99 corresponds to the sequence of the above-mentioned NCBI reference sequence of the PDE4D transcript and also relates to, for example, SEQ ID NO:100 or SEQ ID NO:101 or SEQ ID NO:102 or SEQ ID NO:103 or SEQ ID NO:104 or SEQ ID NO:105 or SEQ ID NO:106 or SEQ ID NO:107 or SEQ ID The corresponding amino acid sequence described in NO:108 corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001098101, NCBI protein identification reference sequence NP_001336171, NCBI protein identification reference sequence NP_001184147, NCBI protein identification reference sequence NP_006194, NCBI protein identification reference sequence NP_001184150, NCBI protein identification reference sequence NP_001184149, NCBI protein identification reference sequence NP_001184152, NCBI protein identification reference sequence NP_001159371, and NCBI protein identification reference sequence NP_001184148 encoding the PDE4D polypeptide.

[0188] The term "PDE4D" also includes nucleotide sequences that show a high degree of homology to PDE4D, 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 shown in SEQ ID NO:91 or SEQ ID NO:92 or SEQ ID NO:93 or SEQ ID NO:94 or SEQ ID NO:95 or SEQ ID NO:96 or SEQ ID NO:97 or SEQ ID NO:98 or SEQ ID NO:99, or 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 shown in SEQ ID NO:100 or SEQ ID NO:101 or SEQ ID NO:102 or SEQ ID NO:103 or SEQ ID NO:104 or SEQ ID NO:105 or SEQ ID NO:106 or SEQ ID NO:107 or SEQ ID NO:108. : 107 or SEQ ID NO: 108, 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 shown in SEQ ID NO: 100 or SEQ ID NO: 101 or SEQ ID NO: 102 or SEQ ID NO: 103 or SEQ ID NO: 104 or SEQ ID NO: 105 or SEQ ID NO: 106 or SEQ ID NO: 107 or SEQ ID NO: 108, or 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 shown in SEQ ID NO: 91 or SEQ ID NO: 92 or SEQ ID NO: 93 or SEQ ID NO: 94 or SEQ ID NO: 95 or SEQ ID NO: 96 or SEQ ID NO: 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 shown in SEQ ID NO:97 or SEQ ID NO:98 or SEQ ID NO:99.

[0189] The term "PRKACA" relates to the protein kinase cAMP-activated catalytic subunit alpha gene (Ensembl: ENSG00000072062), for example, to the sequence defined in the NCBI reference sequence NM_002730 or the NCBI reference sequence NM_207518, specifically, to the nucleotide sequence shown in SEQ ID NO: 109 or SEQ ID NO: 110, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the PRKACA transcript, and also to the corresponding amino acid sequence shown in, for example, SEQ ID NO: 111 or SEQ ID NO: 112, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_002721 and the NCBI protein identification reference sequence NP_997401 encoding the PRKACA polypeptide.

[0190] The term "PRKACA" also includes nucleotide sequences that show a high degree of homology to PRKACA, 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 shown in SEQ ID NO: 109 or SEQ ID NO: 110, 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 shown in SEQ ID NO: 111 or SEQ ID NO: 112, or encodes 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 shown in SEQ ID NO: 111 or SEQ ID NO: 112. 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 shown in SEQ ID NO:109 or SEQ ID NO:110.

[0191] The term "PRKACB" relates to the protein kinase cAMP-activated catalytic subunit beta gene (Ensembl: ENSG00000142875), for example, to the sequence defined in NCBI reference sequence NM_002731 or NCBI reference sequence NM_182948 or NCBI reference sequence NM_001242860 or NCBI reference sequence NM_001242859 or NCBI reference sequence NM_001242858 or NCBI reference sequence NM_001242862 or NCBI reference sequence NM_001242861 or NCBI reference sequence NM_001300915 or NCBI reference sequence NM_207578 or NCBI reference sequence NM_001242857 or NCBI reference sequence NM_001300917, in particular to SEQ ID NO: 113 or SEQ ID NO: 114 or SEQ ID NO: 115 The nucleotide sequence described in SEQ ID NO: 115 or SEQ ID NO: 116 or SEQ ID NO: 117 or SEQ ID NO: 118 or SEQ ID NO: 119 or SEQ ID NO: 120 or SEQ ID NO: 121 or SEQ ID NO: 122 or SEQ ID NO: 123 corresponds to the sequence of the above-mentioned NCBI reference sequence of the PRKACB transcript and also relates to, for example, SEQ ID NO: 124 or SEQ ID NO: 125 or SEQ ID NO: 126 or SEQ ID NO: 127 or SEQ ID NO: 128 or SEQ ID NO: 129 or SEQ ID NO: 130 or SEQ ID NO: 131 or SEQ ID NO: 132 or SEQ ID NO: 133 or SEQ ID NO: The corresponding amino acid sequence described in NO:134 corresponds to the protein sequence defined in NCBI protein identification reference sequence NP_002722 and NCBI protein identification reference sequence NP_891993 and NCBI protein identification reference sequence NP_001229789 and NCBI protein identification reference sequence NP_001229788 and NCBI protein identification reference sequence NP_001229787 and NCBI protein identification reference sequence NP_001229791 and NCBI protein identification reference sequence NP_001229790 and NCBI protein identification reference sequence NP_001287844 and NCBI protein identification reference sequence NP_997461 and NCBI protein identification reference sequence NP_001229786 and NCBI protein identification reference sequence NP_001287846 encoding the PRKACB polypeptide.

[0192] The term "PRKACB" also includes nucleotide sequences that show a high degree of homology to PRKACB, 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 set forth in SEQ ID NO: 113 or SEQ ID NO: 114 or SEQ ID NO: 115 or SEQ ID NO: 116 or SEQ ID NO: 117 or SEQ ID NO: 118 or SEQ ID NO: 119 or SEQ ID NO: 120 or SEQ ID NO: 121 or SEQ ID NO: 122 or SEQ ID NO: 123, or 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 set forth in SEQ ID NO: 124 or SEQ ID NO: 125 or SEQ ID NO: 126 or SEQ ID NO: 127 or SEQ ID NO: 128 or SEQ ID NO: 129 or SEQ ID NO: 130 or SEQ ID NO: 131 or SEQ ID NO: 132 or SEQ ID NO: 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 set forth in SEQ ID NO:124 or SEQ ID NO:125 or SEQ ID NO:126 or SEQ ID NO:127 or SEQ ID NO:128 or SEQ ID NO:129 or SEQ ID NO:130 or SEQ ID NO:131 or SEQ ID NO:132 or SEQ ID NO:133 or SEQ ID NO:134, or 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 set forth in SEQ ID NO:113 or SEQ ID NO:114 or SEQ ID NO:115 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:115 or SEQ ID NO:116 or SEQ ID NO:117 or SEQ ID NO:118 or SEQ ID NO:119 or SEQ ID NO:120 or SEQ ID NO:121 or SEQ ID NO:122 or SEQ ID NO:123.

[0193] The term "PTPRC" relates to the protein tyrosine phosphatase receptor type C gene (Ensembl: ENSG00000081237), for example, to the sequence defined in the NCBI reference sequence NM_002838 or the NCBI reference sequence NM_080921, specifically, to the nucleotide sequence shown in SEQ ID NO: 135 or SEQ ID NO: 136, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the PTPRC transcript, and also to the corresponding amino acid sequence shown in, for example, SEQ ID NO: 137 or SEQ ID NO: 138, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_002829 and the NCBI protein identification reference sequence NP_563578 encoding the PTPRC polypeptide.

[0194] The term "PTPRC" also includes nucleotide sequences that show a high degree of homology to PTPRC, 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 shown in SEQ ID NO: 135 or SEQ ID NO: 136, 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 shown in SEQ ID NO: 137 or SEQ ID NO: 138, or encodes 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 shown in SEQ ID NO: 137 or SEQ ID NO: 138. 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 shown in SEQ ID NO:135 or SEQ ID NO:136.

[0195] The term "RAP1GAP2" relates to the human RAP1 GTPase activating protein 2 gene (ENSG00000132359), for example, to the sequence defined in the NCBI reference sequence NM_015085 or the NCBI reference sequence NM_001100398 or the NCBI reference sequence NM_001330058, specifically, to the nucleotide sequence shown in SEQ ID NO: 139 or SEQ ID NO: 140 or SEQ ID NO: 141, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the RAP1GAP2 transcript, and also to the corresponding amino acid sequence shown in, for example, SEQ ID NO: 142 or SEQ ID NO: 143 or SEQ ID NO: 144, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_055900 and the NCBI protein identification reference sequence NP_001093868 and the NCBI protein identification reference sequence NP_001316987 encoding the RAP1GAP2 polypeptide.

[0196] The term "RAP1GAP2" also includes nucleotide sequences that show a high degree of homology to RAP1GAP2, for example, at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 139 or 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 sequence shown in SEQ ID NO: 142 or SEQ ID NO: 143 or SEQ ID NO: 144, or encodes 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 shown in SEQ ID NO: 142 or SEQ ID NO: 143 or SEQ ID NO: 144. 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 the sequence shown in SEQ ID NO:139, SEQ ID NO:140 or SEQ ID NO:141.

[0197] The term "SLC39A11" relates to the human solute carrier family 39 member 11 gene (Ensembl: ENSG00000133195), for example, to the sequence defined in the NCBI reference sequence NM_139177 or the NCBI reference sequence NM_001352692, specifically, to the nucleotide sequence shown in SEQ ID NO: 145 or SEQ ID NO: 146, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the SLC39A11 transcript, and also to the corresponding amino acid sequence shown in, for example, SEQ ID NO: 147 or SEQ ID NO: 148, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_631916 and the NCBI protein identification reference sequence NP_001339621 encoding the SLC39A11 polypeptide.

[0198] The term "SLC39A11" also includes nucleotide sequences that show a high degree of 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 shown in SEQ ID NO: 145 or SEQ ID NO: 146, 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 shown in SEQ ID NO: 147 or SEQ ID NO: 148, or encodes 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 shown in SEQ ID NO: 147 or SEQ ID NO: 148. NO:148, 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 shown in SEQ ID NO:145 or SEQ ID NO:146.

[0199] The term "TDRD1" relates to the human Tudor domain-containing 1 gene (Ensembl: ENSG00000095627), for example, to the sequence defined in the NCBI reference sequence NM_198795, specifically, to the nucleotide sequence shown in SEQ ID NO:149, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the TDRD1 transcript, and also to the corresponding amino acid sequence shown in, for example, SEQ ID NO:150, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_942090 encoding the TDRD1 polypeptide.

[0200] The term "TDRD1" also includes nucleotide sequences that show a high degree of 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 shown 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 shown in SEQ ID NO:150, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO:150, or 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 shown in SEQ ID NO:150. 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 shown in NO:149.

[0201] The term "TLR8" relates to the Toll-like receptor 8 gene (Ensembl: ENSG00000101916), for example, to the sequence defined in the NCBI reference sequence NM_138636 or the NCBI reference sequence NM_016610, specifically, to the nucleotide sequence listed in SEQ ID NO: 151 or SEQ ID NO: 152, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the TLR8 transcript, and also to the corresponding amino acid sequence shown in, for example, SEQ ID NO: 153 or SEQ ID NO: 154, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_619542 and the NCBI protein identification reference sequence NP_057694 encoding the TLR8 polypeptide.

[0202] The term "TLR8" also includes nucleotide sequences that show a high degree of homology to TLR8, 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 shown in SEQ ID NO: 151 or SEQ ID NO: 152, 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 shown in SEQ ID NO: 153 or SEQ ID NO: 154, or encodes 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 shown in SEQ ID NO: 153 or SEQ ID NO: 154. 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 shown in SEQ ID NO:151 or SEQ ID NO:152.

[0203] The term "VWA2" relates to the human von Willebrand factor A domain-containing 2 gene (Ensembl: ENSG00000165816), for example, to the sequence defined in the NCBI reference sequence NM_001320804, specifically, to the nucleotide sequence shown in SEQ ID NO: 155, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the VWA2 transcript, and also to the corresponding amino acid sequence, for example, shown in SEQ ID NO: 156, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001307733 encoding the VWA2 polypeptide.

[0204] The term "VWA2" also includes nucleotide sequences that show a high degree of homology to VWA2, 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 shown in SEQ ID NO: 155, 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 shown in SEQ ID NO: 156, or a nucleic acid sequence that encodes 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 shown in SEQ ID NO: 156, or 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 shown in SEQ ID NO: 156. 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 shown in NO:155.

[0205] The term "ZAP70" relates to the zeta chain gene of T-cell receptor-associated protein kinase 70 (Ensembl: ENSG00000115085), for example, to the sequence defined in the NCBI reference sequence NM_001079 or the NCBI reference sequence NM_207519, specifically, to the nucleotide sequence shown in SEQ ID NO: 157 or SEQ ID NO: 158, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the ZAP70 transcript, and also to the corresponding amino acid sequence shown in, for example, SEQ ID NO: 159 or SEQ ID NO: 160, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_001070 and the NCBI protein identification reference sequence NP_997402 encoding the ZAP70 polypeptide.

[0206] The term "ZAP70" also includes nucleotide sequences that show a high degree of homology to ZAP70, 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 shown in SEQ ID NO: 157 or SEQ ID NO: 158, 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 shown in SEQ ID NO: 159 or SEQ ID NO: 160, or encodes 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 shown in SEQ ID NO: 159 or SEQ ID NO: 160. 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 shown in SEQ ID NO:157 or SEQ ID NO:158.

[0207] The term "ZBP1" relates to the Z-DNA binding protein 1 gene (Ensembl: ENSG00000124256), for example, to the sequence defined in the NCBI reference sequence NM_030776 or the NCBI reference sequence NM_001160418 or the NCBI reference sequence NM_001160419, specifically, to the nucleotide sequence shown in SEQ ID NO: 161 or SEQ ID NO: 162 or SEQ ID NO: 163, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the ZBP1 transcript, and also to the corresponding amino acid sequence shown in, for example, SEQ ID NO: 164 or SEQ ID NO: 165 or SEQ ID NO: 166, which corresponds to the protein sequence defined in the NCBI protein identification reference sequence NP_110403 and the NCBI protein identification reference sequence NP_001153890 and the NCBI protein identification reference sequence NP_001153891 encoding the ZBP1 polypeptide.

[0208] The term "ZBP1" also includes nucleotide sequences that show a high degree of homology to ZBP1, 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 shown in SEQ ID NO: 161 or SEQ ID NO: 162 or SEQ ID NO: 163, 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 shown in SEQ ID NO: 164 or SEQ ID NO: 165 or SEQ ID NO: 166, or encodes 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 shown in SEQ ID NO: 164 or SEQ ID NO: 165 or SEQ ID NO: 166. 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 shown in SEQ ID NO:161 or SEQ ID NO:162 or SEQ ID NO:163.

[0209] If according to Figures 4 to 6Derived, each individual sub-signature (meaning the immune defense gene set, T cell signaling gene set or PDE4D7-related genes) can be used to predict overall survival. Therefore, it is reasonable to think that predictions (e.g., for survival or treatment response) can be made based on three or more genes from a single sub-signature. Therefore, in one aspect, the present disclosure relates to a method for predicting the outcome of a bladder cancer subject, comprising: determining a gene expression profile comprising three or more gene expression levels (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or even 14 expression levels) or receiving the result of determining a gene expression profile comprising three or more gene expression levels (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or even 14 expression levels), wherein the three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or even 14 expression levels) are selected from the group consisting of: 14 expression levels) gene expression levels are selected from: immune defense response genes, which are selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, the gene expression profile is determined in a biological sample obtained from the subject, and a prediction of an outcome is determined based on the gene expression profile, wherein the prediction is a favorable outcome or an unfavorable outcome for the subject, and optionally, the prediction of the outcome is provided to a medical care provider or the subject.

[0210] Alternatively, the disclosure relates to a method of predicting an outcome in a subject with bladder cancer, comprising: determining a gene expression profile comprising three or more gene expression levels (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or even 17 expression levels) or receiving the results of determining a gene expression profile comprising three or more gene expression levels (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or even 17 expression levels), wherein the three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or even 17 expression levels) of a subject with bladder cancer. 15, 16 or even 17 expression levels) gene expression levels are selected from: T-cell receptor signaling genes, which are 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 gene expression profile is determined in a biological sample obtained from the subject, and a prediction of an outcome is determined based on the gene expression profile, wherein the prediction is a favorable outcome or an unfavorable outcome for the subject, and optionally, the prediction of the outcome is provided to a medical care provider or the subject.

[0211] Alternatively, the present disclosure relates to a method of predicting an outcome in a subject with bladder cancer, comprising: determining a gene expression profile comprising three or more gene expression levels (e.g., 3, 4, 5, 6, 7, or even 8 expression levels) or receiving the results of determining a gene expression profile comprising three or more gene expression levels (e.g., 3, 4, 5, 6, 7, or even 8 expression levels), wherein the three or more (e.g., 3, 4, 5, 6, 7, or even 8 expression levels) gene expression levels are selected from: PDE4D7-related genes selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, the gene expression profile is determined in a biological sample obtained from the subject, determining a prediction of an outcome based on the gene expression profile, wherein the prediction is of a favorable outcome or an unfavorable outcome for the subject, and optionally, providing the prediction of the outcome to a medical care provider or the subject.

[0212] In a preferred aspect, the gene expression profile includes at least one gene from each of an immune defense response gene, a T-cell receptor signaling gene, and a PDE4D7-related gene. Thus, in an embodiment, the three or more genes include one or more (e.g., one, two, three, four, five, six or more) immune defense response genes, one or more (e.g., one, two, three, four, five, six or more) T-cell receptor signaling genes, and one or more (e.g., one, two, three, four, five, six or more) PDE4D7-related genes. For example, the three or more genes include at least one immune defense response gene, a T-cell receptor signaling gene, and a PDE4D7-related gene, or the three or more genes include at least two immune defense response genes, a T-cell receptor signaling gene, and a PDE4D7-related gene, or the three or more genes include at least three immune defense response genes, a T-cell receptor signaling gene, and a PDE4D7-related gene. In embodiments, the one or more immune defense response genes include three or more, preferably six or more, more preferably nine or more, most preferably all immune defense genes, and / or the 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 the one or more PDE4D7-related genes include three or more, preferably six or more, most preferably all PDE4D7-related genes. In embodiments, determining the prediction of the outcome comprises combining the three or more gene expression levels with a regression function derived from a population of bladder cancer subjects.

[0213] As provided herein, the biological sample used can be collected in a clinically acceptable manner, for example, in a manner that preserves nucleic acids (particularly RNA) or proteins.

[0214] (One or more) biological samples can include body tissue and / or fluid, such as but not limited to blood, sweat, saliva and urine. In addition, biological samples can include cell extracts derived from epithelial cells or cell groups including epithelial cells, such as cancerous epithelial cells or epithelial cells derived from suspected cancerous tissues. Biological samples can include cell groups derived from tissues (such as glandular tissues), for example, the sample can be derived from the bladder of the object. In addition, if necessary, cells can be purified from the body tissues and fluids obtained and then used as biological samples. In some implementations, the sample can be a tissue sample, a fluid sample, a blood sample, a saliva sample, a sample including circulating tumor cells, an extracellular vesicle, a sample containing an exosome secreted by the bladder, or a cell line or cancer cell line. In a specific implementation, a biopsy or excision sample can be obtained and / or used. This sample can include cells or cell lysates.

[0215] Thus, in an embodiment, the biological sample obtained from the subject is a biopsy. In another preferred embodiment, the method comprises providing or obtaining a biopsy. In a preferred embodiment, the biopsy is a bladder biopsy, such as tissue or fluid from the bladder.

[0216] It is also conceivable that the contents of the biological sample are submitted to an enrichment step. For example, the sample can be contacted with a ligand specific for the cell membrane or organelles of certain cell types (e.g., bladder cells) functionalized using, for example, magnetic particles. The material concentrated by the magnetic particles can then be used for detection and analysis steps as described above or below in this article.

[0217] Furthermore, cells (eg, tumor cells) can be enriched via a filtration process of a fluid or liquid sample (eg, blood, urine, etc.) Such a filtration process can also be combined with an enrichment step based on ligand-specific interactions as described above.

[0218] Preferably, the biological sample provided herein is obtained from a subject before the start of treatment. Also preferably, the biological sample is a bladder sample or a bladder cancer sample.

[0219] Therefore, in a preferred embodiment, the method according to the present invention comprises obtaining a biological sample from a subject before the start of treatment, preferably wherein the biological sample is a bladder sample or a bladder cancer sample. Alternatively, the method according to the present invention comprises providing a biological sample obtained from a subject before the start of treatment, preferably wherein the biological sample is a bladder sample or a bladder cancer sample.

[0220] It is contemplated herein that the outcome of a bladder cancer subject may be a favorable outcome or an unfavorable outcome. In one aspect of the invention, the prediction of the outcome of a bladder cancer subject may result in determining a favorable risk or unfavorable risk of one or more outcomes. The outcome may include overall death or overall survival, time until disease progression, death associated with bladder cancer, local regional recurrence, and / or distant recurrence. Preferably, the death associated with bladder cancer is a bladder cancer-specific death. The prediction provided by the method of the present invention may provide a prediction of the risk of a bladder cancer subject for a certain outcome. In addition, the method of the present invention may predict whether a subject with bladder cancer has a low risk of a particular outcome or a high risk of a particular outcome. As used herein, the outcome of bladder cancer-related death, local regional recurrence, and / or distant recurrence includes an outcome that is unfavorable to the subject. Another outcome is overall death. As used herein, overall death is an outcome that can be predicted by the method of the present invention, but it cannot be directly associated with the death of a subject due to bladder cancer. Therefore, in an embodiment, the method provides a prediction of a favorable or unfavorable outcome, wherein the favorable or unfavorable outcome is a probability of survival, overall survival, cancer-free survival, overall death, or cancer-specific death.

[0221] The present invention is particularly intended to predict the favorable or unfavorable results of immunotherapy for patients with bladder cancer. In particular, the present invention is intended to identify favorable or unfavorable results for patients with metastatic bladder cancer or high-grade NMIBC. In the case of predicting favorable results, immunotherapy is administered or recommended. In the case of predicting unfavorable results, alternative treatments can be recommended or administered. Such alternative treatments can be a combination of radiotherapy, chemotherapy, tumor resection / surgery, immunotherapy (such as immune checkpoint inhibitor therapy) and chemotherapy or radiotherapy, or experimental therapy, such as CAR-T cell therapy, virotherapy or RNA vaccine-based therapy.

[0222] Therefore, in an embodiment, the present invention relates to a method of treatment, comprising:

[0223] a) predicting the outcome of a subject with bladder cancer, comprising:

[0224] i. determining a gene expression profile comprising gene expression levels or receiving a result of determining a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group consisting of:

[0225] ii. a T cell receptor signaling 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

[0226] iii. an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, wherein the gene expression profile is determined in a biological sample obtained from the subject,

[0227] iv. determining a prediction of the outcome based on the gene expression profile, wherein the prediction is an outcome for the subject, wherein the outcome is a predicted survival time in response to a treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy, and

[0228] b) administering said immunotherapy to said subject when a favorable outcome is predicted.

[0229] In an embodiment, the method further includes when an unfavorable result is predicted, a combination of a therapy selected from radiotherapy, chemotherapy, tumor resection / surgery, immunotherapy (e.g., immune checkpoint inhibitor therapy) and chemotherapy or radiotherapy, or an alternative therapy for experimental therapy such as CAR-T cell therapy, virotherapy, or a therapy based on RNA vaccine is administered. In an embodiment, a favorable result is determined by the above-mentioned average predicted survival time or the time until disease progression. In an embodiment, an unfavorable result is determined by being lower than the average predicted survival time or the time until disease progression. It should be understood that the average predicted survival time can be determined for bladder cancer patients treated with immunotherapy, and responders favorable to immunotherapy can be defined as those patients whose predicted survival time is higher than the average value of the pool of responders and non-responders. Alternatively, the response data to immunotherapy can be related to gene expression data to set a reference value to determine the favorable or unfavorable response in the above method.

[0230] Preferably, the method of the present disclosure provided includes predicting the outcome of a bladder cancer subject, the prediction being a favorable risk or unfavorable risk of bladder cancer-related death, local regional recurrence, and / or distal recurrence after surgery. In other words, surgery is performed on a bladder cancer subject before predicting the risk of a specific outcome. In addition, the method of the present invention provided includes predicting the outcome of a bladder cancer subject, the prediction being a favorable risk or unfavorable risk of bladder cancer-related death, local regional recurrence, and / or distal recurrence after treatment. For example, the treatment may be an immunotherapy, such as BCG therapy, or a treatment using an immune checkpoint inhibitor. Therefore, in an embodiment, the method provides a prediction of a favorable or unfavorable outcome, wherein the favorable or unfavorable outcome is survival in response to treatment. In another embodiment, the treatment is surgery or immunotherapy, preferably wherein the immunotherapy is Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

[0231] As predicted by the methods of the present invention, bladder cancer subjects with a high risk (i.e., above a certain threshold) for one or more of the outcomes of bladder cancer-related death, locoregional recurrence, and / or distant recurrence are expected to be associated with an unfavorable risk of the corresponding outcome, preferably after surgery.

[0232] As predicted by the methods of the present invention, a bladder cancer subject having a low risk (i.e., less than or equal to a certain threshold) for one or more of the outcomes of bladder cancer-related death, locoregional recurrence, and / or distant recurrence is expected to be associated with a favorable risk of the corresponding outcome, preferably after surgery. A favorable risk may include a follow-up strategy that is different from the subject's unfavorable risk, such as a different recommended (follow-up) therapy. For example, a different follow-up strategy, such as a different recommended (follow-up) therapy, may be considered for a bladder cancer subject (preferably, a subject who has undergone bladder (cancer) surgery) to improve the bladder cancer subject's chance of survival.

[0233] Preferably, treatment comprises surgery, radiotherapy, hormone therapy, cytotoxic chemotherapy and / or immunotherapy.Combination therapy in cancer (i.e. therapy combining two or more therapeutic approaches and / or agents, such as combining radiotherapy and chemotherapy) is widely considered to be the cornerstone of cancer treatment.

[0234] Therefore, in a preferred embodiment, the method according to the present invention comprises obtaining a biological sample, preferably a sample of the subject's bladder or a sample of the subject's bladder cancer, prior to starting therapy (including surgery, radiotherapy, hormone therapy, cytotoxic chemotherapy and / or immunotherapy).

[0235] As disclosed herein, an adverse risk of outcome of a broadly defined method for predicting bladder cancer outcome is generally considered to influence the recommended treatment of the bladder cancer subject associated with the adverse risk. In the event that an adverse risk is predicted by the methods of the present invention, the recommended treatment is expected to include one or more of the following:

[0236] (i) providing radiation therapy earlier than is standard; and

[0237] (ii) radiation therapy with increasing radiation doses; and

[0238] (iii) adjuvant therapy, such as cytotoxic chemotherapy, immunotherapy and / or hormonal therapy; and

[0239] (iv) surgery; and

[0240] (v)It is not an alternative to surgery.

[0241] In a preferred aspect, the method according to the present disclosure comprises recommending a treatment based on a prediction, preferably based on a prediction of an outcome, wherein:

[0242] - If the prognosis is unfavorable, the recommended treatment (preferably after surgery) includes one or more of the following:

[0243] (i) providing radiation therapy earlier than is standard; and

[0244] (ii) radiation therapy with increasing radiation doses; and

[0245] (iii) adjuvant therapy, such as cytotoxic chemotherapy, immunotherapy and / or hormonal therapy; and

[0246] (iv) surgery; and

[0247] (v)It is not an alternative to surgery.

[0248] The favorable risk of the expected outcome affects the recommended therapy for the bladder cancer subject associated with the favorable risk. In the event that a favorable risk is predicted by the method of the present disclosure, the expected recommended therapy includes one or more of the following:

[0249] (vi) aggressive radiation therapy; and

[0250] (vii) salvage radiation therapy; and

[0251] (viii) salvage radiation therapy at a reduced dose level; and

[0252] (ix) Watch and wait.

[0253] Therefore, another preferred aspect of the method according to the present disclosure comprises recommending treatment based on the prediction, wherein:

[0254] - If the prognosis is favorable, recommended treatment (preferably after surgery) includes one or more of the following:

[0255] (vi) aggressive radiation therapy; and

[0256] (vii) salvage radiation therapy; and

[0257] (viii) salvage radiation therapy at a reduced dose level; and

[0258] (ix) Watch and wait.

[0259] In an embodiment of the invention, a favorable risk or an unfavorable risk of outcome is predicted for a bladder cancer subject who has undergone surgery, preferably surgery on the bladder, such as but not limited to a lumpectomy, a quadrantectomy, a partial mastectomy, a segmental mastectomy, or a complete mastectomy.

[0260] In another aspect of the invention, there is provided a method according to the invention comprising recommending a secondary treatment for a specific patient, preferably for a patient with a low risk or a high risk of experiencing an outcome selected from bladder cancer specific death or overall death, wherein the recommendation is based on a prediction of an outcome for the bladder cancer patient, wherein:

[0261] - if the prognosis is favourable, no secondary treatment is recommended; and / or

[0262] - If the prognosis is unfavorable, secondary treatment is recommended.

[0263] It is provided herein that by means of the method according to the invention, the effectiveness of secondary treatment can be predicted for patients with a low risk or high risk profile after surgery. Preferably, the secondary treatment comprises one or more, more preferably all, of chemotherapy, hormone therapy and radiotherapy.

[0264] The methods disclosed herein are based on the expression level, e.g., expression profile, of at least one gene selected from immune defense response genes and / or T cell receptor signaling genes and / or PDE4D7-related genes, wherein the immune defense response genes are selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the T cell receptor The signaling gene is 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 PDE4D7-related gene is selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2. It should be understood that the method can be performed on inputs related to the expression level of one or more genes, or determining the expression level can be part of the method.

[0265] It is also envisaged that the method is performed by a processor. Thus, in an embodiment, the present invention relates to a computer-implemented method of predicting the outcome of a bladder cancer subject.

[0266] Preferably, the method for predicting an outcome in a subject with bladder cancer comprises determining the following gene expression profile(s): one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all) 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, and / or one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all) 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 / or one or more (e.g., 1, 2, 3, 4, 5, 6, 7 or all) PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0267] In another aspect, the method for predicting an outcome in a subject with bladder cancer comprises determining the following gene expression profile(s): two or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all) 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, and / or two or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all) 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. , 14, 15, 16 or all) 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 / or two or more (e.g., 1, 2, 3, 4, 5, 6, 7 or all) PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0268] In a preferred aspect, the method according to the present disclosure comprises:

[0269] - the one or more immune defense response genes include three or more, preferably six or more, more preferably nine or more, most preferably all immune defense genes, and / or

[0270] - the 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

[0271] - The one or more PDE4D7-related genes include three or more, preferably six or more, most preferably all PDE4D7-related genes.

[0272] Therefore, preferably, determining the first gene expression profile, the second gene expression profile and / or the third gene expression profile according to the methods of the present disclosure comprises determining or receiving the results of determining:

[0273] - three or more, preferably six or more, more preferably nine or more, and most preferably all genes selected from immune defense response genes, wherein the immune defense response genes are selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1,

[0274] - three or more, preferably six or more, more preferably nine or more, most preferably all genes selected from T-cell receptor signaling genes, wherein the T-cell receptor signaling genes are 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

[0275] - Three or more, preferably six or more, more preferably nine or more, most preferably all genes of PDE4D7-related genes, wherein the PDE4D7-related genes are selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0276] On the other hand, the method according to the present disclosure comprises determining a prediction of an outcome based on one or more immune defense response genes, one or more T-cell receptor signaling genes, and one or more PDE4D7-related genes. On the other hand, the method according to the present disclosure comprises determining a prediction of an outcome based on two or more immune defense response genes, two or more T-cell receptor signaling genes, and two or more PDE4D7-related genes. On the other hand, the method according to the present disclosure comprises determining a prediction of an outcome based on three or more immune defense response genes, three or more T-cell receptor signaling genes, and three or more PDE4D7-related genes.

[0277] As a first reference example, the (first) gene expression profile as disclosed herein may include at least one, at least two, at least three immune defense response genes, and the immune defense response genes are selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1. In a non-limiting example, the gene expression profile as embodied herein includes the expression profiles of genes DDX58, DHX9 and IFI16.

[0278] As another reference example, the (second) gene expression profile as disclosed herein may include at least one, at least two, at least three 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. In a non-limiting example, the gene expression profile as embodied herein includes the expression profiles of genes PRKACA, PRKACB and PTPRC.

[0279] As another reference example, the (third) gene expression profile as disclosed herein may include at least one, at least two, at least three PDE4D7-related genes selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2. In a non-limiting example, the gene expression profile as embodied herein includes the expression profiles of genes KIAA1549, PDE4D and RAP1GAP2.

[0280] As another non-limiting reference example, the gene expression profile disclosed herein may include a first gene expression profile, a second gene expression profile, and a third gene expression profile, wherein the first gene expression profile includes at least one, at least two, or at least three immune defense response genes, and the immune defense response genes are selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and the second gene expression profile includes at least one, at least two, or at least three T-cell receptor signaling genes. The first gene expression profile comprises at least one, at least two, at least three PDE4D7-related genes, the PDE4D7-related genes are selected from the group consisting of: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0281] In another non-limiting reference example disclosed herein, the gene expression profile for predicting the outcome of bladder cancer may consist of the gene expression profiles of TLR8 (IDR gene), CD2 (TCR gene) and PDE4D (PDE4D7-related gene). In another example, the gene expression profile may consist of the gene expression profiles of OAS1 and TLR8 (IDR gene), CD2 (TCR gene) and PDE4D (PDE4D7-related gene), or the gene expression profiles of TLR8 (IDR gene), CD2 and PTPRC (TCR gene) and PDE4D (PDE4D7-related gene), or the gene expression profiles of TLR8 (IDR gene), CD2 (TCR gene) and CUX2 and PDE4D (PDE4D7-related gene).

[0282] Cox proportional hazards regression allows the analysis of the effect of time on several risk factors for a test event (e.g., survival). Thus, risk factors may be dichotomous or discrete variables, such as risk scores or clinical stages, but may 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 hazard. Following information about, for example, whether a subject in a patient group reached a test endpoint (e.g., whether the patient died or not), the time to the endpoint is also taken into account in the regression analysis. Hazard is modeled as: H(t) = H 0 (t)·exp(w 1 ·V 1 +w 2 ·V 2 +w 3 ·V 3 +…), where V 1 ,V 2 ,V 3 …are predictor variables, and H 0 H(t) is the baseline hazard, and H(t) is the hazard at any time t. The hazard ratio (HR) (or risk of reaching an event) is given by Ln[H(t) / H 0 (t)]=w 1 ·V 1 +w 2 ·V 2 +w 3 ·V 3 +… means, where the coefficient or weight w 1 ,w 2 ,w 3 …estimated by Cox regression analysis and can be interpreted in a similar manner to logistic regression analysis.

[0283] Therefore, in a preferred embodiment, the method according to the present invention comprises determining a prediction of an outcome comprising combining the combination of the first gene expression profile, the combination of the second gene expression profile and the combination of the third gene expression profile with a regression function derived from a population of bladder cancer subjects.

[0284] In another preferred embodiment, the method according to the invention comprises determining a prediction of an outcome comprising:

[0285] - combining a first gene expression profile for two or more (e.g. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all) of the immune defence response genes with a regression function that has been derived from a population of bladder cancer subjects, and / or

[0286] combining a second gene expression profile for two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all) of the T-cell receptor signaling genes with a regression function that has been derived from a population of bladder cancer subjects, and / or

[0287] - combining a third gene expression profile for two or more (eg 2, 3, 4, 5, 6, 7 or all) of the PDE4D7-related genes with a regression function that has been derived from a population of bladder cancer subjects.

[0288] In one particular implementation, the prediction of the outcome is determined as follows:

[0289] IDR_14_Model: (1)

[0290] (w 1 ·AIM2)+(w 2 ·APOBEC3A)+(w 3 ·CIAO1)+[…]+( w14 ·ZBP1)

[0291] Among them, w 1 tow 14 is the weight, and AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 are the expression levels of the genes.

[0292] In another specific implementation, the prediction of the outcome is determined as follows:

[0293] TCR_17_Model: (2)

[0294] (w 15 ·CD2)+(w 16·CD247)+(w 17 ·CD28)+[…]+(w 31 ·ZAP70)

[0295] Among them, w 15 tow 31 is the weight, and CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 are the expression levels of the genes.

[0296] In another specific implementation, the prediction of the outcome is determined as follows:

[0297] PDE4D7_CORR_MODEL: (3)

[0298] (w 32 ABCC5)+(w 33 ·CUX2)+(w 34 ·KIAA1549)+[…]+( w39 VWA2)

[0299] Among them, w 32 tow 39 is the weight, and ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 are the expression levels of the genes.

[0300] In another specific implementation, the prediction of the outcome is determined as follows:

[0301] BCAI_Model(4)

[0302] (w 42 ·PDE4D7_CORR)+(w 40 ·IDR_14)+(w 41 ·TCR_17)

[0303] In another implementation, the prediction of the outcome is determined as follows:

[0304] BCAI_Model(5)

[0305] (w 1 ·AIM2)+(w 2 ·APOBEC3A)+(w 3 ·CIAO1)+[…]+( w14 ·ZBP1)+(w 15 ·CD2)+(w 16 ·CD247)+(w 17·CD28)+[…]+(w 31 ·ZAP70)+(w 32 ABCC5)+(w 33 ·CUX2)+(w34·KIAA1549)+[…]+( w39 VWA2),

[0306] Among them, w 1 tow 39 are the weights, and 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 are the expression levels of the genes.

[0307] The prediction of the outcome may also be classified or categorized into one of at least two risk groups based on the predicted value of the outcome. For example, there may be two risk groups, or three risk groups, or four risk groups, or more than four predefined risk groups.

[0308] Each risk group covers a corresponding range of predicted (non-overlapping) values ​​of the outcome. For example, a risk group may indicate the probability of occurrence of a specific clinical event from 0 to 0.1 or from 0.1 to 0.25 or from 0.25 to 0.5 or from 0.5 to 1.0, etc.

[0309] In a preferred embodiment, the method according to the present invention comprises determining the prediction of the result based on one or more clinical parameters obtained from the object. As mentioned above, various measurements based on one or more clinical parameters have been studied. By predicting the result based on such (one or more) clinical parameters, the prediction can be further improved.

[0310] In a preferred embodiment, the one or more clinical parameters include at least the number of tumor-positive lymph nodes. More preferably, the clinical parameter is the number of tumor-positive lymph nodes.

[0311] In embodiments, determining a prediction of therapy response comprises combining gene expression levels for one or more IDR genes, one or more TCR signaling genes, and / or one or more PDE4D7-related genes with a regression function that has been derived from a population of bladder cancer subjects.

[0312] It is further preferred that the gene expression profile for one or more IDR genes, one or more TCR signaling genes and / or one or more PDE4D7-related genes and one or more clinical parameters obtained from the subject is combined with a regression function that has been derived from a population of bladder cancer subjects. Thus, in an embodiment, the prediction of the determined outcome is also based on one or more clinical parameters obtained from the subject.

[0313] It is further preferred that the one or more clinical parameters are combined with one or more of the first gene expression, the second gene expression and the third gene expression using a regression function, the regression function being derived from a population of bladder cancer subjects, so as to provide a method for predicting an outcome in a bladder cancer subject. Thus, in an embodiment, determination of an outcome comprises combining the gene expression profile and the one or more clinical parameters obtained from the subject with a regression function that has been derived from a population of bladder cancer subjects.

[0314] Therefore, in a preferred embodiment of the method according to the invention, the determination of the result comprises combining one or more of the following:

[0315] (i) a first gene expression profile(s) for one or more immune defense response genes;

[0316] (ii) a second gene expression profile(s) for one or more T-cell receptor signaling genes;

[0317] (iii) a third gene expression profile(s) for one or more PDE4D7-related genes; and

[0318] (iv) combining the first gene expression profile, the second gene expression profile, the third gene expression profile and one or more clinical parameters obtained from the subject with a regression function that has been derived from a population of bladder cancer subjects.

[0319] In another specific implementation, the prediction of the outcome is determined as follows:

[0320] BCAI_Clinical Model (6)

[0321] (w 43 BCAI_model)+(w 44 LN_positive)

[0322] Among them, w 43 and w 44is a weight, and BCAI_model is the above regression model based on the expression profile of one or more IDR genes, one or more TCR signaling genes and / or one or more PDE4D7-related genes, and LN_positive represents the number of tumor-positive lymph nodes. Multivariate Cox regression is used to create a clinical model that combines BCAI with clinical data (number of positive lymph nodes).

[0323] An example of a suitable clinical parameter as used and illustrated herein is "LN_Positive", which represents the number of tumor-positive lymph nodes after postoperative pathology. LN-positive bladder tumors are tumors of the bladder in which tumor cells have spread (i.e., metastasized) to nearby lymph nodes. LN-positive tumors are expected to be a precursor to subclinical metastasis of cancer.

[0324] Other examples of clinical parameters are EORTC score, number of tumor-positive regional lymph nodes, metastatic disease status, ECOG score, and FoundationOne mutation load per MB DNA. Thus, in an embodiment, one or more clinical parameters include one or more of the following: EORTC score, number of tumor-positive regional lymph nodes, metastatic disease status, ECOG score, and FoundationOne mutation load per MB DNA, preferably wherein one or more clinical parameters consist of EORTC score, number of tumor-positive regional lymph nodes, or consist of metastatic disease status, ECOG score, and optionally FoundationOne mutation load per MB DNA. In a particularly preferred embodiment, the clinical parameter is the EORTC score.

[0325] When used herein, the EORTC score (he 2006 European Organisation for Research and Treatment of Cancer (EORTC) scoring model) is described in Sylvester, RJ et al., Eur Urol, 2006, 49: 466 (the entire contents of which are incorporated herein by reference). The score uses a scoring system and risk table based on the WHO 1973 classification in 2006 to predict short-term and long-term recurrence risk. The scoring system is based on the 6 most significant clinical and pathological factors in patients treated primarily with intravesical chemotherapy: tumor number; tumor diameter; previous recurrence rate; T category; concurrent CIS; WHO 1973 tumor grade. The score can be calculated as described below: https: / / www.omnicalculator.com / health / eortc-bladder-cancer.

[0326] As used herein, the number of tumor-positive regional lymph nodes reflects the score at which lymph nodes in the bladder region were examined as having tumor, wherein a score of 0 reflects no tumor-positive regional lymph nodes, and any integer above 0 reflects the number of tumor-positive lymph nodes identified. Typically, lymph nodes with only isolated tumor cells (ITCs) are not counted as positive lymph nodes, and only lymph nodes with metastases greater than 0.2 mm (micrometastases or larger) are counted as positive.

[0327] When used in this article, the term metastatic disease status refers to the tumor stage in categories I-IV that clinicians generally use. Typically, the stages are classified as follows: Stage I: The cancer is in a small area and has not spread to lymph nodes or other tissues. Stage II: The cancer has grown, but it has not spread. Stage III: The cancer has grown larger and may have spread to lymph nodes or other tissues. Stage IV: The cancer has spread to other organs or areas of your body.

[0328] When used in this article, the term ECOG score is understood to have its usual meaning. The ECOG score describes the patient's functional level in terms of his or her ability to care for himself or herself, daily activities, and physical ability (walking, working, etc.). The scale was developed by the Eastern Cooperative Oncology Group (ECOG) and published in 1982; it is also known as the WHO or Zubrod score and ranges from 0 to 5, where 0 means perfect health and 5 means death. The score is assigned as:

[0329] 0 - Asymptomatic. Fully active, able to carry out all pre-disease activities without restriction.

[0330] 1-Symptomatic but fully ambulatory. Limited to physically strenuous activities but ambulatory and able to perform work of a light or sedentary nature (e.g., light home work, office work).

[0331] 2-Symptomatic, in bed <50% of the day. Ambulatory and able to perform all self-care but unable to perform any work activities. Awake and active more than 50% of waking hours.

[0332] 3-Symptomatic, spends >50% of the day in bed but is not bedridden. Capable of limited self-care, confined to bed or chair for 50% or more of waking time.

[0333] 4-Bedridden. Totally disabled. Unable to perform any self-care. Totally confined to bed or chair.

[0334] 5-Death.

[0335] When used in this article, the term FoundationOne mutational burden per MB of DNA is defined as the number of somatic mutations per megabase of genomic sequence interrogated and is determined using the FoundationOne CDx Next Generation Sequencing Panel.

[0336] It will be appreciated that for NMIBC or MIBC, the clinical parameter used preferably includes or is the number of tumor-positive regional lymph nodes. In metastatic bladder cancer, the clinical parameter includes or is preferably the EORTC score, metastatic disease status, ECOG score, optionally supplemented with FoundationOne mutational burden per MB DNA.

[0337] The method according to the present invention can be implemented in a computer program product running on a computer. The computer program product may include a non-transitory computer-readable recording medium, such as a disk, a hard disk drive, etc., on which the control program is recorded (stored). Common forms of non-transitory computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, a magnetic tape or any other magnetic storage medium, a CD-ROM, a DVD or any other optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM or other memory chip or a cassette, or any other non-transitory medium from which a computer can read and use.

[0338] Therefore, in a preferred embodiment, the present invention also provides a computer program product comprising instructions, which, when the program is executed by a computer, cause the computer to perform a method, the method comprising receiving data indicating a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group 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, and / or, immune defense A bladder cancer response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, wherein the gene expression profile is determined in a biological sample obtained from a bladder cancer subject, and a prediction of an outcome of the subject is determined based on the gene expression profile, wherein the prediction is a favorable outcome or an unfavorable outcome, wherein the favorable outcome or unfavorable outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy. Optionally, the program product also includes instructions for providing a prediction of the outcome to a medical caregiver or subject.

[0339] The present invention also discloses a computer program product comprising instructions, which, when executed by a computer, cause the computer to perform a method comprising:

[0340] - receiving data indicative of a gene expression profile comprising three or more (e.g., 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, 38 or even 39) gene expression levels, wherein the three or more gene expression levels are selected from: an immune defense response gene selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or 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 / or PDE4D7-related genes 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 a prediction of the outcome is determined based on the gene expression profile, wherein the prediction is a favorable outcome or an unfavorable outcome for the subject. Optionally, the program product also includes instructions for providing the prediction of the outcome to a medical caregiver or the subject.

[0341] Alternatively, one or more steps of the method may be implemented in a transient medium, such as a transmissible carrier wave, wherein the control program is embodied as a data signal using a transmission medium, such as acoustic or light waves, such as those generated during radio wave and infrared data communications, etc.

[0342] The exemplary method may be implemented on one or more general purpose computers, special purpose computers, programmed microprocessors or microcontrollers and peripheral integrated circuit components, ASICs or other integrated circuits, digital signal processors, hardwired electronic or logic circuits (such as discrete component circuits), programmable logic devices (such as PLDs, PLAs, FPGAs, graphics card CPUs (GPUs) or PALs), etc. In general, any device capable of implementing a finite state machine (which in turn is capable of implementing the steps described herein) may be used to implement one or more steps of the risk stratification method for treatment selection in patients with bladder cancer. It should be understood that although the steps of the method may be entirely computer-implemented, in some embodiments, one or more steps may be at least partially performed manually.

[0343] It is herein preferred that the computer program product according to the present invention may be implemented on an apparatus for predicting an outcome in a bladder cancer subject, wherein the apparatus comprises an input adapted to receive data indicative of a gene expression profile(s) of immune defence response genes and / or T-cell receptor signalling genes and / or PDE4D7 related genes, and wherein the apparatus further comprises a processor adapted to determine a prediction of an outcome based on the one or more gene expression profiles, and

[0344] - Optionally, a providing unit adapted to provide said prediction to a medical caregiver or said subject or to provide a therapy suggestion based on said selection.

[0345] In another aspect of the invention, there is provided an apparatus for predicting an outcome in a subject with bladder cancer, comprising: an input adapted to receive data indicative of a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group consisting of: T cell receptor signaling 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 immune defense response genes, wherein selected from the group consisting of: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, the gene expression profile being determined in a biological sample obtained from a subject with bladder cancer; a processor adapted to determine a prediction of an outcome for the subject based on the gene expression profile, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy, and

[0346] - Optionally, a providing unit adapted to provide said prediction or a therapy suggestion based on said prediction to a medical caregiver or to said subject.

[0347] Also disclosed herein is an apparatus for predicting an outcome in a subject with bladder cancer, comprising: an input adapted to receive data indicative of a gene expression profile comprising three or more (e.g., 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, 38 or even 39) gene expression levels, wherein the three or more gene expression levels are selected from:

[0348] - an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or

[0349] - a T-cell receptor signaling 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

[0350] - a PDE4D7-related gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2,

[0351] The gene expression profile is determined in a biological sample obtained from a subject,

[0352] - a processor adapted to determine a prediction of said radiotherapy response based on the gene expression profile(s) for said three or more genes, and

[0353] - Optionally, a providing unit adapted to provide said prediction or a therapy suggestion based on said prediction to a medical caregiver or to said subject.

[0354] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions running on the computer or other programmable apparatus provide a process for implementing the functions / actions specified herein.

[0355] Therefore, in a preferred embodiment, the present invention also provides a diagnostic kit comprising at least one polymerase chain reaction primer and optionally at least one probe for determining the gene expression profile (one or more) in a biological sample and / or a sample obtained from a bladder cancer subject.

[0356] Preferably, the diagnostic kit provided herein comprises at least one of the following: a polymerase chain reaction primer or probe for determining a gene expression profile comprising three or more expression levels in a biological sample and / or in a sample obtained from a bladder cancer subject, wherein the three or more gene expression levels are selected from:

[0357] - an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or

[0358] - a T-cell receptor signaling 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

[0359] - A PDE4D7-related gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2. The kit may be a PCR kit, a quantitative PCR kit, an RNA sequencing kit, a targeted RNA sequencing kit, a sequence analysis of gene expression (SAGE) kit, a DNA microarray or a Tiling array.

[0360] In another preferred embodiment, the present invention provides the use of a diagnostic kit as broadly embodied herein in a method for predicting the outcome of a subject with bladder cancer, preferably in a method for predicting the outcome of a subject with bladder cancer as broadly embodied herein. Thus, in one embodiment, the present invention relates to the use of a diagnostic kit comprising:

[0361] - at least one of a polymerase chain reaction primer or a probe for determining a gene expression profile comprising three or more expression levels in a biological sample and / or in a sample obtained from a subject with bladder cancer, wherein the three or more gene expression levels are selected from:

[0362] - an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or

[0363] - a T-cell receptor signaling 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

[0364] - a PDE4D7-related gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2,

[0365] The use comprises predicting an outcome in a subject with bladder cancer.Preferably, the use comprises using the kit in a method for predicting an outcome as broadly defined herein.

[0366] In another preferred embodiment, the present invention provides a method comprising:

[0367] - receiving a biological sample obtained from a subject with bladder cancer,

[0368] - using a diagnostic kit as broadly embodied herein to determine a gene expression profile comprising the expression levels of genes selected from the group 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, and / or immune defense response genes selected from the group consisting of: AIM2, APOB EC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, wherein the gene expression profile is determined in a biological sample obtained from a bladder cancer subject, and optionally determining a prediction of an outcome for the subject based on the gene expression profile, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

[0369] This article also discloses a method, comprising:

[0370] - receiving a biological sample obtained from a subject with bladder cancer,

[0371] - using a diagnostic kit as broadly embodied herein to determine a gene expression profile comprising three or more expression levels in a biological sample and / or in a sample obtained from a subject with bladder cancer, wherein the three or more gene expression levels are selected from the group consisting of:

[0372] - an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or

[0373] - a T-cell receptor signaling 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

[0374] - A PDE4D7-related gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0375] In another aspect, the present invention provides Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy for treating bladder cancer in a subject, the use comprising performing a method as broadly defined herein, and if a favorable outcome of the treatment is predicted, administering Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy to the subject. Thus, in one aspect, the present invention describes Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy for treating bladder cancer in a subject, the use comprising performing a method for predicting an outcome in a bladder cancer subject, and if a favorable outcome of the treatment is predicted, administering Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy to the subject. Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy, wherein the method for predicting an outcome in a bladder cancer subject comprises: determining a gene expression profile comprising gene expression levels or receiving the results of determining a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group 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, P TPRC and ZAP70, and / or an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, the gene expression profile being determined in a biological sample obtained from the subject, and determining a prediction of the outcome based on the gene expression profile, wherein the prediction is an outcome for the subject, wherein the outcome is survival in response to a treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

[0376] All references cited herein, including journal articles or abstracts, published or corresponding patent applications, patents, or any other references, are incorporated herein by reference in their entirety, including all data, tables, figures, and text presented in the cited references. In addition, the entire contents of the references cited in the references cited herein are also incorporated by reference in their entirety.

[0377] Reference to known process steps, conventional process steps, known methods, or conventional methods is not in any way an admission that any aspect, description, or embodiment of the present invention is disclosed, taught, or suggested in the relevant art.

[0378] It should be understood that the phraseology or terminology herein is for the purpose of description rather than limitation, so that the phraseology or terminology of this specification will be interpreted by skilled artisans based on the teachings and guidance presented herein combined with the knowledge of ordinary skilled in the art.

[0379] It should be understood that all details, embodiments and preferences discussed with respect to one aspect of an embodiment of the invention are equally applicable to any other aspect or embodiment of the invention, and therefore all such details, embodiments and preferences for all aspects need not be separately detailed.

[0380] Having now generally described the invention, the invention will be more readily understood by reference to the following examples, which are provided by way of illustration and are not intended to limit the invention. Additional aspects and embodiments will be apparent to those skilled in the art.

[0381] Other variations to the disclosed implementations can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0382] Any reference signs in the claims should not be construed as limiting the scope.

[0383] Example

[0384] Example 1: Predicting survival outcomes for subjects with non-metastatic (M0) bladder cancer

[0385] Multiple data sets were used to analyze three gene signatures. Gene expression data created by TCGA (Genome Cancer Atlas) were downloaded from the database TCGA Bladder Urothelial Carcinoma Firehose Legacy (accessed on March 6, 2020) along with clinical and survival data. The group included 412 patients (i.e., patients with M>0 disease) with a combination of primary, localized (mainly composed of muscle-invasive bladder cancer) and metastatic disease. The median follow-up in this group was 18 months (up to 13 years) after the initial cancer diagnosis.

[0386] TCGA gene expression values ​​are expressed as log2 data. The log2_expression value for each gene is converted to a z score by calculating the following formula:

[0387] z-score log2_gene = ((log2_gene)-(mean_samples)) / (stdev_samples) (7)

[0388] Where log2_gene is the log2 gene expression value of each gene; mean_samples is the mathematical mean of the log2_gene values ​​of all samples, and stdev_samples is the standard deviation of the log2_gene values ​​of all samples.

[0389] This process distributes the transformed log2_gene values ​​around a mean of 0 with a standard deviation of 1. For multivariate analysis of genes of interest, the log2_gene transformed z-score values ​​for each gene were used as input.

[0390] Cox regression analysis

[0391] We selected 196 patients with M0 disease from the entire TCGA dataset, of which 189 had available survival data. We divided this group into 95 patients for training three gene signatures and constructing the Bladder Cancer Immuno-score (BCAI) to predict overall survival of patients after initial diagnosis of the disease.

[0392] This article provides a test to evaluate whether a combination of 14 immune defense response genes, a combination of 17 T-cell receptor signaling genes, a combination of 8 PDE4D7-related genes, and a combination thereof will show a prognostic value for bladder cancer. By using Cox regression, the expression levels of the 14 immune defense response genes, 17 T-cell receptor signaling genes, and 8 PDE4D7-related genes were modeled to obtain overall survival, respectively.

[0393] The Cox regression function for individual genetic characteristics is derived as follows:

[0394] IDR_14_Model: (8)

[0395] (w 1 ·AIM2)+(w 2 ·APOBEC3A)+(w 3 ·CIAO1)+(w 4 ·DDX58)+

[0396] (w 5 DHX9)+(w 6 ·IFI16)+(w 7 ·IFIH1)+(w 8 IFIT1)+(w 9 IFIT3)

[0397] +(w 10 ·LRRFIP1)+(w 11 MYD88)+(w 12 ·OAS1)+(w13 ·TLR8)+

[0398] (w 14 ·ZBP1)

[0399] TCR_17_Model: (9)

[0400] (w 15 ·C2)+(w 16 ·CD247)+(w 17 ·CD28)+(w 18 ·CD3E)+

[0401] (w 19 ·CD3G)+(w 20 ·CD4)+(w 21 ·CSK)+(w 22 ·EZR)+(w 23 ·FYN)

[0402] +(w 24 ·LAT)+(w 25 ·LCK)+(w 26 ·PAG1)+(w 27 ·PDE4D)+

[0403] (w 28 ·PRKACA)+(w 29 ·PRKACB)+(w 30 ·PTPRC)+(w 31 ·ZAP70)

[0404] PDE4D7_CORR_MODEL: (10)

[0405] (w 32 ABCC5)+(w 33 ·CUX2)+(w 34 ·KIAA1549)+(w 35 ·PDE4D)+

[0406] (w 36 ·RAP1GAP2)+(w 37 ·SLC39A11)+(w 38 TDRD1)+

[0407] (w 39 VWA2)

[0408] Weight w 1 tow 39 The details are shown in Table 4 below.

[0409] Table 4: Variables and weights of three individual Cox regression models, namely, the immune defense response model for bladder cancer (IDR_model), the T cell receptor signaling model (TCR signaling model), and the PDE4D7-related model (PDE4D7_CORR_model); N / A - not available.

[0410]

[0411]

[0412] Finally, the three individual gene signatures were combined for prediction by using Cox regression analysis with overall survival as the clinical endpoint. The Cox regression function was derived as follows:

[0413] BCAI_model (bladder cancer immune score): (11)

[0414] (w 40 ·IDR_14_model)+(w 41 ·TCR_17_model)+

[0415] (w 42 ·PDE4D7_CORR_model)

[0416] The BCAI_clinical score was established on the TCGA discovery cohort (95 patients; see above) and tested on the TCGA validation cohort (94 patients; see above). The clinical endpoint tested in the MV Cox regression was overall death. As inputs to the MV Cox regression analysis, the IDR, TCR, and PDE4D7_CORR signature scores derived from the individual Cox regression models, and the number of tumor-positive lymph nodes (LN_positive) determined in postoperative pathology were used. This resulted in the final BCAI_clinical score.

[0417] BCAI_clinical_model (bladder cancer): (12)

[0418] (w 43 ·IDR_14_model)+(w 44 ·TCR_17_model)+

[0419] (w 45 ·PDE4D7_CORR_model)+(w 46 ·LN_stage = 1)+

[0420] (w 47 ·LN_stage = 2)+(w 48 ·LN_stage = 3)

[0421] The weights w 40 to w 44Details. BCAI_Clinical_Model is also referred to herein as BCAI&Clinical_Model. Table 5: Variables and weights for two combined Cox regression models, namely, Bladder Cancer AI Model (BCAI_Model) and Bladder & Clinical Model (BCAI&Clinical_Model); N / A - not available.

[0422]

[0423] To further validate the BCAI and BCAI_clinical models, we combined the data sets GSE13507, GSE32894, and UROMOL after the z-score transformation of the log2 expression values ​​of the genes representing the three gene signatures with 94 TCGA data samples, which were used to validate the BCAI and BCAI_clinical models in the first step as described above. This super data set consists of a total of 1,262 bladder cancer patients.

[0424] Kaplan-Meier survival analysis

[0425] Two models (BCAI and BCAI&Clinical) developed on the TCGA discovery cohort were tested using Kaplan Meier analysis on the TCGA validation cohort as well as the combined superdataset.Different clinical endpoints were tested (cancer-specific death; overall death; death after treatment).

[0426] For Kaplan-Meier survival curve analysis, the Cox function of the risk model (BCAI_model, BCAI&clinical_model) tested was classified into two subgroups based on the cutoff. The threshold for grouping into low risk and high risk was based on the risk of experiencing clinical endpoints (outcomes) predicted by the corresponding Cox regression model.

[0427] Figure 4-8 Kaplan-Meier survival curve analysis of the TCGA training and validation cohorts for PDE4D7_R2, IDR_14, TCR_17, BCAI, and BCAI_Clinical models, respectively, is shown.

[0428] Figure 9-18 Kaplan-Meier survival curve analysis of the superdataset validation cohort is shown.

[0429] If one or more Figure 4-8 and Figure 9-18The Kaplan-Meier analysis shown in shows that it is possible to predict outcomes, such as selected from bladder cancer-specific death; overall death of bladder cancer subjects. By using a risk model based on a randomly selected combination of genes provided herein, an improved prediction of the outcome of bladder cancer subjects is provided. The prediction of the outcome improves therapy selection and potential survival. By using the risk model developed herein, it is expected that the prediction of the effectiveness of postoperative therapy options for the subject can be improved. In addition, this will reduce the suffering of those patients who will be spared ineffective treatment, and will reduce the cost spent on ineffective treatment. In general, based on a model including variables and weights of genes as presented herein, the outcome of bladder cancer subjects can be predicted methodologically, for example, by distinguishing the risk of a specific outcome in different patient risk groups by means of stratification and statistical methods.

[0430] Example 2: Prediction of metastatic bladder cancer after immunotherapy with anti-PD-L1 immune checkpoint inhibitors Survival results

[0431] In order to analyze the survival outcome prediction in metastatic bladder cancer after immune checkpoint inhibitor treatment, the processing data of the clinical phase II trial was used (https: / / doi.org10.1016 / s0140-6736(16)32455-2). The overall patient group consists of 348 patients, of which 298 patients are eligible for survival analysis due to complete data on treatment response. The group is divided into a training set (224 patients) and a test set (74 patients). The metastatic BCAI (metBCAI) feature is trained using the same process as described above for training three gene features and their combinations. In addition, the metBCAI_clinical immune score is constructed using available clinical features about the metastatic disease stage (classified as lymph node, liver or visceral metastasis) and baseline ECOG (Eastern Cooperative Oncology Group) performance scores.

[0432] Cox regression model

[0433] This article provides a test to evaluate whether a combination of 14 immune defense response genes, a combination of 17 T-cell receptor signaling genes, a combination of 8 PDE4D7-related genes, and a combination thereof will show a prognostic value for metastatic bladder cancer after treatment with the anti-PD-L1 immune checkpoint inhibitor atezolizumab. By using Cox regression, the expression levels of the 14 immune defense response genes, 17 T-cell receptor signaling genes, and 8 PDE4D7-related genes were modeled for overall survival, respectively.

[0434] The Cox regression function for individual genetic characteristics is derived as follows:

[0435] met IDR_14_model: (13)

[0436] (w 49 ·AIM2)+(w 50 ·APOBEC3A)+(w 51 ·CIAO1)+(w 52 ·DDX58)+

[0437] (w 53 ·DHX9)+(w 54 ·IFI16)+(w 55 ·IFIH1)+(w 56 ·IFIT1)+

[0438] (w 57 ·IFIT3)+(w 58 ·LRRFIP1)+(w 59 ·MYD88)+(w 60 ·OAS1)+

[0439] (w 61 ·TLR8)+(w 62 ·ZBP1)

[0440] met TCR_17_model: (14)

[0441] (w 63 ·C2)+(w 64 ·CD247)+(w 65 ·CD28)+(w 66 ·CD3E)+

[0442] (w 67 ·CD3G)+(w 68 ·CD4)+(w 69 ·CSK)+(w 70 ·EZR)+(w 71 ·FYN)

[0443] +(w 72 ·LAT)+(w 73 ·LCK)+(w 74 ·PAG1)+(w 75 ·PDE4D)+

[0444] (w 76 ·PRKACA)+(w 77 ·PRKACB)+(w 78 ·PTPRC)+(w 79 ·ZAP70)

[0445] met PDE4D7_CORR_model: (15)

[0446] (w 80 ABCC5)+(w 81 ·CUX2)+(w 82 ·KIAA1549)+(w 83 ·PDE4D)+

[0447] (w 84 ·RAP1GAP2)+(w 85 ·SLC39A11)+(w 86 TDRD1)+

[0448] (w 87 VWA2)

[0449] The weights w are shown in Table 6 below 1 tow 39 details.

[0450] Table 6: Variables and weights for three separate Cox regression models, namely, the immune defense response model for bladder cancer (metIDR_model), the T-cell receptor signaling model (metTCRsignaling model), and the PDE4D7-related model (metPDE4D7_CORR_model); N / A - not available.

[0451]

[0452]

[0453] Finally, the three individual gene signatures were combined for prediction by Cox regression analysis using overall survival as the clinical endpoint. The Cox regression function was derived as follows:

[0454] BCAI_model (metastatic bladder cancer immune score):

[0455] (w 88 ·IDR_14_model)+(w 89 ·TCR_17_Model)+

[0456] (w 90 PDE4D7_CORR_model) (16)

[0458] BCAI_clinical score is established in training group (224 patients; see above) and tested in validation group (74 patients; see above). The clinical endpoint tested in MV Cox regression is the overall death after treatment with atezolizumab. As the input of MV Cox regression analysis, metIDR, metTCR and metPDE4D7_CORR feature scores derived from independent Cox regression model, and ECOG performance scores at metastatic disease stage (lymph node (LN), liver, visceral metastasis) and baseline are used. This results in final BCAI_clinical score.

[0459] BCAI_Clinical_Model (metastatic bladder cancer): (17)

[0460] (w 91 BCAI_model)+(w 92 ·LN metastatic disease stage)+(w 93 ·Visceral metastatic disease stage)+(w 94 ·ECOG performance score at baseline).

[0461] The weights w are shown in Table 7 below 88 tow 92 The BCAI_Clinical_Model is also referred to herein as the BCAI&Clinical_Model.

[0462] Table 7: Variables and weights for two combined Cox regression models, namely, the Metastatic Bladder Cancer AI Model (BCAI_Model) and the Bladder & Clinical Model (BCAI&Clinical_Model); N / A - not available.

[0463]

[0464] As another variable for the clinical BCAI model, the mutation burden per megabase of DNA measured by the commercial 'FMOne mutation burden test' (Foundation Medicine) was added to the regression model:

[0465] BCAI_Clinical_MB_Model (metastatic bladder cancer): (18)

[0466] (w 95 BCAI_model)+(w 96 ·LN metastatic disease stage)+(w 97 ·Visceral metastatic disease stage)+(w 98 ·ECOG performance score at baseline)+(w 99 Mutational burden per megabase of DNA)

[0467] The following table 9 shows the weight w 40tow 47 The BCAI_Clinical_Model is also referred to herein as the BCAI&Clinical_Model.

[0468] Table 9: Variables and weights for the combined Cox regression model, metastatic bladder cancer & clinical & MB AI model (BCAI&clinical&MB_model); N / A - not available.

[0469]

[0470] Kaplan-Meier survival and AUROC analysis

[0471] For Kaplan-Meier survival curve analysis, the Cox function of the risk model (metBCAI_model, metBCAI&clinical_model, metBCAI&clinical&MB_model) tested was classified into two subgroups based on the cutoff. The threshold for grouping into low risk and high risk was based on the risk of experiencing clinical endpoints (outcomes) predicted by the corresponding Cox regression model.

[0472] Figure 19-25 Kaplan-Meier survival curve analysis of the training and validation cohorts is shown.

[0473] Figure 26-27 AUROC (area under the receiver-operator curve) analysis of the training and validation cohorts is shown.

[0474] If one or more Figure 19-25 The Kaplan-Meier analysis shown in shows that it is possible to predict outcomes, such as selected from metastatic bladder cancer-specific deaths; Overall deaths of metastatic bladder cancer subjects treated with immune checkpoint inhibitors. By using a risk model based on a randomly selected gene combination provided herein, an improved prediction of the outcomes of bladder cancer subjects is provided. The prediction of outcomes improves treatment selection and potential survival. By using the risk model developed herein, it is expected that the prediction of the effectiveness of postoperative treatment options for the subject can be improved. In addition, this will reduce the suffering of those patients who will be spared ineffective treatment, and will reduce the cost spent on ineffective treatment. In general, based on a model including variables and weights of genes as presented herein, the outcomes of bladder cancer subjects can be predicted methodologically, such as by distinguishing the risk of specific outcomes in different patient risk groups by means of stratification and statistical methods.

[0475] Example 3: Predicting the outcome of immunotherapy in patients with high-grade NMIBC or metastatic bladder cancer.

[0476] The IDR_14 and TCR_17 models were tested to predict the outcome of immunotherapy. The models were applied to NMIBC patients treated with BCG ( Fig.28) or metastatic bladder cancer patients treated with immune checkpoint inhibitors (ICIs) ( Fig.29 ). The endpoints used for analysis were overall progression-free survival (high-grade NMIBC) or survival after ICI treatment.

[0477] like Fig.28 and 29 The Kaplan-Meier analysis shown shows that it is possible to predict results, such as progression-free survival or overall survival time of high-grade NMIBC patients with metastatic bladder cancer or treated with immunotherapy (such as BCH or ICI). By using a risk model based on T cell receptor gene signatures or immune defense response gene signatures, an improved prediction of the results of bladder cancer subjects is provided. The prediction of the results improves treatment options and potential survival. By using the risk model developed herein, it is expected to improve the prediction of the effectiveness of the treatment options for the subject. As demonstrated herein in particular, the model allows the prediction of the effectiveness of immunotherapy in bladder cancer (particularly in metastatic bladder cancer or high-grade NMIBC). In addition to predicting patients who respond poorly to these therapies, alternative treatment strategies can also be selected in the early stages to avoid unnecessary treatment using (for the patient) ineffective treatment. In addition, this will reduce the pain of those patients who will save ineffective treatment, and will reduce the cost spent on ineffective treatment. 。

[0478] Example 4: Immunoscore test for BCG response

[0479] Next, immune scores and immune score_clinical models were used to analyze patient samples, as described by De Jong et al., as described above. In short, the data set includes RNA sequencing data obtained from tumor samples of primary HR-NMIBC patients who received ≥5 / 6 BCG-induced instillations between 2000-2018. Group A consists of n=63 BCG responders and n=69 BCG non-responders. Response to BCG is defined as the absence of high-grade recurrence after at least 5 / 6 BCG induction instillations and ≥9 BCG maintenance instillations (i.e., a 1-year schedule with at least 3 BCG maintenance cycles). For Group B, additional patients with HR-NMIBC are included. Group B is similar to Group A, with the goal of having a similar number of tumors that respond to BCG (n=88) and do not respond to BCG (n=63). In order to increase the number of patients in Group B, we also include patients with Ta tumors. In Group B, patients received ≥5 / 6 BCG induction instillations, with a median of 13 instillations.

[0480] The immune score model used herein is a combination of the IDR_14 and TCR_17 features described herein. In the case of the immune score clinical model, the model was further combined with the EORTC score.

[0481] like Fig.31 and 32 It was confirmed that the immune score model can be used to predict disease progression for cohort A and cohort B, respectively. In addition, the immune score_clinical model can be used to predict disease progression, which can be predicted for each BRS subtype (see De Jong et al., supra), but the prediction is most effective in BRS1, see Figure 33-35 .

[0482] The Immune Score_Clinical model also predicts the endpoints cancer-specific death and mortality, see Fig.36 and 37 .

[0483] Immunoscore_Clinical can also be used to stratify patients who smoke, see Figure 38-43 .

[0484] The accompanying sequence listing, entitled 2022PF00548 SEQ LIST, is incorporated herein by reference in its entirety.

Claims

1. A method for predicting the outcome of a subject with bladder cancer, include: - determining a gene expression profile comprising gene expression levels or receiving the result of determining a gene expression profile comprising gene expression levels, wherein the gene expression levels comprise gene expression levels selected from the group consisting of: - a T-cell receptor signaling 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 - an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, The gene expression profile is determined in a biological sample obtained from the subject, - determining a prediction of said outcome based on said gene expression profile, - wherein the prediction is an outcome for the subject, wherein the outcome is survival in response to treatment, cancer-free survival in response to treatment, or time until disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

2. The method of claim 1, further comprising providing the prediction of the outcome to a medical caregiver or the subject.

3. The method according to claim 1 or 2, in, Determining the prediction of the outcome comprises combining the gene expression levels with a regression function that has been derived from a population of bladder cancer subjects.

4. The method according to any one of the preceding claims, in, Determining the prediction of the outcome is also based on one or more clinical parameters obtained from the subject.

5. The method according to any one of the preceding claims, in, Determining the outcome includes combining the gene expression profile and one or more clinical parameters obtained from the subject with a regression function that has been derived from a population of bladder cancer subjects.

6. The method according to any one of the preceding claims, in, The one or more clinical parameters include one or more of the following: EORTC score, the number of tumor-positive regional lymph nodes, metastatic disease status, ECOG score, and FoundationOne mutation load per MB DNA, preferably, wherein the one or more clinical parameters consist of the EORTC score, the number of tumor-positive regional lymph nodes, or consist of the metastatic disease status, ECOG score, and optionally the FoundationOne mutation load per MB DNA.

7. The method according to any one of the preceding claims, in, The biological sample is obtained from the subject before the therapy begins, preferably, wherein the biological sample is a bladder sample or a bladder cancer sample.

8. The method according to any one of the preceding claims, in, A therapy is recommended based on the prediction.

9. The method according to any one of the preceding claims, in, The subject suffers from non-muscle invasive bladder cancer (NMIBC), preferably, high-grade NMIBC or metastatic bladder cancer (mUC).

10. A computer program product comprising instructions which, when executed by a computer, cause the computer to perform a method comprising: - receiving data indicative of a gene expression profile including gene expression levels, in, The gene expression level includes a gene expression level selected from the following: - a T-cell receptor signaling 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 - an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, The gene expression profile is determined in a biological sample obtained from a subject with bladder cancer, - Determining a prediction of an outcome for the subject based on the gene expression profile, wherein the outcome is survival in response to a treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

11. Use of a diagnostic kit, wherein the kit include: - at least one of a polymerase chain reaction primer or a probe for determining a gene expression profile in a biological sample and / or in a sample obtained from a subject with bladder cancer, the gene expression profile comprising expression levels, wherein the expression levels comprise gene expression levels selected from the group consisting of: - a T-cell receptor signaling 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 - an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, The uses include: determining the expression levels of said genes of said gene expression profile, and Predicting an outcome in a subject with bladder cancer, wherein the outcome is survival in response to a treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

12. A Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy for use in treating bladder cancer in a subject, the use comprising performing the method according to any one of claims 1 to 9, and administering the Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy to the subject if a favorable outcome of the treatment is predicted.

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