Predicting outcomes in patients with bladder cancer

Abstract

The present invention relates to a method for predicting an outcome of a patient with bladder cancer, the method comprising determining a gene expression profile, or receiving a result of the determination, comprising gene expression levels selected from 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 being determined in a biological sample obtained from the patient, and determining a prediction of outcome based on the gene expression profile, wherein the prediction is an outcome for the patient.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the outcome of a patient with bladder cancer, and a computer program product for predicting the outcome of a patient with bladder cancer. Further, the present invention relates to a diagnostic kit, a use of the diagnostic kit, a use of the diagnostic kit in a method for predicting the outcome of a patient with bladder cancer, a 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-correlated genes in a method for predicting the outcome of a patient with bladder cancer, and a corresponding computer program product. [Background technology]

[0002] Cancer is a type of disease characterized by uncontrolled cell proliferation, invasion, and sometimes metastasis. These three malignant properties distinguish cancer from benign tumors, which are self-limited and do not invade or metastasize. Bladder cancer is a common malignant tumor, primarily in developed countries, accounting for 3% of cancer diagnoses worldwide. In the United States, bladder cancer is the sixth most common cancer, accounting for 4.4% of all new cancer cases [seer.cancer.gov]. Bladder cancer is also four times more common in men than women. The overall prognosis for this disease is good, with an average 5-year survival rate of 77% in the United States. However, the 5-year survival rate for patients with metastatic disease is very low at 5% [Saginala et al., 2020]. Depending on the degree of tumor invasiveness in the bladder wall, bladder cancer is classified into two main categories: non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC).

[0003] NMIBC accounts for approximately 75–85% of cases and involves tumors confined to the mucosa and submucosa of the bladder without further muscle invasion. Tests to determine clinical stage include physical examination, radiological imaging, and histological evaluation of initial transurethral resection of the bladder tumor (TURBT). Pathological stage is determined after radical cystectomy and pelvic lymphadenectomy. NMIBC consists of three tumor stages: papillary tumors (Ta) confined to the epithelial mucosa; carcinoma in situ (CIS) (Tis), which are difficult to detect, flat, or sessile epithelial layer carcinoma; and papillary tumors (T1), which show invasion of the subepithelial connective tissue [Slovacek et al., 2021].

[0004] Guidelines from the American Urological Association (AUA), European Association of Urology (EAU), and the UK National Institute for Health and Care Excellence (NICE) classify NMIBC into low-, intermediate-, and high-risk groups based on tumor grade, size, number, and recurrence rate. Prognosis is generally favorable for both low- and high-grade lesions, with 10-year cancer-specific survival rates of approximately 70–85% even for high-grade tumors. While 50% of patients with low-grade Ta NMIBC tumors experience recurrence, only 6% of these patients experience disease progression. For patients with high-grade T1 tumors, the recurrence rate is approximately 45%, with a 15–40% risk of cancer progression [Slovacek et al., 2021].

[0005] Typically, all NMIBC is removed by transurethral resection. For certain resections, a combined cystoscopy and biopsy procedure may be used. Platinum-based chemotherapy is the primary treatment for metastatic disease, but newer immunotherapies, such as checkpoint inhibitors, are being used more frequently and may even be used as first-line therapy. To reduce the incidence of recurrence and progression, guidelines recommend continuing a risk-stratified approach to the selection of adjuvant intravesical therapy. Treatment guidelines for low- and intermediate-risk NMIBC recommend immediate postoperative intravesical chemotherapy and / or immunotherapy. Intravesical chemotherapy agents include mitomycin, gemcitabine, epirubicin, and doxorubicin. Immunotherapy typically consists of intravesical administration of live, attenuated bacillus Calmette-Guérin (BCG) bacteria. Patients who experience recurrence after initial intravesical chemotherapy are known to benefit from intravesical BCG therapy, and we will focus on this treatment modality to further emphasize its role.

[0006] Mycobacterium bovis BCG (Mycobacterium bovis Chronicum Guinea-Polygon) has been used since the 1970s as immunotherapy to prevent recurrence and progression in patients with bladder cancer. The mechanism of immune-mediated action of BCG has been extensively studied. Studies suggest that BCG selectively binds to and is internalized by cells, triggering a nonspecific immune response that eliminates tumors via activation of natural killer (NK) cells and CD8+ T cells [Rossel et al., 2021, Guallar-Garrido et al., 2020].

[0007] For low-risk NMIBC lesions, a single postoperative instillation is recommended. For intermediate-risk NMIBC (multifocal low-grade Ta or small-volume high-grade Ta), induction chemotherapy or immunotherapy with BCG is recommended, followed by one year of maintenance therapy in patients who respond to induction therapy. High-risk NMIBC (multifocal Ta, any T1 disease, any CIS) is primarily treated with induction therapy with BCG, followed by up to three years of maintenance therapy [Slovacek et al., 2021]. Due to the recent shortage of BCG in the United States, BCG therapy is prioritized for patients with high-risk NMIBC who are not previously treated with BCG. BCG therapy provides an induction course, followed by up to one year of BCG maintenance therapy, preferably at a reduced dose. Alternatively, intravesical chemotherapy is considered as a first-line treatment for intermediate-risk disease.

[0008] Even after adequate BCG treatment, recurrence occurs in approximately 20–50% of patients. This failure to respond is thought to stem from two scenarios: (1) the predominance of BCG intolerance in patients who cannot tolerate adequate levels of BCG due to BCG toxicity; and (2) the existence of a subgroup of BCG-refractory patients. A portion of this group progresses within 3 months of the initial BCG treatment and exhibits refractory disease with persistent high-grade NMIBC at 6 months posttreatment. The remainder of the refractory group experiences recurrence after a short disease-free period following adequate BCG administration [Slovacek et al., 2021]. Cancers that do not respond to adequate BCG are highly unlikely to respond to additional cycles of BCG. For these patients, radical cystectomy is recommended, with the option of salvage intravesical therapy or clinical trial participation. Although cystectomy for patients with high-grade NMIBC has favorable oncologic outcomes, it is associated with high morbidity and reduced quality of life. Furthermore, delays in cystectomy due to prolonged and continuous ineffective BCG re-administration negatively impact patient outcomes. Unfortunately, no tools exist to predict which patients with high-grade NMIBC will benefit from treatment, leading to all patients being treated with "universal" BCG, leading to overtreatment and unnecessary exposure to toxicity. It would be highly beneficial to prognostically stratify patients more suitable for BCG therapy based on additional patient clinical parameters, such as the presence of biomarkers or gene signatures in the primary tumor.

[0009] Metastases occur in 10–15% of patients diagnosed with bladder cancer [Rosenberg et al., 2005]. More than half of patients presenting with MIBC ultimately die from metastatic disease. The most common sites of metastasis are lymph nodes (69%), bone (47%), lungs (37%), liver (26%), and peritoneum (16%) [Shinagare et al., 2011]. Relatively common clinical manifestations of metastatic disease include obstructive uropathy and ureteral obstruction due to lymphadenopathy, lymphatic obstruction, bone damage, pulmonary damage (hemoptysis, dyspnea with pleural effusion, cough), elevated liver enzymes and dysfunction, and intestinal obstruction. The prognosis for these patients is poor, with a 5-year survival rate of 62–68% after definitive surgery. Neoadjuvant chemotherapy may reduce the associated mortality risk by 33%, but the associated toxicities must also be considered [Chin et al., 2017]. Currently, limited imaging modalities and poor biomarkers make it difficult to make reliable prognostic predictions.

[0010] WO2022 / 069201A1 relates to a method for predicting the outcome of a bladder or kidney cancer patient, the method comprising determining a gene expression profile or receiving the results of that determination. Summary of the Invention

[0011] The present invention aims to overcome these problems, inter alia, by the methods and uses set out in the appended claims.

[0012] Although patients with non-muscle-invasive bladder cancer have a favorable prognosis, a subgroup of NMIBC patients is at high risk of progressing to muscle-invasive or metastatic disease. Patients with high-risk NMIBC (HR-NMIBC) are treated with adjuvant intravesical BCG instillation for 1–3 years after transurethral bladder tumor resection to reduce the probability of recurrence and progression. BCG exerts beneficial effects by inducing a local immune response that promotes the clearance of residual tumor cells. Although the initial response rate to BCG is high, 17–45% of HR-NMIBC patients progress, and approximately 70% of cases die within 5 years of diagnosis. Unfortunately, no tools exist to predict which HR-NMIBC patients will benefit from treatment, leading to overtreatment and unnecessary exposure to toxicity. Similarly, treatment of metastatic bladder cancer patients with immune checkpoint inhibitors results in a binary response pattern: approximately 22% of patients respond very well to treatment, while the remaining patients have a very poor prognosis and appear to not respond at all. Currently, it is not possible to predict or identify non-responders before treatment, but alternative treatment strategies would ideally be explored for this group. The inventors have identified a gene signature related to the immune system that can predict response and survival in such patients.

[0013] In conclusion, the inventors hypothesize that there is a great need for improved prediction of bladder cancer outcome, and therefore of bladder cancer patients, and provide herein methods and means for improved outcome prediction.

[0014] In a first aspect, the present invention relates to a method for predicting an outcome of a patient with bladder cancer, the method comprising determining or receiving a determination result of a gene expression profile comprising gene expression levels 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 AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1. receiving a determination or determination result, wherein the gene expression profile is determined in a biological sample obtained from the patient, the gene expression profile comprising gene expression levels selected from immune defense response genes selected from the group consisting of MYD88, OAS1, TLR8, and ZBP1; and determining a prediction of an outcome based on the gene expression profile, the prediction being an outcome for the patient, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression after treatment, 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 that, when executed by a computer, cause the computer to carry out a method, the method comprising receiving data indicative of a gene expression profile comprising gene expression levels 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 AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIH2, IFIH3, IFIH4, IFIH5, IFIH6, IFIH7, IFIH8, IFIH9, IFIH1, IFIH1, IFIH2, IFIH5, IFIH6, IFIH7, IFIH8, IFIH9, IFIH1, IFIH1, IFIH2, IFIH1, IFIH2, IFIH3, IFIH4, IFIH5, IFIH6, IFIH7, IFIH8, IFIH9, IFIH1, IFIH1, IFIH1, IFIH2 ...1, IFIH2, IFIH1, IFIH1, IFIH1, IFIH2, IFIH1, IFIH1, IFIH1, IFIH2, IFIH1, IFIH1, IFIH1, IFIH2 receiving a biological sample obtained from a bladder cancer patient, the biological sample comprising gene expression levels selected from immune defense response genes selected from the group consisting of T1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the gene expression profile is determined in a biological sample obtained from the bladder cancer patient; and determining a prediction of patient outcome based on the gene expression profile, the prediction being a patient outcome, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression after treatment, 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 diagnostic kit comprising at least one polymerase chain reaction primer or probe for determining a gene expression profile, the gene expression profile comprising expression levels in a biological sample and / or samples obtained from a bladder cancer patient, the expression levels being 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 expression levels of selected from genes 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, and the use includes predicting outcome in a patient with bladder cancer, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression after treatment, and 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 relates to Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy for use in treating bladder cancer in a patient, the use comprising carrying out a method according to the first aspect of the invention and, if a favourable outcome of the treatment is predicted, administering Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy to the patient. [Brief explanation of the drawings]

[0018] [Figure 1] Figure 1 shows the schematic approach to creating an immune score model for predicting bladder cancer patient outcomes. Processed RNAseq data were downloaded from TCGA along with clinical and demographic data for the TCGA BLCA cohort of 412 patients. The main steps of the analysis pipeline are illustrated. [Figure 2]Figure 2 shows a schematic of the analytical approach used to validate the Immunoscore model. We created a bladder cancer superdataset consisting of multiple individual datasets. All BCAI-associated expression values ​​in each dataset were converted to z-scores and centered around the mean expression of all samples in each set, with mean_expression = 0 and SD_expression = 1. After this normalization step, we combined the expression data of BCAI-associated genes to create a superdataset containing over 1,300 patients. Note that not all demographic or survival data were available for all patients. Therefore, the number of patients in downstream data analyses may be smaller than the total number of patients in the superdataset. The number of patients in each analysis is indicated for each analysis performed. [Figure 3] Figure 3 shows a schematic of the analytical approach used to create and test the metastasis immunoscore models (mBCAI and mBCAI_Clinical). We tested the effectiveness of ImmunoScore in patients with metastatic bladder cancer treated with immunotherapy (anti-PD-L1, atezolizumab) (IMVigor210 trial, see references below). To address metastatic cases, we retrained the metastasis model on a subset of the data (training data), while testing the models (mBCAI and mBCAI_Clinical) on a validation set that was not used to train the model. [Figure 4]Figure 4 shows a Kaplan-Meier survival curve analysis of the PDE4D7_R2 model. The clinical endpoint tested was time to all-cause mortality (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (Groups 1-2 of the entire cohort, upper graph) and fixed for validation in the test cohort (Groups 3-4 of the entire cohort, lower graph). In the validation (test) cohort, the PDE4D7_R2 score predicted overall survival, and patients classified in the high-risk group (>0.3 threshold) had a 2.4-fold increased hazard risk of death. [Figure 5] Figure 5 shows a Kaplan-Meier survival curve analysis of the IDR_14 model. The clinical endpoint tested was time to death (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (Groups 1-2 of the entire cohort, upper graph) and fixed for validation in the test cohort (Groups 3-4 of the entire cohort, lower graph). In the validation (test) cohort, the IDR_14 score predicted overall survival, and patients classified into the high-risk group (>0 threshold) had a 2.3-fold increased hazard risk of death. [Figure 6]Figure 6 shows a Kaplan-Meier survival curve analysis of the TCR_17 model. The clinical endpoint tested was time to death (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (Groups 1-2 of the entire cohort, upper graph) and fixed for validation in the test cohort (Groups 3-4 of the entire cohort, lower graph). In the validation (test) cohort, the TCR_14 score predicted overall survival, and patients classified into the high-risk group (>0 threshold) had a two-fold increased hazard risk of death. [Figure 7] Figure 7 shows a Kaplan-Meier survival curve analysis of the BCAI score. The clinical endpoint tested was time to death (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (Groups 1-2 of the entire cohort, upper graph) and fixed for validation in the test cohort (Groups 3-4 of the entire cohort, lower graph). In the validation (test) cohort, the BCAI score predicted overall survival, and patients classified into the high-risk group (>0 threshold) had a 3.2-fold increased hazard risk of death. [Figure 8]Figure 8 shows a Kaplan-Meier survival curve analysis of the BCAI Clinical Score. The clinical endpoint tested was time to death (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (Groups 1-2 of the entire cohort, upper graph) and fixed for validation in the test cohort (Groups 3-4 of the entire cohort, lower graph). In the validation (test) cohort, the BCAI Clinical Score predicted overall survival, and patients classified into the high-risk group (>0 threshold) had a 5.4-fold increased hazard risk of death. [Figure 9] Figure 9 shows a Kaplan-Meier survival curve analysis of the BCAI score. The clinical endpoint tested was bladder cancer-specific death (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (Groups 1-2 of the entire cohort, upper graph) and fixed for validation in the test cohort (Groups 3-4 of the entire cohort, lower graph). In the validation (test) cohort, the BCAI score predicted overall survival, and patients classified into the high-risk group (>0 threshold) had a 5.0-fold increased hazard risk of death. [Figure 10]We tested the survival endpoint (overall survival) of the entire bladder cancer cohort using Cox regression models of BCAI (top graph) and BCAI_Clinical (bottom graph) trained on 95 TCGA data (see previous figures). The same endpoint was tested in the context of different treatments for NMIBC (BCG) and MIBC (cystectomy). The respective TCGA samples used for model training were excluded from the testing analysis. In the validation (test) cohort, BCAI and BCAI clinical scores predicted overall survival, with patients classified as high-risk (>0 and >0.2 thresholds, respectively) having a 2.3-fold and 2.6-fold increased hazard risk of death, respectively. [Figure 11] We tested the survival endpoint (cancer-specific survival) of the entire bladder cancer cohort using Cox regression models of BCAI (top graph) and BCAI_Clinical (bottom graph) trained on 95 TCGA data (see previous figures). Each TCGA sample used for model training was excluded from the test analysis. In the validation (test) cohort, BCAI and BCAI clinical scores predicted cancer-specific survival, with patients classified as high-risk (>0 and >0.2 thresholds, respectively) having a 3.3-fold and 4.2-fold increased hazard risk of death. [Figure 12] We tested survival endpoints (overall survival) for NMIBC subtypes using Cox regression models of BCAI (top graph) and BCAI_Clinical (bottom graph) trained on 95 TCGA data sets (see previous figures). The respective TCGA samples used for model training were excluded from the test analysis. In the validation (test) cohort, BCAI and BCAI clinical scores predicted cancer-specific survival, with patients classified as high-risk (>0 and >0.2 thresholds, respectively) having a 3.5-fold and 3.0-fold increased hazard risk of death. [Figure 13]We tested survival endpoints (overall survival) for NMIBC subtypes after BCG treatment using Cox regression models of BCAI (top graph) and BCAI_Clinical (bottom graph) trained on 95 TCGA data sets (see previous figures). Each TCGA sample used for model training was excluded from the test analysis. In the validation (test) cohort, BCAI and BCAI clinical scores predicted cancer-specific survival, with patients classified as high-risk (>0 and >0.2 thresholds, respectively) having a 3.9-fold and 3.7-fold increased hazard risk of death. [Figure 14] We tested survival endpoints (overall survival) for MIBC subtypes using Cox regression models of BCAI (top graph) and BCAI_Clinical (bottom graph) trained on 95 TCGA data sets (see previous figures). Each TCGA sample used for model training was excluded from the test analysis. In the validation (test) cohort, BCAI and BCAI clinical scores predicted cancer-specific survival, with patients classified as high-risk (>0 and >0.2 thresholds, respectively) having a 1.9-fold and 2.2-fold increased hazard risk of death. [Figure 15] We tested survival endpoints (overall survival) for MIBC subtypes after surgery (cystectomy) using Cox regression models of BCAI (top graph) and BCAI_Clinical (bottom graph) trained on 95 TCGA data sets (see previous figures). The same endpoint was tested in the context of different treatments for NMIBC (BCG) and MIBC (cystectomy). The respective TCGA samples used for model training were excluded from the test analysis. In the validation (test) cohort, BCAI and BCAI clinical scores predicted cancer-specific survival, with patients classified as high-risk (>0 and >0.2 thresholds, respectively) having a 2.3-fold and 2.2-fold increased hazard risk of death. [Figure 16]Based on the BCAI immune score model, TCGA samples were classified into high-risk and low-risk groups. Based on primary treatment outcomes (complete response (CR) / partial response (PR) or progressive disease (PD) / stable disease (SD)), the high-risk and low-risk groups were then plotted on a Kaplan-Meier graph (low-risk group on the top and high-risk group on the bottom, based on BCAI stratification). The low-risk SD / PR group demonstrated an extended median survival time (24 months vs. 22 months). Although the median survival time for CR / PR in the low-risk group could not be calculated, visually, the median survival time appeared to be significantly longer in the CR / PR group of the low-risk group. [Figure 17] Based on the BCAI clinical immune score model, TCGA samples were classified into high-risk and low-risk groups. Based on primary treatment outcomes (complete response (CR) / partial response (PR) or progressive disease (PD) / stable disease (SD)), the high-risk and low-risk groups were then plotted on a Kaplan-Meier graph (low-risk group on the top and high-risk group on the bottom, based on the BCAI clinical stratification). The low-risk SD / PR group demonstrated an extended median survival time (17 months vs. 24 months). Although the median survival time for CR / PR in the low-risk group could not be calculated, visually, the median survival time appeared to be significantly longer in the CR / PR group compared with the low-risk group. [Figure 18] Figure 18 shows the ROC curves for 5-year cancer-specific survival (top graph) and 5-year overall survival (bottom graph). Plots are for both the BCAI model and the BCAI clinical model trained on 95 TCGA data sets. The inset of each plot shows the area under the curve (AUC). [Figure 19]Kaplan-Meier survival curve analysis of metastatic BCAI (mBCAI or metBCAI) scores in patients with metastatic bladder cancer receiving anti-PD-L1 therapy (atezolizumab). The clinical endpoint tested was time to death (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (Groups 1–3 of the entire cohort, upper graph) and fixed for validation in the test cohort (Group 4 of the entire cohort, lower graph). In the validation (test) cohort, the mBCAI score predicted overall survival, and patients classified into the high-risk group (>0 threshold) had a 2.2-fold increased hazard risk of death. [Figure 20] Kaplan-Meier survival curve analysis of metastatic BCAI clinical (mBCAI or metBCAI) scores in patients with metastatic bladder cancer receiving anti-PD-L1 therapy (atezolizumab). The clinical endpoint tested was time to death (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. Model parameters were established in the training set (Groups 1–3 of the entire cohort, upper graph) and fixed for validation in the test cohort (Group 4 of the entire cohort, lower graph). In the validation (test) cohort, the mBCAI clinical score predicted overall survival, and patients classified into the high-risk group (>0 threshold) had a 4.0-fold increased hazard risk of death. [Figure 21] Kaplan-Meier survival curve analysis of TCGA subtype (top graph) or Lund2 classification (bottom graph) in patients with metastatic bladder cancer who received anti-PD-L1 therapy (atezolizumab). No statistically significant stratification was observed between groups based on TCGA or Lund2 subtype. [Figure 22] Patients were divided into anti-PD-L1 responders (top graph) and non-responders (bottom graph). Kaplan-Meier graphs were generated based on the mBCAI clinical model. In the non-responder cohort, the mBCAI clinical score predicted overall survival, and patients classified as high-risk (>threshold 0) had a 2.9-fold increased hazard risk of death. [Figure 23] Figure 23 shows the overall binary response to anti-PD-L1 (atezolizumab) therapy in patients with metastatic bladder cancer. The response rate to this therapy was 22.8%, with responders having a very high median survival time, while non-responders had a median survival time of 7.7 months. [Figure 24] Patients with metastatic bladder cancer treated with anti-PD-L1 antibody (atezolizumab) were classified into two groups based on their risk profile (low risk, upper graph) or risk profile (lower graph) using the mBCAI score. For each group, a Kaplan-Meier graph was created based on the clinical outcome: complete response (CR) / partial response (PR) or progressive disease (PD) / stable disease (SD). In the low-risk group, the response rate to anti-PD-L1 therapy was 41.3%, and the median survival time for non-responders was 16.3 months. In the high-risk group, the response rate to anti-PD-L1 therapy was 10.7%, and the median survival time for non-responders was several months. [Figure 25] Patients with metastatic bladder cancer who received anti-PD-L1 antibody (atezolizumab) therapy were classified into two groups based on low-risk (top graph) or high-risk (bottom graph) using the mBCAI score. For each group, a Kaplan-Meier graph was created based on the clinical outcome: complete response (CR), partial response (PR), progressive disease (PD), or stable disease (SD). [Figure 26]Figure 26 shows the ROC curves. The AUROC training cohort of 161 patients was used as the training set (top graph), and the AUROC test cohort of 49 patients was used to validate the model (bottom graph). The clinical endpoint used was binary response (responders / non-responders to anti-PD-L1 therapy). The models plotted are mBCAI, mBCAI Clinical, and mBCAI Clinical2. The area under the curve (AUC) is shown in the inset of each plot. [Figure 27] Figure 27 shows the ROC curve. The AUROC training cohort of 161 patients was used as the training set (top graph), and the AUROC test cohort of 49 patients was used to validate the model (bottom graph). The clinical endpoint used was all-cause mortality. The plotted models are mBCAI, mBCAI clinical. [Figure 28] Kaplan-Meier survival curve analysis of the IDR_14 model (top graph) or the TCR_17 model (bottom graph) in 165 patients with high-grade NMIBC. The clinical endpoint tested was progression-free survival (in months). Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, cancer-specific survival was predicted by the IDR_14 score or TCR_17 score. Patients classified into the high-risk group (IDR14 > 1 or TCR17 > 2.89) had a 2.0-fold (IDR14) and 1.7-fold (TCR17) increased hazard risk of death. [Figure 29]Kaplan-Meier survival curve analysis of the metIDR_14 model (top graph) or the metTCR_17 model (bottom graph) in 348 patients with metastatic bladder cancer. The clinical endpoint tested was survival time (in months) after immune checkpoint inhibitor (ICI) treatment. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by the respective Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the IDR_14 score or metTCR_17 score predicted survival after ICI. Patients classified as the high-risk group (threshold values ​​> 0 for both metIDR14 and metTCR17) had a 2.9-fold (metIDR14) and 2.0-fold (metTCR17) increased hazard risk of death. [Figure 30] Figure 30 shows Kaplan-Meier survival curves for Erasmus Bladder Cancer Response Types (BRS) 1, 2, and 3 (see De Jong et al, Sci. Transl. Med. 15, eabn4118 (2023)). The clinical endpoint tested was progression after BCG treatment in Cohort B patients. [Figure 31] Kaplan-Meier survival curve analysis of ImmunoScore (IDR_14 and TCR_17 combined model) in Cohort A bladder cancer patients (130). The clinical endpoint tested was progression (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, ImmunoScore predicted progression after BCG, and patients classified in the high-risk group (>4.185 threshold) had a 2.5-fold increased hazard risk of progression. [Figure 32]Kaplan-Meier survival curve analysis of ImmunoScore (IDR_14 and TCR_17 combined model) in Cohort B bladder cancer patients (135). The clinical endpoint tested was progression (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, ImmunoScore predicted progression after BCG, and patients classified in the high-risk group (>4.185 threshold) had a 4.6-fold increased hazard risk of progression. [Figure 33] Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in cohorts A and B of bladder cancer patients (265). The clinical endpoint tested was progression (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted progression after BCR, and patients classified into the high-risk group (>4.025 threshold) had a 2.5-fold increased hazard risk of progression. [Figure 34]Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in cohorts A and B bladder cancer patients (42) with a BRS of 1. The clinical endpoint tested was progression (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted progression after BCR, and patients classified into the high-risk group (>4.025 threshold) had a 10.7-fold increased hazard risk of progression. [Figure 35] Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in cohorts A and B of bladder cancer patients (56) with a BRS of 2. The clinical endpoint tested was progression (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted progression after BCR, and patients classified into the high-risk group (>4.025 threshold) had a 2.9-fold increased hazard risk of progression. [Figure 36]Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (135). The clinical endpoint tested was disease-specific mortality (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted disease-specific mortality after BCG, and patients classified into the high-risk group (>4.025 threshold) had a 3.3-fold increased hazard risk for disease-specific mortality. [Figure 37] Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (135). The clinical endpoint tested was death (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted death after BCR, and patients classified into the high-risk group (>4.025 threshold) had a 3.0-fold increased hazard risk of death. [Figure 38]Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (36) with smoking status "No." The clinical endpoint tested was death (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean value of the prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, ImmunoScore_Clinical did not predict death after BCG in nonsmokers. [Figure 39] Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (91) with a "Yes" smoking status. The clinical endpoint tested was death (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted death after BCG in smokers, and patients with a "Yes" smoking status classified in the high-risk group (>4.025 threshold) had a 3.9-fold increased hazard risk of death. [Figure 40]Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (127). The clinical endpoint tested was progression (in months) after BCG therapy. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The high-risk group included both nonsmokers and smokers, while the high-risk group included only smokers. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted progression after BCR, and patients classified into the high-risk group (>4.025 threshold) had a 7.4-fold increased hazard risk of progression. [Figure 40] Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (127). The clinical endpoint tested was progression (in months) after BCG therapy. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The high-risk group included both nonsmokers and smokers, while the high-risk group included only smokers. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted progression after BCR, and patients classified into the high-risk group (>4.025 threshold) had a 7.4-fold increased hazard risk of progression. [Figure 41]Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (127). The clinical endpoint tested was death (in months) after BCG. Patients were further classified into three groups according to their smoking status ("Yes" or "No"); smokers were further classified according to their risk of experiencing the clinical endpoint predicted by the respective Cox regression model. The mean prognostic risk score derived from the Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, ImmunoScore_Clinical predicted death after BCG. [Figure 42] Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (127). The clinical endpoint tested was death (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The high-risk group included both nonsmokers and smokers, while the high-risk group included only smokers. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted death after BCR, and patients classified into the high-risk group (>4.025 threshold) had a 7.4-fold increased hazard risk of death. [Figure 43]Kaplan-Meier survival curve analysis of ImmunoScore_Clinical (IDR_14 and TCR_17 combined model with clinical parameters) in Cohort B bladder cancer patients (127). The clinical endpoint tested was cancer-specific mortality (in months) after BCG. Patients were classified into two groups according to their risk of experiencing the clinical endpoint predicted by each Cox regression model. The high-risk group included both nonsmokers and smokers, while the high-risk group included only smokers. The mean prognostic risk score derived from Cox regression was used as the cutoff point for group stratification. In the validation (test) cohort, the ImmunoScore_Clinical score predicted cancer-specific mortality after BCR, and patients classified into the high-risk group (>4.025 threshold) had a 7.4-fold increased hazard risk of dying from cancer. DETAILED DESCRIPTION OF THE INVENTION

[0019] definition In the present specification the word "a" or "an" does not exclude a plurality.

[0020] The term "biological sample" or "sample obtained from a patient" refers to any biological material obtained from a patient, for example a bladder cancer patient, by suitable methods known to those skilled in the art.

[0021] As used herein, the term "and / or" indicates that one or more of the listed cases may occur alone or in combination with at least one of the listed cases, and may also occur with all of the listed cases.

[0022] As used herein, the term "at least" a particular value means greater than or equal to the particular value. For example, "at least 2" is understood to be synonymous with "2 or more," i.e., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, etc.

[0023] The term "bladder cancer" refers to cancer of the bladder tissue that develops when cells in the bladder mutate and begin to grow uncontrollably.

[0024] The term "bladder cancer-specific or disease-specific mortality" refers to patient deaths due to bladder cancer.

[0025] The term "clinical recurrence" refers to the presence of clinical signs indicating the presence of tumor cells, as determined, for example, using in vivo imaging.

[0026] It is to be understood that as used herein, the word "comprises" or variations thereof includes the stated element, integer, or step, or group of elements, integers, or steps, but does not exclude other elements, integers, or steps, or group of elements, integers, or steps. The verb "comprises" includes the verbs "consisting essentially of" or "consisting of."

[0027] As 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 AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1.

[0028] The term "metastasis" refers to the presence of metastatic disease in organs other than bladder tissue.

[0029] As used herein, the term "PDE4D7-correlated gene" is used synonymously with "PDE4D7 gene" and refers to one or more genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0030] As used herein, the term "T cell receptor signaling genes" is used interchangeably with "TCR signaling genes" or "TCR genes" and refers to one or more genes selected from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70.

[0031] The immune system in cancer In recent years, the importance of the immune system in cancer initiation, promotion, and metastasis, as well as in cancer suppression, has become increasingly clear (Mantovani et al. Nature. 454(7203):436-44(2008); Br J Cancer. Giraldo et al. 120(1):45-53(2019)). Immune cells and the molecules they secrete form an important part of the tumor microenvironment, and most immune cells can infiltrate tumor tissue. The immune system and tumors influence and shape each other. Thus, antitumor immunity can prevent tumor formation, while an inflammatory tumor environment can promote cancer initiation and growth. At the same time, tumor cells, which may develop independently of the immune system, can shape the immune microenvironment by recruiting immune cells, exerting pro-inflammatory effects while suppressing antitumor immunity.

[0032] Some immune cells within the tumor microenvironment have either general tumor-promoting or general tumor-suppressing effects, while others exhibit plasticity and show both tumor-promoting and tumor-suppressing potential. Thus, 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 within the tumor microenvironment (Giraldo Br J Cancer. 120(1):45-53(2019)).

[0033] Treatment is influenced by immune components of the tumor microenvironment, but RT itself has extensive effects on the composition of these components. Suppressor cell types are relatively unaffected by radiation, increasing their relative abundance. Conversely, radiation damage activates cell survival pathways and stimulates the immune system, leading to inflammatory responses and immune cell recruitment. Whether the net effect is tumor-promoting or tumor-suppressing is still unclear, but the possibility of enhancing cancer immunotherapy is being explored.

[0034] In summary, the state of the immune system and immune microenvironment influences the efficacy of treatment.

[0035] The inventors have identified gene signatures, and combinations of these signatures with clinical parameters, that show a significant association with mortality and thus provide models that are expected to improve prediction of the effectiveness of these treatments.

[0036] immune response defense genes The integrity and stability of genomic DNA are constantly exposed to stress caused by various internal and external factors, such as radiation exposure, viral or bacterial infection, and even oxidative and replicative stress (see Gasser S. et al., "Sensing of dangerous DNA," Mechanisms of Aging and Development, Vol. 165, pp. 33-46, 2017). To maintain DNA structure and stability, cells must be able to recognize all types of DNA damage, including single- or double-strand breaks, caused by various factors. This process involves the involvement of numerous specific proteins as part of the DNA recognition pathway, depending on the type of damage.

[0037] Recent evidence suggests that mislocalized DNA (e.g., DNA that unnaturally appears in the cytoplasm rather than the cell nucleus) and damaged DNA (e.g., due to mutations that occur during cancer development) are used by the immune system to identify infected or other abnormal cells, while genomic and mitochondrial DNA present in healthy cells is ignored by the DNA recognition pathway. In abnormal cells, cytoplasmic DNA sensor proteins have been demonstrated to be involved in the detection of unnaturally occurring DNA in the cell cytoplasm. Detection of such DNA by various nucleic acid sensors leads to similar responses resulting in nuclear factor κB (NF-κB) and type I interferon (type I IFN) signaling, which subsequently activates components of the innate immune system. While it is known that recognition of viral DNA elicits type I IFN responses, evidence that sensing DNA damage can initiate an immune response has only recently accumulated.

[0038] Toll-like receptor 9 (TLR9), located in endosomes, was one of the first DNA sensor molecules identified to be involved in the immune recognition of DNA by signaling downstream through the adaptor protein myeloid differentiation primary response protein 88 (MYD88). This interaction activates mitogen-activated protein kinases (MAPKs) and NF-κB. TLR9 also activates IRF7 via IκB kinase alpha (IKKalpha) in plasmacytoid dendritic cells (pDCs), inducing the production of type I interferons. Various other DNA immune receptors, such as IFI16 (IFN-γ-inducible protein 16), cGAS (cyclic GMP-AMP synthase), DDX41 (DEAD-box helicase 41), and ZBP1 (Z-DNA binding protein 1), interact with STING (stimulator of IFN genes) and activate the IKK complex and IRF3 via TBK1 (TANK-binding kinase 1). ZBP1 also activates NF-κB by recruiting RIP1 and RIP3 (receptor-interacting protein 1 and 3, respectively). The helicase DHX36 (DEAH-box helicase 36) interacts in a complex with TRID to induce NF-κB and IRF-3 / 7, while the DHX9 helicase stimulates MYD88-dependent signaling in plasmacytoid dendritic cells. The DNA sensor LRRFIP1 (leucine-rich repeat flightless-interacting protein) forms a complex with beta-catenin to activate IRF3 transcription, while AIM2 (absent in melanoma 2) recruits the adaptor protein ASC (apoptotic speck-like protein) to induce the caspase-1-activated inflammasome complex, resulting in the secretion of interleukin-1 beta (IL-1 beta) and IL-18. (See Figure 1 in Gasser S. et al., 2017, for a schematic of DNA damage and DNA sensor pathways that lead to the production of inflammatory cytokines and the expression of ligands for activating innate immune receptors. Members of the non-homologous end-joining pathway (orange), homologous recombination (red), inflammasome (dark green), NF-κB, and interferon response (light green) are shown.)

[0039] The factors and mechanisms involved in the activation of DNA sensor pathways in cancer are currently not fully understood. It is important to identify intratumoral DNA species, sensors, and pathways involved in IFN expression in various cancer types at all stages. Such factors may have prognostic and predictive value in addition to therapeutic targets in cancer. Novel DNA sensor pathway agonists and antagonists are currently being developed and tested in preclinical studies. Such compounds will help characterize the role of DNA sensor pathways in the development of cancer, autoimmune, and potentially other diseases.

[0040] T cell receptor signaling genes Immune responses to pathogens are triggered at various levels, and physical barriers, such as the skin, exist to keep invaders out. Once breached, a first, rapid, nonspecific response, the innate immune system, kicks in. If this is not sufficient, the adaptive immune response is triggered, which is much more specific and takes longer to develop the first time a pathogen is encountered. Lymphocytes are activated by interacting with activated antigen-presenting cells from the innate immune system and are also responsible for maintaining memory so that they can respond quickly the next time the same pathogen is encountered.

[0041] Because lymphocytes are highly specific and efficient upon activation, their ability to recognize self makes them subject to negative selection, a process called central immune tolerance. Because not all self-antigens are expressed at select sites, peripheral immune tolerance mechanisms have also evolved, including TCR ligation in the absence of costimulation, expression of inhibitory co-receptors, and suppression by Tregs. An imbalance between activation and suppression can lead to autoimmune diseases, namely immunodeficiency and cancer, respectively.

[0042] T cell activation can have different functional outcomes depending on the location and type of T cell involved: CD8+ T cells differentiate into cytotoxic effector cells, while CD4+ T cells can differentiate into Th1 (secreting IFNγ and promoting cell-mediated immunity) or Th2 (secreting IL4 / 5 / 13 and promoting B cell and humoral immunity). Differentiation into other more recently identified T cell subsets, such as Tregs, which have the effect of suppressing immune activation, is also possible (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, pp. 1009-1016 (2011), in particular Figure 4, which shows 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 pathway in T-cell lipid rafts", Front Biosci, Vol. 11, pp. 2929-2939 (2006)).

[0043] T cell activation can occur in naive and differentiated T cells. At the molecular level, events following ligation of the TCR with cognate antigen and crosstalk with signal transduction induced by costimulatory and co-inhibitory receptors determine whether a T cell becomes activated or becomes apoptotic. Triggering the TCR itself is insufficient, resulting in T cell anergy. The B7:CD28 family of costimulatory molecules plays a central role in controlling the activation state of T cells upon antigen stimulation (Torheim EA, "Immunity Leashed - Mechanisms of Regulation in the Human Immune System," Thesis for the degree of Doctor of Philosophy (PhD), The Biotechnology Centre of Ola, University of Oslo, Norway, 2009).

[0044] Activation occurs through the interaction of the TCR on the T cell surface with an MHC-peptide complex on an APC or target cell (Figure 2). An immune synapse is formed, resulting in the fusion of lipid rafts in the T cell membrane and the activation of Lck and Fyn. These molecules phosphorylate ITAMs within the CD3 subunit of the TCR, promoting the recruitment of Zap-70 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, propagating signaling pathways that include activation of RAS and PKC, mobilization of Ca2+ and 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 NFκB, NFAT, AP1, and ATF families, resulting in the transcription of immune-activating genes (Mosenden et al. Cell Signaling 23:1009-1016 (2011), Tasken et al. Front in Bioscience 11:2929-2939 (2006)).

[0045] Both PKA- and PDE4-regulated signaling intersect and fine-tune TCR-induced T cell activation, exerting opposing effects (see Abrahamsen H. et al., "TCR- and CD28-mediated recruitment of phosphodiesterase 4 to lipid rafts potentiates TCR signaling," J Immunol, Vol. 173, pp. 4847-4848 (2004), especially Figure 6, which shows the opposing effects of PKA and PDE4 on TCR activation). The molecule linking these effectors is cyclic AMP (cAMP), an intracellular second messenger of extracellular ligand action. In T cells, it mediates the effects of prostaglandins, adenosine, histamine, β-adrenergic agonists, neuropeptide hormones, and β-endorphin. Binding of these extracellular molecules to GPCRs leads to conformational changes, release of stimulatory subunits, and activation of adenylate cyclase (AC), which hydrolyzes ATP to cAMP (see Figure 6 in Abrahamsen H. et al., 2004, ibid.). PKA is a major, though not exclusive, effector of cAMP signaling (see Mosenden R. and Tasken K., 2011, ibid. and Tasken K. and Ruppelt A., 2006, ibid.). At the functional level, elevated cAMP levels reduce the production of IFNγ and IL-2 in T cells (see Abrahamsen H. et al., 2004, ibid.). In addition to inhibiting TCR activation, PKA has many other effectors (see Figure 15 in Torheim EA, 2009, ibid.).

[0046] In naive T cells, hyperphosphorylated PAG targets Csk to lipid rafts. PKA targets Csk via the Ezrin-EBP50-PAG scaffold complex. Specific phosphorylation by PKA allows Csk to negatively regulate Lck and Fyn, suppressing their activity and downregulating T cell activation (Abrahamsen H. et al., 2004, see Figure 6). Upon TCR activation, PAG is dephosphorylated, releasing Csk from rafts. Dissociation of Csk is required for T cell activation to proceed. Over the same time course, a Csk-G3BP complex appears to form, sequestering Csk outside of lipid rafts (Mosenden R. and Tasken K., 2011, see ibid.; Tasken K. and Ruppelt A., 2006, see ibid.).

[0047] In contrast, combined stimulation of TCR and CD28 mediates the recruitment of the cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, which promotes the degradation of cAMP (Abrahamsen H. et al., 2004, see Figure 6). This suppresses TCR-induced cAMP production and enhances T cell immune responses. TCR stimulation alone does not result in maximal T cell activation because PDE4 recruitment is too low to fully reduce cAMP levels (Abrahamsen H. et al., 2004, see Figure 6).

[0048] Thus, by actively suppressing proximal TCR signaling, cAMP-PKA-Csk-mediated signaling appears to set a threshold for T cell activation. This suppression can be countered by recruitment of PDEs. Tissue- or cell-type-specific regulation is achieved through the expression of multiple isoforms of AC, PKA, and PDEs. As discussed above, the balance between activation and suppression must be tightly regulated to prevent the development of autoimmune diseases, immune deficiencies, and cancer.

[0049] PDE4D7-related genes Phosphodiesterases (PDEs) provide the only means for degrading the second messenger 3'-5'-cyclic AMP. As such, PDEs play a critical role in regulation. Therefore, aberrant changes in PDE expression, activity, and subcellular location may all contribute to the underlying molecular pathology of specific disease states. Indeed, it has recently been shown that PDE gene mutations are common in prostate cancer patients, leading to enhanced cAMP signaling and predisposing them to prostate cancer. However, the combination of distinct expression profiles in various cell types and the complex array of isoform variants within each PDE family makes it difficult to understand the relationship between aberrant changes in PDE expression and functionality during disease progression. Several studies have been conducted to describe the complement of PDEs in the prostate, all of which have identified significant levels of PDE4 expression alongside other PDEs, leading to the development of the PDE4D7 biomarker (see Alves de Inda M. et al., "Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for Its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes," Eur Urol Focus, Vol. 4, No. 3, pp. 376-384, 2018). Because the PDE4D7 biomarker has proven to be a superior predictor, the ability to identify markers highly correlated with the PDE47 biomarker was deemed potentially useful for predicting outcomes in certain cancer patients.

[0050] Based on the relationship between PDE4D7 expression and pathological features of disease, the goal was defined as identifying a prognostic association between PDE4D7 expression in patient prostate tissue obtained by biopsy or surgery and clinically useful information related to individual patient outcomes. Clinically relevant endpoints or surrogate endpoints significantly correlated with the occurrence of metastasis, cancer-specific mortality, or overall mortality have typically been evaluated as prognostic cancer biomarkers. The most appropriate rationale for using surrogate endpoints involves cases where data on established clinical endpoints are unavailable or the number of events within a data cohort is too small for statistical data analysis. In the development of PDE4D7 prognostic biomarkers, either biochemical recurrence (BCR) progression-free survival or initiation of second-line treatment after surgery was evaluated as surrogate endpoints for metastasis and death from prostate cancer. These specific endpoints were used to identify appropriate event counts (e.g., >30% for BCR) in selected clinical cohorts, which is particularly appropriate for multivariate data analysis.

[0051] The evaluation employed standard methods of multivariate analysis, such as Cox regression and Kaplan-Meier survival analysis, to investigate the additive and independent value of continuous and / or categorical PDE4D7 scores compared with established prognostic clinical variables such as PSA and Gleason score (Alves de Inda, 2018). Logistic regression was used to construct risk models combining the PDE4D7 score with preoperative or postoperative clinical predictors of postoperative progression. The resulting models were then tested in multiple independent patient cohorts with Kaplan-Meier survival and receiver operating characteristic curve analysis to predict progression-free survival after treatment (Alves de Inda, 2018).

[0052] Using this strategy, the prognostic value of the PDE4D7 score in retrospectively collected biopsies of resected prostate tissue was tested in a cohort of patients consecutively managed in the postoperative setting at a single surgical center (Alves de Inda, 2018). The patient population consisted of approximately 500 patients, and longitudinal follow-up was performed for both pathological and biological outcomes. These clinical data were available for all patients and were collected during a mean follow-up of 120 months after treatment. The PDE4D7 score was determined as described above and then tested in both 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 involvement status) to adjust for multivariate outcomes. Here, biochemical progression-free survival after primary intervention was set as the clinical endpoint of interest. Univariate analysis of these clinical samples (Alves de Inda, 2018) found an inverse correlation between PDE4D7 expression (measured as the "PDE4D7 score") and postoperative biological recurrence (HR per unit change = 0.53, 95% CI 0.41-0.67, p<0.0001), strongly confirming previous data (Boettcher, 2015; Boettcher, 2016). In multivariate analysis using these clinical variables, the "PDE4D7 score" remained an independent and valid predictor of clinical outcome (HR per unit change = 0.56, 95% CI 0.43-0.73, p<0.0001). Furthermore, when the "PDE4D7 score" was evaluated in a multivariate analysis using the validated and clinically used risk model CAPRA-S, very similar outcomes were observed (HR=0.54 95% CI 0.42-0.69, p<0.0001). The CAPRA-S score is based on preoperative PSA and pathological parameters determined at the time of surgery and was developed to provide clinicians with information useful for predicting disease recurrence, including BCR, systemic progression, and PCSM, and has been validated in US and other populations.

[0053] Interestingly, when evaluating the hazard ratio (HR) compared with the continuous PDE4D7 score, a linear increase in risk was observed as the PDE4D7 score decreased for score values ​​between 2 and 5. However, a PDE4D7 score below 2 significantly increased the risk of postoperative progression (Alves de Inda, 2018). This was also evident in Kaplan-Meier survival curves, which showed that patients classified in the lowest PDE4D7 score category had the highest risk of disease recurrence. Using logistic regression analysis, the CAPRA-S score was combined with the continuous PDE4D7 score. Testing this model using receiver operating characteristic curve analysis confirmed a significant improvement of 4–6% in both the 2- and 5-year prediction of progression to BCR after treatment compared with CAPRA-S alone. Therefore, a combined Cox regression model combining CAPRA-S and the PDE4D7 score was evaluated in Kaplan-Meier survival analyses and compared with the CAPRA-S score categories alone. This was done and confirmed the added value in risk prediction of using the model "PDE4D7 and CAPRA-S" combined score compared to using the clinical indicator CAPRA-S score alone (Alves de Inda, 2018).

[0054] After a prostate cancer diagnosis, accurate risk assessment is necessary before stratifying patients for a defined first-line treatment. With this in mind, the prognostic use of the PDE4D7 score was tested for its translation to the preoperative setting, where tumor tissue obtained from diagnostic needle biopsy samples was examined (van Strijp 2018). Needle biopsies were performed on 168 patients who underwent surgery as first-line treatment at a single diagnostic clinical center. The minimum follow-up period for each patient was 60 months after this intervention. Clinical covariates used to adjust for the PDE4D7 score in multivariate analyses 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. The utility of the PDE4D7 score and the PDE4D7 & CAPRA composite score compared with the preoperative CAPRA score was evaluated in a Cox regression analysis of biochemical recurrence (van Strijp 2018).

[0055] When evaluating this patient cohort, multivariate analysis showed that the "PDE4D7 score" was inversely associated with BCR when adjusted for clinical variables (HR = 0.43, 95% CI 0.29-0.63, p < 0.0001) and clinical CAPRA score (HR = 0.53, 95% CI 0.38-0.74, p = 0.0001) (van Strijp 2018). Kaplan-Meier analysis, similar to the above, showed that in the postoperative setting, the "PDE4D7 score" category was significantly associated with BCR progression-free survival (logrank p < 0.0001) and second-line treatment-free survival (logrank p = 0.01). Next, a combined logistic regression model developed in the above cohort was used (van Strijp 2018). This consisted of a "CAPRA and PDE4D7" composite score, with patients in the highest "CAPRA and PDE4D7" composite score category demonstrating virtually no risk of biochemical progression or transition to second-line treatment after surgery. This logistic regression model was evaluated using receiver operating characteristic curve analysis to predict 5-year postoperative BCR. This revealed a 5% increase in AUC compared with the CAPRA score alone (AUC = 0.82 vs 0.77, respectively, p = 0.004). Decision curve analysis of the "CAPRA and PDE4D7" composite score model confirmed that using this composite score provided superior net benefit compared with either score alone across all decision thresholds for determining whether to intervene (e.g., surgery) based on an individual patient's risk threshold for postoperative disease progression (van Strijp 2018).

[0056] Predicting treatment outcomes is highly complex because many factors contribute to treatment efficacy and disease recurrence. Important factors likely remain to be identified, while the influence of other factors cannot be accurately determined. To improve response prediction and treatment selection, multiple clinicopathological measures are currently being investigated and applied in clinical settings, resulting in some improvements. However, there remains a need for more accurate prediction of treatment response to increase the success rate of these therapies.

[0057] Gene selection Gene signatures, and combinations of these signatures with clinical parameters, have been identified that yield models that show significant associations with mortality and are therefore expected to improve prediction of the effectiveness of these treatments.

[0058] 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 archived along with clinical parameters (e.g., pathological Gleason Grade Group (pGGG), pathological status (pT stage)) and appropriate outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiation therapy (SRT), salvage androgen deprivation therapy (SADT), and chemotherapy (CTX)). A PDE4D7 score was calculated for each of these patients and classified into four PDE4D7 score classes (Alves de Inda M. et al., 2018, supra). PDE4D7 score class 1 represents patient samples with the lowest PDE4D7 expression levels, while PDE4D7 score class 4 represents patient samples with the highest PDE4D7 expression levels. Next, RNASeq expression data (transcripts per million (TPM)) from 538 prostate cancer patients were examined to examine differential expression between PDE4D7 score classes 1 and 4. Specifically, approximately 20,000 protein-coding transcripts were determined to determine whether the mean expression level of patients with PDE4D7 score class 1 was at least twice that of patients with PDE4D7 score class 4. This analysis identified 637 genes with a PDE4D7 score class 1 / PDE4D7 score class 4 ratio greater than 2 and a minimum mean expression of 1 TPM in each of the four PDE4D7 score classes. These 637 genes were then further subjected to molecular pathway analysis, resulting in various enriched annotation clusters. Annotation cluster #2 showed enrichment in 30 genes with functions in defense responses against viruses, negative regulation of viral genome replication, and type I interferon signaling (enrichment score: 10.8).Further heatmap analysis confirmed that these immune defense response genes generally showed higher expression in samples from PDE4D7 score class 1 patients than in samples from PDE4D7 score class 4 patients. Gene classes with functions in antiviral defense response, negative regulation of viral genome replication, and type I interferon signaling were further enriched to 61 genes through a literature search to identify additional genes with the same molecular functions. Further selection of the 61 genes was performed based on their combined effectiveness in distinguishing between patients who died from prostate cancer and those who did not, resulting in a preferred set of 14 genes. Compared with the entire patient cohort (#538) and a subcohort of 151 patients who underwent salvage radiotherapy (SRT) after postoperative disease recurrence, the subcohort with lower expression of these genes was enriched for the number of events (metastasis, prostate cancer-specific death).

[0059] The identified T cell receptor signaling genes were CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70. A group of 538 prostate cancer patients were treated with RP, and prostate cancer tissue was archived along with clinical parameters (e.g., pathological Gleason grade group (pGGG), pathological status (pT stage)) and appropriate 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)). A PDE4D7 score was calculated for each of these patients and classified into four PDE4D7 score classes (Alves de Inda M. et al., 2018, supra). PDE4D7 score class 1 represents patient samples with the lowest PDE4D7 expression levels, while PDE4D7 score class 4 represents patient samples with the highest PDE4D7 expression levels. Next, RNASeq expression data (transcripts per million (TPM)) from 538 prostate cancer patients were examined to examine differential expression between PDE4D7 score classes 1 and 4. Specifically, approximately 20,000 protein-coding transcripts were determined to determine whether the mean expression level of patients with PDE4D7 score class 1 was at least twice that of patients with PDE4D7 score class 4. This analysis identified 637 genes with a PDE4D7 score class 1 / PDE4D7 score class 4 ratio greater than 2 and a minimum mean expression of 1 TPM in each of the four PDE4D7 score classes. These 637 genes were then further subjected to molecular pathway analysis, resulting in various enriched annotation clusters. Annotation cluster #6 showed enrichment in 17 genes with functions in primary immune deficiency and activation of T cell receptor signaling (enrichment score: 5.9).Further heatmap analysis confirmed that these T cell receptor signaling genes generally showed higher expression in samples from patients with PDE4D7 score class 1 than in samples from patients with PDE4D7 score class 4.

[0060] The identified PDE4D7-correlated genes were ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. RNA-seq data generated from nearly 60,000 transcripts from 571 prostate cancer patients identified various genes correlated with the expression of the known biomarker PDE4D7 within this data set. Correlation of the expression of these genes with PDE4D7 across the 571 samples was performed using Pearson correlation, with values ​​ranging from 0 to 1 for positive correlation and -1 to 0 for negative correlation. The PDE4D7 score (Alves de Inda M. et al., 2018, supra) and the TPM gene expression values ​​for each gene of interest determined by RNA-seq (see below) were used as input data for calculating the correlation coefficients.

[0061] The largest negative correlation coefficient identified between the expression of any of approximately 60,000 transcripts and PDE4D7 expression was -0.38, and the largest positive correlation coefficient identified between the expression of any of approximately 60,000 transcripts and PDE4D7 expression was +0.56. Genes within the correlation ranges of -0.31 to -0.38 and +0.41 to +0.56 were selected. A total of 77 transcripts matching these characteristics were identified. From these 77 transcripts, eight PDE4D7-correlated genes, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, were selected by repeated testing of Cox regression combination models in a subcohort of 186 patients who underwent salvage radiotherapy (SRT) due to postoperative biochemical recurrence. The clinical endpoint tested was prostate cancer-specific death after initiation of SRT. The boundary condition for selecting the eight genes was given by the constraint that the p-value of the multivariate Cox regression be less than 0.1 for all genes retained in the model.

[0062] Herein, these genes are shown to also have prognostic value for the outcome of bladder cancer patients.

[0063] Thus, in a first embodiment, the present invention provides a method for predicting outcome of a patient with bladder cancer, the method comprising determining or receiving a determination result of a gene expression profile comprising gene expression levels 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 AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRF receiving a determination or determination result, wherein the gene expression profile comprises gene expression levels selected from immune defense response genes selected from the group consisting of IP1, MYD88, OAS1, TLR8, and ZBP1, wherein the gene expression profile is determined in a biological sample obtained from the patient; and determining a prediction of an outcome based on the gene expression profile, wherein the prediction is an outcome for the patient, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression after treatment, and the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

[0064] In one embodiment, the invention provides a computer program product comprising instructions that, when executed by a computer, cause the computer to carry out a method, the method comprising receiving data indicative of a gene expression profile comprising gene expression levels 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 AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIH2, IFIH3, IFIH4, IFIH5, IFIH6, IFIH7, IFIH8, IFIH9, IFIH10, IFIH11, IFIH12, IFIH13, IFIH14, IFIH15, IFIH26, IFIH16, IFIH17, IFIH18, IFIH19, IFIH27, IFIH19, IFIH19, IFIH28, IFIH19, IFIH16, IFIH19, IFIH19, IFIH29, IFIH16, IFIH18, IFIH19, IFIH19, IFIH29, IFIH1 ... receiving a biological sample obtained from a bladder cancer patient, the biological sample comprising gene expression levels selected from immune defense response genes selected from the group consisting of T1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the gene expression profile is determined in a biological sample obtained from the bladder cancer patient; and determining a prediction of patient outcome based on the gene expression profile, the prediction being a patient outcome, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression after treatment, wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

[0065] In one embodiment, the method further comprises providing a prediction of outcome to a healthcare caregiver or patient. In one embodiment, determining the prediction of outcome comprises combining the gene expression levels with a regression function derived from a population of bladder cancer patients. In one embodiment, determining the prediction of outcome is further based on one or more clinical parameters obtained from the patient. In one embodiment, determining the outcome comprises combining the gene expression profile and one or more clinical parameters obtained from the patient with a regression function derived from a population of bladder cancer patients. In one embodiment, the one or more clinical parameters comprise one or more of EORTC score, number of tumor-positive regional lymph nodes, metastatic disease status, ECOG score, and FoundationOne mutation burden per MB DNA; preferably, the one or more clinical parameters comprise EORTC score, number of tumor-positive regional lymph nodes, or metastatic disease status, ECOG score, and optionally FoundationOne mutation burden per MB DNA. In one embodiment, a biological sample is obtained from the patient before initiation of treatment; preferably, the biological sample is a bladder sample or a bladder cancer sample. In one embodiment, a treatment is recommended based on the prediction. In one embodiment, the patient has non-muscle invasive bladder cancer (NMIBC), preferably high-grade NMIBC or metastatic bladder cancer (mUC).

[0066] In one embodiment, the invention relates to a method for predicting an outcome of a patient with bladder cancer, the method comprising: determining a gene expression profile comprising gene expression levels or receiving a determination result, wherein the gene expression levels comprise gene expression levels selected from T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and the gene expression profile is determined in a biological sample obtained from the patient; and determining a prediction of an outcome based on the gene expression profile, the prediction being an outcome for the patient, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy. In one embodiment, the method is further based on determining or receiving a determination result of gene expression levels 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, or all 23, selected from 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 PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, as broadly described herein.

[0067] In one embodiment, the present invention relates to a method for predicting an outcome of a patient with bladder cancer, the method comprising: determining a gene expression profile comprising gene expression levels or receiving a determination result, wherein the gene expression levels comprise gene expression levels selected from 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 the gene expression profile is determined in a biological sample obtained from the patient; and determining a prediction of an outcome based on the gene expression profile, the prediction being an outcome for the patient, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression after treatment, and wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy. In one embodiment, the method is further based on determining or receiving a determination result of gene expression levels 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, 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 PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, as broadly described herein.

[0068] Further disclosed herein is a method for predicting outcome of a bladder cancer patient, the method comprising determining or receiving a determination result of 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 39, genes, wherein the three or more gene expression levels are selected from the group consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or CD2, CD247, CD28, CD3 The method includes determining or receiving a gene expression profile, wherein the gene expression profile is determined in a biological sample obtained from the patient, and determining a prediction of an outcome based on the gene expression profile, wherein the prediction is a favorable or unfavorable outcome for the patient. Optionally, the method further includes providing the prediction of the outcome to a healthcare caregiver or the patient.

[0069] Further disclosed herein is a computer-implemented method for predicting outcome of a patient with bladder cancer, the method comprising receiving a determination of a gene expression profile comprising expression levels of three or more genes, wherein the three or more gene expression levels are selected from the group consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTP and ZAP70, and / or PDE4D7-correlated 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 patient, receiving the determination or determination result; determining a prediction of an outcome based on the gene expression profile, wherein the prediction is a favorable or unfavorable outcome for the patient; and optionally providing the prediction of the outcome to a healthcare caregiver or the patient.

[0070] The inventors explain herein that gene signatures can be used to predict survival in patients diagnosed with bladder cancer. Accordingly, the present invention in one embodiment provides a method for predicting outcome of a patient with bladder cancer, the method comprising determining or receiving a determination result of 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 39, genes, wherein the three or more gene expression levels are selected from the group consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or CD2, CD24 The method includes determining a gene expression profile or receiving a determination result, and determining a predicted outcome based on the gene expression profile, wherein the predicted outcome is survival or cancer-specific mortality. Survival may refer to overall survival or cancer-specific mortality. Disease progression, as used herein, refers to an increase in tumor size or tumor spread of at least 20 percent since the start of treatment.

[0071] In one embodiment, the prediction is based on a composite gene signature of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and 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. This combination is also referred to herein as ImmunScore. In a further embodiment, ImmunScore is further combined with clinical parameters, preferably EORTC score.

[0072] When making predictions based on one or more gene signatures described herein, it should be understood that minor changes may be made to the gene signature without affecting or improving the predictive potential.Thus, it should be understood that any gene signature described herein may 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% of the listed genes.The remaining genes may be omitted or replaced with alternative genes.

[0073] The inventors further explain that the gene signature may be used to predict treatment response in bladder cancer patients. Accordingly, the present invention in one embodiment provides a method for predicting outcome of a patient with bladder cancer, the method comprising determining or receiving a determination result of 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 39, genes, wherein the three or more gene expression levels are selected from the group consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or CD2, CD The method includes determining a gene expression profile or receiving a determination result, and determining a prediction of outcome based on the gene expression profile, wherein the gene expression profile is selected from the group consisting of T cell receptor signaling genes selected from the group consisting of 247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and / or PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, and the gene expression profile is determined in a biological sample obtained from the patient. The method includes determining a gene expression profile or receiving a determination result, and determining a prediction of outcome based on the gene expression profile, the prediction being treatment response. The treatment may be surgery, or a therapy such as immunotherapy, or treatment with an immune checkpoint inhibitor. The immunotherapy may be BCG. The immune checkpoint inhibitor may be, for example, an anti-PD-L1 drug such as atezolizumab. Patients may have NMIBC, MIBC, or metastatic bladder cancer.

[0074] The present invention describes the use of gene signature for predicting the outcome of bladder cancer patients.The gene signature comprises a gene expression profile comprising three or more, for example, 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 39 gene expression levels, and three or more gene expression levels are selected from immune defense response genes and / or T cell receptor signaling genes and / or PDE4D7 correlation genes.Therefore, in one embodiment, three or more genes can be selected from immune defense response genes.In one embodiment, three or more genes can be selected from T cell receptor signaling genes.In one embodiment, three or more genes can be selected from PDE4D7 correlation genes. In one embodiment, the three or more genes comprise one or more immune defense response genes and one or more T cell receptor signaling genes. In one embodiment, the three or more genes comprise one or more immune defense response genes and one or more PDE4D7-related genes. In one embodiment, the three or more genes comprise one or more PDE4D7-related genes and one or more T cell receptor signaling genes. In one embodiment, the three or more genes comprise one or more immune defense response genes, one or more T cell receptor signaling genes, and one or more PDE4D7-related genes.

[0075] For the above biological processes, three immune system-related gene signatures were selected, including the genes listed in Tables 1 to 3. The association of these signatures with predicting prostate cancer survival has been previously shown. Immune defense response (IDR) signature. [Table 1] T cell receptor (TCR) signature. [Table 2] PDE4D7 correlation (PDE4D7_R2) signature. [Table 3]

[0076] 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 bladder cancer outcome individually, in combination with one or more genes in a gene panel, in combination with one or more genes in a gene panel, or in all combinations.

[0077] The term "ABCC5" refers to the human ATP-binding cassette subfamily C member 5 gene (Ensembl: ENSG00000114770), e.g., the sequence defined in NCBI Reference Sequence NM_001023587.2 or NCBI Reference Sequence NM_005688.3, ​​specifically the nucleotide sequence set forth in SEQ ID NO: 1 or SEQ ID NO: 2, which correspond to the sequences of the above NCBI Reference Sequences for the ABCC5 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 3 or SEQ ID NO: 4, which correspond to the protein sequences defined in NCBI Protein Accession Reference Sequence NP_001018881.1 and NCBI Protein Accession Reference Sequence NP_005679, which encode the ABCC5 polypeptide.

[0078] The term "ABCC5" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:1 or SEQ ID NO:2, or 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:3 or SEQ ID NO:4. 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:3 or SEQ ID NO:4, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:1 or SEQ ID NO:2.

[0079] The term "AIM2" refers to the Absent in Melanoma 2 gene (Ensembl: ENSG00000163568), e.g., the sequence defined in NCBI Reference Sequence NM_004833, specifically the nucleotide sequence set forth in SEQ ID NO: 5, which corresponds to the sequence of the above NCBI Reference Sequence for the AIM2 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 6, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_004824, which encodes the AIM2 polypeptide.

[0080] The term "AIM2" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:5, or an AIM 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:6. 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:6, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:5.

[0081] The term "APOBEC3A" refers to the apolipoprotein B mRNA editing enzyme catalytic subunit 3A gene (Ensembl: ENSG00000128383), e.g., the sequence defined in NCBI Reference Sequence NM_145699, specifically the nucleotide sequence set forth in SEQ ID NO: 7, which corresponds to the sequence of the above NCBI Reference Sequence for the APOBEC3A transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 8, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_663745, which encodes the APOBEC3A polypeptide.

[0082] The term "APOBEC3A" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:7, or 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:8. This includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:8, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:7.

[0083] The term "CD2" refers to the Cluster Of Differentiation 2 gene (Ensembl: ENSG00000116824), e.g., the sequence defined in NCBI Reference Sequence NM_001767, specifically the nucleotide sequence set forth in SEQ ID NO: 9, which corresponds to the sequence of the above NCBI Reference Sequence for the CD2 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 10, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001758, which encodes the CD2 polypeptide.

[0084] The term "CD2" also includes nucleotide sequences that exhibit high homology to CD2, 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: 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 set forth in SEQ ID NO: 10. 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:10, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:9.

[0085] The term "CD247" refers to the Cluster Of Differentiation 247 gene (Ensembl: ENSG00000198821), e.g., the sequence defined in NCBI Reference Sequence NM_000734 or NCBI Reference Sequence NM_198053, specifically the nucleotide sequence set forth in SEQ ID NO: 11 or SEQ ID NO: 12, which correspond to the sequence of the above NCBI Reference Sequence for the CD247 transcript. It also relates to the corresponding amino acid sequence set forth in, e.g., SEQ ID NO: 13 or SEQ ID NO: 14, which correspond to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000725 and NCBI Protein Accession Reference Sequence NP_932170, which encode the CD247 polypeptide.

[0086] The term "CD247" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO: 11 or SEQ ID NO: 12, or 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: 13 or SEQ ID NO: 14. 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:13 or SEQ ID NO:14, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:11 or SEQ ID NO:12.

[0087] The term "CD28" refers to the Cluster Of Differentiation 28 gene (Ensembl: ENSG00000178562), e.g., the sequence defined in NCBI Reference Sequence NM_006139 or NCBI Reference Sequence NM_001243078, specifically the nucleotide sequence set forth in SEQ ID NO: 15 or SEQ ID NO: 16, which correspond to the sequence of the above NCBI Reference Sequence for the CD28 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 17 or SEQ ID NO: 18, which correspond to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_006130 and NCBI Protein Accession Reference Sequence NP_001230007, which encode the CD28 polypeptide.

[0088] The term "CD28" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO: 15 or SEQ ID NO: 16, or 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: 17 or SEQ ID NO: 18. 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:17 or SEQ ID NO:18, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:15 or SEQ ID NO:16.

[0089] The term "CD3E" refers to the Cluster Of Differentiation 3E gene (Ensembl: ENSG00000198851), e.g., the sequence defined in NCBI Reference Sequence NM_000733, specifically the nucleotide sequence set forth in SEQ ID NO: 19, which corresponds to the sequence of the above NCBI Reference Sequence for the CD3E transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 20, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000724, which encodes the CD3E polypeptide.

[0090] The term "CD3E" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO: 19, 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: 20. 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:20, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:19.

[0091] The term "CD3G" refers to the Cluster Of Differentiation 3G gene (Ensembl: ENSG00000160654), e.g., the sequence defined in NCBI Reference Sequence NM_000073, specifically the nucleotide sequence set forth in SEQ ID NO: 21, which corresponds to the sequence of the above NCBI Reference Sequence for the CD3G transcript. It also relates to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 22, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000064, which encodes the CD3G polypeptide.

[0092] The term "CD3G" also includes nucleotide sequences that exhibit high homology to CD3G, 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:21, 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:22. 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:22, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:21.

[0093] The term "CD4" refers to the Cluster Of Differentiation 4 gene (Ensembl: ENSG00000010610), e.g., the sequence defined in NCBI Reference Sequence NM_000616, specifically the nucleotide sequence set forth in SEQ ID NO: 23, which corresponds to the sequence of the above NCBI Reference Sequence for the CD4 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 24, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000607, which encodes the CD4 polypeptide.

[0094] The term "CD4" also includes nucleotide sequences that exhibit 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 set forth 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 set forth in SEQ ID NO:24. 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:24, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:23.

[0095] The term "CIAO1" refers to the Cytosolic Iron-Sulfur Assembly Component 1 gene (Ensembl: ENSG00000144021), e.g., the sequence defined in NCBI Reference Sequence NM_004804, specifically the nucleotide sequence set forth in SEQ ID NO: 25, which corresponds to the sequence of the above NCBI Reference Sequence for the CIAO1 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 26, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_663745 that encodes the CIAO1 polypeptide.

[0096] The term "CIAO1" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:25, 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:26. 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:26, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:25.

[0097] The term "CSK" refers to the C-Terminal Src Kinase gene (Ensembl: ENSG00000103653), e.g., the sequence defined in NCBI Reference Sequence NM_004383, specifically the nucleotide sequence set forth in SEQ ID NO: 27, which corresponds to the sequence of the above NCBI Reference Sequence for the CSK transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 28, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_004374, which encodes the CSK polypeptide.

[0098] The term "CSK" also includes nucleotide sequences that exhibit high 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 set forth 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 set forth in SEQ ID NO:28. 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:28, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:27.

[0099] The term "CUX2" refers to the human Cut Like Homeobox 2 gene (Ensembl: ENSG00000111249), e.g., the sequence defined in NCBI Reference Sequence NM_015267.3, specifically the nucleotide sequence set forth in SEQ ID NO: 29, which corresponds to the sequence of the above NCBI Reference Sequence for the CUX2 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 30, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_056082.2 that encodes the CUX2 polypeptide.

[0100] The term "CUX2" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:29, 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:30. 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:30, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:29.

[0101] The term "DDX58" refers to the DExD / H-box helicase 58 gene (Ensembl: ENSG00000107201), e.g., the sequence defined in NCBI Reference Sequence NM_014314, specifically the nucleotide sequence set forth in SEQ ID NO: 31, which corresponds to the sequence of the above NCBI Reference Sequence for the DDX58 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 32, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_055129, which encodes the DDX58 polypeptide.

[0102] The term "DDX58" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO: 31, 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: 32. 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: 32, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 31.

[0103] The term "DHX9" refers to the DExD / H-box Helicase 9 gene (Ensembl: ENSG00000135829), e.g., the sequence defined in NCBI Reference Sequence NM_001357, specifically the nucleotide sequence set forth in SEQ ID NO: 33, which corresponds to the sequence of the NCBI Reference Sequence for the DHX9 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 34, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001348, which encodes the DHX9 polypeptide.

[0104] The term "DHX9" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO: 33, 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: 34. 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:34, 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 set forth in SEQ ID NO:33.

[0105] The term "EZR" refers to the Ezrin gene (Ensembl: ENSG00000092820), e.g., the sequence defined in NCBI Reference Sequence NM_003379, specifically the nucleotide sequence set forth in SEQ ID NO: 35, which corresponds to the sequence of the above NCBI Reference Sequence for the EZR transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 36, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_003370 that encodes the EZR polypeptide.

[0106] The term "EZR" also includes nucleotide sequences that exhibit 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 set forth 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 set forth in SEQ ID NO:36. 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:36, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:35.

[0107] The term "FYN" refers to the FYN Proto-Oncogene gene (Ensembl: ENSG00000010810), e.g., the sequence defined in NCBI Reference Sequence NM_002037, NCBI Reference Sequence NM_153047, or NCBI Reference Sequence NM_153048, specifically the nucleotide sequence set forth in SEQ ID NO:37, SEQ ID NO:38, or SEQ ID NO:39, which correspond to the sequences of the above NCBI Reference Sequences for the FYN transcript. It also relates to the corresponding amino acid sequences set forth in, e.g., SEQ ID NO:40, SEQ ID NO:41, or SEQ ID NO:42, which correspond to the protein sequences defined in NCBI Protein Accession Reference Sequence NP_002028, NCBI Protein Accession Reference Sequence NP_694592, and NCBI Protein Accession Reference Sequence XP_005266949, which encode the FYN polypeptide.

[0108] The term "FYN" also includes nucleotide sequences that exhibit high homology to FYN, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:37, SEQ ID NO:38, or SEQ ID NO:39, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:40, SEQ ID NO:41, or SEQ ID NO:42. 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:40, SEQ ID NO:41, or SEQ ID NO:42, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:37, SEQ ID NO:38, or SEQ ID NO:39.

[0109] The term "IFI16" refers to the Interferon Gamma Inducible Protein 16 gene (Ensembl: ENSG00000163565), e.g., the sequence defined in NCBI Reference Sequence NM_005531, specifically the nucleotide sequence set forth in SEQ ID NO: 43, which corresponds to the sequence of the above NCBI Reference Sequence for the IFI16 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 44, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_005522, which encodes the IFI16 polypeptide.

[0110] The term "IFI16" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:43, 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:44. 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:44, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:43.

[0111] The term "IFIH1" refers to the Interferon Induced With Helicase C Domain 1 gene (Ensembl: ENSG00000115267), e.g., the sequence defined in NCBI Reference Sequence NM_022168, specifically the nucleotide sequence set forth in SEQ ID NO: 45, which corresponds to the sequence of the above NCBI Reference Sequence for the IFIH1 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 46, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_071451 that encodes the IFIH1 polypeptide.

[0112] The term "IFIH1" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:45, 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:46. 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:46, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:45.

[0113] The term "IFIT1" refers to the Interferon Induced Protein With Tetratricopeptide Repeats 1 gene (Ensembl: ENSG00000185745), e.g., the sequence defined in NCBI Reference Sequence NM_001270929 or NCBI Reference Sequence NM_001548.5, specifically the nucleotide sequence set forth in SEQ ID NO: 47 or SEQ ID NO: 48, which correspond to the sequence of the above NCBI Reference Sequence for the IFIT1 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 49 or SEQ ID NO: 50, which correspond to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001257858 and NCBI Protein Accession Reference Sequence NP_001539, which encodes the IFIT1 polypeptide.

[0114] The term "IFIT1" also includes nucleotide sequences that exhibit high homology to IFIT1, 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 set forth in SEQ ID NO:47 or SEQ ID NO:48, or 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:49 or SEQ ID NO:50. 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:49 or SEQ ID 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 set forth in SEQ ID NO:47 or SEQ ID NO:48.

[0115] The term "IFIT3" refers to the Interferon Induced Protein With Tetratricopeptide Repeats 3 gene (Ensembl: ENSG00000119917), e.g., the sequence defined in NCBI Reference Sequence NM_001031683, specifically the nucleotide sequence set forth in SEQ ID NO: 51, which corresponds to the sequence of the above NCBI Reference Sequence for the IFIT3 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 52, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001026853 encoding the IFIT3 polypeptide.

[0116] The term "IFIT3" also includes nucleotide sequences that exhibit high homology to IFIT3, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 51, 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: 52. 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:52, 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 set forth in SEQ ID NO:51.

[0117] The term "KIAA1549" refers to the human KIAA1549 gene (Ensembl: ENSG00000122778), for example, the sequence defined in NCBI Reference Sequence NM_020910 or NCBI Reference Sequence NM_001164665, specifically the nucleotide sequence set forth in SEQ ID NO: 53 or SEQ ID NO: 54, which correspond to the sequence of the above NCBI Reference Sequence for the KIAA1549 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 55 or SEQ ID NO: 56, for example, which correspond to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_065961 and NCBI Protein Accession Reference Sequence NP_001158137 that encodes the KIAA1549 polypeptide.

[0118] The term "KIAA1549" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:53 or SEQ ID NO:54, or 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:55 or SEQ ID NO:56. or a nucleic acid sequence encoding an amino acid sequence 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:55 or SEQ ID 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 set forth in SEQ ID NO:53 or SEQ ID NO:54.

[0119] The term "LAT" refers to the Linker For Activation Of T-Cells gene (Ensembl: ENSG00000213658), e.g., the sequence defined in NCBI Reference Sequence NM_001014987 or NCBI Reference Sequence NM_014387, specifically the nucleotide sequence set forth in SEQ ID NO: 57 or SEQ ID NO: 58, which correspond to the sequences of the above NCBI Reference Sequences for the LAT transcript. It also relates to the corresponding amino acid sequence set forth in, e.g., SEQ ID NO: 59 or SEQ ID NO: 60, which correspond to the protein sequences defined in NCBI Protein Accession Reference Sequence NP_001014987 and NCBI Protein Accession Reference Sequence NP_055202, which encode the LAT polypeptide.

[0120] The term "LAT" also includes nucleotide sequences that exhibit 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 set forth 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 set forth in SEQ ID NO:59 or SEQ ID NO:60. 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:59 or SEQ ID 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 set forth in SEQ ID NO:57 or SEQ ID NO:58.

[0121] The term "LCK" refers to the LCK Proto-Oncogene gene (Ensembl: ENSG00000182866), e.g., the sequence defined in NCBI Reference Sequence NM_005356, specifically the nucleotide sequence set forth in SEQ ID NO: 61, which corresponds to the sequence of the above NCBI Reference Sequence for the LCK transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 62, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_005347, which encodes the LCK polypeptide.

[0122] The term "LCK" also includes nucleotide sequences that exhibit high homology to LCK, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth 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 set forth in SEQ ID NO:62. 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:62, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:61.

[0123] The term "LRRFIP1" refers to the LRR Binding FLII Interacting Protein 1 gene (Ensembl: ENSG00000124831), e.g., the sequence defined in NCBI Reference Sequence NM_004735, NCBI Reference Sequence NM_001137550, NCBI Reference Sequence NM_001137553, or NCBI Reference Sequence NM_001137552, specifically the nucleotide sequence set forth in SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, or SEQ ID NO: 66, which correspond to the sequences of the above NCBI Reference Sequences for the LRRFIP1 transcript. It also relates to the corresponding amino acid sequences set forth, for example, in SEQ ID NO:67, SEQ ID NO:68, SEQ ID NO:69, or SEQ ID NO:70, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_004726, NCBI protein accession reference sequence NP_001131022, NCBI protein accession reference sequence NP_001131025, and NCBI protein accession reference sequence NP_001131024, which encode LRRFIP1 polypeptides.

[0124] The term "LRRFIP1" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:63, SEQ ID NO:64, SEQ ID NO:65, or SEQ ID NO:66, or 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:67, SEQ ID NO:68, SEQ ID NO:69, or SEQ ID NO:70. or a nucleic acid sequence encoding an amino acid sequence 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:67, SEQ ID NO:68, SEQ ID NO:69, or SEQ ID NO:70, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:63, SEQ ID NO:64, SEQ ID NO:65, or SEQ ID NO:66.

[0125] The term "MYD88" refers to the MYD88 Innate Immune Signal Transduction Adaptor gene (Ensembl: ENSG00000172936), e.g., the sequence defined in NCBI Reference Sequence NM_001172567, NCBI Reference Sequence NM_001172568, NCBI Reference Sequence NM_001172569, NCBI Reference Sequence NM_001172566, or NCBI Reference Sequence NM_002468, specifically the nucleotide sequence set forth in SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ ID NO:74, or SEQ ID NO:75, which correspond to the sequences of the above NCBI Reference Sequences for the MYD88 transcript. It also relates to the corresponding amino acid sequences set forth, for example, in SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79, or SEQ ID NO:80, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_001166038, NCBI protein accession reference sequence NP_001166039, NCBI protein accession reference sequence NP_001166040, NCBI protein accession reference sequence NP_001166037, and NCBI protein accession reference sequence NP_002459, which encode MYD88 polypeptides.

[0126] The term "MYD88" also includes nucleotide sequences that exhibit high homology to MYD88, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ ID NO:74, or SEQ ID NO:75, or nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79, or SEQ ID NO:80. 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:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79, or SEQ ID NO:80, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ ID NO:74, or SEQ ID NO:75.

[0127] The term "OAS1" refers to the 2'-5'-Oligoadenylate Synthetase 1 gene (Ensembl: ENSG00000089127), e.g., the sequence defined in NCBI Reference Sequence NM_001320151, NCBI Reference Sequence NM_002534, NCBI Reference Sequence NM_001032409, or NCBI Reference Sequence NM_016816, specifically the nucleotide sequence set forth in SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, or SEQ ID NO: 84, which correspond to the sequences of the above NCBI Reference Sequences for the OAS1 transcript. It also relates to the corresponding amino acid sequences set forth, for example, in SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87, or SEQ ID NO:88, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_001307080, NCBI protein accession reference sequence NP_002525, NCBI protein accession reference sequence NP_001027581, and NCBI protein accession reference sequence NP_058132, which encode OAS1 polypeptides.

[0128] The term "OAS1" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:81, SEQ ID NO:82, SEQ ID NO:83, or SEQ ID NO:84, or 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:85, SEQ ID NO:86, SEQ ID NO:87, or SEQ ID NO:88. 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:85, SEQ ID NO:86, SEQ ID NO:87, or SEQ ID NO:88, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:81, SEQ ID NO:82, SEQ ID NO:83, or SEQ ID NO:84.

[0129] The term "PAG1" refers to the Phosphoprotein Membrane Anchor With Glycosphingolipid Microdomains 1 gene (Ensembl: ENSG00000076641), e.g., the sequence defined in NCBI Reference Sequence NM_018440, specifically the nucleotide sequence set forth in SEQ ID NO: 89, which corresponds to the sequence of the above NCBI Reference Sequence for the PAG1 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 90, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_060910 that encodes the PAG1 polypeptide.

[0130] The term "PAG1" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:89, 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:90. 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:90, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:89.

[0131] The term "PDE4D" refers to the human phosphodiesterase PDE4D gene (Ensembl: ENSG00000113448), for example, NCBI Reference Sequence NM_001104631, NCBI Reference Sequence NM_001349242, NCBI Reference Sequence NM_001197218, NCBI Reference Sequence NM_006203, NCBI Reference Sequence NM_001197221, NCBI Reference Sequence NM_001197220, NCBI Reference Sequence NM_001197223, NCBI Reference Sequence NM_001165899, or the sequence defined in NCBI Reference Sequence NM_001165899, in particular the nucleotide sequence set forth in SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, or SEQ ID NO: 99, which correspond to the sequences of the above-mentioned NCBI Reference Sequences for the PDE4D transcripts. Also relates to the corresponding amino acid sequences set forth, for example, in SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, or SEQ ID NO:108, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_001098101, NCBI protein accession reference sequence NP_001336171, NCBI protein accession reference sequence NP_001184147, NCBI protein accession reference sequence NP_006194, NCBI protein accession reference sequence NP_001184150, NCBI protein accession reference sequence NP_001184149, NCBI protein accession reference sequence NP_001184152, NCBI protein accession reference sequence NP_001159371, and NCBI protein accession reference sequence NP_001184148, which encode PDE4D polypeptides.

[0132] The term "PDE4D" also includes nucleotide sequences that exhibit a high degree of homology to PDE4D, 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 set forth in SEQ ID NO:91, SEQ ID NO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ ID NO:98, or SEQ ID NO:99, or an 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:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, or SEQ ID NO:108. or a nucleic acid sequence encoding an amino acid sequence 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:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, or SEQ ID NO:108, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:91, SEQ ID NO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ ID NO:98, or SEQ ID NO:99.

[0133] The term "PRKACA" refers to the Protein Kinase cAMP-Activated Catalytic Subunit Alpha gene (Ensembl: ENSG00000072062), e.g., the sequence defined in NCBI Reference Sequence NM_002730 or NCBI Reference Sequence NM_207518, specifically the nucleotide sequence set forth in SEQ ID NO: 109 or SEQ ID NO: 110, which correspond to the sequences of the above NCBI Reference Sequences for the PRKACA transcript. It also relates to the corresponding amino acid sequence set forth in, e.g., SEQ ID NO: 111 or SEQ ID NO: 112, which correspond to the protein sequences defined in NCBI Protein Accession Reference Sequence NP_002721 and NCBI Protein Accession Reference Sequence NP_997401, which encode the PRKACA polypeptide.

[0134] The term "PRKACA" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:109 or SEQ ID NO:110, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:111 or SEQ ID NO:112. 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:111 or SEQ ID NO:112, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:109 or SEQ ID NO:110.

[0135] The term "PRKACB" refers to the Protein Kinase cAMP-Activated Catalytic Subunit Beta gene (Ensembl: ENSG00000142875), and may be found in, for example, NCBI Reference Sequence NM_002731, NCBI Reference Sequence NM_182948, NCBI Reference Sequence NM_001242860, NCBI Reference Sequence NM_001242859, NCBI Reference Sequence NM_001242858, NCBI Reference Sequence NM_001242862, NCBI Reference Sequence NM_001242861, NCBI Reference Sequence NM_001300915, NCBI Reference Sequence NM_001300916, NCBI Reference Sequence NM_001300918, NCBI Reference Sequence NM_001300919 ... " refers to the sequence defined in column NM_207578, NCBI Reference Sequence NM_001242857, or NCBI Reference Sequence NM_001300917, specifically the nucleotide sequence set forth in SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ ID NO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQ ID NO:122, or SEQ ID NO:123, which correspond to the sequence of the above NCBI Reference Sequence for the PRKACB transcript. Also related are the corresponding amino acid sequences set forth in, for example, SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126, SEQ ID NO: 127, SEQ ID NO: 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131, SEQ ID NO: 132, SEQ ID NO: 133, or SEQ ID NO: 134, which encode PRKACB polypeptides, including NCBI Protein Accession Reference Sequence NP_002722, NCBI Protein Accession Reference Sequence NP_891993, NCBI Protein Accession Reference Sequence NP_001229789, NCBI Protein Accession Reference Sequence NP_00122 9788, NCBI protein accession reference sequence NP_001229787, NCBI protein accession reference sequence NP_001229791, NCBI protein accession reference sequence NP_001229790, NCBI protein accession reference sequence NP_001287844, NCBI protein accession reference sequence NP_997461, NCBI protein accession reference sequence NP_001229786, and NCBI protein accession reference sequence NP_001287846.

[0136] The term "PRKACB" also includes nucleotide sequences that exhibit high homology to PRKACB, e.g., at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 100%, 101%, 102%, 103%, 104%, 105%, 106%, 107%, 108%, 109%, 1109, 112%, 1110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, or 123. a nucleic acid sequence that is 6%, 97%, 98%, or 99% identical to, or 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, SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133, or SEQ ID NO:134. an amino acid sequence or nucleic acid sequence encoding an amino acid sequence 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, SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133, or SEQ ID NO:134; or 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 set forth in SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ ID NO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQ ID NO:122, or SEQ ID NO:123.

[0137] The term "PTPRC" refers to the Protein Tyrosine Phosphatase Receptor Type C gene (Ensembl: ENSG00000081237), e.g., the sequence defined in NCBI Reference Sequence NM_002838 or NCBI Reference Sequence NM_080921, specifically the nucleotide sequence set forth in SEQ ID NO: 135 or SEQ ID NO: 136, which correspond to the sequence of the above NCBI Reference Sequence for the PTPRC transcript. It also relates to the corresponding amino acid sequence set forth in, e.g., SEQ ID NO: 137 or SEQ ID NO: 138, which correspond to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_002829, which encodes a PTPRC polypeptide, and the protein sequence defined in NCBI Protein Accession Reference Sequence NP_563578.

[0138] The term "PTPRC" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO: 135 or SEQ ID NO: 136, or 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: 137 or SEQ ID NO: 138. 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:137 or SEQ ID NO:138, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:135 or SEQ ID NO:136.

[0139] The term "RAP1GAP2" refers to the human RAP1 GTPase Activating Protein 2 gene (ENSG00000132359), e.g., the sequence defined in NCBI Reference Sequence NM_015085, NCBI Reference Sequence NM_001100398, or NCBI Reference Sequence NM_001330058, specifically the nucleotide sequence set forth in SEQ ID NO: 139, SEQ ID NO: 140, or SEQ ID NO: 141, which correspond to the sequences of the above NCBI Reference Sequences for the RAP1GAP2 transcript. It also relates to the corresponding amino acid sequences set forth in, e.g., SEQ ID NO: 142, SEQ ID NO: 143, or SEQ ID NO: 144, which correspond to the protein sequences defined in NCBI Protein Accession Reference Sequence NP_055900, NCBI Protein Accession Reference Sequence NP_001093868, and NCBI Protein Accession Reference Sequence NP_001316987, which encode the RAP1GAP2 polypeptide.

[0140] The term "RAP1GAP2" also includes nucleotide sequences that exhibit high homology to RAP1GAP2, 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:139, SEQ ID NO:140, or SEQ ID NO:141, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:142, SEQ ID NO:143, or SEQ ID NO:144. 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:142, SEQ ID NO:143, or SEQ ID NO:144, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:139, SEQ ID NO:140, or SEQ ID NO:141.

[0141] The term "SLC39A11" refers to the human Solute Carrier family 39 member 11 gene (Ensembl: ENSG00000133195), e.g., the sequence defined in NCBI Reference Sequence NM_139177 or NCBI Reference Sequence NM_001352692, specifically the nucleotide sequence set forth in SEQ ID NO: 145 or SEQ ID NO: 146, which correspond to the sequence of the above NCBI Reference Sequence for the SLC39A11 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 147 or SEQ ID NO: 148, which correspond to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_631916 and NCBI Protein Accession Reference Sequence NP_001339621, which encode the SLC39A11 polypeptide.

[0142] The term "SLC39A11" also includes nucleotide sequences that exhibit 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 set forth in SEQ ID NO: 145 or SEQ ID NO: 146, or 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: 147 or SEQ ID NO: 148. or a nucleic acid sequence encoding an amino acid sequence 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:147 or SEQ ID 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 set forth in SEQ ID NO:145 or SEQ ID NO:146.

[0143] The term "TDRD1" refers to the human Tudor Domain Containing 1 gene (Ensembl: ENSG00000095627), e.g., the sequence defined in NCBI Reference Sequence NM_198795, specifically the nucleotide sequence set forth in SEQ ID NO: 149, which corresponds to the sequence of the above NCBI Reference Sequence for the TDRD1 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 150, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_942090 that encodes the TDRD1 polypeptide.

[0144] The term "TDRD1" also includes nucleotide sequences that exhibit high homology to TDRD1, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 149, 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: 150. 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: 150, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 149.

[0145] The term "TLR8" refers to the Toll Like Receptor 8 gene (Ensembl: ENSG00000101916), e.g., the sequence defined in NCBI Reference Sequence NM_138636 or NCBI Reference Sequence NM_016610, specifically the nucleotide sequence set forth in SEQ ID NO: 151 or SEQ ID NO: 152, which correspond to the sequence of the above NCBI Reference Sequence for the TLR8 transcript. It also relates to the corresponding amino acid sequence set forth in, e.g., SEQ ID NO: 153 or SEQ ID NO: 154, which correspond to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_619542 and NCBI Protein Accession Reference Sequence NP_057694, which encode the TLR8 polypeptide.

[0146] The term "TLR8" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO: 151 or SEQ ID NO: 152, or 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: 153 or SEQ ID NO: 154. 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:153 or SEQ ID NO:154, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:151 or SEQ ID NO:152.

[0147] The term "VWA2" refers to the human Von Willebrand Factor A Domain Containing 2 gene (Ensembl: ENSG00000165816), e.g., the sequence defined in NCBI Reference Sequence NM_001320804, specifically the nucleotide sequence set forth in SEQ ID NO: 155, which corresponds to the sequence of the above NCBI Reference Sequence for the VWA2 transcript. It also relates to the corresponding amino acid sequence set forth in SEQ ID NO: 156, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001307733, which encodes the VWA2 polypeptide.

[0148] The term "VWA2" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO: 155, 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: 156. 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:156, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:155.

[0149] The term "ZAP70" refers to the Zeta Chain of T-Cell Receptor Associated Protein Kinase 70 gene (Ensembl: ENSG00000115085), e.g., the sequence defined in NCBI Reference Sequence NM_001079 or NCBI Reference Sequence NM_207519, specifically the nucleotide sequence set forth in SEQ ID NO: 157 or SEQ ID NO: 158, which correspond to the sequences of the above NCBI Reference Sequences for the ZAP70 transcript. It also relates to the corresponding amino acid sequences set forth in, e.g., SEQ ID NO: 159 or SEQ ID NO: 160, which correspond to the protein sequences defined in NCBI Protein Accession Reference Sequence NP_001070 and NCBI Protein Accession Reference Sequence NP_997402, which encode the ZAP70 polypeptide.

[0150] The term "ZAP70" also includes nucleotide sequences that exhibit high homology to ZAP70, 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 set forth in SEQ ID NO: 157 or SEQ ID NO: 158, or 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: 159 or SEQ ID NO: 160. or a nucleic acid sequence encoding an amino acid sequence 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:159 or SEQ ID NO:160, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:157 or SEQ ID NO:158.

[0151] The term "ZBP1" refers to the Z-DNA Binding Protein 1 gene (Ensembl: ENSG00000124256), e.g., the sequence defined in NCBI Reference Sequence NM_030776, NCBI Reference Sequence NM_001160418, or NCBI Reference Sequence NM_001160419, specifically the nucleotide sequence set forth in SEQ ID NO: 161, SEQ ID NO: 162, or SEQ ID NO: 163, which correspond to the sequences of the above NCBI Reference Sequences for the ZBP1 transcript. It also relates to the corresponding amino acid sequences set forth in, e.g., SEQ ID NO: 164, SEQ ID NO: 165, or SEQ ID NO: 166, which correspond to the protein sequences defined in NCBI Protein Accession Reference Sequence NP_110403, NCBI Protein Accession Reference Sequence NP_001153890, and NCBI Protein Accession Reference Sequence NP_001153891, which encode the ZBP1 polypeptide.

[0152] The term "ZBP1" also includes nucleotide sequences that exhibit high 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 set forth in SEQ ID NO:161, SEQ ID NO:162, or SEQ ID NO:163, or 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:164, SEQ ID NO:165, or SEQ ID NO:166. 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:164, SEQ ID NO:165, or SEQ ID NO:166, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:161, SEQ ID NO:162, or SEQ ID NO:163.

[0153] As can be seen from Figures 4-6, individual sub-signatures, i.e., immune defense gene set, T cell signaling gene set, or PDE4D7-related genes, can be used to predict overall survival. Therefore, it is contemplated that predictions (e.g., survival or treatment response) can be made based on three or more genes from a single sub-signature. Accordingly, in one aspect, the present disclosure relates to a method for predicting the outcome of a bladder cancer patient, comprising determining or receiving 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 14 gene expression levels, wherein the three or more gene expression levels, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 gene expression levels, are selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT2, and IFIT3. The method includes determining or receiving a gene expression profile, wherein the gene expression profile is determined in a biological sample obtained from the patient, and determining a prediction of an outcome based on the gene expression profile, wherein the prediction is a favorable or unfavorable outcome for the patient, and optionally providing the prediction of the outcome to a caregiver or the patient.

[0154] Alternatively, the present disclosure relates to a method for predicting outcome of a bladder cancer patient, the method comprising determining or receiving 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 17 gene expression levels, wherein the three or more gene expression levels, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 gene expression levels, are selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, P The method includes determining or receiving a gene expression profile, wherein the gene expression profile is selected from T cell receptor signaling genes selected from the group consisting of AG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, wherein the gene expression profile is determined in a biological sample obtained from the patient; determining a prediction of an outcome based on the gene expression profile, wherein the prediction is a favorable outcome or an unfavorable outcome for the patient; and optionally providing the prediction of the outcome to a healthcare caregiver or the patient.

[0155] Alternatively, the present disclosure relates to a method for predicting an outcome of a bladder cancer patient, the method comprising: determining a gene expression profile or receiving a determination result comprising three or more gene expression levels, for example, 3, 4, 5, 6, 7, or 8 gene expression levels, wherein the three or more gene expression levels, for example, 3, 4, 5, 6, 7, or 8 gene expression levels, are selected from PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, and the gene expression profile is determined in a biological sample obtained from the patient; determining a prediction of an outcome based on the gene expression profile, wherein the prediction is a favorable or unfavorable outcome for the patient; and optionally providing the prediction of the outcome to a healthcare caregiver or the patient.

[0156] In a preferred aspect, the gene expression profile comprises at least one gene from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-related genes.Therefore, in one embodiment, the three or more genes comprise one or more, for example, 1, 2, 3, 4, 5, or 6 or more immune defense response genes, one or more, for example, 1, 2, 3, 4, 5, or 6 or more T cell receptor signaling genes, and one or more, for example, 1, 2, 3, 4, 5, or 6 or more PDE4D7-related genes.For example, the three or more genes comprise at least one immune defense response gene, T cell receptor signaling gene, and PDE4D7-related gene, or the three or more genes comprise at least two immune defense response genes, T cell receptor signaling gene, and PDE4D7-related gene, or the three or more genes comprise at least three immune defense response genes, T cell receptor signaling gene, and PDE4D7-related gene. In one embodiment, the one or more immune defense response genes include three or more, preferably six or more, more preferably nine or more, and most preferably all of the 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, and most preferably all of the T cell receptor signaling genes, and / or the one or more PDE4D7-correlated genes include three or more, preferably six or more, and most preferably all of the PDE4D7-correlated genes. In one embodiment, determining the prediction of outcome comprises combining the expression levels of three or more genes using a regression function derived from a population of bladder cancer patients.

[0157] As defined herein, the biological sample used may be collected in a clinically acceptable manner, such as in a manner that preserves nucleic acids (particularly RNA) or proteins.

[0158] Biological samples may include bodily tissues and / or bodily fluids, such as, but not limited to, blood, sweat, saliva, and urine. Furthermore, biological samples may include cell extracts derived from epithelial cells, such as epithelial cancer cells or epithelial cells derived from tissue suspected to be cancerous, or cell populations containing such epithelial cells. Biological samples may include cell populations derived from tissues, such as glandular tissues; for example, samples may be derived from a patient's bladder. Furthermore, if necessary, cells may be purified from collected bodily tissues and fluids before use as biological samples. In some implementations, the sample may be a tissue sample, a bodily fluid sample, a blood sample, a saliva sample, a sample containing circulating tumor cells, extracellular vesicles, a sample containing bladder-secreted exosomes, or a cell line or cancer cell line. In certain implementations, biopsy or resection samples may be collected and / or used. Such samples may include cells or cell lysates.

[0159] Thus, in one embodiment, the biological sample obtained from the patient is a biopsy. In a further preferred embodiment, the method comprises providing or obtaining a biopsy. In a preferred embodiment, the biopsy is a bladder biopsy, e.g., tissue or fluid from the bladder.

[0160] It is also conceivable that the contents of the biological sample are subjected to an enrichment step. For example, the sample may be contacted with a ligand specific for the cell membrane or organelles of a particular cell type, such as bladder cells, functionalized with, for example, magnetic particles. The material enriched by the magnetic particles may then be used in the detection and analysis steps described above or below.

[0161] Additionally, cells, e.g., tumor cells, may be enriched by a filtration process of a fluid or liquid sample, e.g., blood, urine, etc. Such a filtration process may be combined with an enrichment step based on ligand-specific interactions as described above.

[0162] The biological samples provided herein are preferably taken from a patient prior to the initiation of treatment, and preferably are bladder samples or bladder cancer samples.

[0163] Thus, in a preferred embodiment, the method of the present invention comprises obtaining a biological sample from the patient prior to the initiation of treatment, preferably the biological sample is a bladder sample or a bladder cancer sample. Alternatively, the method of the present invention comprises providing a biological sample obtained from the patient prior to the initiation of treatment, preferably the biological sample is a bladder sample or a bladder cancer sample.

[0164] As used herein, the outcome of a bladder cancer patient may be a favorable outcome or an unfavorable outcome. In one aspect of the present invention, predicting the outcome of a bladder cancer patient may determine the favorable or unfavorable risk of one or more specific outcomes. The outcome may include overall mortality or overall survival, time to disease progression, bladder cancer-related mortality, locoregional recurrence, and / or distant recurrence. Preferably, bladder cancer-related mortality is bladder cancer-specific mortality. The prediction provided by the method of the present invention can provide a prediction of the risk of a bladder cancer patient for a specific outcome. Furthermore, the method of the present invention can predict whether a patient with bladder cancer is at low risk of a specific outcome or at high risk of a specific outcome. As used herein, the outcomes of bladder cancer-related mortality, locoregional recurrence, and / or distant recurrence include unfavorable outcomes for the patient. A further outcome is overall mortality. As used herein, overall mortality refers to an outcome that can be predicted by the method of the present invention but is not directly related to the patient's death due to bladder cancer. Thus, in one embodiment, the method provides a prediction of a favorable or unfavorable outcome, wherein the favorable or unfavorable outcome is the probability of survival, overall survival, cancer-free survival, overall mortality, or cancer-specific mortality.

[0165] The present invention particularly aims to predict favorable or unfavorable outcomes of immunotherapy for patients suffering from bladder cancer. In particular, the present invention aims to identify favorable or unfavorable outcomes for patients with metastatic bladder cancer or high-grade NMIBC. If a favorable outcome is predicted, immunotherapy is administered or recommended. If an unfavorable outcome is predicted, alternative treatments may be recommended or administered. Such alternative treatments may include radiation therapy, chemotherapy, tumor resection / surgery, immunotherapy (e.g., immune checkpoint inhibitor therapy) combined with chemotherapy or radiation therapy, or experimental therapies such as CAR-T cell therapy, viral therapy, or RNA vaccine-based therapy.

[0166] Thus, in one embodiment, the present invention relates to a method of treatment, the method comprising: a) predicting the outcome of a patient with bladder cancer, wherein predicting i. determining or receiving a gene expression profile comprising gene expression levels, wherein the gene expression levels are 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 iii. determining or receiving a gene expression profile, the gene expression profile comprising gene expression levels selected from 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, wherein the gene expression profile is determined in a biological sample obtained from the patient; and iv. predicting an outcome, including determining a prediction of outcome based on the gene expression profile, the prediction being a patient outcome, the outcome being a predicted survival time in response to a treatment, the treatment being an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy; b) administering immunotherapy to the patient if a favorable outcome is predicted.

[0167] In one embodiment, the method further comprises administering an alternative treatment selected from radiation therapy, chemotherapy, tumor resection / surgery, a combination of immunotherapy (e.g., immune checkpoint inhibitor therapy) with chemotherapy or radiation therapy, or an experimental therapy such as CAR-T cell therapy, virotherapy, or RNA vaccine-based therapy if an unfavorable outcome is predicted. In one embodiment, a favorable outcome is determined by an above-average predicted survival time or time to disease progression. In one embodiment, an unfavorable outcome is determined by a below-average predicted survival time or time to disease progression. It should be understood that the average predicted survival time may be determined for bladder cancer patients receiving immunotherapy, and that patients who respond favorably to immunotherapy may be defined as those with a predicted survival time that exceeds the average of the pool of responding and non-responding patients. Alternatively, immunotherapy response data may be correlated with gene expression data to establish a reference value for determining a favorable or unfavorable response in the above method.

[0168] In the provided methods of the present disclosure, predicting the outcome of a bladder cancer patient preferably includes predicting a favorable or unfavorable risk of bladder cancer-related death, locoregional recurrence, and / or distant recurrence after surgery. In other words, predicting the risk of a particular outcome before surgery is performed on the bladder cancer patient. Furthermore, in the provided methods of the present disclosure, predicting the outcome of a bladder cancer patient includes predicting a favorable or unfavorable risk of bladder cancer-related death, locoregional recurrence, and / or distant recurrence after treatment. For example, the treatment may be immunotherapy, such as Bacillus Calmette-Guérin (BCG) therapy, or treatment with an immune checkpoint inhibitor. Thus, in one embodiment, the method provides a prediction of a favorable or unfavorable outcome, where the favorable or unfavorable outcome is survival from the treatment. In a further embodiment, the treatment is surgery or immunotherapy, preferably Bacillus Calmette-Guérin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

[0169] Bladder cancer patients who are predicted by the methods of the present invention to be at high risk (i.e., above a certain threshold) of one or more of the outcomes of bladder cancer-related death, locoregional recurrence, and / or distant recurrence are believed to be associated with an unfavorable risk (preferably after surgery) for each outcome.

[0170] Bladder cancer patients who are predicted by the method of the present invention to have a low risk (i.e., below a certain threshold) of one or more outcomes of bladder cancer-related death, local-regional recurrence, and / or distant recurrence are considered to be associated with a favorable risk (preferably after surgery) for each outcome. A favorable risk may involve a different follow-up strategy, for example, a different recommended (follow-up) treatment, than a patient with an unfavorable risk. For example, for bladder cancer patients, preferably patients who have undergone bladder (cancer) surgery, a different follow-up strategy, for example, a different recommended (follow-up) treatment, can be considered to improve the survival rate of the bladder cancer patient.

[0171] Preferably, the treatment comprises surgery, radiation therapy, hormone therapy, cytotoxic chemotherapy, and / or immunotherapy. Combination cancer therapy, i.e., the combination of two or more therapies and / or agents, such as the combination of radiation therapy and chemotherapy, is widely recognized as a cornerstone of cancer treatment.

[0172] Thus, in a preferred embodiment, the method of the present invention comprises that a biological sample, preferably a sample of the patient's bladder or the patient's bladder cancer, is obtained prior to the initiation of treatment, including surgery, radiation therapy, hormonal therapy, cytotoxic chemotherapy, and / or immunotherapy.

[0173] As disclosed herein, the risk of an unfavorable outcome in a broad method for predicting bladder cancer outcome generally will influence the recommended treatment for the bladder cancer patient associated with the unfavorable risk. When an unfavorable risk is predicted by the method of the present invention, the recommended treatment is: (i) Radiation therapy is administered earlier than standard; (ii) radiotherapy with increased radiation doses; (iii) adjuvant therapy, such as cytotoxic chemotherapy, immunotherapy, and / or hormonal therapy; (iv) surgery, and (v) is considered to include one or more alternative non-surgical therapies;

[0174] In a preferred aspect, the method according to the present disclosure comprises recommending a treatment based on the prediction, preferably the outcome prediction, - If the prognosis is unfavorable, the recommended treatment, preferably post-operative, is: (i) Radiation therapy is administered earlier than standard; (ii) radiotherapy with increased radiation doses; (iii) adjuvant therapy, such as cytotoxic chemotherapy, immunotherapy, and / or hormonal therapy; (iv) surgery, and (v) includes one or more alternative non-surgical therapies.

[0175] A favorable risk of outcome will influence the recommended treatment for the bladder cancer patient associated with the favorable risk. If a favorable risk is predicted by the methods of the present disclosure, the recommended treatment will be: (vi) definitive radiotherapy; (vii) salvage radiation therapy; (viii) reduced-dose salvage radiation therapy, and (ix) palliative care.

[0176] Thus, in another preferred aspect, the method according to the present disclosure includes recommending a therapy based on the prediction, - If the prognosis is favorable, the recommended treatment, preferably post-operative, is: (vi) definitive radiotherapy; (vii) salvage radiation therapy; (viii) reduced-dose salvage radiation therapy, and (ix) palliative care.

[0177] In one embodiment of the present invention, the risk of a favorable or unfavorable outcome is predicted for bladder cancer patients who have undergone surgery, preferably bladder surgery, such as, but not limited to, lumpectomy, quadrantectomy, partial mastectomy, lumpectomy, or total mastectomy.

[0178] In another aspect of the invention there is provided a method according to the invention comprising recommending a second-line treatment to a particular patient, preferably a patient at low or high risk of suffering an outcome selected from bladder cancer-specific mortality or all-cause mortality, wherein the recommendation is based on a prediction of outcome for the bladder cancer patient; - If the prognosis is favorable, second-line treatment is not recommended, and / or - If the prognosis is unfavorable, second-line treatment is recommended. It is provided herein that the method of the present invention can predict the effectiveness of post-operative secondary treatment for patients with low-risk or high-risk profiles, preferably including one or more, more preferably all, of chemotherapy, hormone therapy, and radiation therapy.

[0179] 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 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-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. It will be appreciated that the method may be performed on input related to the expression level of the one or more genes of interest, or determining the expression level may be part of the method.

[0180] It is further envisaged that the method is executed by a processor.Thus, in one embodiment, the present invention relates to a computer-implemented method for predicting outcome in a bladder cancer patient.

[0181] The method for predicting the outcome of a patient with bladder cancer includes the use of one or more, for example 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 CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, P Preferably, the method comprises determining the gene expression profile of 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 RKACB, PTPRC, and ZAP70, and / or one or more, e.g., 1, 2, 3, 4, 5, 6, 7, or all, PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0182] In a further aspect of the invention, a method of predicting outcome in a bladder cancer patient comprises the step of detecting two or more, for example 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 CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4 The method includes determining the gene expression profile of two 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 T cell receptor D, PRKACA, PRKACB, PTPRC, and ZAP70, and / or two or more, e.g., 1, 2, 3, 4, 5, 6, 7, or all, PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0183] In one preferred aspect, the method according to the present disclosure comprises: - the one or more immune defense response genes include three or more, preferably six or more, more preferably nine or more, and most preferably all immune defense genes; and / or - the one or more T cell receptor signaling genes include three or more, preferably six or more, more preferably nine or more, and most preferably all of the T cell receptor signaling genes; and / or - The one or more PDE4D7-correlated genes include three or more, preferably six or more, and most preferably all of the PDE4D7-correlated genes.

[0184] Thus, determining the first, second, and / or third gene expression profile according to the methods of the present invention includes: - three or more, preferably six or more, more preferably nine or more, and most preferably all of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; - three or more, preferably six or more, more preferably nine or more, and most preferably all 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 T cell receptor signaling genes; and / or - Preferably, the method includes determining or receiving the determination results of three or more, preferably six or more, more preferably nine or more, and most preferably all PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0185] In a further aspect, the method of the present disclosure includes determining the outcome prediction based on one or more immune defense response genes, one or more T cell receptor signaling genes, and one or more PDE4D7-correlated genes. In a further aspect, the method of the present disclosure includes determining the outcome prediction based on two or more immune defense response genes, two or more T cell receptor signaling genes, and two or more PDE4D7-correlated genes. In a further aspect, the method of the present disclosure includes determining the outcome prediction based on three or more immune defense response genes, three or more T cell receptor signaling genes, and three or more PDE4D7-correlated genes.

[0186] As a first illustrative example, the (first) gene expression profile disclosed herein can comprise at least one, at least two, or at least three 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. In a non-limiting example, the gene expression profile embodied herein comprises the expression profiles of the genes DDX58, DHX9, and IFI16.

[0187] By way of further illustrative example, the (second) gene expression profile disclosed herein can comprise at least one, at least two, or 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 embodied herein comprises the expression profiles of the genes PRKACA, PRKACB, and PTPRC.

[0188] As a further illustrative example, the (third) gene expression profile disclosed herein may comprise the gene expression profiles of at least one, at least two, or at least three PDE4D7-correlated 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 embodied herein comprises the expression profiles of the genes KIAA1549, PDE4D, and RAP1GAP2.

[0189] By way of further non-limiting example, the gene expression profile disclosed herein can include first, second, and third gene expression profiles, wherein the first gene expression profile includes at least one, at least two, or at least three 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 the second gene expression profile includes at least one, at least two, or at least three immune defense response genes selected from the group consisting of CD2, CD247, CD28 , CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and the third gene expression profile includes at least one, at least two, or at least three PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0190] In a further non-limiting reference example disclosed herein, the gene expression profile for predicting the outcome of bladder cancer can 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 can 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).

[0191] Cox proportional hazards regression allows for the analysis of the effect of multiple risk factors on the time to a test event, such as survival. Risk factors can be binary or discrete variables, such as risk scores or clinical stages, or continuous variables, such as biomarker measurements or gene expression values. The probability of an endpoint (e.g., death or disease recurrence) is called the hazard. In addition to information about whether patients in a patient cohort reached the test endpoint (e.g., whether they died), the regression analysis also considers the time to the endpoint. Hazard is modeled as H(t) = H0(t)·exp(w1·V1+w2·V2+w3·V3+···), where V1, V2, and V3··· are predictor variables, H0(t) is the baseline hazard, and H(t) is the hazard at any time point t. The hazard ratio (HR) (i.e., the risk of reaching the event) is given by Ln[H(t) / H0(t)]=w1·V1+w2·V2+w3·V3+···, where the coefficients or weights w1, w2, w3··· are estimated by Cox regression analysis and can be interpreted in a similar way to that of logistic regression analysis.

[0192] Thus, in a preferred embodiment of the method of the present invention, determining a prediction of outcome comprises combining the first gene expression profile combination, the second gene expression profile combination, and the third gene expression profile combination using a regression function derived from a population of bladder cancer patients.

[0193] In another preferred embodiment, the method of the present invention comprises determining the prognosis of outcome by: - combining two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, first gene expression profiles of immune defense response genes with a regression function derived from a population of bladder cancer patients; and / or combining a second gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all, T cell receptor signaling genes with a regression function derived from a population of bladder cancer patients; and / or - combining a third gene expression profile of two or more, for example, 2, 3, 4, 5, 6, 7, or all, of the PDE4D7-correlated genes with a regression function derived from a population of bladder cancer patients.

[0194] In one particular implementation, the outcome prediction is determined as follows. IDR_14_model: (1) (w1·AIM2)+(w2·APOBEC3A)+(w3·CIAO1)+[···]+(w 14 ZBP1) Here, w1~w 14 are the weights, and AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 are the expression levels of genes.

[0195] In another particular implementation, the outcome prediction is determined as follows. TCR_17_model: (2) (w 15 CD2)+(w 16 ·CD247)+(w 17 ·CD28)+[···]+(w 31 ZAP70) where w 15 ~w 31 are the weights, and CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 are the expression levels of genes.

[0196] In another particular implementation, the outcome prediction is determined as follows. PDE4D7_CORR_model: (3) (w 32 ·ABCC5)+(w 33 ·CUX2)+(w 34 ·KIAA1549)+[···]+(w 39 VWA2) where w 32 ~w 39 are the weights, and ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 are the expression levels of genes.

[0197] In another particular implementation, the outcome prediction is determined as follows. BCAI_model (4) w 42 ·PDE4D7_CORR)+(w 40 ·IDR_14)+(w 41 ·TCR_17)

[0198] In another implementation, the outcome prediction is determined as follows. BCAI_model (5) (w1·AIM2)+(w2·APOBEC3A)+(w3·CIAO1)+[···]+(w 14 ZBP1)+(w 15 CD2)+(w 16 ·CD247)+(w 17 ·CD28)+[···]+(w 31 ZAP70)+(w 32 ·ABCC5)+(w 33 ·CUX2)+(w 34 ·KIAA1549)+[···]+(w 39 ·VWA2), Here, w1~w 39are 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 gene expression levels.

[0199] The outcome prediction may be classified or categorized into at least two risk groups based on the value of the outcome prediction, for example, there may be two, three, four or more predefined risk groups.

[0200] Each risk group has a distinct (non-overlapping) range of predicted outcomes. For example, risk groups can represent the probability of a particular clinical event occurring, such as 0 to <0.1, 0.1 to <0.25, 0.25 to <0.5, or 0.5 to 1.0.

[0201] In a preferred embodiment, the method of the present invention comprises determining the outcome prediction based on one or more clinical parameters obtained from the patient. As described above, various measures based on one or more clinical parameters have been investigated. Basing the outcome prediction on such clinical parameters may allow for further improvement of the prediction.

[0202] 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.

[0203] In one embodiment, determining a prediction of therapeutic response comprises combining gene expression levels of one or more IDR genes, one or more TCR signaling genes, and / or one or more PDE4D7-correlated genes with a regression function derived from a population of bladder cancer patients.

[0204] In addition, it is preferred that the gene expression profile of one or more IDR genes, one or more TCR signal transduction genes, and / or one or more PDE4D7-related genes obtained from patient and one or more clinical parameters are combined with the regression function derived from a group of bladder cancer patients.Therefore, in one embodiment, determining the prediction of outcome is further based on one or more clinical parameters obtained from patient.

[0205] Also, to provide a method for predicting the outcome of a bladder cancer patient, it is preferred that one or more clinical parameters are combined with one or more of the first, second, and third gene expressions using a regression function derived from a population of bladder cancer patients. Thus, in one embodiment, determining the outcome comprises combining the gene expression profile and one or more clinical parameters obtained from the patient using a regression function derived from a population of bladder cancer patients.

[0206] Thus, in a preferred embodiment of the method of the present invention, determining the outcome comprises: (i) a first gene expression profile of one or more immune defense response genes; (ii) a second gene expression profile of one or more T cell receptor signaling genes; (iii) a third gene expression profile of one or more PDE4D7-correlated genes; and (iv) combining one or more of the first gene expression profile, the second gene expression profile, the third combination of gene expression profiles, and one or more clinical parameters obtained from the patient with a regression function derived from a population of bladder cancer patients.

[0207] In another particular implementation, the outcome prediction is determined as follows. BCAI clinical model (6) (w 43 ·BCAI_model)+(w 44 ·LN_positive) where w 43 and w 44 where is the weight, BCAI_model is the regression model based on the expression profiles of one or more IDR genes, one or more TCR signaling genes, and / or one or more PDE4D7-correlated genes, and LN_positive represents the number of tumor-positive lymph nodes. Multivariate Cox regression was used to create a clinical model combining BCAI with clinical data (number of positive lymph nodes).

[0208] An example of a suitable clinical parameter used and exemplified herein is "LN_positive," which represents the number of tumor-positive lymph nodes after pathology examination following surgery. LN-positive bladder tumors are bladder tumors in which tumor cells have spread to nearby lymph nodes, i.e., metastasized. LN-positive tumors are thought to be a precursor to potential cancer metastasis.

[0209] Other examples of clinical parameters include EORTC score, number of tumor-positive regional lymph nodes, metastatic disease status, ECOG score, and FoundationOne mutation load per MB DNA.Therefore, in one embodiment, one or more clinical parameters include one or more of EORTC score, number of tumor-positive regional lymph nodes, metastatic disease status, ECOG score, and FoundationOne mutation load per MB DNA, and preferably, one or more clinical parameters are comprised of EORTC score, number of tumor-positive regional lymph nodes, or metastatic disease status, ECOG score, and optionally FoundationOne mutation load per MB DNA.In a particularly preferred embodiment, the clinical parameter is EORTC score.

[0210] The EORTC score (2006, European Organization for Research and Treatment of Cancer (EORTC) scoring model) used herein is described in Sylvester, RJ, et al. Eur Urol, 2006.49:466 (incorporated herein in its entirety by reference). This score predicts the short-term and long-term risk of recurrence using a scoring system and risk table based on the 2006 WHO 1973 classification. The scoring system is based on the six most important clinical and pathological factors in patients treated primarily with intravesical chemotherapy: number of tumors, tumor diameter, recurrence frequency, T category, concomitant CIS, and WHO 1973 tumor grade. The score can be calculated as described at https: / / www.omnicalculator.com / health / eortc-bladder-cancer.

[0211] As used herein, the number of tumor-positive regional lymph nodes reflects the score given to lymph nodes in the bladder region tested for tumor, with a score of 0 reflecting no tumor-positive regional lymph nodes and integers greater than 0 reflecting the number of tumor-positive lymph nodes identified. Generally, lymph nodes with only isolated tumor cells (ITCs) are not counted as positive nodes, and only lymph nodes with metastases greater than 0.2 mm (micrometastases or greater) are counted as positive.

[0212] The term metastatic disease state, as used herein, refers to the stage of a tumor in categories I-IV, which are commonly used by clinicians. Generally, the stages are classified as follows: Stage I: The cancer is localized and has not spread to lymph nodes or other tissues. Stage II: The cancer is growing but has not metastasized. Stage III: The cancer is growing larger and may have spread to lymph nodes or other tissues. Stage IV: The cancer has metastasized to other organs or parts of the body.

[0213] The term ECOG score as used herein should be understood to have its usual meaning. The ECOG score describes a patient's level of function in terms of self-care ability, daily activities, and physical ability (walking, work, etc.). This scale, developed by the Eastern Cooperative Oncology Group (ECOG) and published in 1982, is also known as the WHO score or Zubrod score and is rated from 0 to 5, with 0 indicating perfect health and 5 indicating death. Scores are assigned as follows: 0 - Asymptomatic. Fully active and able to perform all pre-illness activities without restriction. 1 - Symptomatic but fully ambulatory. Physically strenuous activity is limited but the patient is able to walk and perform light or sedentary tasks (e.g., light housework, office work). 2 - Symptomatic and spends less than 50% of the day in bed, ambulatory and able to perform all self-care activities but unable to perform any occupational activities, and awake and active more than 50% of the time. 3 - Symptomatic and spends more than 50% of time in bed, but is not bedridden. Has limited self-care and requires more than 50% of waking hours in bed or a chair. 4 - Bedridden. Totally immobile. Unable to perform any self-care. Must be completely confined to bed or a chair. 5-Death.

[0214] As used herein, the term "FoundationOne mutation burden / MB DNA" is defined as the number of somatic mutations per megabase of interrogated genomic sequence, as determined using a FoundationOne CDx next-generation sequencer.

[0215] It will be appreciated that in the case of NMIBC or MIBC, the clinical parameters used preferably include or are the number of tumor-positive regional lymph nodes. In the case of metastatic bladder cancer, the clinical parameters preferably include or are EORTC score, metastatic disease status, ECOG score, optionally complemented by FoundationOne mutation burden per MB DNA.

[0216] The method according to the present invention may be implemented as a computer program product executable on a computer. The computer program product may include a non-transitory computer-readable recording medium, such as a disk or hard drive, on which a 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 other magnetic storage medium, a CD-ROM, a DVD, or other optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM, or other memory chip or cartridge, or other non-transitory medium that can be read and used by a computer.

[0217] Thus, in a preferred embodiment, the present invention provides a computer program product comprising instructions that, when executed by a computer, cause the computer to carry out a method, the method comprising receiving data indicative of a gene expression profile comprising gene expression levels 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 AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIH2, IFIH3, IFIH4, IFIH5, IFIH6, IFIH7, IFIH8, IFIH9, IFIH10, IFIH11, IFIH12, IFIH13, IFIH14, IFIH15, IFIH26, IFIH3, IFIH4, IFIH5, IFIH6, IFIH7, IFIH8, IFIH9, IFIH16, IFIH17, IFIH18, IFIH19, IFIH20, IFIH19, IFIH19, IFIH21, IFIH12, IFIH13, IFIH14, IFIH15, IFIH16, IFIH17, IFIH18, IFIH19, IFIH20, IFIH19, IFIH19, IFIH21, IFIH19 ... receiving a gene expression profile comprising gene expression levels selected from immune defense response genes selected from the group consisting of T3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the gene expression profile is determined in a biological sample obtained from a bladder cancer patient; and determining a prediction of patient outcome based on the gene expression profile, the prediction being a favorable outcome or an unfavorable outcome, the favorable outcome or unfavorable outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression, wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy. Optionally, the program product further includes instructions for providing the outcome prediction to a healthcare caregiver or the patient.

[0218] Further disclosed herein is a computer program product comprising instructions that, when executed by a computer, cause the computer to carry out a method, the method comprising: - receiving data indicative of a gene expression profile, the 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 39, gene expression levels, wherein the three or more gene expression levels are selected from the group consisting of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or CD2, CD247, CD28, CD3E, CD3 and / or PDE4D7-correlated 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 patient; and determining a prediction of an outcome based on the gene expression profile, wherein the prediction is a favorable or unfavorable outcome for the patient. Optionally, the program product further includes instructions for providing the prediction of the outcome to a healthcare caregiver or the patient.

[0219] Alternatively, one or more steps of the method may be implemented as a transitory medium such as a transmissible carrier wave embodied as a data signal using a transmission medium such as sound waves or light waves generated during radio wave communication, infrared data communication, and the like.

[0220] This exemplary method may be implemented on one or more general-purpose computers, special-purpose computers, programmed microprocessors or microcontrollers and peripheral integrated circuit elements, hardwired electronic or logic circuits such as ASICs or other integrated circuits, digital signal processors, discrete element circuits, programmable logic devices such as PLDs, PLAs, FPGAs, graphical card CPUs (GPUs), or PALs. Generally, any device capable of implementing a finite state machine capable of implementing the steps described herein can be used to implement one or more steps of the risk stratification method for treatment selection for bladder cancer patients. While the method steps may all be computer-implemented, it will be understood that in some embodiments, one or more steps may be performed at least in part manually.

[0221] Preferably, the computer program product of the present invention can be implemented in an apparatus for predicting outcome in a bladder cancer patient, the apparatus comprising an input adapted to receive data indicative of gene expression profiles of immune defense response genes and / or T cell receptor signaling genes and / or PDE4D7-correlated genes, the apparatus further comprising a processor adapted to determine a prediction of outcome based on the one or more gene expression profiles; - Optionally, further comprising a delivery unit adapted to provide a prediction or treatment recommendation to a healthcare professional or a patient based on the selection.

[0222] In a further aspect of the present invention, there is provided an apparatus for predicting outcome of a patient with bladder cancer, the apparatus comprising: an input adapted to receive data indicative of a gene expression profile comprising gene expression levels 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 AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and and a processor adapted to determine a prediction of a patient outcome based on the gene expression profile, the prediction being a patient outcome, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression, wherein the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy, - optionally further comprising a delivery unit adapted to provide a prediction or a treatment recommendation to a healthcare professional or a patient based on the prediction.

[0223] Further disclosed is an apparatus for predicting outcome in a bladder cancer patient, the apparatus comprising an input adapted to receive data indicative of a gene expression profile, the 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 39, gene expression levels, wherein the three or more gene expression levels are - 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 - selected from PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; an input, wherein the gene expression profile is determined in a biological sample obtained from the patient; - a processor adapted to determine a prediction of radiotherapy response based on a gene expression profile of three or more genes; - optionally a delivery unit adapted to provide a prediction or a treatment recommendation to a healthcare professional or a patient based on the prediction.

[0224] The computer program instructions may be loaded onto a computer, other programmable data processing apparatus, or other device, and may cause the computer, other programmable apparatus, or other device to perform a series of operational steps to create a computer-implemented process. The instructions executing on the computer or other programmable apparatus provide a process for performing the functions / operations described herein.

[0225] Thus, in one preferred embodiment, the present invention provides a diagnostic kit comprising at least one polymerase chain reaction primer and, optionally, at least one probe for determining the gene expression profile in a biological sample and / or sample obtained from a bladder cancer patient.

[0226] The diagnostic kits provided herein preferably comprise at least one polymerase chain reaction primer or probe for determining a gene expression profile, the gene expression profile comprising three or more expression levels in a biological sample and / or samples obtained from a bladder cancer patient, the three or more gene expression levels being: - 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-correlated genes 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 SAGE (Serial Analysis of Gene Expression) kit, a DNA microarray, or a tiling array.

[0227] In another preferred embodiment, the present invention provides for the use of a diagnostic kit as broadly embodied herein in a method for predicting the outcome of a bladder cancer patient, preferably in a method for predicting the outcome of a bladder cancer patient as broadly embodied herein. Thus, in one embodiment, the present invention relates to the use of a diagnostic kit, the kit comprising: - at least one polymerase chain reaction primer or probe for determining a gene expression profile, wherein the gene expression profile comprises three or more expression levels in a biological sample and / or sample obtained from the bladder cancer patient, and the three or more gene expression levels are 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 - selected from PDE4D7-correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; The use includes predicting outcome in a bladder cancer patient. Preferably, the use includes using the kit in a method of predicting outcome, as broadly defined herein.

[0228] In another preferred embodiment, the present invention provides a method, the method comprising: - receiving biological samples obtained from patients with bladder cancer; and - determining a gene expression profile using a diagnostic kit as broadly embodied herein, wherein the gene expression profile is 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 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 optionally determining a prediction of patient outcome based on the gene expression profile, wherein the prediction is a patient outcome, the outcome being survival to treatment, cancer-free survival to treatment, or time to disease progression, and the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

[0229] Further disclosed herein is a method, the method comprising: - receiving biological samples obtained from patients with bladder cancer; and - determining a gene expression profile using a diagnostic kit as broadly embodied herein, wherein the gene expression profile comprises three or more expression levels in a biological sample and / or sample obtained from the bladder cancer patient, wherein the three or more gene expression levels are: - 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 - determining a gene expression profile selected from PDE4D7 selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0230] In a further aspect, the invention provides Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy for use in treating bladder cancer in a patient, the use comprising carrying out 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 patient. Accordingly, one aspect of the invention describes Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy for use in treating bladder cancer in a patient, the use comprising: performing a method for predicting an outcome of a patient with bladder cancer; and if a favorable outcome of the treatment is predicted, administering Bacillus Calmette-Guerin immunotherapy or immune checkpoint inhibitor therapy to the patient, wherein the method for predicting an outcome of a patient with bladder cancer comprises determining, or receiving a result of the determination, a gene expression profile comprising gene expression levels, the gene expression levels being 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 determining a gene expression profile or receiving a result of the determination, the gene expression profile comprising gene expression levels selected from immune defense response genes 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, wherein the gene expression profile is determined in a biological sample obtained from the patient; and determining a prediction of an outcome based on the gene expression profile, the prediction being an outcome for the patient, the outcome being survival in response to a treatment, and the treatment being an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

[0231] All references cited herein, including journal articles or abstracts, published or corresponding patent applications, patents, or other references, are incorporated herein by reference in their entirety, including all data, tables, figures, and text presented in the cited references. Additionally, the entire contents of the references cited within the references cited herein are also incorporated herein by reference in their entirety.

[0232] Reference to known method steps, conventional method steps, known methods, or conventional methods is not an admission that any aspect, description, or embodiment of the present invention is disclosed, taught, or suggested in the relevant art.

[0233] It is to be understood that the phrases or terminology herein are for purposes of description and not of limitation, and therefore should be interpreted by the skilled artisan in light of the teaching and guidance presented herein, in combination with the knowledge of those skilled in the art.

[0234] It will be understood that all details, embodiments, and preferences described with respect to one aspect of an embodiment of the present invention are equally applicable to other aspects or embodiments of the present invention, and therefore it is not necessary to separately detail all such details, embodiments, and preferences for every aspect.

[0235] Having generally described the invention, the same may be more readily understood by reference to the following examples, which are provided for illustrative purposes and are not intended to limit the invention. Further aspects and embodiments will be apparent to those skilled in the art.

[0236] Other variations of 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.

[0237] Any reference signs in the claims should not be construed as limiting the scope.

[0238] Example Example 1: Predicting survival outcomes in patients with non-metastatic (M0) bladder cancer We analyzed the three gene signatures using multiple datasets. The TCGA Bladder Urothelial sCarcinoma, Firehose legacy database was accessed on March 6, 2020, and gene expression data compiled by The Genome Cancer Atlas (TCGA) were downloaded along with clinical and survival data. This cohort included 412 patients with a combination of primary localized disease (consisting mostly of muscle-invasive bladder cancer) and metastatic disease (i.e., patients with M>0 disease). The average follow-up period for this cohort was 18 months (maximum 13 years) after initial cancer diagnosis.

[0239] TCGA gene expression values ​​were presented as log2 data. The log2_expression value of each gene was converted to a z-score by calculating the following formula: z-score log2_gene=((log2_gene)-(mean_samples)) / (stdev_samples) (7) where log2_gene is the log2 gene expression value per gene, mean_samples is the mathematical mean of the log2_gene values ​​across all samples, and stdev_samples is the standard deviation of the log2_gene values ​​across all samples.

[0240] This process results in the transformed log2_gene values ​​being distributed with a standard deviation of 1 around a mean of 0. For multivariate analysis of genes of interest, the log2_gene transformed z-score values ​​of each gene are used as input.

[0241] Cox regression analysis We selected 196 patients with M0 disease from the entire TCGA dataset, of which survival data were available for 189. We divided this group into 95 patients and trained a three-gene signature to construct the Bladder Cancer Immune Score (BCAI) to predict overall survival after an initial bladder cancer diagnosis.

[0242] Provided herein are combinations of 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-correlated genes, and tests to evaluate whether these combinations exhibit prognostic value for bladder cancer. Cox regression was used to model the expression levels of the 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-correlated genes on overall survival.

[0243] The Cox regression function for each gene signature was derived as follows: IDR_14_model: (8) (w1·AIM2)+(w2·APOBEC3A)+(w3·CIAO1)+(w4·DDX58)+(w5·DHX9)+(w6·IFI16)+(w7·IFIH1)+(w8·IFIT1)+(w9·IFIT3)+(w 10 LRRFIP1)+(w 11 ·MYD88)+(w 12 OAS1)+(w 13 TLR8)+(w 14 ZBP1) TCR_17_model: (9) (w 15 ·C2)+(w 16 ·CD247)+(w 17 CD28)+(w 18 CD3E)+(w 19 CD3G)+(w 20 CD4)+(w 21 CSK)+(w 22 ·EZR)+(w 23 FYN)+(w 24 ·LAT)+(w 25 ·LCK)+(w 26PAG1)+(w 27 ·PDE4D)+(w 28 ·PRKACA)+(w 29 ·PRKACB)+(w 30 ·PTPRC)+(w 31 ZAP70) PDE4D7_CORR_model: (10) (w 32 ·ABCC5)+(w 33 ·CUX2)+(w 34 KIAA1549)+(w 35 ·PDE4D)+(w 36 ·RAP1GAP2)+(w 37 ·SLC39A11)+(w 38 TDRD1)+(w 39 VWA2)

[0244] Weights w1~w 39 The details are shown in Table 4 below. Variables and weights (N / A - not applicable) for three individual Cox regression models: the bladder cancer immune defense response model (IDR_model), the T cell receptor signaling model (TCR SIGNALING model), and the PDE4D7 correlation model (PDE4D7_CORR_model). [Table 4]

[0245] Finally, the three individual gene signatures were combined by Cox regression analysis using overall survival as the predictive clinical endpoint. The Cox regression function was derived as follows: BCAI_mode (Bladder Cancer Immunity Score): (11) w 40 ·IDR_14_model)+(w 41 ·TCR_17_model)+(w 42 ·PDE4D7_CORR_model)

[0246] The BCAI_Clinical score was constructed based on the TCGA discovery cohort (95 patients, see above) and tested in the TCGA validation cohort (94 patients, see above). The clinical endpoint tested in MV Cox regression was all-cause mortality. 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 by postoperative pathology were used as inputs for the MV Cox regression analysis. This resulted in the final BCAI_Clinical score. BCAI_Clinical_model(Bladder Cancer): (12) (w 43 ·IDR_14_model)+(w 44 ·TCR_17_model)+(w 45 ·PDE4D7_CORR_model)+(w 46 LN_stage=1)+(w 47 LN_stage=2)+(w 48 LN_stage=3) Weight w 40 ~w 44 The details are shown in Table 5 below. The BCAI_Clinical_model is also referred to herein as the BCAI&Clinical_model. Variables and weights (N / A - not applicable) of two combined Cox regression models, namely, Bladder Cancer AI model (BCAI_model) and Bladder & Clinical model (BCAI&Clinical_model). [Table 5]

[0247] For further validation of the BCAI and BCAI_Clinical models, we combined the z-score-transformed log2 expression data sets GSE13507, GSE32894, and UROMOL with the 94 TCGA data samples used in the first step to validate the BCAI and BCAI_Clinical models, as described above. This superdataset consisted of a total of 1,262 bladder cancer patients.

[0248] Kaplan-Meier survival analysis Two models (BCAI and BCAI&Clinical) developed in the TCGA discovery cohort were tested by Kaplan-Meier analysis using the TCGA validation cohort and the combined superdataset for various clinical endpoints (cancer-specific mortality, all-cause mortality, and post-treatment mortality).

[0249] For Kaplan-Meier survival curve analysis, the Cox functions of the tested risk models (BCAI_model, BAI&Clinical_model) were classified into two subcohorts based on cutoffs. The thresholds for grouping into low-risk and high-risk groups were determined based on the risk of experiencing the clinical endpoint (outcome) predicted by each Cox regression model.

[0250] Figures 4-8 show Kaplan-Meier survival curve analyses of the TCGA training and validation cohorts for the PDE4D7_R2, IDR_14, TCR_17, BCAI, and BCAI_Clinical models, respectively.

[0251] Figures 9-18 show Kaplan-Meier survival curve analyses of the superdataset validation cohort.

[0252] Kaplan-Meier analyses shown in one or more of Figures 4-8 and 9-18 demonstrate that outcomes selected from, for example, bladder cancer-specific mortality, overall mortality, etc., can be predicted for bladder cancer patients. Using risk models based on randomly selected gene combinations as provided herein improves the prediction of outcomes for bladder cancer patients. Predicting outcomes may improve treatment choices and increase survival. Using the risk models developed herein is expected to improve prediction of the effectiveness of post-surgical treatment options for such patients. This, in turn, reduces the suffering of patients who are spared ineffective treatments and reduces the costs of ineffective treatments. Overall, based on models including the genetic variables and weights presented herein, bladder cancer patient outcomes can be predicted methodologically, for example, by differentiating the risk of specific outcomes among different patient risk groups through stratification and statistical techniques.

[0253] Example 2: Prediction of survival outcomes in metastatic bladder cancer after immunotherapy with anti-PD-L1 immune checkpoint inhibitors To analyze survival outcome prediction for metastatic bladder cancer after immune checkpoint inhibitor treatment, processed data from a phase 2 clinical trial were used (https: / / doi.org / 10.1016 / s0140-6736(16)32455-2). The entire patient cohort consisted of 348 patients, of whom 298 were eligible for survival analysis due to complete data on treatment response. This cohort was divided into a training set (224 patients) and a test set (74 patients). The metastatic BCAI (metBCAI) signature was trained using the same procedures outlined above for the training of the three-gene signature and its combinations. Additional available clinical features regarding metastatic disease stage (classified as lymph node metastasis, liver metastasis, or visceral metastasis) and baseline Eastern Cooperative Oncology Group (ECOG) performance score were used to construct the metBCAI_Clinical ImmunoScore.

[0254] Cox regression model Provided herein are tests to assess whether combinations of 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-correlated genes, and their combinations, exhibit prognostic value for metastatic bladder cancer after treatment with the anti-PD-L1 immune checkpoint inhibitor atezoluzumab. Cox regression was used to model the expression levels of the 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-correlated genes, respectively, on overall survival.

[0255] The Cox regression function for each gene signature was derived as follows: metIDR_14_model: (13) (w 49 AIM2)+(w 50 APOBEC3A)+(w 51 ·CIAO1)+(w 52 DDX58)+(w 53 ·DHX9)+(w54 ·IFI16)+(w 55 ·IFIH1)+(w 56 IFIT1)+(w 57 IFIT3)+(w 58 LRRFIP1)+(w 59 ·MYD88)+(w 60 OAS1)+(w 61 TLR8)+(w 62 ZBP1) metTCR_17_model: (14) (w 63 ·C2)+(w 64 ·CD247)+(w 65 CD28)+(w 66 CD3E)+(w 67 CD3G)+(w 68 CD4)+(w 69 CSK)+(w 70 ·EZR)+(w 71 FYN)+(w 72 ·LAT)+(w 73 ·LCK)+(w 74 PAG1)+(w 75 ·PDE4D)+(w 76 ·PRKACA)+(w 77 ·PRKACB)+(w 78 ·PTPRC)+(w 79 ZAP70) metPDE4D7_CORR_model: (15) (w 80 ·ABCC5)+(w 81 ·CUX2)+(w 82 KIAA1549)+(w 83 ·PDE4D)+(w 84 ·RAP1GAP2)+(w 85 ·SLC39A11)+(w 86 TDRD1)+(w 87 VWA2)

[0256] Weights w1~w 39 The details are shown in Table 6 below. Variables and weights (N / A - not applicable) for three individual Cox regression models: the bladder cancer immune defense response model (metIDR_model), the T cell receptor signaling model (metTCR SIGNALING model), and the PDE4D7 correlation model (metPDE4D7_CORR_model). [Table 6]

[0257] Finally, the three individual gene signatures were combined by Cox regression analysis using overall survival as the predictive clinical endpoint. The Cox regression function was derived as follows: BCAI_mode (Metastatic Bladder Cancer Immunoscore): (w 88 ·IDR_14_model)+(w 89 ·TCR_17_model)+(w 90 ·PDE4D7_CORR_model) (16)

[0258] The BCAI_Clinical score was constructed based on a training cohort (224 patients, see above) and tested in a validation cohort (74 patients, see above). The clinical endpoint tested in MV Cox regression was all-cause mortality after treatment with atezolizumab. The metIDR, metTCR, and metPDE4D7_CORR signature scores derived from the individual Cox regression models, as well as metastatic disease stage (lymph node (LN) metastasis, liver metastasis, visceral metastasis) and ECOG performance score at baseline, were used as inputs for the MV Cox regression analysis. This resulted in the final BCAI_Clinical score. BCAI_Clinical_model (metastatic bladder cancer): (17) (w 91 ·BCAI_model)+(w 92 LN metastatic disease stage)+(w 93 Visceral metastatic disease stage)+(w 94ECOG performance score at baseline

[0259] Weight w 88 ~w 92 The details are shown in Table 7 below. The BCAI_Clinical_model is also referred to herein as the BCAI&Clinical_model. Variables and weights (N / A - not applicable) of two combined Cox regression models, namely, Metastatic Bladder Cancer AI model (BCAI_model) and Bladder & Clinical model (BCAI&Clinical_model). [Table 7]

[0260] In another variation of the clinical BCAI model, mutation burden per megabase of DNA, measured by the commercially available "FMOne mutation burden test" (Foundation Medicine), was added to the regression model. BCAI_Clinical_MB_model (metastatic bladder cancer): (18) (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 Mutation load per megabase of DNA)

[0261] Weight w 40 ~w 47 The details are shown in Table 9 below. The BCAI_Clinical_model is also referred to herein as the BCAI&Clinical_model. Variables and weights (N / A-not applicable) of the combined Cox regression model, i.e., metastatic bladder cancer and clinical and MB AI model (BCAI&Clinical&MB_model). [Table 9]

[0262] Kaplan-Meier survival and AUROC analysis For Kaplan-Meier survival curve analysis, the Cox functions of the tested risk models (metBCAI_model, metBCAI&Clinical_model, metBCAI&Clinical&MB_model) were classified into two subcohorts based on cutoffs. The thresholds for grouping into low-risk and high-risk groups were determined based on the risk of experiencing the clinical endpoint (outcome) predicted by each Cox regression model.

[0263] Figures 19-25 show Kaplan-Meier survival curve analyses of the training and validation cohorts.

[0264] Figures 26-27 show the AUROC (area under the receiver operating curve) analysis of the training and validation cohorts.

[0265] The Kaplan-Meier analyses shown in one or more of Figures 19-25 demonstrate that outcomes selected from, for example, metastatic bladder cancer-specific mortality and all-cause mortality in metastatic bladder cancer patients treated with immune checkpoint inhibitors can be predicted. Using risk models based on randomly selected gene combinations as provided herein improves the prediction of outcomes for bladder cancer patients. Predicting outcomes may improve treatment choices and increase survival. Using the risk models developed herein is expected to improve prediction of the effectiveness of post-surgical treatment options for such patients. This, in turn, may reduce the suffering of patients who are spared ineffective treatments and reduce the costs of ineffective treatments. Overall, based on models including the genetic variables and weights presented herein, bladder cancer patient outcomes can be predicted methodologically, for example, by differentiating the risk of specific outcomes in multiple different patient risk groups through stratification and statistical techniques.

[0266] Example 3: Predicting immunotherapy outcome in patients with high-grade NMIBC or metastatic bladder cancer The IDR_14 and TCR_17 models were tested to predict immunotherapy outcomes. These models were applied to NMIBC patients receiving BCG treatment (Figure 28) or metastatic bladder cancer patients receiving immune checkpoint inhibitor (ICI) treatment (Figure 29). The endpoints used in the analysis were overall progression-free survival (high-grade NMIBC) or survival after ICI treatment.

[0267] Kaplan-Meier analyses shown in Figures 28 and 29 demonstrate that outcomes, such as progression-free survival or overall survival, can be predicted for patients with metastatic bladder cancer or high-grade NMIBC treated with immunotherapy, such as BCH or ICI. Using a risk model based on T cell receptor gene signatures or immune defense response gene signatures improves the prediction of outcomes for bladder cancer patients. Predicting outcomes may improve treatment selection and potentially improve survival. Using the risk model developed herein is expected to improve prediction of the effectiveness of treatment options in those patients. In particular, as demonstrated herein, the model makes it possible to predict the effectiveness of immunotherapy in bladder cancer, particularly metastatic bladder cancer or high-grade NMIBC. Furthermore, for patients predicted to have a poor response to these therapies, alternative treatment strategies can be selected early, avoiding unnecessary treatment with ineffective therapies. This, in turn, reduces the suffering of patients who would otherwise be exposed to ineffective treatments and reduces the costs associated with such treatments.

[0268] Example 4 - ImmunoScore test for BCG response Next, we analyzed patient samples using the Immunoscore and Immunoscore_Clinical models as described by De Jong et al. (ibid.). Briefly, the dataset included RNA sequencing data obtained from tumor samples from primary HR-NMIBC patients who received five or six or more BCG induction infusions between 2000 and 2018. Cohort A consisted of n = 63 BCG responders and n = 69 BCG non-responders. Response to BCG was defined as the absence of high-grade recurrence after at least five or six BCG induction infusions and nine or more BCG maintenance infusions (i.e., a 1-year schedule with at least three BCG maintenance cycles). Cohort B additionally included patients with HR-NMIBC. Cohort B was similar to Cohort A, with a similar number of BCG-responsive tumors (n = 88) and BCG-unresponsive tumors (n = 63). Patients with Ta tumors were also included to increase the number of patients in Cohort B. In Cohort B, patients received five or more than six BCG induction injections, with a median of 13.

[0269] The ImmunoScore model used herein is a combination of the IDR_14 and TCR_17 signatures described herein. In the case of the ImmunoScore Clinical model, the model is further combined with the EORTC score.

[0270] The ImmunoScore model can be used to predict disease progression for Cohort A and Cohort B, as shown in Figures 31 and 32, respectively. The ImmunoScore_Clinical model can be used to predict further disease progression, and although predictions can be made for each BRS subtype (see De Jong et al., supra), it was most effective for BRS1 (see Figures 33-35).

[0271] The ImmunoScore_Clinical model is also predictive of the endpoints cancer-specific mortality and death (see Figures 36 and 37).

[0272] ImmunoScore_Clinical can further be used to stratify smoking patients (see Figures 38-43).

[0273] The attached sequence listing "2022PF00548 SEQ LIST" is hereby incorporated by reference in its entirety.

Claims

1. 1. A method for predicting outcome in a patient with bladder cancer, said method comprising: determining a gene expression profile comprising gene expression levels or receiving results of the determination, The gene expression level is 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 comprising gene expression levels selected from 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; determining a gene expression profile, or receiving results of the determination, wherein the gene expression profile is determined in a biological sample obtained from the patient; determining the prediction of the outcome based on the gene expression profile; The method, wherein the prediction is an outcome for the patient, wherein the outcome is survival to treatment, cancer-free survival to treatment, or time to disease progression after treatment, and the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy, or immune checkpoint inhibitor therapy.

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

3. 3. The method of claim 1 or 2, wherein determining the prediction of the outcome comprises combining the gene expression levels with a regression function derived from the population of bladder cancer patients.

4. 4. The method of claim 1, wherein determining the prediction of the outcome is further based on one or more clinical parameters obtained from the patient.

5. 5. The method of any one of claims 1 to 4, wherein determining the outcome comprises combining the gene expression profile and one or more clinical parameters obtained from the patient with a regression function derived from a population of bladder cancer patients.

6. 6. The method of any one of claims 1 to 5, wherein the one or more clinical parameters comprise one or more of EORTC score, number of tumor-positive regional lymph nodes, metastatic disease status, ECOG score, and FoundationOne mutational burden per MB DNA, preferably wherein said one or more clinical parameters consist of EORTC score, number of tumor-positive regional lymph nodes, or metastatic disease status, ECOG score, and optionally FoundationOne mutational burden per MB DNA.

7. 7. The method of any one of claims 1 to 6, wherein the biological sample is obtained from the patient before the start of the treatment, preferably the biological sample is a bladder sample or a bladder cancer sample.

8. The method of claim 1 , wherein a therapy is recommended based on the prediction.

9. 9. The method of any one of claims 1 to 8, wherein the patient is suffering from non-muscle invasive bladder cancer (NMIBC), preferably high-grade NMIBC or metastatic bladder cancer (mUC).

10. 1. A computer program comprising instructions that, when executed by a computer, cause the computer to carry out a method, the method comprising: receiving data indicative of a gene expression profile comprising gene expression levels; The gene expression level is 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 comprising gene expression levels selected from 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; receiving data indicative of a gene expression profile, wherein the gene expression profile is determined in a biological sample obtained from a bladder cancer patient; and determining a prediction of an outcome for the patient based on the gene expression profile, wherein the outcome is survival to a treatment, and the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or an immune checkpoint inhibitor therapy.

11. Use of a diagnostic kit, said diagnostic kit comprising: at least one polymerase chain reaction primer or probe for determining a gene expression profile, said gene expression profile comprising expression levels in a biological sample and / or samples obtained from a bladder cancer patient; The expression level is 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 comprising gene expression levels selected from 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 use determining the gene expression levels of the gene expression profile; 1. Use of a diagnostic kit comprising: predicting an outcome of a bladder cancer patient, wherein the outcome is survival to a treatment, and the treatment is an immunotherapy selected from Bacillus Calmette-Guerin (BCG) immunotherapy or immune checkpoint inhibitor therapy.

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

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