Predicting outcome in subjects with bladder or kidney cancer

Abstract

To predict outcome of a bladder or kidney cancer subject.SOLUTION: A method comprises: determining, or receiving a result of a determination of, a first gene expression profile for each of one or more immune defense response genes, a second gene expression profile for each of one or more T-cell receptor signaling genes, and / or a third gene expression profile for each of one or more PDE4D7 correlated genes, where the first, second and third expression profiles are determined in a biological sample obtained from the subject; determining the prediction of the outcome based either on the first gene expression profile, on the second gene expression profile, on the third gene expression profile, or on the first, second and third gene expression profiles; and optionally providing the prediction to a medical caregiver or the subject.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the outcome of a subject with bladder or kidney cancer, and an apparatus for predicting the outcome of a subject with bladder or kidney cancer. Additionally, the present invention relates to a diagnostic kit, the use of the kit, the use of the kit in a method for predicting the outcome of a subject with bladder or kidney cancer, the use of a first, second, and / or third gene expression profile in a method for predicting the outcome of a subject with bladder or kidney cancer, and corresponding computer program products. [Background technology]

[0002] Cancer is a class of diseases in which groups of cells exhibit uncontrolled growth, invasion, and sometimes metastasis. These three malignant properties of cancer distinguish it from benign tumors, which are self-limited and do not invade or metastasize.

[0003] Bladder cancer is fairly common, ranking sixth, accounting for 4.5% of new cancer cases annually in the United States. It is estimated that there will be more than 81,000 new cases in 2020, and approximately 18,000 people will die from this type of cancer. The median age at diagnosis is 74, with a 5-year relative survival rate of 76.9%. Patients diagnosed with bladder cancer earlier are more likely to survive for more than five years after diagnosis; the 5-year survival rate for in situ bladder cancer is 95.8%, while only 5.5% of patients with metastatic bladder cancer survive at least five years. Approximately one-third of patients present with localized disease. More men than women are affected, and incidence increases with age. The main risk factors for bladder cancer are smoking, having a family history of bladder cancer, exposure to certain chemicals, and previous treatment with radiation therapy to the pelvic area (see National Cancer Institute, Surveillance, Epidemology, and End Results Program, "Cancer Stat Facts: Bladder Cancer," Bethesda, MD).

[0004] The four standard treatments for bladder cancer are surgery to remove the tumor, radiation therapy to kill cancer cells, chemotherapy to stop or slow cell division, and immunotherapy to help the patient's own immune system fight the cancer. In addition, new treatments are being tested in clinical trials. Bladder cancer often recurs, even if it is noninvasive at the time of diagnosis. Therefore, it is standard practice to perform urinary tract surveillance after initial diagnosis (see National Cancer Institute, PDQ® Adult Treatment Editorial Board, "PDQ Bladder Cancer Treatment," Bethesda, MD).

[0005] Bladder cancer is classified as muscle-invasive (MIBC) or non-muscle-invasive (NMIBC). NMIBC often recurs and progresses to MIBC; approximately 25% of bladder cancer patients have muscle-invasive bladder cancer, or MIBC, which is at high risk for progression and ultimately life-threatening. 20-25% of MIBC patients who undergo radical cystectomy still develop microscopic spread to lymph nodes, which usually significantly reduces the chances of cure. Therefore, treatment options for MIBC target both the local tumor and potential spread to unsuspected lymph nodes. Possible options include chemotherapy, radical cystectomy, and radical radiation therapy. There is considerable variation in UK practice due to uncertainty over the relative effectiveness and indications for each of these treatments (see National Collaborating Centre for Cancer, "Bladder Cancer: Diagnosis and Management", National Institute for Health and Care Excellence, London, UK, 2015).

[0006] For bladder cancer patients who cannot undergo cystectomy or who refuse surgery, radiation therapy is an alternative treatment with relatively good outcomes. For example, in patients with MIBC, there is no difference in survival rate between those who undergo cystectomy and those who undergo radiation therapy. Approximately 25% of patients who undergo radiation therapy survive for 5 years while retaining their bladder. Improving quality of life through organ preservation is an important consideration for bladder cancer patients. Optimizing radiation therapy delivery in combination with novel systemic therapies, which requires close cooperation between the involved clinical disciplines, will enable the future improvement and adoption of organ preservation strategies for more patients (see Zhang S. et al., "Radiotherapy in muscle-invasive bladder: the latest research progress and clinical application," Am J Cancer Res, Vol. 5, No. 2, pp. 854-868, 2015).

[0007] Improved prediction of the efficacy of RT for each patient will lead to improved treatment choices and improved chances of survival, whether in the curative or salvage setting. This can be achieved by 1) optimizing RT for patients predicted to benefit from it (e.g., by dose escalation or altering initiation timing) and 2) directing patients predicted not to benefit from it to alternative, potentially more effective, forms of treatment. This, in turn, reduces patient suffering by avoiding ineffective treatment and reduces costs otherwise spent on ineffective treatment.

[0008] Instead of treating tumors, stimulating the patient's own immune system to fight tumors more vigorously is possible. One immunotherapy that has been used for over 40 years is intravesical bacillus Calmette-Guérin (BCG) to prevent recurrence and progression of NMIBC. Although it appears to be successful, the mechanisms are still poorly understood, and it still fails in up to 40% of patients. There is no gold standard intravesical treatment after BCG failure.

[0009] Emerging immunotherapies, such as checkpoint inhibitors, are expected to meet this unmet need (see Alhunaidi O. and Zlotta AR, "The use of intravesical BCG in urothelial carcinoma of the bladder," Cancer Medical Science, Vol. 13, p. 905, 2019). Clinical trials of immune checkpoint inhibitors have shown promising results, and many more trials are underway or in development (see Fakhrejahani F. et al., "Immunotherapies for bladder cancer: a new hope," Curr Opin Urol, Vol. 25, No. 6, pp. 586-596, 2015). Combining immune checkpoint inhibitors with other treatments, such as chemotherapy, targeted therapy, or radiation therapy, may lead to the development of novel therapeutic strategies with synergistic antitumor activity in advanced bladder cancer. Nevertheless, there is a great need for predictive biomarkers that can identify patients likely to benefit from immunotherapy to help guide precision cancer treatment.

[0010] Detecting bladder cancer requires uncomfortable and expensive cystoscopy and biopsy, which are often associated with several adverse effects. Therefore, novel diagnostic methods for early detection and monitoring are needed. While several FDA-approved urine tests are currently available, their sensitivity, specificity, and diagnostic accuracy remain suboptimal. DNA and RNA-based markers, particularly those derived from liquid biopsies, hold promise for the diagnosis, prognosis, prediction, and monitoring of urological malignancies. However, clinical validation of recently discovered biomarkers in well-designed multicenter studies is urgently needed (see Santoni G. et al., "Urinary Markers in Bladder Cancer: An Update," Front Oncol, Vol. 8, p. 362, 2018).

[0011] Kidney cancer is one of the 10 most common cancers in both men and women, accounting for 4.1% of cancer cases per year in the United States. It is estimated that approximately 74,000 new cases of kidney cancer will be diagnosed in 2020, and approximately 15,000 people will die from the disease. The median age at diagnosis is 64, with a 5-year relative survival rate of 75.2%. Patients diagnosed with kidney cancer earlier are more likely to survive for more than five years after diagnosis; the 5-year survival rate for localized kidney cancer is 92.6%, while only 13% of patients with distant kidney cancer survive at least five years. Approximately 65% ​​of patients have localized disease. Men are affected twice as often as women, and incidence increases with age. Major risk factors for kidney cancer are smoking, obesity, hypertension, a family history of kidney cancer, workplace exposures, sex, race, certain medications, advanced kidney disease, and genetic and inherited risk factors. Since the 1990s, incidence has increased, which can be explained in part by the use of newer imaging tests that find previously undetected cancers (see National Cancer Institute, Surveillance, Epidemology, and End Results Program, "Cancer Stat Facts: Kidney Cancer," Bethesda, MD).

[0012] Approximately 90% of kidney cancers are renal cell carcinomas (RCCs). They usually grow as a single tumor in one kidney, but multiple tumors can appear in one or both kidneys at the same time. 70% of RCCs are clear cell RCCs (ccRCCs), named after the cellular phenotype when viewed under a microscope. The remaining non-ccRCCs include papillary renal cell carcinomas (pRCCs), chromophobe renal cell carcinomas (chRCCs), and other rare types of renal cell carcinomas.

[0013] Identification and differentiation of kidney cancer subtypes is important for patient management and treatment.As explained above, kidney cancer can be classified into subtypes according to histology.However, this does not provide insight into the underlying mechanism of its specific subtype, and a better understanding of each subtype is needed to make use of the many new therapies that have emerged.

[0014] Comprehensive genomic and phenotypic analyses of RCC subtypes (ccRCC, pRCC, and chRCC) have revealed distinct features of each histological subtype (see Ricketts CJ et al., "The Cancer Genome Atlas: Comprehensive Molecular Characterization of Renal Cell Carcinoma," Cell Rep, Vol. 23, No. 1, pp. 313-326, 2018). Here, the combination of histology and genomics provides unique insights into patient-centered management. Some characteristics are shared by several, but not all, RCC subtypes. Somatic mutations in BAP1, PBRM1, and PTEN, as well as altered metabolic pathways, correlated with subtype-specific decreased survival, whereas CDKN2A mutations, increased DNA hypermethylation, and increased immune-related Th2 gene expression signatures correlated with decreased survival within all major histological subtypes (see Ricketts et al., 2018, supra). The major mutations in ccRCC can be grouped according to the associated mechanisms (see Linehan WM and Ricketts CJ, "The Cancer Genome Atlas of Renal Cell Carcinoma: Findings and Clinical Implications," Nat Rev Urol, Vol. 16, 9, pp. 539-552, 2019). Most ccRCCs lose the VHL complex, which leads to stabilization of HIF1-α and HIF-2α, creating a pseudohypoxia state that induces the expression of angiogenic and growth factors. Activating mutations in the mTOR pathway frequently occur in ccRCC and promote protein synthesis and cell survival. Mutations in chromatin-remodeling genes are also common and have been proposed to alter gene transcription. Loss of CDKN2A results in increased cell cycle activation and correlates with lower survival rates (see Linehan WM and Ricketts CJ, 2019, supra).

[0015] In contrast to most other cancer types, a biopsy is sometimes not necessary to diagnose kidney tumors, and imaging can provide sufficient information to determine whether surgery is necessary. In these cases, the diagnosis is confirmed in a portion of the kidney that is removed. In cases of insufficient imaging, small tumors, or when other treatments are considered, a biopsy may be performed.

[0016] Because most kidney cancers grow slowly, active surveillance may be an option for patients with small tumors. Surgery, when tumor resection is necessary, is the primary treatment for most kidney cancers. Alternatives to surgery are cryoablation and radiofrequency ablation. If a patient is unlikely to tolerate surgery, other treatments, such as external beam radiation therapy (EBRT), are first attempted. However, radiation therapy is typically used in a palliative setting for kidney cancer.

[0017] Kidney cancer cells usually do not respond well to chemotherapy, so chemotherapy is not a standard treatment for kidney cancer. Targeted therapy is usually used to shrink tumors or slow tumor growth, or as adjuvant therapy after surgery. For more advanced kidney cancer, immune checkpoint inhibitors are sometimes used in combination with targeted therapy to help the immune system better fight tumors. In certain cases, cytokines IL-2 and interferon-α can be used to shrink tumors.

[0018] The treatment paradigm for ccRCC has recently evolved with the addition of cabozantinib (a tyrosine kinase inhibitor) and nivolumab / ipilimumab (anti-PD-1 / anti-CTLA-4), particularly for intermediate- or poor-risk patients (see Atkins MB and Tannir NM, "Current and emerging therapies for first-line treatment of metastatic clear cell renal cell carcinoma," Cancer Treat Rev, Vol. 70, pp. 127-137, 2018).

[0019] Treatment selection for ccRCC remains based on available clinical evidence and expert opinion. Currently, no validated predictive biomarkers are available. Although PD-L1 overexpression has been proposed to correlate with poor outcomes, its role as a biomarker is unclear. Other potential biomarkers for immunotherapy include PD-L2 expression, IDO-1 expression, and CD8+ T cell infiltration. Further hope lies in different genetic signatures, such as loss-of-function mutations in PBRM1, which are involved in JAK / STAT transcriptional regulation, oxygen deprivation, and immune signaling pathways. More data are needed to reach a consensus on the optimal treatment sequence in ccRCC (e.g., switching from targeted therapy to immunotherapy and sequential versus combination therapy). Prospectively validated biomarkers are needed to match patients to treatment and determine optimal strategies for treatment sequencing (see Atkins MB and Tannir NM, 2018, supra).

[0020] Because pRCC accounts for approximately 15-20% of RCC cases and is further divided into type 1 and type 2, and because treatment for RCC patients is based on studies with minimal participation of patients with pRCC, conventional therapies tend to be less effective for non-ccRCC patients. As explained above, MET is a known mutation in pRCC, making it a potential target for directed therapy (see Rhoades Smith KE and Bilen MA, "A Review of Papillary Renal Cell Carcinoma and MET Inhibitors," Kidney Cancer, Vol. 3, No. 3, pp. 151-161, 2019). Because outcomes are typically poor for pRCC when treated with conventional therapy, the development of effective MET-targeted therapies is necessary. Drugs targeting MET in pRCC are still under active investigation. Furthermore, it has been suggested that immune checkpoint inhibitors alone or in combination with MET inhibitors may be beneficial in the treatment of pRCC (see Rhoades Smith KE and Bilen MA, 2019, supra).

[0021] WO 2007 / 146668(A2) discloses a method for determining the prognosis of a subject with renal cell carcinoma (RCC) or bladder carcinoma, the method comprising determining the presence or level of IMP3 in the subject's primary tumor, wherein the presence of IMP3 in the subject's primary tumor indicates a poor prognosis for the subject, whereas a substantially undetectable level of IMP3 in the subject's primary tumor indicates a good prognosis for the subject.

[0022] International Publication Nos. WO 2016 / 049276(A1) and WO 2018 / 104147(A1) disclose immune response-related gene signatures that correlate with the prognosis of bladder cancer, and International Publication No. WO 2014 / 194078(A1) discloses immune response-related gene signatures that correlate with the prognosis of kidney cancer.

[0023] In conclusion, a better understanding of the (molecular) mechanisms of the disease and the new availability of treatments highlight that there remains a strong need for better prediction of response to treatment for primary bladder and kidney cancer as well as the post-operative setting. Summary of the Invention

[0024] It is an object of the present invention to provide a method for predicting the outcome of a subject with bladder or kidney cancer, as well as an apparatus for predicting the outcome of a subject with bladder or kidney cancer, which allows for better treatment decisions. Further aspects of the present invention provide diagnostic kits, uses of the kits, use of the kits in methods for predicting the outcome of a subject with bladder or kidney cancer, use of the first, second, and / or third gene expression profiles in methods for predicting the outcome of a subject with bladder or kidney cancer, and corresponding computer program products.

[0025] In a first aspect of the present invention, there is provided a method of predicting outcome in a subject with bladder or kidney cancer, comprising: - identifying or receiving results of identifying a first gene expression profile for each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the first expression profile is identified in a biological sample obtained from the subject; and / or - identifying or receiving a second gene expression profile for each 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 CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, wherein the second expression profile is identified in a biological sample obtained from the subject; and / or - identifying or receiving a third gene expression profile for each of 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. wherein the third expression profile is identified in a biological sample obtained from the subject; - identifying a prediction of outcome based on the first gene expression profile, the second gene expression profile, or the third gene expression profile, or the first, second, and third gene expression profiles; and - providing the prediction to a caregiver or subject, as appropriate. A method is presented that includes:

[0026] Thus, in one embodiment, the present invention provides a method for predicting outcome in a subject with bladder or kidney cancer, comprising: - identifying a first gene expression profile for each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the first expression profile is identified in a biological sample obtained from the subject; and / or - identifying a second gene expression profile for each 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 CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, wherein the second expression profile is identified in a biological sample obtained from the subject; and / or - identifying a third gene expression profile for each of 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, wherein the third expression profile is identified in a biological sample obtained from the subject; - identifying a prediction of outcome based on the first gene expression profile, the second gene expression profile, or the third gene expression profile, or the first, second, and third gene expression profiles; and - providing the prediction to a caregiver or subject, as appropriate. The present invention relates to a method comprising:

[0027] In an alternative embodiment, the present invention provides a computer-implemented method for predicting outcome in a subject with bladder or kidney cancer, comprising: - receiving results identifying a first gene expression profile for each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the first expression profile is identified in a biological sample obtained from the subject; and / or - receiving results of identifying a second gene expression profile for each 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 CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, wherein the second expression profile is identified in a biological sample obtained from the subject; and / or - receiving results identifying a third gene expression profile for each of 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, wherein the third expression profile is identified in a biological sample obtained from the subject; - identifying a prediction of outcome based on the first gene expression profile, the second gene expression profile, or the third gene expression profile, or the first, second, and third gene expression profiles; and - providing the prediction to a caregiver or subject, as appropriate. The present invention relates to a method comprising:

[0028] In one embodiment of the present invention, there is provided a method for predicting outcome in a subject with bladder or kidney cancer, comprising: - determining a gene expression profile of three or more genes, wherein the three or more genes are selected from the following: - 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; 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; or - a PDE4D7-correlated gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; or - the three or more genes include at least one or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-related genes; - a gene expression profile of the three or more genes is identified in a biological sample obtained from the subject; Step, - identifying a prediction of outcome based on the gene expression profile of the three or more genes; and - providing the prediction to a caregiver or subject, as appropriate. A method is presented that includes:

[0029] In an alternative embodiment, the present invention provides a computer-implemented method for predicting outcome in a subject with bladder or kidney cancer, comprising: - receiving results of identifying gene expression profiles of three or more genes, the three or more genes being selected from the following: - 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; 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; or - a PDE4D7-correlated gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; or - the three or more genes include at least one or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-related genes; - a gene expression profile of the three or more genes is identified in a biological sample obtained from the subject; Steps; - identifying a prediction of outcome based on the gene expression profile of the three or more genes; and - providing the prediction to a caregiver or subject, as appropriate. The present invention relates to a method comprising:

[0030] In recent years, the importance of the immune system in cancer suppression, as well as in cancer initiation, promotion, and metastasis, has become abundantly clear (see Mantovani A. et al., "Cancer-related inflammation," Nature, Vol. 454, No. 7203, pp. 436-444, 2008; and Giraldo NA et al., "The clinical role of the TME in solid cancer," Br J Cancer, Vol. 120, No. 1, pp. 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 interact and shape each other. Thus, while antitumor immunity can prevent tumor formation, an inflammatory tumor environment promotes cancer initiation and growth. At the same time, tumor cells that develop in an immune-independent manner can shape the immune microenvironment by recruiting immune cells and exert pro-inflammatory effects while also suppressing antitumor immunity.

[0031] Some immune cells in the tumor microenvironment have either general tumor-promoting or general tumor-suppressing effects, while others exhibit flexibility 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 in the tumor microenvironment (see Giraldo NA et al., 2019, supra).

[0032] The above principles regarding the role of the immune system in cancer in general also apply to prostate cancer. Chronic inflammation has been linked to the formation of benign and malignant prostate tissue (see Hall WA et al., 2016, supra), and most prostate cancer tissue samples show immune cell infiltrates. The presence of specific immune cells with tumor-promoting effects has been correlated with poor prognosis, while tumors with more activated natural killer cells have shown better responses to treatment and longer recurrence-free intervals (see Shiao SL et al., "Regulation of prostate cancer progression by tumor microenvironment," Cancer Lett., Vol. 380, No. 1, pp. 340-348, 2016).

[0033] Treatment is influenced by immune components of the tumor microenvironment, and RT itself has extensive effects on the composition of these components (see Barker HE et al., "The tumor microenvironment after radiotherapy: Mechanisms of resistance or recurrence," Nat Rev Cancer, Vol. 15, No. 7, pp. 409-425, 2015). Because suppressive cell types are relatively radiation-insensitive, their relative numbers increase. In response, a given radiation insult activates cell survival pathways and stimulates the immune system, which triggers an inflammatory response and immune cell recruitment. Whether the net effect is tumor-promoting or tumor-suppressing is currently unknown, but its potential for enhancing cancer immunotherapy is being explored.

[0034] The present invention is based on the idea that the state of the immune system and immune microenvironment has an impact on therapeutic efficacy, and therefore the ability to identify markers that predict this effect would be useful to better predict overall outcome.

[0035] immune response defense genes Genomic DNA integrity and stability are constantly under stress caused by various internal and external factors, such as radiation exposure, viral or bacterial infection, as well as oxidative and replicative stresses (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- and double-strand breaks, caused by various factors. This process involves the participation of numerous specific proteins as part of the DNA recognition pathway, depending on the type of damage.

[0036] Recent evidence suggests that mislocalized DNA (e.g., DNA that unnaturally appears in the cytosolic compartment of cells as opposed to the nucleus) and damaged DNA (e.g., due to mutations that occur during cancer development) are used by the immune system to identify infected or otherwise diseased cells, whereas genomic and mitochondrial DNA present in normal cells is ignored by DNA recognition pathways. In diseased cells, cytosolic DNA sensor proteins have been demonstrated to be involved in the detection of non-naturally occurring DNA in the cytosol of cells. Detection of such DNA by various nucleic acid sensors results in similar responses resulting in nuclear factor kappa B (NF-kB) and interferon type I (IFN type I) signaling, subsequently activating components of the innate immune system. While recognition of viral DNA is known to trigger IFN type I responses, more recently, evidence has emerged that sensing DNA damage can initiate immune responses.

[0037] The endosome-localized TLR9 (Toll-like receptor 9) was one of the first DNA sensor molecules identified to be involved in the immune recognition of DNA by downstream signaling through the adaptor protein myeloid differentiation primary response protein 88 (MYD88). This interaction subsequently activates mitogen-activated protein kinases (MAPKs) and NF-kB. TLR9 also induces the production of type I interferons by activating IRF7 via IkB kinase α (IKKα) in plasmacytoid dendritic cells (pDCs). Various other DNA immune receptors, such as IFI16 (IFN-γ-inducible protein 16), cGAS (cyclic DMP-AMP synthase), DDX41 (DEAD-box helicase 41), and ZBP1 (Z-DNA-binding protein 1), interact with STING (stimulator of IFN genes) and activate the IKK complex and IRF3 via TBK1 (TANK-binding kinase 1). ZBP1 also activates NF-kB through the recruitment of RIP1 and RIP3 (receptor-interacting proteins 1 and 3, respectively). The helicase DHX36 (DEAH-box helicase 36) interacts in a complex manner with TRID to induce NF-kB and IRF-3 / 7, whereas the DHX9 helicase stimulates MYD88-dependent signaling in plasmacytoid dendritic cells. The DNA sensor LRRFIP1 (leucine-rich repeat flightless-interacting protein 1) complexes with β-catenin and activates the transcription of IRF3, whereas AIM2 (absent in melanoma 2) recruits the adaptor protein ASC (apoptosis-associated speck-like protein) to induce the caspase-1-activating inflammasome complex, which leads to the secretion of interleukin-1β (IL-1β) and IL-18 (see Gasser S. et al., 2017, supra, which provides a schematic overview of DNA damage and DNA sensor pathways leading to the production of inflammatory cytokines and the expression of ligands for activating innate immune receptors).Members of the non-homologous end-joining pathway (orange), homologous recombination (red), inflammasome (dark green), NF-kB and interferon response (light green) are shown).

[0038] The factors and mechanisms responsible for activating the DNA sensor pathway in cancer are currently poorly understood. It will be important to identify intratumoral DNA species, sensors, and pathways involved in IFN expression in different cancer types at all stages of the disease. In addition to being therapeutic targets in cancer, such factors also have prognostic and predictive value. Novel DNA sensor pathway agonists and antagonists are currently being developed and tested in preclinical studies. Such compounds will be useful in characterizing the role of the DNA sensor pathway in the pathogenesis of cancer, autoimmunity, and potentially other diseases.

[0039] T cell receptor signaling genes The immune response to pathogens can be triggered at various stages: There is a physical barrier, such as the skin, that keeps out invaders. If this is breached, the innate immune system kicks in, which is a first, fast, non-specific response. If this is insufficient, the adaptive immune response is triggered, which is much more specific but takes longer to develop when a pathogen is first encountered. Lymphocytes are activated by interacting with activated antigen-presenting cells from the innate immune system. They are also involved in maintaining memory, which allows them to respond more quickly the next time the same pathogen is encountered.

[0040] Upon activation, lymphocytes become highly specific and efficient, and therefore undergo negative selection for their ability to recognize themselves, a process known as central tolerance. Because not all self-antigens are expressed at selected sites, mechanisms of peripheral tolerance have also developed, such as TCR ligation in the absence of costimulation, expression of inhibitory co-receptors, and suppression by Tregs. Disruption of the balance between activation and suppression can lead to autoimmune disorders, immune deficiencies, and cancer, respectively.

[0041] 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 recently identified T cell subsets, such as Tregs, which have an inhibitory effect on 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, T cell activation and its modulation 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).

[0042] Both PKA- and PDE4-regulated signaling intersect with TCR-induced T cell activation, fine-tuning its regulation with 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; see 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. Within T cells, cAMP mediates the effects of prostaglandins, adenosine, histamine, beta-adrenergic agonists, neuropeptide hormones, and beta-endorphin. Binding of these extracellular molecules to GPCRs leads to a conformational change in the GPCR, the release of stimulatory subunits, and the subsequent activation of adenylate cyclase (AC), which hydrolyzes ATP to cAMP (see Figure 6 in Abrahamsen H. et al., 2004, supra). PKA is a major, though not exclusive, effector of cAMP signaling (see Mosenden R. and Tasken K., 2011, supra; Tasken K. and Ruppelt A., 2006, supra). At the functional level, increased levels of cAMP lead to decreased production of IFNγ and IL-2 in T cells (see Abrahamsen H. et al., 2004, supra). In addition to interfering with TCR activation, PKA has many other effects (see Figure 15 in "Immunity Leashed - Mechanisms of Regulation in the Human Immune System" by Torheim EA, PhD thesis, The Biotechnology Centre of Ola, University of Oslo, Norway, 2009).

[0043] In naive T cells, hyperphosphorylated PAG targets Csk to lipid rafts. PKA targets Csk via the ezrin-EBP50-PAG scaffolding complex. Specifically phosphorylated by PKA, Csk negatively regulates Lck and Fyn, attenuating their activity and downregulating T cell activation (see Figure 6 in Abrahamsen H. et al., 2004, supra). Upon TCR activation, PAG is dephosphorylated, and Csk is released from rafts. Csk dissociation is required for T cell activation to proceed. Over the same time course, a Csk-G3BP complex appears to form, thereby sequestering Csk outside of lipid rafts (see Mosenden R. and Tasken K., 2011, supra; Tasken K. and Ruppelt A., 2006, supra).

[0044] On the other hand, combined stimulation of TCR and CD28 mediates the recruitment of the cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, which enhances the degradation of cAMP (see Figure 6 in Abrahamsen H. et al., 2004, supra). This prevents TCR-induced cAMP production and enhances T cell immune responses. Upon TCR stimulation alone, PDE4 recruitment is too low to sufficiently reduce cAMP levels, and therefore maximal T cell activation cannot occur (see Abrahamsen H. et al., 2004, supra).

[0045] Thus, by actively suppressing proximal TCR signaling, cAMP-PKA-Csk-mediated signaling appears to establish a threshold for T cell activation. Recruitment of PDEs can counteract this suppression. Tissue- or cell-type-specific regulation is achieved through the expression of multiple isoforms of AC, PKA, and PDEs. As mentioned above, the balance between activation and suppression must be tightly regulated to prevent the development of autoimmune disorders, immunodeficiencies, and cancer.

[0046] PDE4D7-related genes Phosphodiesterases (PDEs) provide the sole means for the degradation of the second messenger 3'-5'-cyclic AMP. As such, they play a key regulatory role. Therefore, abnormal changes in their expression, activity, and subcellular location all contribute to the fundamental molecular pathways of specific disease states. Indeed, it has recently been revealed that mutations in PDE genes are abundant in prostate cancer patients, leading to elevated cAMP signaling and a potential predisposition to prostate cancer. However, the diverse expression profiles in different cell types, with complex multiple isoform variants within each PDE family, allow us to understand the link between abnormal changes in PDE expression and their functionality during challenging disease progression. Several studies have attempted to describe the complement of PDEs in the prostate, all of which have identified significant levels of PDE4 expression in collaboration with 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 good predictor, we hypothesized that the ability to identify markers highly correlated with the PDE47 biomarker would also be useful in predicting outcomes in certain cancer subjects.

[0047] Gene selection The list of genes was originally selected for prognosis in subjects with prostate cancer. Herein, they are shown to also be prognostic for outcome in subjects with bladder or kidney cancer.

[0048] The identified immune defense response genes ZBP1, as well as AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, respectively, 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 related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiation treatment (SRT), salvage androgen deprivation treatment (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes (see Alves de Inda M. et al., 2018, supra). PDE4D7 score class 1 represents patient samples with the lowest PDE4D7 expression level, while PDE4D7 score class 4 represents patient samples with the highest PDE4D7 expression level. Then, the RNASeq expression data (TPM - transcripts per million) of 538 prostate cancer subjects was examined for differential gene expression between PDE4D7 score class 1 and class 4. In particular, for approximately 20,000 protein-coding transcripts, it was determined whether the average expression level of patients with PDE4D7 score class 1 was more than twice that of patients with PDE4D7 score class 4. This analysis resulted in 637 genes with a PDE4D7 score class 1 / PDE4D7 score class 4 ratio of greater than 2 at the minimum mean expression of one TPM in each of the four PDE4D7 score classes. These 637 genes were then further subjected to molecular pathway analysis (www.david.ncifcrf.gov), resulting in various enriched annotation clusters.Annotation cluster #2 showed enrichment (enrichment score: 10.8) for 30 genes with functions in defense against viruses, negative regulation of viral genome replication, and type I interferon signaling. Further heatmap analysis confirmed that these immune defense response genes were generally more highly expressed in samples from patients with PDE4D7 score class 1 than in samples from patients with PDE4D7 score class 4. A literature search to identify additional genes with the same molecular function further expanded the class of genes with functions in defense against viruses, negative regulation of viral genome replication, and type I interferon signaling to 61 genes. Further selection of the 61 genes based on combinatorial power to separate patients who died from prostate cancer from those who did not was performed, resulting in a preferred set of 14 genes. The number of events (metastasis, prostate cancer-specific death) was found to be increased in the subcohort with low expression of these genes compared to the total patient cohort (#538) and a subcohort of 151 patients undergoing salvage RT (SRT) after postoperative disease recurrence.

[0049] The identified T cell receptor signaling genes, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, were identified as follows: A group of 538 prostate cancer patients was 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 related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiation therapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes (see Alves de Inda M. et al., 2018, supra). PDE4D7 score class 1 represents the patient sample with the lowest PDE4D7 expression level, while PDE4D7 score class 4 represents the patient sample with the highest PDE4D7 expression level.Then, the RNASeq expression data (TPM-transcripts per million) of 538 prostate cancer subjects was investigated for the differential gene expression between PDE4D7 score class 1 and class 4.Specifically, for approximately 20,000 transcripts encoding proteins, it was determined whether the average expression level of patients with PDE4D7 score class 1 is more than twice that of patients with PDE4D7 score class 4.This analysis resulted in 637 genes with PDE4D7 score class 1 / PDE4D7 score class 4 ratio greater than 2, with the lowest average expression in each of the four PDE4D7 score classes being 1 TPM. These 637 genes were then further subjected to molecular pathway analysis (www.david.ncifcrf.gov), resulting in various enriched annotation clusters. Annotation cluster #6 showed enrichment in 17 genes (enrichment score: 5.9) with functions in primary immune deficiency and activation of T cell receptor signaling.Further heatmap analysis confirmed that these T cell receptor signaling genes were generally more highly expressed in samples from patients in PDE4D7 score class 1 than in samples from patients in PDE4D7 score class 4.

[0050] The identified PDE4D7-correlated genes, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, were identified as follows: RNA-seq data generated for 571 prostate cancer patients was identified for approximately 60,000 transcripts of various genes that correlated with the expression of the known biomarker PDE4D7 in this data. The correlation between the expression of any of these genes and PDE4D7 in the 571 samples was calculated by Pearson correlation, expressed as a value between 0 and 1 for a positive correlation or between -1 and 0 for a negative correlation. The input data for calculating the correlation coefficient were the PDE4D7 score (see Alves de Inda M. et al., 2018, supra) and the RNA-seq-determined TPM gene expression value for each gene of interest (see below).

[0051] The largest negative correlation coefficient identified between the expression of any of approximately 60,000 transcripts and the expression of PDE4D7 was -0.38, while the largest positive correlation coefficient identified between the expression of any of approximately 60,000 transcripts and the expression of PDE4D7 was +0.56. Genes were selected in the correlation ranges of -0.31 to -0.38 and +0.41 to +0.56. 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 iterative Cox regression binding model testing in a subcohort of 186 patients undergoing salvage radiotherapy (SRT) due to postoperative biochemical recurrence. The clinical endpoint tested was prostate cancer-specific death after the initiation of SRT. The boundary condition for the selection of the eight genes was given by the restriction that the p-value in the multivariate Cox regression be <0.1 for all genes retained in the model.

[0052] The term "ABCC5" refers to the nucleotide sequence as set forth in SEQ ID NO: 1 or SEQ ID NO: 2 of 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, ​​in particular the sequence of the NCBI Reference Sequence for the ABCC5 transcript shown above; it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 3 or SEQ ID NO: 4, e.g. corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001018881.1 and NCBI Protein Accession Reference Sequence NP_005679 which encodes the ABCC5 polypeptide.

[0053] The term "ABCC5" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:1 or SEQ ID NO:2.

[0054] The term "AIM2" refers to the nucleotide sequence as set forth in SEQ ID NO: 5, which corresponds to the sequence of the Absent in Melanoma 2 gene (Ensembl: ENSG00000163568), e.g., the sequence defined in NCBI Reference Sequence NM_004833, in particular the sequence of the NCBI Reference Sequence for the AIM2 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0055] The term "AIM2" also refers to a nucleotide sequence that shows 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 as set forth in SEQ ID NO:5, 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 as 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 as 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 as set forth in SEQ ID NO:5.

[0056] The term "APOBEC3A" refers to the nucleotide sequence as set forth in SEQ ID NO: 7, which corresponds to the sequence of the Apolipoprotein B mRNA Editing Enzyme Catalytic Subunit 3A gene (Ensembl: ENSG00000128383), e.g., the sequence defined in NCBI Reference Sequence NM_145699, in particular the NCBI Reference Sequence for the APOBEC3A transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0057] The term "APOBEC3A" also refers to a nucleotide sequence that exhibits 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 as 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 as set forth in SEQ ID NO:8. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:8, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:7.

[0058] The term "CD2" refers to the nucleotide sequence as set forth in SEQ ID NO: 9, which corresponds to the sequence of the Cluster of Differentiation 2 gene (Ensembl: ENSG00000116824), e.g., the sequence defined in NCBI Reference Sequence NM_001767, in particular the NCBI Reference Sequence for the CD2 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0059] The term "CD2" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO: 9.

[0060] The term "CD247" refers to the nucleotide sequence as set forth in SEQ ID NO: 11 or SEQ ID NO: 12, which corresponds to the sequence of 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, in particular the NCBI Reference Sequence for the CD247 transcript shown above; it also relates to the corresponding amino acid sequence, e.g. as set forth in SEQ ID NO: 13 or SEQ ID NO: 14, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000725 and NCBI Protein Accession Reference Sequence NP_932170, which encodes the CD247 polypeptide.

[0061] The term "CD247" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO: 11 or SEQ ID NO: 12, 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 as 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 as 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 as set forth in SEQ ID NO:11 or SEQ ID NO:12.

[0062] The term "CD28" refers to the nucleotide sequence as set forth in SEQ ID NO: 15 or SEQ ID NO: 16, which corresponds to the sequence of the Cluster of Differentiation 28 gene (Ensembl: ENSG00000178562), e.g. as defined in NCBI Reference Sequence NM_006139 or NCBI Reference Sequence NM_001243078, in particular the NCBI Reference Sequence for the CD28 transcript shown above; it also relates to the corresponding amino acid sequence, e.g. as set forth in SEQ ID NO: 17 or SEQ ID NO: 18, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_006130 and NCBI Protein Accession Reference Sequence NP_001230007, which encodes the CD28 polypeptide.

[0063] The term "CD28" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO: 15 or SEQ ID NO: 16, 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 as 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 as 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 as set forth in SEQ ID NO:15 or SEQ ID NO:16.

[0064] The term "CD3E" refers to the nucleotide sequence as set forth in SEQ ID NO: 19, which corresponds to the sequence of the Cluster of Differentiation 3E gene (Ensembl: ENSG00000198851), e.g., the sequence defined in NCBI Reference Sequence NM_000733, in particular the NCBI Reference Sequence for the CD3E transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0065] The term "CD3E" also refers to a nucleotide sequence that shows 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 as 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 as 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 as 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 as set forth in SEQ ID NO:19.

[0066] The term "CD3G" refers to the nucleotide sequence as set forth in SEQ ID NO: 21, which corresponds to the sequence of the Cluster of Differentiation 3G gene (Ensembl: ENSG00000160654), e.g. the sequence defined in NCBI Reference Sequence NM_000073, in particular the sequence of the NCBI Reference Sequence for the CD3G transcript shown above; it also relates to the corresponding amino acid sequence, e.g. as 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.

[0067] The term "CD3G" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:21.

[0068] The term "CD4" refers to the nucleotide sequence as set forth in SEQ ID NO: 23, which corresponds to the sequence of the Cluster of Differentiation 4 gene (Ensembl: ENSG00000010610), e.g., the sequence defined in NCBI Reference Sequence NM_000616, in particular the NCBI Reference Sequence for the CD4 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0069] The term "CD4" also refers to a nucleotide sequence that exhibits high 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 as 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 as 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 as 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 as set forth in SEQ ID NO:23.

[0070] The term "CIAO1" refers to the nucleotide sequence as set forth in SEQ ID NO: 25, which corresponds to the sequence of the Cytosolic Iron-Sulfur Assembly Component 1 gene (Ensembl: ENSG00000144021), e.g., the sequence defined in NCBI Reference Sequence NM_004804, in particular the NCBI Reference Sequence for the CIAO1 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 26, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_663745, which encodes the CIAO1 polypeptide.

[0071] The term "CIAO1" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO:25, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as 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 as 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 as set forth in SEQ ID NO:25.

[0072] The term "CSK" refers to the nucleotide sequence as set forth in SEQ ID NO: 27, which corresponds to the sequence of the C-terminal Src kinase gene (Ensembl: ENSG00000103653), e.g. the sequence defined in NCBI Reference Sequence NM_004383, in particular the NCBI Reference Sequence for the CSK transcript shown above; it also relates to the corresponding amino acid sequence, e.g. as 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.

[0073] The term "CSK" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:27.

[0074] The term "CUX2" refers to the nucleotide sequence as set forth in SEQ ID NO: 29, which corresponds to the sequence of the human Cut Like Homeobox 2 gene (Ensembl: ENSG00000111249), e.g., the sequence defined in NCBI Reference Sequence NM_015267.3, in particular the sequence of the NCBI Reference Sequence for the CUX2 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 30, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_056082.2, which encodes the CUX2 polypeptide.

[0075] The term "CUX2" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:29.

[0076] The term "DDX58" refers to the nucleotide sequence as set forth in SEQ ID NO: 31, which corresponds to the sequence of the DExD / H-box Helicase 58 gene (Ensembl: ENSG00000107201), e.g., the sequence defined in NCBI Reference Sequence NM_014314, in particular the NCBI Reference Sequence for the DDX58 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0077] The term "DDX58" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO: 31, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as 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 as 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 as set forth in SEQ ID NO:31.

[0078] The term "DHX9" refers to the nucleotide sequence as set forth in SEQ ID NO: 33, which corresponds to the sequence of the DExD / H-box Helicase 9 gene (Ensembl: ENSG00000135829), e.g., the sequence defined in NCBI Reference Sequence NM_001357, in particular the NCBI Reference Sequence for the DHX9 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0079] The term "DHX9" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:33.

[0080] The term "EZR" refers to the nucleotide sequence as set forth in SEQ ID NO: 35, which corresponds to the sequence of the Ezrin gene (Ensembl: ENSG00000092820), e.g., the sequence defined in NCBI Reference Sequence NM_003379, in particular the sequence of the NCBI Reference Sequence for the EZR transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 36, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_003370, which encodes the EZR polypeptide.

[0081] The term "EZR" also refers to a nucleotide sequence that exhibits high 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 as 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 as 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 as 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 as set forth in SEQ ID NO:35.

[0082] The term "FYN" refers to the nucleotide sequence as set forth in SEQ ID NO: 37, SEQ ID NO: 38 or SEQ ID NO: 39, corresponding to the sequence of 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, in particular the NCBI Reference Sequence for the FYN transcript shown above; it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 40, SEQ ID NO: 41 or SEQ ID NO: 42, corresponding to the protein sequence 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 encodes the FYN polypeptide.

[0083] The term "FYN" also refers to a nucleotide sequence that exhibits high homology to FYN, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:37, SEQ ID NO:38 or SEQ ID NO:39, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as 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 as 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 as set forth in SEQ ID NO:37, SEQ ID NO:38 or SEQ ID NO:39.

[0084] The term "IFI16" refers to the nucleotide sequence as set forth in SEQ ID NO: 43, which corresponds to the sequence of the Interferon Gamma Inducible Protein 16 gene (Ensembl: ENSG00000163565), e.g., the sequence defined in NCBI Reference Sequence NM_005531, in particular the NCBI Reference Sequence of the IFI16 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0085] The term "IFI16" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO: 43, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as 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 as 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 as set forth in SEQ ID NO:43.

[0086] The term "IFIH1" refers to the nucleotide sequence as set forth in SEQ ID NO: 45, which corresponds to the sequence of the Interferon Induced With Helicase C Domain 1 gene (Ensembl: ENSG00000115267), e.g., the sequence defined in NCBI Reference Sequence NM_022168, in particular the NCBI Reference Sequence for the IFIH1 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 46, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_071451, which encodes the IFIH1 polypeptide.

[0087] The term "IFIH1" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO: 45, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as 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 as 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 as set forth in SEQ ID NO:45.

[0088] The term "IFIT1" refers to the nucleotide sequence as set forth in SEQ ID NO: 47 or SEQ ID NO: 48, which corresponds to the sequence of 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, in particular the NCBI Reference Sequence for the IFIT1 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 49 or SEQ ID NO: 50, which corresponds 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.

[0089] The term "IFIT1" also refers to a nucleotide sequence that exhibits high homology to IFIT1, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as 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 as 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 as 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 as set forth in SEQ ID NO:47 or SEQ ID NO:48.

[0090] The term "IFIT3" refers to the nucleotide sequence as set forth in SEQ ID NO: 51, which corresponds to the sequence of the Interferon Induced Protein With Tetratricopeptide Repeats 3 gene (Ensembl: ENSG00000119917), e.g., the sequence defined in NCBI Reference Sequence NM_00103168, in particular the NCBI Reference Sequence for the IFIT3 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 52, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001026853, which encodes the IFIT3 polypeptide.

[0091] The term "IFIT3" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO: 51, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as 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 as 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 as set forth in SEQ ID NO:51.

[0092] The term "KIAA1549" refers to the nucleotide sequence as set forth in SEQ ID NO: 53 or SEQ ID NO: 54 of the human KIAA1549 gene (Ensembl: ENSG00000122778), for example the sequence defined in NCBI Reference Sequence NM_020910 or NCBI Reference Sequence NM_00116466, in particular the sequence of the NCBI Reference Sequence of the KIAA1549 transcript shown above; it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 55 or SEQ ID NO: 56, for example, corresponding 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.

[0093] The term "KIAA1549" also refers to a nucleotide sequence that shows 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 as 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 as set forth in SEQ ID NO:55 or SEQ ID NO:56. or an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:55 or SEQ ID NO:56, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:53 or SEQ ID NO:54.

[0094] The term "LAT" refers to the nucleotide sequence as set forth in SEQ ID NO: 57 or SEQ ID NO: 58 of the gene for Linker For Activation Of T-Cells (Ensembl: ENSG00000213658), e.g. the sequence defined in NCBI Reference Sequence NM_001014987 or NCBI Reference Sequence NM_014387, in particular the sequence of the NCBI Reference Sequence for the LAT transcript shown above; it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 59 or SEQ ID NO: 60, e.g. corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001014987 and NCBI Protein Accession Reference Sequence NP_055202 that encodes the LAT polypeptide.

[0095] The term "LAT" also refers to nucleotide sequences that exhibit high 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 as 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 as 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 as 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 as set forth in SEQ ID NO:57 or SEQ ID NO:58.

[0096] The term "LCK" refers to the nucleotide sequence as set forth in SEQ ID NO: 61, which corresponds to the sequence of the gene of the LCK Proto-Oncogene (Ensembl: ENSG00000182866), e.g., the sequence defined in NCBI Reference Sequence NM_005356, in particular the NCBI Reference Sequence of the LCK transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0097] The term "LCK" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:61.

[0098] The term "LRRFIP1" refers to the nucleotide sequence as set forth in SEQ ID NO: 63 or SEQ ID NO: 64 or SEQ ID NO: 65 or SEQ ID NO: 66, which corresponds to the sequence defined in the NCBI Reference Sequence NM_004735 or NCBI Reference Sequence NM_001137550 or NCBI Reference Sequence NM_001137553 or NCBI Reference Sequence NM_001137552, specifically the NCBI Reference Sequence of the LRRFIP1 transcript shown above, and it also refers to the LR It also relates to the corresponding amino acid sequences as set forth in SEQ ID NO: 67 or SEQ ID NO: 68 or SEQ ID NO: 69 or SEQ ID NO: 70, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_004726 and NCBI protein accession reference sequence NP_001131022 and NCBI protein accession reference sequence NP_001131025 and NCBI protein accession reference sequence NP_001131024, which encode RFIP1 polypeptides.

[0099] The term "LRRFIP1" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO:63 or SEQ ID NO:64 or 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 as set forth in SEQ ID NO:67 or SEQ ID NO:68 or SEQ ID NO:69 or SEQ ID NO:70. This includes an amino acid sequence, or a nucleic acid sequence that encodes an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:67 or SEQ ID NO:68 or SEQ ID NO:69 or SEQ ID NO:70, or the 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 as set forth in SEQ ID NO:63 or SEQ ID NO:64 or SEQ ID NO:65 or SEQ ID NO:66.

[0100] 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 or NCBI Reference Sequence NM_001172568 or NCBI Reference Sequence NM_001172569 or NCBI Reference Sequence NM_001172566 or NCBI Reference Sequence NM_002468, in particular the nucleotide sequence as set forth in SEQ ID NO: 71 or SEQ ID NO: 72 or SEQ ID NO: 73 or SEQ ID NO: 74 or SEQ ID NO: 75, which corresponds to the sequence of the NCBI Reference Sequence of the MYD88 transcript shown above; it also refers to the MYD88 polypeptide. It also relates to the corresponding amino acid sequences as set forth in SEQ ID NO: 76 or SEQ ID NO: 77 or SEQ ID NO: 78 or SEQ ID NO: 79 or SEQ ID NO: 80, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_001166038 and NCBI protein accession reference sequence NP_001166039 and NCBI protein accession reference sequence NP_001166040 and NCBI protein accession reference sequence NP_001166037 and NCBI protein accession reference sequence NP_002459 which encode peptides.

[0101] The term "MYD88" also refers to a nucleotide sequence that exhibits high homology to MYD88, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:71 or SEQ ID NO:72 or SEQ ID NO:73 or SEQ ID NO:74 or SEQ ID NO:75, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:76 or SEQ ID NO:77 or SEQ ID NO:78 or SEQ ID NO:79 or SEQ ID NO:80. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:76 or SEQ ID NO:77 or SEQ ID NO:78 or SEQ ID NO:79 or SEQ ID NO:80, or the amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:71 or SEQ ID NO:72 or SEQ ID NO:73 or SEQ ID NO:74 or SEQ ID NO:75.

[0102] The term "OAS1" refers to 2'-5'-Oligoadenylate Synthetase 1 gene (Ensembl: ENSG00000089127), for example the sequence defined in NCBI Reference Sequence NM_001320151 or NCBI Reference Sequence NM_002534 or NCBI Reference Sequence NM_001032409 or NCBI Reference Sequence NM_016816, in particular the nucleotide sequence as set forth in SEQ ID NO: 81 or SEQ ID NO: 82 or SEQ ID NO: 83 or SEQ ID NO: 84 corresponding to the sequence of the NCBI Reference Sequence of the OAS1 transcript shown above; and it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 85 or SEQ ID NO: 86 or SEQ ID NO: 87 or SEQ ID NO: 88 corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001307080 and NCBI Protein Accession Reference Sequence NP_002525 and NCBI Protein Accession Reference Sequence NP_001027581 and NCBI Protein Accession Reference Sequence NP_058132 which encodes the OAS1 polypeptide.

[0103] The term "OAS1" also refers to a nucleotide sequence that shows 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 as set forth in SEQ ID NO:81 or SEQ ID NO:82 or SEQ ID NO:83 or SEQ ID NO:84, 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 as set forth in SEQ ID NO:85 or SEQ ID NO:86 or SEQ ID NO:87 or SEQ ID NO:88. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:85 or SEQ ID NO:86 or SEQ ID NO:87 or SEQ ID NO:88, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:81 or SEQ ID NO:82 or SEQ ID NO:83 or SEQ ID NO:84.

[0104] The term "PAG1" refers to the nucleotide sequence as set forth in SEQ ID NO: 89, which corresponds to the sequence of the Phosphoprotein Membrane Anchor With Glycosphingolipid Microdomains 1 gene (Ensembl: ENSG00000076641), e.g., the sequence defined in NCBI Reference Sequence NM_018440, in particular the NCBI Reference Sequence for the PAG1 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 90, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_060910, which encodes the PAG1 polypeptide.

[0105] The term "PAG1" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:89.

[0106] The term "PDE4D" refers to the human Phosphodiesterase 4D gene (Ensembl: ENSG00000113448), e.g., NCBI Reference Sequence NM_001104631 or NCBI Reference Sequence NM_001349242 or NCBI Reference Sequence NM_001197218 or NCBI Reference Sequence NM_006203 or NCBI Reference Sequence NM_001197221 or NCBI Reference Sequence NM_001197220 or NCBI Reference Sequence NM_001197223 or NCBI Reference Sequence NM_001165 899 or NCBI Reference Sequence NM_001165899, specifically the nucleotide sequence as set forth in SEQ ID NO: 91 or SEQ ID NO: 92 or SEQ ID NO: 93 or SEQ ID NO: 94 or SEQ ID NO: 95 or SEQ ID NO: 96 or SEQ ID NO: 97 or SEQ ID NO: 98 or SEQ ID NO: 99, which correspond to the sequences of the NCBI Reference Sequences for the PDE4D transcripts set forth above, and which also refer to the nucleotide sequence as set forth in NCBI Protein Accession Reference Sequence N P_001098101 and NCBI protein accession reference sequence NP_001336171 and NCBI protein accession reference sequence NP_001184147 and NCBI protein accession reference sequence NP_006194 and NCBI protein accession reference sequence NP_001184150 and NCBI protein accession reference sequence NP_001184149 and NCBI protein accession reference sequence NP_001184152 and NCBI protein accession reference sequence NP_001159371 and NCBI protein accession reference sequence NP_001184148, such as the corresponding amino acid sequence as set forth in SEQ ID NO:100 or SEQ ID NO:101 or SEQ ID NO:102 or SEQ ID NO:103 or SEQ ID NO:104 or SEQ ID NO:105 or SEQ ID NO:106 or SEQ ID NO:107 or SEQ ID NO:108.

[0107] The term "PDE4D" also refers to nucleotide sequences that exhibit high homology to PDE4D, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences as 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, SEQ ID NO:99, 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 sequences as 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, SEQ ID NO:108. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as 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 which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as 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.

[0108] The term "PRKACA" refers to the nucleotide sequence as set forth in SEQ ID NO: 109 or SEQ ID NO: 110 of the gene for Protein Kinase cAMP-Activated Catalytic Subunit Alpha (Ensembl: ENSG00000072062), for example the sequence defined in NCBI Reference Sequence NM_002730 or NCBI Reference Sequence NM_207518, in particular the sequence of the NCBI Reference Sequence for the PRKACA transcript shown above; it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 111 or SEQ ID NO: 112, for example, corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_002721 and NCBI Protein Accession Reference Sequence NP_997401 which encodes the PRKACA polypeptide.

[0109] The term "PRKACA" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:109 or SEQ ID NO:110.

[0110] The term "PRKACB" refers to the gene for Protein Kinase cAMP-Activated Catalytic Subunit Beta (Ensembl: ENSG00000142875), e.g., 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_207578, NCBI Reference Sequence NM_00 1242857 or NCBI Reference Sequence NM_001300917, specifically the nucleotide sequence as 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 NCBI Reference Sequence for the PRKACB transcript set forth above, and which also refers to the nucleotide sequence as set forth in NCBI Protein Accession Reference Sequence NP_001300917, which encodes the PRKACB polypeptide. _002722, NCBI protein accession reference sequence NP_891993, NCBI protein accession reference sequence NP_001229789, NCBI protein accession reference sequence NP_001229788, NCBI protein accession reference sequence NP_001229787, NCBI protein accession reference sequence NP_001229791, NCBI protein accession reference sequence NP_001229790, NCBI protein accession reference sequence NP_001287 844, NCBI Protein Accession Reference Sequence NP_997461, NCBI Protein Accession Reference Sequence NP_001229786 and NCBI Protein Accession Reference Sequence NP_001287846, e.g., the corresponding amino acid sequence as 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.

[0111] The term "PRKACB" also refers to a nucleotide sequence that exhibits high homology to PRKACB, for example, 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%, 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, a sequence as 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 a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to a sequence as 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 a sequence as 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.

[0112] The term "PTPRC" refers to the nucleotide sequence as set forth in SEQ ID NO: 135 or SEQ ID NO: 136, which corresponds to the sequence of 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, in particular the NCBI Reference Sequence for the PTPRC transcript shown above; it also relates to NCBI Protein Accession Reference Sequence NP_002829, which encodes a PTPRC polypeptide, and the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 137 or SEQ ID NO: 138, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_563578.

[0113] The term "PTPRC" also refers to a nucleotide sequence that exhibits high homology to a 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 as set forth in SEQ ID NO: 135 or SEQ ID NO: 136, 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 as 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 as 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 as set forth in SEQ ID NO:135 or SEQ ID NO:136.

[0114] The term "RAP1GAP2" refers to the nucleotide sequence as set forth in SEQ ID NO: 139 or SEQ ID NO: 140 or SEQ ID NO: 141 of the human RAP1 GTPase Activating Protein 2 gene (ENSG00000132359), for example the sequence defined in NCBI Reference Sequence NM_015085 or NCBI Reference Sequence NM_001100398 or NCBI Reference Sequence NM_001330058, in particular the sequence of the NCBI Reference Sequence for the RAP1GAP2 transcript shown above; it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 142 or SEQ ID NO: 143 or SEQ ID NO: 144, for example, corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_055900 and NCBI Protein Accession Reference Sequence NP_001093868 and NCBI Protein Accession Reference Sequence NP_001316987 which encodes the RAP1GAP2 polypeptide.

[0115] The term "RAP1GAP2" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO: 139 or SEQ ID NO: 140 or SEQ ID NO: 141, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO: 142 or SEQ ID NO: 143 or SEQ ID NO: 144. or an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:142 or SEQ ID NO:143 or SEQ ID NO:144, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO:139 or SEQ ID NO:140 or SEQ ID NO:141.

[0116] The term "SLC39A11" refers to the nucleotide sequence as set forth in SEQ ID NO: 145 or SEQ ID NO: 146, which corresponds to the sequence of 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, in particular the NCBI Reference Sequence of the SLC39A11 transcript shown above; it also relates to the corresponding amino acid sequence, e.g. as set forth in SEQ ID NO: 147 or SEQ ID NO: 148, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_631916 and NCBI Protein Accession Reference Sequence NP_001339621, which encodes the SLC39A11 polypeptide.

[0117] The term "SLC39A11" also refers to a nucleotide sequence that shows high homology to SLC39A11, for example a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as 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 as set forth in SEQ ID NO: 147 or SEQ ID NO: 148. or an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:147 or SEQ ID NO:148, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:145 or SEQ ID NO:146.

[0118] The term "TDRD1" refers to the nucleotide sequence as set forth in SEQ ID NO: 149, which corresponds to the sequence of the human Tudor Domain Containing 1 gene (Ensembl: ENSG00000095627), e.g., the sequence defined in NCBI Reference Sequence NM_198795, in particular the sequence of the NCBI Reference Sequence for the TDRD1 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as set forth in SEQ ID NO: 150, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_942090, which encodes the TDRD1 polypeptide.

[0119] The term "TDRD1" also refers to a nucleotide sequence that exhibits 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 as set forth in SEQ ID NO: 149, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as 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 as 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 as set forth in SEQ ID NO:149.

[0120] The term "TLR8" refers to the nucleotide sequence as set forth in SEQ ID NO: 151 or SEQ ID NO: 152 of 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, in particular the sequence of the NCBI Reference Sequence of the TLR8 transcript shown above; it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 153 or SEQ ID NO: 154, e.g. corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_619542 and NCBI Protein Accession Reference Sequence NP_057694 which encodes the TLR8 polypeptide.

[0121] The term "TLR8" also refers to a nucleotide sequence that shows 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 as set forth in SEQ ID NO: 151 or SEQ ID NO: 152, 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 as 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 as 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 as set forth in SEQ ID NO:151 or SEQ ID NO:152.

[0122] The term "VWA2" refers to the nucleotide sequence as set forth in SEQ ID NO: 155, which corresponds to the sequence of the Human Von Willebrand Factor A Domain Containing 2 gene (Ensembl: ENSG00000165816), e.g., the sequence defined in NCBI Reference Sequence NM_001320804, in particular the NCBI Reference Sequence of the VWA2 transcript shown above; it also relates to the corresponding amino acid sequence, e.g., as 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.

[0123] The term "VWA2" also refers to a nucleotide sequence that exhibits 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 as 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 as 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 as 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 as set forth in SEQ ID NO:155.

[0124] The term "ZAP70" refers to the nucleotide sequence as set forth in SEQ ID NO: 157 or SEQ ID NO: 158, which corresponds to the sequence of the Zeta Chain Of T-Cell Receptor Associated Protein Kinase 70 gene (Ensembl: ENSG00000115085), for example as defined in NCBI Reference Sequence NM_001079 or NCBI Reference Sequence NM_207519, in particular the NCBI Reference Sequence for the ZAP70 transcript shown above; it also relates to the corresponding amino acid sequence, for example as set forth in SEQ ID NO: 159 or SEQ ID NO: 160, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001070 and NCBI Protein Accession Reference Sequence NP_997402, which encodes the ZAP70 polypeptide.

[0125] The term "ZAP70" also refers to a nucleotide sequence that exhibits high homology to ZAP70, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as set forth in SEQ ID NO: 157 or SEQ ID NO: 158, 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 as set forth in SEQ ID NO: 159 or SEQ ID NO: 160. 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 as 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 as set forth in SEQ ID NO:157 or SEQ ID NO:158.

[0126] The term "ZBP1" refers to the nucleotide sequence as set forth in SEQ ID NO: 161 or SEQ ID NO: 162 or SEQ ID NO: 163 of the Z-DNA Binding Protein 1 gene (Ensembl: ENSG00000124256), for example the sequence defined in NCBI Reference Sequence NM_030776 or NCBI Reference Sequence NM_001160418 or NCBI Reference Sequence NM_001160419, in particular the sequence of the NCBI Reference Sequence of the ZBP1 transcript shown above; it also relates to the corresponding amino acid sequence as set forth in SEQ ID NO: 164 or SEQ ID NO: 165 or SEQ ID NO: 166, for example, corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_110403 and NCBI Protein Accession Reference Sequence NP_001153890 and NCBI Protein Accession Reference Sequence NP_001153891, which encodes the ZBP1 polypeptide.

[0127] The term "ZBP1" also refers to a nucleotide sequence that shows 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 as set forth in SEQ ID NO:161 or SEQ ID NO:162 or SEQ ID NO:163, 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 as set forth in SEQ ID NO:164 or SEQ ID NO:165 or SEQ ID NO:166. or a nucleic acid sequence encoding an amino acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:164 or SEQ ID NO:165 or SEQ ID NO:166, or an amino acid sequence encoded by a nucleic acid sequence which is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as set forth in SEQ ID NO:161 or SEQ ID NO:162 or SEQ ID NO:163.

[0128] The term "biological sample" or "sample obtained from a subject" refers to any biological material obtained from a subject, e.g., a bladder or kidney cancer patient, by a suitable method known to those skilled in the art. The biological sample used is collected in a clinically acceptable manner, e.g., in a manner that preserves nucleic acids (especially RNA) or proteins.

[0129] Biological samples include bodily tissues and / or bodily fluids, such as, but not limited to, blood (or blood-derived samples, such as, for example, serum, plasma, or peripheral blood mononuclear cells (PBMCs)), sweat, saliva, urine, and needle or excision biopsies. Furthermore, biological samples contain cell extracts derived from epithelial cells, such as cancerous epithelial cells or epithelial cells derived from tissue suspected to be cancer, or cell populations containing same. Biological samples contain cell populations derived from glandular tissues, for example, samples derived from the bladder or kidney of a subject. Additionally, if necessary, cells are purified from the obtained bodily tissues and fluids and then used as the biological sample. In some implementations, the sample is a tissue sample, a urine sample, a urine sediment sample, a blood sample, a saliva sample, a semen sample, a sample containing circulating tumor cells, a sample containing extracellular vesicles, exosomes secreted by the prostate, or a cell line or cancer cell line.

[0130] In one particular realization, a biopsy or resection sample is obtained and / or used, such a sample comprising a cell or cell lysate.

[0131] It is also conceivable that the contents of the biological sample are subjected to an enrichment step, for example by contacting the sample with a ligand specific for the cell membrane or organelles of a particular cell type, e.g., bladder or kidney cells, functionalized with, for example, magnetic particles. The material enriched by the magnetic particles is then used for the detection and analysis steps described herein above or below.

[0132] Furthermore, cells, e.g., tumor cells, can also be enriched through a filtration process of a liquid or liquid sample, e.g., blood, urine, etc. Such a filtration process can also be combined with an enrichment step based on ligand-specific interactions as described herein above.

[0133] The term "bladder cancer" refers to cancer of the tissues of the bladder. Bladder cancer usually begins in the cells lining the inside of the bladder (urothelial cells).

[0134] The term "kidney cancer" refers to a group of renal cancers that begin in the kidney. The primary type of kidney cancer is renal cell carcinoma (RCC), accounting for approximately 90-95% of all cases. Renal cell carcinomas are kidney cancers that arise in the lining of the proximal convoluted tubules, which are very small tubes in the kidney that transport urine. Renal cell carcinomas (RCC) comprise a heterogeneous group of tumors and can be further subclassified into papillary RCC (pRCC) and clear cell RCC (ccRCC), which differ in survival outcomes.

[0135] The term "TNM" refers to a classification system used to describe the characteristics of malignant tumors.

[0136] The term "T stage" refers to the extent and size of a tumor according to the TNM classification system. T stage can have the following attributes: T1 (small localized tumor; typically <2 cm in size); T2 (larger localized tumor; typically 2-5 cm in size); T3 (larger locally advanced tumor; typically >5 cm in size); T4 (advanced / metastatic tumor).

[0137] The term "N stage" refers to the presence and extent of tumor-positive lymph nodes according to the TNM classification system. N stage can have the following attributes: N0 (no evidence of tumor-positive lymph nodes); N1 (evidence of tumor-positive lymph nodes); N2 / N3 (an increased number of tumor-positive lymph nodes); NX (no possible assessment of lymph node status).

[0138] The term "M stage" refers to the presence and extent of tumor metastasis according to the TNM classification system. M stage can have the following attributes: M0 (no evidence of distant metastasis); M1 (evidence of distant metastasis).

[0139] The term "survival rate" refers to the survival rate of a patient from bladder or kidney cancer.

[0140] It is preferable that: - 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 response 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.

[0141] Furthermore, in the method of the present invention, the following are preferred: - the three or more immune defense response genes include six or more, preferably nine or more, more preferably all of the immune defense response genes; or - the three or more T cell receptor signaling genes include six or more, preferably nine or more, and more preferably all of the T cell receptor signaling genes; or - the three or more PDE4D7-correlated genes include six or more, preferably nine or more, and most preferably all of the PDE4D7-correlated genes; or - The three or more genes include at least two or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-correlated genes, preferably three or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-correlated genes, most preferably all of the genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-correlated genes.

[0142] identifying an outcome - combining the first gene expression profile for two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes with a regression function obtained from a population of bladder or kidney cancer subjects; and / or - combining a second gene expression profile for two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all, T cell receptor signaling genes with a regression function obtained from a population of bladder or kidney cancer subjects; and / or - combining a third gene expression profile for two or more, e.g., 2, 3, 4, 5, 6, 7, or all, of the PDE4D7-correlated genes with a regression function obtained from a population of bladder or kidney cancer subjects; It is preferred to include:

[0143] Further, the step of determining a prediction of outcome comprises: - combining gene expression profiles for two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes with a regression function obtained from a population of bladder or kidney cancer subjects; and / or - combining gene expression profiles for 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 obtained from a population of bladder or kidney cancer subjects; and / or - Combining gene expression profiles for two or more, e.g., 2, 3, 4, 5, 6, 7, or all, of the PDE4D7-correlated genes with a regression function obtained from a population of bladder or kidney cancer subjects. It is preferred to include:

[0144] Cox proportional hazards regression allows for the real-time analysis of the impact of multiple risk factors on a tested event, such as survival. In this context, risk factors can be binary or discrete variables, such as risk scores or clinical stage, but can also be continuous variables, such as biomarker measurements or gene expression values. The probability of an endpoint (e.g., death or disease recurrence) is called the hazard. In regression analysis, next to information about whether subjects in a patient cohort reached or failed to reach the tested endpoint (e.g., whether the patient died or not), the time to the endpoint is also considered. The 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 t. The hazard rate (i.e., the risk of achieving an 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 similarly to logistic regression analysis.

[0145] In one particular implementation, the combination of the first gene expression profile for two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes and the regression function is specified as follows:

number

[0146] In one example for bladder cancer, w1 is about −0.1 to 0.9, such as −0.4645, w2 is about −0.4 to 0.6, such as 0.06517, w3 is about −0.7 to 0.3, such as −0.1792, w4 is about −1.5 to −0.3, such as −0.7515, and w5 is about 0. w6 is about 0.1 to 1.1, such as 0.6215, w7 is about -0.4 to 0.6, such as 0.06787, w8 is about -0.4 to 0.6, such as 0.08374, w9 is about -0.3 to 0.7, such as 0.2078, and w 10 is approximately -0.1 to 0.9, e.g., 0.3887, and w 11 is approximately -0.8 to 0.2, e.g., -0.3025, and w 12 is approximately -1.0 to 0.0, e.g., -0.487, and w 13 is about -0.3 to 0.7, e.g., 0.1532, and w 14 is approximately -0.5 to 0.5, such as -0.00899.

[0147] In one example for ccRCC kidney cancer, w1 is about -0.2 to 0.8, such as 0.2961, w2 is about -0.4 to 0.6, such as 0.0749, w3 is about -0.4 to 0.6, such as 0.05333, w4 is about -0.8 to 0.2, such as -0.2559, and w5 is about -0.5 to w6 is about -0.5 to 0.5, for example, 0.005466, w7 is about -0.5 to 0.5, for example, -0.02134, w8 is about -0.5 to 0.5, for example, -0.00236, w9 is about -0.5 to 0.5, for example, -0.06985, and w 10 is approximately -0.5 to 0.5, e.g., -0.04794, and w 11 is approximately -0.3 to 0.8, e.g., 0.2408, and w 12 is approximately -0.6 to 0.4, e.g., -0.1432, and w 13 is approximately -0.7 to 0.3, e.g., -0.2362, and w 14 is approximately -0.3 to 0.8, such as 0.1874.

[0148] In one example for pRCC kidney cancer, w1 is about −0.3 to 0.7, such as 0.1518, w2 is about 0.9 to 1.9, such as 1.4194, w3 is about −0.3 to 0.7, such as 0.1616, w4 is about 0.1 to 1.1, such as 0.5588, w5 is about −1.5 to −0.5, such as −1.008, w6 is about 0.1 to 1.1, such as 0.594, w7 is about 0.8 to 1.8, such as 1.3409, w8 is about −0.8 to 0.2, such as −0.2564, and w9 is about −1.0 to 0.0, such as −0.5356; 10 is about 0.4 to 1.4, e.g., 0.9293, and w 11 is approximately -0.4 to 0.6, e.g., 0.07879, and w 12is approximately -1.0 to 0.0, e.g., -0.4654, and w 13 is approximately -1.6 to -0.6, e.g., -1.0803, and w 14 is approximately -0.5 to 0.5, such as 0.00752.

[0149] In one particular implementation, the combination of the second gene expression profile for two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all, T cell receptor signaling genes and the regression function is specified as follows:

number

[0150] In one example, for bladder cancer, 15 is approximately -0.2 to 0.8, e.g., 0.3242, and w 16 is approximately -0.5 to 0.5, e.g., -0.03061, and w 17 is approximately 0.0 to 1.0, e.g., 0.4636, and w 18 is approximately -0.8 to 0.2, e.g., -0.3372, and w 19 is approximately -0.6 to 0.4, e.g., -0.09887, and w 20 is approximately -0.3 to 0.7, e.g., 0.1825, and w 21 is approximately -0.7 to 0.3, e.g., -0.1961, and w 22 is approximately -0.5 to 0.5, e.g., -0.03954, and w 23 is approximately -0.4 to 0.6, e.g., 0.1259, and w 24is approximately -0.6 to 0.4, e.g., -0.06249, and w 25 is approximately -0.6 to 0.4, e.g., -0.07393, and w 26 is approximately -1.2 to -0.2, e.g., -0.705, and w 27 is approximately -0.2 to 0.8, e.g., 0.3454, and w 28 is about 0.0 to 1.0, e.g., 0.4765, and w 29 is approximately -0.2 to 0.8, e.g., 0.2676, and w 30 is about 0.2 to 1.2, such as 0.6749, and w 31 is approximately -1.4 to -0.4, such as -0.8557.

[0151] In one example, for ccRCC kidney cancer, 15 is approximately -0.6 to 0.4, e.g., -0.1186, and w 16 is approximately -0.8 to 0.2, e.g., -0.3247, and w 17 is approximately -0.6 to 0.4, e.g., -0.09282, and w 18 is approximately -0.6 to 0.4, e.g., -0.06851, and w 19 is approximately -1.3 to -0.3, e.g., -0.8094, and w 20 is approximately -0.8 to 0.2, e.g., -0.2527, and w 21 is approximately -0.4 to 0.6, e.g., 0.09508, and w 22 is approximately -0.7 to 0.3, e.g., -0.1908, and w 23 is approximately -0.5 to 0.5, e.g., -0.0408, and w 24 is approximately -0.1 to 0.9, e.g., 0.3905, and w 25 is about 0.3 to 1.3, e.g., 0.79, and w 26 is approximately -0.3 to 0.7, e.g., 0.169, and w 27 is approximately -0.5 to 0.5, e.g., -0.05041, and w 28is approximately -0.3 to 0.7, e.g., 0.1539, and w 29 is approximately -0.6 to 0.3, e.g., -0.07244, and w 30 is about -0.1 to 0.9, for example, 0.3935, and w 31 is approximately -0.4 to 0.9, such as 0.0895.

[0152] In one example, for pRCC kidney cancer, 15 is about 0.9 to 1.9, e.g., 1.3546, and w 16 is approximately 3.2 to 4.2, for example, 3.7462, and w 17 is approximately -0.6 to 0.4, e.g., -0.134, and w 18 is approximately -3.0 to -2.0, e.g., -2.5246, and w 19 is approximately -0.1 to 0.9, e.g., 0.3545, and w 20 is approximately -0.9 to 0.1, e.g., -0.4082, and w 21 is approximately -2.6 to -1.6, e.g., -2.1136, and w 22 is approximately -0.4 to 0.6, e.g., 0.06057, and w 23 is approximately -0.8 to 0.2, e.g., -0.2527, and w 24 is approximately -1.2 to -0.2, e.g., -0.7183, and w 25 is approximately -1.4 to -0.4, e.g., -0.9357, and w 26 is approximately -1.1 to -0.1, e.g., -0.6145, and w 27 is approximately -1.4 to -0.4, e.g., -0.9477, and w 28 is between -1.5 and -0.5, e.g., -1.0039, and w 29 is approximately -1.0 to 0.0, e.g., -0.538, and w 30 is about 0.3 to 1.3, such as 0.7621, and w 31 is approximately 0.0 to 1.0, such as 0.5318.

[0153] In one particular implementation, a combination of a third gene expression profile for two or more, e.g., 2, 3, 4, 5, 6, 7, or all, PDE4D7-correlated genes and a regression function is specified as follows:

number

[0154] In one example, for bladder cancer, 32 is approximately -0.6 to 0.4, e.g., -0.112, and w 33 is approximately -0.8 to 0.2, e.g., -0.3117, and w 34 is approximately 0.0 to 1.0, e.g., -0.00355, and w 35 is approximately -0.2 to 0.8, e.g., 0.2887, and w 36 is approximately -0.6 to 0.4, e.g., -0.08836, and w 37 is approximately -0.7 to 0.3, e.g., -0.1632, and w 38 is about -0.3 to 0.7, e.g., 0.2415, and w 39 is about -0.3 to 0.7, such as 0.209.

[0155] In one example, for ccRCC kidney cancer, 32 is approximately -0.3 to 0.8, e.g., 0.2267, and w 33 is approximately -0.7 to 0.3, e.g., 0.2039, and w 34 is approximately -0.6 to 0.4, e.g., -0.1044, and w 35 is approximately -0.8 to 0.2, e.g., -0.2691, and w 36is approximately -0.8 to 0.2, e.g., -0.2746, and w 37 is approximately -0.4 to 0.6, e.g., 0.07656, and w 38 is approximately -0.5 to 0.5, e.g., -0.02863, and w 39 is about -0.4 to 0.6, such as 0.111.

[0156] In one example, for pRCC kidney cancer, 32 is approximately -0.9 to 0.1, e.g., -0.3819, and w 33 is approximately -2.2 to -1.2, e.g., -1.7267, and w 34 is approximately -0.5 to 0.5, e.g., -0.03515, and w 35 is approximately -1.1 to -0.1, e.g., -0.5589, and w 36 is approximately -1.4 to -0.4, e.g., -0.8972, and w 37 is approximately -0.5 to 0.5, e.g., -0.04555, and w 38 is approximately -0.1 to 0.9, for example, 0.4498, and w 39 is approximately -0.8 to 0.2, such as -0.2879.

[0157] It is further preferred that the step of determining a prediction of outcome further comprises combining the first gene expression profile combination, the second gene expression profile combination, and the third gene expression profile combination with a regression function obtained from a population of bladder or kidney cancer subjects.

[0158] Furthermore, it is preferred that the step of determining a prediction of outcome further comprises combining the combination of gene expression profiles with a regression function obtained from a population of bladder or kidney cancer subjects.

[0159] In one particular realization, the outcome prediction is determined as follows:

number

[0160] In one example for bladder cancer, 40 is about 0.2 to 1.2, e.g., 0.7474, and w 41 is about 0.1 to 1.1, such as 0.6412, and w 42 is approximately -0.2 to 0.8, such as 0.2693.

[0161] In one example for ccRCC kidney cancer, w 40 is approximately -0.2 to 0.8, e.g., 0.3436, and w 41 is about 0.2 to 1.2, such as 0.6589, and w 42 is approximately -0.2 to 0.8, such as 0.2746.

[0162] In one example for pRCC kidney cancer, w 40 is about 0.3 to 1.2, e.g., 0.7855, and w 41 is about 0.1 to 1.1, such as 0.5792, and w 42 is approximately 0.0 to 1.0, such as 0.4559.

[0163] The outcome predictions are also classified or categorized into one of at least two risk groups based on the value of the outcome prediction. For example, there may be two risk groups, or three risk groups, or four risk groups, or five or more predicted risk groups. Each risk group encompasses a respective range of (non-overlapping) values ​​of the outcome prediction. For example, the risk groups may indicate the likelihood of occurrence of a particular clinical event, such as 0 to <0.1, 0.1 to <0.25, 0.25 to <0.5, or 0.5 to 1.0.

[0164] Preferably, the step of determining the outcome prediction is further based on one or more clinical parameters obtained from the subject.

[0165] As mentioned above, various indices based on clinical parameters have been investigated. Furthermore, by making predictions of outcome based on such clinical parameters, predictions can be further improved.

[0166] Preferably, the clinical parameters include one or more of the following: (i) a T stage attribute (T1, T2, T3, or T4); (ii) an N stage attribute (N0, N1, N2, or N3); and (iii) an M stage attribute (M0, M1). Additionally or alternatively, the clinical parameters include one or more other clinical parameters associated with the diagnosis and / or prognosis of bladder or kidney cancer.

[0167] It is further preferred that the step of determining a prediction of outcome comprises combining one or more of the following: (i) a first gene expression profile for one or more immune defense response genes; (ii) a second gene expression profile for one or more T cell receptor signaling genes; (iii) a third gene expression profile for one or more PDE4D7-correlated genes; (iv) a combination of the first gene expression profile, a combination of the second gene expression profile, and a combination of the third gene expression profile, and one or more clinical parameters obtained from the subject, with a regression function obtained from a population of bladder cancer or kidney cancer subjects.

[0168] Furthermore, it is preferable that the step of determining the outcome prediction includes combining one or more of the gene expression profiles of: (i) the three or more immune defense response genes; (ii) the three or more T cell receptor signaling genes; (iii) the three or more PDE4D7-correlated genes; and (iv) a combination of one or more immune defense response genes, one or more T cell receptor signaling genes, and one or more PDE4D7-correlated genes, and one or more clinical parameters obtained from the subject with a regression function obtained from a population of bladder cancer or kidney cancer subjects.

[0169] In one particular realization, the outcome prediction is specified as follows:

number

[0170] In one example, w 40 is about 0.3 to 1.1, e.g., 0.809, and w 41 is about 0.1 to 1.1, e.g., 0.6018, and w 42 is approximately -0.1 to 0.9, e.g., 0.3501, and w 43 is about 0.3 to 1.3, e.g., 0.7607, and w 44 is about -0.2 to 0.8, for example, 0.3335, and w 45 is about 1.9 to 2.9, such as 2.4497.

[0171] In one particular realization, the outcome prediction is determined as follows:

number

[0172] In one example, w 40 is approximately -0.2 to 0.8, e.g., 0.3117, and w 41 is about 0.1 to 1.1, e.g., 0.6033, and w 42 is approximately -0.3 to 0.7, e.g., 0.1932, and w 43 is approximately -0.3 to 0.8, e.g., 0.1509, and w 44 is about 0.2 to 1.2, such as 0.6923, and w 45 is about 1.2 to 2.2, such as 1.6854.

[0173] Preferably, a biological sample is obtained from a subject before the start of therapy. The gene expression profile is determined in the form of mRNA or protein in prostate cancer tissue. Alternatively, if the gene is present in a soluble form, the gene expression profile is determined in blood.

[0174] The therapy may be surgery, radiation therapy, cytotoxic chemotherapy (CTX), or More preferably, it is immunotherapy.

[0175] The prediction of therapy response may be negative or positive for the effectiveness of the therapy, in which case therapy is recommended based on the prediction, and if the prediction is negative, the recommended therapy preferably includes one or more of the following: (i) therapy provided earlier than standard; (ii) therapy with an increased effective dose; (iii) adjuvant therapy, such as chemotherapy; and (iv) alternative therapy, such as immunotherapy.

[0176] In a further aspect of the present invention, there is provided a device for predicting outcome in a subject with bladder or kidney cancer, comprising: an input adapted to receive data indicative of a first gene expression profile for each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the first gene expression profile is identified in a biological sample obtained from the subject; and / or one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; , 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all T cell receptor signaling genes, wherein the second gene expression profile is identified in a biological sample obtained from the subject; and / or an input adapted to receive data indicating a third gene expression profile for each of 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, wherein the third gene expression profile is identified in a biological sample obtained from the subject; - a processor adapted to determine a prediction of outcome based on the first gene expression profile, the second gene expression profile, the third gene expression profile, or the first, second, and third gene expression profiles; and - a delivery device adapted to deliver the prediction to a caregiver or a subject, as appropriate; An apparatus is presented that includes:

[0177] In a further aspect of the present invention, there is provided a device for predicting outcome in a subject with bladder or kidney cancer, comprising: - an input adapted to receive data indicative of a gene expression profile of three or more genes, wherein the three or more genes are selected from the following: - 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; 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; or - a PDE4D7-correlated gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; or - the three or more genes include at least one or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-related genes; - a gene expression profile is identified in a biological sample obtained from a subject with bladder or kidney cancer; input - a processor adapted to determine a prediction of outcome based on the gene expression profile of the three or more genes. - a computer program according to claim 11, and - a delivery device adapted to deliver the prediction to a caregiver or a subject, as appropriate; An apparatus is presented that includes:

[0178] In a further aspect of the present invention, the program when executed by a computer performs the following: - receiving data indicative of a first gene expression profile for each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, wherein the first gene expression profile is identified in a biological sample obtained from a subject with bladder cancer or kidney cancer; and / or receiving data indicative of a first gene expression profile for each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70. , 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all T cell receptor signaling genes, wherein the second gene expression profile is identified in a biological sample obtained from the bladder or kidney cancer subject; and / or receiving data indicative of a third gene expression profile for each of 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, wherein the third gene expression profile is identified in a biological sample obtained from the bladder or kidney cancer subject; - determining a prediction of outcome for the subject based on the first gene expression profile, the second gene expression profile, the third gene expression profile, or the first, second, and third gene expression profiles; and - providing the prediction to a caregiver or subject, as appropriate. A computer program product is presented that includes instructions that cause a computer to perform a method that includes:

[0179] In a further aspect of the present invention, the program when executed by a computer performs the following: - receiving data indicative of a gene expression profile of three or more genes, wherein the three or more genes are selected from the following: - 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; - 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; or - a PDE4D7-correlated gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; or - the three or more genes include at least one or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-related genes; and - the gene expression profile is identified in a biological sample obtained from a subject with bladder cancer or kidney cancer; Step, - determining a prediction of outcome based on the gene expression profile of the three or more genes; and - providing the prediction to a caregiver or subject, as appropriate. A computer program product is presented that includes instructions that cause a computer to perform a method that includes:

[0180] In a further aspect of the present invention, - at least one primer and / or probe for identifying in a biological sample obtained from the subject a first gene expression profile for each of one or more, such as 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 - at least one primer and / or probe for identifying a second gene expression profile for each 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 CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 in a biological sample obtained from the subject; and / or - at least one primer and / or probe for identifying a gene expression profile for each of 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 in a biological sample obtained from the subject; and - apparatus as defined in claim 12 or a computer program as defined in claim 11, as appropriate A diagnostic kit is presented comprising:

[0181] In a further aspect of the present invention, - at least three primers and probes for identifying gene expression profiles of three or more genes, wherein the three or more genes are selected from the following: - 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; 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; or - a PDE4D7-correlated gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; or - the three or more genes include at least one or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-related genes; and - the gene expression profile is identified in a biological sample obtained from a subject with bladder cancer or kidney cancer; at least three primers and probes; - apparatus as defined in claim 12 or a computer program as defined in claim 11, as appropriate A diagnostic kit is presented comprising:

[0182] In a further embodiment of the present invention, the use of a kit as defined in claim 13 is provided.

[0183] Preferably, the use defined in claim 14 is in a method for predicting the outcome of a subject with bladder cancer or kidney cancer.

[0184] In a further aspect of the present invention, - receiving one or more biological samples obtained from a subject with bladder cancer or a subject with kidney cancer; - using the kit as defined in claim 13 to identify in a biological sample obtained from the subject a first gene expression profile for each of one or more, such as 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 - using the kit as defined in claim 13 to identify in a biological sample obtained from the subject a second gene expression profile for each of one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all, T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; and / or - using the kit as defined in claim 13 to identify in a biological sample obtained from the subject a third gene expression profile for each of 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; A method is presented that includes:

[0185] In a further aspect of the present invention, - receiving one or more biological samples obtained from a subject with bladder cancer or a subject with kidney cancer; - using the kit as defined in claim 13 to determine the gene expression profile of three or more genes, wherein the three or more genes are selected from the following: - 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; 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; or - a PDE4D7-correlated gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; or - the three or more genes include at least one or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-related genes; Steps A method is presented that includes:

[0186] In a further aspect of the present invention, the present invention relates to the use of a first gene expression profile for each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D in a method of predicting outcome in a subject with bladder cancer or kidney cancer. , PRKACA, PRKACB, PTPRC, and ZAP70, respectively, and / or a third gene expression profile for 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 ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, respectively, comprising the following: - determining a prediction of outcome based on the first gene expression profile, the second gene expression profile, the third gene expression profile, or the first, second, and third gene expression profiles; and - Provide predictions or personalizations or treatment recommendations to a caregiver or subject, as appropriate, based on the predictions or personalizations Uses including:

[0187] In a further aspect of the invention, there is provided the use of a computer program according to claim 11 or an apparatus according to claim 12 for using a gene expression profile of three or more genes in a method for predicting outcome in a subject with bladder or kidney cancer, wherein the three or more genes are selected from the group consisting of: - 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; 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; or - a PDE4D7-correlated gene selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; or - the three or more genes include at least one or more genes selected from each of immune defense response genes, T cell receptor signaling genes, and PDE4D7-related genes; below: - identifying a prediction of outcome based on the gene expression profile of the three or more genes; and - Providing such predictions to caregivers or subjects, as appropriate It is to be understood that the method according to claim 1, the device according to claim 12, the computer program according to claim 11, the diagnostic kit according to claim 13, the use of the diagnostic kit according to claim 14, the method according to claim 16, and the use of the computer program according to claim 11 or the device according to claim 12 for using the gene expression profile according to claim 17 have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.

[0188] It shall be understood that a preferred embodiment of the invention can also be any combination of the dependent claims or above embodiments with the respective independent claim.

[0189] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. [Brief explanation of the drawings]

[0190] [Figure 1] FIG. 1 shows, diagrammatically and exemplarily, a flow chart of an embodiment of a method for predicting outcome in a subject with bladder or kidney cancer. [Figure 2] FIG. 1 shows Kaplan-Meier curves for the IDR_model in the TCGA bladder cancer cohort of 95 patients (the training set used to develop the IDR_model). [Figure 3] FIG. 1 shows Kaplan-Meier curves of the IDR_model in the TCGA bladder cancer cohort of 94 patients (test set used to validate the IDR_model developed in the training set of 95 patients). [Figure 4] FIG. 1 shows Kaplan-Meier curves for the TCR_SIGNALING_model in the 95 patient TCGA bladder cancer cohort (the training set used to develop the TCR_SIGNALING_model). [Figure 5] Figure 1 shows Kaplan-Meier curves for the TCR_SIGNALING_model in the TCGA bladder cancer cohort of 94 patients (test set used to validate the TCR_SIGNALING_model developed in the training set of 95 patients). [Figure 6] FIG. 1 shows Kaplan-Meier curves for the PDE4D7_CORR_model in the 95 patient TCGA bladder cancer cohort (the training set used to develop the PDE4D7_CORR_model). [Figure 7] FIG. 1 shows Kaplan-Meier curves for the PDE4D7_CORR_model in the TCGA bladder cancer cohort of 94 patients (test set used to validate the PDE4D7_CORR_model developed in the training set of 95 patients). [Figure 8] Figure 1 shows Kaplan-Meier curves for the BCAI_model in the TCGA bladder cancer cohort of 95 patients (the training set used to develop the BCAI_model). [Figure 9] Figure 1 shows the Kaplan-Meier curve of the BCAI_model in the TCGA bladder cancer cohort of 94 patients (test set used to validate the BCAI_model developed in the training set of 95 patients). [Figure 10] Figure 1 shows Kaplan-Meier curves of BCAI&Clinical_model in the TCGA bladder cancer cohort of 95 patients (training set used to develop BCAI&Clinical_model). [Figure 11] Figure 1 shows Kaplan-Meier curves of BCAI&Clinical_model in the TCGA bladder cancer cohort of 94 patients (test set used to validate the BCAI&Clinical_model developed in the training set of 95 patients). [Figure 12] Figure 1 shows Kaplan-Meier curves for the IDR_model in the TCGA ccRCC kidney cancer cohort of 311 patients (the training set used to develop the IDR_model). [Figure 13] Figure 1 shows Kaplan-Meier curves of the IDR_model in the 100 patient TCGA ccRCC kidney cancer cohort (test set used to validate the IDR_model developed in the 311 patient training set). [Figure 14] Figure 1 shows Kaplan-Meier curves for the TCR_SIGNALING_model in the TCGA ccRCC kidney cancer cohort of 311 patients (the training set used to develop the TCR_SIGNALING_model). [Figure 15]Figure 1 shows Kaplan-Meier curves for the TCR_SIGNALING_model in the 100 patient TCGA ccRCC kidney cancer cohort (test set used to validate the TCR_SIGNALING_model developed in the 311 patient training set). [Figure 16] Figure 1 shows Kaplan-Meier curves for PDE4D7_CORR_model in the TCGA ccRCC kidney cancer cohort of 311 patients (training set used to develop PDE4D7_CORR_model). [Figure 17] FIG. 1 shows Kaplan-Meier curves for the PDE4D7_CORR_model in the 100 patient TCGA ccRCC kidney cancer cohort (test set used to validate the PDE4D7_CORR_model developed in the 311 patient training set). [Figure 18] Figure 1 shows Kaplan-Meier curves for the KCAI_model in the TCGA ccRCC kidney cancer cohort of 311 patients (the training set used to develop the KCAI_model). [Figure 19] Figure 1 shows Kaplan-Meier curves of the KCAI_model in the TCGA ccRCC kidney cancer cohort of 100 patients (test set used to validate the KCAI_model developed in the training set of 311 patients). [Figure 20] Figure 1 shows Kaplan-Meier curves of KCAI&Clinical_model in the TCGA ccRCC kidney cancer cohort of 311 patients (training set used to develop KCAI&Clinical_model). [Figure 21] Figure 1 shows Kaplan-Meier curves of KCAI&Clinical_model in the TCGA ccRCC kidney cancer cohort of 100 patients (test set used to validate the KCAI&Clinical_model developed in the training set of 311 patients). [Figure 22]FIG. 1 shows Kaplan-Meier curves for the IDR_model in the 91 patient TCGA pRCC kidney cancer cohort (the training set used to develop the IDR_model). [Figure 23] FIG. 1 shows Kaplan-Meier curves for the TCR_SIGNALING_model in the 91 patient TCGA pRCC kidney cancer cohort (the training set used to develop the TCR_SIGNALING_model). [Figure 24] FIG. 1 shows Kaplan-Meier curves for the PDE4D7_CORR_model in the 91 patient TCGA pRCC kidney cancer cohort (the training set used to develop the PDE4D7_CORR_model). [Figure 25] FIG. 1 shows Kaplan-Meier curves for PKCAI_model in the 91 patient TCGA pRCC kidney cancer cohort (the training set used to develop PKCAI_model). [Figure 26] Figure 1 shows Kaplan-Meier curves for the BCAI_3.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 27] Figure 1 shows Kaplan-Meier curves for the BCAI_3.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 28] Figure 1 shows Kaplan-Meier curves for the BCAI_3.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 29] Figure 1 shows Kaplan-Meier curves for the BCAI_3.4 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 30]Figure 1 shows Kaplan-Meier curves for the BCAI_3.5 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 31] Figure 1 shows Kaplan-Meier curves for the BCAI_3.6 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 32] Figure 1 shows Kaplan-Meier curves for the BCAI_4.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 33] Figure 1 shows Kaplan-Meier curves for the BCAI_4.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 34] Figure 1 shows Kaplan-Meier curves for the BCAI_4.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 35] Figure 1 shows Kaplan-Meier curves for the BCAI_4.4 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 36] Figure 1 shows Kaplan-Meier curves for the BCAI_4.5 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 37] Figure 1 shows Kaplan-Meier curves for the BCAI_4.6 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 38]Figure 1 shows Kaplan-Meier curves for the KCAI_3.1 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 39] Figure 1 shows Kaplan-Meier curves for the KCAI_3.2 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 40] Figure 1 shows Kaplan-Meier curves for the KCAI_3.3 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 41] Figure 1 shows Kaplan-Meier curves for the KCAI_3.4 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 42] Figure 1 shows Kaplan-Meier curves for the KCAI_3.5 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 43] Figure 1 shows Kaplan-Meier curves for the KCAI_3.6 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 44] Figure 1 shows Kaplan-Meier curves for the KCAI_4.1 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 45] Figure 1 shows Kaplan-Meier curves for the KCAI_4.2 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 46] Figure 1 shows Kaplan-Meier curves for the KCAI_4.3 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 47] Figure 1 shows Kaplan-Meier curves for the KCAI_4.4 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 48] Figure 1 shows Kaplan-Meier curves for the KCAI_4.5 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 49] Figure 1 shows Kaplan-Meier curves for the KCAI_4.6 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 50] Figure 1 shows Kaplan-Meier curves for the IDR_3.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 51] Figure 1 shows Kaplan-Meier curves for the IDR_3.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 52] Figure 1 shows Kaplan-Meier curves for the IDR_3.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 53] Figure 1 shows Kaplan-Meier curves for the TCR_3.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 54] Figure 1 shows Kaplan-Meier curves for the TCR_3.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 55] Figure 1 shows Kaplan-Meier curves for the TCR_3.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 56] FIG. 1 shows Kaplan-Meier curves for the PDE4D7_3.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 57] FIG. 1 shows Kaplan-Meier curves for the PDE4D7_3.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 58] FIG. 1 shows Kaplan-Meier curves for the PDE4D7_3.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 59] Figure 1 shows Kaplan-Meier curves for the IDR_3.1 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 60] Figure 1 shows Kaplan-Meier curves for the IDR_3.2 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 61] Figure 1 shows Kaplan-Meier curves for the IDR_3.3 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 62] Figure 1 shows Kaplan-Meier curves for the TCR_3.1 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 63] Figure 1 shows Kaplan-Meier curves for the TCR_3.2 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 64] Figure 1 shows Kaplan-Meier curves for the TCR_3.3 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both training and validation sets). [Figure 65] Figure 1 shows Kaplan-Meier curves for the PDE4D7_3.1 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 66] Figure 1 shows Kaplan-Meier curves for the PDE4D7_3.2 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). [Figure 67] Figure 1 shows Kaplan-Meier curves for the PDE4D7_3.3 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0) including both training and validation sets). DETAILED DESCRIPTION OF THE INVENTION

[0191] Overview of outcome prediction FIG. 1 shows, diagrammatically and exemplarily, a flow chart of an embodiment of a method for predicting outcome in a subject with bladder or kidney cancer.

[0192] The method begins in step S100.

[0193] In step S102, a biological sample is obtained from each of a first set of patients (subjects) diagnosed with bladder cancer or kidney cancer. Preferably, after obtaining the biological sample, the bladder cancer or kidney cancer patients are monitored for bladder cancer or kidney cancer for a period of time, for example, at least one year, at least two years, or about five years.

[0194] In step S104, a first gene expression profile is generated for each 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 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 CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70. A second gene expression profile for each of 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all T cell receptor signaling genes and / or a third gene expression profile for each of two or more, e.g., 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, is obtained for each of the biological samples obtained from the first set of patients, e.g., by performing RT-qPCR (real-time quantitative PCR) on RNA extracted from each biological sample. Exemplary gene expression profiles include expression levels (e.g., values) for each of the two or more genes, which can be normalized using values ​​for each of a set of reference genes, e.g., B2M, HPRT1, POLR2A, and / or PUM1. In one realization, the gene expression levels for each of the two or more genes in the first gene expression profile, and / or the second gene expression profile, and / or the third gene expression profile are normalized to one or more reference genes selected from the group consisting of ACTB, ALAS1, B2M, HPRT1, POLR2A, PUM1, RPLP0, TBP, TUBA1B, and / or YWHAZ, such as at least one, at least two, at least three, or preferably all of these reference genes.

[0195] In step S106, a regression function for assigning a prediction of outcome is calculated based on a first gene expression profile for two or more immune defense response genes, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and / or ZBP1, and / or two or more T cell receptor signaling genes, CD2, CD24, and / or T cell receptor signaling genes, obtained for at least some of the biological samples obtained for the first set of patients. 7, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and / or ZAP7, and / or a third gene expression profile for two or more PDE4D7-correlated genes, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and / or VWA2, and the respective results obtained from monitoring. In a specific implementation, the regression function is determined as specified in equation (4) above.

[0196] In step S108, a biological sample is obtained from a patient (subject or individual), which can be a new patient or one of a first set.

[0197] In step S110, a first gene expression profile for each 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, PA A second gene expression profile for each 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 G1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and / or a third gene expression profile for each of two or more, e.g., 2, 3, 4, 5, 6, 7, or all, PDE4D7-correlated genes, is obtained, for example, by performing PCR on the biological sample. In one realization, the gene expression levels for each of the two or more genes in the first gene expression profile and / or the second gene expression profile and / or the third gene expression profile are normalized to one or more reference genes selected from the group consisting of ACTB, ALAS1, B2M, HPRT1, POLR2A, PUM1, RPLP0, TBP, TUBA1B, and / or YWHAZ, e.g., at least one, at least two, at least three, or preferably all of these reference genes. This is substantially the same as step S104.

[0198] In step S112, a prediction of outcome based on the first, second, and third gene expression profiles is determined for the patient using a regression function, which will be explained in more detail later in the description.

[0199] In S114, a therapy recommendation may be provided, for example, to the patient or their guardian, a physician, or another medical professional, based on the prediction or personalization. To this end, the prediction or personalization may be categorized into one of a set of predefined risk groups based on the value of the prediction or personalization. In one particular implementation, the therapy is radiation therapy, and the prediction of therapy response may be negative or positive for the effectiveness of the therapy. If the prediction is negative, the recommended therapy includes one or more of the following: (i) therapy provided earlier than standard; (ii) therapy with an increased effective dose; (iii) adjuvant therapy, such as chemotherapy; and (iv) alternative therapy, such as immunotherapy.

[0200] At S116, the method ends.

[0201] In one embodiment, the gene expression profile in steps S104 and S110 is determined by detecting mRNA expression using two or more primers and / or probes and / or two or more sets thereof.

[0202] In one embodiment, steps S104 and S110 further include obtaining clinical parameters from the first set of patients and the patients, respectively. The clinical parameters include one or more of the following: (i) a T-stage attribute (T1, T2, T3, or T4); (ii) an N-stage attribute (N0, N1, N2, or N3); and (iii) an M-stage attribute (M0, M1). Additionally or alternatively, the clinical parameters include one or more other clinical parameters related to the diagnosis and / or prognosis of bladder cancer or kidney cancer. The regression function for assigning the outcome prediction determined in step S106 is further based on one or more clinical parameters obtained from at least some of the first set of patients. In step S112, the outcome prediction is further based on one or more clinical parameters obtained from the patients, e.g., a T-stage attribute or an N-stage attribute, and is determined for the patients using the regression function. In one particular implementation, the regression function is determined as specified in equation (5) or equation (6) above.

[0203] Based on the significant correlation with survival outcomes following therapy, it is anticipated that the identified molecules will provide predictive value for the efficacy of treatment of primary bladder or kidney cancer.

[0204] result For each gene, the respective TCGA database (TCGA Bladder Urothelial Carcinoma, Firehose Legacy, http: / / www.linkedomics.org Log2 expression values ​​were obtained as provided in downloads from: Renal Clear Cell Carcinoma, TCGA, Firehorse Legacy, http: / / www.cbioportal.org / , accessed March 6, 2020; Papillary Renal Cell Carcinoma, TCGA, Firehorse Legacy, http: / / www.linkedomics.org, accessed February 2, 2020.

[0205] The log2 expression value for each gene was converted to a z-score by calculating:

number

[0206] This process distributes the transformed log2_gene values ​​around a mean of 0 with a standard deviation of 1.

[0207] For multivariate analysis of genes of interest, the log2_gene transformed z-score values ​​of each gene were used as input.

[0208] Cox regression analysis We next investigated whether the combinations of 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-related genes, as well as their combinations, exhibited prognostic value for bladder and kidney 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-related genes against overall survival in the TCGA cohort of 412 bladder cancer patients, 533 ccRCC kidney cancer patients, and 290 pRCC kidney cancer patients, respectively.

[0209] From the TCGA bladder cancer cohort, only samples for which clinical parameters, gene expression levels, and survival information were available were included. From this subset, only samples from patients with non-metastatic disease (m0) were included, resulting in a total of 189 patients. These 189 patients were randomly divided into four groups. Cohort 1 (n = 95) consisted of groups 1 and 2 and served as the training cohort. Cohort 2 (n = 94) consisted of groups 3 and 4 and was used to validate the risk model derived from the training cohort.

[0210] From the TCGA ccRCC kidney cancer cohort, only samples for which clinical parameters, gene expression levels, and survival information were available were included. From this subset, only samples from patients with non-metastatic disease (m0) were included, resulting in a total of 411 patients. These 411 patients were randomly divided into four groups. Cohort 1 (n = 311) consisted of groups 1, 2, and 3 and served as the training cohort. Cohort 2 (n = 100) consisted of group 4 and was used to validate the risk model derived from the training cohort.

[0211] From the TCGA ccRCC kidney cancer cohort, only samples for which clinical parameters, gene expression levels, and survival information were available were included. From this subset, only samples from patients with non-metastatic disease (m0) were included, resulting in a total of 91 patients. This cohort was not further divided into training and testing cohorts because both were underpowered. Therefore, all n = 91 patients were retained in the training cohort.

[0212] The Cox regression function was obtained as follows: IDR_model:

number

number

number

[0213] [Table 1] TIFF0007729438000012.tif255150

[0214] [Table 2] TIFF0007729438000014.tif255149

[0215] [Table 3] TIFF0007729438000016.tif255149

[0216] Based on the three individual Cox regression models (IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model), we again used Cox regression to model their combinations on overall survival in the presence (BCAI&Clinical_model and KCAI&Clinical_model) or absence (BCAI_model, KCAI_model, and PKCAI_model) of clinical variables (N stage attribute, T stage attribute) in each cohort of bladder and kidney cancer patients. The two models were examined in Kaplan-Meier survival analyses.

[0217] The Cox regression function was obtained as follows: BCAI_model (bladder cancer) or KCAI_model (ccRCC kidney cancer) or PKCAI_model (pRCC kidney cancer):

number

number

number

[0218] [Table 4]

[0219] [Table 5]

[0220] [Table 6]

[0221] Kaplan-Meier survival analysis For Kaplan-Meier survival curve analysis, the Cox functions of the risk models (IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model, BCAI_model, BCAI&Clinical_model, KCAI_model, KCAI&Clinical_model, and PKCAI_model) were categorized into two subcohorts based on cutoffs. The thresholds for separating the low-risk and high-risk groups were based on the risk of experiencing the clinical endpoint (outcome) predicted by each Cox regression model.

[0222] Figure 2 shows the Kaplan-Meier curves for the IDR_model in the 95-patient TCGA bladder cancer cohort (the training set used to develop the IDR_model). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=4.7; 95% CI=2.4-9.2). The supplemental list below shows the number of patients at risk (threshold=0) for each IDR_model class analyzed, i.e., for all +20-month periods: low-risk: 46, 26, 15, 8, 4, 3, 2, 1, 1, 0; high-risk: 49, 17, 7, 1, 1, 1, 0, 0, 0.

[0223] Figure 3 shows the Kaplan-Meier curves of the IDR_model in the TCGA bladder cancer cohort of 94 patients (the test set used to validate the IDR_model developed in the training set of 95 patients). The clinical endpoint tested was all-cause mortality (log-rank p=0.01; HR=2.3; 95% CI=1.2-4.5). The supplementary list below shows the number of patients at risk (threshold=0) for the analyzed IDR_model classes, i.e., at all risk patients in the +20-month period: low risk: 48, 23, 14, 10, 3, 1, 1, 1, 1; High risk: 46, 22, 9, 5, 3, 3, 2, 2, 1, 0.

[0224] Figure 4 shows the Kaplan-Meier curve for the TCR_SIGNALING_model in the 95-patient TCGA bladder cancer cohort (the training set used to develop the TCR_SIGNALING_model). The clinical endpoint tested was all-cause mortality (log-rank p = 0.002; HR = 2.8; 95% CI = 1.5-5.5). The supplemental list below shows the number of patients at risk (threshold = 0) for the TCR_SIGNALING_model classes analyzed, i.e., for all +20-month periods: low-risk: 38, 19, 8, 4, 2, 2, 1, 1, 1, 0; high-risk: 57, 24, 14, 5, 3, 2, 1, 0, 0, 0.

[0225] Figure 5 shows the Kaplan-Meier curves for the TCR_SIGNALING_model in the 94-patient TCGA bladder cancer cohort (the test set used to validate the TCR_SIGNALING_model developed in the training set of 95 patients). The clinical endpoint tested was all-cause mortality (log-rank p = 0.03; HR = 2.0; 95% CI = 1.0-4.0). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed TCR_SIGNALING_model classes, i.e., for all +20-month periods: low-risk: 51, 26, 13, 8, 2, 1, 1, 1, 1, 0; high-risk: 43, 19, 10, 7, 4, 3, 2, 2, 1, 0.

[0226] Figure 6 shows the Kaplan-Meier curve for the PDE4D7_CORR_model in the 95-patient TCGA bladder cancer cohort (the training set used to develop the PDE4D7_CORR_model). The clinical endpoint tested was all-cause mortality (log-rank p = 0.0007; HR = 4.5; 95% CI = 1.9-10.7). The supplemental list below shows the number of patients at risk (threshold = 0.3) for the PDE4D7_CORR_model classes analyzed, i.e., for all +20-month periods: low-risk: 71, 38, 19, 8, 4, 4, 2, 1, 1, 0; high-risk: 24, 5, 3, 1, 1, 0, 0, 0, 0, 0.

[0227] Figure 7 shows the Kaplan-Meier curves for the PDE4D7_CORR_model in the 94-patient TCGA bladder cancer cohort (a test set used to validate the PDE4D7_CORR_model developed in the 95-patient training set). The clinical endpoint tested was all-cause mortality (log-rank p=0.026; HR=2.4; 95% CI=1.1-5.2). The following supplementary list shows the number of patients at risk for the PDE4D7_CORR_model classes analyzed (threshold=0.3), i.e., for all +20-month periods, the risk patients are shown: low risk: 68, 35, 20, 13, 5, 3, 2, 2, 1, 0; high risk: 26, 10, 3, 2, 1, 1, 1, 1, 1, 0.

[0228] Figure 8 shows the Kaplan-Meier curve for the BCAI_model in the 95-patient TCGA bladder cancer cohort (the training set used to develop the BCAI_model). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=5.5; 95% CI=2.9-10.7). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk: 42, 26, 14, 7, 3, 2, 1, 1, 1, 0; high-risk: 53, 17, 8, 2, 2, 2, 1, 0, 0, 0.

[0229] Figure 9 shows the Kaplan-Meier curves for the BCAI_model in the 94-patient TCGA bladder cancer cohort (the test set used to validate the BCAI_model developed in the training set of 95 patients). The clinical endpoint tested was all-cause mortality (log-rank p = 0.0007; HR = 3.2; 95% CI = 1.6-6.4). The supplementary list below shows the number of patients at risk (threshold = 0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk: 55, 28, 16, 11, 3, 2, 2, 2, 2, 0; high-risk: 39, 17, 7, 4, 3, 2, 1, 1, 0, 0.

[0230] Figure 10 shows the Kaplan-Meier curves for the BCAI&Clinical_model in the 95-patient TCGA bladder cancer cohort (the training set used to develop the BCAI&Clinical_model). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=5.1; 95% CI=2.6-10.0). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed BCAI&Clinical_model classes, i.e., for all +20-month periods: low-risk: 37, 22, 11, 6, 3, 2, 1, 0, 0, 0; high-risk: 51, 15, 8, 2, 2, 2, 1, 1, 1, 0.

[0231] Figure 11 shows the Kaplan-Meier curves for the BCAI&Clinical_model in the 94-patient TCGA bladder cancer cohort (the test set used to validate the BCAI&Clinical_model developed in the training set of 95 patients). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=5.4; 95% CI=2.6-11.2). The supplementary list below shows the number of patients at risk (threshold=0) for the analyzed BCAI&Clinical_model classes, i.e., for all +20-month periods: low-risk: 44, 22, 15, 10, 3, 2, 2, 2, 2, 0; high-risk: 38, 15, 6, 4, 3, 2, 1, 1, 0, 0.

[0232] Figure 12 shows the Kaplan-Meier curves for the IDR_model in the TCGA ccRCC kidney cancer cohort of 311 patients (the training set used to develop the IDR_model). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=3.6; 95% CI=2.1-6.3). The supplemental list below shows the number of patients at risk for each IDR_model class analyzed (threshold=0.3), i.e., for all +20-month periods: low-risk: 242, 199, 141, 81, 39, 18, 6, 0; high-risk: 69, 54, 39, 18, 9, 6, 2, 0.

[0233] Figure 13 shows the Kaplan-Meier curves for the IDR_model in the 100-patient TCGA ccRCC kidney cancer cohort (the test set used to validate the IDR_model developed in the training set of 311 patients). The clinical endpoint tested was all-cause mortality (log-rank p = 0.02; HR = 2.5; 95% CI = 1.1-5.3). The supplemental list below shows the number of patients at risk for the analyzed IDR_model classes (threshold = 0.3), i.e., for all +20-month periods: low-risk: 68, 57, 38, 24, 12, 6, 3, 1, 0; high-risk: 32, 23, 16, 10, 4, 3, 1, 0, 0.

[0234] Figure 14 shows the Kaplan-Meier curves for the TCR_SIGNALING_model in the TCGA ccRCC kidney cancer cohort of 311 patients (the training set used to develop the TCR_SIGNALING_model). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=4.5; 95% CI=2.6-7.8). The supplemental list below shows the number of patients at risk (threshold=0.4) for the TCR_SIGNALING_model classes analyzed, i.e., for all +20-month periods: low-risk: 235, 196, 140, 81, 41, 20, 7, 0; high-risk: 76, 57, 40, 18, 7, 4, 1, 0.

[0235] Figure 15 shows the Kaplan-Meier curves for the TCR_SIGNALING_model in the 100-patient TCGA ccRCC kidney cancer cohort (the test set used to validate the TCR_SIGNALING_model developed in the training set of 311 patients). The clinical endpoint tested was all-cause mortality (log-rank p = 0.06; HR = 2.5; 95% CI = 0.96-6.4). The supplemental list below shows the number of patients at risk (threshold = 0.4) for the TCR_SIGNALING_model classes analyzed, i.e., for all +20-month periods: low-risk: 79, 63, 45, 29, 14, 8, 3, 1, 0; high-risk: 21, 17, 9, 5, 2, 1, 1, 0, 0.

[0236] Figure 16 shows the Kaplan-Meier curve for the PDE4D7_CORR_model in the TCGA ccRCC kidney cancer cohort of 311 patients (the training set used to develop the PDE4D7_CORR_model). The clinical endpoint tested was all-cause mortality (log-rank p=0.0003; HR=2.3; 95% CI=1.5-3.7). The supplemental list below shows the number of patients at risk (threshold=0) for the PDE4D7_CORR_model classes analyzed, i.e., for all +20-month periods: low risk: 174, 142, 102, 62, 33, 17, 7, 0; high risk: 137, 111, 78, 37, 15, 7, 1, 0.

[0237] Figure 17 shows the Kaplan-Meier curves for the PDE4D7_CORR_model in the 100-patient TCGA ccRCC kidney cancer cohort (the test set used to validate the PDE4D7_CORR_model developed in the training set of 311 patients). The clinical endpoint tested was all-cause mortality (log-rank p=0.001; HR=3.2; 95% CI=1.6-6.5). The following supplementary list shows the number of patients at risk (threshold=0) for the PDE4D7_CORR_model classes analyzed, i.e., for all +20-month periods, the risk patients are shown: low risk: 51, 44, 28, 19, 11, 5, 3, 1, 0; high risk: 49, 36, 26, 15, 5, 4, 1, 0, 0.

[0238] Figure 18 shows the Kaplan-Meier curves for the KCAI_model in the TCGA ccRCC kidney cancer cohort of 311 patients (the training set used to develop the KCAI_model). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=3.0; 95% CI=1.9-4.8). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk: 179, 145, 98, 64, 35, 17, 6, 0; high-risk: 132, 108, 82, 35, 13, 7, 2, 0.

[0239] Figure 19 shows the Kaplan-Meier curves for the KCAI_model in the 100-patient TCGA ccRCC kidney cancer cohort (the test set used to validate the KCAI_model developed in the training set of 311 patients). The clinical endpoint tested was all-cause mortality (log-rank p = 0.003; HR = 3.0; 95% CI = 1.4-6.1). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low risk: 56, 47, 33, 20, 11, 6, 3, 1, 0; high risk: 44, 33, 21, 14, 5, 3, 1, 0, 0.

[0240] Figure 20 shows the Kaplan-Meier curves for the KCAI&Clinical_model in the TCGA ccRCC kidney cancer cohort of 311 patients (the training set used to develop the KCAI&Clinical_model). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=4.5; 95% CI=2.7-7.6). The supplemental list below shows the number of patients at risk (threshold=0.5) for the analyzed KCAI&Clinical_model classes, i.e., for all +20-month periods: low-risk: 218, 180, 131, 77, 42, 21, 6, 0; high-risk: 93, 73, 49, 22, 6, 3, 2, 0.

[0241] Figure 21 shows the Kaplan-Meier curves for the KCAI&Clinical_model in the 100-patient TCGA ccRCC kidney cancer cohort (the test set used to validate the KCAI&Clinical_model developed in the training set of 311 patients). The clinical endpoint tested was all-cause mortality (log-rank p=0.03; HR=2.7; 95% CI=1.1-6.5). The supplemental list below shows the number of patients at risk (threshold=0.5) for the analyzed KCAI&Clinical_model classes, i.e., for all +20-month periods: low-risk: 75, 64, 44, 28, 13, 6, 2, 1, 0; high-risk: 25, 16, 10, 6, 3, 3, 2, 0, 0.

[0242] Figure 22 shows the Kaplan-Meier curves for the IDR_model in the 91-patient TCGA pRCC kidney cancer cohort (the training set used to develop the IDR_model). The clinical endpoint tested was all-cause mortality (log-rank p=0.05; HR=2.9; 95% CI=1.0-8.8). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed IDR_model classes, i.e., for all +20-month periods: low risk: 41, 26, 14, 6, 2, 1, 1, 0; high risk: 50, 36, 20, 8, 3, 0, 0, 0.

[0243] Figure 23 shows the Kaplan-Meier curves for the TCR_SIGNALING_model in the 91-patient TCGA pRCC kidney cancer cohort (the training set used to develop the TCR_SIGNALING_model). The clinical endpoint tested was all-cause mortality (log-rank p=0.0008; HR=NA; 95% CI=NA). The supplemental list below shows the number of patients at risk (threshold=0) for the TCR_SIGNALING_model classes analyzed, i.e., for all +20-month periods, the risk patients are shown: low risk: 44, 31, 16, 6, 3, 0, 0, 0; high risk: 47, 31, 18, 8, 2, 1, 1, 0.

[0244] Figure 24 shows the Kaplan-Meier curves for the PDE4D7_CORR_model in the 91-patient TCGA pRCC kidney cancer cohort (the training set used to develop the PDE4D7_CORR_model). The clinical endpoint tested was all-cause mortality (log-rank p=0.05; HR=2.9; 95% CI=1.0-8.8). The supplemental list below shows the number of patients at risk (threshold=0) for the PDE4D7_CORR_model classes analyzed, i.e., for all +20-month periods, the risk patients are shown: low risk: 41, 26, 14, 6, 2, 1, 1, 0; high risk: 50, 36, 20, 8, 3, 0, 0, 0.

[0245] Figure 25 shows the Kaplan-Meier curves for the PKCAI_model in the 91-patient TCGA pRCC kidney cancer cohort (the training set used to develop the PKCAI_model). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=11.8; 95% CI=3.7-37.4). The supplemental list below shows the number of patients at risk (threshold=0) for the PKCAI_model classes analyzed, i.e., for all +20-month periods, the risk patients are shown: low-risk: 45, 35, 22, 11, 5, 1, 1, 0; high-risk: 46, 27, 12, 3, 0, 0, 0, 0.

[0246] Kaplan-Meier survival curve analyses shown in Figures 2 through 25 demonstrate the existence of distinct patient risk groups. Patient risk groups are determined by the probability of experiencing each clinical endpoint (all-cause mortality) calculated by each risk model shown in the figure. Depending on the patient's predicted risk of dying from bladder or kidney cancer (i.e., depending on the risk group to which the patient belongs), different types of interventions are indicated. In lower risk groups, standard of care (SOC) provides acceptable long-term oncological suppression. For patient groups exhibiting a higher risk of ultimately dying from bladder or kidney cancer, treatment escalation (e.g., multimodal treatment with radiation and chemotherapy) may provide improved long-term survival outcomes. Alternative options for treatment escalation include alternative therapies such as cytotoxic drug radiation or immunotherapy (e.g., atezolizumab; pembrolizumab; nivolumab; avelumab; durvalumab) or other experimental therapies.

[0247] Further Results This section presents additional results for Cox regression models based on multiple gene models for both bladder and kidney cancer, including combinations of randomly selected three genes (Tables 7 & 9) or four genes (Tables 8 & 10), where the three or four genes include at least one immune defense response gene (IDR), at least one T cell receptor (TCR) signaling gene, and at least one PDE4D7-correlated gene for each random combination of genes. The variables and corresponding weights are provided in Tables 7-10. The Cox regression models are plotted in Kaplan-Meier curve analyses in Figures 26-49.

[0248] [Table 7] TIFF0007729438000024.tif247170

[0249] [Table 8] TIFF0007729438000026.tif251170TIFF0007729438000027.tif24170

[0250] [Table 9] TIFF0007729438000029.tif251170TIFF0007729438000030.tif25170

[0251] [Table 10] TIFF0007729438000032.tif251170TIFF0007729438000033.tif60170

[0252] For Kaplan-Meier curve analysis, the Cox regression functions of the 24 risk models shown in Tables 7 to 10 (BCAI-3.1 to 3.6, BCAI-4.1 to 4.6, KCAI-3.1 to 3.6, and KCAI-4.1 to 4.6) were categorized into two subcohorts (low risk vs. high risk) based on cutoffs (see the descriptions in Figures 26 to 49 below). The thresholds for separating the low-risk and high-risk groups were based on the risk of experiencing the clinical endpoints (outcomes) predicted by each Cox regression model.

[0253] In Figures 26-37, the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0)) was examined for the endpoint: all-cause mortality.

[0254] In Figures 38-49, the 411 patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0)) was examined for the endpoint: all-cause mortality.

[0255] Figure 26 shows the Kaplan-Meier curve for the BCAI_3.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.05; HR = 1.6; 95% CI = 1.0-2.5). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk (<= 0): 99, 49, 27, 15, 6, 4, 2, 1, 1, 0; high-risk (> 0): 90, 39, 18, 9, 5, 4, 3, 3, 2, 0.

[0256] Figure 27 shows the Kaplan-Meier curves for the BCAI_3.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.003; HR = 2.0; 95% CI = 1.3-3.2). The supplemental list below shows the number of patients at risk (threshold = 0.1) for the BCAI_model classes analyzed, i.e., for all +20-month periods: low-risk (<=0.1): 104, 52, 28, 16, 9, 7, 4, 3, 2, 0; high-risk (>0.1): 85, 36, 17, 8, 2, 1, 1, 1, 1, 0.

[0257] Figure 28 shows the Kaplan-Meier curves for the BCAI_3.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.05; HR = 1.6; 95% CI = 1.0-2.5). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 83, 39, 24, 11, 2, 1, 0, 0, 0, 0; high-risk (>0): 106, 49, 21, 13, 9, 7, 5, 4, 3, 0.

[0258] Figure 29 shows the Kaplan-Meier curves for the BCAI_3.4 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.04; HR=1.6; 95% CI=1.0-2.6). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 84, 41, 22, 13, 6, 4, 2, 2, 1, 0; high-risk (>0): 105, 47, 23, 11, 5, 4, 3, 2, 2, 0.

[0259] Figure 30 shows the Kaplan-Meier curves for the BCAI_3.5 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.03; HR = 1.7; 95% CI = 1.0-2.7). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 107, 59, 27, 16, 6, 4, 2, 2, 1, 0; high-risk (>0): 82, 29, 18, 8, 5, 4, 3, 2, 2, 0.

[0260] Figure 31 shows the Kaplan-Meier curves for the BCAI_3.6 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.02; HR = 1.9; 95% CI = 1.1-3.1). The supplemental list below shows the number of patients at risk (threshold = 0.1) for the BCAI_model classes analyzed, i.e., for all +20-month periods: low-risk (<=0.1): 122, 64, 34, 18, 8, 7, 5, 4, 3, 0; high-risk (>0.1): 67, 24, 11, 6, 3, 1, 0, 0, 0, 0.

[0261] Figure 32 shows the Kaplan-Meier curves for the BCAI_4.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.0006; HR=2.3; 95% CI=1.4-3.6). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 92, 49, 26, 12, 5, 5, 4, 3, 2, 0; high-risk (>0): 97, 39, 19, 12, 6, 3, 1, 1, 1, 0.

[0262] Figure 33 shows the Kaplan-Meier curves for the BCAI_4.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.05; HR=1.6; 95% CI=1.0-2.6). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 111, 54, 30, 16, 7, 6, 3, 2, 1, 0; high-risk (>0): 78, 34, 15, 8, 4, 2, 2, 2, 2, 0.

[0263] Figure 34 shows the Kaplan-Meier curves for the BCAI_4.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.02; HR=1.7; 95% CI=1.1-2.7). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 89, 44, 24, 13, 6, 4, 2, 2, 1, 0; high-risk (>0): 100, 44, 21, 11, 5, 4, 3, 2, 2, 0.

[0264] Figure 35 shows the Kaplan-Meier curves for the BCAI_4.4 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.03; HR=1.7; 95% CI=1.1-2.7). The supplemental list below shows the number of patients at risk (threshold=0) for the BCAI_model classes analyzed, i.e., for all +20-month periods: low-risk (<=0): 78, 41, 22, 12, 5, 4, 3, 2, 1, 0; high-risk (>0): 111, 47, 23, 12, 6, 4, 2, 2, 2, 0.

[0265] Figure 36 shows the Kaplan-Meier curves for the BCAI_4.5 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.01; HR=1.8; 95% CI=1.1-2.9). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed BCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 90, 46, 18, 11, 4, 3, 2, 2, 2, 0; high-risk (>0): 99, 42, 27, 13, 7, 5, 3, 2, 1, 0.

[0266] Figure 37 shows the Kaplan-Meier curves for the BCAI_4.6 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.03; HR = 1.7; 95% CI = 1.0-2.6). The supplemental list below shows the number of patients at risk (threshold = -0.1) for the BCAI_model classes analyzed, i.e., for all +20-month periods: low-risk (<=-0.1): 82, 48, 22, 14, 7, 5, 3, 2, 1, 0; high-risk (>-0.1): 107, 40, 23, 10, 4, 3, 2, 2, 2, 0.

[0267] Figure 38 shows the Kaplan-Meier curves for the KCAI_3.1 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.0001; HR = 2.1; 95% CI = 1.4-3.1). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 202, 172, 122, 74, 42, 20, 5, 1, 0; high-risk (>0): 209, 161, 112, 59, 22, 13, 7, 0, 0.

[0268] Figure 39 shows the Kaplan-Meier curves for the KCAI_3.2 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.03; HR = 1.5; 95% CI = 1.0-2.3). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 199, 175, 128, 70, 32, 18, 7, 1, 0; high-risk (>0): 212, 158, 106, 63, 32, 15, 5, 0, 0.

[0269] Figure 40 shows the Kaplan-Meier curves for the KCAI_3.3 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.0002; HR = 2.1; 95% CI = 1.4-3.1). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 221, 184, 128, 79, 39, 20, 8, 1, 0; high-risk (>0): 190, 149, 106, 54, 25, 13, 4, 0, 0.

[0270] Figure 41 shows the Kaplan-Meier curves for the KCAI_3.4 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.01; HR=1.6; 95% CI=1.1-2.4). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 205, 167, 132, 82, 41, 24, 10, 1, 0; high-risk (>0): 206, 166, 111, 51, 23, 9, 2, 0, 0.

[0271] Figure 42 shows the Kaplan-Meier curves for the KCAI_3.5 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.0002; HR = 2.1; 95% CI = 1.4-3.1). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 245, 197, 135, 79, 38, 21, 7, 1, 0; high-risk (>0): 166, 136, 99, 54, 26, 12, 5, 0, 0.

[0272] Figure 43 shows the Kaplan-Meier curves for the KCAI_3.6 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = 0.0001; HR = 2.2; 95% CI = 1.5-3.2). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 215, 174, 121, 72, 34, 18, 6, 1, 0; high-risk (>0): 196, 159, 113, 61, 30, 15, 6, 0, 0.

[0273] Figure 44 shows the Kaplan-Meier curves for the KCAI_4.1 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.0006; HR=2.0; 95% CI=1.3-3.0). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 232, 189, 135, 89, 45, 26, 11, 1, 0; high-risk (>0): 179, 144, 99, 44, 19, 7, 1, 0, 0.

[0274] Figure 45 shows the Kaplan-Meier curves for the KCAI_4.2 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=2.3; 95% CI=1.6-3.5). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 206, 171, 122, 76, 40, 22, 7, 1, 0; high-risk (>0): 205, 162, 112, 57, 24, 11, 5, 0, 0.

[0275] Figure 46 shows the Kaplan-Meier curves for the KCAI_4.3 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.0005; HR=2.0; 95% CI=1.3-2.9). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 221, 178, 124, 73, 39, 19, 5, 1, 0; high-risk (>0): 190, 155, 110, 60, 25, 14, 7, 0, 0.

[0276] Figure 47 shows the Kaplan-Meier curves for the KCAI_4.4 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.0004; HR=2.0; 95% CI=1.4-2.9). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 205, 175, 120, 70, 35, 16, 6, 1, 0; high-risk (>0): 206, 158, 114, 63, 29, 17, 6, 0, 0.

[0277] Figure 48 shows the Kaplan-Meier curves for the KCAI_4.5 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.05; HR=1.6; 95% CI=1.0-2.1). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed KCAI_model classes, i.e., for all +20-month periods: low-risk (<=0): 214, 173, 124, 76, 41, 22, 10, 1, 0; high-risk (>0): 197, 160, 110, 57, 23, 11, 2, 0, 0.

[0278] Figure 49 shows the Kaplan-Meier curves for the KCAI_4.6 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p = ….; HR = …; 95% CI = ….). The supplemental list below shows the number of patients at risk (threshold = 0) for the analyzed KCAI_model classes, i.e., at all +20-month periods: low risk (<=0): 215, 174, 121, 72, 34, 18, 6, 1, 0; high risk (>0): 196, 159, 113, 61, 30, 15, 6, 0, 0.

[0279] The Kaplan-Meier survival curve analyses shown in Figures 26 through 49 demonstrate the existence of different patient risk groups. Patient risk groups are determined by the likelihood of experiencing each clinical endpoint, i.e., all-cause mortality, predicted by each risk model shown in the figures (see Tables 7-10). Depending on the patient's predicted risk of experiencing a clinical endpoint due to bladder or kidney cancer (i.e., depending on the risk group to which the patient belongs), different types of interventions are indicated.

[0280] In low / lower risk groups, standard of care (SOC) protocols provide acceptable long-term oncological suppression. For patient groups exhibiting a high / higher risk of ultimately dying from bladder or kidney cancer, treatment escalation (e.g., multimodality treatment with radiation and chemotherapy) may provide improved long-term survival outcomes. Alternative options for treatment escalation are alternative therapies such as adjuvant therapy with cytotoxic drug radiation or immunotherapy (e.g., atezolizumab; pembrolizumab; nivolumab; avelumab; durvalumab) or other experimental therapies. In other words, the separation of bladder or kidney cancer into low-risk and high-risk patient risk groups based on the BCAI or KCAI risk models provided herein (Tables 7-10) enables methods for predicting the outcome of bladder or kidney cancer subjects, as well as predicting treatment response.

[0281] This next section shows additional results for Cox regression models based on multiple gene models for both bladder and kidney cancer, including randomly selected combinations of at least three genes selected from either immune defense genes (Tables 13 & 16), TCR signaling genes (Tables 14 & 17), or PDE4D7-related genes (Tables 15 & 18). The variables and corresponding weights are provided in Tables 13-18. The Cox regression models are plotted in Kaplan-Meier curve analyses in Figures 50-67.

[0282] [Table 11]

[0283] [Table 12]

[0284] [Table 13]

[0285] [Table 14]

[0286] [Table 15]

[0287] [Table 16]

[0288] For Kaplan-Meier curve analysis, the Cox regression functions of the 18 risk models shown in Tables 13-18 (bladder cancer: IDR 3.1-3.3, TCR 3.1-3.3, and PDE4D 73.1-3.3; kidney cancer: IDR 3.1-3.3, TCR 3.1-3.3, and PDE4D 73.1-3.3) were categorized into two subcohorts (low risk vs. high risk) based on cutoffs (see the descriptions in Figures 50-67 below). The thresholds for separating the low-risk and high-risk groups were based on the risk of experiencing the clinical endpoints (outcomes) predicted by each Cox regression model.

[0289] In Figures 50 through 58, the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0)) was examined for the endpoint: all-cause mortality.

[0290] In Figures 59 to 67, the 411 patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0)) was examined for the endpoint: all-cause mortality.

[0291] Figure 50 shows the Kaplan-Meier curves for the IDR_3.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.03; HR=1.7; 95% CI=1.0-2.7). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed IDR_model classes, i.e., for all +20-month periods: low-risk (<=0): 103, 54, 26, 16, 7, 5, 3, 2, 1, 0; high-risk (>0): 86, 34, 19, 8, 4, 3, 2, 2, 2, 0.

[0292] Figure 51 shows the Kaplan-Meier curves for the IDR_3.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.006; HR=1.9; 95% CI=1.2-3.0). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed IDR_model classes, i.e., for all +20-month periods: low-risk (<=0): 86, 42, 23, 15, 6, 4, 2, 1, 1, 0; high-risk (>0): 103, 46, 22, 9, 5, 4, 3, 3, 2, 0.

[0293] Figure 52 shows the Kaplan-Meier curves for the IDR_3.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.02; HR=1.7; 95% CI=1.1-2.7). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed IDR_model classes, i.e., for all +20-month periods: low-risk (<=0): 92, 46, 22, 13, 6, 4, 3, 2, 2, 0; high-risk (>0): 97, 42, 23, 11, 5, 4, 2, 2, 1, 0.

[0294] Figure 53 shows the Kaplan-Meier curve for the TCR_3.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.02; HR=1.8; 95% CI=1.1-3.0). The supplemental list below shows the number of patients at risk (threshold=0.1) for the TCR_model classes analyzed, i.e., for all +20-month periods: low-risk (<=0.1): 120, 62, 34, 20, 9, 8, 5, 4, 3, 0; high-risk (>0.1): 69, 26, 11, 4, 2, 0, 0, 0, 0, 0.

[0295] Figure 54 shows the Kaplan-Meier curve for the TCR_3.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.01; HR=1.9; 95% CI=1.2-3.2). The supplemental list below shows the number of patients at risk (threshold=0.1) for the TCR_model classes analyzed, i.e., for all +20-month periods: low-risk (<=0.1): 129, 64, 32, 17, 9, 6, 3, 2, 2, 0; high-risk (>0.1): 60, 24, 13, 7, 2, 2, 2, 2, 1, 0.

[0296] Figure 55 shows the Kaplan-Meier curve for the TCR_3.3 model in the 189-patient TCGA bladder cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.03; HR=1.7; 95% CI=1.1-2.7). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed TCR_model classes, i.e., for all +20-month periods: low-risk (<=0): 100, 59, 28, 14, 6, 4, 3, 3, 2, 0; high-risk (>0): 89, 29, 17, 10, 5, 4, 2, 1, 1, 0.

[0297] Figure 56 shows the Kaplan-Meier curve for the PDE4D7_3.1 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.004; HR=2.0; 95% CI=1.2-3.1). The following supplemental list shows the number of patients at risk (threshold=0) for the PDE4D7_model classes analyzed, i.e., at-risk patients for all +20 months: low risk (<=0): 82, 42, 26, 14, 7, 6, 4, 3, 2, 0; high risk (>0): 107, 46, 19, 10, 4, 2, 1, 1, 1, 0.

[0298] Figure 57 shows the Kaplan-Meier curve for the PDE4D7_3.2 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.04; HR=1.6; 95% CI=1.0-2.7). The following supplemental list shows the number of patients at risk (threshold=0) for the PDE4D7_model classes analyzed, i.e., at-risk patients for all +20 months: low risk (<=0): 118, 60, 27, 17, 6, 4, 1, 1, 1; high risk (>0): 71, 28, 18, 7, 5, 4, 4, 3, 2.

[0299] Figure 58 shows the Kaplan-Meier curve for the PDE4D7_3.3 model in the TCGA bladder cancer cohort of 189 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.04; HR=1.8; 95% CI=1.0-3.2). The following supplemental list shows the number of patients at risk (threshold=0) for the PDE4D7_model classes analyzed, i.e., at-risk patients for all +20 months: low risk (<=0): 141, 71, 38, 19, 10, 8, 5, 4, 3, 0; high risk (>0): 48, 17, 7, 5, 1, 0, 0, 0, 0.0.

[0300] Figure 59 shows the Kaplan-Meier curves for the IDR_3.1 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.01; HR=1.7; 95% CI=1.1-2.5). The supplemental list below shows the number of patients at risk (threshold=0.1) for the IDR_model classes analyzed, i.e., for all +20-month periods: low-risk (<=0.1): 252, 203, 154, 86, 47, 23, 8, 1, 0; high-risk (>0.1): 159, 130, 80, 47, 17, 10, 4, 0, 0.

[0301] Figure 60 shows the Kaplan-Meier curves for the IDR_3.2 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.0001; HR=2.1; 95% CI=1.5-3.2). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed IDR_model classes, i.e., for all +20-month periods: low-risk (<=0): 240, 199, 133, 76, 38, 22, 9, 1, 0; high-risk (>0): 171, 134, 101, 57, 26, 11, 3, 0, 0, 0.

[0302] Figure 61 shows the Kaplan-Meier curves for the IDR_3.3 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=2.36; 95% CI=1.6-3.5). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed IDR_model classes, i.e., for all +20-month periods: low-risk (<=0): 251, 210, 154, 95, 48, 24, 9, 1, 0; high-risk (>0): 160, 123, 80, 38, 16, 9, 3, 0, 0.

[0303] Figure 62 shows the Kaplan-Meier curves for the TCR_3.1 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=2.4; 95% CI=1.6-3.6). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed TCR_model classes, i.e., for all +20-month periods: low-risk (<=0): 239, 201, 137, 88, 48, 24, 8, 1, 0; high-risk (>0): 172, 132, 97, 45, 16, 9, 4, 0, 0.

[0304] Figure 63 shows the Kaplan-Meier curves for the TCR_3.2 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p<0.0001; HR=2.4; 95% CI=1.6-3.6). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed TCR_model classes, i.e., for all +20-month periods: low-risk (<=0): 239, 201, 137, 88, 48, 24, 8, 1, 0; high-risk (>0): 172, 132, 97, 45, 16, 9, 4, 0, 0.

[0305] Figure 64 shows the Kaplan-Meier curves for the TCR_3.3 model in the 411-patient TCGA ccRCC kidney cancer cohort (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.004; HR=1.8; 95% CI=1.2-2.6). The supplemental list below shows the number of patients at risk (threshold=0) for the analyzed TCR_model classes, i.e., for all +20-month periods: low-risk (<=0): 209, 174, 126, 83, 45, 24, 11, 1, 0; high-risk (>0): 202, 159, 108, 50, 19, 9, 1, 0, 0.

[0306] Figure 65 shows the Kaplan-Meier curves for the PDE4D7_3.1 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.0005; HR=2.0; 95% CI=1.3-2.9). The following supplemental list shows the number of patients at risk (threshold=0) for the PDE4D7_model classes analyzed, i.e., at-risk patients for all +20 months: low risk (<=0): 224, 190, 134, 80, 41, 20, 10, 1, 0; high risk (>0): 187, 143, 100, 53, 23, 13, 2, 0, 0.

[0307] Figure 66 shows the Kaplan-Meier curves for the PDE4D7_3.2 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.0009; HR=1.6; 95% CI=1.9-2.8). The following supplemental list shows the number of patients at risk (threshold=0) for the PDE4D7_model classes analyzed, i.e., at-risk patients for all +20 months: low risk (<=0): 229, 186, 134, 77, 38, 20, 8, 1, 0; high risk (>0): 182, 147, 100, 56, 26, 13, 4, 0, 0.

[0308] Figure 67 shows the Kaplan-Meier curves for the PDE4D7_3.3 model in the TCGA ccRCC kidney cancer cohort of 411 patients (total number of patients with non-metastatic disease (m0), including both the training and validation sets). The clinical endpoint tested was all-cause mortality (log-rank p=0.002; HR=1.9; 95% CI=1.3-2.7). The following supplemental list shows the number of patients at risk (threshold=0) for the PDE4D7_model classes analyzed, i.e., at-risk patients for all +20 months: low risk (<=0): 231, 189, 128, 72, 36, 19, 6, 1, 0; high risk (>0): 180, 144, 106, 61, 28, 14, 6, 0, 0.

[0309] Kaplan-Meier survival curve analyses shown in Figures 50 through 67 demonstrate the existence of distinct patient risk groups. Patient risk groups are determined by the likelihood of experiencing each clinical endpoint, i.e., all-cause mortality, predicted by each risk model shown in the figures (see Tables 13-18). Depending on the patient's predicted risk of dying from bladder or kidney cancer (i.e., depending on the risk group (low or high) to which the patient belongs), different types of interventions are indicated.

[0310] In lower-risk groups, standard of care (SOC) provides acceptable long-term oncological control. For patient groups at higher risk of ultimately dying from bladder or kidney cancer, treatment escalation (e.g., multimodality treatment with radiation and chemotherapy) may provide improved long-term survival outcomes. Alternative options for treatment escalation are alternative therapies such as adjuvant therapy with cytotoxic drug radiation or immunotherapy (e.g., atezolizumab; pembrolizumab; nivolumab; avelumab; durvalumab) or other experimental therapies.

[0311] In other words, the separation of bladder cancer or kidney cancer into low-risk and high-risk patient risk groups based on any one of the IDR models, any one of the TCR models, or any one of the PDE4D7 models provided herein (Tables 13-18) enables methods of predicting the outcome of bladder cancer or kidney cancer subjects, as well as enabling prediction of therapy response.

[0312] explanation Therapies for primary bladder and kidney cancer have limited efficacy, resulting in disease progression and eventual patient death, particularly in patients at high risk of disease recurrence after initial treatment. Predicting therapy outcomes is challenging because many factors contribute to therapy efficacy and disease recurrence. Perhaps important factors have yet to be identified, and the effects of others may not be precisely determined. Multiple clinicopathological tools are currently being investigated and applied in clinical settings to improve response prediction and treatment selection, providing some improvement. Nevertheless, there is a strong need for better prediction of treatment response to increase the success rate of bladder and kidney cancer therapy.

[0313] It is anticipated that identifying molecules whose expression correlates significantly with mortality after first-line therapy for bladder or kidney cancer will improve prediction of second-line treatment efficacy. This can be achieved by 1) directing these patients at low risk of progressive disease correlated with cancer death to the standard of care, and / or 2) directing patients at high risk of progressive disease and subsequent cancer death to alternative treatments that are potentially more effective than the currently applied standard of care. This will reduce patient suffering by avoiding ineffective treatments and reduce costs otherwise spent on ineffective treatments.

[0314] Those skilled in the art can understand and effect other variations to the disclosed implementations in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0315] In the claims, the words "comprises" and "have" do not exclude other elements or steps, and the singular does not exclude a plurality.

[0316] 1 is implemented in a computer program product running on a computer. The computer program product includes a non-transitory computer-readable recording medium, such as a disk, hard drive, etc., on which a control program is recorded (stored). Common forms of non-transitory computer-readable media include, for example, a floppy disk, flexible disk, hard disk, magnetic tape or other magnetic storage medium, CD-ROM, DVD or other optical medium, RAM, PROM, EPROM, FLASH®-EPROM or other memory chip or cartridge, or other non-transitory medium that can be read from and used by a computer.

[0317] Alternatively, one or more steps of the method may be implemented in a transitory medium such as a transmittable carrier wave in which the control program is embodied as a data signal using a transmission medium such as sound or light waves, such as those generated during radio wave and infrared data communications.

[0318] Exemplary methods are implemented on one or more general-purpose computers, special-purpose computer(s), hardwired electronic or logic circuitry such as a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a digital signal processor, a discrete element circuit, or a programmable logic device such as a PLD, PLA, FPGA, graphics card CPU (GPU), or PAL. Generally, any device capable of implementing a finite machine capable of sequentially performing the flowchart shown in Figure 1 can be used to implement one or more steps of the illustrative risk stratification method for treatment selection in prostate cancer patients. As will be appreciated, while the method steps may all be computer-implemented, in some embodiments, one or more steps are performed at least in part manually.

[0319] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of work steps to be executed on the computer, other programmable apparatus, or other device, creating a computer-implemented process such that the instructions running on the computer or other programmable apparatus provide a process for performing the functions / operations specified herein.

[0320] Any reference signs in the claims should not be construed as limiting the scope.

[0321] The present invention provides a method for predicting outcome in a subject with bladder cancer or kidney cancer, comprising the steps of: identifying, or receiving the results of identifying, a first gene expression profile for each of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1. wherein the first gene expression profile is identified in a biological sample obtained from the subject; and / or one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all, T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70. identifying or receiving results of the identification of a second gene expression profile for each of PDE4D7-correlated genes, wherein the second gene expression profile is identified in a biological sample obtained from the subject; and / or identifying or receiving results of the identification of a third gene expression profile for each of 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, wherein the third gene expression profile is identified in a biological sample obtained from the subject; determining a prediction of outcome based on the first gene expression profile or the second gene expression profile or the third gene expression profile or the first, second, and third gene expression profiles; and, as appropriate, providing the prediction to a healthcare caregiver or the subject.

[0322] In some embodiments, the outcome prediction can also be determined based on the first gene expression profile and the second gene expression profile. In some embodiments, the outcome prediction can also be determined based on the first gene expression profile and the third gene expression profile. In some embodiments, the outcome prediction can also be determined based on the second gene expression profile and the third gene expression profile. The accompanying Sequence Listing entitled 2020PF00580_Sequence Listing_ST25 is incorporated herein by reference in its entirety.

Claims

1. A device for predicting outcome in a subject with kidney cancer, the device comprising: an input adapted to receive data indicative of a gene expression profile of a gene, said gene comprising: immune defense response genes, including AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; T cell receptor signaling genes, including CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70; and PDE4D7-correlated genes, including ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; Including, The gene expression profile is identified in a biological sample derived from the kidney of a subject with renal cancer. Input and A processor for determining a prediction of outcome, the processor comprising: calculating a first combination (IDR_model) of a first gene expression profile for the immune defense response genes and a first regression function; calculating a second combination of a second gene expression profile for the T cell receptor signaling genes and a second regression function (TCR_SIGNALING_model); calculating a third combination (PDE4D7_CORR_model) of a third gene expression profile for the PDE4D7-correlated genes and a third regression function; the first, second, and third regression functions are obtained from a population of kidney cancer subjects; a processor that determines a prediction of outcome for a subject with kidney cancer based on a weighted combination of the IDR model, the TCR SIGNALING model, and the PDE4D7 CORR model; a providing device adapted to provide the prediction to a healthcare professional or a subject; Including, equipment.

2. The device described in claim 1, wherein the processor determines the prediction further based on one or more clinical parameters obtained from the kidney cancer subject.

3. The device of claim 2, wherein the clinical parameters include one or more of: (i) a T stage attribute (T1, T2, T3 or T4); (ii) an N stage attribute (N0, N1, N2 or N3); and (iii) an M stage attribute (M0, M1).

4. An apparatus described in any one of claims 1 to 3, wherein the processor classifies the prediction of outcome into one of at least two risk groups based on the value of the prediction of outcome.

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