Predicting the outcome of neoadjuvant androgen deprivation therapy in subjects with prostate cancer

Abstract

The present invention relates to a method for predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy. The method is based on predicting outcome based on the expression levels of the genes identified herein. The present invention provides a method for determining whether neoadjuvant androgen deprivation therapy is useful for a subject and therefore whether or not it should be administered. The present invention further provides the use of a kit for determining expression levels to predict the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, and a computer program for predicting the response of a subject with prostate cancer to radiation therapy. Further, the present invention relates to a diagnostic kit, the use of the kit, the use of the kit in a method for predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, the use of a respective gene expression profile of one or more PDE4D7-related genes in a method for predicting the response of a subject with prostate cancer to radiation therapy, and corresponding computer programs. [Background technology]

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

[0003] Prostate cancer (PCa) is the second most common non-cutaneous malignancy in men, with an estimated 1.3 million new cases diagnosed worldwide in 2018, resulting in 360,000 deaths (see Bray F. et al., “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA Cancer J Clin, Vol. 68, No. 6, pages 394–424, 2018). In the United States, approximately 90% of new cases involve localized cancer, meaning that metastases have not yet formed (see ACS (American Cancer Society), “Cancer Facts & Figures 2010,” 2010). Several radiation treatments are available for the treatment of primary localized prostate cancer, with surgery (radical prostatectomy, RP) and radiation therapy (RT) being the most commonly used. RT is administered by external beam radiation, by implantation of radioactive seeds into the prostate (brachytherapy), or a combination of both. It is particularly preferred for patients who are not eligible for surgery or who have been diagnosed with advanced-stage tumors, either localized or regional. Definitive RT is offered to up to 50% of patients diagnosed with localized prostate cancer in the United States (ASC, 2010 ibid.). After treatment, prostate cancer antigen (PSA) levels in the blood are measured to monitor the disease. An increase in blood PSA levels provides a biochemical surrogate measure of cancer recurrence or progression.

[0004] Androgen deprivation therapy (ADT), also known as androgen suppression therapy, is an antihormonal treatment primarily used to treat prostate cancer. Prostate cancer cells typically require androgen hormones, such as testosterone, to grow. ADT reduces androgen hormone levels, either through drugs or surgery, preventing the proliferation of prostate cancer cells. Neoadjuvant androgen deprivation therapy (NADT) is a systemic treatment administered after a prostate cancer diagnosis but before locoregional treatments, such as radical prostatectomy (RP) or radiation. NADT before RP aims to eradicate malignant androgen-dependent cells in the hope that sufficient tumor regression will allow for complete removal of remaining prostate cancer, improving disease outcomes and survival. However, the role of preoperative androgen deprivation remains controversial.

[0005] WO2022 / 043299A1 discloses a gene signature that can be used to predict response to salvage androgen deprivation therapy (SADT).

[0006] However, the analysis for predicting SADT response is based on significantly pre-treated patients. All of these men first underwent surgery to remove the prostate, then experienced biochemical recurrence, then underwent salvage radiation to the pelvic bed, and then were treated with ADT. This means that the recurrent tumors treated with RT are no longer the same tumors, molecularly and phenotypically, compared to the tumors the same men had when they were treatment-naive (i.e., before surgery). Therefore, a gene signature that is predictive of SADT does not necessarily predict ADT treatment response.

[0007] Pechlivanis et al. (Cancers, vol. 14, no. 1, 29 December 2021 (2021-12-29), page 166) and Tewari et al. (Cell Reports, vol. 36, no. 10, 1 September 2021 (2021-09-01), pages 109665-109665) disclose the correlation or association of certain genes with pathological minimal residual disease response to neoadjuvant suppressive treatment.

[0008] Wilkinson et al. (EUROPEAN UROLOGY, vol. 80, no. 6, 27 March 2021 (2021-03-27), pages 746-757) disclose that weak neoadjuvant androgen deprivation therapy is associated with mutations in the q10 genomic region (PTEN), TP53, and loss of ERG expression.

[0009] Patients diagnosed with high-risk localized prostate cancer exhibit variable outcomes after surgery. Studies of potent NADT have shown low recurrence rates among patients with minimal residual disease after treatment. There are no known molecular characteristics that distinguish excellent responders from poor responders. Summary of the Invention [Problem to be solved by the invention]

[0010] Therefore, there is a need for new and improved methods for predicting neoadjuvant androgen deprivation treatment response. This unmet need is met by the methods and uses defined in the appended claims. [Means for solving the problem]

[0011] In a first aspect, the present invention provides a method of predicting a prostate cancer subject's response to neoadjuvant androgen deprivation therapy, said method comprising: PDE4D gene expression levels, and / or the expression levels of three or more genes selected from the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; determining a gene expression profile or receiving a result of determining a gene expression profile, comprising: determining the gene expression profile in a biological sample obtained from the subject; determining a prediction of outcome based on said gene expression profile, the prediction being a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; The present invention relates to a method comprising:

[0012] In a second aspect, the present invention relates to the use of a diagnostic kit, said diagnostic kit comprising: A biological sample obtained from a subject with prostate cancer and / or at least one polymerase chain reaction primer or probe for determining a gene expression profile in the sample, wherein the gene expression profile comprises: PDE4D gene expression levels, and / or the expression levels of three or more genes selected from the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; Use of the diagnostic kit includes predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, the outcome being a favorable or unfavorable response to neoadjuvant androgen deprivation therapy. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 shows the experimental setup for testing the prostate cancer gene signature in a first cohort of patients. [Figure 2]FIG. 2 shows the experimental setup for testing the prostate cancer gene signature in a second cohort of patients. [Figure 3] Figure 3 shows 35 patients with intermediate / high-risk prostate cancer; all patients received neoadjuvant antiandrogen therapy (6 months of ADT + enzalutamide). The figure shows the ROC curve. The model was evaluated using a test cohort of 35 patients. The clinical endpoint used was overall mortality. The models plotted are the PCAI model and the reference model, based on a selection of genes regulated by the androgen receptor. The area under the curve is shown in the legend. [Figure 4] Figure 4 shows 24 patients (43 samples) with localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, and apalutamide). The figure shows the receiver operating characteristic curve. The model was evaluated using a test cohort of 35 patients. The clinical endpoint used was overall mortality. The models plotted are the PCAI model and the reference model, based on a selection of genes regulated by the androgen receptor. The area under the curve is shown in the legend. [Figure 5] Figure 5 shows the AUROC analysis of Model_1 for a cohort of 24 patients, all with localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint tested was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 6] Figure 6 shows the AUROC analysis of Model_2 for a cohort of 24 patients, all with localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). [Figure 7]Figure 7 shows the AUROC analysis of Model_3 for a cohort of 24 patients, all with localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint tested was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 8] Figure 8 shows the AUROC analysis of Model_4 for a cohort of 24 patients, all with localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint tested was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 9] Figure 9 shows the AUROC analysis of Model_5 for a cohort of 24 patients, all of whom had localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint studied was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 10] Figure 10 shows the AUROC analysis of Model_6 for a cohort of 24 patients, all with localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint tested was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 11] Figure 11 shows the AUROC analysis of Model_7 for a cohort of 24 patients, all of whom had localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint studied was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 12]Figure 12 shows the AUROC analysis of Model_8 for a cohort of 24 patients, all of whom had localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint tested was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 13] Figure 13 shows the AUROC analysis of Model_9 for a cohort of 24 patients, all of whom had localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint studied was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 14] Figure 14 shows the AUROC analysis of Model_10 for a cohort of 24 patients, all of whom had localized, high-risk prostate cancer; all patients received 6 months of neoadjuvant antiandrogen therapy (6 months of enzalutamide, lupron, abiraterone, apalutamide). The clinical endpoint studied was overall mortality. The area under the curve and P values ​​are included in the figure. [Figure 15] Figure 15 shows the AUROC analysis curve of the comparison between the PDE4D7 correlation model (PDE4D7_R2) and the PDE4D7 correlation model combined with Baseline Tumor Volume (PDE4D7_BaseTumorVol) of a cohort of 35 patients, all of whom have localized, high-risk prostate cancer; all patients receive 6 months of neoadjuvant antiandrogen therapy (6 months of ADT+enzalutamide).The clinical endpoint tested is overall mortality.The area under the curve and P value are included in the figure. [Figure 16]Figure 16 shows the AUROC analysis curve of the comparison between the PDE4D7 correlation model (PDE4D7_R2) and the PDE4D7 correlation model (PDE4D7_BaseTumorVol&ERG&PTEN) combined with Baseline Tumor Volume and ERG and PTEN expression levels in a cohort of 35 patients, all of whom have localized, high-risk prostate cancer; all patients receive 6 months of neoadjuvant antiandrogen therapy (6 months of ADT+enzalutamide).The clinical endpoint tested is overall mortality.The area under the curve is included in the figure. [Figure 17] Figure 17 shows the Kaplan-Meier curves for the PDE4D7_R2_log_BCR model for a cohort of 41 patients, all of whom had intermediate- to high-risk prostate cancer; all patients received 3 months of neoadjuvant treatment with an antiandrogen (enzalutamide). The clinical endpoint tested was biochemical recurrence. P values ​​are included in the figure. [Figure 18] Figure 18 shows a Kaplan-Meier curve using PDE4D_expression levels for a cohort of 41 patients, all of whom had intermediate- to high-risk prostate cancer; all patients received 3 months of neoadjuvant treatment with an antiandrogen (enzalutamide). The clinical endpoint tested was biochemical recurrence. P values ​​are included in the figure. [Figure 19] Figure 19 shows Kaplan-Meier curves of different ISUP Gleason scores at baseline for a cohort of 41 patients, all of whom had intermediate- to high-risk prostate cancer; all patients received 3 months of neoadjuvant treatment with an antiandrogen (enzalutamide). The clinical endpoint studied was biochemical recurrence. P values ​​are included in the figure. DETAILED DESCRIPTION OF THE INVENTION

[0014] definition As used in this specification the word "a" or "an" in the singular does not exclude a plural.

[0015] The term "biological sample" or "sample obtained from a subject" refers to any biological material obtained from a subject, for example, a prostate cancer subject, by a suitable method known to those skilled in the art.

[0016] As used herein, the term "and / or" indicates that one or more of the stated examples may occur alone or in combination with at least one of the stated examples to all of the stated examples.

[0017] As used herein, the term "at least" a particular value means the particular value or more. For example, "at least two" is understood to be the same as "two or more," i.e., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ... etc.

[0018] The term "prostate cancer" refers to cancer of the prostate tissue that occurs when cells in the prostate mutate and begin to grow uncontrollably.

[0019] The term "prostate cancer-specific mortality or disease-specific mortality" refers to patient death from prostate cancer.

[0020] The term "clinical recurrence" refers to the presence of clinical signs indicating the presence of tumor cells, as measured, for example, using in vivo imaging.

[0021] As used herein, the word "comprise" or variations thereof, such as "comprising," is understood to include a stated element, integer, or step, or group of elements, integers, or steps, but not to exclude any other element, integer, or step, or group of elements, integers, or steps. The verb "comprise" includes the verbs "consisting essentially of" and "consisting of."

[0022] As used herein, the term "immune defense response gene" is used interchangeably with "IDR gene" or "immune defense gene" and refers to one or more genes selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1.

[0023] The term "metastasis" refers to the presence of metastatic disease in organs other than prostate tissue.

[0024] As used herein, the term "PDE4D7-related gene" is used interchangeably with "PDE4D7 gene" and refers to one or more of the genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0025] As used herein, the term "T cell receptor signaling genes" is used interchangeably with "TCR signaling genes" or "TCR genes" and refers to one or more of the genes selected from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70.

[0026] As used herein, the terms "outcome" and "prediction of outcome" refer to a prostate cancer subject's response to androgen deprivation therapy, preferably neoadjuvant androgen deprivation therapy.

[0027] As used herein, " PCAI immune score " refers to a model based on PDE4D7-related genes.Therefore, PCAI immune score provides a model based on the expression level of each of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, and each expression level assigns a weight to the model.The model used is described in WO2022 / 043120, the entire contents of which are incorporated herein by reference.

[0028] As used herein, the term "gene expression profile" refers to one or more expression levels, specifically the PDE4D isoform 7 expression level defined herein and / or three or more signatures of PDE4D7-related genes.Thus, the gene expression profile may simply include the expression level of PDE4D7 (i.e., isoform 7 of the phosphodiesterase 4D gene).The gene expression profile may also include, for example, the PDE4D7 expression level and one or more additional expression levels, for example, but not limited to, one or more expression levels selected from the PDE4D7-related genes defined herein, one or more expression levels selected from immune defense response genes and / or one or more expression levels of T cell receptor signaling genes.

[0029] The immune system in cancer In recent years, the importance of the immune system in cancer inhibition and cancer initiation, promotion, and metastasis has become increasingly clear (Mantovani et al. Nature. 454(7203):436-44 (2008); Giraldo et al. Br J Cancer. 120(1):45-53 (2019)). Immune cells and the molecules they secrete form 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, antitumor immunity can prevent tumor formation, while an inflammatory tumor environment can promote cancer initiation and growth. At the same time, tumor cells, which can be generated in an immune system-dependent manner, shape the immune microenvironment by recruiting immune cells and can have pro-inflammatory effects, but also suppress anticancer immunity.

[0030] Some immune cells in the tumor microenvironment have either general tumor-promoting or general tumor-inhibiting effects, while other immune cells exhibit plasticity and exhibit both tumor-promoting and tumor-inhibiting capabilities. 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 (Giraldo Br J Cancer. 120(1):45-53 (2019)).

[0031] In summary, the state of the immune system and the immune microenvironment has an impact on therapeutic efficacy.

[0032] We have identified genetic signatures and the combination of these signatures with clinical parameters, and the resulting models show significant associations with mortality and are therefore expected to improve prediction of treatment efficacy.

[0033] PDE4D7-related genes Phosphodiesterases (PDEs) provide the sole means for the degradation of the second messenger 3'-5'-cyclic AMP. As such, they may play an important regulatory role. Therefore, abnormal changes in their expression, activity, and subcellular location may all contribute to the molecular pathology underlying certain disease states. Indeed, recent studies have shown that mutations in PDE genes are common in prostate cancer patients, leading to elevated cAMP signaling and potentially predisposing to prostate cancer. However, the diverse expression profiles of different cell types, combined with the complex and diverse isoform variants within each PDE family, make it difficult to understand the relationship between abnormal changes in PDE expression and their functionality during disease progression. Several studies have attempted to describe the total amount of PDEs in the prostate, all of which have identified significant levels of PDE4 expression alongside other PDEs, leading to the development of the PDE4D7 biomarker (see Alves de Inda M. et al., “Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for Its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes”, Eur Urol Focus, Vol. 4, No. 3, pages 376-384, 2018). Because the PDE4D7 biomarker has been shown to be a good predictor, the ability to identify markers that highly correlate with the PDE47 biomarker may also be useful in predicting outcome in certain cancer subjects.

[0034] Based on the correlation between PDE4D7 expression and pathological features of disease, our explicit goal was to identify prognostic associations between PDE4D7 expression in patient prostate tissue obtained by biopsy or surgery and clinically useful information related to individual patient outcomes. Clinically meaningful endpoints or surrogate endpoints significantly correlated with the development of metastasis, cancer-specific, or overall mortality have typically been evaluated as prognostic cancer biomarkers. The most appropriate rationale for using surrogate endpoints relates to situations where established clinical endpoint data are unavailable or the number of events in a data cohort is too limited for statistical data analysis. For the development of PDE4D7 prognostic biomarkers, we evaluated either biochemical recurrence (BCR) progression-free survival (PFS) or initiation of secondary treatment after surgery as surrogate endpoints for metastasis and prostate cancer death. These specific endpoints were used to identify associated event numbers (e.g., BCR > 30%) in selected clinical cohorts, which are particularly relevant for multivariate data analysis.

[0035] In the evaluation conducted, standard methods of multivariate analysis, such as Cox regression and Kaplan-Meier survival analysis, were selected to examine the additive and independent value of continuous and / or categorical "PDE4D7 scores" compared with established prognostic clinical variables, such as PSA and Gleason score (Alves de Inda, 2018). Logistic regression analysis was used to construct risk models combining the "PDE4D7 score" with either preoperative or postoperative clinical predictors of postoperative progression. The resulting models were subsequently tested in multiple independent patient cohorts using Kaplan-Meier survival and receiver operating characteristic curve analysis to predict posttreatment progression-free survival (Alves de Inda, 2018).

[0036] Using such a strategy, we began examining the prognostic value of PDE4D7 scores in retrospectively collected biopsies from resected prostate tissue in a consecutively managed cohort of patients from a single surgical center in the postoperative setting (Alves de Inda, 2018). The patient population included approximately 500 individuals with long-term follow-up for both pathological and biological outcomes. These clinical data were available for all patients and were collected during a median follow-up of 120 months after surgery. The PDE4D7 scores were determined as described above and then examined in both univariate and multivariate analyses using available postoperative covariates (i.e., pathological Gleason score, pT stage, surgical margin status, seminal vesicle invasion status, and lymph node involvement status) to adjust for multivariable settings. In this example, biochemical progression-free survival after primary intervention was set as the clinical endpoint to be evaluated. Univariate analysis of these clinical samples (Alves de Inda, 2018) showed an inverse correlation between PDE4D7 expression (in terms of the "PDE4D7 score") and postoperative biological recurrence (HR per unit change = 0.53; 95% CI 0.41-0.67; p<0.0001), confirming previous data (Boettcher 2015; Boettcher, 2016). In multivariate analysis with such clinical variables, the "PDE4D7 score" remained an independent and valid means for predicting clinical outcome (HR per unit change = 0.56; 95% CI 0.43-0.73; p<0.0001). Furthermore, in multivariate analysis, highly similar outcomes were observed when the "PDE4D7 score" was assessed using the validated and clinically used risk model CAPRA-S (HR=0.54 95% CI 0.42-0.69; p<0.0001). The CAPRA-S score, based on preoperative PSA and pathological parameters determined at the time of surgery, was developed to provide physicians with information aimed at helping predict disease recurrence, including BCR, systemic progression, and PCSM, and has been validated in US and other populations.

[0037] Interestingly, when assessing hazard ratios (HRs) compared with the continuous PDE4D7 score, we found a linear increase in risk with decreasing PDE4D7 score for score values ​​between 2 and 5. However, for PDE4D7 scores below 2, the risk of postoperative progression increases sharply (Alves de Inda, 2018). This is also evident in Kaplan-Meier survival curves, where patients classified in the lowest PDE4D7 score category have the highest risk of disease recurrence. We used logistic regression analysis to combine the CAPRA-S score with the continuous PDE4D7 score. We tested this model using receiver operating characteristic curve analysis and found a significant improvement in area under the curve (AUC) of 4–6% compared with CAPRA-S alone for both 2- and 5-year prediction of progression after treatment for BCR. Therefore, we evaluated a Cox regression combination model of the combined CAPRA-S and PDE4D7 score in Kaplan-Meier survival analyses and compared it with the CAPRA-S score categories alone. Taking this into consideration, we confirmed the added value in risk prediction when using a model of the combined “PDE4D7&CAPRA-S” score compared to using the clinical metric of the CAPRA-S score alone ( Alves de Inda, 2018 ).

[0038] Following a prostate cancer diagnosis, accurate risk assessment is necessary before stratification to a defined primary treatment. With this in mind, we examined the translatability of the prognostic use of the PDE4D7 score in the preoperative setting, examining tumor tissue obtained from diagnostic needle biopsy samples (van Strijp 2018). Here, needle biopsies were performed on 168 patients from a single diagnostic clinical center who underwent surgery as a primary treatment. The minimum follow-up period for each patient was 60 months after this intervention. In multivariable analyses, clinical covariates used to adjust for the PDE4D7 score included age at surgery, preoperative PSA, PSA density, biopsy Gleason score, percentage of tumor-positive biopsy cores, percentage of tumor in the biopsy, and clinical cT stage. Here, we evaluated the utility of the PDE4D7 score and the combined PDE4D7 and CAPRA scores compared with the preoperative CAPRA score in a Cox regression analysis for biochemical recurrence (van Strijp 2018).

[0039] Evaluating this patient cohort, we found that the PDE4D7 score was inversely associated with BCR in multivariate analysis, adjusting for clinical variables (HR = 0.43; 95% CI 0.29-0.63; p < 0.0001) and clinical CAPRA score (HR = 0.53; 95% CI 0.38-0.74; p = 0.0001) (van Strijp 2018). Kaplan-Meier analysis, as previously reported, showed that the PDE4D7 score category was significantly associated with BCR progression-free survival (log-rank p = 0.0001) and secondary treatment-free survival (log-rank p = 0.01) in the postoperative setting. A combined logistic regression model developed in the previous cohort was then used (van Strijp 2018). This combined CAPRA and PDE4D7 score demonstrated that patients in the highest combined CAPRA & PDE4D7 score category were virtually free from risk of postoperative biochemical progression or transition to any secondary treatment. This logistic regression model was also evaluated using receiver operating characteristic curve analysis to predict 5-year postoperative BCR. This revealed a 5% increase in area under the curve compared with the CAPRA score alone (AUC = 0.82 vs. 0.77, respectively; p = 0.004). Decision curve analysis of the combined CAPRA & PDE4D7 score model to determine whether to perform an intervention (e.g., surgery) based on an individual patient's risk threshold for experiencing postoperative disease progression confirmed a superior net benefit of using this combined score compared with either score alone across all decision thresholds (van Strijp 2018).

[0040] Predicting treatment outcomes is very complex because many factors contribute to treatment efficacy and disease recurrence. Important factors may not yet be identified, and the influence of other factors cannot be precisely determined. Multiple clinicopathological measurements are currently being investigated and applied in clinical practice to improve response prediction and treatment selection, and have brought about some improvements. Nevertheless, better prediction of treatment response is still strongly needed to increase the success rate of these treatments.

[0041] The identification and role of PDE4D-related genes is described in WO2022 / 0431210, which is incorporated herein by reference in its entirety.

[0042] Ideally, the expression level of PDE4D isoform 7 is therefore used in prognostic model, but depending on the detection method, isoform-specific expression data is not always available.In such cases, only the general expression level covering all PDE4D isoforms is available.Therefore, PDE4D7-specific gene signature (PDE4D7-related gene) is used, which is based on the expression level of the gene that is specifically correlated with PDE4D7 isoform expression level.

[0043] immune response defense genes The integrity and stability of genomic DNA are permanently subject to stresses induced by various intracellular and extracellular 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, pages 33-46, 2017). To maintain DNA structure and stability, cells must be able to recognize all types of DNA damage, such as single- or double-strand breaks, induced 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.

[0044] Recent evidence indicates that mislocalized DNA (e.g., DNA that aberrantly appears in the cytoplasmic compartment of a cell 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 the DNA recognition pathway. In diseased cells, cytoplasmic DNA sensor proteins have been demonstrated to be involved in the detection of aberrantly occurring DNA in the cell cytoplasm. Detection of such DNA by different nucleic acid sensors triggers similar responses resulting in nuclear factor kappa-B (NF-κB) and type I interferon (type I IFN) signaling, subsequently activating components of the innate immune system. While recognition of viral DNA is known to induce type I IFN responses, evidence has recently emerged that sensing DNA damage can also initiate immune responses.

[0045] Endosome-localized Toll-like receptor 9 (TLR9) was one of the first DNA sensor molecules identified to be involved in immune recognition of DNA through downstream signaling via the adaptor protein, myeloid differentiation primary response protein 88 (MYD88). This interaction then activates mitogen-activated protein kinase (MAPK) and NF-κB. TLR9 also induces the production of type I interferons by activating IRF7 via IkB kinase alpha (IKKalpha) in plasmacytoid dendritic cells (pDCs). Various other DNA immunoreceptors, including IFI16 (IFN-gamma-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-κB 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 with TRID to induce NF-κB and IRF-3 / 7, whereas the DHX9 helicase is involved in plasma cell proliferation. It stimulates MYD88-dependent signaling in cytoplasmic dendritic cells. The DNA sensor, LRRFIP1 (leucine-rich repeat flightless-interacting protein), complexes with beta-catenin to activate IRF3 transcription, while AIM2 (melanoma deficiency factor 2) recruits the adaptor protein ASC (apoptosis-associated speck-like protein), inducing caspase-1-activated inflammasome complexes and resulting in the secretion of interleukin-1 beta (IL-1 beta) and IL-18. (See Figure 1 in 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-κB, and interferon response (light green) are shown.)

[0046] The factors and mechanisms responsible for the activation of DNA sensor pathways in cancer are currently not fully understood. It is important to identify intratumoral DNA species, sensors, and pathways involved in IFN expression in different cancer types at all stages of the disease. In addition to being therapeutic targets in cancer, such factors may 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 DNA sensor pathways in the pathogenesis of cancer, autoimmunity, and potentially other diseases.

[0047] The identification and role of immune response defense genes is described in WO2021 / 175746, which is incorporated herein by reference in its entirety.

[0048] T cell receptor signaling genes Immune responses to pathogens can be triggered at various levels: there is a physical barrier, such as the skin, that keeps out invaders. If this is breached, the innate immune system kicks in; an initial, fast, non-specific response. If this is insufficient, the adaptive immune response is triggered. This 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, and are also responsible for maintaining memory to respond more quickly the next time the same pathogen is encountered.

[0049] Once activated, lymphocytes are highly specific and efficient, so they 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, or immune deficiencies and cancer, respectively.

[0050] 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, whereas 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 a suppressive 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, pages 1009-1016 (2011), specifically, T cell activation and its modulation by PKA in Figure 4, and Tasken K. and Ruppelt A., "Negative regulation of T-cell receptor activation by the cAMP-PKA-Csk signaling pathway in T-cell lipid rafts", Front Biosci, Vol. 11, pages 2929-2939 (2006)).

[0051] T cell activation can occur in naive and differentiated T cells. At the molecular level, events following ligation of the TCR with cognate antigen and crosstalk with signal transduction induced by costimulatory and co-inhibitory receptors determine whether a T cell is activated or anergic. Triggering the TCR itself is not sufficient; this results in T cell anergy. The B7:CD28 family of costimulatory molecules plays a central role in regulating the activation state of T cells upon antigen stimulation (Torheim EA, "Immunity Leashed - Mechanisms of Regulation in the Human Immune System," Thesis for the degree of Doctor of Philosophy (PhD), The Biotechnology Centre of Ola, University of Oslo, Norway, 2009).

[0052] Activation occurs upon interaction of the TCR on the surface of T cells with MHC-peptide complexes on APCs or target cells (Figure 2). An immune synapse is formed, activating lipid rafts in the T cell membrane fusion and Lck and Fyn. These molecules phosphorylate ITAMs on the CD3 subunit of the TCR, promoting the recruitment of Zap-70 in close proximity to Lck. Lck phosphorylates and activates Zap-70, which then phosphorylates LAT, SLP76, and PLCγ1. LAT is a docking site for other signaling molecules and is essential for downstream TCR signaling. Grb2, Gads, PI3K, and NCK are recruited to LAT, increasing signaling, including activation of RAS and PKC, Ca2+ recruitment, calcineurin recruitment, and actin cytoskeleton multimerization (Tasken et al. Front in Bioscience 11:2929-2939 (2006)). This ultimately leads to the activation of transcription factors of the NFκB, NFAT, AP1, and ATF families, resulting in the transcription of genes for immune activation (Mosenden et al. Cell Signalling 23:1009-1016 (2011); Tasken et al. Front in Bioscience 11:2929-2939 (2006)).

[0053] PKA- and PDE4-regulated signaling intersects with TCR-induced T cell activation, fine-tuning its regulation with opposing effects (Abrahamsen H. et al., "TCR- and CD28-mediated recruitment of phosphodiesterase 4 to lipid rafts potentiates TCR signaling," J Immunol, Vol. 173, pages 4847-4848 (2004); specifically, see Figure 6, which illustrates 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, and Tasken K. and Ruppelt A., 2006, supra). At the functional level, increased levels of cAMP result in 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 effectors (see Figure 15 in Torheim EA, 2009, supra).

[0054] In naive T cells, hyperphosphorylated PAG targets Csk to lipid rafts. PKA targets Csk via the ezrin-EBP50-PAG scaffolding complex. Specifically, PKA phosphorylates Csk, which negatively regulates Lck and Fyn, attenuating their activity and downregulating T cell activation (see Figure 6 in Abrahamsen H. et al., 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, ibid., and Tasken K. and Ruppelt A., 2006, supra).

[0055] Conversely, combined stimulation of TCR and CD28 mediates the recruitment of the cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, which enhances cAMP degradation (see Figure 6 in Abrahamsen H. et al., 2004, 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).

[0056] 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 controlled to prevent the development of autoimmune disorders, immunodeficiencies, and cancer.

[0057] The identification and role of T cell receptor signaling genes is described in WO2021 / 175986, which is incorporated herein by reference in its entirety.

[0058] Gene selection Gene signatures and combinations of these signatures with clinical parameters have been identified, and the resulting models are believed to show significant associations with mortality and therefore improve prediction of the efficacy of these treatments.

[0059] The identified immune defense response genes, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, were identified as follows: A group of 538 prostate cancer patients was treated with RP, and prostate cancer tissues were archived along with clinical parameters (e.g., pathological Gleason grade group (pGGG) and pathological status (pT stage)) and related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiotherapy (SRT), salvage androgen deprivation therapy (SADT), and 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. Next, the RNASeq expression data (TPM - transcription per million) of 538 prostate cancer subjects was examined for differential gene expression between PDE4D7 score class 1 and class 4. Specifically, 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 average expression of one TPM in each of the four PDE4D7 score classes. These 637 genes were then further subjected to molecular pathway analysis to obtain various enriched annotation clusters. Annotation cluster #2 showed enrichment in 30 genes (enrichment score: 10.8) with functions in defense responses against viruses, negative regulation of viral genome replication, and type I interferon signaling.Further heatmap analysis confirmed that these immune defense response genes were generally more highly expressed in samples from patients with 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 antiviral defense responses, negative control of viral genome replication, and type I interferon signaling to 61 genes. Further selection of the 61 genes based on combinatorial power was performed to separate patients who died from prostate cancer from those who did not, resulting in a preferred set of 14 genes. The number of events (metastasis, prostate cancer-specific death) was found to be increased in the subcohort with low expression of these genes compared with the total patient cohort (#538) and a subcohort of 151 patients receiving salvage RT (SRT) after postoperative stage recurrence.

[0060] 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) and pathological status (pT stage)) and related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiotherapy (SRT), salvage androgen deprivation therapy (SADT), and 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. Next, the RNASeq expression data (TPM - transcription per million) of 538 prostate cancer subjects was examined for differential gene expression between PDE4D7 score class 1 and class 4. Specifically, 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 average expression of one TPM in each of the four PDE4D7 score classes. These 637 genes were then further subjected to molecular pathway analysis to obtain 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 with PDE4D7 score class 1 than in samples from patients with PDE4D7 score class 4.

[0061] The identified PDE4D7-related 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. Correlations between the expression of any of these genes and PDE4D7 in the 571 samples were 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 coefficients were the PDE4D7 score (see Alves de Inda M. et al., 2018, supra) and the RNA-seq-determined TPM gene expression values ​​for each gene of interest (see below).

[0062] The largest negative correlation coefficient identified between the expression of any of approximately 60,000 transcripts and PDE4D7 expression was -0.38, while the largest positive correlation coefficient identified between the expression of any of approximately 60,000 transcripts and PDE4D7 expression 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 fitting these characteristics were identified. From these 77 transcripts, eight PDE4D7-related 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 studied 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.

[0063] This document shows that PDE4D7-related genes are also predictive of the outcome of prostate cancer subjects, preferably the outcome of neoadjuvant androgen deprivation therapy.Since a set of correlated PDE4D7 genes is identified as specifically responding to PDE4D7 expression level, it is further theorized that PDE4D7 expression level itself has the same predictive effect.The examples further demonstrate that a subset of three genes selected from PDE4D7-related genes is sufficient to predict the outcome of neoadjuvant ADT (Example 3 and Figures 5 to 14).The examples further support the hypothesis that BCR is predicted based on PDE4D (total) expression level, and therefore, long-term isoform-specific expression levels (e.g., PDE4D5, PDE4D7, and / or PDE4D9) similarly predict BCR (Example 5 and Figures 17 and 18, and comparative Figure 19).

[0064] Thus, in a first embodiment, the present invention provides a method of predicting a prostate cancer subject's response to neoadjuvant androgen deprivation treatment, the method comprising: the gene expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or the expression levels of three or more genes selected from the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; determining a gene expression profile or receiving a result of determining a gene expression profile, comprising: determining the gene expression profile in a biological sample obtained from the subject; determining a prediction of outcome based on the gene expression profile, the prediction being a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; The present invention provides a method comprising:

[0065] Optionally, the method further comprises providing a prediction of the outcome to a medical caregiver or the subject.

[0066] In one embodiment, the present invention provides a computer-implemented method for predicting a prostate cancer subject's response to neoadjuvant androgen deprivation treatment, the method comprising: the gene expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or the expression levels of three or more genes selected from the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; receiving the results of determining the gene expression profile, comprising: determining the gene expression profile in a biological sample obtained from the subject; determining a prediction of outcome based on the gene expression profile, the prediction being a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; Optionally, the method further comprises providing a prediction of the outcome to a healthcare caregiver or the subject.

[0067] In an alternative embodiment, the present invention provides a method of treating a subject having prostate cancer, the method comprising: the gene expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or the expression levels of three or more genes selected from the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; determining a gene expression profile or receiving a result of determining a gene expression profile, comprising: determining the gene expression profile in a biological sample obtained from the subject; determining a prediction of outcome based on the gene expression profile, the prediction is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; If the prediction is that a neoadjuvant androgen deprivation treatment is preferred, it is administered to the subject prior to the topical treatment; if the prediction is that neoadjuvant androgen deprivation treatment is undesirable, then it is not administered to the subject prior to the topical treatment. In one embodiment, the local treatment is radical prostatectomy (RP) or radiation therapy (RT).

[0068] In one embodiment, the three or more PDE4D7-related genes are or include a combination of three PDE4D7-related genes disclosed in Tables 4-13 below.

[0069] The inventors found that using the eight-gene signature of PDE4D7-related gene set produces very good prediction of NADT outcome (AUC of 0.85 and 0.92 in two independent data sets), as demonstrated in Examples 1 and 2, which correspond to Figures 3 and 4. Furthermore, the inventors tested 10 random selections of three PDE4D7-related genes, and each of these selections showed good to very good prediction, with an average AUC of 0.8, and the lowest-scoring model still had a well-accepted AUC of 0.718.From these data, the inventors confirmed that any random selection of three genes can be used to predict NADT outcome, and therefore concluded that the present invention is not limited to the eight-gene signature or specific three-gene selection presented herein.

[0070] The present inventors have found that BCR can be reliably predicted solely based on PDE4D (total) expression levels, and have theorized that it is largely due to long-term isoform expression levels such as PDE4D5, PDE4D7 and PDE4D9. Thus, in one embodiment, the present invention provides a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, comprising: receiving the results of determining the gene expression level or determining the gene expression profile of PDE4D, determining the gene expression level in a biological sample obtained from the subject; determining a prediction of outcome based on gene expression levels, the prediction being a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; Preferably, the prediction is the chance of biochemical recurrence.

[0071] In one embodiment, the present invention provides a method of predicting a prostate cancer subject's response to neoadjuvant androgen deprivation treatment, the method comprising: determining gene expression or receiving results of determining gene expression levels of a PDE4D isoform selected from PDE4D5, PDE4D7 and / or PDE4D9, or a combination thereof; determining the gene expression level in a biological sample obtained from the subject; determining a prediction of outcome based on gene expression levels, the prediction being a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; Preferably, the prediction is the chance of biochemical recurrence. In one embodiment, the method is based on PDE4D5 expression levels. In one embodiment, the method is based on PDE4D7 expression levels. In one embodiment, the method is based on PDE4D9 expression levels. In one embodiment, the method is based on PDE4D5 and PDE4D7 gene expression levels. In one embodiment, the method is based on PDE4D5 and PDE4D9 gene expression levels. In one embodiment, the method is based on PDE4D7 and PDE4D9 gene expression levels. In one embodiment, the method is based on PDE4D5, PDE4D7, and PDE4D9 gene expression levels.

[0072] As used herein, the term "PDE4D expression" or "total PDE4D7 expression" refers to the expression level of the PDE4D gene, which does not distinguish between different isoforms and is therefore the accumulation of all expressed isoforms detected.

[0073] In one embodiment, the gene expression profile includes PDE4D expression levels and one or more, e.g., 1, 2, 3, 4, 5, 6, 7, or all, of the PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. In one embodiment, the gene expression profile includes three or more, e.g., 3, 4, 5, 6, 7, or all, of the PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. Instead of total PDE4D expression levels, the gene expression profile may use one or more isoform-specific expression levels selected from PDE4D5, PDE4D7, and / or PDE4D9, or a combination thereof.

[0074] It was theorized that the model could be improved by including one or more expression levels from a set of immune defense response genes and / or T cell receptor signaling genes. Thus, in one aspect of the present disclosure, the gene expression profile is: an immune defense response gene selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; and / or T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 The method further comprises one or more gene expression levels selected from:

[0075] In the present invention, prostate cancer-related death is preferably prostate cancer-specific death.

[0076] With respect to the above biological processes, three immune system-related gene signatures were selected, including the genes listed in Tables 1 to 3. Previously, the relevance of these signatures to the prediction of prostate cancer survival was shown.

[0077] [Table 1]

[0078] [Table 2]

[0079] [Table 3]

[0080] It has been found that the genes AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, ZBP1, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, ZAP70, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, each individually, one or more per gene panel, in combination one or more per gene panel, or all combined, can predict outcome in subjects with prostate cancer.

[0081] The term "ABCC5" refers to the nucleotide sequence 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, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 3 or SEQ ID NO: 4, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001018881.1 and NCBI Protein Accession Reference Sequence NP_005679 that encodes the ABCC5 polypeptide.

[0082] 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 set forth in SEQ ID NO:1 or SEQ ID NO:2, or a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:3 or SEQ ID NO:4. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:3 or SEQ ID NO:4, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:1 or SEQ ID NO:2.

[0083] The term "AIM2" refers to the nucleotide sequence set forth in SEQ ID NO: 5, which corresponds to the sequence defined in the NCBI Reference Sequence NM_004833, specifically the NCBI Reference Sequence for the AIM2 transcript shown above, of the Absent in Melanoma 2 gene (Ensembl: ENSG00000163568), and also to the corresponding amino acid sequence, set forth in SEQ ID NO: 6, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_004824 that encodes the AIM2 polypeptide.

[0084] The term "AIM2" also refers to a nucleotide sequence that exhibits high homology to AIM2, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:5, or an AIM sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:6. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:6, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:5.

[0085] The term "APOBEC3A" refers to the nucleotide sequence 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, specifically the sequence of the NCBI Reference Sequence for the APOBEC3A transcript shown above, and also to the corresponding amino acid sequence, e.g., 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.

[0086] 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 set forth in SEQ ID NO:7, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:8. This includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:8, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:7.

[0087] The term "CD2" refers to the nucleotide sequence 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 sequence of the NCBI Reference Sequence for the CD2 transcript shown above, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 10, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001758 encoding the CD2 polypeptide.

[0088] 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 set forth in SEQ ID NO:9, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:10. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:10, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:9.

[0089] The term "CD247" refers to the nucleotide sequence set forth in SEQ ID NO: 11 or SEQ ID NO: 12 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 sequence of the NCBI Reference Sequence for the CD247 transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 13 or SEQ ID NO: 14, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000725 and NCBI Protein Accession Reference Sequence NP_932170 that encodes the CD247 polypeptide.

[0090] 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 set forth in SEQ ID NO: 11 or SEQ ID NO: 12, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 13 or SEQ ID NO: 14. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:13 or SEQ ID NO:14, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:11 or SEQ ID NO:12.

[0091] The term "CD28" refers to the nucleotide sequence set forth in SEQ ID NO: 15 or SEQ ID NO: 16 of the Cluster of Differentiation 28 gene (Ensembl: ENSG00000178562), e.g., the sequence defined in NCBI Reference Sequence NM_006139 or NCBI Reference Sequence NM_001243078, in particular the sequence of the NCBI Reference Sequence for the CD28 transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 17 or SEQ ID NO: 18, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_006130 and NCBI Protein Accession Reference Sequence NP_001230007 that encodes the CD28 polypeptide.

[0092] 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 set forth in SEQ ID NO: 15 or SEQ ID NO: 16, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 17 or SEQ ID NO: 18. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:17 or SEQ ID NO:18, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:15 or SEQ ID NO:16.

[0093] The term "CD3E" refers to the nucleotide sequence 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 sequence of the NCBI Reference Sequence for the CD3E transcript shown above, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 20, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000724 encoding the CD3E polypeptide.

[0094] The term "CD3E" also refers to a nucleotide sequence that exhibits high homology to CD3E, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 19, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 20. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:20, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:19.

[0095] The term "CD3G" refers to the nucleotide sequence 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, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 22, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000064 encoding the CD3G polypeptide.

[0096] 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 set forth in SEQ ID NO:21, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:22. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:22, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:21.

[0097] The term "CD4" refers to the nucleotide sequence 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 sequence of the NCBI Reference Sequence for the CD4 transcript shown above, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 24, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_000607 that encodes the CD4 polypeptide.

[0098] The term "CD4" also refers to a nucleotide sequence that exhibits high homology to CD4, e.g., a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:23, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:24. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:24, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:23.

[0099] The term "CIAO1" refers to the nucleotide sequence set forth in SEQ ID NO: 25, which corresponds to the sequence of the Cytosolic Iron-Sulfer Assembly Component 1 gene (Ensembl: ENSG00000144021), e.g., as defined in NCBI Reference Sequence NM_004804, in particular the sequence of the NCBI Reference Sequence for the CIAO1 transcript shown above, and also 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 that encodes the CIAO1 polypeptide.

[0100] The term "CIAO1" also refers to a nucleotide sequence that exhibits high homology to CAIO1, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:25, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:26. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:26, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:25.

[0101] The term "CSK" refers to the nucleotide sequence 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 sequence of the NCBI Reference Sequence for the CSK transcript shown above, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 28, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_004374 encoding the CSK polypeptide.

[0102] 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 set forth in SEQ ID NO:27, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:28. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:28, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:27.

[0103] The term "CUX2" refers to the nucleotide sequence set forth in SEQ ID NO: 29, which corresponds to the sequence defined in the NCBI Reference Sequence NM_015267.3 of the human Cut Like Homeobox 2 gene (Ensembl: ENSG00000111249), for example, in the NCBI Reference Sequence for the CUX2 transcript shown above, and also to the corresponding amino acid sequence, for example set forth in SEQ ID NO: 30, which corresponds to the protein sequence defined in the NCBI Protein Accession Reference Sequence NP_056082.2 that encodes the CUX2 polypeptide.

[0104] 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 set forth in SEQ ID NO:29, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:30. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:30, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:29.

[0105] The term "DDX58" refers to the nucleotide sequence set forth in SEQ ID NO: 31, which corresponds to the sequence defined in NCBI Reference Sequence NM_014314, specifically the NCBI Reference Sequence for the DDX58 transcript shown above, of the DExD / H-box Helicase 58 gene (Ensembl: ENSG00000107201), and also to the corresponding amino acid sequence, set forth in SEQ ID NO: 32, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_055129 encoding the DDX58 polypeptide.

[0106] 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 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 set forth in SEQ ID NO: 32. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:32, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:31.

[0107] The term "DHX9" refers to the nucleotide sequence set forth in SEQ ID NO: 33 of the DExD / H-box Helicase 9 gene (Ensembl: ENSG00000135829), e.g., the sequence defined in NCBI Reference Sequence NM_001357, in particular the sequence of the NCBI Reference Sequence for the DHX9 transcript shown above, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 34, corresponding to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001348 encoding the DHX9 polypeptide.

[0108] 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 set forth in SEQ ID NO: 33, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 34. The present invention also includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:34, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:33.

[0109] The term "EZR" refers to the nucleotide sequence 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, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 36, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_003370 that encodes the EZR polypeptide.

[0110] 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 set forth in SEQ ID NO:35, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:36. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:36, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:35.

[0111] The term "FYN" refers to the nucleotide sequence set forth in SEQ ID NO: 37, SEQ ID NO: 38 or SEQ ID NO: 39, which corresponds to 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, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 40, SEQ ID NO: 41 or SEQ ID NO: 42, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_002028, NCBI Protein Accession Reference Sequence NP_694592 and NCBI Protein Accession Reference Sequence XP_005266949 that encodes the FYN polypeptide.

[0112] The term "FYN" also refers to a nucleotide sequence that exhibits high homology to FYN, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: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 set forth in SEQ ID NO:40, SEQ ID NO:41, or SEQ ID NO:42. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:40, SEQ ID NO:41, or SEQ ID NO:42, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:37, SEQ ID NO:38, or SEQ ID NO:39.

[0113] The term "IFI16" refers to the nucleotide sequence set forth in SEQ ID NO: 43, which corresponds to the sequence of the Interferon Gamma Inducible protein 16 gene (Ensembl: ENSG00000163565), e.g., as defined in NCBI Reference Sequence NM_005531, in particular the sequence of the NCBI Reference Sequence for the IFI16 transcript shown above, and also 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 encoding the IFI16 polypeptide.

[0114] The term "IFI16" also refers to a nucleotide sequence that exhibits high homology to IFI16, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 43, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 44. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:44, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:43.

[0115] The term "IFIH1" refers to the nucleotide sequence set forth in SEQ ID NO: 45, which corresponds to the sequence of the Interferon Inducible With Helicase C Domain 1 gene (Ensembl: ENSG00000115267), e.g., as defined in NCBI Reference Sequence NM_022168, in particular the sequence of the NCBI Reference Sequence for the IFIH1 transcript shown above, and also 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 encoding the IFIH1 polypeptide.

[0116] 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 set forth in SEQ ID NO:45, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:46. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:46, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:45.

[0117] The term "IFIT1" refers to the nucleotide sequence set forth in SEQ ID NO: 47 or SEQ ID NO: 48 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 sequence of the NCBI Reference Sequence for the IFIT1 transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 49 or SEQ ID NO: 50, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001257858 and NCBI Protein Accession Reference Sequence NP_001539 that encodes the IFIT1 polypeptide.

[0118] The term "IFIT1" also refers to a nucleotide sequence that exhibits high homology to IFIT1, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:47 or SEQ ID NO:48, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:49 or SEQ ID NO:50. The present invention also includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:49 or SEQ ID NO:50, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:47 or SEQ ID NO:48.

[0119] The term "IFIT3" refers to the nucleotide sequence 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., as defined in NCBI Reference Sequence NM_001031683, in particular the sequence of the NCBI Reference Sequence for the IFIT3 transcript shown above, and also 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 encoding the IFIT3 polypeptide.

[0120] The term "IFIT3" 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 set forth in SEQ ID NO: 51, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 52. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:52, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:51.

[0121] The term "KIAA1549" refers to the nucleotide sequence 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 for the KIAA1549 transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 55 or SEQ ID NO: 56, for example, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_065961 and NCBI Protein Accession Reference Sequence NP_001158137 encoding the KIAA1549 polypeptide.

[0122] The term "KIAA1549" also refers to a nucleotide sequence that exhibits high homology to KIAA1549, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 53 or SEQ ID NO: 54, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 55 or SEQ ID NO: 56. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:55 or SEQ ID NO:56, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:53 or SEQ ID NO:54.

[0123] The term "LAT" refers to the nucleotide sequence set forth in SEQ ID NO: 57 or SEQ ID NO: 58 of the Linker For Activation Of T-Cells gene (Ensembl: ENSG00000213658), e.g., the sequence defined in NCBI Reference Sequence NM_001014987 or NCBI Reference Sequence NM_014387, in particular the sequence of the NCBI Reference Sequence for the LAT transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 59 or SEQ ID NO: 60, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001014987 and NCBI Protein Accession Reference Sequence NP_055202, which encodes the LAT polypeptide.

[0124] The term "LAT" also refers to a nucleotide sequence that exhibits 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 set forth in SEQ ID NO:57 or SEQ ID NO:58, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:59 or SEQ ID NO:60. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:59 or SEQ ID NO:60, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:57 or SEQ ID NO:58.

[0125] The term "LCK" refers to the nucleotide sequence set forth in SEQ ID NO: 61, which corresponds to the sequence of the LCK Proto-Oncogene gene (Ensembl: ENSG00000182866), e.g., the sequence defined in NCBI Reference Sequence NM_005356, in particular the sequence of the NCBI Reference Sequence for the LCK transcript shown above, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 62, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_005347 that encodes the LCK polypeptide.

[0126] 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 set forth in SEQ ID NO: 61, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 62. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:62, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:61.

[0127] The term "LRRFIP1" stands for LRR Binding FLII Interacting Protein 1 gene (Ensembl: ENSG00000124831), for example, the sequence defined in NCBI Reference Sequence NM_004735, NCBI Reference Sequence NM_001137550, NCBI Reference Sequence NM_001137553, or NCBI Reference Sequence NM_001137552, specifically the nucleotide sequence set forth in SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, or SEQ ID NO: 66, which corresponds to the sequence of the NCBI Reference Sequence for the LRRFIP1 transcript set forth above; and also the corresponding amino acid sequence set forth in SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, or SEQ ID NO: 70, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_004726, NCBI Protein Accession Reference Sequence NP_001131022, NCBI Protein Accession Reference Sequence NP_001131025, and NCBI Protein Accession Reference Sequence NP_001131024, which encodes the LRRFIP1 polypeptide.

[0128] The term "LRRFIP1" also refers to a nucleotide sequence that exhibits high homology to LRRFIP1, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:63, SEQ ID NO:64, SEQ ID NO:65, or SEQ ID NO:66, or a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:67, SEQ ID NO:68, SEQ ID NO:69, or SEQ ID NO:70. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:67, SEQ ID NO:68, SEQ ID NO:69, or SEQ ID NO:70, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:63, SEQ ID NO:64, SEQ ID NO:65, or SEQ ID NO:66.

[0129] The term "MYD88" refers to the MYD88 Innate Immune Signal Transduction Adaptor gene (Ensembl: ENSG00000172936), e.g., the sequence defined in NCBI Reference Sequence NM_001172567, NCBI Reference Sequence NM_001172568, NCBI Reference Sequence NM_001172569, NCBI Reference Sequence NM_001172566, or NCBI Reference Sequence NM_002468, specifically the nucleotide sequence set forth in SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, or SEQ ID NO: 75, which corresponds to the sequence of the NCBI Reference Sequence of the MYD88 transcript shown above; The present invention also relates to the corresponding amino acid sequences as set forth in SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79 or SEQ ID NO:80, which correspond to the protein sequences defined in NCBI Protein Accession Reference Sequence NP_001166038, NCBI Protein Accession Reference Sequence NP_001166039, NCBI Protein Accession Reference Sequence NP_001166040, NCBI Protein Accession Reference Sequence NP_001166037 and NCBI Protein Accession Reference Sequence NP_002459, encoding the peptides.

[0130] 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 set forth in SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ ID NO:74, or SEQ ID NO:75, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79, or SEQ ID NO:80. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:76, SEQ ID NO:77, SEQ ID NO:78, SEQ ID NO:79, or SEQ ID NO:80, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:71, SEQ ID NO:72, SEQ ID NO:73, SEQ ID NO:74, or SEQ ID NO:75.

[0131] The term "OAS1" refers to 2'-5'-Oligoadenylate Synthetase 1 gene (Ensembl: ENSG00000089127), for example, the sequence defined in NCBI Reference Sequence NM_001320151, NCBI Reference Sequence NM_002534, NCBI Reference Sequence NM_001032409 or NCBI Reference Sequence NM_016816, in particular the nucleotide sequence set forth in SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83 or SEQ ID NO: 84, which correspond to the sequence of the NCBI Reference Sequence for the OAS1 transcript shown above; and also the corresponding amino acid sequence set forth in SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87 or SEQ ID NO: 88, which correspond to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001307080, NCBI Protein Accession Reference Sequence NP_002525, NCBI Protein Accession Reference Sequence NP_001027581 and NCBI Protein Accession Reference Sequence NP_058132, which encodes the OAS1 polypeptide.

[0132] The term "OAS1" also refers to a nucleotide sequence that exhibits high homology to OAS1, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:81, SEQ ID NO:82, SEQ ID NO:83, or SEQ ID NO:84, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87, or SEQ ID NO:88. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:85, SEQ ID NO:86, SEQ ID NO:87 or SEQ ID NO:88, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:81, SEQ ID NO:82, SEQ ID NO:83 or SEQ ID NO:84.

[0133] The term "PAG1" refers to the nucleotide sequence 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 sequence of the NCBI Reference Sequence for the PAG1 transcript shown above, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 90, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_060910 encoding the PAG1 polypeptide.

[0134] The term "PAG1" 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 set forth in SEQ ID NO: 89, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 90. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:90, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:89.

[0135] The term "PDE4D" refers to human phosphodiesterase 4D gene (Ensembl:ENSG00000113448), e.g. NCBI reference sequence NM_001104631, NCBI reference sequence NM_001349242, NCBI reference sequence NM_00119721 8, NCBI reference sequence NM_006203, NCBI reference sequence NM_001197221, NCBI reference sequence NM_001197220, NCBI reference sequence NM_001197223, NCBI reference sequence NM_00 1165899 or NCBI Reference Sequence NM_001165899, specifically the nucleotide sequence set forth in SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98 or SEQ ID NO: 99, which correspond to the sequences of the NCBI Reference Sequences for the PDE4D transcripts set forth above, and which are also referred to as NCBI Protein Accession Reference Sequence NP encoding a PDE4D polypeptide. NCBI protein accession reference sequence NP_001098101, NCBI protein accession reference sequence NP_001336171, NCBI protein accession reference sequence NP_001184147, NCBI protein accession reference sequence NP_006194, NCBI protein accession reference sequence NP_001184150, NCBI protein accession reference sequence NP_001184149, NCBI protein accession reference sequence NP_001184152, NCBI protein accession reference sequence NP_001159371 and NCBI protein accession reference sequence NP_001184148, e.g. 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.

[0136] 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 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 amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences 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 that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107, SEQ ID NO:108, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:91, SEQ ID NO:92, SEQ ID NO:93, SEQ ID NO:94, SEQ ID NO:95, SEQ ID NO:96, SEQ ID NO:97, SEQ ID NO:98 or SEQ ID NO:99.

[0137] The term "PRKACA" refers to the nucleotide sequence set forth in SEQ ID NO: 109 or SEQ ID NO: 110 of the Protein Kinase cAMP-Activated Catalytic Subunit Alpha gene (Ensembl: ENSG00000072062), e.g., the sequence defined in NCBI Reference Sequence NM_002730 or NCBI Reference Sequence NM_207518, in particular the sequence of the NCBI Reference Sequence for the PRKACA transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 111 or SEQ ID NO: 112, e.g., 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.

[0138] The term "PRKACA" also refers to a nucleotide sequence that exhibits high homology to PRKACA, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:109 or SEQ ID NO:110, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:111 or SEQ ID NO:112. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:111 or SEQ ID NO:112, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:109 or SEQ ID NO:110.

[0139] 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_001242863, NCBI Reference Sequence NM_001242865, NCBI Reference Sequence NM_001242866, NCBI Reference Sequence NM_001242867, NCBI Reference Sequence NM_001300915, NCBI Reference Sequence NM_207578, NCBI Reference Sequence NM_001242868, NCBI Reference Sequence NM_001242869, NCBI Reference Sequence NM_001242870, NCBI Reference Sequence NM_001242871, NCBI Reference Sequence NM_001300915, NCBI Reference Sequence NM_207578, NCBI Reference Sequence NM_001242872, NCBI Reference Sequence NM_001242873, NCBI Reference Sequence NM_001242874, NCBI Reference Sequence NM_001242875, NCBI Reference Sequence NM_001242876, NCBI Reference Sequence M_001242857 or NCBI Reference Sequence NM_001300917, specifically the nucleotide sequence set forth in SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, SEQ ID NO: 118, SEQ ID NO: 119, SEQ ID NO: 120, SEQ ID NO: 121, SEQ ID NO: 122, or SEQ ID NO: 123, which correspond to the sequences of the NCBI Reference Sequences for the PRKACB transcripts set forth above, and which encode a PRKACB polypeptide, as set forth in NCBI Protein Accession Reference Sequence NP_002 722, NCBI protein accession reference sequence NP_891993, NCBI protein accession reference sequence NP_001229789, NCBI protein accession reference sequence NP_001229788, NCBI protein accession reference sequence NP_001229787, NCBI protein accession reference sequence NP_001229791, NCBI protein accession reference sequence NP_001229790, NCBI protein accession reference sequence NP_00128 7844, 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 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.

[0140] The term "PRKACB" also refers to a nucleotide sequence that exhibits high homology to PRKACB, such as 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 97%, 98%, or 99% identical, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133, or SEQ ID NO:134. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:124, SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133, or SEQ ID NO:134; or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:113, SEQ ID NO:114, SEQ ID NO:115, SEQ ID NO:116, SEQ ID NO:117, SEQ ID NO:118, SEQ ID NO:119, SEQ ID NO:120, SEQ ID NO:121, SEQ ID NO:122, or SEQ ID NO:123.

[0141] The term "PTPRC" refers to the nucleotide sequence set forth in SEQ ID NO: 135 or SEQ ID NO: 136, which corresponds to the sequence defined in the gene for Protein Tyrosine Phosphatase Receptor Type C (Ensembl: ENSG00000081237), e.g., in NCBI Reference Sequence NM_002838 or NCBI Reference Sequence NM_080921, in particular the NCBI Reference Sequence for the PTPRC transcript shown above, and also to the corresponding amino acid sequence 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_002829 and NCBI Protein Accession Reference Sequence NP_563578 that encodes a PTPRC polypeptide.

[0142] The term "PTPRC" also refers to a nucleotide sequence that exhibits high homology to PTPRC, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 135 or SEQ ID NO: 136, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 137 or SEQ ID NO: 138. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:137 or SEQ ID NO:138, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:135 or SEQ ID NO:136.

[0143] The term "RAP1GAP2" refers to the nucleotide sequence set forth in SEQ ID NO: 139, SEQ ID NO: 140 or SEQ ID NO: 141 of the human RAP1 GTPase Activating Protein 2 gene (ENSG00000132359), e.g., the sequence defined in NCBI Reference Sequence NM_015085, NCBI Reference Sequence NM_001100398 or NCBI Reference Sequence NM_001330058, in particular the sequence of the NCBI Reference Sequence for the RAP1GAP2 transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 142, SEQ ID NO: 143 or SEQ ID NO: 144, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_055900, NCBI Protein Accession Reference Sequence NP_001093868 and NCBI Protein Accession Reference Sequence NP_001316987, which encodes the RAP1GAP2 polypeptide.

[0144] The term "RAP1GAP2" also refers to a nucleotide sequence that exhibits high homology to RAP1GAP2, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 139, SEQ ID NO: 140, or SEQ ID NO: 141, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 142, SEQ ID NO: 143, or SEQ ID NO: 144. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:142, SEQ ID NO:143, or SEQ ID NO:144, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:139, SEQ ID NO:140, or SEQ ID NO:141.

[0145] The term "SLC39A11" refers to the nucleotide sequence set forth in SEQ ID NO: 145 or SEQ ID NO: 146 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 sequence of the NCBI Reference Sequence for the SLC39A11 transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 147 or SEQ ID NO: 148, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_631916 and NCBI Protein Accession Reference Sequence NP_001339621 that encodes the SLC39A11 polypeptide.

[0146] The term "SLC39A11" also refers to a nucleotide sequence that exhibits high homology to SLC39A11, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 145 or SEQ ID NO: 146, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 147 or SEQ ID NO: 148. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:147 or SEQ ID NO:148, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:145 or SEQ ID NO:146.

[0147] The term "TDRD1" refers to the nucleotide sequence set forth in SEQ ID NO: 149 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, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 150, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_942090 that encodes the TDRD1 polypeptide.

[0148] 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 set forth in SEQ ID NO: 149, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 150. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 150, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 149.

[0149] The term "TLR8" refers to the nucleotide sequence 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 for the TLR8 transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 153 or SEQ ID NO: 154, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_619542 and NCBI Protein Accession Reference Sequence NP_057694 that encodes the TLR8 polypeptide.

[0150] The term "TLR8" also refers to a nucleotide sequence that exhibits high homology to TLR8, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 151 or SEQ ID NO: 152, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 153 or SEQ ID NO: 154. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:153 or SEQ ID NO:154, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:151 or SEQ ID NO:152.

[0151] The term "VWA2" refers to the nucleotide sequence 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., as defined in NCBI Reference Sequence NM_001320804, in particular the sequence of the NCBI Reference Sequence for the VWA2 transcript shown above, and also 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 encoding the VWA2 polypeptide.

[0152] The term "VWA2" also refers to a nucleotide sequence that exhibits high homology to VWA2, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 155, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 156. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:156, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence set forth in SEQ ID NO:155.

[0153] The term "ZAP70" refers to the nucleotide sequence set forth in SEQ ID NO: 157 or SEQ ID NO: 158, which corresponds to the sequence 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, of the Zeta Chain of T-Cell Receptor Associated Protein Kinase 70 gene (Ensembl: ENSG00000115085), and also to the corresponding amino acid sequence 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.

[0154] The term "ZAP70" also refers to nucleotide sequences that exhibit high homology to ZAP70, such as a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 157 or SEQ ID NO: 158, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 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 set forth in SEQ ID NO:159 or SEQ ID NO:160, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:157 or SEQ ID NO:158.

[0155] The term "ZBP1" refers to the nucleotide sequence set forth in SEQ ID NO: 161, SEQ ID NO: 162 or SEQ ID NO: 163 of the Z-DNA Binding Protein 1 gene (Ensembl: ENSG00000124256), e.g., the sequence defined in NCBI Reference Sequence NM_030776, NCBI Reference Sequence NM_001160418 or NCBI Reference Sequence NM_001160419, in particular the NCBI Reference Sequence for the ZBP1 transcript shown above, and also to the corresponding amino acid sequence set forth in SEQ ID NO: 164, SEQ ID NO: 165 or SEQ ID NO: 166, e.g., the protein sequence defined in NCBI Protein Accession Reference Sequence NP_110403, NCBI Protein Accession Reference Sequence NP_001153890 and NCBI Protein Accession Reference Sequence NP_001153891, which encodes the ZBP1 polypeptide.

[0156] The term "ZBP1" also refers to a nucleotide sequence that exhibits high homology to ZBP1, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:161, SEQ ID NO:162, or SEQ ID NO:163, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:164, SEQ ID NO:165, or SEQ ID NO:166. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:164, SEQ ID NO:165, or SEQ ID NO:166, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:161, SEQ ID NO:162, or SEQ ID NO:163.

[0157] The term "PDE4D7" refers to the nucleotide sequence set forth in SEQ ID NO: 167, which corresponds to the sequence of the phosphodiesterase 4D isoform 7 gene (Ensembl: ENSG00000113448), e.g., the sequence defined in NCBI Reference Sequence NM_001364599, in particular the sequence of the NCBI Reference Sequence for the PDE4D7 transcript shown above, and also to the corresponding amino acid sequence, e.g., set forth in SEQ ID NO: 168, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_001351528 encoding the PDE4D7 polypeptide.

[0158] The term "PDE4D7" also refers to a nucleotide sequence that is highly homologous to PDE4D7, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 167, or at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO: 168. or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:168, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence set forth in SEQ ID NO:167.

[0159] As provided herein, the biological sample used may be collected in a clinically acceptable manner, for example, in a manner that preserves nucleic acids (particularly RNA) or proteins.

[0160] The biological sample(s) may include bodily tissues and / or bodily fluids, such as, but not limited to, blood, sweat, saliva, and urine. Furthermore, the biological sample may contain a cell extract or a cell population containing epithelial cells, such as cancerous epithelial cells or epithelial cells from tissue suspected to be cancerous. The biological sample may contain a cell population derived from tissue, such as a glandular tissue; for example, the sample may be derived from the subject's prostate. Additionally, if necessary, cells may be purified from the obtained bodily tissues and fluids and then used as a biological sample. In some implementations, the sample may be a tissue sample, a bodily fluid sample, a blood sample, a saliva sample, a sample containing circulating tumor cells, extracellular vesicles, a sample containing exosomes secreted by the prostate, or a cell line or cancer cell line. In a particular implementation, a biopsy or resection sample may be obtained and / or used. Such a sample may include cells or cell lysates.

[0161] Thus, in one embodiment, the biological sample obtained from the subject is a biopsy. In a further preferred embodiment, the method includes providing or obtaining a biopsy. In a preferred embodiment, the biopsy is a prostate biopsy, e.g., tissue or fluid from the prostate, or a prostate cancer biopsy.

[0162] It is also conceivable that the contents of the biological sample are subjected to a concentration step. For example, the sample may be contacted with a ligand specific for the cell membrane or organelles of a particular cell type, e.g., prostate cells, and functionalized with, for example, magnetic particles. The material concentrated by the magnetic particles may then be used for the detection and analysis steps described herein above or below.

[0163] Furthermore, cells, e.g., tumor cells, can be enriched through a filtration process of a body fluid 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.

[0164] The biological sample provided herein is preferably obtained from a subject prior to the initiation of treatment, and is also preferably a prostate sample or a prostate cancer sample.

[0165] Thus, in a preferred embodiment, the method according to the invention comprises a biological sample obtained from the subject before the start of treatment, preferably the biological sample is a prostate sample or a prostate cancer sample. Alternatively, the method according to the invention comprises the step of providing a biological sample obtained from the subject before the start of treatment, preferably the biological sample is a prostate sample or a prostate cancer sample.

[0166] It is contemplated herein that the outcome of a subject with prostate cancer may be favorable or unfavorable. In one aspect of the present invention, predicting the outcome of a subject with prostate cancer may result in determining the favorable or unfavorable risk of one or more specific outcomes. The outcome may include prostate cancer-related death, local recurrence and / or distant recurrence. Preferably, the prostate cancer-related death is prostate cancer-specific death.

[0167] Thus, the present method provides for predicting a subject's response to neoadjuvant androgen deprivation therapy. The goal of neoadjuvant androgen deprivation therapy is to eradicate malignant androgen-dependent cells and improve pathological outcomes and survival, with the hope that sufficient tumor regression will allow for complete resection of any remaining prostate cancer. Thus, for example, response can be measured as overall tumor size, tumor burden, number of tumor-positive lymph nodes, survival probability, overall survival time, cancer-free survival time, overall mortality rate, or cancer-specific mortality rate in response to neoadjuvant androgen deprivation therapy. Thus, response can be measured by increasing or decreasing the predicted overall survival time when neoadjuvant androgen deprivation therapy is administered to a patient compared to when neoadjuvant androgen deprivation therapy is not administered, and response can be measured by increasing or decreasing the predicted frequency of cancer-specific mortality when neoadjuvant androgen deprivation therapy is administered to a patient compared to when neoadjuvant androgen deprivation therapy is not administered. It is understood that other parameters listed above can be used to determine the outcome of neoadjuvant androgen deprivation therapy. Thus, in one embodiment, the prediction is based on at least one of: predicted residual tumor burden; predicted tumor size; predicted frequency of tumor metastasis; and / or predicted frequency of BCR. The prediction may indicate an increased or decreased frequency of occurrence. In one embodiment, the prediction is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy, wherein the favorable or unfavorable response is tumor size, tumor burden, number of tumor-positive lymph nodes, probability of survival, overall survival, cancer-free survival, overall mortality, or cancer-specific mortality. In one embodiment, the favorable or unfavorable outcome is an improved frequency of survival in response to treatment.

[0168] As used herein, tumor size refers to the volume of a tumor at a given time point after treatment. As used herein, tumor burden refers to the number of cancer cells, tumor size, or number of metastases at a given time point after treatment. As used herein, tumor metastasis frequency refers to the frequency of one or more metastases occurring from prostate cancer at a given time point after treatment. As used herein, the number of positive lymph nodes refers to the number of lymph nodes in a prostate cancer subject in which at least one tumor cell can be identified. Preferably, the cancer cells are cancer cells derived from prostate cancer. The positive lymph nodes can be local or peripheral. As used herein, survival probability refers to the frequency of a prostate cancer subject surviving at a given time point after treatment. As used herein, the term overall survival refers to the (average) length of survival from the date of collection of a biological sample from a subject. Cancer-free survival refers to the (average) time a subject survives without detectable cancer after the date of collection of a biological sample from the subject. As used herein, the term overall mortality refers to the (average) length of time from the date of collection of a biological sample from the subject to death. As used herein, the term cancer-specific mortality refers to the (average) length of time from the date a biological sample is taken from a subject to death resulting from prostate cancer (or its metastasis).

[0169] The inventors have further shown that the methods of predicting outcome described herein can be further improved by including clinical parameters. Examples of clinical parameters that have been shown to improve the method are: tumor size or volume; TMPRSS2-ERG fusion status; ERG and / or PTEN expression level(s); and / or the presence of metastases. Thus, in one embodiment, the prediction is further based on at least one of the following parameters: tumor size or volume; TMPRSS2-ERG fusion status; ERG and / or PTEN expression level(s); and / or the presence of metastases.

[0170] The ETS-related gene (ERG) is a member of the E-26 transformation specific (ETS) family of transcription factors that play a role in development, including vasculogenesis, angiogenesis, hematopoiesis, and bone development. The oncogenic potential of ERG is well known due to its involvement in Ewing's sarcoma and leukemia. However, over the past decade, ERG has become significantly associated with the development of prostate cancer, specifically as a result of gene fusion with the promoter region of the androgen-inducible TMPRSS2 gene (see, e.g., Adamo and Ladomery, Oncogene volume 35, pages 403-414 (2016)). The use of TMPRSS2-ERG fusion status as a clinical parameter has been previously described, and those skilled in the art are well aware of how fusion status can be determined, for example, as described in WO2019185773A1, which is incorporated herein by reference in its entirety.

[0171] As used herein, androgen deprivation therapy (ADT), also known as androgen suppression therapy, refers to antihormonal therapy. Androgen deprivation therapy aims to reduce the level of androgen hormones (such as testosterone) in patients, since prostate cancer cells typically depend on androgen hormones for growth. ADT can include surgical or drug-based methods. For example, because the testes are the primary organs where androgens are produced, orchiectomy (surgical removal of the testes) can be used as ADT. Alternatively, drug-based methods can be used, and some non-limiting examples are chemical castration and antiandrogen treatment. Chemical castration can be achieved, for example, by agonists or antagonists of gonadotropin-releasing hormone (GnRH). GnRH induces the production of luteinizing hormone, which in turn induces testosterone synthesis. GnRH agonists and antagonists used in androgen deprivation therapy include leuprolide (leuprolide), goserelin, triptorelin, histrelin, buserelin, and degarelix. Antiandrogen treatment aims to reduce androgen receptor (AR) signaling-induced androgen synthesis, or target testosterone synthesis or AR nuclear translocation, by blocking the AR signaling pathway, for example, by disrupting the positive feedback loop created by testosterone. Some illustrative examples of antiandrogens that can be used as ADT are cyproterone acetate, flutamide, nilutamide, bicalutamide, enzalutamide, abiraterone, abiraterone acetate, seviteronel, apalutamide, darolutamide, leuprolide, and galeterone. Thus, in one embodiment, androgen deprivation therapy includes treatment with an antiandrogen. In one embodiment, the antiandrogen is selected from cyproterone acetate, flutamide, nilutamide, bicalutamide, enzalutamide, abiraterone, abiraterone acetate, ceviteronel, apalutamide, darolutamide, leuprorelin, and galeterone, more preferably selected from enzalutamide, leuprorelin, abiraterone, or apalutamide.

[0172] As used herein, the term neoadjuvant androgen deprivation therapy (NADT) refers to a systemic treatment administered after the diagnosis of prostate cancer but before localized treatment such as radical prostatectomy (RP) or radiation. NADT before RP is intended to eradicate malignant androgen-dependent cells, with the hope that sufficient tumor regression will allow for complete removal of remaining prostate cancer, improving the outcome and survival of the condition. Therefore, according to the present invention, androgen deprivation therapy is neoadjuvant androgen deprivation therapy. The listed examples of androgen deprivation therapy can also be used as neoadjuvant androgen deprivation therapy.

[0173] The prediction provided by the method of the present invention can provide a prediction of the risk of a prostate cancer subject for the outcome of ADT. Furthermore, the method of the present invention can predict whether a subject with prostate cancer will have a low-risk or high-risk outcome. The outcomes of prostate cancer-related death, local recurrence and / or distant recurrence as used herein include unfavorable outcomes for the subject. Another outcome is overall mortality. As used herein, overall mortality is an outcome that can be predicted by the method of the present invention, but is not directly related to the subject's death due to prostate cancer.

[0174] Prostate cancer subjects at high risk (i.e., above a certain threshold) of one or more of the outcomes of prostate cancer-related death, local recurrence, and / or distant recurrence, when predicted by the methods of the present invention, are considered to be associated with a preferably unfavorable post-operative risk of each outcome.

[0175] A prostate cancer subject who is at low risk (i.e., lower than or equal to a certain threshold) of one or more of the outcomes of prostate cancer-related death, local recurrence, and / or distant recurrence, when predicted by the method of the present invention, is considered to be associated with a favorable risk, preferably after surgery, for each outcome. A favorable risk may include a different follow-up strategy, e.g., a different recommended (follow-up) treatment, than a subject with an unfavorable risk. For example, a different follow-up strategy, e.g., a different recommended (follow-up) treatment, is considered to improve the survival frequency of a prostate cancer subject who has preferably undergone prostate (cancer) surgery.

[0176] Preferably, the treatment includes neoadjuvant androgen deprivation therapy before locoregional treatment, such as radical prostatectomy (RP) or radiation, if a favorable outcome is predicted by the methods defined herein. For example, if a subject is predicted to respond favorably to ADT, ADT is administered before subsequent treatment. Preferred treatment can be indicated by predicted long-term survival, long-term cancer-free survival, etc., as outlined above.

[0177] Thus, in a preferred embodiment, the method according to the present invention comprises the step of obtaining a biological sample, preferably a sample of the subject's prostate or prostate cancer in a subject, prior to the initiation of treatment, including surgery, radiation therapy, hormonal therapy, cytotoxic chemotherapy and / or immunotherapy.

[0178] The risk of an unfavorable outcome may influence the recommended treatment for the prostate cancer subject associated with the unfavorable risk. In cases where an unfavorable risk is predicted by the methods of the present invention, the recommended treatment may not include providing the subject with neoadjuvant androgen deprivation therapy.

[0179] In a preferred embodiment, the method according to the invention comprises the step of: recommending a treatment based on a prediction, preferably a prediction of an outcome; If the prognosis is unfavorable, the recommended treatment does not include providing neoadjuvant androgen deprivation therapy; If the prognosis is favorable, the recommended treatment includes providing neoadjuvant androgen deprivation therapy.

[0180] A favorable risk of outcome may influence the recommended treatment for said prostate cancer subject associated with the favorable risk. In cases where a favorable risk is predicted by the methods of the present invention, the recommended treatment may include neoadjuvant androgen deprivation therapy, as broadly defined herein.

[0181] Therefore, another preferred embodiment of the method according to the present invention is characterized in that the treatment is recommended based on the prediction, If the prognosis is favorable, the recommended treatment includes neoadjuvant androgen deprivation therapy as defined herein.

[0182] Another aspect of the invention includes a second-line treatment recommended for certain patients, preferably patients with a low or high risk of experiencing an outcome selected from prostate cancer-specific death or overall mortality, wherein the recommendation is based on a prediction of outcome for the prostate cancer patient; If the prognosis is favorable, secondary treatment is not recommended; If the prognosis is unfavorable, secondary treatment is recommended The present invention provides a method.

[0183] It is provided herein that by means of the method described in the present invention, the effectiveness of secondary treatments can be predicted after surgery for patients with low-risk or high-risk profiles. Preferably, the secondary treatments include one or more, more preferably all, of chemotherapy, hormone therapy and radiation therapy.

[0184] The methods provided herein are based on the expression levels, e.g., expression profiles, of at least three genes selected from PDE4D, or PDE4D isoforms 5 (PDE4D5), 7 (PDE4D7), or 9 (PDE4D9), or a combination thereof, and / or PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. The method may further be based on at least one gene selected from immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, SCK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70. It will be appreciated that the method may be performed with input related to the expression level of one or more genes, or that the step of determining the expression level may be part of the method.

[0185] It is further envisaged that the method is executed by a processor.Accordingly, in one embodiment, the present invention relates to a computer-implemented method for predicting outcome in a subject with prostate cancer.

[0186] Preferably, the method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy comprises determining the gene expression profile(s) of PDE4D, preferably the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or the gene expression of three or more, for example 3, 4, 5, 6, 7 or all, of the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. As further disclosed herein, the gene expression profile may include one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all, of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1. 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, of the T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, SCK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70.

[0187] In a further embodiment, a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy comprises determining the gene expression profile(s) of PDE4D, preferably the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and one or more, for example 1, 2, 3, 4, 5, 6, 7 or all, of the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0188] In a further aspect of the disclosure, the gene expression profile comprises two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, of immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1; or optionally two or more, for example 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all, of the 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.

[0189] In one aspect of the disclosure, the method according to the present invention comprises the step of: one or more immune defense response genes, preferably three or more, preferably six or more, more preferably nine or more, most preferably all immune defense response genes; and / or one or more T cell receptor signaling genes, preferably three or more, preferably six or more, more preferably nine or more, and most preferably all of the T cell receptor signaling genes; and Three or more PDE4D7-related genes, preferably four or more, preferably six or more, most preferably all PDE4D7-related genes Includes:

[0190] Thus, the step of determining the first, second and / or third gene expression profile(s) according to the method of the present disclosure may comprise: Three or more, preferably six or more, more preferably nine or more, and most preferably all of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1. three or more, preferably six or more, more preferably nine or more, and most preferably all of the 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 Three or more, preferably six or more, more preferably nine or more, and most preferably all of the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. or receiving a result of the determination.

[0191] In a further aspect, the method disclosed herein comprises determining a prognosis of outcome based on one or more immune defense response genes, one or more T cell receptor signaling genes, and three or more, e.g., three, four, five, six, seven, or all, of the PDE4D and / or PDE4D7-related genes. In a further aspect, the method disclosed herein comprises determining a prognosis of outcome based on two or more immune defense response genes, two or more T cell receptor signaling genes, and three or more, e.g., three, four, five, six, seven, or all, of the PDE4D7 and / or PDE4D7-related genes. In a further aspect, the method disclosed herein comprises determining a prognosis of outcome based on three or more immune defense response genes, three or more T cell receptor signaling genes, and three or more, e.g., three, four, five, six, seven, or all, of the PDE4D and / or PDE4D7-related genes.

[0192] As a first reference example, the gene expression profile disclosed herein may further comprise at least one, at least two, or at least three immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1. In one non-limiting example, the gene expression profile embodied herein comprises the expression profiles of the genes DDX58, DHX9, and IFI16.

[0193] By way of further reference, the gene expression profiles disclosed herein may further comprise at least one, at least two, or at least three T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70. In one non-limiting example, the gene expression profile embodied herein comprises the expression profiles of the genes PRKACA, PRKACB, and PTPRC.

[0194] As a further example, the gene expression profile disclosed herein may include PDE4D7 expression levels and at least one, at least two, or at least three PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. In one non-limiting example, the gene expression profile embodied herein includes the expression profiles of the genes KIAA1549, PDE4D, and RAP1GAP2.

[0195] As a further example, a gene expression profile embodied herein may include at least three, at least four, or at least five PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. In one non-limiting example, a gene expression profile embodied herein includes the expression profiles of the genes CUX2, SLC39A11, and TDRD1.

[0196] Cox proportional hazards regression allows for the real-time analysis of the impact of multiple risk factors on a tested event, such as survival. 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, V3 ... are predictor variables, H0(t) is the baseline hazard, and H(t) is the hazard at any time t. The hazard ratio (HR) (or risk of reaching an event) is given by Ln[H(t) / 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.

[0197] Thus, in one preferred embodiment, the method of the present invention, wherein determining a prediction of outcome comprises combining a gene expression profile with a regression function derived from a population of prostate cancer subjects. Thus, in one embodiment, the method of the present invention, wherein determining a prediction of outcome comprises combining three or more gene expression levels with a regression function derived from a population of prostate cancer subjects.

[0198] In another aspect, the method described herein comprises determining a prediction of outcome by: combining a first gene expression profile of 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 derived from a population of prostate cancer subjects; combining a second gene expression profile of two or more, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all, T cell receptor signaling genes with a regression function derived from a population of prostate cancer subjects; and / or combining a third gene expression profile of two or more, e.g., two, three, four, five, six, seven, or all, of the PDE4D7-related genes with a regression function derived from a population of prostate cancer subjects. Includes:

[0199] In one particular realization of the present invention, the prediction of outcome is determined as follows: PDE4D7_CORR_model: (w1·ABCC5)+(w2·CUX2)+(w3·KIAA1549)+[...]+( w8 VWA2) (1) Here, from w1 w8 are weights, and ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 are the expression levels of genes.

[0200] In one particular realization of the present disclosure, the prediction of outcome is supplemented by an additional model as follows: IDR_14_model: (w9·AIM2)+(w 10 APOBEC3A)+(w 11 ·CIAO1)+[...]+( w22 ZBP1) (2) Here, from w9 w22 are weights, and AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 are the expression levels of genes.

[0201] In another particular realization of the present disclosure, the prediction of outcome is supplemented by an additional model as follows: IDR_17_model: (w 23 CD2)+(w 24 ·CD247)+(w 25 CD28)+[...]+( w39 ZAP70) (3) where w 23 from w39 are weights, and CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 are the expression levels of genes.

[0202] In another particular realization of the invention, the outcome prediction is determined as follows: PCAI_model: (w 40 ·PDE4D7_CORR)+(w 41 PDE4D7) (4)

[0203] In another realization of the invention, the outcome prediction is determined as follows: PCAI_model: (w1·ABCC5)+(w2·CUX2)+(w3·KIAA1549)+[...]+( w8 ·VWA2)+( w41 PDE4D7) (5) Here, from w1 w8 are weights, and ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 are the expression levels of genes.

[0204] The outcome predictions may also be classified or categorized into one of at least two risk groups based on the value of the outcome predictions, for example, there may be two risk groups, three risk groups, four risk groups, or more than four predefined risk groups.

[0205] Each risk group covers a distinct range of (non-overlapping) values ​​predicting outcome. For example, a risk group may represent the probability 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.

[0206] In a preferred embodiment, the method according to the present invention, the step of determining the outcome prediction is based on one or more clinical parameters obtained from the subject.As mentioned above, various measurements based on one or more clinical parameters have been considered.By basing the outcome prediction on such clinical parameter(s), the prediction can be further improved.

[0207] In one embodiment, determining a prediction of treatment response comprises combining gene expression levels of PDE4D and / or three or more PDE4D7-related genes with a regression function derived from a population of prostate cancer subjects.

[0208] Preferably, the PDE4D expression level and / or gene expression profile of three or more PDE4D7-related genes and one or more clinical parameters obtained from the subject are combined with a regression function derived from a population of prostate cancer subjects.

[0209] It is also preferred that one or more clinical parameters are combined with gene expression profiles and regression functions derived from a population of prostate cancer subjects to provide a method for predicting a prostate cancer subject's response to neoadjuvant androgen deprivation therapy.

[0210] Therefore, in a preferred embodiment of the method according to the present invention, the step of determining the outcome comprises a combination of one or more of the following steps: (i) PDE4D expression levels, preferably gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9 or a combination thereof; (ii) the expression levels of three or more PDE4D7-related genes; (iii) A regression function derived from a population of prostate cancer subjects and one or more clinical parameters obtained from the subjects.

[0211] In another particular realization, the outcome prediction is determined as follows: PCAI_Clinical Model (w 43 ·PDE4D7_CORR)+(w 44 ·LN_positive) (6) where w 43 and w 43 where σ is the weight, PCAI_model is the regression model described above based on the expression profile of one or more IDR genes, one or more TCR signaling genes, and / or one or more PDE4D7-related genes, and LN_positive represents the number of tumor-positive lymph nodes. Multivariate Cox regression was used to create a clinical model that combines PCAI with clinical data (number of positive lymph nodes).

[0212] An example of a suitable clinical parameter, as used and exemplified herein, is "LN_positive," which represents the number of tumor-positive lymph nodes after postoperative lesion. LN-positive prostate tumors are tumors of the prostate whose tumor cells have spread to nearby lymph nodes, i.e., metastasized. LN-positive tumors are considered to have occult metastasis of cancer.

[0213] It will be appreciated by those skilled in the art that a corresponding model may be formed, for example, of three or more PDE4D7-related genes, in which case an exemplary model would be as follows: PDE4D7_CORR_EX1-3 gene (w3·KIAA1549)+(w5·RAP1GAP2)+(w7·TDRD1) (7).

[0214] In a similar manner, components can be added to the model (e.g., one or more immune defense genes, or one or more T cell receptor signaling genes). When components are removed or added from the model, it is understood that the weights are preferably recalculated for the corrected model. Those skilled in the art will appreciate that the model can be calculated using a ground truth dataset comprising, for example, expression data obtained before ADT treatment in prostate cancer patients, where the dataset contains expression data from responders and non-responders to ADT treatment.

[0215] The methods described in the present invention can be implemented in a computer program that runs on a computer. The computer program can include a non-transitory computer-readable recording medium, such as a disk, a hard drive, etc., on which a control program is recorded (saved). Common forms of non-transitory computer-readable medium include, for example, a floppy disk, a flexible disk, a hard disk, a magnetic tape or any other magnetic storage medium, a CD-ROM, a DVD, or any other optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM, or other memory chip or cartridge, or any other non-transitory medium that can be read from and used by a computer.

[0216] In a preferred embodiment, the present invention therefore also provides a computer program comprising instructions, which when the program is run by a computer, perform the following: Gene expression levels of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9 the expression levels of three or more genes selected from the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; receiving a result of the determination of the gene expression profile, comprising: determining said gene expression profile in a biological sample obtained from the subject; determining a prediction of outcome based on the gene expression profile, The method further comprises receiving a gene expression level of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1. In one aspect of the disclosure, the method further comprises receiving gene expression levels of one or more, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all, of T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70. In one embodiment, the method comprises receiving gene expression levels of three or more, e.g., 3, 4, 5, 6, 7, or all, of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0217] Alternatively, one or more steps of the method may be implemented in a transmissible medium such as a transmissible carrier wave in which the control program is embodied as a data signal using a transmission medium such as acoustic or light waves, such as those generated during radio wave and infrared data communications.

[0218] Exemplary methods can be implemented on one or more general-purpose computers, dedicated computer(s), programmed microprocessors or microcontrollers and peripheral integrated circuit elements, hardwired electronic or logic circuits such as ASICs or other integrated circuits, digital signal processors, discrete element circuits, programmable logic devices such as PLDs, PLAs, FPGAs, graphic cards, CPUs (GPUs), or PALs. Generally, any device capable of implementing a finite machine capable of sequentially performing the steps described herein can be used to implement one or more steps of the exemplary risk stratification method for treatment selection in patients with prostate cancer. As will be appreciated, although all steps of the method can be implemented on a computer, in some embodiments, one or more steps can be performed at least partially manually.

[0219] As used herein, the computer program product of the present invention may preferably be implemented on a device for predicting a response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, the device comprising an input suitable for receiving data indicative of a gene expression profile comprising PDE4D expression levels, preferably gene expression levels of specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or expression levels of three or more selected from PDE4D7-related genes, the device further comprising a processor suitable for determining a prediction of response based on the gene expression profile; Optionally, a unit suitable for providing a selection-based prediction or treatment recommendation to a healthcare caregiver or subject is provided.

[0220] In a further aspect of the present invention, there is provided an apparatus for predicting a prostate cancer subject's response to neoadjuvant androgen deprivation therapy, comprising: an input suitable for receiving data indicative of a gene expression profile comprising gene expression levels of PDE4D, preferably gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or gene expression levels of three or more genes selected from each of one or more, e.g., one, two, three, four, five, six, seven or all of PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, an input, wherein the gene expression profile is determined in a biological sample obtained from the subject; a processor suitable for determining a prediction of outcome based on the gene expression profile, wherein the prediction is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; and optionally a delivery unit suitable for providing a prognosis or treatment recommendation based on the prognosis to a caregiver or subject. An apparatus is provided that includes:

[0221] The 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 performed on the computer, other programmable apparatus, or other device, creating a computer-implemented process, such that the instructions executing on the computer or other programmable apparatus provide a process for performing the functions / acts specified herein.

[0222] In a preferred embodiment, therefore, the present invention also provides a diagnostic kit comprising at least one polymerase chain reaction primer and, optionally, at least one probe for determining gene expression profile(s) in a biological sample and / or samples obtained from a prostate cancer subject.

[0223] The diagnostic kit provided herein preferably comprises at least one polymerase chain reaction primer or probe for determining a gene expression profile in a biological sample and / or sample obtained from a subject with prostate cancer, the gene expression profile comprising: the gene expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or Expression levels of three or more PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. Includes:

[0224] In another preferred embodiment, the present invention provides the use of a diagnostic kit as broadly embodied herein in a method for predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, preferably in a method for predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy as broadly embodied herein.

[0225] In one embodiment, the present invention relates to the use of a diagnostic kit, comprising: the gene expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or Expression levels of three or more PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. at least one polymerase chain reaction primer or probe for determining a gene expression profile in a biological sample and / or sample obtained from a subject with prostate cancer, The present invention relates to a use of a kit, the use of which comprises predicting a subject's response to neoadjuvant androgen deprivation therapy, wherein the outcome is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy. In one embodiment, the use comprises using the kit in a method as defined in a method for predicting a subject's response to neoadjuvant androgen deprivation therapy, as broadly defined herein.

[0226] In another preferred embodiment, the present invention provides receiving a biological sample obtained from a subject with prostate cancer; the gene expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or and expression levels of three or more genes selected from PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 in a biological sample and / or sample obtained from a subject with prostate cancer. using a diagnostic kit, as broadly embodied herein, to determine a gene expression profile, comprising: The present invention provides a method comprising:

[0227] All references cited herein, including articles or abstracts, publications or corresponding patent specifications, patents, or any other references, are incorporated herein by reference in their entirety, including all data, tables, figures, and text present in the cited references. Additionally, the entire contents of references cited within references cited herein are also incorporated by reference in their entirety.

[0228] Reference to known method steps or conventional method steps is not an admission that any aspect, description, or embodiment of the present invention is disclosed, taught, or suggested in the relevant art.

[0229] It is understood that the terminology or terminology used herein is for purposes of description and not limitation, and that the terminology or terminology used herein will be interpreted by those skilled in the art in light of the teaching and guidance presented herein, in combination with the knowledge of those skilled in the art.

[0230] It will be understood that all details, embodiments and references discussed with respect to one aspect of an embodiment of the present invention may likewise be applied to any other aspect or embodiment of the present invention, and therefore it is not necessary to recite all such details, embodiments and references of all aspects separately.

[0231] Having now generally described the invention, it will be more readily understood by reference to the following examples, which are provided by way of illustration and are not intended to be limiting of the invention. Further aspects and embodiments will be apparent to those skilled in the art.

[0232] Other variations of the disclosed implementations can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0233] Any reference signs in the claims shall not be construed as limiting the scope. [Example]

[0234] The generation of different models based on immune response defense genes, T cell receptor signaling genes or PDE4D7 related genes or combinations thereof is described below: Immune response defense gene WO2021 / 175746 T cell receptor signal transduction gene WO2021 / 175986 PDE4D7-related genes WO2022 / 043120 Combination WO2022 / 043299

[0235] These applications further provide experimental evidence that predictions can be made based on portions of a gene signature, for example, 3, 4, 5, 6, 7, or 8 randomly selected genes from immune response defense genes, T cell receptor signaling genes, or PDE4D-related genes, or 3, 4, 5, 6, 7, or 8 randomly selected genes from a combination of these signatures.

[0236] Example 1: Heterogeneity of early stage prostate cancer drives evolution and resistance to intensive hormonal therapy background Patients diagnosed with high-risk localized prostate cancer exhibit variable outcomes after surgery. Trials of intensive neoadjuvant androgen deprivation therapy (NADT) have shown low recurrence rates among patients with minimal residual disease after treatment. There are no known molecular characteristics that distinguish excellent responders from poor responders.

[0237] the purpose Genomic and histological features associated with treatment resistance at baseline will be identified.

[0238] Design, setting, and participants Targeted biopsies were obtained from 37 men with intermediate- to high-risk prostate cancer before receiving 6 months of ADT plus enzalutamide. Biopsy tissue was used for RNA sequencing. The publicly available dataset is described in Wilkinson et al. Nascent Prostate Cancer Heterogeneity Drives Evolution and Resistance to Intense Hormonal Therapy. Eur Urol 80 (2021), 746-757, which is incorporated herein by reference in its entirety.

[0239] Outcome measures and statistical analysis 0.05 cm of residual cancer burden 3 Evaluation of the relationship between molecular features of pre-treatment biopsy tissue samples (n=35) and final pathologic response, comparing excellent responders to incomplete and non-responders using a cut-point of .

[0240] Research Overview RNAseq-based gene expression matrix from a study with n=35 eligible samples. Gene expression values ​​for genes of interest were used as input for data analysis. Calculation of the Prostate Cancer AI ImmunoScore based on a regression model previously developed with independent data (see, e.g., WO2022 / 043299). Examination of the relationship between the PCAI Immunoscore and response to 6 months of neoadjuvant NADT. A schematic diagram is shown in Figure 1. The PCAI Immunoscore uses PDE4D7-related genes and the model described in WO2022 / 043120.

[0241] result The results are shown in Figure 3. The Prostate Cancer AI ImmunoScore accurately predicts exceptional treatment response to NADT (AUC=0.85) compared to a selection of genes regulated by the androgen receptor (AUC=0.56).

[0242] Example 2: Molecular characteristics of exceptional response to NADT in high-risk localized prostate cancer background High-risk localized prostate cancer is associated with a substantial risk of recurrence and disease mortality. Recent clinical trials have shown that augmenting antiandrogen therapy administered prior to prostatectomy can induce pathological complete responses or minimal residual disease, so-called exceptional responses, although the molecular determinants of these clinical outcomes are largely unknown.

[0243] the purpose Genomic and histological features associated with treatment resistance at baseline will be identified.

[0244] Design, setting, and participants Here, we performed whole-exome and transcriptome sequencing on pre-treatment multiregional tumor biopsies from exceptional responders (ER) and non-responders (NR, pathological T3 or node-positive disease) to strong neoadjuvant antiandrogen therapy (including apalutamide). The publicly available dataset is described in Tewari et al. Molecular features of exceptional response to NADT in high-risk localized prostate cancer. Cell Reports 36 (2021), 109665, which is incorporated herein by reference in its entirety.

[0245] Outcome measures and statistical analysis Evaluation of the relationship between molecular features of pretreatment biopsy tissue samples (n=43) taken from n=24 patients and final pathologic response, using a cutpoint of <5mm tumor tissue, defined as exceptional responders compared with non-responders.

[0246] Research Overview RNAseq-based gene expression matrix from a study with n=43 eligible samples. Gene expression values ​​for genes of interest were used as input for data analysis. Prostate Cancer AI ImmunoScore based on a regression model previously developed with independent data. Examination of the relationship between PCAI ImmunoScore and response to 6 months of neoadjuvant NADT. A schematic diagram is shown in Figure 2. PCAI Immunoscore uses PDE4D7-related genes and the model described in WO2022 / 043120.

[0247] result The results are shown in Figure 4. The Prostate Cancer AI ImmunoScore accurately predicts exceptional treatment response to NADT (AUC=0.92) compared to a selection of genes regulated by the androgen receptor (AUC=0.75).

[0248] Example 3-3 Gene Model To establish a random selection of three PDE4D7-related genes sufficient to predict the response of prostate cancer subjects to neoadjuvant androgen deprivation therapy, 10 random selections of three PDE4D7-related genes were performed, and these are listed below as Model_1 to Model_10. Each model was trained on a training cohort of 35 patients (as above) and then validated on a validation cohort of 24 patients (as above). For each model, Kaplan-Meier curves were plotted based on the results obtained by the validation cohort. The results are plotted in Figures 5 to 14. Each randomly selected set of three PDE4D7-related genes can also provide good prediction of treatment response to neoadjuvant androgen deprivation therapy. The average AUC of the different models was approximately 0.8, which is considered very good.

[0249] The genes used in each model and the weights assigned are listed in Tables 4-13 below.

[0250] [Table 4]

[0251] [Table 5]

[0252] [Table 6]

[0253] [Table 7]

[0254] [Table 8]

[0255] [Table 9]

[0256] [Table 10]

[0257] [Table 11]

[0258] [Table 12]

[0259] [Table 13]

[0260] These results were obtained by randomly selecting 10 different PDE4D7-related genes. Each model gave good to very good predictions, ensuring that any random selection of three PDE4D7-related genes (ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2) was predictive. From these results, we conclude that random selection of three genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 can be used to predict the response of prostate cancer subjects to neoadjuvant androgen deprivation therapy.

[0261] Example 4 The addition of clinical parameters will further improve prediction. To demonstrate that the model can further improve the regression model, the PDE4D7_R2 model was used in combination with baseline tumor volume (before NADT initiation; Figure 15), or this model (PDE4D7_R2+baseline_tumor_volume) was further combined with gene expression data for two additional genes (ERG and PTEN; Figure 16).

[0262] The model was constructed as follows.

[0263] [Table 14]

[0264] [Table 15]

[0265] The same cohort as in Example 1 was used to generate and evaluate the model, and the data were plotted as Kaplan-Meier curves, shown in Figures 15 and 16. Combining baseline tumor volume parameters with the PDE4D7-related gene model increased the AUC to 0.85-0.90, and including ERG and PTEN expression levels further increased the AUC to 0.94.

[0266] From these data it can be concluded that additional data on clinical parameters such as Baseline Tumor Volume and ERG and / or PTEN expression levels may further improve the predictive power of the model.

[0267] Example 5 - PDE4D(7) Expression as a Predictor of Biochemical Recurrence In this example, we used publicly available data from the DARANA Trial to test whether PDE4D7 expression levels are sufficient to predict BCR. The data are available as NCT03297385 and published in Linder et al. Drug-induced epigenomic plasticity reprograms circadian rhythm regulation to drive prostate cancer toward androgen independence. Cancer Discov. 2022 September 2; 12(9): 2074-2097.

[0268] In the DARANA study, 56 patients were selected from patients with intermediate- to high-risk prostate cancer with localized disease and ISUP Gleason grades 2-5 (ca. 55% ISUP grades 4-5). Patients were treated with an antiandrogen (enzalutamide) in the neoadjuvant setting for 3 months. Biopsies were taken from the prostate (MRI-guided) before treatment initiation. At the end of hormonal treatment, the prostate was removed by RP, and tissue punches were taken from the resected prostate tissue. Pre- and post-treatment tissues were subjected to RNA-seq expression analysis. Baseline ISUP Gleason grade was available in the dataset, as were BCR events and time to BCR for each patient. The dataset includes RNA-seq and clinical variables for N=42 samples.

[0269] In this dataset, PDE4D expression levels were evaluated as a predictor of BCR. The results are shown in Figures 17 and 18 and reference Figure 19.

[0270] Briefly, the primary expression of PDE4D (all isoforms) was evaluated in a cohort of 35 patients in the DARANA data set. The results are plotted as Kaplan-Meier curves in Figure 17, showing that groups with low BCR frequency can be identified using the model. The figure shows log-rank and confidence intervals.

[0271] It is concluded that these data based on PDE4D expression level can reliably predict BCR.Furthermore, since PDE4D7 (or the long transcript combination such as PDE4D5, 4D7 and / or 4D9) is assumed to be the most contributing factor to the variation in expression level between responders, the inventors believe that isoform-specific PDE4D7 (or PDE4D5, 4D7 and / or 4D9) expression level is at least equally, if not more, predictive.Unfortunately, isoform-specific sequence data is not available in the public data to verify this assumption.

[0272] Next, we reviewed whether the predictive model could be improved by including additional PDE4D7-related genes. We constructed a combined logistic regression model using the PDE4D and ABCC5 expression levels listed in Table 16 below.

[0273] [Table 16]

[0274] The model predicted postoperative PSA recurrence after 3 months of neoadjuvant ADT (enzalutamide) in men with high-risk, localized prostate cancer and was evaluated in 35 patients within the DARANA cohort. Results are shown in Figure 18, which shows the Kaplan-Meier curves separating patients with predicted high-risk and low-risk BCR. Including ABCC5 as an additional marker improved prediction. We believe that any PDE4D7-related gene, not just ABCC5, could be combined with PDE4D to improve prediction.

[0275] For reference, Figure 19 shows the Kaplan-Meier curve based on the Baseline ISUP Gleason score, which does not predict BCR (P value 0.3).

[0276] The attached Sequence Listing, entitled 2022PF00781 SEQ LIST.xml, is hereby incorporated by reference in its entirety.

Claims

1. 1. A method for predicting a response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, the method comprising: the gene expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or the expression levels of three or more genes selected from the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; determining a gene expression profile or receiving a result of determining a gene expression profile, comprising: determining the gene expression profile in a biological sample obtained from the subject; determining a prediction of outcome based on said gene expression profile, the prediction being a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; optionally providing a prediction of outcome to a healthcare caregiver or subject; A method comprising:

2. the method is based on the expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and the prediction is the frequency of biochemical relapse (BCR), The method of claim 1, wherein the expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, is combined with the expression level of one or more selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

3. The prediction is tumor size or volume, TMPRSS2-ERG fusion status, ERG and / or PTEN expression levels, and / or Presence of metastases The method of claim 1 or 2, further based on at least one of:

4. the neoadjuvant androgen deprivation therapy comprises treatment with an antiandrogen; 4. The method according to any one of claims 1 to 3, wherein the antiandrogen is selected from cyproterone acetate, flutamide, nilutamide, bicalutamide, enzalutamide, abiraterone, abiraterone acetate, ceviteronel, apalutamide, darolutamide, leuprorelin and galeterone.

5. The outcome is predicted residual tumor burden, predicted tumor size, predicted frequency of tumor metastasis, or Predicted frequency of biochemical recurrence 5. The method according to claim 1, wherein the method is at least one of:

6. 6. The method of any one of claims 1 to 5, wherein the favorable or unfavorable response is tumor size, tumor burden, number of tumor-positive lymph nodes, survival probability, overall survival, cancer-free survival, overall mortality, or cancer-specific mortality.

7. 7. The method of any one of claims 1 to 6, wherein the favorable or unfavorable outcome is an improved frequency of survival in response to treatment.

8. 8. The method of any one of claims 1 to 7, wherein the three or more genes comprise one or more immune defense response genes, one or more T cell receptor signaling genes, and three or more PDE4D7-related genes.

9. 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; 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 three or more PDE4D7-related genes include four or more, preferably six or more, and most preferably all of the PDE4D7-related genes; The method according to claim 2 or 8.

10. 10. The method of any one of claims 1 to 9, wherein determining a prediction of outcome comprises combining three or more gene expression levels with a regression function derived from a population of prostate cancer subjects.

11. 11. The method of any one of claims 1 to 10, wherein determining a prediction of outcome is further based on one or more clinical parameters obtained from the subject.

12. 12. The method of any one of claims 1 to 11, wherein determining the outcome comprises combining the gene expression profile and one or more clinical parameters obtained from the subject with a regression function derived from a population of prostate cancer subjects.

13. 13. The method of any one of claims 1 to 12, wherein a biological sample is obtained from the subject before the start of treatment, preferably the biological sample is a prostate sample or a prostate cancer sample.

14. 14. The method of any one of claims 1 to 13, wherein treatment is recommended based on the prediction.

15. Use of a diagnostic kit, the diagnostic kit comprising: A biological sample obtained from a subject with prostate cancer and / or at least one polymerase chain reaction primer or probe for determining a gene expression profile in the sample, wherein the gene expression profile comprises: the gene expression level of PDE4D, preferably the gene expression of the specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or comprising expression levels of three or more genes selected from the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2; using the diagnostic kit includes predicting a response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, the outcome being a favorable or unfavorable response to neoadjuvant androgen deprivation therapy; Preferably, the use of a diagnostic kit comprises using the diagnostic kit in a method according to any one of claims 1 to 14.

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