Prediction of results of neoadjuvant androgen deprivation therapy for prostate cancer subjects

Abstract

The present invention relates to methods of predicting the response of prostate cancer subjects to neohelper androgen deprivation therapy. The method is based on prediction results based on gene expression levels identified herein. The present invention provides a method for determining whether neoadjuvant androgen deprivation therapy is useful for a subject and thus should be administered. Further provided is the use of a kit for determining the expression level for predicting the response of a prostate cancer subject to neohelper androgen deprivation therapy.

Description

Field of the Invention

[0001] The present invention relates to methods for predicting the response of prostate cancer subjects to neoadjuvant androgen deprivation therapy, and computer program products for predicting the response of prostate cancer subjects to radiotherapy. In addition, the present invention relates to diagnostic kits, the use of such kits, the use of the kits in methods for predicting the response of prostate cancer subjects to neoadjuvant androgen deprivation therapy, the use of the gene expression profiles of each of one or more PDE4D7-related genes in methods for predicting the response of prostate cancer subjects to radiotherapy, and corresponding computer program products. Background of the Invention

[0003] Cancer is a class of diseases in which a group of cells exhibit uncontrolled growth, invasion, and sometimes metastasis. These three malignant characteristics of cancer distinguish it from benign tumors, which are self-limiting and do not invade or metastasize.

[0004] Prostate cancer (PCa) is the second most common non-skin malignancy in men, with an estimated 1.3 million new diagnoses and 360,000 deaths globally in 2018 (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 local cancer, meaning that metastasis has not yet occurred (see ACS (American Cancer Society), “Cancer Facts & Figures 2010”, 2010). For the treatment of primary localized prostate cancer, several radical therapies are available, with surgery (radical prostatectomy, RP) and radiotherapy (RT) being the most commonly used. RT is administered by external beam or by implanting radioactive seeds into the prostate (brachytherapy) or a combination of both. This is particularly desirable for patients who are not eligible for surgery or who are diagnosed with advanced local or regional tumors. In the United States, up to 50% of patients diagnosed with localized prostate cancer are eligible for radical RT treatment (see ACS, 2010, ibid.). After treatment, the level of prostate cancer antigen (PSA) in the blood is measured for disease monitoring. An increase in the blood PSA level provides a biochemical surrogate measure for cancer recurrence or progression.

[0005] Androgen deprivation therapy (ADT), also known as androgen suppression therapy, is an antihormonal therapy mainly used to treat prostate cancer. Prostate cancer cells usually require androgens such as testosterone to grow. ADT reduces androgen levels through medications or surgery to prevent the growth of prostate cancer cells. Neoadjuvant androgen deprivation therapy (NADT) is a systemic treatment conducted after the diagnosis of prostate cancer but before local treatment such as radical prostatectomy (RP) or radiotherapy. The use of NADT before RP aims to eradicate malignant androgen-dependent cells, hoping that sufficient tumor regression can enable complete resection of residual prostate cancer, improving the pathological outcome and survival rate. However, the role of preoperative androgen deprivation remains controversial.

[0006] WO 2022 / 043299A1 discloses a gene signature that can be used to predict the response to salvage androgen deprivation therapy (SADT).

[0007] However, the analysis for predicting the SADT response is based on those patients who have undergone significant pretreatment. These men all first underwent surgical resection of the prostate, then experienced biochemical recurrence, then received salvage radiotherapy to the pelvic bed, and then received ADT. This means that the recurrent tumors treated with RT are no longer the same tumor from a molecular and phenotypic perspective compared to the tumor the same man had at the beginning of treatment (i.e., before surgery). Therefore, the gene signature for predicting SADT may not necessarily predict the response to NADT treatment.

[0008] 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) disclosed the correlation or association of certain genes with the pathological minimal residual disease response to neoadjuvant deprivation therapy.

[0009] Wilkinson et al. (EUROPEAN UROLOGY, vol. 80, no. 6, 27 March 2021 (2021-03-27), pages 746-757) disclosed that poor neoadjuvant androgen deprivation therapy is associated with the deletion of the q10 genomic region (PTEN), TP53 mutations, and ERG expression.

[0010] Patients diagnosed with high-risk localized prostate cancer have different outcomes after surgery. Trials of intensified NADT have shown that patients with minimal residual disease after treatment have a lower recurrence rate. The molecular characteristics that distinguish responders from non-responders are not clear.

[0011] Accordingly, there is a continuing need for new and improved methods for predicting response to neoadjuvant androgen deprivation therapy. The methods and uses defined in the appended claims meet this unmet need. SUMMARY OF THE INVENTION

[0013] In a first aspect, the present invention relates to a method for predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, comprising:

[0014] - determining or receiving a determination result of a gene expression profile,

[0015] wherein the expression profile comprises: the gene expression level of PDE4D, and / or

[0016] - the gene expression levels of three or more genes, wherein the three or more gene expression levels are selected from:

[0017] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2,

[0018] wherein the gene expression profile is determined in a biological sample obtained from the subject,

[0019] - a prediction based on the determination result of the gene expression profile,

[0020] - wherein the prediction is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy.

[0021] In a second aspect, the present invention relates to the use of a diagnostic kit, the kit comprising:

[0022] - at least one of polymerase chain reaction primers or probes 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:

[0023] the gene expression level of PDE4D, and / or

[0024] the expression levels of three or more genes, wherein the three or more gene expression levels are selected from:

[0025] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2,

[0026] wherein the use comprises predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, wherein the result is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Describes the experimental setup for testing the prostate cancer gene signature in a first patient cohort.

[0029] Figure 2 Describes the experimental setup for testing the prostate cancer gene signature in a second patient cohort.

[0030] Figure 3 ; 35 patients with intermediate / high-risk prostate cancer; all patients received anti-androgen neoadjuvant therapy (6 months of ADT + enzalutamide). The figure depicts the ROC curve. A test cohort of 35 patients was used to validate the model. The clinical endpoint used was overall death. The models plotted were the PCAI model and a reference model based on androgen receptor-regulated gene selection. The area under the curve is shown in the inset.

[0031] Figure 4 ; 24 patients (43 samples) with localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The figure depicts the ROC curve. A test cohort of 35 patients was used to validate the model. The clinical endpoint used was overall death. The models plotted were the PCAI model and a reference model based on androgen receptor-regulated gene selection. The area under the curve is shown in the inset.

[0032] Figure 5 Shows the AUROC analysis of Model_1 in a cohort of 24 patients, where all patients had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P value.

[0033] Figure 6 Shows the AUROC analysis of Model_2 in a cohort of 24 patients, where all patients had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). Colleagues,

[0034] Last week, the US Food and Drug Administration (FDA) notified Philips Respironics that they believe additional testing is necessary to support the test conclusions for affected sleep therapy devices. You may have read about it in the Dutch news.

[0035] Philips Respironics has agreed to the FDA's recommendation to conduct additional testing on certain sleep and respiratory care devices to supplement current test data. The FDA stated that the testing was extensive, conducted by independent parties, and that it had no concerns about the validity or objectivity of the testing. We are discussing the details of the further testing with the FDA.

[0036] We are confident in the methods and results to date. We have worked with five independent accredited test laboratories in Europe and the United States, as well as third-party experts with extensive scientific, toxicology, and medical expertise. Our testing uses comprehensive scientific methods and appropriate ISO standards. Based on the results to date, Philips Respironics has concluded that the use of its sleep therapy devices is not expected to cause significant harm to patients' health. Philips Respironics has been working with the FDA on a testing and research program and regularly releases test updates in accordance with its agreement with the FDA.

[0037] Philips and the FDA, along with other regulatory agencies globally, share the common goal of ensuring the highest standards of patient safety and quality in the delivery of healthcare. We will continue to invest all necessary resources to ensure that patients receive the remedial devices and that the testing and research program is completed. In the Netherlands, we have provided solutions for approximately 107,000 of the 110,000 registered and affected devices.

[0038] Let me emphasize that our top priority is the health and well-being of patients, including providing alternative devices and testing to further clarify the safety of sleep and respiratory care devices under the field safety notice. We understand how important these sleep and respiratory care devices are to the patients who use them.

[0039] Figure 7 Shows the AUROC analysis of Model_3 in a cohort of 24 patients, all of whom had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P-value.

[0040] Figure 8 Shows the AUROC analysis of Model_4 in a cohort of 24 patients, all of whom had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P-value.

[0041] Figure 9Shows the AUROC analysis of Model_5 in a cohort of 24 patients, all of whom had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P value.

[0042] Figure 10 . Shows the AUROC analysis of Model_6 in a cohort of 24 patients, all of whom had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P value.

[0043] Figure 11 Shows the AUROC analysis of Model_7 in a cohort of 24 patients, all of whom had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P value.

[0044] Figure 12 Shows the AUROC analysis of Model_8 in a cohort of 24 patients, all of whom had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P value.

[0045] Figure 13 Shows the AUROC analysis of Model_9 in a cohort of 24 patients, all of whom had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P value.

[0046] Figure 14 Shows the AUROC analysis of Model_10 in a cohort of 24 patients, all of whom had localized high-risk prostate cancer; all patients received 6 months of anti-androgen neoadjuvant therapy (6 months of enzalutamide, leuprorelin acetate, abiraterone, apalutamide). The clinical endpoint tested was overall death. The figure includes the area under the curve and the P value.

[0047] Figure 15Displays the AUROC analysis curve showing the comparison between the PDE4D7-related model (PDE4D7_R2) and the baseline tumor volume of the PDE4D7-related model combination (PDE4D7_BaseTumorVol) in a cohort of 35 patients, where all patients had localized high-risk prostate cancer; all patients received neoadjuvant anti-androgen therapy for 6 months (6 months ADT + enzalutamide). The clinical endpoint tested was overall death. The area under the curve value is included in the figure.

[0048] Figure 16 Displays the AUROC analysis curve showing the comparison between the PDE4D7-related model (PDE4D7_R2) and the baseline tumor volume and the expression levels of ERG and PTEN in a cohort of 35 patients, where all patients had localized, high-risk prostate cancer; all patients received neoadjuvant anti-androgen therapy for 6 months (6 months ADT + enzalutamide). The clinical endpoint tested was overall death. The area under the curve value is included in the figure.

[0049] Figure 17 Displays the Kaplan-Meier curve of the PDE4D7_R2_log_BCR model in a cohort of 41 patients, where all patients had intermediate-high risk prostate cancer; all patients received neoadjuvant anti-androgen (enzalutamide) therapy for 3 months. The clinical endpoint tested was biochemical recurrence. The P value is included in the figure.

[0050] Figure 18 Displays the Kaplan-Meier curve using the PDE4D expression level in a cohort of 41 patients, where all patients had intermediate-high risk prostate cancer; all patients received neoadjuvant anti-androgen (enzalutamide) therapy for 3 months. The clinical endpoint tested was biochemical recurrence. The P value is included in the figure.

[0051] Figure 19 Displays the Kaplan-Meier curve of different ISUP Gleason scores at baseline in a cohort of 41 patients, where all patients had intermediate-high risk prostate cancer; all patients received neoadjuvant anti-androgen (enzalutamide) therapy for 3 months. The clinical endpoint tested was biochemical recurrence. The P value is included in the figure.

[0052] Definition

[0053] As used herein, the indefinite terms "a" or "an" do not exclude a plurality.

[0054] The term "biological sample" or "sample obtained from a subject" refers to any biological material obtained from a subject (such as a prostate cancer subject) by a suitable method known to those skilled in the art.

[0055] As used herein, the term "and / or" means that one or more of the stated circumstances may occur alone, or in combination with at least one of the stated circumstances, up to and including all of the stated circumstances occurring together.

[0056] As used herein, the term "at least" a particular value means that particular value or more. For example, "at least 2" is understood to be the same as "2 or more", i.e., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15... etc.

[0057] The term "prostate cancer" refers to cancer of the prostate tissue, which occurs when cells in the prostate mutate and begin to grow out of control.

[0058] The term "prostate cancer-specific death or disease-specific death" refers to the death of a prostate cancer patient.

[0059] The term "clinical recurrence" refers to the presence of clinical symptoms indicating the presence of tumor cells, such as measured using in vivo imaging.

[0060] As used herein, the word "comprising" or variants thereof such as "including" is understood to include the stated elements, integers or steps, or a group of elements, integers and steps, but not to exclude any other elements, integers, steps or group of elements, integers and steps. The verb "comprising" includes the verbs "consisting essentially of" and "consisting of".

[0061] When used herein, the term "immune defense response gene" may be used interchangeably with "IDR gene", or "immune defense gene" refers to one or more genes selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1.

[0062] The term "metastasis" refers to the presence of metastatic disease in organs outside the prostate tissue.

[0063] When used herein, the term "PDE4D7-related gene" may be used interchangeably with "PDE4D7 gene", and refers to one or more genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2.

[0064] As used herein, the term "T cell receptor signaling gene" may be used interchangeably with "TCR signaling gene", or "TCR gene" refers to one or more genes selected from the following: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70.

[0065] As used herein, the term "outcome" or "prediction of outcome" refers to the response of a prostate cancer subject to androgen deprivation therapy, preferably neoadjuvant androgen deprivation therapy.

[0066] As used herein, "PCAI immune score" refers to a model based on PDE4D7-related genes. Thus, the PCAI immune score provides a model based on the expression levels of each of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, where each expression level is weighted in the model. The model used is described in WO2022 / 043120, the entire content of which is incorporated herein by reference.

[0067] As used herein, the term "gene expression profile" refers to the characterization of one or more expression levels, particularly the expression level of PDE4D subtype 7 and / or three or more of the PDE4D7-related genes defined herein. Thus, the gene expression profile may include only the expression level of PDE4D7 (i.e., subtype 7 of the phosphodiesterase 4D gene). The gene expression profile may also, for example, include the PDE4D7 expression level and one or more additional expression levels, such as, but not limited to, one or more expression levels selected from the PDE4D7-related genes, one or more expression levels selected from the immune defense response genes, and / or one or more expression levels of the T cell receptor signaling genes defined herein. DETAILED DESCRIPTION OF THE INVENTION

[0069] The Immune System in Cancer

[0070] In recent years, the importance of the immune system in cancer suppression as well as cancer initiation, promotion, and metastasis has become very evident (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 constitute a key part of the tumor microenvironment, and most immune cells can infiltrate tumor tissues. The immune system and tumors interact and shape each other. Thus, anti - tumor immunity can prevent tumor formation, while the inflammatory tumor environment can promote cancer development and proliferation. Meanwhile, tumor cells that may originate in an immune - system - independent manner will shape the immune microenvironment by recruiting immune cells and can have a pro - inflammatory effect while suppressing anti - cancer immunity.

[0071] Some immune cells in the tumor microenvironment will have a general tumor - promoting or general tumor - suppressing effect, while other immune cells exhibit plasticity and show both tumor - promoting and tumor - suppressing potential. Thus, the overall immune microenvironment of the 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)).

[0072] In summary, the state of the immune system and the immune microenvironment affects the treatment outcome.

[0073] The inventors have identified gene signatures, as well as combinations of these signatures with clinical parameters, where the resulting models show a significant association with mortality and thus hold promise for improving the prediction of treatment effectiveness.

[0074] PDE4D7 - related genes

[0075] Phosphodiesterases (PDEs) are the sole means of degrading the second messenger 3'-5'-cyclic AMP. As such, they are poised to provide critical regulatory roles. Thus, aberrant changes in their expression, activity, and intracellular location can all contribute to the underlying molecular pathology of specific disease states. In fact, recent studies have shown that PDE gene mutations are enriched in prostate cancer patients, leading to elevated cAMP signaling and potential prostate cancer susceptibility. However, the diverse expression profiles in different cell types, coupled with the complex array of isoform variants within each PDE family, make it challenging to understand the link between PDE expression and functional aberrations during disease progression. Several studies have attempted to characterize the complementarity of PDEs in the prostate, all of which have identified significant expression levels of PDE4 relative to other PDEs, leading to the development of the PDE4D7 biomarker (see Alves de Inda M. et al., “Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes”, Eur Urol Focus, Vol. 4, No. 3, pages 376-384, 2018). Since the PDE4D7 biomarker has proven to be a good predictor, it is hypothesized that the ability to identify markers highly correlated with the PDE4D7 biomarker may also aid in prognosticating the outcomes of certain cancer subjects.

[0076] Based on the correlation between PDE4D7 expression and disease pathologic features, the aim was to determine the prognostic association between the expression of PDE4D7 in prostate tissue collected from patients by biopsy or surgery and clinically useful information related to individual patient outcomes. Clinically relevant endpoints, or surrogate endpoints that are significantly associated with the development of metastasis, cancer-specific, or overall mortality, are commonly evaluated as prognostic cancer biomarkers. The most relevant rationale for using surrogate endpoints is related to situations where established clinical endpoint data are unavailable or the number of events in the data cohort is too limited to perform statistical data analysis. To develop the PDE4D7 prognostic biomarker, progression-free survival without biochemical recurrence (BCR) or the initiation of secondary treatment after surgery was evaluated as a surrogate endpoint for metastasis and prostate cancer death. Using these specific endpoints, a relevant number of events (e.g., >30% for BCR) were identified in the selected clinical cohort, which is particularly relevant for multivariate data analysis.

[0077] In the evaluation conducted, standard methods of multivariate analysis such as Cox regression and Kaplan-Meier survival analysis were selected to study the added value and independent value of the continuous and / or categorical "PDE4D7 score" compared to established prognostic clinical variables such as PSA and Gleason score (Alves de Inda, 2018). A risk model was established in which logistic regression was used to combine the "PDE4D7 score" with clinical predictors of preoperative or postoperative surgical progression. Subsequently, in Kaplan-Meier survival and ROC curve analyses, the resulting model was tested on multiple independent patient cohorts to predict progression-free survival after treatment (Alves de Inda, 2018).

[0078] Using this strategy, the prognostic value of the PDE4D7 score was tested on retrospectively collected, resected prostate tissue biopsies from a cohort of patients continuously managed in a single surgical center in the postoperative setting (Alves de Inda, 2018). The patient population consisted of approximately 500 individuals who had longitudinal follow-up of pathological and biological outcomes. These clinical data were available for all patients and were collected during a median follow-up of 120 months after treatment. The "PDE4D7 score" was determined as described above and then tested in univariate and multivariate analyses using available postoperative covariates (i.e., pathological Gleason score, pT stage, surgical margin status, seminal vesicle invasion status, and lymph node invasion status) to adjust the multivariate setting. In this context, biochemical progression-free survival after primary intervention was set as the clinical endpoint for evaluation. Univariate analysis of these clinical samples (Alves de Inda, 2018) showed a negative correlation between PDE4D7 expression (represented by the "PDE4D7 score") and postoperative biochemical recurrence (HR = 0.53 per unit change; 95% CI 0.41 - 0.67; p < 0.0001), strongly confirming previous data (Boettcher 2015; Boettcher, 2016). In multivariate analysis with such clinical variables, the "PDE4D7 score" remained an independent and effective means of predicting clinical outcomes (HR = 0.56 per unit change; 95% CI 0.43 - 0.73; p < 0.0001). Additionally, when the "PDE4D7 score" in multivariate analysis was evaluated using the validated and clinically used risk model CAPRA-S, very similar results were obtained (HR = 0.54 95% CI 0.42 - 0.69; p < 0.0001). The CAPRA-S score is based on preoperative PSA and pathological parameters determined at the time of surgery and is designed to provide clinicians with information to help predict disease recurrence, including BCR, systemic progression, and PCSM, and has been validated in the United States and other populations.

[0079] Interestingly, when evaluating the hazard ratio (HR) compared to the continuous “PDE4D7 score”, a linear increase in risk was found as the “PDE4D score” decreased between 2 and 5. However, when the PDE4D7 score was below 2, the risk of postoperative progression increased sharply (Alves de Inda, 2018). This was also evident in the Kaplan-Meier survival curves, where patients grouped in the lowest “PDE4D7 score” category showed the highest risk of disease recurrence. Using logistic regression analysis, the CAPRA-S score was combined with the continuous “PDE4D7 score”. The model was tested using ROC curve analysis and showed a significant 4-6% increase in the AUC for predicting BCR progression at 2 and 5 years after treatment compared to CAPRA-S alone. Therefore, the combined CAPRA-S&‘PDE4D7 score’ Cox regression combined model was evaluated in Kaplan-Meier survival analysis and compared to the CAPRA-S score categories alone. After conducting this study, the added value of using the model (combined ‘PDE4D7&CAPRA-S’ score) for risk prediction compared to the clinical metrics of using the CAPRA-S score alone was confirmed (Alves de Inda, 2018).

[0080] After diagnosing prostate cancer, accurate risk assessment is needed before stratifying to definitive primary treatment. With this in mind, it was tested whether it was possible to convert the prognostic use of the “PDE4D7 score” in the preoperative setting of testing tumor tissue obtained from diagnostic needle biopsy samples (van Strijp 2018). In this study, 168 patients from a diagnostic clinical center underwent needle biopsy and received surgery as primary treatment. The shortest follow-up period for each patient was 60 months after this intervention. The clinical covariates used to adjust the “PDE4D7 score” in the multivariable analysis were age at surgery, preoperative PSA, PSA density, biopsy Gleason score, percentage of positive biopsy cores for tumor, percentage of biopsy tumor, and clinical cT stage. In this study, the utility of the “PDE4D7 score” and the combined “PDE4D7&CAPRA” score was evaluated compared to the preoperative CAPRA score in Cox regression analysis for biochemical recurrence (van Strijp 2018).

[0081] Evaluation of this patient cohort revealed (van Strijp 2018) that the "PDE4D7 score" in multivariable analysis was negatively correlated with BCR when 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). Kaplan-Meier analysis showed that, as before, in the postoperative setting, the "PDE4D7 score" categories were significantly associated with progression-free BCR survival (logrank p < 0.0001) and second-line treatment-free survival (logrank p = 0.01). Next, the combined logistic regression model developed in the previous cohort (van Strijp 2018) was employed. This included the combined "CAPRA&PDE4D7" score, indicating that patients in the highest combined 'CAPRA&PDE4D7' combined score category had little risk of biochemical progression or metastasis to any second-line treatment after surgery. To predict 5-year BCR after surgery, the logistic regression model was also evaluated using ROC curve analysis. This showed a 5% increase in AUC compared to the CAPRA score alone (AUCs were 0.82 and 0.77 respectively; p = 0.004). Decision curve analysis of the combined "CAPRA&PDE4D7" score model confirmed that the net benefit of using this combined score was higher at all decision thresholds compared to using either score alone, for deciding whether to intervene (such as surgery) based on the risk threshold of individual patients experiencing postoperative disease progression (van Strijp 2018).

[0082] Predicting treatment outcomes is very complex because many factors play a role in treatment efficacy and disease recurrence. Important factors may not have been identified, and the impact of other factors cannot be accurately determined. Currently, multiple clinicopathological measures are being studied and applied in the clinical setting to improve response prediction and treatment selection, providing a degree of improvement. However, there is still a strong need for better prediction of treatment response to improve the success rate of these therapies.

[0083] The identification and role of PDE4D7-related genes have been described in WO 2022 / 043120, the entire content of which is incorporated herein by reference.

[0084] Ideally, the expression level of PDE4D isoform 7 is thus used in the prediction model, but depending on the detection method, subtype-specific expression data may not always be available. In such cases, there may only be a general expression level covering all PDE4D isoforms. Therefore, a PDE4D7-specific gene signature (PDE4D7-related genes) can be used, which is based on the expression levels of genes specifically associated with the expression level of the PDE4D7 isoform.

[0085] Immune response defense genes

[0086] The integrity and stability of genomic DNA are constantly under pressure from various intra- and extracellular factors, such as exposure to radiation, viral or bacterial infections, and oxidative and replication stress (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 caused by various factors, such as single-strand or double-strand breaks, etc. This process involves the participation of multiple specific proteins, depending on the type of damage as part of the DNA recognition pathway.

[0087] Recent evidence suggests that mislocalized DNA (e.g., DNA that appears unnaturally in the cytoplasmic part of the cell, as opposed to the nucleus) and damaged DNA (such as through mutations that occur during cancer development) are used by the immune system to identify infected or otherwise diseased cells, while genomic and mitochondrial DNA present in healthy cells are ignored by the DNA recognition pathway. In diseased cells, cytoplasmic DNA sensor proteins have been shown to be involved in detecting DNA that appears unnaturally in the cell cytoplasm. The detection of such DNA by different nucleic acid sensors is translated into similar responses, leading to nuclear factor κB (NF-κB) and type I interferon (IFN I) signaling, and subsequently activating components of the innate immune system. While it is known that the recognition of viral DNA induces a type I IFN response, recent evidence suggests that sensing DNA damage can trigger an immune response.

[0088] TLR9 (Toll-like receptor 9) located in endosomes is one of the first identified DNA sensor molecules that signal downstream through the adaptor protein myeloid differentiation primary response protein 88 (MYD88) to participate in DNA immune recognition. This interaction in turn activates mitogen-activated protein kinase (MAPK) and NF-kB. TLR9 also activates IRF7 in plasmacytoid dendritic cells (pDCs) through IkB kinase α (IKKalpha), inducing the production of type I interferons. Various other DNA immune receptors, including IFI16 (IFN-γ-inducible protein 16), cGAS (cyclic GMP-AMP synthase), DDX41 (DEAD-box helicase 41), and ZBP1 (Z-DNA binding protein 1), interact with STING (stimulator of IFN genes), and STING activates the IKK complex and IRF3 through TBK1 (TANK-binding kinase 1). ZBP1 also activates NF-kB by recruiting RIP1 and RIP3 (receptor-interacting protein 1 and 3, respectively). When the helicase DHX36 (DEAH-box helicase 36) interacts with the complex of TRID to induce NF-kB and IRF-3 / 7, the DHX9 helicase stimulates MYD88-dependent signaling in plasmacytoid dendritic cells. The DNA sensor LRRFIP1 (leucine-rich repeat flightless-interacting protein) complexed with β-catenin activates the transcription of IRF3, while AIM2 (absent in melanoma 2) recruits the adaptor protein ASC (apoptosis speck-like protein) to induce caspase-1 activation of the inflammasome complex, leading to the secretion of interleukin-1β (IL-1β) and IL-18 (see Gasser S. et al., 2017, ibid., Figure 1 Schematic representation of DNA damage and DNA sensor pathways leading to the production of inflammatory cytokines and the expression of ligands activating innate immune receptors. Members of the non-homologous end joining pathway (orange), homologous recombination (red), inflammasome (dark green), NF-kB, and interferon response (light green) are shown.

[0089] The factors and mechanisms responsible for activating DNA sensor pathways in cancer are not well elucidated at present. Identifying the intratumoral DNA species, sensors, and pathways involved in IFN expression in different cancer types will be important at various stages of the disease. In addition to being therapeutic targets for cancer, these factors may also have prognostic and predictive value. Novel DNA sensor pathway agonists and antagonists are currently being developed and tested in preclinical trials. Such compounds will help to characterize the role of DNA sensor pathways in cancer pathogenesis, autoimmunity, and potentially other diseases.

[0090] The identification and role of immune response defense genes have been described in WO 2021 / 175746, the entire content of which is incorporated herein by reference.

[0091] T cell receptor signaling genes

[0092] The immune response against pathogens can be triggered at different levels: there are physical barriers, such as the skin, that block invaders. If breached, innate immunity comes into play; the first rapid non-specific response. If this is not sufficient, an adaptive immune response is triggered. This is much more specific and takes time to develop upon the first encounter with a pathogen. Lymphocytes are activated by interacting with activated antigen-presenting cells from the innate immune system and are also responsible for maintaining memory in order to respond more quickly upon the next encounter with the same pathogen.

[0093] Since lymphocytes are highly specific and efficient upon activation, they are subject to negative selection in their ability to recognize self, a process known as central tolerance. Since not all self-antigens are expressed at the selection sites, peripheral tolerance mechanisms also develop, such as ligation of the TCR in the absence of co-stimulation, expression of inhibitory co-receptors, and suppression by Tregs. Imbalance between activation and inhibition can lead to autoimmune disorders or immunodeficiency and cancer, respectively.

[0094] T cell activation can result in different functional consequences, depending on the location and type of T cells involved. CD8+ T cells differentiate into cytotoxic effector cells, while CD4+ T cells can differentiate into Th1 (secreting IFNγ and promoting cell-mediated immunity) or Th2 (secreting IL4 / 5 / 13 and promoting B cell and humoral immunity). Differentiation into other recently identified T cell subsets is also possible, such as Tregs, which have an inhibitory effect on immune activation (see Mosenden R. and Tasken K., “Cyclic AMP-mediated immune regulation – Overview of mechanisms of action in T-cells”, Cell Signal, Vol. 23, No. 6, pages 1009-1016 (2011), especially Figure 4, the activation of T cells by PKA and its regulation, and Tasken K. and Ruppelt A., “Negative regulation of T-cell receptor activation by the cAMP-PKA-Csk signalling pathway in T-cell lipid rafts”, Front Biosci, Vol. 11, pages 2929-2939 (2006)).

[0095] T cell activation can occur in naive and differentiated T cells. At the molecular level, the events following the ligation of the TCR to cognate antigen and the crosstalk with the signalling induced by co-stimulatory and co-inhibitory receptors determine whether a T cell will be activated or become unresponsive. Merely triggering the TCR itself is insufficient, which leads to T cell anergy. The B7:CD28 family of co-stimulatory molecules plays a central role in controlling the activation state of T cells upon antigen stimulation (Torheim E.A., “Immunity Leashed–Mechanisms of Regulation in the Human Immune System”, Thesis for the degree of Philosophiae Doctor (PhD), The Biotechnology Centre of Ola, University of Oslo, Norway, 2009).

[0096] Activation occurs when the TCR on the T cell surface interacts with the MHC peptide complex on the APC or target cell. Figure 2)。An immunological synapse is formed, lipid rafts in the T cell membrane coalesce, and Lck and Fyn are activated. These molecules phosphorylate the ITAMs in the CD3 subunits of the TCR, which aids in the recruitment of Zap-70 near 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 crucial for downstream TCR signaling. Grb2, Gads, PI3K, and NCK are recruited to LAT, propagating signals involving RAS, PKC activation, Ca2+ mobilization, calcineurin, and actin cytoskeleton polymerization (Tasken et al. Front in Bioscience 11:2929-2939 (2006)). This ultimately leads to the activation of NFkB, NFAT, AP1, and ATF family transcription factors, resulting in the transcription of immune activation genes (Mosenden et al. Cell Signalling 23:1009-1016 (2011); Tasken et al. Front in Bioscience 11:2929-2939 (2006)).

[0097] Signal transduction regulated by PKA and PDE4 intersects with TCR-induced T cell activation to fine-tune its regulation, with opposing effects (see Abrahamsen H. et al., “TCR-and CD28-mediated recruitment of phosphodiesterase 4 to lipid rafts potentiates TCR signaling”, J Immunol, Vol. 173, pages 4847-4848 (2004), especially Figure 6 , showing the opposing effects of PKA and PDG4 on TCR activation). The molecule that links these effectors is cyclic AMP (cAMP), which is an intracellular second messenger of the action of extracellular ligands. In T cells, it mediates the actions of prostaglandins, adenosine, histamine, β-adrenergic agonists, neuropeptide hormones, and β-endorphin. The binding of these extracellular molecules to GPCRs leads to their conformational change, the release of the stimulatory subunit, and subsequent activation of adenylyl cyclase (AC), which hydrolyzes ATP to cAMP (see Figure 6)。Although not the only one, PKA is the main effector of cAMP signaling (see Mosenden R. and Tasken K., 2011, ibid, and Tasken K. and Ruppelt A., 2006, ibid). At the functional level, an increase in cAMP levels results in a decrease in the production of IFNγ and IL-2 in T cells (see Abrahamsen H. et al., 2004, ibid). In addition to interfering with TCR activation, PKA has more effectors (see Figure 15 ).

[0098] In naive T cells, hyperphosphorylated PAG targets Csk to lipid rafts. Through the Ezrin-EBP50-PAG scaffold complex, PKA targets Csk. Through specific phosphorylation by PKA, Csk can negatively regulate Lck and Fyn to inhibit their activities and downregulate T cell activation (see Figure 6 ) of Abrahamsen H. et al. (2004, ibid). After TCR activation, PAG is dephosphorylated and Csk is released from the raft. Dissociation of Csk is required for T cell activation. During the same time course, a Csk-G3BP complex forms, which seems to sequester Csk outside the lipid raft (see Mosenden R. and Tasken K., 2011, ibid, and Tasken K. and Ruppelt A., 2006, ibid).

[0099] In contrast, co-stimulation of TCR and CD28 mediates the recruitment of the cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, enhancing cAMP degradation (see Figure 6 ) of Abrahamsen H. et al. (2004, ibid). Thus, TCR-induced cAMP production is counteracted and the T cell immune response is enhanced. When only TCR is stimulated, the recruitment of PDE4 may be too low to completely reduce cAMP levels, and thus maximal T cell activation cannot occur (see Abrahamsen H. et al., 2004, ibid).

[0100] Therefore, by actively inhibiting proximal TCR signaling, the signaling through cAMP-PKA-Csk is thought to set the threshold for T cell activation. Recruitment of PDE can counteract this inhibition. Tissue or cell type-specific regulation is achieved through the expression of multiple subtypes of AC, PKA, and PDE. As described above, a strict balance between activation and inhibition needs to be regulated to prevent the development of autoimmune disorders, immunodeficiencies, and cancer.

[0101] The identification and role of T cell receptor signaling genes have been described in WO 2021 / 175986, the entire content of which is incorporated herein by reference.

[0102] Selection of genes

[0103] Gene signatures and combinations of these signatures with clinical parameters have been determined, and the resulting models show a significant relationship with mortality, thus promising to improve the prediction of the effectiveness of these treatments.

[0104] 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 received RP treatment, and prostate cancer tissues were stored together with clinical (such as pathological Gleason grade group (pGGG), pathological status (pT stage)) and relevant outcome parameters (such as biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiotherapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, the PDE4D7 score was calculated and divided into four PDE4D7 score categories (see Alves de Inda M. et al., 2018, ibid.). PDE4D7 score category 1 represents the patient samples with the lowest PDE4D7 expression level, while PDE4D7 score category 4 represents the patient samples with the highest PDE4D7 expression level. RNASeq expression data (TPM - transcripts per million) of 538 prostate cancer subjects were studied to understand the differential gene expression between PDE4D7 score category 1 and 4. In particular, it was determined whether the average expression level of PDE4D7 score category 1 patients of approximately 20,000 protein-coding transcripts was more than twice that of PDE4D7 score category 4 patients. This analysis yielded a PDE4D7 score category 1 / PDE4D7 score category 4 ratio > 2 for 637 genes, with a minimum average expression of 1 TPM for each of the four PDE4D7 score categories. Then, molecular pathway analysis was further performed on these 637 genes, resulting in a series of enriched annotation clusters. Annotation cluster #2 showed enrichment of 30 genes (enrichment score: 10.8), which have functions of defense response to viruses, negative regulation of viral genome replication, and type I interferon signaling. Further heatmap analysis confirmed that the expression of these immune defense response genes was generally higher in the patient samples of PDE4D7 score category 1 than in those of PDE4D7 score category 4. By literature search to identify other genes with the same molecular function, the gene category with functions of viral defense response, negative regulation of viral genome replication, and type I interferon signaling was further enriched to 61 genes. Further selection from the 61 genes based on combinatorial power separated patients who died of prostate cancer from those who did not, resulting in a preferred set of 14 genes. It was found that the number of events (metastasis, prostate cancer-specific death) in the sub-cohort with low expression of these genes increased compared to the total patient cohort (#538) and the sub-cohort of 151 patients who received salvage RT (SRT) after postoperative disease recurrence.

[0105] 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 who underwent RP treatment had their prostate cancer tissues stored together with clinical (such as pathological Gleason grade group (pGGG), pathological status (pT stage)) and relevant outcome parameters (such as biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiotherapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, the PDE4D7 score was calculated and classified into four PDE4D7 score categories (see Alves de Inda M. et al., 2018, ibid.). PDE4D7 score category 1 represents the patient samples with the lowest PDE4D7 expression level, while PDE4D7 score category 4 represents the patient samples with the highest PDE4D7 expression level. Then, the RNASeq expression data (TPM – transcripts per million) of 538 prostate cancer subjects were studied to understand the differential gene expression between PDE4D7 score category 1 and 4. In particular, it was determined whether the average expression level of PDE4D7 score category 1 patients for approximately 20,000 protein-coding transcripts was more than twice the average expression level of PDE4D7 score category 4 patients. This analysis yielded a PDE4D7 score category 1 / PDE4D7 score category 4 ratio > 2 for 637 genes, with a minimum average expression of 1 TPM for each of the four PDE4D7 score categories. Then, molecular pathway analysis was further performed on these 637 genes, resulting in a series of enriched annotation clusters. Annotation cluster #6 showed an enrichment of 17 genes (enrichment score: 5.9), which have functions in primary immunodeficiency and activation of T cell receptor signaling. Further heatmap analysis confirmed that the expression of these T cell receptor signaling genes was generally higher in the samples of PDE4D7 score category 1 patients than in those of PDE4D7 score category 4 patients.

[0106] The identified PDE4D7-related genes ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 were identified as follows: In the RNAseq data of nearly 60,000 transcripts generated from 571 prostate cancer patients, a series of genes related to the expression of the known biomarker PDE4D7 in this data were identified. In the 571 samples, the correlation between the expression of any of these genes and PDE4D7 was calculated by Pearson correlation. If it was a positive correlation, it was represented as a value between 0 and 1, and if it was a negative correlation, it was represented as a value between -1 and 0. As the input data for calculating the correlation coefficient, the PDE4D7 score (see Alves de Inda M. et al., 2018, ibid.) and the TPM gene expression values / genes of interest determined by RNAseq (see below) were used.

[0107] The maximum negative correlation coefficient determined between the expression of any one of approximately 60,000 transcripts and the expression of PDE4D7 was -0.38, while the maximum positive correlation coefficient identified between the expression of any one of approximately 60,000 transcripts and the expression of PDE4D7 was +0.56. Genes with a correlation range of -0.31 to -0.38 and +0.41 to +0.56 were selected. A total of 77 transcripts that met these characteristics were identified. In a subcohort of 186 patients who received salvage radiotherapy (SRT) due to postoperative biochemical recurrence, eight PDE4D7-related genes ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 were selected from these 77 transcripts by iterative testing of the Cox regression combined model. The clinical endpoint tested was prostate cancer-specific death after the start of SRT. The boundary condition for selecting the eight genes was that for all genes retained in the model, the p-value in the multivariate Cox regression < 0.1.

[0108] In this literature, it is shown that the PDE4D7-related genes also have predictive value for the outcomes of prostate cancer subjects, preferably the outcomes of neoadjuvant androgen deprivation therapy. Since the PDE4D7-related group was determined to have a specific response to the PDE4D7 expression level, it was further speculated that the PDE4D7 expression level itself has the same predictive effect. The examples further demonstrated that a subset of three genes selected from the PDE4D7-related genes was sufficient to predict the outcomes of neoadjuvant ADT (Examples 3 and Figure 5-14 ). In addition, the examples also showed that BCR can be predicted based on the PDE4D (overall) expression level, thus further supporting the hypothesis that the long isoform-specific expression levels (such as PDE4D5, PDE4D7, and / or PDE4D9) can also predict BCR (Examples 5 and Figure 17 and18 , and comparing Figure 19 ).

[0109] Thus, in a first embodiment, the present invention provides a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, comprising:

[0110] - determining or receiving a determination result of a gene expression profile, the gene expression profile comprising: the gene expression level of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9, and / or

[0111] the gene expression levels of three or more genes, wherein the three or more gene expression levels are selected from:

[0112] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2,

[0113] the gene expression profile being determined in a biological sample obtained from the subject,

[0114] - a prediction based on the determination result of the gene expression profile,

[0115] - wherein the prediction is of a favorable or unfavorable response to neoadjuvant androgen deprivation therapy.

[0116] Optionally, the method further comprises the step of providing the result prediction to a healthcare provider or the subject.

[0117] In one embodiment, the present invention provides a computer-implemented method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, comprising:

[0118] - receiving a determination result of a gene expression profile

[0119] the gene expression profile comprising the gene expression level of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9, and / or the gene expression levels of three or more genes, wherein the three or more gene expression levels are selected from:

[0120] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2,

[0121] the gene expression profile being determined in a biological sample obtained from the subject,

[0122] - a prediction based on the determination result of the gene expression profile,

[0123] - wherein the prediction is of a favorable or unfavorable response to neoadjuvant androgen deprivation therapy. Optionally, the method further comprises the step of providing the result prediction to a healthcare provider or the subject.

[0124] In an alternative embodiment, the present invention relates to a method of treating a subject having prostate cancer, the method comprising:

[0125] - determining or receiving a determination of a gene expression profile

[0126] The gene expression profile comprises: the gene expression level of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9, and / or

[0127] the gene expression levels of three or more genes, wherein the three or more gene expression levels are selected from:

[0128] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2,

[0129] The gene expression profile is determined in a biological sample obtained from the subject,

[0130] - predicting based on the determination result of the gene expression profile,

[0131] - wherein the prediction is of a favorable or unfavorable response to neoadjuvant androgen deprivation therapy,

[0132] wherein, if the prediction is favorable, neoadjuvant androgen deprivation therapy is administered to the subject prior to local treatment; and

[0133] if the prediction is unfavorable, neoadjuvant androgen deprivation therapy is not administered to the subject prior to local treatment. In one embodiment, the local treatment is radical prostatectomy (RP) or radiotherapy (RT).

[0134] In one embodiment, the three or more PDE4D7-related genes are or comprise a combination of three PDE4D7-related genes disclosed in Tables 4 to 13 below.

[0135] The inventors found that an 8-gene signature using the PDE4D7-related gene set can well predict the outcome of NADT (in two independent datasets, the AUCs are 0.85 and 0.92, respectively), as shown in Examples 1 and 2 and the corresponding Figure 3 and 4As shown. In addition, the inventors tested 10 randomly selected combinations of three PDE4D7-related genes, and each selection gave a good to very good prediction, with an average AUC of 0.8. The AUC of the lowest scoring model was still 0.718, which is very acceptable. From these data, it can be concluded that the inventors have demonstrated that any random selection of three genes can be used to predict the NADT outcome, and thus the present invention is not limited to the 8-gene signature or the specific three-gene selection presented herein.

[0136] The inventors found that BCR can be reliably predicted based solely on the PDE4D (overall) expression level, and speculated that this is mainly due to the expression levels of the long isoforms such as PDE4D5, PDE4D7, and PDE4D9. Accordingly, in one embodiment, the present invention provides a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, comprising:

[0137] - determining or receiving a determination result of the PDE4D gene expression level,

[0138] wherein the gene expression level is determined in a biological sample obtained from the subject,

[0139] - predicting based on the determination result of the gene expression level,

[0140] - wherein the prediction is of a favorable or unfavorable response to neoadjuvant androgen deprivation therapy. Preferably, wherein the prediction is the chance of biochemical recurrence.

[0141] In one embodiment, the present invention provides a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, comprising:

[0142] - determining or receiving a determination result of the gene expression level of a PDE4D isoform selected from PDE4D5, PDE4D7, and PDE4D9 or a combination thereof,

[0143] wherein the gene expression level is determined in a biological sample obtained from the subject,

[0144] - predicting based on the determination result of the gene expression level,

[0145] - wherein said prediction is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy. Preferably, wherein said prediction is the chance of biochemical recurrence. In one embodiment, the method is based on the PDE4D5 expression level. In one embodiment, the method is based on the PDE4D7 expression level. In one embodiment, the method is based on the PDE4D9 expression level. In one embodiment, the method is based on the PDE4D5 and PDE4D7 gene expression levels. In one embodiment, the method is based on the PDE4D5 and PDE4D9 gene expression levels. In one embodiment, the method is based on the PDE4D7 and PDE4D9 gene expression levels. In one embodiment, the method is based on the PDE4D5, PDE4D7 and PDE4D9 gene expression levels.

[0146] As used herein, the term "PDE4D expression" or "PDE4D7 overall expression" refers to the expression level of the PDE4D gene, which does not distinguish between different subtypes and is thus the cumulative of all detected expression subtypes.

[0147] In one embodiment, the gene expression profile includes the PDE4D expression level and one or more, such as one, two, three, four, five, six, seven 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 comprises three or more, such as three, four, five, six, seven or all, of the PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2. Instead of the overall PDE4D expression level, the gene expression profile can use one or more subtype-specific expression levels selected from PDE4D5, PDE4D7 and / or PDE4D9 or combinations thereof.

[0148] Theoretically, the model can be improved by including the expression levels of one or more of 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 further includes the expression levels of one or more genes selected from:

[0149] - immune defense response genes, selected from: AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1, and / or

[0150] - T cell receptor signaling genes, selected from: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70.

[0151] In the present invention, prostate cancer-related death is preferably prostate cancer-specific death.

[0152] Regarding the above biological processes, three immune system-related gene signatures were selected, including the genes listed in Tables 1-3. Previously, the correlation of these signatures with predicting prostate cancer survival has been shown.

[0153] Table 1: Immune Defense Response (IDR) signature.

[0154]

[0155]

[0156] Table 2: T cell receptor (TCR) signature.

[0157] Gene Symbol Ensembl_ID CD2 ENSG00000116824 CD247 ENSG00000198821 CD28 ENSG00000178562 CD3E ENSG00000198851 CD3G ENSG00000160654 CD4 ENSG00000010610 CSK ENSG00000103653 EZR ENSG00000092820 FYN ENSG00000010810 LAT ENSG00000213658 LCK ENSG00000182866 PAG1 ENSG00000076641 PDE4D ENSG00000113448 PRKACA ENSG00000072062 PRKACB ENSG00000142875 PTPRC ENSG00000081237 ZAP70 ENSG00000115085

[0158] Table 3: PDE4D7-related (PDE4D7-R2) signature.

[0159] Gene Symbol Ensembl_ID ABCC5 ENSG00000114770 CUX2 ENSG00000111249 KIAA1549 ENSG00000122778 PDE4D ENSG00000113448 RAP1GAP2 ENSG00000132359 SLC39A11 ENSG00000133195 TDRD1 ENSG00000095627 VWA2 ENSG00000165816

[0160] It was 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 independently, one or more / gene panels, in combinations of one or more / gene panels, or when combined in their entirety, are capable of predicting the outcome of prostate cancer subjects.

[0161] The term "ABCC5" refers to the human ATP-binding cassette sub-family C member 5 gene (Ensembl: ENSG00000114770), for example, the sequence defined in NCBI reference sequence NM_001023587.2 or NCBI reference sequence NM_005688.3. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:1 or SEQ ID NO:2, which corresponds to the above NCBI reference sequences of the ABCC5 transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:3 or SEQ ID NO:4, which correspond to the protein sequences defined in NCBI protein accession reference sequences NP_001018881.1 and NP_005679 encoding the ABCC5 polypeptide.

[0162] The term "ABCC5" also includes nucleotide sequences that show high homology with ABCC5, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:1 or SEQ ID NO:2, 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 shown in SEQ ID NO:3 or SEQ ID NO:4, or nucleic acid sequences encoding 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 shown in SEQ ID NO:3 or SEQ ID NO:4, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:1 or SEQ ID NO:2.

[0163] The term "AIM2" refers to absent in melanoma 2 gene (Ensembl: ENSG00000163568), for example, the sequence defined in NCBI reference sequence NM_004833. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:5, which corresponds to the above NCBI reference sequence of the AIM2 transcript, and also relates to the corresponding amino acid sequences, such as the sequence shown in SEQ ID NO:6, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_004824 encoding the AIM2 polypeptide.

[0164] The term "AIM2" also includes nucleotide sequences that show a high degree of homology to AIM2, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:5, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:6, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:6, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:5.

[0165] The term "APOBEC3A" refers to the apolipoprotein B mRNA editing enzyme catalytic subunit 3A gene (Ensembl: ENSG00000128383), such as the sequence defined in NCBI reference sequence NM_145699. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:7, which corresponds to the sequence of the above-mentioned APOBEC3A transcript in the NCBI reference sequence, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:8, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_663745 encoding the APOBEC3A polypeptide.

[0166] The term "APOBEC3A" also includes nucleotide sequences that show a high degree of homology to APOBEC3A, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:7, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:8, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:8, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:7.

[0167] The term "CD2" refers to the cluster of differentiation 2 gene (Ensembl: ENSG00000116824), such as the sequence defined in NCBI reference sequence NM_001767. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 9, which corresponds to the sequence of the above NCBI reference sequence of the CD2 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 10, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_001758 encoding the CD2 polypeptide.

[0168] The term "CD2" also includes nucleotide sequences that show high homology with CD2, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 9, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 10, or nucleic acid sequences encoding amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 10, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 9.

[0169] The term "CD247" refers to the cluster of differentiation 247 gene (Ensembl: ENSG00000198821), such as the sequence defined in NCBI reference sequence NM_000734 or NCBI reference sequence NM_198053. In particular, it refers to the nucleotide sequences shown in SEQ ID NO: 11 or SEQ ID NO: 12, which correspond to the sequences of the above NCBI reference sequences of the CD247 transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO: 13 or SEQ ID NO: 14, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_000725 and NCBI protein accession reference sequence NP_932170 encoding the CD247 polypeptide.

[0170] The term "CD247" also includes nucleotide sequences that show a high degree of homology with CD247, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:11 or SEQ ID NO:12, 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 shown in SEQ ID NO:13 or SEQ ID NO:14, or nucleic acid sequences that encode 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 shown in SEQ ID NO:13 or SEQ ID NO:14, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:11 or SEQ ID NO:12.

[0171] The term "CD 28" refers to the cluster of differentiation 28 gene (Ensembl: ENSG00000178562), such as the sequences defined in NCBI reference sequence NM_006139 or NCBI reference sequence NM_001243078. In particular, it refers to the nucleotide sequences shown in SEQ ID NO:15 or SEQ ID NO:16, which correspond to the sequences of the above NCBI reference sequences of the CD28 transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:17 or SEQ ID NO:18, which correspond to the protein sequences defined in NCBI protein accession reference sequences NP_006130 and NCBI protein accession reference sequence NP_001230007 encoding the CD28 polypeptide.

[0172] The term "CD28" also includes nucleotide sequences that show a high degree of homology with CD28, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:15 or SEQ ID NO:16, 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 shown in SEQ ID NO:17 or SEQ ID NO:18, or nucleic acid sequences encoding 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 shown in SEQ ID NO:17 or SEQ ID NO:18, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ IDNO:15 or SEQ ID NO:16.

[0173] The term "CD3E" refers to the cluster of differentiation 3E gene (Ensembl: ENSG00000198851), such as the sequence defined in NCBI reference sequence NM_000733. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:19, which corresponds to the sequence of the above NCBI reference sequence of the CD3E transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:20, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000724 encoding the CD3E polypeptide.

[0174] The term "CD3E" also includes nucleotide sequences that show a high degree of homology to CD3E, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:19, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:20, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:20, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:19.

[0175] The term "CD3G" refers to the cluster of differentiation 3G gene (Ensembl: ENSG00000160654), such as the sequence defined in NCBI reference sequence NM_000073. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:21, which corresponds to the sequence of the above NCBI reference sequence of the CD3G transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:22. It corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000064 encoding the CD3G polypeptide.

[0176] The term "CD3G" also includes nucleotide sequences that show a high degree of homology to CD3G, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:21, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:22, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:22, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:21.

[0177] The term "CD4" refers to the cluster of differentiation 4 gene (Ensembl: ENSG00000010610), for example, the sequence defined in NCBI reference sequence NM_000616. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 23, which corresponds to the sequence of the above NCBI reference sequence of the CD4 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 24, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_000607 encoding the CD4 polypeptide.

[0178] The term "CD4" also includes nucleotide sequences that show a high degree of homology with CD4, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 23, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 24, or nucleic acid sequences encoding amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 24, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 23.

[0179] The term "CIAO1" refers to the cytoplasmic iron-sulfur assembly component 1 gene (Ensembl: ENSG00000144021), for example, the sequence defined in NCBI reference sequence NM_004804. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 25, which corresponds to the sequence of the above NCBI reference sequence of the CIAO1 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 26, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_663745 encoding the CIAO1 polypeptide.

[0180] The term "CIAO1" also includes nucleotide sequences that show a high degree of homology with CIAO1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:25, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:26, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:26, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:25.

[0181] The term "CSK" refers to the C-terminal Src kinase gene (Ensembl: ENSG00000103653), such as the sequence defined in NCBI reference sequence NM_004383. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:27, which corresponds to the sequence of the above NCBI reference sequence of the CSK transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:28, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_004374 encoding the CSK polypeptide.

[0182] The term "CSK" also includes nucleotide sequences that show a high degree of homology with CSK, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:27, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:28, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:28, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:27.

[0183] The term "CUX2" refers to the human cut-like homeobox 2 gene (Ensembl: ENSG00000111249), such as the sequence defined in NCBI reference sequence NM_015267.3. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:29, which corresponds to the sequence of the above NCBI reference sequence of the CUX2 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:30, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_056082.2 encoding the CUX2 polypeptide.

[0184] The term "CUX2" also includes nucleotide sequences that show high homology with CUX2, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:29, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:30, or nucleic acid sequences encoding amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:30, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:29.

[0185] The term "DDX58" refers to the DExD / H-box helicase 58 gene (Ensembl: ENSG00000107201), such as the sequence defined in NCBI reference sequence NM_014314. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:31, which corresponds to the sequence of the above NCBI reference sequence of the DDX58 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:32, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_055129 encoding the DDX58 polypeptide.

[0186] The term "DDX58" also includes nucleotide sequences that show a high degree of homology with DDX58, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence shown in SEQ ID NO:31, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence shown in SEQ ID NO:32, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence shown in SEQ ID NO:32, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence shown in SEQ ID NO:31.

[0187] The term "DHX9" refers to the DExD / H-box helicase 9 gene (Ensembl: ENSG00000135829), such as the sequence defined in NCBI reference sequence NM_001357. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:33, which corresponds to the sequence of the above NCBI reference sequence of the DHX9 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:34, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001348 encoding the DHX9 polypeptide.

[0188] The term "DHX9" also includes nucleotide sequences that show a high degree of homology with DHX9, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence shown in SEQ ID NO:33, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence shown in SEQ ID NO:34, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence shown in SEQ ID NO:34, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence shown in SEQ ID NO:33.

[0189] The term "EZR" refers to the Ezrin gene (Ensembl: ENSG00000092820), such as the sequence defined in NCBI Reference Sequence NM_003379. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 35, which corresponds to the sequence of the above NCBI reference sequence of the EZR transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 36, which corresponds to the protein sequence defined in NCBI Protein Accession Reference Sequence NP_003370 encoding the EZR polypeptide.

[0190] The term "EZR" also includes nucleotide sequences that show high homology with EZR, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 35, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 36, or nucleic acid sequences encoding amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 36, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 35.

[0191] The term "FYN" refers to the FYN proto-oncogene (Ensembl: ENSG00000010810), such as the sequence defined in NCBI Reference Sequence NM_002037 or NCPI Reference Sequence NM_153047 or NCBI Reference Sequence NM_153048. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 37 or SEQ ID NO: 38 or SEQ ID NO: 39, which corresponds to the sequence of the above NCBI reference series of the FYN transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 40 or 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 encoding the FYN polypeptide.

[0192] The term "FYN" also includes nucleotide sequences that show a high degree of homology to the FYN display, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:37 or SEQ ID NO:38 or SEQ ID NO:39, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:40 or SEQ ID NO:41 or SEQ ID NO:42, or nucleic acid sequences that encode 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 shown in SEQ ID NO:40 or SEQ ID NO:41 or SEQ ID NO:42, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:37 or SEQ ID NO:38 or SEQ ID NO:39.

[0193] The term "IFI16" refers to the interferon gamma-induced protein 16 gene (Ensembl: ENSG00000163565), such as the sequence defined in NCBI reference sequence NM_005531. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:43, which corresponds to the sequence of the NCBI reference sequence of the above IFI16 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:44, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_005522 encoding the IFI16 polypeptide.

[0194] The term "IFI16" also includes nucleotide sequences that show a high degree of homology with IFI16, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:43, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:44, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:44, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:43.

[0195] The term "IFIH1" refers to the interferon-induced helicase C domain 1 gene (Ensembl: ENSG00000115267), such as the sequence defined in NCBI reference sequence NM_022168. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:45, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the IFIH1 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:46, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_071451 encoding the IFIH1 polypeptide.

[0196] The term "IFIH1" also includes nucleotide sequences that show a high degree of homology with IFIH1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:45, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:46, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:46, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:45.

[0197] The term "IFIT1" refers to the interferon-induced protein with tetratricopeptide repeats 1 gene (Ensembl: ENSG00000185745), such as the sequence defined in NCBI reference sequence NM_001270929 or NCBI reference sequence NM_001548.5. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 47 or SEQ ID NO: 48, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the IFIT1 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 49 or SEQ ID NO: 50, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_001257858 and NCBI protein accession reference sequence NP_001539 encoding the IFIT1 polypeptide.

[0198] The term "IFIT1" also includes nucleotide sequences that show a high degree of homology with IFIT1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 47 or SEQ ID NO: 48, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 49 or SEQ ID NO: 50, or nucleic acid sequences encoding amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 49 or SEQ ID NO: 50, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 47 or SEQ ID NO: 48.

[0199] The term "IFIT3" refers to the interferon-induced protein with tetratricopeptide repeats 3 gene (Ensembl: ENSG00000119917), such as the sequence defined in NCBI reference sequence NM_001031683. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 51, which corresponds to the sequence of the above-mentioned NCBI reference sequence of the IFIT3 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 52, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_001026853 encoding the IFIT3 polypeptide.

[0200] The term "IFIT3" also includes nucleotide sequences that show a high degree of homology with IFIT3, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:51, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:52, or nucleic acid sequences encoding amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:52, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:51.

[0201] The term "KIAA1549" refers to the human KIAA1549 gene (Ensembl: ENSG00000122778), such as the sequences defined in NCBI reference sequence NM_020910 or NCBI reference sequence NM_001164665. In particular, it refers to the nucleotide sequences shown in SEQ ID NO:53 or SEQ ID NO:54, which correspond to the sequences of the NCBI reference sequences of the above KIAA1549 transcripts, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:55 or SEQ ID NO:56, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_065961 and NCBI protein accession reference sequence NP_001158137 encoding the KIAA1549 polypeptide.

[0202] The term "KIAA1549" also includes nucleotide sequences that show a high degree of homology with KIAA1549, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:53 or SEQ ID NO:54, 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 shown in SEQ ID NO:55 or SEQ ID NO:56, or nucleic acid sequences that encode 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 shown in SEQ ID NO:55 or SEQ ID NO:56, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:53 or SEQ ID NO:54.

[0203] The term "LAT" refers to the T cell activation linker protein gene (Ensembl: ENSG00000213658), such as the sequences defined in NCBI reference sequence NM_001014987 or NCBI reference sequence NM_014387. In particular, it refers to the nucleotide sequences shown in SEQ ID NO:57 or SEQ ID NO:58, which correspond to the sequences of the above NCBI reference sequences of the LAT transcript, and also relates to the corresponding amino acid sequences, such as the amino acid sequences shown in SEQ ID NO:59 or SEQ ID NO:60, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_001014987 and NCBI protein accession reference sequence NP_055202 encoding the LAT polypeptide.

[0204] The term "LAT" also includes nucleotide sequences that show a high degree of homology with the LAT display, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:57 or SEQ ID NO:58, 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 shown in SEQ ID NO:59 or SEQ ID NO:60, or nucleic acid sequences that encode 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 shown in SEQ ID NO:59 or SEQ ID NO:60, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:57 or SEQ ID NO:58.

[0205] The term "LCK" refers to the LCK proto-oncogene (Ensembl: ENSG00000182866), such as the sequence defined in NCBI reference sequence NM_005356. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:61, which corresponds to the sequence of the above NCBI reference sequence of the LCK transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:62, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_005347 encoding the LCK polypeptide.

[0206] The term "LCK" also includes nucleotide sequences that show a high degree of homology with the LCK display, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:61, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:62, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:62, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:61.

[0207] The term "LRRFIP1" refers to the LRR-binding FLII interaction protein 1 gene (Ensembl: ENSG00000124831), such as the sequences defined in NCBI reference sequence NM_004735 or NCBI reference sequence NM_001137550 or NCBI reference sequence NM_001137553 or NCBI reference sequence NM_001137552. In particular, it refers to the nucleotide sequences shown in SEQ ID NO:63 or SEQ ID NO:64 or SEQ ID NO:65 or SEQ ID NO:66, which correspond to the sequences of the above NCBI reference sequences of the LRRFIP1 transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:67 or SEQ ID NO:68 or SEQ ID NO:69 or SEQ ID NO:70, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_004726, NCBI protein accession reference sequence NP_001131022, NCBI protein accession reference sequence NP_001131025 and NCBI protein accession reference sequence NP_001131024 that encode the LRRFIP1 polypeptide.

[0208] The term "LRRFIP1" also includes nucleotide sequences that show a high degree of homology with LRRFIP1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:63 or SEQ ID NO:64 or SEQ ID NO:65 or SEQ ID NO:66, 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 shown in SEQ ID NO:67 or SEQ ID NO:68 or SEQ ID NO:69 or SEQ ID NO:70, or nucleic acid sequences encoding 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 shown in SEQ ID NO:67 or SEQ ID NO:68 or SEQ ID NO:69 or SEQ ID NO:70, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:63 or SEQ ID NO:64 or SEQ ID NO:65 or SEQ ID NO:66.

[0209] The term "MYD88" refers to the MYD88 innate immune signaling adaptor protein gene (Ensembl: ENSG00000172936), for example, the sequence defined in NCBI reference sequence NM_001172567 or NCBI reference sequence NM_001172568 or NCBI reference sequence NM_001172569 or NCBI reference sequence NM_001172566 or NCBI reference sequence NM_002468, and in particular, refers to the nucleotide sequences shown in SEQ ID NO:71 or SEQ ID NO:72 or SEQ ID NO:73 or SEQ ID NO:74 or SEQ ID NO:75, which correspond to the sequences of the above NCBI reference sequences of the MYD88 transcript, and also refers to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:76 or SEQ ID NO:77 or SEQ ID NO:78 or SEQ ID NO:79 or SEQ ID NO:80, 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 that encode the MYD88 polypeptide.

[0210] The term "MYD88" also includes nucleotide sequences that show a high degree of homology with MYD88, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:71 or SEQ ID NO:72 or SEQ ID NO:73 or SEQ ID NO:74 or SEQ ID NO:75, 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 shown in SEQ ID NO:76 or SEQ ID NO:77 or SEQ ID NO:78 or SEQ ID NO:79 or SEQ ID NO:80, or nucleic acid sequences encoding 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 shown in SEQ ID NO:76 or SEQ ID NO:77 or SEQ ID NO:78 or SEQ ID NO:79 or SEQ ID NO:80, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:71 or SEQ ID NO:72 or SEQ ID NO:73 or SEQ ID NO:74 or SEQ ID NO:75.

[0211] The term "OAS1" refers to the 2'-5'-oligoadenylate synthetase 1 gene (Ensembl: ENSG00000089127), such as the sequence defined in NCBI reference sequence NM_001320151 or NCBI reference sequence NM_002534 or NCBI reference sequence NM_001032409 or NCBI reference sequence NM_016816. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:81 or SEQ ID NO:82 or SEQ ID NO:83 or SEQ ID NO:84, which corresponds to the sequence of the above NCBI reference sequences of the OAS1 transcript. It also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:85 or SEQ ID NO:86 or SEQ ID NO:87 or SEQ ID NO:88, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_001307080, NCBI protein accession reference sequence NP_0002525, NCBI protein accession reference sequence NP_001027581, and NCBI protein accession reference sequence NP_058132 encoding the OAS1 polypeptide.

[0212] The term "OAS1" also includes nucleotide sequences that show a high degree of homology with OAS1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences shown in SEQ ID NO:81 or SEQ ID NO:82 or SEQ ID NO:83 or SEQ ID NO:84, 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 shown in SEQ ID NO:85 or SEQ ID NO:86 or SEQ ID NO:87 or SEQ ID NO:88, or nucleic acid sequences encoding 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 shown in SEQ ID NO:85 or SEQ ID NO:86 or SEQ ID NO:87 or SEQ ID NO:88, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences shown in SEQ ID NO:81 or SEQ ID NO:82 or SEQ ID NO:83 or SEQ ID NO:84.

[0213] The term "PAG1" refers to the phosphoprotein associated with glycosphingolipid microdomains 1 gene (Ensembl: ENSG00000076641), for example, the sequence defined in NCBI reference sequence NM_018440. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 89, which corresponds to the sequence of the above NCBI reference sequence of the PAG1 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 90, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_060910 encoding the PAG1 polypeptide.

[0214] The term "PAG1" also includes nucleotide sequences that show a high degree of homology with PAG1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 89, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 90, or nucleic acid sequences encoding amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 90, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 89.

[0215] The term "PDE4D" refers to the human phosphodiesterase 4D gene (Ensembl: ENSG00000113448), such as the NCBI reference sequence NM_001104631 or the NCBI reference sequence NM_001349242 or the NCBI reference sequence NM_001197218 or the NCBI reference sequence NM_006203 or the NCBI reference sequence NM_0001197221 or the NCBI reference sequence NM_001197220 or the NCBI reference sequence NM_001197223 or the NCBI reference sequence NM_001165899 or the NCBI reference sequence NM_001165899, and in particular, refers to the nucleotide sequences shown in SEQ ID NO:91 or SEQ ID NO:92 or SEQ ID NO:93 or SEQ ID NO:94 or SEQ ID NO:95 or SEQ ID NO:96 or SEQ ID NO:97 or SEQ ID NO:98 or SEQ ID NO:99, which correspond to the sequences of the above NCBI reference sequences of the PDE4D transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107 or SEQ ID NO:108, which correspond to the protein sequences defined in the NCBI protein accession reference sequences NP_001098101, NCBI protein accession reference sequence NP_001336171, NCBI protein accession reference sequence NP_001184147, NCBI protein accession reference sequence NP_006194 and 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 encoding the PDE4D polypeptide.

[0216] The term "PDE4D" also includes nucleotide sequences that show a high degree of homology with PDE4D, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:91 or SEQ ID NO:92 or SEQ ID NO:93 or SEQ ID NO:94 or SEQ ID NO:95 or SEQ ID NO:96 or SEQ ID NO:97 or SEQ ID NO:98 or SEQ ID NO:99, or 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 shown in SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107 or SEQ ID NO:108, or nucleic acid sequences encoding 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 shown in SEQ ID NO:100, SEQ ID NO:101, SEQ ID NO:102, SEQ ID NO:103, SEQ ID NO:104, SEQ ID NO:105, SEQ ID NO:106, SEQ ID NO:107 or SEQ ID NO:108, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:91 or SEQ ID NO:92 or SEQ ID NO:93 or SEQ ID NO:94 or SEQ ID NO:95 or SEQ ID NO:96 or SEQ ID NO:97 or SEQ ID NO:98 or SEQ ID NO:99.

[0217] The term "PRKACA" refers to the protein kinase cAMP-activated catalytic subunit alpha gene (Ensembl: ENSG00000072062), such as the sequences defined in NCBI reference sequence NM_002730 or NCBI reference sequence NM_207518. In particular, it refers to the nucleotide sequences shown in SEQ ID NO: 109 or SEQ ID NO: 110, which correspond to the sequences of the above NCBI reference sequences of the PRKACA transcript. It also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO: 111 or SEQ ID NO: 112, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_002721 and NCBI protein accession reference sequence NP_997401 encoding the PRKACA polypeptide.

[0218] The term "PRKACA" also includes nucleotide sequences that show a high degree of homology with PRKACA, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO: 109 or SEQ ID NO: 110, 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 shown in SEQ ID NO: 111 or SEQ ID NO: 112, or nucleic acid sequences encoding 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 shown in SEQ ID NO: 111 or SEQ ID NO: 112, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO: 109 or SEQ ID NO: 110.

[0219] The term "PRKACB" refers to the protein kinase cAMP-activated catalytic subunit beta gene (Ensembl: ENSG00000142875), such as the sequence defined in NCBI reference sequence NM_002731 or NCBI reference sequence NM_182948 or NCBI reference sequence NM_001242860 or NCBI reference sequence NM_001242859 or NCBI reference sequence NM_001242858 or NCBI reference sequence NM_001242862 or NCBI reference sequence NM_001242861 or NCBI reference sequence NM_001300915 or NCBI reference sequence NM_207578 or NCBI reference sequence NM_001242857 or NCBI reference sequence NM_001300917. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:113 or SEQ ID NO:114 or SEQ ID NO:115 or SEQ ID NO:116 or SEQ ID NO:117 or SEQ ID NO:118 or SEQ ID NO:119 or SEQ ID NO:120 or SEQ ID NO:121 or SEQ ID NO:122 or SEQ ID NO:123, which corresponds to the sequence of the above NCBI reference sequences of the PRKACB transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:124 or SEQ ID NO:125, SEQ ID NO:126, SEQ ID NO:127, SEQ ID NO:128, SEQ ID NO:129, SEQ ID NO:130, SEQ ID NO:131, SEQ ID NO:132, SEQ ID NO:133 or SEQ ID NO:134, which corresponds to the protein sequences defined in NCBI protein accession reference sequence NP_002722, NCBI protein accession reference sequence NP_891993, NCBI protein accession reference sequence NP_001229789, NCBI protein accession reference sequence NP_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_001287844, NCBI protein accession reference sequence NP_997461, NCBI protein accession reference sequence NP_001229786 and NCBI protein accession reference sequence NP_001287846 that encode the PRKACB polypeptide.

[0220] The term "PRKACB" also includes nucleotide sequences that show a high degree of homology to PRKACB, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:113 or SEQ ID NO:114 or SEQ ID NO:115 or SEQ ID NO:116 or SEQ ID NO:117 or SEQ ID NO:118 or SEQ ID NO:119 or SEQ ID NO:120 or SEQ ID NO:121 or SEQ ID NO:122 or SEQ ID NO:123, or 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 shown in SEQ ID NO:124 or 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 nucleic acid sequences that encode 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 shown in SEQ ID NO:124 or 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 amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:113 or SEQ ID NO:114 or SEQ ID NO:115 or SEQ ID NO:116 or SEQ ID NO:117 or SEQ ID NO:118 or SEQ ID NO:119 or SEQ ID NO:120 or SEQ ID NO:121 or SEQ ID NO:122 or SEQ ID NO:123.

[0221] The term "PTPRC" refers to the protein tyrosine phosphatase receptor type C gene (Ensembl: ENSG00000081237), for example, the sequences defined in NCBI reference sequence NM_002838 or NCBI reference sequence NM_080921, and in particular, refers to the sequences shown in SEQ ID NO: 135 or SEQ ID NO: 136, which correspond to the sequences of the above NCBI reference sequences of PTPRC transcripts, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO: 137 or SEQ ID NO: 138, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_002829 and NCBI protein accession reference sequence NP_563578 encoding PTPRC polypeptides.

[0222] The term "PTPRC" also includes nucleotide sequences that show a high degree of homology with PTPRC, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO: 135 or SEQ ID NO: 136, 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 shown in SEQ ID NO: 137 or SEQ ID NO: 138, or nucleic acid sequences encoding 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 shown in SEQ ID NO: 137 or SEQ ID NO: 138, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO: 135 or SEQ ID NO: 136.

[0223] The term "RAP1GAP2" refers to the human RAP1 GTPase activating protein 2 gene (ENSG00000132359), such as the sequences defined in NCBI reference sequence NM_015085 or NCBI reference sequence NM_001100398 or NCBI reference sequence NM_001330058, and in particular, refers to the nucleotide sequences shown in SEQ ID NO:139 or SEQ ID NO:140 or SEQ ID NO:141, which correspond to the sequences of the above NCBI reference sequences of the RAP1GAP2 transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:142 or SEQ ID NO:143 or SEQ ID NO:144, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_055900, NCBI protein accession reference sequence NP_001093868, and NCBI protein accession reference sequence NP_001316987 encoding the RAP1GAP2 polypeptide.

[0224] The term "RAP1GAP2" also includes nucleotide sequences that show a high degree of homology with RAP1GAP2, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences shown in SEQ ID NO:139 or SEQ ID NO:140 or SEQ ID NO:141, 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 shown in SEQ ID NO:142 or SEQ ID NO:143 or SEQ ID NO:144, or nucleic acid sequences encoding 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 shown in SEQ ID NO:142 or SEQ ID NO:143 or SEQ ID NO:144, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences shown in SEQ ID NO:139 or SEQ ID NO:140 or SEQ ID NO:141.

[0225] The term "SLC39A11" refers to the human solute carrier family 39 member 11 gene (Ensembl: ENSG00000133195), such as the sequence defined in NCBI reference sequence NM_139177 or NCBI reference sequence NM_001352692. In particular, it refers to the nucleotide sequence shown in SEQ ID NO: 145 or SEQ ID NO: 146, which corresponds to the above NCBI reference sequence of the SLC39A11 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 147 or SEQ ID NO: 148, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_631916 and NCBI protein accession reference sequence NP_001339621 encoding the SLC39A11 polypeptide.

[0226] The term "SLC39A11" also includes nucleotide sequences that show a high degree of homology with SLC39A11, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 145 or SEQ ID NO: 146, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 147 or SEQ ID NO: 148, or nucleic acid sequences encoding amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 147 or SEQ ID NO: 148, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO: 145 or SEQ ID NO: 146.

[0227] The term "TDRD1" refers to the human tudor domain containing 1 gene (Ensembl: ENSG00000095627), such as the sequence defined in NCBI reference sequence NM_198795. In particular, it is the nucleotide sequence shown in SEQ ID NO: 149, which corresponds to the above NCBI reference sequence of the TDRD1 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO: 150, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_942090 encoding the TDRD1 polypeptide.

[0228] The term "TDRD1" also includes nucleotide sequences that show a high degree of homology with TDRD1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:149, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:150, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:150, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:149.

[0229] The term "TLR8" refers to the Toll-like receptor 8 gene (Ensembl: ENSG00000101916), such as the sequences defined in NCBI reference sequence NM_138636 or NCBI reference sequence NM_016610. In particular, it refers to the nucleotide sequences shown in SEQ ID NO:151 or SEQ ID NO:152, which correspond to the sequences of the above NCBI reference sequences of the TLR8 transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:153 or SEQ ID NO:154, which correspond to the protein sequences defined in NCBI protein accession reference sequence NP_619542 and NCBI protein accession reference sequence NP_057694 encoding the TLR8 polypeptide.

[0230] The term "TLR8" also includes nucleotide sequences that show a high degree of homology with TLR8, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:151 or SEQ ID NO:152, 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 shown in SEQ ID NO:153 or SEQ ID NO:154, or nucleic acid sequences that encode 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 shown in SEQ ID NO:153 or SEQ ID NO:154, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:151 or SEQ ID NO:152.

[0231] The term "VWA2" refers to the human Von Willebrand factor A domain 2 gene (Ensembl: ENSG00000165816), such as the sequence defined in NCBI reference sequence NM_001320804. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:155, which corresponds to the sequence of the above NCBI reference sequence of the VWA2 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:156, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001307733 encoding the VWA2 polypeptide.

[0232] The term "VWA2" also includes nucleotide sequences that show a high degree of homology with the VWA2 display, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:155, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:156, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:156, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:155.

[0233] The term "ZAP70" refers to the T cell receptor-associated protein kinase 70 Zeta chain gene (Ensembl: ENSG00000115085), such as the sequences defined in NCBI reference sequence NM_001079 or NCBI reference sequence NM_207519. In particular, it refers to the nucleotide sequences shown in SEQ ID NO:157 or SEQ ID NO:158, which correspond to the sequences of the above NCBI reference sequences of the ZAP70 transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:159 or SEQ ID NO:160, which correspond to the protein sequences defined in NCBI protein accession reference sequences NP_001070 and NCBI protein accession reference sequence NP_997402 encoding the ZAP70 polypeptide.

[0234] The term "ZAP70" also includes nucleotide sequences that show a high degree of homology with ZAP70, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:157 or SEQ ID NO:158, 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 shown in SEQ ID NO:159 or SEQ ID NO:160, or nucleic acid sequences that encode 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 shown in SEQ ID NO:159 or SEQ ID NO:160, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:157 or SEQ ID NO:158.

[0235] The term "ZBP1" refers to the Z-DNA binding protein 1 gene (Ensembl: ENSG00000124256), such as the sequences defined in NCBI reference sequence NM_030776 or NCBI reference sequence NM_001160418 or NCBI reference sequence NM_001160419. In particular, it refers to the nucleotide sequences shown in SEQ ID NO:161 or SEQ ID NO:162 or SEQ ID NO:163, which correspond to the sequences of the above NCBI reference sequences of the ZBP1 transcript, and also relates to the corresponding amino acid sequences, such as the sequences shown in SEQ ID NO:164 or SEQ ID NO:165 or SEQ ID NO:166, which correspond to the protein sequences defined in NCBI protein accession reference sequences NP_110403, NCBI protein accession reference sequence NP_001153890 and NCBI protein accession reference sequence NP_001153891 that encode the ZBP1 polypeptide.

[0236] The term "ZBP1" also includes nucleotide sequences that show a high degree of homology with ZBP1, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:161 or SEQ ID NO:162 or SEQ ID NO:163, 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 shown in SEQ ID NO:164 or SEQ ID NO:165 or SEQ ID NO:166, or nucleic acid sequences encoding 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 shown in SEQ ID NO:164 or SEQ ID NO:165 or SEQ ID NO:166, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequences shown in SEQ ID NO:161 or SEQ ID NO:162 or SEQ ID NO:163.

[0237] The term "PDE4D7" refers to the phosphodiesterase 4D subtype 7 gene (Ensembl: ENSG00000113448), such as the sequence defined in NCBI reference sequence NM_001364599. In particular, it refers to the nucleotide sequence shown in SEQ ID NO:167, which corresponds to the sequence of the above NCBI reference sequence of the PDE4D7 transcript, and also relates to the corresponding amino acid sequence, such as the sequence shown in SEQ ID NO:168, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001351528 encoding the PDE4D7 polypeptide.

[0238] The term "PDE4D7" also includes nucleotide sequences that show a high degree of homology to PDE4D7, such as nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:167, or amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:168, or nucleic acid sequences that encode amino acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:168, or amino acid sequences encoded by nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence shown in SEQ ID NO:167.

[0239] As described herein, the biological samples used can be collected in a clinically acceptable manner, such as in a way that preserves nucleic acids (especially RNA) or proteins.

[0240] Biological samples can include body tissues and / or fluids, such as but not limited to blood, sweat, saliva, and urine. In addition, biological samples can contain cell extracts derived from epithelial cells or cell populations including epithelial cells, such as cancerous epithelial cells or epithelial cells derived from suspected cancerous tissues. Biological samples can contain cell populations derived from tissues (such as glandular tissues), for example, the sample can be derived from the prostate of a subject. In addition, if needed, cells can be purified from the obtained body tissues and fluids and then used as biological samples. In some implementations, the sample can be a tissue sample, a liquid sample, a blood sample, a saliva sample, a sample including circulating tumor cells, extracellular vesicles, a sample containing exosomes secreted by the prostate, or a cell line or a cancer cell line. In a particular implementation, a biopsy or resection sample can be obtained and / or used. Such samples may include cells or cell lysates.

[0241] Thus, in one embodiment, the biological sample obtained from a subject is a biopsy. In another preferred embodiment, the method shown includes providing or obtaining a biopsy. In a preferred embodiment, the biopsy is a prostate biopsy, such as tissue or fluid from the prostate, or a prostate cancer biopsy.

[0242] It is also contemplated that an enrichment step is performed on the contents of the biological sample. For example, the sample can be contacted with a ligand (e.g., functionalized with magnetic particles) specific for the cell membrane or organelles of a particular cell type, such as prostate cells. The material concentrated by the magnetic particles can then be used for the detection and analysis steps as described above or below.

[0243] In addition, cells, such as tumor cells, can be enriched by a filtration process of a fluid or liquid sample (e.g., blood, urine, etc.). As described above, such a filtration process can also be combined with an enrichment step based on ligand-specific interactions.

[0244] Preferably, the biological sample provided herein is obtained from a subject prior to the start of treatment. It is also preferred that the biological sample is a prostate sample or a prostate cancer sample.

[0245] Thus, in a preferred embodiment, the method according to the invention comprises obtaining a biological sample from a subject prior to the start of treatment, preferably wherein the biological sample is a prostate sample or a prostate cancer sample. Alternatively, the method according to the invention comprises providing a biological sample obtained from a subject prior to the start of treatment, preferably wherein the biological sample is a prostate sample or a prostate cancer sample.

[0246] It is contemplated herein that the outcome for a prostate cancer subject can be a favorable outcome or an unfavorable outcome. In one aspect of the invention, the prediction of the outcome for a prostate cancer subject can lead to the determination of a favorable risk or an unfavorable risk for one or more of the outcomes. The outcomes may include prostate cancer-related death, local recurrence, and / or distant recurrence. Preferably, the prostate cancer-related death is prostate cancer-specific death.

[0247] Accordingly, the present method provides for predicting an object's response to neoadjuvant androgen deprivation therapy. The purpose of neoadjuvant androgen deprivation therapy is to eradicate malignant androgen-dependent cells, with the hope that sufficient tumor regression will enable complete resection of residual prostate cancer, improving the pathologic outcome and survival rate. Thus, for example, the response can be measured as the overall tumor size, tumor burden, number of tumor-positive lymph nodes, probability of survival, overall survival time, cancer-free survival time, overall chance of death, or cancer-specific chance of death in response to neoadjuvant androgen deprivation therapy. Thus, compared to the situation where neoadjuvant androgen deprivation therapy is not administered, when neoadjuvant androgen deprivation therapy is administered to a patient, the response can be an increased or decreased predicted overall survival time, or compared to the situation where neoadjuvant androgen deprivation therapy is not administered, when neoadjuvant androgen deprivation therapy is administered to a patient, the response can be an increased or decreased predicted cancer-specific chance of death. It is understood that the other parameters described 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 the following: predicted remaining tumor burden; predicted tumor size; predicted chance of tumor metastasis; and / or predicted chance of BCR. The prediction can indicate an increased or decreased chance of occurrence. In one embodiment, the prediction is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy, where the favorable or unfavorable response is tumor size, tumor burden, number of tumor-positive lymph nodes, probability of survival, overall survival rate, cancer-free survival rate, overall death, or cancer-specific death. In one embodiment, the favorable outcome or unfavorable outcome is an increased chance of survival in response to treatment.

[0248] As used herein, tumor size refers to the volume of the tumor at a predetermined time point after treatment. As used herein, tumor burden refers to the number of cancer cells, the size of the tumor, or the number of metastases at a predetermined time point after treatment. As used herein, the chance of tumor metastasis refers to the chance of developing one or more metastases from prostate cancer at a predetermined 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. Positive lymph nodes can be local or peripheral. As used herein, probability of survival refers to the chance that a prostate cancer subject will still be alive at a predetermined time point after treatment. As used herein, the term overall survival refers to the (average) survival time from the date a biological sample is obtained from the subject. Cancer-free survival is the (average) survival time after a biological sample is obtained from the subject during which the subject has no detectable cancer. As used herein, the term overall death refers to the (average) time from the date a biological sample is obtained from the subject until death. As used herein, the term cancer-specific death refers to the (average) time from the date a biological sample is obtained from the subject until death due to prostate cancer (or its metastases).

[0249] The inventors further show that the methods for predicting the outcomes 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 levels; 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 levels; and / or the presence of metastases.

[0250] The ETS-related gene (ERG) is a member of the E-twenty-six transformation-specific (ETS) family of transcription factors that plays a role in development, including angiogenesis, vasculogenesis, hematopoiesis, and bone development. The oncogenic potential of ERG is well known as it is associated with Ewing's sarcoma and leukemia. However, in the past decade, ERG has been highly associated with prostate cancer development, particularly due to gene fusions with the promoter region of the androgen-induced TMPRSS2 gene (see, for example, Adamo and Ladomery, Oncogene volume 35, pages 403–414 (2016)). The use of the TMPRSS2-ERG fusion status as a clinical parameter has been previously described, and it is well known to those skilled in the art how to determine the fusion status, for example, as described in WO2019185773A1, the entire content of which is incorporated herein by reference.

[0251] When 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 androgens (such as testosterone) in a patient, as prostate cancer cells typically rely on androgens for growth. ADT can include surgery-based methods or drug-based methods. For example, orchiectomy (surgical removal of the testicles) can be used as ADT, as the testicles are the main organs that produce androgens. Alternatively, drug-based methods can be used, and some non-limiting examples are chemical castration and antiandrogen therapy. For example, chemical castration can be achieved 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 for androgen deprivation therapy include leuprolide, goserelin, triptorelin, histrelin, buserelin, and degarelix. Antiandrogen therapy aims to reduce androgen synthesis induced by AR signaling by blocking the androgen receptor (AR) signaling pathway, for example, by disrupting the positive feedback loop of testosterone production, or alternatively by targeting testosterone synthesis or AR nuclear translocation. Some exemplary instances 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, seviteronel, apalutamide, darolutamide, leuprolide, and galeterone, more preferably selected from enzalutamide, leuprolide, abiraterone, or apalutamide.

[0252] When used herein, the term neoadjuvant androgen deprivation therapy (NADT) refers to systemic therapy administered after the diagnosis of prostate cancer but before local treatment (such as radical prostatectomy (RP) or radiation). The use of NADT before RP aims to eradicate malignant androgen-dependent cells, with the hope that sufficient tumor regression will enable complete resection of residual prostate cancer, improving the pathological outcome and survival rate. Thus, according to the present invention, androgen deprivation therapy is neoadjuvant androgen deprivation therapy. The examples of androgen deprivation therapy listed can also be used as neoadjuvant androgen deprivation therapy.

[0253] The predictions provided by the methods of the present invention can provide a prediction of the risk of ADT outcome for prostate cancer subjects. In addition, the methods of the present invention can predict whether a subject with prostate cancer is at low risk for a certain outcome or high risk for a certain consequence. Outcomes such as prostate cancer-related death, local regional recurrence, and / or distant recurrence as used herein include outcomes that are adverse to the subject. Another outcome is overall death. Overall death as used herein is an outcome that can be predicted by the methods of the present invention, but is not directly related to the subject dying from prostate cancer.

[0254] As predicted by the methods of the present invention, prostate cancer subjects at high risk (i.e., above a specific threshold) for one or more of the outcomes of prostate cancer-related death, local regional recurrence, and / or distant recurrence are considered to be associated with an adverse risk for the respective outcome, preferably after surgery.

[0255] As predicted by the methods of the present invention, prostate cancer subjects at low risk (i.e., below or equal to a specific threshold) for one or more of the outcomes of prostate cancer-related death, local regional recurrence, and / or distant recurrence are considered to be associated with a favorable risk for the corresponding outcome, preferably after surgery. Favorable risks may include different follow-up strategies than the adverse risks of the subject, such as different recommended (follow-up) treatments. For example, for prostate cancer subjects, preferably those who have undergone prostate (cancer) surgery, different follow-up strategies, such as different recommended (follow-up) treatments, may be considered to improve the survival chances of the prostate cancer subject.

[0256] When a favorable outcome is predicted by the methods defined herein, the preferred treatment includes neoadjuvant androgen deprivation therapy prior to local treatment such as radical prostatectomy (RP) or radiotherapy. For example, if it is predicted that the subject will respond well to ADT, ADT is administered prior to subsequent treatment. As described above, a predicted durable survival period, a longer cancer-free survival period, etc. may imply favorable treatment.

[0257] Thus, in a preferred embodiment, the method according to the present invention includes obtaining a biological sample, preferably a sample of the subject's prostate or the subject's prostate cancer, prior to initiating treatment including surgery, radiotherapy, hormone therapy, cytotoxic chemotherapy, and / or immunotherapy.

[0258] The adverse risk of the expected outcome affects the recommended treatment for the prostate cancer subject associated with the adverse risk. In the case where an adverse risk is predicted by the methods of the present invention, it is contemplated that the recommended treatment includes not providing the subject with neoadjuvant androgen deprivation therapy.

[0259] In a preferred embodiment, the method according to the present invention includes recommending treatment based on the prediction, preferably based on the prediction of the outcome, wherein:

[0260] - If the prediction is unfavorable, recommended treatments include not providing neoadjuvant androgen deprivation therapy; and

[0261] - If the prediction is favorable, recommended treatments include providing neoadjuvant androgen deprivation therapy.

[0262] The favorable risk impact on the expected outcome is related to the recommended treatment for the prostate cancer subject with a favorable risk. In the case of predicting a favorable risk by the method of the present invention, it is contemplated that the recommended treatment includes neoadjuvant androgen deprivation therapy as broadly defined herein.

[0263] Thus, another preferred embodiment of the method according to the present invention includes recommending treatment based on the prediction, wherein:

[0264] - If the prediction is favorable, the recommended treatment includes neoadjuvant androgen deprivation therapy as defined herein.

[0265] In another aspect of the present invention, there is provided a method according to the present invention, which includes recommending secondary treatment for a specific patient, preferably for a patient with a low or high risk of an outcome selected from prostate cancer-specific death or overall death, wherein the recommendation is based on a prediction of the outcome of a prostate cancer patient, wherein:

[0266] - If the prediction is favorable, secondary treatment is not recommended; and / or

[0267] - If the prediction is unfavorable, secondary treatment is recommended.

[0268] It is provided herein that by the method of the present invention, the effectiveness of secondary treatment after surgery for low-risk or high-risk patients can be predicted. Preferably, the secondary treatment includes one or more of chemotherapy, hormone therapy, and radiotherapy, more preferably all.

[0269] The methods provided herein are based on the expression levels, such as expression profiles, of PDE4D or PDE4D isoform 5 (PDE4D5), 7 (PDE4D7), or 9 (PDE4D9) or combinations thereof, and / or at least three genes selected from the group of PDE4D7-related genes, where the PDE4D7-related genes are selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. The method can also be based on at least one 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 genes 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. It should be understood that the method can be performed on an input related to the expression levels of the one or more genes, or determining the expression levels can be part of the method.

[0270] It is further contemplated that the method is performed by a processor. Thus, in one embodiment, the invention relates to a computer-implemented method for predicting the outcome of a prostate cancer subject.

[0271] Preferably, a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy includes determining the gene expression profile of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9 and / or the gene expression of three or more, such as 3, 4, 5, 6, 7, or all, PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. As further disclosed herein, the gene expression profile may further comprise one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, immune defense response genes selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 and / or one or more, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all, T cell receptor signaling genes selected from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70.

[0272] In another embodiment, a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy includes determining the gene expression profile of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9 and the gene expression of one or more, such as 1, 2, 3, 4, 5, 6, 7, or all, PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0273] In another aspect of the present disclosure, the gene expression profile may optionally comprise two or more, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all, of the immune defense response genes selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or two or more, such as 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 CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70.

[0274] In one aspect of the present disclosure, according to the method of the present invention, the gene expression profile comprises:

[0275] - one or more immune defense response genes, preferably three or more, preferably six or more, more preferably nine or more, and most preferably all of the immune defense genes, and / or

[0276] - 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

[0277] - three or more PDE4D7-related genes, preferably four or more, preferably six or more, and most preferably all of the PDE4D7-related genes.

[0278] Thus, it is disclosed that determining the first, second, and / or third gene expression profile according to the method of the present disclosure comprises determining or receiving the results of the following determinations:

[0279] - three or more, preferably six or more, more preferably nine or more, and most preferably all of the genes of the immune defense response genes selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1,

[0280] - Three or more, preferably six or more, more preferably nine or more, most preferably all genes of T cell receptor signaling genes selected from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and

[0281] - Three or more, preferably six or more, more preferably nine or more, most preferably all genes of PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0282] In another aspect, the method according to the present disclosure includes determining that the prediction of the result is based on one or more immune defense response genes, one or more T cell receptor signaling genes other than PDE4D, and / or three or more, such as three, four, five, six, seven, or all, PDE4D7-related genes. In another aspect, the method according to the present disclosure includes determining that the prediction of the result is based on two or more immune defense response genes, two or more T cell receptor signaling genes other than PDE4D7, and / or three or more, such as three, four, five, six, seven, or all, PDE4D7-related genes. In another aspect, the method according to the present disclosure includes determining that the prediction of the result is based on three or more immune defense response genes, three or more T cell receptor signaling genes other than PDE4D7, and / or three or more, such as three, four, five, six, seven, or all, PDE4D7-related genes.

[0283] As a first reference example, the gene expression profiles disclosed herein may also include at least one, at least two, at least three immune defense response genes selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1. In a non-limiting example, the gene expression profile as described herein includes the expression profiles of the genes DDX58, DHX9, and IFI16.

[0284] As another reference example, 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 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 as described herein includes the expression profiles of the genes PRKACA, PRKACB, and PTPRC.

[0285] As another reference example, the gene expression profiles disclosed herein may include the expression level of PDE4D7 and at least one, at least two, or at least three PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. In one non-limiting example, the gene expression profile as described herein includes the expression profiles of the genes KIAA1549, PDE4D, and RAP1GAP2.

[0286] As another example, the gene expression profiles described herein may comprise at least three, at least four, or at least five PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2. In one non-limiting example, the gene expression profile as described herein includes the expression profiles of the genes CUX2, SLC39A11, and TDRD1.

[0287] Cox proportional hazards regression allows the analysis of the effect of several risk factors on the time to a test event such as survival. Thus, a risk factor may be a binary or discrete variable such as a risk score or clinical stage, but may also be a continuous variable such as a biomarker measurement or gene expression value. The probability of an endpoint (such as death or disease recurrence) is called the hazard. In addition to information on whether an individual in a patient cohort has reached the test endpoint (e.g., whether the patient has died), the regression analysis also takes into account the time to reach the endpoint. The hazard is modeled as: H(t) = H 0 (t)·exp(w 1 ·V 1 +w 2 ·V 2 +w 3 ·V 3 +…), where V 1 、V 2 、V 3 … are the predictor variables, and H 0(t) is the baseline hazard, and H(t) is the hazard at any time t. The hazard ratio (HR) (or the risk of achieving the said event) is expressed as Ln[H(t) / H 0 (t)] = w 1 ·V 1 +w 2 ·V 2 +w 3 ·V 3 +…, where the coefficients or weights w 1 、w 2 、w 3 … are estimated by Cox regression analysis and can be interpreted in a manner similar to logistic regression analysis.

[0288] Thus, in a preferred embodiment, the method according to the invention for determining a prediction of an outcome comprises combining the combination of said gene expression profiles with a regression function derived from a population of prostate cancer subjects. Thus, in one embodiment, the method of the invention for predicting an outcome comprises combining the expression levels of three or more genes with a regression function derived from a population of prostate cancer subjects.

[0289] In another aspect, the method according to the present disclosure for determining a prediction of an outcome comprises:

[0290] - combining a first gene expression profile of two or more, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of said immune defense response genes, with a regression function derived from a population of prostate cancer subjects, and / or

[0291] - combining a second gene expression profile of two or more, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of said T cell receptor signaling genes, with a regression function derived from a population of prostate cancer subjects, and / or

[0292] - combining a third gene expression profile of two or more, such as 2, 3, 4, 5, 6, 7, or all of said PDE4D7-related genes, with a regression function derived from a population of prostate cancer subjects.

[0293] In a particular implementation of the invention, the prediction of the outcome is determined as follows:

[0294] PDE4D7_CORR_ model: (1)

[0295] (w 1 ·ABCC5)+(w 2 ·CUX2)+(w 3 ·KIAA1549)+[…]+(w 8 ·VWA2)

[0296] where w 1 to w 8 are weights and the expression levels of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 are genes.

[0297] In a specific implementation of the present disclosure, the prediction of the result is supplemented by the following additional model:

[0298] IDR_14_Model: (2)

[0299] (w 9 ·AIM2)+(w 10 ·APOBEC3A)+(w 11 ·CIAO1)+[…]+(w 22 ·ZBP1)

[0300] where w 9 to w 22 are weights, and the expression levels of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 are genes.

[0301] In another specific implementation of the present disclosure, the prediction of the result is supplemented by the following additional model:

[0302] TCR_17_Model: (3)

[0303] (w 23 ·CD2)+(w 24 ·CD247)+(w 25 ·CD28)+[…]+(w 39 ·ZAP70)

[0304] where w 23 to w 39 are weights, and the expression levels of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 are genes.

[0305] In another specific implementation of the present invention, the prediction of the result is determined as follows:

[0306] PCAI_Model (4)

[0307] (w 40 ·PDE4D7_CORR)+(w41 ·PDE4D7)

[0308] In another implementation of the present invention, the prediction of the result is determined as follows:

[0309] PCAI_model(5)

[0310] (w 1 ·ABCC5)+(w 2 ·CUX2)+(w 3 ·KIAA1549)+[…]+(w 8 ·VWA2)+(w 41 ·PDE4D7),

[0311] where w 1 to w 8 are weights, and ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 are the expression levels of genes.

[0312] Based on the predicted value of the result, the prediction of the result can also be classified or categorized into one of at least two risk groups. For example, there may be two risk groups or three risk groups or four risk groups, or more than four predefined risk groups.

[0313] Each risk group covers a corresponding range (non-overlapping) of values of the result prediction. For example, the risk group can indicate the probability of a specific clinical event occurring, from 0 to <0.1 or from 0.1 to <0.25 or from 0.25 to <0.5 or from 0.5 to 1.0, etc.

[0314] In a preferred embodiment, the method according to the present invention includes determining the prediction of the result based on one or more clinical parameters obtained from the subject. As described above, various measurements based on one or more clinical parameters have been studied. By predicting the result based on these clinical parameters, the prediction can be further improved.

[0315] In one embodiment, determining the prediction of treatment response includes combining the 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.

[0316] More preferably, the PDE4D expression level obtained from the subject and / or the gene expression profile of three or more PDE4D7-related genes and the one or more clinical parameters are combined with a regression function derived from a population of prostate cancer subjects.

[0317] It is also preferred to combine the one or more clinical parameters with a gene expression profile and a regression function derived from a population of prostate cancer subjects in another context to provide a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy.

[0318] Thus, in a preferred embodiment of the method according to the invention, the determination of the result comprises combining one or more of the following:

[0319] (i) The PDE4D expression level, preferably the gene expression of specific subtypes PDE4D5, PDE4D7 and / or PDE4D9 or a combination thereof;

[0320] (ii) The expression levels of three or more PDE4D7-related genes;

[0321] (iii) The one or more clinical parameters obtained from the subject and a regression function derived from a population of prostate cancer subjects.

[0322] In another specific implementation, the prediction of the result is determined as follows:

[0323] PCAI_clinical model (6)

[0324] (w 43 ·PDE4D7_CORR)+(w 44 ·LN_positive)

[0325] where w 43 and w 44 are weights, the PCAI_model is the above regression model based on the expression profiles of one or more IDR genes, one or more TCR signaling genes and / or one or more PDE4D7-related genes, and LN_positive represents the number of tumor-positive lymph nodes. Multivariate Cox regression is used to create the clinical model, combining PCAI with clinical data (number of lymph node positives).

[0326] As used and illustrated herein, an example of a suitable clinical parameter is "LN_positive", which represents the number of tumor-positive lymph nodes after postoperative pathological examination. An LN-positive prostate tumor is a tumor of the prostate in which the tumor cells have spread, i.e., metastasized, to nearby lymph nodes. An LN-positive tumor is a harbinger of subclinical metastasis of cancer.

[0327] Those skilled in the art should understand that, for example, a corresponding model can be formed for three or more PDE4D7-related genes. In this case, an exemplary model is as follows:

[0328] PDE4D7_COR_EX1-3 genes (7)

[0329] (w 3·KIAA1549)+(w 5 ·RAP1GAP2)+(w 7 ·TDRD1).

[0330] 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). It should be understood that when the model removes or adds components, the weights are preferably recalibrated for the modified model. Those skilled in the art know that, for example, a ground truth dataset including expression data obtained prior to ADT treatment in prostate cancer patients can be used to calibrate the model, where the dataset contains expression data from responders and non-responders to ADT treatment.

[0331] The method according to the present invention can be implemented in a computer program product that can be executed on a computer. The computer program product can include a non-transitory computer-readable recording medium that records (stores) a control program, such as a magnetic disk, a hard disk drive, etc. Common forms of non-transitory computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, or any other magnetic storage medium, CD-ROM, DVD, or any other optical medium, RAM, PROM, EPROM, FLASH-EPROM, or other storage chips or cartridges, or any other non-transitory medium that can be read and used by a computer.

[0332] Thus, in a preferred embodiment, the present invention also provides a computer program product that includes instructions that, when executed by a computer, cause the computer to execute a method that includes:

[0333] - receiving

[0334] the determination result of a gene expression profile

[0335] The gene expression profile includes: the gene expression level of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9, and / or

[0336] the gene expression levels of three or more genes, where the three or more gene expression levels are selected from:

[0337] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2,

[0338] The gene expression profile is determined in a biological sample obtained from a subject,

[0339] - a prediction based on the determination result of the gene expression profile,

[0340] -wherein the prediction is of a favorable or unfavorable response to neoadjuvant androgen deprivation therapy, and optionally, the method also provides a prediction of the result to a healthcare provider or the subject. In one aspect of the present disclosure, the method further comprises receiving the gene expression level of each of one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all) immune defense response genes selected from AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1. In one aspect of the present disclosure, the method further comprises receiving the gene expression level of each of one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all) T cell receptor signaling genes selected from 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 the gene expression level of each of three or more (e.g., 3, 4, 5, 6, 7 or all) PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

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

[0342] Exemplary methods may be implemented on one or more general-purpose computers, special-purpose computers, programmed microprocessors or microcontrollers and peripheral integrated circuit elements, ASICs or other integrated circuits, digital signal processors, hard-wired electronic or logic circuits (such as discrete element circuits), programmable logic devices such as PLDs, PLAs, FPGAs, graphics card CPUs (GPUs) or PALs, etc. Generally, any device capable of implementing a finite state machine (which in turn can implement the steps described herein) can be used to implement one or more steps of the risk stratification method for selecting a treatment in a patient with prostate cancer. As will be appreciated, although the steps of the method may be implemented entirely by a computer, in some embodiments, one or more steps may be performed at least in part manually.

[0343] Preferably, the computer program product according to the present invention can be implemented on a device for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, wherein the device includes an input adapted to receive data indicating a gene expression profile, the gene expression profile including the PDE4D expression level, preferably the gene expression levels of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9 and / or three or more gene expression levels, wherein the three or more gene expression levels are selected from PDE4D7-related genes, and wherein the device further includes a processor adapted to determine a prediction of the response based on the gene manifestation profile, and

[0344] - Optionally, a unit adapted to provide the prediction or treatment advice based on the selection to a healthcare provider or the subject.

[0345] In another aspect of the present invention, there is provided a device for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, comprising:

[0346] - An input adapted to receive data indicating a gene expression profile

[0347] The gene expression profile contains the gene expression level of PDE4D, preferably the gene expression and / or three or more gene expression levels of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9, wherein the three or more gene expression levels are selected from:

[0348] - Each of one or more, such as 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;

[0349] The gene expression profile is determined in a biological sample obtained from the subject,

[0350] - A processor adapted to determine a prediction of the result based on the gene expression profile, wherein the prediction is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy, and

[0351] - Optionally, a unit is provided, adapted to provide the prediction or treatment advice based on the prediction to a healthcare provider or the subject.

[0352] Computer program instructions can also be loaded onto a computer, other programmable data processing device, or other device to perform a series of operating steps on the computer, other programmable device, or other device to produce a computer-implemented process, such that the instructions executed on the computer or other programmable device provide a process for implementing the functions / actions described herein.

[0353] Accordingly, in a preferred embodiment, the present invention also provides a diagnostic kit comprising at least one polymerase chain reaction primer and optionally comprising at least one probe for determining the gene expression profile in a biological sample and / or sample obtained from a prostate cancer subject.

[0354] Preferably, the diagnostic kit provided herein comprises at least one of a polymerase chain reaction primer or a probe for determining the gene expression profile in a biological sample and / or sample obtained from a prostate cancer subject, wherein the gene expression profile comprises:

[0355] The gene expression level of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9, and / or

[0356] Three or more expression levels, wherein the three or more gene expression levels are selected from:

[0357] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2.

[0358] In another preferred embodiment, the present invention provides the use of the diagnostic kit broadly described herein in a method for predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, preferably for use in a method for predicting the response of a prostate cancer subject to neoadjuvant androgen ablation therapy broadly described herein.

[0359] In one embodiment, the present invention relates to the use of a diagnostic kit comprising:

[0360] - At least one of a polymerase chain reaction primer or a probe for determining the gene expression profile in a biological sample and / or sample obtained from a prostate cancer subject, wherein the gene expression profile comprises:

[0361] The gene expression level of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9, and / or

[0362] Three or more expression levels, wherein the three or more gene expression levels are selected from:

[0363] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2,

[0364] The uses include predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, wherein the outcome is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy. In one embodiment, the uses include using the kit in a method as defined in the methods for predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy as broadly described herein.

[0365] In another preferred embodiment, the present invention provides a method comprising:

[0366] - receiving a biological sample obtained from a subject with prostate cancer,

[0367] - using a diagnostic kit as broadly described herein to determine a gene expression profile, the gene expression profile comprising:

[0368] the gene expression level of PDE4D, preferably the gene expression of specific subtypes PDE4D5, PDE4D7, and / or PDE4D9, and / or

[0369] three or more expression levels, wherein the three or more gene expression levels are selected from:

[0370] - PDE4D7-related genes selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 in a biological sample obtained from a subject with prostate cancer and / or in the sample.

[0371] All references cited herein, including journal articles or abstracts, published or corresponding patent applications, patents, or any other references, are hereby incorporated by reference in their entirety, including all data, tables, figures, and text provided in the cited references. In addition, the entire contents of the references cited in the references cited herein are also incorporated by reference in their entirety.

[0372] Reference to known method steps, conventional method steps, known methods, or conventional methods does not in any way admit that any aspect, description, or embodiment of the present invention is disclosed, taught, or suggested in the relevant art.

[0373] It should be understood that the language or terms herein are for descriptive purposes and not for limiting purposes, such that the terms or language of this specification will be interpreted by those skilled in the art in light of the teachings and guidance presented herein in combination with the knowledge of those skilled in the art.

[0374] It should be understood that all details, embodiments, and preferences discussed with respect to one aspect or embodiment of the present invention equally apply to any other aspect or embodiment of the present invention, and thus all such details, embodiments, and preferences of all aspects do not need to be separately described in detail.

[0375] The present invention has been generally described, and the present invention will be more easily understood by reference to the following embodiments, which are provided by way of illustration and are not intended to limit the present invention. Other aspects and embodiments will be apparent to those skilled in the art.

[0376] When practicing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed implementation by studying the drawings, the disclosure, and the appended claims.

[0377] Any reference signs in the claims shall not be construed as limiting the scope. Examples

[0378] The generation of different models based on immune response defense genes, T cell receptor signaling genes, or PDE4D7-related genes or combinations thereof has been described in the following documents:

[0379]

[0380] These applications further provide experimental evidence indicating that predictions can be made based on partial gene signatures, such as 3, 4, 5, 6, 7, or 8 genes randomly selected from immune response defense genes, cell receptor signaling genes, or PDE4D7-related genes, or 3, 4, 5, 6, 7, or 8 genes randomly selected from combinations of these signatures.

[0381] Example 1: Neoadjuvant prostate cancer heterogeneity drives evolution and resistance to intensive hormonal therapy

[0382] Background

[0383] Patients diagnosed with high-risk localized prostate cancer have different outcomes after surgery. Trials of intensive neoadjuvant androgen deprivation therapy (NADT) have shown that patients with minimal residual disease after treatment have a lower recurrence rate. The molecular signatures that distinguish responders from non-responders are not well understood.

[0384] Objective

[0385] To identify genomic and histological features associated with baseline treatment resistance.

[0386] Design, setting, and participants

[0387] Thirty-seven men with intermediate- to high-risk prostate cancer underwent targeted biopsies before receiving ADT plus enzalutamide for 6 months. Biopsy tissues were used for RNA sequencing. A 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, the entire content of which is incorporated herein by reference.

[0388] Outcome Measures and Statistical Analysis

[0389] The relationship between the molecular characteristics of pretreatment biopsy tissue samples (n = 35) and the final pathologic response was evaluated, using a cut-off of 0.05 cm3 for residual cancer burden to compare abnormal responders with incomplete and non-responders.

[0390] Study Overview

[0391] This study was based on an RNAseq gene expression matrix with n = 35 eligible samples. Gene expression values of genes of interest were used as input for data analysis. The Prostate Cancer AI Immune Score was calculated based on a regression model developed previously on independent data (see, for example, WO 2022 / 043299). The correlation between the PCAI Immune Score and the response to 6-month neoadjuvant NADT was tested. A schematic overview is as Figure 1 shown. The PCAI Immune Score uses the PDE4D7-related genes and model described in WO 2022 / 043120.

[0392] Results

[0393] Results are as Figure 3 shown. The Prostate Cancer AI Immune Score accurately predicted abnormal treatment response to NADT (AUC = 0.85), compared to androgen receptor-regulated gene selection (AUC = 0.56).

[0394] Example 2: Molecular Characteristics of Abnormal Response to NADT in High-Risk Localized Prostate Cancer

[0395] Background

[0396] High-risk localized prostate cancer is associated with a substantial risk of recurrence and disease mortality. Recent clinical trials have shown that intensified anti-androgen therapy administered before prostatectomy can induce pathologic complete response or minimal residual disease, termed an abnormal response, although the molecular determinants of these clinical outcomes are largely unknown.

[0397] Objective

[0398] Identify genomic and histological features associated with baseline treatment resistance.

[0399] Design, setting, and participants

[0400] Here, we performed whole-exome and transcriptome sequencing on pre-treatment multi-region tumor biopsies from exceptional responders (ERs) and non-responders (NRs, pathologic T3 or node-positive disease) to intensified neoadjuvant anti-androgen therapy, including apalutamide. The publicly available dataset described by Tewari et al. Molecular features of exceptional response to NADT in high-risk localized prostate cancer. Cell Reports 36 (2021), 109665 is incorporated herein by reference in its entirety.

[0401] Outcome measures and statistical analysis

[0402] The relationship between the molecular features of pre-treatment biopsy tissue samples (n = 43) collected from n = 24 patients and the final pathologic response was evaluated, defining exceptional responders using <5 mm of tumor tissue as the cut-off point compared to non-responders.

[0403] Study overview

[0404] This study was based on an RNAseq gene expression matrix with n = 43 eligible samples. The gene expression values of genes of interest were used as input for data analysis. The prostate cancer AI immune score was calculated based on a regression model developed previously on independent data. The correlation between the PCAI immune score and the response to 6-month neoadjuvant NADT was tested. A schematic overview is as Figure 2 shown. The PCAI immune score uses the PDE4D7-related genes and model described in WO 2022 / 043120.

[0405] Results

[0406] Results are as Figure 4 shown. The prostate cancer AI immune score accurately predicted exceptional treatment response to NADT (AUC = 0.92) compared to androgen receptor-regulated gene selection (AUC = 0.75).

[0407] Example 3-3 Gene Model

[0408] To determine that a random selection of three genes chosen from the PDE4D7-related genes is sufficient to predict the response of prostate cancer subjects to neoadjuvant androgen deprivation therapy, three genes were randomly selected 10 times from the PDE4D7-related genes, listed below as Model_1 to Model_10 respectively. Each model was trained on a training cohort of 35 patients (the same as above), and then validated on a validation cohort of 24 patients (the same as above). For each model, Kaplan-Meier curves were plotted based on the results obtained from the validation cohort. The results are as Figures 5 to 14 shown. Each randomly selected set of three PDE4D7-related genes was able to well predict the treatment response to neoadjuvant androgen deprivation therapy, and the average AUC of different models was approximately 0.8, which is considered very good.

[0409] Table 4-13 below lists the genes used and the assigned weights in each model.

[0410] Table 4 - Model_1

[0411] Variable Coefficient ABCC5 0,47548 KIAA1549 -0,033557 TDRD1 0,53527 Constant -4,23419

[0412] Table 5 - Model_2

[0413] Variable Coefficient CUX2 0,78268 RAP1GAP2 -0,099723 TDRD1 0,5245 Constant -5,1238

[0414] Table 6 - Model_3

[0415] Variable Coefficient KIAA1549 0,63567 RAP1GAP2 0,49901 SLC39A11 0,86667 Constant -8,65365

[0416] Table 7 - Model_4

[0417] Variable Coefficient ABCC5 0,78955 RAP1GAP2 0,82978 VWA2 0,58433 Constant -11,46623

[0418] Table 8 - Model_5

[0419] Variable Coefficient KIAA1549 0,43965 PDE4D -0,82439 RAP1GAP2 0,82473 Constant -2,14272

[0420] Table 9 - Model_6

[0421] Variable Coefficient CUX2 0,7378 SLC39A11 0,85395 VWA2 0,6956 Constant -10,19711

[0422] Table 10 - Model_7

[0423] Variable Coefficient CUX2 0,54868 RAP1GAP2 0,36851 SLC39A11 0,6335 Constant -7,48616

[0424] Table 11 - Model_8

[0425] Variable Coefficient ABCC5 0,27836 SLC39A11 1,15051 TDRD1 0,57252 Constant -8,26492

[0426] Table 12 - Model_9

[0427] Variable Coefficient RAP1GAP2 0,50033 TDRD1 0,53609 VWA2 -0,086614 Constant -3,84015

[0428] Table 13 - Model_10

[0429] Variable Coefficient ABCC5 0,76585 RAP1GAP2 0,68022 TDRD1 0,46563 Constant -9,71884

[0430] These results were obtained from ten different random selections of three genes of the PDE4D7 - related genes. Each model gave a good to very good prediction, making it possible that any random selection of three of the PDE4D7 - related genes (ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2) is predictive. From these results, it was concluded that three genes randomly 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.

[0431] Example 4

[0432] The prediction was further improved by adding clinical parameters.

[0433] To demonstrate that the model can be further improved, a regression model using the PDE4D7_R2 model was combined with the tumor volume at baseline (before the start of NADT; Figure 15 ) or alternatively, the model (PDE4D7_R2 + baseline_tumor_volume) was combined with the gene expression data of two additional genes (ERG and PTEN; Figure 16 ).

[0434] The model was constructed as follows:

[0435] Table 14 - PDE4D7 R2 + Tumor Volume Model

[0436] Coefficients and Standard Deviations

[0437] Variable Coefficient Standard Deviation Wald P PDE4D7_R2_log_SRT 0.81343 0.43405 3.5120 0.0609 Baseline_Tumor_Volume 0.28454 0.15951 3.1821 0.0744 Constant -1.02377 0.70827 2.0894 0.1483

[0438] Table 15 - PDE4D7 R2 + Tumor Volume + ERG&PTEN Model

[0439] Coefficients and Standard Deviations

[0440] Variable Coefficient Standard Deviation Wald P PDE4D7_BaseTumorVol 5.61225 2.20191 6.4964 0.0108 ERG 0.43462 0.24480 3.1521 0.0758 PTEN -1.77404 1.29773 1.8688 0.1716 Constant 5.58220 7.88444 0.5013 0.4789

[0441] The same cohort as in Example 1 was used to develop and validate the model, and the data was plotted as Kaplan - Meier curves, as Figure 15 and 16As shown, by combining the baseline tumor volume parameter with the PDE4D7-related gene model, the AUC was increased from 0.85 to 0.90. Additionally, including the ERG and PTEN expression levels further increased the AUC to 0.94.

[0442] From these data, it can be concluded that clinical parameters such as baseline tumor volume and additional data on ERG and / or PTEN expression levels can further improve the predictive ability of the model.

[0443] Example 5 - Prediction of PDE4D(7) Expression as Biochemical Recurrence

[0444] For this example, publicly available data from the DARANA trial were used to test whether PDE4D7 expression levels are sufficient to predict BCR. The data are available under the identifier NCT03297385 and were published in Linder et al. Drug-induced epigenomic plasticity reprograms circadian rhythm regulation to drive prostate cancer towards androgen-independence. Cancer Discov. 2022 September 2;12(9):2074–2097.

[0445] In the DARANA trial, 56 patients with locally advanced, intermediate- to high-risk prostate cancer and ISUP Gleason 2-5 (approx. 55% ISUP 4-5) were selected. The patients received 3 months of anti-androgen (enzalutamide) treatment in neoadjuvant therapy. Biopsies (MRI-guided) were collected from the prostate before the start of treatment. At the end of hormonal treatment, the prostate was resected by RP and tissue punches were collected from the resected prostate tissue. RNAseq expression analysis was performed on the tissue before and after treatment. The baseline ISUP Gleason grade was available for the dataset. Additionally, for each patient, BCR events and time to BCR were also available. This dataset included N = 42 samples with RNAseq and clinical variables.

[0446] On this dataset, PDE4D expression levels were evaluated as predictors of BCR. The results are as Figure 17 and 18 and reference Figure 19 shown.

[0447] In short, the first expression of PDE4D (all subtypes) was evaluated in a cohort of 35 patients in the DARANA dataset. The results are as Figure 17As shown by the Kaplan-Meier curve in , it indicates that the use of this model can identify groups with low risk of BCR. This figure shows the logrank and confidence intervals.

[0448] From these data, it can be concluded that BCR can be reliably predicted based on the PDE4D expression level. Additionally, since it is hypothesized that PDE4D7 (or a combination of long transcripts such as PDE4D5, 4D7, and / or 4D9) contributes the most to the variation in expression levels among responders, the inventors believe that the expression level of subtype-specific PDE4D7 (or a combination of long transcripts such as PDE4D5, 4D7, and / or 4D9) may be at least equally predictive, if not more predictive. Unfortunately, there is no subtype-specific sequencing data in the public dataset to validate this hypothesis.

[0449] Next, the inventors reviewed whether the prediction model could be improved by including additional PDE4D7-related genes. The inventors constructed a combined logistic regression model using the PDE4D and ABCC5 expression levels according to Table 16 below:

[0450] Table 16 - PDE4D7_R2_log_BCR regression model

[0451]

[0452] Three months after receiving neoadjuvant ADT (enzalutamide) treatment, this model predicted postoperative PSA recurrence in men with high-risk localized prostate cancer and was evaluated in 35 patients in the DARANA cohort. The results are as Figure 18 shown, showing the Kaplan-Meier curve that separates the predicted high-risk and low-risk BCR patients. Including ABCC5 as an additional marker improved the prediction. The applicant believes that any PDE4D7-related gene, not just ABCC5, can be combined with PDE4D to improve the prediction.

[0453] For reference, Figure 19 the Kaplan-Meier curve based on the baseline ISUP Gleason score is depicted, which cannot predict BCR (P value = 0.3).

[0454] The attached sequence listing named 2022PF00781 SEQ LIST.xml is incorporated herein by reference in its entirety.

Claims

1. A method for predicting the response of a subject with prostate cancer to neoadjuvant androgen deprivation therapy, include: - determining or receiving the determination of a gene expression profile, said gene expression profile comprising: gene expression levels of PDE4D, preferably gene expression of specific isoforms PDE4D5, PDE4D7 and / or PDE4D9, and / or Three or more gene expression levels, wherein the three or more gene expression levels are selected from: - a PDE4D7-related gene selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, The gene expression profile is determined in a biological sample obtained from the subject, - predictions based on the results of said gene expression profiling determination, - wherein the prediction is a favorable or unfavorable response to neoadjuvant androgen deprivation therapy, - Optionally, providing a prediction of outcome to a medical care provider or the subject.

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

3. The method according to claim 1 or 2, wherein 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 levels; and / or The presence of transfer.

4. The method according to any one of the preceding claims, wherein the neoadjuvant androgen deprivation therapy comprises treatment with an anti-androgen, Preferably, the antiandrogen is selected from cyproterone acetate, flutamide, nilutamide, bicalutamide, enzalutamide, abiraterone, abiraterone acetate, serveterol, apalutamide, darutamide, leuprolide and galactone.

5. The method according to any one of the preceding claims, wherein the result is at least one of: - predicted residual tumor burden; - predicted tumor size; - predicted chance of tumor metastasis; or - Predicted chance of biochemical recurrence.

6. The method of any of the preceding claims, 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, total mortality, or cancer-specific mortality.

7. The method according to any one of the preceding claims, wherein the favorable or unfavorable outcome is an improved chance of survival in response to treatment.

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

9. The method according to claim 2 or 8, in: - the one or more immune defense response genes comprise three or more, preferably six or more, more preferably nine or more, most preferably all immune defense genes, and / or - the one or more T cell receptor signaling genes comprise three or more, preferably six or more, more preferably nine or more, most preferably all T cell receptor signaling genes, and / or - The three or more PDE4D7-related genes comprise four or more, preferably six or more, most preferably all PDE4D7-related genes.

10. The method of any one of the preceding claims, wherein prediction of the determination result comprises combining the three or more gene expression levels with a regression function derived from a population of prostate cancer subjects.

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

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

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

14. The method according to any one of the preceding claims, wherein a treatment is recommended based on the prediction.

15. Use of a diagnostic kit, the kit comprising: include: - at least one of a polymerase chain reaction primer or a probe for determining a gene expression profile in a biological sample and / or a 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 Three or more gene expression levels, wherein the three or more gene expression levels are selected from: - a PDE4D7-related gene selected from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1 and VWA2, The use comprises predicting the response of a prostate cancer subject to neoadjuvant androgen deprivation therapy, wherein the outcome is a favorable or unfavorable response to the neoadjuvant androgen deprivation therapy, Preferably, the use comprises using the kit in a method as defined in any one of claims 1 to 14.

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