Selection of radiation therapy dose for prostate cancer

Abstract

The present invention relates to a method for selecting a dose for radiation therapy for a subject with prostate cancer, comprising the steps of determining, or receiving results of determining, a first gene expression profile for each of one or more immune defense response genes, and / or a second gene expression profile for each of one or more T cell receptor signaling genes, and / or a third gene expression profile for each of one or more PDE4D7-correlated genes, wherein the first, second, and third expression profiles are determined in a biological sample obtained from the subject, and selecting a radiation therapy dose based on the first gene expression profile, or the second gene expression profile, or the third gene expression profile, or the first, second, and third gene expression profiles, and, as appropriate, providing the selection or a treatment recommendation based on the selection to a healthcare caregiver or the subject.

Description

[Technical Field]

[0001] The present invention relates to a method for selecting a radiation therapy dose for prostate cancer patients, and an apparatus for selecting a radiation therapy dose for prostate cancer patients. Furthermore, the present invention relates to a diagnostic kit, the use of the kit, the use of the kit in the method for selecting a radiation therapy dose for prostate cancer patients, the use of a first, second, and / or third gene expression profile(s) in the method for selecting a radiation therapy dose for prostate cancer patients, and a corresponding computer program product. [Background technology]

[0002] Cancer is a class of diseases characterized by uncontrolled proliferation, invasion, and sometimes metastasis of cells. These three malignant characteristics of cancer distinguish it from benign tumors that are self-limited and do not invade or metastasize. Prostate cancer (PCa) is the second most common non-cutaneous malignant tumor in men, with an estimated 1.3 million new cases diagnosed worldwide and 360,000 deaths 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, pp. 394-424, 2018). In the United States, approximately 90% of new cases relate to localized cancer, meaning that metastasis has not yet formed (see "Cancer Facts & Figures 2010" by the ACS (American Cancer Society), 2010).

[0003] Several curative treatments are available for primary localized prostate cancer, with surgery (radical resection of the prostate, RP) and radiation therapy (RT) being the most commonly used. RT is administered by external beam, by implantation of radioactive seeds into the prostate (brachytherapy), or a combination of both. RT is particularly preferred for patients who are not candidates for surgery or who have been diagnosed with localized or regional advanced tumors. Radical RT is available to up to 50% of patients diagnosed with localized prostate cancer in the United States (see ACS, ibid., 2010).

[0004] After treatment, prostate cancer antigen (PSA) levels in the blood are measured for disease monitoring. An increase in blood PSA levels provides a biochemical surrogate indicator of cancer recurrence or progression. However, there is considerable variability in reported biochemical progression-free survival (bPFS) (see Grimm P. et al., "Comparative analysis of prostate-specific antigen-free survival outcomes for patients with low, intermediate and high risk prostate cancer treatment by radical therapy. Results from the Prostate Cancer Results Study Group," BJU Int, Suppl. 1, pp. 22-29, 2012). In many patients, bPFS after radical RT exceeds 90% at 5 years or even 10 years. Unfortunately, in patients with a moderate risk of recurrence, and especially in those at a higher risk of recurrence, bPFS can drop to approximately 40% at 5 years, depending on the type of RT used (see Grimm P. et al., ibid., 2012).

[0005] Many patients with primary, localized prostate cancer who have not been treated with radiotherapy (RT) will undergo RP (see ibid., 2010, by ACS). After RP, an average of 60% of patients in the highest-risk group experience biochemical recurrence at 5 and 10 years (see ibid., 2012, by Grimm P. et al.). In cases of biochemical progression after RP, one of the main challenges is the uncertainty of whether it is due to a recurrence of a localized disease, one or more metastases, or even a painless disease that does not lead to clinical disease progression (see "Contemporary role of postoperative radiotherapy for prostate cancer" by Dal Pra A. et al., Transl Androl Urol, Vol. 7, No. 3, pp. 399-413, 2018, and "Radiation therapy after radical prostatectomy: Implications for clinicians" by Herrera FG and Berthold DR, Front Oncol, Vol. 6, No. 117, 2016). RT to eradicate cancer cells remaining in the prostate bed is one of the main treatment options to save patients after PSA increases following RP. The effectiveness of salvage radiotherapy (SRT) resulted in a 5-year bPFS in 18% to 90% of patients, depending on multiple factors (see Herrera FG and Berthold DR, ibid., 2016, and Pisansky TM et al., "Salvage radiation therapy dose response for biochemical failure of prostate cancer after prostatectomy - A multi-institutional observational study," Int J Radiat Oncol Biol Phys, Vol. 96, No. 5, pp. 1046-1053, 2016).

[0006] It is clear that radical or salvage radiotherapy is ineffective for certain patient groups. Their condition is further exacerbated by serious side effects that radiotherapy can cause, such as intestinal inflammation and dysfunction, urinary incontinence, and erectile dysfunction (see Resnick MJ et al., "Long-term functional outcomes after treatment for localized prostate cancer," N Engl J Med, Vol.368, No.5, pp. 436-445, 2013, and Hegarty SE et al., "Radiation therapy after radical prostatectomy for prostate cancer: Evaluation of complications and influence of radiation timing on outcomes in a large, population-based cohort," PLoS One, Vol.10, No.2, 2015). Furthermore, the median cost of one course of RT, based on Medicare reimbursements, is $18,000, and can vary significantly up to approximately $40,000 (see "Variation in the cost of radiation therapy among medicare patients with cancer" by Paravati AJ et al., J Oncol Pract, Vol. 11, No. 5, pp. 403-409, 2015). These figures do not include the substantial long-term costs of follow-up care after curative and salvage RT.

[0007] Improving the prediction of RT effectiveness for each patient leads to improved treatment choices and increased survival rates, whether in a curative or salvage setting. This can be achieved by 1) optimizing RT for patients who are predicted to respond well to it (e.g., by changing dose escalation or initiation timing), and 2) guiding patients who are predicted not to respond well to alternative, potentially more effective forms of treatment. Furthermore, this reduces patient suffering by avoiding ineffective treatments and lowers the costs previously spent on ineffective treatments.

[0008] Numerous studies have been conducted on measurements for predicting responses to radical RT (see "Biomarkers of outcome in patients with localized prostate cancer treated with radiotherapy" by Hall WA et al., Semin Radiat Oncol, Vol. 27, pp. 11-20, 2016, and "An appraisal of analytical tools used in predicting clinical outcomes following radiation therapy treatment of men with prostate cancer: A systematic review" by Raymond E. et al., Vol. 12, No. 1, p. 56, 2017) and radical SRT (see the same by Herrera FG and Berthold DR, 2016). Many of these measurements depend on the concentration of PSA, a blood-based biomarker. Numerical indicators examined for predicting responses before the initiation of RT (radical and rescue) include absolute values ​​of PSA concentration, absolute values ​​related to prostate volume, absolute increase over a period of time, and doubling period. Other factors often considered include the Gleason score and the clinical stage of the tumor. In the case of SRT settings, further factors such as the condition of the resection margin, the time to recurrence after RP, pre-surgical / peri-surgical (peri-surgical) PSA levels, and clinicopathological parameters are also appropriate.

[0009] While these clinical variables have provided limited improvements in stratifying patients across diverse risk groups, better predictive tools are needed.

[0010] A wide range of biomarker candidates in tissues and body fluids have been investigated, but validation is often limited and generally demonstrates prognostic information rather than predictive values (specific to treatment) (see Hall W.A. et al. 2016, ibid.). A few gene expression panels are currently being validated by private groups. One or some of these may show predictive values for RT in the future (see Dal Pra A. et al. 2018, ibid.).

[0011] In conclusion, for primary prostate cancer and postoperative situations, there remains a strong need for better prediction of response to RT and personalization of radiotherapy, such as the selection of dose for radiotherapy.

Summary of the Invention

Problems to be Solved by the Invention

[0012] An object of the present invention is to provide a method for selecting a dose for radiotherapy of a prostate cancer subject and an apparatus for selecting a dose for radiotherapy of a prostate cancer subject, which enable the application of better salvage radiotherapy. Further aspects of the present invention are a diagnostic kit, use of the kit, use of the kit in a method for selecting a dose for radiotherapy of a prostate cancer subject, use of a first, second, and / or third gene expression profile(s) in a method for selecting a dose for radiotherapy of a prostate cancer subject, and corresponding computer program products.

Means for Solving the Problems

[0013] [[ID=二十一]]In a first aspect of the present invention, a method for selecting a dose for radiotherapy of a prostate cancer subject, comprising: [[ID=二十二]] Determining or receiving the result of determination of a first gene expression profile for each of one or more selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 species or all immune defense response genes, wherein the first gene expression profile(s) is determined in a biological sample obtained from a subject, step, and / or Determining or receiving the result of determination of a second gene expression profile for each of one or more selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 species or all T cell receptor signaling genes, wherein the second gene expression profile(s) is determined in a biological sample obtained from a subject, step, and / or Determining or receiving the result of determination of a third gene expression profile for each of one or more selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, for example, 1, 2, 3, 4, 5, 6, 7 species or all PDE4D7-related genes, wherein the third gene expression profile(s) is determined in a biological sample obtained from a subject, step Determining the selection of a radiotherapy dose based on the first gene expression profile(s), or the second gene expression profile(s), or the third gene expression profile(s), or the first, second, and third gene expression profile(s), and There is provided a method having the step of appropriately providing a medical caregiver or subject with a recommended treatment method based on the selection or the selection.

[0014] Therefore, in one embodiment, the present invention relates to a method for selecting a dose for radiotherapy in a patient with prostate cancer: the method is A step of determining a first gene expression profile for each of one or more immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, wherein the first gene expression profile is determined in a biological sample obtained from a subject, and / or A step of determining a second gene expression profile for each of one or more 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, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them, wherein the second gene expression profile(s) is determined in a biological sample obtained from a subject, and / or A step of determining a third gene expression profile for each of one or more PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, for example, 1, 2, 3, 4, 5, 6, 7, or all of them, wherein the third gene expression profile (or more) is determined in a biological sample obtained from a subject. A step of determining the selection of the radiotherapy dose based on the first gene expression profile (or multiple gene expression profiles), or the second gene expression profile (or multiple gene expression profiles), or the third gene expression profile (or multiple gene expression profiles), and The present invention relates to a method that includes, as appropriate, a step of providing a medical assistant or patient with recommendations for treatment options or treatment options based on those options.

[0015] In an alternative embodiment, the present invention relates to a computer implementation method for selecting a dose for radiotherapy in a patient with prostate cancer: A step of receiving the results of determining a first gene expression profile for each of one or more immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of them, wherein the first gene expression profile (or more) is determined in a biological sample obtained from the subject, and / or A step of receiving the results of determining a second gene expression profile for each of one or more 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, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, wherein the second gene expression profile (or multiple) is determined in a biological sample obtained from the subject, and / or A step of receiving the results of determining a third gene expression profile (or multiple profiles) for each of one or more PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, for example, 1, 2, 3, 4, 5, 6, 7, or all PDE4D7-related genes, wherein the third gene expression profile (or multiple profiles) is determined in a biological sample obtained from the subject. A step of determining the selection of the radiotherapy dose based on the first gene expression profile (or multiple gene expression profiles), or the second gene expression profile (or multiple gene expression profiles), or the third gene expression profile (or multiple gene expression profiles), and The method includes, as appropriate, a step of providing medical assistants or patients with recommendations for treatment options or treatment options based on those options.

[0016] In recent years, the importance of the immune system in cancer suppression, as well as in cancer development, promotion, and metastasis, has become very clear (see "Cancer-related inflammation" by Mantovani A. et al., Nature, Vol. 454, No. 7203, pp. 436-444, 2008, and "The clinical role of the TME in solid cancer" by Giraldo NA et al., Br J Cancer, Vol. 120, No. 1, pp. 45-53, 2019). Immune cells and the molecules they secrete form an important part of the tumor microenvironment, and most immune cells can infiltrate tumor tissue. The immune system and tumors influence and shape each other. Thus, while anti-tumor immunity can prevent tumor formation, an inflammatory tumor environment promotes cancer development and growth. At the same time, tumor cells that develop independently of the immune system can mobilize immune cells to form an immune microenvironment and have pro-inflammatory effects while suppressing anti-cancer immunity.

[0017] Some immune cells in the tumor microenvironment exhibit either general tumor-promoting or general tumor-suppressing effects, while others are more flexible, showing potential for both tumor-promoting and tumor-suppressing behaviors. Thus, the overall immune microenvironment of a tumor is a complex mixture of the diverse immune cells present, the cytokines they produce, and their interactions with tumor cells and other cells in the tumor microenvironment (see Giraldo NA et al., ibid., 2019).

[0018] The above principles regarding the role of the immune system in cancer in general also apply to prostate cancer. Chronic inflammation is associated with the formation of both benign and malignant prostate tissue (see Hall WA et al., ibid., 2016), and most prostate cancer tissue samples show infiltration of immune cells. While the presence of certain immune cells with tumor-promoting effects is correlated with a poorer prognosis, tumors with more activated natural killer cells have shown a better response to treatment and a longer recurrence-free period (see Shiao SL et al., "Regulation of prostate cancer progression by tumor microenvironment," Cancer Lett, Vol. 380, No. 1, pp. 340-348, 2016).

[0019] Treatment is influenced by the immune components of the tumor microenvironment, but RT itself also broadly influences the composition of these components (see Barker HE et al., "The tumor microenvironment after radiotherapy: Mechanisms of resistance or recurrence," Nat Rev Cancer, Vol. 15, No. 7, pp. 409-425, 2015). Suppressive cell types are relatively insensitive to radiation, so their relative numbers increase. Conversely, the administered radiation damage activates cell survival pathways and stimulates the immune system, which triggers an inflammatory response and the recruitment of immune cells. It is currently unclear whether the net effect is tumor-promoting or tumor-suppressing, but its potential for enhancing cancer immunotherapy is being investigated.

[0020] The present invention is based on the idea that, since the state of the immune system and the state of the immune microenvironment greatly influence the effectiveness of treatment, the ability to identify markers that predict this influence can help enable better dose selection for radiotherapy.

[0021] Furthermore, since known PDE4D7 biomarkers have proven to be excellent predictors of radiotherapy response, the present invention is based on the idea that the ability to identify markers highly correlated with PDE47 biomarkers may help enable better dose selection for radiotherapy.

[0022] Immune response defense genes The integrity and stability of genomic DNA are constantly under stress caused by various intracellular and extracellular factors, including not only exposure to radiation and viral or bacterial infections, but also oxidative stress and replication stress (see Gasser S. et al., "Sensing of dangerous DNA," Mechanisms of Aging and Development, 165, pp. 33-46, 2017). To maintain the structure and stability of DNA, cells must be able to recognize all types of DNA damage, such as single-strand or double-strand breaks, caused by various factors. This process involves numerous specific proteins, depending on the type of damage, as part of the DNA recognition pathway.

[0023] Recent findings have shown that the immune system utilizes mislocalized DNA (e.g., DNA unnaturally occurring in the cytoplasmic fraction as opposed to the nucleus) and damaged DNA (e.g., those resulting from mutations associated with cancer development) to identify infected cells or other diseased cells, while genomic and mitochondrial DNA present in healthy cells is ignored by the DNA recognition pathway. In diseased cells, cytoplasmic DNA sensor proteins have been shown to be involved in detecting DNA unnaturally occurring in the cell's cytoplasm. Detection of such DNA by different nucleic acid sensors leads to similar responses that result in the activation of innate immune system components following signaling of nuclear factor κB (NF-κB) and interferon type I (IFN type I). While it is known that an IFN type I response occurs upon recognition of viral DNA, there is a growing understanding in recent years that an immune response can be initiated by sensing DNA damage.

[0024] Endosome-localized TLR9 (Toll-like receptor 9) was one of the first DNA sensor molecules to be shown to be involved in the immune recognition of DNA by signaling downstream via the adapter protein myeloid differentiation primary response protein 88 (MYD88). This interaction subsequently activates mitogen-activated protein kinase (MAPK) and NF-κB. TLR9 also induces the production of type I interferon by activating IRF7 via IκB kinase alpha (IKK alpha) in plasmacytoid dendritic cells (pDCs). Various other DNA immune receptors such as IFI16 (IFN-gamma-inducible protein 16), cGAS (cyclic DMP-AMP synthase), DDX41 (DEAD-box helicase 41), and ZBP1 (Z-DNA binding protein 1) interact with STING (stimulator of IFN genes) and TBK1 ZBP1 activates the IKK complex and IRF3 via TANK-binding kinase 1. ZBP1 also activates NF-κB through the recruitment of RIP1 and RIP3 (receptor-coupled proteins 1 and 3, respectively). Helicase DHX36 (DEAH-box helicase 36) interacts with TRID in complex to induce NF-κB and IRF-3 / 7, while DHX9 helicase activates MYD88-dependent signaling in plasmacytoid dendritic cells. The DNA sensor LRRFIP1 (leucine-rich repeat flightless interaction protein) complexes with beta-catenin to activate IRF3 transcription, while AIM2 (absent in melanoma 2) recruits the adapter protein ASC (apoptotic speck-like protein) to induce the caspase-1 activated Inlamaserm complex, leading to the secretion of interleukin-1 beta (IL-1 beta) and IL-18 (Gasser). See Figure 1 in the 2017 book by S. et al., which provides an overview of DNA damage and DNA sensor pathways leading to the production of inflammatory cytokines and the expression of ligands for activation of innate immune receptors. (Members of the non-homologous end junction pathway (orange), homologous recombination (red), inflammasome (dark green), and NF-κB and interferon response (light green) are shown).

[0025] The factors and mechanisms that activate DNA sensor pathways in cancer are not yet well understood. Identifying tumor DNA species, sensors, and pathways involved in IFN expression across different cancer types and stages of the disease is crucial. In addition to being therapeutic targets for cancer, such factors may also have prognostic and predictive value. Novel DNA sensor pathway agonists and antagonists are currently being developed and tested in preclinical trials. Such compounds may be useful in characterizing the role of DNA sensor pathways in the pathogenesis of cancer, autoimmunity, and potentially other diseases.

[0026] The identified immune defense response genes AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 were identified as follows: A group of 538 prostate cancer patients were treated with RP, and prostate cancer tissue was stored along with clinical parameters (e.g., pathological Gleason grade group (pGGG), pathological status (pT stage)) and related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiation therapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes, as described by Alves de Inda M. et al., "Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes," Eur Urol Focus, Vol. 4, No. 3, pp. 376-384, 2018. PDE4D7 score class 1 corresponds to the patient sample with the lowest PDE4D7 expression level, while PDE4D7 score class 4 corresponds to the patient sample with the highest PDE4D7 expression level. Next, RNASeq expression data (Transcripts Per Million) from 538 prostate cancer subjects were examined for differential gene expression between PDE4D7 score classes 1 and 4. Specifically, for approximately 20,000 protein-coding transcripts, we determined whether the average expression level in patients with a PDE4D7 score of class 1 was more than twice the average expression level in patients with a PDE4D7 score of class 4.This analysis yielded 637 genes with a PDE4D7 score class 1 / PDE4D7 score class 4 ratio greater than 2, with a minimum mean expression of 1 TPM in each of the four PDE4D7 score classes. These 637 genes were then subjected to further molecular pathway analysis (www.david.ncifcrf.gov), resulting in various enriched annotation clusters. Annotation cluster #2 showed enrichment (enrichment score: 10.8) in 30 genes with functions related to the defense response against viruses, negative regulation of viral genome replication, and type I interferon signaling. Further heatmap analysis confirmed that these immune defense response genes were generally more highly expressed in patient-derived samples with PDE4D7 score class 1 than in patient-derived samples with PDE4D7 score class 4. The class of genes with functions related to the defense response against viruses, negative regulation of viral genome replication, and type I interferon signaling was further enriched to 61 genes through a literature search to further identify genes with the same molecular function. Further selection from 61 genes, based on a combinatorial power of separating patients who died from prostate cancer from those who did not, yielded a favorable set of 14 genes. Subcohorts with low expression of these genes were found to have increased event (metastasis, prostate cancer-specific death) compared to the overall patient cohort (#538) and a subcohort of 151 patients who received salvage RT (SRT) after postoperative disease recurrence.

[0027] T cell receptor signaling genes The immune response to pathogens can be triggered at various stages: there are physical barriers, such as the skin, that prevent invaders from entering. If these are breached, the first rapid, nonspecific response, innate immunity, begins to work. If this is insufficient, an adaptive immune response is triggered. This is far more specific, but it takes time to manifest when encountering a pathogen for the first time. Lymphocytes are activated by interacting with activated antigen-presenting cells derived from the innate immune system. They are also involved in maintaining memory to respond more quickly the next time the same pathogen is encountered.

[0028] When activated, lymphocytes become highly specific and effective, leading to negative selection of their self-recognition in a process known as central tolerance. Since not all self-antigens are expressed at the selected sites, peripheral tolerance mechanisms have also developed, such as ligation of the TCR in the absence of co-stimulation, expression of inhibitory co-receptors, and inhibition by Tregs. An imbalance between activation and inhibition can lead to autoimmune disorders, immunodeficiency, and cancer, respectively.

[0029] T cell activation can have various 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 cellular immunity) or Th2 (secreting IL4 / 5 / 13 and promoting B cell and humoral immunity). It is also possible to differentiate into other T cell subsets that have been recently identified, 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, pp. 1009-1016, 2011, in particular, T cell activation and its modulation by PKA in Figure 4, and Tasken K. and Ruppelt A., "Negative regulation of T-cell receptor activation by the cAMP-PKA-Csk signaling pathway in T-cell lipid rafts," Front Biosci, Vol. 11, pp. 2929-2939, 2006).

[0030] Both signaling pathways regulated by PKA and PDE4 interact with TCR-induced T cell activation, fine-tuning its regulation through opposing effects (see Abrahamsen H. et al., "TCR- and CD28-mediated recruitment of phosphodiesterase 4 to lipid rafts potentiates TCR signaling," J Immunol, Vol. 173, pp. 4847-4848, 2004, in particular Figure 6 illustrating the opposing effects of PKA and PDE4 on TCR activation). The molecule that links these effectors is cyclic AMP (cAMP), an intracellular second messenger of the action of extracellular ligands. Within T cells, cAMP mediates the effects of prostaglandins, adenosine, histamine, beta-adrenergic agonists, neuropeptide hormones, and beta-endorphins. The binding of these extracellular molecules to GPCRs results in conformational changes of the GPCRs, the release of stimulatory subunits, and subsequent activation of adenylyl cyclase (AC), which hydrolyzes ATP to cAMP (see Figure 6 in Abrahamsen H. et al., 2004, ibid.). PKA is a major effector of cAMP signaling, though not the only one (see Mosenden R. and Tasken K., ibid., 2011, and Tasken K. and Ruppelt A., ibid., 2006). At a functional level, increased levels of cAMP lead to decreased production of IFNγ and IL-2 in T cells (see Abrahamsen H. et al., ibid., 2004). In addition to inhibiting TCR activation, PKA has many other effects (see Figure 15 in "Immunity Leashed-Mechanisms of Regulation in the Human Immune System" by Torheim EA, PhD dissertation, The Biotechnology Centre of Ola, University of Oslo, Norway, 2009).

[0031] In naive T cells, highly phosphorylated PAG targets Csk to lipid rafts. PKA targets Csk via the ezrin-EBP50-PAG scaffold complex. By being specifically phosphorylated by PKA, Csk can negatively modulate Lck and Fyn, weakening their activity and downregulating T cell activation (see Figure 6, ibid., 2004, by Abrahamsen H. et al.). Upon TCR activation, PAG is dephosphorylated and Csk is released from the raft. Dissociation of Csk is necessary for T cell activation to proceed. Within the same time course, the Csk-G3BP complex appears to be formed, thereby sequestering Csk outside the lipid raft (see ibid., 2011, by Mosenden R. and Tasken K., and ibid., 2006, by Tasken K. and Ruppelt A.).

[0032] On the other hand, combined stimulation of the TCR and CD28 mediates the recruitment of the cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, which enhances the degradation of cAMP (see Figure 6 in the same paper by Abrahamsen H. et al., 2004). This inhibits TCR-induced cAMP production and enhances the immune response of T cells. When the TCR is stimulated alone, the recruitment of PDE4 is insufficient to sufficiently reduce cAMP levels, and therefore maximal T cell activation cannot occur (see the same paper by Abrahamsen H. et al., 2004).

[0033] Thus, by actively suppressing proximal TCR signaling, cAMP-PKA-Csk-mediated signaling is thought to establish a threshold for T cell activation. PDE recruitment can block this suppression. Tissue or cell type-specific regulation is achieved through the expression of multiple isoforms of AC, PKA, and PDE. As mentioned above, a tight balance between activation and suppression is necessary to prevent the development of autoimmune disorders, immunodeficiency, and cancer.

[0034] The identified T cell receptor signaling genes, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, were identified as follows: A group of 538 prostate cancer patients were treated with RP, and prostate cancer tissue was stored along with clinical parameters (e.g., pathological Gleason grade group (pGGG), pathological status (pT stage)) and related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiation therapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes, as described by Alves de Inda M. et al., "Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes," Eur Urol Focus, Vol. 4, No. 3, pp. 376-384, 2018. PDE4D7 score class 1 corresponds to the patient sample with the lowest PDE4D7 expression level, while PDE4D7 score class 4 corresponds to the patient sample with the highest PDE4D7 expression level. Next, RNASeq expression data (Transcripts Per Million) from 538 prostate cancer subjects were examined for differential gene expression between PDE4D7 score classes 1 and 4. Specifically, for approximately 20,000 protein-coding transcripts, we determined whether the average expression level in patients with a PDE4D7 score of class 1 was more than twice the average expression level in patients with a PDE4D7 score of class 4.This analysis yielded 637 genes with a PDE4D7 score class 1 / PDE4D7 score class 4 ratio greater than 2, with a minimum mean expression of 1 TPM in each of the four PDE4D7 score classes. These 637 genes were then subjected to further molecular pathway analysis (www.david.ncifcrf.gov), resulting in various enriched annotation clusters. Annotation cluster #6 showed enrichment (enrichment score: 5.9) in 17 genes with functions in primary immunodeficiency and T cell receptor signaling activation. Further heatmap analysis confirmed that these T cell receptor signaling genes were generally more highly expressed in patient-derived samples with PDE4D7 score class 1 than in patient-derived samples with PDE4D7 score class 4.

[0035] PDE4D7 correlated gene The identified PDE4D7-related genes, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, were identified as follows: From RNA-seq data created from 571 prostate cancer patients, a range of genes correlated with the expression of the known biomarker PDE4D7 was identified from approximately 60,000 transcripts. The correlation between any of these genes and PDE4D7 expression across the 571 samples was performed using Pearson correlation, expressed as a value between 0 and 1 for positive correlation, or between -1 and 0 for negative correlation.

[0036] The formula for the correlation coefficient is as follows:

number

[0037] The largest negative correlation coefficient identified between the expression of any of the approximately 60,000 transcripts and the expression of PDE4D7 was -0.38, and the largest positive correlation coefficient identified between the expression of any of the approximately 60,000 transcripts and the expression of PDE4D7 was +0.56. Genes with correlations in the range of -0.31 to -0.38 and +0.41 to +0.56 were selected. A total of 77 transcripts matching these characteristics were identified. From these 77 transcripts, eight PDE4D7-associated genes—ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2—were selected by repeatedly testing a Cox regression combination model in a subcohort of 186 patients who received salvage radiotherapy (SRT) due to postoperative biochemical recurrence. The clinical endpoint tested was prostate cancer-specific mortality after initiation of SRT. The boundary condition for selecting the 8 genes was given by the constraint that the p-value of the multivariate Cox regression was less than 0.1 for all genes retained in the model.

[0038] The term "ABCC5" refers to the human ATP-binding cassette subfamily C member 5 gene (Ensembl:ENSG00000114770), specifically the sequence defined by the NCBI reference sequence NM_001023587.2 or NCBI reference sequence NM_005688.3, ​​and more specifically, the nucleotide sequence as defined in SEQ ID NO: 1 or SEQ ID NO: 2, which corresponds to the sequence of the NCBI reference sequence of the ABCC5 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 3 or SEQ ID NO: 4, which corresponds to the protein sequence defined by the NCBI protein accession reference sequences NP_001018881.1 and NCBI protein accession reference sequences NP_005679, which encode the ABCC5 polypeptide.

[0039] The term "ABCC5" also includes nucleotide sequences that exhibit high homology to 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 sequence defined in SEQ ID NO: 1 or SEQ ID NO: 2, 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 defined 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 sequence defined in SEQ ID NO: 1 or SEQ ID NO: 2.

[0040] "AIM2" refers to the sequence defined in the Absent in Melanoma 2 gene (Ensembl:ENSG00000163568), for example, the sequence defined in NCBI reference sequence NM_004833, specifically the sequence as defined in Sequence ID No. 5, which corresponds to the sequence of the NCBI reference sequence of the AIM2 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 6, for example, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_004824, which encodes the AIM2 polypeptide.

[0041] The term "AIM2" also refers to nucleotide sequences that show high homology to AIM2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 5, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 6. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the amino acid sequence or the sequence specified in Sequence ID No. 6, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 5.

[0042] The term "APOBEC3A" refers to the apolipoprotein B mRNA editing enzyme catalytic subunit 3A gene (Ensembl:ENSG00000128383), specifically the sequence defined in the NCBI reference sequence NM_145699, and more precisely, the nucleotide sequence as defined in Sequence ID No. 7, which corresponds to the sequence of the NCBI reference sequence of the APOBEC3A transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 8, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_663745 that encodes the APOBEC3A polypeptide.

[0043] The term "APOBEC3A" also refers to nucleotide sequences that show high homology to APOBEC3A, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence 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 defined in Sequence ID No. 8, or the sequence defined in Sequence ID No. 8. The sequence includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 7, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 7.

[0044] The term "CD2" refers to the gene for differentiation antigen group 2 (Ensembl:ENSG00000116824), for example, the sequence defined in the NCBI reference sequence NM_001767, specifically the nucleotide sequence as defined in Sequence ID No. 9, which corresponds to the sequence of the NCBI reference sequence of the CD2 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 10, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001758 that encodes the CD2 polypeptide.

[0045] The term "CD2" also refers to nucleotide sequences that show high homology to AIM2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 9, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 10. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 10, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 9.

[0046] The term "CD247" refers to the gene for differentiation antigen group 247 (Ensembl:ENSG00000198821), for example, the sequence defined in NCBI reference sequence NM_000734 or NCBI reference sequence NM_198053, specifically the nucleotide sequence as defined in SEQ ID NO: 11 or SEQ ID NO: 12, which corresponds to the sequence of the NCBI reference sequence of the CD247 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 13 or SEQ ID NO: 14, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_000725 and NCBI protein accession reference sequences NP_932170 that encode the CD247 polypeptide.

[0047] The term "CD247" also refers to nucleotide sequences that show high homology to CD247, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 11 or SEQ ID NO: 12, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 13 or SEQ ID NO: 14. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91%, 91%,

[0048] The term "CD28" refers to the sequence defined in the gene for differentiation antigen group 28 (Ensembl:ENSG00000178562), for example, the sequence defined in NCBI reference sequence NM_006139 or NCBI reference sequence NM_001243078, specifically the nucleotide sequence as defined in SEQ ID NO: 15 or SEQ ID NO: 16, which corresponds to the sequence of the NCBI reference sequence of the CD28 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 17 or SEQ ID NO: 18, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_006130 and NCBI protein accession reference sequences NP_001230007 that encode the CD28 polypeptide.

[0049] The term "CD28" also refers to nucleotide sequences that show high homology to CD28, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 15 or SEQ ID NO: 16, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 17 or SEQ ID NO: 18. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence, or a nucleic acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91%, 91%, 91%, 91%, 91

[0050] The term "CD3E" refers to the gene for differentiation antigen group 3E (Ensembl:ENSG00000198851), for example, the sequence defined in the NCBI reference sequence NM_000733, specifically the nucleotide sequence as defined in Sequence ID No. 19, which corresponds to the sequence of the NCBI reference sequence of the CD3E transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 20, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000724 that encodes the CD3E polypeptide.

[0051] The term "CD3E" also refers to nucleotide sequences that show high homology to CD3E, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 19, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 20. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 20, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 19.

[0052] The term "CD3G" refers to the gene for differentiation antigen group 3G (Ensembl:ENSG00000160654), for example, the sequence defined in NCBI reference sequence NM_000073, specifically the nucleotide sequence as defined in SEQ ID NO: 21, which corresponds to the sequence of the NCBI reference sequence of the CD3G transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 22, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_000064, which encodes the CD3G polypeptide.

[0053] The term "CD3G" also refers to nucleotide sequences that show high homology to CD3G, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 21, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 22. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 22, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 21.

[0054] The term "CD4" refers to the gene for differentiation antigen group 4 (Ensembl:ENSG00000010610), for example, the sequence defined in the NCBI reference sequence NM_000616, specifically the nucleotide sequence as defined in SEQ ID NO: 23, which corresponds to the sequence of the NCBI reference sequence of the CD4 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 24, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000607 that encodes the CD4 polypeptide.

[0055] The term "CD4" also refers to nucleotide sequences that show high homology to CD4, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 23, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 24. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in Sequence ID No. 24, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 23.

[0056] The term "CIAO1" refers to the cytoplasmic iron-sulfur assembly component 1 gene (Ensembl:ENSG00000144021), specifically the nucleotide sequence defined by the NCBI reference sequence NM_004804, and more specifically, the nucleotide sequence as defined in Sequence ID No. 25, which corresponds to the sequence of the NCBI reference sequence of the CIAO1 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 26, which corresponds to the protein sequence defined by the NCBI protein accession reference sequence NP_663745 encoding the CIAO1 polypeptide.

[0057] The term "CIAO1" also refers to nucleotide sequences that show high homology to CIAO1, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 25, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 26. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 26, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 25.

[0058] The term "CSK" refers to the C-terminal Src kinase gene (Ensembl:ENSG00000103653), specifically the nucleotide sequence defined in NCBI reference sequence NM_004383, and more specifically, the nucleotide sequence as defined in Sequence ID No. 27, which corresponds to the NCBI reference sequence of the CSK transcript shown above. It also refers to the corresponding amino acid sequence as defined in NCBI protein accession reference sequence NP_004374, which encodes the CSK polypeptide, and more specifically, the nucleotide sequence as defined in Sequence ID No. 28.

[0059] The term "CSK" also refers to nucleotide sequences that show high homology to CSK, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 27, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 28. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in Sequence ID No. 28, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 27.

[0060] The term "CUX2" refers to the human Cut-like homeobox 2 gene (Ensembl:ENSG00000111249), for example, the sequence defined by the NCBI reference sequence NM_015267.3, specifically the nucleotide sequence as defined in Sequence ID No. 29, which corresponds to the sequence of the NCBI reference sequence of the CUX2 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 30, for example, which corresponds to the protein sequence defined by the NCBI protein accession reference sequence NP_056082.2 encoding the CUX2 polypeptide.

[0061] The term "CUX2" also refers to nucleotide sequences that show high homology to CUX2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 29, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 30. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 30, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 29.

[0062] The term "DDX58" refers to the DExD / H-box helicase 58 gene (Ensembl:ENSG00000107201), specifically the sequence defined in the NCBI reference sequence NM_014314, and more precisely, the nucleotide sequence as defined in Sequence ID No. 31, which corresponds to the sequence of the NCBI reference sequence of the DDX58 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 32, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_055129 that encodes the DDX58 polypeptide.

[0063] The term "DDX58" also refers to nucleotide sequences that show high homology to DDX58, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 31, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 32. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 32, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 31.

[0064] The term "DHX9" refers to the DExD / H-box helicase 9 gene (Ensembl:ENSG00000135829), specifically the sequence defined in the NCBI reference sequence NM_001357, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 33, which corresponds to the sequence of the NCBI reference sequence of the DHX9 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 34, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001348 that encodes the DHX9 polypeptide.

[0065] The term "DHX9" also refers to nucleic acid sequences that show high homology to DHX9, for example, sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 33, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 34. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 34, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 33.

[0066] The term "EZR" refers to the Ezrin gene (Ensembl:ENSG00000092820), specifically the nucleotide sequence defined in NCBI reference sequence NM_003379, and more specifically, the nucleotide sequence as defined in Sequence ID No. 35, which corresponds to the NCBI reference sequence of the EZR transcript shown above. It also refers to the corresponding amino acid sequence as defined in NCBI protein accession reference sequence NP_003370, which encodes the EZR polypeptide, and more specifically, the nucleotide sequence as defined in Sequence ID No. 36.

[0067] The term "EZR" also refers to nucleotide sequences that show high homology to EZR, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 35, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 36. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in Sequence ID No. 36, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 35.

[0068] The term "FYN" refers to the sequence defined in the FYN Proto-Oncogene gene (Ensembl:ENSG00000010810), for example, NCBI reference sequence NM_002037, NCBI reference sequence NM_153047, or NCBI reference sequence NM_153048, specifically the nucleotide sequence as defined in SEQ ID NO: 37, SEQ ID NO: 38, or SEQ ID NO: 39, corresponding to the NCBI reference sequence of the FYN transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 40, SEQ ID NO: 41, or SEQ ID NO: 42, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_002028, NCBI protein accession reference sequences NP_694592, and NCBI protein accession reference sequences XP_005266949, which encode the FYN polypeptide.

[0069] The term "FYN" also refers to a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a nucleotide sequence that shows high homology to FYN, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a nucleotide sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a nucleotide sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a nucleotide sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a nucleotide sequence that is at least 75%, 80%, 85%, 90%, 91%, 9 The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence, or an amino acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91%, 91%, 91%, 91

[0070] The term "IFI16" refers to the nucleotide sequence defined in Sequence ID No. 43, which corresponds to the sequence of the interferon-gamma-inducible protein 16 gene (Ensembl: ENSG00000163565), for example, the NCBI reference sequence NM_005531, specifically the sequence of the NCBI reference sequence of the IFI16 transcript shown above. It also refers to the corresponding amino acid sequence defined in Sequence ID No. 44, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_005522 that encodes the IFI16 polypeptide.

[0071] The term "IFI16" also refers to nucleotide sequences that show high homology to IFI16, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 43, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 44. The amino acid sequence includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 44, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 43.

[0072] The term "IFIH1" refers to the sequence defined in the Interferon Induced With Helicase C Domain 1 gene (Ensembl:ENSG00000115267), for example, NCBI reference NM_022168, specifically the nucleotide sequence as defined in Sequence ID No. 45, which corresponds to the sequence of the NCBI reference sequence of the IFIH1 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 46, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_071451 that encodes the IFIH1 polypeptide.

[0073] The term "IFIH1" also refers to a nucleotide sequence that shows high homology to IFIH1, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 45, or a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 46. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 46, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 45.

[0074] The term "IFIT1" refers to the Interferon Induced Protein With Tetratricopeptide Repeats 1 gene (Ensembl:ENSG00000185745), specifically the sequence defined in NCBI reference sequence NM_001270929 or NCBI reference sequence NM_001548.5, and more precisely, the nucleotide sequence defined in SEQ ID NO: 47 or SEQ ID NO: 48, which corresponds to the sequence of the NCBI reference sequence of the IFIT1 transcript shown above. It also refers to the corresponding amino acid sequence defined in SEQ ID NO: 49 or SEQ ID NO: 50, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_001257858 and NCBI protein accession reference sequences NP_001539, which encode the IFIT1 polypeptide.

[0075] The term "IFIT1" also refers to nucleotide sequences that show high homology to IFIT1, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 47 or SEQ ID NO: 48, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 49 or SEQ ID NO: 50. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91%, 91%,

[0076] The term "IFIT3" refers to the sequence defined in the Interferon Induced Protein With Tetratricopeptide Repeats 3 gene (Ensembl:ENSG00000119917), for example, the NCBI reference sequence NM_001031683, specifically the nucleotide sequence as defined in Sequence ID No. 51, which corresponds to the NCBI reference sequence of the IFIT3 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 52, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001026853 that encodes the IFIT3 polypeptide.

[0077] The term "IFIT3" also refers to nucleic acid sequences that show high homology to IFIT3, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in nucleic acid sequence 51, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in sequence number 52. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in Sequence ID No. 52, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 51.

[0078] The term "KIAA1549" refers to the human KIAA1549 gene (Ensembl:ENSG00000122778), specifically the sequence defined by the NCBI reference sequence NM_020910 or NM_001164665, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 53 or SEQ ID NO: 54, which corresponds to the sequence of the NCBI reference sequence of the KIAA1549 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 55 or SEQ ID NO: 56, which corresponds to the protein sequence defined by the NCBI protein accession reference sequences NP_065961 and NPBI protein accession reference sequences NP_001158137, which encode the KIAA1549 polypeptide.

[0079] The term "KIAA1549" also refers to nucleotide sequences that show high homology to KIAA1549, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 53 or SEQ ID NO: 54, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 55 or SEQ ID NO: 56. The invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a certain amino acid sequence, or to a sequence as defined in SEQ ID NO: 55 or SEQ ID NO: 56, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence as defined in SEQ ID NO: 53 or SEQ ID NO: 54.

[0080] The term "LAT" refers to the Linker For Activation Of T-Cells gene (Ensembl:ENSG00000213658), for example, the sequence defined in NCBI reference sequence NM_001014987 or NCBI reference sequence NM_014387, specifically the nucleotide sequence as defined in SEQ ID NO: 57 or SEQ ID NO: 58, corresponding to the sequence of the NCBI reference sequence of the LAT transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 59 or SEQ ID NO: 60, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_001014987 and NCBI protein accession reference sequences NP_055202, which encode the LAT polypeptide.

[0081] The term "LAT" also refers to a nucleotide sequence that exhibits high homology to LAT, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 57 or SEQ ID NO: 58, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 59 or SEQ ID NO: 60. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in SEQ ID NO: 59 or SEQ ID NO: 60, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 57 or SEQ ID NO: 58.

[0082] The term "LCK" refers to the LCK Proto-Oncogene gene (Ensembl:ENSG00000182866), specifically the nucleotide sequence defined in NCBI reference sequence NM_005356, as defined in Sequence ID No. 61, which corresponds to the NCBI reference sequence of the LCK transcript shown above. It also refers to the corresponding amino acid sequence defined in NCBI protein accession reference sequence NP_005347, which encodes the LCK polypeptide, as defined in Sequence ID No. 62.

[0083] The term "LCK" also refers to nucleotide sequences that show high homology to LCK, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 61, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 62. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in Sequence ID No. 62, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 61.

[0084] The term "LRRFIP1" refers to the LRR-binding FLII interaction protein 1 gene (Ensembl: ENSG00000124831), for example, refers to the sequence defined by NCBI reference sequence NM_004735 or NCBI reference sequence NM_001137550 or NCBI reference sequence NM_001137553 or NCBI reference sequence NM_001137552, specifically the nucleotide sequence as defined in SEQ ID NO: 63 or SEQ ID NO: 64 or SEQ ID NO: 65 or SEQ ID NO: 66, which corresponds to the sequence of the NCBI reference sequence of the LRRFIP1 transcript shown above. It also relates to the corresponding amino acid sequence as defined in SEQ ID NO: 67 or SEQ ID NO: 68 or SEQ ID NO: 69 or SEQ ID NO: 70, which corresponds to the protein sequence defined by NCBI protein accession reference sequences NP_004726, NCBI protein accession reference sequences NP_001131022, NCBI protein accession reference sequences NP_001131025 and NCBI protein accession reference sequences NP_001131024 encoding the LRRFIP1 polypeptide.

[0085] The term "LRRFIP1" also refers to nucleic acid sequences that show high homology to 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 sequences as defined in SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, or SEQ ID NO: 66, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to sequences as defined in SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, or SEQ ID NO: 70. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino

[0086] The term "MYD88" refers to the MYD88 innate immune signaling adapter gene (Ensembl: ENSG00000172936), specifically the sequence defined by NCBI reference sequence NM_001172567 or NM_001172568 or NM_001172569 or NM_001172566 or NM_002468, and more precisely, the nucleotide sequence as defined in SEQ ID NO: 71 or SEQ ID NO: 72 or SEQ ID NO: 73 or SEQ ID NO: 74 or SEQ ID NO: 75, which corresponds to the sequence of the NCBI reference sequence of the MYD88 transcript shown above. This also relates to the corresponding amino acid sequences, for example, as defined in SEQ ID NO: 76, SEQ ID NO: 77, SEQ ID NO: 78, SEQ ID NO: 79, or SEQ ID NO: 80, corresponding to the protein sequences defined in the NCBI protein accession reference sequences NP_001166038, NP_001166039, NP_001166040, NP_001166037, and NP_002459 that encode the YD88 polypeptide.

[0087] The term "MYD88" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to MYD88, such as sequences defined in SEQ ID NO: 71, 72, 73, 74, or 75, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to sequences defined in SEQ ID NO: 76, 77, 78, 79, or 80. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the amino acid sequence or the sequence specified in SEQ ID NO 76, SEQ ID NO 77, SEQ ID NO 78, SEQ ID NO 79, or SEQ ID NO 80, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO 71, SEQ ID NO 72, SEQ ID NO 73, SEQ ID NO 74, or SEQ ID NO 75.

[0088] The term "OAS1" refers to the 2'-5'-oligoadenylate synthase 1 gene (Ensembl: ENSG00000089127), for example, the sequence defined by NCBI reference sequence NM_001320151 or NCBI reference sequence NM_002534 or NCBI reference sequence NM_001032409 or NCBI reference sequence NM_016816, specifically the nucleotide sequence as defined 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 NCBI reference sequence of the OAS1 transcript shown above, and also the corresponding amino acid sequence as defined in SEQ ID NO: 85 or SEQ ID NO: 86 or SEQ ID NO: 87 or SEQ ID NO: 88, which corresponds to the protein sequence defined by NCBI protein accession reference sequences NP_001307080 and NCBI protein accession reference sequences NP_002525 and NCBI protein accession reference sequences NP_001027581 and NCBI protein accession reference sequences NP_058132 encoding the OAS1 polypeptide.

[0089] The term "OAS1" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to OAS1, such as the sequences defined in SEQ ID NO: 81, 82, 83, or 84, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences defined in SEQ ID NO: 85, 86, 87, or 88. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence, or a nucleic acid sequence encoding an amino2%, 83, or 84.

[0090] The term "PAG1" refers to the gene for Phosphoprotein Membrane Anchor With Glycosphingolipid Microdomains 1 (Ensembl:ENSG00000076641), for example, the sequence defined in the NCBI reference sequence NM_018440, specifically the nucleotide sequence as defined in Sequence ID No. 89, which corresponds to the sequence of the NCBI reference sequence of the PAG1 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 90, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_060910 that encodes the PAG1 polypeptide.

[0091] The term "PAG1" also refers to nucleotide sequences that show high homology to PAG1, for example: nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined 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 defined in SEQ ID NO: 90, or The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence as defined in Sequence ID No. 89.

[0092] The term "PDE4D" refers to the human phosphodiesterase 4D gene (Ensembl: ENSG00000113448), for example, the sequence defined by NCBI reference sequence NM_001104631 or NCBI reference sequence NM_001349242 or NCBI reference sequence NM_001197218 or NCBI reference sequence NM_006203 or NCBI reference sequence NM_001197221 or NCBI reference sequence NM_001197220 or NCBI reference sequence NM_001197223 or NCBI reference sequence NM_001165899 or NCBI reference sequence NM_001165899, specifically the nucleotide sequence as defined 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 corresponds to the sequence of the NCBI reference sequence of the PDE4D transcript shown above, and it also refers to PDE4D This also relates to the corresponding amino acid sequences, for example, as defined 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, corresponding to the protein sequences defined in the NCBI protein accession reference sequences NP_001098101, NP_001336171, NP_001184147, NP_006194, NP_001184150, NP_001184149, NP_001184152, NP_1159371, and NP_001184148, which encode polypeptides.

[0093] The term "PDE4D" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to PDE4D, for example, sequences defined in SEQ ID NOs. 91, 92, 93, 94, 95, 96, 97, 98, or 99, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to sequences defined in SEQ ID NOs. 100, 101, 102, 103, 104, 105, 106, 107, or 108. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, or SEQ ID NO: 108, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, or SEQ ID NO: 99.

[0094] The term "PRKACA" refers to the gene for Protein Kinase cAMP-Activated Catalytic Subunit Alpha (Ensembl:ENSG00000072062), specifically the sequence defined in NCBI reference sequence NM_002730 or NCBI reference sequence NM_207518, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 109 or SEQ ID NO: 110, corresponding to the sequence of the NCBI reference sequence of the PRKACA transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 111 or SEQ ID NO: 112, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_002721 and NCBI protein accession reference sequences NP_997401, which encode the PRKACA polypeptide.

[0095] The term "PRKACA" also refers to nucleotide sequences that show high homology to PRKACA, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 109 or SEQ ID NO: 110, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 111 or SEQ ID NO: 112. The present invention includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91

[0096] The term "PRKACB" refers to the gene for Protein Kinase cAMP-Activated Catalytic Subunit Beta (Ensembl:ENSG00000142875), for example, NCBI reference sequences NM_002731, NM_182948, NM_001242860, NM_001242859, NM_001242858, NM_001242862, NM_001242861, NM_001300915, NM_207578, NM_00 The sequence defined in 1242857 or NCBI reference sequence NM_001300917, specifically the nucleotide sequence as defined in SEQ ID NOs. 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, or 123, which corresponds to the sequence of the NCBI reference sequence of the PRKACB transcript shown above, and also refers to the NCBI protein accession reference sequence NP encoding the PRKACB polypeptide. _002722, NCBI protein accession reference sequence NP_891993, NCBI protein accession reference sequence NP_001229789, NCBI protein accession reference sequence NP_001229788, NCBI protein accession reference sequence NP_001229787, NCBI protein accession reference sequence NP_001229791, NCBI protein accession reference sequence NP_001229790, NCBI protein accession reference sequence NP_001287 This also relates to the corresponding amino acid sequences, for example, as defined in SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126, SEQ ID NO: 127, SEQ ID NO: 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131, SEQ ID NO: 132, SEQ ID NO: 133, or SEQ ID NO: 134, corresponding to the protein sequences defined in NCBI protein accession reference sequence NP_997461, NCBI protein accession reference sequence NP_001229786, and NCBI protein accession reference sequence NP_001287846.

[0097] The term "PRKACB" also refers to nucleotide sequences that show high homology to PRKACB, for example, sequences as defined in SEQ ID NOs: 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, or 123, with at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, and 9 Nucleic acid sequences that are 6%, 97%, 98%, or 99% identical, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to sequences as defined in SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126, SEQ ID NO: 127, SEQ ID NO: 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131, SEQ ID NO: 132, SEQ ID NO: 133, or SEQ ID NO: 134 An amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 124, SEQ ID NO: 125, SEQ ID NO: 126, SEQ ID NO: 127, SEQ ID NO: 128, SEQ ID NO: 129, SEQ ID NO: 130, SEQ ID NO: 131, SEQ ID NO: 132, SEQ ID NO: 133, or SEQ ID NO: 134, or i includes an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, SEQ ID NO: 118, SEQ ID NO: 119, SEQ ID NO: 120, SEQ ID NO: 121, SEQ ID NO: 122, or SEQ ID NO: 123.

[0098] The term "PTPRC" refers to the gene for protein tyrosine phosphatase receptor type C (Ensembl:ENSG00000081237), for example, the sequence defined in NCBI reference sequence NM_002838 or NCBI reference sequence NM_080921, specifically the nucleotide sequence as defined in SEQ ID NO: 135 or SEQ ID NO: 136, corresponding to the sequence of the NCBI reference sequence of the PTPRC transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 137 or SEQ ID NO: 138, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_002829 and NCBI protein accession reference sequences NP_563578, which encode the PTPRC polypeptide.

[0099] The term "PTPRC" also refers to a nucleotide sequence that exhibits high homology to a PTPRC, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 135 or SEQ ID NO: 136, or a sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 137 or SEQ ID NO: 138. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91%, 91%,

[0100] The term "RAP1GAP2" refers to the sequence defined in the human RAP1 GTPase Activating Protein 2 gene (ENSG00000132359), for example, NCBI reference sequence NM_015085, NCBI reference sequence NM_001100398, or NCBI reference sequence NM_001330058, specifically the nucleotide sequence as defined in SEQ ID NO: 139, SEQ ID NO: 140, or SEQ ID NO: 141, which corresponds to the sequence of the NCBI reference sequence of the RAP1GAP2 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 142, SEQ ID NO: 143, or SEQ ID NO: 144, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_055900, NCBI protein accession reference sequences NP_001093868, and NCBI protein accession reference sequences NP_001316987, which encodes the RAP1GAP2 polypeptide.

[0101] The term "RAP1GAP2" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to RAP1GAP2, for example, sequences as defined in SEQ ID NO: 139, SEQ ID NO: 140, or SEQ ID NO: 141, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to sequences as defined in SEQ ID NO: 142, SEQ ID NO: 143, or SEQ ID NO: 144. The invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a certain amino acid sequence, or to a sequence as defined in SEQ ID NO: 142, SEQ ID NO: 143, or SEQ ID NO: 144, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence as defined in SEQ ID NO: 139, SEQ ID NO: 140, or SEQ ID NO: 141.

[0102] The term "SLC39A11" refers to the nucleotide sequence defined in SEQ ID NO: 145 or SEQ ID NO: 148, corresponding to the NCBI reference sequence NM_139177 or NM_001352692, which is a member of the human solute carrier family 39, and specifically to the nucleotide sequence defined in SEQ ID NO: 145 or SEQ ID NO: 146, which corresponds to the NCBI reference sequence of the SLC39A11 transcript shown above. It also refers to the corresponding amino acid sequence defined in SEQ ID NO: 147 or SEQ ID NO: 148, which corresponds to the protein sequence defined in the NCBI protein accession reference sequences NP_631916 and NP_001339621, which encode the SLC39A11 polypeptide.

[0103] The term "SLC39A11" also refers to nucleotide sequences that show high homology to SLC39A11, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 145 or SEQ ID NO: 146, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 147 or SEQ ID NO: 148. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a certain amino acid sequence, or to a sequence as defined in SEQ ID NO: 147 or SEQ ID NO: 148, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence as defined in SEQ ID NO: 145 or SEQ ID NO: 146.

[0104] The term "TDRD1" refers to the human Tudor Domain Containing 1 gene (Ensembl:ENSG00000095627), specifically the sequence defined in the NCBI reference sequence NM_198795, and more precisely, the nucleotide sequence as defined in Sequence ID No. 149, which corresponds to the sequence of the NCBI reference sequence of the TDRD1 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 150, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_942090 that encodes the TDRD1 polypeptide.

[0105] The term "TDRD1" also refers to nucleotide sequences that show high homology to TDRD1, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence 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 defined in Sequence ID No. 150, or as defined in Sequence ID No. 150 A nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 149, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 149.

[0106] The term "TLR8" refers to the Toll-like receptor 8 gene (Ensembl:ENSG00000101916), specifically the nucleotide sequence defined in, for example, NCBI reference sequence NM_138636 or NCBI reference sequence NM_016610, as defined in SEQ ID NO: 151 or SEQ ID NO: 152, which corresponds to the NCBI reference sequence of the TLR8 transcript shown above. It also refers to the corresponding amino acid sequence defined in SEQ ID NO: 153 or SEQ ID NO: 154, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_619542 and NCBI protein accession reference sequences NP_057694, which encode the TLR8 polypeptide.

[0107] The term "TLR8" also refers to nucleotide sequences that show high homology to TLR8, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences defined in SEQ ID NO: 151 or SEQ ID NO: 152, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences defined in SEQ ID NO: 153 or SEQ ID NO: 154. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence, or a nucleic acid sequence encoding an amino sequence as defined in SEQ ID NO: 153 or SEQ ID NO: 154, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 91%, 91%, 91%,

[0108] The term "VWA2" refers to the human Von Willebrand Factor A Domain Containing 2 gene (Ensembl:ENSG00000165816), specifically the sequence defined in the NCBI reference sequence NM_001320804, and more specifically, the nucleotide sequence as defined in Sequence ID No. 155, which corresponds to the sequence of the NCBI reference sequence of the VWA2 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 156, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001307733 that encodes the VWA2 polypeptide.

[0109] The term "VWA2" also refers to nucleic acid sequences that show high homology to VWA2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 155, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 156. A nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 156, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 155.

[0110] The term "ZAP70" refers to the gene (Ensembl:ENSG00000115085) of the zeta chain of T-cell receptor-associated protein kinase 70, for example, the sequence defined in NCBI reference sequence NM_001079 or NCBI reference sequence NM_207519, specifically the nucleotide sequence as defined in SEQ ID NO: 157 or SEQ ID NO: 158, corresponding to the sequence of the NCBI reference sequence of the ZAP70 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 159 or SEQ ID NO: 160, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_001070 and NCBI protein accession reference sequences NP_997402, which encode the ZAP70 polypeptide.

[0111] The term "ZAP70" also refers to nucleotide sequences that show high homology to ZAP70, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 157 or SEQ ID NO: 158, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 159 or SEQ ID NO: 160. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or a nucleic acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91%, 91%,

[0112] The term "ZBP1" refers to the sequence defined in the Z-DNA binding protein 1 gene (Ensembl: ENSG00000124256), for example, NCBI reference sequence NM_030776 or NCBI reference sequence NM_001160418 or NCBI reference sequence NM_001160419, specifically the nucleotide sequence as defined in SEQ ID NO: 161, SEQ ID NO: 162, or SEQ ID NO: 163, which corresponds to the sequence of the NCBI reference sequence of the ZBP1 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 164, SEQ ID NO: 165, or SEQ ID NO: 166, which corresponds to the protein sequence defined in NCBI protein accession reference sequences NP_110403, NP_001153890, and NP_001153891, which encode the ZBP1 polypeptide.

[0113] The term "ZBP1" also refers to nucleic acid sequences that show high homology to ZBP1, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequences defined in SEQ ID NO: 161, 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%, 96%, 97%, 98%, or 99% identical to the sequences defined in SEQ ID NO: 164, SEQ ID NO: 165, or SEQ ID NO: 166 or comprising a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 164, SEQ ID NO: 165, or SEQ ID NO: 166, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 161, SEQ ID NO: 162, or SEQ ID NO: 163.

[0114] The terms “biological sample” or “sample obtained from subject” refer to any biological material obtained from a subject, for example, a prostate cancer patient, by a suitable method known to those skilled in the art. The term “prostate cancer subject” refers to a person who has or is suspected of having prostate cancer. Biological samples used should be collected in a clinically acceptable manner, for example, by a method that preserves nucleic acids (especially RNA) or proteins.

[0115] Biological samples may include body tissues and / or bodily fluids, such as, but not limited to, blood, sweat, saliva, and urine. Furthermore, biological samples may contain cell extracts or cell populations derived from epithelial cells, such as cancerous epithelial cells or epithelial cells derived from tissue suspected of being cancerous. Biological samples may also contain cell populations derived from glandular tissue; for example, a sample may be derived from the prostate of a male subject. In addition, if necessary, cells may be purified from the acquired body tissues and bodily fluids and then used as biological samples. In some realizations, samples may be tissue samples, urine samples, urine sediment samples, blood samples, saliva samples, semen samples, samples containing circulating tumor cells, extracellular vesicles, samples containing exosomes secreted by the prostate, or cell lines or cancer cell lines.

[0116] Furthermore, it was demonstrated that using the full model (i.e., using all eight PDE4D7-related genes, all 14 immune defense response genes, and all 17 T-cell receptor signaling genes) is not essential to obtain a significant predictive effect, and that significant results have already been obtained by randomly selecting one PDE4D7-related gene and combining it with one randomly selected immune defense response gene and one randomly selected T-cell receptor signaling gene. Examples and Figures 15-32 demonstrate that randomly selecting one PDE4D7-related gene from the full set of genes and combining it with one randomly selected immune defense response gene and one randomly selected T-cell receptor signaling gene is sufficient to make significant predictions. It is reasonable that these random selections make it possible to significantly predict the response of prostate cancer patients to radiotherapy by arbitrarily selecting one gene from each group.

[0117] In certain realizations, biopsy or excision samples are obtained and / or used. Such samples may include cells or cell lysates.

[0118] It is also conceivable to subject the contents of a biological sample to a concentration step. For example, the sample is brought into contact with a ligand that is specific to a particular cell type, such as the cell membrane or organelle of prostate cells, and is functionalized with, for example, magnetic particles. The material concentrated with magnetic particles is then used for the detection and analysis steps described above or below in this specification.

[0119] Furthermore, cells, such as tumor cells, can also be concentrated through a filtration process of a liquid or liquid sample, such as blood or urine. Such a filtration process can also be combined with the concentration step based on ligand-specific interactions described above.

[0120] The term “prostate cancer” refers to cancer of the prostate gland in the male reproductive system, which occurs when cells in the prostate mutate and begin to multiply uncontrollably. Typically, prostate cancer is associated with elevated levels of prostate-specific antigen (PSA). In one embodiment, the term “prostate cancer” refers to cancer exhibiting a PSA level greater than 3.0. In another embodiment, the term refers to cancer exhibiting a PSA level greater than 2.0. The term “PSA level” refers to the concentration of PSA in the blood in ng / ml.

[0121] The term "non-progressive prostate cancer state" means that an individual sample does not exhibit parameter values ​​representing "biochemical recurrence," "clinical recurrence," "metastasis," "castration-resistant disease," and / or "prostate cancer, i.e., disease-specific death."

[0122] The term “progressive prostate cancer state” means that a sample of an individual exhibits parameter values ​​representing “biochemical recurrence,” “clinical recurrence,” “metastasis,” “castration-resistant disease,” and / or “prostate cancer, i.e., disease-specific death.”

[0123] The term "biochemical recurrence" generally refers to a recurrence characterized by an increase in the biological value of PSA, indicating the presence of prostate cancer cells in the sample. However, other markers that can be used to detect such presence or to raise suspicion of such presence may also be used.

[0124] The term "clinical recurrence" refers to the presence of clinical signs indicating the presence of tumor cells, for example, as measured using in vivo imaging.

[0125] The term "metastasis" refers to the presence of metastatic disease in organs other than the prostate gland.

[0126] The term "castration-resistant disease" refers to the presence of hormone-insensitive prostate cancer, that is, cancer in the prostate that no longer responds to androgen deprivation therapy (ADT).

[0127] The term "prostate cancer-specific death, or disease-specific death," refers to a patient's death resulting from prostate cancer.

[0128] What is preferable is, One or more immune defense response genes include three or more, preferably six or more, more preferably nine or more, most preferably all immune defense genes, and / or One or more T cell receptor signaling genes include three or more, preferably six or more, more preferably nine or more, most preferably all T cell receptor signaling genes, and / or One or more PDE4D7-related genes include three or more, preferably six or more, and most preferably all PDE4D7-related genes, and one or more genes include three or more, preferably six or more, and most preferably all genes.

[0129] Determining the appropriate radiation therapy dose is The steps include combining a first gene expression profile for two or more immune defense response genes, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of them, with a regression function derived from a population of prostate cancer subjects, and / or The steps of combining a second gene expression profile for two or more T cell receptor signaling genes, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them, with a regression function derived from a population of prostate cancer subjects, and / or Preferably, the process includes a step of combining a third gene expression profile for two or more PDE4D7-related genes, for example, 2, 3, 4, 5, 6, 7, or all of them, with a regression function derived from a population of prostate cancer subjects.

[0130] Cox proportional hazards regression allows for real-time analysis of the impact of multiple risk factors on a tested event, such as survival. These risk factors can be binary or discrete variables, such as risk scores or clinical stage, or continuous variables, such as biomarker measurements or gene expression values. The probability of reaching an endpoint (e.g., death or disease recurrence) is called the hazard. In regression analysis, the time to the endpoint is considered, in addition to information about whether a subject in a patient cohort reached the tested endpoint (e.g., whether the patient died or not). The hazard is modeled as H(t) = H0(t)·exp(w1·V1+w2·V2+w3·V3+···), where V1, V2, V3··· are predictors, H0(t) is the baseline hazard, and H(t) is the hazard at any given time t. The hazard rate (i.e., the risk of reaching an event) is expressed as Ln[H(t) / H0(t)]=w1·V1+w2·V2+w3·V3+···, where the coefficients or weights w1, w2, w3··· are estimated by Cox regression analysis and can be interpreted similarly to logistic regression analysis.

[0131] In a particular realization, the combination of two or more immune defense response genes, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of the first gene expression profiles and the regression function is determined as follows:

number

[0132] In one example, w1 may be about -0.8 to 0.2, such as -0.313, w2 may be about -0.7 to 0.3, such as -0.1417, w3 may be about -0.4 to 0.6, such as 0.1008, w4 may be about -0.5 to 0.5, such as -0.07478, w5 may be about -0.2 to 0.8, such as 0.277, w6 may be about -0.6 to 0.4, such as -0.07944, w7 may be about -0.2 to 0.8, such as 0.3036, w8 may be about -0.6 to 0.4, such as -0.09188, w9 may be about -0.3 to 0.7, such as 0.1661, w 10 may be about -1.2 to 0.2, such as -0.7105, w 11 may be about -0.3 to 0.7, such as 0.1615, w 12 may be about -0.6 to 0.4, such as -0.07468, w 13 may be about -0.6 to 0.4, such as -0.06677, w 14 may be about -0.7 to 0.3, such as -0.155.

[0133] In a particular implementation, the combination of the second gene expression profile and the regression function for two or more, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 species or all, of the T cell receptor signaling genes is determined as follows:

Number

[0134] In one example, w 15 may be about -0.1 to 0.9, such as 0.4137, w 16It can be approximately -0.6 to 0.4, for example, -0.1323, w 17 w can be approximately -0.1 to 0.9, for example, 0.3695. 18 It can be approximately -0.8 to 0.2, for example, -0.267, w 19 It can be approximately -0.7 to 0.3, for example, -0.1984, w 20 w can be approximately -0.2 to 0.8, for example, 0.3018. 21 w can be approximately 0.1 to 1.1, for example, 0.6169. 22 It can be approximately -0.8 to 0.2, for example, -0.2789, w 23 It can be approximately -0.7 to 0.3, for example, -0.1842, w 24 It can be approximately 0.5 to 1.5, for example, 0.4672, w 25 w can be -0.5 to 0.5, for example, -0.07028. 26 w can be -0.2 to 0.8, for example, 0.3278. 27 w can be -1.3 to -0.3, for example, -0.8253, 28 w can be approximately 0.1 to 1.1, for example, 0.6212. 29 w can be approximately -0.9 to 0.1, for example, -0.4462, 30 w can be approximately -0.9 to 0.1, for example, -0.4622. 31 It can be approximately -0.1 to 0.9, for example, -0.3702.

[0135] In a particular realization, the combination of a third gene expression profile and regression function for two or more PDE4D7-related genes, e.g., 2, 3, 4, 5, 6, 7, or all of them, is determined as follows:

number

[0136] For example, w 32 w can be approximately -0.1 to 0.9, for example, 0.368. 33 It can be approximately -0.3 to -1.3, for example, -0.7675, and w 34 w can be approximately -0.1 to 0.9, for example, 0.4108. 35 w can be approximately -0.1 to 0.9, for example, 0.4007. 36 It can be approximately -1.2 to -0.2, for example, -0.679, and w 37 w can be approximately 0.0 to 0.1, for example, 0.5433. 38 w can be approximately 0.1 to 1.1, for example, 0.6366. 39 It can be approximately -1.0 to 0.0, for example, -0.4749.

[0137] It is even more preferable that the selection of the radiotherapy dose further includes combining a first combination of gene expression profiles, a second combination of gene expression profiles, and a third combination of gene expression profiles with a regression function derived from a population of prostate cancer patients.

[0138] In a particular realization, the combination is determined as follows:

number

[0139] In one example, w 40 It can be approximately 0.2 to 1.2, for example, 0.674, w 41 w can be approximately 0.0 to 1.0, for example, 0.5474. 42 It can be approximately 0.1 to 1.1, for example, 0.6372.

[0140] The predicted response to radiotherapy may also be classified or categorized into one of at least two risk groups based on the predicted value of the radiotherapy outcome. For example, there may be two, three, four, or more than four predefined risk groups. Each risk group covers a distinct range of (non-overlapping) values ​​of the predicted radiotherapy outcome. For example, the risk groups represent the probability of a specific clinical event occurring, such as 0 to <0.1, 0.1 to <0.25, 0.25 to <0.5, or 0.5 to 1.0.

[0141] It is even more preferable that the selection of the radiation therapy dose be based on one or more clinical parameters obtained from the subject.

[0142] As mentioned above, various measurements based on clinical parameters are being considered. Further selection of radiotherapy doses based on these clinical parameters (multiple parameters are possible) may allow for further improvement of the selection process.

[0143] Clinical parameters include: (i) prostate-specific antigen (PSA) level; (ii) pathological Gleason score (pGS); (iii) clinical tumor stage; (iv) pathological Gleason grade group (pGGG); (v) pathological status; (vi) one or more pathological variables, e.g., surgical margin status and / or lymph node metastasis and / or extraprostatic growth and / or seminal vesicle invasion; (vii) CAPRA; (viii) CAPRA-S; (ix) EAU-BCR risk group; and (x) one or more of the other clinical risk scores.

[0144] Predicting treatment response or determining treatment personalization more preferably involves: (i) a first gene expression profile (multiple) for one or more immune defense response genes; (ii) a second gene expression profile (multiple) for one or more T cell receptor signaling genes; (iii) a third gene expression profile (multiple) for one or more PDE4D7-related genes; and (iv) combining one or more combinations of the first gene expression profiles, the second gene expression profiles, and the third gene expression profiles, and one or more clinical parameters obtained from subjects, with a regression function derived from a population of prostate cancer subjects.

[0145] In a particular realization, the combination is determined as follows:

number

[0146] In one example, w 43 It can be approximately 0.4 to 1.4, for example, 0.8887, w 44 It can be approximately 1.1 to 2.1, for example, 1.6085.

[0147] Biological samples are preferably obtained from the subject before the start of radiotherapy. Gene expression profiles may be determined in the form of mRNA or protein in prostate cancer tissue. Alternatively, if the genes are present in a soluble form, gene expression profiles may be determined in the blood.

[0148] It is even more preferable that the radiation therapy be curative radiation therapy or salvage radiation therapy.

[0149] In a further embodiment of the present invention, a device for selecting the dose for radiotherapy in a patient with prostate cancer, A first gene expression profile for each of the immune defense response genes, one or more selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, which is determined in a biological sample obtained from the subject, and / or one or more selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example An input unit adapted to receive data showing a second gene expression profile for each of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, the said second gene expression profile(s) determined in a biological sample obtained from the subject, and / or a third gene expression profile(s) selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, for example, 1, 2, 3, 4, 5, 6, 7 or all PDE4D7 correlated genes, the said third gene expression profile(s) determined in a biological sample obtained from the subject. A processor adapted to determine the selection of a radiotherapy dose based on the first gene expression profile(s), or the second gene expression profile(s), or the third gene expression profile(s), or the first, second, and third gene expression profiles(s), and A device is presented that includes a delivery unit adapted to provide, as appropriate, recommendations for treatment options or treatment options based on those options to medical assistants or patients.

[0150] In a further aspect of the present invention, when a program is executed by a computer, Steps include receiving data showing a first gene expression profile for each of one or more immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, wherein the first gene expression profile (or more) is determined in a biological sample obtained from the subject and / or A step of receiving data showing a second gene expression profile for each of one or more 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, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them, wherein the second gene expression profile (or more) is determined in a biological sample obtained from a subject, and / or A step of receiving data showing a third gene expression profile for each of one or more PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, e.g., 1, 2, 3, 4, 5, 6, 7, or all of them, wherein the third gene expression profile (or more) is determined in a biological sample obtained from the subject. A step of determining the dose for radiotherapy for a prostate cancer patient based on a first gene expression profile (multiple), a second gene expression profile (multiple), a third gene expression profile (multiple), or the first, second, and third gene expression profiles (multiple), and Steps to provide healthcare assistants or patients with recommendations for treatment options or treatment options based on those options, as appropriate. A computer program product is presented that includes instructions for causing a computer to perform a method that has [a certain characteristic].

[0151] In a further aspect of the present invention, At least one primer and / or probe for determining a first gene expression profile for each of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all immunodefense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 in a biological sample obtained from a subject, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all immunodefense response genes, and / or At least one primer and / or probe for determining a second gene expression profile for each of the following T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70 in biological samples obtained from subjects, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, and / or At least one primer and / or probe for determining the gene expression profile for each of the PDE4D7-related genes, selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 in biological samples obtained from subjects, for example, 1, 2, 3, 4, 5, 6, 7, or all of them, and A diagnostic kit including the apparatus described in claim 10 or the computer program product described in claim 11 may be presented as appropriate.

[0152] In a further embodiment of the present invention, the use of the kit defined in claim 12 is presented.

[0153] The use described in claim 13 is preferably in a method for selecting a dose for radiotherapy in patients with prostate cancer.

[0154] In a further aspect of the present invention, The step of receiving one or more biological samples obtained from prostate cancer subjects, A step of determining a first gene expression profile for each of the following immune defense response genes, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of them, in a biological sample obtained from a subject using the kit described in claim 12, and / or A step of determining a second gene expression profile for each of one or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, in a biological sample obtained from a subject, using the kit described in claim 12, and / or A method is presented that uses the kit described in claim 12 to determine a third gene expression profile for each of the PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2 in a biological sample obtained from a subject.

[0155] In a further aspect of the present invention, a method for selecting a dose for radiotherapy in a patient with prostate cancer, comprising: a first gene expression profile for each of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8 and ZBP1; CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D The method involves using a second gene expression profile for each of one or more T cell receptor signaling genes selected from the group consisting of PRKACA, PRKACB, PTPRC, and ZAP70, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all T cell receptor signaling genes, and a third gene expression profile for each of one or more PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, for example, 1, 2, 3, 4, 5, 6, 7, or all PDE4D7-related genes, wherein the method is The steps include determining the selection of the radiotherapy dose based on a first gene expression profile (multiple possible), a second gene expression profile (multiple possible), a third gene expression profile (multiple possible), or the first, second, and third gene expression profiles (multiple possible), and The use is presented, which includes, as appropriate, the step of providing the medical assistant or patient with recommendations for treatment options or treatment options based on those options.

[0156] It will be understood that the method according to claim 1, the apparatus according to claim 10, the computer program product according to claim 11, the diagnostic kit according to claim 12, the use of the diagnostic kit according to claim 13, the method according to claim 15, and the use of the first, second, and third gene expression profiles (plural) according to claim 16 have similar and / or identical preferred embodiments, particularly as defined in the dependent claims.

[0157] Preferred embodiments of the present invention may be any combination of the dependent claims or the embodiments described above and each independent claim.

[0158] These and other aspects of the present invention will become apparent from the embodiments described below and will be explained by reference to the embodiments described below. [Brief explanation of the drawing]

[0159] [Figure 1] This diagram schematically and illustratively shows a flowchart of an embodiment of a method for predicting the response of prostate cancer patients to radiation therapy, or a method for personalizing the treatment of prostate cancer patients. [Figure 2] This figure shows the prostate cancer-specific survival rate in the low-risk group (IDR_model <= -0.5) as defined by the IDR_model. [Figure 3] This figure shows the prostate cancer-specific survival rate in the high-risk group (IDR_model > -0.5) as defined by the IDR_model. [Figure 4] This figure shows the prostate cancer-specific survival rate in the low-risk group (TCR_SIGNALING_model <= 0.0) as defined by the TCR_SIGNALING_model. [Figure 5] This figure shows the prostate cancer-specific survival rate for the high-risk group (TCR_SIGNALING_model > 0.0) as defined by the TCR_SIGNALING_model. [Figure 6] This figure shows the prostate cancer-specific survival rate in the low-risk group (PDE4D7_CORR_model <= 0.0) as defined by the PDE4D7_CORR_model. [Figure 7] This figure shows the prostate cancer-specific survival rate in the high-risk group (PDE4D7_CORR_model > 0.0) as defined by the PDE4D7_CORR_model. [Figure 8]This figure shows the prostate cancer-specific survival rate in the low-risk group (PCAI_model <= 0.5) as defined by the PCAI_model. [Figure 9] This figure shows the prostate cancer-specific survival rate in the high-risk group (PCAI_model > 0.5) as defined by the PCAI_model. [Figure 10] This figure shows the prostate cancer-specific survival rate in the low-risk group (PCAI&Clinical_model<=0.5) as defined by PCAI&Clinical_model. [Figure 11] This figure shows the prostate cancer-specific survival rate in the high-risk group (PCAI&Clinical_model > 0.5) as defined by PCAI&Clinical_model. [Figure 12] This is a Kaplan-Meier curve of the PCAI-3.1 model in a low-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Figure 13] This is a Kaplan-Meier curve of the PCAI-3.1 model in a high-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Figure 14] This is a Kaplan-Meier curve of the PCAI-3.2 model in a low-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Figure 15] This is a Kaplan-Meier curve of the PCAI-3.2 model in a high-risk patient cohort, encompassing all patients who underwent SRT (salvage radiotherapy) after postoperative BCR (biochemical recurrence). [Figure 16] This is a reference figure including Kaplan-Meier curves for the PCAI-3.3 model in a low-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative BCR (biochemical recurrence). [Figure 17]This is a reference figure including Kaplan-Meier curves for the PCAI-3.3 model in a high-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Figure 18] This is a Kaplan-Meier curve of the PCAI-3.4 model in a low-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Figure 19] This is a Kaplan-Meier curve of the PCAI-3.4 model in a high-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Figure 20] This is a Kaplan-Meier curve of the PCAI-3.5 model in a low-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Figure 21] This is a Kaplan-Meier curve of the PCAI-3.5 model in a high-risk patient cohort, encompassing all patients who underwent SRT (salvage radiotherapy) after postoperative BCR (biochemical recurrence). [Figure 22] This is a Kaplan-Meier curve of the PCAI-3.6 model in a low-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Figure 23] This is a Kaplan-Meier curve of the PCAI-3.6 model in a high-risk patient cohort, encompassing all patients who received salvage radiotherapy (SRT) after postoperative biochemical recurrence (BCR). [Modes for carrying out the invention]

[0160] Overview of radiation therapy dose selection Figure 1 schematically and illustratively shows a flowchart of one embodiment of a method for selecting the radiation dose for prostate cancer patients.

[0161] In step S100, the method is implemented.

[0162] In step S102, a biological sample is obtained from each of the first set of patients (subjects) diagnosed with prostate cancer. Preferably, after obtaining the biological sample, these prostate cancer patients are monitored for prostate cancer for a period of at least one year, at least two years, or about five years.

[0163] In step S104, a first gene expression profile is obtained for each of the following immune defense response genes, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, and / or one or more of the following genes are obtained, for example, 1, 2 A second gene expression profile for each of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes, and / or a third gene expression profile for each of two or more PDE4D7-related genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, for example, 2, 3, 4, 5, 6, 7 or all PDE4D7-related genes, are obtained for each of the biological samples obtained from the first patient set, for example by performing RT-qPCR (real-time quantitative PCR) on RNA extracted from each biological sample. An exemplary gene expression profile includes expression levels (e.g., values) for each of two or more genes that can be normalized using values ​​(or more) for each of a set of reference genes such as B2M, HPRT1, POLR2A, and / or PUM1. In one implementation, the gene expression levels of each of two or more genes in the first gene expression profile and / or the second gene expression profile and / or the third gene expression profile are normalized to one or more reference genes selected from the group consisting of ACTB, ALAS1, B2M, HPRT1, POLR2A, PUM1, RPLP0, TBP, TUBA1B, and / or YWHAZ, for example, at least one, at least two, or at least three, or preferably all of these reference genes.

[0164] In step S106, the regression function for assigning the selection of radiotherapy response is based on a first gene expression profile for two or more immune defense response genes, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and / or ZBP1, obtained for at least a portion of the biological samples obtained for a first set of patients, and / or two or more T cell receptor signaling genes, CD2, The results are determined based on a second gene expression profile for CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and / or ZAP70, and / or a third expression profile for two or more PDE4D7 correlated genes, ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and / or VWA2, with each result obtained from monitoring. In a particular realization, the regression function is determined as specified in equation (5) above.

[0165] In step S108, a biological sample is obtained from the patient (subject, i.e., individual). The patient may be a new patient or a patient from the first set.

[0166] In step S110, a first gene expression profile for each of one or more immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, and / or CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, A second gene expression profile and / or a third gene expression profile for each of the following T cell receptor signaling genes, selected from the group consisting of PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP701, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them, is obtained, for example, by performing PCR on a biological sample, for two or more, for example, 2, 3, 4, 5, 6, 7, or all of the PDE4D7-related genes, respectively. In one implementation, the gene expression levels of each of two or more genes in the first gene expression profile and / or the second gene expression profile and / or the third gene expression profile are normalized to one or more reference genes selected from the group consisting of ACTB, ALAS1, B2M, HPRT1, POLR2A, PUM1, RPLP0, TBP, TUBA1B, and / or YWHAZ, for example, at least one, at least two, at least three, or preferably all of these reference genes. This is substantially the same as step S104.

[0167] In step S112, the prediction of treatment response or personalization of treatment based on the first, second, or third gene expression profiles, or the first, second, and third gene expression profiles, is determined for the patient using a regression function. This will be described in more detail later in this document.

[0168] In S114, based on the selection, a recommendation for treatment is provided, for example, to the patient or their guardian, to the physician, or to another healthcare professional. For this purpose, the selection may be categorized into one of a predefined set of risk groups based on the value of the selection. In a particular realization, the treatment may be radiotherapy, and the prediction of the treatment outcome may be negative or positive regarding the effectiveness of the treatment. If the prediction is negative, the recommended treatment may include (i) radiotherapy performed earlier than standard radiotherapy; (ii) radiotherapy with increased radiation dose; (iii) the use of non-standard EBRT radiotherapy techniques such as IMRT (intensity-modulated RT); IGRT (image-guided RT); SBRT (stereotactic body RT); or fractionated RT; (iv) adjuvant therapy such as androgen deprivation therapy; and (v) one or more other treatments other than radiotherapy. Furthermore, in order to personalize RT, a recommendation for the total dose to be applied to the patient may be provided. In one specific realization, the selected total RT dose may be 6 Gy or less for patients who are part of a predetermined low-risk group in order to minimize radiation-induced toxicity. In other realizations, the selected total RT dose may be greater than 66 Gy for patients who are part of a predetermined high-risk group in order to maximize radiation-induced tumor control and patient outcomes.

[0169] In S116, the method is terminated.

[0170] In one embodiment, the gene expression profile in steps S104 and S110 is determined by detecting mRNA expression using two or more primers and / or probes and / or sets thereof.

[0171] In one embodiment, steps S104 and S110 further include obtaining clinical parameters from a first set of patients and patients, respectively. The clinical parameters may include: (i) prostate-specific antigen (PSA) levels; (ii) pathological Gleason score (pGS); (iii) clinical tumor status; (iv) pathological Gleason grade group (pGGG); (v) pathological status; (vi) one or more pathological variables, e.g., surgical margin status and / or lymph node metastasis and / or extraprostatic proliferation and / or seminal vesicle invasion; (vii) CAPRA; (viii) CAPRA-S; (ix) EAU-BCR risk group; and (x) one or more other clinical risk scores. The regression function for assigning the selection of radiotherapy doses determined in step S106 is then further based on one or more clinical parameters obtained from at least a portion of the first set of patients. In step S112, the selection of the radiotherapy dose is then determined for the patient using a regression function, further based on one or more clinical parameters obtained from the patient, for example, the EAU-BCR risk group. In a particular realization, the regression function is determined as specified in equation (6) above.

[0172] Based on the significant correlation with outcomes after RT, the identified molecules are expected to provide predictive values ​​regarding the effectiveness of radical RT and / or SRT. Furthermore, based on the significant correlation between SRT dose and patient outcomes in different risk groups defined by the identified genes, it is expected that SRT dose can predict the effectiveness of radical RT and / or SRT.

[0173] result TPM gene expression levels For each gene, the TPM (total productivity per million transcripts) expression value was calculated based on the following steps: 1. Divide the read count derived from RNAseq fastq raw data by the length (kilobases) of each gene after mapping and alignment to the human genome. This yields the number of reads per kilobase (RPK). 2. Sum all the RPK values ​​in the sample and divide this value by 1,000,000. This will give you the scale factor "per million". 3. Divide the RPK value by the "per million" scale factor. This will give you the TPM expression value for each gene.

[0174] The TPM values ​​per gene were used to calculate the reference normalized gene expression for each gene.

[0175] Normalized gene expression levels In the Cox regression model, expression values ​​based on TPM (total per million transcripts) for all genes from RNA-seq data were log2 normalized using the following transformation:

number

[0176] As the second step in normalizing TPM_log2, the expression values ​​were normalized against the mean (mean(ref_genes)) of the four reference genes as follows:

number

[0177] The following reference genes were considered (Table 1):

[0178] [Table 1]

[0179] For these reference genes, we selected the following four: B2M, HPRT1, POLR2A, and PUM1, and calculated the following:

number

[0180] In the final step, the reference gene-normalized TPM_log2_norm expression values ​​for each gene were converted to z-scores by calculating them as follows:

number

[0181] This process causes the TPM_log2_norm values ​​to be distributed around a mean of 0 and a standard deviation of 1.

[0182] For multivariate analysis of the genes of interest, the reference gene-normalized log2(TPM) z-score value for each gene was used as input.

[0183] Cox regression analysis Next, we began investigating whether combinations of 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-related genes, and whether these combinations would have greater prognostic value. Using Cox regression, we modeled the expression levels of 14 immune defense response genes, 17 T cell receptor signaling genes, and 8 PDE4D7-related genes against prostate cancer-specific mortality after postoperative salvage RT in a cohort of 571 prostate cancer patients.

[0184] The Cox regression function was derived as follows:

number

[0185] weight w1~w 39 Details are shown in Table 2 below.

[0186] [Table 2] TIFF0007838574000014.tif255161

[0187] Based on three separate Cox regression models (IDR_model, TCR_SIGNALING_model, and PDE4D7_CORR_model), Cox regression was again used to model the combination of prostate cancer-specific mortality after postoperative salvage RT with or without the variable EAU_BCR in a cohort of 571 prostate cancer patients. These two models were validated using Kaplan-Meier survival analysis.

[0188] The Cox regression function was derived as follows:

number

[0189] weight w 40 ~w 44 Details are shown in Table 2 below.

[0190] [Table 3]

[0191] Kaplan-Meier Survival Analysis In the Kaplan-Meier survival curve analysis, the Cox functions of the risk models (IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model, PCAI_model, PCAI&Clinical_model) categorized patients into two subcohorts based on a cutoff. The thresholds for grouping patients into low-risk and high-risk categories were based on the mean prognostic index (PI) of each of the IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model, PCAI_model, and PCAI&Clinical_model, calculated using Cox regression models for each patient across the entire cohort.

[0192] The patient classes represent an increased risk of experiencing the tested clinical endpoints—prostate cancer-specific death (PCa Death) after initiation of salvage radiotherapy (SRT) due to postoperative disease recurrence (Figures 2-11) and death after initiation of salvage radiotherapy (SRT) due to postoperative disease recurrence—for each of the risk models created (IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model, PCAI_model, PCAI&Clinical_model).

[0193] Figure 2 shows the prostate cancer-specific survival rate in the low-risk group (IDR_model <= -0.5) as defined by the IDR_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total dose applied <= 66 Gy) and the other with high-dose SRT (defined as total dose applied > 66 Gy) (Ohri N. et al., "Salvage Radiotherapy for prostate cancer - Finding a way forward using radiobiological modeling," Cancer Biol Ther., Vol. 13, No. 4, pp. 1449-1453, 2012). Survival curves for the two patient groups are shown; no statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p=0.2; HR low-risk vs. high-risk = NA; 95% CI = NA). The following supplementary list shows the number of patients at risk for each dose class of the SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 10, 10, 10, 7, 3, 2, 1, 1, 0; High dose: 25, 24, 22, 18, 14, 8, 7, 5, 2, 2, 0.

[0194] Figure 3 shows the prostate cancer-specific survival rates in the high-risk group (IDR_model > -0.5) as defined by the IDR_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total dose applied <= 66 Gy) and the other with high-dose SRT (defined as total dose applied > 66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; a statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p=0.05; HR low-risk vs. high-risk = 4.1; 95% CI = 1.0~16.7). The following supplementary list shows the number of patients at risk for each dose class of SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 25, 24, 23, 20, 18, 11, 7, 5, 2, 0; High dose: 38, 38, 36, 29, 25, 14, 10, 7, 5, 0.

[0195] Figure 4 shows the prostate cancer-specific survival rate in the low-risk group (TCR_SIGNALING_model<=0.0) as defined by the TCR_SIGNALING_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total dose applied <= 66 Gy) and the other with high-dose SRT (defined as total dose applied > 66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; no statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p=NA; HR low-risk vs. high-risk=NA; 95%CI=NA). The following supplementary list shows the number of patients at risk for each dose class of the SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 13, 13, 13, 11, 5, 4, 2, 1, 0; High dose: 26, 25, 24, 20, 15, 7, 5, 3, 3, 0.

[0196] Figure 5 shows the prostate cancer-specific survival rates in the high-risk group (TCR_SIGNALING_model>0.0) as defined by the TCR_SIGNALING_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total dose applied <= 66 Gy) and the other with high-dose SRT (defined as total dose applied > 66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; a statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p=0.01; HR low-risk vs. high-risk = 5.6; 95%CI=1.4~21.4). The following supplementary list shows the number of patients at risk for each dose class of SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 22, 21, 20, 17, 14, 9, 5, 4, 2, 1, 0; High dose: 37, 37, 34, 27, 24, 15, 12, 9, 4, 2, 0.

[0197] Figure 6 shows the prostate cancer-specific survival rates in the low-risk group (PDE4D7_CORR_model<=0.0) as defined by the PDE4D7_CORR_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total dose applied <= 66 Gy) and the other with high-dose SRT (defined as total dose applied > 66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; no statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p=0.9; HR low-risk vs. high-risk = 1.2; 95%CI=0.07~19.2). The following supplementary list shows the number of patients at risk for each dose class of SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 18, 18, 17, 14, 9, 6, 4, 2, 1, 0; High dose: 26, 26, 25, 21, 16, 10, 9, 6, 5, 2, 0.

[0198] Figure 7 shows the prostate cancer-specific survival rates in the high-risk group (PDE4D7_CORR_model>0.0) as defined by the PDE4D7_CORR_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total dose applied <= 66 Gy) and the other with high-dose SRT (defined as total dose applied > 66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; a statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p=0.002; HR low-risk vs. high-risk = 12.3; 95%CI=2.5~60.7). The following supplementary list shows the number of patients at risk for each dose class of SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 16, 15, 14, 12, 10, 4, 3, 2, 1, 0; High dose: 36, 35, 32, 25, 22, 12, 8, 6, 2, 0.

[0199] Figure 8 shows the prostate cancer-specific survival rates in the low-risk group (PCAI_model <= 0.5) as defined by the PCAI_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total dose applied <= 66 Gy) and the other with high-dose SRT (defined as total dose applied > 66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; no statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p=NA; HR low-risk vs. high-risk=NA; 95% CI=NA). The following supplementary list shows the number of patients at risk for each dose class of the SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 18, 18, 18, 16, 9, 5, 3, 2, 1, 0; High dose: 37, 36, 34, 28, 22, 12, 11, 7, 5, 2, 0.

[0200] Figure 9 shows the prostate cancer-specific survival rates in the high-risk group (PCAI_model > 0.5) as defined by the PCAI_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total dose applied <= 66 Gy) and the other with high-dose SRT (defined as total dose applied > 66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; a statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p = 0.01; HR low-risk vs. high-risk = 5.6; 95% CI = 1.5~21.7). The following supplementary list shows the number of patients at risk for each dose class of SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 17, 16, 15, 12, 9, 5, 4, 3, 1, 0; High dose: 26, 26, 24, 19, 17, 10, 6, 5, 2, 0.

[0201] Figure 10 shows the prostate cancer-specific survival rate in the low-risk group (PCAI&Clinical_model<=0.5) as defined by the PCAI&Clinical_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total applied dose <=66 Gy) and the other with high-dose SRT (defined as total applied dose >66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; no statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p=NA; HR low-risk vs. high-risk=NA; 95%CI=NA). The following supplementary list shows the number of patients at risk for each dose class of the SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 14, 14, 14, 12, 5, 2, 1, 0, 0; High dose: 26, 26, 25, 19, 16, 7, 6, 5, 3, 1, 0.

[0202] Figure 11 shows the prostate cancer-specific survival rate in the high-risk group (PCAI&Clinical_model > 0.5) as defined by the PCAI&Clinical_model. This group was further divided into two cohorts: one treated with low-dose SRT (defined as total applied dose <= 66 Gy) and the other with high-dose SRT (defined as total applied dose > 66 Gy) (see Ohri N. et al., 2012). Survival curves for the two cohorts are shown; a statistically significant difference in prostate cancer-specific survival rates was observed in this cohort (logrank p = 0.009; HR low-risk vs. high-risk = 9.8; 95% CI = 1.8~53.9). The following supplementary list shows the number of patients at risk for each dose class of SRT analyzed, i.e., patients at risk at any time interval of 20 months or more after SRT: Low dose: 15, 14, 13, 11, 8, 5, 3, 1, 0; High dose: 31, 30, 27, 22, 19, 11, 7, 5, 2, 0.

[0203] The Kaplan-Meier survival curve analyses shown in Figures 2-11 illustrate the impact of the total dose applied during SRT on the risk of death from prostate cancer in patients. A patient's risk group is determined by the probability of suffering each clinical endpoint (death, prostate cancer-specific death) calculated by the risk models IDR_model, TCR_SIGNALING_model, PDE4D7_CORR_model, PCAI_model, or PCAI&Clinical_model. Depending on the patient's predicted risk of death from prostate cancer (i.e., which risk group the patient belongs to), different types of total dose levels for SRT may be indicated. For low-risk groups, standard care (SOC) with a total dose tapered to less than 66 Gy may be indicated to ensure tumor control while minimizing radiation-induced toxicity. For high-risk groups, standard care (SOC) with doses exceeding 66 Gy may be indicated, and the use of non-SOC techniques (i.e., EBRT) such as IMRT, IGRT, SBRT, or fractionated RT may be indicated. Furthermore, combining SRT with chemotherapy may improve long-term survival. Treatment options for expanding the treatment regimen include SRT (considering higher-dose regimens), SADT, and early combinations of chemotherapy, or alternative therapies such as immunotherapy (e.g., Sipuleucil-T) or other experimental treatments.

[0204] Further results It has been further demonstrated that the use of the full model (i.e., the use of all 8 PDE4D7-related genes, all 14 immune defense response genes, and all 17 T-cell receptor signaling genes) is not essential to obtain a significant predictive effect, and that significant results can already be obtained by randomly selecting one of the PDE4D7-related genes and combining it with a randomly selected immune defense response gene and a randomly selected T-cell receptor signaling gene. Examples and Figures 15–32 demonstrate that randomly selecting one of the PDE4D7-related genes selected from the full set of genes and combining it with a randomly selected immune defense response gene and a randomly selected T-cell receptor signaling gene is sufficient to make significant predictions. With these random selections, it is reasonable that the response of prostate cancer to radiotherapy can be significantly predicted by simply arbitrarily selecting one gene from each group.

[0205] This section presents further results for Cox regression models based on six different gene models, each containing a randomly selected combination of three genes. In five of the six gene models (models 3.1, 3.2, 3.4-3.6), the random combination of genes includes one immune defense response gene, one TCR signaling gene, and one PDE4D7 correlated gene. In one reference model (model 3.3), the random combination of genes includes two TCR signaling genes and one PDE4D7 correlated gene. Details of the variables and weights are shown in Table 3.

[0206] [Table 4] TIFF0007838574000018.tif253170

[0207] In the Kaplan-Meier curve analysis, the Cox regression functions of six risk models (PCAI-3.1 to 3.6) were tested for two RT dose levels (low (i.e., <= 66 Gy) and high (i.e., > 66 Gy)) based on a 66 Gy cutoff value (see explanation of the figures below). Prior to the Kaplan-Meier curve analysis, the patient cohort was categorized into two subcohorts (low risk vs. high risk) based on the cutoff value (see explanation of the figures). For a cohort of 186 patients, including all patients who received salvage radiotherapy (SRT) after postoperative BCR (biochemical recurrence), the endpoint of prostate cancer-specific death (PCa Death) after the initiation of SRT was tested (Figures 12 to 23).

[0208] Figures 12 and 13 show the Kaplan-Meier curves for the PCAI-3.1 model. The clinical endpoint tested was PCa mortality due to postoperative disease recurrence after initiation of SRT. Participants were stratified into two cohorts according to their risk of experiencing the clinical endpoint predicted by the PCAI-3.1 model (low risk (Figure 12) vs. high risk (Figure 13)). A cutoff value of 0.04 was used.

[0209] Figure 12 shows the stratification of low-risk subjects by two RT dose levels (low (i.e., <=66 Gy) vs. high (i.e., >66 Gy)) for the risk of experiencing the clinical endpoint predicted by the PCAI-3.1 model. 66 Gy was used as the cutoff value (logrank p=0.6; HR=1.9.1; CI=0.2~18.3). The following supplementary list shows the number of subjects at risk for each class of the PCAI-3.1 model analyzed, i.e., subjects at risk at any time interval 20 months post-surgery: Low dose class (<=66): 21, 21, 21, 19, 16, 9, 5, 3, 1, 0, 0; High dose class (>66): 29, 28, 25, 20, 20, 15, 8, 7, 5, 2, 1, 0.

[0210] Figure 13 shows the stratification of high-risk subjects by two RT dose levels (low (i.e., <=66 Gy) vs. high (i.e., >66 Gy)) regarding the risk of experiencing the clinical endpoint predicted by the PCAI-3.1 model. 66 Gy was used as the cutoff value (logrank p=0.002; HR=16.4; CI=2.7~99). The following supplementary lists show the number of subjects at risk, i.e., subjects at risk at any time interval of 20 months or more postoperatively, for each class of the PCAI-3.1 model analyzed: Low dose class (<=66): 14, 13, 12, 11, 9, 5, 4, 3, 2, 1, 0; High dose class (>66): 34, 34, 33, 27, 24, 14, 10, 7, 5, 1, 0.

[0211] Figures 14 and 15 show the Kaplan-Meier curves for the PCAI-3.2 model. The clinical endpoint tested was PCa mortality due to postoperative disease recurrence after initiation of SRT. Participants were stratified into two cohorts based on their risk of experiencing the clinical endpoint predicted by the PCAI-3.2 model (low risk (Figure 14) vs. high risk (Figure 15)). A cutoff value of -0.1 was used.

[0212] Figure 14 shows the stratification of low-risk subjects by two RT dose levels (low (i.e., <=66 Gy) vs. high (i.e., >66 Gy)) with respect to the risk of experiencing the clinical endpoint predicted by the PCAI-3.2 model. 66 Gy was used as the cutoff value (logrank p=N / A; HR=N / A; CI=N / A). The following supplementary lists show the number of subjects at risk, i.e., subjects at risk at any time interval of 20 months or more postoperatively, for each class of the PCAI-3.2 model analyzed: Low-dose class (<=66): 15, 15, 15, 14, 11, 7, 4, 2, 0, 0, 0; High-dose class (>66): 30, 30, 29, 25, 21, 9, 7, 4, 2, 2, 0.

[0213] Figure 15 shows the stratification of high-risk subjects by two RT dose levels (low (i.e., <= 66 Gy) vs. high (>66 Gy)) for the risk of experiencing the clinical endpoint predicted by the PCAI-3.2 model. 66 Gy was used as the cutoff value (logrank p=0.03; HR=4.5; CI=1.2~17.0). The following supplementary lists show the number of subjects at risk, i.e., subjects at risk at any time interval of 20 months or more postoperatively, for each class of the PCAI-3.2 model analyzed: Low dose class (<= 66): 20, 19, 18, 16, 14, 7, 5, 4, 3, 1, 0; High dose class (>66): 33, 32, 29, 22, 18, 13, 10, 8, 5, 0, 0.

[0214] Figures 16 and 17 show reference Kaplan-Meier curves for the PCAI-3.3 model. The clinical endpoint tested was PCa mortality due to postoperative disease recurrence after initiation of SRT. Participants were stratified into two cohorts (low (Figure 16) vs. high (Figure 17) risk) according to their risk of experiencing the clinical endpoint predicted by the reference PCAI-3.3 model. A cutoff value of 0.1 was used.

[0215] Figure 16 shows the stratification of low-risk subjects by two RT dose levels (low (i.e., <=66 Gy) vs. high (i.e., >66 Gy)) regarding the risk of experiencing the clinical endpoint predicted by the PCAI-3.3 model. 66 Gy was used as the cutoff value (logrank p=0.6; HR=1.7; CI=0.2~12.9). The supplementary list below shows the number of subjects at risk, i.e., subjects at risk at any time interval of 20 months or more postoperatively, for each class of the PCAI-3.3 model analyzed: Low dose class (<=66): 19, 19, 19, 17, 14, 9, 6, 4, 2, 1, 0; High dose class (<66 or greater): 39, 38, 35, 30, 23, 13, 11, 7, 6, 1, 0.

[0216] Figure 17 shows the stratification of high-risk subjects by two RT dose levels (low (i.e., <= 66 Gy) vs. high (< 66 Gy)) for the risk of experiencing the clinical endpoint predicted by the PCAI-3.3 model. 66 Gy was used as the cutoff value (logrank p=N / A; HR=N / A; CI=N / A). The following supplementary lists show the number of subjects at risk, i.e., subjects at risk at any point in time 20 months or more postoperatively, for each class of the PCAI-3.3 model analyzed: Low dose class (<= 66): 16, 15, 14, 13, 11, 5, 3, 2, 1, 0, 0; High dose class (<66 or greater): 24, 24, 23, 17, 16, 9, 6, 5, 1, 1, 0.

[0217] Figures 18 and 19 show the Kaplan-Meier curves for the PCAI-3.4 model. The clinical endpoint tested was PCa mortality due to postoperative disease recurrence after initiation of SRT. Participants were stratified into two cohorts according to their risk of experiencing the clinical endpoint predicted by the PCAI-3.4 model (low (Figure 18) vs. high (Figure 19) risk). A cutoff value of 0.08 was used.

[0218] Figure 18 shows the stratification of low-risk subjects by two RT dose levels (low (i.e., <=66 Gy) vs. high (i.e., >66 Gy)) for the risk of experiencing the clinical endpoint predicted by the PCAI-3.4 model. 66 Gy was used as the cutoff value (logrank p=0.9; HR=1.9; CI=0.08~8.9). The following supplementary lists show the number of subjects at risk, i.e., subjects at risk at any time interval of 20 months or more postoperatively, for each class of the PCAI-3.4 model analyzed: Low dose class (<=66): 15, 15, 15, 14, 12, 9, 7, 4, 3, 1, 0; High dose class (<66 or greater): 31, 30, 27, 22, 20, 12, 10, 8, 5, 1, 0.

[0219] Figure 19 shows the stratification of high-risk subjects by two RT dose levels (i.e., low (<= 66 Gy) vs. high (> 66 Gy)) for the risk of experiencing the clinical endpoints predicted by the PCAI-3.4 model. A cutoff value of 66 Gy was used (logrank p = N / A; HR = N / A; CI = N / A). The following supplementary list shows the number of at-risk subjects, i.e., subjects at risk at any time interval more than 20 months after surgery, for the classes of the PCAI-3.4 model analyzed: low-dose class (<= 66): 20, 19, 18, 16, 13, 5, 2, 2, 0, 0, 0; high-dose class (> 66): 32, 32, 31, 25, 19, 10, 7, 4, 2, 1, 0.

[0220] Figures 20 and 21 show the Kaplan-Meier curves of the PCAI-3.5 model. The clinical endpoint tested was PCa death due to postoperative disease recurrence after SRT initiation. Subjects were stratified into two cohorts according to the risk of experiencing the clinical endpoints predicted by the PCAI-3.5 model (low (Figure 20) vs. high (Figure 21) risk). A cutoff value of 0.08 was used.

[0221] Figure 20 shows the stratification of low-risk subjects by two RT dose levels (low (i.e., <= 66 Gy) vs. high (i.e., > 66 Gy)) for the risk of experiencing the clinical endpoints predicted by the PCAI-3.5 model. A cutoff value of 66 Gy was used (logrank p = 0.5; HR = 2.2; CI = 0.3 - 17.7). The following supplementary list shows the number of at-risk subjects, i.e., subjects at risk at any time interval more than 20 months after surgery, for the classes of the PCAI-3.5 model analyzed: low-dose class (<= 66): 16, 16, 16, 14, 12, 8, 5, 3, 2, 1, 0; high-dose class (> 66): 41, 40, 37, 32, 24, 12, 11, 7, 6, 2, 0.

[0222] Figure 21 shows the stratification of high-risk subjects by two RT dose levels (low (i.e., <=66 Gy) vs. high (i.e., >66 Gy)) for the risk of experiencing clinical endpoints predicted by the PCAI-3.5 model. A cutoff value of 66 Gy was used (logrank p = N / A; HR = N / A; CI = N / A). The following supplementary list shows the number of at-risk subjects, i.e., at-risk subjects at any time interval more than 20 months after surgery, for the classes of the PCAI-3.5 model analyzed: low-dose class (<=66): 19, 18, 17, 6, 13, 6, 4, 3, 1, 0; high-dose class (>66): 22, 22, 21, 15, 15, 10, 6, 5, 1, 0.

[0223] Figures 22 and 23 show the Kaplan-Meier curves of the PCAI-3.5 model. The clinical endpoint tested was PCa death due to postoperative disease recurrence after SRT initiation. Subjects were stratified into two cohorts according to the risk of experiencing clinical endpoints predicted by the PCAI-3.6 model (low (Figure 22) vs. high (Figure 23) risk). A cutoff value of -0.06 was used.

[0224] Figure 22 shows the stratification of low-risk subjects by two RT dose levels (low (i.e., <=66 Gy) vs. high (i.e., >66 Gy)) for the risk of experiencing clinical endpoints predicted by the PCAI-3.6 model. A cutoff value of 66 Gy was used (logrank p = N / A; HR = N / A; CI = N / A). The following supplementary list shows the number of at-risk subjects for the classes of the PCAI-3.6 model analyzed, i.e., at-risk subjects at any time interval more than 20 months after surgery: low-dose class (<=66): 20, 19, 19, 16, 7, 5, 3, 1, 0, 0; high-dose class (>66 or more): 39, 38, 36, 31, 26, 13, 9, 7, 6, 2, 0.

[0225] Figure 23 shows the stratification of high-risk subjects by two RT dose levels (i.e., low (<=66 Gy) vs. high (<66 Gy)) for the risk of experiencing the clinical endpoint predicted by the PCAI-3.6 model. 66 Gy was used as the cutoff value (logrank p=0.05; HR=4.1; CI=1.0~17.0). The following supplementary lists show the number of subjects at risk, i.e., subjects at risk at any time interval of 20 months or more postoperatively, for each class of the PCAI-3.6 model analyzed: Low dose class (<=66): 15, 15, 14, 11, 9, 7, 4, 3, 2, 1, 0; High dose class (<66): 24, 24, 22, 16, 13, 9, 8, 5, 1, 0, 0.

[0226] The Kaplan-Meier analyses shown in Figures 12-23 demonstrate that the response of prostate cancer patients to radiotherapy can be predicted by using a risk model based on randomly selected combinations of three genes, thereby allowing for the selection of the appropriate dose (low or high dose) for the patients. Each random combination has one immune defense response gene, one TCR signaling gene, and one PDE4D7 correlated gene, and / or two TCR signaling genes and one PDE4D7 correlated gene (Figures 16 and 17).

[0227] Consideration The efficacy of both radical radiotherapy (RT) and steroidal respiration (SRT) for localized prostate cancer is limited, and in patients at high risk of disease progression, disease progression and ultimately death are common. Predicting treatment outcomes is highly complex, as many factors influence treatment efficacy and disease recurrence. Several key factors may still be unidentified, and the influence of other factors has not been accurately determined. Multiple clinicopathological measures are currently being investigated and applied in clinical practice to improve response prediction and treatment selection, and some improvements have been achieved. Nevertheless, better prediction of responses to radical RT and SRT remains strongly needed to increase the success rates of these treatments.

[0228] The expression of this molecule was shown to be significantly associated with mortality after curative RT and SRT, and therefore a molecule that is expected to improve the prediction of the effectiveness of these treatments was identified. Improved prediction of RT effectiveness for each patient leads to improved treatment choices and increased survival chances, whether in a curative or rescue setting. This can be achieved by 1) optimizing RT for patients for whom it is predicted to be effective (e.g., by changing dose escalation or initiation timing), and 2) guiding patients for whom RT is predicted to be ineffective to alternative, potentially more effective forms of treatment. Furthermore, this reduces patient suffering by avoiding ineffective treatments and reduces the costs previously spent on ineffective treatments.

[0229] Furthermore, in addition to applying risk stratification to patients with recurrent or advanced prostate cancer, individualized treatment is also an unmet need. Current radiation dose regimens are arbitrarily selected based on factors such as tumor size and location, without considering tumor biology. However, dose levels to pelvic beads used to treat recurrent prostate cancer exceeding a specific total dose range typically induce serious side effects, such as urinary or gastrointestinal adverse reactions. The number of patients potentially affected by these side effects increases exponentially with each 1 Gy increase in the applied RT dose. Therefore, it is paramount for patients to keep the dose as low as possible while simultaneously controlling tumor growth over the long term. Thus, gradually increasing the dose for biologically high-risk patients and gradually decreasing the dose for biologically low-risk patients is key to optimizing and individualizing the balance between tumor progression and treatment toxicity in prostate SRT.

[0230] A person skilled in the art can, in carrying out the claimed invention, understand and perform other modifications to the disclosed realization by examining the drawings, disclosures, and appended claims.

[0231] In the claims, the words “have,” “include,” and “equip” do not exclude other elements or steps, and the singular form does not exclude the plural.

[0232] One or more steps of the method illustrated in Figure 1 are implemented in a computer program product that runs on a computer. The computer program product includes a non-transient, computer-readable recording medium on which the control program is recorded (stored), such as a disk or hard drive. Common forms of non-transient, computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes or other magnetic storage media, CD-ROMs, DVDs or other optical media, RAM, PROMs, EPROMs, FLASH®-EPROMs or other memory chips or cartridges, or other non-transient media from which a computer can read and use.

[0233] Alternatively, one or more steps of this method may be implemented in a transient medium such as a transmittable carrier wave, in which a control program is embodied as a data signal, using a transmission medium such as sound waves or light waves generated between radio waves and infrared data communications.

[0234] Typical methods are implemented on one or more general-purpose computers, dedicated computers, programmed microprocessors or microcontrollers and peripheral integrated circuit elements, ASICs or other integrated circuits, hardwired electronic or logic circuits such as digital signal processors, discrete element circuits, programmable logic devices such as PLDs, PLAs, FPGAs, graphics card CPUs (GPUs), or PALs. Generally, any device capable of implementing a finite machine that can sequentially perform the flowchart shown in Figure 1 can be used to implement one or more steps of the illustrated risk stratification method for treatment selection in patients with prostate cancer. As will be understood, all steps of the method can be implemented on a computer, although in some embodiments, one or more steps are performed at least partially manually.

[0235] A computer implementation process can also be generated such that computer program instructions are placed on a computer, other programmable data processing device, or other device, causing a series of work steps to be executed on the computer, other programmable device, or other device, thereby providing a process for instructions running on the computer or other programmable device to perform the functions / operations specified herein.

[0236] No reference symbol in the claims should be interpreted as limiting its scope.

[0237] The present invention relates to a method for selecting a dose for radiotherapy in a patient with prostate cancer, the method comprising determining or receiving the results of a first gene expression profile for each of, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or all of the immune defense response genes selected from the group consisting of AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1. Step, wherein the first gene expression profile (multiple possible) is determined in a biological sample obtained from the subject, and / or one or more selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all T cell receptor signaling genes The present invention relates to a method comprising the steps of: determining or receiving the results of a second gene expression profile for each of the following: the second gene expression profile(s) are determined in a biological sample obtained from the subject; and / or determining or receiving the results of a third gene expression profile for each of the following PDE4D7 correlated genes selected from the group consisting of ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, for example, 1, 2, 3, 4, 5, 6, 7, or all of them.Since the state of the immune system and the immune microenvironment influences treatment outcomes, identifying markers that predict these effects could be useful in better predicting the overall RT response. The identified genes were found to show a significant correlation with outcomes after SRT. Furthermore, SRT dose was identified as a predictor of prostate cancer-specific survival after SRT. Therefore, these genes are expected to provide predictive values ​​regarding the effectiveness of curative RT and / or SRT, while also providing decision-making support for selecting SRT dose regimens based on the patient's biological disease progression risk.

[0238] The attached sequence listing, titled 2020PF00494_Sequence Listing_ST25, is incorporated herein by reference in its entirety.

Claims

1. A method for selecting the dose in radiation therapy for patients with prostate cancer, wherein the method is: A step of determining an immune defense response model, which is a weighted sum of first gene expression levels for each of one or more immune defense response genes from among AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, weighted by a regression function derived from a population of prostate cancer subjects, or receiving the result of such determination, wherein the first gene expression levels are determined in a biological sample obtained from the subjects. A step of determining or receiving the result of a T cell receptor signaling model, which is a weighted sum of second gene expression levels for each of one or more T cell receptor signaling genes from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, weighted by a regression function derived from a population of prostate cancer subjects, wherein the second gene expression levels are determined in biological samples obtained from the subjects. A step of determining or receiving the result of determining a PDE4D7 correlation model, which is a weighted sum of third gene expression levels for each of one or more PDE4D7-related genes from ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, weighted by the weights of a regression function derived from a population of prostate cancer subjects, wherein the third gene expression levels are determined in biological samples obtained from the subjects. The steps include determining a risk model which is a weighted sum of the aforementioned immune defense response model, T cell receptor signaling model, and PDE4D7 correlation model, weighted by regression functions derived from a population of prostate cancer patients, and determining the selection of the radiotherapy dose based on the risk model. A method comprising the step of providing a medical assistant with a recommendation for the selection or a treatment method based on the selection.

2. The one or more immune defense response genes include all of the immune defense genes, and / or The one or more T cell receptor signaling genes include all of the T cell receptor signaling genes, and / or The method according to claim 1, wherein the one or more PDE4D7 correlated genes comprise all of the PDE4D7 correlated genes.

3. The method according to claim 1 or 2, wherein the step of determining the selection of the radiotherapy dose is further based on one or more clinical parameters obtained from the subject.

4. The aforementioned clinical parameters include (i) prostate-specific antigen (PSA) level, (ii) pathological Gleason score (pGS), (iii) clinical oncological stage, (iv) pathological Gleason grade group (pGGG), (v) pathological status, (vi) surgical margin status, and / or lymph node metastasis, and / or extraprostatic growth, and / or seminal vesicle invasion (one or more pathological variables), (vii) CAPR, (viiii) CAPR-S, (ix) EAU-BCR risk group, and (x) other clinical risk scores. The method according to claim 3, comprising one or more of the above.

5. The method according to claim 3 or 4, wherein the step of determining the selection of the radiotherapy dose is based on a weighted sum of the risk model and the one or more clinical parameters, weighted by a regression function derived from a population of prostate cancer patients.

6. The method according to any one of claims 1 to 5, wherein the biological sample is obtained from the subject before the commencement of the radiotherapy.

7. The method according to any one of claims 1 to 6, wherein the radiotherapy is curative radiotherapy or salvage radiotherapy.

8. The method according to any one of claims 1 to 7, wherein the subject with prostate cancer is a subject with prostate cancer who is receiving salvage radiotherapy (SRT) after a recurrence of the disease after surgery.

9. When a computer program is executed by a computer, A step of receiving data representing an immune defense response model, which is a weighted sum of first gene expression levels for each of one or more immune defense response genes from among AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1, weighted by a regression function derived from a population of prostate cancer subjects, wherein the first gene expression levels are determined in biological samples obtained from the subjects, and A step of receiving data representing a T cell receptor signaling model, which is a weighted sum of second gene expression levels for each of one or more T cell receptor signaling genes from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, weighted by a regression function derived from a population of prostate cancer subjects, wherein the second gene expression levels are determined in biological samples obtained from the subjects, and A step of receiving data representing a PDE4D7 correlation model, which is a weighted sum of third gene expression levels for each of one or more PDE4D7-related genes from BCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, weighted by a regression function derived from a population of prostate cancer subjects, wherein the third gene expression levels are determined in biological samples obtained from the subjects. The steps include determining a risk model as a weighted sum of the regression functions derived from a population of prostate cancer patients, which are the aforementioned immune defense response model, the T cell receptor signaling model, and the PDE4D7 correlation model, and determining the appropriate dose for radiotherapy for prostate cancer patients based on the risk model, A step of providing the medical assistant or subject with the aforementioned selection or a recommendation of a treatment method based on the aforementioned selection. A computer program that contains instructions that cause a computer to execute something.

10. A device for selecting the radiation dose for radiation therapy in patients with prostate cancer. An immune defense response model in which the first gene expression level for each of one or more immune defense response genes from among AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 is a weighted sum of the first gene expression levels determined in biological samples obtained from the subject, weighted by a regression function derived from a population of prostate cancer subjects, and A T cell receptor signaling model in which the second gene expression level is for each of one or more T cell receptor signaling genes from CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, and is a weighted sum of the second gene expression levels determined in biological samples obtained from the subject, weighted by a regression function derived from a population of prostate cancer subjects, and The PDE4D7 correlation model is a weighted sum of the third gene expression levels for each of one or more PDE4D7-related genes from among ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, which are determined in biological samples obtained from the subjects, and weighted by a regression function derived from a population of prostate cancer subjects. An input unit that receives, A processor that determines a risk model which is a weighted sum of the aforementioned immune defense response model, T cell receptor signaling model, and PDE4D7 correlation model, weighted by regression functions derived from a population of prostate cancer patients, and determines the selection of the radiotherapy dose based on the risk model. An apparatus comprising a providing unit that provides a medical assistant or subject with the aforementioned selection or a recommendation of a treatment method based on the aforementioned selection.

11. It is a diagnostic kit, For each of the one or more immune defense response genes among AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and ZBP1 in a biological sample obtained from the subject, at least one primer and probe for determining the first gene expression level, A primer and probe for determining the second gene expression level for each of the following T cell receptor signaling genes in biological samples obtained from subjects: CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, For each of the PDE4D7-related genes, one or more of the following: ABCC5, CUX2, KIAA1549, PDE4D, RAP1GAP2, SLC39A11, TDRD1, and VWA2, at least one primer and probe for determining a third gene expression level, A diagnostic kit comprising the apparatus described in claim 10 or the computer program described in claim 9.

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