Predicting radiotherapy response in prostate cancer patients based on immune defense response genes.

Abstract

The present invention relates to a method of predicting a response of a prostate cancer subject to radiation therapy, the method comprising the steps of determining or receiving results of determining a gene expression profile for each of five 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, wherein the gene expression profiles are determined in a biological sample obtained from the prostate cancer subject; and determining, by a processor, a prediction of radiation therapy response based on the gene expression profiles of the five or more immune defense response genes.

Description

Technical Field

[0001] The present invention relates to a method for predicting the response of prostate cancer subjects to radiation therapy. Further, the present invention relates to a diagnostic kit, the use of the diagnostic kit in a method for predicting the response of prostate cancer subjects to radiation therapy, the use of the gene expression profile of each of five or more immune defense response genes in predicting radiation therapy for prostate cancer subjects, and a corresponding computer program.

Background Art

[0002] Cancer is a class of diseases in which cell groups exhibit uncontrolled growth, invasion, and sometimes metastasis. Due to these three malignant properties of cancer, cancer is distinguished from benign tumors that are self-limiting and do not invade or metastasize. Prostate cancer (PCa) is the second most common non-skin malignant tumor in men, with an estimated 1.3 million new cases diagnosed worldwide and 360,000 deaths in 2018 (see "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries" by Bray F. et al., CA Cancer J Clin, Vol. 68, No. 6, pp. 394-424, 2018). In the United States, approximately 90% of new cases are related to localized cancer, which means that no metastasis has yet formed (see "Cancer Facts & Figures 2010" by 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 (ACS, 2010, see ibid.).

[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 ACS, ibid., 2010). After RP, an average of 60% of patients in the highest-risk group experience biochemical recurrence at 5 and 10 years (see Grimm P. et al., 2012, ibid.). 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 bodily fluids have been investigated, but validation has often been limited, and generally, they demonstrate prognostic information rather than treatment-specific predictive values ​​(see Hall WA et al., ibid., 2016). A small number of gene expression panels are currently being validated by private organizations. One or more of these may potentially provide predictive values ​​for RT in the future (see Dal Pra A. et al., ibid., 2018).

[0011] In conclusion, there is still a strong need for better prediction of the response to RT for primary prostate cancer and for postoperative settings.

[0012] The paper "Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis" by Zhao SG et al., The Lancet Oncology, Vol. 17, No. 11, pp. 1612-1620, 2016, begins with the premise that postoperative radiotherapy plays an important role in the treatment of prostate cancer, and that individualized patient selection may improve outcomes and avoid unnecessary toxicity. The aforementioned document aims to develop and validate gene expression signatures to predict which patients will benefit most from postoperative radiotherapy.

[0013] WO2019 / 028285A2 discloses methods, systems, and kits for the diagnosis, prognosis, and determination of cancer progression of prostate cancer in subjects. In particular, the disclosure relates to the use of immune cell-specific gene expression in determining prognosis and identifying individuals who require treatment for prostate cancer in response to radiotherapy. [Overview of the Initiative] [Problems that the invention aims to solve]

[0014] The object of the present invention is a method for predicting the response of prostate cancer patients to radiotherapy, which enables better treatment decisions. A further object of the present invention is to provide a diagnostic kit, the use of the diagnostic kit in a method for predicting the response of prostate cancer patients to radiotherapy, the use of gene expression profiles of five or more immunoprotective response genes in radiotherapy prediction for prostate cancer patients, and a corresponding computer program. [Means for solving the problem]

[0015] In a first aspect of the present invention, a method for predicting the response of a prostate cancer patient to radiotherapy, wherein the method is A step of determining or receiving the results of determining the gene expression profile of each of five 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, wherein the gene expression profile is determined in a biological sample obtained from the prostate cancer subject. Preferably, the processor determines the prediction of the radiotherapy response based on the gene expression profiles of the five or more immune defense response genes, The steps include, as appropriate, providing a medical assistant or the prostate cancer patient with a prediction of the radiotherapy response or a recommendation of a treatment method based on the prediction of the radiotherapy response, A method is presented that has this feature.

[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 (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 (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 have a significant impact on therapeutic efficacy, the ability to identify markers that predict this impact can help enable better prediction of the overall RT response.

[0021] The integrity and stability of genomic DNA are perpetually under stress not only induced by a variety of intracellular and extracellular factors such as radiation exposure and viral or bacterial infection, but also under oxidative and replication stress (see Gasser S. et al., "Sensing of dangerous DNA," Mechanisms of Aging and Development, Vol. 165, pp. 33-46, 2017). To maintain DNA structure and stability, cells must be able to recognize all types of DNA damage, such as single-strand or double-strand breaks induced by various factors. This process requires the involvement of numerous specific proteins as part of a DNA recognition pathway, depending on the type of damage.

[0022] Recent evidence suggests that mislocalized DNA (e.g., DNA unnaturally appearing in the cytoplasmic fraction of a cell rather than the nucleus) and damaged DNA (e.g., due to mutations present in cancer development) are used by the immune system to identify infected or other diseased cells, while genomic and mitochondrial DNA present in healthy cells are ignored by DNA recognition pathways. In diseased cells, cytoplasmic DNA sensor proteins have been shown to be involved in detecting DNA unnaturally present in the cell's cytoplasm. Detection of such DNA by different nucleic acid sensors translates into similar responses leading to nuclear factor-κB (NF-κB) and type I interferon (type I IFN) signaling, followed by activation of components of the innate immune system. While the recognition of viral DNA is known to induce type I IFN responses, the evidence that sensing DNA damage can initiate an immune response has only recently begun to accumulate.

[0023] TLR9 (Toll-like receptor 9), located within endosomes, was one of the first DNA sensor molecules identified as being involved in the immune recognition of DNA through downstream signaling via the adapter protein myeloid differentiation primary-response protein 88 (MYD88). This interaction sequentially activates mitogen-activated protein kinase (MAPK) and NF-κB. TLR9 also induces type I interferon production in plasmacytoid dendritic cells (pDCs) by activating IRF7 via IκB kinase alpha (IKK alpha). Other diverse DNA immune receptors, including IFI16 (IFN gamma-inducible protein 16), cGAS (cyclic DMP-AMP synthase), DDX41 (DEAD box helicase 41), and ZBP1 (Z-DNA binding protein 1), interact with STING (IFN gene stimulant), which activates the IKK complex and IRF3 via TBK1 (TANK-binding kinase 1). ZBP1 also activates NF-κB through the recruitment of RIP1 and RIP3 (receptor-coupled proteins 1 and 3, respectively). In plasmacytoid dendritic cells, helicase DHX36 (DEAH-box helicase 36) interacts with TRID in the complex to induce NF-κB and IRF-3 / 7, while DHX9 helicase stimulates MYD88-dependent signaling. The DNA sensor LRRFIP1 (leucine-rich repeat flightless-interacting protein) forms a complex with beta-catenin to activate IRF3 transcription, while AIM2 (absent in melanoma 2) recruits the adapter protein ASC (apoptosis-related speck-like protein) to induce the caspase-1-activating inflammasome complex, which leads to the secretion of interleukin-1 beta (IL1 beta) and IL18 (see Figure 1 in Gasser S. et al., 2017, ibid.).This figure provides an overview of the DNA damage and DNA sensor pathways leading to the production of inflammatory cytokines and the expression of ligands that activate innate immune receptors. Members of the non-homologous end joining pathway (orange), homologous recombination (red), inflammasome (dark green), NFkB and interferon response (light green) are shown).

[0024] The factors and mechanisms involved in the activation of the DNA sensor pathway in cancer are not currently well understood. Identifying the DNA species, sensors and pathways within tumors that have been suggested to be involved in IFN expression in all stages of the disease in various cancer types is important. In addition to therapeutic targets in cancer, such factors also have prognostic and predictive value. Novel agonists and antagonists of the DNA sensor pathway are currently under development and being tested in preclinical trials. Such compounds are useful for characterizing the role of the DNA sensor pathway in the pathogenesis of cancer, autoimmune diseases and potentially other diseases.

[0025] 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 in 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. A class of genes with functions in the defense response against viruses, negative regulation of viral genome replication, and type I interferon signaling was identified through a literature search to identify further genes with the same molecular function, resulting in 61 genes (APOBEC3A, AIM2, APOBEC3B, CASP1, CIAO1, CTNNB1, CXCL10, CXCL9, DDX41, DDX58, DDX60, DHX36, DHX58, DHX9, GBP1, HERC5, HIST2H2BE, IFI16, IFI44L, IFIH1, IFIT1, I The genes were further enriched with FIT1B, IFIT2, IFIT3, IFITM1, IFITM3, IL1B, IRF3, IRF5, IRF7, ISG15, ITGAX, LILRB1, LRRFIP1, MAVS, MB21D1, MX1, MX2, MYD88, NLRP3, NOD2, OAS1, OAS3, OASL, OPRK1, POLR3A, PTPRC, PYCARD, RSAD2, STAT1, TBK1, TICAM1, TLR2, TLR3, TLR4, TLR7, TLR8, TLR9, TMEM173, TRIM22, and ZBP1). Further selection from 61 genes was performed based on combinatorial power to differentiate between patients who died from prostate cancer and those who did not, resulting in a preferred set of 14 genes.In a sub-cohort with low expression of these genes compared to the entire patient cohort (#538), and in a sub-cohort of 151 patients who received salvage RT (SRT) after disease recurrence following surgery, the number of events (metastasis, prostate cancer-specific death) was found to be concentrated.

[0026] The term "AIM2" refers to the gene absent in melanoma 2 (Ensembl: ENSG00000163568), for example, the sequence defined in NCBI reference sequence NM_004833, specifically, the nucleotide sequence as defined in SEQ ID NO: 1 corresponding to the sequence of the NCBI reference sequence of the AIM2 transcript shown above, and it also relates to the corresponding amino acid sequence as defined in SEQ ID NO: 2 corresponding to the protein sequence defined in NCBI protein accession reference sequence NP_004824 that encodes the AIM2 polypeptide.

[0027] The term "AIM2" also includes a nucleotide sequence showing high homology to AIM2, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as defined in SEQ ID NO: 1, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as defined in SEQ ID NO: 2, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the sequence as defined in SEQ ID NO: 2, 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 SEQ ID NO: 1.

[0028] The term "APOBEC3A" refers to the gene for apolipoprotein B mRNA editing enzyme catalytic subunit 3A (Ensembl:ENSG00000128383), for example, the sequence defined in the NCBI reference sequence NM_145699, specifically the nucleotide sequence as defined in Sequence ID No. 3, 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. 4, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_663745 that encodes the APOBEC3A polypeptide.

[0029] 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. 3, 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. 4. 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 the sequence specified in Sequence ID No. 4, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 3.

[0030] The term "CIAO1" refers to the gene for Cytosolic Iron-Sulfur Assembly Component 1 (Ensembl:ENSG00000144021), for example, the sequence defined in the NCBI reference sequence NM_004804, specifically the nucleotide sequence as defined in Sequence ID No. 5, 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. 6, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_663745 that encodes the CIAO1 polypeptide.

[0031] 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. 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 an 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.

[0032] The term "DDX58" refers to the DExD / H box helicase 58 gene (Ensembl:ENSG00000107201), specifically the nucleotide sequence defined in NCBI reference sequence NM_014314, and more precisely, the nucleotide sequence as defined in Sequence ID No. 7, which corresponds to the NCBI reference sequence of the DDX58 transcript shown above. It also refers to the corresponding amino acid sequence as defined in NCBI protein accession reference sequence NP_055129, which encodes the DDX58 polypeptide, and more precisely, the nucleotide sequence as defined in Sequence ID No. 8.

[0033] 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. 7, 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. 8. 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. 8, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 7.

[0034] The term "DHX9" refers to the DExD / H box helicase 9 gene (Ensembl:ENSG00000135829), for example, the sequence defined in the NCBI reference sequence NM_001357, specifically the nucleotide sequence as defined in SEQ ID NO: 9, 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: 10, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001348 that encodes the DHX9 polypeptide.

[0035] The term "DHX9" also refers to nucleotide sequences that show high homology to DHX9, 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.

[0036] The term "IFI16" refers to the gene for interferon-gamma-inducible protein 16 (Ensembl:ENSG00000163565), for example, the sequence defined in the NCBI reference sequence NM_005531, specifically the nucleotide sequence as defined in SEQ ID NO: 11, which corresponds to the sequence of the NCBI reference sequence of the IFI16 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 12, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_005522 that encodes the IFI16 polypeptide.

[0037] 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. 11, 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. 12. 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. 12, 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. 11.

[0038] The term "IFIH1" refers to the gene for Interferon Induced With Helicase C Domain 1 (Ensembl:ENSG00000115267), specifically the nucleotide sequence defined in NCBI reference sequence NM_022168, as defined in Sequence ID No. 13, 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 defined in NCBI protein accession reference sequence NP_071451, which encodes the IFIH1 polypeptide, as defined in Sequence ID No. 14.

[0039] The term "IFIH1" also refers to nucleotide sequences that show high homology to IFIH1, 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: 13, 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: 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 the sequence specified in Sequence ID No. 14, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 13.

[0040] The term "IFIT1" refers to the gene for Interferon Induced Protein With Tetratricopeptide Repeats 1 (Ensembl:ENSG00000185745), specifically the sequence defined in NCBI reference sequence NM_001270929 or NCBI reference sequence NM_001548.5, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 15 or SEQ ID NO: 16, corresponding to the sequence of the NCBI reference sequence of the IFIT1 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 17 or SEQ ID NO: 18, corresponding 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.

[0041] 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: 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 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%,

[0042] The term "IFIT3" refers to the gene for Interferon Induced Protein With Tetratricopeptide Repeats 3 (Ensembl:ENSG00000119917), specifically the nucleotide sequence defined in NCBI reference sequence NM_001031683, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 19, 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 NCBI protein accession reference sequence NP_001026853, which encodes the IFIT3 polypeptide, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 20.

[0043] The term "IFIT3" also refers to nucleotide 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 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.

[0044] The term "LRRFIP1" refers to the sequence defined in the LRR Binding FLII Interacting Protein 1 gene (Ensembl:ENSG00000124831), for example, NCBI reference sequence NM_004735, NCBI reference sequence NM_001137550, NCBI reference sequence NM_001137553, or NCBI reference sequence NM_001137552, specifically the nucleotide sequence as defined in SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, or SEQ ID NO: 24, which corresponds to the sequence of the NCBI reference sequence of the LRRFIP1 transcript shown above, and also This also relates to the corresponding amino acid sequences, for example, as defined in SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, or SEQ ID NO: 28, corresponding to the protein sequences defined in the NCBI protein accession reference sequences NP_004726, NP_001131022, NP_001131025, and NP_001131024 that encode the LRRFIP1 polypeptide.

[0045] The term "LRRFIP1" also refers to nucleotide sequences that show high homology to LRRFIP1, 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: 21, SEQ ID NO: 22, SEQ ID NO: 23, or SEQ ID NO: 24, 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: 25, SEQ ID NO: 26, SEQ ID NO: 27, or SEQ ID NO: 28. 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 the sequence specified in SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, or SEQ ID NO: 28, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, or SEQ ID NO: 24.

[0046] The term "MYD88" refers to the MYD88 Innate Immune Signal Transduction Adaptor gene (Ensembl:ENSG00000172936), specifically the sequence defined in NCBI reference sequences NM_001172567, NM_001172568, NM_001172569, NM_001172566, or NM_002468, and more precisely, the nucleotide sequence as defined in SEQ ID NOs. 29, 30, 31, 32, or 33, which corresponds to 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: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, or SEQ ID NO: 38, corresponding to the protein sequences defined in the NCBI protein accession reference sequences NP_001166038, NP_001166039, NP_001166040, NP_001166037, and NP_002459, which encode 88 polypeptides.

[0047] The term "MYD88" also refers to nucleotide sequences that show high homology to MYD88, 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: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, or SEQ 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 sequences defined in SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, or SEQ ID NO: 38. 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 amino

[0048] The term "OAS1" refers to the gene for 2'-5'-oligoadenylate synthetase 1 (Ensembl:ENSG00000089127), specifically the sequence defined in NCBI reference sequences NM_001320151, NM_002534, NM_001032409, or NM_016816, and more precisely, the nucleo as defined in SEQ ID NOs. 39, 40, 41, or 42, corresponding to the NCBI reference sequence of the OAS1 transcript shown above. This refers to the cydode sequence, which also relates to the corresponding amino acid sequence as defined in, for example, SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45, or SEQ ID NO: 46, corresponding to the protein sequence defined in the NCBI protein accession reference sequences NP_001307080, NCBI protein accession reference sequences NP_002525, NCBI protein accession reference sequences NP_001027581, and NCBI protein accession reference sequences NP_058132 that encode the OAS1 polypeptide.

[0049] The term "OAS1" also refers to nucleotide sequences that show high homology to OAS1, 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: 39, SEQ ID NO: 40, SEQ ID NO: 41, or SEQ ID NO: 42, 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: 43, SEQ ID NO: 44, SEQ ID NO: 45, or SEQ 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 to a sequence as defined in SEQ ID NO 43, SEQ ID NO 44, SEQ ID NO 45, or SEQ ID NO 46, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence as defined in SEQ ID NO 39, SEQ ID NO 40, SEQ ID NO 41, or SEQ ID NO 42.

[0050] The term "TLR8" refers to the Toll-like receptor 8 gene (Ensembl:ENSG00000101916), specifically the sequence defined in NCBI reference sequence NM_138636 or NCBI reference sequence NM_016610, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 47 or SEQ ID NO: 48, corresponding to the sequence of the NCBI reference sequence of the TLR8 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 49 or SEQ ID NO: 50, corresponding 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.

[0051] 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 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 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: 49 or SEQ ID NO: 50.

[0052] The term "ZBP1" refers to the gene for Z-DNA Binding Protein 1 (Ensembl:ENSG00000124256), specifically the sequence defined in NCBI reference sequences NM_030776, NM_001160418, or NM_001160419, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 51, SEQ ID NO: 52, or SEQ ID NO: 53, corresponding to 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: 54, SEQ ID NO: 55, or SEQ ID NO: 56, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_110403, NP_001153890, and NP_001153891, which encode the ZBP1 polypeptide.

[0053] The term "ZBP1" also refers to nucleic acid sequences that show high homology to ZBP1, for example, sequences defined in SEQ ID NO: 51, SEQ ID NO: 52, or SEQ ID NO: 53, which 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: 54, SEQ ID NO: 55, or SEQ ID NO: 56, which 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: 54, SEQ ID NO: 55, or SEQ ID NO: 56. 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

[0054] The terms “biological sample” or “sample obtained from subject” refer to any biological material obtained from a subject, such as a prostate cancer patient, through a suitable method known to those skilled in the art. The biological sample used should be collected in a clinically acceptable manner, for example, by a method that preserves nucleic acids (especially RNA) or proteins.

[0055] 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.

[0056] In certain realizations, biopsy or excision samples are obtained and / or used. Such samples may include cells or cell lysates.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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."

[0061] 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."

[0062] 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.

[0063] 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.

[0064] The term "metastasis" refers to the presence of metastatic disease in organs other than the prostate gland.

[0065] 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).

[0066] The term "prostate cancer-specific death, or disease-specific death," refers to a patient's death resulting from prostate cancer.

[0067] Preferably, the five or more immune defense response genes include six or more of the immune defense response genes.

[0068] It is even more preferable that the five or more immune defense response genes include nine or more of the aforementioned immune defense response genes.

[0069] It is even more preferable that the five or more immune defense response genes mentioned above include twelve or more.

[0070] Preferably, the step of determining the prediction of the radiotherapy response includes combining the gene expression profiles of five 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, with a regression function derived from a population of prostate cancer patients.

[0071] 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.

[0072] In a specific realization, the prediction of the radiotherapy response is determined as follows:

number

[0073] For example, w1 is approximately -0.5 to 0.5, e.g., -0.02116, w2 is approximately -0.5 to 0.5, e.g., -0.01195, w3 is approximately -0.5 to 0.5, e.g., -0.02092, w4 is approximately -0.5 to 0.5, e.g., -0.009288, w5 is approximately -0.5 to 0.5, e.g., 0.002662, w6 is approximately -0.5 to 0.5, e.g., -0.01924, w7 is approximately -0.5 to 0.5, e.g., 0.007922, w8 is approximately -0.5 to 0.5, e.g., -0.06404, w9 is approximately -0.5 to 0.5, e.g., 0.01869, w 10 It is approximately -0.5 to 0.5, for example -0.005425, and w 11 It is approximately -0.5 to 0.5, for example 0.002878, and w 12 It is approximately -0.5 to 0.5, for example 0.001348, and w 13 It is approximately -0.5 to 0.5, for example -0.04764, and w 14 The value is approximately -0.5 to 0.5, for example, -0.01494.

[0074] Alternatively, the step of determining the prediction of the radiotherapy response preferably includes a step of combining the gene expression profiles of five 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, with a summation function that sums the discretized gene expression profiles of the five or more immune defense response genes, wherein the gene expression profiles are discretized using individual cut-off values ​​derived from a population of prostate cancer subjects.

[0075] In a specific realization, the prediction of the radiotherapy response is determined as follows:

number

[0076] For example, the cutoff value for AIM2 is approximately 30-40, for example 33.44; the cutoff value for APOBEC3A is approximately 5-15, for example 8.55; the cutoff value for CIAO1 is approximately 60-70, for example 64.49; the cutoff value for DDX58 is approximately 40-50, for example 45.87; the cutoff value for DHX9 is approximately 200-250, for example 230.03; the cutoff value for IFI16 is approximately 50-60, for example 53.56; the cutoff value for IFIH1 is approximately 90-110, for example 99.63; and IFI The cutoff value for T1 is approximately 10-20, for example 14.57; the cutoff value for IFIT3 is approximately 5-15, for example 11.75; the cutoff value for LRRFIP1 is approximately 260-310, for example 283.82; the cutoff value for MYD88 is approximately 20-30, for example 23.03; the cutoff value for OAS1 is approximately 30-40, for example 33.93; the cutoff value for TLR8 is approximately 5-15, for example 9.89; the cutoff value for ZBP1 is approximately 15-25, for example 20.16; and the threshold is approximately 0-5, for example 1.

[0077] The predicted radiotherapy response may also be classified or categorized into one of at least two risk groups based on the predicted value of the radiotherapy response. For example, there may be two risk groups, three risk groups, four risk groups, or more than four predefined risk groups. Each risk group covers a distinct range of (non-overlapping) values ​​of the predicted radiotherapy response. 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.

[0078] 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 immune defense response genes are present in a soluble form, gene expression profiles may be determined in the blood.

[0079] It is even more preferable that the radiation therapy be curative radiation therapy or salvage radiation therapy.

[0080] If the prediction of the radiotherapy response is negative or positive for the effectiveness of the radiotherapy, and a treatment is recommended based on the prediction of the radiotherapy response, and the prediction of the radiotherapy response is negative, the recommended treatment preferably includes one or more of the following: (i) radiotherapy provided earlier than standard; (ii) radiotherapy with increased radiation dose; (iii) adjuvant therapy such as androgen deprivation; and iv) alternative treatment other than radiotherapy. The degree to which the prediction of the radiotherapy response is negative determines the extent to which the recommended treatment deviates from the standard form of radiotherapy.

[0081] In a further aspect of the present invention, there is an apparatus for predicting the response of a prostate cancer patient to radiotherapy, the apparatus being An input unit adapted to receive data showing the gene expression profiles of five 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, wherein the gene expression profiles are determined in biological samples obtained from the prostate cancer subjects. A processor adapted to determine the prediction of the radiotherapy response based on the gene expression profiles of the five or more immune defense response genes, A providing unit adapted to provide, as appropriate, a prediction of the radiotherapy response or a recommendation of treatment based on the prediction of the radiotherapy response to a medical assistant or the prostate cancer patient, A device equipped with the following features is presented.

[0082] In a further aspect of the present invention, a computer program comprising a plurality of instructions, wherein when the computer program is run by a computer, the plurality of instructions A step of receiving data showing the gene expression profiles of five 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, wherein the gene expression profiles are determined in biological samples obtained from the prostate cancer subject. A step of determining the prediction of the radiotherapy response based on the gene expression profiles of the five or more immune defense response genes, The steps include, as appropriate, providing a medical assistant or the prostate cancer patient with a prediction of the radiotherapy response or a recommendation of a treatment method based on the prediction of the radiotherapy response, A computer program is presented that causes the computer to perform a method having the above.

[0083] In a further aspect of the present invention, In the biological sample obtained from the aforementioned prostate cancer subject, at least five primers and / or probes are used to determine the gene expression profile of each of five 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them. The apparatus according to claim 11 or the computer program according to claim 12, A diagnostic kit containing [the specified ingredient / method] is presented.

[0084] In a further aspect of the present invention, the use of the diagnostic kit described in claim 13 is presented.

[0085] The use of the diagnostic kit described in claim 14 is preferably in a method for predicting the response of a prostate cancer patient to radiotherapy.

[0086] In a further aspect of the present invention, The steps include receiving biological samples obtained from prostate cancer patients, The step of using the diagnostic kit described in claim 12 to determine the gene expression profile of each of five 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, in the biological sample obtained from the prostate cancer subject, A method is presented that has this feature.

[0087] A further aspect of the present invention is a method for predicting the response of a prostate cancer patient to radiotherapy, Preferably, the processor determines the prediction of the radiotherapy response based on the gene expression profiles of the five or more immune defense response genes, The steps include, as appropriate, providing a medical assistant or the prostate cancer patient with a prediction of the radiotherapy response or a recommendation of a treatment method based on the prediction of the radiotherapy response, The method involves the use of gene expression profiles for five or more immunodefense 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them.

[0088] The method described in claim 1, the apparatus described in claim 10, the computer program described in claim 11, the diagnostic kit described in claim 12, the use of the diagnostic kit described in claim 13, the method described in claim 15, and the use of the gene expression profile described in claim 16 are understood to have similar and / or identical preferred embodiments, particularly those described in the dependent claims.

[0089] Preferred embodiments of the present invention may be any combination of the dependent claims or the embodiments described above and each independent claim.

[0090] 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]

[0091] [Figure 1] This diagram schematically and typically illustrates a flowchart of an embodiment of a method for predicting the response of prostate cancer patients to radiation therapy. [Figure 2]This figure shows the Kaplan-Meier curve analysis of the immune defense response model (IDR_model). The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 3] This figure shows the Kaplan-Meier curve analysis of the immune defense response model (IDR_model). The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 4] This figure shows a Kaplan-Meier curve analysis of immune defense response scores (IDR_score) in men with recurrent prostate cancer after surgery who are receiving salvage radiation therapy (SRT). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after initiation of SRT. [Figure 5] This figure shows a Kaplan-Meier curve analysis of CARPA-S score categories in men with recurrent prostate cancer after surgery who are receiving salvage radiation therapy (SRT). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after initiation of SRT. [Figure 6] This figure shows a Kaplan-Meier curve analysis of the EAU BCR risk group (EAU_BCR_Risk) in men with recurrent prostate cancer after surgery who are receiving salvage radiation therapy (SRT). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after initiation of SRT. [Figure 7] This figure shows a Kaplan-Meier curve analysis of immune defense response scores (IDR_score) in men with recurrent prostate cancer after surgery who were receiving salvage androgen deprivation therapy (SADT). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after the initiation of SADT. [Figure 8]This figure shows a Kaplan-Meier curve analysis of CARPA-S score categories in men with recurrent prostate cancer after surgery who are receiving salvage androgen deprivation therapy (SADT). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after the initiation of SADT. [Figure 9] This figure shows a Kaplan-Meier curve analysis of the EAU BCR risk group (EAU_BCR_Risk) in men with recurrent prostate cancer after surgery who are receiving salvage radiation therapy (SRT). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after initiation of SRT. [Figure 10] This figure shows a Kaplan-Meier curve analysis of immune defense response scores (IDR_score) in men with recurrent prostate cancer after surgery who were receiving cytotoxic therapy (CTX). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after initiation of CTX. [Figure 11] This figure shows a Kaplan-Meier curve analysis of CARPA-S score categories in men with recurrent prostate cancer after surgery who are receiving cytotoxic therapy (CTX). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after CTX initiation. [Figure 12] This figure shows a Kaplan-Meier curve analysis of the EAU BCR risk group (EAU_BCR_Risk) in men with recurrent prostate cancer after surgery who are receiving cytotoxic therapy (CTX). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after CTX initiation. [Figure 13] This figure shows a Kaplan-Meier curve analysis of immune defense response scores (IDR_score) in men with recurrent prostate cancer after surgery. The clinical endpoint tested was metastasis-free survival after first-line treatment. [Figure 14] This figure shows a Kaplan-Meier curve analysis of CARPA-S score categories in men with recurrent prostate cancer after surgery. The clinical endpoint tested was metastasis-free survival after first-line treatment. [Figure 15] This figure shows a Kaplan-Meier curve analysis of immune defense response scores (IDR_score) in men with recurrent prostate cancer after surgery. The clinical endpoint tested was metastasis-free survival after biochemical recurrence (BCR) following surgery. [Figure 16] This figure shows a Kaplan-Meier curve analysis of CARPA-S score categories in men with recurrent prostate cancer after surgery. The clinical endpoint tested was metastasis-free survival after biochemical recurrence (BCR) following surgery. [Figure 17] This figure shows a Kaplan-Meier curve analysis of immune defense response scores (IDR_score) in men with recurrent prostate cancer after surgery. The clinical endpoint tested was overall survival after first-line treatment. [Figure 18] This figure shows a Kaplan-Meier curve analysis of the pathological Gleason grade group (pGGG) in men with recurrent prostate cancer after surgery. The clinical endpoint tested was overall survival after first-line treatment. [Figure 19] This figure shows a Kaplan-Meier curve analysis of immune defense response scores (IDR_score) in men with recurrent prostate cancer after surgery. The endpoint tested was overall survival after biochemical recurrence (BCR) following surgery. [Figure 20] This figure shows a Kaplan-Meier curve analysis of the pathological Gleason grade group (pGGG) in men with recurrent prostate cancer after surgery. The endpoint tested was overall survival after biochemical recurrence (BCR) following surgery. [Figure 21] This figure shows the Kaplan-Meier curves for the five-gene immune defense response model (IDR_5.1_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 22]This figure shows Kaplan-Meier curves for another immune defense response 5-gene model (IDR_5.2_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 23] This figure shows Kaplan-Meier curves for another immune defense response 5-gene model (IDR_5.3_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 24] This figure shows Kaplan-Meier curves for another immune defense response 5-gene model (IDR_5.4_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 25] This figure shows Kaplan-Meier curves for another immune defense response 5-gene model (IDR_5.5_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 26] This figure shows Kaplan-Meier curves for another immune defense response 5-gene model (IDR_5.6_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 27] This figure shows Kaplan-Meier curves for another immune defense response 5-gene model (IDR_5.7_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 28]This figure shows Kaplan-Meier curves for another immune defense response 5-gene model (IDR_5.8_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 29] This figure shows the Kaplan-Meier curves for the six-gene immune defense response model (IDR_6.1_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 30] This figure shows Kaplan-Meier curves for another six-gene immune defense response model (IDR_6.2_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 31] This figure shows Kaplan-Meier curves for another six-gene immune defense response model (IDR_6.3_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Figure 32] This figure shows Kaplan-Meier curves for another six-gene immune defense response model (IDR_6.4_model). The clinical endpoint tested was the time from initiation of salvage radiation therapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). [Modes for carrying out the invention]

[0092] Overview of Radiation Therapy Response Prediction Figure 1 schematically and typically shows a flowchart of an embodiment of a method for predicting the response of prostate cancer patients to radiotherapy.

[0093] In step S100, the method is implemented.

[0094] 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.

[0095] In step S104, gene expression profiles are obtained for each of the five 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, by performing, for example, RT-qPCR (real-time quantitative PCR) on the RNA extracted from each biological sample, thereby obtaining gene expression profiles for each of the five or more immune defense response genes for each of the biological samples obtained from the first set of patients. For example, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them. A typical gene expression profile includes the expression levels (e.g., values) of each of the five or more immune defense response genes.

[0096] In step S106, a regression function is determined to predict the radiotherapy response based on the results obtained from gene expression profiles and monitoring of five or more immune defense response genes, AIM2, APOBEC3A, CIAO1, DDX58, DHX9, IFI16, IFIH1, IFIT1, IFIT3, LRRFIP1, MYD88, OAS1, TLR8, and / or ZBP1, obtained from at least a portion of the biological samples obtained from the first set of patients. In a particular realization, the regression function is determined as specified in equation (1) above.

[0097] 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.

[0098] In step S110, for example, PCR is performed on the biological sample to obtain gene expression profiles for five or more, for example, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of the immune defense response genes.

[0099] In step S112, the prediction of the radiotherapy response based on the gene expression profiles of five or more immune defense response genes is determined for the patient using the regression function described above. This will be described in more detail later in this document.

[0100] In S114, based on the prediction, 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 prediction may be categorized into one of a predefined set of risk groups based on the value of the prediction. In a particular realization, the prediction of the radiotherapy response is negative or positive for the effectiveness of the radiotherapy. If the prediction of the radiotherapy response is negative, the recommended treatment includes one or more of the following: (i) radiotherapy provided earlier than standard; (ii) radiotherapy with increased radiation dose; (iii) adjunctive therapy such as androgen deprivation; and (iv) alternative treatment other than radiotherapy.

[0101] In S116, the method is terminated.

[0102] In one embodiment, the gene expression profile in steps S104 and S110 is determined by detecting mRNA expression using five or more primers and / or probes and / or sets thereof.

[0103] In the alternative method, the function for giving the prediction of the radiotherapy response determined in step S106 is not based on a regression function, but rather combines the gene expression profiles of the five or more immune defense response genes with a summation function that sums the discretized gene expression profiles of the five or more immune defense response genes, where the gene expression profiles are discretized using individual cutoff values ​​derived from a population of prostate cancer subjects. In a particular realization, the summation function is determined as specified in equation (2) above. Then, in step S112, the prediction of the radiotherapy response is determined for the patient using the summation function.

[0104] The immune system interacts powerfully with prostate cancer, both at the systemic level and within the tumor microenvironment. Immune defense response genes play a central role in regulating immune activity. Therefore, immune defense response genes provide information about the effectiveness of RT. However, estimating which immune defense response genes have predictive values ​​in this application from existing literature is extremely difficult due to the many factors that influence the precise function of immune defense response genes.

[0105] We investigated the extent to which the expression of immune defense response genes in prostate cancer tissue correlates with disease recurrence after curative radiotherapy or steroidal resuscitation (SRT).

[0106] In a cohort of 151 prostate cancer patients, we identified 14 immune defense response genes whose expression levels in prostate cancer tissue were significantly correlated with mortality after SRT.

[0107] 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.

[0108] result Cox regression analysis Next, we began testing whether combinations of these 14 immune defense response genes would provide better prognostic indications. Using Cox regression, we modeled the expression levels of the 14 immune defense response genes against prostate cancer-specific mortality after salvage RT following surgery (IDR_model). This model was then tested using Kaplan-Meier survival analysis.

[0109] The patient cohort studied, consisting of 538 subjects, underwent prostatectomy to remove their primary prostate tumors. Approximately 30% of all patients experienced PSA relapse (i.e., biochemical recurrence) within 10 years of prostate surgery. Consequently, many of these patients with PSA recurrence (#151) received salvage radiation therapy (SRT) alone or in combination with anti-androgen therapy as a second-line treatment to address the recurrent cancer cells. Despite this treatment, approximately 40–50% of these patients developed regional or distant metastases within 5–10 years of starting SRT, and approximately 25% died from prostate cancer.

[0110] The Cox regression function was derived as follows.

number

[0111] [Table 1]

[0112] ROC curve analysis Next, we tested the logistic regression model outlined above for its statistical power in predicting prostate cancer-specific death (PCa Death) at 5 years after the initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery. We compared the model's performance with that of the EAU BCR risk group (see Tilki D. et al., "External validation of the European Association of Urology Biochemical Recurrence Risk groups to predict metastasis and mortality after radical prostatectomy in a European cohort," Eur Urol, Vol. 75, No. 6, pp. 896-900, 2019).

[0113] Figure 2 shows the ROC curve analysis of the two prediction models. IDR_model (AUC=0.83) is a Cox regression model based on 14 immune defense response genes. EAU_BCR_Risk (AUC=0.76) is the EAU_BCR_Risk group (European Association of Urology Biochemical Recurrence Risk group).

[0114] Kaplan-Meier Survival Analysis For Kaplan-Meier curve analysis, the Cox function of the risk model (TCR_signaling_model) was categorized into two subcohorts based on a cutoff value (see the explanation of the figure below). The goal was to create classes of patients by separating them based on the median risk score calculated from each patient's IDR model, using a somewhat similar number of patients within each group.

[0115] The aforementioned patient class corresponds to an increased risk of experiencing the tested clinical endpoint (Figure 3), which is the time from the initiation of salvage radiation therapy (SRT) to prostate cancer-specific death, in the constructed risk model (IDR_model).

[0116] Figure 3 shows the Kaplan-Meier curve for the IDR_model. The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). Using a cutoff value of -2.3, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR regression model (log-rank p=0.0001). The following supplementary list represents the number of risk patients in each of the TCR_signaling_model classes analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 78, 77, 71, 68, 60, 46, 43, 22, 6, 3, 0; High risk: 73, 67, 59, 46, 35, 30, 28, 15, 0, 0, 0.

[0117] The analysis of Kaplan-Meier curves shown in Figure 3 demonstrates the existence of different patient risk groups. Patient risk groups are determined by the probability of suffering individual clinical endpoints (e.g., prostate cancer-specific death) calculated by the risk model IDR_model. Different types of therapeutic interventions are indicated depending on the patient's predicted risk (i.e., whether the patient belongs to risk group 1 or risk group 2).

[0118] Immune defense response score As an alternative to the regression model (IDR_model), we adopted a combination of each gene expression profile using a summation function (IDR_score). The model was extensively tested in Kaplan-Meier survival analysis.

[0119] The summing function was derived as follows.

number

[0120] [Table 2]

[0121] Kaplan-Meier Survival Analysis In Kaplan-Meier curve analysis, the performance of the immune protective response score (IDR_score) was compared with the clinical risk score CAPRA-S (see "The CAPRA-S score: A straightforward tool for improved prediction of outcomes after radical prostatectomy" by Cooperberg MR et al., Cancer, Vol. 117, No. 22, pp. 5039-5046, 2011) and the EAU BCR risk group ("External validation of the European Association of Urology Biochemical Recurrence Risk groups to predict metastasis and mortality after radical prostatectomy in a European cohort" by Tilki D. et al., Eur Urol, Vol. 75, No. 6, pp. 896-900, 2019) for different patient groups and clinical endpoints.

[0122] Figure 4 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving salvage radiation therapy (SRT). The entire SRT patient subcohort of 151 men was stratified into two cohorts (low score vs. high score) according to their immune defense response score (IDR_score) level. The clinical endpoint tested was prostate cancer-specific death (PCa Death) after initiation of SRT (log-rank p=0.0001). The following supplementary list represents the number of risk patients in each IDR_score class analyzed. That is, risk patients are shown for all +20 months post-surgery periods: High score: 76, 74, 70, 66, 59, 45, 41, 21, 3, 2, 0; Low score: 75, 70, 60, 48, 36, 31, 30, 16, 3, 1, 0.

[0123] Figure 5 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving salvage radiation therapy (SRT). The entire SRT patient subcohort of 151 men was stratified into three cohorts according to their CARPA-S score category level (low, intermediate, and high risk). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after initiation of SRT (log-rank p=0.003). The following supplementary list represents the number of risk patients in each CARPA-S class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 23, 23, 23, 23, 20, 18, 18, 12, 1, 1, 0; Intermediate risk: 76, 73, 66, 58, 50, 41, 38, 19, 4, 2, 0; High risk: 52, 48, 41, 33, 25, 17, 15, 6, 1, 0, 0.

[0124] Figure 6 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving salvage radiation therapy (SRT). The entire SRT patient subcohort of 151 men was stratified into two cohorts (low risk vs. high risk) according to their EAU BCR risk level (EAU_BCR_Risk). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after initiation of SRT (log-rank p=0.0001). The following supplementary list shows the number of risk patients in each EAU_BCR_Risk class analyzed. That is, the number of risk patients in all +20 months post-surgery periods is shown: Low risk: 72, 72, 69, 66, 58, 48, 45, 25, 6, 3, 0; High risk: 79, 72, 61, 48, 37, 28, 26, 12, 0, 0, 0.

[0125] Figure 7 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving androgen deprivation therapy (SADT). The entire SADT patient subcohort of 106 men was stratified into two cohorts (low score vs. high score) according to their immune defense response score (IDR_score) level. The clinical endpoint tested was prostate cancer-specific death (PCa Death) after SADT initiation (log-rank p=0.0001). The following supplementary list represents the number of risk patients in each IDR_score class analyzed. That is, risk patients are shown for all +20 months post-surgery periods: High score: 48, 47, 46, 43, 38, 29, 27, 17, 3, 2, 0; Low score: 58, 52, 43, 33, 24, 21, 19, 13, 2, 1, 0.

[0126] Figure 8 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving androgen deprivation therapy (SADT). The entire SADT patient subcohort of 106 men was stratified into three cohorts according to their CARPA-S score category level (low, intermediate, and high risk). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after the initiation of SADT (log-rank p=0.05). The following supplementary list represents the number of risk patients in each CARPA-S class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 11, 11, 11, 11, 11, 10, 9, 6, 1, 1, 0; Intermediate risk: 51, 48, 44, 38, 33, 28, 26, 18, 3, 2, 0; High risk: 44, 40, 34, 27, 18, 12, 11, 6, 1, 0, 0.

[0127] Figure 9 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving androgen deprivation therapy (SADT). The entire SADT patient subcohort of 106 men was stratified into two cohorts (low risk vs. high risk) according to their EAU BCR risk level (EAU_BCR_Risk). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after SADT initiation (log-rank p=0.004). The following supplementary list shows the number of risk patients in each EAU_BCR_Risk class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 43, 43, 42, 40, 35, 29, 27, 19, 5, 3, 0; High risk: 63, 56, 47, 36, 27, 21, 19, 11, 0, 0, 0.

[0128] Figure 10 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving cytotoxic therapy (CTX). A subcohort of 10 men with all SADT patients was stratified into two cohorts (low score vs. high score) according to their immune defense response score (IDR_score) levels. The clinical endpoint tested was prostate cancer-specific death (PCa Death) after CTX initiation (log-rank p=0.005). The following supplementary list represents the number of risk patients in each IDR_score class analyzed. That is, risk patients are shown for all +20 months post-surgery: High score: 2, 2, 2, 1, 1, 1, 0, 0, 0, 0, 0; Low score: 17, 6, 2, 1, 1, 1, 1, 1, 1, 0.

[0129] Figure 11 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving cytotoxic therapy (CTX). The all-SADT subcohort of 19 men was stratified into three cohorts according to their CARPA-S score category levels (low, intermediate, and high risk). (In this example, there were no patients in the low-risk class.) The clinical endpoint tested was prostate cancer-specific death (PCa Death) after CTX initiation (log-rank p=0.38). The following supplementary list represents the number of risk patients in each CARPA-S class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: -; Intermediate risk: 10, 3, 2, 1, 1, 1, 1, 1, 1, 1, 0; High risk: 9, 5, 2, 1, 1, 1, 0, 0, 0, 0, 0.

[0130] Figure 12 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery who are receiving cytotoxic therapy (CTX). The all-SADT subcohort of 19 men was stratified into two cohorts (low risk vs. high risk) according to their EAU BCR risk level (EAU_BCR_Risk). The clinical endpoint tested was prostate cancer-specific death (PCa Death) after CTX initiation (log-rank p=0.4). The following supplementary list represents the number of risk patients in each EAU_BCR_Risk class analyzed. That is, the number of risk patients in all +20 months post-surgery periods is shown: Low risk: 5, 3, 2, 1, 1, 1, 1, 1, 1, 1, 0; High risk: 14, 5, 2, 1, 1, 1, 0, 0, 0, 0, 0.

[0131] Figure 13 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery. The whole patient cohort of 130 men was stratified into two cohorts (low score vs. high score) according to the level of immune protective response score (IDR_score). The clinical endpoint tested was metastasis-free survival after first-line treatment (log-rank p=0.0004). The following supplementary list represents the number of risk patients in each IDR_score class analyzed. That is, risk patients are shown for all +20 months post-surgery: High score: 87, 81, 66, 36, 19, 11, 2, 1, 0; Low score: 43, 37, 29, 17, 8, 5, 2, 0, 0.

[0132] Figure 14 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery. The whole patient cohort of 130 men was stratified into three cohorts according to their CARPA-S score category (low, intermediate, and high risk). The clinical endpoint tested was metastasis-free survival after first-line treatment (log-rank p=0.04). The following supplementary list represents the number of risk patients in each CARPA-S class analyzed. That is, risk patients are shown for all +20 months after surgery: Low risk: 64, 61, 46, 22, 12, 6, 2, 0, 0; Intermediate risk: 42, 37, 31, 21, 10, 6, 1, 1, 0; High risk: 23, 20, 18, 10, 5, 4, 1, 0, 0.

[0133] Figure 15 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery. The whole patient cohort of 130 men was stratified into two cohorts (low score vs. high score) according to the level of immune protective response score (IDR_score). The clinical endpoint tested was metastasis-free survival after biochemical recurrence (BCR) after surgery (log-rank p=0.004). The following supplementary list represents the number of risk patients in each IDR_score class analyzed. That is, risk patients are shown for all +20 months after surgery: High score: 15, 14, 12, 10, 6, 4, 1, 0; Low score: 11, 10, 9, 6, 3, 1, 0, 0.

[0134] Figure 16 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery. The whole patient cohort of 130 men was stratified into three cohorts according to their CARPA-S score category (low, intermediate, and high risk). The clinical endpoint tested was metastasis-free survival after biochemical recurrence (BCR) after surgery (log-rank p=0.39). The following supplementary list represents the number of risk patients in each CARPA-S class analyzed. That is, risk patients are shown for all +20 months after surgery: Low risk: 4, 4, 3, 3, 2, 2, 0, 0; Intermediate risk: 11, 10, 9, 7, 4, 1, 0, 0; High risk: 11, 10, 9, 6, 3, 2, 1, 0.

[0135] Figure 17 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery. The whole patient cohort of 56 men was stratified into two cohorts (low score vs. high score) according to the level of immune protective response score (IDR_score). The clinical endpoint tested was overall survival after first treatment (log-rank p<0.0001). The following supplementary list represents the number of risk patients in each IDR_score class analyzed. That is, risk patients are shown for all +20 months post-surgery: High score: 42, 41, 39, 37, 37, 35, 26, 15, 9, 8, 5, 4, 1, 0; Low score: 14, 14, 14, 12, 11, 9, 7, 4, 2, 0, 0, 0, 0, 0.

[0136] Figure 18 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery. The whole patient cohort of 56 men was stratified into two cohorts (pGGG ≤ 2 vs. pGGG ≥ 3) according to the level of the pathological Gleason grade group. The clinical endpoint tested was overall survival after first treatment (log-rank p < 0.02). The following supplementary list represents the number of risk patients in each pGGG class analyzed. That is, risk patients are shown for all +20 months post-surgery: pGGG ≤ 2: 42, 42, 40, 38, 37, 35, 26, 18, 10, 7, 5, 4, 1, 0; pGGG ≥ 3: 14, 13, 13, 11, 11, 9, 7, 1, 1, 1, 0, 0, 0, 0.

[0137] Figure 19 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery. The whole patient cohort of 56 men was stratified into two cohorts (low score vs. high score) according to the level of immune protective response score (IDR_score). The clinical endpoint tested was overall survival after biochemical recurrence (BCR) after surgery (log-rank p<0.0001). The following supplementary list represents the number of risk patients in each IDR_score class analyzed. That is, risk patients are shown for all +20 months after surgery: High score: 23, 22, 21, 20, 20, 18, 14, 10, 5, 5, 4, 3, 0; Low score: 11, 10, 8, 8, 7, 6, 4, 2, 1, 0, 0, 0, 0.

[0138] Figure 20 shows a Kaplan-Meier curve analysis of men with recurrent prostate cancer after surgery. The whole patient cohort of 56 men was stratified into two cohorts (pGGG ≤ 2 vs. pGGG ≥ 3) according to the level of the pathological Gleason grade group. The clinical endpoint tested was overall survival after biochemical recurrence (BCR) after surgery (log-rank p = 0.13). The following supplementary list represents the number of risk patients in each pGGG class analyzed. That is, risk patients are shown for all +20 months after surgery: pGGG ≤ 2: 22, 21, 20, 19, 18, 17, 13, 12, 6, 5, 4, 3, 0; pGGG ≥ 3: 12, 11, 9, 9, 9, 7, 5, 0, 0, 0, 0, 0, 0.

[0139] The data presented in Figures 2–20 demonstrate that measurements and combinations of immune defense response genes provide a means to define patient groups with different risks of disease recurrence after surgery and death from prostate cancer after the initiation of secondary treatments such as radiation therapy. Furthermore, the presented data show that by selecting 14 of these immune defense genes, patient groups can be segmented by different risks of progressive disease in multiple independent datasets.

[0140] Further results This section presents additional results for Cox regression models based on only five and six of the identified immune defense response genes, respectively. In total, eight different five-gene models and four different six-gene models were tested. Details regarding the weights are shown in Tables 3 and 4 below.

[0141] [Table 3]

[0142] [Table 4]

[0143] Figure 21 shows the Kaplan-Meier curve for the IDR_5.1 model. The clinical endpoint tested was the time to prostate cancer-specific death (PCa Death) after initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery. Using a cutoff value of -0.1, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_5.1 model (log-rank p=0.003; HR=3.3; CI=1.5–7.2). The following supplementary list represents the number of risk patients in each IDR_5.1 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 53, 49, 43, 34, 22, 13, 4, 3, 0; High risk: 132, 114, 88, 64, 38, 20, 11, 5, 0.

[0144] Figure 22 shows the Kaplan-Meier curve for the IDR_5.2 model. The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). Using a cutoff value of 0.16, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_5.2 model (log-rank p=0.004; HR=3.2; CI=1.5–7.0). The following supplementary list represents the number of risk patients in each IDR_5.2 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 90, 81, 67, 51, 34, 19, 7, 3, 0; High risk: 95, 82, 64, 47, 26, 14, 8, 5, 0.

[0145] Figure 23 shows the Kaplan-Meier curve for the IDR_5.3 model. The clinical endpoint tested was the time to prostate cancer-specific death (PCa Death) after initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery. Using a cutoff value of 0.2, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_5.3 model (log-rank p=0.005; HR=3.1; CI=1.4–6.9). The following supplementary list represents the number of risk patients in each IDR_5.3 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 96, 86, 71, 56, 34, 21, 8, 5, 0; High risk: 89, 77, 60, 42, 26, 12, 7, 3, 0.

[0146] Figure 24 shows the Kaplan-Meier curve for the IDR_5.4 model. The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). Using a cutoff value of -0.1, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_5.4 model (log-rank p=0.007; HR=2.0; CI=1.3–6.4). The following supplementary list represents the number of risk patients in each IDR_5.4 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 90, 80, 67, 53, 32, 17, 8, 3, 0; High risk: 95, 83, 64, 45, 28, 16, 7, 5, 0.

[0147] Figure 25 shows the Kaplan-Meier curve for the IDR_5.5 model. The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). Using a cutoff value of -0.1, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_5.5 model (log-rank p=0.004; HR=3.2; CI=1.5–7.0). The following supplementary list represents the number of risk patients in each IDR_5.5 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 67, 63, 56, 43, 26, 13, 6, 2, 0; High risk: 118, 100, 75, 55, 34, 20, 9, 6, 0.

[0148] Figure 26 shows the Kaplan-Meier curve for the IDR_5.6 model. The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). Using a cutoff value of -0.12, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_5.6 model (log-rank p=0.0004; HR=4.1; CI=1.9–9.0). The following supplementary list represents the number of risk patients in each IDR_5.6 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 66, 62, 55, 42, 25, 12, 6, 2, 0; High risk: 119, 101, 76, 56, 35, 21, 9, 6, 0.

[0149] Figure 27 shows the Kaplan-Meier curve for the IDR_5.7 model. The clinical endpoint tested was the time to prostate cancer-specific death (PCa Death) after initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery. Using a cutoff value of 0.1, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_5.7 model (log-rank p=0.01; HR=2.9; CI=1.3–6.7). The following supplementary list represents the number of risk patients in each IDR_5.7 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 117, 194, 84, 67, 44, 21, 9, 5, 0; High risk: 68, 59, 47, 31, 16, 12, 6, 3, 0.

[0150] Figure 28 shows the Kaplan-Meier curve for the IDR_5.8 model. The clinical endpoint tested was the time to prostate cancer-specific death (PCa Death) after initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery. Using a cutoff value of 0.0, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_5.8 model (log-rank p=0.005; HR=3.0; CI=1.4–6.7). The following supplementary list represents the number of risk patients in each IDR_5.8 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 88, 79, 68, 54, 33, 17, 7, 3, 0; High risk: 97, 84, 63, 44, 27, 16, 8, 5, 0.

[0151] Figure 29 shows the Kaplan-Meier curve for the IDR_6.1 model. The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). Using a cutoff value of -0.1, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_6.1 model (log-rank p=0.02; HR=2.5; CI=1.2–5.6). The following supplementary list represents the number of risk patients in each IDR_6.1 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 67, 62, 53, 42, 23, 15, 7, 5, 0; High risk: 118, 101, 78, 56, 37, 18, 8, 3, 0.

[0152] Figure 30 shows the Kaplan-Meier curve for the IDR_6.2 model. The clinical endpoint tested was the time to prostate cancer-specific death (PCa Death) after initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery. Using a cutoff value of 0.17, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_6.2 model (log-rank p=0.004; HR=3.2; CI=1.5–7.1). The following supplementary list represents the number of risk patients in each IDR_6.2 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 90, 81, 67, 51, 34, 19, 7, 3, 0; High risk: 95, 82, 64, 47, 26, 14, 8, 5, 0.

[0153] Figure 31 shows the Kaplan-Meier curve for the IDR_6.3 model. The clinical endpoint tested was the time from initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery to prostate cancer-specific death (PCa Death). Using a cutoff value of 0.16, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_6.3 model (log-rank p=0.008; HR=2.9; CI=1.3–6.4). The following supplementary list represents the number of risk patients in each IDR_6.3 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 92, 85, 71, 56, 32, 19, 7, 4, 0; High risk: 93, 78, 60, 42, 28, 14, 8, 4, 0.

[0154] Figure 32 shows the Kaplan-Meier curve for the IDR_6.4 model. The clinical endpoint tested was the time to prostate cancer-specific death (PCa Death) after initiation of salvage radiotherapy (SRT) due to disease recurrence after surgery. Using a cutoff value of 0.1, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the IDR_6.4 model (log-rank p=0.006; HR=3.0; CI=1.4–6.6). The following supplementary list represents the number of risk patients in each IDR_6.4 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 88, 79, 66, 53, 31, 18, 7, 5, 0; High risk: 97, 84, 65, 45, 29, 15, 8, 3, 0.

[0155] The Kaplan-Meier analyses shown in Figures 21-32 demonstrate that different patient risk groups can also be identified using risk models based on only a subset of identified immune defense response genes, e.g., five or six genes.

[0156] Consideration The efficacy of both radical radiotherapy (RT) and steroidal resuscitation (SRT) for localized prostate cancer is limited, often leading to disease progression and ultimately death, particularly in patients at high risk of recurrence. Predicting treatment outcomes is highly complex due to the numerous factors involved in treatment efficacy and disease recurrence. Several key factors may still be unidentified, and the influence of other factors has not been precisely determined. Multiple clinicopathological measures are currently being investigated and applied in clinical practice to improve response prediction and treatment selection, yielding some improvements. Nevertheless, better prediction of responses to radical RT and SRT remains strongly needed to increase the success rates of these treatments.

[0157] 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.

[0158] 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.

[0159] In several aspects of the present invention that are not currently claimed, APOBEC3A, AIM2, APOBEC3B, CASP1, CIAO1, CTNNB1, CXCL10, CXCL9, DDX41, DDX58, DDX60, DHX36, DHX58, DHX9, GBP1, HERC5, HIST2H2BE, IFI16, IFI44L, IFIH1, IFIT1, IFIT1B, IFIT2, IFIT3, IFITM1, IFITM3, IL1B, IRF3, IRF5, IRF7, ISG15, ITGAX, LILRB1, LRRFIP1, MAVS, MB21D1, MX1, MX2, MYD88, NLRP3, NOD2, OAS1, OAS3, OASL, OPRK1, POLR3A, PTPRC, PYCAR Five or more types selected from the group consisting of D, RSAD2, STAT1, TBK1, TICAM1, TLR2, TLR3, TLR4, TLR7, TLR8, TLR9, TMEM173, TRIM22, and ZBP1, for example, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 2 The gene expression profiles of each of the 7, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 or all of the immunoprotective response genes are used to determine the prediction of the radiotherapy response.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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, but in some embodiments, one or more steps are performed at least partially manually.

[0164] 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.

[0165] No reference symbol in the claims should be interpreted as limiting its scope.

[0166] The present invention relates to a method for predicting the response of a prostate cancer patient to radiotherapy, the method comprising: determining the gene expression profile of each of five 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, or all of them, or receiving the result of such determination, wherein the gene expression profile is determined in a biological sample obtained from the prostate cancer patient; preferably, determining a prediction of the radiotherapy response based on the gene expression profiles of the five or more immune defense response genes by a processor; and optionally, providing the prediction of the radiotherapy response or a recommendation of treatment based on the prediction of the radiotherapy response to a medical assistant or the prostate cancer patient. Since the state of the immune system and the immune microenvironment significantly impact treatment efficacy, the ability to identify markers that predict this impact can help enable better prediction of the overall RT response. Immune defense response genes play a central role in regulating immune activity. Identified immune defense response genes were found to show a significant correlation with post-RT outcomes. Therefore, identified immune defense response genes are expected to provide predictive values ​​regarding the efficacy of radical RT and / or SRT.

[0167] The attached sequence listing, titled 2019PF00711_Sequence Listing_ST25, is incorporated herein by reference in its entirety.

Claims

1. A method for assisting in predicting the response of prostate cancer patients to radiotherapy, wherein the method is A step of determining the gene expression profile of six 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, wherein the gene expression profiles are determined in biological samples obtained from the prostate cancer subjects. A step of determining the prediction of the radiotherapy response based on the gene expression profiles of the six or more immune defense response genes, The above prediction, It is determined as the weighted sum of the expression levels of each of the six or more immune defense response genes, or The expression levels of each of the six or more immune defense response genes are discretized based on one or more cutoff values, and the sum of these discretized values ​​is determined as an immune defense response score, which is then binarized based on whether or not the sum of these values ​​exceeds a threshold. Steps and The steps include providing the aforementioned prediction of the radiation therapy response to a medical assistant, and A method having

2. A method for assisting in predicting the response of prostate cancer patients to radiotherapy, wherein the method is A step of receiving the results of determining the gene expression profiles of six 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, wherein the gene expression profiles are determined in biological samples obtained from the prostate cancer subjects. A step of determining the prediction of the radiotherapy response based on the gene expression profiles of the six or more immune defense response genes using a processor, The above prediction, It is determined as the weighted sum of the expression levels of each of the six or more immune defense response genes, or The expression levels of each of the six or more immune defense response genes are discretized based on one or more cutoff values, and the sum of these discretized values ​​is determined as an immune defense response score, which is then binarized based on whether or not the sum of these values ​​exceeds a threshold. Steps and The steps include providing the medical assistant with a prediction of the radiation therapy response, A method of having.

3. A method for assisting in predicting the response of prostate cancer patients to radiotherapy, wherein the method is A step of determining or receiving the results of determining the gene expression profiles of six 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, wherein the gene expression profiles are determined in biological samples obtained from the prostate cancer subject. A step of determining the prediction of the radiotherapy response based on the gene expression profiles of the six or more immune defense response genes using a processor, The above prediction, It is determined as the weighted sum of the expression levels of each of the six or more immune defense response genes, or The expression levels of each of the six or more immune defense response genes are discretized based on one or more cutoff values, and the sum of these discretized values ​​is determined as an immune defense response score, which is then binarized based on whether or not the sum of these values ​​exceeds a threshold. Steps and The steps include providing the aforementioned prediction of the radiation therapy response to a medical assistant, and A method having

4. The method according to any one of claims 1 to 3, wherein the step of determining the prediction of the radiotherapy response includes the step of combining the gene expression profiles of six or more of the immune defense response genes with a regression function derived from a population of prostate cancer subjects.

5. The method according to any one of claims 1 to 3, wherein the step of determining the prediction of the radiotherapy response is to combine the gene expression profiles of six or more immune defense response genes with a summation function that sums the discretized gene expression profiles of the six or more immune defense response genes, wherein the gene expression profiles are discretized using individual cutoff values ​​derived from a population of prostate cancer subjects.

6. The method according to any one of claims 1 to 5, wherein the radiotherapy is curative radiotherapy or salvage radiotherapy.

7. The method according to any one of claims 1 to 6, wherein the prediction of the radiotherapy response is negative or positive for the effectiveness of the radiotherapy, and a negative prediction of the radiotherapy response indicates that one or more of the following are recommended: (i) radiotherapy provided earlier than standard, (ii) radiotherapy with increased radiation dose, (iii) adjunctive therapy such as androgen deprivation therapy, and (iv) alternative therapy other than radiotherapy.

8. A device for predicting the response of a prostate cancer patient to radiation therapy, wherein the device is An input unit that receives data showing the gene expression profiles of six 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, wherein the gene expression profiles are determined in biological samples obtained from the prostate cancer subjects. A processor for determining the prediction of the radiotherapy response based on the gene expression profiles of the six or more immune defense response genes, The aforementioned forecast is, The weighted sum of the expression levels of the six or more immune defense response genes, or The immune defense response score is determined based on whether the sum of the discretized values ​​obtained by analyzing the expression levels of the six or more immune defense response genes, based on one or more cutoff values, exceeds a threshold. Processor and A provision unit that provides a prediction of the radiotherapy response or a recommendation of treatment based on the prediction of the radiotherapy response to a medical assistant or the prostate cancer patient. A device equipped with the following features.

9. A computer program containing multiple instructions, wherein when the computer program is run by a computer, the multiple instructions are A step of receiving data showing the gene expression profiles of six 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, wherein the gene expression profiles are determined in biological samples obtained from prostate cancer subjects. A step of determining the prediction of the radiotherapy response based on the gene expression profiles of the six or more immune defense response genes, The aforementioned forecast is, The weighted sum of the expression levels of the six or more immune defense response genes, or The immune defense response score is determined based on whether the sum of the discretized values ​​obtained by analyzing the expression levels of the six or more immune defense response genes, based on one or more cutoff values, exceeds a threshold. Steps and A step of providing a medical assistant or the prostate cancer patient with a prediction of the radiotherapy response or a recommendation of treatment based on the prediction of the radiotherapy response, A computer program that causes the computer to perform a method having the above-mentioned method.

10. A method for determining the gene expression profiles of six 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 in biological samples obtained from prostate cancer subjects, comprising at least six primers and / or probes, The apparatus according to claim 8 or the computer program according to claim 9, A diagnostic kit that includes this.

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