Predicting radiotherapy response in prostate cancer patients based on T cell receptor signaling genes

Abstract

The present invention relates to a method of predicting a prostate cancer subject's response to radiation therapy, the method comprising the steps of determining or receiving results of determining a gene expression profile for each of eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, 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 the radiation therapy response based on the gene expression profiles of the eight or more T cell receptor signaling genes.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the response of prostate cancer patients to radiotherapy. Furthermore, the present invention relates to 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 eight or more T cell receptor signaling genes in radiotherapy prediction for prostate cancer patients, and a corresponding computer program. [Background technology]

[0002] Cancer is a class of diseases characterized by uncontrolled proliferation, invasion, and sometimes metastasis of cells. These three malignant characteristics of cancer distinguish it from benign tumors that are self-limited and do not invade or metastasize. Prostate cancer (PCa) is the second most common non-cutaneous malignant tumor in men, with an estimated 1.3 million new cases diagnosed worldwide and 360,000 deaths in 2018 (see Bray F. et al., "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA Cancer J Clin, Vol. 68, No. 6, pp. 394-424, 2018). In the United States, approximately 90% of new cases relate to localized cancer, meaning that metastasis has not yet formed (see "Cancer Facts & Figures 2010" by the ACS (American Cancer Society), 2010).

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

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

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

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

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

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

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

[0010] A wide range of biomarker candidates in tissues and 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] 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.

[0013] WO2017 / 216559A1 discloses a method for predicting the response of subjects with prostate cancer to mitotic inhibitors and / or DNA damage treatments, comprising the step of measuring the expression level of at least one gene from a given number of genes in a sample derived from the subject with prostate cancer. [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 eight or more T cell receptor signaling 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 eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16, 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 eight or more T cell receptor signaling genes, The steps include 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, as appropriate. 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 (see Shiao SL et al., "Regulation of prostate cancer progression by tumor microenvironment," Cancer Lett, Vol. 380, No. 1, pp. 340-348, 2016).

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

[0020] The present invention is based on the idea that, since the state of the immune system and the state of the immune microenvironment 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 immune response against pathogens can be induced at various stages: there are physical barriers such as the skin to keep invaders out. If breached, innate immunity, the first rapid non-specific response, begins to act. If this is insufficient, an adaptive immune response is induced. This is much more specific but takes time to develop upon first encounter with a pathogen. Lymphocytes are activated by interacting with activated antigen-presenting cells derived from the innate immune system. They are also involved in maintaining memory to respond more quickly upon subsequent encounter with the same pathogen.

[0022] Once activated, lymphocytes become highly specific and effective, so they undergo negative selection on their ability to recognize self, a process known as central tolerance. Since not all self-antigens are expressed at the selection sites, peripheral tolerance mechanisms such as TCR ligation in the absence of co-stimulation, expression of inhibitory co-receptors, and suppression by Tregs also develop similarly. Imbalances between activation and suppression can lead to autoimmune disorders or immunodeficiency and cancer, respectively.

[0023] T cell activation can have various functional consequences depending on the location and type of T cells involved. CD8+ T cells differentiate into cytotoxic effector cells, while CD4+ T cells can differentiate into Th1 (secreting IFNγ and promoting cellular immunity) or Th2 (secreting IL4 / 5 / 13 and promoting B cell and humoral immunity). It is also possible to differentiate into other T cell subsets that have been recently identified, such as Tregs, which have an inhibitory effect on immune activation (see Mosenden R. and Tasken K., "Cyclic AMP-mediated immune regulation - Overview of mechanisms of action in T-cells," Cell Signal, Vol. 23, No. 6, pp. 1009-1016, 2011, in particular, T cell activation and its modulation by PKA in Figure 4, and Tasken K. and Ruppelt A., "Negative regulation of T-cell receptor activation by the cAMP-PKA-Csk signaling pathway in T-cell lipid rafts," Front Biosci, Vol. 11, pp. 2929-2939, 2006).

[0024] Both PKA- and PDE4-regulated signaling intersects with TCR-induced T cell activation and fine-tunes its regulation with opposing effects (see Figure 6 in Abrahamsen H. et al., "TCR- and CD28-mediated recruitment of phosphodiesterase 4 to lipid rafts potentiates TCR signaling," J Immunol, Vol. 173, pp. 4847-4848, 2004, which shows opposing actions of PKA and PDE4 in TCR activation). The molecule that links these effectors is cyclic AMP (cAMP), an intracellular second messenger of extracellular ligand action. In T cells, cAMP mediates the effects of prostaglandins, adenosine, histamine, beta-adrenergic agonists, neuropeptide hormones, and beta-endorphin. Binding of these extracellular molecules to GPCRs results in conformational changes of the GPCR, release of stimulatory subunits, and subsequent activation of adenylate cyclase (AC), which hydrolyzes ATP to cAMP (see Figure 6 in Abrahamsen H. et al., 2004, ibid.). Although not the only one, PKA is a major effector of cAMP signaling (see Mosenden R. and Tasken K., ibid., 2011, and Tasken K. and Ruppelt A., ibid., 2006). At the functional level, an increase in cAMP levels leads to a decrease in the production of IFNγ and IL-2 in T cells (see Abrahamsen H. et al., ibid., 2004). In addition to interfering with TCR activation, PKA has further effects (see Figure 15 in Torheim E.A., "Immunity Leashed-Mechanisms of Regulation in the Human Immune System," PhD thesis, The Biotechnology Centre of Ola, University of Oslo, Norway, 2009).

[0025] In naive T cells, highly phosphorylated PAG targets Csk to lipid rafts. PKA targets Csk via the ezrin-EBP50-PAG scaffold complex. By being specifically phosphorylated by PKA, Csk can negatively modulate Lck and Fyn, weakening their activity and downregulating T cell activation (see Figure 6, ibid., 2004, by Abrahamsen H. et al.). Upon TCR activation, PAG is dephosphorylated and Csk is released from the raft. Dissociation of Csk is necessary for T cell activation to proceed. Within the same time course, the Csk-G3BP complex appears to be formed, thereby sequestering Csk outside the lipid raft (see ibid., 2011, by Mosenden R. and Tasken K., and ibid., 2006, by Tasken K. and Ruppelt A.).

[0026] On the other hand, combined stimulation of the TCR and CD28 mediates the recruitment of the cyclic nucleotide phosphodiesterase PDE4 to lipid rafts, which enhances the degradation of cAMP (see Figure 6 in the same paper by Abrahamsen H. et al., 2004). This inhibits TCR-induced cAMP production and enhances the immune response of T cells. When the TCR is stimulated alone, the recruitment of PDE4 is insufficient to sufficiently reduce cAMP levels, and therefore maximal T cell activation cannot occur (see the same paper by Abrahamsen H. et al., 2004).

[0027] Thus, by actively suppressing proximal TCR signaling, cAMP-PKA-Csk-mediated signaling is thought to establish a threshold for T cell activation. PDE recruitment can block this suppression. Tissue or cell type-specific regulation is achieved through the expression of multiple isoforms of AC, PKA, and PDE. As mentioned above, a tight balance between activation and suppression is necessary to prevent the development of autoimmune disorders, immunodeficiency, and cancer.

[0028] The identified T cell receptor signaling genes, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, were identified as follows: A group of 538 prostate cancer patients were treated with RP, and prostate cancer tissue was stored along with clinical parameters (e.g., pathological Gleason grade group (pGGG), pathological status (pT stage)) and related outcome parameters (e.g., biochemical recurrence (BCR), metastatic recurrence, prostate cancer-specific death (PCa death), salvage radiation therapy (SRT), salvage androgen deprivation therapy (SADT), chemotherapy (CTX)). For each of these patients, a PDE4D7 score was calculated and categorized into four PDE4D7 score classes, as described by Alves de Inda M. et al., "Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes," Eur Urol Focus, Vol. 4, No. 3, pp. 376-384, 2018. PDE4D7 score class 1 corresponds to the patient sample with the lowest PDE4D7 expression level, while PDE4D7 score class 4 corresponds to the patient sample with the highest PDE4D7 expression level. Next, RNASeq expression data (Transcripts Per Million) from 538 prostate cancer subjects were examined for differential gene expression between PDE4D7 score classes 1 and 4. Specifically, for approximately 20,000 protein-coding transcripts, we determined whether the average expression level in patients with a PDE4D7 score of class 1 was more than twice the average expression level in patients with a PDE4D7 score of class 4.This analysis yielded 637 genes with a PDE4D7 score class 1 / PDE4D7 score class 4 ratio greater than 2, with a minimum mean expression of 1 TPM in each of the four PDE4D7 score classes. These 637 genes were then subjected to further molecular pathway analysis (www.david.ncifcrf.gov), resulting in various enriched annotation clusters. Annotation cluster #6 showed enrichment (enrichment score: 5.9) in 17 genes with functions in primary immunodeficiency and T cell receptor signaling activation. Further heatmap analysis confirmed that these T cell receptor signaling genes were generally more highly expressed in patient-derived samples with PDE4D7 score class 1 than in patient-derived samples with PDE4D7 score class 4.

[0029] The term "CD2" refers to the gene for differentiation antigen group 2 (Ensembl:ENSG00000116824), for example, the sequence defined in the NCBI reference sequence NM_001767, specifically the nucleotide sequence as defined in Sequence ID No. 1, which corresponds to the sequence of the NCBI reference sequence of the CD2 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 2, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_001758 that encodes the CD2 polypeptide.

[0030] The term "CD2" also refers to nucleotide sequences that show high homology to AIM2, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 1, 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. 2. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the amino acid sequence or the sequence specified in Sequence ID No. 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 specified in Sequence ID No. 1.

[0031] The term "CD247" refers to the gene for differentiation antigen group 247 (Ensembl:ENSG00000198821), for example, the sequence defined in NCBI reference sequence NM_000734 or NCBI reference sequence NM_198053, specifically the nucleotide sequence as defined in SEQ ID NO: 3 or SEQ ID NO: 4, corresponding to the sequence of the NCBI reference sequence of the CD247 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 5 or SEQ ID NO: 6, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_000725 and NCBI protein accession reference sequences NP_932170, which encode the CD247 polypeptide.

[0032] The term "CD247" also refers to nucleotide sequences that show high homology to CD247, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 3 or SEQ ID NO: 4, 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: 5 or SEQ 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 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 an amino 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 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 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 amino acid sequence, or a sequence as defined in SEQ ID NO: 5 or SEQ ID NO: 6, or a sequence as defined in SEQ ID NO: 6, or a sequence as defined in SEQ ID NO: 4.

[0033] The term "CD28" refers to the sequence defined in the gene for differentiation antigen group 28 (Ensembl:ENSG00000178562), for example, the sequence defined in NCBI reference sequence NM_006139 or NCBI reference sequence NM_001243078, specifically the nucleotide sequence as defined in SEQ ID NO: 7 or SEQ ID NO: 8, corresponding to the sequence of the NCBI reference sequence of the CD28 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 9 or SEQ ID NO: 10, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_006130 and NCBI protein accession reference sequences NP_001230007, which encode the CD28 polypeptide.

[0034] The term "CD28" also refers to nucleotide sequences that show high homology to CD28, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 7 or SEQ ID NO: 8, 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: 9 or SEQ 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 a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence, or a nucleic acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91%, 91%, 91%, 91%, 91

[0035] The term "CD3E" refers to the gene for differentiation antigen group 3E (Ensembl:ENSG00000198851), for example, the sequence defined in the NCBI reference sequence NM_000733, specifically the nucleotide sequence as defined in Sequence ID No. 11, which corresponds to the sequence of the NCBI reference sequence of the CD3E transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 12, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000724 that encodes the CD3E polypeptide.

[0036] The term "CD3E" also refers to nucleotide sequences that show high homology to CD3E, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 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.

[0037] The term "CD3G" refers to the gene for differentiation antigen group 3G (Ensembl:ENSG00000160654), for example, the sequence defined in NCBI reference sequence NM_000073, specifically the nucleotide sequence as defined in SEQ ID NO: 13, which corresponds to the sequence of the NCBI reference sequence of the CD3G transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 14, which corresponds to the protein sequence defined in NCBI protein accession reference sequence NP_000064 that encodes the CD3G polypeptide.

[0038] The term "CD3G" also refers to nucleotide sequences that show high homology to CD3G, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 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 Sequence 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.

[0039] The term "CD4" refers to the gene for differentiation antigen group 4 (Ensembl:ENSG00000010610), for example, the sequence defined in the NCBI reference sequence NM_000616, specifically the nucleotide sequence as defined in SEQ ID NO: 15, which corresponds to the sequence of the NCBI reference sequence of the CD4 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 16, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_000607 that encodes the CD4 polypeptide.

[0040] The term "CD4" also refers to nucleotide sequences that show high homology to CD4, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 15, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 16. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in Sequence ID No. 16, 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. 15.

[0041] The term "CSK" refers to the C-terminal Src kinase gene (Ensembl:ENSG00000103653), specifically the nucleotide sequence defined in NCBI reference sequence NM_004383, and more precisely, the nucleotide sequence as defined in Sequence ID No. 17, which corresponds to the NCBI reference sequence of the CSK transcript shown above. It also refers to the corresponding amino acid sequence as defined in NCBI protein accession reference sequence NP_004374, which encodes the CSK polypeptide, and more precisely, the nucleotide sequence as defined in Sequence ID No. 18.

[0042] The term "CSK" also refers to nucleotide sequences that show high homology to CSK, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 17, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 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 acid sequence or the sequence specified in Sequence ID No. 18, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 17.

[0043] The term "EZR" refers to the Ezrin gene (Ensembl:ENSG00000092820), specifically the nucleotide sequence defined in NCBI reference sequence NM_003379, and more specifically, the nucleotide sequence as defined in Sequence ID No. 19, which corresponds to the NCBI reference sequence of the EZR transcript shown above. It also refers to the corresponding amino acid sequence as defined in NCBI protein accession reference sequence NP_003370, which encodes the EZR polypeptide, and more specifically, the amino acid sequence as defined in Sequence ID No. 20.

[0044] The term "EZR" also refers to nucleotide sequences that show high homology to EZR, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 19, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 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 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.

[0045] The term "FYN" refers to the sequence defined in the FYN Proto-Oncogene gene (Ensembl:ENSG00000010810), for example, the NCBI reference sequence NM_002037, NCBI reference sequence NM_153047, or NCBI reference sequence NM_153048, specifically the nucleotide sequence as defined in SEQ ID NO: 21, SEQ ID NO: 22, or SEQ ID NO: 23, corresponding to the NCBI reference sequence of the FYN transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 24, SEQ ID NO: 25, or SEQ ID NO: 26, corresponding to the protein sequence defined in the NCBI protein accession reference sequences NP_004726, NCBI protein accession reference sequences NP_001131022, NCBI protein accession reference sequences NP_001131025, and NCBI protein accession reference sequences NP_001131024, which encodes the FYN polypeptide.

[0046] The term "FYN" also refers to a nucleotide sequence that exhibits high homology to FYN, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 21, SEQ ID NO: 22, or SEQ ID NO: 23, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 24, SEQ ID NO: 25, or SEQ ID NO: 26. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence, or an amino acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91%, 91%, 91%, 91

[0047] The term "LAT" refers to the Linker For Activation Of T-Cells gene (Ensembl:ENSG00000213658), for example, the sequence defined in NCBI reference sequence NM_001014987 or NCBI reference sequence NM_014387, specifically the nucleotide sequence as defined in SEQ ID NO: 27 or SEQ ID NO: 28, corresponding to the sequence of the NCBI reference sequence of the LAT transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 29 or SEQ ID NO: 30, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_001014987 and NCBI protein accession reference sequences NP_055202, which encode the LAT polypeptide.

[0048] The term "LAT" also refers to a nucleotide sequence that exhibits high homology to LAT, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 27 or SEQ ID NO: 28, or an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 29 or SEQ ID NO: 30. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in SEQ ID NO: 29 or SEQ ID NO: 30, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 27 or SEQ ID NO: 28.

[0049] The term "LCK" refers to the LCK Proto-Oncogene gene (Ensembl:ENSG00000182866), specifically the nucleotide sequence defined in NCBI reference sequence NM_005356, as defined in Sequence ID No. 31, which corresponds to the NCBI reference sequence of the LCK transcript shown above. It also refers to the corresponding amino acid sequence defined in NCBI protein accession reference sequence NP_005347, which encodes the LCK polypeptide, as defined in Sequence ID No. 32.

[0050] The term "LCK" also refers to nucleotide sequences that show high homology to LCK, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 31, or amino acids that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 32. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an acid sequence or the sequence specified in Sequence ID No. 32, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 31.

[0051] The term "PAG1" refers to the gene for Phosphoprotein Membrane Anchor With Glycosphingolipid Microdomains 1 (Ensembl:ENSG00000076641), for example, the sequence defined in the NCBI reference sequence NM_018440, specifically the nucleotide sequence as defined in Sequence ID No. 33, which corresponds to the sequence of the NCBI reference sequence of the PAG1 transcript shown above. It also refers to the corresponding amino acid sequence as defined in Sequence ID No. 34, for example, which corresponds to the protein sequence defined in the NCBI protein accession reference sequence NP_060910 that encodes the PAG1 polypeptide.

[0052] The term "PAG1" also refers to nucleotide sequences that show high homology to PAG1, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 33, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in Sequence ID No. 34. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence or the sequence specified in Sequence ID No. 34, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in Sequence ID No. 33.

[0053] The term "PDE4D" refers to the gene for phosphodiesterase 4D (Ensembl:ENSG00000113448), such as NCBI reference sequences NM_001104631, NM_001349242, NM_001197218, NM_006203, NM_001197221, NM_001197220, and NM_001197 223, refers to the sequence defined in NCBI reference sequence NM_001165899 or NCBI reference sequence NM_001197219, specifically the nucleotide sequence as defined in SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, SEQ ID NO: 42, or SEQ ID NO: 43, which corresponds to the sequence of the NCBI reference sequence of the PDE4D transcript shown above, and which also encodes the PDE4D polypeptide. This also relates to the corresponding amino acid sequences, for example, as defined in SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ ID NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, or SEQ ID NO: 52, corresponding to the protein sequences defined in NCBI protein accession reference sequences NP_001098101, NP_001336171, NP_001184147, NP_006194, NP_001184150, NP_001184149, NP_001184152, NP_001159371, and NP_001184148.

[0054] The term "PDE4D" also refers to nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to nucleotide sequences that show high homology to PDE4D, for example, sequences defined in SEQ ID NOs. 35, 36, 37, 38, 39, 40, 41, 42, or 43, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to sequences defined in SEQ ID NOs. 44, 45, 46, 47, 48, 49, 50, 51, or 52. The present invention includes a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence as defined in SEQ ID NOs. 44, 45, 46, 47, 48, 49, 50, 51, or 52, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence as defined in SEQ ID NOs. 45, 36, 37, 38, 39, 40, 41, 42, or 43.

[0055] The term "PRKACA" refers to the gene for Protein Kinase cAMP-Activated Catalytic Subunit Alpha (Ensembl:ENSG00000072062), specifically the sequence defined in NCBI reference sequence NM_002730 or NCBI reference sequence NM_207518, and more specifically, the nucleotide sequence as defined in SEQ ID NO: 53 or SEQ ID NO: 54, corresponding to the sequence of the NCBI reference sequence of the PRKACA transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 55 or SEQ ID NO: 56, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_002721 and NCBI protein accession reference sequences NP_997401, which encode the PRKACA polypeptide.

[0056] The term "PRKACA" also refers to nucleotide sequences that show high homology to PRKACA, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 53 or SEQ ID NO: 54, or sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 55 or SEQ ID NO: 56. The present invention includes an amino acid sequence, or a nucleic acid sequence encoding an amino acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to an amino acid sequence, or an amino acid sequence encoding an amino1%, 91%, 91%, 91%, 91%, 91

[0057] The term "PRKACB" refers to the gene for Protein Kinase cAMP-Activated Catalytic Subunit Beta (Ensembl:ENSG00000142875), for example, NCBI reference sequences NM_002731, NM_182948, NM_001242860, NM_001242859, NM_001242858, NM_001242862, NM_001242861, NM_001300915, NM_207578, and NCBI reference sequences. The sequence defined in NM_001242857 or NCBI reference sequence NM_001300917, specifically the nucleotide sequence as defined in SEQ ID NOs. 57, SEQ ID NOs. 58, SEQ ID NOs. 59, SEQ ID NOs. 60, SEQ ID NOs. 61, SEQ ID NOs. 62, SEQ ID NOs. 63, SEQ ID NOs. 64, SEQ ID NOs. 65, SEQ ID NOs. 66, or SEQ ID NOs. 67, which corresponds to the NCBI protein accession reference sequence NP encoding the PRKACB polypeptide, also refers to the NCBI protein accession reference sequence NP encoding the PRKACB polypeptide. _002722, NCBI protein accession reference sequence NP_891993, NCBI protein accession reference sequence NP_001229789, NCBI protein accession reference sequence NP_001229788, NCBI protein accession reference sequence NP_001229787, NCBI protein accession reference sequence NP_001229791, NCBI protein accession reference sequence NP_001229790, NCBI protein accession reference sequence NP_ This also relates to the corresponding amino acid sequences, for example, as defined in SEQ ID NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, or SEQ ID NO: 78, corresponding to the protein sequences defined in 001287844, NCBI protein accession reference sequence NP_997461, NCBI protein accession reference sequence NP_001229786, and NCBI protein accession reference sequence NP_001287846.

[0058] The term "PRKACB" also refers to a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a nucleotide sequence that shows high homology to PRKACB, for example, a sequence as defined in SEQ ID NOs. 57, SEQ ID NOs. 58, SEQ ID NOs. 59, SEQ ID NOs. 60, SEQ ID NOs. 61, SEQ ID NOs. 62, SEQ ID NOs. 63, SEQ ID NOs. 64, SEQ ID NOs. 65, SEQ ID NOs. 66, or SEQ ID NOs. 98, or 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to a sequence as defined in SEQ ID NOs. 68, SEQ ID NOs. 79, SEQ ID NOs. 70, SEQ ID NOs. 71, SEQ ID NOs. 72, SEQ ID NOs. 73, SEQ ID NOs. 74, SEQ ID NOs. 75, SEQ ID NOs. 76, SEQ ID NOs. 77, or SEQ ID NOs. 98, 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: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ ID NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, or SEQ ID NO: 78, or an amino acid sequence encoded by a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence specified in SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, or SEQ ID NO: 67.

[0059] The term "PTPRC" refers to the gene for protein tyrosine phosphatase receptor type C (Ensembl:ENSG00000081237), for example, the sequence defined in NCBI reference sequence NM_002838 or NCBI reference sequence NM_080921, specifically the nucleotide sequence as defined in SEQ ID NO: 79 or SEQ ID NO: 80, corresponding to the sequence of the NCBI reference sequence of the PTPRC transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 81 or SEQ ID NO: 82, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_002829 and NCBI protein accession reference sequences NP_563578, which encode the PTPRC polypeptide.

[0060] The term "PTPRC" also refers to a nucleotide sequence that exhibits high homology to a PTPRC, for example, a nucleic acid sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 79 or SEQ ID NO: 80, or a sequence that is at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 81 or SEQ ID NO: 82. 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%, 93%, 94%, 95%, 96%, 97%, 98%, or

[0061] The term "ZAP70" refers to the gene (Ensembl:ENSG00000115085) of the zeta chain of T-cell receptor-associated protein kinase 70, for example, the sequence defined in NCBI reference sequence NM_001079 or NCBI reference sequence NM_207519, specifically the nucleotide sequence as defined in SEQ ID NO: 83 or SEQ ID NO: 84, corresponding to the sequence of the NCBI reference sequence of the ZAP70 transcript shown above. It also refers to the corresponding amino acid sequence as defined in SEQ ID NO: 85 or SEQ ID NO: 86, corresponding to the protein sequence defined in NCBI protein accession reference sequences NP_001070 and NCBI protein accession reference sequences NP_997402, which encode the ZAP70 polypeptide.

[0062] The term "ZAP70" also refers to nucleotide sequences that show high homology to ZAP70, for example, nucleic acid sequences that are at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to the sequence defined in SEQ ID NO: 83 or SEQ ID NO: 84, 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: 85 or SEQ ID NO: 86. 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%,

[0063] 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.

[0064] 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.

[0065] In certain realizations, biopsy or excision samples are obtained and / or used. Such samples may include cells or cell lysates.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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."

[0070] 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.”

[0071] 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.

[0072] 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.

[0073] The term "metastasis" refers to the presence of metastatic disease in organs other than the prostate gland.

[0074] 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).

[0075] The term "prostate cancer-specific death, or disease-specific death," refers to a patient's death resulting from prostate cancer.

[0076] Preferably, the eight or more T cell receptor signaling genes include nine or more of the aforementioned T cell receptor signaling genes.

[0077] It is even more preferable that the eight or more T cell receptor signaling genes include twelve or more T cell receptor signaling genes.

[0078] It is even more preferable that the eight or more T cell receptor signaling genes include 15 or more, preferably all T cell receptor signaling genes.

[0079] Preferably, the step of determining the prediction of the radiotherapy response includes combining the gene expression profiles of eight or more, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16 or all of the T cell receptor signaling genes with a regression function derived from a population of prostate cancer patients.

[0080] 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.

[0081] In a specific realization, the prediction of the radiotherapy response is determined as follows:

number

[0082] In one example, w1 is from about -0.5 to 0.5, such as -0.07388, w2 is from about -3.5 to -2.5, such as -3.1496, w3 is from about -1.0 to 0.0, such as -0.4443, w4 is from about -0.5 to 0.5, such as -0.005986, w5 is from about -1.0 to 0.0, such as -0.2943, w6 is from about 0.0 to 1.0, such as 0.5738, w7 is from about -0.5 to 0.5, such as 0.1856, w8 is from about 0.0 to 1.0, such as 0.3914, w9 is from about -0.5 to 0.5, such as 0.02173, w 10 is from about 0.5 to 1.5, such as 0.7811, w 11 is from about -0.5 to 0.5, such as 0.2116, w 12 is from about -0.5 to 0.5, such as 0.2432, w 13 is from about -0.5 to -3.0, such as -4.0934, w 14 is from about 0.5 to 1.5, such as 1.176, w 15 is from about -1.0 to 0.0, such as -0.4655, w 16 is from about -1.5 to -0.5, such as -1.153, w 17 is from about -1.0 to 0.0, such as -0.2942.

[0083] The prediction of radiotherapy response may also be classified or categorized into one of at least two risk groups based on the predicted value of radiotherapy response. For example, there are two risk groups, three risk groups, four risk groups or more than four predefined risk groups. Each risk group covers the (non-overlapping) values of individual ranges of the prediction of radiotherapy response. For example, the risk groups represent the probability of occurrence of specific clinical events such as 0 to <0.1, 0.1 to <0.25, 0.25 to <0.5, or 0.5 to 1.0.

[0084] The biological sample is preferably obtained from the subject before the start of radiotherapy. The gene expression profile may be determined in the form of mRNA or protein in the prostate cancer tissue. Alternatively, if the T cell receptor signaling gene exists in a soluble form, the gene expression profile may be determined in the blood.

[0085] It is even more preferable that the radiation therapy be curative radiation therapy or salvage radiation therapy.

[0086] 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.

[0087] In a further aspect of the present invention, a device for predicting the response of a prostate cancer patient to radiotherapy, An input unit adapted to receive data showing the gene expression profiles of eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16, 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 eight or more T cell receptor signaling 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 is presented.

[0088] 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 eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them, wherein the gene expression profiles are determined in biological samples obtained from the prostate cancer subjects. The steps include determining the prediction of the radiotherapy response based on the gene expression profiles of the eight or more T cell receptor signaling genes, The steps include 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, as appropriate. A computer program is presented that causes the computer to perform a method having the above.

[0089] In a further aspect of the present invention, In a biological sample obtained from the aforementioned prostate cancer subject, at least eight primers and / or probes are used to determine the gene expression profile of eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of the T cell receptor signaling genes, for determining the gene expression profile of each of these T cell receptor signaling genes, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them, in a biological sample obtained from the aforementioned prostate cancer subject. Appropriately, the apparatus described in claim 9 or the computer program described in claim 10 A diagnostic kit containing [the specified ingredient / method] is presented.

[0090] In a further aspect of the present invention, the use of the diagnostic kit described in claim 11 is presented.

[0091] The use of the diagnostic kit described in claim 12 is preferably in a method for predicting the response of a prostate cancer patient to radiotherapy.

[0092] In a further aspect of the present invention, The steps include receiving biological samples obtained from prostate cancer patients, The steps of using the diagnostic kit described in claim 10 to determine the gene expression profile of each of eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of the T cell receptor signaling genes in the biological sample obtained from the aforementioned prostate cancer subject, and A method is presented that has this feature.

[0093] 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 eight or more T cell receptor signaling 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 eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them.

[0094] The method described in claim 1, the apparatus described in claim 9, the computer program described in claim 10, the diagnostic kit described in claim 11, the use of the diagnostic kit described in claim 12, the method described in claim 14, and the use of the gene expression profile described in claim 15 are understood to have similar and / or identical preferred embodiments, particularly those described in the dependent claims.

[0095] Preferred embodiments of the present invention may be any combination of the dependent claims or the embodiments described above and each independent claim.

[0096] 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]

[0097] [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 ROC curve analysis of two prediction models. [Figure 3] This figure shows a Kaplan-Meier curve analysis of a T cell receptor signaling model (TCR_signaling_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 4] This figure shows the Kaplan-Meier curves for the T cell receptor signaling 8-gene model (TCR_8.1_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 5]This figure shows Kaplan-Meier curves for an alternative T-cell receptor signaling 8-gene model (TCR_8.2_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 6] This figure shows Kaplan-Meier curves for an alternative T-cell receptor signaling 8-gene model (TCR_8.3_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 7] This figure shows Kaplan-Meier curves for an alternative T-cell receptor signaling 8-gene model (TCR_8.4_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 8] This figure shows Kaplan-Meier curves for an alternative T-cell receptor signaling 8-gene model (TCR_8.5_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 9] This figure shows Kaplan-Meier curves for an alternative T-cell receptor signaling 8-gene model (TCR_8.6_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 10] This figure shows Kaplan-Meier curves for an alternative T-cell receptor signaling 8-gene model (TCR_8.7_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 11]This figure shows Kaplan-Meier curves for another T-cell receptor signaling 8-gene model (TCR_8.8_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 12] This figure shows Kaplan-Meier curves for another T-cell receptor signaling 9-gene model (TCR_9.1_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 13] This figure shows Kaplan-Meier curves for another T-cell receptor signaling 9-gene model (TCR_9.2_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 14] This figure shows the Kaplan-Meier curves for the T cell receptor signaling 9 gene model (TCR_9.3_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 15] This figure shows Kaplan-Meier curves for another T-cell receptor signaling 9-gene model (TCR_9.4_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]

[0098] 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.

[0099] In step S100, the method is implemented.

[0100] 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.

[0101] In step S104, gene expression profiles are obtained for each of the eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for each of the RNAs extracted from each biological sample, by performing, for example, RT-qPCR (real-time quantitative PCR), to obtain gene expression profiles for each of the eight or more T cell receptor signaling genes, for example, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, or all of them, for each of the biological samples obtained from the first set of patients. A typical gene expression profile includes the expression level (e.g., value) of one or more T cell receptor signaling genes.

[0102] 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 eight or more T cell receptor signaling genes, CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and / or ZAP70, 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.

[0103] 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.

[0104] In step S110, for example, PCR is performed on the biological sample to obtain gene expression profiles for eight or more T cell receptor signaling genes, such as 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them.

[0105] In step S112, the prediction of the radiotherapy response based on the gene expression profiles of eight or more T cell receptor signaling genes is determined for the patient using the regression function described above. This will be described in more detail later in this document.

[0106] 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.

[0107] In S116, the method is terminated.

[0108] In one embodiment, the gene expression profile in steps S104 and S110 is determined by detecting mRNA expression using eight or more primers and / or probes and / or sets thereof.

[0109] The immune system interacts powerfully with prostate cancer, both at the systemic level and within the tumor microenvironment. T cell receptor signaling genes play a central role in regulating immune activity. Therefore, T cell receptor signaling genes provide information about the effectiveness of RT. However, estimating which T cell receptor signaling genes have predictive values ​​in this application from existing literature is extremely difficult due to the many factors that influence the precise function of T cell receptor signaling genes.

[0110] We investigated the extent to which the expression of T cell receptor signaling genes in prostate cancer tissue correlates with disease recurrence after curative radiotherapy or steroidal resuscitation (SRT).

[0111] In a cohort of 151 prostate cancer patients, we identified 17 T-cell receptor signaling genes whose expression levels in prostate cancer tissue were significantly correlated with mortality after SRT.

[0112] 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.

[0113] result Cox regression analysis Next, we began testing whether combinations of these 17 T-cell receptor signaling genes provided better prognostic indications. Using Cox regression, we modeled the expression levels of the 17 T-cell receptor signaling genes against prostate cancer-specific mortality after salvage RT following surgery (TCR_signaling_model). We tested this model in ROC curve analysis and Kaplan-Meier survival analysis.

[0114] 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 after prostate surgery. Consequently, many of these patients with PSA recurrence (#151) received salvage radiation therapy (SRT) alone or in combination with anti-androgen treatment as a second-line therapy to treat 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.

[0115] The Cox regression function was derived as follows.

number

[0116] [Table 1]

[0117] 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).

[0118] Figure 2 shows the ROC curve analysis of the two prediction models. TCR_signaling_model (AUC=0.88) is a Cox regression model based on 17 T cell receptor signaling genes. EAU_BCR_Risk (AUC=0.77) is the EAU_BCR_Risk group (European Association of Urology Biochemical Recurrence Risk group).

[0119] 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.

[0120] 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, according to the constructed risk model (TCR_signaling_model).

[0121] Figure 3 shows the Kaplan-Meier curves for the TCR_signaling_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 TCR regression model (log-rank p<0.0001; HR=6,4; CI=2.9~14.1). The following supplementary list represents the number of risk patients in each TCR_signaling_model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 76, 76, 73, 72, 61, 48, 45, 24, 3, 2, 0; High risk: 75, 68, 57, 42, 34, 28, 26, 13, 3, 1, 0.

[0122] 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 TCR_signaling_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).

[0123] Further results This section presents additional results for Cox regression models based on only eight and nine of the identified T cell receptor signaling genes, respectively. In total, eight different eight-gene models and four different nine-gene models were tested. Details regarding the weights are shown in Tables 2 and 3 below, respectively.

[0124] [Table 2]

[0125]

number

[0126] For Kaplan-Meier curve analysis, the Cox regression functions of 12 risk models (TCR_8.1_model~TCR_8.8_model and TCR_9.1_model~TCR_9.4_model) were categorized into two subcohorts based on the cutoff value, as described above (see the explanation of the figure below).

[0127] The aforementioned patient class corresponds to an increased risk of experiencing the tested clinical endpoint, time from the initiation of rescue RT to prostate cancer-specific death, in the risk model we developed.

[0128] Figure 4 shows the Kaplan-Meier curves for the TCR_8.1_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 0.08, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_8.1_model (log-rank p=0.005; HR=4.1; CI=1.5–10.8). The following supplementary list represents the number of risk patients in each TCR_8.1_model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 79, 72, 62, 49, 27, 17, 8, 4, 0; High risk: 106, 91, 69, 49, 33, 16, 7, 4, 0.

[0129] Figure 5 shows the Kaplan-Meier curves for the TCR_8.2_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 -0.02, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_8.2 model (log-rank p=0.003; HR=3.2; CI=1.5–7.1). The following supplementary list represents the number of risk patients in each TCR_8.2 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 69, 62, 52, 41, 23, 15, 6, 3, 0; High risk: 116, 101, 79, 57, 37, 18, 9, 5, 0.

[0130] Figure 6 shows the Kaplan-Meier curve for the TCR_8.3 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 -0.1, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_8.3 model (log-rank p=0.005; HR=3.1; CI=1.4–6.8). The following supplementary list represents the number of risk patients in each TCR_8.3 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 60, 54, 47, 36, 23, 15, 6, 3, 0; High risk: 125, 109, 84, 62, 37, 18, 9, 5, 0.

[0131] Figure 7 shows the Kaplan-Meier curves for the TCR_8.4 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 0.25, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_8.4 model (log-rank p=0.0002; HR=4.5; CI=2.0–9.8). The following supplementary list represents the number of risk patients in each TCR_8.4 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 92, 82, 68, 54, 31, 20, 10, 5, 0; High risk: 93, 81, 63, 44, 29, 13, 5, 3, 0.

[0132] Figure 8 shows the Kaplan-Meier curves for the TCR_8.5_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 -0.3, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_8.5 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 TCR_8.5 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 68, 60, 50, 39, 24, 17, 7, 3, 0; High risk: 117, 103, 81, 59, 36, 16, 8, 5, 0.

[0133] Figure 9 shows the Kaplan-Meier curves for the TCR_8.6_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 TCR_8.6_model (log-rank p=0.0002; HR=4.5; CI=2.9–9.8). The following supplementary list represents the number of risk patients in each TCR_8.6_model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 89, 78, 66, 49, 31, 20, 7, 3, 0; High risk: 96, 85, 65, 49, 29, 13, 8, 5, 0.

[0134] Figure 10 shows the Kaplan-Meier curves for the TCR_8.7_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 TCR_8.7 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 TCR_8.7 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 92, 81, 68, 51, 33, 21, 8, 4, 0; High risk: 93, 82, 63, 47, 27, 12, 7, 4, 0.

[0135] Figure 11 shows the Kaplan-Meier curve for the TCR_8.8 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 0.2, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_8.8 model (log-rank p<0.0001; HR=5.3; CI=2.4~11.8). The following supplementary list represents the number of risk patients in each TCR_8.8 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 92, 81, 68, 51, 33, 21, 8, 4, 0; High risk: 93, 82, 63, 47, 27, 12, 7, 4, 0.

[0136] Figure 12 shows the Kaplan-Meier curve for the TCR_9.1 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 0.14, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_9.1 model (log-rank p=0.0002; HR=4.4; CI=2.0–9.7). The following supplementary list represents the number of risk patients in each TCR_9.1 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 85, 76, 63, 50, 28, 18, 9, 5, 0; High risk: 100, 87, 68, 48, 32, 15, 6, 3, 0.

[0137] Figure 13 shows the Kaplan-Meier curves for the TCR_9.2 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 0.06, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_9.2 model (log-rank p=0.0007; HR=3.9; CI=1.8–8.6). The following supplementary list represents the number of risk patients in each TCR_9.2 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 90, 79, 65, 50, 26, 15, 7, 3, 0; High risk: 95, 84, 66, 48, 34, 18, 8, 5, 0.

[0138] Figure 14 shows the Kaplan-Meier curves for the TCR_9.3 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 0.0, patients were stratified into two cohorts (low vs. high) according to their risk of experiencing their clinical endpoint as predicted by the TCR_9.3 model (log-rank p=0.0006; HR=4.0; CI=1.8–8.7). The following supplementary list represents the number of risk patients in each TCR_9.3 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 87, 77, 63, 48, 24, 15, 7, 3, 0; High risk: 98, 86, 68, 50, 36, 18, 8, 5, 0.

[0139] Figure 15 shows the Kaplan-Meier curve for the TCR_9.4 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 TCR_9.4 model (log-rank p=0.0003; HR=4.3; CI=2.0–9.6). The following supplementary list represents the number of risk patients in each TCR_9.4 model class analyzed. That is, risk patients are shown for all +20 months post-surgery: Low risk: 89, 82, 69, 55, 29, 19, 10, 5, 0; High risk: 96, 81, 62, 43, 31, 14, 5, 3, 0.

[0140] The Kaplan-Meier analyses shown in Figures 4-15 demonstrate that different patient risk groups can also be identified using risk models based on only a subset of identified T cell receptor signaling genes, e.g., eight or nine genes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In a preferred embodiment, one or more T cell receptor signaling genes are selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PRKACA, PRKACB, PTPRC, and ZAP70. For example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or all of these T cell receptor signaling genes are selected from this group, and the prediction of the radiotherapy response is determined based on the gene expression profile (multiple possible) of one or more of the T cell receptor signaling genes. Furthermore, the gene expression profiles of eight or more selected T cell receptor signaling genes, for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or all of these T cell receptor signaling genes are combined with a regression function derived from a population of prostate cancer patients.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Typical methods are implemented on one or more general-purpose computers, dedicated computers, programmed microprocessors or microcontrollers and peripheral integrated circuit elements, ASICs or other integrated circuits, hardwired electronic or logic circuits such as digital signal processors, discrete element circuits, programmable logic devices such as PLDs, PLAs, FPGAs, graphics card CPUs (GPUs), or PALs. Generally, any device capable of implementing a finite machine that can sequentially perform the flowchart shown in Figure 1 can be used to implement one or more steps of the illustrated risk stratification method for treatment selection in patients with prostate cancer. As will be understood, all steps of the method can be implemented on a computer, although in some embodiments, one or more steps are performed at least partially manually.

[0149] 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.

[0150] No reference symbol in the claims should be interpreted as limiting its scope.

[0151] The present invention relates to a method for predicting the response of a prostate cancer patient to radiotherapy, the method comprising the steps of: determining or receiving the results of determining the gene expression profile of each of eight or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, for example, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all of them, 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 eight or more T cell receptor signaling genes using a processor; and optionally, providing a medical assistant or the prostate cancer patient with the prediction of the radiotherapy response or a recommendation of treatment based on the prediction of the radiotherapy response. 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. T cell receptor signaling genes play a central role in regulating immune activity. Identified T cell receptor signaling genes were found to show a significant correlation with post-RT outcomes. Therefore, identified T cell receptor signaling genes are expected to provide predictive values ​​for the efficacy of radical RT and / or SRT.

[0152] The attached sequence listing, titled 2019PF00712_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 nine or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, wherein the gene expression profile is determined in a biological sample obtained from the prostate cancer subject. A step of determining the prediction of the radiotherapy response based on the gene expression profiles of the nine or more T cell receptor signaling genes, The above prediction, This is determined as the weighted sum of the expression levels of the nine or more T cell receptor signaling genes mentioned above. 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 nine or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, 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 nine or more T cell receptor signaling genes using a processor, The above prediction, This is determined as the weighted sum of the expression levels of the nine or more T cell receptor signaling genes mentioned above. Steps and The steps include providing the aforementioned prediction of the radiation therapy response to a medical assistant, and A method having

3. 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 nine or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, or receiving the result of such determination, wherein the gene expression profile is determined in a biological sample obtained from the prostate cancer subject. A step of determining the prediction of the radiotherapy response based on the gene expression profiles of the nine or more T cell receptor signaling genes using a processor, The above prediction, This is determined as the weighted sum of the expression levels of the nine or more T cell receptor signaling genes mentioned above. 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 nine or more T cell receptor signaling genes with a regression function derived from a population of prostate cancer subjects.

5. The method according to any one of claims 1 to 4, wherein the radiotherapy is curative radiotherapy or salvage radiotherapy.

6. The method according to any one of claims 1 to 5, wherein 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 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 treatment such as androgen deprivation therapy, and iv) alternative treatment other than radiotherapy.

7. 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 nine or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, 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 nine or more T cell receptor signaling genes, The aforementioned forecast is, The weighted sum of the expression levels of the nine or more T cell receptor signaling genes mentioned above, 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.

8. 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 nine or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, 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 nine or more T cell receptor signaling genes, The aforementioned forecast is, The weighted sum of the expression levels of the nine or more T cell receptor signaling genes mentioned above, Steps and The steps include 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 that causes the computer to perform a method having the above-mentioned method.

9. In a biological sample obtained from the aforementioned prostate cancer subject, at least nine primers and / or probes are provided to determine the gene expression profiles of nine or more T cell receptor signaling genes selected from the group consisting of CD2, CD247, CD28, CD3E, CD3G, CD4, CSK, EZR, FYN, LAT, LCK, PAG1, PDE4D, PRKACA, PRKACB, PTPRC, and ZAP70, respectively. The apparatus according to claim 7 or the computer program according to claim 8 A diagnostic kit that includes this.

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